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e4c74025e5 .. fb53fdf1df

Author SHA1 Message Date
fahricansecer fb53fdf1df Merge pull request 'v26-shadow' (#2) from v26-shadow into main
Deploy Iddaai Backend / build-and-deploy (push) Successful in 2m51s
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Reviewed-on: #2
 2026-04-23 22:29:23 +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
70 changed files with 440541 additions and 1177 deletions
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ai-engine/catboost_info/catboost_training.json
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375 13205 57036
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381 13396 56741
382 13421 56666
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568 19016 47824
569 19037 47761
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601 19925 46271
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603 19991 46204
604 20019 46159
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619 20423 45459
620 20454 45420
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631 20752 44919
632 20782 44881
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635 20862 44742
636 20892 44704
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639 20994 44612
640 21022 44570
641 21052 44531
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651 21315 44068
652 21343 44027
653 21377 43997
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664 21688 43539
665 21714 43493
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667 21771 43413
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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
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686 22452 42912
687 22484 42878
688 22511 42834
689 22537 42789
690 22571 42757
691 22598 42714
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695 22708 42545
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699 22816 42373
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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
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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
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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
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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
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83 81 3381 79089
84 82 3416 78911
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86 84 3535 79649
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89 87 3644 79185
90 88 3678 78975
91 89 3712 78785
92 90 3743 78531
93 91 3775 78297
94 92 3806 78047
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98 96 3945 77403
99 97 3989 77433
100 98 4020 77204
101 99 4053 77020
102 100 4084 76789
103 101 4116 76597
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105 103 4176 76141
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111 109 4351 74766
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113 111 4424 74577
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123 121 4756 73220
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ai-engine/features/odds_band_analyzer.py
+1224
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File diff suppressed because it is too large Load Diff
ai-engine/features/rolling_features.py
+243
View File
@@ -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
+10 -1
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()
@@ -38,6 +39,7 @@ async def lifespan(_: FastAPI):
try:
print("🚀 Initializing V25 orchestrator...", flush=True)
get_single_match_orchestrator()
get_v26_shadow_engine()
print("✅ V25 orchestrator ready", flush=True)
except Exception as error:
print(f"❌ Failed to initialize orchestrator: {error}", flush=True)
@@ -104,6 +106,7 @@ def read_root() -> dict[str, Any]:
return {
"status": "Suggest-Bet AI Engine v25",
"engine": "V25 Single Match Orchestrator",
"mode": os.getenv("AI_ENGINE_MODE", "v25"),
"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",
"mode": os.getenv("AI_ENGINE_MODE", "v25"),
"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", "v25"),
}
except Exception as error:
return {"status": "unhealthy", "ready": False, "error": str(error)}
ai-engine/reports/backtest_v26_shadow.json
+902
View File
@@ -0,0 +1,902 @@
[
{
"match_id": "2b1jyd72hogojec5j50fd9gr8",
"v25": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v26": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v25_main": "\u00dcst",
"v26_main": "1.5 \u00dcst"
},
{
"match_id": "1b2chhfsohmulm85sb95y189g",
"v25": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v26": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v25_main": null,
"v26_main": "X"
},
{
"match_id": "dg84sd1wkmtfrtdm9od7wy7f8",
"v25": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v26": {
"playable_count": 0.0,
"avg_edge": 0.0,
"avg_confidence": 0.0
},
"v25_main": "Alt",
"v26_main": "1.5 \u00dcst"
},
{
"match_id": "dydrdtrxi3dsomph1at54jaxg",
"v25": {
"playable_count": 1.0,
"avg_edge": 0.0264,
"avg_confidence": 69.0
},
"v26": {
"playable_count": 2.0,
"avg_edge": 0.1559,
"avg_confidence": 71.4
},
"v25_main": "\u00dcst",
"v26_main": "2.5 \u00dcst"
},
{
"match_id": "b6uzz042mizu0dqpci538z4lw",
"v25": {
"playable_count": 1.0,
"avg_edge": 0.1515,
"avg_confidence": 66.3
},
"v26": {
"playable_count": 2.0,
"avg_edge": 0.1103,
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ai-engine/reports/backtest_v26_shadow_htft_upset.csv
+35
View File
@@ -0,0 +1,35 @@
match_id,date,league,match,ht_score,final_score,strategy,market,pick,odds,playable,confidence,result,counted_in_roi,profit_flat,resolution_note,source,reversal_pick,reversal_prob,favorite_gap,favorite_odd,support_score,odds_band_score,odds_band_label,league_reversal_rate,league_strict_rev_rate,referee_strict_rev_rate,surprise_score,reason_codes,pick_reason
b6uzz042mizu0dqpci538z4lw,2025-09-06,Süper Kupa,V. Sarsfield vs C. Cordoba,0-0,2-0,v25_aggressive,HTFT,2/1,34.5,True,21.0,LOST,True,-1.0,actual=X/1,v25.aggressive_pick,2/1,,,,,,,,,,,,
b6uzz042mizu0dqpci538z4lw,2025-09-06,Süper Kupa,V. Sarsfield vs C. Cordoba,0-0,2-0,v26_aggressive,HTFT,1/1,3.26,True,16.0,LOST,True,-1.0,actual=X/1,v26.aggressive_pick,1/1,,,,,,,,,,,,
ytsc38rm4j22govgwo3as6j8,2025-09-06,DK Elemeler,Avusturya vs G. Kıbrıs Rum Kesimi,0-0,1-0,v25_aggressive,HTFT,X/1,4.01,True,11.4,WON,True,3.01,actual=X/1,v25.aggressive_pick,X/1,,,,,,,,,,,,
cfar57gsu6hy770n7e58u8duc,2025-09-06,Eerste Divisie,Cambuur vs Willem II,0-1,2-2,v25_aggressive,HTFT,2/1,20.7,True,15.6,LOST,True,-1.0,actual=2/X,v25.aggressive_pick,2/1,,,,,,,,,,,,
cfar57gsu6hy770n7e58u8duc,2025-09-06,Eerste Divisie,Cambuur vs Willem II,0-1,2-2,v26_aggressive,HTFT,1/1,2.13,True,14.4,LOST,True,-1.0,actual=2/X,v26.aggressive_pick,1/1,,,,,,,,,,,,
99r0d7ggi1169dhklo4eroopg,2025-09-06,2. Lig,Bromley vs Gillingham,2-0,2-2,v25_aggressive,HTFT,2/1,30.5,True,25.6,LOST,True,-1.0,actual=1/X,v25.aggressive_pick,2/1,,,,,,,,,,,,
99r0d7ggi1169dhklo4eroopg,2025-09-06,2. Lig,Bromley vs Gillingham,2-0,2-2,v26_surprise,HTFT,1/2,34.5,False,4.9,LOST,False,0.0,actual=1/X,v26.surprise_pick,1/2,0.0679,1.02,1.92,73.5,0.642,,0.04,0.0,0.0,62.7,"favorite_gap_large,favorite_price_supported,reversal_prob_warm,quality_supports_reversal,favorite_odds_band_reversal_window,favorite_streak_break_window",
99r0d7ggi1169dhklo4eroopg,2025-09-06,2. Lig,Bromley vs Gillingham,2-0,2-2,v26_aggressive,HTFT,1/1,3.16,True,16.1,LOST,True,-1.0,actual=1/X,v26.aggressive_pick,1/1,,,,,,,,,,,,
790qnaweqoyffb5ndxnb4hlas,2025-09-06,Ulusal Lig,Aldershot vs Brackley,1-0,2-2,v25_aggressive,HTFT,X/2,6.07,True,9.0,LOST,True,-1.0,actual=1/X,v25.aggressive_pick,X/2,,,,,,,,,,,,
790qnaweqoyffb5ndxnb4hlas,2025-09-06,Ulusal Lig,Aldershot vs Brackley,1-0,2-2,v26_aggressive,HTFT,2/2,3.8,True,37.1,LOST,True,-1.0,actual=1/X,v26.aggressive_pick,2/2,,,,,,,,,,,,
8808y1x2hmz52k3mr598orqc4,2025-09-06,DK Elemeler,Ermenistan vs Portekiz,0-3,0-5,v25_aggressive,HTFT,X/2,4.03,True,11.6,LOST,True,-1.0,actual=2/2,v25.aggressive_pick,X/2,,,,,,,,,,,,
7gfnwoxqz5o2clin40a85uqdw,2025-09-06,1. Lig,Port Vale vs Leyton Orient,1-2,2-3,v25_aggressive,HTFT,X/1,4.84,True,12.3,LOST,True,-1.0,actual=2/2,v25.aggressive_pick,X/1,,,,,,,,,,,,
7gfnwoxqz5o2clin40a85uqdw,2025-09-06,1. Lig,Port Vale vs Leyton Orient,1-2,2-3,v26_surprise,HTFT,1/2,33.0,False,5.4,LOST,False,0.0,actual=2/2,v26.surprise_pick,1/2,0.0749,0.62,1.97,52.1,0.642,,0.044,0.0,0.0,57.0,"favorite_gap_large,favorite_price_supported,reversal_prob_warm,draw_swing_support,quality_supports_reversal,favorite_odds_band_reversal_window",
7gfnwoxqz5o2clin40a85uqdw,2025-09-06,1. Lig,Port Vale vs Leyton Orient,1-2,2-3,v26_aggressive,HTFT,2/2,4.51,True,17.3,WON,True,3.51,actual=2/2,v26.aggressive_pick,2/2,,,,,,,,,,,,
7fld91ykj1kfuoc8wn4r2frbo,2025-09-06,1. Lig,Lincoln City vs Wigan Ath,2-1,2-2,v25_aggressive,HTFT,X/1,4.7,True,19.2,LOST,True,-1.0,actual=1/X,v25.aggressive_pick,X/1,,,,,,,,,,,,
7fld91ykj1kfuoc8wn4r2frbo,2025-09-06,1. Lig,Lincoln City vs Wigan Ath,2-1,2-2,v26_surprise,HTFT,1/2,34.5,False,7.1,LOST,False,0.0,actual=1/X,v26.surprise_pick,1/2,0.0984,0.71,2.0,61.5,0.642,,0.044,0.0,0.0,61.7,"favorite_gap_large,favorite_price_supported,reversal_prob_hot,upset_risk_detected,quality_supports_reversal,favorite_odds_band_reversal_window",
7fld91ykj1kfuoc8wn4r2frbo,2025-09-06,1. Lig,Lincoln City vs Wigan Ath,2-1,2-2,v26_aggressive,HTFT,1/1,3.36,True,19.5,LOST,True,-1.0,actual=1/X,v26.aggressive_pick,1/1,,,,,,,,,,,,
7i4nhkex1qssyp3x6rsgj03ro,2025-09-06,1. Lig,Wycombe vs Mansfield,1-0,2-0,v25_aggressive,HTFT,X/2,6.92,True,11.1,LOST,True,-1.0,actual=1/1,v25.aggressive_pick,X/2,,,,,,,,,,,,
7i4nhkex1qssyp3x6rsgj03ro,2025-09-06,1. Lig,Wycombe vs Mansfield,1-0,2-0,v26_main_htft,HTFT,2/2,5.22,False,39.9,LOST,False,0.0,actual=1/1,v26.main_pick,,,,,,,,,,,0.0,,
7hagai8xazmsj5exw7idwhhck,2025-09-06,1. Lig,Rotherham vs Exeter City,1-0,1-0,v25_aggressive,HTFT,X/2,6.28,True,6.7,LOST,True,-1.0,actual=1/1,v25.aggressive_pick,X/2,,,,,,,,,,,,
7hagai8xazmsj5exw7idwhhck,2025-09-06,1. Lig,Rotherham vs Exeter City,1-0,1-0,v26_aggressive,HTFT,2/2,4.47,True,29.6,LOST,True,-1.0,actual=1/1,v26.aggressive_pick,2/2,,,,,,,,,,,,
6e37x17qvk0snpokd1698lkb8,2025-09-06,Premiership,Crusaders vs Coleraine,0-3,0-4,v25_aggressive,HTFT,X/2,4.24,True,11.5,LOST,True,-1.0,actual=2/2,v25.aggressive_pick,X/2,,,,,,,,,,,,
6e37x17qvk0snpokd1698lkb8,2025-09-06,Premiership,Crusaders vs Coleraine,0-3,0-4,v26_aggressive,HTFT,2/2,2.32,True,37.7,WON,True,1.32,actual=2/2,v26.aggressive_pick,2/2,,,,,,,,,,,,
6f0fqlafaei9oj8yd9hdi6rdg,2025-09-06,Premiership,Linfield vs Portadown,0-0,3-0,v25_aggressive,HTFT,2/1,23.2,True,18.6,LOST,True,-1.0,actual=X/1,v25.aggressive_pick,2/1,,,,,,,,,,,,
6f0fqlafaei9oj8yd9hdi6rdg,2025-09-06,Premiership,Linfield vs Portadown,0-0,3-0,v26_aggressive,HTFT,1/1,1.74,True,20.4,LOST,True,-1.0,actual=X/1,v26.aggressive_pick,1/1,,,,,,,,,,,,
6dny1bmxcj6shc5382dno2al0,2025-09-06,Premiership,Carrick vs Cliftonville,0-1,1-2,v25_aggressive,HTFT,2/1,30.5,True,14.4,LOST,True,-1.0,actual=2/2,v25.aggressive_pick,2/1,,,,,,,,,,,,
6dny1bmxcj6shc5382dno2al0,2025-09-06,Premiership,Carrick vs Cliftonville,0-1,1-2,v26_surprise,HTFT,2/1,30.5,False,10.1,LOST,False,0.0,actual=2/2,v26.surprise_pick,2/1,0.1401,0.59,1.97,62.8,0.642,,0.0667,0.0,0.0,66.2,"favorite_price_supported,reversal_prob_hot,quality_supports_reversal,favorite_odds_band_reversal_window,league_strict_reversal_prior,draw_pressure_supports_swing",
6dny1bmxcj6shc5382dno2al0,2025-09-06,Premiership,Carrick vs Cliftonville,0-1,1-2,v26_aggressive,HTFT,1/1,4.36,True,14.6,LOST,True,-1.0,actual=2/2,v26.aggressive_pick,1/1,,,,,,,,,,,,
9d9nwo82prx06riy5odgnr190,2025-09-06,2. Lig,Walsall vs Chesterfield,1-0,1-0,v25_aggressive,HTFT,X/2,5.38,True,10.3,LOST,True,-1.0,actual=1/1,v25.aggressive_pick,X/2,,,,,,,,,,,,
9d9nwo82prx06riy5odgnr190,2025-09-06,2. Lig,Walsall vs Chesterfield,1-0,1-0,v26_aggressive,HTFT,2/2,3.78,True,35.1,LOST,True,-1.0,actual=1/1,v26.aggressive_pick,2/2,,,,,,,,,,,,
7dwtx5tr7g66bsqkoc1wos9as,2025-09-06,1. Lig,Bolton vs Wimbledon,1-0,3-0,v25_aggressive,HTFT,X/1,3.76,True,12.1,LOST,True,-1.0,actual=1/1,v25.aggressive_pick,X/1,,,,,,,,,,,,
7dwtx5tr7g66bsqkoc1wos9as,2025-09-06,1. Lig,Bolton vs Wimbledon,1-0,3-0,v26_aggressive,HTFT,1/1,1.81,True,33.3,WON,True,0.81,actual=1/1,v26.aggressive_pick,1/1,,,,,,,,,,,,
7c85gguekhbhadtm7qdbgir6c,2025-09-06,Ulusal Lig,Tamworth vs Eastleigh,0-0,1-0,v25_aggressive,HTFT,1/2,34.5,True,14.1,LOST,True,-1.0,actual=X/1,v25.aggressive_pick,1/2,,,,,,,,,,,,
7c85gguekhbhadtm7qdbgir6c,2025-09-06,Ulusal Lig,Tamworth vs Eastleigh,0-0,1-0,v26_aggressive,HTFT,2/2,5.53,True,14.8,LOST,True,-1.0,actual=X/1,v26.aggressive_pick,2/2,,,,,,,,,,,,
1 match_id date league match ht_score final_score strategy market pick odds playable confidence result counted_in_roi profit_flat resolution_note source reversal_pick reversal_prob favorite_gap favorite_odd support_score odds_band_score odds_band_label league_reversal_rate league_strict_rev_rate referee_strict_rev_rate surprise_score reason_codes pick_reason
2 b6uzz042mizu0dqpci538z4lw 2025-09-06 Süper Kupa V. Sarsfield vs C. Cordoba 0-0 2-0 v25_aggressive HTFT 2/1 34.5 True 21.0 LOST True -1.0 actual=X/1 v25.aggressive_pick 2/1
3 b6uzz042mizu0dqpci538z4lw 2025-09-06 Süper Kupa V. Sarsfield vs C. Cordoba 0-0 2-0 v26_aggressive HTFT 1/1 3.26 True 16.0 LOST True -1.0 actual=X/1 v26.aggressive_pick 1/1
4 ytsc38rm4j22govgwo3as6j8 2025-09-06 DK Elemeler Avusturya vs G. Kıbrıs Rum Kesimi 0-0 1-0 v25_aggressive HTFT X/1 4.01 True 11.4 WON True 3.01 actual=X/1 v25.aggressive_pick X/1
5 cfar57gsu6hy770n7e58u8duc 2025-09-06 Eerste Divisie Cambuur vs Willem II 0-1 2-2 v25_aggressive HTFT 2/1 20.7 True 15.6 LOST True -1.0 actual=2/X v25.aggressive_pick 2/1
6 cfar57gsu6hy770n7e58u8duc 2025-09-06 Eerste Divisie Cambuur vs Willem II 0-1 2-2 v26_aggressive HTFT 1/1 2.13 True 14.4 LOST True -1.0 actual=2/X v26.aggressive_pick 1/1
7 99r0d7ggi1169dhklo4eroopg 2025-09-06 2. Lig Bromley vs Gillingham 2-0 2-2 v25_aggressive HTFT 2/1 30.5 True 25.6 LOST True -1.0 actual=1/X v25.aggressive_pick 2/1
8 99r0d7ggi1169dhklo4eroopg 2025-09-06 2. Lig Bromley vs Gillingham 2-0 2-2 v26_surprise HTFT 1/2 34.5 False 4.9 LOST False 0.0 actual=1/X v26.surprise_pick 1/2 0.0679 1.02 1.92 73.5 0.642 0.04 0.0 0.0 62.7 favorite_gap_large,favorite_price_supported,reversal_prob_warm,quality_supports_reversal,favorite_odds_band_reversal_window,favorite_streak_break_window
9 99r0d7ggi1169dhklo4eroopg 2025-09-06 2. Lig Bromley vs Gillingham 2-0 2-2 v26_aggressive HTFT 1/1 3.16 True 16.1 LOST True -1.0 actual=1/X v26.aggressive_pick 1/1
10 790qnaweqoyffb5ndxnb4hlas 2025-09-06 Ulusal Lig Aldershot vs Brackley 1-0 2-2 v25_aggressive HTFT X/2 6.07 True 9.0 LOST True -1.0 actual=1/X v25.aggressive_pick X/2
11 790qnaweqoyffb5ndxnb4hlas 2025-09-06 Ulusal Lig Aldershot vs Brackley 1-0 2-2 v26_aggressive HTFT 2/2 3.8 True 37.1 LOST True -1.0 actual=1/X v26.aggressive_pick 2/2
12 8808y1x2hmz52k3mr598orqc4 2025-09-06 DK Elemeler Ermenistan vs Portekiz 0-3 0-5 v25_aggressive HTFT X/2 4.03 True 11.6 LOST True -1.0 actual=2/2 v25.aggressive_pick X/2
13 7gfnwoxqz5o2clin40a85uqdw 2025-09-06 1. Lig Port Vale vs Leyton Orient 1-2 2-3 v25_aggressive HTFT X/1 4.84 True 12.3 LOST True -1.0 actual=2/2 v25.aggressive_pick X/1
14 7gfnwoxqz5o2clin40a85uqdw 2025-09-06 1. Lig Port Vale vs Leyton Orient 1-2 2-3 v26_surprise HTFT 1/2 33.0 False 5.4 LOST False 0.0 actual=2/2 v26.surprise_pick 1/2 0.0749 0.62 1.97 52.1 0.642 0.044 0.0 0.0 57.0 favorite_gap_large,favorite_price_supported,reversal_prob_warm,draw_swing_support,quality_supports_reversal,favorite_odds_band_reversal_window
15 7gfnwoxqz5o2clin40a85uqdw 2025-09-06 1. Lig Port Vale vs Leyton Orient 1-2 2-3 v26_aggressive HTFT 2/2 4.51 True 17.3 WON True 3.51 actual=2/2 v26.aggressive_pick 2/2
16 7fld91ykj1kfuoc8wn4r2frbo 2025-09-06 1. Lig Lincoln City vs Wigan Ath 2-1 2-2 v25_aggressive HTFT X/1 4.7 True 19.2 LOST True -1.0 actual=1/X v25.aggressive_pick X/1
17 7fld91ykj1kfuoc8wn4r2frbo 2025-09-06 1. Lig Lincoln City vs Wigan Ath 2-1 2-2 v26_surprise HTFT 1/2 34.5 False 7.1 LOST False 0.0 actual=1/X v26.surprise_pick 1/2 0.0984 0.71 2.0 61.5 0.642 0.044 0.0 0.0 61.7 favorite_gap_large,favorite_price_supported,reversal_prob_hot,upset_risk_detected,quality_supports_reversal,favorite_odds_band_reversal_window
18 7fld91ykj1kfuoc8wn4r2frbo 2025-09-06 1. Lig Lincoln City vs Wigan Ath 2-1 2-2 v26_aggressive HTFT 1/1 3.36 True 19.5 LOST True -1.0 actual=1/X v26.aggressive_pick 1/1
19 7i4nhkex1qssyp3x6rsgj03ro 2025-09-06 1. Lig Wycombe vs Mansfield 1-0 2-0 v25_aggressive HTFT X/2 6.92 True 11.1 LOST True -1.0 actual=1/1 v25.aggressive_pick X/2
20 7i4nhkex1qssyp3x6rsgj03ro 2025-09-06 1. Lig Wycombe vs Mansfield 1-0 2-0 v26_main_htft HTFT 2/2 5.22 False 39.9 LOST False 0.0 actual=1/1 v26.main_pick 0.0
21 7hagai8xazmsj5exw7idwhhck 2025-09-06 1. Lig Rotherham vs Exeter City 1-0 1-0 v25_aggressive HTFT X/2 6.28 True 6.7 LOST True -1.0 actual=1/1 v25.aggressive_pick X/2
22 7hagai8xazmsj5exw7idwhhck 2025-09-06 1. Lig Rotherham vs Exeter City 1-0 1-0 v26_aggressive HTFT 2/2 4.47 True 29.6 LOST True -1.0 actual=1/1 v26.aggressive_pick 2/2
23 6e37x17qvk0snpokd1698lkb8 2025-09-06 Premiership Crusaders vs Coleraine 0-3 0-4 v25_aggressive HTFT X/2 4.24 True 11.5 LOST True -1.0 actual=2/2 v25.aggressive_pick X/2
24 6e37x17qvk0snpokd1698lkb8 2025-09-06 Premiership Crusaders vs Coleraine 0-3 0-4 v26_aggressive HTFT 2/2 2.32 True 37.7 WON True 1.32 actual=2/2 v26.aggressive_pick 2/2
25 6f0fqlafaei9oj8yd9hdi6rdg 2025-09-06 Premiership Linfield vs Portadown 0-0 3-0 v25_aggressive HTFT 2/1 23.2 True 18.6 LOST True -1.0 actual=X/1 v25.aggressive_pick 2/1
26 6f0fqlafaei9oj8yd9hdi6rdg 2025-09-06 Premiership Linfield vs Portadown 0-0 3-0 v26_aggressive HTFT 1/1 1.74 True 20.4 LOST True -1.0 actual=X/1 v26.aggressive_pick 1/1
27 6dny1bmxcj6shc5382dno2al0 2025-09-06 Premiership Carrick vs Cliftonville 0-1 1-2 v25_aggressive HTFT 2/1 30.5 True 14.4 LOST True -1.0 actual=2/2 v25.aggressive_pick 2/1
28 6dny1bmxcj6shc5382dno2al0 2025-09-06 Premiership Carrick vs Cliftonville 0-1 1-2 v26_surprise HTFT 2/1 30.5 False 10.1 LOST False 0.0 actual=2/2 v26.surprise_pick 2/1 0.1401 0.59 1.97 62.8 0.642 0.0667 0.0 0.0 66.2 favorite_price_supported,reversal_prob_hot,quality_supports_reversal,favorite_odds_band_reversal_window,league_strict_reversal_prior,draw_pressure_supports_swing
29 6dny1bmxcj6shc5382dno2al0 2025-09-06 Premiership Carrick vs Cliftonville 0-1 1-2 v26_aggressive HTFT 1/1 4.36 True 14.6 LOST True -1.0 actual=2/2 v26.aggressive_pick 1/1
30 9d9nwo82prx06riy5odgnr190 2025-09-06 2. Lig Walsall vs Chesterfield 1-0 1-0 v25_aggressive HTFT X/2 5.38 True 10.3 LOST True -1.0 actual=1/1 v25.aggressive_pick X/2
31 9d9nwo82prx06riy5odgnr190 2025-09-06 2. Lig Walsall vs Chesterfield 1-0 1-0 v26_aggressive HTFT 2/2 3.78 True 35.1 LOST True -1.0 actual=1/1 v26.aggressive_pick 2/2
32 7dwtx5tr7g66bsqkoc1wos9as 2025-09-06 1. Lig Bolton vs Wimbledon 1-0 3-0 v25_aggressive HTFT X/1 3.76 True 12.1 LOST True -1.0 actual=1/1 v25.aggressive_pick X/1
33 7dwtx5tr7g66bsqkoc1wos9as 2025-09-06 1. Lig Bolton vs Wimbledon 1-0 3-0 v26_aggressive HTFT 1/1 1.81 True 33.3 WON True 0.81 actual=1/1 v26.aggressive_pick 1/1
34 7c85gguekhbhadtm7qdbgir6c 2025-09-06 Ulusal Lig Tamworth vs Eastleigh 0-0 1-0 v25_aggressive HTFT 1/2 34.5 True 14.1 LOST True -1.0 actual=X/1 v25.aggressive_pick 1/2
35 7c85gguekhbhadtm7qdbgir6c 2025-09-06 Ulusal Lig Tamworth vs Eastleigh 0-0 1-0 v26_aggressive HTFT 2/2 5.53 True 14.8 LOST True -1.0 actual=X/1 v26.aggressive_pick 2/2
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# HT/FT + Upset Backtest
- Sample: last 120 finished football matches
- Scope: only HT/FT reversal and upset-oriented picks
- ROI: flat `1 unit` per played pick
- Generated at: 2026-04-21T12:32:14.419378+00:00
## Strategy Summary
| Strategy | Candidates | Played | Won | Lost | Hit Rate | Profit | ROI |
|---|---:|---:|---:|---:|---:|---:|---:|
| v25_aggressive | 16 | 16 | 1 | 15 | 6.25% | -11.99 | -74.94% |
| v26_surprise | 4 | 0 | 0 | 0 | 0.0% | +0.00 | +0.00% |
| v26_aggressive | 13 | 13 | 3 | 10 | 23.08% | -4.36 | -33.54% |
| v26_main_htft | 1 | 0 | 0 | 0 | 0.0% | +0.00 | +0.00% |
## v26 Surprise By Reversal Type
| Reversal | Candidates | Played | Won | Lost | Profit | ROI |
|---|---:|---:|---:|---:|---:|---:|
| 1/2 | 3 | 0 | 0 | 0 | +0.00 | +0.00% |
| 2/1 | 1 | 0 | 0 | 0 | +0.00 | +0.00% |
| X/1 | 0 | 0 | 0 | 0 | +0.00 | +0.00% |
| X/2 | 0 | 0 | 0 | 0 | +0.00 | +0.00% |
## Match Detail
| Date | Match | HT | FT | v25 aggressive | v26 surprise | v26 aggressive | v26 main HTFT |
|---|---|---|---|---|---|---|---|
| 2025-09-06 | Forge FC vs Hfx Wan | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Estoril vs Santa Clara | 0-0 | 0-1 | - | - | - | - |
| 2025-09-06 | Tenerife vs Merida AD | 1-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Ibiza vs Hercules | 2-1 | 2-1 | - | - | - | - |
| 2025-09-06 | V. Sarsfield vs C. Cordoba | 0-0 | 2-0 | 2/1 (LOST, played, -1.00) | - | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Botafogo vs Paranaense | 0-1 | 1-3 | - | - | - | - |
| 2025-09-06 | San Felipe vs Curico Unido | 2-0 | 4-2 | - | - | - | - |
| 2025-09-06 | Nacional Potosi vs Independiente | 0-1 | 0-3 | - | - | - | - |
| 2025-09-06 | Avusturya vs G. Kıbrıs Rum Kesimi | 0-0 | 1-0 | X/1 (WON, played, +3.01) | - | - | - |
| 2025-09-06 | CD Estepona FS vs Linares | 1-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Pergolettese vs Cittadella | 2-1 | 3-1 | - | - | - | - |
| 2025-09-06 | Union Brescia vs Pro Vercelli | 2-0 | 5-0 | - | - | - | - |
| 2025-09-06 | Casertana vs Potenza | 1-1 | 3-2 | - | - | - | - |
| 2025-09-06 | Montevideo vs Danubio | 0-2 | 0-2 | - | - | - | - |
| 2025-09-06 | Hoogstraten vs W. Beveren | 0-0 | 1-3 | - | - | - | - |
| 2025-09-06 | Cambuur vs Willem II | 0-1 | 2-2 | 2/1 (LOST, played, -1.00) | - | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Tlaxcala vs A. Oaxaca | 1-0 | 2-1 | - | - | - | - |
| 2025-09-06 | JS Kabylie vs Olympique Akbou | 0-0 | 0-0 | - | - | - | - |
| 2025-09-06 | CD A. Baleares vs Castellon II | 3-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Karlovac 1919 vs Dugopolje | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | San Sebastian R. vs RSD Alcala | 1-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Hindistan U23 vs Katar U23 | 0-1 | 1-2 | - | - | - | - |
| 2025-09-06 | Estradense vs Boiro | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Yeclano II vs El Palmar | 1-0 | 2-1 | - | - | - | - |
| 2025-09-06 | Montlouis vs La Roche | 0-3 | 1-4 | - | - | - | - |
| 2025-09-06 | L'Hospitalet vs San Cristobal | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Marchamalo vs Guadalajara II | 1-1 | 1-1 | - | - | - | - |
| 2025-09-06 | Real Zaragoza vs R. Valladolid | 0-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Bromley vs Gillingham | 2-0 | 2-2 | 2/1 (LOST, played, -1.00) | 1/2 (LOST, not played, +0.00) | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Ajman Club vs Al Wahda | 2-1 | 2-4 | - | - | - | - |
| 2025-09-06 | Aldershot vs Brackley | 1-0 | 2-2 | X/2 (LOST, played, -1.00) | - | 2/2 (LOST, played, -1.00) | - |
| 2025-09-06 | Barbastro vs UE Olot | 1-0 | 1-1 | - | - | - | - |
| 2025-09-06 | CA Atlanta vs Guemes | 1-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Pulpileno vs UCAM Murcia II | 0-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Mojados vs Santa Marta | 0-2 | 1-3 | - | - | - | - |
| 2025-09-06 | Ermenistan vs Portekiz | 0-3 | 0-5 | X/2 (LOST, played, -1.00) | - | - | - |
| 2025-09-06 | USM Khenchela vs CR Belouizdad | 1-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Nafta vs Gorica | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | CS Cerrito vs Atenas | 0-0 | 0-3 | - | - | - | - |
| 2025-09-06 | VfB Oldenburg vs Hannoverscher | 4-1 | 6-1 | - | - | - | - |
| 2025-09-06 | Lealtad vs Numancia | 1-1 | 1-1 | - | - | - | - |
| 2025-09-06 | Astorga vs Real Avila | 0-0 | 1-3 | - | - | - | - |
| 2025-09-06 | Grindavik vs IR Reykjavik | 2-1 | 3-1 | - | - | - | - |
| 2025-09-06 | Andratx vs Sant Andreu | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Arenas Getxo vs Cacereno | 2-1 | 2-2 | - | - | - | - |
| 2025-09-06 | Aegir vs Dalvik / Reynir | 1-1 | 2-1 | - | - | - | - |
| 2025-09-06 | Laval II vs Cesson | 0-0 | 0-1 | - | - | - | - |
| 2025-09-06 | Palencia CF vs Arandina | 3-0 | 4-0 | - | - | - | - |
| 2025-09-06 | Leioa vs Pasaia | 0-0 | 0-1 | - | - | - | - |
| 2025-09-06 | Vic vs UE Cornella | 0-0 | 0-1 | - | - | - | - |
| 2025-09-06 | Voltigeurs vs Granville | 1-2 | 3-2 | - | - | - | - |
| 2025-09-06 | Bobigny vs Creteil | 0-1 | 1-1 | - | - | - | - |
| 2025-09-06 | Beauvais vs Furiani Agliani | 0-0 | 2-2 | - | - | - | - |
| 2025-09-06 | Oissel vs Caen II | 0-1 | 0-2 | - | - | - | - |
| 2025-09-06 | Cartagena LU II vs Un. Molinense | 0-2 | 1-2 | - | - | - | - |
| 2025-09-06 | Hercules II vs Atzeneta | 0-1 | 0-3 | - | - | - | - |
| 2025-09-06 | Sassari vs Pianese | 0-0 | 0-2 | - | - | - | - |
| 2025-09-06 | Giugliano vs Foggia | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Sorrento vs Trapani 1905 | 0-0 | 1-2 | - | - | - | - |
| 2025-09-06 | Ascoli vs Juventus U23 | 0-0 | 0-0 | - | - | - | - |
| 2025-09-06 | Arezzo vs Vis Pesaro | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Ath. Carpi vs Campobasso | 0-0 | 2-2 | - | - | - | - |
| 2025-09-06 | Oviedo II vs Sarriana | 2-0 | 3-2 | - | - | - | - |
| 2025-09-06 | Llosetense vs Platges | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Le Havre (K) vs Strasbourg (K) | 2-2 | 2-2 | - | - | - | - |
| 2025-09-06 | Montpellier (K) vs Fleury 91 (K) | 0-1 | 1-2 | - | - | - | - |
| 2025-09-06 | Bistrica vs NK Krsko | 3-0 | 6-0 | - | - | - | - |
| 2025-09-06 | Bilje vs Dravinja | 1-0 | 2-0 | - | - | - | - |
| 2025-09-06 | Nantes (K) vs Saint-Etienne (K) | 2-1 | 2-1 | - | - | - | - |
| 2025-09-06 | Jadran Dekani vs NK Ilirija | 3-1 | 3-1 | - | - | - | - |
| 2025-09-06 | Tabor Sezana vs Jesenice | 0-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Alaves II vs Logrones | 0-0 | 2-0 | - | - | - | - |
| 2025-09-06 | Unionistas II vs Tordesillas | 1-1 | 1-1 | - | - | - | - |
| 2025-09-06 | Leganes II vs Alcorcon II | 0-0 | 0-0 | - | - | - | - |
| 2025-09-06 | Volna Pinsk vs Bumprom | 0-1 | 0-1 | - | - | - | - |
| 2025-09-06 | L. Mikulas vs S. Bratislava II | 0-0 | 2-1 | - | - | - | - |
| 2025-09-06 | Dubrava vs Hrvace | 0-1 | 2-1 | - | - | - | - |
| 2025-09-06 | Plymouth vs Stockport | 2-1 | 4-2 | - | - | - | - |
| 2025-09-06 | Port Vale vs Leyton Orient | 1-2 | 2-3 | X/1 (LOST, played, -1.00) | 1/2 (LOST, not played, +0.00) | 2/2 (WON, played, +3.51) | - |
| 2025-09-06 | Huddersfield vs Peterborough | 0-0 | 3-2 | - | - | - | - |
| 2025-09-06 | Lincoln City vs Wigan Ath | 2-1 | 2-2 | X/1 (LOST, played, -1.00) | 1/2 (LOST, not played, +0.00) | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Wycombe vs Mansfield | 1-0 | 2-0 | X/2 (LOST, played, -1.00) | - | - | 2/2 (LOST, not played, +0.00) |
| 2025-09-06 | Rotherham vs Exeter City | 1-0 | 1-0 | X/2 (LOST, played, -1.00) | - | 2/2 (LOST, played, -1.00) | - |
| 2025-09-06 | Ballymena Utd vs Glentoran | 0-2 | 0-2 | - | - | - | - |
| 2025-09-06 | Crusaders vs Coleraine | 0-3 | 0-4 | X/2 (LOST, played, -1.00) | - | 2/2 (WON, played, +1.32) | - |
| 2025-09-06 | Glenavon vs Dungannon | 0-2 | 0-2 | - | - | - | - |
| 2025-09-06 | Linfield vs Portadown | 0-0 | 3-0 | 2/1 (LOST, played, -1.00) | - | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Colchester vs Crewe | 0-1 | 1-1 | - | - | - | - |
| 2025-09-06 | G. Morton vs Raith Rovers | 0-0 | 0-1 | - | - | - | - |
| 2025-09-06 | Puchov vs Pohronie | 0-1 | 3-1 | - | - | - | - |
| 2025-09-06 | Carrick vs Cliftonville | 0-1 | 1-2 | 2/1 (LOST, played, -1.00) | 2/1 (LOST, not played, +0.00) | 1/1 (LOST, played, -1.00) | - |
| 2025-09-06 | Harrogate vs Crawley Town | 0-1 | 0-1 | - | - | - | - |
| 2025-09-06 | Walsall vs Chesterfield | 1-0 | 1-0 | X/2 (LOST, played, -1.00) | - | 2/2 (LOST, played, -1.00) | - |
| 2025-09-06 | Banik Lehota vs Samorin | 1-0 | 2-1 | - | - | - | - |
| 2025-09-06 | Bolton vs Wimbledon | 1-0 | 3-0 | X/1 (LOST, played, -1.00) | - | 1/1 (WON, played, +0.81) | - |
| 2025-09-06 | Edinburgh vs Hearts II | 4-1 | 4-2 | - | - | - | - |
| 2025-09-06 | Kelty Hearts vs Stranraer | 0-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Forfar vs Dundee II | 2-1 | 4-1 | - | - | - | - |
| 2025-09-06 | Spartans vs Hibernian II | 2-0 | 5-1 | - | - | - | - |
| 2025-09-06 | East Kilbride vs Hamilton | 0-2 | 2-4 | - | - | - | - |
| 2025-09-06 | Annan Ath vs St. Mirren II | 0-1 | 2-2 | - | - | - | - |
| 2025-09-06 | East Fife vs Dumbarton | 1-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Clyde vs Motherwell II | 3-0 | 4-0 | - | - | - | - |
| 2025-09-06 | Peterhead vs Dundee United II | 2-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Montrose vs Aberdeen II | 2-1 | 3-1 | - | - | - | - |
| 2025-09-06 | Elgin City vs Cove Rangers | 1-0 | 1-0 | - | - | - | - |
| 2025-09-06 | Queen Of S. vs Rangers II | 0-0 | 2-1 | - | - | - | - |
| 2025-09-06 | Boston Utd vs Solihull | 1-1 | 1-2 | - | - | - | - |
| 2025-09-06 | Carlisle vs Truro | 2-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Southend vs Halifax | 1-0 | 3-0 | - | - | - | - |
| 2025-09-06 | Woking vs Gateshead | 2-0 | 5-0 | - | - | - | - |
| 2025-09-06 | Tamworth vs Eastleigh | 0-0 | 1-0 | 1/2 (LOST, played, -1.00) | - | 2/2 (LOST, played, -1.00) | - |
| 2025-09-06 | Alcorcon vs Teruel | 1-1 | 2-1 | - | - | - | - |
| 2025-09-06 | Merthyr vs Worksop | 0-0 | 2-0 | - | - | - | - |
| 2025-09-06 | Chesham Utd vs Bath | 0-0 | 0-0 | - | - | - | - |
| 2025-09-06 | Southport vs South Shields | 0-0 | 0-0 | - | - | - | - |
| 2025-09-06 | Kidderminster vs Macclesfield | 0-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Throttur Vogar vs Höttur / Huginn | 0-0 | 2-1 | - | - | - | - |
| 2025-09-06 | Alfreton vs Kings Lynn | 0-0 | 1-1 | - | - | - | - |
| 2025-09-06 | Buxton vs Oxford City | 1-0 | 2-1 | - | - | - | - |
ai-engine/reports/backtest_v26_shadow_roi_detail.json
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ai-engine/reports/backtest_v26_shadow_roi_picks.csv
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ai-engine/reports/backtest_v26_shadow_roi_report.md
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ai-engine/reports/training_v26_shadow.json
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{
"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
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@@ -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_niche.py
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@@ -0,0 +1,215 @@
"""
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/backtest_v26_shadow.py
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@@ -0,0 +1,94 @@
from __future__ import annotations
import json
import sys
from pathlib import Path
import psycopg2
from psycopg2.extras import RealDictCursor
AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_DIR) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_DIR))
from services.single_match_orchestrator import SingleMatchOrchestrator
def _resolve_dsn() -> str:
env_path = AI_ENGINE_DIR / ".env"
if env_path.exists():
for line in env_path.read_text(encoding="utf-8").splitlines():
if line.startswith("DATABASE_URL="):
return line.split("=", 1)[1].strip().split("?schema=")[0]
raise SystemExit("DATABASE_URL not found in ai-engine/.env")
def _fetch_matches(dsn: str, limit: int = 60) -> list[str]:
query = """
SELECT m.id
FROM matches m
WHERE m.status = 'FT'
AND m.sport = 'football'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT %s
"""
with psycopg2.connect(dsn) as conn:
with conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute(query, (limit,))
return [str(row["id"]) for row in cur.fetchall()]
def _score_prediction(package: dict) -> dict[str, float]:
rows = package.get("bet_summary", []) or []
playable = [row for row in rows if row.get("playable")]
return {
"playable_count": float(len(playable)),
"avg_edge": round(
sum(float(row.get("ev_edge", 0.0)) for row in playable) / len(playable),
4,
)
if playable
else 0.0,
"avg_confidence": round(
sum(float(row.get("calibrated_confidence", 0.0)) for row in playable)
/ len(playable),
2,
)
if playable
else 0.0,
}
def main() -> None:
dsn = _resolve_dsn()
match_ids = _fetch_matches(dsn)
orchestrator = SingleMatchOrchestrator()
results: list[dict[str, object]] = []
for match_id in match_ids:
orchestrator.engine_mode = "v25"
v25 = orchestrator.analyze_match(match_id)
orchestrator.engine_mode = "v26"
v26 = orchestrator.analyze_match(match_id)
if not v25 or not v26:
continue
results.append(
{
"match_id": match_id,
"v25": _score_prediction(v25),
"v26": _score_prediction(v26),
"v25_main": (v25.get("main_pick") or {}).get("pick"),
"v26_main": (v26.get("main_pick") or {}).get("pick"),
}
)
out_path = AI_ENGINE_DIR / "reports" / "backtest_v26_shadow.json"
out_path.write_text(json.dumps(results, indent=2), encoding="utf-8")
print(f"[OK] Shadow backtest summary written to {out_path}")
if __name__ == "__main__":
main()
ai-engine/scripts/backtest_v26_shadow_htft_upset.py
+505
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from __future__ import annotations
import argparse
import csv
import json
import sys
from collections import defaultdict
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Optional
import psycopg2
from psycopg2.extras import RealDictCursor
AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_DIR) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_DIR))
from services.single_match_orchestrator import SingleMatchOrchestrator
STRATEGIES = ("v25_aggressive", "v26_surprise", "v26_aggressive", "v26_main_htft")
REVERSAL_LABELS = ("1/2", "2/1", "X/1", "X/2")
@dataclass
class MatchContext:
match_id: str
match_date_ms: int
league: str
home_team: str
away_team: str
final_home: int
final_away: int
ht_home: Optional[int]
ht_away: Optional[int]
@property
def match_name(self) -> str:
return f"{self.home_team} vs {self.away_team}"
@property
def final_score(self) -> str:
return f"{self.final_home}-{self.final_away}"
@property
def ht_score(self) -> str:
if self.ht_home is None or self.ht_away is None:
return "-"
return f"{self.ht_home}-{self.ht_away}"
def _resolve_dsn() -> str:
env_path = AI_ENGINE_DIR / ".env"
if env_path.exists():
for line in env_path.read_text(encoding="utf-8").splitlines():
if line.startswith("DATABASE_URL="):
return line.split("=", 1)[1].strip().split("?schema=")[0]
raise SystemExit("DATABASE_URL not found in ai-engine/.env")
def _fetch_matches(dsn: str, limit: int) -> list[MatchContext]:
query = """
SELECT
m.id,
m.mst_utc,
COALESCE(l.name, 'Unknown League') AS league,
COALESCE(ht.name, 'Home') AS home_team,
COALESCE(at.name, 'Away') AS away_team,
COALESCE(m.score_home, 0) AS score_home,
COALESCE(m.score_away, 0) AS score_away,
m.ht_score_home,
m.ht_score_away
FROM matches m
LEFT JOIN leagues l ON l.id = m.league_id
LEFT JOIN teams ht ON ht.id = m.home_team_id
LEFT JOIN teams at ON at.id = m.away_team_id
WHERE m.status = 'FT'
AND m.sport = 'football'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
AND m.ht_score_home IS NOT NULL
AND m.ht_score_away IS NOT NULL
ORDER BY m.mst_utc DESC
LIMIT %s
"""
with psycopg2.connect(dsn) as conn:
with conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute(query, (limit,))
rows = cur.fetchall()
return [
MatchContext(
match_id=str(row["id"]),
match_date_ms=int(row["mst_utc"] or 0),
league=str(row["league"] or "Unknown League"),
home_team=str(row["home_team"] or "Home"),
away_team=str(row["away_team"] or "Away"),
final_home=int(row["score_home"] or 0),
final_away=int(row["score_away"] or 0),
ht_home=int(row["ht_score_home"]) if row.get("ht_score_home") is not None else None,
ht_away=int(row["ht_score_away"]) if row.get("ht_score_away") is not None else None,
)
for row in rows
]
def _safe_float(value: Any) -> float:
try:
return float(value)
except (TypeError, ValueError):
return 0.0
def _outcome_symbol(home: int, away: int) -> str:
if home > away:
return "1"
if home < away:
return "2"
return "X"
def _resolve_htft(pick: str, context: MatchContext) -> Dict[str, Any]:
if not pick or "/" not in str(pick):
return {"result": "UNRESOLVED", "won": None, "note": "htft_pick_invalid"}
actual = f"{_outcome_symbol(context.ht_home or 0, context.ht_away or 0)}/{_outcome_symbol(context.final_home, context.final_away)}"
won = str(pick).strip().upper() == actual
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
def _market_odds(odds: Dict[str, Any], market: str, pick: str) -> float:
mapping = {
"HTFT": {
"1/1": "htft_11",
"1/X": "htft_1x",
"1/2": "htft_12",
"X/1": "htft_x1",
"X/X": "htft_xx",
"X/2": "htft_x2",
"2/1": "htft_21",
"2/X": "htft_2x",
"2/2": "htft_22",
},
"MS": {"1": "ms_h", "X": "ms_d", "2": "ms_a"},
}
key = mapping.get(market, {}).get(str(pick))
if not key:
return 0.0
value = _safe_float((odds or {}).get(key))
return value if value > 1.0 else 0.0
def _evaluate_pick(
*,
strategy: str,
market: str,
pick: str,
odds: Any,
playable: bool,
confidence: Any,
extra: Optional[Dict[str, Any]],
context: MatchContext,
) -> Dict[str, Any]:
odds_value = _safe_float(odds)
if market == "HT/FT":
market = "HTFT"
resolution = _resolve_htft(pick, context) if market == "HTFT" else {
"result": "UNRESOLVED",
"won": None,
"note": "non_htft_market",
}
counted = bool(playable and market == "HTFT" and odds_value > 1.01 and resolution["result"] in {"WON", "LOST"})
profit = 0.0
if counted:
profit = (odds_value - 1.0) if resolution["result"] == "WON" else -1.0
row = {
"strategy": strategy,
"market": market,
"pick": pick,
"odds": round(odds_value, 2),
"playable": playable,
"confidence": round(_safe_float(confidence), 1),
"result": resolution["result"],
"counted_in_roi": counted,
"profit_flat": round(profit, 4),
"resolution_note": resolution["note"],
}
if extra:
row.update(extra)
return row
def _extract_strategy_rows(
*,
context: MatchContext,
odds_data: Dict[str, Any],
v25: Dict[str, Any],
v26: Dict[str, Any],
) -> Dict[str, Optional[Dict[str, Any]]]:
strategies: Dict[str, Optional[Dict[str, Any]]] = {name: None for name in STRATEGIES}
v25_aggressive = v25.get("aggressive_pick") or {}
if v25_aggressive.get("pick"):
pick = str(v25_aggressive.get("pick"))
strategies["v25_aggressive"] = _evaluate_pick(
strategy="v25_aggressive",
market=str(v25_aggressive.get("market") or "HTFT"),
pick=pick,
odds=_market_odds(odds_data, "HTFT", pick),
playable=True,
confidence=v25_aggressive.get("confidence"),
extra={
"source": "v25.aggressive_pick",
"reversal_pick": pick,
},
context=context,
)
v26_surprise = v26.get("surprise_pick") or {}
v26_hunter = v26.get("surprise_hunter") or {}
if v26_surprise.get("pick"):
pick = str(v26_surprise.get("raw_pick") or v26_surprise.get("pick"))
strategies["v26_surprise"] = _evaluate_pick(
strategy="v26_surprise",
market=str(v26_surprise.get("market") or "HTFT"),
pick=pick,
odds=v26_surprise.get("odds") or _market_odds(odds_data, "HTFT", pick),
playable=bool(v26_surprise.get("playable")),
confidence=v26_surprise.get("calibrated_confidence", v26_surprise.get("confidence")),
extra={
"source": "v26.surprise_pick",
"surprise_score": round(_safe_float(v26_surprise.get("surprise_score")), 1),
"support_score": round(_safe_float(v26_surprise.get("support_score")), 1),
"reversal_pick": v26_hunter.get("reversal_pick"),
"reversal_prob": round(_safe_float(v26_hunter.get("reversal_prob")), 4),
"favorite_gap": round(_safe_float(v26_hunter.get("favorite_gap")), 3),
"favorite_odd": round(_safe_float(v26_hunter.get("favorite_odd")), 2),
"odds_band_score": round(_safe_float(v26_hunter.get("odds_band_score")), 3),
"odds_band_label": str(v26_hunter.get("odds_band_label") or ""),
"league_reversal_rate": round(_safe_float(v26_hunter.get("league_reversal_rate")), 4),
"league_strict_rev_rate": round(_safe_float(v26_hunter.get("league_strict_rev_rate")), 4),
"referee_strict_rev_rate": round(_safe_float(v26_hunter.get("referee_strict_rev_rate")), 4),
"reason_codes": ",".join(v26_hunter.get("reason_codes", [])),
},
context=context,
)
v26_aggressive = v26.get("aggressive_pick") or {}
if v26_aggressive.get("pick"):
pick = str(v26_aggressive.get("pick"))
strategies["v26_aggressive"] = _evaluate_pick(
strategy="v26_aggressive",
market=str(v26_aggressive.get("market") or "HTFT"),
pick=pick,
odds=v26_aggressive.get("odds") or _market_odds(odds_data, "HTFT", pick),
playable=True,
confidence=v26_aggressive.get("confidence"),
extra={
"source": "v26.aggressive_pick",
"reversal_pick": pick,
},
context=context,
)
v26_main = v26.get("main_pick") or {}
if str(v26_main.get("market") or "") == "HTFT" and v26_main.get("pick"):
pick = str(v26_main.get("raw_pick") or v26_main.get("pick"))
strategies["v26_main_htft"] = _evaluate_pick(
strategy="v26_main_htft",
market="HTFT",
pick=pick,
odds=v26_main.get("odds") or _market_odds(odds_data, "HTFT", pick),
playable=bool(v26_main.get("playable")),
confidence=v26_main.get("calibrated_confidence", v26_main.get("confidence")),
extra={
"source": "v26.main_pick",
"pick_reason": v26_main.get("pick_reason"),
"surprise_score": round(_safe_float(v26_main.get("surprise_score")), 1),
},
context=context,
)
return strategies
def _summarize_bucket(bucket: Dict[str, float]) -> Dict[str, Any]:
played = int(bucket["played"])
won = int(bucket["won"])
lost = int(bucket["lost"])
candidate = int(bucket["candidate"])
profit = round(bucket["profit"], 4)
roi = round((profit / played) * 100.0, 2) if played else 0.0
hit = round((won / played) * 100.0, 2) if played else 0.0
return {
"candidates": candidate,
"played": played,
"won": won,
"lost": lost,
"profit_flat": profit,
"roi_flat_pct": roi,
"hit_rate_pct": hit,
}
def _format_date(ms: int) -> str:
return datetime.fromtimestamp(ms / 1000, tz=timezone.utc).strftime("%Y-%m-%d")
def _build_markdown(report: Dict[str, Any]) -> str:
lines: list[str] = []
lines.append("# HT/FT + Upset Backtest")
lines.append("")
lines.append(f"- Sample: last {report['sample_size']} finished football matches")
lines.append("- Scope: only HT/FT reversal and upset-oriented picks")
lines.append("- ROI: flat `1 unit` per played pick")
lines.append(f"- Generated at: {report['generated_at']}")
lines.append("")
lines.append("## Strategy Summary")
lines.append("")
lines.append("| Strategy | Candidates | Played | Won | Lost | Hit Rate | Profit | ROI |")
lines.append("|---|---:|---:|---:|---:|---:|---:|---:|")
for strategy in STRATEGIES:
payload = report["summary"]["strategies"][strategy]
lines.append(
f"| {strategy} | {payload['candidates']} | {payload['played']} | {payload['won']} | "
f"{payload['lost']} | {payload['hit_rate_pct']}% | {payload['profit_flat']:+.2f} | {payload['roi_flat_pct']:+.2f}% |"
)
lines.append("")
lines.append("## v26 Surprise By Reversal Type")
lines.append("")
lines.append("| Reversal | Candidates | Played | Won | Lost | Profit | ROI |")
lines.append("|---|---:|---:|---:|---:|---:|---:|")
for reversal, payload in report["summary"]["v26_surprise_by_pick"].items():
lines.append(
f"| {reversal} | {payload['candidates']} | {payload['played']} | {payload['won']} | "
f"{payload['lost']} | {payload['profit_flat']:+.2f} | {payload['roi_flat_pct']:+.2f}% |"
)
lines.append("")
lines.append("## Match Detail")
lines.append("")
lines.append("| Date | Match | HT | FT | v25 aggressive | v26 surprise | v26 aggressive | v26 main HTFT |")
lines.append("|---|---|---|---|---|---|---|---|")
for match in report["matches"]:
lines.append(
f"| {_format_date(match['match_date_ms'])} | {match['match_name']} | {match['ht_score']} | {match['final_score']} | "
f"{match['v25_aggressive']} | {match['v26_surprise']} | {match['v26_aggressive']} | {match['v26_main_htft']} |"
)
lines.append("")
return "\n".join(lines)
def main() -> None:
parser = argparse.ArgumentParser(description="HT/FT + upset focused backtest.")
parser.add_argument("--limit", type=int, default=120, help="Number of finished matches to analyze.")
args = parser.parse_args()
dsn = _resolve_dsn()
orchestrator = SingleMatchOrchestrator()
matches = _fetch_matches(dsn, max(1, args.limit))
strategy_buckets: Dict[str, Dict[str, float]] = {name: defaultdict(float) for name in STRATEGIES}
v26_reversal_buckets: Dict[str, Dict[str, float]] = {label: defaultdict(float) for label in REVERSAL_LABELS}
report_matches: list[Dict[str, Any]] = []
csv_rows: list[Dict[str, Any]] = []
for context in matches:
data = orchestrator._load_match_data(context.match_id) # noqa: SLF001
if data is None:
continue
orchestrator.engine_mode = "v25"
v25 = orchestrator.analyze_match(context.match_id) or {}
orchestrator.engine_mode = "v26"
v26 = orchestrator.analyze_match(context.match_id) or {}
extracted = _extract_strategy_rows(
context=context,
odds_data=data.odds_data or {},
v25=v25,
v26=v26,
)
match_row: Dict[str, Any] = {
"match_id": context.match_id,
"match_name": context.match_name,
"league": context.league,
"match_date_ms": context.match_date_ms,
"ht_score": context.ht_score,
"final_score": context.final_score,
}
for strategy, payload in extracted.items():
if payload:
strategy_buckets[strategy]["candidate"] += 1
if payload["counted_in_roi"]:
strategy_buckets[strategy]["played"] += 1
if payload["result"] == "WON":
strategy_buckets[strategy]["won"] += 1
else:
strategy_buckets[strategy]["lost"] += 1
strategy_buckets[strategy]["profit"] += payload["profit_flat"]
if strategy == "v26_surprise":
reversal_label = str(payload.get("reversal_pick") or "")
if reversal_label in v26_reversal_buckets:
v26_reversal_buckets[reversal_label]["candidate"] += 1
if payload["counted_in_roi"]:
v26_reversal_buckets[reversal_label]["played"] += 1
if payload["result"] == "WON":
v26_reversal_buckets[reversal_label]["won"] += 1
else:
v26_reversal_buckets[reversal_label]["lost"] += 1
v26_reversal_buckets[reversal_label]["profit"] += payload["profit_flat"]
summary = (
f"{payload['pick']} ({payload['result']}, {'played' if payload['counted_in_roi'] else 'not played'}, {payload['profit_flat']:+.2f})"
)
match_row[strategy] = summary
csv_rows.append(
{
"match_id": context.match_id,
"date": _format_date(context.match_date_ms),
"league": context.league,
"match": context.match_name,
"ht_score": context.ht_score,
"final_score": context.final_score,
**payload,
}
)
else:
match_row[strategy] = "-"
report_matches.append(match_row)
report = {
"generated_at": datetime.now(timezone.utc).isoformat(),
"sample_size": len(report_matches),
"summary": {
"strategies": {
strategy: _summarize_bucket(bucket)
for strategy, bucket in strategy_buckets.items()
},
"v26_surprise_by_pick": {
label: _summarize_bucket(bucket)
for label, bucket in v26_reversal_buckets.items()
},
},
"matches": report_matches,
}
report_dir = AI_ENGINE_DIR / "reports"
json_path = report_dir / "backtest_v26_shadow_htft_upset.json"
csv_path = report_dir / "backtest_v26_shadow_htft_upset.csv"
md_path = report_dir / "backtest_v26_shadow_htft_upset.md"
json_path.write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding="utf-8")
with csv_path.open("w", encoding="utf-8", newline="") as handle:
writer = csv.DictWriter(
handle,
fieldnames=[
"match_id",
"date",
"league",
"match",
"ht_score",
"final_score",
"strategy",
"market",
"pick",
"odds",
"playable",
"confidence",
"result",
"counted_in_roi",
"profit_flat",
"resolution_note",
"source",
"reversal_pick",
"reversal_prob",
"favorite_gap",
"favorite_odd",
"support_score",
"odds_band_score",
"odds_band_label",
"league_reversal_rate",
"league_strict_rev_rate",
"referee_strict_rev_rate",
"surprise_score",
"reason_codes",
"pick_reason",
],
)
writer.writeheader()
writer.writerows(csv_rows)
md_path.write_text(_build_markdown(report), encoding="utf-8")
print(f"[OK] JSON report written to {json_path}")
print(f"[OK] CSV report written to {csv_path}")
print(f"[OK] Markdown report written to {md_path}")
if __name__ == "__main__":
main()
ai-engine/scripts/backtest_v26_shadow_roi_detail.py
+810
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@@ -0,0 +1,810 @@
from __future__ import annotations
import argparse
import csv
import json
import sys
from collections import defaultdict
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Iterable, Optional
import psycopg2
from psycopg2.extras import RealDictCursor
AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
if str(AI_ENGINE_DIR) not in sys.path:
sys.path.insert(0, str(AI_ENGINE_DIR))
from services.single_match_orchestrator import SingleMatchOrchestrator
from utils.top_leagues import load_top_league_ids
MARKET_ORDER = [
"MS",
"DC",
"OU15",
"OU25",
"OU35",
"BTTS",
"HT",
"HT_OU05",
"HT_OU15",
"HTFT",
"OE",
"CARDS",
"HCAP",
]
@dataclass
class MatchContext:
match_id: str
match_date_ms: int
league_id: Optional[str]
league: str
home_team: str
away_team: str
final_home: int
final_away: int
ht_home: Optional[int]
ht_away: Optional[int]
total_cards: Optional[float]
@property
def match_name(self) -> str:
return f"{self.home_team} vs {self.away_team}"
@property
def final_score(self) -> str:
return f"{self.final_home}-{self.final_away}"
@property
def ht_score(self) -> Optional[str]:
if self.ht_home is None or self.ht_away is None:
return None
return f"{self.ht_home}-{self.ht_away}"
@property
def total_goals(self) -> int:
return self.final_home + self.final_away
@property
def total_ht_goals(self) -> Optional[int]:
if self.ht_home is None or self.ht_away is None:
return None
return self.ht_home + self.ht_away
def _resolve_dsn() -> str:
env_path = AI_ENGINE_DIR / ".env"
if env_path.exists():
for line in env_path.read_text(encoding="utf-8").splitlines():
if line.startswith("DATABASE_URL="):
return line.split("=", 1)[1].strip().split("?schema=")[0]
raise SystemExit("DATABASE_URL not found in ai-engine/.env")
def _fetch_matches(
dsn: str,
limit: int,
top_league_ids: Optional[list[str]] = None,
) -> list[MatchContext]:
query = """
SELECT
m.id,
m.mst_utc,
m.league_id,
COALESCE(l.name, 'Unknown League') AS league,
COALESCE(ht.name, 'Home') AS home_team,
COALESCE(at.name, 'Away') AS away_team,
COALESCE(m.score_home, 0) AS score_home,
COALESCE(m.score_away, 0) AS score_away,
m.ht_score_home,
m.ht_score_away,
cards.total_cards
FROM matches m
LEFT JOIN leagues l ON l.id = m.league_id
LEFT JOIN teams ht ON ht.id = m.home_team_id
LEFT JOIN teams at ON at.id = m.away_team_id
LEFT JOIN (
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
)::float AS total_cards
FROM match_player_events mpe
WHERE mpe.event_type::text LIKE '%%card%%'
GROUP BY mpe.match_id
) cards ON cards.match_id = m.id
WHERE m.status = 'FT'
AND m.sport = 'football'
AND m.score_home IS NOT NULL
AND m.score_away IS NOT NULL
"""
params: list[Any] = []
if top_league_ids:
query += " AND m.league_id = ANY(%s)"
params.append(top_league_ids)
query += """
ORDER BY m.mst_utc DESC
LIMIT %s
"""
params.append(limit)
with psycopg2.connect(dsn) as conn:
with conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute(query, params)
rows = cur.fetchall()
results: list[MatchContext] = []
for row in rows:
results.append(
MatchContext(
match_id=str(row["id"]),
match_date_ms=int(row["mst_utc"] or 0),
league_id=str(row["league_id"]) if row.get("league_id") else None,
league=str(row["league"] or "Unknown League"),
home_team=str(row["home_team"] or "Home"),
away_team=str(row["away_team"] or "Away"),
final_home=int(row["score_home"] or 0),
final_away=int(row["score_away"] or 0),
ht_home=(
int(row["ht_score_home"])
if row.get("ht_score_home") is not None
else None
),
ht_away=(
int(row["ht_score_away"])
if row.get("ht_score_away") is not None
else None
),
total_cards=(
float(row["total_cards"])
if row.get("total_cards") is not None
else None
),
)
)
return results
def _odds_band(odds: float) -> str:
if odds < 1.5:
return "<1.50"
if odds < 1.8:
return "1.50-1.79"
if odds < 2.1:
return "1.80-2.09"
if odds < 2.5:
return "2.10-2.49"
return "2.50+"
def _confidence_band(confidence: float) -> str:
if confidence < 55.0:
return "<55"
if confidence < 65.0:
return "55-64.9"
if confidence < 75.0:
return "65-74.9"
return "75+"
def _edge_band(edge: float) -> str:
if edge < 0.03:
return "<0.03"
if edge < 0.06:
return "0.03-0.059"
if edge < 0.10:
return "0.06-0.099"
return "0.10+"
def _top_n_buckets(rows: Iterable[tuple[str, float]], limit: int = 10) -> list[dict[str, Any]]:
ranked = sorted(rows, key=lambda item: (-item[1], item[0]))
return [
{"label": label, "count": int(count)}
for label, count in ranked[:limit]
]
def _summarize_v26_losses(csv_rows: list[Dict[str, Any]]) -> Dict[str, Any]:
losses = [
row for row in csv_rows
if row.get("model") == "v26.shadow"
and bool(row.get("counted_in_roi"))
and row.get("result") == "LOST"
]
by_market: Dict[str, float] = defaultdict(float)
by_league: Dict[str, float] = defaultdict(float)
by_pick: Dict[str, float] = defaultdict(float)
by_odds_band: Dict[str, float] = defaultdict(float)
by_conf_band: Dict[str, float] = defaultdict(float)
by_edge_band: Dict[str, float] = defaultdict(float)
for row in losses:
market = str(row.get("market") or "UNKNOWN")
league = str(row.get("league") or "Unknown League")
pick = str(row.get("pick") or "")
odds = _safe_float(row.get("odds"))
confidence = _safe_float(row.get("confidence"))
edge = _safe_float(row.get("edge"))
by_market[market] += 1
by_league[league] += 1
by_pick[f"{market} {pick}".strip()] += 1
by_odds_band[_odds_band(odds)] += 1
by_conf_band[_confidence_band(confidence)] += 1
by_edge_band[_edge_band(edge)] += 1
return {
"lost_bets": len(losses),
"by_market": _top_n_buckets(by_market.items(), limit=20),
"by_league": _top_n_buckets(by_league.items(), limit=15),
"by_pick": _top_n_buckets(by_pick.items(), limit=15),
"by_odds_band": _top_n_buckets(by_odds_band.items(), limit=10),
"by_confidence_band": _top_n_buckets(by_conf_band.items(), limit=10),
"by_edge_band": _top_n_buckets(by_edge_band.items(), limit=10),
}
def _safe_float(value: Any) -> float:
try:
return float(value)
except (TypeError, ValueError):
return 0.0
def _normalize_text(value: Any) -> str:
text = str(value or "").strip().upper()
return (
text.replace("İ", "I")
.replace("İ", "I")
.replace("Ş", "S")
.replace("Ğ", "G")
.replace("Ü", "U")
.replace("Ö", "O")
.replace("Ç", "C")
)
def _outcome_symbol(home: int, away: int) -> str:
if home > away:
return "1"
if home < away:
return "2"
return "X"
def _resolve_pick(
market: str,
pick: str,
context: MatchContext,
) -> Dict[str, Any]:
market_code = _normalize_text(market).replace("/", "")
pick_text = str(pick or "").strip()
pick_norm = _normalize_text(pick_text)
if not market_code or not pick_norm:
return {"result": "UNRESOLVED", "won": None, "note": "pick_missing"}
if market_code == "HTFT":
market_code = "HTFT"
if market_code == "HTFT" or market_code == "HTFT":
if context.ht_home is None or context.ht_away is None:
return {"result": "UNRESOLVED", "won": None, "note": "ht_score_missing"}
if "/" not in pick_text:
return {"result": "UNRESOLVED", "won": None, "note": "htft_pick_invalid"}
ht_pick, ft_pick = pick_text.split("/", 1)
actual = f"{_outcome_symbol(context.ht_home, context.ht_away)}/{_outcome_symbol(context.final_home, context.final_away)}"
won = f"{_normalize_text(ht_pick)}/{_normalize_text(ft_pick)}" == actual
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
if market_code == "MS":
actual = _outcome_symbol(context.final_home, context.final_away)
won = pick_norm in {actual, f"MS {actual}"}
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
if market_code == "DC":
actual = _outcome_symbol(context.final_home, context.final_away)
winning = {
"1X": {"1", "X"},
"X2": {"X", "2"},
"12": {"1", "2"},
}
won = actual in winning.get(pick_norm, set())
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
if market_code in {"OU15", "OU25", "OU35", "HTOU05", "HTOU15", "HT_OU05", "HT_OU15"}:
if market_code in {"HTOU05", "HTOU15", "HT_OU05", "HT_OU15"}:
if context.total_ht_goals is None:
return {"result": "UNRESOLVED", "won": None, "note": "ht_score_missing"}
total = context.total_ht_goals
line = 0.5 if "05" in market_code else 1.5
else:
total = context.total_goals
line = {"OU15": 1.5, "OU25": 2.5, "OU35": 3.5}[market_code]
if "UST" in pick_norm or "OVER" in pick_norm:
won = total > line
side = "OVER"
elif "ALT" in pick_norm or "UNDER" in pick_norm:
won = total < line
side = "UNDER"
else:
return {"result": "UNRESOLVED", "won": None, "note": "ou_side_unknown"}
return {
"result": "WON" if won else "LOST",
"won": won,
"note": f"actual_total={total} side={side} line={line}",
}
if market_code == "BTTS":
both_scored = context.final_home > 0 and context.final_away > 0
if "VAR" in pick_norm or "YES" in pick_norm:
won = both_scored
side = "YES"
elif "YOK" in pick_norm or pick_norm.endswith("NO") or pick_norm == "NO":
won = not both_scored
side = "NO"
else:
return {"result": "UNRESOLVED", "won": None, "note": "btts_side_unknown"}
return {
"result": "WON" if won else "LOST",
"won": won,
"note": f"actual_btts={'YES' if both_scored else 'NO'} side={side}",
}
if market_code == "HT":
if context.ht_home is None or context.ht_away is None:
return {"result": "UNRESOLVED", "won": None, "note": "ht_score_missing"}
actual = _outcome_symbol(context.ht_home, context.ht_away)
won = pick_norm == actual
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
if market_code == "OE":
actual = "EVEN" if context.total_goals % 2 == 0 else "ODD"
if pick_norm in {"CIFT", "EVEN"}:
wanted = "EVEN"
elif pick_norm in {"TEK", "ODD"}:
wanted = "ODD"
else:
return {"result": "UNRESOLVED", "won": None, "note": "oe_pick_unknown"}
won = actual == wanted
return {"result": "WON" if won else "LOST", "won": won, "note": f"actual={actual}"}
if market_code == "CARDS":
if context.total_cards is None:
return {"result": "UNRESOLVED", "won": None, "note": "cards_missing"}
if "UST" in pick_norm or "OVER" in pick_norm:
won = context.total_cards > 4.5
side = "OVER"
elif "ALT" in pick_norm or "UNDER" in pick_norm:
won = context.total_cards < 4.5
side = "UNDER"
else:
return {"result": "UNRESOLVED", "won": None, "note": "cards_side_unknown"}
return {
"result": "WON" if won else "LOST",
"won": won,
"note": f"actual_cards={context.total_cards:.1f} side={side} line=4.5",
}
if market_code == "HCAP":
adjusted_home = context.final_home - 1.0
adjusted_away = float(context.final_away)
if adjusted_home > adjusted_away:
actual = "1"
elif adjusted_home < adjusted_away:
actual = "2"
else:
actual = "X"
won = pick_norm == actual
return {
"result": "WON" if won else "LOST",
"won": won,
"note": f"actual={actual} line_home=-1.0",
}
return {"result": "UNRESOLVED", "won": None, "note": "market_not_supported"}
def _evaluate_row(
market: str,
pick: str,
odds: Any,
playable: bool,
stake_units: Any,
context: MatchContext,
) -> Dict[str, Any]:
resolution = _resolve_pick(market, pick, context)
odds_value = _safe_float(odds)
stake_value = _safe_float(stake_units)
counted = bool(playable and odds_value > 1.01 and resolution["result"] in {"WON", "LOST"})
flat_profit = 0.0
stake_profit = 0.0
if counted:
flat_profit = (odds_value - 1.0) if resolution["result"] == "WON" else -1.0
stake_profit = flat_profit * (stake_value if stake_value > 0 else 1.0)
return {
"result": resolution["result"],
"won": resolution["won"],
"resolution_note": resolution["note"],
"counted_in_roi": counted,
"profit_flat": round(flat_profit, 4),
"profit_stake": round(stake_profit, 4),
}
def _summarize_bucket(bucket: Dict[str, float]) -> Dict[str, Any]:
played = int(bucket["played"])
won = int(bucket["won"])
lost = int(bucket["lost"])
unresolved = int(bucket["unresolved"])
profit = round(bucket["profit"], 4)
roi = round((profit / played) * 100.0, 2) if played else 0.0
win_rate = round((won / played) * 100.0, 2) if played else 0.0
return {
"played": played,
"won": won,
"lost": lost,
"unresolved": unresolved,
"profit_flat": profit,
"roi_flat_pct": roi,
"win_rate_pct": win_rate,
}
def _format_date(ms: int) -> str:
if ms <= 0:
return "-"
dt = datetime.fromtimestamp(ms / 1000, tz=timezone.utc)
return dt.strftime("%Y-%m-%d")
def _build_markdown_report(report: Dict[str, Any]) -> str:
lines: list[str] = []
lines.append("# v25 vs v26.shadow ROI Report")
lines.append("")
lines.append(f"- Sample: last {report['sample_size']} finished football matches")
if report.get("top_leagues_only"):
lines.append("- Filter: top leagues only")
lines.append("- ROI calculation: flat `1 unit` per playable and resolvable bet")
lines.append(f"- Generated at: {report['generated_at']}")
lines.append("")
lines.append("## Overall Summary")
lines.append("")
lines.append("| Model | Played | Won | Lost | Win Rate | Profit | ROI | Main Pick ROI | Main Pick W/L |")
lines.append("|---|---:|---:|---:|---:|---:|---:|---:|---|")
for model_name, payload in report["summary"]["models"].items():
main = payload["main_pick"]
lines.append(
f"| {model_name} | {payload['all_playable']['played']} | {payload['all_playable']['won']} | "
f"{payload['all_playable']['lost']} | {payload['all_playable']['win_rate_pct']}% | "
f"{payload['all_playable']['profit_flat']:+.2f} | {payload['all_playable']['roi_flat_pct']:+.2f}% | "
f"{main['roi_flat_pct']:+.2f}% | {main['won']}/{main['played']} |"
)
lines.append("")
lines.append("## Market Summary")
lines.append("")
lines.append("| Model | Market | Played | Won | Lost | Profit | ROI |")
lines.append("|---|---|---:|---:|---:|---:|---:|")
for model_name, markets in report["summary"]["markets"].items():
for market_name in MARKET_ORDER:
payload = markets.get(market_name)
if not payload or payload["played"] == 0:
continue
lines.append(
f"| {model_name} | {market_name} | {payload['played']} | {payload['won']} | {payload['lost']} | "
f"{payload['profit_flat']:+.2f} | {payload['roi_flat_pct']:+.2f}% |"
)
lines.append("")
loss_summary = report["summary"].get("v26_loss_analysis", {})
if loss_summary:
lines.append("## v26 Loss Analysis")
lines.append("")
lines.append(f"- Lost bets: {loss_summary.get('lost_bets', 0)}")
lines.append("")
lines.append("| Bucket | Top Items |")
lines.append("|---|---|")
for label, key in (
("By market", "by_market"),
("By league", "by_league"),
("By pick", "by_pick"),
("By odds band", "by_odds_band"),
("By confidence band", "by_confidence_band"),
("By edge band", "by_edge_band"),
):
items = loss_summary.get(key) or []
rendered = ", ".join(f"{item['label']} ({item['count']})" for item in items[:6]) or "-"
lines.append(f"| {label} | {rendered} |")
lines.append("")
lines.append("## Match By Match")
lines.append("")
lines.append("| Date | Match | Score | v25 Main | v25 Played Picks | v25 Profit | v26 Main | v26 Played Picks | v26 Profit |")
lines.append("|---|---|---|---|---|---:|---|---|---:|")
for match in report["matches"]:
v25 = match["models"]["v25"]
v26 = match["models"]["v26.shadow"]
lines.append(
f"| {_format_date(match['match_date_ms'])} | {match['match_name']} | {match['final_score']} | "
f"{v25['main_pick']['summary']} | {v25['played_picks_summary']} | {v25['profit_flat']:+.2f} | "
f"{v26['main_pick']['summary']} | {v26['played_picks_summary']} | {v26['profit_flat']:+.2f} |"
)
lines.append("")
return "\n".join(lines)
def main() -> None:
parser = argparse.ArgumentParser(
description="Detailed ROI backtest for v25 vs v26.shadow.",
)
parser.add_argument("--limit", type=int, default=60, help="Number of finished matches to analyze.")
parser.add_argument(
"--top-leagues-only",
action="store_true",
help="Only analyze matches whose league_id exists in top_leagues.json.",
)
args = parser.parse_args()
dsn = _resolve_dsn()
top_league_ids = sorted(load_top_league_ids()) if args.top_leagues_only else None
matches = _fetch_matches(dsn, max(1, args.limit), top_league_ids=top_league_ids)
orchestrator = SingleMatchOrchestrator()
report_matches: list[Dict[str, Any]] = []
model_aggregate: Dict[str, Dict[str, float]] = {
"v25": defaultdict(float),
"v26.shadow": defaultdict(float),
}
main_pick_aggregate: Dict[str, Dict[str, float]] = {
"v25": defaultdict(float),
"v26.shadow": defaultdict(float),
}
market_aggregate: Dict[str, Dict[str, Dict[str, float]]] = {
"v25": defaultdict(lambda: defaultdict(float)),
"v26.shadow": defaultdict(lambda: defaultdict(float)),
}
csv_rows: list[Dict[str, Any]] = []
for context in matches:
match_payload = {
"match_id": context.match_id,
"match_name": context.match_name,
"league": context.league,
"match_date_ms": context.match_date_ms,
"final_score": context.final_score,
"ht_score": context.ht_score,
"total_cards": context.total_cards,
"models": {},
}
for model_name, mode in (("v25", "v25"), ("v26.shadow", "v26")):
orchestrator.engine_mode = mode
package = orchestrator.analyze_match(context.match_id) or {}
rows = package.get("bet_summary") or []
evaluated_rows: list[Dict[str, Any]] = []
match_profit = 0.0
for row in rows:
market = str(row.get("market") or "")
pick = str(row.get("pick") or "")
evaluation = _evaluate_row(
market=market,
pick=pick,
odds=row.get("odds"),
playable=bool(row.get("playable")),
stake_units=row.get("stake_units"),
context=context,
)
combined = {
"market": market,
"pick": pick,
"playable": bool(row.get("playable")),
"bet_grade": row.get("bet_grade"),
"odds": round(_safe_float(row.get("odds")), 2),
"calibrated_confidence": round(_safe_float(row.get("calibrated_confidence")), 1),
"edge": round(_safe_float(row.get("ev_edge", row.get("edge"))), 4),
"stake_units": round(_safe_float(row.get("stake_units")), 2),
**evaluation,
}
evaluated_rows.append(combined)
if combined["counted_in_roi"]:
bucket = market_aggregate[model_name][market]
bucket["played"] += 1
if combined["result"] == "WON":
bucket["won"] += 1
else:
bucket["lost"] += 1
bucket["profit"] += combined["profit_flat"]
model_bucket = model_aggregate[model_name]
model_bucket["played"] += 1
if combined["result"] == "WON":
model_bucket["won"] += 1
else:
model_bucket["lost"] += 1
model_bucket["profit"] += combined["profit_flat"]
match_profit += combined["profit_flat"]
elif combined["playable"]:
model_aggregate[model_name]["unresolved"] += 1
market_aggregate[model_name][market]["unresolved"] += 1
csv_rows.append(
{
"match_id": context.match_id,
"date": _format_date(context.match_date_ms),
"league": context.league,
"match": context.match_name,
"final_score": context.final_score,
"ht_score": context.ht_score or "",
"model": model_name,
"market": market,
"pick": pick,
"playable": combined["playable"],
"bet_grade": combined["bet_grade"],
"odds": combined["odds"],
"confidence": combined["calibrated_confidence"],
"edge": combined["edge"],
"result": combined["result"],
"counted_in_roi": combined["counted_in_roi"],
"profit_flat": combined["profit_flat"],
"resolution_note": combined["resolution_note"],
}
)
main_pick = package.get("main_pick") or {}
main_eval = _evaluate_row(
market=str(main_pick.get("market") or ""),
pick=str(main_pick.get("pick") or ""),
odds=main_pick.get("odds"),
playable=bool(main_pick.get("playable")),
stake_units=main_pick.get("stake_units"),
context=context,
)
main_pick_summary = {
"market": main_pick.get("market"),
"pick": main_pick.get("pick"),
"playable": bool(main_pick.get("playable")),
"odds": round(_safe_float(main_pick.get("odds")), 2),
"confidence": round(
_safe_float(
main_pick.get("calibrated_confidence", main_pick.get("confidence"))
),
1,
),
"edge": round(_safe_float(main_pick.get("ev_edge", main_pick.get("edge"))), 4),
**main_eval,
}
if main_pick_summary["counted_in_roi"]:
summary_suffix = (
f"{main_pick_summary['result']}, played, {main_pick_summary['profit_flat']:+.2f}"
)
elif main_pick_summary.get("market") and main_pick_summary.get("pick"):
summary_suffix = f"{main_pick_summary['result']}, not played"
else:
summary_suffix = ""
if main_pick_summary["counted_in_roi"]:
bucket = main_pick_aggregate[model_name]
bucket["played"] += 1
if main_pick_summary["result"] == "WON":
bucket["won"] += 1
else:
bucket["lost"] += 1
bucket["profit"] += main_pick_summary["profit_flat"]
elif main_pick_summary["playable"]:
main_pick_aggregate[model_name]["unresolved"] += 1
main_pick_summary["summary"] = (
f"{main_pick_summary['market']} {main_pick_summary['pick']} "
f"({summary_suffix})"
if main_pick_summary.get("market") and main_pick_summary.get("pick")
else "No main pick"
)
played_rows = [row for row in evaluated_rows if row["counted_in_roi"]]
played_picks_summary = (
"; ".join(
f"{row['market']} {row['pick']}={row['result']} ({row['profit_flat']:+.2f})"
for row in played_rows
)
if played_rows
else "-"
)
match_payload["models"][model_name] = {
"main_pick": main_pick_summary,
"profit_flat": round(match_profit, 4),
"played_picks_summary": played_picks_summary,
"played_picks": played_rows,
"all_picks": evaluated_rows,
}
report_matches.append(match_payload)
summary = {
"models": {
model_name: {
"all_playable": _summarize_bucket(model_aggregate[model_name]),
"main_pick": _summarize_bucket(main_pick_aggregate[model_name]),
}
for model_name in ("v25", "v26.shadow")
},
"markets": {
model_name: {
market_name: _summarize_bucket(bucket)
for market_name, bucket in sorted(
market_aggregate[model_name].items(),
key=lambda item: (
MARKET_ORDER.index(item[0]) if item[0] in MARKET_ORDER else 999,
item[0],
),
)
}
for model_name in ("v25", "v26.shadow")
},
"v26_loss_analysis": _summarize_v26_losses(csv_rows),
}
report = {
"generated_at": datetime.now(timezone.utc).isoformat(),
"sample_size": len(report_matches),
"top_leagues_only": bool(args.top_leagues_only),
"summary": summary,
"matches": report_matches,
}
report_dir = AI_ENGINE_DIR / "reports"
json_path = report_dir / "backtest_v26_shadow_roi_detail.json"
csv_path = report_dir / "backtest_v26_shadow_roi_picks.csv"
md_path = report_dir / "backtest_v26_shadow_roi_report.md"
json_path.write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding="utf-8")
with csv_path.open("w", encoding="utf-8", newline="") as handle:
writer = csv.DictWriter(
handle,
fieldnames=[
"match_id",
"date",
"league",
"match",
"final_score",
"ht_score",
"model",
"market",
"pick",
"playable",
"bet_grade",
"odds",
"confidence",
"edge",
"result",
"counted_in_roi",
"profit_flat",
"resolution_note",
],
)
writer.writeheader()
writer.writerows(csv_rows)
md_path.write_text(_build_markdown_report(report), encoding="utf-8")
print(f"[OK] JSON report written to {json_path}")
print(f"[OK] CSV report written to {csv_path}")
print(f"[OK] Markdown report written to {md_path}")
if __name__ == "__main__":
main()
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_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
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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_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
+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()
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
+590 -73
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]]:
"""
@@ -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",
"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}",
@@ -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
View File
File diff suppressed because it is too large Load Diff
ai-engine/tests/test_single_match_orchestrator.py
+2
View File
@@ -90,8 +90,10 @@ class _RouterCursor:
def _build_orchestrator() -> SingleMatchOrchestrator:
orchestrator = SingleMatchOrchestrator.__new__(SingleMatchOrchestrator)
orchestrator.v25_predictor = MagicMock()
orchestrator.v26_shadow_engine = None
orchestrator.basketball_predictor = MagicMock()
orchestrator.dsn = "postgresql://unit-test"
orchestrator.engine_mode = "v25"
orchestrator.league_reliability = {}
orchestrator.market_calibration = {
"MS": 0.82,
ai-engine/tests/test_v26_shadow_engine.py
+286
View File
@@ -0,0 +1,286 @@
import sys
import unittest
from pathlib import Path
from types import SimpleNamespace
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.v26_shadow_engine import V26ShadowEngine
def _build_prediction():
return SimpleNamespace(
risk_level="MEDIUM",
risk_score=42.0,
is_surprise_risk=False,
surprise_type="",
surprise_score=0.0,
surprise_comment="",
surprise_reasons=[],
risk_warnings=[],
team_confidence=71.0,
player_confidence=64.0,
odds_confidence=75.0,
referee_confidence=58.0,
predicted_ft_score="2-1",
predicted_ht_score="1-0",
home_xg=1.72,
away_xg=1.08,
total_xg=2.8,
ft_scores_top5=[
{"score": "2-1", "prob": 0.093},
{"score": "1-1", "prob": 0.086},
],
ms_home_prob=0.52,
ms_draw_prob=0.24,
ms_away_prob=0.24,
)
def _build_data(referee_name="Ref A", lineup_source="confirmed_live", league_id="league1"):
return SimpleNamespace(
match_id="m1",
home_team_name="Home",
away_team_name="Away",
league_id=league_id,
league_name="League",
match_date_ms=1710000000000,
sport="football",
home_lineup=["h"] * 11,
away_lineup=["a"] * 11,
lineup_source=lineup_source,
referee_name=referee_name,
odds_data={
"ms_h": 2.1,
"ms_d": 3.4,
"ms_a": 3.7,
"dc_1x": 1.28,
"dc_x2": 1.68,
"dc_12": 1.34,
"ou15_o": 1.24,
"ou15_u": 4.1,
"ou25_o": 1.77,
"ou25_u": 2.05,
"ou35_o": 2.95,
"ou35_u": 1.4,
"btts_y": 1.74,
"btts_n": 2.04,
"ht_h": 2.72,
"ht_d": 2.05,
"ht_a": 4.8,
"ht_ou05_o": 1.38,
"ht_ou05_u": 2.85,
"ht_ou15_o": 2.48,
"ht_ou15_u": 1.48,
"oe_odd": 1.92,
"oe_even": 1.9,
"cards_o": 1.98,
"cards_u": 1.84,
"hcap_h": 3.3,
"hcap_d": 3.7,
"hcap_a": 1.93,
"htft_11": 3.8,
"htft_1x": 5.1,
"htft_12": 16.5,
"htft_x1": 5.6,
"htft_xx": 4.8,
"htft_x2": 7.4,
"htft_21": 22.0,
"htft_2x": 12.0,
"htft_22": 6.2,
},
)
class V26ShadowEngineTests(unittest.TestCase):
def setUp(self):
self.engine = V26ShadowEngine()
self.engine.top_league_ids = {"top1"}
self.prediction = _build_prediction()
self.quality = {
"label": "HIGH",
"score": 0.88,
"home_lineup_count": 11,
"away_lineup_count": 11,
"lineup_source": "confirmed_live",
"flags": [],
}
self.v25_signal = {
"MS": {"probs": {"1": 0.46, "X": 0.27, "2": 0.27}},
"HT": {"probs": {"1": 0.39, "X": 0.41, "2": 0.20}},
"HTFT": {"probs": {"1/1": 0.22, "X/X": 0.18, "2/2": 0.14}},
"HCAP": {"probs": {"1": 0.21, "X": 0.19, "2": 0.60}},
"CARDS": {"probs": {"Under": 0.53, "Over": 0.47}},
}
def test_build_package_exposes_shadow_metadata(self):
package = self.engine.build_package(
data=_build_data(),
prediction=self.prediction,
v25_signal=self.v25_signal,
quality=self.quality,
)
self.assertEqual(package["model_version"], "v26.shadow.2")
self.assertIn("calibration_version", package)
self.assertIn("decision_trace_id", package)
self.assertIn("market_reliability", package)
self.assertTrue(package["bet_summary"])
def test_cards_defaults_to_pass_when_referee_missing(self):
package = self.engine.build_package(
data=_build_data(referee_name=None),
prediction=self.prediction,
v25_signal=self.v25_signal,
quality=self.quality,
)
cards = next(item for item in package["bet_summary"] if item["market"] == "CARDS")
self.assertFalse(cards["playable"])
self.assertEqual(cards["bet_grade"], "PASS")
def test_select_main_pick_prioritizes_ms_when_playable(self):
rows = [
{
"market": "OU25",
"pick": "2.5 Üst",
"playable": True,
"selection_score": 86.0,
"play_score": 83.0,
"edge": 0.15,
"calibrated_confidence": 72.0,
},
{
"market": "MS",
"pick": "1",
"playable": True,
"selection_score": 81.0,
"play_score": 82.0,
"edge": 0.08,
"calibrated_confidence": 64.0,
},
]
main_pick = self.engine._select_main_pick(rows)
self.assertIsNotNone(main_pick)
self.assertEqual(main_pick["market"], "MS")
self.assertEqual(main_pick["pick_reason"], "ms_priority_market")
def test_build_package_exposes_surprise_pick_when_reversal_is_hot(self):
prediction = _build_prediction()
prediction.is_surprise_risk = True
prediction.surprise_score = 82.0
prediction.surprise_type = "favorite_reversal"
v25_signal = dict(self.v25_signal)
v25_signal["HTFT"] = {
"probs": {
"1/2": 0.24,
"X/2": 0.14,
"1/1": 0.12,
"X/X": 0.10,
}
}
package = self.engine.build_package(
data=_build_data(),
prediction=prediction,
v25_signal=v25_signal,
quality=self.quality,
)
self.assertIn("surprise_hunter", package)
self.assertIn("surprise_pick", package)
self.assertTrue(package["surprise_hunter"]["playable"])
self.assertEqual(package["surprise_pick"]["market"], "HTFT")
self.assertEqual(package["surprise_pick"]["strategy_channel"], "surprise_sidecar")
self.assertEqual(package["surprise_hunter"]["strategy_channel"], "surprise_sidecar")
self.assertGreaterEqual(package["surprise_pick"]["surprise_score"], 66.0)
self.assertEqual(package["main_pick"]["strategy_channel"], "standard")
self.assertNotEqual(package["main_pick"].get("strategy_channel"), package["surprise_pick"].get("strategy_channel"))
self.assertNotEqual(package["main_pick"].get("pick_reason"), "favorite_reversal_signal")
def test_top_league_policy_suppresses_early_and_extra_goal_markets(self):
package = self.engine.build_package(
data=_build_data(league_id="top1"),
prediction=self.prediction,
v25_signal=self.v25_signal,
quality=self.quality,
)
summary = {item["market"]: item for item in package["bet_summary"]}
self.assertFalse(summary["HT_OU05"]["playable"])
self.assertTrue(
"top_league_early_market_suppressed" in summary["HT_OU05"]["reasons"]
or "top_league_ht_ou05_over_disabled" in summary["HT_OU05"]["reasons"]
)
playable_goal_cluster = [
item for item in package["bet_summary"]
if item["market"] in {"OU15", "OU25", "OU35", "BTTS"} and item["playable"]
]
self.assertLessEqual(len(playable_goal_cluster), 1)
def test_scoreline_consistency_blocks_conflicting_markets(self):
rows = [
{
"market": "MS",
"raw_pick": "1",
"pick": "1",
"playable": True,
"bet_grade": "A",
"stake_units": 1.0,
"decision_reasons": [],
},
{
"market": "BTTS",
"raw_pick": "Yes",
"pick": "KG Var",
"playable": True,
"bet_grade": "A",
"stake_units": 1.0,
"decision_reasons": [],
},
{
"market": "OU25",
"raw_pick": "Over",
"pick": "2.5 Üst",
"playable": True,
"bet_grade": "A",
"stake_units": 1.0,
"decision_reasons": [],
},
{
"market": "OU25",
"raw_pick": "Under",
"pick": "2.5 Alt",
"playable": True,
"bet_grade": "A",
"stake_units": 1.0,
"decision_reasons": [],
},
]
prediction = _build_prediction()
prediction.predicted_ft_score = "1-0"
prediction.predicted_ht_score = "1-0"
controlled = self.engine._apply_scoreline_consistency_controls(rows, prediction)
by_market_pick = {(row["market"], row["raw_pick"]): row for row in controlled}
self.assertTrue(by_market_pick[("MS", "1")]["playable"])
self.assertIn(
"scoreline_scenario_aligned",
by_market_pick[("MS", "1")]["decision_reasons"],
)
self.assertFalse(by_market_pick[("BTTS", "Yes")]["playable"])
self.assertFalse(by_market_pick[("OU25", "Over")]["playable"])
self.assertTrue(by_market_pick[("OU25", "Under")]["playable"])
self.assertIn(
"scoreline_scenario_conflict",
by_market_pick[("BTTS", "Yes")]["decision_reasons"],
)
if __name__ == "__main__":
unittest.main()
mds/changelog-2026-04-22.md
+155
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@@ -0,0 +1,155 @@
# 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
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@@ -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
+331 -696
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src/common/constants/roles.ts
+12
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@@ -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/utils/ai-engine-client.ts
+267
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@@ -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/match-status.util.ts
+203
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@@ -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/env.validation.ts
+1
View File
@@ -23,6 +23,7 @@ export const envSchema = z.object({
DATABASE_URL: z.string().url(),
// AI Engine
AI_ENGINE_URL: z.string().url().default("http://localhost:8000"),
AI_ENGINE_MODE: z.enum(["v25", "dual", "v26"]).default("v25"),
// JWT
JWT_SECRET: z.string().min(32),
src/modules/admin/admin.controller.ts
+6
View File
@@ -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.match.count(),
this.prisma.prediction.count(),
this.prisma.userCoupon.count(),
]);
return createSuccessResponse({
totalUsers,
activeUsers,
totalPredictions,
totalCoupons,
users: {
total: totalUsers,
active: activeUsers,
src/modules/auth/guards/auth.guards.ts
+21 -2
View File
@@ -13,11 +13,13 @@ import {
ROLES_KEY,
PERMISSIONS_KEY,
} from "../../../common/decorators";
import { normalizeRole } from "../../../common/constants/roles";
interface AuthenticatedUser {
id: string;
email: string;
roles: string[];
role?: string;
permissions: string[];
}
@@ -88,11 +90,28 @@ 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");
}
src/modules/auth/strategies/jwt.strategy.ts
+6 -1
View File
@@ -3,6 +3,7 @@ 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) {
@@ -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
+37 -1
View File
@@ -27,6 +27,7 @@ import {
AnalyzeMatchDto,
DailyBankoDto,
SuggestCouponDto,
FrequencyCouponDto,
} from "./dto/coupons-request.dto";
import { Public } from "../../common/decorators";
import { JwtAuthGuard } from "../auth/guards/auth.guards"; // Assuming standard guard
@@ -188,8 +189,43 @@ export class CouponsController {
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
// ============================================
/**
src/modules/coupons/coupons.module.ts
+14 -2
View File
@@ -2,6 +2,7 @@ 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";
@@ -10,7 +11,18 @@ 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/dto/coupons-request.dto.ts
+44
View File
@@ -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
+265 -14
View File
@@ -1,6 +1,14 @@
import { HttpException, HttpStatus, Injectable, Logger } from "@nestjs/common";
import axios from "axios";
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";
@@ -126,24 +134,37 @@ 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) {
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: ${detail}`,
error.response?.status || HttpStatus.SERVICE_UNAVAILABLE,
error.status || HttpStatus.SERVICE_UNAVAILABLE,
);
}
throw new HttpException(
@@ -205,8 +226,8 @@ export class SmartCouponService {
options: { maxMatches?: number; minConfidence?: number } = {},
): Promise<SmartCouponResult> {
try {
const response = await axios.post<SmartCouponResult>(
`${this.aiEngineUrl}/v20plus/coupon`,
const response = await this.aiEngineClient.post<SmartCouponResult>(
"/v20plus/coupon",
{
match_ids: matchIds,
strategy,
@@ -215,13 +236,14 @@ export class SmartCouponService {
},
);
return response.data;
} catch (error) {
} catch (error: unknown) {
this.logger.error("Failed to generate smart coupon", error);
if (axios.isAxiosError(error)) {
const detail = error.response?.data?.detail || error.message;
if (error instanceof AiEngineRequestError) {
const detail =
typeof error.detail === "string" ? error.detail : error.message;
throw new HttpException(
`Coupon generation failed: ${detail}`,
error.response?.status || HttpStatus.SERVICE_UNAVAILABLE,
error.status || HttpStatus.SERVICE_UNAVAILABLE,
);
}
throw new HttpException(
@@ -230,6 +252,235 @@ export class SmartCouponService {
);
}
}
// ─────────────────────────────────────────────────────────────
// FREQUENCY-BASED COUPON ENGINE
// ─────────────────────────────────────────────────────────────
async generateFrequencyBasedCoupon(options: {
matchIds?: string[];
maxMatches?: number;
minSignal?: number;
markets?: string[];
}): Promise<FrequencyCouponResult> {
const maxMatches = options.maxMatches ?? 3;
const minSignal = options.minSignal ?? 0.70;
const allowedMarkets = options.markets?.map((m) => m.toUpperCase()) || null;
this.logger.log(
`[FrequencyCoupon] Starting — max=${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/feeder/feeder-persistence.service.ts
+10 -31
View File
@@ -22,6 +22,7 @@ import {
BasketballTeamStats,
} from "./feeder.types";
import { ImageUtils } from "../../common/utils/image.util";
import { deriveStoredMatchStatus } from "../../common/utils/match-status.util";
@Injectable()
export class FeederPersistenceService {
@@ -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 },
@@ -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: {} } },
src/modules/feeder/feeder.service.ts
+31 -50
View File
@@ -24,6 +24,7 @@ import {
DbEventPayload,
DbMarketPayload,
} from "./feeder.types";
import { isMatchCompleted } from "../../common/utils/match-status.util";
interface ProcessDateOptions {
onlyCompletedMatches?: boolean;
@@ -113,51 +114,16 @@ export class FeederService {
};
}
private parseScoreValue(value: unknown): number | null {
if (value === null || value === undefined || value === "") return null;
const parsed = Number(value);
return Number.isFinite(parsed) ? parsed : null;
}
private isCompletedMatchSummary(match: MatchSummary): boolean {
if (match.statusBoxContent === "ERT") return false;
const normalizedState = String(match.state || "")
.trim()
.toLowerCase();
const normalizedStatus = String(match.status || "")
.trim()
.toLowerCase();
const normalizedSubstate = String(match.substate || "")
.trim()
.toLowerCase();
if (["postgame", "post"].includes(normalizedState)) return true;
if (
["played", "finished", "ft", "afterpenalties", "penalties"].includes(
normalizedStatus,
)
) {
return true;
}
if (
["postgame", "post", "played", "finished", "ft"].includes(
normalizedSubstate,
)
) {
return true;
}
const homeScore = this.parseScoreValue(
match.score?.home ?? match.homeScore,
);
const awayScore = this.parseScoreValue(
match.score?.away ?? match.awayScore,
);
return homeScore !== null && awayScore !== null;
return isMatchCompleted({
state: match.state,
status: match.status,
substate: match.substate,
statusBoxContent: match.statusBoxContent,
score: match.score,
scoreHome: match.homeScore,
scoreAway: match.awayScore,
});
}
async runPreviousDayCompletedMatchesScan(
@@ -957,15 +923,30 @@ export class FeederService {
*/
// ==========================================
if (saved && hasCriticalError) {
// Collect missing components
const missingParts: string[] = [];
if (!stats) missingParts.push("Stats");
const completedMatch = isMatchCompleted({
state: headerData?.matchStatus ?? matchSummary.state,
status: matchSummary.status,
substate: matchSummary.substate,
statusBoxContent: matchSummary.statusBoxContent,
scoreHome: headerData?.scoreHome ?? matchSummary.score?.home,
scoreAway: headerData?.scoreAway ?? matchSummary.score?.away,
});
const missingParts: string[] = [];
if (scope === "all" && completedMatch) {
if (sport === "football" && !stats) missingParts.push("Stats");
if (sport === "basketball" && !basketballTeamStats)
missingParts.push("BoxScore");
if (oddsArray.length === 0) missingParts.push("Odds");
if (officialsData.length === 0) missingParts.push("Officials");
}
if (saved && (hasCriticalError || missingParts.length > 0)) {
const reason = hasCriticalError
? "missing data after upstream errors"
: "incomplete completed-match payload";
this.logger.warn(
`[${matchId}] Saved with MISSING DATA (502). Missing: [${missingParts.join(", ")}]. Scheduled for retry.`,
`[${matchId}] Saved with ${reason}. Missing: [${missingParts.join(", ")}]. Scheduled for retry.`,
);
return { success: false, retryable: true };
}
src/modules/health/health.controller.ts
+64 -18
View File
@@ -1,44 +1,90 @@
import { Controller, Get } from "@nestjs/common";
import { Controller, Get, Res } from "@nestjs/common";
import { ApiTags, ApiOperation } from "@nestjs/swagger";
import {
HealthCheck,
HealthCheckService,
PrismaHealthIndicator,
} from "@nestjs/terminus";
import type { Response } from "express";
import { Public } from "../../common/decorators";
import { PrismaService } from "../../database/prisma.service";
import { PredictionsService } from "../predictions/predictions.service";
@ApiTags("Health")
@Controller("health")
export class HealthController {
constructor(
private health: HealthCheckService,
private prismaHealth: PrismaHealthIndicator,
private prisma: PrismaService,
private readonly predictionsService: PredictionsService,
) {}
@Get()
@Public()
@HealthCheck()
@ApiOperation({ summary: "Basic health check" })
check() {
return this.health.check([]);
async check(@Res() response: Response) {
const database = await this.getDatabaseHealth();
const aiEngine = await this.predictionsService.checkHealth();
const ok = database.status === "up" && aiEngine.predictionServiceReady;
return response.status(ok ? 200 : 503).json({
status: ok ? "ok" : "degraded",
timestamp: new Date().toISOString(),
checks: {
database,
aiEngine,
},
});
}
@Get("ready")
@Public()
@HealthCheck()
@ApiOperation({ summary: "Readiness check (includes database)" })
readiness() {
return this.health.check([
() => this.prismaHealth.pingCheck("database", this.prisma),
]);
async readiness(@Res() response: Response) {
const database = await this.getDatabaseHealth();
const aiEngine = await this.predictionsService.checkHealth();
const ready = database.status === "up" && aiEngine.predictionServiceReady;
return response.status(ready ? 200 : 503).json({
status: ready ? "ready" : "not_ready",
timestamp: new Date().toISOString(),
checks: {
database,
aiEngine,
},
});
}
@Get("live")
@Public()
@ApiOperation({ summary: "Liveness check" })
liveness() {
return { status: "ok", timestamp: new Date().toISOString() };
liveness(@Res() response: Response) {
return response
.status(200)
.json({ status: "ok", timestamp: new Date().toISOString() });
}
@Get("dependencies")
@Public()
@ApiOperation({ summary: "Dependency-level health details" })
async dependencies(@Res() response: Response) {
const database = await this.getDatabaseHealth();
const aiEngine = await this.predictionsService.checkHealth();
return response.status(200).json({
timestamp: new Date().toISOString(),
checks: {
database,
aiEngine,
},
});
}
private async getDatabaseHealth() {
try {
await this.prisma.$queryRaw`SELECT 1`;
return {
status: "up",
};
} catch (error: unknown) {
return {
status: "down",
detail: error instanceof Error ? error.message : "Unknown database error",
};
}
}
}
src/modules/health/health.module.ts
+2 -4
View File
@@ -1,11 +1,9 @@
import { Module } from "@nestjs/common";
import { TerminusModule } from "@nestjs/terminus";
import { PrismaHealthIndicator } from "@nestjs/terminus";
import { HealthController } from "./health.controller";
import { PredictionsModule } from "../predictions/predictions.module";
@Module({
imports: [TerminusModule],
imports: [PredictionsModule],
controllers: [HealthController],
providers: [PrismaHealthIndicator],
})
export class HealthModule {}
src/modules/leagues/leagues.controller.ts
+7 -4
View File
@@ -119,20 +119,23 @@ export class LeaguesController {
/**
* GET /leagues/teams/:id/matches
* Get team's recent matches
* Get team's recent matches (paginated)
*/
@Get("teams/:id/matches")
@Public()
@ApiOperation({ summary: "Get team's recent matches" })
@ApiOperation({ summary: "Get team's recent matches (paginated)" })
@ApiParam({ name: "id", description: "Team ID" })
@ApiQuery({ name: "limit", required: false, type: Number })
@ApiQuery({ name: "page", required: false, type: Number, description: "Page number (default: 1)" })
@ApiQuery({ name: "limit", required: false, type: Number, description: "Items per page (default: 20)" })
async getTeamMatches(
@Param("id") id: string,
@Query("page") page?: string,
@Query("limit") limit?: string,
) {
return this.leaguesService.getTeamRecentMatches(
id,
parseInt(limit || "10", 10),
parseInt(page || "1", 10),
parseInt(limit || "20", 10),
);
}
src/modules/leagues/leagues.service.ts
+33 -14
View File
@@ -99,21 +99,40 @@ export class LeaguesService {
}
/**
* Get team's matches (past + upcoming)
* Get team's matches (past + upcoming) with pagination
*/
async getTeamRecentMatches(teamId: string, limit: number = 50) {
return this.prisma.match.findMany({
where: {
OR: [{ homeTeamId: teamId }, { awayTeamId: teamId }],
},
include: {
homeTeam: true,
awayTeam: true,
league: { include: { country: true } },
},
orderBy: { mstUtc: "desc" },
take: limit,
});
async getTeamRecentMatches(
teamId: string,
page: number = 1,
limit: number = 20,
) {
const skip = (page - 1) * limit;
const where = {
OR: [{ homeTeamId: teamId }, { awayTeamId: teamId }],
};
const [data, total] = await this.prisma.$transaction([
this.prisma.match.findMany({
where,
include: {
homeTeam: true,
awayTeam: true,
league: { include: { country: true } },
},
orderBy: { mstUtc: "desc" },
skip,
take: limit,
}),
this.prisma.match.count({ where }),
]);
return {
data,
total,
page,
limit,
totalPages: Math.ceil(total / limit),
};
}
/**
src/modules/matches/matches.service.ts
+34 -35
View File
@@ -9,6 +9,13 @@ import {
ActiveLeagueDto,
} from "./dto";
import { Prisma } from "@prisma/client";
import {
FINISHED_STATE_VALUES_FOR_DB,
FINISHED_STATUS_VALUES_FOR_DB,
LIVE_STATE_VALUES_FOR_DB,
LIVE_STATUS_VALUES_FOR_DB,
getDisplayMatchStatus,
} from "../../common/utils/match-status.util";
@Injectable()
export class MatchesService {
@@ -38,23 +45,12 @@ export class MatchesService {
OR: [
{
status: {
in: [
"LIVE",
"1H",
"2H",
"HT",
"1Q",
"2Q",
"3Q",
"4Q",
"Playing",
"Half Time",
],
in: LIVE_STATUS_VALUES_FOR_DB,
},
},
{
state: {
in: ["live", "firsthalf", "secondhalf"],
in: LIVE_STATE_VALUES_FOR_DB,
},
},
],
@@ -66,14 +62,23 @@ export class MatchesService {
OR: [
{
status: {
in: ["Finished", "Played", "FT", "AET", "PEN", "Ended"],
in: FINISHED_STATUS_VALUES_FOR_DB,
},
},
{
state: {
in: ["Finished", "post", "FT", "postGame"],
in: FINISHED_STATE_VALUES_FOR_DB,
},
},
{
AND: [
{ scoreHome: { not: null } },
{ scoreAway: { not: null } },
{
NOT: this.getLiveFilter(),
},
],
},
],
};
}
@@ -325,16 +330,13 @@ export class MatchesService {
}
// Map status for frontend
let displayStatus = match.status || "NS";
if (match.state === "live") {
displayStatus = "LIVE";
} else if (
match.state === "post" ||
match.state === "FT" ||
match.status === "Finished"
) {
displayStatus = "Finished";
}
const displayStatus = getDisplayMatchStatus({
state: match.state,
status: match.status,
substate: match.substate,
scoreHome: match.scoreHome,
scoreAway: match.scoreAway,
});
league.matches.push({
id: match.id,
@@ -562,16 +564,13 @@ export class MatchesService {
if (liveMatch) {
// Map liveMatch status
let displayStatus = liveMatch.status || "NS";
if (liveMatch.state === "live") {
displayStatus = "LIVE";
} else if (
liveMatch.state === "post" ||
liveMatch.state === "FT" ||
liveMatch.status === "Finished"
) {
displayStatus = "Finished";
}
const displayStatus = getDisplayMatchStatus({
state: liveMatch.state,
status: liveMatch.status,
substate: liveMatch.substate,
scoreHome: liveMatch.scoreHome,
scoreAway: liveMatch.scoreAway,
});
match = {
...liveMatch,
src/modules/predictions/dto/index.ts
+50
View File
@@ -115,6 +115,9 @@ export class MatchPickDto {
@ApiProperty()
market: string;
@ApiProperty({ required: false, default: "standard" })
strategy_channel?: string;
@ApiProperty()
pick: string;
@@ -350,6 +353,15 @@ export class MatchPredictionDto {
@ApiProperty()
model_version: string;
@ApiProperty({ required: false, nullable: true })
calibration_version?: string | null;
@ApiProperty({ required: false, nullable: true })
shadow_engine_version?: string | null;
@ApiProperty({ required: false, nullable: true })
decision_trace_id?: string | null;
@ApiProperty({ type: MatchInfoDto })
match_info: MatchInfoDto;
@@ -368,6 +380,9 @@ export class MatchPredictionDto {
@ApiProperty({ type: MatchPickDto, nullable: true })
value_pick: MatchPickDto | null;
@ApiProperty({ type: MatchPickDto, nullable: true, required: false })
surprise_pick?: MatchPickDto | null;
@ApiProperty({ type: MatchBetAdviceDto })
bet_advice: MatchBetAdviceDto;
@@ -394,6 +409,23 @@ export class MatchPredictionDto {
@ApiProperty({ type: [String] })
reasoning_factors: string[];
@ApiProperty({ type: Object, required: false })
market_reliability?: Record<string, number>;
@ApiProperty({ type: Object, required: false })
shadow_engine?: Record<string, unknown>;
@ApiProperty({ type: Object, required: false })
surprise_hunter?: Record<string, unknown>;
@ApiProperty({
type: Object,
required: false,
description:
"V28 Odds-Band engine output: historical band analytics, triple-value detection, cards profiling, and HTFT 9-combo analysis",
})
v27_engine?: Record<string, unknown>;
}
export class ValueBetDto {
@@ -461,6 +493,24 @@ export class AIHealthDto {
@ApiProperty()
predictionServiceReady: boolean;
@ApiProperty({ required: false, default: true })
aiEngineReachable?: boolean;
@ApiProperty({ required: false, enum: ["closed", "open"] })
circuitState?: "closed" | "open";
@ApiProperty({ required: false, default: 0 })
consecutiveFailures?: number;
@ApiProperty({ required: false })
endpoint?: string;
@ApiProperty({ required: false, nullable: true })
detail?: string | null;
@ApiProperty({ required: false, nullable: true })
mode?: string | null;
}
export * from "./smart-coupon.dto";
src/modules/predictions/predictions.service.ts
+207 -21
View File
@@ -19,11 +19,14 @@ import {
ValueBetDto,
AIHealthDto,
} from "./dto";
import axios, { AxiosError } from "axios";
import { Prisma } from "@prisma/client";
import { FeederService } from "../feeder/feeder.service";
import * as fs from "node:fs";
import * as path from "node:path";
import {
AiEngineClient,
AiEngineRequestError,
} from "../../common/utils/ai-engine-client";
type ConfidenceBand = "HIGH" | "MEDIUM" | "LOW";
@@ -45,6 +48,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
private readonly logger = new Logger(PredictionsService.name);
private queueEvents: QueueEvents | null = null;
private readonly aiEngineUrl: string;
private readonly aiEngineClient: AiEngineClient;
private readonly topLeagueIds = new Set<string>();
private readonly reasonTranslations: Record<string, string> = {
confidence_below_threshold: "Güven eşiğin altında",
@@ -125,6 +129,14 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
"AI_ENGINE_URL",
"http://localhost:8000",
);
this.aiEngineClient = new AiEngineClient({
baseUrl: this.aiEngineUrl,
logger: this.logger,
serviceName: PredictionsService.name,
timeoutMs: 60000,
maxRetries: 2,
retryDelayMs: 750,
});
this.topLeagueIds = this.loadTopLeagueIds();
}
@@ -149,12 +161,55 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
}
}
checkHealth(): Promise<AIHealthDto> {
return Promise.resolve({
status: "healthy",
modelLoaded: true,
predictionServiceReady: true,
});
async checkHealth(): Promise<AIHealthDto> {
const circuit = this.aiEngineClient.getSnapshot();
try {
const response = await this.aiEngineClient.get<{
status?: string;
model_loaded?: boolean;
prediction_service_ready?: boolean;
}>("/health", {
timeout: 5000,
retryCount: 0,
});
return {
status: response.data?.status || "healthy",
modelLoaded: response.data?.model_loaded ?? true,
predictionServiceReady:
response.data?.prediction_service_ready ?? true,
aiEngineReachable: true,
circuitState: circuit.state,
consecutiveFailures: circuit.consecutiveFailures,
endpoint: this.aiEngineUrl,
mode:
typeof (response.data as Record<string, unknown>)?.mode === "string"
? String((response.data as Record<string, unknown>).mode)
: this.configService.get("AI_ENGINE_MODE", "v25"),
};
} catch (error: unknown) {
const requestError =
error instanceof AiEngineRequestError
? error
: new AiEngineRequestError("AI health check failed");
return {
status: requestError.isCircuitOpen ? "circuit_open" : "unhealthy",
modelLoaded: false,
predictionServiceReady: false,
aiEngineReachable: false,
circuitState: this.aiEngineClient.getSnapshot().state,
consecutiveFailures:
this.aiEngineClient.getSnapshot().consecutiveFailures,
endpoint: this.aiEngineUrl,
detail:
typeof requestError.detail === "string"
? requestError.detail
: requestError.message,
mode: this.configService.get("AI_ENGINE_MODE", "v25"),
};
}
}
async getPredictionById(matchId: string): Promise<MatchPredictionDto | null> {
@@ -169,6 +224,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
if (!data || data.error) {
return null;
}
await this.recordPredictionRun(matchId, data as MatchPredictionDto);
return this.enrichPredictionResponse(
data as MatchPredictionDto,
matchContext,
@@ -182,22 +238,22 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
// Direct HTTP mode (no Redis)
try {
const response = await axios.post(
`${this.aiEngineUrl}/v20plus/analyze/${matchId}`,
const response = await this.aiEngineClient.post<MatchPredictionDto>(
`/v20plus/analyze/${matchId}`,
{},
{ timeout: 60000 },
);
await this.recordPredictionRun(matchId, response.data);
return this.enrichPredictionResponse(
response.data as MatchPredictionDto,
matchContext,
);
} catch (e: unknown) {
const error = e as AxiosError<Record<string, unknown>>;
const status = error?.response?.status;
const detail =
error?.response?.data?.detail ||
error?.response?.data ||
error?.message;
const requestError =
e instanceof AiEngineRequestError
? e
: new AiEngineRequestError("AI Engine request failed");
const status = requestError.status;
const detail = requestError.detail || requestError.message;
this.logger.error(
`Direct AI Engine call failed for ${matchId}: status=${status}, detail=${JSON.stringify(detail)}`,
);
@@ -988,14 +1044,18 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
// Direct HTTP mode
try {
const response = await axios.post(
`${this.aiEngineUrl}/smart-coupon`,
const response = await this.aiEngineClient.post(
"/smart-coupon",
{ match_ids: matchIds, strategy, ...options },
{ timeout: 60000 },
);
return response.data;
} catch (error) {
const message = error instanceof Error ? error.message : String(error);
} catch (error: unknown) {
const message =
error instanceof AiEngineRequestError
? error.message
: error instanceof Error
? error.message
: String(error);
this.logger.error(`Direct smart coupon call failed: ${message}`);
this.throwAiError(message);
}
@@ -1018,6 +1078,12 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
HttpStatus.BAD_GATEWAY,
);
}
if (message.includes("circuit breaker is open")) {
throw new HttpException(
"AI Engine is temporarily unavailable",
HttpStatus.SERVICE_UNAVAILABLE,
);
}
throw new HttpException(
"Failed to get prediction from AI Engine",
HttpStatus.SERVICE_UNAVAILABLE,
@@ -1169,4 +1235,124 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
HttpStatus.UNPROCESSABLE_ENTITY,
);
}
private async recordPredictionRun(
matchId: string,
payload: MatchPredictionDto,
): Promise<void> {
try {
const oddsSnapshot = await this.getPredictionOddsSnapshot(matchId);
const payloadSummary = this.buildPredictionPayloadSummary(payload);
await this.prisma.$executeRawUnsafe(
`
INSERT INTO prediction_runs (
match_id,
engine_version,
decision_trace_id,
odds_snapshot,
payload_summary
)
VALUES ($1, $2, $3, $4::jsonb, $5::jsonb)
`,
matchId,
String(payload.model_version || "unknown"),
typeof payload.decision_trace_id === "string"
? payload.decision_trace_id
: null,
JSON.stringify(oddsSnapshot),
JSON.stringify(payloadSummary),
);
} catch (error) {
const message = error instanceof Error ? error.message : String(error);
this.logger.warn(`Prediction run audit skipped for ${matchId}: ${message}`);
}
}
private async getPredictionOddsSnapshot(
matchId: string,
): Promise<Record<string, unknown>> {
const liveMatch = await this.prisma.liveMatch.findUnique({
where: { id: matchId },
select: {
odds: true,
oddsUpdatedAt: true,
state: true,
status: true,
scoreHome: true,
scoreAway: true,
},
});
if (liveMatch) {
return {
source: "live_match",
odds: liveMatch.odds ?? {},
odds_updated_at: liveMatch.oddsUpdatedAt?.toISOString() ?? null,
state: liveMatch.state ?? null,
status: liveMatch.status ?? null,
score_home: liveMatch.scoreHome ?? null,
score_away: liveMatch.scoreAway ?? null,
};
}
const oddCategoryCount = await this.prisma.oddCategory.count({
where: { matchId },
});
return {
source: "historical_match",
odd_category_count: oddCategoryCount,
};
}
private buildPredictionPayloadSummary(
payload: MatchPredictionDto,
): Record<string, unknown> {
const topSummary = Array.isArray(payload.bet_summary)
? payload.bet_summary.slice(0, 5).map((item) => ({
market: item.market,
pick: item.pick,
playable: item.playable,
bet_grade: item.bet_grade,
calibrated_confidence: item.calibrated_confidence,
ev_edge: item.ev_edge ?? 0,
stake_units: item.stake_units,
}))
: [];
return {
model_version: payload.model_version,
calibration_version: payload.calibration_version ?? null,
shadow_engine_version: payload.shadow_engine_version ?? null,
decision_trace_id: payload.decision_trace_id ?? null,
main_pick: payload.main_pick
? {
market: payload.main_pick.market,
pick: payload.main_pick.pick,
playable: payload.main_pick.playable,
bet_grade: payload.main_pick.bet_grade,
calibrated_confidence: payload.main_pick.calibrated_confidence,
ev_edge: payload.main_pick.ev_edge ?? 0,
stake_units: payload.main_pick.stake_units,
}
: null,
value_pick: payload.value_pick
? {
market: payload.value_pick.market,
pick: payload.value_pick.pick,
playable: payload.value_pick.playable,
bet_grade: payload.value_pick.bet_grade,
calibrated_confidence: payload.value_pick.calibrated_confidence,
ev_edge: payload.value_pick.ev_edge ?? 0,
}
: null,
bet_advice: {
playable: payload.bet_advice?.playable ?? false,
suggested_stake_units:
payload.bet_advice?.suggested_stake_units ?? 0,
reason: payload.bet_advice?.reason ?? null,
},
top_summary: topSummary,
market_reliability: payload.market_reliability ?? {},
shadow_engine: payload.shadow_engine ?? null,
};
}
}
src/modules/social-poster/social-poster.controller.ts
+1 -1
View File
@@ -8,7 +8,7 @@ import { RolesGuard } from "../auth/guards/auth.guards";
@ApiTags("Social Poster")
@ApiBearerAuth()
@UseGuards(RolesGuard)
@Roles("admin")
@Roles("superadmin")
@Controller("social-poster")
export class SocialPosterController {
constructor(private readonly socialPosterService: SocialPosterService) {}
src/modules/spor-toto/spor-toto.controller.ts
+3 -3
View File
@@ -43,7 +43,7 @@ export class SporTotoController {
@Post("sync")
@UseGuards(JwtAuthGuard)
@Roles("admin")
@Roles("superadmin")
@ApiBearerAuth()
@HttpCode(HttpStatus.OK)
@ApiOperation({
@@ -114,7 +114,7 @@ export class SporTotoController {
@Post("bulletins")
@UseGuards(JwtAuthGuard)
@Roles("admin")
@Roles("superadmin")
@ApiBearerAuth()
@HttpCode(HttpStatus.CREATED)
@ApiOperation({
@@ -135,7 +135,7 @@ export class SporTotoController {
@Patch("bulletins/:id/results")
@UseGuards(JwtAuthGuard)
@Roles("admin")
@Roles("superadmin")
@ApiBearerAuth()
@HttpCode(HttpStatus.OK)
@ApiOperation({
src/modules/users/users.controller.ts
+2 -2
View File
@@ -84,7 +84,7 @@ export class UsersController extends BaseController<
}
// Override create to require admin role
@Roles("admin")
@Roles("superadmin")
async create(
...args: Parameters<
BaseController<User, CreateUserDto, UpdateUserDto>["create"]
@@ -94,7 +94,7 @@ export class UsersController extends BaseController<
}
// Override delete to require admin role
@Roles("admin")
@Roles("superadmin")
async delete(
...args: Parameters<
BaseController<User, CreateUserDto, UpdateUserDto>["delete"]
src/services/ai.service.ts
+29 -16
View File
@@ -1,7 +1,9 @@
import { Injectable, Logger } from "@nestjs/common";
import { HttpService } from "@nestjs/axios";
import { ConfigService } from "@nestjs/config";
import { firstValueFrom } from "rxjs";
import {
AiEngineClient,
AiEngineRequestError,
} from "../common/utils/ai-engine-client";
export interface AIPredictionResult {
matchId: string;
@@ -40,13 +42,21 @@ export interface AIPredictionResult {
export class AiService {
private readonly logger = new Logger(AiService.name);
private readonly pythonEngineUrl: string;
private readonly aiEngineClient: AiEngineClient;
constructor(
private readonly httpService: HttpService,
private readonly configService: ConfigService,
) {
this.pythonEngineUrl =
this.configService.get("AI_ENGINE_URL") || "http://127.0.0.1:8000";
this.aiEngineClient = new AiEngineClient({
baseUrl: this.pythonEngineUrl,
logger: this.logger,
serviceName: AiService.name,
timeoutMs: 30000,
maxRetries: 2,
retryDelayMs: 500,
});
}
/**
@@ -71,14 +81,9 @@ export class AiService {
`Calling Python V25 Engine for ${matchDetails.homeTeam} vs ${matchDetails.awayTeam}`,
);
const response = await firstValueFrom(
this.httpService.post(
`${this.pythonEngineUrl}/v20plus/analyze/${matchId}`,
{},
{
timeout: 30000,
},
),
const response = await this.aiEngineClient.post(
`/v20plus/analyze/${matchId}`,
{},
);
if (response.data) {
@@ -86,8 +91,14 @@ export class AiService {
}
return null;
} catch (error: any) {
this.logger.warn(`Python Engine error: ${error.message}`);
} catch (error: unknown) {
const message =
error instanceof AiEngineRequestError
? error.message
: error instanceof Error
? error.message
: "Unknown AI engine error";
this.logger.warn(`Python Engine error: ${message}`);
return null;
}
}
@@ -286,10 +297,12 @@ export class AiService {
*/
async checkHealth(): Promise<boolean> {
try {
const response = await firstValueFrom(
this.httpService.get(`${this.pythonEngineUrl}/health`, {
const response = await this.aiEngineClient.get<{ status?: string }>(
"/health",
{
timeout: 5000,
}),
retryCount: 0,
},
);
return response.data?.status === "healthy";
} catch {
src/tasks/data-fetcher.task.ts
+177 -98
View File
@@ -1,4 +1,4 @@
import { Injectable, Logger } from "@nestjs/common";
import { Injectable, Logger } from "@nestjs/common";
import { Cron } from "@nestjs/schedule";
import { HttpService } from "@nestjs/axios";
import { PrismaService } from "../database/prisma.service";
@@ -8,10 +8,22 @@ import * as fs from "fs";
import * as path from "path";
import { Prisma } from "@prisma/client";
import { SidelinedResponse } from "../modules/feeder/feeder.types";
import {
FINISHED_STATE_VALUES_FOR_DB,
FINISHED_STATUS_VALUES_FOR_DB,
LIVE_STATE_VALUES_FOR_DB,
LIVE_STATUS_VALUES_FOR_DB,
} from "../common/utils/match-status.util";
import {
getDateStringInTimeZone,
getDayBoundsForTimeZone,
getShiftedDateStringInTimeZone,
} from "../common/utils/timezone.util";
import { TaskLockService } from "./task-lock.service";
// ────────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────────
// Types
// ────────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────────
interface LiveScoreTeamPayload {
id: string;
@@ -64,75 +76,119 @@ interface LiveLineupsJson {
type SportType = "football" | "basketball";
// ────────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────────
// Service
// ────────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────────
@Injectable()
export class DataFetcherTask {
private readonly logger = new Logger(DataFetcherTask.name);
private readonly timeZone = "Europe/Istanbul";
constructor(
private readonly httpService: HttpService,
private readonly prisma: PrismaService,
private readonly scraper: FeederScraperService,
private readonly taskLock: TaskLockService,
) {}
// ────────────────────────────────────────────────────────────
// CRON 1: Main sync — every 15 minutes
// Phases: match list → live scores → odds → lineups
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// CRON 1: Main sync — every 15 minutes
// Phases: match list → live scores → odds → lineups
// ────────────────────────────────────────────────────────────
@Cron("*/15 * * * *")
async syncLiveMatches(): Promise<void> {
if (this.shouldSkipInHistoricalMode("syncLiveMatches")) return;
this.logger.log("━━━ syncLiveMatches START ━━━");
const today = new Date().toISOString().split("T")[0];
// Phase 1: Match list (football + basketball)
await this.syncMatchList(today);
// Phase 2: Live score updates
await this.updateLiveScores();
// Phase 3: Odds + referee + lineups + sidelined (via processMatchOdds)
await this.fetchOddsForMatches();
// Phase 4: Fill missing lineups (backup for edge cases)
await this.fillMissingLineups();
this.logger.log("━━━ syncLiveMatches END ━━━");
await this.taskLock.runWithLease(
"syncLiveMatches",
30 * 60 * 1000,
async () => {
await this.runLiveSync();
},
this.logger,
);
}
// ────────────────────────────────────────────────────────────
// CRON 2: Daily cleanup + full sync — 07:00 Istanbul
// Truncates live_matches, then runs full sync
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// CRON 2: Daily cleanup + full sync — 07:00 Istanbul
// Preserve yesterday as a fallback until the 08:00 archive job completes.
// ────────────────────────────────────────────────────────────
@Cron("0 7 * * *", { timeZone: "Europe/Istanbul" })
async cleanAndFullSync(): Promise<void> {
if (this.shouldSkipInHistoricalMode("cleanAndFullSync")) return;
this.logger.log("🧹 cleanAndFullSync: Truncating live_matches...");
await this.taskLock.runWithLease(
"cleanAndFullSync",
2 * 60 * 60 * 1000,
async () => {
this.logger.log(
"cleanAndFullSync: Pruning stale live_matches while preserving yesterday for archive fallback...",
);
try {
const deleted = await this.prisma.liveMatch.deleteMany({});
this.logger.log(
`🧹 Deleted ${deleted.count} live matches. Starting full sync...`,
);
} catch (error: unknown) {
const message = error instanceof Error ? error.message : String(error);
this.logger.error(`Truncate failed: ${message}`);
return;
}
try {
const yesterdayDate = getShiftedDateStringInTimeZone(
-1,
this.timeZone,
);
const { startMs: yesterdayStartMs } = getDayBoundsForTimeZone(
yesterdayDate,
this.timeZone,
);
const cutoffDate = new Date(yesterdayStartMs);
// Run full sync immediately after cleanup
await this.syncLiveMatches();
const deleted = await this.prisma.liveMatch.deleteMany({
where: {
OR: [
{ mstUtc: { lt: BigInt(yesterdayStartMs) } },
{
AND: [
{ mstUtc: null },
{ updatedAt: { lt: cutoffDate } },
{
OR: [
{ status: { in: FINISHED_STATUS_VALUES_FOR_DB } },
{ state: { in: FINISHED_STATE_VALUES_FOR_DB } },
],
},
],
},
],
},
});
this.logger.log(
`Pruned ${deleted.count} stale live matches. Starting full sync...`,
);
} catch (error: unknown) {
const message = error instanceof Error ? error.message : String(error);
this.logger.error(`Stale live_match cleanup failed: ${message}`);
return;
}
await this.runLiveSync();
},
this.logger,
);
}
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Phase 1: Fetch match list for all sports
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
private async runLiveSync(): Promise<void> {
if (this.shouldSkipInHistoricalMode("syncLiveMatches")) return;
this.logger.log("syncLiveMatches START");
const today = getDateStringInTimeZone(new Date(), this.timeZone);
await this.syncMatchList(today);
await this.updateLiveScores();
await this.fetchOddsForMatches();
await this.fillMissingLineups();
this.logger.log("syncLiveMatches END");
}
private async syncMatchList(date: string): Promise<void> {
// Football
@@ -141,7 +197,7 @@ export class DataFetcherTask {
await this.fetchMatchesForSport("football", date, footballLeagues);
} else {
this.logger.warn(
"top_leagues.json is missing/empty — writing ALL football matches",
"top_leagues.json is missing/empty — writing ALL football matches",
);
await this.fetchMatchesForSport("football", date, new Set());
}
@@ -170,17 +226,18 @@ export class DataFetcherTask {
}
}
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Phase 2: Live score updates (merged from live-updater.task)
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
private async updateLiveScores(): Promise<void> {
try {
const liveMatches = await this.prisma.liveMatch.findMany({
where: {
state: {
in: ["live", "firsthalf", "secondhalf", "1H", "2H", "HT", "LIVE"],
},
OR: [
{ state: { in: LIVE_STATE_VALUES_FOR_DB } },
{ status: { in: LIVE_STATUS_VALUES_FOR_DB } },
],
},
select: { id: true, matchSlug: true },
});
@@ -191,7 +248,7 @@ export class DataFetcherTask {
}
this.logger.log(
`📡 Updating scores for ${liveMatches.length} live matches`,
`📡 Updating scores for ${liveMatches.length} live matches`,
);
for (const match of liveMatches) {
@@ -219,19 +276,19 @@ export class DataFetcherTask {
}
}
this.logger.log("📡 Live score update complete");
this.logger.log("📡 Live score update complete");
} catch (error: unknown) {
const message = error instanceof Error ? error.message : String(error);
this.logger.error(`Live score update failed: ${message}`);
}
}
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Phase 3: Odds + referee + lineups + sidelined
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
private async fetchOddsForMatches(): Promise<void> {
this.logger.log("💰 Fetching odds for live matches...");
this.logger.log("💰 Fetching odds for live matches...");
try {
// Load both league filters
@@ -266,11 +323,11 @@ export class DataFetcherTask {
});
if (matchesToFetch.length === 0) {
this.logger.log("💰 No matches to fetch odds for");
this.logger.log("💰 No matches to fetch odds for");
return;
}
this.logger.log(`💰 Fetching odds for ${matchesToFetch.length} matches`);
this.logger.log(`💰 Fetching odds for ${matchesToFetch.length} matches`);
let successCount = 0;
let errorCount = 0;
@@ -299,7 +356,7 @@ export class DataFetcherTask {
// Retry failed matches (502/Timeout)
if (failedMatches.length > 0) {
this.logger.warn(
`⚠️ Retrying ${failedMatches.length} failed matches (502/Timeout)...`,
`⚠️ Retrying ${failedMatches.length} failed matches (502/Timeout)...`,
);
for (const match of failedMatches) {
@@ -307,19 +364,19 @@ export class DataFetcherTask {
try {
await this.processMatchOdds(match);
successCount++;
this.logger.log(`✅ Retry successful for match ${match.id}`);
this.logger.log(`✅ Retry successful for match ${match.id}`);
} catch (retryErr: unknown) {
const message =
retryErr instanceof Error ? retryErr.message : String(retryErr);
this.logger.error(
`❌ Retry failed for match ${match.id}: ${message}`,
`❌ Retry failed for match ${match.id}: ${message}`,
);
}
}
}
this.logger.log(
`💰 Odds complete: ${successCount} success, ${errorCount} errors (initially)`,
`💰 Odds complete: ${successCount} success, ${errorCount} errors (initially)`,
);
} catch (error: unknown) {
const message = error instanceof Error ? error.message : String(error);
@@ -327,14 +384,36 @@ export class DataFetcherTask {
}
}
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Phase 4: Fill missing lineups (backup)
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
private async fillMissingLineups(): Promise<void> {
try {
const matchesToUpdate = await this.prisma.liveMatch.findMany({
where: { status: { notIn: ["FT", "post", "postGame"] } },
where: {
sport: "football",
NOT: {
OR: [
{ status: { in: FINISHED_STATUS_VALUES_FOR_DB } },
{ state: { in: FINISHED_STATE_VALUES_FOR_DB } },
{
AND: [
{ scoreHome: { not: null } },
{ scoreAway: { not: null } },
{
NOT: {
OR: [
{ status: { in: LIVE_STATUS_VALUES_FOR_DB } },
{ state: { in: LIVE_STATE_VALUES_FOR_DB } },
],
},
},
],
},
],
},
},
select: { id: true, matchSlug: true, lineups: true, sport: true },
take: 30,
});
@@ -345,11 +424,11 @@ export class DataFetcherTask {
);
if (toUpdate.length === 0) {
this.logger.debug("👕 All lineups already filled");
this.logger.debug("👕 All lineups already filled");
return;
}
this.logger.log(`👕 Filling lineups for ${toUpdate.length} matches...`);
this.logger.log(`👕 Filling lineups for ${toUpdate.length} matches...`);
for (const match of toUpdate) {
try {
@@ -374,7 +453,7 @@ export class DataFetcherTask {
},
});
this.logger.log(`👕 Lineups filled for match ${match.id}`);
this.logger.log(`👕 Lineups filled for match ${match.id}`);
await this.delay(500);
} catch (err: unknown) {
const message = err instanceof Error ? err.message : String(err);
@@ -387,9 +466,9 @@ export class DataFetcherTask {
}
}
// ────────────────────────────────────────────────────────────
// Unified match fetcher — DRY for football + basketball
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Unified match fetcher — DRY for football + basketball
// ────────────────────────────────────────────────────────────
private async fetchMatchesForSport(
sport: SportType,
@@ -650,7 +729,7 @@ export class DataFetcherTask {
upsertCount + skippedCount === targetMatches.length
) {
this.logger.log(
`[${sport}] ⏳ Progress: ${upsertCount + skippedCount}/${targetMatches.length} (Saved: ${upsertCount}, Skipped: ${skippedCount})`,
`[${sport}] ⏳ Progress: ${upsertCount + skippedCount}/${targetMatches.length} (Saved: ${upsertCount}, Skipped: ${skippedCount})`,
);
}
} catch (err: unknown) {
@@ -668,10 +747,10 @@ export class DataFetcherTask {
}
}
// ────────────────────────────────────────────────────────────
// processMatchOdds — odds + referee + lineups + sidelined
// (Preserved from original — no logic changes)
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// processMatchOdds — odds + referee + lineups + sidelined
// (Preserved from original — no logic changes)
// ────────────────────────────────────────────────────────────
private async processMatchOdds(match: LiveMatchOddsTarget): Promise<void> {
const matchSlug = match.matchSlug || "match";
@@ -687,7 +766,7 @@ export class DataFetcherTask {
let lineups: LiveLineupsJson | null = null;
let sidelined: SidelinedResponse | null = null;
// 1. Fetch Odds from İddaa page
// 1. Fetch Odds from Ä°ddaa page
const oddsUrl = `https://www.mackolik.com/${sportPath}/${matchSlug}/iddaa/${match.id}`;
try {
const response = await firstValueFrom(
@@ -722,7 +801,7 @@ export class DataFetcherTask {
typeof mainResp.data === "string" ? mainResp.data : "",
);
} catch {
// Non-critical — referee is optional
// Non-critical — referee is optional
}
}
@@ -751,7 +830,7 @@ export class DataFetcherTask {
subs: substitutions?.stats?.away || [],
},
};
this.logger.log(`👥 Lineups found for ${match.matchName}`);
this.logger.log(`👥 Lineups found for ${match.matchName}`);
} else {
this.logger.debug(`No lineups (yet) for ${match.matchName}`);
}
@@ -779,7 +858,7 @@ export class DataFetcherTask {
sidelined.awayTeam?.totalSidelined > 0
) {
this.logger.log(
`🚑 Sidelined: ${sidelined.homeTeam.totalSidelined}(H) - ${sidelined.awayTeam.totalSidelined}(A) for ${match.matchName}`,
`🚑 Sidelined: ${sidelined.homeTeam.totalSidelined}(H) - ${sidelined.awayTeam.totalSidelined}(A) for ${match.matchName}`,
);
}
}
@@ -813,22 +892,22 @@ export class DataFetcherTask {
sidelined.awayTeam.totalSidelined > 0))
) {
this.logger.log(
`✅ Loop update: ${match.matchName} | Odds: ${Object.keys(odds).length} | Ref: ${refereeName || "N/A"} | Lineups: ${lineups ? "Yes" : "No"} | Sidelined: ${sidelined ? "Yes" : "No"}`,
`✅ Loop update: ${match.matchName} | Odds: ${Object.keys(odds).length} | Ref: ${refereeName || "N/A"} | Lineups: ${lineups ? "Yes" : "No"} | Sidelined: ${sidelined ? "Yes" : "No"}`,
);
} else {
this.logger.debug(
`❕ No detailed data for ${match.matchName}, marked check.`,
`❕ No detailed data for ${match.matchName}, marked check.`,
);
}
}
// ────────────────────────────────────────────────────────────
// HTML Extraction Helpers (preserved — no logic changes)
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// HTML Extraction Helpers (preserved — no logic changes)
// ────────────────────────────────────────────────────────────
/**
* Extract odds from Mackolik HTML page
* Returns structured odds object: { "MS": {"1": 2.10, "X": 3.40}, "AU25": {"Alt": 2.05, "Üst": 1.75} }
* Returns structured odds object: { "MS": {"1": 2.10, "X": 3.40}, "AU25": {"Alt": 2.05, "Ãœst": 1.75} }
*/
private extractOddsFromHtml(
html: string,
@@ -914,17 +993,17 @@ export class DataFetcherTask {
const lower = name.toLowerCase();
// Specific & Compound names FIRST
if (lower.includes("ilk yarı/maç sonucu")) return "HTFT";
if (lower.includes("1. yarı sonucu")) return "HT";
if (lower.includes("çifte şans")) return "CS";
if (lower.includes("ilk yarı/maç sonucu")) return "HTFT";
if (lower.includes("1. yarı sonucu")) return "HT";
if (lower.includes("çifte şans")) return "CS";
// General names LATER
if (lower.includes("maç sonucu") && !lower.includes("handikap"))
if (lower.includes("maç sonucu") && !lower.includes("handikap"))
return "MS";
if (lower.includes("karşılıklı gol")) return "KG";
if (lower.includes("2,5 alt/üst") || lower.includes("2.5")) return "AU25";
if (lower.includes("1,5 alt/üst") || lower.includes("1.5")) return "AU15";
if (lower.includes("3,5 alt/üst") || lower.includes("3.5")) return "AU35";
if (lower.includes("karşılıklı gol")) return "KG";
if (lower.includes("2,5 alt/üst") || lower.includes("2.5")) return "AU25";
if (lower.includes("1,5 alt/üst") || lower.includes("1.5")) return "AU15";
if (lower.includes("3,5 alt/üst") || lower.includes("3.5")) return "AU35";
return null;
}
@@ -934,7 +1013,7 @@ export class DataFetcherTask {
*/
private extractRefereeFromHtml(html: string): string | null {
try {
// Strategy 1: Mackolik officials section — head referee in '--main' list item
// Strategy 1: Mackolik officials section — head referee in '--main' list item
const mainOfficialPattern =
/official-list-item--main[^>]*>\s*(?:<[^>]*>\s*)*?<span[^>]*official-name[^>]*>\s*([^<]+)/i;
const mainMatch = mainOfficialPattern.exec(html);
@@ -970,9 +1049,9 @@ export class DataFetcherTask {
return null;
}
// ────────────────────────────────────────────────────────────
// Low-level Helpers (preserved — no logic changes)
// ────────────────────────────────────────────────────────────
// ────────────────────────────────────────────────────────────
// Low-level Helpers (preserved — no logic changes)
// ────────────────────────────────────────────────────────────
private shouldSkipInHistoricalMode(jobName: string): boolean {
if (process.env.FEEDER_MODE === "historical") {
src/tasks/historical-results-sync.task.ts
+23 -12
View File
@@ -1,12 +1,16 @@
import { Injectable, Logger } from "@nestjs/common";
import { Cron } from "@nestjs/schedule";
import { FeederService } from "../modules/feeder/feeder.service";
import { TaskLockService } from "./task-lock.service";
@Injectable()
export class HistoricalResultsSyncTask {
private readonly logger = new Logger(HistoricalResultsSyncTask.name);
constructor(private readonly feederService: FeederService) {}
constructor(
private readonly feederService: FeederService,
private readonly taskLock: TaskLockService,
) {}
private shouldSkipInHistoricalMode(jobName: string): boolean {
if (process.env.FEEDER_MODE === "historical") {
@@ -25,17 +29,24 @@ export class HistoricalResultsSyncTask {
return;
}
this.logger.log(
"Starting previous-day completed match sync for football and basketball...",
);
await this.taskLock.runWithLease(
"syncPreviousDayCompletedMatches",
6 * 60 * 60 * 1000,
async () => {
this.logger.log(
"Starting previous-day completed match sync for football and basketball...",
);
try {
await this.feederService.runPreviousDayCompletedMatchesScan();
this.logger.log("Previous-day completed match sync finished");
} catch (error: any) {
this.logger.error(
`Previous-day completed match sync failed: ${error.message}`,
);
}
try {
await this.feederService.runPreviousDayCompletedMatchesScan();
this.logger.log("Previous-day completed match sync finished");
} catch (error: any) {
this.logger.error(
`Previous-day completed match sync failed: ${error.message}`,
);
}
},
this.logger,
);
}
}
src/tasks/limit-resetter.task.ts
+126 -70
View File
@@ -1,12 +1,28 @@
import { Injectable, Logger } from "@nestjs/common";
import { Cron } from "@nestjs/schedule";
import { PrismaService } from "../database/prisma.service";
import {
FINISHED_STATE_VALUES_FOR_DB,
FINISHED_STATUS_VALUES_FOR_DB,
LIVE_STATE_VALUES_FOR_DB,
LIVE_STATUS_VALUES_FOR_DB,
} from "../common/utils/match-status.util";
import {
getDateOnlyValueForTimeZone,
getShiftedDateStringInTimeZone,
getDayBoundsForTimeZone,
} from "../common/utils/timezone.util";
import { TaskLockService } from "./task-lock.service";
@Injectable()
export class LimitResetterTask {
private readonly logger = new Logger(LimitResetterTask.name);
private readonly timeZone = "Europe/Istanbul";
constructor(private readonly prisma: PrismaService) {}
constructor(
private readonly prisma: PrismaService,
private readonly taskLock: TaskLockService,
) {}
private shouldSkipInHistoricalMode(jobName: string): boolean {
if (process.env.FEEDER_MODE === "historical") {
@@ -22,34 +38,39 @@ export class LimitResetterTask {
@Cron("0 3 * * *", { timeZone: "Europe/Istanbul" })
async resetUsageLimits() {
if (this.shouldSkipInHistoricalMode("resetUsageLimits")) return;
this.logger.log("Starting daily usage limit reset job...");
await this.taskLock.runWithLease(
"resetUsageLimits",
30 * 60 * 1000,
async () => {
this.logger.log("Starting daily usage limit reset job...");
try {
const today = new Date();
today.setHours(0, 0, 0, 0);
try {
const today = getDateOnlyValueForTimeZone(this.timeZone);
// Reset all limits that were last reset before today
const result = await this.prisma.usageLimit.updateMany({
where: {
lastResetDate: { lt: today },
},
data: {
analysisCount: 0,
couponCount: 0,
lastResetDate: today,
},
});
const result = await this.prisma.usageLimit.updateMany({
where: {
lastResetDate: { lt: today },
},
data: {
analysisCount: 0,
couponCount: 0,
lastResetDate: today,
},
});
if (result.count > 0) {
this.logger.log(
`Usage limits for ${result.count} users have been reset`,
);
} else {
this.logger.log("No user limits needed resetting");
}
} catch (error: any) {
this.logger.error(`Limit reset job failed: ${error.message}`);
}
if (result.count > 0) {
this.logger.log(
`Usage limits for ${result.count} users have been reset`,
);
} else {
this.logger.log("No user limits needed resetting");
}
} catch (error: any) {
this.logger.error(`Limit reset job failed: ${error.message}`);
}
},
this.logger,
);
}
/**
@@ -58,37 +79,65 @@ export class LimitResetterTask {
@Cron("0 4 * * *", { timeZone: "Europe/Istanbul" })
async cleanupOldData() {
if (this.shouldSkipInHistoricalMode("cleanupOldData")) return;
this.logger.log("Starting data cleanup job...");
await this.taskLock.runWithLease(
"cleanupOldData",
60 * 60 * 1000,
async () => {
this.logger.log("Starting data cleanup job...");
try {
const thirtyDaysAgo = new Date();
thirtyDaysAgo.setDate(thirtyDaysAgo.getDate() - 30);
try {
const thirtyDaysAgo = new Date();
thirtyDaysAgo.setDate(thirtyDaysAgo.getDate() - 30);
// Delete old AI prediction logs
const deletedLogs = await this.prisma.aiPredictionsLog.deleteMany({
where: {
createdAt: { lt: thirtyDaysAgo },
},
});
const deletedLogs = await this.prisma.aiPredictionsLog.deleteMany({
where: {
createdAt: { lt: thirtyDaysAgo },
},
});
// Delete old live matches (finished more than 1 day ago)
// Historical data is already persisted in the 'matches' table
const oneDayAgo = new Date();
oneDayAgo.setDate(oneDayAgo.getDate() - 1);
const yesterdayDate = getShiftedDateStringInTimeZone(
-1,
this.timeZone,
);
const { startMs: yesterdayStartMs } = getDayBoundsForTimeZone(
yesterdayDate,
this.timeZone,
);
const liveMatchCutoff = new Date(yesterdayStartMs);
const deletedLiveMatches = await this.prisma.liveMatch.deleteMany({
where: {
state: "Finished",
updatedAt: { lt: oneDayAgo },
},
});
const deletedLiveMatches = await this.prisma.liveMatch.deleteMany({
where: {
updatedAt: { lt: liveMatchCutoff },
OR: [
{ status: { in: FINISHED_STATUS_VALUES_FOR_DB } },
{ state: { in: FINISHED_STATE_VALUES_FOR_DB } },
{
AND: [
{ scoreHome: { not: null } },
{ scoreAway: { not: null } },
{
NOT: {
OR: [
{ status: { in: LIVE_STATUS_VALUES_FOR_DB } },
{ state: { in: LIVE_STATE_VALUES_FOR_DB } },
],
},
},
],
},
],
},
});
this.logger.log(
`Cleanup complete: ${deletedLogs.count} old logs, ${deletedLiveMatches.count} old live matches`,
);
} catch (error: any) {
this.logger.error(`Cleanup job failed: ${error.message}`);
}
this.logger.log(
`Cleanup complete: ${deletedLogs.count} old logs, ${deletedLiveMatches.count} old live matches`,
);
} catch (error: any) {
this.logger.error(`Cleanup job failed: ${error.message}`);
}
},
this.logger,
);
}
/**
@@ -97,26 +146,33 @@ export class LimitResetterTask {
@Cron("0 0 * * *", { timeZone: "Europe/Istanbul" })
async checkSubscriptions() {
if (this.shouldSkipInHistoricalMode("checkSubscriptions")) return;
this.logger.log("Checking expired subscriptions...");
await this.taskLock.runWithLease(
"checkSubscriptions",
30 * 60 * 1000,
async () => {
this.logger.log("Checking expired subscriptions...");
try {
const now = new Date();
try {
const now = new Date();
const result = await this.prisma.user.updateMany({
where: {
subscriptionStatus: "active",
subscriptionExpiresAt: { lt: now },
},
data: {
subscriptionStatus: "expired",
},
});
const result = await this.prisma.user.updateMany({
where: {
subscriptionStatus: "active",
subscriptionExpiresAt: { lt: now },
},
data: {
subscriptionStatus: "expired",
},
});
if (result.count > 0) {
this.logger.log(`${result.count} subscriptions marked as expired`);
}
} catch (error: any) {
this.logger.error(`Subscription check failed: ${error.message}`);
}
if (result.count > 0) {
this.logger.log(`${result.count} subscriptions marked as expired`);
}
} catch (error: any) {
this.logger.error(`Subscription check failed: ${error.message}`);
}
},
this.logger,
);
}
}
src/tasks/task-lock.service.ts
+80
View File
@@ -0,0 +1,80 @@
import { Injectable, Logger } from "@nestjs/common";
import { Prisma } from "@prisma/client";
import { PrismaService } from "../database/prisma.service";
@Injectable()
export class TaskLockService {
private readonly logger = new Logger(TaskLockService.name);
private readonly activeTasks = new Set<string>();
constructor(private readonly prisma: PrismaService) {}
async runWithLease<T>(
key: string,
ttlMs: number,
task: () => Promise<T>,
logger: Logger,
): Promise<T | null> {
if (this.activeTasks.has(key)) {
logger.warn(`Skipping ${key}: task is already running in this process`);
return null;
}
const owner = `${process.pid}-${Date.now()}-${Math.random().toString(36).slice(2, 10)}`;
const acquired = await this.acquireLease(key, owner, ttlMs);
if (!acquired) {
logger.warn(`Skipping ${key}: lease is already held by another instance`);
return null;
}
this.activeTasks.add(key);
try {
return await task();
} finally {
this.activeTasks.delete(key);
await this.releaseLease(key, owner);
}
}
private async acquireLease(
key: string,
owner: string,
ttlMs: number,
): Promise<boolean> {
const rows = await this.prisma.$queryRaw<{ key: string }[]>(
Prisma.sql`
INSERT INTO app_settings (key, value, updated_at)
VALUES (${this.getDbKey(key)}, ${owner}, NOW() + (${ttlMs} * INTERVAL '1 millisecond'))
ON CONFLICT (key) DO UPDATE
SET value = EXCLUDED.value,
updated_at = EXCLUDED.updated_at
WHERE app_settings.updated_at < NOW()
OR app_settings.value = ${owner}
RETURNING key
`,
);
return rows.length > 0;
}
private async releaseLease(key: string, owner: string): Promise<void> {
try {
await this.prisma.$executeRaw(
Prisma.sql`
DELETE FROM app_settings
WHERE key = ${this.getDbKey(key)}
AND value = ${owner}
`,
);
} catch (error) {
const message = error instanceof Error ? error.message : String(error);
this.logger.warn(`Failed to release task lease ${key}: ${message}`);
}
}
private getDbKey(key: string): string {
return `task_lock:${key}`;
}
}
src/tasks/tasks.module.ts
+7 -3
View File
@@ -1,15 +1,14 @@
import { Module } from "@nestjs/common";
import { ScheduleModule } from "@nestjs/schedule";
import { HttpModule } from "@nestjs/axios";
import { DataFetcherTask } from "./data-fetcher.task";
import { HistoricalResultsSyncTask } from "./historical-results-sync.task";
import { LimitResetterTask } from "./limit-resetter.task";
import { TaskLockService } from "./task-lock.service";
import { DatabaseModule } from "../database/database.module";
import { FeederModule } from "../modules/feeder/feeder.module";
@Module({
imports: [
ScheduleModule.forRoot(),
HttpModule.register({
timeout: 30000,
headers: {
@@ -20,7 +19,12 @@ import { FeederModule } from "../modules/feeder/feeder.module";
DatabaseModule,
FeederModule,
],
providers: [DataFetcherTask, HistoricalResultsSyncTask, LimitResetterTask],
providers: [
TaskLockService,
DataFetcherTask,
HistoricalResultsSyncTask,
LimitResetterTask,
],
exports: [DataFetcherTask, HistoricalResultsSyncTask, LimitResetterTask],
})
export class TasksModule {}