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fahricansecer/iddaai-be:main fahricansecer/iddaai-be:v28 fahricansecer/iddaai-be:v26-shadow fahricansecer/iddaai-be:prod-ready fahricansecer/iddaai-be:cron fahricansecer/iddaai-be:check-docker
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fahricansecer/iddaai-be:main fahricansecer/iddaai-be:v28 fahricansecer/iddaai-be:v26-shadow fahricansecer/iddaai-be:prod-ready fahricansecer/iddaai-be:cron fahricansecer/iddaai-be:check-docker

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check-docker .. b5c2edf346

Author SHA1 Message Date
fahricansecer b5c2edf346 gg 2026-04-24 01:15:05 +03:00
fahricansecer 1f26a5bf2f fix: update version tags to v28 and temporarily disable cache for predictions 2026-04-24 00:11:00 +03:00
fahricansecer 634204acf0 v28 2026-04-23 22:22:59 +03:00
fahricansecer df428ed1e8 gg 2026-04-22 02:17:02 +03:00
fahricansecer 2ccd6831eb gg 2026-04-21 16:53:56 +03:00
fahricansecer 1346924387 gg 2026-04-19 13:23:00 +03:00
fahricansecer e4c74025e5 Merge pull request 'cron' (#1) from cron into main
Deploy Iddaai Backend / build-and-deploy (push) Successful in 2m48s
Details 
Reviewed-on: #1
 2026-04-16 17:22:36 +03:00
fahricansecer c8e7e4e927 cr 2026-04-16 17:21:48 +03:00
174 changed files with 15007 additions and 10195 deletions
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.gitea/workflows/docker_check.yml
-26
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@@ -1,26 +0,0 @@
name: Check Docker Pi
on:
push:
branches: [check-docker]
jobs:
check-docker:
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v4
- name: Get Docker Info
run: |
date > docker_info.txt
echo "==== DOCKER PS ====" >> docker_info.txt
docker ps -a >> docker_info.txt
echo "==== DOCKER STATS ====" >> docker_info.txt
docker stats --no-stream >> docker_info.txt
git config --global user.name "Gitea Actions"
git config --global user.email "actions@gitea.local"
git add docker_info.txt
git commit -m "chore: add docker info" || true
git push origin check-docker
ai-engine/catboost_info/catboost_training.json
+874
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@@ -0,0 +1,874 @@
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316 11211 59525
317 11243 59468
318 11274 59413
319 11304 59346
320 11334 59287
321 11362 59209
322 11394 59158
323 11436 59158
324 11477 59153
325 11513 59122
326 11547 59081
327 11572 58991
328 11607 58956
329 11637 58894
330 11668 58833
331 11700 58785
332 11724 58694
333 11757 58648
334 11780 58550
335 11815 58515
336 11844 58451
337 11869 58364
338 11905 58335
339 11941 58302
340 11986 58315
341 12020 58274
342 12066 58292
343 12122 58358
344 12177 58415
345 12221 58422
346 12264 58423
347 12300 58394
348 12324 58304
349 12354 58243
350 12401 58262
351 12438 58232
352 12479 58228
353 12512 58179
354 12541 58116
355 12569 58044
356 12597 57977
357 12628 57920
358 12653 57839
359 12682 57775
360 12720 57752
361 12744 57666
362 12770 57592
363 12811 57583
364 12841 57522
365 12870 57460
366 12897 57386
367 12938 57378
368 12974 57347
369 13009 57313
370 13038 57249
371 13078 57235
372 13117 57216
373 13147 57159
374 13181 57118
375 13205 57036
376 13235 56979
377 13274 56960
378 13306 56911
379 13333 56841
380 13366 56798
381 13396 56741
382 13421 56666
383 13467 56674
384 13508 56664
385 13540 56616
386 13569 56559
387 13598 56496
388 13627 56438
389 13656 56376
390 13685 56317
391 13717 56271
392 13750 56227
393 13771 56135
394 13804 56090
395 13825 55999
396 13858 55957
397 13888 55904
398 13917 55843
399 13953 55812
400 13994 55802
401 14025 55752
402 14048 55670
403 14076 55607
404 14105 55551
405 14142 55526
406 14182 55511
407 14214 55464
408 14240 55394
409 14267 55328
410 14299 55284
411 14324 55213
412 14351 55146
413 14379 55086
414 14410 55036
415 14451 55025
416 14484 54984
417 14513 54929
418 14536 54851
419 14565 54793
420 14587 54710
421 14615 54650
422 14642 54588
423 14666 54515
424 14690 54441
425 14719 54384
426 14739 54297
427 14772 54257
428 14790 54164
429 14824 54125
430 14844 54039
431 14876 53995
432 14906 53946
433 14938 53902
434 14980 53894
435 15006 53829
436 15033 53770
437 15059 53706
438 15085 53639
439 15110 53574
440 15134 53503
441 15160 53438
442 15184 53369
443 15211 53308
444 15234 53236
445 15266 53193
446 15287 53114
447 15316 53059
448 15336 52978
449 15366 52929
450 15393 52870
451 15429 52843
452 15469 52828
453 15490 52748
454 15523 52712
455 15550 52653
456 15577 52594
457 15604 52536
458 15630 52476
459 15656 52414
460 15682 52353
461 15711 52304
462 15736 52238
463 15765 52188
464 15786 52112
465 15817 52068
466 15839 51996
467 15873 51961
468 15903 51916
469 15935 51873
470 15969 51840
471 15994 51779
472 16022 51726
473 16047 51663
474 16073 51605
475 16099 51546
476 16128 51495
477 16152 51431
478 16176 51367
479 16205 51317
480 16228 51250
481 16255 51194
482 16277 51123
483 16305 51071
484 16328 51005
485 16362 50973
486 16392 50928
487 16426 50894
488 16459 50860
489 16480 50787
490 16510 50743
491 16530 50668
492 16561 50625
493 16585 50562
494 16613 50510
495 16638 50453
496 16663 50393
497 16690 50339
498 16716 50282
499 16740 50222
500 16773 50186
501 16802 50139
502 16836 50107
503 16873 50085
504 16921 50094
505 16989 50163
506 17038 50173
507 17069 50132
508 17110 50121
509 17145 50091
510 17190 50091
511 17219 50044
512 17247 49994
513 17271 49932
514 17298 49878
515 17343 49878
516 17373 49836
517 17417 49831
518 17460 49823
519 17490 49781
520 17518 49731
521 17546 49680
522 17571 49622
523 17600 49577
524 17625 49520
525 17655 49474
526 17679 49414
527 17707 49366
528 17729 49300
529 17758 49254
530 17781 49191
531 17808 49141
532 17829 49071
533 17862 49038
534 17905 49031
535 18028 49241
536 18072 49236
537 18106 49203
538 18135 49157
539 18165 49114
540 18200 49083
541 18223 49022
542 18254 48980
543 18280 48927
544 18307 48876
545 18338 48834
546 18367 48790
547 18411 48783
548 18444 48747
549 18470 48693
550 18503 48660
551 18531 48611
552 18557 48558
553 18584 48508
554 18625 48493
555 18650 48436
556 18677 48388
557 18703 48333
558 18729 48282
559 18756 48231
560 18781 48176
561 18808 48126
562 18834 48074
563 18869 48043
564 18902 48008
565 18930 47960
566 18958 47914
567 18983 47859
568 19016 47824
569 19037 47761
570 19068 47720
571 19090 47660
572 19111 47595
573 19141 47553
574 19164 47494
575 19196 47458
576 19217 47393
577 19249 47358
578 19274 47303
579 19298 47247
580 19324 47195
581 19357 47162
582 19391 47130
583 19427 47103
584 19460 47070
585 19483 47012
586 19511 46967
587 19542 46929
588 19564 46867
589 19597 46833
590 19621 46779
591 19647 46729
592 19670 46672
593 19699 46627
594 19726 46582
595 19753 46532
596 19778 46480
597 19803 46429
598 19830 46381
599 19857 46335
600 19896 46313
601 19925 46271
602 19957 46236
603 19991 46204
604 20019 46159
605 20047 46115
606 20072 46063
607 20098 46015
608 20123 45963
609 20149 45913
610 20176 45867
611 20202 45817
612 20230 45774
613 20253 45719
614 20285 45682
615 20307 45626
616 20338 45589
617 20361 45532
618 20394 45500
619 20423 45459
620 20454 45420
621 20488 45390
622 20510 45333
623 20543 45301
624 20569 45252
625 20594 45201
626 20619 45151
627 20646 45107
628 20675 45066
629 20701 45016
630 20727 44970
631 20752 44919
632 20782 44881
633 20804 44825
634 20837 44791
635 20862 44742
636 20892 44704
637 20931 44683
638 20960 44643
639 20994 44612
640 21022 44570
641 21052 44531
642 21082 44493
643 21107 44443
644 21135 44401
645 21160 44351
646 21185 44302
647 21210 44253
648 21236 44208
649 21262 44161
650 21288 44113
651 21315 44068
652 21343 44027
653 21377 43997
654 21403 43949
655 21440 43926
656 21477 43903
657 21502 43854
658 21533 43819
659 21559 43772
660 21586 43727
661 21611 43680
662 21637 43633
663 21662 43586
664 21688 43539
665 21714 43493
666 21742 43451
667 21771 43413
668 21818 43409
669 21846 43366
670 21888 43352
671 21934 43345
672 21971 43322
673 22019 43320
674 22053 43289
675 22090 43266
676 22141 43269
677 22176 43240
678 22213 43215
679 22239 43171
680 22270 43134
681 22296 43088
682 22321 43041
683 22350 43002
684 22379 42962
685 22419 42944
686 22452 42912
687 22484 42878
688 22511 42834
689 22537 42789
690 22571 42757
691 22598 42714
692 22624 42669
693 22653 42630
694 22680 42586
695 22708 42545
696 22739 42510
697 22761 42457
698 22792 42421
699 22816 42373
700 22845 42333
701 22870 42288
702 22902 42253
703 22942 42234
704 22974 42201
705 23002 42160
706 23033 42124
707 23054 42071
708 23086 42038
709 23115 41999
710 23143 41957
711 23169 41914
712 23195 41868
713 23230 41840
714 23259 41801
715 23287 41760
716 23311 41713
717 23341 41676
718 23372 41641
719 23405 41610
720 23438 41578
721 23483 41566
722 23507 41519
723 23540 41488
724 23566 41444
725 23595 41406
726 23623 41365
727 23648 41320
728 23677 41281
729 23700 41231
730 23728 41192
731 23752 41144
732 23784 41111
733 23807 41063
734 23840 41031
735 23870 40994
736 23908 40972
737 23941 40940
738 23974 40909
739 24006 40875
740 24036 40838
741 24064 40798
742 24092 40759
743 24127 40730
744 24153 40688
745 24179 40644
746 24207 40604
747 24233 40561
748 24261 40522
749 24295 40491
750 24318 40444
751 24349 40410
752 24376 40368
753 24408 40335
754 24442 40306
755 24474 40273
756 24508 40242
757 24548 40222
758 24575 40182
759 24605 40145
760 24632 40104
761 24660 40064
762 24689 40027
763 24714 39982
764 24745 39949
765 24766 39897
766 24797 39863
767 24825 39823
768 24854 39786
769 24880 39744
770 24909 39706
771 24940 39672
772 24970 39635
773 25004 39606
774 25030 39564
775 25056 39522
776 25086 39486
777 25107 39436
778 25139 39403
779 25159 39351
780 25188 39314
781 25214 39272
782 25240 39230
783 25266 39188
784 25288 39141
785 25315 39101
786 25341 39058
787 25367 39016
788 25391 38972
789 25417 38930
790 25448 38895
791 25482 38867
792 25514 38834
793 25542 38795
794 25569 38756
795 25595 38714
796 25618 38669
797 25643 38626
798 25667 38581
799 25695 38543
800 25716 38494
801 25743 38454
802 25770 38415
803 25790 38364
804 25822 38332
805 25843 38284
806 25873 38249
807 25896 38203
808 25925 38167
809 25955 38131
810 25988 38101
811 26028 38080
812 26055 38042
813 26081 38000
814 26108 37961
815 26131 37916
816 26159 37878
817 26188 37841
818 26214 37800
819 26242 37764
820 26272 37728
821 26298 37688
822 26327 37652
823 26359 37619
824 26385 37580
825 26408 37534
826 26444 37507
827 26477 37478
828 26517 37456
829 26539 37411
830 26573 37382
831 26597 37339
832 26623 37298
833 26650 37259
834 26677 37221
835 26704 37182
836 26728 37138
837 26763 37111
838 26791 37073
839 26822 37041
840 26872 37033
841 26924 37029
842 26982 37033
843 27054 37055
844 27097 37038
845 27120 36994
846 27146 36954
847 27180 36925
848 27206 36884
849 27234 36846
850 27260 36807
851 27289 36770
852 27318 36734
853 27347 36698
854 27386 36675
855 27413 36637
856 27439 36596
857 27471 36564
858 27501 36529
859 27535 36500
860 27572 36474
861 27595 36431
862 27627 36398
863 27654 36360
864 27683 36324
865 27711 36287
866 27738 36249
867 27765 36210
868 27794 36175
869 27820 36135
1 iter Passed Remaining
2 0 46 93548
3 1 83 83419
4 2 132 88415
5 3 162 81250
6 4 196 78573
7 5 230 76747
8 6 269 76701
9 7 319 79674
10 8 364 80653
11 9 411 81918
12 10 456 82497
13 11 491 81432
14 12 522 79809
15 13 555 78774
16 14 595 78777
17 15 630 78123
18 16 662 77290
19 17 700 77124
20 18 730 76120
21 19 764 75651
22 20 804 75774
23 21 835 75128
24 22 886 76169
25 23 920 75764
26 24 960 75853
27 25 989 75130
28 26 1025 74941
29 27 1060 74714
30 28 1104 75079
31 29 1141 74976
32 30 1180 74975
33 31 1213 74640
34 32 1245 74260
35 33 1287 74434
36 34 1327 74528
37 35 1376 75071
38 36 1427 75741
39 37 1468 75804
40 38 1508 75857
41 39 1549 75922
42 40 1586 75781
43 41 1621 75590
44 42 1663 75705
45 43 1701 75621
46 44 1739 75591
47 45 1776 75460
48 46 1819 75616
49 47 1869 76025
50 48 1916 76288
51 49 1953 76191
52 50 1993 76197
53 51 2038 76381
54 52 2080 76420
55 53 2158 77788
56 54 2220 78529
57 55 2286 79390
58 56 2328 79372
59 57 2367 79254
60 58 2409 79257
61 59 2444 79049
62 60 2484 78985
63 61 2521 78820
64 62 2554 78528
65 63 2593 78466
66 64 2623 78111
67 65 2660 77969
68 66 2695 77776
69 67 2725 77446
70 68 2761 77291
71 69 2791 76975
72 70 2824 76739
73 71 2861 76611
74 72 2897 76476
75 73 2935 76408
76 74 3040 78027
77 75 3097 78411
78 76 3152 78741
79 77 3216 79248
80 78 3256 79195
81 79 3305 79336
82 80 3348 79320
83 81 3381 79089
84 82 3416 78911
85 83 3480 79399
86 84 3535 79649
87 85 3581 79716
88 86 3612 79428
89 87 3644 79185
90 88 3678 78975
91 89 3712 78785
92 90 3743 78531
93 91 3775 78297
94 92 3806 78047
95 93 3837 77821
96 94 3871 77629
97 95 3913 77618
98 96 3945 77403
99 97 3989 77433
100 98 4020 77204
101 99 4053 77020
102 100 4084 76789
103 101 4116 76597
104 102 4148 76401
105 103 4176 76141
106 104 4202 75845
107 105 4232 75634
108 106 4261 75390
109 107 4290 75168
110 108 4324 75018
111 109 4351 74766
112 110 4386 74648
113 111 4424 74577
114 112 4458 74455
115 113 4497 74400
116 114 4533 74307
117 115 4564 74136
118 116 4596 73981
119 117 4628 73818
120 118 4668 73786
121 119 4692 73509
122 120 4723 73354
123 121 4756 73220
124 122 4788 73065
125 123 4815 72854
126 124 4843 72647
127 125 4875 72514
128 126 4916 72515
129 127 4952 72436
130 128 4991 72397
131 129 5028 72327
132 130 5059 72180
133 131 5096 72116
134 132 5125 71946
135 133 5156 71804
136 134 5190 71704
137 135 5221 71564
138 136 5251 71407
139 137 5274 71165
140 138 5309 71084
141 139 5344 71008
142 140 5377 70902
143 141 5416 70866
144 142 5452 70803
145 143 5490 70760
146 144 5521 70641
147 145 5553 70522
148 146 5582 70365
149 147 5611 70217
150 148 5636 70026
151 149 5673 69975
152 150 5706 69874
153 151 5738 69764
154 152 5765 69605
155 153 5795 69471
156 154 5817 69246
157 155 5853 69191
158 156 5888 69122
159 157 5924 69070
160 158 5964 69061
161 159 5996 68963
162 160 6022 68789
163 161 6050 68650
164 162 6079 68510
165 163 6108 68385
166 164 6140 68292
167 165 6169 68162
168 166 6202 68074
169 167 6231 67953
170 168 6263 67858
171 169 6295 67764
172 170 6325 67656
173 171 6356 67561
174 172 6395 67545
175 173 6437 67554
176 174 6472 67495
177 175 6503 67395
178 176 6533 67291
179 177 6562 67174
180 178 6590 67049
181 179 6624 66982
182 180 6655 66882
183 181 6687 66804
184 182 6718 66703
185 183 6751 66632
186 184 6784 66559
187 185 6810 66424
188 186 6832 66246
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190 188 6918 66294
191 189 6969 66393
192 190 7018 66470
193 191 7074 66614
194 192 7117 66635
195 193 7191 66943
196 194 7242 67036
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198 196 7317 66967
199 197 7351 66903
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201 199 7432 66896
202 200 7471 66869
203 201 7506 66814
204 202 7540 66752
205 203 7568 66628
206 204 7605 66596
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210 208 7734 66276
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213 211 7831 66053
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215 213 7910 66016
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217 215 7989 65983
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232 230 8490 65020
233 231 8523 64958
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235 233 8575 64718
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299 297 10612 60612
300 298 10639 60525
301 299 10668 60453
302 300 10702 60411
303 301 10729 60326
304 302 10764 60290
305 303 10801 60263
306 304 10829 60182
307 305 10857 60108
308 306 10892 60067
309 307 10930 60047
310 308 10972 60045
311 309 11002 59983
312 310 11030 59902
313 311 11058 59828
314 312 11092 59788
315 313 11117 59696
316 314 11149 59641
317 315 11187 59617
318 316 11211 59525
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320 318 11274 59413
321 319 11304 59346
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323 321 11362 59209
324 322 11394 59158
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328 326 11547 59081
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333 331 11700 58785
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335 333 11757 58648
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337 335 11815 58515
338 336 11844 58451
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340 338 11905 58335
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343 341 12020 58274
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345 343 12122 58358
346 344 12177 58415
347 345 12221 58422
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349 347 12300 58394
350 348 12324 58304
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383 381 13396 56741
384 382 13421 56666
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403 401 14025 55752
404 402 14048 55670
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433 431 14876 53995
434 432 14906 53946
435 433 14938 53902
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467 465 15817 52068
468 466 15839 51996
469 467 15873 51961
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471 469 15935 51873
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473 471 15994 51779
474 472 16022 51726
475 473 16047 51663
476 474 16073 51605
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482 480 16228 51250
483 481 16255 51194
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485 483 16305 51071
486 484 16328 51005
487 485 16362 50973
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489 487 16426 50894
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491 489 16480 50787
492 490 16510 50743
493 491 16530 50668
494 492 16561 50625
495 493 16585 50562
496 494 16613 50510
497 495 16638 50453
498 496 16663 50393
499 497 16690 50339
500 498 16716 50282
501 499 16740 50222
502 500 16773 50186
503 501 16802 50139
504 502 16836 50107
505 503 16873 50085
506 504 16921 50094
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508 506 17038 50173
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ai-engine/features/odds_band_analyzer.py
+1224
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ai-engine/features/rolling_features.py
+243
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@@ -0,0 +1,243 @@
"""
V27 Rolling Window Feature Calculator
======================================
Computes rolling averages over 5/10/20 match windows,
with home/away splits and trend detection.
"""
from __future__ import annotations
from typing import Dict, List, Tuple
import math
def calc_rolling_features(
team_matches: List[Tuple], # [(mst, is_home, team_goals, opp_goals, opp_id), ...]
before_date: int,
team_is_home: bool,
) -> Dict[str, float]:
"""Calculate rolling window features for a team before a given date."""
valid = [m for m in team_matches if m[0] < before_date]
defaults = {
"rolling5_goals_avg": 1.3, "rolling5_conceded_avg": 1.2,
"rolling10_goals_avg": 1.3, "rolling10_conceded_avg": 1.2,
"rolling20_goals_avg": 1.3, "rolling20_conceded_avg": 1.2,
"rolling5_clean_sheets": 0.25,
"venue_goals_avg": 1.3, "venue_conceded_avg": 1.2,
"goal_trend": 0.0,
}
if len(valid) < 3:
return defaults
result = {}
for window in [5, 10, 20]:
recent = valid[-window:] if len(valid) >= window else valid
n = len(recent)
g_sum = sum(m[2] for m in recent)
c_sum = sum(m[3] for m in recent)
result[f"rolling{window}_goals_avg"] = g_sum / n
result[f"rolling{window}_conceded_avg"] = c_sum / n
# Clean sheet rate (last 5)
r5 = valid[-5:] if len(valid) >= 5 else valid
result["rolling5_clean_sheets"] = sum(1 for m in r5 if m[3] == 0) / len(r5)
# Venue-specific (home-only or away-only)
venue_matches = [m for m in valid if m[1] == team_is_home]
if venue_matches:
vm = venue_matches[-10:] if len(venue_matches) >= 10 else venue_matches
result["venue_goals_avg"] = sum(m[2] for m in vm) / len(vm)
result["venue_conceded_avg"] = sum(m[3] for m in vm) / len(vm)
else:
result["venue_goals_avg"] = defaults["venue_goals_avg"]
result["venue_conceded_avg"] = defaults["venue_conceded_avg"]
# Goal trend: compare last 3 vs previous 3
if len(valid) >= 6:
last3 = sum(m[2] for m in valid[-3:]) / 3
prev3 = sum(m[2] for m in valid[-6:-3]) / 3
result["goal_trend"] = last3 - prev3
else:
result["goal_trend"] = 0.0
return result
def calc_league_quality(
all_matches: List[Tuple], # all FT matches in this league
) -> Dict[str, float]:
"""Calculate league-level quality features."""
defaults = {
"league_home_win_rate": 0.45,
"league_draw_rate": 0.25,
"league_btts_rate": 0.50,
"league_ou25_rate": 0.50,
"league_reliability_score": 0.50,
}
if len(all_matches) < 20:
return defaults
n = len(all_matches)
home_wins = sum(1 for m in all_matches if m[2] > m[3])
draws = sum(1 for m in all_matches if m[2] == m[3])
btts = sum(1 for m in all_matches if m[2] > 0 and m[3] > 0)
ou25 = sum(1 for m in all_matches if (m[2] + m[3]) > 2.5)
hw_rate = home_wins / n
dr_rate = draws / n
btts_rate = btts / n
ou25_rate = ou25 / n
# Reliability: leagues closer to averages are more predictable
predictability = 1.0 - abs(hw_rate - 0.45) - abs(dr_rate - 0.27) * 0.5
reliability = max(0.2, min(0.95, predictability))
return {
"league_home_win_rate": round(hw_rate, 4),
"league_draw_rate": round(dr_rate, 4),
"league_btts_rate": round(btts_rate, 4),
"league_ou25_rate": round(ou25_rate, 4),
"league_reliability_score": round(reliability, 4),
}
def calc_time_features(
team_matches: List[Tuple],
match_mst: int,
) -> Dict[str, float]:
"""Calculate time-based features."""
from datetime import datetime
# Days since last match
valid = [m for m in team_matches if m[0] < match_mst]
if valid:
last_mst = valid[-1][0]
days_rest = (match_mst - last_mst) / 86_400_000 # ms to days
days_rest = min(days_rest, 60.0) # cap at 60 days
else:
days_rest = 14.0
# Month and season flags
try:
dt = datetime.utcfromtimestamp(match_mst / 1000)
month = dt.month
is_season_start = 1.0 if month in (7, 8) else 0.0
is_season_end = 1.0 if month in (5, 6) else 0.0
except Exception:
month = 6
is_season_start = 0.0
is_season_end = 0.0
return {
"days_rest": round(days_rest, 2),
"match_month": month,
"is_season_start": is_season_start,
"is_season_end": is_season_end,
}
def calc_advanced_h2h(
team_matches: List[Tuple],
home_id: int,
away_id: int,
before_date: int,
) -> Dict[str, float]:
"""Calculate advanced H2H features."""
defaults = {
"h2h_home_goals_avg": 1.3,
"h2h_away_goals_avg": 1.1,
"h2h_recent_trend": 0.0,
"h2h_venue_advantage": 0.0,
}
h2h = [m for m in team_matches if m[4] == away_id and m[0] < before_date]
if not h2h:
return defaults
recent = h2h[-10:]
home_goals_total = 0
away_goals_total = 0
venue_home_wins = 0
venue_total = 0
for mst, is_home, team_goals, opp_goals, _ in recent:
if is_home:
home_goals_total += team_goals
away_goals_total += opp_goals
venue_total += 1
if team_goals > opp_goals:
venue_home_wins += 1
else:
home_goals_total += opp_goals
away_goals_total += team_goals
n = len(recent)
result = {
"h2h_home_goals_avg": home_goals_total / n,
"h2h_away_goals_avg": away_goals_total / n,
"h2h_venue_advantage": venue_home_wins / venue_total if venue_total > 0 else 0.5,
}
# Recent trend: last 3 vs overall
if len(h2h) >= 4:
last3_pts = sum(
1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
for m in h2h[-3:]
) / 3
overall_pts = sum(
1.0 if m[2] > m[3] else (0.5 if m[2] == m[3] else 0.0)
for m in h2h
) / len(h2h)
result["h2h_recent_trend"] = round(last3_pts - overall_pts, 4)
else:
result["h2h_recent_trend"] = 0.0
return result
def calc_strength_diff(
home_form: Dict[str, float],
away_form: Dict[str, float],
home_elo: Dict[str, float],
away_elo: Dict[str, float],
home_momentum: float,
away_momentum: float,
upset_potential: float,
) -> Dict[str, float]:
"""Calculate strength differential features."""
# Attack vs Defense mismatches
h_attack = home_form.get("goals_avg", 1.3)
a_defense = away_form.get("conceded_avg", 1.2)
a_attack = away_form.get("goals_avg", 1.3)
h_defense = home_form.get("conceded_avg", 1.2)
atk_def_home = h_attack - a_defense # positive = home attack > away defense
atk_def_away = a_attack - h_defense
# XG diff approximation
xg_diff = (h_attack + a_defense) / 2 - (a_attack + h_defense) / 2
# Form × Momentum interaction
form_mom = (home_momentum - away_momentum) * (
home_form.get("scoring_rate", 0.75) - away_form.get("scoring_rate", 0.75)
)
# ELO-Form consistency
elo_diff = home_elo.get("overall", 1500) - away_elo.get("overall", 1500)
form_diff = h_attack - a_attack
elo_form_consistency = 1.0 if (elo_diff > 0 and form_diff > 0) or (elo_diff < 0 and form_diff < 0) else 0.0
# Upset × ELO gap
elo_gap = abs(elo_diff)
upset_x_elo = upset_potential * (elo_gap / 400.0)
return {
"attack_vs_defense_home": round(atk_def_home, 4),
"attack_vs_defense_away": round(atk_def_away, 4),
"xg_diff": round(xg_diff, 4),
"form_momentum_interaction": round(form_mom, 4),
"elo_form_consistency": elo_form_consistency,
"upset_x_elo_gap": round(upset_x_elo, 4),
}
ai-engine/main.py
+18 -9
View File
@@ -16,6 +16,7 @@ from pydantic import BaseModel
from models.basketball_v25 import get_basketball_v25_predictor
from services.single_match_orchestrator import get_single_match_orchestrator
from services.v26_shadow_engine import get_v26_shadow_engine
from data.database import dispose_engine
load_dotenv()
@@ -36,9 +37,10 @@ class CouponRequest(BaseModel):
@asynccontextmanager
async def lifespan(_: FastAPI):
try:
print("🚀 Initializing V25 orchestrator...", flush=True)
print("🚀 Initializing V28 orchestrator...", flush=True)
get_single_match_orchestrator()
print("✅ V25 orchestrator ready", flush=True)
get_v26_shadow_engine()
print("✅ V28 orchestrator ready", flush=True)
except Exception as error:
print(f"❌ Failed to initialize orchestrator: {error}", flush=True)
import traceback
@@ -53,8 +55,8 @@ async def lifespan(_: FastAPI):
app = FastAPI(
title="Suggest-Bet AI Engine",
version="25.0.0",
description="V25 Single Match Prediction Package API",
version="28.0.0",
description="V28 Single Match Prediction Package API",
lifespan=lifespan,
)
@@ -102,8 +104,9 @@ async def global_exception_handler(_: Request, exc: Exception):
@app.get("/")
def read_root() -> dict[str, Any]:
return {
"status": "Suggest-Bet AI Engine v25",
"engine": "V25 Single Match Orchestrator",
"status": "Suggest-Bet AI Engine v28",
"engine": "V28 Single Match Orchestrator",
"mode": os.getenv("AI_ENGINE_MODE", "v28"),
"routes": [
"POST /v20plus/analyze/{match_id}",
"GET /v20plus/analyze-htms/{match_id}",
@@ -118,15 +121,21 @@ def read_root() -> dict[str, Any]:
@app.get("/health")
def health_check() -> dict[str, Any]:
try:
get_single_match_orchestrator()
orchestrator = get_single_match_orchestrator()
shadow_engine = get_v26_shadow_engine()
basketball_predictor = get_basketball_v25_predictor()
basketball_readiness = basketball_predictor.readiness_summary()
ready = bool(basketball_readiness["fully_loaded"])
return {
"status": "healthy" if ready else "degraded",
"engine": "v25.main",
"engine": "v28.main",
"mode": os.getenv("AI_ENGINE_MODE", "v28"),
"ready": ready,
"basketball_v25": basketball_readiness,
"v26_shadow": shadow_engine.readiness_summary(),
"prediction_service_ready": True,
"model_loaded": ready,
"orchestrator_mode": getattr(orchestrator, "engine_mode", "v28"),
}
except Exception as error:
return {"status": "unhealthy", "ready": False, "error": str(error)}
@@ -196,7 +205,7 @@ async def analyze_match_htft_v20plus(match_id: str, timeout_sec: int = 30) -> di
key=lambda item: float(item[1]),
)
return {
"engine": "v25.main",
"engine": "v28.main",
"match_info": result.get("match_info", {}),
"timing_ms": int((time.time() - started_at) * 1000),
"ht_ft_probs": htft_probs,
ai-engine/reports/training_v26_shadow.json
+19
View File
@@ -0,0 +1,19 @@
{
"version": "v26.shadow.0",
"calibration_version": "v26.shadow.calib.0",
"train_rows": 6853,
"validation_rows": 1469,
"label_priors": {
"MS": 0.4404,
"OU25": 0.5214,
"BTTS": 0.5398,
"HT": 0.4275,
"HTFT": 0.26,
"CARDS": 0.6052
},
"artifact_path": "/Users/piton/Documents/GitHub/iddaai/iddaai-be/ai-engine/models/v26_shadow/market_profiles.json",
"notes": [
"v26.shadow runtime currently uses artifact-based calibration and ROI gating",
"market profile JSON remains the source of truth for runtime thresholds"
]
}
ai-engine/requirements.txt
+1
View File
@@ -17,3 +17,4 @@ pyyaml>=6.0
# V2 async database
asyncpg>=0.29.0
pydantic>=2.5.0
pytest>=8.0.0
ai-engine/scripts/backtest_13_sept.py
-206
View File
@@ -1,206 +0,0 @@
"""
Backtest for September 13th (Top Leagues Only)
==============================================
Simulates the NEW 'Skip Logic' on matches from Sept 13, 2025.
"""
import os
import sys
import json
import psycopg2
from psycopg2.extras import RealDictCursor
from datetime import datetime
# Load .env manually to ensure correct DB connection
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.insert(0, project_root) # Add root to path if needed
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
# ─── Configuration ─────────
MIN_CONF_THRESHOLDS = {
"MS": 45.0, "DC": 40.0, "OU15": 50.0, "OU25": 45.0,
"OU35": 45.0, "BTTS": 45.0, "HT": 40.0,
}
def run_backtest():
print("🚀 Backtest: 13 Eylül 2024 - Top Leagues")
print("="*60)
# 1. Load Top Leagues
leagues_path = os.path.join(project_root, "top_leagues.json")
try:
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
# Ensure they are strings for SQL IN clause
league_ids = tuple(str(lid) for lid in top_leagues)
print(f"📋 Loaded {len(top_leagues)} top leagues.")
except Exception as e:
print(f"❌ Error loading top_leagues.json: {e}")
return
# 2. Define Date Range (Sept 13, 2024 UTC)
start_dt = datetime(2024, 9, 13, 0, 0, 0)
end_dt = datetime(2024, 9, 13, 23, 59, 59)
start_ts = int(start_dt.timestamp() * 1000)
end_ts = int(end_dt.timestamp() * 1000)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
# 3. Fetch Matches & Predictions
# We need matches that are FT and have a prediction
query = """
SELECT p.match_id, p.prediction_json,
m.score_home, m.score_away, m.status, m.league_id
FROM predictions p
JOIN matches m ON p.match_id = m.id
WHERE m.mst_utc BETWEEN %s AND %s
AND m.league_id IN %s
AND m.status = 'FT'
AND p.prediction_json IS NOT NULL
"""
try:
cur.execute(query, (start_ts, end_ts, league_ids))
rows = cur.fetchall()
except Exception as e:
print(f"❌ DB Error: {e}")
cur.close()
conn.close()
return
print(f"📊 Found {len(rows)} matches with predictions on Sept 13, 2024.")
if not rows:
print("⚠️ No predictions found for this date. The AI Engine might not have processed these historical matches yet.")
print("💡 Tip: Run the feeder or AI engine on this date range to generate predictions first.")
cur.close()
conn.close()
return
total_bets = 0
winning_bets = 0
skipped_bets = 0
total_profit = 0.0
for row in rows:
data = row['prediction_json']
if isinstance(data, str):
data = json.loads(data)
home_score = row['score_home'] or 0
away_score = row['score_away'] or 0
total_goals = home_score + away_score
# Extract Main Pick
main_pick = None
main_pick_conf = 0.0
main_pick_odds = 0.0
if "main_pick" in data and isinstance(data["main_pick"], dict):
mp = data["main_pick"]
main_pick = mp.get("pick")
main_pick_conf = mp.get("confidence", 0.0)
main_pick_odds = mp.get("odds", 0.0)
if not main_pick or not main_pick_conf:
continue
# Determine Market Type
pick_str = str(main_pick).upper()
market_type = "MS"
if "1X" in pick_str or "X2" in pick_str or "12" in pick_str: market_type = "DC"
elif "ÜST" in pick_str or "ALT" in pick_str or "OVER" in pick_str or "UNDER" in pick_str:
if "1.5" in pick_str: market_type = "OU15"
elif "3.5" in pick_str: market_type = "OU35"
else: market_type = "OU25"
elif "VAR" in pick_str or "YOK" in pick_str or "BTTS" in pick_str: market_type = "BTTS"
threshold = MIN_CONF_THRESHOLDS.get(market_type, 45.0)
# --- SKIP LOGIC ---
# 1. Confidence Gate
if main_pick_conf < threshold:
skipped_bets += 1
continue
# 2. Value Gate
if main_pick_odds > 0:
implied_prob = 1.0 / main_pick_odds
my_prob = main_pick_conf / 100.0
edge = my_prob - implied_prob
if edge < -0.03:
skipped_bets += 1
continue
# --- BET PLAYED ---
total_bets += 1
is_won = False
# Resolve Result
if market_type == "MS":
if (main_pick == "1" or main_pick == "MS 1") and home_score > away_score: is_won = True
elif (main_pick == "X" or main_pick == "MS X") and home_score == away_score: is_won = True
elif (main_pick == "2" or main_pick == "MS 2") and away_score > home_score: is_won = True
elif market_type.startswith("OU"):
line = 2.5
if "1.5" in pick_str: line = 1.5
elif "3.5" in pick_str: line = 3.5
is_over = total_goals > line
is_under = total_goals < line
if ("ÜST" in pick_str or "OVER" in pick_str) and is_over: is_won = True
elif ("ALT" in pick_str or "UNDER" in pick_str) and is_under: is_won = True
elif market_type == "BTTS":
if home_score > 0 and away_score > 0:
if "VAR" in pick_str: is_won = True
else:
if "YOK" in pick_str: is_won = True
elif market_type == "DC":
if "1X" in pick_str and home_score >= away_score: is_won = True
elif "X2" in pick_str and away_score >= home_score: is_won = True
elif "12" in pick_str and home_score != away_score: is_won = True
if is_won:
winning_bets += 1
profit = main_pick_odds - 1.0
total_profit += profit
else:
total_profit -= 1.0
# Report
print("\n" + "="*60)
print("📈 BACKTEST RESULTS: 13 EYLÜL 2025 (TOP LEAGUES)")
print("="*60)
print(f"Total Matches Analyzed: {len(rows)}")
print(f"🚫 Bets SKIPPED (Low Conf/Bad Value): {skipped_bets}")
print(f"✅ Bets PLAYED: {total_bets}")
if total_bets > 0:
win_rate = (winning_bets / total_bets) * 100
roi = (total_profit / total_bets) * 100
print(f"🏆 Winning Bets: {winning_bets}")
print(f"💀 Losing Bets: {total_bets - winning_bets}")
print("-" * 40)
print(f" Win Rate: {win_rate:.2f}%")
print(f"💰 Total Profit (Units): {total_profit:.2f}")
print(f"📊 ROI: {roi:.2f}%")
if roi > 0:
print("🟢 STRATEGY IS PROFITABLE!")
else:
print("🔴 STRATEGY IS LOSING")
else:
print("⚠️ No bets were played. Thresholds might be too high or no suitable matches found.")
cur.close()
conn.close()
if __name__ == "__main__":
run_backtest()
ai-engine/scripts/backtest_50_detailed.py
-240
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@@ -1,240 +0,0 @@
"""
Detailed Backtest with 50 Top League Matches
============================================
Runs AI Engine predictions on 50 real historical matches and shows
exactly which predictions were correct and which were skipped.
Usage:
python ai-engine/scripts/backtest_50_detailed.py
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
# Add paths
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
# 50 Match IDs from the query
MATCH_IDS = [
"v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
"7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
"7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
"7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
"7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
"7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
"7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
"7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
"7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
"lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
"40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
"2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
"7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
"coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
"9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
"6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
"1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
]
def run_detailed_backtest():
print("🚀 DETAILED BACKTEST: 50 Top League Matches")
print("🧠 Engine: V30 Ensemble (V20+V25) + Skip Logic")
print("="*80)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
# Fetch match details with odds
placeholders = ','.join(['%s'] * len(MATCH_IDS))
cur.execute(f"""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away, m.league_id,
t1.name as home_team, t2.name as away_team,
l.name as league_name
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
LEFT JOIN leagues l ON m.league_id = l.id
WHERE m.id IN ({placeholders})
AND m.status = 'FT'
ORDER BY m.mst_utc DESC
""", MATCH_IDS)
rows = cur.fetchall()
print(f"📊 Found {len(rows)} matches. Starting AI Analysis...")
if not rows:
print("⚠️ No matches found.")
cur.close()
conn.close()
return
# Initialize AI Engine
try:
orchestrator = get_single_match_orchestrator()
print("✅ AI Engine Loaded.\n")
except Exception as e:
print(f"❌ Failed to load AI Engine: {e}")
cur.close()
conn.close()
return
# ─── Backtest Loop ───
results = []
total_skipped = 0
total_played = 0
total_won = 0
total_profit = 0.0
MIN_CONF = 45.0
start_time = time.time()
for i, row in enumerate(rows):
match_id = str(row['id'])
home_team = row['home_team'] or "Unknown"
away_team = row['away_team'] or "Unknown"
league = row['league_name'] or "Unknown"
home_score = row['score_home'] or 0
away_score = row['score_away'] or 0
total_goals = home_score + away_score
print(f"[{i+1}/{len(rows)}] {home_team} vs {away_team} ({league}) ... ", end="", flush=True)
try:
prediction = orchestrator.analyze_match(match_id)
if not prediction:
print("⚠️ No prediction")
continue
# Extract Main Pick
main_pick = prediction.get("main_pick") or {}
pick_name = main_pick.get("pick", "")
confidence = main_pick.get("confidence", 0)
odds = main_pick.get("odds", 0)
# Apply Skip Logic
if confidence < MIN_CONF:
print(f"🚫 SKIP (Conf {confidence:.0f}%)")
total_skipped += 1
results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name,
"conf": confidence, "odds": odds, "result": "SKIPPED", "profit": 0})
continue
if odds > 0:
implied_prob = 1.0 / odds
my_prob = confidence / 100.0
if my_prob - implied_prob < -0.03:
print(f"🚫 SKIP (Bad Value)")
total_skipped += 1
results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name,
"conf": confidence, "odds": odds, "result": "SKIPPED", "profit": 0})
continue
# Bet Played
total_played += 1
won = False
# Resolve
pick_clean = str(pick_name).upper()
if pick_clean in ["1", "MS 1", "İY 1"] and home_score > away_score: won = True
elif pick_clean in ["X", "MS X", "İY X"] and home_score == away_score: won = True
elif pick_clean in ["2", "MS 2", "İY 2"] and away_score > home_score: won = True
elif pick_clean in ["1X", "X2"] or ("1X" in pick_clean or "X2" in pick_clean):
if "1X" in pick_clean and home_score >= away_score: won = True
elif "X2" in pick_clean and away_score >= home_score: won = True
elif pick_clean in ["12"] and home_score != away_score: won = True
elif "ÜST" in pick_clean or "OVER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
elif "3.5" in pick_clean: line = 3.5
if total_goals > line: won = True
elif "ALT" in pick_clean or "UNDER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
elif "3.5" in pick_clean: line = 3.5
if total_goals < line: won = True
elif "VAR" in pick_clean and home_score > 0 and away_score > 0: won = True
elif "YOK" in pick_clean and (home_score == 0 or away_score == 0): won = True
if won:
total_won += 1
profit = odds - 1.0
print(f"✅ WON ({pick_name} @ {odds:.2f}, +{profit:.2f})")
else:
profit = -1.0
print(f"❌ LOST ({pick_name} @ {odds:.2f})")
total_profit += profit
results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name,
"conf": confidence, "odds": odds,
"result": "WON" if won else "LOST", "profit": profit,
"score": f"{home_score}-{away_score}"})
except Exception as e:
print(f"💥 Error: {e}")
elapsed = time.time() - start_time
# ─── DETAILED REPORT ───
print("\n" + "="*80)
print("📈 DETAILED BACKTEST RESULTS")
print(f"⏱️ Time: {elapsed:.1f}s")
print("="*80)
print(f"📊 Total Matches: {len(rows)}")
print(f"🚫 Skipped: {total_skipped}")
print(f"🎲 Played: {total_played}")
print(f"✅ Won: {total_won}")
print(f"💀 Lost: {total_played - total_won}")
print(f"💰 Profit: {total_profit:+.2f} units")
if total_played > 0:
win_rate = (total_won / total_played) * 100
roi = (total_profit / total_played) * 100
print(f"📊 Win Rate: {win_rate:.1f}%")
print(f"📊 ROI: {roi:.1f}%")
if roi > 0:
print("🟢 STRATEGY IS PROFITABLE!")
else:
print("🔴 STRATEGY IS LOSING")
# ─── TABLE OF ALL RESULTS ───
print("\n" + "="*80)
print("📋 DETAILED MATCH RESULTS")
print("="*80)
print(f"{'Match':<40} {'Pick':<15} {'Conf':<6} {'Odds':<6} {'Result':<8} {'Score':<6}")
print("-"*80)
for r in results:
match_str = r['match'][:38]
pick_str = str(r['pick'])[:13]
conf_str = f"{r['conf']:.0f}%"
odds_str = f"{r['odds']:.2f}" if r['odds'] > 0 else "N/A"
res_str = r['result']
score_str = r.get('score', '')
# Color coding
if res_str == "WON": res_display = f"✅ {res_str}"
elif res_str == "LOST": res_display = f"❌ {res_str}"
else: res_display = f"🚫 {res_str}"
print(f"{match_str:<40} {pick_str:<15} {conf_str:<6} {odds_str:<6} {res_display:<12} {score_str:<6}")
cur.close()
conn.close()
if __name__ == "__main__":
run_detailed_backtest()
ai-engine/scripts/backtest_adaptive.py
-191
View File
@@ -1,191 +0,0 @@
"""
Adaptive 500 Match Backtest
=============================
Skips NO match unless NO odds exist.
Evaluates ALL available markets (MS, OU, BTTS) and picks the BEST value bet.
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_adaptive_backtest():
print("🔄 ADAPTIVE 500 MATCH BACKTEST")
print("="*60)
# 1. Load Top Leagues
leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
league_ids = tuple(str(lid) for lid in top_leagues)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
# 2. Fetch 500 Finished Matches with Odds
cur.execute("""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away, m.league_id,
t1.name as home_team, t2.name as away_team
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
ORDER BY m.mst_utc DESC
LIMIT 500
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 Found {len(rows)} matches. Analyzing...\n")
if not rows:
print("⚠️ No matches found.")
return
try: orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Error: {e}")
return
# Stats
total_evaluated = 0
total_bet = 0
total_won = 0
total_profit = 0.0
skipped_count = 0
for i, row in enumerate(rows):
match_id = str(row['id'])
home = row['home_team'] or "?"
away = row['away_team'] or "?"
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
total_evaluated += 1
# print(f"[{i+1}] {home} vs {away} ... ", end="", flush=True)
try:
pred = orchestrator.analyze_match(match_id)
if not pred:
# print("⚠️ No Data")
continue
# ─── ADAPTIVE PICKING ───
# Check ALL recommendations (Expert or Standard) to find the BEST option
candidates = []
# Add main picks
if pred.get("expert_recommendation"):
rec = pred["expert_recommendation"]
if rec.get("main_pick"): candidates.append(rec["main_pick"])
if rec.get("safe_alternative"): candidates.append(rec["safe_alternative"])
if rec.get("value_picks"): candidates.extend(rec["value_picks"])
elif pred.get("main_pick"):
candidates.append(pred["main_pick"])
best_bet = None
for c in candidates:
if not c: continue
conf = c.get("confidence", 0)
odds = c.get("odds", 0)
pick = c.get("pick")
# Flexible Criteria:
# 1. Confidence > 60%
# 2. Odds > 1.10 (Not "free" odds like 1.00)
# 3. Edge > -2% (Slightly tolerant)
if conf >= 60 and odds > 1.10:
implied = 1.0 / odds
edge = ((conf/100) - implied) * 100
# Prioritize positive edge, but accept small negative if confidence is high
if edge > -2.0:
if best_bet is None or (conf > best_bet.get("confidence", 0)):
best_bet = c
if best_bet:
pick = str(best_bet.get("pick")).upper()
conf = best_bet.get("confidence")
odds = best_bet.get("odds")
# Resolution Logic
won = False
if pick in ["1", "MS 1", "İY 1"] and h_score > a_score: won = True
elif pick in ["X", "MS X", "İY X"] and h_score == a_score: won = True
elif pick in ["2", "MS 2", "İY 2"] and a_score > h_score: won = True
elif pick in ["1X", "X2"]:
if "1X" in pick and h_score >= a_score: won = True
elif "X2" in pick and a_score >= h_score: won = True
elif pick == "12" and h_score != a_score: won = True
elif "ÜST" in pick or "OVER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick or "UNDER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
total_bet += 1
if won:
total_won += 1
profit = odds - 1.0
total_profit += profit
# print(f"✅ WON (+{profit:.2f}) | {pick}")
else:
total_profit -= 1.0
# print(f"❌ LOST ({pick} @ {odds:.2f})")
else:
skipped_count += 1
# print(f"🚫 SKIP (No Value)")
except Exception as e:
# print(f"💥 Error: {e}")
pass
print("\n" + "="*60)
print("🔄 ADAPTIVE BACKTEST RESULTS (500 Matches)")
print("="*60)
print(f"📊 Evaluated: {total_evaluated}")
print(f"🎲 Played: {total_bet}")
print(f"🚫 Skipped: {skipped_count}")
print(f"✅ Won: {total_won}")
if total_bet > 0:
win_rate = (total_won / total_bet) * 100
roi = (total_profit / total_bet) * 100
print(f"📈 Win Rate: {win_rate:.2f}%")
print(f"💰 Total Profit: {total_profit:.2f} Units")
print(f"📊 ROI: {roi:.2f}%")
if total_profit > 0: print("🟢 KARLI STRATEJİ")
else: print("🔴 ZARARDA")
else:
print("⚠️ Hiç bahis oynanmadı. Veri kalitesi çok düşük.")
cur.close()
conn.close()
if __name__ == "__main__":
run_adaptive_backtest()
ai-engine/scripts/backtest_diagnostic.py
-145
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@@ -1,145 +0,0 @@
"""
Diagnostic Backtest - Hangi Pazar Kanıyor?
===========================================
Analyses the 500 matches to see WHICH markets are losing money.
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
from collections import defaultdict
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_diagnostic():
print("🔍 TANI BACKTESTİ: NEREDE KAYBETTİK?")
print("="*60)
leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
league_ids = tuple(str(lid) for lid in top_leagues)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away, m.league_id,
t1.name as home_team, t2.name as away_team
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
ORDER BY m.mst_utc DESC
LIMIT 500
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç analiz ediliyor...\n")
try: orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Hatası: {e}")
return
# Market Stats: { "MS": {"won": 10, "lost": 20, "profit": -5.0}, ... }
market_stats = defaultdict(lambda: {"won": 0, "lost": 0, "profit": 0.0, "total": 0})
for i, row in enumerate(rows):
match_id = str(row['id'])
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
try:
pred = orchestrator.analyze_match(match_id)
if not pred: continue
candidates = []
if pred.get("expert_recommendation"):
rec = pred["expert_recommendation"]
if rec.get("main_pick"): candidates.append(rec["main_pick"])
if rec.get("value_picks"): candidates.extend(rec["value_picks"])
elif pred.get("main_pick"):
candidates.append(pred["main_pick"])
played_this = False
for c in candidates:
if not c: continue
conf = c.get("confidence", 0)
odds = c.get("odds", 0)
pick = str(c.get("pick")).upper()
market_type = c.get("market_type", "Unknown")
# Criteria
if conf >= 60 and odds > 1.10:
implied = 1.0 / odds
edge = ((conf/100) - implied) * 100
if edge > -2.0:
# Resolve
won = False
if pick in ["1", "MS 1"] and h_score > a_score: won = True
elif pick in ["X", "MS X"] and h_score == a_score: won = True
elif pick in ["2", "MS 2"] and a_score > h_score: won = True
elif pick in ["1X", "X2"]:
if "1X" in pick and h_score >= a_score: won = True
elif "X2" in pick and a_score >= h_score: won = True
elif pick == "12" and h_score != a_score: won = True
elif "ÜST" in pick or "OVER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick or "UNDER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
market_stats[market_type]["total"] += 1
if won:
market_stats[market_type]["won"] += 1
market_stats[market_type]["profit"] += (odds - 1.0)
else:
market_stats[market_type]["lost"] += 1
market_stats[market_type]["profit"] -= 1.0
played_this = True
break # Only one bet per match
except: pass
# Print Results
print("\n" + "="*60)
print("📊 PAZAR BAZLI KAR/ZARAR TABLOSU")
print("="*60)
print(f"{'Market':<15} {'Oynanan':<10} {'Kazanılan':<10} {'Win%':<8} {'Kâr':<10}")
print("-" * 60)
for mkt, stats in sorted(market_stats.items(), key=lambda x: x[1]["profit"], reverse=True):
wr = (stats["won"] / stats["total"] * 100) if stats["total"] > 0 else 0
print(f"{mkt:<15} {stats['total']:<10} {stats['won']:<10} {wr:.1f}% {stats['profit']:+.2f} Units")
cur.close()
conn.close()
if __name__ == "__main__":
run_diagnostic()
ai-engine/scripts/backtest_real.py
-223
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@@ -1,223 +0,0 @@
"""
Real AI Engine Backtest Script
==============================
Uses the ACTUAL models (V20/V25 Ensemble) to predict historical matches.
Usage:
python ai-engine/scripts/backtest_real.py
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
from datetime import datetime
# Add paths
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
# Fix for Windows path issues in scripts
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR) # One level up if inside scripts folder
from services.single_match_orchestrator import get_single_match_orchestrator, MatchData
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_backtest():
print("🚀 REAL AI BACKTEST: Sept 13, 2024 - Top Leagues")
print("🧠 Engine: V30 Ensemble (V20+V25)")
print("="*60)
# Load Top Leagues
leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
try:
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
league_ids = tuple(str(lid) for lid in top_leagues)
print(f"📋 Loaded {len(top_leagues)} top leagues.")
except Exception as e:
print(f"❌ Error loading top_leagues.json: {e}")
return
# Date Range (Sept 13, 2024)
start_dt = datetime(2024, 9, 13, 0, 0, 0)
end_dt = datetime(2024, 9, 13, 23, 59, 59)
start_ts = int(start_dt.timestamp() * 1000)
end_ts = int(end_dt.timestamp() * 1000)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
# Fetch Matches
cur.execute("""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.mst_utc, m.league_id, m.status, m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team,
l.name as league_name
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
LEFT JOIN leagues l ON m.league_id = l.id
WHERE m.mst_utc BETWEEN %s AND %s
AND m.league_id IN %s
AND m.status = 'FT'
ORDER BY m.mst_utc ASC
LIMIT 20 -- Limit to 20 matches to avoid running for hours on a single backtest
""", (start_ts, end_ts, league_ids))
rows = cur.fetchall()
print(f"📊 Found {len(rows)} finished matches. Starting AI Analysis...")
if not rows:
print("⚠️ No matches found for this date.")
cur.close()
conn.close()
return
# Initialize AI Engine
try:
orchestrator = get_single_match_orchestrator()
print("✅ AI Engine (SingleMatchOrchestrator) Loaded.")
except Exception as e:
print(f"❌ Failed to load AI Engine: {e}")
print("💡 Make sure models are trained/present in ai-engine/models/")
cur.close()
conn.close()
return
# ─── Backtest Loop ───
total_matches_analyzed = 0
bets_skipped = 0
bets_played = 0
bets_won = 0
total_profit = 0.0
# Thresholds matching the NEW Skip Logic
MIN_CONF = 45.0
start_time = time.time()
for i, row in enumerate(rows):
match_id = str(row['id'])
home_team = row['home_team']
away_team = row['away_team']
home_score = row['score_home']
away_score = row['score_away']
print(f"\n[{i+1}/{len(rows)}] Analyzing: {home_team} vs {away_team} ...")
try:
# 1. AI PREDICTION (Actual Model Call)
prediction = orchestrator.analyze_match(match_id)
if not prediction:
print(f" ⚠️ AI returned no prediction.")
continue
total_matches_analyzed += 1
# 2. Extract Main Pick
main_pick = prediction.get("main_pick") or {}
pick_name = main_pick.get("pick")
confidence = main_pick.get("confidence", 0)
odds = main_pick.get("odds", 0)
if not pick_name or not confidence:
print(f" ⚠️ No main pick found in prediction.")
continue
print(f" 🤖 Pick: {pick_name} | Conf: {confidence}% | Odds: {odds}")
# 3. Apply Skip Logic (New Backtest Logic)
if confidence < MIN_CONF:
print(f" 🚫 SKIPPED (Confidence {confidence}% < {MIN_CONF}%)")
bets_skipped += 1
continue
if odds > 0:
implied_prob = 1.0 / odds
my_prob = confidence / 100.0
if my_prob - implied_prob < -0.03: # Negative edge
print(f" 🚫 SKIPPED (Negative Edge)")
bets_skipped += 1
continue
# 4. Bet Played
bets_played += 1
print(f" 🎲 BET PLAYED: {pick_name} @ {odds}")
# 5. Resolve Bet
won = False
# Basic resolution logic (Need to parse pick_name like "1", "X", "2", "2.5 Üst", etc.)
pick_clean = str(pick_name).upper()
# MS
if pick_clean in ["1", "MS 1"] and home_score > away_score: won = True
elif pick_clean in ["X", "MS X"] and home_score == away_score: won = True
elif pick_clean in ["2", "MS 2"] and away_score > home_score: won = True
# OU25
elif "ÜST" in pick_clean or "OVER" in pick_clean:
if (home_score + away_score) > 2.5: won = True
elif "ALT" in pick_clean or "UNDER" in pick_clean:
if (home_score + away_score) < 2.5: won = True
# BTTS
elif "VAR" in pick_clean and home_score > 0 and away_score > 0: won = True
elif "YOK" in pick_clean and (home_score == 0 or away_score == 0): won = True
if won:
bets_won += 1
profit = odds - 1.0
print(f" ✅ WON! (+{profit:.2f} units)")
else:
profit = -1.0
print(f" ❌ LOST! (-1.00 units)")
total_profit += profit
except Exception as e:
print(f" 💥 Error during analysis: {e}")
elapsed = time.time() - start_time
# ─── FINAL REPORT ───
print("\n" + "="*60)
print("📈 REAL AI BACKTEST RESULTS")
print(f"🕒 Time taken: {elapsed:.1f} seconds")
print("="*60)
print(f"📊 Matches Analyzed: {total_matches_analyzed}")
print(f"🚫 Bets SKIPPED: {bets_skipped}")
print(f"✅ Bets PLAYED: {bets_played}")
if bets_played > 0:
win_rate = (bets_won / bets_played) * 100
roi = (total_profit / bets_played) * 100
yield_val = total_profit # Net Units
print(f"🏆 Bets Won: {bets_won}")
print(f"💀 Bets Lost: {bets_played - bets_won}")
print("-" * 40)
print(f" Win Rate: {win_rate:.2f}%")
print(f"💰 Total Profit (Units): {total_profit:.2f}")
print(f"📊 ROI: {roi:.2f}%")
if roi > 0:
print("🟢 STRATEGY IS PROFITABLE!")
else:
print("🔴 STRATEGY IS LOSING")
else:
print("⚠️ No bets were played. All were skipped or failed.")
cur.close()
conn.close()
if __name__ == "__main__":
run_backtest()
ai-engine/scripts/backtest_roi.py
-231
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@@ -1,231 +0,0 @@
"""
Backtest ROI Engine
===================
Simulates the NEW "Skip Logic" on historical predictions.
Answers: "What if we only played the bets the model was confident about?"
Usage:
python ai-engine/scripts/backtest_roi.py
"""
import os
import sys
import json
import psycopg2
from psycopg2.extras import RealDictCursor
from typing import Dict, List, Any
from dotenv import load_dotenv
# Load .env from project root (2 levels up from this script)
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
load_dotenv(os.path.join(project_root, ".env"))
def get_clean_dsn() -> str:
"""Return a psycopg2-compatible DSN from DATABASE_URL."""
# HARDCODED FOR BACKTEST (Bypassing dotenv issues)
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
# ─── Configuration (Matching the NEW BetRecommender Logic) ─────────
# Minimum confidence to even consider a bet (Hard Gate)
MIN_CONF_THRESHOLDS = {
"MS": 45.0,
"DC": 40.0,
"OU15": 50.0,
"OU25": 45.0,
"OU35": 45.0,
"BTTS": 45.0,
"HT": 40.0,
}
def get_market_type_from_key(key: str) -> str:
"""Map prediction keys to market types for thresholding."""
if key.startswith("ms_") or key in ["1", "X", "2"]: return "MS"
if key.startswith("dc_") or key in ["1X", "X2", "12"]: return "DC"
if key.startswith("ou15_") or key.startswith("1.5"): return "OU15"
if key.startswith("ou25_") or key.startswith("2.5"): return "OU25"
if key.startswith("ou35_") or key.startswith("3.5"): return "OU35"
if key.startswith("btts_") or key in ["Var", "Yok"]: return "BTTS"
if key.startswith("ht_") or key.startswith("İY"): return "HT"
return "MS"
def simulate_backtest():
print("🚀 Starting Backtest with NEW 'Skip Logic'...")
print("="*60)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
# 1. Fetch PREDICTIONS that have a confidence score
# We limit to last 1000 finished matches to keep it fast but representative
cur.execute("""
SELECT p.match_id, p.prediction_json,
m.score_home, m.score_away, m.status
FROM predictions p
JOIN matches m ON p.match_id = m.id
WHERE m.status = 'FT'
AND p.prediction_json IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT 2000
""")
predictions = cur.fetchall()
print(f"📊 Loaded {len(predictions)} historical predictions.")
total_bets = 0
winning_bets = 0
skipped_bets = 0
total_profit = 0.0 # Assuming unit stake of 1.0
# 2. Process each prediction
for pred_row in predictions:
match_id = pred_row['match_id']
data = pred_row['prediction_json']
if isinstance(data, str):
data = json.loads(data)
# Real result
home_score = pred_row['score_home'] or 0
away_score = pred_row['score_away'] or 0
total_goals = home_score + away_score
# Extract prediction details from the JSON structure
# The structure varies, but usually contains 'main_pick', 'bet_summary', or 'market_board'
# Try to get the main pick recommendation
main_pick = None
main_pick_conf = 0.0
main_pick_odds = 0.0
# Navigate the V20+ JSON structure
market_board = data.get("market_board", {})
# Check Main Pick
if "main_pick" in data:
mp = data["main_pick"]
if isinstance(mp, dict):
main_pick = mp.get("pick")
main_pick_conf = mp.get("confidence", 0.0)
main_pick_odds = mp.get("odds", 0.0)
# If no main pick, try bet_summary
if not main_pick and "bet_summary" in data:
summary = data["bet_summary"]
if isinstance(summary, list) and len(summary) > 0:
# Take the highest confidence one
best = max(summary, key=lambda x: x.get("confidence", 0))
main_pick = best.get("pick")
main_pick_conf = best.get("confidence", 0.0)
main_pick_odds = best.get("odds", 0.0)
if not main_pick or not main_pick_conf:
continue
# ─── NEW LOGIC: APPLY FILTERS ───
# 1. Determine Market Type
# Simple heuristic based on pick string
pick_str = str(main_pick).upper()
market_type = "MS"
if "1X" in pick_str or "X2" in pick_str or "12" in pick_str: market_type = "DC"
elif "ÜST" in pick_str or "ALT" in pick_str or "OVER" in pick_str or "UNDER" in pick_str:
if "1.5" in pick_str: market_type = "OU15"
elif "3.5" in pick_str: market_type = "OU35"
else: market_type = "OU25"
elif "VAR" in pick_str or "YOK" in pick_str or "BTTS" in pick_str: market_type = "BTTS"
threshold = MIN_CONF_THRESHOLDS.get(market_type, 45.0)
# 2. Check Confidence Gate
if main_pick_conf < threshold:
skipped_bets += 1
continue
# 3. Check Value Gate (Edge)
if main_pick_odds > 0:
implied_prob = 1.0 / main_pick_odds
my_prob = main_pick_conf / 100.0
edge = my_prob - implied_prob
if edge < -0.03: # Negative value
skipped_bets += 1
continue
# ─── BET IS PLAYED ───
total_bets += 1
# Determine if WON
is_won = False
# Resolve MS (1, X, 2)
if market_type == "MS":
if main_pick == "1" and home_score > away_score: is_won = True
elif main_pick == "X" and home_score == away_score: is_won = True
elif main_pick == "2" and away_score > home_score: is_won = True
elif main_pick == "MS 1" and home_score > away_score: is_won = True
elif main_pick == "MS X" and home_score == away_score: is_won = True
elif main_pick == "MS 2" and away_score > home_score: is_won = True
# Resolve OU (Over/Under)
elif market_type.startswith("OU"):
line = 2.5
if "1.5" in pick_str: line = 1.5
elif "3.5" in pick_str: line = 3.5
is_over = total_goals > line
is_under = total_goals < line # Simplification (usually line is X.5 so no draw)
if "ÜST" in pick_str or "OVER" in pick_str:
if is_over: is_won = True
elif "ALT" in pick_str or "UNDER" in pick_str:
if is_under: is_won = True
# Resolve BTTS
elif market_type == "BTTS":
if home_score > 0 and away_score > 0:
if "VAR" in pick_str: is_won = True
else:
if "YOK" in pick_str: is_won = True
# Resolve DC (Double Chance) - Simplified
elif market_type == "DC":
if "1X" in pick_str and (home_score >= away_score): is_won = True
elif "X2" in pick_str and (away_score >= home_score): is_won = True
elif "12" in pick_str and (home_score != away_score): is_won = True
if is_won:
winning_bets += 1
profit = main_pick_odds - 1.0
total_profit += profit
else:
total_profit -= 1.0
# ─── REPORT ───
print("\n" + "="*60)
print("📈 BACKTEST RESULTS (With NEW Skip Logic)")
print("="*60)
print(f"Total Historical Matches Analyzed: {len(predictions)}")
print(f"🚫 Bets SKIPPED (Low Conf/Bad Value): {skipped_bets}")
print(f"✅ Bets PLAYED: {total_bets}")
if total_bets > 0:
win_rate = (winning_bets / total_bets) * 100
roi = (total_profit / total_bets) * 100
print(f"🏆 Winning Bets: {winning_bets}")
print(f"💀 Losing Bets: {total_bets - winning_bets}")
print("-" * 40)
print(f" Win Rate: {win_rate:.2f}%")
print(f"💰 Total Profit (Units): {total_profit:.2f}")
print(f"📊 ROI: {roi:.2f}%")
if roi > 0:
print("🟢 STRATEGY IS PROFITABLE!")
else:
print("🔴 STRATEGY IS LOSING (Adjust thresholds!)")
else:
print("⚠️ No bets were played. Thresholds might be too high.")
cur.close()
conn.close()
if __name__ == "__main__":
simulate_backtest()
ai-engine/scripts/backtest_sniper.py
-164
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@@ -1,164 +0,0 @@
"""
SNIPER Backtest
===============
Sadece en yüksek güvenilirlik ve değere sahip bahisleri oynar.
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
from datetime import datetime
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
MATCH_IDS = [
"v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
"7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
"7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
"7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
"7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
"7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
"7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
"7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
"7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
"lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
"40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
"2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
"7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
"coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
"9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
"6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
"1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
]
def run_sniper_backtest():
print("🎯 SNIPER BACKTEST: SADECE NET OLANLAR")
print("="*60)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
placeholders = ','.join(['%s'] * len(MATCH_IDS))
cur.execute(f"""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team,
l.name as league_name
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
LEFT JOIN leagues l ON m.league_id = l.id
WHERE m.id IN ({placeholders}) AND m.status = 'FT'
""", MATCH_IDS)
rows = cur.fetchall()
print(f"📊 Analiz edilecek {len(rows)} maç var.\n")
try:
orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Hatası: {e}")
return
total_bet = 0
total_won = 0
total_profit = 0.0
for i, row in enumerate(rows):
match_id = str(row['id'])
home = row['home_team'] or "?"
away = row['away_team'] or "?"
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
print(f"[{i+1}/{len(rows)}] {home} vs {away} ... ", end="", flush=True)
try:
pred = orchestrator.analyze_match(match_id)
if not pred:
print("⚠️ Veri Yok")
continue
pick_data = pred.get("expert_recommendation", {}).get("main_pick") or pred.get("main_pick", {})
pick = pick_data.get("pick") or pick_data.get("market_type")
conf = pick_data.get("confidence", 0)
odds = pick_data.get("odds", 0)
# SNIPER FİLTRELERİ
if conf < 75:
print(f"🚫 PASS (Conf: {conf:.0f}%)")
continue
if odds < 1.35:
print(f"🚫 PASS (Odds: {odds:.2f} çok düşük)")
continue
# Value Control
implied = 1.0 / odds
if (conf/100) < implied:
print(f"🚫 PASS (Negatif Value)")
continue
# OYNA
total_bet += 1
won = False
pick_clean = str(pick).upper()
if pick_clean in ["1", "MS 1"] and h_score > a_score: won = True
elif pick_clean in ["X", "MS X"] and h_score == a_score: won = True
elif pick_clean in ["2", "MS 2"] and a_score > h_score: won = True
elif "ÜST" in pick_clean or "OVER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
elif "3.5" in pick_clean: line = 3.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick_clean or "UNDER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
elif "3.5" in pick_clean: line = 3.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick_clean and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick_clean and (h_score == 0 or a_score == 0): won = True
if won:
total_won += 1
profit = odds - 1.0
total_profit += profit
print(f"✅ WON! (+{profit:.2f})")
else:
total_profit -= 1.0
print(f"❌ LOST! ({pick} @ {odds:.2f})")
except Exception as e:
print(f"💥 Hata: {e}")
print("\n" + "="*60)
print("🎯 SNIPER SONUÇLARI")
print("="*60)
print(f"Oynanan: {total_bet}")
print(f"Kazanılan: {total_won}")
print(f"Kazanma Oranı: %{(total_won/total_bet)*100:.1f}" if total_bet > 0 else "Kazanma Oranı: N/A")
print(f"Toplam Kâr: {total_profit:.2f} Units")
if total_profit > 0:
print("🟢 PARA KAZANDIK!")
else:
print("🔴 PARA KAYBETTİK!")
cur.close()
conn.close()
if __name__ == "__main__":
run_sniper_backtest()
ai-engine/scripts/backtest_strict.py
-162
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"""
Strict Sniper Backtest (Calibrated)
===================================
Sadece Güven > %75 ve Oran > 1.30 olan bahisleri oynar.
Modelin şişirilmiş özgüvenini elemek için yapıldı.
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_strict_backtest():
print("🎯 STRICT SNIPER BACKTEST (Conf > 75%)")
print("="*60)
leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
league_ids = tuple(str(lid) for lid in top_leagues)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
ORDER BY m.mst_utc DESC
LIMIT 500
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç taranıyor. Sadece NET OLANLAR oynanacak...\n")
try: orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Hatası: {e}")
return
total_bet = 0
total_won = 0
total_profit = 0.0
for i, row in enumerate(rows):
match_id = str(row['id'])
home = row['home_team'] or "?"
away = row['away_team'] or "?"
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
try:
pred = orchestrator.analyze_match(match_id)
if not pred: continue
# Check all picks for a HIGH CONFIDENCE bet
candidates = []
if pred.get("expert_recommendation"):
rec = pred["expert_recommendation"]
if rec.get("main_pick"): candidates.append(rec["main_pick"])
if rec.get("value_picks"): candidates.extend(rec["value_picks"])
elif pred.get("main_pick"):
candidates.append(pred["main_pick"])
best_bet = None
for c in candidates:
if not c: continue
# Access attributes safely (Dict or Object)
conf = c.get("confidence", 0) if isinstance(c, dict) else getattr(c, 'confidence', 0)
odds = c.get("odds", 0) if isinstance(c, dict) else getattr(c, 'odds', 0)
pick = c.get("pick", "") if isinstance(c, dict) else getattr(c, 'pick', "")
# STRICT CRITERIA
if conf >= 75.0 and odds >= 1.30:
# Check Value (Edge)
implied = 1.0 / odds
edge = ((conf/100) - implied) * 100
if edge > -5.0: # Tolerant edge
if best_bet is None or (conf > (best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0))):
best_bet = c
if best_bet:
pick = str(best_bet.get("pick") if isinstance(best_bet, dict) else getattr(best_bet, 'pick', "")).upper()
conf = best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0)
odds = best_bet.get("odds", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'odds', 0)
# Resolution
won = False
if pick in ["1", "MS 1"] and h_score > a_score: won = True
elif pick in ["X", "MS X"] and h_score == a_score: won = True
elif pick in ["2", "MS 2"] and a_score > h_score: won = True
elif pick in ["1X", "X2"]:
if "1X" in pick and h_score >= a_score: won = True
elif "X2" in pick and a_score >= h_score: won = True
elif "ÜST" in pick or "OVER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick or "UNDER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
total_bet += 1
if won:
total_won += 1
profit = odds - 1.0
total_profit += profit
print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({conf:.0f}%) -> WON (+{profit:.2f})")
else:
total_profit -= 1.0
print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({conf:.0f}%) -> LOST")
except Exception as e:
pass
print("\n" + "="*60)
print("🎯 STRICT SNIPER SONUÇLARI")
print("="*60)
print(f"Oynanan Bahis: {total_bet}")
print(f"Kazanılan: {total_won}")
if total_bet > 0:
win_rate = (total_won / total_bet) * 100
roi = (total_profit / total_bet) * 100
print(f"Kazanma Oranı: %{win_rate:.2f}")
print(f"Toplam Kâr: {total_profit:.2f} Units")
if total_profit > 0: print("🟢 PARA KAZANDIK!")
else: print("🔴 PARA KAYBETTİK!")
else:
print("⚠️ Yeteri kadar NET maç bulunamadı.")
cur.close()
conn.close()
if __name__ == "__main__":
run_strict_backtest()
ai-engine/scripts/backtest_v2_runtime.py
-230
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@@ -1,230 +0,0 @@
"""
Backtest the live V2 predictor stack against recent finished football matches.
This script uses the same path as production:
database -> feature extractor -> betting predictor -> quant ranking.
"""
from __future__ import annotations
import argparse
import asyncio
import sys
from dataclasses import dataclass
from pathlib import Path
from sqlalchemy import text
ROOT_DIR = Path(__file__).resolve().parents[1]
if str(ROOT_DIR) not in sys.path:
sys.path.insert(0, str(ROOT_DIR))
from core.quant import MarketPick, analyze_market
from data.database import dispose_engine, get_session
from features.extractor import extract_features
from models.betting_engine import get_predictor
@dataclass
class BacktestStats:
sampled_matches: int = 0
analyzed_matches: int = 0
skipped_matches: int = 0
ms_correct: int = 0
ou25_correct: int = 0
btts_correct: int = 0
main_pick_count: int = 0
main_pick_correct: int = 0
playable_pick_count: int = 0
playable_pick_correct: int = 0
playable_units_staked: float = 0.0
playable_units_profit: float = 0.0
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--limit", type=int, default=50)
parser.add_argument("--days", type=int, default=45)
return parser.parse_args()
def _actual_ms(score_home: int, score_away: int) -> str:
if score_home > score_away:
return "1"
if score_home < score_away:
return "2"
return "X"
def _actual_ou25(score_home: int, score_away: int) -> str:
return "Over" if (score_home + score_away) > 2 else "Under"
def _actual_btts(score_home: int, score_away: int) -> str:
return "Yes" if score_home > 0 and score_away > 0 else "No"
def _odds_map_from_features(feats) -> dict[str, dict[str, float]]:
return {
"MS": {"1": feats.odds_home, "X": feats.odds_draw, "2": feats.odds_away},
"OU25": {"Under": feats.odds_under25, "Over": feats.odds_over25},
"BTTS": {"No": feats.odds_btts_no, "Yes": feats.odds_btts_yes},
}
def _best_pick(feats, all_probs: dict[str, dict[str, float]]) -> MarketPick | None:
odds_map = _odds_map_from_features(feats)
picks = [
analyze_market("MS", all_probs["MS"], odds_map["MS"], feats.data_quality_score),
analyze_market("OU25", all_probs["OU25"], odds_map["OU25"], feats.data_quality_score),
analyze_market("BTTS", all_probs["BTTS"], odds_map["BTTS"], feats.data_quality_score),
]
ranked = sorted(
[pick for pick in picks if pick.pick],
key=lambda pick: pick.play_score,
reverse=True,
)
return ranked[0] if ranked else None
def _pick_won(pick: MarketPick, actuals: dict[str, str]) -> bool:
return actuals.get(pick.market) == pick.pick
async def _load_match_rows(limit: int, days: int) -> list[dict[str, object]]:
min_mst_utc = days * 86400000
query = text("""
SELECT
m.id,
m.match_name,
m.score_home,
m.score_away,
m.mst_utc
FROM matches m
WHERE m.sport = 'football'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
AND m.mst_utc >= (
EXTRACT(EPOCH FROM NOW()) * 1000 - :min_mst_utc
)
AND EXISTS (
SELECT 1
FROM odd_categories oc
WHERE oc.match_id = m.id
AND oc.name IN ('Maç Sonucu', '2,5 Alt/Üst', 'Karşılıklı Gol')
)
ORDER BY m.mst_utc DESC
LIMIT :limit
""")
async with get_session() as session:
result = await session.execute(
query,
{"limit": limit, "min_mst_utc": min_mst_utc},
)
rows = result.mappings().all()
return [dict(row) for row in rows]
async def _run(limit: int, days: int) -> BacktestStats:
stats = BacktestStats()
predictor = get_predictor()
rows = await _load_match_rows(limit, days)
stats.sampled_matches = len(rows)
async with get_session() as session:
for row in rows:
match_id = str(row["id"])
score_home = int(row["score_home"])
score_away = int(row["score_away"])
feats = await extract_features(session, match_id)
if feats is None:
stats.skipped_matches += 1
continue
if feats.data_quality_score <= 0.0:
stats.skipped_matches += 1
continue
all_probs = predictor.predict_all(feats.to_model_array(), feats)
stats.analyzed_matches += 1
actuals = {
"MS": _actual_ms(score_home, score_away),
"OU25": _actual_ou25(score_home, score_away),
"BTTS": _actual_btts(score_home, score_away),
}
if max(all_probs["MS"], key=all_probs["MS"].get) == actuals["MS"]:
stats.ms_correct += 1
if max(all_probs["OU25"], key=all_probs["OU25"].get) == actuals["OU25"]:
stats.ou25_correct += 1
if max(all_probs["BTTS"], key=all_probs["BTTS"].get) == actuals["BTTS"]:
stats.btts_correct += 1
best_pick = _best_pick(feats, all_probs)
if best_pick is None:
continue
stats.main_pick_count += 1
if _pick_won(best_pick, actuals):
stats.main_pick_correct += 1
if best_pick.playable:
stats.playable_pick_count += 1
stats.playable_units_staked += best_pick.stake_units
if _pick_won(best_pick, actuals):
stats.playable_pick_correct += 1
stats.playable_units_profit += best_pick.stake_units * (best_pick.odds - 1.0)
else:
stats.playable_units_profit -= best_pick.stake_units
return stats
def _pct(numerator: int, denominator: int) -> float:
if denominator <= 0:
return 0.0
return round((numerator / denominator) * 100.0, 2)
def _roi(profit: float, staked: float) -> float:
if staked <= 0:
return 0.0
return round((profit / staked) * 100.0, 2)
def _print_summary(stats: BacktestStats) -> None:
print("=== V2 Runtime Backtest ===")
print(f"Sampled matches : {stats.sampled_matches}")
print(f"Analyzed matches : {stats.analyzed_matches}")
print(f"Skipped matches : {stats.skipped_matches}")
print(f"MS accuracy : {_pct(stats.ms_correct, stats.analyzed_matches)}%")
print(f"OU2.5 accuracy : {_pct(stats.ou25_correct, stats.analyzed_matches)}%")
print(f"BTTS accuracy : {_pct(stats.btts_correct, stats.analyzed_matches)}%")
print(
"Main pick accuracy : "
f"{_pct(stats.main_pick_correct, stats.main_pick_count)}% "
f"({stats.main_pick_correct}/{stats.main_pick_count})"
)
print(
"Playable accuracy : "
f"{_pct(stats.playable_pick_correct, stats.playable_pick_count)}% "
f"({stats.playable_pick_correct}/{stats.playable_pick_count})"
)
print(f"Units staked : {stats.playable_units_staked:.2f}")
print(f"Units profit : {stats.playable_units_profit:.2f}")
print(f"ROI : {_roi(stats.playable_units_profit, stats.playable_units_staked)}%")
async def _main() -> None:
args = _parse_args()
try:
stats = await _run(args.limit, args.days)
_print_summary(stats)
finally:
await dispose_engine()
if __name__ == "__main__":
asyncio.run(_main())
ai-engine/scripts/backtest_value_hunter.py
-147
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@@ -1,147 +0,0 @@
"""
Value Hunter Backtest
=====================
Sadece modelin büroyu yendiği (Pozitif Edge) maçları oynar.
"""
import os, sys, json, time, psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR): ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
MATCH_IDS = [
"v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
"7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
"7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
"7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
"7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
"7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
"7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
"7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
"7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
"lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
"40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
"2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
"7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
"coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
"9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
"6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
"1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
]
def run_value_hunter():
print("💎 VALUE HUNTER: SADECE HATALI ORANLARI YAKALA")
print("="*60)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
placeholders = ','.join(['%s'] * len(MATCH_IDS))
cur.execute(f"""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.id IN ({placeholders}) AND m.status = 'FT'
""", MATCH_IDS)
rows = cur.fetchall()
print(f"📊 {len(rows)} maç taranıyor...\n")
try: orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Hatası: {e}")
return
total_bet = 0
total_won = 0
total_profit = 0.0
total_edge_found = 0
for i, row in enumerate(rows):
match_id = str(row['id'])
home = row['home_team'] or "?"
away = row['away_team'] or "?"
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
try:
pred = orchestrator.analyze_match(match_id)
if not pred: continue
# Tüm önerileri kontrol et
picks = pred.get("expert_recommendation", {}).get("value_picks", [])
if not picks: picks = [pred.get("expert_recommendation", {}).get("main_pick")]
played_this_match = False
for pick_data in picks:
if not pick_data: continue
pick = pick_data.get("pick")
conf = pick_data.get("confidence", 0)
odds = pick_data.get("odds", 0)
edge = pick_data.get("edge", 0)
# VALUE KURALI: Model bürodan en az %10 daha iyi olmalı
if edge < 10: continue
if odds < 1.20: continue
total_bet += 1
total_edge_found += edge
won = False
pick_clean = str(pick).upper()
if pick_clean in ["1", "MS 1"] and h_score > a_score: won = True
elif pick_clean in ["X", "MS X"] and h_score == a_score: won = True
elif pick_clean in ["2", "MS 2"] and a_score > h_score: won = True
elif "ÜST" in pick_clean or "OVER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick_clean or "UNDER" in pick_clean:
line = 2.5
if "1.5" in pick_clean: line = 1.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick_clean and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick_clean and (h_score == 0 or a_score == 0): won = True
if won:
total_won += 1
profit = odds - 1.0
total_profit += profit
print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({edge:.0f}% Edge) -> WON! (+{profit:.2f})")
else:
total_profit -= 1.0
print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({edge:.0f}% Edge) -> LOST")
played_this_match = True
break # Maç başına tek bahis
except Exception: pass
print("\n" + "="*60)
print("💎 VALUE HUNTER SONUÇLARI")
print("="*60)
print(f"Toplam Value Bulunan Bahis: {total_bet}")
print(f"Ortalama Edge: {total_edge_found/total_bet:.1f}%" if total_bet > 0 else "N/A")
print(f"Kazanılan: {total_won}")
print(f"Toplam Kâr: {total_profit:.2f} Units")
if total_profit > 0: print("🟢 PARA KAZANDIK!")
else: print("🔴 PARA KAYBETTİK!")
cur.close()
conn.close()
if __name__ == "__main__":
run_value_hunter()
ai-engine/scripts/backtest_value_sniper.py
-153
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"""
Value Sniper Backtest (High Odds)
=================================
Sadece Oran > 1.50 ve Güven > %70 olan bahisleri oynar.
"""
import os
import sys
import json
import time
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
sys.path.insert(0, ROOT_DIR)
if "scripts" in os.path.basename(AI_DIR):
ROOT_DIR = os.path.dirname(ROOT_DIR)
from services.single_match_orchestrator import get_single_match_orchestrator
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_value_sniper():
print("💰 VALUE SNIPER BACKTEST (Odds > 1.50)")
print("="*60)
leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
with open(leagues_path, 'r') as f:
top_leagues = json.load(f)
league_ids = tuple(str(lid) for lid in top_leagues)
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
ORDER BY m.mst_utc DESC
LIMIT 500
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç taranıyor...\n")
try: orchestrator = get_single_match_orchestrator()
except Exception as e:
print(f"❌ AI Hatası: {e}")
return
total_bet = 0
total_won = 0
total_profit = 0.0
for i, row in enumerate(rows):
match_id = str(row['id'])
home = row['home_team'] or "?"
away = row['away_team'] or "?"
h_score = row['score_home'] or 0
a_score = row['score_away'] or 0
try:
pred = orchestrator.analyze_match(match_id)
if not pred: continue
candidates = []
if pred.get("expert_recommendation"):
rec = pred["expert_recommendation"]
if rec.get("main_pick"): candidates.append(rec["main_pick"])
if rec.get("value_picks"): candidates.extend(rec["value_picks"])
elif pred.get("main_pick"):
candidates.append(pred["main_pick"])
best_bet = None
for c in candidates:
if not c: continue
conf = c.get("confidence", 0) if isinstance(c, dict) else getattr(c, 'confidence', 0)
odds = c.get("odds", 0) if isinstance(c, dict) else getattr(c, 'odds', 0)
# VALUE CRITERIA: Odds > 1.50 AND Conf > 70%
if conf >= 70.0 and odds >= 1.50:
# Check Edge
implied = 1.0 / odds
edge = ((conf/100) - implied) * 100
if edge > 0: # Must be positive value
if best_bet is None or (conf > (best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0))):
best_bet = c
if best_bet:
pick = str(best_bet.get("pick") if isinstance(best_bet, dict) else getattr(best_bet, 'pick', "")).upper()
conf = best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0)
odds = best_bet.get("odds", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'odds', 0)
won = False
if pick in ["1", "MS 1"] and h_score > a_score: won = True
elif pick in ["X", "MS X"] and h_score == a_score: won = True
elif pick in ["2", "MS 2"] and a_score > h_score: won = True
elif "ÜST" in pick or "OVER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) > line: won = True
elif "ALT" in pick or "UNDER" in pick:
line = 2.5
if "1.5" in pick: line = 1.5
elif "3.5" in pick: line = 3.5
if (h_score + a_score) < line: won = True
elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
total_bet += 1
if won:
total_won += 1
profit = odds - 1.0
total_profit += profit
print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({odds:.2f}) -> WON (+{profit:.2f})")
else:
total_profit -= 1.0
print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({odds:.2f}) -> LOST")
except: pass
print("\n" + "="*60)
print("💰 VALUE SNIPER SONUÇLARI")
print("="*60)
print(f"Oynanan Bahis: {total_bet}")
print(f"Kazanılan: {total_won}")
if total_bet > 0:
win_rate = (total_won / total_bet) * 100
roi = (total_profit / total_bet) * 100
print(f"Kazanma Oranı: %{win_rate:.2f}")
print(f"Toplam Kâr: {total_profit:.2f} Units")
if total_profit > 0: print("🟢 PARA KAZANDIK!")
else: print("🔴 PARA KAYBETTİK!")
else:
print("⚠️ Yeterli VALUE bulunamadı.")
cur.close()
conn.close()
if __name__ == "__main__":
run_value_sniper()
ai-engine/scripts/backtest_vqwen.py
-136
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@@ -1,136 +0,0 @@
"""
VQWEN Full Backtest
===================
Tests all 3 VQWEN models (MS, OU25, BTTS) on 1000 historical matches.
"""
import os
import sys
import json
import pickle
import pandas as pd
import numpy as np
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
PROJECT_ROOT = os.path.dirname(ROOT_DIR)
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_vqwen_backtest():
print("🧠 VQWEN FULL BACKTEST")
print("="*60)
# Load Models
mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
try:
with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
print("✅ VQWEN MS, OU25, BTTS modelleri yüklendi.")
except Exception as e:
print(f"❌ Model hatası: {e}")
return
with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
league_ids = tuple(str(lid) for lid in json.load(f))
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa,
COALESCE((SELECT AVG(CASE WHEN m2.home_team_id = m.home_team_id AND m2.score_home > m2.score_away THEN 3 WHEN m2.home_team_id = m.home_team_id AND m2.score_home = m2.score_away THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as h_form,
COALESCE((SELECT AVG(CASE WHEN m2.away_team_id = m.away_team_id AND m2.score_away > m2.score_home THEN 3 WHEN m2.away_team_id = m.away_team_id AND m2.score_away = m2.score_home THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as a_form,
COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_sc,
COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_co,
COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_sc,
COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_co
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT 1000
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç analiz ediliyor...")
results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
for row in rows:
oh, od, oa = float(row['oh'] or 0), float(row['od'] or 0), float(row['oa'] or 0)
if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
h_xg = (float(row['h_sc'] or 1.2) + float(row['a_co'] or 1.2)) / 2
a_xg = (float(row['a_sc'] or 1.2) + float(row['h_co'] or 1.2)) / 2
h_p = (float(row['h_form'] or 0)*10) + (float(row['h_sc'] or 1.2)*5) - (float(row['h_co'] or 1.2)*5)
a_p = (float(row['a_form'] or 0)*10) + (float(row['a_sc'] or 1.2)*5) - (float(row['a_co'] or 1.2)*5)
margin = (1/oh) + (1/od) + (1/oa)
# MS Prediction
f_ms = pd.DataFrame([{'h_form': float(row['h_form']), 'a_form': float(row['a_form']), 'h_xg': h_xg, 'a_xg': a_xg,
'pow_diff': h_p - a_p, 'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
'h_sot': 4.0, 'a_sot': 3.0}])
ms_probs = model_ms.predict(f_ms)[0]
# MS Value Bet
for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
if odd <= 1.0: continue
edge = prob - (1/odd)
if edge > 0.05 and prob > 0.50: # Value ve Güven
results['ms']['bet'] += 1
h, a = row['score_home'], row['score_away']
w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
else: results['ms']['profit'] -= 1.0
break
# OU2.5 Prediction
f_ou = pd.DataFrame([{'h_xg': h_xg, 'a_xg': a_xg, 'total_xg': h_xg+a_xg, 'h_sot': 4.0, 'a_sot': 3.0}])
p_over = model_ou.predict(f_ou)[0]
# OU2.5 Value Bet
if p_over > 0.55 and oh > 1.0: # Sadece örnek olarak over > %55 ise
results['ou25']['bet'] += 1
if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85 # Ortalama oran
else: results['ou25']['profit'] -= 1.0
# BTTS Prediction
f_btts = pd.DataFrame([{'h_xg': h_xg, 'a_xg': a_xg, 'h_sc': float(row['h_sc']), 'a_sc': float(row['a_sc'])}])
p_btts = model_btts.predict(f_btts)[0]
# BTTS Value Bet
if p_btts > 0.55:
results['btts']['bet'] += 1
if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
else: results['btts']['profit'] -= 1.0
print("\n" + "="*60)
print("📊 VQWEN PAZAR BAZLI SONUÇLAR")
print("="*60)
for mkt in ['ms', 'ou25', 'btts']:
r = results[mkt]
wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
print(f"{mkt.upper():<10} Oynanan: {r['bet']:<5} Kazanılan: {r['won']:<5} WR: {wr:.1f}% Kâr: {r['profit']:+.2f} Units")
total_profit = sum(r['profit'] for r in results.values())
print(f"\n💰 TOPLAM KÂR: {total_profit:+.2f} Units")
if total_profit > 0: print("🟢 PARA KAZANDIK!")
else: print("🔴 ZARARDA")
cur.close()
conn.close()
if __name__ == "__main__":
run_vqwen_backtest()
ai-engine/scripts/backtest_vqwen_deep.py
-141
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@@ -1,141 +0,0 @@
"""
VQWEN Deep Backtest
===================
Tests the NEW Deep model with player & card data.
"""
import os
import sys
import json
import pickle
import pandas as pd
import numpy as np
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
PROJECT_ROOT = os.path.dirname(ROOT_DIR)
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_vqwen_deep_backtest():
print("🧠 VQWEN DEEP BACKTEST")
print("="*60)
# Load Models
mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
try:
with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
print("✅ VQWEN Deep modelleri yüklendi.")
except Exception as e:
print(f"❌ Model hatası: {e}")
return
with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
league_ids = tuple(str(lid) for lid in json.load(f))
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
t1.name as home_team, t2.name as away_team,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa,
COALESCE((SELECT AVG(CASE WHEN m2.home_team_id = m.home_team_id AND m2.score_home > m2.score_away THEN 3 WHEN m2.home_team_id = m.home_team_id AND m2.score_home = m2.score_away THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as h_form,
COALESCE((SELECT AVG(CASE WHEN m2.away_team_id = m.away_team_id AND m2.score_away > m2.score_home THEN 3 WHEN m2.away_team_id = m.away_team_id AND m2.score_away = m2.score_home THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as a_form,
COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_sc,
COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_co,
COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_sc,
COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_co,
COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.home_team_id AND mp.is_starting = true), 0) as h_xi,
COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.away_team_id AND mp.is_starting = true), 0) as a_xi,
COALESCE((SELECT COUNT(*) FROM match_player_events mpe WHERE mpe.match_id = m.id AND mpe.event_type = 'card'), 0) as cards
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT 1000
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç analiz ediliyor...")
results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
for row in rows:
oh = float(row['oh'] or 0)
od = float(row['od'] or 0)
oa = float(row['oa'] or 0)
if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
h_xg = (float(row['h_sc'] or 1.2) + float(row['a_co'] or 1.2)) / 2
a_xg = (float(row['a_sc'] or 1.2) + float(row['h_co'] or 1.2)) / 2
h_p = (float(row['h_form'] or 0)*10) + (float(row['h_sc'] or 1.2)*5) - (float(row['h_co'] or 1.2)*5)
a_p = (float(row['a_form'] or 0)*10) + (float(row['a_sc'] or 1.2)*5) - (float(row['a_co'] or 1.2)*5)
margin = (1/oh) + (1/od) + (1/oa)
h_sot, a_sot = 4.0, 3.0
# Features
f = pd.DataFrame([{
'h_form': float(row['h_form']), 'a_form': float(row['a_form']),
'h_xg': h_xg, 'a_xg': a_xg, 'pow_diff': h_p - a_p,
'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
'h_sot': h_sot, 'a_sot': a_sot,
'h_xi': float(row['h_xi']), 'a_xi': float(row['a_xi']),
'xi_diff': float(row['h_xi'] - row['a_xi']),
'cards': float(row['cards'])
}])
# MS
ms_probs = model_ms.predict(f)[0]
for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
if odd <= 1.0: continue
edge = prob - (1/odd)
if edge > 0.05 and prob > 0.50:
results['ms']['bet'] += 1
h, a = row['score_home'], row['score_away']
w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
else: results['ms']['profit'] -= 1.0
break
# OU2.5
p_over = float(model_ou.predict(f)[0])
if p_over > 0.55:
results['ou25']['bet'] += 1
if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85
else: results['ou25']['profit'] -= 1.0
# BTTS
p_btts = float(model_btts.predict(f)[0])
if p_btts > 0.55:
results['btts']['bet'] += 1
if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
else: results['btts']['profit'] -= 1.0
print("\n" + "="*60)
print("📊 VQWEN DEEP SONUÇLAR")
print("="*60)
for mkt in ['ms', 'ou25', 'btts']:
r = results[mkt]
wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
print(f"{mkt.upper():<10} Oyn: {r['bet']:<5} Kaz: {r['won']:<5} WR: {wr:.1f}% Kâr: {r['profit']:+.2f}")
total = sum(r['profit'] for r in results.values())
print(f"\n💰 TOPLAM: {total:+.2f} Units")
print("🟢 PARA KAZANDIK!" if total > 0 else "🔴 ZARARDA")
cur.close()
conn.close()
if __name__ == "__main__":
run_vqwen_deep_backtest()
ai-engine/scripts/backtest_vqwen_final.py
-159
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@@ -1,159 +0,0 @@
"""
VQWEN Final Backtest
====================
Tests the Final Model (ELO + Rest + Context).
"""
import os
import sys
import json
import pickle
import pandas as pd
import numpy as np
import psycopg2
from psycopg2.extras import RealDictCursor
AI_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(AI_DIR)
PROJECT_ROOT = os.path.dirname(ROOT_DIR)
def get_clean_dsn() -> str:
return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
def run_final_backtest():
print("🧠 VQWEN FINAL BACKTEST (ELO + REST)")
print("="*60)
# Load Models
mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
try:
with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
print("✅ VQWEN Final modelleri yüklendi.")
except Exception as e:
print(f"❌ Model hatası: {e}")
return
with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
league_ids = tuple(str(lid) for lid in json.load(f))
dsn = get_clean_dsn()
conn = psycopg2.connect(dsn)
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
m.mst_utc,
t1.name as home_team, t2.name as away_team,
maf.home_elo, maf.away_elo,
COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as h_home_goals,
COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as a_away_goals,
COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as h_rest,
COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as a_rest,
COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.home_team_id AND mp.is_starting = true), 11) as h_xi,
COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.away_team_id AND mp.is_starting = true), 11) as a_xi,
COALESCE((SELECT COUNT(*) FROM match_player_events mpe WHERE mpe.match_id = m.id AND mpe.event_type = 'card'), 4) as cards,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
(SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa
FROM matches m
LEFT JOIN teams t1 ON m.home_team_id = t1.id
LEFT JOIN teams t2 ON m.away_team_id = t2.id
LEFT JOIN football_ai_features maf ON maf.match_id = m.id
WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT 1000
""", (league_ids,))
rows = cur.fetchall()
print(f"📊 {len(rows)} maç analiz ediliyor...")
results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
for row in rows:
oh = float(row['oh'] or 0)
od = float(row['od'] or 0)
oa = float(row['oa'] or 0)
if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
# Features
h_elo = float(row['home_elo'] or 1500)
a_elo = float(row['away_elo'] or 1500)
h_home_goals = float(row['h_home_goals'] or 1.2)
a_away_goals = float(row['a_away_goals'] or 1.2)
h_rest = float(row['h_rest'] or 7)
a_rest = float(row['a_rest'] or 7)
h_xi = float(row['h_xi'] or 11)
a_xi = float(row['a_xi'] or 11)
cards = float(row['cards'] or 4)
def fatigue(rest):
if rest < 3: return 0.85
if rest < 5: return 0.95
return 1.0
h_fat = fatigue(h_rest)
a_fat = fatigue(a_rest)
h_xg = h_home_goals * h_fat
a_xg = a_away_goals * a_fat
total_xg = h_xg + a_xg
margin = (1/oh) + (1/od) + (1/oa)
f = pd.DataFrame([{
'elo_diff': h_elo - a_elo,
'h_xg': h_xg, 'a_xg': a_xg,
'total_xg': total_xg,
'pow_diff': (h_elo/100)*h_fat - (a_elo/100)*a_fat,
'rest_diff': h_rest - a_rest,
'h_fatigue': h_fat, 'a_fatigue': a_fat,
'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
'h_xi': h_xi, 'a_xi': a_xi,
'cards': cards
}])
# MS
ms_probs = model_ms.predict(f)[0]
for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
if odd <= 1.0: continue
edge = prob - (1/odd)
if edge > 0.05 and prob > 0.45:
results['ms']['bet'] += 1
h, a = row['score_home'], row['score_away']
w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
else: results['ms']['profit'] -= 1.0
break
# OU2.5
p_over = float(model_ou.predict(f)[0])
if p_over > 0.55:
results['ou25']['bet'] += 1
if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85
else: results['ou25']['profit'] -= 1.0
# BTTS
p_btts = float(model_btts.predict(f)[0])
if p_btts > 0.55:
results['btts']['bet'] += 1
if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
else: results['btts']['profit'] -= 1.0
print("\n" + "="*60)
print("📊 VQWEN FINAL SONUÇLAR")
print("="*60)
for mkt in ['ms', 'ou25', 'btts']:
r = results[mkt]
wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
print(f"{mkt.upper():<10} Oyn: {r['bet']:<5} Kaz: {r['won']:<5} WR: {wr:.1f}% Kâr: {r['profit']:+.2f}")
total = sum(r['profit'] for r in results.values())
print(f"\n💰 TOPLAM: {total:+.2f} Units")
print("🟢 PARA KAZANDIK!" if total > 0 else "🔴 ZARARDA")
cur.close()
conn.close()
if __name__ == "__main__":
run_final_backtest()
ai-engine/scripts/backtest_vqwen_v3.py
-182
View File
@@ -1,182 +0,0 @@
"""
VQWEN v3 Shared-Contract Backtest
=================================
Evaluates the retrained VQWEN models on the temporal validation slice using
the exact same pre-match feature contract as training/runtime.
"""
from __future__ import annotations
import json
import os
import pickle
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import psycopg2
from dotenv import load_dotenv
AI_DIR = Path(__file__).resolve().parent
ENGINE_DIR = AI_DIR.parent
REPO_DIR = ENGINE_DIR.parent
MODELS_DIR = ENGINE_DIR / "models" / "vqwen"
if str(ENGINE_DIR) not in sys.path:
sys.path.insert(0, str(ENGINE_DIR))
from features.vqwen_contract import FEATURE_COLUMNS # noqa: E402
from train_vqwen_v3 import ( # noqa: E402
_enrich_pre_match_context,
_fetch_dataframe,
_prepare_features,
_temporal_split,
load_top_league_ids,
)
def _load_env() -> None:
load_dotenv(REPO_DIR / ".env", override=False)
load_dotenv(ENGINE_DIR / ".env", override=False)
def get_clean_dsn() -> str:
_load_env()
raw = os.getenv("DATABASE_URL", "").strip().strip('"').strip("'")
if not raw:
raise RuntimeError("DATABASE_URL is missing.")
return raw.split("?", 1)[0]
def _accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
if len(y_true) == 0:
return 0.0
return float((y_true == y_pred).mean())
def _binary_metrics(prob: np.ndarray, y_true: np.ndarray) -> tuple[float, float]:
pred = (prob >= 0.5).astype(int)
acc = _accuracy(y_true, pred)
brier = float(np.mean((prob - y_true) ** 2)) if len(y_true) else 1.0
return acc, brier
def _multiclass_brier(prob: np.ndarray, y_true: np.ndarray, n_classes: int = 3) -> float:
if len(y_true) == 0:
return 1.0
target = np.zeros((len(y_true), n_classes), dtype=np.float64)
target[np.arange(len(y_true)), y_true.astype(int)] = 1.0
return float(np.mean(np.sum((prob - target) ** 2, axis=1)))
def _band_label(probability: float) -> str:
if probability >= 0.70:
return "HIGH"
if probability >= 0.60:
return "MEDIUM"
if probability >= 0.50:
return "LOW"
return "NO_BET"
def _summarize_bands(
name: str,
confidence: np.ndarray,
is_correct: np.ndarray,
) -> list[str]:
lines: list[str] = []
for band in ("HIGH", "MEDIUM", "LOW"):
mask = np.array([_band_label(float(p)) == band for p in confidence], dtype=bool)
count = int(mask.sum())
accuracy = float(is_correct[mask].mean()) if count else 0.0
avg_conf = float(confidence[mask].mean()) if count else 0.0
lines.append(
f"{name} {band:<6} count={count:<4} accuracy={accuracy*100:5.1f}% avg_conf={avg_conf*100:5.1f}%"
)
return lines
def run_v3_backtest() -> None:
print("VQWEN v3 SHARED-CONTRACT BACKTEST")
print("=" * 60)
league_ids = load_top_league_ids()
dsn = get_clean_dsn()
with psycopg2.connect(dsn) as conn:
with conn.cursor() as cur:
df = _fetch_dataframe(cur, league_ids)
df = _enrich_pre_match_context(cur, df)
df = _prepare_features(df)
train_df, valid_df = _temporal_split(df)
print(f"Toplam ornek: {len(df)} | Train: {len(train_df)} | Valid: {len(valid_df)}")
with (MODELS_DIR / "vqwen_ms.pkl").open("rb") as handle:
model_ms = pickle.load(handle)
with (MODELS_DIR / "vqwen_ou25.pkl").open("rb") as handle:
model_ou25 = pickle.load(handle)
with (MODELS_DIR / "vqwen_btts.pkl").open("rb") as handle:
model_btts = pickle.load(handle)
X_valid = valid_df[FEATURE_COLUMNS]
y_ms = valid_df["t_ms"].to_numpy(dtype=np.int64)
y_ou25 = valid_df["t_ou"].to_numpy(dtype=np.int64)
y_btts = valid_df["t_btts"].to_numpy(dtype=np.int64)
ms_prob = np.asarray(model_ms.predict(X_valid), dtype=np.float64)
ou25_prob = np.asarray(model_ou25.predict(X_valid), dtype=np.float64).reshape(-1)
btts_prob = np.asarray(model_btts.predict(X_valid), dtype=np.float64).reshape(-1)
ms_pred = np.argmax(ms_prob, axis=1)
ms_conf = np.max(ms_prob, axis=1)
ms_correct = (ms_pred == y_ms).astype(np.int64)
ou25_pred = (ou25_prob >= 0.5).astype(np.int64)
ou25_conf = np.where(ou25_prob >= 0.5, ou25_prob, 1.0 - ou25_prob)
ou25_correct = (ou25_pred == y_ou25).astype(np.int64)
btts_pred = (btts_prob >= 0.5).astype(np.int64)
btts_conf = np.where(btts_prob >= 0.5, btts_prob, 1.0 - btts_prob)
btts_correct = (btts_pred == y_btts).astype(np.int64)
ms_acc = _accuracy(y_ms, ms_pred)
ou25_acc, ou25_brier = _binary_metrics(ou25_prob, y_ou25)
btts_acc, btts_brier = _binary_metrics(btts_prob, y_btts)
ms_brier = _multiclass_brier(ms_prob, y_ms)
print("\nGenel metrikler")
print(f"MS accuracy : {ms_acc*100:.2f}% | multiclass_brier={ms_brier:.4f}")
print(f"OU25 accuracy : {ou25_acc*100:.2f}% | brier={ou25_brier:.4f}")
print(f"BTTS accuracy : {btts_acc*100:.2f}% | brier={btts_brier:.4f}")
print("\nConfidence band")
for line in _summarize_bands("MS", ms_conf, ms_correct):
print(line)
for line in _summarize_bands("OU25", ou25_conf, ou25_correct):
print(line)
for line in _summarize_bands("BTTS", btts_conf, btts_correct):
print(line)
summary = {
"validation_samples": int(len(valid_df)),
"metrics": {
"ms_accuracy": round(ms_acc, 4),
"ms_brier": round(ms_brier, 4),
"ou25_accuracy": round(ou25_acc, 4),
"ou25_brier": round(ou25_brier, 4),
"btts_accuracy": round(btts_acc, 4),
"btts_brier": round(btts_brier, 4),
},
}
(MODELS_DIR / "vqwen_backtest_v3_summary.json").write_text(
json.dumps(summary, indent=2),
encoding="utf-8",
)
print("\nKaydedildi: vqwen_backtest_v3_summary.json")
if __name__ == "__main__":
run_v3_backtest()
ai-engine/scripts/conditional_frequency_engine.py
+312
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@@ -0,0 +1,312 @@
"""
V28 — CONDITIONAL FREQUENCY ENGINE
====================================
User's strategy automated at scale:
For every match (e.g. Beşiktaş vs Konya):
1. Look at Beşiktaş's HOME history when their MS1 odds were in the same band (e.g. 1.30-1.40)
→ What % of those matches ended OU 1.5 over? OU 2.5 over? MS1?
2. Look at Konya's AWAY history when their MS2 odds were in the same band (e.g. 2.00-2.20)
→ Same questions
3. COMBINE both signals:
→ If BOTH teams historically produce >80% OU1.5 over at these odds → BET OU1.5 over
→ This is the user's exact Excel strategy, now running on 104K matches
CRITICAL: Only uses PAST matches for each prediction (no future leakage)
"""
import pandas as pd
import numpy as np
from collections import defaultdict
import warnings
warnings.filterwarnings('ignore')
# ─── Load Data ───
print("Loading data...")
df = pd.read_csv('data/training_data_v27.csv', low_memory=False)
KEEP_STR = ['match_id', 'league_name', 'home_team', 'away_team',
'home_team_id', 'away_team_id', 'league_id', 'mst_utc']
for c in df.columns:
if c not in KEEP_STR:
df[c] = pd.to_numeric(df[c], errors='coerce')
# Ensure chronological order (by match_id or date)
if 'mst_utc' in df.columns:
df['mst_utc'] = pd.to_datetime(df['mst_utc'], errors='coerce')
df = df.sort_values('mst_utc').reset_index(drop=True)
# Filter: need valid odds + scores
df = df.dropna(subset=['odds_ms_h', 'odds_ms_a', 'score_home', 'score_away',
'home_team_id', 'away_team_id', 'label_ms'])
# Compute actual goal labels
df['total_goals'] = df['score_home'] + df['score_away']
df['ou15_actual'] = (df['total_goals'] > 1.5).astype(int)
df['ou25_actual'] = (df['total_goals'] > 2.5).astype(int)
df['ou35_actual'] = (df['total_goals'] > 3.5).astype(int)
df['btts_actual'] = ((df['score_home'] > 0) & (df['score_away'] > 0)).astype(int)
df['ms_result'] = df['label_ms'].astype(int) # 0=H, 1=D, 2=A
N = len(df)
print(f"Total matches: {N}")
print(f"Unique home teams: {df.home_team_id.nunique()}")
print(f"Unique away teams: {df.away_team_id.nunique()}")
# ─── Odds Band Helper ───
def get_odds_band(odds, band_width=0.10):
"""Round odds to nearest band. E.g. 1.35 → (1.30, 1.40)"""
lower = round(np.floor(odds / band_width) * band_width, 2)
upper = round(lower + band_width, 2)
return (lower, upper)
def get_odds_band_wide(odds):
"""Wider band for less common teams. E.g. 1.35 → (1.20, 1.50)"""
if odds < 1.50:
return (1.01, 1.50)
elif odds < 2.00:
return (1.50, 2.00)
elif odds < 2.50:
return (2.00, 2.50)
elif odds < 3.00:
return (2.50, 3.00)
elif odds < 4.00:
return (3.00, 4.00)
elif odds < 6.00:
return (4.00, 6.00)
else:
return (6.00, 20.00)
# ─── Build Conditional Frequency Lookup (Expanding Window) ───
print("\nBuilding conditional frequency features (expanding window)...")
# We'll compute features for each match using only past data
MIN_MATCHES = 5 # minimum historical matches to generate a signal
# Pre-allocate feature arrays
feat_names = [
'home_ou15_rate_at_band', 'home_ou25_rate_at_band', 'home_ou35_rate_at_band',
'home_btts_rate_at_band', 'home_win_rate_at_band', 'home_n_at_band',
'away_ou15_rate_at_band', 'away_ou25_rate_at_band', 'away_ou35_rate_at_band',
'away_btts_rate_at_band', 'away_win_rate_at_band', 'away_n_at_band',
'combined_ou15', 'combined_ou25', 'combined_ou35', 'combined_btts',
'home_goals_at_band', 'away_goals_at_band', 'combined_goals_at_band',
'home_conceded_at_band', 'away_conceded_at_band',
]
features = np.full((N, len(feat_names)), np.nan)
# Historical ledger: team_id → list of (odds_band, ou15, ou25, ou35, btts, ms_result, goals_scored, goals_conceded)
home_history = defaultdict(list) # team performances when playing HOME
away_history = defaultdict(list) # team performances when playing AWAY
for i in range(N):
row = df.iloc[i]
ht_id = row.home_team_id
at_id = row.away_team_id
h_odds = row.odds_ms_h
a_odds = row.odds_ms_a
if pd.isna(h_odds) or pd.isna(a_odds):
continue
h_band = get_odds_band_wide(h_odds)
a_band = get_odds_band_wide(a_odds)
# ── Look up HOME team's historical performance at this odds band ──
h_hist = [x for x in home_history[ht_id] if h_band[0] <= x[0] < h_band[1]]
if len(h_hist) >= MIN_MATCHES:
features[i, 0] = np.mean([x[1] for x in h_hist]) # ou15 rate
features[i, 1] = np.mean([x[2] for x in h_hist]) # ou25 rate
features[i, 2] = np.mean([x[3] for x in h_hist]) # ou35 rate
features[i, 3] = np.mean([x[4] for x in h_hist]) # btts rate
features[i, 4] = np.mean([x[5] for x in h_hist]) # win rate (home win = 1 if ms==0)
features[i, 5] = len(h_hist)
features[i, 16] = np.mean([x[6] for x in h_hist]) # avg goals scored
features[i, 19] = np.mean([x[7] for x in h_hist]) # avg goals conceded
# ── Look up AWAY team's historical performance at this odds band ──
a_hist = [x for x in away_history[at_id] if a_band[0] <= x[0] < a_band[1]]
if len(a_hist) >= MIN_MATCHES:
features[i, 6] = np.mean([x[1] for x in a_hist]) # ou15 rate
features[i, 7] = np.mean([x[2] for x in a_hist]) # ou25 rate
features[i, 8] = np.mean([x[3] for x in a_hist]) # ou35 rate
features[i, 9] = np.mean([x[4] for x in a_hist]) # btts rate
features[i, 10] = np.mean([x[5] for x in a_hist]) # away win rate
features[i, 11] = len(a_hist)
features[i, 17] = np.mean([x[6] for x in a_hist]) # avg goals scored (away)
features[i, 20] = np.mean([x[7] for x in a_hist]) # avg goals conceded (away)
# ── Combined signals ──
if not np.isnan(features[i, 0]) and not np.isnan(features[i, 6]):
features[i, 12] = (features[i, 0] + features[i, 6]) / 2 # combined ou15
features[i, 13] = (features[i, 1] + features[i, 7]) / 2 # combined ou25
features[i, 14] = (features[i, 2] + features[i, 8]) / 2 # combined ou35
features[i, 15] = (features[i, 3] + features[i, 9]) / 2 # combined btts
features[i, 18] = features[i, 16] + features[i, 17] # combined goals
# ── Add THIS match to history (for future lookups) ──
ou15 = int(row.total_goals > 1.5)
ou25 = int(row.total_goals > 2.5)
ou35 = int(row.total_goals > 3.5)
btts = int(row.score_home > 0 and row.score_away > 0)
h_won = int(row.label_ms == 0)
a_won = int(row.label_ms == 2)
home_history[ht_id].append((h_odds, ou15, ou25, ou35, btts, h_won,
row.score_home, row.score_away))
away_history[at_id].append((a_odds, ou15, ou25, ou35, btts, a_won,
row.score_away, row.score_home))
if (i+1) % 20000 == 0:
valid = np.sum(~np.isnan(features[:i+1, 12]))
print(f" Processed {i+1}/{N} matches, {valid} with combined signals")
# Count valid features
valid_mask = ~np.isnan(features[:, 12])
print(f"\nMatches with combined conditional signals: {valid_mask.sum()} / {N}")
# ─── BACKTEST: Walk-Forward ───
print("\n" + "="*70)
print(" CONDITIONAL FREQUENCY BACKTEST")
print("="*70)
# Only test on last 20% of data (to avoid early sparse data)
test_start = int(N * 0.7)
test_idx = range(test_start, N)
test_valid = [i for i in test_idx if valid_mask[i]]
print(f"Test window: matches {test_start}-{N} ({len(test_valid)} with signals)")
# Strategy: bet on OU1.5 over when combined_ou15 > threshold
markets = [
('OU 1.5 Over', 'combined_ou15', 12, 'ou15_actual', 'odds_ou15_o'),
('OU 2.5 Over', 'combined_ou25', 13, 'ou25_actual', 'odds_ou25_o'),
('OU 3.5 Over', 'combined_ou35', 14, 'ou35_actual', 'odds_ou35_o'),
('BTTS Yes', 'combined_btts', 15, 'btts_actual', 'odds_btts_y'),
]
for market_name, feat_key, feat_idx, label_col, odds_col in markets:
print(f"\n ── {market_name} ──")
if odds_col not in df.columns:
print(f" No odds column '{odds_col}', skipping")
continue
for threshold in [0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90]:
bets = 0
wins = 0
pnl = 0.0
for i in test_valid:
signal = features[i, feat_idx]
if np.isnan(signal) or signal < threshold:
continue
odds_val = df.iloc[i][odds_col]
if pd.isna(odds_val) or odds_val < 1.05:
continue
actual = df.iloc[i][label_col]
if pd.isna(actual):
continue
bets += 1
if actual == 1:
wins += 1
pnl += odds_val - 1
else:
pnl -= 1
if bets >= 20:
roi = pnl / bets * 100
hit = wins / bets * 100
ev = (wins/bets) * (pnl/wins + 1) if wins > 0 else 0
marker = " *** PROFITABLE ***" if roi > 0 else ""
print(f" Threshold>{threshold:.2f}: {bets:5d} bets, "
f"hit={hit:.1f}%, ROI={roi:+.1f}%{marker}")
# Also test MS (1X2) market
print(f"\n ── Maç Sonucu (1X2) ──")
# Home win when home_win_rate_at_band > X AND away team loses often at that band
for threshold in [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80]:
bets = wins = 0
pnl = 0.0
for i in test_valid:
h_wr = features[i, 4] # home win rate at band
a_lr = 1 - features[i, 10] if not np.isnan(features[i, 10]) else np.nan # away loss rate
if np.isnan(h_wr) or np.isnan(a_lr):
continue
combined = (h_wr + a_lr) / 2
if combined < threshold:
continue
odds_val = df.iloc[i].odds_ms_h
if pd.isna(odds_val) or odds_val < 1.10 or odds_val > 5.0:
continue
bets += 1
if df.iloc[i].label_ms == 0:
wins += 1
pnl += odds_val - 1
else:
pnl -= 1
if bets >= 20:
roi = pnl / bets * 100
hit = wins / bets * 100
marker = " *** PROFITABLE ***" if roi > 0 else ""
print(f" Home win comb>{threshold:.2f}: {bets:5d} bets, "
f"hit={hit:.1f}%, ROI={roi:+.1f}%{marker}")
# ─── DEEP DIVE: Best performing niches ───
print("\n" + "="*70)
print(" DEEP DIVE: Combined OU15 + Odds Value Filter")
print("="*70)
# The user's strategy: high confidence + the odds must pay enough
for threshold in [0.75, 0.80, 0.85, 0.90]:
for min_odds in [1.10, 1.20, 1.30, 1.40]:
bets = wins = 0
pnl = 0.0
for i in test_valid:
signal = features[i, 12] # combined ou15
if np.isnan(signal) or signal < threshold:
continue
odds_val = df.iloc[i].get('odds_ou15_o', np.nan) if 'odds_ou15_o' in df.columns else np.nan
if pd.isna(odds_val) or odds_val < min_odds:
continue
actual = df.iloc[i].ou15_actual
bets += 1
if actual == 1:
wins += 1
pnl += odds_val - 1
else:
pnl -= 1
if bets >= 30:
roi = pnl / bets * 100
hit = wins / bets * 100
if roi > -5: # show near-profitable too
marker = " *** PROFITABLE ***" if roi > 0 else ""
print(f" OU15 sig>{threshold:.2f} odds>{min_odds}: "
f"{bets:5d} bets, hit={hit:.1f}%, ROI={roi:+.1f}%{marker}")
# ─── Additional: Goal expectation accuracy ───
print("\n" + "="*70)
print(" GOAL PREDICTION ACCURACY")
print("="*70)
valid_goals = [i for i in test_valid if not np.isnan(features[i, 18])]
if valid_goals:
pred_goals = [features[i, 18] for i in valid_goals]
actual_goals = [df.iloc[i].total_goals for i in valid_goals]
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(actual_goals, pred_goals)
corr = np.corrcoef(pred_goals, actual_goals)[0, 1]
print(f" Combined goal prediction MAE: {mae:.3f}")
print(f" Correlation: {corr:.4f}")
print(f" Avg predicted: {np.mean(pred_goals):.2f}, Avg actual: {np.mean(actual_goals):.2f}")
# Bucket analysis
print("\n Goal prediction buckets:")
for low, high in [(0, 1.5), (1.5, 2.0), (2.0, 2.5), (2.5, 3.0), (3.0, 3.5), (3.5, 5.0)]:
bucket = [i for i, pg in zip(valid_goals, pred_goals) if low <= pg < high]
if len(bucket) >= 20:
avg_actual = np.mean([df.iloc[i].total_goals for i in bucket])
ou25_rate = np.mean([df.iloc[i].ou25_actual for i in bucket])
print(f" Predicted {low:.1f}-{high:.1f}: n={len(bucket)}, "
f"actual_avg={avg_actual:.2f}, OU25%={ou25_rate*100:.1f}%")
print("\nDone!")
ai-engine/scripts/extract_training_data.py
+5 -5
View File
@@ -1071,13 +1071,13 @@ class FeatureExtractor:
for mst, poss, sot, total_shots, corners, team_goals in rows:
if poss and poss > 0:
poss_sum += poss
poss_sum += float(poss)
poss_count += 1
sot_sum += sot or 0
shots_sum += total_shots or 0
corners_sum += corners or 0
sot_sum += float(sot or 0)
shots_sum += float(total_shots or 0)
corners_sum += float(corners or 0)
goals_scored += team_goals or 0
goals_scored += float(team_goals or 0)
return {
"possession": (poss_sum / poss_count / 100) if poss_count > 0 else 0.50,
ai-engine/scripts/extract_training_data_v26.py
+93
View File
@@ -0,0 +1,93 @@
from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
SOURCE_CSV = AI_ENGINE_DIR / "data" / "training_data.csv"
TARGET_DIR = AI_ENGINE_DIR / "data" / "v26_shadow"
TARGET_DIR.mkdir(parents=True, exist_ok=True)
def _rolling_windows(frame: pd.DataFrame) -> list[dict[str, int]]:
ordered = frame.sort_values("mst_utc").reset_index(drop=True)
windows: list[dict[str, int]] = []
if ordered.empty:
return windows
size = len(ordered)
cuts = [0.55, 0.7, 0.85]
for idx, cut in enumerate(cuts, start=1):
end_ix = max(int(size * cut), 1)
test_end = min(size - 1, end_ix + max(int(size * 0.10), 1))
windows.append(
{
"window": idx,
"train_end_ix": end_ix - 1,
"test_start_ix": end_ix,
"test_end_ix": test_end,
"train_end_mst_utc": int(ordered.iloc[end_ix - 1]["mst_utc"]),
"test_end_mst_utc": int(ordered.iloc[test_end]["mst_utc"]),
}
)
return windows
def main() -> None:
if not SOURCE_CSV.exists():
raise SystemExit(f"Missing source CSV: {SOURCE_CSV}")
frame = pd.read_csv(SOURCE_CSV)
if "mst_utc" not in frame.columns:
raise SystemExit("training_data.csv must include mst_utc")
ordered = frame.sort_values("mst_utc").reset_index(drop=True)
ordered["lineup_completeness"] = 1.0
ordered["referee_available"] = (
ordered.get("referee_experience", pd.Series([0] * len(ordered))).fillna(0) > 0
).astype(float)
ordered["league_reliability"] = ordered.get("league_zero_goal_rate", 0).fillna(0).apply(
lambda value: round(max(0.25, min(0.95, 0.85 - float(value))), 4)
)
ordered["odds_snapshot_freshness"] = 1.0
train_end = max(int(len(ordered) * 0.70), 1)
validation_end = max(int(len(ordered) * 0.85), train_end + 1)
validation_end = min(validation_end, len(ordered) - 1)
train_df = ordered.iloc[:train_end].copy()
validation_df = ordered.iloc[train_end:validation_end].copy()
holdout_df = ordered.iloc[validation_end:].copy()
train_df.to_csv(TARGET_DIR / "train.csv", index=False)
validation_df.to_csv(TARGET_DIR / "validation.csv", index=False)
holdout_df.to_csv(TARGET_DIR / "holdout.csv", index=False)
meta = {
"source": str(SOURCE_CSV),
"rows": int(len(ordered)),
"train_rows": int(len(train_df)),
"validation_rows": int(len(validation_df)),
"holdout_rows": int(len(holdout_df)),
"rolling_windows": _rolling_windows(ordered),
"derived_columns": [
"lineup_completeness",
"referee_available",
"league_reliability",
"odds_snapshot_freshness",
],
"feature_policy": "prediction_time_only",
}
(TARGET_DIR / "dataset_meta.json").write_text(
json.dumps(meta, indent=2),
encoding="utf-8",
)
print(f"[OK] V26 dataset written to {TARGET_DIR}")
if __name__ == "__main__":
main()
ai-engine/scripts/extract_training_data_v27.py
+305
View File
@@ -0,0 +1,305 @@
"""
V27 Training Data Extraction - Value Sniper
Extends V25 to ALL matches with odds (~104K).
Adds rolling window, league quality, time, H2H, strength features.
Usage: python3 scripts/extract_training_data_v27.py
"""
import os, sys, csv, time
from collections import defaultdict
AI_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, AI_DIR)
from scripts.extract_training_data import (
BatchDataLoader as V25Loader,
FeatureExtractor as V25Extractor,
FEATURE_COLS as V25_COLS,
get_conn,
)
from features.rolling_features import (
calc_rolling_features, calc_league_quality,
calc_time_features, calc_advanced_h2h, calc_strength_diff,
)
OUTPUT = os.path.join(AI_DIR, "data", "training_data_v27.csv")
os.makedirs(os.path.dirname(OUTPUT), exist_ok=True)
V27_NEW = [
"home_rolling5_goals","home_rolling5_conceded",
"home_rolling10_goals","home_rolling10_conceded",
"home_rolling20_goals","home_rolling20_conceded",
"away_rolling5_goals","away_rolling5_conceded",
"away_rolling10_goals","away_rolling10_conceded",
"home_rolling5_cs","away_rolling5_cs",
"home_venue_goals","home_venue_conceded",
"away_venue_goals","away_venue_conceded",
"home_goal_trend","away_goal_trend",
"league_home_win_rate","league_draw_rate",
"league_btts_rate","league_ou25_rate",
"league_reliability_score",
"home_days_rest","away_days_rest",
"match_month","is_season_start","is_season_end",
"h2h_home_goals_avg","h2h_away_goals_avg",
"h2h_recent_trend","h2h_venue_advantage",
"attack_vs_defense_home","attack_vs_defense_away",
"xg_diff","form_momentum_interaction",
"elo_form_consistency","upset_x_elo_gap",
]
ALL_COLS = V25_COLS + V27_NEW
class V27Loader(V25Loader):
"""Load ALL matches with odds, not just top leagues."""
def __init__(self, conn):
super().__init__(conn, [])
self.league_matches_cache = {}
def _load_matches(self):
self.cur.execute("""
SELECT m.id, m.home_team_id, m.away_team_id,
m.score_home, m.score_away,
m.ht_score_home, m.ht_score_away,
m.mst_utc, m.league_id,
ht.name, at.name, l.name
FROM matches m
JOIN teams ht ON m.home_team_id = ht.id
JOIN teams at ON m.away_team_id = at.id
JOIN leagues l ON m.league_id = l.id
WHERE m.status='FT' AND m.score_home IS NOT NULL
AND m.sport='football'
AND EXISTS(SELECT 1 FROM odd_categories oc WHERE oc.match_id=m.id)
ORDER BY m.mst_utc ASC
""")
self.matches = self.cur.fetchall()
def _load_odds(self):
self.cur.execute("""
SELECT oc.match_id, oc.name, os.name, os.odd_value
FROM odd_selections os
JOIN odd_categories oc ON os.odd_category_db_id=oc.db_id
JOIN matches m ON oc.match_id=m.id
WHERE m.status='FT' AND m.sport='football'
""")
for mid, cat, sel, val in self.cur.fetchall():
try:
v = float(val) if val else 0
if v <= 0 or not cat or not sel: continue
if mid not in self.odds_cache: self.odds_cache[mid] = {}
c = cat.lower().strip()
s = sel.lower().strip()
o = self.odds_cache[mid]
if c == 'maç sonucu':
if sel=='1': o['ms_h']=v
elif sel in('0','X'): o['ms_d']=v
elif sel=='2': o['ms_a']=v
elif c == '1. yarı sonucu':
if sel=='1': o['ht_ms_h']=v
elif sel in('0','X'): o['ht_ms_d']=v
elif sel=='2': o['ht_ms_a']=v
elif c == 'karşılıklı gol':
if 'var' in s: o['btts_y']=v
elif 'yok' in s: o['btts_n']=v
elif c == '2,5 alt/üst':
if 'alt' in s: o['ou25_u']=v
elif 'üst' in s: o['ou25_o']=v
elif c == '1,5 alt/üst':
if 'alt' in s: o['ou15_u']=v
elif 'üst' in s: o['ou15_o']=v
elif c == '3,5 alt/üst':
if 'alt' in s: o['ou35_u']=v
elif 'üst' in s: o['ou35_o']=v
elif c == '0,5 alt/üst':
if 'alt' in s: o['ou05_u']=v
elif 'üst' in s: o['ou05_o']=v
elif c == '1. yarı 0,5 alt/üst':
if 'alt' in s: o['ht_ou05_u']=v
elif 'üst' in s: o['ht_ou05_o']=v
elif c == '1. yarı 1,5 alt/üst':
if 'alt' in s: o['ht_ou15_u']=v
elif 'üst' in s: o['ht_ou15_o']=v
except (ValueError, TypeError): pass
def _load_league_stats(self):
self.cur.execute("""
SELECT league_id,
AVG(score_home+score_away), AVG(CASE WHEN score_home=0 AND score_away=0 THEN 1.0 ELSE 0.0 END),
COUNT(*)
FROM matches WHERE status='FT' AND score_home IS NOT NULL AND sport='football'
GROUP BY league_id
""")
for lid, ag, zr, cnt in self.cur.fetchall():
self.league_stats_cache[lid] = {
"avg_goals": float(ag) if ag else 2.5,
"zero_rate": float(zr) if zr else 0.07,
"match_count": cnt
}
def _load_squad_data(self):
self.cur.execute("""
SELECT mpp.match_id, mpp.team_id,
COUNT(*) FILTER(WHERE mpp.is_starting=true),
COUNT(*),
COUNT(*) FILTER(WHERE mpp.is_starting=true
AND LOWER(COALESCE(mpp.position::TEXT,''))~'(forward|fwd|forvet|striker)')
FROM match_player_participation mpp
JOIN matches m ON mpp.match_id=m.id
WHERE m.status='FT' AND m.sport='football'
GROUP BY mpp.match_id, mpp.team_id
""")
part = {}
for mid,tid,st,tot,fwd in self.cur.fetchall():
part[(mid,tid)]={'starting_count':st or 0,'total_squad':tot or 0,'fwd_count':fwd or 0}
self.cur.execute("""
SELECT mpe.match_id, mpe.team_id,
COUNT(*) FILTER(WHERE mpe.event_type='goal' AND COALESCE(mpe.event_subtype,'') NOT ILIKE '%%penaltı kaçırma%%'),
COUNT(DISTINCT mpe.assist_player_id) FILTER(WHERE mpe.event_type='goal' AND mpe.assist_player_id IS NOT NULL),
COUNT(DISTINCT mpe.player_id) FILTER(WHERE mpe.event_type='goal' AND COALESCE(mpe.event_subtype,'') NOT ILIKE '%%penaltı kaçırma%%')
FROM match_player_events mpe
JOIN matches m ON mpe.match_id=m.id
WHERE m.status='FT' AND m.sport='football'
GROUP BY mpe.match_id, mpe.team_id
""")
evts = {}
for mid,tid,g,a,sc in self.cur.fetchall():
evts[(mid,tid)]={'goals':g or 0,'assists':a or 0,'unique_scorers':sc or 0}
self.cur.execute("""
SELECT mpe.team_id, mpe.player_id, COUNT(*)
FROM match_player_events mpe JOIN matches m ON mpe.match_id=m.id
WHERE m.status='FT' AND m.sport='football' AND mpe.event_type='goal'
AND COALESCE(mpe.event_subtype,'') NOT ILIKE '%%penaltı kaçırma%%'
GROUP BY mpe.team_id, mpe.player_id HAVING COUNT(*)>=3
""")
kp_by_team = defaultdict(set)
for tid,pid,_ in self.cur.fetchall(): kp_by_team[tid].add(pid)
self.cur.execute("""
SELECT mpp.match_id, mpp.team_id, mpp.player_id
FROM match_player_participation mpp JOIN matches m ON mpp.match_id=m.id
WHERE mpp.is_starting=true AND m.status='FT' AND m.sport='football'
""")
starters = defaultdict(list)
for mid,tid,pid in self.cur.fetchall(): starters[(mid,tid)].append(pid)
for key in set(part)|set(evts):
mid,tid = key
p = part.get(key,{'starting_count':0,'total_squad':0,'fwd_count':0})
e = evts.get(key,{'goals':0,'assists':0,'unique_scorers':0})
s = starters.get(key,[])
kp_in = sum(1 for x in s if x in kp_by_team.get(tid,set()))
kp_tot = len(kp_by_team.get(tid,set()))
kp_miss = max(0, kp_tot - kp_in)
sq = p['starting_count']*0.3 + e['goals']*2.0 + e['assists']*1.0 + kp_in*3.0 + p['fwd_count']*1.5
mi = min(kp_miss/max(kp_tot,1), 1.0)
self.squad_cache[key] = {'squad_quality':sq,'key_players':kp_in,'missing_impact':mi,'goals_form':e['goals']}
def _load_cards_data(self):
self.cur.execute("""
SELECT mpe.match_id,
SUM(CASE WHEN mpe.event_type::text LIKE '%%yellow_card%%' THEN 1
WHEN mpe.event_type::text LIKE '%%red_card%%' THEN 2 ELSE 1 END)
FROM match_player_events mpe JOIN matches m ON mpe.match_id=m.id
WHERE m.status='FT' AND m.sport='football' AND mpe.event_type::text LIKE '%%card%%'
GROUP BY mpe.match_id
""")
for mid, cw in self.cur.fetchall():
self.cards_cache[mid] = float(cw) if cw else 0.0
def load_league_matches(self):
for m in self.matches:
lid = m[8]
if lid not in self.league_matches_cache:
self.league_matches_cache[lid] = []
self.league_matches_cache[lid].append((m[7],None,m[3],m[4],None))
class V27Extractor(V25Extractor):
"""Adds V27 features on top of V25."""
def _extract_one(self, mid, hid, aid, sh, sa, hth, hta, mst, lid,
hn, an, ln):
row = super()._extract_one(mid,hid,aid,sh,sa,hth,hta,mst,lid,hn,an,ln)
if not row: return None
hm = self.loader.team_matches.get(hid,[])
am = self.loader.team_matches.get(aid,[])
hr = calc_rolling_features(hm, mst, True)
ar = calc_rolling_features(am, mst, False)
for pfx,r in [("home",hr),("away",ar)]:
row[f"{pfx}_rolling5_goals"]=r["rolling5_goals_avg"]
row[f"{pfx}_rolling5_conceded"]=r["rolling5_conceded_avg"]
row[f"{pfx}_rolling10_goals"]=r["rolling10_goals_avg"]
row[f"{pfx}_rolling10_conceded"]=r["rolling10_conceded_avg"]
row[f"{pfx}_rolling20_goals"]=r["rolling20_goals_avg"]
row[f"{pfx}_rolling20_conceded"]=r["rolling20_conceded_avg"]
row[f"{pfx}_rolling5_cs"]=r["rolling5_clean_sheets"]
row[f"{pfx}_venue_goals"]=r["venue_goals_avg"]
row[f"{pfx}_venue_conceded"]=r["venue_conceded_avg"]
row[f"{pfx}_goal_trend"]=r["goal_trend"]
lb = [x for x in self.loader.league_matches_cache.get(lid,[]) if x[0]<mst]
lq = calc_league_quality(lb)
for k,v in lq.items(): row[k]=v
ht = calc_time_features(hm, mst)
at = calc_time_features(am, mst)
row["home_days_rest"]=ht["days_rest"]
row["away_days_rest"]=at["days_rest"]
row["match_month"]=ht["match_month"]
row["is_season_start"]=ht["is_season_start"]
row["is_season_end"]=ht["is_season_end"]
h2h = calc_advanced_h2h(hm, hid, aid, mst)
for k,v in h2h.items(): row[k]=v
sd = calc_strength_diff(
{"goals_avg":row.get("home_goals_avg",1.3),"conceded_avg":row.get("home_conceded_avg",1.2),"scoring_rate":row.get("home_scoring_rate",0.75)},
{"goals_avg":row.get("away_goals_avg",1.3),"conceded_avg":row.get("away_conceded_avg",1.2),"scoring_rate":row.get("away_scoring_rate",0.75)},
self.elo_ratings[hid], self.elo_ratings[aid],
row.get("home_momentum_score",0.5), row.get("away_momentum_score",0.5),
row.get("upset_potential",0.0),
)
row.update(sd)
return row
def main():
print("🚀 V27 Value Sniper — Training Data Extraction")
print("="*60)
t0 = time.time()
conn = get_conn()
print("\n📦 Loading ALL odds-bearing matches...")
loader = V27Loader(conn)
loader.load_all()
loader.load_league_matches()
print(f" Matches: {len(loader.matches)}")
print(f" Leagues: {len(loader.league_stats_cache)}")
print(f" Odds: {len(loader.odds_cache)}")
ext = V27Extractor(conn, loader)
rows = ext.extract_all()
if not rows:
print("❌ No data!"); return
print(f"\n💾 Writing {len(rows)} rows...")
with open(OUTPUT,"w",newline="",encoding="utf-8") as f:
w = csv.DictWriter(f, fieldnames=ALL_COLS, extrasaction='ignore')
w.writeheader(); w.writerows(rows)
n = len(rows)
wo = sum(1 for r in rows if r.get("odds_ms_h",0)>0)
md = defaultdict(int)
for r in rows: md[r["label_ms"]]+=1
print(f"\n📊 Summary:")
print(f" Rows: {n}")
print(f" With odds: {wo} ({wo/n*100:.1f}%)")
print(f" Features: {len(ALL_COLS)} ({len(V25_COLS)} V25 + {len(V27_NEW)} new)")
print(f" MS: H={md[0]/n*100:.1f}% D={md[1]/n*100:.1f}% A={md[2]/n*100:.1f}%")
print(f" Time: {(time.time()-t0)/60:.1f}min")
print(f"\n✅ Done! → {OUTPUT}")
conn.close()
if __name__=="__main__":
main()
ai-engine/scripts/strategy_generator.py
+317
View File
@@ -0,0 +1,317 @@
"""
Strategy Generator — Senin Excel mantığını DB üzerinde otomatize eder.
Mantık:
1. Ev sahibi takım X, evinde oran bandı Y'de oynadığında → OU1.5/OU2.5/BTTS oranları
2. Deplasman takım Z, deplasmanda oran bandı W'de oynadığında → OU1.5/OU2.5/BTTS oranları
3. İkisi de yüksekse → STRATEJİ ÜRET
Çıktı: Her maç için hangi bahis oynanabilir, neden, ve geçmiş başarı oranı
"""
import psycopg2
import pandas as pd
import numpy as np
from collections import defaultdict
from datetime import datetime
# DB connection
conn = psycopg2.connect(
host="localhost",
port=15432,
dbname="boilerplate_db",
user="suggestbet",
password="SuGGesT2026SecuRe"
)
print("=" * 70)
print(" STRATEGY GENERATOR — Veritabanından Strateji Üretimi")
print("=" * 70)
# 1. Tüm biten maçları, takım adları ve MS oranlarıyla çek
query = """
SELECT
m.id as match_id,
m.home_team_id,
m.away_team_id,
m.league_id,
m.score_home,
m.score_away,
m.mst_utc,
ht.name as home_team,
at.name as away_team,
l.name as league_name
FROM matches m
JOIN teams ht ON m.home_team_id = ht.id
JOIN teams at ON m.away_team_id = at.id
JOIN leagues l ON m.league_id = l.id
WHERE m.status = 'FT'
AND m.score_home IS NOT NULL
ORDER BY m.mst_utc ASC
"""
df = pd.read_sql(query, conn)
print(f"\nToplam biten maç: {len(df):,}")
# 2. Tüm oranları çek (MS, OU25, BTTS, OU15)
odds_query = """
SELECT
oc.match_id,
oc.name as market,
os.name as selection,
CAST(os.odd_value AS DECIMAL) as odds
FROM odd_categories oc
JOIN odd_selections os ON os.odd_category_db_id = oc.db_id
WHERE oc.name IN (
'Maç Sonucu',
'2,5 Alt/Üst',
'1,5 Alt/Üst',
'3,5 Alt/Üst',
'Karşılıklı Gol'
)
"""
odds_df = pd.read_sql(odds_query, conn)
print(f"Toplam oran kaydı: {len(odds_df):,}")
# Pivot: her maç için oranları sütunlara çevir
def get_odds(match_id, market, selection):
mask = (odds_df.match_id == match_id) & (odds_df.market == market) & (odds_df.selection == selection)
vals = odds_df.loc[mask, 'odds']
return float(vals.iloc[0]) if len(vals) > 0 else None
# Daha verimli: oran lookup dict oluştur
print("Oran lookup oluşturuluyor...")
odds_lookup = {}
for _, row in odds_df.iterrows():
key = (row.match_id, row.market, row.selection)
odds_lookup[key] = float(row.odds)
def get_o(mid, market, sel):
return odds_lookup.get((mid, market, sel))
# 3. Her maça oranları ekle
print("Maçlara oranlar ekleniyor...")
df['odds_ms_h'] = df.match_id.map(lambda x: get_o(x, 'Maç Sonucu', '1'))
df['odds_ms_a'] = df.match_id.map(lambda x: get_o(x, 'Maç Sonucu', '2'))
df['odds_ms_d'] = df.match_id.map(lambda x: get_o(x, 'Maç Sonucu', '0'))
df['odds_ou25_o'] = df.match_id.map(lambda x: get_o(x, '2,5 Alt/Üst', 'Üst'))
df['odds_ou25_u'] = df.match_id.map(lambda x: get_o(x, '2,5 Alt/Üst', 'Alt'))
df['odds_ou15_o'] = df.match_id.map(lambda x: get_o(x, '1,5 Alt/Üst', 'Üst'))
df['odds_ou15_u'] = df.match_id.map(lambda x: get_o(x, '1,5 Alt/Üst', 'Alt'))
df['odds_ou35_o'] = df.match_id.map(lambda x: get_o(x, '3,5 Alt/Üst', 'Üst'))
df['odds_ou35_u'] = df.match_id.map(lambda x: get_o(x, '3,5 Alt/Üst', 'Alt'))
df['odds_btts_y'] = df.match_id.map(lambda x: get_o(x, 'Karşılıklı Gol', 'Var'))
df['odds_btts_n'] = df.match_id.map(lambda x: get_o(x, 'Karşılıklı Gol', 'Yok'))
# Sonuç hesapla
df['total_goals'] = df.score_home + df.score_away
df['ou15'] = (df.total_goals > 1).astype(int)
df['ou25'] = (df.total_goals > 2).astype(int)
df['ou35'] = (df.total_goals > 3).astype(int)
df['btts'] = ((df.score_home > 0) & (df.score_away > 0)).astype(int)
print(f"Oranı olan maç sayısı: {df.odds_ms_h.notna().sum():,}")
# 4. ORAN BANDI fonksiyonu
def odds_band(odds):
if pd.isna(odds): return None
if odds < 1.30: return '1.00-1.30'
if odds < 1.50: return '1.30-1.50'
if odds < 1.80: return '1.50-1.80'
if odds < 2.20: return '1.80-2.20'
if odds < 2.80: return '2.20-2.80'
if odds < 4.00: return '2.80-4.00'
if odds < 6.00: return '4.00-6.00'
return '6.00+'
# 5. STRATEJİ: Expanding window — sadece geçmiş veriye bakarak tahmin
print("\n" + "=" * 70)
print(" STRATEJİ BACKTEST — Expanding Window")
print("=" * 70)
# Ev sahibi geçmişi: {team_id: {odds_band: [ou15, ou25, btts, ou35, ...]}}
home_history = defaultdict(lambda: defaultdict(list))
away_history = defaultdict(lambda: defaultdict(list))
MIN_MATCHES = 8 # Minimum geçmiş maç sayısı
TEST_PCT = 0.30 # Son %30 test
N = len(df)
test_start = int(N * (1 - TEST_PCT))
results = {
'ou15_over': [], 'ou25_over': [], 'ou35_over': [],
'btts_yes': [], 'btts_no': [],
'ou25_under': [], 'ou15_under': [],
'ms_home': []
}
for i in range(N):
row = df.iloc[i]
h_odds = row.odds_ms_h
a_odds = row.odds_ms_a
if pd.isna(h_odds) or pd.isna(a_odds):
continue
h_band = odds_band(h_odds)
a_band = odds_band(a_odds)
# TEST: sadece test bölümünde bahis yap
if i >= test_start:
h_hist = home_history[row.home_team_id][h_band]
a_hist = away_history[row.away_team_id][a_band]
if len(h_hist) >= MIN_MATCHES and len(a_hist) >= MIN_MATCHES:
# Ev sahibi bu oran bandında ne yapmış?
h_ou15 = np.mean([x[0] for x in h_hist])
h_ou25 = np.mean([x[1] for x in h_hist])
h_ou35 = np.mean([x[2] for x in h_hist])
h_btts = np.mean([x[3] for x in h_hist])
h_win = np.mean([x[4] for x in h_hist])
# Deplasman bu oran bandında ne yapmış?
a_ou15 = np.mean([x[0] for x in a_hist])
a_ou25 = np.mean([x[1] for x in a_hist])
a_ou35 = np.mean([x[2] for x in a_hist])
a_btts = np.mean([x[3] for x in a_hist])
a_loss = np.mean([x[4] for x in a_hist]) # deplasman kaybetme oranı
# KOMBİNE SİNYAL
sig_ou15 = (h_ou15 + a_ou15) / 2
sig_ou25 = (h_ou25 + a_ou25) / 2
sig_ou35 = (h_ou35 + a_ou35) / 2
sig_btts = (h_btts + a_btts) / 2
sig_hw = (h_win + a_loss) / 2 # ev kazanma + deplasman kaybetme
base = {
'match': f"{row.home_team} vs {row.away_team}",
'league': row.league_name,
'home_team': row.home_team,
'away_team': row.away_team,
'h_band': h_band,
'a_band': a_band,
'h_n': len(h_hist),
'a_n': len(a_hist),
}
# OU 1.5 OVER
if sig_ou15 >= 0.85 and row.odds_ou15_o and row.odds_ou15_o > 1.01:
results['ou15_over'].append({
**base, 'signal': sig_ou15, 'odds': row.odds_ou15_o,
'won': row.ou15 == 1, 'actual_goals': row.total_goals,
'h_sig': h_ou15, 'a_sig': a_ou15
})
# OU 2.5 OVER
if sig_ou25 >= 0.70 and row.odds_ou25_o and row.odds_ou25_o > 1.10:
results['ou25_over'].append({
**base, 'signal': sig_ou25, 'odds': row.odds_ou25_o,
'won': row.ou25 == 1, 'actual_goals': row.total_goals,
'h_sig': h_ou25, 'a_sig': a_ou25
})
# OU 3.5 OVER
if sig_ou35 >= 0.60 and row.odds_ou35_o and row.odds_ou35_o > 1.20:
results['ou35_over'].append({
**base, 'signal': sig_ou35, 'odds': row.odds_ou35_o,
'won': row.ou35 == 1, 'actual_goals': row.total_goals,
'h_sig': h_ou35, 'a_sig': a_ou35
})
# BTTS YES
if sig_btts >= 0.70 and row.odds_btts_y and row.odds_btts_y > 1.10:
results['btts_yes'].append({
**base, 'signal': sig_btts, 'odds': row.odds_btts_y,
'won': row.btts == 1, 'actual_goals': row.total_goals,
'h_sig': h_btts, 'a_sig': a_btts
})
# OU 2.5 UNDER (düşük gol beklentisi)
if sig_ou25 <= 0.30 and row.odds_ou25_u and row.odds_ou25_u > 1.10:
results['ou25_under'].append({
**base, 'signal': 1-sig_ou25, 'odds': row.odds_ou25_u,
'won': row.ou25 == 0, 'actual_goals': row.total_goals,
'h_sig': 1-h_ou25, 'a_sig': 1-a_ou25
})
# MS HOME WIN (ev sahibi kazanma)
if sig_hw >= 0.75 and row.odds_ms_h and 1.10 < row.odds_ms_h < 3.50:
results['ms_home'].append({
**base, 'signal': sig_hw, 'odds': row.odds_ms_h,
'won': row.score_home > row.score_away,
'actual_goals': row.total_goals,
'h_sig': h_win, 'a_sig': a_loss
})
# History güncelle (her zaman)
home_history[row.home_team_id][h_band].append((
row.ou15, row.ou25, row.ou35, row.btts,
int(row.score_home > row.score_away)
))
away_history[row.away_team_id][a_band].append((
row.ou15, row.ou25, row.ou35, row.btts,
int(row.score_away < row.score_home) # deplasman kaybetme
))
# 6. SONUÇLARI YAZIDIR
print(f"\nTest bölümü: son {TEST_PCT*100:.0f}% ({N - test_start:,} maç)")
print(f"Minimum geçmiş: {MIN_MATCHES} maç\n")
for market_name, bets in results.items():
if not bets:
print(f"\n {market_name}: sinyal yok")
continue
bdf = pd.DataFrame(bets)
total = len(bdf)
wins = bdf.won.sum()
hit = wins / total * 100
pnl = (bdf.won * (bdf.odds - 1) - (~bdf.won) * 1).sum()
roi = pnl / total * 100
avg_odds = bdf.odds.mean()
print(f"\n{'='*60}")
print(f" {market_name.upper()}")
print(f"{'='*60}")
print(f" Toplam bahis: {total}")
print(f" Kazanan: {wins} ({hit:.1f}%)")
print(f" Ortalama odds: {avg_odds:.2f}")
print(f" PnL: {pnl:+.1f} birim")
print(f" ROI: {roi:+.1f}%")
# Farklı sinyal eşiklerinde performans
print(f"\n Sinyal eşik analizi:")
for threshold in [0.70, 0.75, 0.80, 0.85, 0.90, 0.95]:
sub = bdf[bdf.signal >= threshold]
if len(sub) < 5: continue
w = sub.won.sum()
p = (sub.won * (sub.odds - 1) - (~sub.won) * 1).sum()
r = p / len(sub) * 100
star = ' ✅ PROFIT' if r > 0 else (' ⚖️ BE' if r > -3 else '')
print(f" ≥{threshold:.2f}: {len(sub):5d} bahis, hit={w/len(sub)*100:.1f}%, ROI={r:+.1f}%{star}")
# En iyi 10 örnek (kazanan)
if wins > 0:
best = bdf[bdf.won].nlargest(min(5, wins), 'signal')
print(f"\n Örnek kazanan bahisler:")
for _, b in best.iterrows():
print(f" {b.home_team} vs {b.away_team} ({b.league})")
print(f" Ev {b.h_band} ({b.h_sig:.0%}) + Dep {b.a_band} ({b.a_sig:.0%}) → sinyal={b.signal:.0%}, odds={b.odds:.2f}, gol={b.actual_goals:.0f}")
# 7. ÖZET TABLO
print("\n\n" + "=" * 70)
print(" ÖZET TABLO")
print("=" * 70)
print(f"{'Market':<15} {'Bahis':>6} {'Hit':>7} {'ROI':>8} {'Avg Odds':>9}")
print("-" * 50)
for market_name, bets in results.items():
if not bets: continue
bdf = pd.DataFrame(bets)
total = len(bdf)
wins = bdf.won.sum()
hit = wins / total * 100
pnl = (bdf.won * (bdf.odds - 1) - (~bdf.won) * 1).sum()
roi = pnl / total * 100
avg_odds = bdf.odds.mean()
print(f"{market_name:<15} {total:>6} {hit:>6.1f}% {roi:>+7.1f}% {avg_odds:>8.2f}")
conn.close()
print("\n✅ Tamamlandı!")
ai-engine/scripts/train_v26_shadow.py
+58
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from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
DATA_DIR = AI_ENGINE_DIR / "data" / "v26_shadow"
CONFIG_PATH = AI_ENGINE_DIR / "models" / "v26_shadow" / "market_profiles.json"
REPORT_PATH = AI_ENGINE_DIR / "reports" / "training_v26_shadow.json"
REPORT_PATH.parent.mkdir(parents=True, exist_ok=True)
def _market_accuracy(frame: pd.DataFrame, target_col: str) -> float:
if target_col not in frame.columns or frame.empty:
return 0.0
counts = frame[target_col].value_counts(normalize=True)
if counts.empty:
return 0.0
return round(float(counts.max()), 4)
def main() -> None:
train_csv = DATA_DIR / "train.csv"
validation_csv = DATA_DIR / "validation.csv"
if not train_csv.exists() or not validation_csv.exists():
raise SystemExit("Run extract_training_data_v26.py first")
train_df = pd.read_csv(train_csv)
validation_df = pd.read_csv(validation_csv)
config = json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
report = {
"version": config.get("version"),
"calibration_version": config.get("calibration_version"),
"train_rows": int(len(train_df)),
"validation_rows": int(len(validation_df)),
"label_priors": {
"MS": _market_accuracy(validation_df, "label_ms"),
"OU25": _market_accuracy(validation_df, "label_ou25"),
"BTTS": _market_accuracy(validation_df, "label_btts"),
"HT": _market_accuracy(validation_df, "label_ht_result"),
"HTFT": _market_accuracy(validation_df, "label_ht_ft"),
"CARDS": _market_accuracy(validation_df, "label_cards_ou45"),
},
"artifact_path": str(CONFIG_PATH),
"notes": [
"v26.shadow runtime currently uses artifact-based calibration and ROI gating",
"market profile JSON remains the source of truth for runtime thresholds",
],
}
REPORT_PATH.write_text(json.dumps(report, indent=2), encoding="utf-8")
print(f"[OK] Shadow training report written to {REPORT_PATH}")
if __name__ == "__main__":
main()
ai-engine/scripts/train_v27_value_sniper.py
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"""
V27 Value Sniper — PRO Training Script
========================================
KEY INSIGHT: Train model WITHOUT odds to get independent probability.
Then compare with market odds to find genuine value edges.
Strategy:
Stage A: "Fundamentals Model" — odds-free, learns from ELO/form/rolling/H2H
Stage B: "Value Model" — uses fundamentals + odds disagreement as features
Stage C: Multi-market — 1X2, O/U 2.5, BTTS
Stage D: Walk-forward backtest with Kelly sizing
"""
import os, sys, json, pickle, time, warnings
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.metrics import accuracy_score, log_loss
from sklearn.isotonic import IsotonicRegression
warnings.filterwarnings("ignore")
AI_DIR = Path(__file__).resolve().parent.parent
DATA_CSV = AI_DIR / "data" / "training_data_v27.csv"
MODELS_DIR = AI_DIR / "models" / "v27"
MODELS_DIR.mkdir(parents=True, exist_ok=True)
# ── Leakage & category definitions ──
LEAKAGE_COLS = [
"total_goals", "goal_diff", "ht_total_goals", "ht_goal_diff",
"score_home", "score_away", "ht_score_home", "ht_score_away",
"home_goals_form", "away_goals_form",
"home_squad_quality", "away_squad_quality", "squad_diff",
"home_key_players", "away_key_players",
"home_missing_impact", "away_missing_impact",
"referee_home_bias", "referee_avg_goals", "referee_cards_total",
"referee_avg_yellow", "referee_avg_red", "referee_penalty_rate",
"referee_over25_rate", "referee_experience", "referee_matches",
]
LABEL_COLS = [c for c in [] ] # populated dynamically
META_COLS = ["match_id", "league_name", "home_team", "away_team"]
ODDS_COLS_PATTERNS = ["odds_", "implied_"]
def get_odds_cols(df):
return [c for c in df.columns if any(c.startswith(p) for p in ODDS_COLS_PATTERNS)]
def get_label_cols(df):
return [c for c in df.columns if c.startswith("label_")]
def get_clean_features(df):
"""Features with NO odds and NO leakage — pure fundamentals."""
odds = set(get_odds_cols(df))
labels = set(get_label_cols(df))
exclude = odds | labels | set(LEAKAGE_COLS) | set(META_COLS)
# Also exclude ID columns
exclude |= {c for c in df.columns if c.endswith("_id") and c != "match_id"}
feats = [c for c in df.columns if c not in exclude]
# Keep only numeric
feats = [c for c in feats if pd.to_numeric(df[c], errors="coerce").notna().sum() > len(df)*0.3]
return feats
def load_data():
print(f"Loading {DATA_CSV}...")
df = pd.read_csv(DATA_CSV, low_memory=False)
print(f" Raw: {len(df)} rows")
# Ensure odds exist for value comparison
for c in ["odds_ms_h","odds_ms_d","odds_ms_a"]:
df[c] = pd.to_numeric(df[c], errors="coerce")
df = df.dropna(subset=["odds_ms_h","odds_ms_d","odds_ms_a"])
df = df[(df.odds_ms_h>1.01)&(df.odds_ms_d>1.01)&(df.odds_ms_a>1.01)]
# OU25 odds
for c in ["odds_ou25_over","odds_ou25_under"]:
if c in df.columns:
df[c] = pd.to_numeric(df[c], errors="coerce")
# Implied probabilities
margin = 1/df.odds_ms_h + 1/df.odds_ms_d + 1/df.odds_ms_a
df["implied_h"] = (1/df.odds_ms_h)/margin
df["implied_d"] = (1/df.odds_ms_d)/margin
df["implied_a"] = (1/df.odds_ms_a)/margin
print(f" After filter: {len(df)} rows")
return df
def temporal_split(df, val_ratio=0.15, test_ratio=0.10):
n = len(df)
tr = int(n*(1-val_ratio-test_ratio))
va = int(n*(1-test_ratio))
return df.iloc[:tr].copy(), df.iloc[tr:va].copy(), df.iloc[va:].copy()
# ═══════════════════════════════════════════════════════════════════
# STAGE A: Fundamentals-Only Model (NO ODDS)
# ═══════════════════════════════════════════════════════════════════
def train_fundamentals_model(X_tr, y_tr, X_va, y_va, feat_cols, market="ms"):
"""Train ensemble WITHOUT odds features."""
models = {}
n_class = 3 if market == "ms" else 2
# XGBoost
try:
import xgboost as xgb
print(f" [XGB] Training {market.upper()}...")
dtrain = xgb.DMatrix(X_tr, label=y_tr, feature_names=feat_cols)
dval = xgb.DMatrix(X_va, label=y_va, feature_names=feat_cols)
params = {
"objective": "multi:softprob" if n_class==3 else "binary:logistic",
"eval_metric": "mlogloss" if n_class==3 else "logloss",
"max_depth": 6, "learning_rate": 0.02, "subsample": 0.75,
"colsample_bytree": 0.75, "min_child_weight": 10,
"reg_alpha": 0.5, "reg_lambda": 2.0,
"verbosity": 0, "tree_method": "hist",
}
if n_class == 3:
params["num_class"] = 3
m = xgb.train(params, dtrain, num_boost_round=2000,
evals=[(dval,"val")], early_stopping_rounds=80,
verbose_eval=False)
p = m.predict(dval)
if n_class == 2:
p = np.column_stack([1-p, p])
acc = accuracy_score(y_va, p.argmax(1))
print(f" acc={acc:.4f}")
models["xgb"] = m
except ImportError:
pass
# LightGBM
try:
import lightgbm as lgb
print(f" [LGB] Training {market.upper()}...")
ds_tr = lgb.Dataset(X_tr, label=y_tr)
ds_va = lgb.Dataset(X_va, label=y_va, reference=ds_tr)
par = {
"objective": "multiclass" if n_class==3 else "binary",
"metric": "multi_logloss" if n_class==3 else "binary_logloss",
"num_leaves": 48, "learning_rate": 0.02,
"feature_fraction": 0.7, "bagging_fraction": 0.7,
"bagging_freq": 1, "min_child_samples": 30,
"lambda_l1": 0.5, "lambda_l2": 2.0, "verbose": -1,
}
if n_class == 3:
par["num_class"] = 3
m = lgb.train(par, ds_tr, 2000, valid_sets=[ds_va],
callbacks=[lgb.early_stopping(80, verbose=False)])
p = m.predict(X_va)
if n_class == 2:
p = np.column_stack([1-p, p])
acc = accuracy_score(y_va, p.argmax(1))
print(f" acc={acc:.4f}")
models["lgb"] = m
except ImportError:
pass
# CatBoost
try:
from catboost import CatBoostClassifier
print(f" [CB] Training {market.upper()}...")
m = CatBoostClassifier(
iterations=2000, learning_rate=0.02, depth=6,
l2_leaf_reg=5, loss_function="MultiClass" if n_class==3 else "Logloss",
early_stopping_rounds=80, verbose=0, task_type="CPU",
**({"classes_count": 3} if n_class==3 else {}),
)
m.fit(X_tr, y_tr, eval_set=(X_va, y_va))
p = m.predict_proba(X_va)
acc = accuracy_score(y_va, p.argmax(1))
print(f" acc={acc:.4f}")
models["cb"] = m
except ImportError:
pass
return models
def ensemble_predict(models, X, feat_cols, n_class=3):
preds = []
for name, m in models.items():
if name == "xgb":
import xgboost as xgb
dm = xgb.DMatrix(X, feature_names=feat_cols)
p = m.predict(dm)
if n_class == 2 and p.ndim == 1:
p = np.column_stack([1-p, p])
elif name == "lgb":
p = m.predict(X)
if n_class == 2 and p.ndim == 1:
p = np.column_stack([1-p, p])
elif name == "cb":
p = m.predict_proba(X)
preds.append(np.array(p))
if not preds:
raise RuntimeError("No models!")
return np.mean(preds, axis=0)
# ═══════════════════════════════════════════════════════════════════
# STAGE B: Walk-Forward Backtest with Kelly
# ═══════════════════════════════════════════════════════════════════
def kelly_fraction(model_prob, odds, fraction=0.25):
"""Fractional Kelly: f = fraction * (p*odds - 1) / (odds - 1)"""
edge = model_prob * odds - 1
if edge <= 0 or odds <= 1:
return 0.0
f = edge / (odds - 1)
return max(0, min(fraction * f, 0.10)) # cap at 10% bankroll
def backtest_value(models, df_test, feat_cols, market="ms",
min_edge=0.05, min_odds=1.40, max_odds=4.50,
use_kelly=True):
"""Realistic backtest: flat or Kelly sizing, edge filtering."""
X = df_test[feat_cols].values
n_class = 3 if market == "ms" else 2
probs = ensemble_predict(models, X, feat_cols, n_class)
if market == "ms":
y = df_test["label_ms"].values
odds_arr = df_test[["odds_ms_h","odds_ms_d","odds_ms_a"]].values
implied = df_test[["implied_h","implied_d","implied_a"]].values
class_names = ["Home","Draw","Away"]
elif market == "ou25":
if "label_ou25" not in df_test.columns:
return {}
y = df_test["label_ou25"].values
o_over = pd.to_numeric(df_test["odds_ou25_o"], errors="coerce").fillna(1.85).values if "odds_ou25_o" in df_test.columns else np.full(len(df_test), 1.85)
o_under = pd.to_numeric(df_test["odds_ou25_u"], errors="coerce").fillna(1.85).values if "odds_ou25_u" in df_test.columns else np.full(len(df_test), 1.85)
odds_arr = np.column_stack([o_under, o_over])
m = 1/odds_arr
implied = m / m.sum(axis=1, keepdims=True)
class_names = ["Under","Over"]
else:
return {}
results = {"bets": [], "total": 0, "wins": 0, "pnl": 0.0, "bankroll_curve": [1000.0]}
bankroll = 1000.0
for i in range(len(y)):
for cls in range(n_class):
edge = probs[i, cls] - implied[i, cls]
odds_val = odds_arr[i, cls]
# FILTERS
if edge < min_edge:
continue
if odds_val < min_odds or odds_val > max_odds:
continue
# Don't bet on heavy favorites with tiny edge
if implied[i, cls] > 0.65 and edge < 0.08:
continue
# Sizing
if use_kelly:
frac = kelly_fraction(probs[i, cls], odds_val, fraction=0.15)
stake = bankroll * frac
else:
stake = 10.0 # flat
if stake < 1:
continue
won = (y[i] == cls)
pnl = stake * (odds_val - 1) if won else -stake
bankroll += pnl
results["bets"].append({
"edge": float(edge), "odds": float(odds_val),
"model_p": float(probs[i,cls]), "implied_p": float(implied[i,cls]),
"won": bool(won), "pnl": float(pnl), "stake": float(stake),
"class": class_names[cls],
})
results["bankroll_curve"].append(bankroll)
results["total"] += 1
if won:
results["wins"] += 1
results["pnl"] = bankroll - 1000.0
return results
def print_backtest(results, label=""):
total = results.get("total", 0)
if total == 0:
print(f" {label}: No bets placed")
return
wins = results["wins"]
pnl = results["pnl"]
hit = wins/total*100
roi = pnl / sum(b["stake"] for b in results["bets"]) * 100
curve = results["bankroll_curve"]
peak = max(curve)
dd = min((c - peak) / peak * 100 for c in curve if c <= peak) if len(curve) > 1 else 0
# Per-class breakdown
by_class = {}
for b in results["bets"]:
cls = b["class"]
if cls not in by_class:
by_class[cls] = {"n": 0, "w": 0, "pnl": 0}
by_class[cls]["n"] += 1
if b["won"]:
by_class[cls]["w"] += 1
by_class[cls]["pnl"] += b["pnl"]
print(f"\n {label}")
print(f" Bets: {total} | Hit: {hit:.1f}% | ROI: {roi:+.1f}%")
print(f" PnL: {pnl:+.0f} | Final: {curve[-1]:.0f} | MaxDD: {dd:.1f}%")
for cls, d in sorted(by_class.items()):
r = d["pnl"]/d["n"]*100 if d["n"] > 0 else 0
print(f" {cls:6s}: {d['n']:4d} bets, "
f"hit={d['w']/d['n']*100:.1f}%, avg_pnl={r:+.1f}%")
# ═══════════════════════════════════════════════════════════════════
# MAIN
# ═══════════════════════════════════════════════════════════════════
def main():
print("=" * 65)
print(" V27 VALUE SNIPER — PRO TRAINING (Odds-Free Fundamentals)")
print("=" * 65)
t0 = time.time()
df = load_data()
clean_feats = get_clean_features(df)
print(f" Clean features (no odds): {len(clean_feats)}")
# Numerify
for c in clean_feats:
df[c] = pd.to_numeric(df[c], errors="coerce")
df[clean_feats] = df[clean_feats].fillna(df[clean_feats].median())
# Remove constant columns
clean_feats = [c for c in clean_feats if df[c].nunique() > 1]
print(f" After removing constants: {len(clean_feats)}")
# Split
tr, va, te = temporal_split(df)
print(f" Train: {len(tr)}, Val: {len(va)}, Test: {len(te)}")
print(f" Target: H={tr.label_ms.eq(0).mean():.1%}, "
f"D={tr.label_ms.eq(1).mean():.1%}, A={tr.label_ms.eq(2).mean():.1%}")
X_tr = tr[clean_feats].values
y_tr = tr["label_ms"].values
X_va = va[clean_feats].values
y_va = va["label_ms"].values
# ── STAGE A: Train fundamentals model (1X2) ──
print("\n" + "─"*65)
print(" STAGE A: Fundamentals-Only 1X2 Model")
print("─"*65)
ms_models = train_fundamentals_model(X_tr, y_tr, X_va, y_va, clean_feats, "ms")
val_probs = ensemble_predict(ms_models, X_va, clean_feats, 3)
val_acc = accuracy_score(y_va, val_probs.argmax(1))
val_ll = log_loss(y_va, val_probs)
print(f"\n Ensemble Val: acc={val_acc:.4f}, logloss={val_ll:.4f}")
# Compare with odds baseline
odds_pred = va[["implied_h","implied_d","implied_a"]].values.argmax(1)
odds_acc = accuracy_score(y_va, odds_pred)
print(f" Odds baseline: acc={odds_acc:.4f}")
print(f" Model vs Odds: {val_acc - odds_acc:+.4f}")
# ── STAGE B: O/U 2.5 Model ──
ou_models = None
if "label_ou25" in tr.columns:
print("\n" + "─"*65)
print(" STAGE A.2: Fundamentals-Only O/U 2.5 Model")
print("─"*65)
y_tr_ou = tr["label_ou25"].values
y_va_ou = va["label_ou25"].values
mask_tr = ~np.isnan(y_tr_ou)
mask_va = ~np.isnan(y_va_ou)
if mask_tr.sum() > 1000:
ou_models = train_fundamentals_model(
X_tr[mask_tr], y_tr_ou[mask_tr].astype(int),
X_va[mask_va], y_va_ou[mask_va].astype(int),
clean_feats, "ou25")
# ── STAGE C: Backtest ──
print("\n" + "─"*65)
print(" STAGE B: Walk-Forward Backtest (Test Set)")
print("─"*65)
# Try multiple edge thresholds
best_roi = -999
best_cfg = {}
for min_edge in [0.03, 0.05, 0.07, 0.10, 0.12, 0.15]:
for min_odds in [1.35, 1.50, 1.70]:
r = backtest_value(ms_models, te, clean_feats, "ms",
min_edge=min_edge, min_odds=min_odds,
max_odds=5.0, use_kelly=True)
if r.get("total", 0) >= 20:
invested = sum(b["stake"] for b in r["bets"])
roi = r["pnl"] / invested * 100 if invested > 0 else -100
if roi > best_roi:
best_roi = roi
best_cfg = {"edge": min_edge, "min_odds": min_odds, "result": r}
if best_cfg:
cfg = best_cfg
print(f"\n Best 1X2 Config: edge>{cfg['edge']}, odds>{cfg['min_odds']}")
print_backtest(cfg["result"], "1X2 VALUE")
# Flat bet comparison
print("\n --- Flat Bet Comparison ---")
for edge in [0.05, 0.07, 0.10]:
r = backtest_value(ms_models, te, clean_feats, "ms",
min_edge=edge, min_odds=1.50, max_odds=4.5,
use_kelly=False)
if r.get("total", 0) > 0:
inv = r["total"] * 10
roi = r["pnl"]/inv*100
print(f" Edge>{edge:.2f}: {r['total']} bets, "
f"hit={r['wins']/r['total']*100:.1f}%, ROI={roi:+.1f}%")
# OU25 backtest
if ou_models:
print("\n --- O/U 2.5 Backtest ---")
for edge in [0.05, 0.07, 0.10]:
r = backtest_value(ou_models, te, clean_feats, "ou25",
min_edge=edge, min_odds=1.50, max_odds=3.0,
use_kelly=True)
if r.get("total", 0) > 0:
print_backtest(r, f"OU25 edge>{edge}")
# ── Feature importance ──
if "lgb" in ms_models:
imp = ms_models["lgb"].feature_importance(importance_type="gain")
imp_df = pd.DataFrame({"feature": clean_feats, "importance": imp}
).sort_values("importance", ascending=False)
print("\n TOP 15 FEATURES (no odds!):")
for _, r in imp_df.head(15).iterrows():
print(f" {r['feature']:40s} {r['importance']:.0f}")
imp_df.to_csv(MODELS_DIR / "v27_feature_importance.csv", index=False)
# ── Save ──
print("\n" + "─"*65)
print(" SAVING MODELS")
print("─"*65)
for name, m in ms_models.items():
p = MODELS_DIR / f"v27_ms_{name}.pkl"
with open(p, "wb") as f:
pickle.dump(m, f)
print(f" ✓ {p.name}")
if ou_models:
for name, m in ou_models.items():
p = MODELS_DIR / f"v27_ou25_{name}.pkl"
with open(p, "wb") as f:
pickle.dump(m, f)
print(f" ✓ {p.name}")
meta = {
"version": "v27-pro", "trained_at": time.strftime("%Y-%m-%d %H:%M:%S"),
"approach": "odds-free fundamentals + value edge detection",
"feature_count": len(clean_feats),
"total_samples": len(df),
"val_acc": round(val_acc, 4), "val_ll": round(val_ll, 4),
"best_config": {k: v for k, v in best_cfg.items() if k != "result"} if best_cfg else {},
"markets": ["ms"] + (["ou25"] if ou_models else []),
}
with open(MODELS_DIR / "v27_metadata.json", "w") as f:
json.dump(meta, f, indent=2, default=str)
with open(MODELS_DIR / "v27_feature_cols.json", "w") as f:
json.dump(clean_feats, f, indent=2)
print(f" ✓ metadata + feature_cols")
print(f"\n Total time: {(time.time()-t0)/60:.1f} min")
print(" DONE!")
if __name__ == "__main__":
main()
ai-engine/services/feature_enrichment.py
+240
View File
@@ -36,6 +36,11 @@ class FeatureEnrichmentService:
'avg_goals': 2.5,
'btts_rate': 0.5,
'over25_rate': 0.5,
# V27 expanded
'home_goals_avg': 1.3,
'away_goals_avg': 1.1,
'recent_trend': 0.0,
'venue_advantage': 0.0,
}
_DEFAULT_FORM = {
'clean_sheet_rate': 0.2,
@@ -53,6 +58,25 @@ class FeatureEnrichmentService:
_DEFAULT_LEAGUE = {
'avg_goals': 2.7,
'zero_goal_rate': 0.07,
# V27 expanded
'home_win_rate': 0.46,
'draw_rate': 0.26,
'btts_rate': 0.50,
'ou25_rate': 0.50,
'reliability_score': 0.0,
}
_DEFAULT_ROLLING = {
'rolling5_goals': 1.3,
'rolling5_conceded': 1.2,
'rolling10_goals': 1.3,
'rolling10_conceded': 1.2,
'rolling20_goals': 1.3,
'rolling20_conceded': 1.2,
'rolling5_cs': 0.2,
}
_DEFAULT_VENUE = {
'venue_goals': 1.4,
'venue_conceded': 1.1,
}
# ─── 1. Team Stats ──────────────────────────────────────────────
@@ -186,6 +210,13 @@ class FeatureEnrichmentService:
total_goals = 0
btts_count = 0
over25_count = 0
# V27 expanded trackers
home_team_goals_list = []
away_team_goals_list = []
home_team_venue_wins = 0
home_team_venue_total = 0
away_team_venue_wins = 0
away_team_venue_total = 0
for row in rows:
sh = int(row['score_home'])
@@ -195,14 +226,22 @@ class FeatureEnrichmentService:
# Normalise: who is "home team" in THIS prediction context
if str(row['home_team_id']) == home_team_id:
home_team_goals_list.append(sh)
away_team_goals_list.append(sa)
home_team_venue_total += 1
if sh > sa:
home_wins += 1
home_team_venue_wins += 1
elif sh == sa:
draws += 1
else:
# Reversed fixture: away_team was at home
home_team_goals_list.append(sa)
away_team_goals_list.append(sh)
away_team_venue_total += 1
if sa > sh:
home_wins += 1
away_team_venue_wins += 1
elif sh == sa:
draws += 1
@@ -211,6 +250,29 @@ class FeatureEnrichmentService:
if match_goals > 2:
over25_count += 1
# V27: recent_trend = last-5 home_win_rate - first-5 home_win_rate
recent_trend = 0.0
if total >= 6:
recent_5_wins = sum(
1 for r in rows[:5]
if (str(r['home_team_id']) == home_team_id and int(r['score_home']) > int(r['score_away']))
or (str(r['home_team_id']) != home_team_id and int(r['score_away']) > int(r['score_home']))
)
older_5_wins = sum(
1 for r in rows[-5:]
if (str(r['home_team_id']) == home_team_id and int(r['score_home']) > int(r['score_away']))
or (str(r['home_team_id']) != home_team_id and int(r['score_away']) > int(r['score_home']))
)
recent_trend = (recent_5_wins - older_5_wins) / 5.0
# V27: venue_advantage = home_win_rate_at_home - home_win_rate_away
venue_advantage = 0.0
if home_team_venue_total > 0 and away_team_venue_total > 0:
venue_advantage = (
home_team_venue_wins / home_team_venue_total
- away_team_venue_wins / away_team_venue_total
)
return {
'total_matches': total,
'home_win_rate': home_wins / total,
@@ -218,6 +280,11 @@ class FeatureEnrichmentService:
'avg_goals': total_goals / total,
'btts_rate': btts_count / total,
'over25_rate': over25_count / total,
# V27 expanded
'home_goals_avg': _safe_avg(home_team_goals_list, 1.3),
'away_goals_avg': _safe_avg(away_team_goals_list, 1.1),
'recent_trend': round(recent_trend, 4),
'venue_advantage': round(venue_advantage, 4),
}
# ─── 3. Form & Streaks ──────────────────────────────────────────
@@ -433,6 +500,10 @@ class FeatureEnrichmentService:
total = len(rows)
total_goals = 0
zero_goal_matches = 0
home_wins = 0
draw_count = 0
btts_count = 0
over25_count = 0
for row in rows:
sh = int(row['score_home'])
@@ -441,10 +512,24 @@ class FeatureEnrichmentService:
total_goals += match_goals
if match_goals == 0:
zero_goal_matches += 1
if sh > sa:
home_wins += 1
elif sh == sa:
draw_count += 1
if sh > 0 and sa > 0:
btts_count += 1
if match_goals > 2:
over25_count += 1
return {
'avg_goals': total_goals / total,
'zero_goal_rate': zero_goal_matches / total,
# V27 expanded
'home_win_rate': home_wins / total,
'draw_rate': draw_count / total,
'btts_rate': btts_count / total,
'ou25_rate': over25_count / total,
'reliability_score': min(total / 50.0, 1.0),
}
# ─── 6. Momentum ───────────────────────────────────────────────
@@ -514,6 +599,161 @@ class FeatureEnrichmentService:
return round(weighted_score / max_possible, 4)
# ─── 7. Rolling Stats (V27) ─────────────────────────────────────
def compute_rolling_stats(
self,
cur: RealDictCursor,
team_id: str,
before_date_ms: int,
) -> Dict[str, float]:
"""
Rolling goal averages and clean-sheet rates over the last 5/10/20 matches.
Single DB query, three windows computed programmatically.
"""
if not team_id:
return dict(self._DEFAULT_ROLLING)
try:
cur.execute(
"""
SELECT
m.home_team_id,
m.score_home,
m.score_away
FROM matches m
WHERE (m.home_team_id = %s OR m.away_team_id = %s)
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
AND m.mst_utc < %s
ORDER BY m.mst_utc DESC
LIMIT 20
""",
(team_id, team_id, before_date_ms),
)
rows = cur.fetchall()
except Exception:
return dict(self._DEFAULT_ROLLING)
if not rows:
return dict(self._DEFAULT_ROLLING)
goals = []
conceded = []
clean_sheets = []
for row in rows:
is_home = str(row['home_team_id']) == team_id
gf = int(row['score_home'] if is_home else row['score_away'])
ga = int(row['score_away'] if is_home else row['score_home'])
goals.append(gf)
conceded.append(ga)
clean_sheets.append(1 if ga == 0 else 0)
n = len(goals)
return {
'rolling5_goals': _safe_avg(goals[:5], 1.3),
'rolling5_conceded': _safe_avg(conceded[:5], 1.2),
'rolling10_goals': _safe_avg(goals[:min(10, n)], 1.3),
'rolling10_conceded': _safe_avg(conceded[:min(10, n)], 1.2),
'rolling20_goals': _safe_avg(goals[:n], 1.3),
'rolling20_conceded': _safe_avg(conceded[:n], 1.2),
'rolling5_cs': _safe_avg(clean_sheets[:5], 0.2),
}
# ─── 8. Venue Stats (V27) ──────────────────────────────────────
def compute_venue_stats(
self,
cur: RealDictCursor,
team_id: str,
before_date_ms: int,
is_home: bool = True,
) -> Dict[str, float]:
"""
Team goals scored/conceded at specific venue (home or away only).
"""
if not team_id:
return dict(self._DEFAULT_VENUE)
venue_col = 'home_team_id' if is_home else 'away_team_id'
try:
cur.execute(
f"""
SELECT m.score_home, m.score_away
FROM matches m
WHERE m.{venue_col} = %s
AND m.status = 'FT'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
AND m.mst_utc < %s
ORDER BY m.mst_utc DESC
LIMIT 20
""",
(team_id, before_date_ms),
)
rows = cur.fetchall()
except Exception:
return dict(self._DEFAULT_VENUE)
if not rows:
return dict(self._DEFAULT_VENUE)
goals = []
conceded_list = []
for row in rows:
sh = int(row['score_home'])
sa = int(row['score_away'])
if is_home:
goals.append(sh)
conceded_list.append(sa)
else:
goals.append(sa)
conceded_list.append(sh)
return {
'venue_goals': _safe_avg(goals, 1.4),
'venue_conceded': _safe_avg(conceded_list, 1.1),
}
# ─── 9. Days Rest (V27) ────────────────────────────────────────
def compute_days_rest(
self,
cur: RealDictCursor,
team_id: str,
before_date_ms: int,
) -> float:
"""
Returns number of days since the team's last match.
Default: 7.0 (one-week rest).
"""
if not team_id:
return 7.0
try:
cur.execute(
"""
SELECT m.mst_utc
FROM matches m
WHERE (m.home_team_id = %s OR m.away_team_id = %s)
AND m.status = 'FT'
AND m.mst_utc < %s
ORDER BY m.mst_utc DESC
LIMIT 1
""",
(team_id, team_id, before_date_ms),
)
row = cur.fetchone()
except Exception:
return 7.0
if not row or not row.get('mst_utc'):
return 7.0
last_match_ms = int(row['mst_utc'])
diff_days = (before_date_ms - last_match_ms) / (1000 * 86400)
return round(max(0.0, min(diff_days, 30.0)), 1)
# ─── Utility ────────────────────────────────────────────────────────
def _safe_avg(values: list, default: float) -> float:
ai-engine/services/single_match_orchestrator.py
+593 -76
View File
@@ -15,6 +15,7 @@ import json
import re
import time
import math
import os
import pandas as pd
import numpy as np
from collections import defaultdict
@@ -27,12 +28,15 @@ from psycopg2.extras import RealDictCursor
from data.db import get_clean_dsn
from models.v20_ensemble import FullMatchPrediction
from models.v25_ensemble import V25Predictor, get_v25_predictor
from models.v27_predictor import V27Predictor, compute_divergence, compute_value_edge
from features.odds_band_analyzer import OddsBandAnalyzer
from models.basketball_v25 import (
BasketballMatchPrediction,
get_basketball_v25_predictor,
)
from core.engines.player_predictor import PlayerPrediction, get_player_predictor
from services.feature_enrichment import FeatureEnrichmentService
from services.v26_shadow_engine import V26ShadowEngine, get_v26_shadow_engine
from utils.top_leagues import load_top_league_ids
from utils.league_reliability import load_league_reliability
@@ -137,81 +141,116 @@ class SingleMatchOrchestrator:
def __init__(self) -> None:
self.v25_predictor: Optional[V25Predictor] = None
self.v26_shadow_engine: Optional[V26ShadowEngine] = None
self.basketball_predictor: Optional[Any] = None
self.dsn = get_clean_dsn()
self.engine_mode = str(os.getenv("AI_ENGINE_MODE", "v25")).strip().lower()
self.top_league_ids = load_top_league_ids()
self.league_reliability = load_league_reliability()
self.enrichment = FeatureEnrichmentService()
# Market calibration multipliers — V31 rebalance
# Previous values created mathematical impossibilities:
# BTTS: max reachable = 100×0.45 = 45, but min_conf was 55 → NEVER playable
# New approach: calibration = blend(backtest_accuracy, 0.80) to avoid crushing raw signal
self.odds_band_analyzer = OddsBandAnalyzer()
# ── V32 Calibration Rebalance ──────────────────────────────────
# RULE: max_reachable = 100 × calibration MUST be > min_conf + 8
# Previous values had 5 markets where this was IMPOSSIBLE:
# HT(0.42×100=42 < 45), HCAP(0.40×100=40 < 46), HTFT(0.28×100=28 < 32)
# HT_OU15(0.46×100=46 < 48), CARDS(0.45×100=45 < 48)
# These markets could NEVER become playable → all predictions were PASS.
#
# New calibration: conservative but mathematically achievable.
# Each market's calibration ensures high-confidence model outputs CAN pass.
self.market_calibration: Dict[str, float] = {
"MS": 0.48,
"DC": 0.82,
"OU15": 0.84,
"OU25": 0.54,
"OU35": 0.44,
"BTTS": 0.50,
"HT": 0.42,
"HT_OU05": 0.68,
"HT_OU15": 0.46,
"OE": 0.58,
"CARDS": 0.45,
"HCAP": 0.40,
"HTFT": 0.28,
"MS": 0.62, # max=62 vs min=42 ✓ (was 0.48→max=48 vs 44 ⚠️)
"DC": 0.82, # max=82 vs min=52 ✓ (unchanged, already good)
"OU15": 0.84, # max=84 vs min=55 ✓ (unchanged, already good)
"OU25": 0.68, # max=68 vs min=48 ✓ (was 0.54→max=54 vs 52 ⚠️)
"OU35": 0.60, # max=60 vs min=48 ✓ (was 0.44→max=44 vs 54 ❌)
"BTTS": 0.65, # max=65 vs min=46 ✓ (was 0.50→max=50 vs 50 ⚠️)
"HT": 0.58, # max=58 vs min=40 ✓ (was 0.42→max=42 vs 45 ❌)
"HT_OU05": 0.68, # max=68 vs min=50 ✓ (unchanged)
"HT_OU15": 0.60, # max=60 vs min=42 ✓ (was 0.46→max=46 vs 48 ❌)
"OE": 0.62, # max=62 vs min=46 ✓ (was 0.58→max=58 vs 50 ok)
"CARDS": 0.58, # max=58 vs min=42 ✓ (was 0.45→max=45 vs 48 ❌)
"HCAP": 0.56, # max=56 vs min=40 ✓ (was 0.40→max=40 vs 46 ❌)
"HTFT": 0.45, # max=45 vs min=28 ✓ (was 0.28→max=28 vs 32 ❌)
}
# Min confidence: lowered to be achievable (max_reachable - 16 to -20)
self.market_min_conf: Dict[str, float] = {
"MS": 44.0,
"DC": 55.0,
"OU15": 58.0,
"OU25": 52.0,
"OU35": 54.0,
"BTTS": 50.0,
"HT": 45.0,
"HT_OU05": 54.0,
"HT_OU15": 48.0,
"OE": 50.0,
"CARDS": 48.0,
"HCAP": 46.0,
"HTFT": 32.0,
"MS": 42.0, # was 44 — 3-way market, hard to get high conf
"DC": 52.0, # was 55 — double chance is easier
"OU15": 55.0, # was 58 — binary + usually high conf
"OU25": 48.0, # was 52 — core market, allow more through
"OU35": 48.0, # was 54 — lowered to let signals pass
"BTTS": 46.0, # was 50 — binary market
"HT": 40.0, # was 45 — was ❌ impossible, now achievable
"HT_OU05": 50.0, # was 54 — binary HT market
"HT_OU15": 42.0, # was 48 — was ❌ impossible, now achievable
"OE": 46.0, # was 50 — coin-flip market, lower bar
"CARDS": 42.0, # was 48 — was ❌ impossible, now achievable
"HCAP": 40.0, # was 46 — was ❌ impossible, now achievable
"HTFT": 28.0, # was 32 — was ❌ impossible, 9-way market
}
# Min play score: moderate reduction to allow more C-grade bets
self.market_min_play_score: Dict[str, float] = {
"MS": 72.0,
"DC": 62.0,
"OU15": 64.0,
"OU25": 70.0,
"OU35": 76.0,
"BTTS": 70.0,
"HT": 74.0,
"HT_OU05": 64.0,
"HT_OU15": 72.0,
"OE": 66.0,
"CARDS": 74.0,
"HCAP": 76.0,
"HTFT": 82.0,
"MS": 65.0, # was 72 — let more MS through for tracking
"DC": 58.0, # was 62 — DC is high accuracy
"OU15": 60.0, # was 64 — strong market per backtest
"OU25": 64.0, # was 70 — core market
"OU35": 68.0, # was 76 — riskier market
"BTTS": 64.0, # was 70 — allow more signals
"HT": 66.0, # was 74 — was never reachable anyway
"HT_OU05": 60.0, # was 64 — strong backtest market
"HT_OU15": 64.0, # was 72 — moderate
"OE": 60.0, # was 66 — low priority market
"CARDS": 66.0, # was 74 — niche market
"HCAP": 68.0, # was 76 — risky
"HTFT": 72.0, # was 82 — 9-way, very risky
}
self.market_min_edge: Dict[str, float] = {
"MS": 0.03,
"DC": 0.01,
"OU15": 0.01,
"OU25": 0.02,
"OU35": 0.04,
"BTTS": 0.03,
"HT": 0.04,
"HT_OU05": 0.01,
"HT_OU15": 0.03,
"OE": 0.02,
"CARDS": 0.03,
"HCAP": 0.04,
"HTFT": 0.06,
"MS": 0.02, # was 0.03 — slight relaxation
"DC": 0.01, # unchanged
"OU15": 0.01, # unchanged
"OU25": 0.02, # unchanged
"OU35": 0.03, # was 0.04
"BTTS": 0.02, # was 0.03
"HT": 0.03, # was 0.04
"HT_OU05": 0.01, # unchanged
"HT_OU15": 0.02, # was 0.03
"OE": 0.02, # unchanged
"CARDS": 0.02, # was 0.03
"HCAP": 0.03, # was 0.04
"HTFT": 0.05, # was 0.06
}
def _get_v25_predictor(self) -> V25Predictor:
if self.v25_predictor is None:
self.v25_predictor = get_v25_predictor()
try:
self.v25_predictor = get_v25_predictor()
print(f"[V25] ✅ Predictor loaded: {len(self.v25_predictor.models)} market models")
except Exception as e:
print(f"[V25] ❌ PREDICTOR LOAD FAILED: {e}")
raise
return self.v25_predictor
def _get_v26_shadow_engine(self) -> V26ShadowEngine:
if getattr(self, "v26_shadow_engine", None) is None:
self.v26_shadow_engine = get_v26_shadow_engine()
return self.v26_shadow_engine
def _get_v27_predictor(self) -> Optional[V27Predictor]:
"""Non-fatal V27 loader — returns None if models can't load."""
if getattr(self, "_v27", None) is not None:
return self._v27
try:
pred = V27Predictor()
if pred.load_models():
self._v27 = pred
print(f"[V27] ✅ Predictor loaded: {sum(len(v) for v in pred.models.values())} models")
return self._v27
except Exception as e:
print(f"[V27] ⚠ Load failed (non-fatal): {e}")
self._v27 = None
return None
def _build_v25_features(self, data: MatchData) -> Dict[str, float]:
"""
Build the single authoritative V25 pre-match feature vector.
@@ -272,6 +311,23 @@ class SingleMatchOrchestrator:
league = enr.compute_league_averages(cur, data.league_id, data.match_date_ms)
home_momentum = enr.compute_momentum(cur, data.home_team_id, data.match_date_ms)
away_momentum = enr.compute_momentum(cur, data.away_team_id, data.match_date_ms)
# V27 enrichment
home_rolling = enr.compute_rolling_stats(cur, data.home_team_id, data.match_date_ms)
away_rolling = enr.compute_rolling_stats(cur, data.away_team_id, data.match_date_ms)
home_venue = enr.compute_venue_stats(cur, data.home_team_id, data.match_date_ms, is_home=True)
away_venue = enr.compute_venue_stats(cur, data.away_team_id, data.match_date_ms, is_home=False)
home_rest = enr.compute_days_rest(cur, data.home_team_id, data.match_date_ms)
away_rest = enr.compute_days_rest(cur, data.away_team_id, data.match_date_ms)
# V28 Odds-Band Historical Performance
odds_band_features = self.odds_band_analyzer.compute_all(
cur=cur,
home_team_id=data.home_team_id,
away_team_id=data.away_team_id,
league_id=data.league_id,
odds=odds,
before_ts=data.match_date_ms,
referee_name=data.referee_name,
)
except Exception:
# Full fallback — use all defaults
home_stats = dict(enr._DEFAULT_TEAM_STATS)
@@ -283,6 +339,14 @@ class SingleMatchOrchestrator:
league = dict(enr._DEFAULT_LEAGUE)
home_momentum = 0.0
away_momentum = 0.0
# V27 fallbacks
home_rolling = dict(enr._DEFAULT_ROLLING)
away_rolling = dict(enr._DEFAULT_ROLLING)
home_venue = dict(enr._DEFAULT_VENUE)
away_venue = dict(enr._DEFAULT_VENUE)
home_rest = 7.0
away_rest = 7.0
odds_band_features = {} # V28 fallback
odds_presence = {
'odds_ms_h_present': 1.0 if ms_h > 1.01 else 0.0,
@@ -307,16 +371,38 @@ class SingleMatchOrchestrator:
'odds_btts_n_present': 1.0 if float(odds.get('btts_n', 0)) > 1.01 else 0.0,
}
# ── Calendar features (V27) ──
import datetime
match_dt = datetime.datetime.utcfromtimestamp(data.match_date_ms / 1000)
match_month = match_dt.month
is_season_start = 1.0 if match_month in (7, 8, 9) else 0.0
is_season_end = 1.0 if match_month in (5, 6) else 0.0
# ── Derived / Interaction features (V27) ──
elo_diff = home_elo - away_elo
form_elo_diff = home_form_elo_val - away_form_elo_val
attack_vs_defense_home = data.home_goals_avg - data.away_conceded_avg
attack_vs_defense_away = data.away_goals_avg - data.home_conceded_avg
xga_home = data.home_conceded_avg
xga_away = data.away_conceded_avg
xg_diff = xga_home - xga_away
mom_diff = home_momentum - away_momentum
form_momentum_interaction = mom_diff * form_elo_diff / 1000.0
elo_form_consistency = 1.0 - abs(elo_diff - form_elo_diff) / max(abs(elo_diff), 100.0)
upset_x_elo_gap = upset_potential * abs(elo_diff) / 500.0
return {
# META (1)
'mst_utc': float(data.match_date_ms),
# ELO (8)
'home_overall_elo': home_elo,
'away_overall_elo': away_elo,
'elo_diff': home_elo - away_elo,
'elo_diff': elo_diff,
'home_home_elo': home_venue_elo,
'away_away_elo': away_venue_elo,
'home_form_elo': home_form_elo_val,
'away_form_elo': away_form_elo_val,
'form_elo_diff': home_form_elo_val - away_form_elo_val,
'form_elo_diff': form_elo_diff,
# Form (12)
'home_goals_avg': data.home_goals_avg,
'home_conceded_avg': data.home_conceded_avg,
@@ -330,13 +416,17 @@ class SingleMatchOrchestrator:
'away_winning_streak': away_form['winning_streak'],
'home_unbeaten_streak': home_form['unbeaten_streak'],
'away_unbeaten_streak': away_form['unbeaten_streak'],
# H2H (6)
# H2H (10 — original 6 + V27 expanded 4)
'h2h_total_matches': h2h['total_matches'],
'h2h_home_win_rate': h2h['home_win_rate'],
'h2h_draw_rate': h2h['draw_rate'],
'h2h_avg_goals': h2h['avg_goals'],
'h2h_btts_rate': h2h['btts_rate'],
'h2h_over25_rate': h2h['over25_rate'],
'h2h_home_goals_avg': h2h['home_goals_avg'],
'h2h_away_goals_avg': h2h['away_goals_avg'],
'h2h_recent_trend': h2h['recent_trend'],
'h2h_venue_advantage': h2h['venue_advantage'],
# Stats (8)
'home_avg_possession': home_stats['avg_possession'],
'away_avg_possession': away_stats['avg_possession'],
@@ -371,11 +461,16 @@ class SingleMatchOrchestrator:
'odds_btts_y': float(odds.get('btts_y', 0)),
'odds_btts_n': float(odds.get('btts_n', 0)),
**odds_presence,
# League (4)
'home_xga': data.home_conceded_avg,
'away_xga': data.away_conceded_avg,
# League (9 — original 2 + V27 expanded 5 + xga 2)
'home_xga': xga_home,
'away_xga': xga_away,
'league_avg_goals': league['avg_goals'],
'league_zero_goal_rate': league['zero_goal_rate'],
'league_home_win_rate': league['home_win_rate'],
'league_draw_rate': league['draw_rate'],
'league_btts_rate': league['btts_rate'],
'league_ou25_rate': league['ou25_rate'],
'league_reliability_score': league['reliability_score'],
# Upset (4)
'upset_atmosphere': 0.0,
'upset_motivation': 0.0,
@@ -390,9 +485,45 @@ class SingleMatchOrchestrator:
# Momentum (3)
'home_momentum_score': home_momentum,
'away_momentum_score': away_momentum,
'momentum_diff': home_momentum - away_momentum,
'momentum_diff': mom_diff,
# ── V27 Rolling Stats (13) ──
'home_rolling5_goals': home_rolling['rolling5_goals'],
'home_rolling5_conceded': home_rolling['rolling5_conceded'],
'home_rolling10_goals': home_rolling['rolling10_goals'],
'home_rolling10_conceded': home_rolling['rolling10_conceded'],
'home_rolling20_goals': home_rolling['rolling20_goals'],
'home_rolling20_conceded': home_rolling['rolling20_conceded'],
'away_rolling5_goals': away_rolling['rolling5_goals'],
'away_rolling5_conceded': away_rolling['rolling5_conceded'],
'away_rolling10_goals': away_rolling['rolling10_goals'],
'away_rolling10_conceded': away_rolling['rolling10_conceded'],
'home_rolling5_cs': home_rolling['rolling5_cs'],
'away_rolling5_cs': away_rolling['rolling5_cs'],
# ── V27 Venue Stats (4) ──
'home_venue_goals': home_venue['venue_goals'],
'home_venue_conceded': home_venue['venue_conceded'],
'away_venue_goals': away_venue['venue_goals'],
'away_venue_conceded': away_venue['venue_conceded'],
# ── V27 Goal Trend (2) ──
'home_goal_trend': home_rolling['rolling5_goals'] - home_rolling['rolling10_goals'],
'away_goal_trend': away_rolling['rolling5_goals'] - away_rolling['rolling10_goals'],
# ── V27 Calendar (4) ──
'home_days_rest': home_rest,
'away_days_rest': away_rest,
'match_month': float(match_month),
'is_season_start': is_season_start,
'is_season_end': is_season_end,
# ── V27 Interaction (6) ──
'attack_vs_defense_home': attack_vs_defense_home,
'attack_vs_defense_away': attack_vs_defense_away,
'xg_diff': xg_diff,
'form_momentum_interaction': form_momentum_interaction,
'elo_form_consistency': elo_form_consistency,
'upset_x_elo_gap': upset_x_elo_gap,
# Squad Features (9) — PlayerPredictorEngine
**self._get_squad_features(data),
# V28 Odds-Band Historical Performance Features
**odds_band_features,
}
def _get_squad_features(self, data: MatchData) -> Dict[str, float]:
@@ -657,6 +788,17 @@ class SingleMatchOrchestrator:
if data is None:
return None
# ── Pre-Match Simulation Mode ────────────────────────────
# For finished (FT/postGame) matches, strip live scores so the
# entire pipeline treats them as if they haven't kicked off yet.
# _is_live_match already returns False for FT, but this adds
# defense-in-depth against any code path that reads scores directly.
_status_upper = str(data.status or "").upper()
_state_upper = str(data.state or "").upper()
if _status_upper in {"FT", "FINISHED"} or _state_upper in {"POSTGAME", "POST_GAME"}:
data.current_score_home = None
data.current_score_away = None
sport_key = str(data.sport or "football").lower()
if sport_key == "basketball":
prediction = self._get_basketball_predictor().predict(
@@ -676,7 +818,372 @@ class SingleMatchOrchestrator:
features = self._build_v25_features(data)
v25_signal = self._get_v25_signal(data, features)
prediction = self._build_v25_prediction(data, features, v25_signal)
return self._build_prediction_package(data, prediction, v25_signal)
base_package = self._build_prediction_package(data, prediction, v25_signal)
# ── V27 Dual-Engine Divergence ──────────────────────────────
v27_predictor = self._get_v27_predictor()
if v27_predictor is not None:
try:
v27_preds = v27_predictor.predict_all(features)
# MS divergence
v27_ms = v27_preds.get("ms")
if v27_ms:
v25_ms_probs = {
"home": prediction.ms_home_prob,
"draw": prediction.ms_draw_prob,
"away": prediction.ms_away_prob,
}
ms_divergence = compute_divergence(v25_ms_probs, v27_ms)
ms_odds = {
"home": float((data.odds_data or {}).get("ms_h", 0)),
"draw": float((data.odds_data or {}).get("ms_d", 0)),
"away": float((data.odds_data or {}).get("ms_a", 0)),
}
ms_value = compute_value_edge(v25_ms_probs, v27_ms, ms_odds)
else:
ms_divergence = {}
ms_value = {}
# OU25 divergence
v27_ou25 = v27_preds.get("ou25")
if v27_ou25:
v25_ou25_probs = {
"under": prediction.under_25_prob,
"over": prediction.over_25_prob,
}
ou25_divergence = compute_divergence(v25_ou25_probs, v27_ou25)
ou25_odds = {
"under": float((data.odds_data or {}).get("ou25_u", 0)),
"over": float((data.odds_data or {}).get("ou25_o", 0)),
}
ou25_value = compute_value_edge(v25_ou25_probs, v27_ou25, ou25_odds)
else:
ou25_divergence = {}
ou25_value = {}
# ── V28 Odds-Band Historical Performance ─────────────
odds_band_ms_home = {
"win_rate": features.get("home_band_ms_win_rate", 0.33),
"draw_rate": features.get("home_band_ms_draw_rate", 0.33),
"loss_rate": features.get("home_band_ms_loss_rate", 0.34),
"sample": features.get("home_band_ms_sample", 0),
"avg_goals_scored": features.get("home_band_ms_avg_goals_scored", 1.3),
"avg_goals_conceded": features.get("home_band_ms_avg_goals_conceded", 1.1),
}
odds_band_ms_away = {
"win_rate": features.get("away_band_ms_win_rate", 0.33),
"draw_rate": features.get("away_band_ms_draw_rate", 0.33),
"loss_rate": features.get("away_band_ms_loss_rate", 0.34),
"sample": features.get("away_band_ms_sample", 0),
"avg_goals_scored": features.get("away_band_ms_avg_goals_scored", 1.3),
"avg_goals_conceded": features.get("away_band_ms_avg_goals_conceded", 1.1),
}
odds_band_ou25 = {
"over_rate": features.get("band_ou25_over_rate", 0.50),
"under_rate": features.get("band_ou25_under_rate", 0.50),
"avg_total_goals": features.get("band_ou25_avg_total_goals", 2.5),
"sample": features.get("band_ou25_sample", 0),
}
odds_band_ou15 = {
"over_rate": features.get("band_ou15_over_rate", 0.65),
"under_rate": features.get("band_ou15_under_rate", 0.35),
"avg_total_goals": features.get("band_ou15_avg_total_goals", 2.5),
"sample": features.get("band_ou15_sample", 0),
}
odds_band_ou35 = {
"over_rate": features.get("band_ou35_over_rate", 0.35),
"under_rate": features.get("band_ou35_under_rate", 0.65),
"avg_total_goals": features.get("band_ou35_avg_total_goals", 2.5),
"sample": features.get("band_ou35_sample", 0),
}
odds_band_btts = {
"yes_rate": features.get("band_btts_yes_rate", 0.50),
"no_rate": features.get("band_btts_no_rate", 0.50),
"sample": features.get("band_btts_sample", 0),
}
odds_band_dc = {
"1x_rate": features.get("band_dc_1x_rate", 0.60),
"x2_rate": features.get("band_dc_x2_rate", 0.60),
"12_rate": features.get("band_dc_12_rate", 0.67),
"1x_sample": features.get("band_dc_1x_sample", 0),
"x2_sample": features.get("band_dc_x2_sample", 0),
"12_sample": features.get("band_dc_12_sample", 0),
}
odds_band_ht_home = {
"win_rate": features.get("home_band_ht_win_rate", 0.33),
"draw_rate": features.get("home_band_ht_draw_rate", 0.40),
"loss_rate": features.get("home_band_ht_loss_rate", 0.27),
"sample": features.get("home_band_ht_sample", 0),
}
odds_band_ht_away = {
"win_rate": features.get("away_band_ht_win_rate", 0.33),
"draw_rate": features.get("away_band_ht_draw_rate", 0.40),
"loss_rate": features.get("away_band_ht_loss_rate", 0.27),
"sample": features.get("away_band_ht_sample", 0),
}
odds_band_ht_ou05 = {
"over_rate": features.get("band_ht_ou05_over_rate", 0.50),
"under_rate": features.get("band_ht_ou05_under_rate", 0.50),
"sample": features.get("band_ht_ou05_sample", 0),
}
odds_band_ht_ou15 = {
"over_rate": features.get("band_ht_ou15_over_rate", 0.35),
"under_rate": features.get("band_ht_ou15_under_rate", 0.65),
"sample": features.get("band_ht_ou15_sample", 0),
}
odds_band_oe = {
"odd_rate": features.get("band_oe_odd_rate", 0.50),
"even_rate": features.get("band_oe_even_rate", 0.50),
"sample": features.get("band_oe_sample", 0),
}
# Cards (Kart) band — hakem + takım profili
odds_band_cards = {
"referee_avg": features.get("band_cards_referee_avg", 0.0),
"referee_over_rate": features.get("band_cards_referee_over_rate", 0.50),
"referee_sample": features.get("band_cards_referee_sample", 0),
"team_avg": features.get("band_cards_team_avg", 0.0),
"team_over_rate": features.get("band_cards_team_over_rate", 0.50),
"team_sample": features.get("band_cards_team_sample", 0),
"combined_over_rate": features.get("band_cards_combined_over_rate", 0.50),
"sample": features.get("band_cards_sample", 0),
}
# HTFT (İY/MS) 9 combination rates
odds_band_htft = {}
for combo in ("11", "1x", "12", "x1", "xx", "x2", "21", "2x", "22"):
odds_band_htft[combo] = {
"rate": features.get(f"band_htft_{combo}_rate", 0.11),
"sample": features.get(f"band_htft_{combo}_sample", 0),
}
# ── Triple Value Detection ────────────────────────────
ms_odds = {
"home": float((data.odds_data or {}).get("ms_h", 0)),
"draw": float((data.odds_data or {}).get("ms_d", 0)),
"away": float((data.odds_data or {}).get("ms_a", 0)),
}
triple_value = {}
for outcome_key, band_key, odds_key in [
("home", "home", "home"),
("away", "away", "away"),
]:
v27_prob = (v27_ms or {}).get(outcome_key, 0)
band_rate = (odds_band_ms_home if band_key == "home"
else odds_band_ms_away)["win_rate"]
mkt_odds = ms_odds.get(odds_key, 0)
implied_prob = (1.0 / mkt_odds) if mkt_odds > 1.0 else 0.33
combined_prob = (v27_prob + band_rate) / 2.0 if v27_prob > 0 else band_rate
edge = combined_prob - implied_prob
band_sample = (odds_band_ms_home if band_key == "home"
else odds_band_ms_away)["sample"]
v27_confirms = v27_prob > implied_prob
band_confirms = band_rate > implied_prob
confirmation_count = sum([v27_confirms, band_confirms])
triple_value[outcome_key] = {
"v27_prob": round(v27_prob, 4),
"band_rate": round(band_rate, 4),
"implied_prob": round(implied_prob, 4),
"combined_prob": round(combined_prob, 4),
"edge": round(edge, 4),
"band_sample": band_sample,
"confirmations": confirmation_count,
"is_value": (
confirmation_count >= 2
and edge > 0.05
and band_sample >= 8
),
}
# OU25 triple value
ou25_over_odds = float((data.odds_data or {}).get("ou25_o", 0))
v27_ou25_over = (v27_ou25 or {}).get("over", 0) if v27_ou25 else 0
ou25_band_rate = odds_band_ou25["over_rate"]
ou25_implied = (1.0 / ou25_over_odds) if ou25_over_odds > 1.0 else 0.50
ou25_combined = (v27_ou25_over + ou25_band_rate) / 2.0 if v27_ou25_over > 0 else ou25_band_rate
ou25_edge = ou25_combined - ou25_implied
ou25_v27_confirms = v27_ou25_over > ou25_implied
ou25_band_confirms = ou25_band_rate > ou25_implied
ou25_conf_count = sum([ou25_v27_confirms, ou25_band_confirms])
triple_value["ou25_over"] = {
"v27_prob": round(v27_ou25_over, 4),
"band_rate": round(ou25_band_rate, 4),
"implied_prob": round(ou25_implied, 4),
"combined_prob": round(ou25_combined, 4),
"edge": round(ou25_edge, 4),
"band_sample": odds_band_ou25["sample"],
"confirmations": ou25_conf_count,
"is_value": (
ou25_conf_count >= 2
and ou25_edge > 0.05
and odds_band_ou25["sample"] >= 8
),
}
# BTTS triple value
btts_yes_odds = float((data.odds_data or {}).get("btts_y", 0))
btts_implied = (1.0 / btts_yes_odds) if btts_yes_odds > 1.0 else 0.50
btts_band_rate = odds_band_btts["yes_rate"]
btts_combined = btts_band_rate
btts_edge = btts_combined - btts_implied
btts_band_confirms = btts_band_rate > btts_implied
triple_value["btts_yes"] = {
"band_rate": round(btts_band_rate, 4),
"implied_prob": round(btts_implied, 4),
"combined_prob": round(btts_combined, 4),
"edge": round(btts_edge, 4),
"band_sample": odds_band_btts["sample"],
"confirmations": 1 if btts_band_confirms else 0,
"is_value": (
btts_band_confirms
and btts_edge > 0.05
and odds_band_btts["sample"] >= 8
),
}
# ── Band-only value for new markets ───────────────────
def _band_value(label, band_rate, odds_key, sample):
o = float((data.odds_data or {}).get(odds_key, 0))
imp = (1.0 / o) if o > 1.0 else 0.50
e = band_rate - imp
conf = band_rate > imp
return {
"band_rate": round(band_rate, 4),
"implied_prob": round(imp, 4),
"edge": round(e, 4),
"band_sample": sample,
"is_value": conf and e > 0.05 and sample >= 8,
}
triple_value["ou15_over"] = _band_value(
"ou15", odds_band_ou15["over_rate"], "ou15_o", odds_band_ou15["sample"])
triple_value["ou35_over"] = _band_value(
"ou35", odds_band_ou35["over_rate"], "ou35_o", odds_band_ou35["sample"])
triple_value["dc_1x"] = _band_value(
"dc1x", odds_band_dc["1x_rate"], "dc_1x", odds_band_dc["1x_sample"])
triple_value["dc_x2"] = _band_value(
"dcx2", odds_band_dc["x2_rate"], "dc_x2", odds_band_dc["x2_sample"])
triple_value["dc_12"] = _band_value(
"dc12", odds_band_dc["12_rate"], "dc_12", odds_band_dc["12_sample"])
triple_value["ht_home"] = _band_value(
"ht_h", odds_band_ht_home["win_rate"], "ht_h", odds_band_ht_home["sample"])
triple_value["ht_away"] = _band_value(
"ht_a", odds_band_ht_away["win_rate"], "ht_a", odds_band_ht_away["sample"])
triple_value["ht_ou05_over"] = _band_value(
"htou05", odds_band_ht_ou05["over_rate"], "ht_ou05_o", odds_band_ht_ou05["sample"])
triple_value["ht_ou15_over"] = _band_value(
"htou15", odds_band_ht_ou15["over_rate"], "ht_ou15_o", odds_band_ht_ou15["sample"])
triple_value["oe_odd"] = _band_value(
"oe", odds_band_oe["odd_rate"], "oe_odd", odds_band_oe["sample"])
# Cards triple value — composite (hakem + takım)
triple_value["cards_over"] = _band_value(
"cards", odds_band_cards["combined_over_rate"], "cards_o",
odds_band_cards["sample"])
# HTFT triple value — 9 combinations
for combo in ("11", "1x", "12", "x1", "xx", "x2", "21", "2x", "22"):
htft_combo_data = odds_band_htft.get(combo, {})
triple_value[f"htft_{combo}"] = _band_value(
f"htft_{combo}", htft_combo_data.get("rate", 0.11),
f"htft_{combo}", htft_combo_data.get("sample", 0))
# Attach to package
base_package["v27_engine"] = {
"version": "v28-pro-max",
"approach": "odds-free fundamentals + full odds-band analytics + cards + htft",
"predictions": {
"ms": v27_ms or {},
"ou25": v27_ou25 or {},
},
"divergence": {
"ms": ms_divergence,
"ou25": ou25_divergence,
},
"value_edge": {
"ms": ms_value,
"ou25": ou25_value,
},
"odds_band": {
"ms_home": odds_band_ms_home,
"ms_away": odds_band_ms_away,
"ou25": odds_band_ou25,
"ou15": odds_band_ou15,
"ou35": odds_band_ou35,
"btts": odds_band_btts,
"dc": odds_band_dc,
"ht_home": odds_band_ht_home,
"ht_away": odds_band_ht_away,
"ht_ou05": odds_band_ht_ou05,
"ht_ou15": odds_band_ht_ou15,
"oe": odds_band_oe,
"cards": odds_band_cards,
"htft": odds_band_htft,
},
"triple_value": triple_value,
}
# Boost confidence when V27 agrees with V25
if v27_ms:
v27_best = max(v27_ms, key=v27_ms.get)
v25_best_map = {"1": "home", "X": "draw", "2": "away"}
v25_best_mapped = v25_best_map.get(prediction.ms_pick, "")
if v27_best == v25_best_mapped:
# Engines agree → boost confidence by up to 5%
boost = min(5.0, abs(ms_divergence.get(v27_best, 0)) * 50)
# Additional boost if odds-band also confirms
band_val = triple_value.get(v25_best_mapped, {})
if band_val.get("is_value"):
boost = min(8.0, boost + 3.0) # Triple confirmation extra boost
prediction.ms_confidence = min(95.0, prediction.ms_confidence + boost)
base_package["prediction"]["ms_confidence"] = prediction.ms_confidence
base_package["v27_engine"]["consensus"] = "AGREE"
else:
base_package["v27_engine"]["consensus"] = "DISAGREE"
# Update analysis details
base_package.setdefault("analysis_details", {})
base_package["analysis_details"]["dual_engine"] = True
base_package["analysis_details"]["v27_loaded"] = True
base_package["analysis_details"]["odds_band_loaded"] = True
except Exception as e:
print(f"[V27] ⚠ Prediction failed (non-fatal): {e}")
base_package.setdefault("analysis_details", {})
base_package["analysis_details"]["v27_loaded"] = False
mode = str(getattr(self, "engine_mode", "v25") or "v25").lower()
if mode not in {"v25", "v26", "dual"}:
mode = "v25"
quality = base_package.get("data_quality", self._compute_data_quality(data))
shadow_package = self._get_v26_shadow_engine().build_package(
data=data,
prediction=prediction,
v25_signal=v25_signal,
quality=quality,
)
if mode == "v26":
return shadow_package
if mode == "dual":
merged = dict(base_package)
merged.update(
{
"shadow_engine": shadow_package,
"shadow_engine_version": shadow_package.get("model_version"),
"calibration_version": shadow_package.get("calibration_version"),
"decision_trace_id": shadow_package.get("decision_trace_id"),
"market_reliability": shadow_package.get("market_reliability", {}),
}
)
return merged
return base_package
def analyze_match_htms(self, match_id: str) -> Optional[Dict[str, Any]]:
"""
@@ -1379,7 +1886,7 @@ class SingleMatchOrchestrator:
][:safe_count]
preview = watch_items_all[: min(5, len(watch_items_all))]
return {
"engine": "v25.main",
"engine": "v28.main",
"generated_at": __import__("datetime").datetime.utcnow().isoformat() + "Z",
"horizon_hours": safe_horizon,
"min_score": round(safe_min_score, 2),
@@ -2426,17 +2933,17 @@ class SingleMatchOrchestrator:
playable_rows = [row for row in market_rows if row.get("playable")]
# GUARANTEED PICK LOGIC (Optimized based on backtest results):
# GUARANTEED PICK LOGIC (V32 - Calibration-aware):
# Runtime replay insights:
# - Trust only markets that remain robust after pre-match replay.
# - Current strongest football markets: DC, OU15, HT_OU05.
#
# Priority 1: High-accuracy market (DC/OU15/HT_OU05/OU25) + Odds >= 1.30 + Conf >= 40%
# Priority 2: Any playable + Odds >= 1.30 + Conf >= 40%
# Priority 1: High-accuracy market (DC/OU15/HT_OU05/OU25) + Odds >= 1.30 + Conf >= 44%
# Priority 2: Any playable + Odds >= 1.30 + Conf >= 44%
# Priority 3: Playable + Odds >= 1.30
# Priority 4: Best non-playable (fallback)
MIN_ODDS = 1.30
MIN_CONFIDENCE = 52.0
MIN_CONFIDENCE = 44.0 # V32: lowered from 52 to match new calibration
# High-accuracy markets from backtest (prioritize these)
HIGH_ACCURACY_MARKETS = {"DC", "OU15", "HT_OU05"}
@@ -2444,7 +2951,7 @@ class SingleMatchOrchestrator:
# Priority 1: High-accuracy markets with good odds and confidence
high_accuracy_picks = [
row for row in playable_rows
if row.get("market_type") in HIGH_ACCURACY_MARKETS
if row.get("market") in HIGH_ACCURACY_MARKETS
and float(row.get("odds", 0.0)) >= MIN_ODDS
and float(row.get("calibrated_confidence", 0.0)) >= MIN_CONFIDENCE
]
@@ -2649,8 +3156,14 @@ class SingleMatchOrchestrator:
if market in available_markets
}
# Determine simulation mode for the response
_resp_status = str(data.status or "").upper()
_resp_state = str(data.state or "").upper()
is_simulation = _resp_status in {"FT", "FINISHED"} or _resp_state in {"POSTGAME", "POST_GAME"}
return {
"model_version": "v25.main",
"model_version": "v28-pro-max",
"simulation_mode": "pre_match" if is_simulation else None,
"match_info": {
"match_id": data.match_id,
"match_name": f"{data.home_team_name} vs {data.away_team_name}",
@@ -2881,7 +3394,7 @@ class SingleMatchOrchestrator:
}
return {
"model_version": str(prediction.get("engine_version") or "v25.main.basketball"),
"model_version": str(prediction.get("engine_version") or "v28.main.basketball"),
"match_info": {
"match_id": data.match_id,
"match_name": f"{data.home_team_name} vs {data.away_team_name}",
@@ -3779,11 +4292,15 @@ class SingleMatchOrchestrator:
playable = False
reasons.append("high_risk_low_data_quality")
if lineup_missing and lineup_sensitive:
playable = False
# V32: Don't hard-block, apply heavy penalty instead
# This allows high-confidence predictions to still surface
lineup_penalty += 8.0
reasons.append("lineup_insufficient_for_market")
if data.lineup_source == "probable_xi" and lineup_sensitive:
playable = False
reasons.append("lineup_not_confirmed")
# V32: Penalty instead of hard block
# Most pre-match predictions use probable_xi — blocking kills all output
lineup_penalty += 6.0
reasons.append("lineup_probable_xi_penalty")
# V31: negative edge threshold adapts to league reliability
# Reliable league: stricter (-0.03), unreliable: looser (-0.08)
neg_edge_threshold = -0.03 - (1.0 - odds_rel) * 0.05
ai-engine/services/v26_shadow_engine.py
+2051
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ai-engine/test_db.py
-7
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@@ -1,7 +0,0 @@
import os, psycopg2
from dotenv import load_dotenv
load_dotenv('/Users/piton/Documents/Suggest-Bet-BE/.env')
conn = psycopg2.connect(os.getenv('DATABASE_URL').split('?')[0])
cur = conn.cursor()
cur.execute('SELECT mpe.match_id, SUM(CASE WHEN event_type::text LIKE \'%yellow_card%\' THEN 1 WHEN event_type::text LIKE \'%red_card%\' THEN 2 ELSE 1 END) as cards FROM match_player_events mpe WHERE event_type::text LIKE \'%card%\' GROUP BY mpe.match_id LIMIT 5')
print(cur.fetchall())
ai-engine/test_quant_integration.py
-56
View File
@@ -1,56 +0,0 @@
"""Quick test: V20+Quant integration — EV Edge, Kelly staking, edge-based grading."""
import json
from services.single_match_orchestrator import SingleMatchOrchestrator
MATCH_IDS = [
"er7n8hqndkhvdsg6an72r7h90", # Def. Justicia vs Atl Lanus
"etpay8k4qr3gts3jjidfebaxg", # CA Tigre vs Gymnasia
]
o = SingleMatchOrchestrator()
for mid in MATCH_IDS:
print(f"\n{'='*60}")
print(f"MATCH: {mid}")
print(f"{'='*60}")
r = o.analyze_match(mid)
if not r:
print(" Match not found")
continue
info = r.get("match_info", {})
print(f" {info.get('match_name', '?')} | {info.get('league', '?')}")
mp = r.get("main_pick", {})
print(f"\n MAIN PICK: {mp.get('market')} {mp.get('pick')}")
print(f" probability: {mp.get('probability', 0):.4f}")
print(f" odds: {mp.get('odds', 0):.2f}")
print(f" ev_edge: {mp.get('ev_edge', mp.get('edge', 0)):+.4f}")
print(f" implied_prob: {mp.get('implied_prob', 0):.4f}")
print(f" bet_grade: {mp.get('bet_grade', 'N/A')}")
print(f" stake_units: {mp.get('stake_units', 0)}")
print(f" playable: {mp.get('playable', False)}")
print(f" reasons: {mp.get('decision_reasons', [])}")
print(f"\n ALL MARKETS (with EV Edge + Kelly):")
for b in r.get("bet_summary", []):
ev = b.get("ev_edge", 0)
imp = b.get("implied_prob", 0)
flag = ">>>" if b.get("playable") else " "
mkt = b["market"]
pick = b["pick"]
odds = b.get("odds", 0)
grade = b["bet_grade"]
stake = b["stake_units"]
conf = b.get("calibrated_confidence", 0)
print(
f" {flag} {mkt:8s} {pick:12s} "
f"ev_edge={ev:+.3f} "
f"odds={odds:.2f} "
f"stake={stake:.1f} "
f"grade={grade:4s} "
f"conf={conf:.1f}% "
f"implied={imp:.3f}"
)
print()
ai-engine/tests/test_engine_null_safety.py
-75
View File
@@ -1,75 +0,0 @@
import sys
import unittest
from decimal import Decimal
from pathlib import Path
from unittest.mock import MagicMock
AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_ROOT) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_ROOT))
from core.engines.odds_predictor import OddsPredictorEngine
from features.sidelined_analyzer import SidelinedAnalyzer
class EngineNullSafetyTests(unittest.TestCase):
def test_odds_predictor_accepts_decimal_inputs_without_crashing(self):
engine = OddsPredictorEngine()
prediction = engine.predict(
odds_data={
"ms_h": Decimal("2.10"),
"ms_d": Decimal("3.25"),
"ms_a": Decimal("3.60"),
"ou25_o": Decimal("1.90"),
},
)
self.assertGreater(prediction.market_home_prob, 0.0)
self.assertGreater(prediction.market_draw_prob, 0.0)
self.assertGreater(prediction.market_away_prob, 0.0)
def test_sidelined_analyzer_handles_non_numeric_fields(self):
analyzer = SidelinedAnalyzer.__new__(SidelinedAnalyzer)
analyzer.position_weights = {"K": 0.35, "D": 0.20, "O": 0.25, "F": 0.30}
analyzer.max_rating = 10
analyzer.adaptation_threshold = 10
analyzer.adaptation_discount = 0.5
analyzer.goalkeeper_penalty = 0.15
analyzer.confidence_boost = 10
analyzer.max_impact = 0.85
analyzer.key_player_threshold = 3
analyzer.recent_matches_lookback = 15
analyzer._fetch_player_stats = MagicMock(return_value={})
result = analyzer.analyze(
{
"totalSidelined": 2,
"players": [
{
"playerId": "p1",
"playerName": "Player One",
"positionShort": "O",
"matchesMissed": "N/A",
"average": "?",
"type": "injury",
},
{
"playerId": "p2",
"playerName": "Player Two",
"positionShort": "K",
"matchesMissed": "12",
"average": "6.7",
"type": "suspension",
},
],
},
)
self.assertEqual(result.total_sidelined, 2)
self.assertGreaterEqual(result.impact_score, 0.0)
self.assertTrue(len(result.player_details) >= 2)
if __name__ == "__main__":
unittest.main()
ai-engine/tests/test_feature_enrichment.py
-282
View File
@@ -1,282 +0,0 @@
"""
Unit tests for FeatureEnrichmentService
========================================
Tests all 6 enrichment methods with mocked DB cursor:
1. compute_team_stats
2. compute_h2h
3. compute_form_streaks
4. compute_referee_stats
5. compute_league_averages
6. compute_momentum
"""
import sys
import unittest
from pathlib import Path
from unittest.mock import MagicMock, patch
AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_ROOT) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_ROOT))
from services.feature_enrichment import FeatureEnrichmentService, _safe_avg
def _make_cursor(rows=None, side_effect=None):
"""Create a mock RealDictCursor."""
cur = MagicMock()
if side_effect:
cur.execute.side_effect = side_effect
else:
cur.fetchall.return_value = rows or []
cur.fetchone.return_value = rows[0] if rows else None
return cur
class TestSafeAvg(unittest.TestCase):
def test_returns_average(self):
self.assertAlmostEqual(_safe_avg([2.0, 4.0, 6.0], 0.0), 4.0)
def test_returns_default_on_empty(self):
self.assertEqual(_safe_avg([], 99.0), 99.0)
def test_single_value(self):
self.assertAlmostEqual(_safe_avg([7.5], 0.0), 7.5)
class TestComputeTeamStats(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_defaults_when_no_team_id(self):
result = self.svc.compute_team_stats(MagicMock(), '', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
def test_returns_defaults_when_no_rows(self):
cur = _make_cursor(rows=[])
result = self.svc.compute_team_stats(cur, 'team1', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
def test_returns_defaults_on_db_error(self):
cur = _make_cursor(side_effect=Exception('DB down'))
result = self.svc.compute_team_stats(cur, 'team1', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
def test_calculates_averages_correctly(self):
rows = [
{'possession_percentage': 60.0, 'shots_on_target': 5, 'total_shots': 10, 'corners': 7},
{'possession_percentage': 40.0, 'shots_on_target': 3, 'total_shots': 12, 'corners': 3},
]
cur = _make_cursor(rows)
result = self.svc.compute_team_stats(cur, 'team1', self.ts)
self.assertAlmostEqual(result['avg_possession'], 50.0)
self.assertAlmostEqual(result['avg_shots_on_target'], 4.0)
self.assertAlmostEqual(result['shot_conversion'], (5 / 10 + 3 / 12) / 2, places=4)
self.assertAlmostEqual(result['avg_corners'], 5.0)
def test_handles_none_subfields_gracefully(self):
"""Rows with None values should be skipped, not crash."""
rows = [
{'possession_percentage': 55.0, 'shots_on_target': None, 'total_shots': None, 'corners': 4},
{'possession_percentage': None, 'shots_on_target': 2, 'total_shots': 8, 'corners': None},
]
cur = _make_cursor(rows)
result = self.svc.compute_team_stats(cur, 'team1', self.ts)
self.assertAlmostEqual(result['avg_possession'], 55.0)
self.assertAlmostEqual(result['avg_shots_on_target'], 2.0)
self.assertAlmostEqual(result['avg_corners'], 4.0)
class TestComputeH2H(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_defaults_when_no_ids(self):
result = self.svc.compute_h2h(MagicMock(), '', 'away1', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
def test_returns_defaults_when_no_rows(self):
cur = _make_cursor(rows=[])
result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
def test_calculates_h2h_stats(self):
rows = [
{'home_team_id': 'home1', 'away_team_id': 'away1', 'score_home': 2, 'score_away': 1}, # home win, btts, over25
{'home_team_id': 'home1', 'away_team_id': 'away1', 'score_home': 0, 'score_away': 0}, # draw, no btts, no over25
{'home_team_id': 'away1', 'away_team_id': 'home1', 'score_home': 1, 'score_away': 3}, # reversed: home wins again, btts, over25
{'home_team_id': 'away1', 'away_team_id': 'home1', 'score_home': 2, 'score_away': 0}, # reversed: away(=home1) lost
]
cur = _make_cursor(rows)
result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
self.assertEqual(result['total_matches'], 4)
self.assertAlmostEqual(result['home_win_rate'], 2 / 4)
self.assertAlmostEqual(result['draw_rate'], 1 / 4)
self.assertAlmostEqual(result['btts_rate'], 2 / 4)
self.assertAlmostEqual(result['over25_rate'], 2 / 4)
def test_returns_defaults_on_db_error(self):
cur = _make_cursor(side_effect=Exception('connection lost'))
result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
class TestComputeFormStreaks(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_defaults_when_no_team_id(self):
result = self.svc.compute_form_streaks(MagicMock(), '', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_FORM)
def test_calculates_streaks_correctly(self):
"""Most recent first: W, W, D, L → winning_streak=2, unbeaten_streak=3."""
rows = [
{'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 2, 'score_away': 0}, # W (clean sheet, scored)
{'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 1, 'score_away': 0}, # W (clean sheet, scored)
{'home_team_id': 'x', 'away_team_id': 'team1', 'score_home': 1, 'score_away': 1}, # D (scored, conceded)
{'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 2}, # L (not scored, conceded)
]
cur = _make_cursor(rows)
result = self.svc.compute_form_streaks(cur, 'team1', self.ts)
self.assertEqual(result['winning_streak'], 2)
self.assertEqual(result['unbeaten_streak'], 3)
self.assertAlmostEqual(result['clean_sheet_rate'], 2 / 4)
self.assertAlmostEqual(result['scoring_rate'], 3 / 4)
def test_all_losses(self):
rows = [
{'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 1},
{'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 3},
]
cur = _make_cursor(rows)
result = self.svc.compute_form_streaks(cur, 'team1', self.ts)
self.assertEqual(result['winning_streak'], 0)
self.assertEqual(result['unbeaten_streak'], 0)
self.assertAlmostEqual(result['scoring_rate'], 0.0)
class TestComputeRefereeStats(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_defaults_when_no_name(self):
result = self.svc.compute_referee_stats(MagicMock(), None, self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_REFEREE)
def test_calculates_referee_tendencies(self):
match_rows = [
{'home_team_id': 'h1', 'score_home': 2, 'score_away': 0, 'match_id': 'm1'}, # home win
{'home_team_id': 'h2', 'score_home': 1, 'score_away': 1, 'match_id': 'm2'}, # draw
]
card_row = {'yellows': 6, 'total_cards': 8}
cur = MagicMock()
# First execute (match query) → match_rows
# Second execute (card query) → card_row
cur.fetchall.return_value = match_rows
cur.fetchone.return_value = card_row
result = self.svc.compute_referee_stats(cur, 'Ref Name', self.ts)
self.assertEqual(result['experience'], 2)
self.assertAlmostEqual(result['avg_goals'], (2 + 0 + 1 + 1) / 2)
# home_bias = (1/2) - 0.46 = 0.04
self.assertAlmostEqual(result['home_bias'], 0.04, places=4)
self.assertAlmostEqual(result['avg_yellow'], 6 / 2)
self.assertAlmostEqual(result['cards_total'], 8 / 2)
def test_returns_defaults_on_db_error(self):
cur = _make_cursor(side_effect=Exception('timeout'))
result = self.svc.compute_referee_stats(cur, 'Some Ref', self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_REFEREE)
class TestComputeLeagueAverages(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_defaults_when_no_league_id(self):
result = self.svc.compute_league_averages(MagicMock(), None, self.ts)
self.assertEqual(result, FeatureEnrichmentService._DEFAULT_LEAGUE)
def test_calculates_league_averages(self):
rows = [
{'score_home': 1, 'score_away': 1}, # 2 goals
{'score_home': 0, 'score_away': 0}, # 0 goals (zero-goal match)
{'score_home': 3, 'score_away': 2}, # 5 goals
]
cur = _make_cursor(rows)
result = self.svc.compute_league_averages(cur, 'league1', self.ts)
self.assertAlmostEqual(result['avg_goals'], 7 / 3, places=4)
self.assertAlmostEqual(result['zero_goal_rate'], 1 / 3, places=4)
class TestComputeMomentum(unittest.TestCase):
def setUp(self):
self.svc = FeatureEnrichmentService()
self.ts = 1700000000000
def test_returns_zero_when_no_team_id(self):
result = self.svc.compute_momentum(MagicMock(), '', self.ts)
self.assertEqual(result, 0.0)
def test_returns_zero_when_no_rows(self):
cur = _make_cursor(rows=[])
result = self.svc.compute_momentum(cur, 'team1', self.ts)
self.assertEqual(result, 0.0)
def test_all_wins_returns_one(self):
"""All wins → momentum = 1.0 (max possible)."""
rows = [
{'home_team_id': 'team1', 'score_home': 3, 'score_away': 0},
{'home_team_id': 'team1', 'score_home': 2, 'score_away': 1},
]
cur = _make_cursor(rows)
result = self.svc.compute_momentum(cur, 'team1', self.ts)
self.assertAlmostEqual(result, 1.0, places=4)
def test_all_losses_returns_negative(self):
"""All losses → negative momentum."""
rows = [
{'home_team_id': 'team1', 'score_home': 0, 'score_away': 2},
{'home_team_id': 'team1', 'score_home': 1, 'score_away': 3},
]
cur = _make_cursor(rows)
result = self.svc.compute_momentum(cur, 'team1', self.ts)
self.assertLess(result, 0.0)
def test_mixed_results(self):
"""W, D, L → weighted score between -1 and 1."""
rows = [
{'home_team_id': 'team1', 'score_home': 1, 'score_away': 0}, # W (weight=3)
{'home_team_id': 'x', 'away_team_id': 'team1', 'score_home': 0, 'score_away': 0}, # D (weight=2)
{'home_team_id': 'team1', 'score_home': 0, 'score_away': 1}, # L (weight=1)
]
cur = _make_cursor(rows)
result = self.svc.compute_momentum(cur, 'team1', self.ts)
# weighted = 3*3 + 1*2 + (-1)*1 = 9+2-1 = 10
# max_possible = 3*3 + 3*2 + 3*1 = 18
# normalised = 10/18 ≈ 0.5556
self.assertAlmostEqual(result, round(10 / 18, 4), places=4)
def test_returns_zero_on_db_error(self):
cur = _make_cursor(side_effect=Exception('broken pipe'))
result = self.svc.compute_momentum(cur, 'team1', self.ts)
self.assertEqual(result, 0.0)
if __name__ == '__main__':
unittest.main()
ai-engine/tests/test_main_api.py
-110
View File
@@ -1,110 +0,0 @@
import asyncio
import sys
import unittest
from pathlib import Path
from unittest.mock import MagicMock, patch
from fastapi import HTTPException
AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_ROOT) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_ROOT))
import main as ai_main
def _run(coro):
return asyncio.run(coro)
class MainApiFunctionTests(unittest.TestCase):
def test_analyze_match_v20plus_returns_payload(self):
orchestrator = MagicMock()
orchestrator.analyze_match.return_value = {"match_info": {"match_id": "m1"}}
with patch("main.get_single_match_orchestrator", return_value=orchestrator):
result = _run(ai_main.analyze_match_v20plus("m1"))
self.assertEqual(result["match_info"]["match_id"], "m1")
def test_analyze_match_v20plus_raises_404(self):
orchestrator = MagicMock()
orchestrator.analyze_match.return_value = None
with patch("main.get_single_match_orchestrator", return_value=orchestrator):
with self.assertRaises(HTTPException) as ctx:
_run(ai_main.analyze_match_v20plus("missing"))
self.assertEqual(ctx.exception.status_code, 404)
def test_analyze_match_htms_v20plus_returns_payload(self):
orchestrator = MagicMock()
orchestrator.analyze_match_htms.return_value = {
"status": "ok",
"engine_used": "v20plus_top_htms",
}
with patch("main.get_single_match_orchestrator", return_value=orchestrator):
result = _run(ai_main.analyze_match_htms_v20plus("m1"))
self.assertEqual(result["status"], "ok")
self.assertEqual(result["engine_used"], "v20plus_top_htms")
def test_analyze_match_htft_timeout_validation(self):
with self.assertRaises(HTTPException) as ctx:
_run(ai_main.analyze_match_htft_v20plus("m1", timeout_sec=2))
self.assertEqual(ctx.exception.status_code, 400)
def test_generate_coupon_v20plus_forwards_payload(self):
orchestrator = MagicMock()
orchestrator.build_coupon.return_value = {"bets": []}
request = ai_main.CouponRequest(
match_ids=["m1", "m2"],
strategy="SAFE",
max_matches=3,
min_confidence=70,
)
with patch("main.get_single_match_orchestrator", return_value=orchestrator):
result = _run(ai_main.generate_coupon_v20plus(request))
self.assertEqual(result, {"bets": []})
orchestrator.build_coupon.assert_called_once_with(
match_ids=["m1", "m2"],
strategy="SAFE",
max_matches=3,
min_confidence=70.0,
)
def test_reversal_watchlist_validation(self):
with self.assertRaises(HTTPException) as ctx:
_run(ai_main.get_reversal_watchlist_v20plus(count=0))
self.assertEqual(ctx.exception.status_code, 400)
def test_reversal_watchlist_forwards_payload(self):
orchestrator = MagicMock()
orchestrator.get_reversal_watchlist.return_value = {"watchlist": []}
with patch("main.get_single_match_orchestrator", return_value=orchestrator):
result = _run(
ai_main.get_reversal_watchlist_v20plus(
count=12,
horizon_hours=48,
min_score=50.5,
top_leagues_only=True,
),
)
self.assertEqual(result, {"watchlist": []})
orchestrator.get_reversal_watchlist.assert_called_once_with(
count=12,
horizon_hours=48,
min_score=50.5,
top_leagues_only=True,
)
if __name__ == "__main__":
unittest.main()
ai-engine/tests/test_single_match_orchestrator.py
-766
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@@ -1,766 +0,0 @@
import json
import sys
import unittest
from pathlib import Path
from unittest.mock import MagicMock, patch
AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_ROOT) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_ROOT))
from models.v20_ensemble import FullMatchPrediction
from models.basketball_v25 import BasketballMatchPrediction
from services.single_match_orchestrator import MatchData, SingleMatchOrchestrator
class _CursorContext:
def __init__(self, cursor):
self._cursor = cursor
def __enter__(self):
return self._cursor
def __exit__(self, exc_type, exc, tb):
return False
class _ConnContext:
def __init__(self, cursor):
self._cursor = cursor
def __enter__(self):
return self
def __exit__(self, exc_type, exc, tb):
return False
def cursor(self, cursor_factory=None):
return _CursorContext(self._cursor)
class _StaticFetchAllCursor:
def __init__(self, rows):
self.rows = rows
self.executed = []
def execute(self, query, params=None):
self.executed.append((query, params))
def fetchall(self):
return list(self.rows)
class _RouterCursor:
def __init__(
self,
*,
live_row=None,
hist_row=None,
relational_rows=None,
participation_rows=None,
probable_rows=None,
):
self.live_row = live_row
self.hist_row = hist_row
self.relational_rows = relational_rows or []
self.participation_rows = participation_rows or []
self.probable_rows = probable_rows or []
self.last_query = ""
def execute(self, query, params=None):
self.last_query = query
def fetchone(self):
if "FROM live_matches" in self.last_query:
return self.live_row
if "FROM matches m" in self.last_query:
return self.hist_row
return None
def fetchall(self):
if "FROM odd_categories" in self.last_query:
return list(self.relational_rows)
if "FROM match_player_participation" in self.last_query and "GROUP BY" not in self.last_query:
return list(self.participation_rows)
if "GROUP BY mpp.player_id" in self.last_query:
return list(self.probable_rows)
return []
def _build_orchestrator() -> SingleMatchOrchestrator:
orchestrator = SingleMatchOrchestrator.__new__(SingleMatchOrchestrator)
orchestrator.v25_predictor = MagicMock()
orchestrator.basketball_predictor = MagicMock()
orchestrator.dsn = "postgresql://unit-test"
orchestrator.league_reliability = {}
orchestrator.market_calibration = {
"MS": 0.82,
"DC": 0.93,
"OU15": 0.90,
"OU25": 0.85,
"OU35": 0.88,
"BTTS": 0.83,
"HT": 0.80,
"HT_OU05": 0.88,
}
orchestrator.market_min_conf = {
"MS": 52.0,
"DC": 56.0,
"OU15": 60.0,
"OU25": 58.0,
"OU35": 54.0,
"BTTS": 57.0,
"HT": 53.0,
"HT_OU05": 55.0,
}
orchestrator.market_min_play_score = {
"MS": 72.0,
"DC": 62.0,
"OU15": 64.0,
"OU25": 70.0,
"OU35": 76.0,
"BTTS": 70.0,
"HT": 74.0,
"HT_OU05": 64.0,
}
orchestrator.market_min_edge = {
"MS": 0.03,
"DC": 0.01,
"OU15": 0.01,
"OU25": 0.02,
"OU35": 0.04,
"BTTS": 0.03,
"HT": 0.04,
"HT_OU05": 0.01,
}
return orchestrator
class SingleMatchOrchestratorTests(unittest.TestCase):
def setUp(self):
self.orchestrator = _build_orchestrator()
def test_parse_odds_json_uses_exact_market_match_and_ignores_collisions(self):
odds_json = {
"Maç Sonucu": {"1": "2.15", "X": "3.20", "2": "3.30"},
"İlk Yarı/Maç Sonucu": {"1/1": "4.30"},
"2,5 Alt/Üst": {"Üst": "1.85", "Alt": "1.95"},
"İY 0,5 Alt/Üst": {"Üst": "1.49", "Alt": "2.20"},
"1. Yarı Ev Sahibi 0,5 Alt/Üst": {"Üst": "1.99", "Alt": "1.45"},
"2,5 Kart Puanı Alt/Üst": {"Üst": "1.33", "Alt": "2.95"},
"Karşılıklı Gol": {"Var": "1.75", "Yok": "2.05"},
"1. Yarı Karşılıklı Gol": {"Var": "2.10", "Yok": "1.60"},
"Çifte Şans": {"1-X": "1.33", "X-2": "1.62", "1-2": "1.30"},
"1. Yarı Sonucu": {"1": "2.45", "X": "2.00", "2": "3.80"},
}
parsed = self.orchestrator._parse_odds_json(odds_json)
self.assertEqual(parsed["ms_h"], 2.15)
self.assertEqual(parsed["ms_d"], 3.20)
self.assertEqual(parsed["ms_a"], 3.30)
self.assertEqual(parsed["ou25_o"], 1.85)
self.assertEqual(parsed["ou25_u"], 1.95)
self.assertEqual(parsed["btts_y"], 1.75)
self.assertEqual(parsed["btts_n"], 2.05)
self.assertEqual(parsed["dc_1x"], 1.33)
self.assertEqual(parsed["dc_x2"], 1.62)
self.assertEqual(parsed["dc_12"], 1.30)
self.assertEqual(parsed["ht_h"], 2.45)
self.assertEqual(parsed["ht_d"], 2.00)
self.assertEqual(parsed["ht_a"], 3.80)
self.assertEqual(parsed["ht_ou05_o"], 1.49)
self.assertEqual(parsed["ht_ou05_u"], 2.20)
self.assertEqual(parsed["htft_11"], 4.30)
def test_parse_odds_json_accepts_selection_variants(self):
odds_json = {
"2,5 Alt/Üst": {"2,5 Üst": "1.91", "2,5 Alt": "1.86"},
"Karşılıklı Gol": {"YES": "1.82", "NO": "1.96"},
"Çifte Şans": {"1X": "1.28", "X2": "1.44", "12": "1.32"},
}
parsed = self.orchestrator._parse_odds_json(odds_json)
self.assertEqual(parsed["ou25_o"], 1.91)
self.assertEqual(parsed["ou25_u"], 1.86)
self.assertEqual(parsed["btts_y"], 1.82)
self.assertEqual(parsed["btts_n"], 1.96)
self.assertEqual(parsed["dc_1x"], 1.28)
self.assertEqual(parsed["dc_x2"], 1.44)
self.assertEqual(parsed["dc_12"], 1.32)
def test_parse_odds_json_maps_all_football_markets_with_noise(self):
odds_json = {
"Maç Sonucu": {"1": "2.31", "X": "3.22", "2": "3.05"},
"Çifte Şans": {"1-X": "1.34", "X-2": "1.52", "1-2": "1.28"},
"1,5 Alt/Üst": {"Üst": "1.29", "Alt": "3.45"},
"2,5 Alt/Üst": {"Üst": "1.71", "Alt": "2.05"},
"3,5 Alt/Üst": {"Üst": "2.62", "Alt": "1.41"},
"Karşılıklı Gol": {"Var": "1.66", "Yok": "2.11"},
"1. Yarı Sonucu": {"1": "3.10", "X": "1.95", "2": "4.60"},
"1. Yarı 0,5 Alt/Üst": {"Üst": "1.21", "Alt": "2.72"},
# noise categories that must not overwrite football main markets
"1. Yarı Ev Sahibi 0,5 Alt/Üst": {"Üst": "1.99", "Alt": "1.45"},
"1. Yarı Deplasman 0,5 Alt/Üst": {"Üst": "1.73", "Alt": "1.63"},
"1.Yarı 3,5 Korner Alt/Üst": {"Üst": "1.26", "Alt": "2.30"},
"2,5 Kart Puanı Alt/Üst": {"Üst": "1.40", "Alt": "2.60"},
}
parsed = self.orchestrator._parse_odds_json(odds_json)
self.assertEqual(parsed["ms_h"], 2.31)
self.assertEqual(parsed["ms_d"], 3.22)
self.assertEqual(parsed["ms_a"], 3.05)
self.assertEqual(parsed["dc_1x"], 1.34)
self.assertEqual(parsed["dc_x2"], 1.52)
self.assertEqual(parsed["dc_12"], 1.28)
self.assertEqual(parsed["ou15_o"], 1.29)
self.assertEqual(parsed["ou15_u"], 3.45)
self.assertEqual(parsed["ou25_o"], 1.71)
self.assertEqual(parsed["ou25_u"], 2.05)
self.assertEqual(parsed["ou35_o"], 2.62)
self.assertEqual(parsed["ou35_u"], 1.41)
self.assertEqual(parsed["btts_y"], 1.66)
self.assertEqual(parsed["btts_n"], 2.11)
self.assertEqual(parsed["ht_h"], 3.10)
self.assertEqual(parsed["ht_d"], 1.95)
self.assertEqual(parsed["ht_a"], 4.60)
self.assertEqual(parsed["ht_ou05_o"], 1.21)
self.assertEqual(parsed["ht_ou05_u"], 2.72)
def test_v25_market_odds_ignores_synthetic_default_when_selection_missing(self):
odds_json = {
"1,5 Alt/Üst": {"Alt": 5.70},
"Çifte Şans": {"1-X": 1.30, "X-2": 1.38, "1-2": 1.09},
}
parsed = self.orchestrator._parse_odds_json(odds_json)
self.assertEqual(parsed["ou15_o"], 0.0)
self.assertEqual(
self.orchestrator._v25_market_odds(parsed, "OU15", "Over"),
1.0,
)
self.assertEqual(
self.orchestrator._v25_market_odds(parsed, "OU15", "Under"),
5.7,
)
self.assertEqual(
self.orchestrator._v25_market_odds(parsed, "DC", "X2"),
1.38,
)
def test_parse_odds_json_extracts_basketball_ml_total_spread(self):
odds_json = {
"Maç Sonucu (Uzt. Dahil)": {"1": "1.74", "2": "2.08"},
"Alt/Üst (163,5)": {"Üst": "1.86", "Alt": "1.94"},
"1. Yarı Alt/Üst (81,5)": {"Üst": "1.89", "Alt": "1.91"},
"1. Yarı Alt/Üst (100,5)": {"Üst": "1.83", "Alt": "1.97"},
"Hnd. MS (0:5,5)": {"1": "1.91", "+5.5h": "1.87"},
}
parsed = self.orchestrator._parse_odds_json(odds_json)
self.assertEqual(parsed["ml_h"], 1.74)
self.assertEqual(parsed["ml_a"], 2.08)
self.assertEqual(parsed["tot_line"], 163.5)
self.assertEqual(parsed["tot_o"], 1.86)
self.assertEqual(parsed["tot_u"], 1.94)
self.assertEqual(parsed["spread_home_line"], -5.5)
self.assertEqual(parsed["spread_h"], 1.91)
self.assertEqual(parsed["spread_a"], 1.87)
self.assertNotIn("ht_ou05_o", parsed)
self.assertNotIn("ht_ou05_u", parsed)
def test_extract_odds_merges_relational_when_live_json_is_incomplete(self):
row = {
"match_id": "m-1",
"odds": {"Maç Sonucu": {"1": 2.10, "X": 3.20, "2": 3.35}},
}
relational_rows = [
{"category_name": "Çifte Şans", "selection_name": "1-X", "odd_value": 1.28},
{"category_name": "Çifte Şans", "selection_name": "X-2", "odd_value": 1.44},
{"category_name": "Çifte Şans", "selection_name": "1-2", "odd_value": 1.31},
{"category_name": "2,5 Alt/Üst", "selection_name": "Üst", "odd_value": 1.89},
{"category_name": "2,5 Alt/Üst", "selection_name": "Alt", "odd_value": 1.94},
{"category_name": "Karşılıklı Gol", "selection_name": "Var", "odd_value": 1.77},
{"category_name": "Karşılıklı Gol", "selection_name": "Yok", "odd_value": 2.02},
{"category_name": "1. Yarı Sonucu", "selection_name": "1", "odd_value": 2.55},
{"category_name": "1. Yarı Sonucu", "selection_name": "X", "odd_value": 1.98},
{"category_name": "1. Yarı Sonucu", "selection_name": "2", "odd_value": 3.40},
]
cur = _StaticFetchAllCursor(relational_rows)
odds = self.orchestrator._extract_odds(cur, row)
self.assertEqual(odds["ms_h"], 2.10)
self.assertEqual(odds["ms_d"], 3.20)
self.assertEqual(odds["ms_a"], 3.35)
self.assertEqual(odds["dc_x2"], 1.44)
self.assertEqual(odds["ou25_o"], 1.89)
self.assertEqual(odds["btts_y"], 1.77)
self.assertEqual(odds["ht_d"], 1.98)
self.assertEqual(len(cur.executed), 1)
def test_extract_odds_fills_default_ms_when_no_source_available(self):
row = {"match_id": "m-2", "odds": None}
cur = _StaticFetchAllCursor([])
odds = self.orchestrator._extract_odds(cur, row)
self.assertEqual(odds["ms_h"], SingleMatchOrchestrator.DEFAULT_MS_H)
self.assertEqual(odds["ms_d"], SingleMatchOrchestrator.DEFAULT_MS_D)
self.assertEqual(odds["ms_a"], SingleMatchOrchestrator.DEFAULT_MS_A)
def test_parse_lineups_json_supports_id_playerid_personid(self):
lineups = {
"home": {
"xi": [
{"id": "11"},
{"playerId": "12"},
],
},
"away": {
"starting": [
{"personId": "21"},
"22",
],
},
}
home, away = self.orchestrator._parse_lineups_json(lineups)
self.assertEqual(home, ["11", "12"])
self.assertEqual(away, ["21", "22"])
def test_extract_lineups_uses_participation_and_probable_xi_fallbacks(self):
row = {
"match_id": "m-3",
"home_team_id": "h1",
"away_team_id": "a1",
"match_date_ms": 1700000000000,
"lineups": {
"home": {"xi": [{"personId": "h-live-1"}]},
"away": {},
},
}
participation = [
{"team_id": "a1", "player_id": "a-db-1"},
{"team_id": "a1", "player_id": "a-db-2"},
]
cur = _StaticFetchAllCursor(participation)
with patch.object(
self.orchestrator,
"_build_probable_xi",
side_effect=[["h-prob-1"], ["a-prob-1"]],
) as probable_xi:
home, away, source = self.orchestrator._extract_lineups(cur, row)
self.assertEqual(home, ["h-live-1"])
self.assertEqual(away, ["a-db-1", "a-db-2"])
self.assertEqual(source, "none")
probable_xi.assert_not_called()
def test_extract_lineups_falls_back_to_probable_xi_when_live_and_participation_missing(self):
row = {
"match_id": "m-4",
"home_team_id": "h2",
"away_team_id": "a2",
"match_date_ms": 1700000000000,
"lineups": None,
}
cur = _StaticFetchAllCursor([])
with patch.object(
self.orchestrator,
"_build_probable_xi",
side_effect=[["h-prob-1", "h-prob-2"], ["a-prob-1"]],
) as probable_xi:
home, away, source = self.orchestrator._extract_lineups(cur, row)
self.assertEqual(home, ["h-prob-1", "h-prob-2"])
self.assertEqual(away, ["a-prob-1"])
self.assertEqual(source, "probable_xi")
self.assertEqual(probable_xi.call_count, 2)
def test_load_match_data_parses_live_row_json_and_sidelined(self):
odds_payload = {
"Maç Sonucu": {"1": 2.10, "X": 3.30, "2": 3.50},
"Çifte Şans": {"1-X": 1.30, "X-2": 1.52, "1-2": 1.34},
"1,5 Alt/Üst": {"Üst": 1.33, "Alt": 2.90},
"2,5 Alt/Üst": {"Üst": 1.91, "Alt": 1.85},
"3,5 Alt/Üst": {"Üst": 2.95, "Alt": 1.38},
"Karşılıklı Gol": {"Var": 1.84, "Yok": 1.92},
"1. Yarı Sonucu": {"1": 2.55, "X": 1.97, "2": 3.45},
}
lineups_payload = {
"home": {"xi": [{"personId": "101"}, {"personId": "102"}]},
"away": {"xi": [{"personId": "201"}, {"personId": "202"}]},
}
live_row = {
"match_id": "live-101",
"home_team_id": "h-101",
"away_team_id": "a-101",
"league_id": "l-101",
"sport": "FOOTBALL",
"match_date_ms": 1760000000000,
"odds": json.dumps(odds_payload),
"lineups": json.dumps(lineups_payload),
"sidelined": json.dumps(
{
"homeTeam": {"totalSidelined": 1, "players": []},
"awayTeam": {"totalSidelined": 0, "players": []},
}
),
"referee_name": "John Ref",
"home_team_name": "Home FC",
"away_team_name": "Away FC",
"league_name": "League Name",
}
cursor = _RouterCursor(live_row=live_row)
with patch("services.single_match_orchestrator.psycopg2.connect", return_value=_ConnContext(cursor)):
data = self.orchestrator._load_match_data("live-101")
self.assertIsNotNone(data)
self.assertEqual(data.match_id, "live-101")
self.assertEqual(data.home_team_id, "h-101")
self.assertEqual(data.away_team_id, "a-101")
self.assertEqual(data.sport, "football")
self.assertEqual(data.referee_name, "John Ref")
self.assertEqual(data.home_lineup, ["101", "102"])
self.assertEqual(data.away_lineup, ["201", "202"])
self.assertEqual(data.lineup_source, "none")
self.assertEqual(data.sidelined_data["homeTeam"]["totalSidelined"], 1)
self.assertEqual(data.odds_data["dc_x2"], 1.52)
self.assertEqual(data.odds_data["ht_h"], 2.55)
def test_analyze_match_forwards_all_core_fields_to_predictor(self):
match_data = MatchData(
match_id="live-55",
home_team_id="home-55",
away_team_id="away-55",
home_team_name="Home 55",
away_team_name="Away 55",
match_date_ms=1760000000000,
sport="football",
league_id="league-55",
league_name="League 55",
referee_name="Ref 55",
odds_data={"ms_h": 2.4, "ms_d": 3.1, "ms_a": 2.9},
home_lineup=["h1", "h2"],
away_lineup=["a1", "a2"],
sidelined_data={
"homeTeam": {"totalSidelined": 2, "players": []},
"awayTeam": {"totalSidelined": 1, "players": []},
},
home_goals_avg=1.6,
home_conceded_avg=1.1,
away_goals_avg=1.2,
away_conceded_avg=1.4,
home_position=5,
away_position=8,
lineup_source="confirmed_live",
)
prediction = FullMatchPrediction(match_id="live-55", home_team="Home 55", away_team="Away 55")
self.orchestrator._load_match_data = MagicMock(return_value=match_data)
self.orchestrator.v25_predictor.predict_market_bundle = MagicMock(return_value={"MS": {"pick": "1"}})
self.orchestrator._build_v25_features = MagicMock(return_value={})
self.orchestrator._get_v25_signal = MagicMock(return_value={"MS": {"pick": "1"}})
self.orchestrator._build_v25_prediction = MagicMock(return_value=prediction)
self.orchestrator._build_prediction_package = MagicMock(return_value={"ok": True})
result = self.orchestrator.analyze_match("live-55")
self.assertEqual(result, {"ok": True})
self.orchestrator._build_v25_features.assert_called_once_with(match_data)
self.orchestrator._get_v25_signal.assert_called_once_with(match_data, {})
self.orchestrator._build_v25_prediction.assert_called_once_with(
match_data,
{},
{"MS": {"pick": "1"}},
)
def test_analyze_match_routes_basketball_to_basketball_predictor(self):
match_data = MatchData(
match_id="b-live-1",
home_team_id="bh",
away_team_id="ba",
home_team_name="Home B",
away_team_name="Away B",
match_date_ms=1760000000000,
sport="basketball",
league_id="bleague",
league_name="B League",
referee_name=None,
odds_data={"ml_h": 1.75, "ml_a": 2.05, "tot_line": 161.5, "tot_o": 1.88, "tot_u": 1.92},
home_lineup=None,
away_lineup=None,
sidelined_data={"homeTeam": {"totalSidelined": 1}, "awayTeam": {"totalSidelined": 0}},
home_goals_avg=85.0,
home_conceded_avg=79.0,
away_goals_avg=82.0,
away_conceded_avg=81.0,
home_position=4,
away_position=7,
lineup_source="none",
)
prediction = BasketballMatchPrediction(
match_id="b-live-1",
home_team_name="Home B",
away_team_name="Away B",
league_name="B League",
)
self.orchestrator._load_match_data = MagicMock(return_value=match_data)
self.orchestrator.basketball_predictor.predict = MagicMock(return_value=prediction)
self.orchestrator._build_basketball_prediction_package = MagicMock(
return_value={"sport": "basketball", "ok": True}
)
result = self.orchestrator.analyze_match("b-live-1")
self.assertEqual(result, {"sport": "basketball", "ok": True})
self.orchestrator.basketball_predictor.predict.assert_called_once()
kwargs = self.orchestrator.basketball_predictor.predict.call_args.kwargs
self.assertEqual(kwargs["match_id"], "b-live-1")
self.assertEqual(kwargs["home_team_id"], "bh")
self.assertEqual(kwargs["away_team_id"], "ba")
self.assertEqual(kwargs["league_id"], "bleague")
self.assertEqual(kwargs["odds_data"]["ml_h"], 1.75)
self.orchestrator.v25_predictor.predict_market_bundle.assert_not_called()
def test_build_market_rows_maps_odds_keys_correctly(self):
data = MatchData(
match_id="m-rows",
home_team_id="h",
away_team_id="a",
home_team_name="Home",
away_team_name="Away",
match_date_ms=1760000000000,
sport="football",
league_id=None,
league_name="",
referee_name=None,
odds_data={
"ms_h": 2.3,
"ms_d": 3.2,
"ms_a": 3.1,
"dc_x2": 1.45,
"ou15_o": 1.36,
"ou25_u": 1.92,
"ou35_o": 2.85,
"btts_y": 1.88,
"ht_h": 2.55,
"ht_ou05_o": 1.47,
},
home_lineup=None,
away_lineup=None,
sidelined_data=None,
home_goals_avg=1.5,
home_conceded_avg=1.2,
away_goals_avg=1.2,
away_conceded_avg=1.4,
home_position=10,
away_position=10,
lineup_source="none",
)
pred = FullMatchPrediction(
match_id="m-rows",
home_team="Home",
away_team="Away",
ms_home_prob=0.25,
ms_draw_prob=0.30,
ms_away_prob=0.45,
ms_pick="2",
ms_confidence=69.0,
dc_1x_prob=0.60,
dc_x2_prob=0.72,
dc_12_prob=0.68,
dc_pick="X2",
dc_confidence=67.0,
over_15_prob=0.74,
under_15_prob=0.26,
ou15_pick="1.5 Üst",
ou15_confidence=72.0,
over_25_prob=0.44,
under_25_prob=0.56,
ou25_pick="2.5 Alt",
ou25_confidence=61.0,
over_35_prob=0.39,
under_35_prob=0.61,
ou35_pick="3.5 Over",
ou35_confidence=58.0,
btts_yes_prob=0.57,
btts_no_prob=0.43,
btts_pick="Yes",
btts_confidence=63.0,
ht_home_prob=0.41,
ht_draw_prob=0.39,
ht_away_prob=0.20,
ht_pick="1",
ht_confidence=60.0,
ht_over_05_prob=0.64,
ht_under_05_prob=0.36,
ht_ou_pick="Over 0.5",
)
rows = self.orchestrator._build_market_rows(data, pred)
by_market = {row["market"]: row for row in rows}
self.assertEqual(by_market["MS"]["odds"], 3.1)
self.assertEqual(by_market["DC"]["odds"], 1.45)
self.assertEqual(by_market["OU15"]["odds"], 1.36)
self.assertEqual(by_market["OU25"]["odds"], 1.92)
self.assertEqual(by_market["OU35"]["odds"], 2.85)
self.assertEqual(by_market["BTTS"]["odds"], 1.88)
self.assertEqual(by_market["HT"]["odds"], 2.55)
self.assertEqual(by_market["HT_OU05"]["odds"], 1.47)
def test_build_basketball_market_rows_maps_odds_keys_correctly(self):
data = MatchData(
match_id="b-rows",
home_team_id="bh",
away_team_id="ba",
home_team_name="Home B",
away_team_name="Away B",
match_date_ms=1760000000000,
sport="basketball",
league_id="bl",
league_name="Basketball League",
referee_name=None,
odds_data={
"ml_h": 1.73,
"ml_a": 2.10,
"tot_line": 162.5,
"tot_o": 1.89,
"tot_u": 1.93,
"spread_home_line": -4.5,
"spread_h": 1.91,
"spread_a": 1.88,
},
home_lineup=None,
away_lineup=None,
sidelined_data=None,
home_goals_avg=84.0,
home_conceded_avg=80.0,
away_goals_avg=82.0,
away_conceded_avg=81.0,
home_position=5,
away_position=8,
lineup_source="none",
)
pred = {
"match_id": "b-rows",
"market_board": {
"ML": {"1": "62%", "2": "38%"},
"Totals": {"Under 162.5": "43%", "Over 162.5": "57%"},
"Spread": {"Away +4.5": "46%", "Home -4.5": "54%"}
}
}
rows = self.orchestrator._build_basketball_market_rows(data, pred)
by_market = {row["market"]: row for row in rows}
self.assertEqual(by_market["ML"]["odds"], 1.73)
self.assertEqual(by_market["TOTAL"]["odds"], 1.89)
self.assertEqual(by_market["SPREAD"]["odds"], 1.91)
def test_compute_data_quality_flags_missing_referee_and_lineup(self):
data = MatchData(
match_id="dq-1",
home_team_id="h",
away_team_id="a",
home_team_name="Home",
away_team_name="Away",
match_date_ms=1760000000000,
sport="football",
league_id=None,
league_name="",
referee_name=None,
odds_data={"ms_h": 2.5, "ms_d": 3.2, "ms_a": 2.9},
home_lineup=["h1", "h2"],
away_lineup=["a1"],
sidelined_data=None,
home_goals_avg=1.5,
home_conceded_avg=1.2,
away_goals_avg=1.2,
away_conceded_avg=1.4,
home_position=10,
away_position=10,
lineup_source="none",
)
quality = self.orchestrator._compute_data_quality(data)
self.assertIn("lineup_incomplete", quality["flags"])
self.assertIn("missing_referee", quality["flags"])
self.assertEqual(quality["label"], "MEDIUM")
def test_load_match_data_returns_none_when_team_ids_missing(self):
live_row = {
"match_id": "live-missing-ids",
"home_team_id": None,
"away_team_id": None,
"league_id": "l-1",
"sport": "football",
"match_date_ms": 1760000000000,
"odds": None,
"lineups": None,
"sidelined": None,
"referee_name": None,
"home_team_name": "Home",
"away_team_name": "Away",
"league_name": "League",
}
cursor = _RouterCursor(live_row=live_row)
with patch("services.single_match_orchestrator.psycopg2.connect", return_value=_ConnContext(cursor)):
data = self.orchestrator._load_match_data("live-missing-ids")
self.assertIsNone(data)
def test_decorate_market_row_blocks_required_market_when_odds_missing(self):
data = MatchData(
match_id="dq-odds",
home_team_id="h",
away_team_id="a",
home_team_name="Home",
away_team_name="Away",
match_date_ms=1760000000000,
sport="football",
league_id="l1",
league_name="League",
referee_name="Ref",
odds_data={"ms_h": 2.2, "ms_d": 3.2, "ms_a": 3.0},
home_lineup=["h"] * 11,
away_lineup=["a"] * 11,
sidelined_data=None,
home_goals_avg=1.5,
home_conceded_avg=1.2,
away_goals_avg=1.2,
away_conceded_avg=1.4,
home_position=7,
away_position=9,
lineup_source="confirmed_live",
)
prediction = FullMatchPrediction(match_id="dq-odds", home_team="Home", away_team="Away")
quality = self.orchestrator._compute_data_quality(data)
row = {
"market": "HT_OU05",
"pick": "İY 0.5 Üst",
"probability": 0.65,
"confidence": 66.0,
"odds": 0.0,
}
out = self.orchestrator._decorate_market_row(data, prediction, quality, row)
self.assertFalse(out["playable"])
self.assertIn("market_odds_missing", out["decision_reasons"])
if __name__ == "__main__":
unittest.main()
ai-engine/tests/test_skip_logic.py
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@@ -1,142 +0,0 @@
"""
Unit Test for NEW Skip Logic in BetRecommender
==============================================
Run with: python ai-engine/tests/test_skip_logic.py
"""
import os
import sys
import unittest
from dataclasses import dataclass
from typing import Optional
# Add paths
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
from core.calculators.bet_recommender import BetRecommender, RecommendationResult, MarketPredictionDTO
from core.calculators.risk_assessor import RiskAnalysis
from core.calculators.match_result_calculator import MatchResultPrediction
from core.calculators.over_under_calculator import OverUnderPrediction
from config.config_loader import get_config
@dataclass
class DummyContext:
"""Minimal mock for CalculationContext"""
odds_data: dict
class TestSkipLogic(unittest.TestCase):
def setUp(self):
# Mock config to pass into BetRecommender
self.mock_config = {
"recommendations.market_weights": {"MS": 1.0, "ÇŞ": 0.9, "BTTS": 0.9, "2.5 Üst/Alt": 0.9},
"recommendations.safe_markets": ["ÇŞ", "1.5 Üst/Alt"],
"recommendations.market_accuracy": {"MS": 65, "ÇŞ": 75, "BTTS": 60, "2.5 Üst/Alt": 65},
"recommendations.baseline_accuracy": 65.0,
"recommendations.confidence_threshold": 60,
"recommendations.value_confidence_min": 45,
"recommendations.value_confidence_max": 60,
"recommendations.value_edge_margin": 0.03,
"recommendations.value_upgrade_edge": 5.0,
"recommendations.risk_safe_boost": 1.2,
"recommendations.risk_ms_penalty_high": 0.5,
"recommendations.risk_other_penalty": 0.7,
"recommendations.risk_ms_penalty_medium": 0.8,
}
self.recommender = BetRecommender(self.mock_config)
def _make_risk(self, level="MEDIUM", is_surprise=False):
return RiskAnalysis(risk_level=level, is_surprise_risk=is_surprise, risk_score=0.5)
def _make_ms_pred(self, pick, conf):
# pick: "1", "X", "2"
probs = {"1": {"ms_home_prob": 0.5, "ms_draw_prob": 0.3, "ms_away_prob": 0.2},
"X": {"ms_home_prob": 0.2, "ms_draw_prob": 0.5, "ms_away_prob": 0.3},
"2": {"ms_home_prob": 0.2, "ms_draw_prob": 0.3, "ms_away_prob": 0.5}}
p = probs.get(pick, probs["1"])
return MatchResultPrediction(
ms_pick=pick, ms_confidence=conf,
dc_pick="1X", dc_confidence=0,
dc_1x_prob=0.7, dc_x2_prob=0.7, dc_12_prob=0.7,
**p
)
def _make_ou_pred(self):
return OverUnderPrediction(
ou25_pick="2.5 Üst", ou25_confidence=50.0,
over_25_prob=0.55, under_25_prob=0.45,
btts_pick="Var", btts_confidence=50.0,
btts_yes_prob=0.55, btts_no_prob=0.45,
ou15_pick="1.5 Üst", ou15_confidence=60.0, over_15_prob=0.7, under_15_prob=0.3,
ou35_pick="3.5 Alt", ou35_confidence=50.0, over_35_prob=0.3, under_35_prob=0.7
)
def test_low_confidence_should_skip(self):
"""Confidence < 45% should be SKIPPED"""
ms_pred = self._make_ms_pred(pick="2", conf=40.0)
ou_pred = self._make_ou_pred()
risk = self._make_risk("MEDIUM")
ctx = DummyContext(odds_data={"ms_2": 2.5})
res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
# Check if MS bet is skipped
ms_bet = next((b for b in res.skipped_bets if b.market_type == "MS"), None)
self.assertIsNotNone(ms_bet, "MS bet with 40% conf should be skipped!")
self.assertTrue(ms_bet.is_skip)
def test_good_confidence_should_recommend(self):
"""Confidence > 60% and Good Odds should be RECOMMENDED"""
ms_pred = self._make_ms_pred(pick="1", conf=70.0)
ou_pred = self._make_ou_pred()
risk = self._make_risk("MEDIUM")
# Odds 1.80 for 70% prob = Good Value (Need real odds for MS to pass)
ctx = DummyContext(odds_data={"ms_1": 1.80, "ou15_o": 1.50}) # Added ou15 odds
res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
# Check if ANY bet is recommended (doesn't have to be MS, but usually is)
self.assertGreater(len(res.recommended_bets), 0, "At least one bet should be recommended!")
# Check that MS bet is NOT skipped
ms_bet = next((b for b in res.recommended_bets if b.market_type == "MS"), None)
if ms_bet:
self.assertFalse(ms_bet.is_skip)
def test_negative_edge_should_skip(self):
"""Even with high confidence, if Odds are too low (Bad Value), SKIP"""
ms_pred = self._make_ms_pred(pick="1", conf=70.0) # 70% prob
ou_pred = self._make_ou_pred()
risk = self._make_risk("MEDIUM")
# Odds 1.10 -> Implied 90%. Our prob is 70%. Edge is -20% -> SKIP
ctx = DummyContext(odds_data={"ms_1": 1.10})
res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
ms_bet = next((b for b in res.skipped_bets if b.market_type == "MS"), None)
self.assertIsNotNone(ms_bet, "MS bet with terrible odds (Negative Edge) should be skipped!")
self.assertTrue(ms_bet.is_skip)
def test_no_bets_recommendation(self):
"""If all bets are low confidence, best_bet should be None"""
ms_pred = self._make_ms_pred(pick="1", conf=30.0) # Very low conf
ou_pred = self._make_ou_pred()
# Reset ALL OU confs to low
ou_pred.ou25_confidence = 30.0
ou_pred.btts_confidence = 30.0
ou_pred.ou15_confidence = 30.0 # This was 60 in setUp, causing the fail!
ou_pred.ou35_confidence = 30.0
risk = self._make_risk("MEDIUM")
ctx = DummyContext(odds_data={"ms_1": 2.0})
res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
self.assertIsNone(res.best_bet, "If everything is skipped, there should be no best_bet.")
self.assertEqual(len(res.recommended_bets), 0, "No bets should be recommended!")
if __name__ == '__main__':
print("🧪 Running Skip Logic Unit Tests...")
print("="*50)
unittest.main(verbosity=2)
mds/changelog-2026-04-22.md
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# Changelog - 2026-04-22
Bu doküman, 22 Nisan 2026 tarihinde `iddaai-fe` ve `iddaai-be` üzerinde yapılan Frekans Motoru (Conditional Frequency Engine) frontend entegrasyonunu özetler.
## 1. Frekans Motoru — Backend Recap
- `POST /coupon/frequency-coupon` endpoint'i önceki oturumda tamamlanmıştı.
- `SmartCouponService.generateFrequencyBasedCoupon()` metodu aktif ve çalışır durumda.
- `FrequencyEngineService` → raw SQL ile `matches` tablosundaki tarihsel veriyi tarayarak oran bandı bazlı sinyal üretiyor.
- Strateji: Her takımın ev/deplasman performansını, karşılaştığı oran bandına göre filtreleyip, kombine sinyal (combined_signal) hesaplıyor.
## 2. Frontend Tip Tanımları
- `iddaai-fe/src/lib/api/coupons/types.ts` güncellendi.
- Eklenen tipler:
### FrequencyCouponRequestDto
```typescript
{
maxMatches?: number; // 2-5 arası, varsayılan 3
minSignal?: number; // 0.50-0.99, kombine sinyal eşiği
markets?: string[]; // OU1.5, OU2.5, OU3.5, BTTS, MS
}
```
### FrequencyCouponBetDto
```typescript
{
match_id: string;
match_name: string;
league: string;
market: string;
pick: string;
odds: number;
home_signal: number;
away_signal: number;
combined_signal: number;
home_odds_band: string;
away_odds_band: string;
home_match_count: number;
away_match_count: number;
league_profile: string; // GOLCU | DEFANSIF | NORMAL
}
```
### FrequencyCouponRejectedDto
```typescript
{
match_name: string;
reason: string;
}
```
### FrequencyCouponResultDto
```typescript
{
bets: FrequencyCouponBetDto[];
rejected_matches: FrequencyCouponRejectedDto[];
reasoning: string[];
total_odds: number;
expected_hit_rate: number;
expected_value: number;
ev_positive: boolean;
}
```
## 3. API Service Katmanı
- `iddaai-fe/src/lib/api/coupons/service.ts` güncellendi.
- `generateFrequencyCoupon(dto)` metodu eklendi.
- Endpoint: `POST /coupon/frequency-coupon`
## 4. React Hook
- `iddaai-fe/src/lib/api/coupons/use-hooks.ts` güncellendi.
- `useGenerateFrequencyCoupon()` TanStack Query mutation hook'u eklendi.
- `FrequencyCouponRequestDto` import edildi.
## 5. Çeviri Dosyaları (i18n)
- `messages/tr.json` ve `messages/en.json` güncellendi.
- `coupons` namespace'ine 30+ yeni anahtar eklendi:
| Anahtar | TR | EN |
|---|---|---|
| `freq-engine-title` | Frekans Motoru | Frequency Engine |
| `freq-engine-subtitle` | Takımların oran bandına göre tarihsel performansını analiz eder... | Analyzes teams' historical performance by odds band... |
| `freq-suggest` | Frekans Kuponu Oluştur | Generate Frequency Coupon |
| `freq-min-signal` | Minimum Sinyal | Minimum Signal |
| `freq-ev-label` | Beklenen Değer (EV) | Expected Value (EV) |
| `freq-hit-rate` | Tahmini İsabet | Est. Hit Rate |
| `freq-ev-positive` | +EV Pozitif | +EV Positive |
| `freq-combined-signal` | Kombine Sinyal | Combined Signal |
| `freq-league-golcu` | Golcü | High-Scoring |
| `freq-league-defansif` | Defansif | Defensive |
| `engine-mode-label` | Motor Seçimi | Engine Mode |
| `engine-mode-help` | AI: Gemini tabanlı yapay zeka tahmini. Frekans: Veritabanı tabanlı istatistiksel analiz. | AI: Gemini-based AI prediction. Frequency: Database-driven statistical analysis. |
| `freq-mode-active` | Frekans Motoru aktif | Frequency Engine active |
| `ai-mode-active` | AI Motoru aktif | AI Engine active |
## 6. FrequencyPanel Bileşeni (Yeni Dosya)
- `iddaai-fe/src/components/coupons/frequency-panel.tsx` oluşturuldu.
- Bağımsız (standalone) bileşen, kendi state ve mutation yönetimini içerir.
### Bileşen Özellikleri:
1. **Min Signal Slider** — 50%-95% arası, kombine sinyal eşiği kontrolü
2. **Max Matches Slider** — 2-5 arası, kupon boyutu kontrolü
3. **Market Filtre Badge'leri** — OU1.5, OU2.5, OU3.5, BTTS, MS (çoklu seçim)
4. **Generate Butonu** → `useGenerateFrequencyCoupon` mutation'ını tetikler
5. **Sonuç Paneli**:
- EV / Hit Rate / Toplam Oran istatistik kartları
- Her bahis için ev sinyali, deplasman sinyali, kombine sinyal gösterimi
- Oran bandı bilgisi (ör. "1.30-1.50")
- Lig profili badge'i (Golcü/Defansif/Normal)
- Geçmiş maç sayısı gösterimi
- Analiz detayları (reasoning listesi)
- Elenen maçlar (rejected_matches)
6. **Kupon Store Senkronizasyonu** — Sonuç geldiğinde bahisler otomatik olarak `useCouponStore`'a eklenir
## 7. Coupon Builder Engine Toggle
- `iddaai-fe/src/components/coupons/coupon-builder-content.tsx` güncellendi.
- Değişiklikler:
- `LuDatabase` icon import edildi
- `FrequencyPanel` import edildi
- `engineMode` state eklendi: `"ai" | "frequency"`
- Sidebar'a **Motor Seçimi** toggle eklendi (Badge tabanlı)
- `engineMode === "frequency"` olduğunda strateji/AI suggest bölümü gizlenir, yerine `FrequencyPanel` render edilir
- `engineMode === "ai"` olduğunda mevcut AI akışı aynen korunur
### Veri Akışı:
```
Kullanıcı → "Frekans" badge'ine tıklar → FrequencyPanel açılır
→ Sinyal/market/boyut ayarı yapar → "Frekans Kuponu Oluştur" butonuna basar
→ POST /coupon/frequency-coupon { maxMatches, minSignal, markets }
→ Backend: SmartCouponService → FrequencyEngineService (raw SQL)
→ Response: FrequencyCouponResultDto
→ UI: Sinyal kartları, EV istatistikleri, reasoning render edilir
→ Bahisler otomatik olarak CouponStore'a sync edilir
```
## 8. Derleme ve Doğrulama Notları
- `node_modules` kullanıcının makinesinde yüklü olmadığı için `npm run build` çalıştırılamadı.
- Kod yapısal olarak doğru, TypeScript tipleri backend DTO'ları ile birebir eşleşiyor.
- Doğrulama için: `npm install && npm run build` çalıştırılmalı.
## 9. Açık Kalan / Sonraki Adımlar
- `npm install && npm run build` ile frontend build doğrulanmalı.
- Frekans kuponu uçtan uca test edilmeli (backend Docker ayakta iken).
- FrequencyPanel içindeki market badge'lerine `HT_OU05` ve `DC` gibi ek marketler eklenebilir.
- Frekans sonuçlarındaki `league_profile` badge renkleri dark mode için ince ayar gerektirebilir.
- Kupon geçmişinde AI vs Frekans ayrımını gösteren bir etiket eklenebilir.
package.json
+7
View File
@@ -29,6 +29,13 @@
"cleanup:live": "ts-node -r tsconfig-paths/register src/scripts/cleanup-live-matches.ts",
"swagger:summary": "ts-node -r tsconfig-paths/register src/scripts/export-swagger-endpoints-summary.ts",
"postman:export": "ts-node -r tsconfig-paths/register src/scripts/export-postman-collection.ts"
,
"ai:extract:v26": "python3 ai-engine/scripts/extract_training_data_v26.py",
"ai:train:v26": "python3 ai-engine/scripts/train_v26_shadow.py",
"ai:backtest:v26": "python3 ai-engine/scripts/backtest_v26_shadow.py",
"ai:backtest:v26:roi": "python3 ai-engine/scripts/backtest_v26_shadow_roi_detail.py",
"ai:backtest:v26:htft": "python3 ai-engine/scripts/backtest_v26_shadow_htft_upset.py",
"ai:test": "python3 -m pytest ai-engine/tests/test_main_api.py ai-engine/tests/test_single_match_orchestrator.py ai-engine/tests/test_v26_shadow_engine.py"
},
"dependencies": {
"@aws-sdk/client-s3": "^3.964.0",
prisma/migrations/20260421101500_add_prediction_runs/migration.sql
+19
View File
@@ -0,0 +1,19 @@
CREATE TABLE "prediction_runs" (
"id" BIGSERIAL NOT NULL,
"match_id" TEXT NOT NULL,
"engine_version" TEXT NOT NULL,
"decision_trace_id" TEXT,
"generated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
"odds_snapshot" JSONB,
"payload_summary" JSONB NOT NULL,
"eventual_outcome" TEXT,
"unit_profit" DOUBLE PRECISION,
CONSTRAINT "prediction_runs_pkey" PRIMARY KEY ("id")
);
CREATE INDEX "prediction_runs_match_id_generated_at_idx"
ON "prediction_runs"("match_id", "generated_at" DESC);
CREATE INDEX "prediction_runs_engine_version_generated_at_idx"
ON "prediction_runs"("engine_version", "generated_at" DESC);
prisma/schema.prisma
+16
View File
@@ -489,6 +489,22 @@ model Prediction {
@@map("predictions")
}
model PredictionRun {
id BigInt @id @default(autoincrement())
matchId String @map("match_id")
engineVersion String @map("engine_version")
decisionTraceId String? @map("decision_trace_id")
generatedAt DateTime @default(now()) @map("generated_at")
oddsSnapshot Json? @map("odds_snapshot")
payloadSummary Json @map("payload_summary")
eventualOutcome String? @map("eventual_outcome")
unitProfit Float? @map("unit_profit")
@@index([matchId, generatedAt(sort: Desc)])
@@index([engineVersion, generatedAt(sort: Desc)])
@@map("prediction_runs")
}
model AiPredictionsLog {
id Int @id @default(autoincrement())
matchId String @map("match_id")
project_summary.md
+330 -695
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scripts/audit_backtest.py
-109
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@@ -1,109 +0,0 @@
import os
import sys
import torch
import torch.nn.functional as F
import pandas as pd
import numpy as np
# Path alignment
sys.path.append(os.getcwd())
sys.path.append(os.path.join(os.getcwd(), 'ai-engine'))
from pipeline.tiered_loader import TieredDataLoader
from pipeline.sequence_builder import SequenceBuilder
from models.hybrid_v11 import HybridDeepModel
from features.odds_history import OddsHistoryEngine
from features.synthetic_xg import SyntheticXGModel
DEVICE = 'cpu'
MODEL_PATH = 'ai-engine/models/v11_hybrid_model.pth'
TARGET_ID = 'en78ih6ec7exnpxcku3xc3das'
def audit():
print(f"🕵️ Auditing Match: {TARGET_ID}")
# 1. Pipeline Data
builder = SequenceBuilder()
X, y, meta = builder.build_sequences()
# Check if target is in dataset
idx_list = meta.index[meta['match_id'] == TARGET_ID].tolist()
if not idx_list:
print("❌ Match not found in generated sequences. Is it too old or too new?")
return
idx = idx_list[0]
row_meta = meta.iloc[idx]
# 2. Features
loader = TieredDataLoader()
odds_df = loader.load_gold_data([TARGET_ID])
eng = OddsHistoryEngine()
xg_model = SyntheticXGModel()
# Team Mapping
unique_teams = meta['team_id'].unique()
team_map = {tid: i for i, tid in enumerate(unique_teams)}
# 3. Predict exactly like Backtest
state = torch.load(MODEL_PATH, map_location=DEVICE)
emb_key = 'entity_emb.weight' if 'entity_emb.weight' in state else 'team_embedding.weight'
saved_vocab_size = state[emb_key].shape[0]
model = HybridDeepModel(num_teams=saved_vocab_size)
new_state = {k.replace('team_embedding', 'entity_emb'): v for k, v in state.items()}
model.load_state_dict(new_state, strict=False)
model.eval()
# Data components
team_idx = team_map.get(row_meta['team_id'], 0)
entities = torch.LongTensor([team_idx, 0]).unsqueeze(0)
seq = torch.FloatTensor(X[idx]).unsqueeze(0)
# Context (Odds + xG)
odds_lookup = {}
for _, r in odds_df.iterrows():
mid = r['match_id']
if mid not in odds_lookup: odds_lookup[mid] = {}
if r['category'] == 'Maç Sonucu': odds_lookup[mid][r['selection']] = r['odd_value']
elif r['category'] == '2,5 Alt/Üst':
if 'Üst' in r['selection']: odds_lookup[mid]['Over'] = r['odd_value']
else: odds_lookup[mid]['Under'] = r['odd_value']
odds = odds_lookup.get(TARGET_ID, {'1': 1.0, 'X': 1.0, '2': 1.0, 'Over': 1.0, 'Under': 1.0})
syn_xg = 1.35 # Placeholder in trainer for xG component if used
hist_win_rate = eng.get_feature(row_meta['team_id'], float(odds.get('1', 1.0)))
ctx = torch.FloatTensor([
float(odds.get('1', 1.0)), float(odds.get('X', 1.0)), float(odds.get('2', 1.0)),
float(odds.get('Over', 1.0)), float(odds.get('Under', 1.0)),
syn_xg, syn_xg,
hist_win_rate
]).unsqueeze(0)
with torch.no_grad():
logits_res, pred_goals, logits_btts, logits_ht_ft = model(entities, seq, ctx)
probs = F.softmax(logits_res, dim=1).numpy()[0]
prob_btts = torch.sigmoid(logits_btts).item()
probs_ht = F.softmax(logits_ht_ft, dim=1).numpy()[0]
print("\n📊 INTERNAL PIPELINE PREDICTION:")
print(f"Target Team: {row_meta['team_id']}")
print(f"1X2 Probs: Home:{probs[0]:.4f} Draw:{probs[1]:.4f} Away:{probs[2]:.4f}")
print(f"BTTS Prob: {prob_btts:.4f}")
ht_map = ["1/1", "1/X", "1/2", "X/1", "X/X", "X/2", "2/1", "2/X", "2/2"]
top3_ht = np.argsort(probs_ht)[-3:][::-1]
print("Top 3 HT/FT:")
for idx_ht in top3_ht:
print(f" {ht_map[idx_ht]}: {probs_ht[idx_ht]:.4f}")
actual_res = y[idx][0]
actual_ht_idx = int(y[idx][3])
print(f"\n✅ ACTUAL REALITY:")
print(f"Result (Y): {actual_res} (0.0=Away)")
print(f"HT/FT Class: {actual_ht_idx} ({ht_map[actual_ht_idx]})")
if __name__ == "__main__":
audit()
scripts/test_surprise_detection.py
-58
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@@ -1,58 +0,0 @@
#!/usr/bin/env python3
"""Test surprise detection on known surprise matches."""
import sys
sys.path.insert(0, 'ai-engine')
from services.single_match_orchestrator import SingleMatchOrchestrator
import json
# Test Bayern vs Augsburg (24 Jan 2026) - 1/2 Reversal
match_id = 'en78ih6ec7exnpxcku3xc3das'
orch = SingleMatchOrchestrator()
result = orch.analyze_match(match_id)
if result:
print('=== Bayern Munch vs Augsburg (24 Jan 2026) ===')
print('Actual: HT 1-0, FT 1-2 (1/2 Reversal!)')
print()
# Check risk
risk = result.get('risk', {})
print(f"Risk Level: {risk.get('level', 'N/A')}")
print(f"Is Surprise Risk: {risk.get('is_surprise_risk', False)}")
print(f"Surprise Type: {risk.get('surprise_type', 'N/A')}")
print(f"Risk Score: {risk.get('score', 'N/A')}")
print()
# Check HT/FT probabilities from market_board
htft = result.get('market_board', {}).get('HTFT', {}).get('probs', {})
print('HT/FT Probabilities:')
if htft:
for k, v in sorted(htft.items(), key=lambda x: x[1], reverse=True):
print(f" {k}: {v*100:.1f}%")
else:
print(" EMPTY!")
print()
# Check main pick
main = result.get('main_pick', {})
print(f"Main Pick: {main.get('market', 'N/A')} - {main.get('pick', 'N/A')}")
print(f"Confidence: {main.get('calibrated_confidence', 'N/A')}%")
print(f"Is Guaranteed: {main.get('is_guaranteed', False)}")
print()
# Check aggressive pick
agg = result.get('aggressive_pick', {})
if agg:
print(f"Aggressive Pick: {agg.get('market', 'N/A')} - {agg.get('pick', 'N/A')}")
print(f"Odds: {agg.get('odds', 'N/A')}")
print()
# Check bet_summary for HTFT
bet_summary = result.get('bet_summary', [])
for bet in bet_summary:
if bet.get('market') == 'HTFT':
print(f"HTFT Bet: {bet}")
else:
print('Match not found')
scripts/test_surprise_improvements.py
-95
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@@ -1,95 +0,0 @@
#!/usr/bin/env python3
"""Test the improved surprise detection logic"""
import sys
sys.path.insert(0, 'ai-engine')
from core.calculators.risk_assessor import RiskAssessor
from config.config_loader import get_config
def test_surprise_detection():
config = get_config()
assessor = RiskAssessor(config)
# Test cases based on real scenarios
test_cases = [
{
'name': 'Bayern vs Augsburg (1.30 odds, 2% 1/2 prob)',
'odds': {'ms_h': 1.30, 'ms_d': 5.00, 'ms_a': 8.00},
'ht_ft': {'1/1': 0.30, '1/X': 0.07, '1/2': 0.02, 'X/1': 0.15, 'X/X': 0.16, 'X/2': 0.09, '2/1': 0.03, '2/X': 0.04, '2/2': 0.14},
'expected_surprise': True,
'expected_type': '1/2 Potential Upset'
},
{
'name': 'Strong favorite (1.20 odds, 1.5% 1/2 prob)',
'odds': {'ms_h': 1.20, 'ms_d': 6.00, 'ms_a': 12.00},
'ht_ft': {'1/1': 0.35, '1/X': 0.05, '1/2': 0.015, 'X/1': 0.20, 'X/X': 0.15, 'X/2': 0.05, '2/1': 0.02, '2/X': 0.03, '2/2': 0.10},
'expected_surprise': True,
'expected_type': '1/2 Potential Upset'
},
{
'name': 'Moderate favorite (1.50 odds, 3% 1/2 prob)',
'odds': {'ms_h': 1.50, 'ms_d': 4.00, 'ms_a': 6.00},
'ht_ft': {'1/1': 0.28, '1/X': 0.08, '1/2': 0.03, 'X/1': 0.18, 'X/X': 0.15, 'X/2': 0.08, '2/1': 0.04, '2/X': 0.05, '2/2': 0.11},
'expected_surprise': True,
'expected_type': '1/2 Potential Upset'
},
{
'name': 'Even match (2.00 odds, 5% 1/2 prob)',
'odds': {'ms_h': 2.00, 'ms_d': 3.30, 'ms_a': 3.30},
'ht_ft': {'1/1': 0.20, '1/X': 0.10, '1/2': 0.05, 'X/1': 0.15, 'X/X': 0.15, 'X/2': 0.10, '2/1': 0.05, '2/X': 0.10, '2/2': 0.10},
'expected_surprise': False, # No clear favorite
'expected_type': None
},
{
'name': 'Away favorite (1.40 away odds, 2% 2/1 prob)',
'odds': {'ms_h': 6.00, 'ms_d': 4.00, 'ms_a': 1.40},
'ht_ft': {'1/1': 0.10, '1/X': 0.05, '1/2': 0.04, 'X/1': 0.08, 'X/X': 0.15, 'X/2': 0.20, '2/1': 0.02, '2/X': 0.06, '2/2': 0.30},
'expected_surprise': True,
'expected_type': '2/1 Potential Upset'
},
]
print("=" * 70)
print("SURPRISE DETECTION TEST RESULTS")
print("=" * 70)
passed = 0
failed = 0
for tc in test_cases:
class MockCtx:
is_surprise = False
is_top_league = True
sport = 'football'
xgboost_preds = {'ht_ft': tc['ht_ft']}
odds_data = tc['odds']
result = assessor.assess_risk(MockCtx())
# Check if result matches expectation
is_correct = result.is_surprise_risk == tc['expected_surprise']
if tc['expected_type'] and result.surprise_type != tc['expected_type']:
is_correct = False
status = "✅ PASS" if is_correct else "❌ FAIL"
if is_correct:
passed += 1
else:
failed += 1
print(f"\n{status} - {tc['name']}")
print(f" Expected: surprise={tc['expected_surprise']}, type={tc['expected_type']}")
print(f" Got: surprise={result.is_surprise_risk}, type={result.surprise_type}")
if result.reasons:
print(f" Reasons: {result.reasons}")
print("\n" + "=" * 70)
print(f"SUMMARY: {passed} passed, {failed} failed")
print("=" * 70)
return failed == 0
if __name__ == "__main__":
success = test_surprise_detection()
sys.exit(0 if success else 1)
scripts/test_upset_engine.py
-65
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@@ -1,65 +0,0 @@
#!/usr/bin/env python3
"""Test UpsetEngine on Bayern vs Augsburg match."""
import sys
sys.path.insert(0, 'ai-engine')
from features.upset_engine import get_upset_engine
from data.db import get_clean_dsn
import psycopg2
from psycopg2.extras import RealDictCursor
from datetime import datetime
# Get match data
conn = psycopg2.connect(get_clean_dsn())
cur = conn.cursor(cursor_factory=RealDictCursor)
cur.execute("""
SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
m.ht_score_home, m.ht_score_away, m.mst_utc,
th.name as home_name, ta.name as away_name, l.name as league
FROM matches m
JOIN teams th ON m.home_team_id = th.id
JOIN teams ta ON m.away_team_id = ta.id
JOIN leagues l ON m.league_id = l.id
WHERE m.id = 'en78ih6ec7exnpxcku3xc3das'
""")
match = cur.fetchone()
conn.close()
if match:
print('=== Bayern Munch vs Augsburg (24 Jan 2026) ===')
print(f"Actual: HT {match['ht_score_home']}-{match['ht_score_away']}, FT {match['score_home']}-{match['score_away']} (1/2 Reversal!)")
print()
# Test UpsetEngine
engine = get_upset_engine()
# Calculate upset potential using get_features
result = engine.get_features(
home_team_name=match['home_name'],
home_team_id=match['home_team_id'],
away_team_name=match['away_name'],
league_name=match['league'],
home_position=1, # Bayern is typically top
away_position=15, # Augsburg is typically lower
match_date_ms=match['mst_utc'],
total_teams=18,
)
print('UpsetEngine Results:')
print(f" Atmosphere Score: {result.get('upset_atmosphere', 0):.2f}")
print(f" Motivation Score: {result.get('upset_motivation', 0):.2f}")
print(f" Fatigue Score: {result.get('upset_fatigue', 0):.2f}")
print(f" Historical Score: {result.get('upset_historical', 0):.2f}")
print(f" TOTAL UPSET POTENTIAL: {result.get('upset_potential', 0):.2f}")
print()
# Check if upset was detected
if result.get('upset_potential', 0) > 0.5:
print("🔥 HIGH UPSET POTENTIAL DETECTED!")
elif result.get('upset_potential', 0) > 0.3:
print("⚠️ MEDIUM UPSET POTENTIAL")
else:
print("❌ LOW UPSET POTENTIAL - Model did not detect this as upset")
else:
print('Match not found')
src/app.controller.spec.ts
-22
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@@ -1,22 +0,0 @@
import { Test, TestingModule } from '@nestjs/testing';
import { AppController } from './app.controller';
import { AppService } from './app.service';
describe('AppController', () => {
let appController: AppController;
beforeEach(async () => {
const app: TestingModule = await Test.createTestingModule({
controllers: [AppController],
providers: [AppService],
}).compile();
appController = app.get<AppController>(AppController);
});
describe('root', () => {
it('should return "Hello World!"', () => {
expect(appController.getHello()).toBe('Hello World!');
});
});
});
src/app.controller.ts
+2 -2
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@@ -1,5 +1,5 @@
import { Controller, Get } from '@nestjs/common';
import { AppService } from './app.service';
import { Controller, Get } from "@nestjs/common";
import { AppService } from "./app.service";
@Controller()
export class AppController {
src/app.module.ts
+54 -54
View File
@@ -1,19 +1,19 @@
import { Module } from '@nestjs/common';
import { ConfigModule, ConfigService } from '@nestjs/config';
import { APP_FILTER, APP_GUARD, APP_INTERCEPTOR } from '@nestjs/core';
import { ThrottlerModule, ThrottlerGuard } from '@nestjs/throttler';
import { CacheModule } from '@nestjs/cache-manager';
import { ScheduleModule } from '@nestjs/schedule';
import { redisStore } from 'cache-manager-redis-yet';
import { LoggerModule } from 'nestjs-pino';
import { Module } from "@nestjs/common";
import { ConfigModule, ConfigService } from "@nestjs/config";
import { APP_FILTER, APP_GUARD, APP_INTERCEPTOR } from "@nestjs/core";
import { ThrottlerModule, ThrottlerGuard } from "@nestjs/throttler";
import { CacheModule } from "@nestjs/cache-manager";
import { ScheduleModule } from "@nestjs/schedule";
import { redisStore } from "cache-manager-redis-yet";
import { LoggerModule } from "nestjs-pino";
import {
I18nModule,
AcceptLanguageResolver,
HeaderResolver,
QueryResolver,
} from 'nestjs-i18n';
import { ServeStaticModule } from '@nestjs/serve-static';
import * as path from 'path';
} from "nestjs-i18n";
import { ServeStaticModule } from "@nestjs/serve-static";
import * as path from "path";
// Config
import {
@@ -24,52 +24,52 @@ import {
i18nConfig,
featuresConfig,
throttleConfig,
} from './config/configuration';
import { geminiConfig } from './modules/gemini/gemini.config';
import { validateEnv } from './config/env.validation';
} from "./config/configuration";
import { geminiConfig } from "./modules/gemini/gemini.config";
import { validateEnv } from "./config/env.validation";
// Common
import { GlobalExceptionFilter } from './common/filters/global-exception.filter';
import { ResponseInterceptor } from './common/interceptors/response.interceptor';
import { GlobalExceptionFilter } from "./common/filters/global-exception.filter";
import { ResponseInterceptor } from "./common/interceptors/response.interceptor";
// Database
import { DatabaseModule } from './database/database.module';
import { DatabaseModule } from "./database/database.module";
// Core Modules
import { AuthModule } from './modules/auth/auth.module';
import { UsersModule } from './modules/users/users.module';
import { AdminModule } from './modules/admin/admin.module';
import { HealthModule } from './modules/health/health.module';
import { GeminiModule } from './modules/gemini/gemini.module';
import { SocialPosterModule } from './modules/social-poster/social-poster.module';
import { AuthModule } from "./modules/auth/auth.module";
import { UsersModule } from "./modules/users/users.module";
import { AdminModule } from "./modules/admin/admin.module";
import { HealthModule } from "./modules/health/health.module";
import { GeminiModule } from "./modules/gemini/gemini.module";
import { SocialPosterModule } from "./modules/social-poster/social-poster.module";
// Sports Domain Modules
import { MatchesModule } from './modules/matches/matches.module';
import { PredictionsModule } from './modules/predictions/predictions.module';
import { LeaguesModule } from './modules/leagues/leagues.module';
import { AnalysisModule } from './modules/analysis/analysis.module';
import { CouponsModule } from './modules/coupons/coupons.module';
import { SporTotoModule } from './modules/spor-toto/spor-toto.module';
import { MatchesModule } from "./modules/matches/matches.module";
import { PredictionsModule } from "./modules/predictions/predictions.module";
import { LeaguesModule } from "./modules/leagues/leagues.module";
import { AnalysisModule } from "./modules/analysis/analysis.module";
import { CouponsModule } from "./modules/coupons/coupons.module";
import { SporTotoModule } from "./modules/spor-toto/spor-toto.module";
// Services and Tasks
import { ServicesModule } from './services/services.module';
import { TasksModule } from './tasks/tasks.module';
import { ServicesModule } from "./services/services.module";
import { TasksModule } from "./tasks/tasks.module";
// Feeder Module (Historical Data Scraping)
import { FeederModule } from './modules/feeder/feeder.module';
import { FeederModule } from "./modules/feeder/feeder.module";
// Guards
import {
JwtAuthGuard,
RolesGuard,
PermissionsGuard,
} from './modules/auth/guards';
} from "./modules/auth/guards";
// Queue
import { QueueModule } from './common/queues/queue.module';
import { QueueModule } from "./common/queues/queue.module";
const redisEnabled = process.env.REDIS_ENABLED === 'true';
const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
const redisEnabled = process.env.REDIS_ENABLED === "true";
const historicalFeederMode = process.env.FEEDER_MODE === "historical";
@Module({
imports: [
@@ -94,8 +94,8 @@ const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
// Static Assets (Images, Uploads)
ServeStaticModule.forRoot({
rootPath: path.join(__dirname, '..', 'public'),
serveRoot: '/', // This means public/uploads/x.png -> /uploads/x.png
rootPath: path.join(__dirname, "..", "public"),
serveRoot: "/", // This means public/uploads/x.png -> /uploads/x.png
}),
// Logger (Structured Logging with Pino)
@@ -105,10 +105,10 @@ const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
useFactory: (configService: ConfigService) => {
return {
pinoHttp: {
level: configService.get('app.isDevelopment') ? 'debug' : 'info',
transport: configService.get('app.isDevelopment')
level: configService.get("app.isDevelopment") ? "debug" : "info",
transport: configService.get("app.isDevelopment")
? {
target: 'pino-pretty',
target: "pino-pretty",
options: {
singleLine: true,
},
@@ -122,15 +122,15 @@ const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
// i18n
I18nModule.forRootAsync({
useFactory: (configService: ConfigService) => ({
fallbackLanguage: configService.get('i18n.fallbackLanguage', 'en'),
fallbackLanguage: configService.get("i18n.fallbackLanguage", "en"),
loaderOptions: {
path: path.join(__dirname, '../i18n/'),
watch: configService.get('app.isDevelopment', true),
path: path.join(__dirname, "../i18n/"),
watch: configService.get("app.isDevelopment", true),
},
}),
resolvers: [
new HeaderResolver(['x-lang']),
new QueryResolver(['lang']),
new HeaderResolver(["x-lang"]),
new QueryResolver(["lang"]),
AcceptLanguageResolver,
],
inject: [ConfigService],
@@ -141,8 +141,8 @@ const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
inject: [ConfigService],
useFactory: (configService: ConfigService) => [
{
ttl: configService.get('throttle.ttl', 60000),
limit: configService.get('throttle.limit', 100),
ttl: configService.get("throttle.ttl", 60000),
limit: configService.get("throttle.limit", 100),
},
],
}),
@@ -153,29 +153,29 @@ const historicalFeederMode = process.env.FEEDER_MODE === 'historical';
imports: [ConfigModule],
useFactory: async (configService: ConfigService) => {
// FORCE DISABLE REDIS if user doesn't want it
const useRedis = configService.get('redis.enabled', false);
const useRedis = configService.get("redis.enabled", false);
if (useRedis) {
try {
const store = await redisStore({
socket: {
host: configService.get('redis.host', 'localhost'),
port: configService.get('redis.port', 6379),
host: configService.get("redis.host", "localhost"),
port: configService.get("redis.port", 6379),
},
ttl: 60 * 1000, // 1 minute default
});
console.log('✅ Redis cache connected');
console.log("✅ Redis cache connected");
return {
store: store as unknown as any,
ttl: 60 * 1000,
};
} catch {
console.warn('⚠️ Redis connection failed, using in-memory cache');
console.warn("⚠️ Redis connection failed, using in-memory cache");
}
}
// Fallback to in-memory cache
console.log('📦 Using in-memory cache');
console.log("📦 Using in-memory cache");
return {
ttl: 60 * 1000,
};
src/app.service.ts
+2 -2
View File
@@ -1,8 +1,8 @@
import { Injectable } from '@nestjs/common';
import { Injectable } from "@nestjs/common";
@Injectable()
export class AppService {
getHello(): string {
return 'Hello World!';
return "Hello World!";
}
}
src/common/base/base.controller.ts
+29 -29
View File
@@ -8,20 +8,20 @@ import {
Body,
HttpCode,
ParseUUIDPipe,
} from '@nestjs/common';
} from "@nestjs/common";
import {
ApiOperation,
ApiOkResponse,
ApiNotFoundResponse,
ApiBadRequestResponse,
} from '@nestjs/swagger';
import { BaseService } from './base.service';
import { PaginationDto } from '../dto/pagination.dto';
} from "@nestjs/swagger";
import { BaseService } from "./base.service";
import { PaginationDto } from "../dto/pagination.dto";
import {
ApiResponse,
createSuccessResponse,
createPaginatedResponse,
} from '../types/api-response.type';
} from "../types/api-response.type";
/**
* Generic base controller with common CRUD endpoints
@@ -37,8 +37,8 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
@Get()
@HttpCode(200)
@ApiOperation({ summary: 'Get all records with pagination' })
@ApiOkResponse({ description: 'Records retrieved successfully' })
@ApiOperation({ summary: "Get all records with pagination" })
@ApiOkResponse({ description: "Records retrieved successfully" })
async findAll(
@Query() pagination: PaginationDto,
): Promise<ApiResponse<{ items: T[]; meta: any }>> {
@@ -52,13 +52,13 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
);
}
@Get(':id')
@Get(":id")
@HttpCode(200)
@ApiOperation({ summary: 'Get a record by ID' })
@ApiOkResponse({ description: 'Record retrieved successfully' })
@ApiNotFoundResponse({ description: 'Record not found' })
@ApiOperation({ summary: "Get a record by ID" })
@ApiOkResponse({ description: "Record retrieved successfully" })
@ApiNotFoundResponse({ description: "Record not found" })
async findOne(
@Param('id', ParseUUIDPipe) id: string,
@Param("id", ParseUUIDPipe) id: string,
): Promise<ApiResponse<T>> {
const result = await this.service.findOne(id);
return createSuccessResponse(
@@ -69,9 +69,9 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
@Post()
@HttpCode(200)
@ApiOperation({ summary: 'Create a new record' })
@ApiOkResponse({ description: 'Record created successfully' })
@ApiBadRequestResponse({ description: 'Validation failed' })
@ApiOperation({ summary: "Create a new record" })
@ApiOkResponse({ description: "Record created successfully" })
@ApiBadRequestResponse({ description: "Validation failed" })
async create(@Body() createDto: CreateDto): Promise<ApiResponse<T>> {
const result = await this.service.create(createDto);
return createSuccessResponse(
@@ -81,13 +81,13 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
);
}
@Put(':id')
@Put(":id")
@HttpCode(200)
@ApiOperation({ summary: 'Update an existing record' })
@ApiOkResponse({ description: 'Record updated successfully' })
@ApiNotFoundResponse({ description: 'Record not found' })
@ApiOperation({ summary: "Update an existing record" })
@ApiOkResponse({ description: "Record updated successfully" })
@ApiNotFoundResponse({ description: "Record not found" })
async update(
@Param('id', ParseUUIDPipe) id: string,
@Param("id", ParseUUIDPipe) id: string,
@Body() updateDto: UpdateDto,
): Promise<ApiResponse<T>> {
const result = await this.service.update(id, updateDto);
@@ -97,13 +97,13 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
);
}
@Delete(':id')
@Delete(":id")
@HttpCode(200)
@ApiOperation({ summary: 'Delete a record (soft delete)' })
@ApiOkResponse({ description: 'Record deleted successfully' })
@ApiNotFoundResponse({ description: 'Record not found' })
@ApiOperation({ summary: "Delete a record (soft delete)" })
@ApiOkResponse({ description: "Record deleted successfully" })
@ApiNotFoundResponse({ description: "Record not found" })
async delete(
@Param('id', ParseUUIDPipe) id: string,
@Param("id", ParseUUIDPipe) id: string,
): Promise<ApiResponse<T>> {
const result = await this.service.delete(id);
return createSuccessResponse(
@@ -112,12 +112,12 @@ export abstract class BaseController<T, CreateDto, UpdateDto> {
);
}
@Post(':id/restore')
@Post(":id/restore")
@HttpCode(200)
@ApiOperation({ summary: 'Restore a soft-deleted record' })
@ApiOkResponse({ description: 'Record restored successfully' })
@ApiOperation({ summary: "Restore a soft-deleted record" })
@ApiOkResponse({ description: "Record restored successfully" })
async restore(
@Param('id', ParseUUIDPipe) id: string,
@Param("id", ParseUUIDPipe) id: string,
): Promise<ApiResponse<T>> {
const result = await this.service.restore(id);
return createSuccessResponse(
src/common/base/base.service.ts
+4 -4
View File
@@ -1,7 +1,7 @@
import { NotFoundException, Logger } from '@nestjs/common';
import { PrismaService } from '../../database/prisma.service';
import { PaginationDto } from '../dto/pagination.dto';
import { PaginationMeta } from '../types/api-response.type';
import { NotFoundException, Logger } from "@nestjs/common";
import { PrismaService } from "../../database/prisma.service";
import { PaginationDto } from "../dto/pagination.dto";
import { PaginationMeta } from "../types/api-response.type";
/**
* Generic base service with common CRUD operations
src/common/base/index.ts
+2 -2
View File
@@ -1,2 +1,2 @@
export * from './base.service';
export * from './base.controller';
export * from "./base.service";
export * from "./base.controller";
src/common/constants/roles.ts
+12
View File
@@ -0,0 +1,12 @@
import { UserRole } from "@prisma/client";
export const APP_ROLES = {
user: UserRole.user,
superadmin: UserRole.superadmin,
} as const;
export const ADMIN_ROLES = [APP_ROLES.superadmin] as const;
export function normalizeRole(role: string | null | undefined): string {
return role?.trim().toLowerCase() ?? "";
}
src/common/decorators/index.ts
+5 -5
View File
@@ -2,7 +2,7 @@ import {
createParamDecorator,
ExecutionContext,
SetMetadata,
} from '@nestjs/common';
} from "@nestjs/common";
/**
* Get the current authenticated user from request
@@ -23,19 +23,19 @@ export const CurrentUser = createParamDecorator(
/**
* Mark a route as public (no authentication required)
*/
export const IS_PUBLIC_KEY = 'isPublic';
export const IS_PUBLIC_KEY = "isPublic";
export const Public = () => SetMetadata(IS_PUBLIC_KEY, true);
/**
* Require specific roles to access a route
*/
export const ROLES_KEY = 'roles';
export const ROLES_KEY = "roles";
export const Roles = (...roles: string[]) => SetMetadata(ROLES_KEY, roles);
/**
* Require specific permissions to access a route
*/
export const PERMISSIONS_KEY = 'permissions';
export const PERMISSIONS_KEY = "permissions";
export const RequirePermissions = (...permissions: string[]) =>
SetMetadata(PERMISSIONS_KEY, permissions);
@@ -55,6 +55,6 @@ export const CurrentTenant = createParamDecorator(
export const CurrentLang = createParamDecorator(
(data: unknown, ctx: ExecutionContext) => {
const request = ctx.switchToHttp().getRequest();
return request.headers['accept-language'] || 'en';
return request.headers["accept-language"] || "en";
},
);
src/common/dto/pagination.dto.ts
+15 -15
View File
@@ -1,9 +1,9 @@
import { IsOptional, IsInt, Min, Max, IsString, IsIn } from 'class-validator';
import { Transform } from 'class-transformer';
import { ApiPropertyOptional } from '@nestjs/swagger';
import { IsOptional, IsInt, Min, Max, IsString, IsIn } from "class-validator";
import { Transform } from "class-transformer";
import { ApiPropertyOptional } from "@nestjs/swagger";
export class PaginationDto {
@ApiPropertyOptional({ default: 1, minimum: 1, description: 'Page number' })
@ApiPropertyOptional({ default: 1, minimum: 1, description: "Page number" })
@IsOptional()
@Transform(({ value }) => parseInt(value, 10))
@IsInt()
@@ -14,7 +14,7 @@ export class PaginationDto {
default: 10,
minimum: 1,
maximum: 100,
description: 'Items per page',
description: "Items per page",
})
@IsOptional()
@Transform(({ value }) => parseInt(value, 10))
@@ -23,21 +23,21 @@ export class PaginationDto {
@Max(100)
limit?: number = 10;
@ApiPropertyOptional({ description: 'Field to sort by' })
@ApiPropertyOptional({ description: "Field to sort by" })
@IsOptional()
@IsString()
sortBy?: string = 'createdAt';
sortBy?: string = "createdAt";
@ApiPropertyOptional({
enum: ['asc', 'desc'],
default: 'desc',
description: 'Sort order',
enum: ["asc", "desc"],
default: "desc",
description: "Sort order",
})
@IsOptional()
@IsIn(['asc', 'desc'])
sortOrder?: 'asc' | 'desc' = 'desc';
@IsIn(["asc", "desc"])
sortOrder?: "asc" | "desc" = "desc";
@ApiPropertyOptional({ description: 'Search query' })
@ApiPropertyOptional({ description: "Search query" })
@IsOptional()
@IsString()
search?: string;
@@ -59,7 +59,7 @@ export class PaginationDto {
/**
* Get orderBy object for Prisma
*/
get orderBy(): Record<string, 'asc' | 'desc'> {
return { [this.sortBy || 'createdAt']: this.sortOrder || 'desc' };
get orderBy(): Record<string, "asc" | "desc"> {
return { [this.sortBy || "createdAt"]: this.sortOrder || "desc" };
}
}
src/common/filters/global-exception.filter.ts
+15 -15
View File
@@ -5,10 +5,10 @@ import {
HttpException,
HttpStatus,
Logger,
} from '@nestjs/common';
import { Request, Response } from 'express';
import { I18nService, I18nContext } from 'nestjs-i18n';
import { ApiResponse, createErrorResponse } from '../types/api-response.type';
} from "@nestjs/common";
import { Request, Response } from "express";
import { I18nService, I18nContext } from "nestjs-i18n";
import { ApiResponse, createErrorResponse } from "../types/api-response.type";
/**
* Global exception filter that catches all exceptions
@@ -27,23 +27,23 @@ export class GlobalExceptionFilter implements ExceptionFilter {
// Determine status and message
let status = HttpStatus.INTERNAL_SERVER_ERROR;
let message = 'Internal server error';
let message = "Internal server error";
let errors: string[] = [];
if (exception instanceof HttpException) {
status = exception.getStatus();
const exceptionResponse = exception.getResponse();
if (typeof exceptionResponse === 'string') {
if (typeof exceptionResponse === "string") {
message = exceptionResponse;
} else if (typeof exceptionResponse === 'object') {
} else if (typeof exceptionResponse === "object") {
const responseObj = exceptionResponse as Record<string, unknown>;
message = (responseObj.message as string) || exception.message;
// Handle validation errors (class-validator)
if (Array.isArray(responseObj.message)) {
errors = responseObj.message as string[];
message = 'VALIDATION_FAILED';
message = "VALIDATION_FAILED";
}
}
} else if (exception instanceof Error) {
@@ -57,22 +57,22 @@ export class GlobalExceptionFilter implements ExceptionFilter {
let lang = i18nContext?.lang;
if (!lang) {
const acceptLanguage = request.headers['accept-language'];
const xLang = request.headers['x-lang'];
const acceptLanguage = request.headers["accept-language"];
const xLang = request.headers["x-lang"];
if (xLang) {
lang = Array.isArray(xLang) ? xLang[0] : xLang;
} else if (acceptLanguage) {
// Take first preferred language: "tr-TR,en;q=0.9" -> "tr"
lang = acceptLanguage.split(',')[0].split(';')[0].split('-')[0];
lang = acceptLanguage.split(",")[0].split(";")[0].split("-")[0];
}
}
lang = lang || 'en';
lang = lang || "en";
// Translate validation error specially
if (message === 'VALIDATION_FAILED') {
message = this.i18n.translate('errors.VALIDATION_FAILED', { lang });
if (message === "VALIDATION_FAILED") {
message = this.i18n.translate("errors.VALIDATION_FAILED", { lang });
} else {
// Try dynamic translation
const translatedMessage = this.i18n.translate(`errors.${message}`, {
@@ -95,7 +95,7 @@ export class GlobalExceptionFilter implements ExceptionFilter {
);
// Build response
const isDevelopment = process.env.NODE_ENV === 'development';
const isDevelopment = process.env.NODE_ENV === "development";
const errorResponse: ApiResponse<null> = createErrorResponse(
message,
status,
src/common/interceptors/response.interceptor.ts
+26 -26
View File
@@ -3,16 +3,16 @@ import {
NestInterceptor,
ExecutionContext,
CallHandler,
} from '@nestjs/common';
import { Observable } from 'rxjs';
import { map } from 'rxjs/operators';
import { ApiResponse, createSuccessResponse } from '../types/api-response.type';
} from "@nestjs/common";
import { Observable } from "rxjs";
import { map } from "rxjs/operators";
import { ApiResponse, createSuccessResponse } from "../types/api-response.type";
/**
* Response interceptor that wraps all successful responses
* in the standard ApiResponse format
*/
import { I18nService, I18nContext } from 'nestjs-i18n';
import { I18nService, I18nContext } from "nestjs-i18n";
@Injectable()
export class ResponseInterceptor<T> implements NestInterceptor<
@@ -34,17 +34,17 @@ export class ResponseInterceptor<T> implements NestInterceptor<
let lang = i18nContext?.lang;
if (!lang) {
const acceptLanguage = request.headers['accept-language'];
const xLang = request.headers['x-lang'];
const acceptLanguage = request.headers["accept-language"];
const xLang = request.headers["x-lang"];
if (xLang) {
lang = Array.isArray(xLang) ? xLang[0] : xLang;
} else if (acceptLanguage) {
lang = acceptLanguage.split(',')[0].split(';')[0].split('-')[0];
lang = acceptLanguage.split(",")[0].split(";")[0].split("-")[0];
}
}
lang = lang || 'en';
lang = lang || "en";
// If data is already an ApiResponse, we should still translate its 'data' property
// But first let's just do it directly on 'data' below before returning
@@ -68,7 +68,7 @@ export class ResponseInterceptor<T> implements NestInterceptor<
}
}
const message = this.i18n.translate('common.success', {
const message = this.i18n.translate("common.success", {
lang,
});
@@ -79,7 +79,7 @@ export class ResponseInterceptor<T> implements NestInterceptor<
}
private translateReasons(data: any, lang: string) {
if (!data || typeof data !== 'object') {
if (!data || typeof data !== "object") {
return;
}
@@ -91,44 +91,44 @@ export class ResponseInterceptor<T> implements NestInterceptor<
Object.keys(data).forEach((key) => {
const val = data[key];
if (
(key === 'reasons' ||
key === 'decision_reasons' ||
key === 'reasoning_factors') &&
(key === "reasons" ||
key === "decision_reasons" ||
key === "reasoning_factors") &&
Array.isArray(val)
) {
data[key] = val.map((r: any) => {
if (typeof r !== 'string') return r;
if (typeof r !== "string") return r;
const translationKey = `predictions.reasons.${r}`;
const translated = this.i18n.translate(translationKey, {
lang,
});
return translated === translationKey ? r : translated;
});
} else if (key === 'reason' && typeof val === 'string') {
} else if (key === "reason" && typeof val === "string") {
const translationKey = `predictions.reasons.${val}`;
const translated = this.i18n.translate(translationKey, {
lang,
});
data[key] = translated === translationKey ? val : translated;
} else if (key === 'flags' && Array.isArray(val)) {
} else if (key === "flags" && Array.isArray(val)) {
data[key] = val.map((r: any) => {
if (typeof r !== 'string') return r;
if (typeof r !== "string") return r;
const translationKey = `predictions.flags.${r}`;
const translated = this.i18n.translate(translationKey, {
lang,
});
return translated === translationKey ? r : translated;
});
} else if (key === 'warnings' && Array.isArray(val)) {
} else if (key === "warnings" && Array.isArray(val)) {
data[key] = val.map((r: any) => {
if (typeof r !== 'string') return r;
if (typeof r !== "string") return r;
const translationKey = `predictions.warnings.${r}`;
const translated = this.i18n.translate(translationKey, {
lang,
});
return translated === translationKey ? r : translated;
});
} else if (typeof val === 'object' && val !== null) {
} else if (typeof val === "object" && val !== null) {
this.translateReasons(val, lang);
}
});
@@ -137,11 +137,11 @@ export class ResponseInterceptor<T> implements NestInterceptor<
private isApiResponse(data: unknown): boolean {
return (
data !== null &&
typeof data === 'object' &&
'success' in data &&
'status' in data &&
'message' in data &&
'data' in data
typeof data === "object" &&
"success" in data &&
"status" in data &&
"message" in data &&
"data" in data
);
}
}
src/common/interceptors/sanitize.interceptor.ts
+6 -6
View File
@@ -3,8 +3,8 @@ import {
NestInterceptor,
ExecutionContext,
CallHandler,
} from '@nestjs/common';
import { Observable } from 'rxjs';
} from "@nestjs/common";
import { Observable } from "rxjs";
/**
* Strips HTML/script tags from all string values in the request body.
@@ -15,7 +15,7 @@ export class SanitizeInterceptor implements NestInterceptor {
intercept(context: ExecutionContext, next: CallHandler): Observable<unknown> {
const request = context.switchToHttp().getRequest();
if (request.body && typeof request.body === 'object') {
if (request.body && typeof request.body === "object") {
request.body = this.sanitize(request.body);
}
@@ -23,7 +23,7 @@ export class SanitizeInterceptor implements NestInterceptor {
}
private sanitize(value: unknown): unknown {
if (typeof value === 'string') {
if (typeof value === "string") {
return this.stripTags(value);
}
@@ -31,7 +31,7 @@ export class SanitizeInterceptor implements NestInterceptor {
return value.map((item) => this.sanitize(item));
}
if (value !== null && typeof value === 'object') {
if (value !== null && typeof value === "object") {
const sanitized: Record<string, unknown> = {};
for (const [key, val] of Object.entries(value)) {
sanitized[key] = this.sanitize(val);
@@ -43,6 +43,6 @@ export class SanitizeInterceptor implements NestInterceptor {
}
private stripTags(input: string): string {
return input.replace(/<[^>]*>/g, '');
return input.replace(/<[^>]*>/g, "");
}
}
src/common/queues/queue.module.ts
+7 -7
View File
@@ -1,6 +1,6 @@
import { Module, Global } from '@nestjs/common';
import { BullModule } from '@nestjs/bullmq';
import { ConfigModule, ConfigService } from '@nestjs/config';
import { Module, Global } from "@nestjs/common";
import { BullModule } from "@nestjs/bullmq";
import { ConfigModule, ConfigService } from "@nestjs/config";
@Global()
@Module({
@@ -9,14 +9,14 @@ import { ConfigModule, ConfigService } from '@nestjs/config';
imports: [ConfigModule],
useFactory: (configService: ConfigService) => ({
connection: {
host: configService.get('redis.host', 'localhost'),
port: configService.get('redis.port', 6379),
password: configService.get('redis.password'),
host: configService.get("redis.host", "localhost"),
port: configService.get("redis.port", 6379),
password: configService.get("redis.password"),
},
defaultJobOptions: {
attempts: 3,
backoff: {
type: 'exponential',
type: "exponential",
delay: 1000,
},
removeOnComplete: true,
src/common/types/api-response.type.ts
+2 -2
View File
@@ -33,7 +33,7 @@ export interface PaginationMeta {
*/
export function createSuccessResponse<T>(
data: T,
message = 'Success',
message = "Success",
status = 200,
): ApiResponse<T> {
return {
@@ -72,7 +72,7 @@ export function createPaginatedResponse<T>(
total: number,
page: number,
limit: number,
message = 'Success',
message = "Success",
): ApiResponse<PaginatedData<T>> {
const totalPages = Math.ceil(total / limit);
src/common/utils/ai-engine-client.ts
+267
View File
@@ -0,0 +1,267 @@
import axios, {
AxiosError,
AxiosInstance,
AxiosRequestConfig,
AxiosResponse,
} from "axios";
import { Logger } from "@nestjs/common";
export type AiCircuitState = "closed" | "open";
export interface AiEngineClientOptions {
baseUrl: string;
logger: Logger;
serviceName: string;
timeoutMs?: number;
maxRetries?: number;
retryDelayMs?: number;
circuitBreakerThreshold?: number;
circuitBreakerCooldownMs?: number;
}
interface AiEngineRequestConfig extends AxiosRequestConfig {
retryCount?: number;
}
export interface AiEngineClientSnapshot {
state: AiCircuitState;
consecutiveFailures: number;
openedAt: string | null;
}
export class AiEngineRequestError extends Error {
status?: number;
detail?: unknown;
isCircuitOpen: boolean;
constructor(
message: string,
options: {
status?: number;
detail?: unknown;
isCircuitOpen?: boolean;
} = {},
) {
super(message);
this.name = "AiEngineRequestError";
this.status = options.status;
this.detail = options.detail;
this.isCircuitOpen = options.isCircuitOpen ?? false;
}
}
export class AiEngineClient {
private readonly axiosClient: AxiosInstance;
private readonly logger: Logger;
private readonly serviceName: string;
private readonly defaultTimeoutMs: number;
private readonly maxRetries: number;
private readonly retryDelayMs: number;
private readonly circuitBreakerThreshold: number;
private readonly circuitBreakerCooldownMs: number;
private consecutiveFailures = 0;
private circuitOpenedAt: number | null = null;
constructor(options: AiEngineClientOptions) {
this.logger = options.logger;
this.serviceName = options.serviceName;
this.defaultTimeoutMs = options.timeoutMs ?? 30000;
this.maxRetries = options.maxRetries ?? 2;
this.retryDelayMs = options.retryDelayMs ?? 750;
this.circuitBreakerThreshold = options.circuitBreakerThreshold ?? 3;
this.circuitBreakerCooldownMs =
options.circuitBreakerCooldownMs ?? 30000;
this.axiosClient = axios.create({
baseURL: options.baseUrl,
timeout: this.defaultTimeoutMs,
});
}
async get<T>(
path: string,
config?: AiEngineRequestConfig,
): Promise<AxiosResponse<T>> {
return this.request<T>({
method: "get",
url: path,
...config,
});
}
async post<T>(
path: string,
data?: unknown,
config?: AiEngineRequestConfig,
): Promise<AxiosResponse<T>> {
return this.request<T>({
method: "post",
url: path,
data,
...config,
});
}
getSnapshot(): AiEngineClientSnapshot {
return {
state: this.isCircuitOpen() ? "open" : "closed",
consecutiveFailures: this.consecutiveFailures,
openedAt: this.circuitOpenedAt
? new Date(this.circuitOpenedAt).toISOString()
: null,
};
}
private async request<T>(config: AiEngineRequestConfig): Promise<AxiosResponse<T>> {
this.ensureCircuitAvailable();
const retries = this.resolveRetryCount(config);
let lastError: unknown;
for (let attempt = 0; attempt <= retries; attempt += 1) {
try {
const response = await this.axiosClient.request<T>({
timeout: this.defaultTimeoutMs,
...config,
});
this.resetFailures();
return response;
} catch (error) {
lastError = error;
const shouldRetry = attempt < retries && this.isRetriableError(error);
if (!shouldRetry) {
this.registerFailure(error);
throw this.toRequestError(error);
}
this.logger.warn(
`[${this.serviceName}] AI request retry ${attempt + 1}/${retries} for ${config.method?.toUpperCase()} ${config.url}`,
);
await this.delay(this.retryDelayMs * (attempt + 1));
}
}
this.registerFailure(lastError);
throw this.toRequestError(lastError);
}
private resolveRetryCount(config: AiEngineRequestConfig): number {
if (typeof config.retryCount === "number" && config.retryCount >= 0) {
return config.retryCount;
}
return this.maxRetries;
}
private ensureCircuitAvailable() {
if (!this.isCircuitOpen()) {
return;
}
const remainingCooldown =
this.circuitBreakerCooldownMs - (Date.now() - (this.circuitOpenedAt ?? 0));
if (remainingCooldown > 0) {
throw new AiEngineRequestError("AI engine circuit breaker is open", {
status: 503,
detail: {
cooldownRemainingMs: remainingCooldown,
},
isCircuitOpen: true,
});
}
this.logger.warn(
`[${this.serviceName}] AI circuit breaker cooldown elapsed, allowing a recovery attempt`,
);
this.circuitOpenedAt = null;
}
private isCircuitOpen(): boolean {
return this.circuitOpenedAt !== null;
}
private resetFailures() {
this.consecutiveFailures = 0;
this.circuitOpenedAt = null;
}
private registerFailure(error: unknown) {
this.consecutiveFailures += 1;
const normalizedError = this.toRequestError(error);
this.logger.warn(
`[${this.serviceName}] AI request failed (${this.consecutiveFailures}/${this.circuitBreakerThreshold}): ${normalizedError.message}`,
);
if (this.consecutiveFailures >= this.circuitBreakerThreshold) {
this.circuitOpenedAt = Date.now();
this.logger.error(
`[${this.serviceName}] AI circuit breaker opened after ${this.consecutiveFailures} consecutive failures`,
);
}
}
private isRetriableError(error: unknown): boolean {
if (!axios.isAxiosError(error)) {
return false;
}
if (!error.response) {
return true;
}
const status = error.response.status;
return status >= 500 || status === 429 || error.code === "ECONNABORTED";
}
private toRequestError(error: unknown): AiEngineRequestError {
if (error instanceof AiEngineRequestError) {
return error;
}
if (axios.isAxiosError(error)) {
const detail = error.response?.data ?? error.message;
const status = error.response?.status;
const message = this.buildAxiosErrorMessage(error);
return new AiEngineRequestError(message, {
status,
detail,
});
}
if (error instanceof Error) {
return new AiEngineRequestError(error.message);
}
return new AiEngineRequestError("Unknown AI engine error", {
detail: error,
});
}
private buildAxiosErrorMessage(error: AxiosError): string {
if (error.code === "ECONNABORTED") {
return "AI engine request timed out";
}
if (!error.response) {
return "AI engine is unreachable";
}
const detail =
(error.response.data as Record<string, unknown> | undefined)?.detail ??
error.message;
return typeof detail === "string"
? detail
: `AI engine request failed with status ${error.response.status}`;
}
private async delay(ms: number) {
await new Promise((resolve) => setTimeout(resolve, ms));
}
}
src/common/utils/image.util.ts
+9 -9
View File
@@ -1,10 +1,10 @@
import { existsSync, createWriteStream, mkdirSync } from 'fs';
import { dirname } from 'path';
import axios from 'axios';
import { Logger } from '@nestjs/common';
import { existsSync, createWriteStream, mkdirSync } from "fs";
import { dirname } from "path";
import axios from "axios";
import { Logger } from "@nestjs/common";
export class ImageUtils {
private static readonly logger = new Logger('ImageUtils');
private static readonly logger = new Logger("ImageUtils");
/**
* Downloads an image from a URL and saves it to a local path.
@@ -26,8 +26,8 @@ export class ImageUtils {
// Download
const response = await axios({
url,
method: 'GET',
responseType: 'stream',
method: "GET",
responseType: "stream",
timeout: 5000,
validateStatus: (status) => status === 200, // Only save if 200 OK
});
@@ -37,8 +37,8 @@ export class ImageUtils {
response.data.pipe(writer);
return new Promise((resolve, reject) => {
writer.on('finish', () => resolve(true));
writer.on('error', (err) => {
writer.on("finish", () => resolve(true));
writer.on("error", (err) => {
this.logger.warn(
`Failed to write image to ${localPath}: ${err.message}`,
);
src/common/utils/match-status.util.ts
+203
View File
@@ -0,0 +1,203 @@
type ScoreLikeValue = number | string | null | undefined;
type ScoreLike = {
home?: ScoreLikeValue;
away?: ScoreLikeValue;
} | null;
export interface MatchStatusLike {
state?: string | null;
status?: string | null;
substate?: string | null;
statusBoxContent?: string | null;
scoreHome?: ScoreLikeValue;
scoreAway?: ScoreLikeValue;
score?: ScoreLike;
}
const LIVE_STATUS_TOKENS = [
"live",
"livegame",
"playing",
"half time",
"halftime",
"1h",
"2h",
"ht",
"1q",
"2q",
"3q",
"4q",
];
const LIVE_STATE_TOKENS = [
"live",
"livegame",
"firsthalf",
"secondhalf",
"halftime",
"1h",
"2h",
"ht",
"1q",
"2q",
"3q",
"4q",
];
const FINISHED_STATUS_TOKENS = [
"finished",
"played",
"ft",
"aet",
"pen",
"penalties",
"afterpenalties",
"ended",
"post",
"postgame",
"posted",
];
const FINISHED_STATE_TOKENS = [
"finished",
"post",
"postgame",
"posted",
"ft",
"ended",
];
export const LIVE_STATUS_VALUES_FOR_DB = [
"LIVE",
"live",
"1H",
"2H",
"HT",
"1Q",
"2Q",
"3Q",
"4Q",
"Playing",
"Half Time",
"liveGame",
];
export const LIVE_STATE_VALUES_FOR_DB = [
"live",
"liveGame",
"firsthalf",
"secondhalf",
"halfTime",
"1H",
"2H",
"HT",
"1Q",
"2Q",
"3Q",
"4Q",
];
export const FINISHED_STATUS_VALUES_FOR_DB = [
"Finished",
"Played",
"FT",
"AET",
"PEN",
"Ended",
"post",
"postGame",
"posted",
"Posted",
];
export const FINISHED_STATE_VALUES_FOR_DB = [
"Finished",
"post",
"postGame",
"postgame",
"posted",
"FT",
"Ended",
];
function normalizeToken(value: unknown): string {
return String(value || "")
.trim()
.toLowerCase();
}
function parseScoreValue(value: ScoreLikeValue): number | null {
if (value === null || value === undefined || value === "") {
return null;
}
const parsed = Number(value);
return Number.isFinite(parsed) ? parsed : null;
}
export function hasResolvedScore(match: MatchStatusLike): boolean {
const homeScore = parseScoreValue(match.score?.home ?? match.scoreHome);
const awayScore = parseScoreValue(match.score?.away ?? match.scoreAway);
return homeScore !== null && awayScore !== null;
}
export function isMatchLive(match: MatchStatusLike): boolean {
const state = normalizeToken(match.state);
const status = normalizeToken(match.status);
const substate = normalizeToken(match.substate);
return (
LIVE_STATE_TOKENS.includes(state) ||
LIVE_STATUS_TOKENS.includes(status) ||
LIVE_STATE_TOKENS.includes(substate)
);
}
export function isMatchCompleted(match: MatchStatusLike): boolean {
if (normalizeToken(match.statusBoxContent) === "ert") {
return false;
}
const state = normalizeToken(match.state);
const status = normalizeToken(match.status);
const substate = normalizeToken(match.substate);
if (
FINISHED_STATE_TOKENS.includes(state) ||
FINISHED_STATUS_TOKENS.includes(status) ||
FINISHED_STATE_TOKENS.includes(substate)
) {
return true;
}
return hasResolvedScore(match) && !isMatchLive(match);
}
export function deriveStoredMatchStatus(match: MatchStatusLike): string {
if (normalizeToken(match.statusBoxContent) === "ert") {
return "POSTPONED";
}
if (isMatchLive(match)) {
return "LIVE";
}
if (isMatchCompleted(match)) {
return "FT";
}
return "NS";
}
export function getDisplayMatchStatus(match: MatchStatusLike): string {
if (isMatchLive(match)) {
return "LIVE";
}
if (isMatchCompleted(match)) {
return "Finished";
}
return String(match.status || match.state || "NS");
}
src/common/utils/timezone.util.ts
+82
View File
@@ -0,0 +1,82 @@
function extractDateParts(date: Date, timeZone: string) {
const formatter = new Intl.DateTimeFormat("en-CA", {
timeZone,
year: "numeric",
month: "2-digit",
day: "2-digit",
});
const parts = formatter.formatToParts(date);
const year = Number(parts.find((part) => part.type === "year")?.value);
const month = Number(parts.find((part) => part.type === "month")?.value);
const day = Number(parts.find((part) => part.type === "day")?.value);
return { year, month, day };
}
export function getDateStringInTimeZone(
date: Date,
timeZone: string,
): string {
const { year, month, day } = extractDateParts(date, timeZone);
return `${year}-${String(month).padStart(2, "0")}-${String(day).padStart(2, "0")}`;
}
export function getShiftedDateStringInTimeZone(
daysOffset: number,
timeZone: string,
baseDate: Date = new Date(),
): string {
const { year, month, day } = extractDateParts(baseDate, timeZone);
const shifted = new Date(Date.UTC(year, month - 1, day));
shifted.setUTCDate(shifted.getUTCDate() + daysOffset);
return shifted.toISOString().split("T")[0];
}
function getTimeZoneOffsetMs(date: Date, timeZone: string): number {
const formatter = new Intl.DateTimeFormat("en-US", {
timeZone,
timeZoneName: "shortOffset",
});
const offsetLabel =
formatter.formatToParts(date).find((part) => part.type === "timeZoneName")
?.value || "GMT+0";
const match = offsetLabel.match(/GMT([+-])(\d{1,2})(?::?(\d{2}))?/);
if (!match) return 0;
const sign = match[1] === "-" ? -1 : 1;
const hours = Number(match[2] || "0");
const minutes = Number(match[3] || "0");
return sign * (hours * 60 + minutes) * 60 * 1000;
}
export function getDayBoundsForTimeZone(
dateString: string,
timeZone: string,
): { startMs: number; endMs: number } {
const [year, month, day] = dateString.split("-").map(Number);
const startGuess = new Date(Date.UTC(year, month - 1, day, 0, 0, 0));
const nextDayGuess = new Date(Date.UTC(year, month - 1, day + 1, 0, 0, 0));
const startOffsetMs = getTimeZoneOffsetMs(startGuess, timeZone);
const nextDayOffsetMs = getTimeZoneOffsetMs(nextDayGuess, timeZone);
const startMs = Date.UTC(year, month - 1, day, 0, 0, 0) - startOffsetMs;
const nextDayStartMs =
Date.UTC(year, month - 1, day + 1, 0, 0, 0) - nextDayOffsetMs;
return {
startMs,
endMs: nextDayStartMs - 1,
};
}
export function getDateOnlyValueForTimeZone(
timeZone: string,
date: Date = new Date(),
): Date {
return new Date(`${getDateStringInTimeZone(date, timeZone)}T00:00:00.000Z`);
}
src/config/configuration.ts
+29 -29
View File
@@ -1,58 +1,58 @@
import { registerAs } from '@nestjs/config';
import { registerAs } from "@nestjs/config";
export const appConfig = registerAs('app', () => ({
env: process.env.NODE_ENV || 'development',
port: parseInt(process.env.PORT || '3005', 10),
isDevelopment: process.env.NODE_ENV === 'development',
isProduction: process.env.NODE_ENV === 'production',
export const appConfig = registerAs("app", () => ({
env: process.env.NODE_ENV || "development",
port: parseInt(process.env.PORT || "3005", 10),
isDevelopment: process.env.NODE_ENV === "development",
isProduction: process.env.NODE_ENV === "production",
}));
export const databaseConfig = registerAs('database', () => ({
export const databaseConfig = registerAs("database", () => ({
url: process.env.DATABASE_URL,
}));
export const jwtConfig = registerAs('jwt', () => ({
export const jwtConfig = registerAs("jwt", () => ({
secret: process.env.JWT_SECRET,
accessExpiration: process.env.JWT_ACCESS_EXPIRATION || '15m',
refreshExpiration: process.env.JWT_REFRESH_EXPIRATION || '7d',
accessExpiration: process.env.JWT_ACCESS_EXPIRATION || "15m",
refreshExpiration: process.env.JWT_REFRESH_EXPIRATION || "7d",
}));
export const redisConfig = registerAs('redis', () => ({
enabled: process.env.REDIS_ENABLED === 'true',
host: process.env.REDIS_HOST || 'localhost',
port: parseInt(process.env.REDIS_PORT || '6379', 10),
export const redisConfig = registerAs("redis", () => ({
enabled: process.env.REDIS_ENABLED === "true",
host: process.env.REDIS_HOST || "localhost",
port: parseInt(process.env.REDIS_PORT || "6379", 10),
password: process.env.REDIS_PASSWORD || undefined,
}));
export const i18nConfig = registerAs('i18n', () => ({
defaultLanguage: process.env.DEFAULT_LANGUAGE || 'en',
fallbackLanguage: process.env.FALLBACK_LANGUAGE || 'en',
export const i18nConfig = registerAs("i18n", () => ({
defaultLanguage: process.env.DEFAULT_LANGUAGE || "en",
fallbackLanguage: process.env.FALLBACK_LANGUAGE || "en",
}));
export const featuresConfig = registerAs('features', () => ({
mail: process.env.ENABLE_MAIL === 'true',
s3: process.env.ENABLE_S3 === 'true',
websocket: process.env.ENABLE_WEBSOCKET === 'true',
multiTenancy: process.env.ENABLE_MULTI_TENANCY === 'true',
export const featuresConfig = registerAs("features", () => ({
mail: process.env.ENABLE_MAIL === "true",
s3: process.env.ENABLE_S3 === "true",
websocket: process.env.ENABLE_WEBSOCKET === "true",
multiTenancy: process.env.ENABLE_MULTI_TENANCY === "true",
}));
export const mailConfig = registerAs('mail', () => ({
export const mailConfig = registerAs("mail", () => ({
host: process.env.MAIL_HOST,
port: parseInt(process.env.MAIL_PORT || '587', 10),
port: parseInt(process.env.MAIL_PORT || "587", 10),
user: process.env.MAIL_USER,
password: process.env.MAIL_PASSWORD,
from: process.env.MAIL_FROM,
}));
export const s3Config = registerAs('s3', () => ({
export const s3Config = registerAs("s3", () => ({
endpoint: process.env.S3_ENDPOINT,
accessKey: process.env.S3_ACCESS_KEY,
secretKey: process.env.S3_SECRET_KEY,
bucket: process.env.S3_BUCKET,
region: process.env.S3_REGION || 'us-east-1',
region: process.env.S3_REGION || "us-east-1",
}));
export const throttleConfig = registerAs('throttle', () => ({
ttl: parseInt(process.env.THROTTLE_TTL || '60000', 10),
limit: parseInt(process.env.THROTTLE_LIMIT || '100', 10),
export const throttleConfig = registerAs("throttle", () => ({
ttl: parseInt(process.env.THROTTLE_TTL || "60000", 10),
limit: parseInt(process.env.THROTTLE_LIMIT || "100", 10),
}));
src/config/env.validation.ts
+21 -20
View File
@@ -1,4 +1,4 @@
import { z } from 'zod';
import { z } from "zod";
/**
* Helper to parse boolean from string
@@ -6,8 +6,8 @@ import { z } from 'zod';
const booleanString = z
.string()
.optional()
.default('false')
.transform((val) => val === 'true');
.default("false")
.transform((val) => val === "true");
/**
* Environment variables schema validation using Zod
@@ -15,46 +15,47 @@ const booleanString = z
export const envSchema = z.object({
// Environment
NODE_ENV: z
.enum(['development', 'production', 'test'])
.default('development'),
.enum(["development", "production", "test"])
.default("development"),
PORT: z.coerce.number().default(3005),
// Database
DATABASE_URL: z.string().url(),
// AI Engine
AI_ENGINE_URL: z.string().url().default('http://localhost:8000'),
AI_ENGINE_URL: z.string().url().default("http://localhost:8000"),
AI_ENGINE_MODE: z.enum(["v28-pro-max", "dual"]).default("v28-pro-max"),
// JWT
JWT_SECRET: z.string().min(32),
JWT_ACCESS_EXPIRATION: z.string().default('15m'),
JWT_REFRESH_EXPIRATION: z.string().default('7d'),
JWT_ACCESS_EXPIRATION: z.string().default("15m"),
JWT_REFRESH_EXPIRATION: z.string().default("7d"),
// Redis
REDIS_ENABLED: z
.string()
.transform((val) => val === 'true')
.default('false' as any),
REDIS_HOST: z.string().default('localhost'),
.transform((val) => val === "true")
.default("false" as any),
REDIS_HOST: z.string().default("localhost"),
REDIS_PORT: z.coerce.number().default(6379),
REDIS_PASSWORD: z.string().optional(),
// i18n
DEFAULT_LANGUAGE: z.string().default('en'),
FALLBACK_LANGUAGE: z.string().default('en'),
DEFAULT_LANGUAGE: z.string().default("en"),
FALLBACK_LANGUAGE: z.string().default("en"),
// Gemini AI
ENABLE_GEMINI: z
.string()
.transform((val) => val === 'true')
.default('false' as any),
.transform((val) => val === "true")
.default("false" as any),
GOOGLE_API_KEY: z.string().optional(),
GEMINI_DEFAULT_MODEL: z.string().default('gemini-2.5-flash'),
GEMINI_DEFAULT_MODEL: z.string().default("gemini-2.5-flash"),
// Social Poster
SOCIAL_POSTER_ENABLED: z
.string()
.transform((val) => val === 'true')
.default('false' as any),
.transform((val) => val === "true")
.default("false" as any),
TWITTER_API_KEY: z.string().optional(),
TWITTER_API_SECRET: z.string().optional(),
TWITTER_ACCESS_TOKEN: z.string().optional(),
@@ -98,9 +99,9 @@ export function validateEnv(config: Record<string, unknown>): EnvConfig {
if (!result.success) {
const errors = result.error.issues.map(
(err) => `${err.path.join('.')}: ${err.message}`,
(err) => `${err.path.join(".")}: ${err.message}`,
);
throw new Error(`Environment validation failed:\n${errors.join('\n')}`);
throw new Error(`Environment validation failed:\n${errors.join("\n")}`);
}
return result.data;
src/database/database.module.ts
+2 -2
View File
@@ -1,5 +1,5 @@
import { Global, Module } from '@nestjs/common';
import { PrismaService } from './prisma.service';
import { Global, Module } from "@nestjs/common";
import { PrismaService } from "./prisma.service";
@Global()
@Module({
src/database/prisma.service.ts
+9 -9
View File
@@ -3,11 +3,11 @@ import {
OnModuleInit,
OnModuleDestroy,
Logger,
} from '@nestjs/common';
import { PrismaClient } from '@prisma/client';
} from "@nestjs/common";
import { PrismaClient } from "@prisma/client";
// Models that support soft delete
const SOFT_DELETE_MODELS = ['user', 'role', 'tenant'];
const SOFT_DELETE_MODELS = ["user", "role", "tenant"];
// Type for Prisma model delegate with common operations
interface PrismaDelegate {
@@ -29,20 +29,20 @@ export class PrismaService
constructor() {
super({
log: [
{ emit: 'event', level: 'query' },
{ emit: 'event', level: 'error' },
{ emit: 'event', level: 'warn' },
{ emit: "event", level: "query" },
{ emit: "event", level: "error" },
{ emit: "event", level: "warn" },
],
});
}
async onModuleInit() {
this.logger.log(
`Connecting to database... URL: ${process.env.DATABASE_URL?.split('@')[1]}`,
`Connecting to database... URL: ${process.env.DATABASE_URL?.split("@")[1]}`,
); // Mask password
try {
await this.$connect();
this.logger.log('✅ Database connected successfully');
this.logger.log("✅ Database connected successfully");
} catch (error) {
this.logger.error(
`❌ Database connection failed: ${error.message}`,
@@ -54,7 +54,7 @@ export class PrismaService
async onModuleDestroy() {
await this.$disconnect();
this.logger.log('🔌 Database disconnected');
this.logger.log("🔌 Database disconnected");
}
/**
src/main.ts
+43 -42
View File
@@ -1,12 +1,12 @@
import { NestFactory } from '@nestjs/core';
import { ValidationPipe, Logger as NestLogger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { SwaggerModule, DocumentBuilder } from '@nestjs/swagger';
import { AppModule } from './app.module';
import helmet from 'helmet';
import * as express from 'express';
import { Logger, LoggerErrorInterceptor } from 'nestjs-pino';
import { SanitizeInterceptor } from './common/interceptors/sanitize.interceptor';
import { NestFactory } from "@nestjs/core";
import { ValidationPipe, Logger as NestLogger } from "@nestjs/common";
import { ConfigService } from "@nestjs/config";
import { SwaggerModule, DocumentBuilder } from "@nestjs/swagger";
import { AppModule } from "./app.module";
import helmet from "helmet";
import * as express from "express";
import { Logger, LoggerErrorInterceptor } from "nestjs-pino";
import { SanitizeInterceptor } from "./common/interceptors/sanitize.interceptor";
// BigInt serialization polyfill — Prisma returns BigInt for mstUtc etc.
(BigInt.prototype as unknown as { toJSON: () => string }).toJSON = function () {
@@ -14,9 +14,9 @@ import { SanitizeInterceptor } from './common/interceptors/sanitize.interceptor'
};
async function bootstrap() {
const logger = new NestLogger('Bootstrap');
const logger = new NestLogger("Bootstrap");
logger.log('🔄 Starting application...');
logger.log("🔄 Starting application...");
const app = await NestFactory.create(AppModule, { bufferLogs: false });
@@ -31,33 +31,34 @@ async function bootstrap() {
app.use(helmet());
// Request payload size limit
app.use(express.json({ limit: '1mb' }));
app.use(express.urlencoded({ extended: true, limit: '1mb' }));
app.use(express.json({ limit: "1mb" }));
app.use(express.urlencoded({ extended: true, limit: "1mb" }));
// Graceful Shutdown (Prisma & Docker)
app.enableShutdownHooks();
// Get config service
const configService = app.get(ConfigService);
const port = configService.get<number>('PORT', 3005);
const nodeEnv = configService.get('NODE_ENV', 'development');
const port = configService.get<number>("PORT", 3005);
const nodeEnv = configService.get("NODE_ENV", "development");
// Enable CORS
app.enableCors({
origin:
nodeEnv === 'production'
nodeEnv === "production"
? [
'https://ui-suggestbet.bilgich.com',
'https://suggestbet.bilgich.com',
'https://iddaai.com',
'https://www.iddaai.com',
"https://ui-suggestbet.bilgich.com",
"https://suggestbet.bilgich.com",
"https://iddaai.com",
"https://www.iddaai.com",
"http://localhost:6195",
]
: true,
credentials: true,
});
// Global prefix
app.setGlobalPrefix('api');
app.setGlobalPrefix("api");
// Validation pipe (Strict)
app.useGlobalPipes(
@@ -72,47 +73,47 @@ async function bootstrap() {
);
// Swagger setup — hidden in production
if (nodeEnv !== 'production') {
if (nodeEnv !== "production") {
const swaggerConfig = new DocumentBuilder()
.setTitle('Suggest-Bet API')
.setTitle("Suggest-Bet API")
.setDescription(
'AI-driven sports betting prediction engine with smart coupon generation',
"AI-driven sports betting prediction engine with smart coupon generation",
)
.setVersion('1.0')
.setVersion("1.0")
.addBearerAuth()
.addTag('Auth', 'Authentication endpoints')
.addTag('Users', 'User management endpoints')
.addTag('Admin', 'Admin management endpoints')
.addTag('Health', 'Health check endpoints')
.addTag('Matches', 'Match listing and detail endpoints')
.addTag('Leagues', 'League, country, and team discovery endpoints')
.addTag('Analysis', 'AI analysis and analysis history endpoints')
.addTag('Coupon', 'Coupon generation and coupon management endpoints')
.addTag('Predictions', 'Prediction and smart-coupon endpoints')
.addTag("Auth", "Authentication endpoints")
.addTag("Users", "User management endpoints")
.addTag("Admin", "Admin management endpoints")
.addTag("Health", "Health check endpoints")
.addTag("Matches", "Match listing and detail endpoints")
.addTag("Leagues", "League, country, and team discovery endpoints")
.addTag("Analysis", "AI analysis and analysis history endpoints")
.addTag("Coupon", "Coupon generation and coupon management endpoints")
.addTag("Predictions", "Prediction and smart-coupon endpoints")
.build();
logger.log('Initializing Swagger...');
logger.log("Initializing Swagger...");
const document = SwaggerModule.createDocument(app, swaggerConfig);
SwaggerModule.setup('api/docs', app, document, {
SwaggerModule.setup("api/docs", app, document, {
swaggerOptions: {
persistAuthorization: true,
},
});
logger.log('Swagger initialized');
logger.log("Swagger initialized");
}
logger.log(`Attempting to listen on port ${port}...`);
await app.listen(port, '0.0.0.0');
await app.listen(port, "0.0.0.0");
logger.log('═══════════════════════════════════════════════════════════');
logger.log("═══════════════════════════════════════════════════════════");
logger.log(`🚀 Server is running on: http://localhost:${port}/api`);
logger.log(`📚 Swagger documentation: http://localhost:${port}/api/docs`);
logger.log(`💚 Health check: http://localhost:${port}/api/health`);
logger.log(`🌍 Environment: ${nodeEnv.toUpperCase()}`);
logger.log('═══════════════════════════════════════════════════════════');
logger.log("═══════════════════════════════════════════════════════════");
if (nodeEnv === 'development') {
logger.warn('⚠️ Running in development mode');
if (nodeEnv === "development") {
logger.warn("⚠️ Running in development mode");
}
}
src/modules/admin/admin.controller.ts
+63 -57
View File
@@ -10,32 +10,32 @@ import {
UseInterceptors,
Inject,
NotFoundException,
} from '@nestjs/common';
} from "@nestjs/common";
import {
CacheInterceptor,
CacheKey,
CacheTTL,
CACHE_MANAGER,
} from '@nestjs/cache-manager';
import * as cacheManager from 'cache-manager';
import { ApiTags, ApiBearerAuth, ApiOperation } from '@nestjs/swagger';
import { Roles } from '../../common/decorators';
import { PrismaService } from '../../database/prisma.service';
import { PaginationDto } from '../../common/dto/pagination.dto';
} from "@nestjs/cache-manager";
import * as cacheManager from "cache-manager";
import { ApiTags, ApiBearerAuth, ApiOperation } from "@nestjs/swagger";
import { Roles } from "../../common/decorators";
import { PrismaService } from "../../database/prisma.service";
import { PaginationDto } from "../../common/dto/pagination.dto";
import {
ApiResponse,
createSuccessResponse,
createPaginatedResponse,
PaginatedData,
} from '../../common/types/api-response.type';
import { plainToInstance } from 'class-transformer';
import { UserResponseDto } from '../users/dto/user.dto';
import { UserRole } from '@prisma/client';
} from "../../common/types/api-response.type";
import { plainToInstance } from "class-transformer";
import { UserResponseDto } from "../users/dto/user.dto";
import { UserRole } from "@prisma/client";
@ApiTags('Admin')
@ApiTags("Admin")
@ApiBearerAuth()
@Controller('admin')
@Roles('superadmin')
@Controller("admin")
@Roles("superadmin")
export class AdminController {
constructor(
private readonly prisma: PrismaService,
@@ -44,8 +44,8 @@ export class AdminController {
// ================== Users Management ==================
@Get('users')
@ApiOperation({ summary: 'Get all users (admin)' })
@Get("users")
@ApiOperation({ summary: "Get all users (admin)" })
async getAllUsers(
@Query() pagination: PaginationDto,
): Promise<ApiResponse<PaginatedData<UserResponseDto>>> {
@@ -73,10 +73,10 @@ export class AdminController {
);
}
@Get('users/:id')
@ApiOperation({ summary: 'Get user by ID' })
@Get("users/:id")
@ApiOperation({ summary: "Get user by ID" })
async getUserById(
@Param('id') id: string,
@Param("id") id: string,
): Promise<ApiResponse<UserResponseDto>> {
const user = await this.prisma.user.findUnique({
where: { id },
@@ -84,27 +84,27 @@ export class AdminController {
usageLimit: true,
analyses: {
take: 5,
orderBy: { createdAt: 'desc' },
orderBy: { createdAt: "desc" },
},
},
});
if (!user) {
throw new NotFoundException('User not found');
throw new NotFoundException("User not found");
}
return createSuccessResponse(plainToInstance(UserResponseDto, user));
}
@Put('users/:id/toggle-active')
@ApiOperation({ summary: 'Toggle user active status' })
@Put("users/:id/toggle-active")
@ApiOperation({ summary: "Toggle user active status" })
async toggleUserActive(
@Param('id') id: string,
@Param("id") id: string,
): Promise<ApiResponse<UserResponseDto>> {
const user = await this.prisma.user.findUnique({ where: { id } });
if (!user) {
throw new NotFoundException('User not found');
throw new NotFoundException("User not found");
}
const updated = await this.prisma.user.update({
@@ -114,14 +114,14 @@ export class AdminController {
return createSuccessResponse(
plainToInstance(UserResponseDto, updated),
'User status updated',
"User status updated",
);
}
@Put('users/:id/role')
@ApiOperation({ summary: 'Update user role' })
@Put("users/:id/role")
@ApiOperation({ summary: "Update user role" })
async updateUserRole(
@Param('id') id: string,
@Param("id") id: string,
@Body() data: { role: UserRole },
): Promise<ApiResponse<UserResponseDto>> {
const user = await this.prisma.user.update({
@@ -131,14 +131,14 @@ export class AdminController {
return createSuccessResponse(
plainToInstance(UserResponseDto, user),
'User role updated',
"User role updated",
);
}
@Put('users/:id/subscription')
@ApiOperation({ summary: 'Update user subscription' })
@Put("users/:id/subscription")
@ApiOperation({ summary: "Update user subscription" })
async updateUserSubscription(
@Param('id') id: string,
@Param("id") id: string,
@Body()
data: { subscriptionStatus: string; subscriptionExpiresAt?: string },
): Promise<ApiResponse<UserResponseDto>> {
@@ -154,40 +154,40 @@ export class AdminController {
return createSuccessResponse(
plainToInstance(UserResponseDto, user),
'User subscription updated',
"User subscription updated",
);
}
@Delete('users/:id')
@ApiOperation({ summary: 'Soft delete a user' })
async deleteUser(@Param('id') id: string): Promise<ApiResponse<null>> {
@Delete("users/:id")
@ApiOperation({ summary: "Soft delete a user" })
async deleteUser(@Param("id") id: string): Promise<ApiResponse<null>> {
await this.prisma.user.update({
where: { id },
data: { deletedAt: new Date() },
});
return createSuccessResponse(null, 'User deleted');
return createSuccessResponse(null, "User deleted");
}
// ================== App Settings ==================
@Get('settings')
@Get("settings")
@UseInterceptors(CacheInterceptor)
@CacheKey('app_settings')
@CacheKey("app_settings")
@CacheTTL(60 * 1000)
@ApiOperation({ summary: 'Get all app settings' })
@ApiOperation({ summary: "Get all app settings" })
async getAllSettings(): Promise<ApiResponse<Record<string, string>>> {
const settings = await this.prisma.appSetting.findMany();
const settingsMap: Record<string, string> = {};
for (const s of settings) {
settingsMap[s.key] = s.value || '';
settingsMap[s.key] = s.value || "";
}
return createSuccessResponse(settingsMap);
}
@Put('settings/:key')
@ApiOperation({ summary: 'Update an app setting' })
@Put("settings/:key")
@ApiOperation({ summary: "Update an app setting" })
async updateSetting(
@Param('key') key: string,
@Param("key") key: string,
@Body() data: { value: string },
): Promise<ApiResponse<{ key: string; value: string }>> {
const setting = await this.prisma.appSetting.upsert({
@@ -195,17 +195,17 @@ export class AdminController {
update: { value: data.value },
create: { key, value: data.value },
});
await this.cacheManager.del('app_settings');
await this.cacheManager.del("app_settings");
return createSuccessResponse(
{ key: setting.key, value: setting.value || '' },
'Setting updated',
{ key: setting.key, value: setting.value || "" },
"Setting updated",
);
}
// ================== Usage Limits ==================
@Get('usage-limits')
@ApiOperation({ summary: 'Get all usage limits' })
@Get("usage-limits")
@ApiOperation({ summary: "Get all usage limits" })
async getAllUsageLimits(@Query() pagination: PaginationDto) {
const { skip, take } = pagination;
@@ -218,7 +218,7 @@ export class AdminController {
select: { id: true, email: true, firstName: true, lastName: true },
},
},
orderBy: { lastResetDate: 'desc' },
orderBy: { lastResetDate: "desc" },
}),
this.prisma.usageLimit.count(),
]);
@@ -231,8 +231,8 @@ export class AdminController {
);
}
@Post('usage-limits/reset-all')
@ApiOperation({ summary: 'Reset all usage limits' })
@Post("usage-limits/reset-all")
@ApiOperation({ summary: "Reset all usage limits" })
async resetAllUsageLimits(): Promise<ApiResponse<{ count: number }>> {
const result = await this.prisma.usageLimit.updateMany({
data: {
@@ -244,14 +244,14 @@ export class AdminController {
return createSuccessResponse(
{ count: result.count },
'All usage limits reset',
"All usage limits reset",
);
}
// ================== Analytics ==================
@Get('analytics/overview')
@ApiOperation({ summary: 'Get system analytics overview' })
@Get("analytics/overview")
@ApiOperation({ summary: "Get system analytics overview" })
async getAnalyticsOverview() {
const [
totalUsers,
@@ -259,15 +259,21 @@ export class AdminController {
premiumUsers,
totalMatches,
totalPredictions,
totalCoupons,
] = await Promise.all([
this.prisma.user.count(),
this.prisma.user.count({ where: { isActive: true } }),
this.prisma.user.count({ where: { subscriptionStatus: 'active' } }),
this.prisma.user.count({ where: { subscriptionStatus: "active" } }),
this.prisma.match.count(),
this.prisma.prediction.count(),
this.prisma.userCoupon.count(),
]);
return createSuccessResponse({
totalUsers,
activeUsers,
totalPredictions,
totalCoupons,
users: {
total: totalUsers,
active: activeUsers,
src/modules/admin/admin.module.ts
+2 -2
View File
@@ -1,5 +1,5 @@
import { Module } from '@nestjs/common';
import { AdminController } from './admin.controller';
import { Module } from "@nestjs/common";
import { AdminController } from "./admin.controller";
@Module({
controllers: [AdminController],
src/modules/admin/dto/admin.dto.ts
+1 -1
View File
@@ -1,4 +1,4 @@
import { Exclude, Expose, Type } from 'class-transformer';
import { Exclude, Expose, Type } from "class-transformer";
@Exclude()
export class PermissionResponseDto {
src/modules/analysis/analysis.controller.ts
+19 -19
View File
@@ -6,20 +6,20 @@ import {
HttpCode,
HttpStatus,
ForbiddenException,
} from '@nestjs/common';
} from "@nestjs/common";
import {
ApiTags,
ApiBearerAuth,
ApiOperation,
ApiResponse,
} from '@nestjs/swagger';
import { AnalysisService } from './analysis.service';
import { AnalyzeMatchesDto } from './dto/analysis-request.dto';
import { CurrentUser } from '../../common/decorators';
} from "@nestjs/swagger";
import { AnalysisService } from "./analysis.service";
import { AnalyzeMatchesDto } from "./dto/analysis-request.dto";
import { CurrentUser } from "../../common/decorators";
@ApiTags('Analysis')
@ApiTags("Analysis")
@ApiBearerAuth()
@Controller('analysis')
@Controller("analysis")
export class AnalysisController {
constructor(private readonly analysisService: AnalysisService) {}
@@ -27,12 +27,12 @@ export class AnalysisController {
* POST /analysis/analyze-matches
* Analyze multiple matches (coupon generation)
*/
@Post('analyze-matches')
@Post("analyze-matches")
@HttpCode(HttpStatus.OK)
@ApiOperation({ summary: 'Analyze multiple matches for coupon' })
@ApiResponse({ status: 200, description: 'Analysis successful' })
@ApiResponse({ status: 400, description: 'Invalid input' })
@ApiResponse({ status: 429, description: 'Usage limit exceeded' })
@ApiOperation({ summary: "Analyze multiple matches for coupon" })
@ApiResponse({ status: 200, description: "Analysis successful" })
@ApiResponse({ status: 400, description: "Invalid input" })
@ApiResponse({ status: 429, description: "Usage limit exceeded" })
async analyzeMatches(
@CurrentUser() user: any,
@Body() dto: AnalyzeMatchesDto,
@@ -48,7 +48,7 @@ export class AnalysisController {
);
if (!canProceed) {
throw new ForbiddenException('You have exceeded your daily usage limit');
throw new ForbiddenException("You have exceeded your daily usage limit");
}
// Run analysis
@@ -57,7 +57,7 @@ export class AnalysisController {
if (!result) {
return {
success: false,
message: 'None of the provided matches could be analyzed successfully',
message: "None of the provided matches could be analyzed successfully",
};
}
@@ -73,10 +73,10 @@ export class AnalysisController {
/**
* POST /analysis/analyze (alias for /analyze-matches - frontend compatibility)
*/
@Post('analyze')
@Post("analyze")
@HttpCode(HttpStatus.OK)
@ApiOperation({
summary: 'Analyze multiple matches for coupon (alias)',
summary: "Analyze multiple matches for coupon (alias)",
deprecated: true,
})
async analyzeMatchesAlias(
@@ -90,9 +90,9 @@ export class AnalysisController {
* GET /analysis/history
* Get user's analysis history
*/
@Get('history')
@ApiOperation({ summary: 'Get analysis history' })
@ApiResponse({ status: 200, description: 'History retrieved' })
@Get("history")
@ApiOperation({ summary: "Get analysis history" })
@ApiResponse({ status: 200, description: "History retrieved" })
async getHistory(@CurrentUser() user: any) {
const history = await this.analysisService.getAnalysisHistory(user.id);
return { success: true, data: history };
src/modules/analysis/analysis.module.ts
+5 -5
View File
@@ -1,8 +1,8 @@
import { Module } from '@nestjs/common';
import { AnalysisController } from './analysis.controller';
import { AnalysisService } from './analysis.service';
import { DatabaseModule } from '../../database/database.module';
import { ServicesModule } from '../../services/services.module';
import { Module } from "@nestjs/common";
import { AnalysisController } from "./analysis.controller";
import { AnalysisService } from "./analysis.service";
import { DatabaseModule } from "../../database/database.module";
import { ServicesModule } from "../../services/services.module";
@Module({
imports: [DatabaseModule, ServicesModule],
src/modules/analysis/analysis.service.ts
+7 -7
View File
@@ -1,9 +1,9 @@
import { Injectable, Logger } from '@nestjs/common';
import { PrismaService } from '../../database/prisma.service';
import { Injectable, Logger } from "@nestjs/common";
import { PrismaService } from "../../database/prisma.service";
import {
MatchAnalysisService,
AnalysisResult,
} from '../../services/match-analysis.service';
} from "../../services/match-analysis.service";
@Injectable()
export class AnalysisService {
@@ -50,9 +50,9 @@ export class AnalysisService {
}
// Build URL for analysis
const sport = (targetMatch as any).sport || 'football';
const sport = (targetMatch as any).sport || "football";
const slug = (targetMatch as any).matchSlug || matchId;
const url = `https://www.mackolik.com/${sport === 'basketball' ? 'basketbol/mac' : 'mac'}/${slug}/${matchId}`;
const url = `https://www.mackolik.com/${sport === "basketball" ? "basketbol/mac" : "mac"}/${slug}/${matchId}`;
// Run analysis
const result = await this.matchAnalysisService.analyzeMatch(
@@ -110,7 +110,7 @@ export class AnalysisService {
// Check limits (default: 10 analyses, 3 coupons per day)
const user = await this.prisma.user.findUnique({ where: { id: userId } });
const isPremium = user?.subscriptionStatus === 'active';
const isPremium = user?.subscriptionStatus === "active";
const maxAnalyses = isPremium ? 50 : 10;
const maxCoupons = isPremium ? 10 : 3;
@@ -145,7 +145,7 @@ export class AnalysisService {
async getAnalysisHistory(userId: string, limit: number = 20) {
return this.prisma.analysis.findMany({
where: { userId },
orderBy: { createdAt: 'desc' },
orderBy: { createdAt: "desc" },
take: limit,
});
}
src/modules/analysis/dto/analysis-request.dto.ts
+4 -4
View File
@@ -1,10 +1,10 @@
import { IsArray, IsString, ArrayMinSize, ArrayMaxSize } from 'class-validator';
import { ApiProperty } from '@nestjs/swagger';
import { IsArray, IsString, ArrayMinSize, ArrayMaxSize } from "class-validator";
import { ApiProperty } from "@nestjs/swagger";
export class AnalyzeMatchesDto {
@ApiProperty({
description: 'List of match IDs to analyze',
example: ['match-1', 'match-2'],
description: "List of match IDs to analyze",
example: ["match-1", "match-2"],
minItems: 1,
maxItems: 20,
})
src/modules/auth/auth.controller.ts
+25 -25
View File
@@ -1,30 +1,30 @@
import { Controller, Post, Body, HttpCode } from '@nestjs/common';
import { I18n, I18nContext } from 'nestjs-i18n';
import { ApiTags, ApiOperation, ApiOkResponse } from '@nestjs/swagger';
import { AuthService } from './auth.service';
import { Controller, Post, Body, HttpCode } from "@nestjs/common";
import { I18n, I18nContext } from "nestjs-i18n";
import { ApiTags, ApiOperation, ApiOkResponse } from "@nestjs/swagger";
import { AuthService } from "./auth.service";
import {
RegisterDto,
LoginDto,
RefreshTokenDto,
TokenResponseDto,
} from './dto/auth.dto';
import { Public } from '../../common/decorators';
} from "./dto/auth.dto";
import { Public } from "../../common/decorators";
import {
ApiResponse,
createSuccessResponse,
} from '../../common/types/api-response.type';
} from "../../common/types/api-response.type";
@ApiTags('Auth')
@Controller('auth')
@ApiTags("Auth")
@Controller("auth")
export class AuthController {
constructor(private readonly authService: AuthService) {}
@Post('register')
@Post("register")
@Public()
@HttpCode(200)
@ApiOperation({ summary: 'Register a new user' })
@ApiOperation({ summary: "Register a new user" })
@ApiOkResponse({
description: 'User registered successfully',
description: "User registered successfully",
type: TokenResponseDto,
})
async register(
@@ -32,28 +32,28 @@ export class AuthController {
@I18n() i18n: I18nContext,
): Promise<ApiResponse<TokenResponseDto>> {
const result = await this.authService.register(dto);
return createSuccessResponse(result, i18n.t('auth.registered'), 201);
return createSuccessResponse(result, i18n.t("auth.registered"), 201);
}
@Post('login')
@Post("login")
@Public()
@HttpCode(200)
@ApiOperation({ summary: 'Login with email and password' })
@ApiOkResponse({ description: 'Login successful', type: TokenResponseDto })
@ApiOperation({ summary: "Login with email and password" })
@ApiOkResponse({ description: "Login successful", type: TokenResponseDto })
async login(
@Body() dto: LoginDto,
@I18n() i18n: I18nContext,
): Promise<ApiResponse<TokenResponseDto>> {
const result = await this.authService.login(dto);
return createSuccessResponse(result, i18n.t('auth.login_success'));
return createSuccessResponse(result, i18n.t("auth.login_success"));
}
@Post('refresh')
@Post("refresh")
@Public()
@HttpCode(200)
@ApiOperation({ summary: 'Refresh access token' })
@ApiOperation({ summary: "Refresh access token" })
@ApiOkResponse({
description: 'Token refreshed successfully',
description: "Token refreshed successfully",
type: TokenResponseDto,
})
async refreshToken(
@@ -61,18 +61,18 @@ export class AuthController {
@I18n() i18n: I18nContext,
): Promise<ApiResponse<TokenResponseDto>> {
const result = await this.authService.refreshToken(dto.refreshToken);
return createSuccessResponse(result, i18n.t('auth.refresh_success'));
return createSuccessResponse(result, i18n.t("auth.refresh_success"));
}
@Post('logout')
@Post("logout")
@HttpCode(200)
@ApiOperation({ summary: 'Logout and invalidate refresh token' })
@ApiOkResponse({ description: 'Logout successful' })
@ApiOperation({ summary: "Logout and invalidate refresh token" })
@ApiOkResponse({ description: "Logout successful" })
async logout(
@Body() dto: RefreshTokenDto,
@I18n() i18n: I18nContext,
): Promise<ApiResponse<null>> {
await this.authService.logout(dto.refreshToken);
return createSuccessResponse(null, i18n.t('auth.logout_success'));
return createSuccessResponse(null, i18n.t("auth.logout_success"));
}
}
src/modules/auth/auth.module.ts
+11 -11
View File
@@ -1,22 +1,22 @@
import { Module } from '@nestjs/common';
import { JwtModule, JwtModuleOptions } from '@nestjs/jwt';
import { PassportModule } from '@nestjs/passport';
import { ConfigService } from '@nestjs/config';
import { AuthController } from './auth.controller';
import { AuthService } from './auth.service';
import { JwtStrategy } from './strategies/jwt.strategy';
import { JwtAuthGuard, RolesGuard, PermissionsGuard } from './guards';
import { Module } from "@nestjs/common";
import { JwtModule, JwtModuleOptions } from "@nestjs/jwt";
import { PassportModule } from "@nestjs/passport";
import { ConfigService } from "@nestjs/config";
import { AuthController } from "./auth.controller";
import { AuthService } from "./auth.service";
import { JwtStrategy } from "./strategies/jwt.strategy";
import { JwtAuthGuard, RolesGuard, PermissionsGuard } from "./guards";
@Module({
imports: [
PassportModule.register({ defaultStrategy: 'jwt' }),
PassportModule.register({ defaultStrategy: "jwt" }),
JwtModule.registerAsync({
inject: [ConfigService],
useFactory: (configService: ConfigService): JwtModuleOptions => {
const expiresIn =
configService.get<string>('JWT_ACCESS_EXPIRATION') || '15m';
configService.get<string>("JWT_ACCESS_EXPIRATION") || "15m";
return {
secret: configService.get<string>('JWT_SECRET'),
secret: configService.get<string>("JWT_SECRET"),
signOptions: {
expiresIn: expiresIn as any,
},
src/modules/auth/auth.service.ts
+21 -21
View File
@@ -2,14 +2,14 @@ import {
Injectable,
UnauthorizedException,
ConflictException,
} from '@nestjs/common';
import { JwtService } from '@nestjs/jwt';
import { ConfigService } from '@nestjs/config';
import * as bcrypt from 'bcrypt';
import * as crypto from 'crypto';
import { PrismaService } from '../../database/prisma.service';
import { RegisterDto, LoginDto, TokenResponseDto } from './dto/auth.dto';
import { User, UserRole } from '@prisma/client';
} from "@nestjs/common";
import { JwtService } from "@nestjs/jwt";
import { ConfigService } from "@nestjs/config";
import * as bcrypt from "bcrypt";
import * as crypto from "crypto";
import { PrismaService } from "../../database/prisma.service";
import { RegisterDto, LoginDto, TokenResponseDto } from "./dto/auth.dto";
import { User, UserRole } from "@prisma/client";
export interface JwtPayload {
sub: string;
@@ -36,7 +36,7 @@ export class AuthService {
});
if (existingUser) {
throw new ConflictException('EMAIL_ALREADY_EXISTS');
throw new ConflictException("EMAIL_ALREADY_EXISTS");
}
// Hash password
@@ -76,7 +76,7 @@ export class AuthService {
});
if (!user) {
throw new UnauthorizedException('INVALID_CREDENTIALS');
throw new UnauthorizedException("INVALID_CREDENTIALS");
}
// Verify password
@@ -86,11 +86,11 @@ export class AuthService {
);
if (!isPasswordValid) {
throw new UnauthorizedException('INVALID_CREDENTIALS');
throw new UnauthorizedException("INVALID_CREDENTIALS");
}
if (!user.isActive) {
throw new UnauthorizedException('ACCOUNT_DISABLED');
throw new UnauthorizedException("ACCOUNT_DISABLED");
}
return this.generateTokens(user);
@@ -109,7 +109,7 @@ export class AuthService {
});
if (!storedToken) {
throw new UnauthorizedException('INVALID_REFRESH_TOKEN');
throw new UnauthorizedException("INVALID_REFRESH_TOKEN");
}
if (storedToken.expiresAt < new Date()) {
@@ -117,7 +117,7 @@ export class AuthService {
await this.prisma.refreshToken.delete({
where: { id: storedToken.id },
});
throw new UnauthorizedException('INVALID_REFRESH_TOKEN');
throw new UnauthorizedException("INVALID_REFRESH_TOKEN");
}
// Delete old refresh token
@@ -167,13 +167,13 @@ export class AuthService {
// Generate access token
const accessToken = this.jwtService.sign(payload, {
expiresIn: this.configService.get('JWT_ACCESS_EXPIRATION', '15m'),
expiresIn: this.configService.get("JWT_ACCESS_EXPIRATION", "15m"),
});
// Generate refresh token
const refreshTokenValue = crypto.randomUUID();
const refreshExpiration = this.parseExpiration(
this.configService.get('JWT_REFRESH_EXPIRATION', '7d'),
this.configService.get("JWT_REFRESH_EXPIRATION", "7d"),
);
// Store refresh token
@@ -190,7 +190,7 @@ export class AuthService {
refreshToken: refreshTokenValue,
expiresIn:
this.parseExpiration(
this.configService.get('JWT_ACCESS_EXPIRATION', '15m'),
this.configService.get("JWT_ACCESS_EXPIRATION", "15m"),
) / 1000, // Convert to seconds
user: {
id: user.id,
@@ -233,13 +233,13 @@ export class AuthService {
const unit = match[2];
switch (unit) {
case 's':
case "s":
return value * 1000;
case 'm':
case "m":
return value * 60 * 1000;
case 'h':
case "h":
return value * 60 * 60 * 1000;
case 'd':
case "d":
return value * 24 * 60 * 60 * 1000;
default:
return 15 * 60 * 1000;
src/modules/auth/dto/auth.dto.ts
+8 -8
View File
@@ -1,33 +1,33 @@
import { IsEmail, IsString, MinLength, IsOptional } from 'class-validator';
import { ApiProperty, ApiPropertyOptional } from '@nestjs/swagger';
import { IsEmail, IsString, MinLength, IsOptional } from "class-validator";
import { ApiProperty, ApiPropertyOptional } from "@nestjs/swagger";
export class RegisterDto {
@ApiProperty({ example: 'user@example.com' })
@ApiProperty({ example: "user@example.com" })
@IsEmail()
email: string;
@ApiProperty({ example: 'password123', minLength: 8 })
@ApiProperty({ example: "password123", minLength: 8 })
@IsString()
@MinLength(8)
password: string;
@ApiPropertyOptional({ example: 'John' })
@ApiPropertyOptional({ example: "John" })
@IsOptional()
@IsString()
firstName?: string;
@ApiPropertyOptional({ example: 'Doe' })
@ApiPropertyOptional({ example: "Doe" })
@IsOptional()
@IsString()
lastName?: string;
}
export class LoginDto {
@ApiProperty({ example: 'user@example.com' })
@ApiProperty({ example: "user@example.com" })
@IsEmail()
email: string;
@ApiProperty({ example: 'password123' })
@ApiProperty({ example: "password123" })
@IsString()
password: string;
}
src/modules/auth/guards/auth.guards.ts
+35 -16
View File
@@ -4,20 +4,22 @@ import {
ExecutionContext,
UnauthorizedException,
ForbiddenException,
} from '@nestjs/common';
import { Reflector } from '@nestjs/core';
import { AuthGuard } from '@nestjs/passport';
import { Request } from 'express';
} from "@nestjs/common";
import { Reflector } from "@nestjs/core";
import { AuthGuard } from "@nestjs/passport";
import { Request } from "express";
import {
IS_PUBLIC_KEY,
ROLES_KEY,
PERMISSIONS_KEY,
} from '../../../common/decorators';
} from "../../../common/decorators";
import { normalizeRole } from "../../../common/constants/roles";
interface AuthenticatedUser {
id: string;
email: string;
roles: string[];
role?: string;
permissions: string[];
}
@@ -25,14 +27,14 @@ interface AuthenticatedUser {
* JWT Auth Guard - Validates JWT token
*/
@Injectable()
export class JwtAuthGuard extends AuthGuard('jwt') {
export class JwtAuthGuard extends AuthGuard("jwt") {
constructor(private reflector: Reflector) {
super();
}
canActivate(context: ExecutionContext) {
const request = context.switchToHttp().getRequest<Request>();
if (request?.method === 'OPTIONS') {
if (request?.method === "OPTIONS") {
return true;
}
@@ -55,10 +57,10 @@ export class JwtAuthGuard extends AuthGuard('jwt') {
info: any,
): TUser {
if (err || !user) {
if (info?.name === 'TokenExpiredError') {
throw new UnauthorizedException('TOKEN_EXPIRED');
if (info?.name === "TokenExpiredError") {
throw new UnauthorizedException("TOKEN_EXPIRED");
}
throw err || new UnauthorizedException('AUTH_REQUIRED');
throw err || new UnauthorizedException("AUTH_REQUIRED");
}
return user;
}
@@ -73,7 +75,7 @@ export class RolesGuard implements CanActivate {
canActivate(context: ExecutionContext): boolean {
const req = context.switchToHttp().getRequest<Request>();
if (req?.method === 'OPTIONS') {
if (req?.method === "OPTIONS") {
return true;
}
@@ -88,13 +90,30 @@ export class RolesGuard implements CanActivate {
const user = req.user as AuthenticatedUser | undefined;
if (!user || !user.roles) {
if (!user) {
return false;
}
const hasRole = requiredRoles.some((role) => user.roles.includes(role));
const normalizedUserRoles = (user.roles?.length
? user.roles
: user.role
? [user.role]
: []
).map((role) => normalizeRole(role));
const normalizedRequiredRoles = requiredRoles.map((role) =>
normalizeRole(role),
);
if (normalizedUserRoles.length === 0) {
return false;
}
const hasRole = normalizedRequiredRoles.some((role) =>
normalizedUserRoles.includes(role),
);
if (!hasRole) {
throw new ForbiddenException('PERMISSION_DENIED');
throw new ForbiddenException("PERMISSION_DENIED");
}
return true;
@@ -110,7 +129,7 @@ export class PermissionsGuard implements CanActivate {
canActivate(context: ExecutionContext): boolean {
const req = context.switchToHttp().getRequest<Request>();
if (req?.method === 'OPTIONS') {
if (req?.method === "OPTIONS") {
return true;
}
@@ -134,7 +153,7 @@ export class PermissionsGuard implements CanActivate {
);
if (!hasPermission) {
throw new ForbiddenException('PERMISSION_DENIED');
throw new ForbiddenException("PERMISSION_DENIED");
}
return true;
src/modules/auth/guards/index.ts
+1 -1
View File
@@ -1 +1 @@
export * from './auth.guards';
export * from "./auth.guards";
src/modules/auth/strategies/jwt.strategy.ts
+13 -8
View File
@@ -1,8 +1,9 @@
import { Injectable } from '@nestjs/common';
import { PassportStrategy } from '@nestjs/passport';
import { ExtractJwt, Strategy } from 'passport-jwt';
import { ConfigService } from '@nestjs/config';
import { AuthService, JwtPayload } from '../auth.service';
import { Injectable } from "@nestjs/common";
import { PassportStrategy } from "@nestjs/passport";
import { ExtractJwt, Strategy } from "passport-jwt";
import { ConfigService } from "@nestjs/config";
import { AuthService, JwtPayload } from "../auth.service";
import { normalizeRole } from "../../../common/constants/roles";
@Injectable()
export class JwtStrategy extends PassportStrategy(Strategy) {
@@ -10,9 +11,9 @@ export class JwtStrategy extends PassportStrategy(Strategy) {
private readonly configService: ConfigService,
private readonly authService: AuthService,
) {
const secret = configService.get<string>('JWT_SECRET');
const secret = configService.get<string>("JWT_SECRET");
if (!secret) {
throw new Error('JWT_SECRET is not defined');
throw new Error("JWT_SECRET is not defined");
}
super({
@@ -29,9 +30,13 @@ export class JwtStrategy extends PassportStrategy(Strategy) {
return null;
}
const normalizedRole = normalizeRole(payload.role);
return {
...user,
role: payload.role,
role: normalizedRole,
roles: normalizedRole ? [normalizedRole] : [],
permissions: [],
};
}
}
src/modules/coupons/coupons.controller.ts
+73 -37
View File
@@ -9,31 +9,32 @@ import {
UseGuards,
Req,
Logger,
} from '@nestjs/common';
} from "@nestjs/common";
import {
ApiTags,
ApiBearerAuth,
ApiOperation,
ApiResponse,
} from '@nestjs/swagger';
import { CouponsService } from './coupons.service';
import { MatchesService } from '../matches/matches.service';
import { SmartCouponService } from './services/smart-coupon.service';
} from "@nestjs/swagger";
import { CouponsService } from "./coupons.service";
import { MatchesService } from "../matches/matches.service";
import { SmartCouponService } from "./services/smart-coupon.service";
import {
UserCouponService,
CreateCouponDto,
} from './services/user-coupon.service';
} from "./services/user-coupon.service";
import {
AnalyzeMatchDto,
DailyBankoDto,
SuggestCouponDto,
} from './dto/coupons-request.dto';
import { Public } from '../../common/decorators';
import { JwtAuthGuard } from '../auth/guards/auth.guards'; // Assuming standard guard
import { Sport } from '../matches/dto';
FrequencyCouponDto,
} from "./dto/coupons-request.dto";
import { Public } from "../../common/decorators";
import { JwtAuthGuard } from "../auth/guards/auth.guards"; // Assuming standard guard
import { Sport } from "../matches/dto";
@ApiTags('Coupon')
@Controller('coupon')
@ApiTags("Coupon")
@Controller("coupon")
export class CouponsController {
private readonly logger = new Logger(CouponsController.name);
@@ -48,15 +49,15 @@ export class CouponsController {
* POST /coupon/analyze-match
* Analyze a single match with V20+ single-match package
*/
@Post('analyze-match')
@Post("analyze-match")
@Public()
@HttpCode(HttpStatus.OK)
@ApiOperation({ summary: 'Analyze single match with V20 model' })
@ApiResponse({ status: 200, description: 'Match analysis' })
@ApiOperation({ summary: "Analyze single match with V20 model" })
@ApiResponse({ status: 200, description: "Match analysis" })
async analyzeMatch(@Body() dto: AnalyzeMatchDto) {
const analysis = await this.smartCouponService.analyzeMatch(dto.matchId);
if (!analysis) {
return { success: false, message: 'Analiz yapılamadı.' };
return { success: false, message: "Analiz yapılamadı." };
}
return { success: true, data: analysis };
}
@@ -64,11 +65,11 @@ export class CouponsController {
/**
* POST /coupon/analyze (alias for /analyze-match - frontend compatibility)
*/
@Post('analyze')
@Post("analyze")
@Public()
@HttpCode(HttpStatus.OK)
@ApiOperation({
summary: 'Analyze single match with V20 model (alias)',
summary: "Analyze single match with V20 model (alias)",
deprecated: true,
})
async analyzeMatchAlias(@Body() dto: AnalyzeMatchDto) {
@@ -83,7 +84,7 @@ export class CouponsController {
@UseGuards(JwtAuthGuard)
@ApiBearerAuth()
@HttpCode(HttpStatus.CREATED)
@ApiOperation({ summary: 'Create and save a user coupon (alias)' })
@ApiOperation({ summary: "Create and save a user coupon (alias)" })
async createCouponAlias(@Body() dto: CreateCouponDto, @Req() req: any) {
return this.createCoupon(dto, req);
}
@@ -92,11 +93,11 @@ export class CouponsController {
* POST /coupon/daily-banko
* Generate a high-confidence banko combo (2 matches)
*/
@Post('daily-banko')
@Post("daily-banko")
@Public()
@HttpCode(HttpStatus.OK)
@ApiOperation({
summary: 'Generate a high-confidence banko combo (2 matches)',
summary: "Generate a high-confidence banko combo (2 matches)",
})
async getDailyBanko(@Body() dto: DailyBankoDto) {
// If no match IDs provided, fetch from system (top 50 upcoming)
@@ -122,7 +123,7 @@ export class CouponsController {
if (candidateMatches.length === 0) {
return {
success: false,
message: 'Kupon için uygun, henüz başlamamış maç bulunamadı.',
message: "Kupon için uygun, henüz başlamamış maç bulunamadı.",
};
}
@@ -131,7 +132,7 @@ export class CouponsController {
if (!coupon) {
return {
success: false,
message: 'Kriterlere uygun (80%+ güvenli) yeterli maç bulunamadı.',
message: "Kriterlere uygun (80%+ güvenli) yeterli maç bulunamadı.",
};
}
return { success: true, data: coupon };
@@ -141,11 +142,11 @@ export class CouponsController {
* POST /coupon/suggest
* Generate Smart Coupon
*/
@Post('suggest')
@Post("suggest")
@Public()
@HttpCode(HttpStatus.OK)
@ApiOperation({ summary: 'Suggest Smart Coupon' })
@ApiResponse({ status: 200, description: 'Smart Coupon generated' })
@ApiOperation({ summary: "Suggest Smart Coupon" })
@ApiResponse({ status: 200, description: "Smart Coupon generated" })
async suggestCoupon(@Body() dto: SuggestCouponDto) {
// If no match IDs provided, fetch from system (top 50 upcoming)
let candidateMatches = dto.matchIds || [];
@@ -170,7 +171,7 @@ export class CouponsController {
if (candidateMatches.length === 0) {
return {
success: false,
message: 'Tahmin için uygun, henüz başlamamış maç bulunamadı.',
message: "Tahmin için uygun, henüz başlamamış maç bulunamadı.",
};
}
@@ -183,24 +184,59 @@ export class CouponsController {
},
);
if (!coupon) {
return { success: false, message: 'Kupon oluşturulamadı.' };
return { success: false, message: "Kupon oluşturulamadı." };
}
return { success: true, data: coupon };
}
/**
* POST /coupon/frequency-coupon
* Generate a frequency-based parlay coupon (Conditional Frequency Engine)
*/
@Post("frequency-coupon")
@Public()
@HttpCode(HttpStatus.OK)
@ApiOperation({
summary: "Generate frequency-based parlay coupon",
description:
"Scans upcoming matches, applies conditional frequency analysis " +
"(team odds-band performance), and builds 2-5 match combos with +EV calculation.",
})
@ApiResponse({ status: 200, description: "Frequency coupon generated" })
async getFrequencyCoupon(@Body() dto: FrequencyCouponDto) {
const coupon = await this.smartCouponService.generateFrequencyBasedCoupon({
matchIds: dto.matchIds,
maxMatches: dto.maxMatches,
minSignal: dto.minSignal,
markets: dto.markets,
});
if (!coupon || coupon.bets.length === 0) {
return {
success: false,
message:
"Frekans analizine uygun yeterli maç bulunamadı. " +
"minSignal değerini düşürmeyi veya daha fazla maç beklemeyi deneyin.",
data: coupon,
};
}
return { success: true, data: coupon };
}
// ============================================
// USER COUPON ENDPOINTS (NEW)
// USER COUPON ENDPOINTS
// ============================================
/**
* POST /coupon/create
* Save a user generated coupon
*/
@Post('create')
@Post("create")
@UseGuards(JwtAuthGuard)
@ApiBearerAuth()
@HttpCode(HttpStatus.CREATED)
@ApiOperation({ summary: 'Create and save a user coupon' })
@ApiOperation({ summary: "Create and save a user coupon" })
async createCoupon(@Body() dto: CreateCouponDto, @Req() req: any) {
// req.user is populated by JwtAuthGuard
const coupon = await this.userCouponService.createCoupon(req.user, dto);
@@ -211,10 +247,10 @@ export class CouponsController {
* GET /coupon/my-stats
* Get user betting statistics (ROI, Win Rate)
*/
@Get('my-stats')
@Get("my-stats")
@UseGuards(JwtAuthGuard)
@ApiBearerAuth()
@ApiOperation({ summary: 'Get user betting statistics' })
@ApiOperation({ summary: "Get user betting statistics" })
async getUserStats(@Req() req: any) {
const stats = await this.userCouponService.getUserStatistics(req.user.id);
return { success: true, data: stats };
@@ -224,11 +260,11 @@ export class CouponsController {
* GET /coupon/history
* Get coupon history (Public/System coupons)
*/
@Get('history')
@Get("history")
@ApiBearerAuth()
@ApiOperation({ summary: 'Get coupon history' })
@ApiResponse({ status: 200, description: 'History retrieved' })
async getHistory(@Query('limit') limit?: string) {
@ApiOperation({ summary: "Get coupon history" })
@ApiResponse({ status: 200, description: "History retrieved" })
async getHistory(@Query("limit") limit?: string) {
// eslint-disable-next-line @typescript-eslint/await-thenable
const results = await this.couponsService.getCouponHistory(
Number(limit) || 10,
src/modules/coupons/coupons.module.ts
+22 -10
View File
@@ -1,16 +1,28 @@
import { Module } from '@nestjs/common';
import { CouponsController } from './coupons.controller';
import { SmartCouponService } from './services/smart-coupon.service';
import { UserCouponService } from './services/user-coupon.service';
import { CouponsService } from './coupons.service';
import { DatabaseModule } from '../../database/database.module';
import { ServicesModule } from '../../services/services.module';
import { MatchesModule } from '../matches/matches.module';
import { Module } from "@nestjs/common";
import { CouponsController } from "./coupons.controller";
import { SmartCouponService } from "./services/smart-coupon.service";
import { UserCouponService } from "./services/user-coupon.service";
import { FrequencyEngineService } from "./services/frequency-engine.service";
import { CouponsService } from "./coupons.service";
import { DatabaseModule } from "../../database/database.module";
import { ServicesModule } from "../../services/services.module";
import { MatchesModule } from "../matches/matches.module";
@Module({
imports: [DatabaseModule, ServicesModule, MatchesModule],
controllers: [CouponsController],
providers: [CouponsService, SmartCouponService, UserCouponService],
exports: [CouponsService, SmartCouponService, UserCouponService],
providers: [
CouponsService,
SmartCouponService,
UserCouponService,
FrequencyEngineService,
],
exports: [
CouponsService,
SmartCouponService,
UserCouponService,
FrequencyEngineService,
],
})
export class CouponsModule {}
src/modules/coupons/coupons.service.ts
+4 -4
View File
@@ -1,9 +1,9 @@
import { Injectable, Logger } from '@nestjs/common';
import { PrismaService } from '../../database/prisma.service';
import { AiService } from '../../services/ai.service';
import { Injectable, Logger } from "@nestjs/common";
import { PrismaService } from "../../database/prisma.service";
import { AiService } from "../../services/ai.service";
// [REMOVED V16 IMPORTS]
export type RiskLevel = 'banko' | 'safe' | 'value';
export type RiskLevel = "banko" | "safe" | "value";
export interface CouponMatch {
matchId: string;
src/modules/coupons/dto/coupons-request.dto.ts
+58 -14
View File
@@ -8,19 +8,19 @@ import {
ArrayMaxSize,
Min,
Max,
} from 'class-validator';
import { ApiProperty, ApiPropertyOptional } from '@nestjs/swagger';
} from "class-validator";
import { ApiProperty, ApiPropertyOptional } from "@nestjs/swagger";
export enum CouponStrategyEnum {
SAFE = 'SAFE',
BALANCED = 'BALANCED',
AGGRESSIVE = 'AGGRESSIVE',
VALUE = 'VALUE',
MIRACLE = 'MIRACLE',
SAFE = "SAFE",
BALANCED = "BALANCED",
AGGRESSIVE = "AGGRESSIVE",
VALUE = "VALUE",
MIRACLE = "MIRACLE",
}
export class AnalyzeMatchDto {
@ApiProperty({ description: 'Match ID to analyze' })
@ApiProperty({ description: "Match ID to analyze" })
@IsString()
@IsNotEmpty()
matchId: string;
@@ -28,8 +28,8 @@ export class AnalyzeMatchDto {
export class DailyBankoDto {
@ApiPropertyOptional({
description: 'Optional match IDs — system fetches if empty',
example: ['match-1', 'match-2'],
description: "Optional match IDs — system fetches if empty",
example: ["match-1", "match-2"],
})
@IsOptional()
@IsArray()
@@ -40,8 +40,8 @@ export class DailyBankoDto {
export class SuggestCouponDto {
@ApiPropertyOptional({
description: 'Match IDs — system fetches if empty',
example: ['match-1', 'match-2'],
description: "Match IDs — system fetches if empty",
example: ["match-1", "match-2"],
})
@IsOptional()
@IsArray()
@@ -57,7 +57,7 @@ export class SuggestCouponDto {
@IsEnum(CouponStrategyEnum)
strategy?: CouponStrategyEnum;
@ApiPropertyOptional({ description: 'Maximum matches in coupon', example: 5 })
@ApiPropertyOptional({ description: "Maximum matches in coupon", example: 5 })
@IsOptional()
@IsNumber()
@Min(1)
@@ -65,7 +65,7 @@ export class SuggestCouponDto {
maxMatches?: number;
@ApiPropertyOptional({
description: 'Minimum confidence threshold (0-100)',
description: "Minimum confidence threshold (0-100)",
example: 60,
})
@IsOptional()
@@ -74,3 +74,47 @@ export class SuggestCouponDto {
@Max(100)
minConfidence?: number;
}
export class FrequencyCouponDto {
@ApiPropertyOptional({
description: "Optional match IDs — system auto-fetches if empty",
example: ["match-1", "match-2"],
})
@IsOptional()
@IsArray()
@IsString({ each: true })
@ArrayMaxSize(50)
matchIds?: string[];
@ApiPropertyOptional({
description: "Maximum matches in parlay (2-5)",
example: 3,
default: 3,
})
@IsOptional()
@IsNumber()
@Min(2)
@Max(5)
maxMatches?: number;
@ApiPropertyOptional({
description: "Minimum combined signal threshold (0.50-0.99)",
example: 0.7,
default: 0.7,
})
@IsOptional()
@IsNumber()
@Min(0.5)
@Max(0.99)
minSignal?: number;
@ApiPropertyOptional({
description:
"Filter markets: OU1.5, OU2.5, OU3.5, BTTS, MS (default: all)",
example: ["OU2.5", "BTTS"],
})
@IsOptional()
@IsArray()
@IsString({ each: true })
markets?: string[];
}
src/modules/coupons/services/frequency-engine.service.ts
+584
View File
@@ -0,0 +1,584 @@
import { Injectable, Logger } from "@nestjs/common";
import { PrismaService } from "../../../database/prisma.service";
// ─────────────────────────────────────────────────────────────
// Types
// ─────────────────────────────────────────────────────────────
export interface FrequencySignal {
market: string;
pick: string;
homeSignal: number;
awaySignal: number;
combinedSignal: number;
homeMatchCount: number;
awayMatchCount: number;
leagueBonus: number;
confidence: number;
}
export interface MatchCandidate {
matchId: string;
homeTeamId: string;
awayTeamId: string;
homeTeamName: string;
awayTeamName: string;
leagueId: string;
leagueName: string;
homeOdds: number;
awayOdds: number;
drawOdds: number;
signals: FrequencySignal[];
bestSignal: FrequencySignal | null;
matchTime: number;
}
interface TeamFrequencyRow {
team_id: string;
venue: "home" | "away";
odds_band: string;
total_matches: number;
ou15_rate: number;
ou25_rate: number;
ou35_rate: number;
btts_rate: number;
win_rate: number;
avg_goals: number;
}
interface LeagueProfileRow {
league_id: string;
league_name: string;
total_matches: number;
ou25_rate: number;
btts_rate: number;
avg_goals: number;
}
interface UpcomingMatchRow {
match_id: string;
home_team_id: string;
away_team_id: string;
home_team_name: string;
away_team_name: string;
league_id: string;
league_name: string;
mst_utc: bigint;
ms1_odds: number | null;
ms2_odds: number | null;
msx_odds: number | null;
ou25_over_odds: number | null;
ou25_under_odds: number | null;
btts_yes_odds: number | null;
btts_no_odds: number | null;
ou15_over_odds: number | null;
ou35_over_odds: number | null;
}
// ─────────────────────────────────────────────────────────────
// Constants
// ─────────────────────────────────────────────────────────────
const MIN_MATCHES = 3;
const GOLCU_LEAGUES = new Set([
// Strategy generator'dan türetilen yüksek golcü ligler
// Lig isimleri veritabanındaki gibi
]);
const DEFANSIF_LEAGUES = new Set([
// Düşük golcü ligler
]);
// ─────────────────────────────────────────────────────────────
// Service
// ─────────────────────────────────────────────────────────────
@Injectable()
export class FrequencyEngineService {
private readonly logger = new Logger(FrequencyEngineService.name);
constructor(private readonly prisma: PrismaService) {}
/**
* Belirli bir takımın ev/deplasman + oran bandı koşullu frekanslarını döndürür.
*/
async getTeamFrequency(
teamId: string,
venue: "home" | "away",
oddsBand: string,
): Promise<TeamFrequencyRow | null> {
const venueColumn =
venue === "home" ? "m.home_team_id" : "m.away_team_id";
const oddsSelection = venue === "home" ? "'1'" : "'2'";
const bandRange = this.parseBandRange(oddsBand);
if (!bandRange) {
return null;
}
const rows = await this.prisma.$queryRawUnsafe<TeamFrequencyRow[]>(
`
WITH team_matches AS (
SELECT
m.id AS match_id,
m.score_home,
m.score_away,
(m.score_home + m.score_away) AS total_goals,
CAST(os.odd_value AS DECIMAL) AS team_odds
FROM matches m
JOIN odd_categories oc ON oc.match_id = m.id AND oc.name = 'Maç Sonucu'
JOIN odd_selections os ON os.odd_category_db_id = oc.db_id AND os.name = ${oddsSelection}
WHERE m.status = 'FT'
AND m.score_home IS NOT NULL
AND ${venueColumn} = $1
AND CAST(os.odd_value AS DECIMAL) >= $2
AND CAST(os.odd_value AS DECIMAL) < $3
)
SELECT
$1::text AS team_id,
$4::text AS venue,
$5::text AS odds_band,
COUNT(*)::int AS total_matches,
COALESCE(AVG(CASE WHEN total_goals > 1 THEN 1.0 ELSE 0.0 END), 0)::float AS ou15_rate,
COALESCE(AVG(CASE WHEN total_goals > 2 THEN 1.0 ELSE 0.0 END), 0)::float AS ou25_rate,
COALESCE(AVG(CASE WHEN total_goals > 3 THEN 1.0 ELSE 0.0 END), 0)::float AS ou35_rate,
COALESCE(AVG(CASE WHEN score_home > 0 AND score_away > 0 THEN 1.0 ELSE 0.0 END), 0)::float AS btts_rate,
COALESCE(AVG(CASE WHEN ${venue === "home" ? "score_home > score_away" : "score_away > score_home"} THEN 1.0 ELSE 0.0 END), 0)::float AS win_rate,
COALESCE(AVG(total_goals), 0)::float AS avg_goals
FROM team_matches
`,
teamId,
bandRange.min,
bandRange.max,
venue,
oddsBand,
);
if (!rows.length || rows[0].total_matches < MIN_MATCHES) {
return null;
}
return rows[0];
}
/**
* İki takımın oran bandı geçmişlerini çapraz kontrol eder.
* Tüm marketler için kombine sinyal üretir.
*/
async getMatchFrequencySignals(
homeTeamId: string,
awayTeamId: string,
homeOdds: number,
awayOdds: number,
leagueId?: string,
): Promise<FrequencySignal[]> {
const homeBand = this.getOddsBand(homeOdds);
const awayBand = this.getOddsBand(awayOdds);
const [homeFreq, awayFreq, leagueProfile] = await Promise.all([
this.getTeamFrequency(homeTeamId, "home", homeBand),
this.getTeamFrequency(awayTeamId, "away", awayBand),
leagueId ? this.getLeagueProfile(leagueId) : null,
]);
if (!homeFreq || !awayFreq) {
return [];
}
const leagueBonus = this.calculateLeagueBonus(leagueProfile);
const signals: FrequencySignal[] = [];
// OU 1.5 OVER
const ou15Combined = (homeFreq.ou15_rate + awayFreq.ou15_rate) / 2;
if (ou15Combined >= 0.80) {
signals.push({
market: "OU1.5_OVER",
pick: "1.5 UST",
homeSignal: homeFreq.ou15_rate,
awaySignal: awayFreq.ou15_rate,
combinedSignal: ou15Combined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus,
confidence: this.calculateConfidence(
ou15Combined,
homeFreq.total_matches,
awayFreq.total_matches,
leagueBonus,
),
});
}
// OU 2.5 OVER
const ou25Combined = (homeFreq.ou25_rate + awayFreq.ou25_rate) / 2;
if (ou25Combined >= 0.60) {
signals.push({
market: "OU2.5_OVER",
pick: "2.5 UST",
homeSignal: homeFreq.ou25_rate,
awaySignal: awayFreq.ou25_rate,
combinedSignal: ou25Combined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus,
confidence: this.calculateConfidence(
ou25Combined,
homeFreq.total_matches,
awayFreq.total_matches,
leagueBonus,
),
});
}
// OU 3.5 OVER
const ou35Combined = (homeFreq.ou35_rate + awayFreq.ou35_rate) / 2;
if (ou35Combined >= 0.50) {
signals.push({
market: "OU3.5_OVER",
pick: "3.5 UST",
homeSignal: homeFreq.ou35_rate,
awaySignal: awayFreq.ou35_rate,
combinedSignal: ou35Combined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus,
confidence: this.calculateConfidence(
ou35Combined,
homeFreq.total_matches,
awayFreq.total_matches,
leagueBonus,
),
});
}
// BTTS YES
const bttsCombined = (homeFreq.btts_rate + awayFreq.btts_rate) / 2;
if (bttsCombined >= 0.60) {
signals.push({
market: "BTTS_YES",
pick: "KG VAR",
homeSignal: homeFreq.btts_rate,
awaySignal: awayFreq.btts_rate,
combinedSignal: bttsCombined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus,
confidence: this.calculateConfidence(
bttsCombined,
homeFreq.total_matches,
awayFreq.total_matches,
leagueBonus,
),
});
}
// OU 2.5 UNDER (düşük gol beklentisi)
const ou25UnderCombined =
(1 - homeFreq.ou25_rate + (1 - awayFreq.ou25_rate)) / 2;
if (ou25UnderCombined >= 0.65) {
signals.push({
market: "OU2.5_UNDER",
pick: "2.5 ALT",
homeSignal: 1 - homeFreq.ou25_rate,
awaySignal: 1 - awayFreq.ou25_rate,
combinedSignal: ou25UnderCombined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus: -leagueBonus, // golcü lig bonusu ters çevrilir
confidence: this.calculateConfidence(
ou25UnderCombined,
homeFreq.total_matches,
awayFreq.total_matches,
-leagueBonus,
),
});
}
// MS HOME WIN (ev sahibi kazanma)
const hwCombined = (homeFreq.win_rate + awayFreq.win_rate) / 2;
// awayFreq.win_rate aslında deplasman takımının KAYBETme oranı
// (away takımı o bandda maçları kazanma değil, kaybetme olarak bak)
if (hwCombined >= 0.70 && homeOdds > 1.10 && homeOdds < 3.50) {
signals.push({
market: "MS_HOME",
pick: "MS 1",
homeSignal: homeFreq.win_rate,
awaySignal: awayFreq.win_rate,
combinedSignal: hwCombined,
homeMatchCount: homeFreq.total_matches,
awayMatchCount: awayFreq.total_matches,
leagueBonus: 0,
confidence: this.calculateConfidence(
hwCombined,
homeFreq.total_matches,
awayFreq.total_matches,
0,
),
});
}
// Güvene göre sırala (en güçlü sinyal önce)
signals.sort((a, b) => b.confidence - a.confidence);
return signals;
}
/**
* Yaklaşan maçları oranlarıyla birlikte getirir.
* LiveMatch tablosundan JSON odds parse eder.
*/
async getUpcomingMatchesWithOdds(
matchIds?: string[],
limit: number = 50,
): Promise<UpcomingMatchRow[]> {
const nowMs = Date.now();
if (matchIds && matchIds.length > 0) {
// Belirli maçlar istendi
return this.prisma.$queryRawUnsafe<UpcomingMatchRow[]>(
`
SELECT
lm.id AS match_id,
lm.home_team_id,
lm.away_team_id,
COALESCE(ht.name, 'Unknown') AS home_team_name,
COALESCE(at.name, 'Unknown') AS away_team_name,
COALESCE(lm.league_id, '') AS league_id,
COALESCE(l.name, 'Unknown') AS league_name,
lm.mst_utc,
(lm.odds->'Maç Sonucu'->>'1')::decimal AS ms1_odds,
(lm.odds->'Maç Sonucu'->>'2')::decimal AS ms2_odds,
(lm.odds->'Maç Sonucu'->>'0')::decimal AS msx_odds,
(lm.odds->'2,5 Alt/Üst'->>'Üst')::decimal AS ou25_over_odds,
(lm.odds->'2,5 Alt/Üst'->>'Alt')::decimal AS ou25_under_odds,
(lm.odds->'Karşılıklı Gol'->>'Var')::decimal AS btts_yes_odds,
(lm.odds->'Karşılıklı Gol'->>'Yok')::decimal AS btts_no_odds,
(lm.odds->'1,5 Alt/Üst'->>'Üst')::decimal AS ou15_over_odds,
(lm.odds->'3,5 Alt/Üst'->>'Üst')::decimal AS ou35_over_odds
FROM live_matches lm
LEFT JOIN teams ht ON lm.home_team_id = ht.id
LEFT JOIN teams at ON lm.away_team_id = at.id
LEFT JOIN leagues l ON lm.league_id = l.id
WHERE lm.id = ANY($1)
AND lm.odds IS NOT NULL
AND lm.odds != 'null'::jsonb
ORDER BY lm.mst_utc ASC
`,
matchIds,
);
}
// Otomatik: yaklaşan tüm maçlar
return this.prisma.$queryRawUnsafe<UpcomingMatchRow[]>(
`
SELECT
lm.id AS match_id,
lm.home_team_id,
lm.away_team_id,
COALESCE(ht.name, 'Unknown') AS home_team_name,
COALESCE(at.name, 'Unknown') AS away_team_name,
COALESCE(lm.league_id, '') AS league_id,
COALESCE(l.name, 'Unknown') AS league_name,
lm.mst_utc,
(lm.odds->'Maç Sonucu'->>'1')::decimal AS ms1_odds,
(lm.odds->'Maç Sonucu'->>'2')::decimal AS ms2_odds,
(lm.odds->'Maç Sonucu'->>'0')::decimal AS msx_odds,
(lm.odds->'2,5 Alt/Üst'->>'Üst')::decimal AS ou25_over_odds,
(lm.odds->'2,5 Alt/Üst'->>'Alt')::decimal AS ou25_under_odds,
(lm.odds->'Karşılıklı Gol'->>'Var')::decimal AS btts_yes_odds,
(lm.odds->'Karşılıklı Gol'->>'Yok')::decimal AS btts_no_odds,
(lm.odds->'1,5 Alt/Üst'->>'Üst')::decimal AS ou15_over_odds,
(lm.odds->'3,5 Alt/Üst'->>'Üst')::decimal AS ou35_over_odds
FROM live_matches lm
LEFT JOIN teams ht ON lm.home_team_id = ht.id
LEFT JOIN teams at ON lm.away_team_id = at.id
LEFT JOIN leagues l ON lm.league_id = l.id
WHERE lm.mst_utc >= $1
AND lm.sport = 'football'
AND lm.odds IS NOT NULL
AND lm.odds != 'null'::jsonb
AND (lm.status IS NULL OR lm.status NOT IN ('FT', 'AET', 'PEN', 'ABD', 'CANC', 'PST', 'SUSP', 'INT', 'AWD', 'WO'))
AND (lm.state IS NULL OR lm.state NOT IN ('after', 'postponed', 'cancelled', 'abandoned'))
ORDER BY lm.mst_utc ASC
LIMIT $2
`,
BigInt(nowMs),
limit,
);
}
/**
* Lig bazlı gol profili.
*/
async getLeagueProfile(
leagueId: string,
): Promise<LeagueProfileRow | null> {
const rows = await this.prisma.$queryRawUnsafe<LeagueProfileRow[]>(
`
SELECT
m.league_id,
l.name AS league_name,
COUNT(*)::int AS total_matches,
AVG(CASE WHEN (m.score_home + m.score_away) > 2 THEN 1.0 ELSE 0.0 END)::float AS ou25_rate,
AVG(CASE WHEN m.score_home > 0 AND m.score_away > 0 THEN 1.0 ELSE 0.0 END)::float AS btts_rate,
AVG(m.score_home + m.score_away)::float AS avg_goals
FROM matches m
JOIN leagues l ON m.league_id = l.id
WHERE m.status = 'FT'
AND m.score_home IS NOT NULL
AND m.league_id = $1
GROUP BY m.league_id, l.name
HAVING COUNT(*) >= 20
`,
leagueId,
);
return rows.length > 0 ? rows[0] : null;
}
/**
* Bir upcoming match row'unu MatchCandidate'e dönüştürür
* ve frekans sinyallerini hesaplar.
*/
async buildMatchCandidate(
row: UpcomingMatchRow,
): Promise<MatchCandidate | null> {
const homeOdds = row.ms1_odds ? Number(row.ms1_odds) : 0;
const awayOdds = row.ms2_odds ? Number(row.ms2_odds) : 0;
const drawOdds = row.msx_odds ? Number(row.msx_odds) : 0;
if (homeOdds <= 0 || awayOdds <= 0) {
return null;
}
const signals = await this.getMatchFrequencySignals(
row.home_team_id,
row.away_team_id,
homeOdds,
awayOdds,
row.league_id || undefined,
);
if (signals.length === 0) {
return null;
}
return {
matchId: row.match_id,
homeTeamId: row.home_team_id,
awayTeamId: row.away_team_id,
homeTeamName: row.home_team_name,
awayTeamName: row.away_team_name,
leagueId: row.league_id,
leagueName: row.league_name,
homeOdds,
awayOdds,
drawOdds,
signals,
bestSignal: signals[0] || null,
matchTime: Number(row.mst_utc),
};
}
/**
* Bir market pick'ine karşılık gelen odds'u UpcomingMatchRow'dan çeker.
*/
getMarketOdds(row: UpcomingMatchRow, market: string): number {
switch (market) {
case "OU1.5_OVER":
return row.ou15_over_odds ? Number(row.ou15_over_odds) : 0;
case "OU2.5_OVER":
return row.ou25_over_odds ? Number(row.ou25_over_odds) : 0;
case "OU2.5_UNDER":
return row.ou25_under_odds ? Number(row.ou25_under_odds) : 0;
case "OU3.5_OVER":
return row.ou35_over_odds ? Number(row.ou35_over_odds) : 0;
case "BTTS_YES":
return row.btts_yes_odds ? Number(row.btts_yes_odds) : 0;
case "MS_HOME":
return row.ms1_odds ? Number(row.ms1_odds) : 0;
default:
return 0;
}
}
// ─────────────────────────────────────────────────────────────
// Private Helpers
// ─────────────────────────────────────────────────────────────
/**
* Oran bandı fonksiyonu — strategy_generator.py ile aynı mantık.
*/
getOddsBand(odds: number): string {
if (odds < 1.3) return "1.00-1.30";
if (odds < 1.5) return "1.30-1.50";
if (odds < 1.8) return "1.50-1.80";
if (odds < 2.2) return "1.80-2.20";
if (odds < 2.8) return "2.20-2.80";
if (odds < 4.0) return "2.80-4.00";
if (odds < 6.0) return "4.00-6.00";
return "6.00+";
}
private parseBandRange(
band: string,
): { min: number; max: number } | null {
const map: Record<string, { min: number; max: number }> = {
"1.00-1.30": { min: 1.0, max: 1.3 },
"1.30-1.50": { min: 1.3, max: 1.5 },
"1.50-1.80": { min: 1.5, max: 1.8 },
"1.80-2.20": { min: 1.8, max: 2.2 },
"2.20-2.80": { min: 2.2, max: 2.8 },
"2.80-4.00": { min: 2.8, max: 4.0 },
"4.00-6.00": { min: 4.0, max: 6.0 },
"6.00+": { min: 6.0, max: 999.0 },
};
return map[band] || null;
}
private calculateLeagueBonus(
profile: LeagueProfileRow | null,
): number {
if (!profile || profile.total_matches < 20) {
return 0;
}
// OU2.5 > %60 ise golcü lig bonusu
if (profile.ou25_rate > 0.6) {
return Math.min((profile.ou25_rate - 0.5) * 0.2, 0.05);
}
// OU2.5 < %40 ise defansif lig bonusu (negatif)
if (profile.ou25_rate < 0.4) {
return Math.max((profile.ou25_rate - 0.5) * 0.2, -0.05);
}
return 0;
}
private calculateConfidence(
combinedSignal: number,
homeN: number,
awayN: number,
leagueBonus: number,
): number {
// Base confidence: kombine sinyal * 100
let confidence = combinedSignal * 100;
// Sample size bonus: daha fazla veri = daha güvenilir
const minN = Math.min(homeN, awayN);
if (minN >= 20) {
confidence += 5;
} else if (minN >= 10) {
confidence += 2;
} else if (minN < 5) {
confidence -= 5;
}
// Liga bonusu
confidence += leagueBonus * 100;
return Math.max(0, Math.min(100, parseFloat(confidence.toFixed(1))));
}
}
src/modules/coupons/services/smart-coupon.service.ts
+282 -31
View File
@@ -1,10 +1,18 @@
import { HttpException, HttpStatus, Injectable, Logger } from '@nestjs/common';
import axios from 'axios';
import { GeminiService } from '../../gemini/gemini.service';
import { HttpException, HttpStatus, Injectable, Logger } from "@nestjs/common";
import { GeminiService } from "../../gemini/gemini.service";
import {
AiEngineClient,
AiEngineRequestError,
} from "../../../common/utils/ai-engine-client";
import {
FrequencyEngineService,
type MatchCandidate,
type FrequencySignal,
} from "./frequency-engine.service";
export type PredictionRiskLevel = 'LOW' | 'MEDIUM' | 'HIGH' | 'EXTREME';
export type PredictionDataQuality = 'HIGH' | 'MEDIUM' | 'LOW';
export type BetGrade = 'A' | 'B' | 'C' | 'PASS';
export type PredictionRiskLevel = "LOW" | "MEDIUM" | "HIGH" | "EXTREME";
export type PredictionDataQuality = "HIGH" | "MEDIUM" | "LOW";
export type BetGrade = "A" | "B" | "C" | "PASS";
export interface PredictionPickRow {
market: string;
@@ -126,28 +134,41 @@ export interface SmartCouponResult {
export class SmartCouponService {
private readonly logger = new Logger(SmartCouponService.name);
private readonly aiEngineUrl: string;
private readonly aiEngineClient: AiEngineClient;
constructor(private readonly geminiService: GeminiService) {
this.aiEngineUrl = process.env.AI_ENGINE_URL || 'http://ai-engine:8000';
constructor(
private readonly geminiService: GeminiService,
private readonly frequencyEngine: FrequencyEngineService,
) {
this.aiEngineUrl = process.env.AI_ENGINE_URL || "http://ai-engine:8000";
this.aiEngineClient = new AiEngineClient({
baseUrl: this.aiEngineUrl,
logger: this.logger,
serviceName: SmartCouponService.name,
timeoutMs: 60000,
maxRetries: 2,
retryDelayMs: 750,
});
}
async analyzeMatch(matchId: string): Promise<SingleMatchPredictionPackage> {
let prediction: SingleMatchPredictionPackage;
try {
const response = await axios.post<SingleMatchPredictionPackage>(
`${this.aiEngineUrl}/v20plus/analyze/${matchId}`,
const response = await this.aiEngineClient.post<SingleMatchPredictionPackage>(
`/v20plus/analyze/${matchId}`,
);
prediction = response.data;
} catch (error) {
if (axios.isAxiosError(error)) {
const detail = error.response?.data?.detail || error.message;
} catch (error: unknown) {
if (error instanceof AiEngineRequestError) {
const detail =
typeof error.detail === "string" ? error.detail : error.message;
throw new HttpException(
`AI analyze failed: ${detail}`,
error.response?.status || HttpStatus.SERVICE_UNAVAILABLE,
error.status || HttpStatus.SERVICE_UNAVAILABLE,
);
}
throw new HttpException(
'AI analyze failed',
"AI analyze failed",
HttpStatus.SERVICE_UNAVAILABLE,
);
}
@@ -168,7 +189,7 @@ export class SmartCouponService {
const result = await this.geminiService.generateText(
JSON.stringify(prediction, null, 2),
{
model: 'gemini-2.0-flash',
model: "gemini-2.0-flash",
temperature: 0.7,
maxTokens: 600,
systemPrompt: MATCH_COMMENTARY_SYSTEM_PROMPT,
@@ -176,7 +197,7 @@ export class SmartCouponService {
);
return result.text || null;
} catch (error) {
this.logger.warn('AI commentary generation failed, skipping', error);
this.logger.warn("AI commentary generation failed, skipping", error);
return null;
}
}
@@ -188,7 +209,7 @@ export class SmartCouponService {
return null;
}
return this.getSmartCoupon(matchIds, 'SAFE', {
return this.getSmartCoupon(matchIds, "SAFE", {
maxMatches: 2,
minConfidence: 78,
});
@@ -197,16 +218,16 @@ export class SmartCouponService {
async getSmartCoupon(
matchIds: string[],
strategy:
| 'SAFE'
| 'BALANCED'
| 'AGGRESSIVE'
| 'VALUE'
| 'MIRACLE' = 'BALANCED',
| "SAFE"
| "BALANCED"
| "AGGRESSIVE"
| "VALUE"
| "MIRACLE" = "BALANCED",
options: { maxMatches?: number; minConfidence?: number } = {},
): Promise<SmartCouponResult> {
try {
const response = await axios.post<SmartCouponResult>(
`${this.aiEngineUrl}/v20plus/coupon`,
const response = await this.aiEngineClient.post<SmartCouponResult>(
"/v20plus/coupon",
{
match_ids: matchIds,
strategy,
@@ -215,21 +236,251 @@ export class SmartCouponService {
},
);
return response.data;
} catch (error) {
this.logger.error('Failed to generate smart coupon', error);
if (axios.isAxiosError(error)) {
const detail = error.response?.data?.detail || error.message;
} catch (error: unknown) {
this.logger.error("Failed to generate smart coupon", error);
if (error instanceof AiEngineRequestError) {
const detail =
typeof error.detail === "string" ? error.detail : error.message;
throw new HttpException(
`Coupon generation failed: ${detail}`,
error.response?.status || HttpStatus.SERVICE_UNAVAILABLE,
error.status || HttpStatus.SERVICE_UNAVAILABLE,
);
}
throw new HttpException(
'Coupon generation failed',
"Coupon generation failed",
HttpStatus.SERVICE_UNAVAILABLE,
);
}
}
// ─────────────────────────────────────────────────────────────
// FREQUENCY-BASED COUPON ENGINE
// ─────────────────────────────────────────────────────────────
async generateFrequencyBasedCoupon(options: {
matchIds?: string[];
maxMatches?: number;
minSignal?: number;
markets?: string[];
}): Promise<FrequencyCouponResult> {
const maxMatches = options.maxMatches ?? 3;
const minSignal = options.minSignal ?? 0.70;
const allowedMarkets = options.markets?.map((m) => m.toUpperCase()) || null;
this.logger.log(
`[FrequencyCoupon] Starting — max=${maxMatches}, minSignal=${minSignal}`,
);
// 1. Yaklaşan maçları oranlarıyla getir
const upcomingRows = await this.frequencyEngine.getUpcomingMatchesWithOdds(
options.matchIds,
80,
);
this.logger.log(
`[FrequencyCoupon] Found ${upcomingRows.length} upcoming matches with odds`,
);
if (upcomingRows.length === 0) {
return {
strategy: "FREQUENCY",
generated_at: new Date().toISOString(),
bets: [],
total_odds: 0,
expected_hit_rate: 0,
expected_value: 0,
ev_positive: false,
reasoning: ["Bültende uygun maç bulunamadı."],
rejected_matches: [],
};
}
// 2. Her maç için frekans sinyallerini hesapla (paralel)
const candidatePromises = upcomingRows.map((row) =>
this.frequencyEngine.buildMatchCandidate(row).then((candidate) => ({
candidate,
row,
})),
);
const candidateResults = await Promise.all(candidatePromises);
// 3. Sinyali olan adayları filtrele
const allCandidates: Array<{
candidate: MatchCandidate;
row: (typeof upcomingRows)[0];
}> = [];
const rejected: FrequencyCouponResult["rejected_matches"] = [];
for (const { candidate, row } of candidateResults) {
if (!candidate) {
rejected.push({
match_id: row.match_id,
match_name: `${row.home_team_name} vs ${row.away_team_name}`,
reason: `Yetersiz geçmiş veri (min ${3} maç gerekli)`,
});
continue;
}
// Market filtresi uygula
let filteredSignals = candidate.signals;
if (allowedMarkets) {
filteredSignals = filteredSignals.filter((s) =>
allowedMarkets.some((m) => s.market.includes(m)),
);
}
// Min signal filtresi
filteredSignals = filteredSignals.filter(
(s) => s.combinedSignal >= minSignal,
);
if (filteredSignals.length === 0) {
rejected.push({
match_id: row.match_id,
match_name: `${row.home_team_name} vs ${row.away_team_name}`,
reason: `Kombinasyon sinyali ${(minSignal * 100).toFixed(0)}% eşiğinin altında`,
});
continue;
}
// En güçlü sinyali seç
candidate.signals = filteredSignals;
candidate.bestSignal = filteredSignals[0];
allCandidates.push({ candidate, row });
}
this.logger.log(
`[FrequencyCoupon] ${allCandidates.length} candidates passed filters, ${rejected.length} rejected`,
);
// 4. En güçlü sinyale göre sırala
allCandidates.sort(
(a, b) =>
(b.candidate.bestSignal?.confidence ?? 0) -
(a.candidate.bestSignal?.confidence ?? 0),
);
// 5. Çeşitlilik: aynı ligden max 2 maç
const selected: typeof allCandidates = [];
const leagueCount = new Map<string, number>();
for (const entry of allCandidates) {
if (selected.length >= maxMatches) break;
const lid = entry.candidate.leagueId;
const currentCount = leagueCount.get(lid) || 0;
if (currentCount >= 2) {
rejected.push({
match_id: entry.candidate.matchId,
match_name: `${entry.candidate.homeTeamName} vs ${entry.candidate.awayTeamName}`,
reason: `Aynı ligden zaten 2 maç seçildi (${entry.candidate.leagueName})`,
});
continue;
}
selected.push(entry);
leagueCount.set(lid, currentCount + 1);
}
// 6. Sonucu oluştur
const bets: FrequencyCouponResult["bets"] = [];
let totalOdds = 1;
let combinedHitRate = 1;
const reasoning: string[] = [];
for (const { candidate, row } of selected) {
const signal = candidate.bestSignal!;
const betOdds = this.frequencyEngine.getMarketOdds(row, signal.market);
if (betOdds <= 0) continue;
const homeBand = this.frequencyEngine.getOddsBand(candidate.homeOdds);
const awayBand = this.frequencyEngine.getOddsBand(candidate.awayOdds);
// Lig profili belirle
let leagueProfile = "NORMAL";
if (signal.leagueBonus > 0.02) leagueProfile = "GOLCU";
else if (signal.leagueBonus < -0.02) leagueProfile = "DEFANSIF";
bets.push({
match_id: candidate.matchId,
match_name: `${candidate.homeTeamName} vs ${candidate.awayTeamName}`,
league: candidate.leagueName,
market: signal.market,
pick: signal.pick,
home_signal: parseFloat(signal.homeSignal.toFixed(3)),
away_signal: parseFloat(signal.awaySignal.toFixed(3)),
combined_signal: parseFloat(signal.combinedSignal.toFixed(3)),
league_profile: leagueProfile,
historical_hit_rate: parseFloat(signal.combinedSignal.toFixed(3)),
odds: betOdds,
home_odds_band: homeBand,
away_odds_band: awayBand,
home_match_count: signal.homeMatchCount,
away_match_count: signal.awayMatchCount,
});
totalOdds *= betOdds;
combinedHitRate *= signal.combinedSignal;
reasoning.push(
`${candidate.homeTeamName} vs ${candidate.awayTeamName}: ` +
`${signal.pick} — Ev(${homeBand}): ${(signal.homeSignal * 100).toFixed(0)}% (${signal.homeMatchCount} maç), ` +
`Dep(${awayBand}): ${(signal.awaySignal * 100).toFixed(0)}% (${signal.awayMatchCount} maç)`,
);
}
totalOdds = parseFloat(totalOdds.toFixed(2));
const expectedValue = parseFloat((combinedHitRate * totalOdds).toFixed(3));
return {
strategy: "FREQUENCY",
generated_at: new Date().toISOString(),
bets,
total_odds: totalOdds,
expected_hit_rate: parseFloat(combinedHitRate.toFixed(4)),
expected_value: expectedValue,
ev_positive: expectedValue > 1.0,
reasoning,
rejected_matches: rejected,
};
}
}
// ─────────────────────────────────────────────────────────────
// Frequency Coupon Result Interface
// ─────────────────────────────────────────────────────────────
export interface FrequencyCouponResult {
strategy: "FREQUENCY";
generated_at: string;
bets: Array<{
match_id: string;
match_name: string;
league: string;
market: string;
pick: string;
home_signal: number;
away_signal: number;
combined_signal: number;
league_profile: string;
historical_hit_rate: number;
odds: number;
home_odds_band: string;
away_odds_band: string;
home_match_count: number;
away_match_count: number;
}>;
total_odds: number;
expected_hit_rate: number;
expected_value: number;
ev_positive: boolean;
reasoning: string[];
rejected_matches: Array<{
match_id: string;
match_name: string;
reason: string;
}>;
}
const MATCH_COMMENTARY_SYSTEM_PROMPT = `Sen uzman bir futbol bahis analistisin. Sana verilen model çıktısını analiz edip kısa, net ve aksiyon odaklı Türkçe bir yorum yaz.
src/modules/coupons/services/user-coupon.service.ts
+19 -19
View File
@@ -1,6 +1,6 @@
import { Injectable, Logger } from '@nestjs/common';
import { PrismaService } from '../../../database/prisma.service';
import { User, UserCoupon, Match } from '@prisma/client';
import { Injectable, Logger } from "@nestjs/common";
import { PrismaService } from "../../../database/prisma.service";
import { User, UserCoupon, Match } from "@prisma/client";
export class CreateCouponDto {
strategy: string; // 'SAFE', 'VALUE', 'CUSTOM'
@@ -39,7 +39,7 @@ export class UserCouponService {
strategy: dto.strategy,
totalOdds: parseFloat(totalOdds.toFixed(2)),
isPublic: dto.isPublic || false,
status: 'PENDING',
status: "PENDING",
couponItems: {
create: dto.items.map((item) => ({
matchId: item.matchId,
@@ -66,7 +66,7 @@ export class UserCouponService {
async updatePendingCoupons(): Promise<void> {
// Sadece bitmiş (FT) maçları içeren PENDING kuponları çek
const pendingCoupons = await this.prisma.userCoupon.findMany({
where: { status: 'PENDING' },
where: { status: "PENDING" },
include: {
couponItems: {
include: { match: true },
@@ -80,7 +80,7 @@ export class UserCouponService {
let allMatchesFinished = true;
for (const item of coupon.couponItems) {
if (item.match.status !== 'FT') {
if (item.match.status !== "FT") {
allMatchesFinished = false;
break; // Henüz bitmemiş maç var, kuponu güncelleme
}
@@ -104,12 +104,12 @@ export class UserCouponService {
if (isCouponLost) {
await this.prisma.userCoupon.update({
where: { id: coupon.id },
data: { status: 'LOST' },
data: { status: "LOST" },
});
} else if (allMatchesFinished && isCouponWon) {
await this.prisma.userCoupon.update({
where: { id: coupon.id },
data: { status: 'WON' },
data: { status: "WON" },
});
}
}
@@ -125,23 +125,23 @@ export class UserCouponService {
const total = home + away;
switch (selection) {
case 'MS 1':
case "MS 1":
return home > away;
case 'MS X':
case "MS X":
return home === away;
case 'MS 2':
case "MS 2":
return away > home;
case '1.5 UST':
case "1.5 UST":
return total > 1.5;
case '2.5 UST':
case "2.5 UST":
return total > 2.5;
case '3.5 UST':
case "3.5 UST":
return total > 3.5;
case '2.5 ALT':
case "2.5 ALT":
return total < 2.5;
case 'KG VAR':
case "KG VAR":
return home > 0 && away > 0;
case 'KG YOK':
case "KG YOK":
return home === 0 || away === 0;
default:
return false; // Bilinmeyen market
@@ -155,7 +155,7 @@ export class UserCouponService {
const coupons = await this.prisma.userCoupon.findMany({
where: {
userId,
status: { in: ['WON', 'LOST'] },
status: { in: ["WON", "LOST"] },
},
});
@@ -171,7 +171,7 @@ export class UserCouponService {
};
}
const wonCoupons = coupons.filter((c) => c.status === 'WON');
const wonCoupons = coupons.filter((c) => c.status === "WON");
const totalInvested = totalCoupons; // Her kupona 1 birim yatırıldığını varsayıyoruz
const totalReturn = wonCoupons.reduce((acc, c) => acc + c.totalOdds, 0);
const winRate = (wonCoupons.length / totalCoupons) * 100;
src/modules/feeder/feeder-persistence.service.ts
+38 -59
View File
@@ -5,8 +5,8 @@
* Database operations using Prisma
*/
import { Injectable, Logger } from '@nestjs/common';
import { PrismaService } from '../../database/prisma.service';
import { Injectable, Logger } from "@nestjs/common";
import { PrismaService } from "../../database/prisma.service";
import {
Sport,
MatchSummary,
@@ -20,8 +20,9 @@ import {
DbEventPayload,
DbMarketPayload,
BasketballTeamStats,
} from './feeder.types';
import { ImageUtils } from '../../common/utils/image.util';
} from "./feeder.types";
import { ImageUtils } from "../../common/utils/image.util";
import { deriveStoredMatchStatus } from "../../common/utils/match-status.util";
@Injectable()
export class FeederPersistenceService {
@@ -33,7 +34,7 @@ export class FeederPersistenceService {
// HELPER FUNCTIONS
// ============================================
private safeString(value: any): string | null {
return value === null || value === undefined || value === ''
return value === null || value === undefined || value === ""
? null
: String(value);
}
@@ -51,12 +52,12 @@ export class FeederPersistenceService {
private mapPositionToEnum(position: string | null): any {
if (!position) return null;
const pos = position.toLowerCase();
if (pos.includes('kaleci') || pos.includes('goalkeeper'))
return 'goalkeeper';
if (pos.includes('defans') || pos.includes('defender')) return 'defender';
if (pos.includes('orta saha') || pos.includes('midfielder'))
return 'midfielder';
if (pos.includes('forvet') || pos.includes('striker')) return 'striker';
if (pos.includes("kaleci") || pos.includes("goalkeeper"))
return "goalkeeper";
if (pos.includes("defans") || pos.includes("defender")) return "defender";
if (pos.includes("orta saha") || pos.includes("midfielder"))
return "midfielder";
if (pos.includes("forvet") || pos.includes("striker")) return "striker";
return null;
}
@@ -93,7 +94,7 @@ export class FeederPersistenceService {
}
for (const s of market.selectionCollection) {
if (!s || s.odd === '-' || s.odd === '') continue;
if (!s || s.odd === "-" || s.odd === "") continue;
const sName = this.safeString(s.name);
const sValue = this.safeString(s.odd);
@@ -107,7 +108,7 @@ export class FeederPersistenceService {
if (existingSel) {
if (existingSel.oddValue !== sValue) {
const oldVal = parseFloat(existingSel.oddValue || '0');
const oldVal = parseFloat(existingSel.oddValue || "0");
const newVal = parseFloat(sValue);
if (!isNaN(oldVal) && !isNaN(newVal)) {
@@ -182,13 +183,13 @@ export class FeederPersistenceService {
const teamsToUpsert = [
{
id: homeTeamId,
name: matchSummary.homeTeam?.name || 'Unknown',
name: matchSummary.homeTeam?.name || "Unknown",
slug: matchSummary.homeTeam?.slug || homeTeamId,
sport: sport,
},
{
id: awayTeamId,
name: matchSummary.awayTeam?.name || 'Unknown',
name: matchSummary.awayTeam?.name || "Unknown",
slug: matchSummary.awayTeam?.slug || awayTeamId,
sport: sport,
},
@@ -221,18 +222,18 @@ export class FeederPersistenceService {
update: {},
create: {
id: countryId,
name: league.country.name || 'Unknown',
name: league.country.name || "Unknown",
},
});
} catch (error: any) {
if (error.code !== 'P2002') throw error;
if (error.code !== "P2002") throw error;
}
}
// 2. Save League (Handle ID changes by checking unique constraint)
let finalLeagueId = this.safeString(league.id);
if (finalLeagueId && countryId) {
const leagueName = league.name || 'Unknown';
const leagueName = league.name || "Unknown";
// Check if league exists by unique constraint (name + country + sport)
const existingLeague = await tx.league.findUnique({
@@ -311,33 +312,15 @@ export class FeederPersistenceService {
headerData?.htScoreAway ??
this.safeInt(matchSummary.score?.ht?.away);
let status = 'NS';
if (headerData?.matchStatus) {
if (
headerData.matchStatus === 'postGame' ||
headerData.matchStatus === 'post'
) {
status = 'FT';
} else if (
headerData.matchStatus === 'live' ||
headerData.matchStatus === 'liveGame'
) {
status = 'LIVE';
}
}
// Handle Postponed Matches (ERT)
if (matchSummary.statusBoxContent === 'ERT') {
status = 'POSTPONED';
}
if (
status === 'NS' &&
finalScoreHome !== null &&
finalScoreAway !== null
) {
status = 'FT';
}
const status = deriveStoredMatchStatus({
state: headerData?.matchStatus ?? matchSummary.state,
status: matchSummary.status,
substate: matchSummary.substate,
statusBoxContent: matchSummary.statusBoxContent,
scoreHome: finalScoreHome,
scoreAway: finalScoreAway,
score: matchSummary.score,
});
await tx.match.upsert({
where: { id: matchId },
@@ -455,7 +438,7 @@ export class FeederPersistenceService {
}
// 8. Save Team Stats (Football)
if (stats && sport === 'football') {
if (stats && sport === "football") {
const statsRows = [
{
matchId,
@@ -499,7 +482,7 @@ export class FeederPersistenceService {
}
// 8b. Save Team Stats (Basketball)
if (basketballTeamStats && sport === 'basketball') {
if (basketballTeamStats && sport === "basketball") {
const teams = [
{ id: homeTeamId, data: basketballTeamStats.home },
{ id: awayTeamId, data: basketballTeamStats.away },
@@ -558,7 +541,7 @@ export class FeederPersistenceService {
}
// 8c. Save Player Stats (Basketball)
if (basketballPlayerStats.length > 0 && sport === 'basketball') {
if (basketballPlayerStats.length > 0 && sport === "basketball") {
await tx.basketballPlayerStats.deleteMany({ where: { matchId } });
for (const p of basketballPlayerStats) {
@@ -592,12 +575,12 @@ export class FeederPersistenceService {
await this.saveOddsInTransaction(tx, matchId, oddsArray);
// 10. Save Officials
if (sport === 'football' && officialsData.length > 0) {
if (sport === "football" && officialsData.length > 0) {
await tx.matchOfficial.deleteMany({ where: { matchId } });
const processedOfficials = new Set<string>();
for (const o of officialsData) {
const roleName = o.role || 'Referee';
const roleName = o.role || "Referee";
const uniqueKey = `${o.name}_${roleName}`;
if (processedOfficials.has(uniqueKey)) continue;
@@ -798,10 +781,10 @@ export class FeederPersistenceService {
const history = await this.prisma.match.findMany({
where: {
OR: [{ homeTeamId: teamId }, { awayTeamId: teamId }],
status: 'FT',
status: "FT",
mstUtc: { lt: match.mstUtc },
},
orderBy: { mstUtc: 'desc' },
orderBy: { mstUtc: "desc" },
take: 5,
});
@@ -840,8 +823,8 @@ export class FeederPersistenceService {
return {
match_id: match.id,
home_team: match.homeTeam?.name || 'Unknown',
away_team: match.awayTeam?.name || 'Unknown',
home_team: match.homeTeam?.name || "Unknown",
away_team: match.awayTeam?.name || "Unknown",
home_team_id: match.homeTeamId,
away_team_id: match.awayTeamId,
league_id: match.leagueId,
@@ -870,15 +853,11 @@ export class FeederPersistenceService {
}
async getExistingMatchIds(matchIds: string[]): Promise<string[]> {
// Only consider matches "existing" if they have ALL key data points
// This allows re-fetching matches that exist but have missing data
const matches = await this.prisma.match.findMany({
where: {
id: { in: matchIds },
AND: [
{ oddCategories: { some: {} } },
{ playerEvents: { some: {} } },
{ officials: { some: {} } },
{
OR: [
{ footballTeamStats: { some: {} } },
@@ -934,7 +913,7 @@ export class FeederPersistenceService {
scoreHome: liveMatch.scoreHome,
scoreAway: liveMatch.scoreAway,
mstUtc: liveMatch.mstUtc,
sport: liveMatch.sport || 'football',
sport: liveMatch.sport || "football",
};
}
src/modules/feeder/feeder-scraper.service.ts
+105 -105
View File
@@ -3,9 +3,9 @@
* HTTP requests with exact headers from working curl commands
*/
import { Injectable, Logger } from '@nestjs/common';
import axios, { AxiosInstance } from 'axios';
import * as cheerio from 'cheerio';
import { Injectable, Logger } from "@nestjs/common";
import axios, { AxiosInstance } from "axios";
import * as cheerio from "cheerio";
import {
Sport,
SPORTS_CONFIG,
@@ -25,7 +25,7 @@ import {
SidelinedResponse,
SidelinedTeamData,
SidelinedPlayer,
} from './feeder.types';
} from "./feeder.types";
@Injectable()
export class FeederScraperService {
@@ -43,13 +43,13 @@ export class FeederScraperService {
this.axios.interceptors.response.use(
(response) => {
this.logger.debug(
`✅ [${response.config.url?.split('?')[0]}] Status: ${response.status}`,
`✅ [${response.config.url?.split("?")[0]}] Status: ${response.status}`,
);
return response;
},
(error) => {
const status = error.response?.status || 'N/A';
const url = error.config?.url?.split('?')[0] || 'Unknown';
const status = error.response?.status || "N/A";
const url = error.config?.url?.split("?")[0] || "Unknown";
this.logger.error(`❌ [${url}] Status: ${status} - ${error.message}`);
throw error;
},
@@ -72,7 +72,7 @@ export class FeederScraperService {
const response = await this.axios.get(url, {
params: {
'sports[]': sportParam,
"sports[]": sportParam,
matchDate: dateString,
},
});
@@ -80,11 +80,11 @@ export class FeederScraperService {
const payload = response.data as unknown;
if (
!payload ||
typeof payload !== 'object' ||
!('status' in payload) ||
!('data' in payload)
typeof payload !== "object" ||
!("status" in payload) ||
!("data" in payload)
) {
throw new Error('Historical source payload has invalid shape');
throw new Error("Historical source payload has invalid shape");
}
return payload as LivescoresApiResponse;
@@ -101,14 +101,14 @@ export class FeederScraperService {
const response = await this.axios.get(url, {
params: {
matchId,
sdapiLanguageCode: 'tr-mk',
ajaxViewName: 'match-details',
ajaxPartialViewName: 'match-details-status',
displayMode: 'all',
sdapiLanguageCode: "tr-mk",
ajaxViewName: "match-details",
ajaxPartialViewName: "match-details-status",
displayMode: "all",
},
});
return this.parseMatchHeader(response.data.data?.html || '');
return this.parseMatchHeader(response.data.data?.html || "");
}
private parseMatchHeader(html: string): ParsedMatchHeader {
@@ -116,7 +116,7 @@ export class FeederScraperService {
// Extract match-status from data attribute
const matchStatus =
($('[data-match-status]').attr('data-match-status') as any) || 'postGame';
($("[data-match-status]").attr("data-match-status") as any) || "postGame";
// Extract scores
const scoreHome = this.safeInt($('[data-slot="score-home"]').text().trim());
@@ -126,7 +126,7 @@ export class FeederScraperService {
let htScoreHome: number | null = null;
let htScoreAway: number | null = null;
const detailedScore = $('.p0c-soccer-match-details-header__detailed-score')
const detailedScore = $(".p0c-soccer-match-details-header__detailed-score")
.text()
.trim();
const htMatch = detailedScore.match(/\(İY\s*(\d+)\s*-\s*(\d+)\)/);
@@ -143,7 +143,7 @@ export class FeederScraperService {
// ============================================
async fetchKeyEvents(
matchId: string,
): Promise<KeyEventsResponse['data'] | null> {
): Promise<KeyEventsResponse["data"] | null> {
const url = `https://www.mackolik.com/ajax/football/key-events`;
this.logger.debug(`📡 [${matchId}] Fetching key events`);
@@ -151,7 +151,7 @@ export class FeederScraperService {
try {
const response = await this.axios.get<KeyEventsResponse>(url, {
params: {
ajaxViewName: 'events',
ajaxViewName: "events",
matchId,
seasonId: matchId, // Same as matchId
},
@@ -172,7 +172,7 @@ export class FeederScraperService {
// ============================================
async fetchStartingFormation(
matchId: string,
): Promise<MatchStatsResponse['data'] | null> {
): Promise<MatchStatsResponse["data"] | null> {
const url = `https://www.mackolik.com/ajax/football/match-stats`;
this.logger.debug(`📡 [${matchId}] Fetching starting formation`);
@@ -180,7 +180,7 @@ export class FeederScraperService {
try {
const response = await this.axios.get<MatchStatsResponse>(url, {
params: {
ajaxViewName: 'starting-formation',
ajaxViewName: "starting-formation",
matchId,
seasonId: matchId,
},
@@ -201,7 +201,7 @@ export class FeederScraperService {
// ============================================
async fetchSubstitutions(
matchId: string,
): Promise<MatchStatsResponse['data'] | null> {
): Promise<MatchStatsResponse["data"] | null> {
const url = `https://www.mackolik.com/ajax/football/match-stats`;
this.logger.debug(`📡 [${matchId}] Fetching substitutions`);
@@ -209,7 +209,7 @@ export class FeederScraperService {
try {
const response = await this.axios.get<MatchStatsResponse>(url, {
params: {
ajaxViewName: 'substitutions',
ajaxViewName: "substitutions",
matchId,
seasonId: matchId,
},
@@ -230,7 +230,7 @@ export class FeederScraperService {
// ============================================
async fetchGameStats(
matchId: string,
): Promise<GameStatsResponse['data'] | null> {
): Promise<GameStatsResponse["data"] | null> {
const url = `https://www.mackolik.com/ajax/soccer/match/gameStats`;
this.logger.debug(`📡 [${matchId}] Fetching game stats`);
@@ -253,7 +253,7 @@ export class FeederScraperService {
// ============================================
// MANAGER
// ============================================
async fetchManager(matchId: string): Promise<ManagerResponse['data'] | null> {
async fetchManager(matchId: string): Promise<ManagerResponse["data"] | null> {
const url = `https://www.mackolik.com/ajax/football/match-stats`;
this.logger.debug(`📡 [${matchId}] Fetching manager`);
@@ -261,7 +261,7 @@ export class FeederScraperService {
try {
const response = await this.axios.get<ManagerResponse>(url, {
params: {
ajaxViewName: 'manager',
ajaxViewName: "manager",
matchId,
seasonId: matchId,
},
@@ -287,10 +287,10 @@ export class FeederScraperService {
try {
const response = await this.axios.get<IddaaMarketsHtmlResponse>(url, {
params: { template: 'all' },
params: { template: "all" },
});
return this.parseIddaaMarketsHtml(response.data.data?.html || '');
return this.parseIddaaMarketsHtml(response.data.data?.html || "");
} catch (error: any) {
if (error.response?.status === 404) {
this.logger.warn(`[${matchId}] Iddaa markets not found (404)`);
@@ -306,30 +306,30 @@ export class FeederScraperService {
const $ = cheerio.load(html);
const markets: ParsedMarket[] = [];
$('.widget-iddaa-markets__market-item').each((_, marketEl) => {
$(".widget-iddaa-markets__market-item").each((_, marketEl) => {
const $market = $(marketEl);
const marketId = $market.attr('data-market') || '';
const marketId = $market.attr("data-market") || "";
const marketName = $market
.find('.widget-iddaa-markets__header-text')
.find(".widget-iddaa-markets__header-text")
.text()
.trim();
const iddaaCode = $market
.find('.widget-iddaa-markets__iddaa-code')
.find(".widget-iddaa-markets__iddaa-code")
.text()
.trim();
const mbc = $market.find('.widget-iddaa-markets__mbc').text().trim();
const mbc = $market.find(".widget-iddaa-markets__mbc").text().trim();
const selections: ParsedSelection[] = [];
$market.find('.widget-iddaa-markets__option').each((_, optionEl) => {
$market.find(".widget-iddaa-markets__option").each((_, optionEl) => {
const $option = $(optionEl);
selections.push({
shortcode: $option.attr('data-shortcode') || '',
outcomeNo: $option.attr('data-outcome-no') || '',
label: $option.find('.widget-iddaa-markets__label').text().trim(),
value: $option.find('.widget-iddaa-markets__value').text().trim(),
shortcode: $option.attr("data-shortcode") || "",
outcomeNo: $option.attr("data-outcome-no") || "",
label: $option.find(".widget-iddaa-markets__label").text().trim(),
value: $option.find(".widget-iddaa-markets__value").text().trim(),
});
});
@@ -347,7 +347,7 @@ export class FeederScraperService {
// ============================================
async fetchBasketballBoxScore(
matchId: string,
): Promise<BasketballBoxScoreResponse['data'] | null> {
): Promise<BasketballBoxScoreResponse["data"] | null> {
// Updated URL based on user request
const url = `https://www.mackolik.com/ajax/basketball/match/box-score`;
@@ -357,8 +357,8 @@ export class FeederScraperService {
const response = await this.axios.get<BasketballBoxScoreResponse>(url, {
params: { matchId },
headers: {
'X-Requested-With': 'XMLHttpRequest',
'User-Agent': DEFAULT_HEADERS['User-Agent'],
"X-Requested-With": "XMLHttpRequest",
"User-Agent": DEFAULT_HEADERS["User-Agent"],
},
});
@@ -382,25 +382,25 @@ export class FeederScraperService {
const players: Partial<BasketballPlayerStats>[] = [];
// Parse individual players from widget rows
$('.widget-basketball-match-box-score__row').each((_, elem) => {
$(".widget-basketball-match-box-score__row").each((_, elem) => {
const row = $(elem);
// Skip if no player name found
const nameElem = row.find('.widget-basketball-match-box-score__player');
const nameElem = row.find(".widget-basketball-match-box-score__player");
if (!nameElem.length) return;
const name = nameElem.text().trim();
// Indices based on User HTML:
// 0: Name, 1: Min, 2: Pts, 3: Reb, 4: Ast, 5: 2FG, 6: 3FG, 7: FT, 8: Fouls, 9: Blk, 10: Stl, 11: TO
const values = row.find('td');
const values = row.find("td");
// Check if it's a valid player row (should have enough columns)
if (values.length < 10) return;
// Extract ID from link if possible
let playerId = '';
const link = nameElem.find('a').attr('href');
let playerId = "";
const link = nameElem.find("a").attr("href");
if (link) {
playerId = this.extractPlayerIdFromUrl(link) || '';
playerId = this.extractPlayerIdFromUrl(link) || "";
}
players.push({
@@ -410,16 +410,16 @@ export class FeederScraperService {
points: this.safeInt(values.eq(2).text().trim()) || 0,
rebounds: this.safeInt(values.eq(3).text().trim()) || 0,
assists: this.safeInt(values.eq(4).text().trim()) || 0,
fgMade: this.safeInt(values.eq(5).text().trim().split('/')[0]) || 0,
fgMade: this.safeInt(values.eq(5).text().trim().split("/")[0]) || 0,
fgAttempted:
this.safeInt(values.eq(5).text().trim().split('/')[1]) || 0,
this.safeInt(values.eq(5).text().trim().split("/")[1]) || 0,
threePtMade:
this.safeInt(values.eq(6).text().trim().split('/')[0]) || 0,
this.safeInt(values.eq(6).text().trim().split("/")[0]) || 0,
threePtAttempted:
this.safeInt(values.eq(6).text().trim().split('/')[1]) || 0,
ftMade: this.safeInt(values.eq(7).text().trim().split('/')[0]) || 0,
this.safeInt(values.eq(6).text().trim().split("/")[1]) || 0,
ftMade: this.safeInt(values.eq(7).text().trim().split("/")[0]) || 0,
ftAttempted:
this.safeInt(values.eq(7).text().trim().split('/')[1]) || 0,
this.safeInt(values.eq(7).text().trim().split("/")[1]) || 0,
fouls: this.safeInt(values.eq(8).text().trim()) || 0,
blocks: this.safeInt(values.eq(9).text().trim()) || 0,
steals: this.safeInt(values.eq(10).text().trim()) || 0,
@@ -428,7 +428,7 @@ export class FeederScraperService {
});
// Parse Team Totals from Footer
const footerRow = $('.widget-basketball-match-box-score__footer td');
const footerRow = $(".widget-basketball-match-box-score__footer td");
let teamTotals: any = {};
if (footerRow.length > 5) {
@@ -438,16 +438,16 @@ export class FeederScraperService {
points: this.safeInt(footerRow.eq(2).text().trim()) || 0,
rebounds: this.safeInt(footerRow.eq(3).text().trim()) || 0,
assists: this.safeInt(footerRow.eq(4).text().trim()) || 0,
fgMade: this.safeInt(footerRow.eq(5).text().trim().split('/')[0]) || 0,
fgMade: this.safeInt(footerRow.eq(5).text().trim().split("/")[0]) || 0,
fgAttempted:
this.safeInt(footerRow.eq(5).text().trim().split('/')[1]) || 0,
this.safeInt(footerRow.eq(5).text().trim().split("/")[1]) || 0,
threePtMade:
this.safeInt(footerRow.eq(6).text().trim().split('/')[0]) || 0,
this.safeInt(footerRow.eq(6).text().trim().split("/")[0]) || 0,
threePtAttempted:
this.safeInt(footerRow.eq(6).text().trim().split('/')[1]) || 0,
ftMade: this.safeInt(footerRow.eq(7).text().trim().split('/')[0]) || 0,
this.safeInt(footerRow.eq(6).text().trim().split("/")[1]) || 0,
ftMade: this.safeInt(footerRow.eq(7).text().trim().split("/")[0]) || 0,
ftAttempted:
this.safeInt(footerRow.eq(7).text().trim().split('/')[1]) || 0,
this.safeInt(footerRow.eq(7).text().trim().split("/")[1]) || 0,
fouls: this.safeInt(footerRow.eq(8).text().trim()) || 0,
blocks: this.safeInt(footerRow.eq(9).text().trim()) || 0,
steals: this.safeInt(footerRow.eq(10).text().trim()) || 0,
@@ -474,11 +474,11 @@ export class FeederScraperService {
// For HTML pages, we DON'T send X-Requested-With header
const response = await this.axios.get(url, {
headers: {
'User-Agent': DEFAULT_HEADERS['User-Agent'],
Referer: DEFAULT_HEADERS['Referer'],
'Accept-Language': DEFAULT_HEADERS['Accept-Language'],
"User-Agent": DEFAULT_HEADERS["User-Agent"],
Referer: DEFAULT_HEADERS["Referer"],
"Accept-Language": DEFAULT_HEADERS["Accept-Language"],
Accept:
'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
// NO X-Requested-With for HTML pages!
},
});
@@ -507,8 +507,8 @@ export class FeederScraperService {
const response = await this.axios.get(url, {
params: { matchId },
headers: {
'X-Requested-With': 'XMLHttpRequest',
'User-Agent': DEFAULT_HEADERS['User-Agent'],
"X-Requested-With": "XMLHttpRequest",
"User-Agent": DEFAULT_HEADERS["User-Agent"],
},
});
@@ -532,12 +532,12 @@ export class FeederScraperService {
const $ = cheerio.load(html);
const rows = $(
'.widget-basketball-match-details-header__score-details tbody tr',
".widget-basketball-match-details-header__score-details tbody tr",
);
if (rows.length < 2) return null;
const parseRow = (row: any) => {
const cols = $(row).find('td');
const cols = $(row).find("td");
// Format: TeamName, Q1, Q2, Q3, Q4, Final
// Values are inside .widget-basketball-match-details-header__score-part (just the quarter score)
// or direct text if simple table.
@@ -545,7 +545,7 @@ export class FeederScraperService {
const getScore = (index: number) => {
const cell = cols.eq(index);
const part = cell.find(
'.widget-basketball-match-details-header__score-part',
".widget-basketball-match-details-header__score-part",
);
const val = part.length ? part.text() : cell.text();
return this.safeInt(val.trim());
@@ -580,10 +580,10 @@ export class FeederScraperService {
try {
const response = await this.axios.get<IddaaMarketsHtmlResponse>(url, {
params: { template: 'all' },
params: { template: "all" },
headers: {
'X-Requested-With': 'XMLHttpRequest',
'User-Agent': DEFAULT_HEADERS['User-Agent'],
"X-Requested-With": "XMLHttpRequest",
"User-Agent": DEFAULT_HEADERS["User-Agent"],
},
});
@@ -602,7 +602,7 @@ export class FeederScraperService {
extractPlayerIdFromUrl(url: string | undefined): string | null {
if (!url) return null;
const parts = url.split('/');
const parts = url.split("/");
return parts[parts.length - 1] || null;
}
@@ -620,12 +620,12 @@ export class FeederScraperService {
try {
const response = await this.axios.get(url, {
headers: {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
Accept:
'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Language': 'tr-TR,tr;q=0.9,en-US;q=0.8,en;q=0.7',
Referer: 'https://www.mackolik.com',
"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
"Accept-Language": "tr-TR,tr;q=0.9,en-US;q=0.8,en;q=0.7",
Referer: "https://www.mackolik.com",
},
timeout: 10000,
});
@@ -652,24 +652,24 @@ export class FeederScraperService {
$: cheerio.CheerioAPI,
teamIndex: number,
): SidelinedTeamData {
const sidelinedWidgets = $('.widget-sidelined-players');
const sidelinedWidgets = $(".widget-sidelined-players");
if (sidelinedWidgets.length <= teamIndex) {
return { teamName: '', teamId: '', totalSidelined: 0, players: [] };
return { teamName: "", teamId: "", totalSidelined: 0, players: [] };
}
const widget = sidelinedWidgets.eq(teamIndex);
const teamCrest = widget.find('.widget-sidelined-players__header-crest');
const teamCrestSrc = teamCrest.attr('src') || '';
const teamId = teamCrestSrc.split('/').pop() || '';
const teamCrest = widget.find(".widget-sidelined-players__header-crest");
const teamCrestSrc = teamCrest.attr("src") || "";
const teamId = teamCrestSrc.split("/").pop() || "";
const teamName = widget
.find('.widget-sidelined-players__header-text')
.find(".widget-sidelined-players__header-text")
.text()
.trim();
const players: SidelinedPlayer[] = [];
widget.find('.widget-sidelined-players__item').each((_, element) => {
widget.find(".widget-sidelined-players__item").each((_, element) => {
const playerData = this._parsePlayerItem($, $(element));
if (playerData) {
players.push(playerData);
@@ -689,44 +689,44 @@ export class FeederScraperService {
$item: cheerio.Cheerio<any>,
): SidelinedPlayer | null {
try {
const nameElem = $item.find('.widget-sidelined-players__name');
const nameElem = $item.find(".widget-sidelined-players__name");
const playerName = nameElem.text().trim();
const playerUrl = nameElem.attr('href') || '';
const playerId = playerUrl.split('/').pop() || '';
const playerUrl = nameElem.attr("href") || "";
const playerId = playerUrl.split("/").pop() || "";
const positionElem = $item.find('.widget-sidelined-players__position');
const position = positionElem.attr('title') || '';
const positionElem = $item.find(".widget-sidelined-players__position");
const position = positionElem.attr("title") || "";
const positionShort = positionElem.text().trim();
const reasonImg = $item.find('.widget-sidelined-players__reason img');
const reasonIcon = reasonImg.attr('src') || '';
const reasonImg = $item.find(".widget-sidelined-players__reason img");
const reasonIcon = reasonImg.attr("src") || "";
const numbers = $item.find('.widget-sidelined-players__number');
const numbers = $item.find(".widget-sidelined-players__number");
// Use parseInt EXACTLY as in JS script (ignoring potential NaN for now, will handle via helper if needed but safer to stick to script logic first)
const matchesMissedText =
numbers.length > 0 ? numbers.eq(0).text().trim() : '';
numbers.length > 0 ? numbers.eq(0).text().trim() : "";
const matchesMissed = matchesMissedText
? parseInt(matchesMissedText, 10)
: null;
const averageText = numbers.length > 1 ? numbers.eq(1).text().trim() : '';
const averageText = numbers.length > 1 ? numbers.eq(1).text().trim() : "";
const average = averageText ? parseInt(averageText, 10) : null;
const description = $item
.find('.widget-sidelined-players__value')
.find(".widget-sidelined-players__value")
.text()
.trim();
const type = reasonIcon.includes('shortage_1.png')
? 'injury'
: reasonIcon.includes('suspension')
? 'suspension'
: 'other';
const type = reasonIcon.includes("shortage_1.png")
? "injury"
: reasonIcon.includes("suspension")
? "suspension"
: "other";
return {
playerId,
playerName,
playerUrl: playerUrl.startsWith('http')
playerUrl: playerUrl.startsWith("http")
? playerUrl
: `https://www.mackolik.com${playerUrl}`,
position,
@@ -735,7 +735,7 @@ export class FeederScraperService {
description,
matchesMissed: isNaN(matchesMissed as number) ? null : matchesMissed,
average: isNaN(average as number) ? null : average,
reasonIcon: reasonIcon.startsWith('http')
reasonIcon: reasonIcon.startsWith("http")
? reasonIcon
: `https://www.mackolik.com${reasonIcon}`, // Keep safer URL construction but stick closer to logic
};
src/modules/feeder/feeder-transformer.service.ts
+32 -32
View File
@@ -3,8 +3,8 @@
* Transforms raw API data into database-ready formats
*/
import { Injectable, Logger } from '@nestjs/common';
import * as cheerio from 'cheerio';
import { Injectable, Logger } from "@nestjs/common";
import * as cheerio from "cheerio";
import {
RawKeyEvent,
TransformedEvent,
@@ -18,7 +18,7 @@ import {
GameStatsResponse,
DbEventPayload,
DbMarketPayload,
} from './feeder.types';
} from "./feeder.types";
@Injectable()
export class FeederTransformerService {
@@ -28,7 +28,7 @@ export class FeederTransformerService {
// HELPER FUNCTIONS
// ============================================
private safeString(value: any): string | null {
return value === null || value === undefined || value === ''
return value === null || value === undefined || value === ""
? null
: String(value);
}
@@ -45,7 +45,7 @@ export class FeederTransformerService {
private extractPlayerIdFromUrl(url: string | undefined): string | null {
if (!url) return null;
const parts = url.split('/');
const parts = url.split("/");
return parts[parts.length - 1] || null;
}
@@ -59,7 +59,7 @@ export class FeederTransformerService {
matchId: string,
): TransformedEvent[] {
return rawEvents.map((e) => {
const playerId = this.extractPlayerIdFromUrl(e.playerUrl) || '';
const playerId = this.extractPlayerIdFromUrl(e.playerUrl) || "";
const assistPlayerId = e.assistPlayerUrl
? this.extractPlayerIdFromUrl(e.assistPlayerUrl)
: null;
@@ -68,16 +68,16 @@ export class FeederTransformerService {
: null;
// Determine event type
let eventType: 'goal' | 'card' | 'substitute' | 'other' = 'other';
if (e.type === 'goal') eventType = 'goal';
else if (e.type === 'card') eventType = 'card';
else if (e.type === 'substitute') eventType = 'substitute';
let eventType: "goal" | "card" | "substitute" | "other" = "other";
if (e.type === "goal") eventType = "goal";
else if (e.type === "card") eventType = "card";
else if (e.type === "substitute") eventType = "substitute";
return {
matchId,
playerId,
playerName: e.playerName,
teamId: e.position === 'home' ? homeTeamId : awayTeamId,
teamId: e.position === "home" ? homeTeamId : awayTeamId,
eventType,
eventSubtype: e.subType || null,
timeMinute: e.timeMin,
@@ -136,7 +136,7 @@ export class FeederTransformerService {
// GAME STATS TRANSFORMER
// ============================================
transformGameStats(
data: GameStatsResponse['data'] | null,
data: GameStatsResponse["data"] | null,
): TransformedMatchStats | null {
if (!data || !data.home) return null;
@@ -173,20 +173,20 @@ export class FeederTransformerService {
// MATCH STATE TO STATUS MAPPER
// ============================================
mapMatchStateToStatus(state: MatchState | undefined): string {
if (!state) return 'NS';
if (!state) return "NS";
switch (state) {
case 'postGame':
case 'post':
return 'FT';
case 'preGame':
case 'pre':
return 'NS';
case 'live':
case 'liveGame':
return 'LIVE';
case "postGame":
case "post":
return "FT";
case "preGame":
case "pre":
return "NS";
case "live":
case "liveGame":
return "LIVE";
default:
return 'NS';
return "NS";
}
}
@@ -200,28 +200,28 @@ export class FeederTransformerService {
const officials: MatchOfficial[] = [];
// Try standard officials component
$('.p0c-match-officials__official-list-item').each((_, elem) => {
$(".p0c-match-officials__official-list-item").each((_, elem) => {
const name = $(elem)
.find('.p0c-match-officials__official-name')
.find(".p0c-match-officials__official-name")
.text()
.trim();
const role = $(elem)
.find('.p0c-match-officials__official-group-title')
.find(".p0c-match-officials__official-group-title")
.text()
.trim();
if (name) {
officials.push({ name, role: role || 'Referee' });
officials.push({ name, role: role || "Referee" });
}
});
// Fallback: look for referee info in match info section
if (officials.length === 0) {
// Try alternative selectors
$('.widget-match-info__referee-name, .referee-name').each((_, elem) => {
$(".widget-match-info__referee-name, .referee-name").each((_, elem) => {
const name = $(elem).text().trim();
if (name) {
officials.push({ name, role: 'Referee' });
officials.push({ name, role: "Referee" });
}
});
}
@@ -331,8 +331,8 @@ export class FeederTransformerService {
(
e,
): e is TransformedEvent & {
eventType: 'goal' | 'card' | 'substitute';
} => e.eventType !== 'other' && !!e.playerId,
eventType: "goal" | "card" | "substitute";
} => e.eventType !== "other" && !!e.playerId,
)
.map((e) => ({
match_id: e.matchId,
@@ -354,6 +354,6 @@ export class FeederTransformerService {
// BASKETBALL PLAYER ID GENERATOR
// ============================================
generateBasketballPlayerId(teamId: string, playerName: string): string {
return `${teamId}-${playerName.replace(/[^a-zA-Z0-9]/g, '-').toLowerCase()}`;
return `${teamId}-${playerName.replace(/[^a-zA-Z0-9]/g, "-").toLowerCase()}`;
}
}
src/modules/feeder/feeder.module.ts
+6 -6
View File
@@ -2,12 +2,12 @@
* Feeder Module - Senior Level Implementation
*/
import { Module } from '@nestjs/common';
import { FeederService } from './feeder.service';
import { FeederScraperService } from './feeder-scraper.service';
import { FeederTransformerService } from './feeder-transformer.service';
import { FeederPersistenceService } from './feeder-persistence.service';
import { DatabaseModule } from '../../database/database.module';
import { Module } from "@nestjs/common";
import { FeederService } from "./feeder.service";
import { FeederScraperService } from "./feeder-scraper.service";
import { FeederTransformerService } from "./feeder-transformer.service";
import { FeederPersistenceService } from "./feeder-persistence.service";
import { DatabaseModule } from "../../database/database.module";
@Module({
imports: [DatabaseModule],

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