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295 10534 60645
296 10576 60646
297 10612 60612
298 10639 60525
299 10668 60453
300 10702 60411
301 10729 60326
302 10764 60290
303 10801 60263
304 10829 60182
305 10857 60108
306 10892 60067
307 10930 60047
308 10972 60045
309 11002 59983
310 11030 59902
311 11058 59828
312 11092 59788
313 11117 59696
314 11149 59641
315 11187 59617
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
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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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194 192 7117 66635
195 193 7191 66943
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203 201 7506 66814
204 202 7540 66752
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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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467 465 15817 52068
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469 467 15873 51961
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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
484 482 16277 51123
485 483 16305 51071
486 484 16328 51005
487 485 16362 50973
488 486 16392 50928
489 487 16426 50894
490 488 16459 50860
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ai-engine/features/rolling_features.py
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"""
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/scripts/backtest_niche.py
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"""
V27 FINAL BACKTEST — Conservative Flat Bet
Only the strongest validated edges. No Kelly compounding.
"""
import pandas as pd, numpy as np
df = pd.read_csv('data/training_data_v27.csv', low_memory=False)
for c in df.columns:
if c not in ['match_id','league_name','home_team','away_team']:
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)]
n = len(df)
# 5-fold walk-forward: train on 60%, validate patterns, test on remaining
folds = 5
fold_size = n // folds
all_results = []
print("="*65)
print(" V27 WALK-FORWARD FLAT-BET BACKTEST")
print("="*65)
for fold in range(2, folds): # start from fold 2 so we have enough training data
train_end = fold * fold_size
test_start = train_end
test_end = (fold+1)*fold_size if fold < folds-1 else n
train_df = df.iloc[:train_end]
test_df = df.iloc[test_start:test_end]
print(f"\n --- Fold {fold}: train={len(train_df)}, test={len(test_df)} ---")
# Discover REST edges from training data
strategies = []
for hr in [5, 7, 10, 14]:
for ar in [3, 4, 5]:
for cls, col in [(0,'odds_ms_h'), (2,'odds_ms_a')]:
idx = (train_df.home_days_rest > hr) & (train_df.away_days_rest < ar)
sub = train_df[idx]
if len(sub) < 50:
continue
rate = (sub.label_ms == cls).mean()
avg_odds = sub[col].mean()
ev = rate * avg_odds
if ev > 1.02: # only strong edges (>2% edge)
strategies.append((hr, ar, cls, rate, avg_odds, ev, len(sub)))
if not strategies:
print(" No strong edges found in training data")
continue
# Apply best strategies to test
strategies.sort(key=lambda x: x[5], reverse=True)
best = strategies[:3] # top 3 only
fold_bets = 0
fold_wins = 0
fold_pnl = 0
stake = 10 # flat 10 units
for _, row in test_df.iterrows():
for hr, ar, cls, est_p, _, _, _ in best:
if pd.isna(row.home_days_rest) or pd.isna(row.away_days_rest):
continue
if row.home_days_rest <= hr or row.away_days_rest >= ar:
continue
odds_col = ['odds_ms_h','odds_ms_d','odds_ms_a'][cls]
odds_val = row[odds_col]
if pd.isna(odds_val) or odds_val < 1.50 or odds_val > 5.0:
continue
# Additional filter: only bet when odds give reasonable EV
if est_p * odds_val < 1.0:
continue
won = (row.label_ms == cls)
pnl = stake * (odds_val - 1) if won else -stake
fold_bets += 1
if won:
fold_wins += 1
fold_pnl += pnl
all_results.append({'fold': fold, 'won': won, 'pnl': pnl,
'odds': odds_val, 'stake': stake,
'cls': ['H','D','A'][cls]})
if fold_bets > 0:
roi = fold_pnl / (fold_bets * stake) * 100
print(f" Best strategies: {[(h,a,['H','D','A'][c],f'EV={e:.3f}') for h,a,c,_,_,e,_ in best]}")
print(f" Bets: {fold_bets}, Wins: {fold_wins} ({fold_wins/fold_bets*100:.1f}%), "
f"ROI: {roi:+.1f}%, PnL: {fold_pnl:+.0f}")
# Overall
print("\n" + "="*65)
print(" OVERALL RESULTS")
print("="*65)
if all_results:
total = len(all_results)
wins = sum(1 for r in all_results if r['won'])
total_pnl = sum(r['pnl'] for r in all_results)
total_staked = sum(r['stake'] for r in all_results)
roi = total_pnl / total_staked * 100
print(f" Total bets: {total}")
print(f" Wins: {wins} ({wins/total*100:.1f}%)")
print(f" Total staked: {total_staked:.0f}")
print(f" PnL: {total_pnl:+.0f}")
print(f" ROI: {roi:+.1f}%")
print(f" Avg odds: {np.mean([r['odds'] for r in all_results]):.2f}")
# By class
print("\n --- By Bet Type ---")
for cls in ['H','A']:
cb = [r for r in all_results if r['cls'] == cls]
if cb:
cw = sum(1 for r in cb if r['won'])
cp = sum(r['pnl'] for r in cb)
cs = sum(r['stake'] for r in cb)
print(f" {cls}: {len(cb)} bets, hit={cw/len(cb)*100:.1f}%, ROI={cp/cs*100:+.1f}%")
# Cumulative PnL curve
print("\n --- Cumulative PnL ---")
cum = 0
step = max(1, total // 15)
for j in range(0, total, step):
cum = sum(r['pnl'] for r in all_results[:j+1])
print(f" After bet {j+1:4d}: PnL={cum:+.0f}")
cum = sum(r['pnl'] for r in all_results)
print(f" After bet {total:4d}: PnL={cum:+.0f} (FINAL)")
else:
print(" No bets placed!")
# ── Now combine with MODEL for smarter filtering ──
print("\n" + "="*65)
print(" COMBINED: Rest Rules + Fundamentals Model")
print("="*65)
import pickle, json
from pathlib import Path
MODELS_DIR = Path("models/v27")
feat_cols = json.load(open(MODELS_DIR / "v27_feature_cols.json"))
ms_models = {}
for name in ['xgb','lgb','cb']:
p = MODELS_DIR / f"v27_ms_{name}.pkl"
if p.exists():
with open(p,'rb') as f:
ms_models[name] = pickle.load(f)
if ms_models:
test_df = df.iloc[int(n*0.8):].copy()
X_test = test_df[feat_cols].values
# Get model predictions
preds = []
for name, m in ms_models.items():
if name == 'xgb':
import xgboost as xgb
dm = xgb.DMatrix(X_test, feature_names=feat_cols)
preds.append(m.predict(dm))
elif name == 'lgb':
preds.append(m.predict(X_test))
elif name == 'cb':
preds.append(m.predict_proba(X_test))
model_probs = np.mean(preds, axis=0) # (n, 3)
# Now apply rest rules + model agreement
margin = 1/test_df.odds_ms_h.values + 1/test_df.odds_ms_d.values + 1/test_df.odds_ms_a.values
impl = np.column_stack([
(1/test_df.odds_ms_h.values)/margin,
(1/test_df.odds_ms_d.values)/margin,
(1/test_df.odds_ms_a.values)/margin,
])
combo_bets = 0
combo_wins = 0
combo_pnl = 0
for j in range(len(test_df)):
row = test_df.iloc[j]
for hr, ar in [(14,5),(10,5),(7,5),(5,5)]:
if pd.isna(row.home_days_rest) or pd.isna(row.away_days_rest):
continue
if row.home_days_rest <= hr or row.away_days_rest >= ar:
continue
for cls in [0, 2]:
odds_val = [row.odds_ms_h, row.odds_ms_d, row.odds_ms_a][cls]
if pd.isna(odds_val) or odds_val < 1.50 or odds_val > 5.0:
continue
model_p = model_probs[j, cls]
impl_p = impl[j, cls]
# DOUBLE FILTER: rest rule + model agrees (model_prob > implied)
if model_p <= impl_p:
continue # model disagrees, skip
edge = model_p - impl_p
if edge < 0.03:
continue # too small
won = (row.label_ms == cls)
pnl = 10 * (odds_val - 1) if won else -10
combo_bets += 1
if won:
combo_wins += 1
combo_pnl += pnl
if combo_bets > 0:
roi = combo_pnl / (combo_bets * 10) * 100
print(f" Bets: {combo_bets}")
print(f" Wins: {combo_wins} ({combo_wins/combo_bets*100:.1f}%)")
print(f" PnL: {combo_pnl:+.0f}")
print(f" ROI: {roi:+.1f}%")
else:
print(" No combined bets triggered")
ai-engine/scripts/conditional_frequency_engine.py
+312
View File
@@ -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_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
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"""
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_v27_value_sniper.py
+480
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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()