Merge pull request 'v26-shadow' (#4) from v26-shadow into main

Reviewed-on: #4
2026-04-24 01:15:54 +03:00
parent bf7473c1e7 9e2edd590c
commit 6880eb92f5
48 changed files with 1 additions and 434106 deletions
@@ -1,871 +0,0 @@
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-    "v25_main": "2/2",
-    "v26_main": "2.5 \u00dcst"
-  },
-  {
-    "match_id": "55bckv97w88qeqymhqgwg9fdg",
-    "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": "1X",
-    "v26_main": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "ir507rn2anyknjvm1xua1c7o",
-    "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": "2.5 Alt"
-  },
-  {
-    "match_id": "d9h5hdhwum2s04ma722emljx0",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 1.2061,
-      "avg_confidence": 79.9
-    },
-    "v25_main": null,
-    "v26_main": "1"
-  },
-  {
-    "match_id": "3riltqclv2llihnklpcw6u784",
-    "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": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "8808y1x2hmz52k3mr598orqc4",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 0.2221,
-      "avg_confidence": 67.9
-    },
-    "v25_main": "KG Var",
-    "v26_main": "KG Var"
-  },
-  {
-    "match_id": "5qypw3tl5qkx16ihhbifv9jis",
-    "v25": {
-      "playable_count": 1.0,
-      "avg_edge": 0.0979,
-      "avg_confidence": 66.9
-    },
-    "v26": {
-      "playable_count": 3.0,
-      "avg_edge": 0.1071,
-      "avg_confidence": 66.3
-    },
-    "v25_main": "\u00dcst",
-    "v26_main": "2"
-  },
-  {
-    "match_id": "710vie8tgz5ccs7sq9xwrjmz8",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 1.2892,
-      "avg_confidence": 82.9
-    },
-    "v25_main": null,
-    "v26_main": "1"
-  },
-  {
-    "match_id": "2vkbdwkxyjj1invl5877bz0us",
-    "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": "7caglruhdiegg9xrc20fzhrtg",
-    "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": "\u00c7ift",
-    "v26_main": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "gl3nvw4hprtny0aby1z40duc",
-    "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": "\u00c7ift",
-    "v26_main": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "f87f0jsazmdhlq3wzz7ngp3o",
-    "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": "X2",
-    "v26_main": "2.5 Alt"
-  },
-  {
-    "match_id": "chb58495fc1o6ek9t9kdfbyfo",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 0.0572,
-      "avg_confidence": 75.5
-    },
-    "v25_main": "1",
-    "v26_main": "KG Var"
-  },
-  {
-    "match_id": "57h5qfr5mr4qoxix7vhiwd2j8",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 1.1927,
-      "avg_confidence": 79.4
-    },
-    "v25_main": null,
-    "v26_main": "2"
-  },
-  {
-    "match_id": "de76ipnpgaznxvl9tk9ewquqc",
-    "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": "5pjdtn5bb9v64cj4ceh4ei1hw",
-    "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": "\u00c7ift",
-    "v26_main": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "6orrk7jpn3ucpybdieh6oin84",
-    "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": "1"
-  },
-  {
-    "match_id": "3shwt6aecvnu9utm2go9diq6s",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 0.674,
-      "avg_confidence": 60.6
-    },
-    "v25_main": null,
-    "v26_main": "1"
-  },
-  {
-    "match_id": "5kukkba6u6wq1xj8oi118q1hw",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 0.8906,
-      "avg_confidence": 56.7
-    },
-    "v25_main": null,
-    "v26_main": "X"
-  },
-  {
-    "match_id": "9fzu7f8aybsqy88r3nam79p1w",
-    "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": "2"
-  },
-  {
-    "match_id": "bxcnq91mnr9f6bhicdxgzbims",
-    "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": "1.5 \u00dcst"
-  },
-  {
-    "match_id": "dueticp36nbckn2pnpnt6az8",
-    "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": "5m9do9pcggnl0tgmeolvnph5g",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 0.6914,
-      "avg_confidence": 61.3
-    },
-    "v25_main": null,
-    "v26_main": "1"
-  },
-  {
-    "match_id": "avu5vzpdi1wy7mrneodb3gw7o",
-    "v25": {
-      "playable_count": 0.0,
-      "avg_edge": 0.0,
-      "avg_confidence": 0.0
-    },
-    "v26": {
-      "playable_count": 1.0,
-      "avg_edge": 1.1608,
-      "avg_confidence": 78.3
-    },
-    "v25_main": null,
-    "v26_main": "1"
-  }
-]
@@ -1,35 +0,0 @@
-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,,,,,,,,,,,,
@@ -1,149 +0,0 @@
-# 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 | - | - | - | - |
@@ -1,206 +0,0 @@
-"""
-Backtest for September 13th (Top Leagues Only)
-==============================================
-Simulates the NEW 'Skip Logic' on matches from Sept 13, 2025.
-"""
-
-import os
-import sys
-import json
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from datetime import datetime
-
-# Load .env manually to ensure correct DB connection
-project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.insert(0, project_root) # Add root to path if needed
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-# ─── Configuration ─────────
-MIN_CONF_THRESHOLDS = {
-    "MS": 45.0, "DC": 40.0, "OU15": 50.0, "OU25": 45.0, 
-    "OU35": 45.0, "BTTS": 45.0, "HT": 40.0,
-}
-
-def run_backtest():
-    print("🚀 Backtest: 13 Eylül 2024 - Top Leagues")
-    print("="*60)
-
-    # 1. Load Top Leagues
-    leagues_path = os.path.join(project_root, "top_leagues.json")
-    try:
-        with open(leagues_path, 'r') as f:
-            top_leagues = json.load(f)
-        # Ensure they are strings for SQL IN clause
-        league_ids = tuple(str(lid) for lid in top_leagues)
-        print(f"📋 Loaded {len(top_leagues)} top leagues.")
-    except Exception as e:
-        print(f"❌ Error loading top_leagues.json: {e}")
-        return
-
-    # 2. Define Date Range (Sept 13, 2024 UTC)
-    start_dt = datetime(2024, 9, 13, 0, 0, 0)
-    end_dt = datetime(2024, 9, 13, 23, 59, 59)
-    start_ts = int(start_dt.timestamp() * 1000)
-    end_ts = int(end_dt.timestamp() * 1000)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    # 3. Fetch Matches & Predictions
-    # We need matches that are FT and have a prediction
-    query = """
-        SELECT p.match_id, p.prediction_json,
-               m.score_home, m.score_away, m.status, m.league_id
-        FROM predictions p
-        JOIN matches m ON p.match_id = m.id
-        WHERE m.mst_utc BETWEEN %s AND %s
-          AND m.league_id IN %s
-          AND m.status = 'FT'
-          AND p.prediction_json IS NOT NULL
-    """
-    
-    try:
-        cur.execute(query, (start_ts, end_ts, league_ids))
-        rows = cur.fetchall()
-    except Exception as e:
-        print(f"❌ DB Error: {e}")
-        cur.close()
-        conn.close()
-        return
-
-    print(f"📊 Found {len(rows)} matches with predictions on Sept 13, 2024.")
-
-    if not rows:
-        print("⚠️ No predictions found for this date. The AI Engine might not have processed these historical matches yet.")
-        print("💡 Tip: Run the feeder or AI engine on this date range to generate predictions first.")
-        cur.close()
-        conn.close()
-        return
-
-    total_bets = 0
-    winning_bets = 0
-    skipped_bets = 0
-    total_profit = 0.0
-
-    for row in rows:
-        data = row['prediction_json']
-        if isinstance(data, str):
-            data = json.loads(data)
-
-        home_score = row['score_home'] or 0
-        away_score = row['score_away'] or 0
-        total_goals = home_score + away_score
-
-        # Extract Main Pick
-        main_pick = None
-        main_pick_conf = 0.0
-        main_pick_odds = 0.0
-        
-        if "main_pick" in data and isinstance(data["main_pick"], dict):
-            mp = data["main_pick"]
-            main_pick = mp.get("pick")
-            main_pick_conf = mp.get("confidence", 0.0)
-            main_pick_odds = mp.get("odds", 0.0)
-        
-        if not main_pick or not main_pick_conf:
-            continue
-
-        # Determine Market Type
-        pick_str = str(main_pick).upper()
-        market_type = "MS"
-        if "1X" in pick_str or "X2" in pick_str or "12" in pick_str: market_type = "DC"
-        elif "ÜST" in pick_str or "ALT" in pick_str or "OVER" in pick_str or "UNDER" in pick_str:
-            if "1.5" in pick_str: market_type = "OU15"
-            elif "3.5" in pick_str: market_type = "OU35"
-            else: market_type = "OU25"
-        elif "VAR" in pick_str or "YOK" in pick_str or "BTTS" in pick_str: market_type = "BTTS"
-        
-        threshold = MIN_CONF_THRESHOLDS.get(market_type, 45.0)
-
-        # --- SKIP LOGIC ---
-        # 1. Confidence Gate
-        if main_pick_conf < threshold:
-            skipped_bets += 1
-            continue
-
-        # 2. Value Gate
-        if main_pick_odds > 0:
-            implied_prob = 1.0 / main_pick_odds
-            my_prob = main_pick_conf / 100.0
-            edge = my_prob - implied_prob
-            if edge < -0.03:
-                skipped_bets += 1
-                continue
-
-        # --- BET PLAYED ---
-        total_bets += 1
-        is_won = False
-        
-        # Resolve Result
-        if market_type == "MS":
-            if (main_pick == "1" or main_pick == "MS 1") and home_score > away_score: is_won = True
-            elif (main_pick == "X" or main_pick == "MS X") and home_score == away_score: is_won = True
-            elif (main_pick == "2" or main_pick == "MS 2") and away_score > home_score: is_won = True
-            
-        elif market_type.startswith("OU"):
-            line = 2.5
-            if "1.5" in pick_str: line = 1.5
-            elif "3.5" in pick_str: line = 3.5
-            is_over = total_goals > line
-            is_under = total_goals < line
-            if ("ÜST" in pick_str or "OVER" in pick_str) and is_over: is_won = True
-            elif ("ALT" in pick_str or "UNDER" in pick_str) and is_under: is_won = True
-
-        elif market_type == "BTTS":
-            if home_score > 0 and away_score > 0:
-                if "VAR" in pick_str: is_won = True
-            else:
-                if "YOK" in pick_str: is_won = True
-        
-        elif market_type == "DC":
-            if "1X" in pick_str and home_score >= away_score: is_won = True
-            elif "X2" in pick_str and away_score >= home_score: is_won = True
-            elif "12" in pick_str and home_score != away_score: is_won = True
-
-        if is_won:
-            winning_bets += 1
-            profit = main_pick_odds - 1.0
-            total_profit += profit
-        else:
-            total_profit -= 1.0
-
-    # Report
-    print("\n" + "="*60)
-    print("📈 BACKTEST RESULTS: 13 EYLÜL 2025 (TOP LEAGUES)")
-    print("="*60)
-    print(f"Total Matches Analyzed: {len(rows)}")
-    print(f"🚫 Bets SKIPPED (Low Conf/Bad Value): {skipped_bets}")
-    print(f"✅ Bets PLAYED: {total_bets}")
-    
-    if total_bets > 0:
-        win_rate = (winning_bets / total_bets) * 100
-        roi = (total_profit / total_bets) * 100
-        
-        print(f"🏆 Winning Bets: {winning_bets}")
-        print(f"💀 Losing Bets: {total_bets - winning_bets}")
-        print("-" * 40)
-        print(f" Win Rate: {win_rate:.2f}%")
-        print(f"💰 Total Profit (Units): {total_profit:.2f}")
-        print(f"📊 ROI: {roi:.2f}%")
-        
-        if roi > 0:
-            print("🟢 STRATEGY IS PROFITABLE!")
-        else:
-            print("🔴 STRATEGY IS LOSING")
-    else:
-        print("⚠️ No bets were played. Thresholds might be too high or no suitable matches found.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_backtest()
@@ -1,240 +0,0 @@
-"""
-Detailed Backtest with 50 Top League Matches
-============================================
-Runs AI Engine predictions on 50 real historical matches and shows
-exactly which predictions were correct and which were skipped.
-
-Usage:
-    python ai-engine/scripts/backtest_50_detailed.py
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-# Add paths
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-# 50 Match IDs from the query
-MATCH_IDS = [
-    "v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
-    "7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
-    "7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
-    "7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
-    "7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
-    "7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
-    "7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
-    "7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
-    "7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
-    "lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
-    "40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
-    "2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
-    "7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
-    "coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
-    "9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
-    "6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
-    "1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
-]
-
-def run_detailed_backtest():
-    print("🚀 DETAILED BACKTEST: 50 Top League Matches")
-    print("🧠 Engine: V30 Ensemble (V20+V25) + Skip Logic")
-    print("="*80)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    # Fetch match details with odds
-    placeholders = ','.join(['%s'] * len(MATCH_IDS))
-    cur.execute(f"""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away, m.league_id,
-               t1.name as home_team, t2.name as away_team,
-               l.name as league_name
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        LEFT JOIN leagues l ON m.league_id = l.id
-        WHERE m.id IN ({placeholders})
-          AND m.status = 'FT'
-        ORDER BY m.mst_utc DESC
-    """, MATCH_IDS)
-    
-    rows = cur.fetchall()
-    print(f"📊 Found {len(rows)} matches. Starting AI Analysis...")
-
-    if not rows:
-        print("⚠️ No matches found.")
-        cur.close()
-        conn.close()
-        return
-
-    # Initialize AI Engine
-    try:
-        orchestrator = get_single_match_orchestrator()
-        print("✅ AI Engine Loaded.\n")
-    except Exception as e:
-        print(f"❌ Failed to load AI Engine: {e}")
-        cur.close()
-        conn.close()
-        return
-
-    # ─── Backtest Loop ───
-    results = []
-    total_skipped = 0
-    total_played = 0
-    total_won = 0
-    total_profit = 0.0
-    MIN_CONF = 45.0
-
-    start_time = time.time()
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home_team = row['home_team'] or "Unknown"
-        away_team = row['away_team'] or "Unknown"
-        league = row['league_name'] or "Unknown"
-        home_score = row['score_home'] or 0
-        away_score = row['score_away'] or 0
-        total_goals = home_score + away_score
-
-        print(f"[{i+1}/{len(rows)}] {home_team} vs {away_team} ({league}) ... ", end="", flush=True)
-
-        try:
-            prediction = orchestrator.analyze_match(match_id)
-            
-            if not prediction:
-                print("⚠️ No prediction")
-                continue
-
-            # Extract Main Pick
-            main_pick = prediction.get("main_pick") or {}
-            pick_name = main_pick.get("pick", "")
-            confidence = main_pick.get("confidence", 0)
-            odds = main_pick.get("odds", 0)
-
-            # Apply Skip Logic
-            if confidence < MIN_CONF:
-                print(f"🚫 SKIP (Conf {confidence:.0f}%)")
-                total_skipped += 1
-                results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name, 
-                               "conf": confidence, "odds": odds, "result": "SKIPPED", "profit": 0})
-                continue
-
-            if odds > 0:
-                implied_prob = 1.0 / odds
-                my_prob = confidence / 100.0
-                if my_prob - implied_prob < -0.03:
-                    print(f"🚫 SKIP (Bad Value)")
-                    total_skipped += 1
-                    results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name, 
-                                   "conf": confidence, "odds": odds, "result": "SKIPPED", "profit": 0})
-                    continue
-
-            # Bet Played
-            total_played += 1
-            won = False
-
-            # Resolve
-            pick_clean = str(pick_name).upper()
-            if pick_clean in ["1", "MS 1", "İY 1"] and home_score > away_score: won = True
-            elif pick_clean in ["X", "MS X", "İY X"] and home_score == away_score: won = True
-            elif pick_clean in ["2", "MS 2", "İY 2"] and away_score > home_score: won = True
-            elif pick_clean in ["1X", "X2"] or ("1X" in pick_clean or "X2" in pick_clean):
-                if "1X" in pick_clean and home_score >= away_score: won = True
-                elif "X2" in pick_clean and away_score >= home_score: won = True
-            elif pick_clean in ["12"] and home_score != away_score: won = True
-            elif "ÜST" in pick_clean or "OVER" in pick_clean:
-                line = 2.5
-                if "1.5" in pick_clean: line = 1.5
-                elif "3.5" in pick_clean: line = 3.5
-                if total_goals > line: won = True
-            elif "ALT" in pick_clean or "UNDER" in pick_clean:
-                line = 2.5
-                if "1.5" in pick_clean: line = 1.5
-                elif "3.5" in pick_clean: line = 3.5
-                if total_goals < line: won = True
-            elif "VAR" in pick_clean and home_score > 0 and away_score > 0: won = True
-            elif "YOK" in pick_clean and (home_score == 0 or away_score == 0): won = True
-
-            if won:
-                total_won += 1
-                profit = odds - 1.0
-                print(f"✅ WON ({pick_name} @ {odds:.2f}, +{profit:.2f})")
-            else:
-                profit = -1.0
-                print(f"❌ LOST ({pick_name} @ {odds:.2f})")
-            
-            total_profit += profit
-            results.append({"match": f"{home_team} vs {away_team}", "pick": pick_name, 
-                           "conf": confidence, "odds": odds, 
-                           "result": "WON" if won else "LOST", "profit": profit,
-                           "score": f"{home_score}-{away_score}"})
-
-        except Exception as e:
-            print(f"💥 Error: {e}")
-
-    elapsed = time.time() - start_time
-
-    # ─── DETAILED REPORT ───
-    print("\n" + "="*80)
-    print("📈 DETAILED BACKTEST RESULTS")
-    print(f"⏱️  Time: {elapsed:.1f}s")
-    print("="*80)
-    print(f"📊 Total Matches: {len(rows)}")
-    print(f"🚫 Skipped: {total_skipped}")
-    print(f"🎲 Played: {total_played}")
-    print(f"✅ Won: {total_won}")
-    print(f"💀 Lost: {total_played - total_won}")
-    print(f"💰 Profit: {total_profit:+.2f} units")
-    
-    if total_played > 0:
-        win_rate = (total_won / total_played) * 100
-        roi = (total_profit / total_played) * 100
-        print(f"📊 Win Rate: {win_rate:.1f}%")
-        print(f"📊 ROI: {roi:.1f}%")
-        if roi > 0:
-            print("🟢 STRATEGY IS PROFITABLE!")
-        else:
-            print("🔴 STRATEGY IS LOSING")
-
-    # ─── TABLE OF ALL RESULTS ───
-    print("\n" + "="*80)
-    print("📋 DETAILED MATCH RESULTS")
-    print("="*80)
-    print(f"{'Match':<40} {'Pick':<15} {'Conf':<6} {'Odds':<6} {'Result':<8} {'Score':<6}")
-    print("-"*80)
-    for r in results:
-        match_str = r['match'][:38]
-        pick_str = str(r['pick'])[:13]
-        conf_str = f"{r['conf']:.0f}%"
-        odds_str = f"{r['odds']:.2f}" if r['odds'] > 0 else "N/A"
-        res_str = r['result']
-        score_str = r.get('score', '')
-        
-        # Color coding
-        if res_str == "WON": res_display = f"✅ {res_str}"
-        elif res_str == "LOST": res_display = f"❌ {res_str}"
-        else: res_display = f"🚫 {res_str}"
-            
-        print(f"{match_str:<40} {pick_str:<15} {conf_str:<6} {odds_str:<6} {res_display:<12} {score_str:<6}")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_detailed_backtest()
@@ -1,191 +0,0 @@
-"""
-Adaptive 500 Match Backtest
-=============================
-Skips NO match unless NO odds exist.
-Evaluates ALL available markets (MS, OU, BTTS) and picks the BEST value bet.
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_adaptive_backtest():
-    print("🔄 ADAPTIVE 500 MATCH BACKTEST")
-    print("="*60)
-    
-    # 1. Load Top Leagues
-    leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
-    with open(leagues_path, 'r') as f:
-        top_leagues = json.load(f)
-    league_ids = tuple(str(lid) for lid in top_leagues)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    # 2. Fetch 500 Finished Matches with Odds
-    cur.execute("""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away, m.league_id,
-               t1.name as home_team, t2.name as away_team
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s
-          AND m.status = 'FT'
-          AND m.score_home IS NOT NULL
-          AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
-        ORDER BY m.mst_utc DESC
-        LIMIT 500
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 Found {len(rows)} matches. Analyzing...\n")
-
-    if not rows:
-        print("⚠️ No matches found.")
-        return
-
-    try: orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Error: {e}")
-        return
-
-    # Stats
-    total_evaluated = 0
-    total_bet = 0
-    total_won = 0
-    total_profit = 0.0
-    skipped_count = 0
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home = row['home_team'] or "?"
-        away = row['away_team'] or "?"
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-        
-        total_evaluated += 1
-        # print(f"[{i+1}] {home} vs {away} ... ", end="", flush=True)
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: 
-                # print("⚠️ No Data")
-                continue
-
-            # ─── ADAPTIVE PICKING ───
-            # Check ALL recommendations (Expert or Standard) to find the BEST option
-            candidates = []
-            
-            # Add main picks
-            if pred.get("expert_recommendation"):
-                rec = pred["expert_recommendation"]
-                if rec.get("main_pick"): candidates.append(rec["main_pick"])
-                if rec.get("safe_alternative"): candidates.append(rec["safe_alternative"])
-                if rec.get("value_picks"): candidates.extend(rec["value_picks"])
-            elif pred.get("main_pick"):
-                candidates.append(pred["main_pick"])
-
-            best_bet = None
-            for c in candidates:
-                if not c: continue
-                conf = c.get("confidence", 0)
-                odds = c.get("odds", 0)
-                pick = c.get("pick")
-                
-                # Flexible Criteria:
-                # 1. Confidence > 60%
-                # 2. Odds > 1.10 (Not "free" odds like 1.00)
-                # 3. Edge > -2% (Slightly tolerant)
-                if conf >= 60 and odds > 1.10:
-                    implied = 1.0 / odds
-                    edge = ((conf/100) - implied) * 100
-                    
-                    # Prioritize positive edge, but accept small negative if confidence is high
-                    if edge > -2.0:
-                        if best_bet is None or (conf > best_bet.get("confidence", 0)):
-                            best_bet = c
-
-            if best_bet:
-                pick = str(best_bet.get("pick")).upper()
-                conf = best_bet.get("confidence")
-                odds = best_bet.get("odds")
-                
-                # Resolution Logic
-                won = False
-                if pick in ["1", "MS 1", "İY 1"] and h_score > a_score: won = True
-                elif pick in ["X", "MS X", "İY X"] and h_score == a_score: won = True
-                elif pick in ["2", "MS 2", "İY 2"] and a_score > h_score: won = True
-                elif pick in ["1X", "X2"]:
-                    if "1X" in pick and h_score >= a_score: won = True
-                    elif "X2" in pick and a_score >= h_score: won = True
-                elif pick == "12" and h_score != a_score: won = True
-                elif "ÜST" in pick or "OVER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) > line: won = True
-                elif "ALT" in pick or "UNDER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) < line: won = True
-                elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
-                elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
-
-                total_bet += 1
-                if won:
-                    total_won += 1
-                    profit = odds - 1.0
-                    total_profit += profit
-                    # print(f"✅ WON (+{profit:.2f}) | {pick}")
-                else:
-                    total_profit -= 1.0
-                    # print(f"❌ LOST ({pick} @ {odds:.2f})")
-            else:
-                skipped_count += 1
-                # print(f"🚫 SKIP (No Value)")
-
-        except Exception as e:
-            # print(f"💥 Error: {e}")
-            pass
-
-    print("\n" + "="*60)
-    print("🔄 ADAPTIVE BACKTEST RESULTS (500 Matches)")
-    print("="*60)
-    print(f"📊 Evaluated: {total_evaluated}")
-    print(f"🎲 Played: {total_bet}")
-    print(f"🚫 Skipped: {skipped_count}")
-    print(f"✅ Won: {total_won}")
-    
-    if total_bet > 0:
-        win_rate = (total_won / total_bet) * 100
-        roi = (total_profit / total_bet) * 100
-        print(f"📈 Win Rate: {win_rate:.2f}%")
-        print(f"💰 Total Profit: {total_profit:.2f} Units")
-        print(f"📊 ROI: {roi:.2f}%")
-        if total_profit > 0: print("🟢 KARLI STRATEJİ")
-        else: print("🔴 ZARARDA")
-    else:
-        print("⚠️ Hiç bahis oynanmadı. Veri kalitesi çok düşük.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_adaptive_backtest()
@@ -1,145 +0,0 @@
-"""
-Diagnostic Backtest - Hangi Pazar Kanıyor?
-===========================================
-Analyses the 500 matches to see WHICH markets are losing money.
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from collections import defaultdict
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_diagnostic():
-    print("🔍 TANI BACKTESTİ: NEREDE KAYBETTİK?")
-    print("="*60)
-    
-    leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
-    with open(leagues_path, 'r') as f:
-        top_leagues = json.load(f)
-    league_ids = tuple(str(lid) for lid in top_leagues)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away, m.league_id,
-               t1.name as home_team, t2.name as away_team
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s
-          AND m.status = 'FT'
-          AND m.score_home IS NOT NULL
-          AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
-        ORDER BY m.mst_utc DESC
-        LIMIT 500
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç analiz ediliyor...\n")
-
-    try: orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Hatası: {e}")
-        return
-
-    # Market Stats: { "MS": {"won": 10, "lost": 20, "profit": -5.0}, ... }
-    market_stats = defaultdict(lambda: {"won": 0, "lost": 0, "profit": 0.0, "total": 0})
-    
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: continue
-
-            candidates = []
-            if pred.get("expert_recommendation"):
-                rec = pred["expert_recommendation"]
-                if rec.get("main_pick"): candidates.append(rec["main_pick"])
-                if rec.get("value_picks"): candidates.extend(rec["value_picks"])
-            elif pred.get("main_pick"):
-                candidates.append(pred["main_pick"])
-
-            played_this = False
-            for c in candidates:
-                if not c: continue
-                conf = c.get("confidence", 0)
-                odds = c.get("odds", 0)
-                pick = str(c.get("pick")).upper()
-                market_type = c.get("market_type", "Unknown")
-
-                # Criteria
-                if conf >= 60 and odds > 1.10:
-                    implied = 1.0 / odds
-                    edge = ((conf/100) - implied) * 100
-                    if edge > -2.0:
-                        # Resolve
-                        won = False
-                        if pick in ["1", "MS 1"] and h_score > a_score: won = True
-                        elif pick in ["X", "MS X"] and h_score == a_score: won = True
-                        elif pick in ["2", "MS 2"] and a_score > h_score: won = True
-                        elif pick in ["1X", "X2"]:
-                            if "1X" in pick and h_score >= a_score: won = True
-                            elif "X2" in pick and a_score >= h_score: won = True
-                        elif pick == "12" and h_score != a_score: won = True
-                        elif "ÜST" in pick or "OVER" in pick:
-                            line = 2.5
-                            if "1.5" in pick: line = 1.5
-                            elif "3.5" in pick: line = 3.5
-                            if (h_score + a_score) > line: won = True
-                        elif "ALT" in pick or "UNDER" in pick:
-                            line = 2.5
-                            if "1.5" in pick: line = 1.5
-                            elif "3.5" in pick: line = 3.5
-                            if (h_score + a_score) < line: won = True
-                        elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
-                        elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
-
-                        market_stats[market_type]["total"] += 1
-                        if won:
-                            market_stats[market_type]["won"] += 1
-                            market_stats[market_type]["profit"] += (odds - 1.0)
-                        else:
-                            market_stats[market_type]["lost"] += 1
-                            market_stats[market_type]["profit"] -= 1.0
-                        
-                        played_this = True
-                        break # Only one bet per match
-
-        except: pass
-
-    # Print Results
-    print("\n" + "="*60)
-    print("📊 PAZAR BAZLI KAR/ZARAR TABLOSU")
-    print("="*60)
-    print(f"{'Market':<15} {'Oynanan':<10} {'Kazanılan':<10} {'Win%':<8} {'Kâr':<10}")
-    print("-" * 60)
-
-    for mkt, stats in sorted(market_stats.items(), key=lambda x: x[1]["profit"], reverse=True):
-        wr = (stats["won"] / stats["total"] * 100) if stats["total"] > 0 else 0
-        print(f"{mkt:<15} {stats['total']:<10} {stats['won']:<10} {wr:.1f}%   {stats['profit']:+.2f} Units")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_diagnostic()
@@ -1,215 +0,0 @@
-"""
-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")
@@ -1,223 +0,0 @@
-"""
-Real AI Engine Backtest Script
-==============================
-Uses the ACTUAL models (V20/V25 Ensemble) to predict historical matches.
-
-Usage:
-    python ai-engine/scripts/backtest_real.py
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from datetime import datetime
-
-# Add paths
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-
-# Fix for Windows path issues in scripts
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR) # One level up if inside scripts folder
-
-from services.single_match_orchestrator import get_single_match_orchestrator, MatchData
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_backtest():
-    print("🚀 REAL AI BACKTEST: Sept 13, 2024 - Top Leagues")
-    print("🧠 Engine: V30 Ensemble (V20+V25)")
-    print("="*60)
-
-    # Load Top Leagues
-    leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
-    try:
-        with open(leagues_path, 'r') as f:
-            top_leagues = json.load(f)
-        league_ids = tuple(str(lid) for lid in top_leagues)
-        print(f"📋 Loaded {len(top_leagues)} top leagues.")
-    except Exception as e:
-        print(f"❌ Error loading top_leagues.json: {e}")
-        return
-
-    # Date Range (Sept 13, 2024)
-    start_dt = datetime(2024, 9, 13, 0, 0, 0)
-    end_dt = datetime(2024, 9, 13, 23, 59, 59)
-    start_ts = int(start_dt.timestamp() * 1000)
-    end_ts = int(end_dt.timestamp() * 1000)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    # Fetch Matches
-    cur.execute("""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id, 
-               m.mst_utc, m.league_id, m.status, m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team,
-               l.name as league_name
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        LEFT JOIN leagues l ON m.league_id = l.id
-        WHERE m.mst_utc BETWEEN %s AND %s
-          AND m.league_id IN %s
-          AND m.status = 'FT'
-        ORDER BY m.mst_utc ASC
-        LIMIT 20  -- Limit to 20 matches to avoid running for hours on a single backtest
-    """, (start_ts, end_ts, league_ids))
-    
-    rows = cur.fetchall()
-    print(f"📊 Found {len(rows)} finished matches. Starting AI Analysis...")
-
-    if not rows:
-        print("⚠️ No matches found for this date.")
-        cur.close()
-        conn.close()
-        return
-
-    # Initialize AI Engine
-    try:
-        orchestrator = get_single_match_orchestrator()
-        print("✅ AI Engine (SingleMatchOrchestrator) Loaded.")
-    except Exception as e:
-        print(f"❌ Failed to load AI Engine: {e}")
-        print("💡 Make sure models are trained/present in ai-engine/models/")
-        cur.close()
-        conn.close()
-        return
-
-    # ─── Backtest Loop ───
-    total_matches_analyzed = 0
-    bets_skipped = 0
-    bets_played = 0
-    bets_won = 0
-    total_profit = 0.0
-    
-    # Thresholds matching the NEW Skip Logic
-    MIN_CONF = 45.0 
-
-    start_time = time.time()
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home_team = row['home_team']
-        away_team = row['away_team']
-        home_score = row['score_home']
-        away_score = row['score_away']
-        
-        print(f"\n[{i+1}/{len(rows)}] Analyzing: {home_team} vs {away_team} ...")
-
-        try:
-            # 1. AI PREDICTION (Actual Model Call)
-            prediction = orchestrator.analyze_match(match_id)
-            
-            if not prediction:
-                print(f"   ⚠️ AI returned no prediction.")
-                continue
-
-            total_matches_analyzed += 1
-            
-            # 2. Extract Main Pick
-            main_pick = prediction.get("main_pick") or {}
-            pick_name = main_pick.get("pick")
-            confidence = main_pick.get("confidence", 0)
-            odds = main_pick.get("odds", 0)
-
-            if not pick_name or not confidence:
-                print(f"   ⚠️ No main pick found in prediction.")
-                continue
-
-            print(f"   🤖 Pick: {pick_name} | Conf: {confidence}% | Odds: {odds}")
-
-            # 3. Apply Skip Logic (New Backtest Logic)
-            if confidence < MIN_CONF:
-                print(f"   🚫 SKIPPED (Confidence {confidence}% < {MIN_CONF}%)")
-                bets_skipped += 1
-                continue
-
-            if odds > 0:
-                implied_prob = 1.0 / odds
-                my_prob = confidence / 100.0
-                if my_prob - implied_prob < -0.03: # Negative edge
-                    print(f"   🚫 SKIPPED (Negative Edge)")
-                    bets_skipped += 1
-                    continue
-
-            # 4. Bet Played
-            bets_played += 1
-            print(f"   🎲 BET PLAYED: {pick_name} @ {odds}")
-
-            # 5. Resolve Bet
-            won = False
-            # Basic resolution logic (Need to parse pick_name like "1", "X", "2", "2.5 Üst", etc.)
-            pick_clean = str(pick_name).upper()
-            
-            # MS
-            if pick_clean in ["1", "MS 1"] and home_score > away_score: won = True
-            elif pick_clean in ["X", "MS X"] and home_score == away_score: won = True
-            elif pick_clean in ["2", "MS 2"] and away_score > home_score: won = True
-            
-            # OU25
-            elif "ÜST" in pick_clean or "OVER" in pick_clean:
-                if (home_score + away_score) > 2.5: won = True
-            elif "ALT" in pick_clean or "UNDER" in pick_clean:
-                if (home_score + away_score) < 2.5: won = True
-            
-            # BTTS
-            elif "VAR" in pick_clean and home_score > 0 and away_score > 0: won = True
-            elif "YOK" in pick_clean and (home_score == 0 or away_score == 0): won = True
-
-            if won:
-                bets_won += 1
-                profit = odds - 1.0
-                print(f"   ✅ WON! (+{profit:.2f} units)")
-            else:
-                profit = -1.0
-                print(f"   ❌ LOST! (-1.00 units)")
-            
-            total_profit += profit
-
-        except Exception as e:
-            print(f"   💥 Error during analysis: {e}")
-
-    elapsed = time.time() - start_time
-
-    # ─── FINAL REPORT ───
-    print("\n" + "="*60)
-    print("📈 REAL AI BACKTEST RESULTS")
-    print(f"🕒 Time taken: {elapsed:.1f} seconds")
-    print("="*60)
-    print(f"📊 Matches Analyzed: {total_matches_analyzed}")
-    print(f"🚫 Bets SKIPPED: {bets_skipped}")
-    print(f"✅ Bets PLAYED: {bets_played}")
-    
-    if bets_played > 0:
-        win_rate = (bets_won / bets_played) * 100
-        roi = (total_profit / bets_played) * 100
-        yield_val = total_profit  # Net Units
-        
-        print(f"🏆 Bets Won: {bets_won}")
-        print(f"💀 Bets Lost: {bets_played - bets_won}")
-        print("-" * 40)
-        print(f" Win Rate: {win_rate:.2f}%")
-        print(f"💰 Total Profit (Units): {total_profit:.2f}")
-        print(f"📊 ROI: {roi:.2f}%")
-        
-        if roi > 0:
-            print("🟢 STRATEGY IS PROFITABLE!")
-        else:
-            print("🔴 STRATEGY IS LOSING")
-    else:
-        print("⚠️ No bets were played. All were skipped or failed.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_backtest()
@@ -1,231 +0,0 @@
-"""
-Backtest ROI Engine
-===================
-Simulates the NEW "Skip Logic" on historical predictions.
-Answers: "What if we only played the bets the model was confident about?"
-
-Usage:
-    python ai-engine/scripts/backtest_roi.py
-"""
-
-import os
-import sys
-import json
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from typing import Dict, List, Any
-from dotenv import load_dotenv
-
-# Load .env from project root (2 levels up from this script)
-project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-load_dotenv(os.path.join(project_root, ".env"))
-
-def get_clean_dsn() -> str:
-    """Return a psycopg2-compatible DSN from DATABASE_URL."""
-    # HARDCODED FOR BACKTEST (Bypassing dotenv issues)
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-# ─── Configuration (Matching the NEW BetRecommender Logic) ─────────
-# Minimum confidence to even consider a bet (Hard Gate)
-MIN_CONF_THRESHOLDS = {
-    "MS": 45.0,
-    "DC": 40.0,
-    "OU15": 50.0,
-    "OU25": 45.0,
-    "OU35": 45.0,
-    "BTTS": 45.0,
-    "HT": 40.0,
-}
-
-def get_market_type_from_key(key: str) -> str:
-    """Map prediction keys to market types for thresholding."""
-    if key.startswith("ms_") or key in ["1", "X", "2"]: return "MS"
-    if key.startswith("dc_") or key in ["1X", "X2", "12"]: return "DC"
-    if key.startswith("ou15_") or key.startswith("1.5"): return "OU15"
-    if key.startswith("ou25_") or key.startswith("2.5"): return "OU25"
-    if key.startswith("ou35_") or key.startswith("3.5"): return "OU35"
-    if key.startswith("btts_") or key in ["Var", "Yok"]: return "BTTS"
-    if key.startswith("ht_") or key.startswith("İY"): return "HT"
-    return "MS"
-
-def simulate_backtest():
-    print("🚀 Starting Backtest with NEW 'Skip Logic'...")
-    print("="*60)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    # 1. Fetch PREDICTIONS that have a confidence score
-    # We limit to last 1000 finished matches to keep it fast but representative
-    cur.execute("""
-        SELECT p.match_id, p.prediction_json,
-               m.score_home, m.score_away, m.status
-        FROM predictions p
-        JOIN matches m ON p.match_id = m.id
-        WHERE m.status = 'FT'
-        AND p.prediction_json IS NOT NULL
-        ORDER BY m.mst_utc DESC
-        LIMIT 2000
-    """)
-    predictions = cur.fetchall()
-
-    print(f"📊 Loaded {len(predictions)} historical predictions.")
-
-    total_bets = 0
-    winning_bets = 0
-    skipped_bets = 0
-    total_profit = 0.0  # Assuming unit stake of 1.0
-
-    # 2. Process each prediction
-    for pred_row in predictions:
-        match_id = pred_row['match_id']
-        data = pred_row['prediction_json']
-        if isinstance(data, str):
-            data = json.loads(data)
-
-        # Real result
-        home_score = pred_row['score_home'] or 0
-        away_score = pred_row['score_away'] or 0
-        total_goals = home_score + away_score
-
-        # Extract prediction details from the JSON structure
-        # The structure varies, but usually contains 'main_pick', 'bet_summary', or 'market_board'
-        
-        # Try to get the main pick recommendation
-        main_pick = None
-        main_pick_conf = 0.0
-        main_pick_odds = 0.0
-        
-        # Navigate the V20+ JSON structure
-        market_board = data.get("market_board", {})
-        
-        # Check Main Pick
-        if "main_pick" in data:
-            mp = data["main_pick"]
-            if isinstance(mp, dict):
-                main_pick = mp.get("pick")
-                main_pick_conf = mp.get("confidence", 0.0)
-                main_pick_odds = mp.get("odds", 0.0)
-        
-        # If no main pick, try bet_summary
-        if not main_pick and "bet_summary" in data:
-            summary = data["bet_summary"]
-            if isinstance(summary, list) and len(summary) > 0:
-                # Take the highest confidence one
-                best = max(summary, key=lambda x: x.get("confidence", 0))
-                main_pick = best.get("pick")
-                main_pick_conf = best.get("confidence", 0.0)
-                main_pick_odds = best.get("odds", 0.0)
-
-        if not main_pick or not main_pick_conf:
-            continue
-
-        # ─── NEW LOGIC: APPLY FILTERS ───
-        # 1. Determine Market Type
-        # Simple heuristic based on pick string
-        pick_str = str(main_pick).upper()
-        market_type = "MS"
-        if "1X" in pick_str or "X2" in pick_str or "12" in pick_str: market_type = "DC"
-        elif "ÜST" in pick_str or "ALT" in pick_str or "OVER" in pick_str or "UNDER" in pick_str:
-            if "1.5" in pick_str: market_type = "OU15"
-            elif "3.5" in pick_str: market_type = "OU35"
-            else: market_type = "OU25"
-        elif "VAR" in pick_str or "YOK" in pick_str or "BTTS" in pick_str: market_type = "BTTS"
-        
-        threshold = MIN_CONF_THRESHOLDS.get(market_type, 45.0)
-
-        # 2. Check Confidence Gate
-        if main_pick_conf < threshold:
-            skipped_bets += 1
-            continue
-
-        # 3. Check Value Gate (Edge)
-        if main_pick_odds > 0:
-            implied_prob = 1.0 / main_pick_odds
-            my_prob = main_pick_conf / 100.0
-            edge = my_prob - implied_prob
-            if edge < -0.03: # Negative value
-                skipped_bets += 1
-                continue
-
-        # ─── BET IS PLAYED ───
-        total_bets += 1
-        
-        # Determine if WON
-        is_won = False
-        
-        # Resolve MS (1, X, 2)
-        if market_type == "MS":
-            if main_pick == "1" and home_score > away_score: is_won = True
-            elif main_pick == "X" and home_score == away_score: is_won = True
-            elif main_pick == "2" and away_score > home_score: is_won = True
-            elif main_pick == "MS 1" and home_score > away_score: is_won = True
-            elif main_pick == "MS X" and home_score == away_score: is_won = True
-            elif main_pick == "MS 2" and away_score > home_score: is_won = True
-            
-        # Resolve OU (Over/Under)
-        elif market_type.startswith("OU"):
-            line = 2.5
-            if "1.5" in pick_str: line = 1.5
-            elif "3.5" in pick_str: line = 3.5
-            
-            is_over = total_goals > line
-            is_under = total_goals < line # Simplification (usually line is X.5 so no draw)
-            
-            if "ÜST" in pick_str or "OVER" in pick_str:
-                if is_over: is_won = True
-            elif "ALT" in pick_str or "UNDER" in pick_str:
-                if is_under: is_won = True
-
-        # Resolve BTTS
-        elif market_type == "BTTS":
-            if home_score > 0 and away_score > 0:
-                if "VAR" in pick_str: is_won = True
-            else:
-                if "YOK" in pick_str: is_won = True
-        
-        # Resolve DC (Double Chance) - Simplified
-        elif market_type == "DC":
-            if "1X" in pick_str and (home_score >= away_score): is_won = True
-            elif "X2" in pick_str and (away_score >= home_score): is_won = True
-            elif "12" in pick_str and (home_score != away_score): is_won = True
-
-        if is_won:
-            winning_bets += 1
-            profit = main_pick_odds - 1.0
-            total_profit += profit
-        else:
-            total_profit -= 1.0
-
-    # ─── REPORT ───
-    print("\n" + "="*60)
-    print("📈 BACKTEST RESULTS (With NEW Skip Logic)")
-    print("="*60)
-    print(f"Total Historical Matches Analyzed: {len(predictions)}")
-    print(f"🚫 Bets SKIPPED (Low Conf/Bad Value): {skipped_bets}")
-    print(f"✅ Bets PLAYED: {total_bets}")
-    
-    if total_bets > 0:
-        win_rate = (winning_bets / total_bets) * 100
-        roi = (total_profit / total_bets) * 100
-        
-        print(f"🏆 Winning Bets: {winning_bets}")
-        print(f"💀 Losing Bets: {total_bets - winning_bets}")
-        print("-" * 40)
-        print(f" Win Rate: {win_rate:.2f}%")
-        print(f"💰 Total Profit (Units): {total_profit:.2f}")
-        print(f"📊 ROI: {roi:.2f}%")
-        
-        if roi > 0:
-            print("🟢 STRATEGY IS PROFITABLE!")
-        else:
-            print("🔴 STRATEGY IS LOSING (Adjust thresholds!)")
-    else:
-        print("⚠️ No bets were played. Thresholds might be too high.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    simulate_backtest()
@@ -1,164 +0,0 @@
-"""
-SNIPER Backtest
-===============
-Sadece en yüksek güvenilirlik ve değere sahip bahisleri oynar.
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from datetime import datetime
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-MATCH_IDS = [
-    "v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
-    "7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
-    "7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
-    "7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
-    "7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
-    "7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
-    "7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
-    "7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
-    "7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
-    "lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
-    "40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
-    "2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
-    "7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
-    "coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
-    "9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
-    "6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
-    "1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
-]
-
-def run_sniper_backtest():
-    print("🎯 SNIPER BACKTEST: SADECE NET OLANLAR")
-    print("="*60)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    placeholders = ','.join(['%s'] * len(MATCH_IDS))
-    cur.execute(f"""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team,
-               l.name as league_name
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        LEFT JOIN leagues l ON m.league_id = l.id
-        WHERE m.id IN ({placeholders}) AND m.status = 'FT'
-    """, MATCH_IDS)
-    
-    rows = cur.fetchall()
-    print(f"📊 Analiz edilecek {len(rows)} maç var.\n")
-
-    try:
-        orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Hatası: {e}")
-        return
-
-    total_bet = 0
-    total_won = 0
-    total_profit = 0.0
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home = row['home_team'] or "?"
-        away = row['away_team'] or "?"
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-
-        print(f"[{i+1}/{len(rows)}] {home} vs {away} ... ", end="", flush=True)
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: 
-                print("⚠️ Veri Yok")
-                continue
-
-            pick_data = pred.get("expert_recommendation", {}).get("main_pick") or pred.get("main_pick", {})
-            pick = pick_data.get("pick") or pick_data.get("market_type")
-            conf = pick_data.get("confidence", 0)
-            odds = pick_data.get("odds", 0)
-
-            # SNIPER FİLTRELERİ
-            if conf < 75:
-                print(f"🚫 PASS (Conf: {conf:.0f}%)")
-                continue
-            if odds < 1.35:
-                print(f"🚫 PASS (Odds: {odds:.2f} çok düşük)")
-                continue
-            
-            # Value Control
-            implied = 1.0 / odds
-            if (conf/100) < implied:
-                print(f"🚫 PASS (Negatif Value)")
-                continue
-
-            # OYNA
-            total_bet += 1
-            won = False
-            pick_clean = str(pick).upper()
-
-            if pick_clean in ["1", "MS 1"] and h_score > a_score: won = True
-            elif pick_clean in ["X", "MS X"] and h_score == a_score: won = True
-            elif pick_clean in ["2", "MS 2"] and a_score > h_score: won = True
-            elif "ÜST" in pick_clean or "OVER" in pick_clean:
-                line = 2.5
-                if "1.5" in pick_clean: line = 1.5
-                elif "3.5" in pick_clean: line = 3.5
-                if (h_score + a_score) > line: won = True
-            elif "ALT" in pick_clean or "UNDER" in pick_clean:
-                line = 2.5
-                if "1.5" in pick_clean: line = 1.5
-                elif "3.5" in pick_clean: line = 3.5
-                if (h_score + a_score) < line: won = True
-            elif "VAR" in pick_clean and h_score > 0 and a_score > 0: won = True
-            elif "YOK" in pick_clean and (h_score == 0 or a_score == 0): won = True
-
-            if won:
-                total_won += 1
-                profit = odds - 1.0
-                total_profit += profit
-                print(f"✅ WON! (+{profit:.2f})")
-            else:
-                total_profit -= 1.0
-                print(f"❌ LOST! ({pick} @ {odds:.2f})")
-
-        except Exception as e:
-            print(f"💥 Hata: {e}")
-
-    print("\n" + "="*60)
-    print("🎯 SNIPER SONUÇLARI")
-    print("="*60)
-    print(f"Oynanan: {total_bet}")
-    print(f"Kazanılan: {total_won}")
-    print(f"Kazanma Oranı: %{(total_won/total_bet)*100:.1f}" if total_bet > 0 else "Kazanma Oranı: N/A")
-    print(f"Toplam Kâr: {total_profit:.2f} Units")
-    
-    if total_profit > 0:
-        print("🟢 PARA KAZANDIK!")
-    else:
-        print("🔴 PARA KAYBETTİK!")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_sniper_backtest()
@@ -1,162 +0,0 @@
-"""
-Strict Sniper Backtest (Calibrated)
-===================================
-Sadece Güven > %75 ve Oran > 1.30 olan bahisleri oynar.
-Modelin şişirilmiş özgüvenini elemek için yapıldı.
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_strict_backtest():
-    print("🎯 STRICT SNIPER BACKTEST (Conf > 75%)")
-    print("="*60)
-    
-    leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
-    with open(leagues_path, 'r') as f:
-        top_leagues = json.load(f)
-    league_ids = tuple(str(lid) for lid in top_leagues)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s
-          AND m.status = 'FT'
-          AND m.score_home IS NOT NULL
-          AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
-        ORDER BY m.mst_utc DESC
-        LIMIT 500
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç taranıyor. Sadece NET OLANLAR oynanacak...\n")
-
-    try: orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Hatası: {e}")
-        return
-
-    total_bet = 0
-    total_won = 0
-    total_profit = 0.0
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home = row['home_team'] or "?"
-        away = row['away_team'] or "?"
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: continue
-
-            # Check all picks for a HIGH CONFIDENCE bet
-            candidates = []
-            if pred.get("expert_recommendation"):
-                rec = pred["expert_recommendation"]
-                if rec.get("main_pick"): candidates.append(rec["main_pick"])
-                if rec.get("value_picks"): candidates.extend(rec["value_picks"])
-            elif pred.get("main_pick"):
-                candidates.append(pred["main_pick"])
-
-            best_bet = None
-            for c in candidates:
-                if not c: continue
-                # Access attributes safely (Dict or Object)
-                conf = c.get("confidence", 0) if isinstance(c, dict) else getattr(c, 'confidence', 0)
-                odds = c.get("odds", 0) if isinstance(c, dict) else getattr(c, 'odds', 0)
-                pick = c.get("pick", "") if isinstance(c, dict) else getattr(c, 'pick', "")
-
-                # STRICT CRITERIA
-                if conf >= 75.0 and odds >= 1.30:
-                    # Check Value (Edge)
-                    implied = 1.0 / odds
-                    edge = ((conf/100) - implied) * 100
-                    if edge > -5.0: # Tolerant edge
-                        if best_bet is None or (conf > (best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0))):
-                            best_bet = c
-
-            if best_bet:
-                pick = str(best_bet.get("pick") if isinstance(best_bet, dict) else getattr(best_bet, 'pick', "")).upper()
-                conf = best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0)
-                odds = best_bet.get("odds", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'odds', 0)
-                
-                # Resolution
-                won = False
-                if pick in ["1", "MS 1"] and h_score > a_score: won = True
-                elif pick in ["X", "MS X"] and h_score == a_score: won = True
-                elif pick in ["2", "MS 2"] and a_score > h_score: won = True
-                elif pick in ["1X", "X2"]:
-                    if "1X" in pick and h_score >= a_score: won = True
-                    elif "X2" in pick and a_score >= h_score: won = True
-                elif "ÜST" in pick or "OVER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) > line: won = True
-                elif "ALT" in pick or "UNDER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) < line: won = True
-                elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
-                elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
-
-                total_bet += 1
-                if won:
-                    total_won += 1
-                    profit = odds - 1.0
-                    total_profit += profit
-                    print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({conf:.0f}%) -> WON (+{profit:.2f})")
-                else:
-                    total_profit -= 1.0
-                    print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({conf:.0f}%) -> LOST")
-
-        except Exception as e:
-            pass
-
-    print("\n" + "="*60)
-    print("🎯 STRICT SNIPER SONUÇLARI")
-    print("="*60)
-    print(f"Oynanan Bahis: {total_bet}")
-    print(f"Kazanılan: {total_won}")
-    
-    if total_bet > 0:
-        win_rate = (total_won / total_bet) * 100
-        roi = (total_profit / total_bet) * 100
-        print(f"Kazanma Oranı: %{win_rate:.2f}")
-        print(f"Toplam Kâr: {total_profit:.2f} Units")
-        if total_profit > 0: print("🟢 PARA KAZANDIK!")
-        else: print("🔴 PARA KAYBETTİK!")
-    else:
-        print("⚠️ Yeteri kadar NET maç bulunamadı.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_strict_backtest()
@@ -1,94 +0,0 @@
-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()
@@ -1,505 +0,0 @@
-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()
@@ -1,810 +0,0 @@
-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()
@@ -1,230 +0,0 @@
-"""
-Backtest the live V2 predictor stack against recent finished football matches.
-
-This script uses the same path as production:
-database -> feature extractor -> betting predictor -> quant ranking.
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-
-from sqlalchemy import text
-
-ROOT_DIR = Path(__file__).resolve().parents[1]
-if str(ROOT_DIR) not in sys.path:
-    sys.path.insert(0, str(ROOT_DIR))
-
-from core.quant import MarketPick, analyze_market
-from data.database import dispose_engine, get_session
-from features.extractor import extract_features
-from models.betting_engine import get_predictor
-
-
-@dataclass
-class BacktestStats:
-    sampled_matches: int = 0
-    analyzed_matches: int = 0
-    skipped_matches: int = 0
-    ms_correct: int = 0
-    ou25_correct: int = 0
-    btts_correct: int = 0
-    main_pick_count: int = 0
-    main_pick_correct: int = 0
-    playable_pick_count: int = 0
-    playable_pick_correct: int = 0
-    playable_units_staked: float = 0.0
-    playable_units_profit: float = 0.0
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--limit", type=int, default=50)
-    parser.add_argument("--days", type=int, default=45)
-    return parser.parse_args()
-
-
-def _actual_ms(score_home: int, score_away: int) -> str:
-    if score_home > score_away:
-        return "1"
-    if score_home < score_away:
-        return "2"
-    return "X"
-
-
-def _actual_ou25(score_home: int, score_away: int) -> str:
-    return "Over" if (score_home + score_away) > 2 else "Under"
-
-
-def _actual_btts(score_home: int, score_away: int) -> str:
-    return "Yes" if score_home > 0 and score_away > 0 else "No"
-
-
-def _odds_map_from_features(feats) -> dict[str, dict[str, float]]:
-    return {
-        "MS": {"1": feats.odds_home, "X": feats.odds_draw, "2": feats.odds_away},
-        "OU25": {"Under": feats.odds_under25, "Over": feats.odds_over25},
-        "BTTS": {"No": feats.odds_btts_no, "Yes": feats.odds_btts_yes},
-    }
-
-
-def _best_pick(feats, all_probs: dict[str, dict[str, float]]) -> MarketPick | None:
-    odds_map = _odds_map_from_features(feats)
-    picks = [
-        analyze_market("MS", all_probs["MS"], odds_map["MS"], feats.data_quality_score),
-        analyze_market("OU25", all_probs["OU25"], odds_map["OU25"], feats.data_quality_score),
-        analyze_market("BTTS", all_probs["BTTS"], odds_map["BTTS"], feats.data_quality_score),
-    ]
-    ranked = sorted(
-        [pick for pick in picks if pick.pick],
-        key=lambda pick: pick.play_score,
-        reverse=True,
-    )
-    return ranked[0] if ranked else None
-
-
-def _pick_won(pick: MarketPick, actuals: dict[str, str]) -> bool:
-    return actuals.get(pick.market) == pick.pick
-
-
-async def _load_match_rows(limit: int, days: int) -> list[dict[str, object]]:
-    min_mst_utc = days * 86400000
-    query = text("""
-        SELECT
-            m.id,
-            m.match_name,
-            m.score_home,
-            m.score_away,
-            m.mst_utc
-        FROM matches m
-        WHERE m.sport = 'football'
-          AND m.score_home IS NOT NULL
-          AND m.score_away IS NOT NULL
-          AND m.mst_utc >= (
-            EXTRACT(EPOCH FROM NOW()) * 1000 - :min_mst_utc
-          )
-          AND EXISTS (
-            SELECT 1
-            FROM odd_categories oc
-            WHERE oc.match_id = m.id
-              AND oc.name IN ('Maç Sonucu', '2,5 Alt/Üst', 'Karşılıklı Gol')
-          )
-        ORDER BY m.mst_utc DESC
-        LIMIT :limit
-    """)
-    async with get_session() as session:
-        result = await session.execute(
-            query,
-            {"limit": limit, "min_mst_utc": min_mst_utc},
-        )
-        rows = result.mappings().all()
-    return [dict(row) for row in rows]
-
-
-async def _run(limit: int, days: int) -> BacktestStats:
-    stats = BacktestStats()
-    predictor = get_predictor()
-    rows = await _load_match_rows(limit, days)
-    stats.sampled_matches = len(rows)
-
-    async with get_session() as session:
-        for row in rows:
-            match_id = str(row["id"])
-            score_home = int(row["score_home"])
-            score_away = int(row["score_away"])
-            feats = await extract_features(session, match_id)
-
-            if feats is None:
-                stats.skipped_matches += 1
-                continue
-
-            if feats.data_quality_score <= 0.0:
-                stats.skipped_matches += 1
-                continue
-
-            all_probs = predictor.predict_all(feats.to_model_array(), feats)
-            stats.analyzed_matches += 1
-
-            actuals = {
-                "MS": _actual_ms(score_home, score_away),
-                "OU25": _actual_ou25(score_home, score_away),
-                "BTTS": _actual_btts(score_home, score_away),
-            }
-
-            if max(all_probs["MS"], key=all_probs["MS"].get) == actuals["MS"]:
-                stats.ms_correct += 1
-            if max(all_probs["OU25"], key=all_probs["OU25"].get) == actuals["OU25"]:
-                stats.ou25_correct += 1
-            if max(all_probs["BTTS"], key=all_probs["BTTS"].get) == actuals["BTTS"]:
-                stats.btts_correct += 1
-
-            best_pick = _best_pick(feats, all_probs)
-            if best_pick is None:
-                continue
-
-            stats.main_pick_count += 1
-            if _pick_won(best_pick, actuals):
-                stats.main_pick_correct += 1
-
-            if best_pick.playable:
-                stats.playable_pick_count += 1
-                stats.playable_units_staked += best_pick.stake_units
-                if _pick_won(best_pick, actuals):
-                    stats.playable_pick_correct += 1
-                    stats.playable_units_profit += best_pick.stake_units * (best_pick.odds - 1.0)
-                else:
-                    stats.playable_units_profit -= best_pick.stake_units
-
-    return stats
-
-
-def _pct(numerator: int, denominator: int) -> float:
-    if denominator <= 0:
-        return 0.0
-    return round((numerator / denominator) * 100.0, 2)
-
-
-def _roi(profit: float, staked: float) -> float:
-    if staked <= 0:
-        return 0.0
-    return round((profit / staked) * 100.0, 2)
-
-
-def _print_summary(stats: BacktestStats) -> None:
-    print("=== V2 Runtime Backtest ===")
-    print(f"Sampled matches      : {stats.sampled_matches}")
-    print(f"Analyzed matches     : {stats.analyzed_matches}")
-    print(f"Skipped matches      : {stats.skipped_matches}")
-    print(f"MS accuracy          : {_pct(stats.ms_correct, stats.analyzed_matches)}%")
-    print(f"OU2.5 accuracy       : {_pct(stats.ou25_correct, stats.analyzed_matches)}%")
-    print(f"BTTS accuracy        : {_pct(stats.btts_correct, stats.analyzed_matches)}%")
-    print(
-        "Main pick accuracy   : "
-        f"{_pct(stats.main_pick_correct, stats.main_pick_count)}% "
-        f"({stats.main_pick_correct}/{stats.main_pick_count})"
-    )
-    print(
-        "Playable accuracy    : "
-        f"{_pct(stats.playable_pick_correct, stats.playable_pick_count)}% "
-        f"({stats.playable_pick_correct}/{stats.playable_pick_count})"
-    )
-    print(f"Units staked         : {stats.playable_units_staked:.2f}")
-    print(f"Units profit         : {stats.playable_units_profit:.2f}")
-    print(f"ROI                  : {_roi(stats.playable_units_profit, stats.playable_units_staked)}%")
-
-
-async def _main() -> None:
-    args = _parse_args()
-    try:
-        stats = await _run(args.limit, args.days)
-        _print_summary(stats)
-    finally:
-        await dispose_engine()
-
-
-if __name__ == "__main__":
-    asyncio.run(_main())
@@ -1,147 +0,0 @@
-"""
-Value Hunter Backtest
-=====================
-Sadece modelin büroyu yendiği (Pozitif Edge) maçları oynar.
-"""
-
-import os, sys, json, time, psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR): ROOT_DIR = os.path.dirname(ROOT_DIR)
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-MATCH_IDS = [
-    "v2ljcst50nk37x04xwimpi50", "7gz0bhb5yvdssazl3y5946kno", "7ftj7kbu4rzpewxravf3luuc4",
-    "7f1z4e8ch1dm5q677644cky6s", "7ffq3aq3so22iymfdzch63nys", "rrkmeuymz7gzvoz8mplikzdg",
-    "7hegc9covicy699bxsi81xkb8", "7gl7rpr1hjayk3e5ut0gr613o", "7g7d86i3738287xfvyfeffcwk",
-    "7hs4boe4hv80muawocevvx2j8", "7ijhsloieg4t9yp5cxp0duln8", "7ixaiiptli5ek32kuybuni4gk",
-    "7i5sfh41cjpwg4l972dm487x0", "eo7g4wunxxxr8uv45q8p5x638", "7dinds2937w4645wva2rddlas",
-    "7b5ukdhvqh62wtndeqfg01ixg", "7bjptsj24gndoydn7n0202g44", "7cqxf3vo58ewrwmoom5xiyexg",
-    "7bxjl9h2hnf165rlp3o1vfztg", "7eo8zrez08c342rqsezpvq39w", "7as1muhs98vdarlhsean4bspg",
-    "7dwhj8cfxv6v6bzxpu5e3h05w", "7d4vq4417ps84yjzh95bnvvv8", "7ea9z501jgp9kxw3gay4myrkk",
-    "7cd3401itlty6ded7c1wct0yc", "ebgpz9mcije2snv986n6587pw", "i7ar1dkhvcwpxmkyks65ib6c",
-    "lyek7tyy6qk2xjs9vblucnx0", "hdn9qtyn3ysjwbc3i2trantg", "3y2bnssfqlajosiz2gpkn6xhw",
-    "40pehd14s9djjtycujavbex3o", "3xnbfjznzmnwml20akbgnis5w", "2eovi2rcc2l4ha7fpb2w7e1hw",
-    "2bwuikdjyyuithhru8ka8o00k", "2d3pcd76ya9ihi9yotxc553is", "1e9it04z4epy2etdxsffe7m6s",
-    "7af49jgo4iulv1k8cplj9smj8", "5k3vrz619hdu9nx4rnx6uim1g", "amjppgpetnyr0iisi241kgkyc",
-    "coqrhq09kxd16iejvgtzj3mz8", "d8ysan1qdctmkvjaz2adw7aqc", "9ttciz0gtb0z09ev1q5fe0ro4",
-    "9u720o37yaddqu1w6hlszpnh0", "7ijezdjp8t0rjti91ac63hyxg", "72gvdvztbb3dn79jidzzxzcb8",
-    "6uof1v2s6vrpieeml2bwo9tlg", "91dd8ia3m0bxoqzjgyo3ptsk", "3tj1nt3udsbvb9soqn2cs6gpg",
-    "1br5g88o5idtjxka1fr6zg4k4", "akuesquthbmxlzckvnqmgles4"
-]
-
-def run_value_hunter():
-    print("💎 VALUE HUNTER: SADECE HATALI ORANLARI YAKALA")
-    print("="*60)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    placeholders = ','.join(['%s'] * len(MATCH_IDS))
-    cur.execute(f"""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.id IN ({placeholders}) AND m.status = 'FT'
-    """, MATCH_IDS)
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç taranıyor...\n")
-
-    try: orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Hatası: {e}")
-        return
-
-    total_bet = 0
-    total_won = 0
-    total_profit = 0.0
-    total_edge_found = 0
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home = row['home_team'] or "?"
-        away = row['away_team'] or "?"
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: continue
-
-            # Tüm önerileri kontrol et
-            picks = pred.get("expert_recommendation", {}).get("value_picks", [])
-            if not picks: picks = [pred.get("expert_recommendation", {}).get("main_pick")]
-            
-            played_this_match = False
-
-            for pick_data in picks:
-                if not pick_data: continue
-                pick = pick_data.get("pick")
-                conf = pick_data.get("confidence", 0)
-                odds = pick_data.get("odds", 0)
-                edge = pick_data.get("edge", 0)
-
-                # VALUE KURALI: Model bürodan en az %10 daha iyi olmalı
-                if edge < 10: continue
-                if odds < 1.20: continue
-
-                total_bet += 1
-                total_edge_found += edge
-                won = False
-                pick_clean = str(pick).upper()
-
-                if pick_clean in ["1", "MS 1"] and h_score > a_score: won = True
-                elif pick_clean in ["X", "MS X"] and h_score == a_score: won = True
-                elif pick_clean in ["2", "MS 2"] and a_score > h_score: won = True
-                elif "ÜST" in pick_clean or "OVER" in pick_clean:
-                    line = 2.5
-                    if "1.5" in pick_clean: line = 1.5
-                    if (h_score + a_score) > line: won = True
-                elif "ALT" in pick_clean or "UNDER" in pick_clean:
-                    line = 2.5
-                    if "1.5" in pick_clean: line = 1.5
-                    if (h_score + a_score) < line: won = True
-                elif "VAR" in pick_clean and h_score > 0 and a_score > 0: won = True
-                elif "YOK" in pick_clean and (h_score == 0 or a_score == 0): won = True
-
-                if won:
-                    total_won += 1
-                    profit = odds - 1.0
-                    total_profit += profit
-                    print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({edge:.0f}% Edge) -> WON! (+{profit:.2f})")
-                else:
-                    total_profit -= 1.0
-                    print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({edge:.0f}% Edge) -> LOST")
-                
-                played_this_match = True
-                break # Maç başına tek bahis
-
-        except Exception: pass
-
-    print("\n" + "="*60)
-    print("💎 VALUE HUNTER SONUÇLARI")
-    print("="*60)
-    print(f"Toplam Value Bulunan Bahis: {total_bet}")
-    print(f"Ortalama Edge: {total_edge_found/total_bet:.1f}%" if total_bet > 0 else "N/A")
-    print(f"Kazanılan: {total_won}")
-    print(f"Toplam Kâr: {total_profit:.2f} Units")
-    
-    if total_profit > 0: print("🟢 PARA KAZANDIK!")
-    else: print("🔴 PARA KAYBETTİK!")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_value_hunter()
@@ -1,153 +0,0 @@
-"""
-Value Sniper Backtest (High Odds)
-=================================
-Sadece Oran > 1.50 ve Güven > %70 olan bahisleri oynar.
-"""
-
-import os
-import sys
-import json
-import time
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-sys.path.insert(0, ROOT_DIR)
-if "scripts" in os.path.basename(AI_DIR):
-    ROOT_DIR = os.path.dirname(ROOT_DIR)
-
-from services.single_match_orchestrator import get_single_match_orchestrator
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_value_sniper():
-    print("💰 VALUE SNIPER BACKTEST (Odds > 1.50)")
-    print("="*60)
-    
-    leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
-    with open(leagues_path, 'r') as f:
-        top_leagues = json.load(f)
-    league_ids = tuple(str(lid) for lid in top_leagues)
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
-               m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s
-          AND m.status = 'FT'
-          AND m.score_home IS NOT NULL
-          AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
-        ORDER BY m.mst_utc DESC
-        LIMIT 500
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç taranıyor...\n")
-
-    try: orchestrator = get_single_match_orchestrator()
-    except Exception as e:
-        print(f"❌ AI Hatası: {e}")
-        return
-
-    total_bet = 0
-    total_won = 0
-    total_profit = 0.0
-
-    for i, row in enumerate(rows):
-        match_id = str(row['id'])
-        home = row['home_team'] or "?"
-        away = row['away_team'] or "?"
-        h_score = row['score_home'] or 0
-        a_score = row['score_away'] or 0
-
-        try:
-            pred = orchestrator.analyze_match(match_id)
-            if not pred: continue
-
-            candidates = []
-            if pred.get("expert_recommendation"):
-                rec = pred["expert_recommendation"]
-                if rec.get("main_pick"): candidates.append(rec["main_pick"])
-                if rec.get("value_picks"): candidates.extend(rec["value_picks"])
-            elif pred.get("main_pick"):
-                candidates.append(pred["main_pick"])
-
-            best_bet = None
-            for c in candidates:
-                if not c: continue
-                conf = c.get("confidence", 0) if isinstance(c, dict) else getattr(c, 'confidence', 0)
-                odds = c.get("odds", 0) if isinstance(c, dict) else getattr(c, 'odds', 0)
-                
-                # VALUE CRITERIA: Odds > 1.50 AND Conf > 70%
-                if conf >= 70.0 and odds >= 1.50:
-                    # Check Edge
-                    implied = 1.0 / odds
-                    edge = ((conf/100) - implied) * 100
-                    if edge > 0: # Must be positive value
-                        if best_bet is None or (conf > (best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0))):
-                            best_bet = c
-
-            if best_bet:
-                pick = str(best_bet.get("pick") if isinstance(best_bet, dict) else getattr(best_bet, 'pick', "")).upper()
-                conf = best_bet.get("confidence", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'confidence', 0)
-                odds = best_bet.get("odds", 0) if isinstance(best_bet, dict) else getattr(best_bet, 'odds', 0)
-                
-                won = False
-                if pick in ["1", "MS 1"] and h_score > a_score: won = True
-                elif pick in ["X", "MS X"] and h_score == a_score: won = True
-                elif pick in ["2", "MS 2"] and a_score > h_score: won = True
-                elif "ÜST" in pick or "OVER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) > line: won = True
-                elif "ALT" in pick or "UNDER" in pick:
-                    line = 2.5
-                    if "1.5" in pick: line = 1.5
-                    elif "3.5" in pick: line = 3.5
-                    if (h_score + a_score) < line: won = True
-                elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
-                elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
-
-                total_bet += 1
-                if won:
-                    total_won += 1
-                    profit = odds - 1.0
-                    total_profit += profit
-                    print(f"[{i+1}] ✅ {home} vs {away} | {pick} ({odds:.2f}) -> WON (+{profit:.2f})")
-                else:
-                    total_profit -= 1.0
-                    print(f"[{i+1}] ❌ {home} vs {away} | {pick} ({odds:.2f}) -> LOST")
-
-        except: pass
-
-    print("\n" + "="*60)
-    print("💰 VALUE SNIPER SONUÇLARI")
-    print("="*60)
-    print(f"Oynanan Bahis: {total_bet}")
-    print(f"Kazanılan: {total_won}")
-    
-    if total_bet > 0:
-        win_rate = (total_won / total_bet) * 100
-        roi = (total_profit / total_bet) * 100
-        print(f"Kazanma Oranı: %{win_rate:.2f}")
-        print(f"Toplam Kâr: {total_profit:.2f} Units")
-        if total_profit > 0: print("🟢 PARA KAZANDIK!")
-        else: print("🔴 PARA KAYBETTİK!")
-    else:
-        print("⚠️ Yeterli VALUE bulunamadı.")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_value_sniper()
@@ -1,136 +0,0 @@
-"""
-VQWEN Full Backtest
-===================
-Tests all 3 VQWEN models (MS, OU25, BTTS) on 1000 historical matches.
-"""
-
-import os
-import sys
-import json
-import pickle
-import pandas as pd
-import numpy as np
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-PROJECT_ROOT = os.path.dirname(ROOT_DIR)
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_vqwen_backtest():
-    print("🧠 VQWEN FULL BACKTEST")
-    print("="*60)
-    
-    # Load Models
-    mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
-    try:
-        with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
-        print("✅ VQWEN MS, OU25, BTTS modelleri yüklendi.")
-    except Exception as e:
-        print(f"❌ Model hatası: {e}")
-        return
-
-    with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
-        league_ids = tuple(str(lid) for lid in json.load(f))
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa,
-               COALESCE((SELECT AVG(CASE WHEN m2.home_team_id = m.home_team_id AND m2.score_home > m2.score_away THEN 3 WHEN m2.home_team_id = m.home_team_id AND m2.score_home = m2.score_away THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as h_form,
-               COALESCE((SELECT AVG(CASE WHEN m2.away_team_id = m.away_team_id AND m2.score_away > m2.score_home THEN 3 WHEN m2.away_team_id = m.away_team_id AND m2.score_away = m2.score_home THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as a_form,
-               COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_sc,
-               COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_co,
-               COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_sc,
-               COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_co
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
-        ORDER BY m.mst_utc DESC
-        LIMIT 1000
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç analiz ediliyor...")
-
-    results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
-
-    for row in rows:
-        oh, od, oa = float(row['oh'] or 0), float(row['od'] or 0), float(row['oa'] or 0)
-        if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
-
-        h_xg = (float(row['h_sc'] or 1.2) + float(row['a_co'] or 1.2)) / 2
-        a_xg = (float(row['a_sc'] or 1.2) + float(row['h_co'] or 1.2)) / 2
-        h_p = (float(row['h_form'] or 0)*10) + (float(row['h_sc'] or 1.2)*5) - (float(row['h_co'] or 1.2)*5)
-        a_p = (float(row['a_form'] or 0)*10) + (float(row['a_sc'] or 1.2)*5) - (float(row['a_co'] or 1.2)*5)
-        
-        margin = (1/oh) + (1/od) + (1/oa)
-        
-        # MS Prediction
-        f_ms = pd.DataFrame([{'h_form': float(row['h_form']), 'a_form': float(row['a_form']), 'h_xg': h_xg, 'a_xg': a_xg, 
-                              'pow_diff': h_p - a_p, 'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
-                              'h_sot': 4.0, 'a_sot': 3.0}])
-        ms_probs = model_ms.predict(f_ms)[0]
-        
-        # MS Value Bet
-        for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
-            if odd <= 1.0: continue
-            edge = prob - (1/odd)
-            if edge > 0.05 and prob > 0.50: # Value ve Güven
-                results['ms']['bet'] += 1
-                h, a = row['score_home'], row['score_away']
-                w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
-                if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
-                else: results['ms']['profit'] -= 1.0
-                break
-
-        # OU2.5 Prediction
-        f_ou = pd.DataFrame([{'h_xg': h_xg, 'a_xg': a_xg, 'total_xg': h_xg+a_xg, 'h_sot': 4.0, 'a_sot': 3.0}])
-        p_over = model_ou.predict(f_ou)[0]
-        
-        # OU2.5 Value Bet
-        if p_over > 0.55 and oh > 1.0: # Sadece örnek olarak over > %55 ise
-            results['ou25']['bet'] += 1
-            if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85 # Ortalama oran
-            else: results['ou25']['profit'] -= 1.0
-
-        # BTTS Prediction
-        f_btts = pd.DataFrame([{'h_xg': h_xg, 'a_xg': a_xg, 'h_sc': float(row['h_sc']), 'a_sc': float(row['a_sc'])}])
-        p_btts = model_btts.predict(f_btts)[0]
-        
-        # BTTS Value Bet
-        if p_btts > 0.55:
-            results['btts']['bet'] += 1
-            if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
-            else: results['btts']['profit'] -= 1.0
-
-    print("\n" + "="*60)
-    print("📊 VQWEN PAZAR BAZLI SONUÇLAR")
-    print("="*60)
-    for mkt in ['ms', 'ou25', 'btts']:
-        r = results[mkt]
-        wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
-        print(f"{mkt.upper():<10} Oynanan: {r['bet']:<5} Kazanılan: {r['won']:<5} WR: {wr:.1f}%  Kâr: {r['profit']:+.2f} Units")
-    
-    total_profit = sum(r['profit'] for r in results.values())
-    print(f"\n💰 TOPLAM KÂR: {total_profit:+.2f} Units")
-    if total_profit > 0: print("🟢 PARA KAZANDIK!")
-    else: print("🔴 ZARARDA")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_vqwen_backtest()
@@ -1,141 +0,0 @@
-"""
-VQWEN Deep Backtest
-===================
-Tests the NEW Deep model with player & card data.
-"""
-
-import os
-import sys
-import json
-import pickle
-import pandas as pd
-import numpy as np
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-PROJECT_ROOT = os.path.dirname(ROOT_DIR)
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_vqwen_deep_backtest():
-    print("🧠 VQWEN DEEP BACKTEST")
-    print("="*60)
-    
-    # Load Models
-    mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
-    try:
-        with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
-        print("✅ VQWEN Deep modelleri yüklendi.")
-    except Exception as e:
-        print(f"❌ Model hatası: {e}")
-        return
-
-    with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
-        league_ids = tuple(str(lid) for lid in json.load(f))
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
-               t1.name as home_team, t2.name as away_team,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa,
-               COALESCE((SELECT AVG(CASE WHEN m2.home_team_id = m.home_team_id AND m2.score_home > m2.score_away THEN 3 WHEN m2.home_team_id = m.home_team_id AND m2.score_home = m2.score_away THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as h_form,
-               COALESCE((SELECT AVG(CASE WHEN m2.away_team_id = m.away_team_id AND m2.score_away > m2.score_home THEN 3 WHEN m2.away_team_id = m.away_team_id AND m2.score_away = m2.score_home THEN 1 ELSE 0 END) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc LIMIT 5), 0) as a_form,
-               COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_sc,
-               COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.home_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as h_co,
-               COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_sc,
-               COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.away_team_id AND m2.status = 'FT' LIMIT 10), 1.2) as a_co,
-               COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.home_team_id AND mp.is_starting = true), 0) as h_xi,
-               COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.away_team_id AND mp.is_starting = true), 0) as a_xi,
-               COALESCE((SELECT COUNT(*) FROM match_player_events mpe WHERE mpe.match_id = m.id AND mpe.event_type = 'card'), 0) as cards
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
-        ORDER BY m.mst_utc DESC
-        LIMIT 1000
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç analiz ediliyor...")
-
-    results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
-
-    for row in rows:
-        oh = float(row['oh'] or 0)
-        od = float(row['od'] or 0)
-        oa = float(row['oa'] or 0)
-        if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
-
-        h_xg = (float(row['h_sc'] or 1.2) + float(row['a_co'] or 1.2)) / 2
-        a_xg = (float(row['a_sc'] or 1.2) + float(row['h_co'] or 1.2)) / 2
-        h_p = (float(row['h_form'] or 0)*10) + (float(row['h_sc'] or 1.2)*5) - (float(row['h_co'] or 1.2)*5)
-        a_p = (float(row['a_form'] or 0)*10) + (float(row['a_sc'] or 1.2)*5) - (float(row['a_co'] or 1.2)*5)
-        
-        margin = (1/oh) + (1/od) + (1/oa)
-        h_sot, a_sot = 4.0, 3.0
-        
-        # Features
-        f = pd.DataFrame([{
-            'h_form': float(row['h_form']), 'a_form': float(row['a_form']),
-            'h_xg': h_xg, 'a_xg': a_xg, 'pow_diff': h_p - a_p,
-            'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
-            'h_sot': h_sot, 'a_sot': a_sot,
-            'h_xi': float(row['h_xi']), 'a_xi': float(row['a_xi']),
-            'xi_diff': float(row['h_xi'] - row['a_xi']),
-            'cards': float(row['cards'])
-        }])
-
-        # MS
-        ms_probs = model_ms.predict(f)[0]
-        for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
-            if odd <= 1.0: continue
-            edge = prob - (1/odd)
-            if edge > 0.05 and prob > 0.50:
-                results['ms']['bet'] += 1
-                h, a = row['score_home'], row['score_away']
-                w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
-                if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
-                else: results['ms']['profit'] -= 1.0
-                break
-
-        # OU2.5
-        p_over = float(model_ou.predict(f)[0])
-        if p_over > 0.55:
-            results['ou25']['bet'] += 1
-            if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85
-            else: results['ou25']['profit'] -= 1.0
-
-        # BTTS
-        p_btts = float(model_btts.predict(f)[0])
-        if p_btts > 0.55:
-            results['btts']['bet'] += 1
-            if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
-            else: results['btts']['profit'] -= 1.0
-
-    print("\n" + "="*60)
-    print("📊 VQWEN DEEP SONUÇLAR")
-    print("="*60)
-    for mkt in ['ms', 'ou25', 'btts']:
-        r = results[mkt]
-        wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
-        print(f"{mkt.upper():<10} Oyn: {r['bet']:<5} Kaz: {r['won']:<5} WR: {wr:.1f}%  Kâr: {r['profit']:+.2f}")
-    
-    total = sum(r['profit'] for r in results.values())
-    print(f"\n💰 TOPLAM: {total:+.2f} Units")
-    print("🟢 PARA KAZANDIK!" if total > 0 else "🔴 ZARARDA")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_vqwen_deep_backtest()
@@ -1,159 +0,0 @@
-"""
-VQWEN Final Backtest
-====================
-Tests the Final Model (ELO + Rest + Context).
-"""
-
-import os
-import sys
-import json
-import pickle
-import pandas as pd
-import numpy as np
-import psycopg2
-from psycopg2.extras import RealDictCursor
-
-AI_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.dirname(AI_DIR)
-PROJECT_ROOT = os.path.dirname(ROOT_DIR)
-
-def get_clean_dsn() -> str:
-    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
-
-def run_final_backtest():
-    print("🧠 VQWEN FINAL BACKTEST (ELO + REST)")
-    print("="*60)
-    
-    # Load Models
-    mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
-    try:
-        with open(os.path.join(mdir, 'vqwen_ms.pkl'), 'rb') as f: model_ms = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_ou25.pkl'), 'rb') as f: model_ou = pickle.load(f)
-        with open(os.path.join(mdir, 'vqwen_btts.pkl'), 'rb') as f: model_btts = pickle.load(f)
-        print("✅ VQWEN Final modelleri yüklendi.")
-    except Exception as e:
-        print(f"❌ Model hatası: {e}")
-        return
-
-    with open(os.path.join(PROJECT_ROOT, "top_leagues.json"), 'r') as f:
-        league_ids = tuple(str(lid) for lid in json.load(f))
-
-    dsn = get_clean_dsn()
-    conn = psycopg2.connect(dsn)
-    cur = conn.cursor(cursor_factory=RealDictCursor)
-
-    cur.execute("""
-        SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away,
-               m.mst_utc,
-               t1.name as home_team, t2.name as away_team,
-               maf.home_elo, maf.away_elo,
-               COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as h_home_goals,
-               COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as a_away_goals,
-               COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as h_rest,
-               COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as a_rest,
-               COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.home_team_id AND mp.is_starting = true), 11) as h_xi,
-               COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.away_team_id AND mp.is_starting = true), 11) as a_xi,
-               COALESCE((SELECT COUNT(*) FROM match_player_events mpe WHERE mpe.match_id = m.id AND mpe.event_type = 'card'), 4) as cards,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
-               (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa
-        FROM matches m
-        LEFT JOIN teams t1 ON m.home_team_id = t1.id
-        LEFT JOIN teams t2 ON m.away_team_id = t2.id
-        LEFT JOIN football_ai_features maf ON maf.match_id = m.id
-        WHERE m.league_id IN %s AND m.status = 'FT' AND m.score_home IS NOT NULL
-        ORDER BY m.mst_utc DESC
-        LIMIT 1000
-    """, (league_ids,))
-    
-    rows = cur.fetchall()
-    print(f"📊 {len(rows)} maç analiz ediliyor...")
-
-    results = {'ms': {'bet': 0, 'won': 0, 'profit': 0}, 'ou25': {'bet': 0, 'won': 0, 'profit': 0}, 'btts': {'bet': 0, 'won': 0, 'profit': 0}}
-
-    for row in rows:
-        oh = float(row['oh'] or 0)
-        od = float(row['od'] or 0)
-        oa = float(row['oa'] or 0)
-        if oh <= 1.0 or od <= 1.0 or oa <= 1.0: continue
-
-        # Features
-        h_elo = float(row['home_elo'] or 1500)
-        a_elo = float(row['away_elo'] or 1500)
-        h_home_goals = float(row['h_home_goals'] or 1.2)
-        a_away_goals = float(row['a_away_goals'] or 1.2)
-        h_rest = float(row['h_rest'] or 7)
-        a_rest = float(row['a_rest'] or 7)
-        h_xi = float(row['h_xi'] or 11)
-        a_xi = float(row['a_xi'] or 11)
-        cards = float(row['cards'] or 4)
-
-        def fatigue(rest):
-            if rest < 3: return 0.85
-            if rest < 5: return 0.95
-            return 1.0
-
-        h_fat = fatigue(h_rest)
-        a_fat = fatigue(a_rest)
-
-        h_xg = h_home_goals * h_fat
-        a_xg = a_away_goals * a_fat
-        total_xg = h_xg + a_xg
-        
-        margin = (1/oh) + (1/od) + (1/oa)
-        f = pd.DataFrame([{
-            'elo_diff': h_elo - a_elo,
-            'h_xg': h_xg, 'a_xg': a_xg,
-            'total_xg': total_xg,
-            'pow_diff': (h_elo/100)*h_fat - (a_elo/100)*a_fat,
-            'rest_diff': h_rest - a_rest,
-            'h_fatigue': h_fat, 'a_fatigue': a_fat,
-            'imp_h': (1/oh)/margin, 'imp_d': (1/od)/margin, 'imp_a': (1/oa)/margin,
-            'h_xi': h_xi, 'a_xi': a_xi,
-            'cards': cards
-        }])
-
-        # MS
-        ms_probs = model_ms.predict(f)[0]
-        for i, (pick, prob, odd) in enumerate(zip(['1', 'X', '2'], ms_probs, [oh, od, oa])):
-            if odd <= 1.0: continue
-            edge = prob - (1/odd)
-            if edge > 0.05 and prob > 0.45:
-                results['ms']['bet'] += 1
-                h, a = row['score_home'], row['score_away']
-                w = (pick=='1' and h>a) or (pick=='X' and h==a) or (pick=='2' and a>h)
-                if w: results['ms']['won'] += 1; results['ms']['profit'] += (odd - 1.0)
-                else: results['ms']['profit'] -= 1.0
-                break
-
-        # OU2.5
-        p_over = float(model_ou.predict(f)[0])
-        if p_over > 0.55:
-            results['ou25']['bet'] += 1
-            if (row['score_home'] + row['score_away']) > 2.5: results['ou25']['won'] += 1; results['ou25']['profit'] += 0.85
-            else: results['ou25']['profit'] -= 1.0
-
-        # BTTS
-        p_btts = float(model_btts.predict(f)[0])
-        if p_btts > 0.55:
-            results['btts']['bet'] += 1
-            if row['score_home'] > 0 and row['score_away'] > 0: results['btts']['won'] += 1; results['btts']['profit'] += 0.85
-            else: results['btts']['profit'] -= 1.0
-
-    print("\n" + "="*60)
-    print("📊 VQWEN FINAL SONUÇLAR")
-    print("="*60)
-    for mkt in ['ms', 'ou25', 'btts']:
-        r = results[mkt]
-        wr = (r['won'] / r['bet'] * 100) if r['bet'] > 0 else 0
-        print(f"{mkt.upper():<10} Oyn: {r['bet']:<5} Kaz: {r['won']:<5} WR: {wr:.1f}%  Kâr: {r['profit']:+.2f}")
-    
-    total = sum(r['profit'] for r in results.values())
-    print(f"\n💰 TOPLAM: {total:+.2f} Units")
-    print("🟢 PARA KAZANDIK!" if total > 0 else "🔴 ZARARDA")
-
-    cur.close()
-    conn.close()
-
-if __name__ == "__main__":
-    run_final_backtest()
@@ -1,182 +0,0 @@
-"""
-VQWEN v3 Shared-Contract Backtest
-=================================
-
-Evaluates the retrained VQWEN models on the temporal validation slice using
-the exact same pre-match feature contract as training/runtime.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import pickle
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import psycopg2
-from dotenv import load_dotenv
-
-AI_DIR = Path(__file__).resolve().parent
-ENGINE_DIR = AI_DIR.parent
-REPO_DIR = ENGINE_DIR.parent
-MODELS_DIR = ENGINE_DIR / "models" / "vqwen"
-
-if str(ENGINE_DIR) not in sys.path:
-    sys.path.insert(0, str(ENGINE_DIR))
-
-from features.vqwen_contract import FEATURE_COLUMNS  # noqa: E402
-from train_vqwen_v3 import (  # noqa: E402
-    _enrich_pre_match_context,
-    _fetch_dataframe,
-    _prepare_features,
-    _temporal_split,
-    load_top_league_ids,
-)
-
-
-def _load_env() -> None:
-    load_dotenv(REPO_DIR / ".env", override=False)
-    load_dotenv(ENGINE_DIR / ".env", override=False)
-
-
-def get_clean_dsn() -> str:
-    _load_env()
-    raw = os.getenv("DATABASE_URL", "").strip().strip('"').strip("'")
-    if not raw:
-        raise RuntimeError("DATABASE_URL is missing.")
-    return raw.split("?", 1)[0]
-
-
-def _accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    if len(y_true) == 0:
-        return 0.0
-    return float((y_true == y_pred).mean())
-
-
-def _binary_metrics(prob: np.ndarray, y_true: np.ndarray) -> tuple[float, float]:
-    pred = (prob >= 0.5).astype(int)
-    acc = _accuracy(y_true, pred)
-    brier = float(np.mean((prob - y_true) ** 2)) if len(y_true) else 1.0
-    return acc, brier
-
-
-def _multiclass_brier(prob: np.ndarray, y_true: np.ndarray, n_classes: int = 3) -> float:
-    if len(y_true) == 0:
-        return 1.0
-    target = np.zeros((len(y_true), n_classes), dtype=np.float64)
-    target[np.arange(len(y_true)), y_true.astype(int)] = 1.0
-    return float(np.mean(np.sum((prob - target) ** 2, axis=1)))
-
-
-def _band_label(probability: float) -> str:
-    if probability >= 0.70:
-        return "HIGH"
-    if probability >= 0.60:
-        return "MEDIUM"
-    if probability >= 0.50:
-        return "LOW"
-    return "NO_BET"
-
-
-def _summarize_bands(
-    name: str,
-    confidence: np.ndarray,
-    is_correct: np.ndarray,
-) -> list[str]:
-    lines: list[str] = []
-    for band in ("HIGH", "MEDIUM", "LOW"):
-        mask = np.array([_band_label(float(p)) == band for p in confidence], dtype=bool)
-        count = int(mask.sum())
-        accuracy = float(is_correct[mask].mean()) if count else 0.0
-        avg_conf = float(confidence[mask].mean()) if count else 0.0
-        lines.append(
-            f"{name} {band:<6} count={count:<4} accuracy={accuracy*100:5.1f}% avg_conf={avg_conf*100:5.1f}%"
-        )
-    return lines
-
-
-def run_v3_backtest() -> None:
-    print("VQWEN v3 SHARED-CONTRACT BACKTEST")
-    print("=" * 60)
-
-    league_ids = load_top_league_ids()
-    dsn = get_clean_dsn()
-
-    with psycopg2.connect(dsn) as conn:
-        with conn.cursor() as cur:
-            df = _fetch_dataframe(cur, league_ids)
-            df = _enrich_pre_match_context(cur, df)
-            df = _prepare_features(df)
-
-    train_df, valid_df = _temporal_split(df)
-    print(f"Toplam ornek: {len(df)} | Train: {len(train_df)} | Valid: {len(valid_df)}")
-
-    with (MODELS_DIR / "vqwen_ms.pkl").open("rb") as handle:
-        model_ms = pickle.load(handle)
-    with (MODELS_DIR / "vqwen_ou25.pkl").open("rb") as handle:
-        model_ou25 = pickle.load(handle)
-    with (MODELS_DIR / "vqwen_btts.pkl").open("rb") as handle:
-        model_btts = pickle.load(handle)
-
-    X_valid = valid_df[FEATURE_COLUMNS]
-    y_ms = valid_df["t_ms"].to_numpy(dtype=np.int64)
-    y_ou25 = valid_df["t_ou"].to_numpy(dtype=np.int64)
-    y_btts = valid_df["t_btts"].to_numpy(dtype=np.int64)
-
-    ms_prob = np.asarray(model_ms.predict(X_valid), dtype=np.float64)
-    ou25_prob = np.asarray(model_ou25.predict(X_valid), dtype=np.float64).reshape(-1)
-    btts_prob = np.asarray(model_btts.predict(X_valid), dtype=np.float64).reshape(-1)
-
-    ms_pred = np.argmax(ms_prob, axis=1)
-    ms_conf = np.max(ms_prob, axis=1)
-    ms_correct = (ms_pred == y_ms).astype(np.int64)
-
-    ou25_pred = (ou25_prob >= 0.5).astype(np.int64)
-    ou25_conf = np.where(ou25_prob >= 0.5, ou25_prob, 1.0 - ou25_prob)
-    ou25_correct = (ou25_pred == y_ou25).astype(np.int64)
-
-    btts_pred = (btts_prob >= 0.5).astype(np.int64)
-    btts_conf = np.where(btts_prob >= 0.5, btts_prob, 1.0 - btts_prob)
-    btts_correct = (btts_pred == y_btts).astype(np.int64)
-
-    ms_acc = _accuracy(y_ms, ms_pred)
-    ou25_acc, ou25_brier = _binary_metrics(ou25_prob, y_ou25)
-    btts_acc, btts_brier = _binary_metrics(btts_prob, y_btts)
-    ms_brier = _multiclass_brier(ms_prob, y_ms)
-
-    print("\nGenel metrikler")
-    print(f"MS accuracy   : {ms_acc*100:.2f}% | multiclass_brier={ms_brier:.4f}")
-    print(f"OU25 accuracy : {ou25_acc*100:.2f}% | brier={ou25_brier:.4f}")
-    print(f"BTTS accuracy : {btts_acc*100:.2f}% | brier={btts_brier:.4f}")
-
-    print("\nConfidence band")
-    for line in _summarize_bands("MS", ms_conf, ms_correct):
-        print(line)
-    for line in _summarize_bands("OU25", ou25_conf, ou25_correct):
-        print(line)
-    for line in _summarize_bands("BTTS", btts_conf, btts_correct):
-        print(line)
-
-    summary = {
-        "validation_samples": int(len(valid_df)),
-        "metrics": {
-            "ms_accuracy": round(ms_acc, 4),
-            "ms_brier": round(ms_brier, 4),
-            "ou25_accuracy": round(ou25_acc, 4),
-            "ou25_brier": round(ou25_brier, 4),
-            "btts_accuracy": round(btts_acc, 4),
-            "btts_brier": round(btts_brier, 4),
-        },
-    }
-    (MODELS_DIR / "vqwen_backtest_v3_summary.json").write_text(
-        json.dumps(summary, indent=2),
-        encoding="utf-8",
-    )
-    print("\nKaydedildi: vqwen_backtest_v3_summary.json")
-
-
-if __name__ == "__main__":
-    run_v3_backtest()
@@ -1,7 +0,0 @@
-import os, psycopg2
-from dotenv import load_dotenv
-load_dotenv('/Users/piton/Documents/Suggest-Bet-BE/.env')
-conn = psycopg2.connect(os.getenv('DATABASE_URL').split('?')[0])
-cur = conn.cursor()
-cur.execute('SELECT mpe.match_id, SUM(CASE WHEN event_type::text LIKE \'%yellow_card%\' THEN 1 WHEN event_type::text LIKE \'%red_card%\' THEN 2 ELSE 1 END) as cards FROM match_player_events mpe WHERE event_type::text LIKE \'%card%\' GROUP BY mpe.match_id LIMIT 5')
-print(cur.fetchall())
@@ -1,56 +0,0 @@
-"""Quick test: V20+Quant integration — EV Edge, Kelly staking, edge-based grading."""
-import json
-from services.single_match_orchestrator import SingleMatchOrchestrator
-
-MATCH_IDS = [
-    "er7n8hqndkhvdsg6an72r7h90",  # Def. Justicia vs Atl Lanus
-    "etpay8k4qr3gts3jjidfebaxg",  # CA Tigre vs Gymnasia
-]
-
-o = SingleMatchOrchestrator()
-
-for mid in MATCH_IDS:
-    print(f"\n{'='*60}")
-    print(f"MATCH: {mid}")
-    print(f"{'='*60}")
-    r = o.analyze_match(mid)
-    if not r:
-        print("  Match not found")
-        continue
-
-    info = r.get("match_info", {})
-    print(f"  {info.get('match_name', '?')} | {info.get('league', '?')}")
-
-    mp = r.get("main_pick", {})
-    print(f"\n  MAIN PICK: {mp.get('market')} {mp.get('pick')}")
-    print(f"    probability:  {mp.get('probability', 0):.4f}")
-    print(f"    odds:         {mp.get('odds', 0):.2f}")
-    print(f"    ev_edge:      {mp.get('ev_edge', mp.get('edge', 0)):+.4f}")
-    print(f"    implied_prob: {mp.get('implied_prob', 0):.4f}")
-    print(f"    bet_grade:    {mp.get('bet_grade', 'N/A')}")
-    print(f"    stake_units:  {mp.get('stake_units', 0)}")
-    print(f"    playable:     {mp.get('playable', False)}")
-    print(f"    reasons:      {mp.get('decision_reasons', [])}")
-
-    print(f"\n  ALL MARKETS (with EV Edge + Kelly):")
-    for b in r.get("bet_summary", []):
-        ev = b.get("ev_edge", 0)
-        imp = b.get("implied_prob", 0)
-        flag = ">>>" if b.get("playable") else "   "
-        mkt = b["market"]
-        pick = b["pick"]
-        odds = b.get("odds", 0)
-        grade = b["bet_grade"]
-        stake = b["stake_units"]
-        conf = b.get("calibrated_confidence", 0)
-        print(
-            f"    {flag} {mkt:8s} {pick:12s} "
-            f"ev_edge={ev:+.3f}  "
-            f"odds={odds:.2f}  "
-            f"stake={stake:.1f}  "
-            f"grade={grade:4s}  "
-            f"conf={conf:.1f}%  "
-            f"implied={imp:.3f}"
-        )
-
-    print()
@@ -1,75 +0,0 @@
-import sys
-import unittest
-from decimal import Decimal
-from pathlib import Path
-from unittest.mock import MagicMock
-
-AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
-if str(AI_ENGINE_ROOT) not in sys.path:
-    sys.path.insert(0, str(AI_ENGINE_ROOT))
-
-from core.engines.odds_predictor import OddsPredictorEngine
-from features.sidelined_analyzer import SidelinedAnalyzer
-
-
-class EngineNullSafetyTests(unittest.TestCase):
-    def test_odds_predictor_accepts_decimal_inputs_without_crashing(self):
-        engine = OddsPredictorEngine()
-
-        prediction = engine.predict(
-            odds_data={
-                "ms_h": Decimal("2.10"),
-                "ms_d": Decimal("3.25"),
-                "ms_a": Decimal("3.60"),
-                "ou25_o": Decimal("1.90"),
-            },
-        )
-
-        self.assertGreater(prediction.market_home_prob, 0.0)
-        self.assertGreater(prediction.market_draw_prob, 0.0)
-        self.assertGreater(prediction.market_away_prob, 0.0)
-
-    def test_sidelined_analyzer_handles_non_numeric_fields(self):
-        analyzer = SidelinedAnalyzer.__new__(SidelinedAnalyzer)
-        analyzer.position_weights = {"K": 0.35, "D": 0.20, "O": 0.25, "F": 0.30}
-        analyzer.max_rating = 10
-        analyzer.adaptation_threshold = 10
-        analyzer.adaptation_discount = 0.5
-        analyzer.goalkeeper_penalty = 0.15
-        analyzer.confidence_boost = 10
-        analyzer.max_impact = 0.85
-        analyzer.key_player_threshold = 3
-        analyzer.recent_matches_lookback = 15
-        analyzer._fetch_player_stats = MagicMock(return_value={})
-
-        result = analyzer.analyze(
-            {
-                "totalSidelined": 2,
-                "players": [
-                    {
-                        "playerId": "p1",
-                        "playerName": "Player One",
-                        "positionShort": "O",
-                        "matchesMissed": "N/A",
-                        "average": "?",
-                        "type": "injury",
-                    },
-                    {
-                        "playerId": "p2",
-                        "playerName": "Player Two",
-                        "positionShort": "K",
-                        "matchesMissed": "12",
-                        "average": "6.7",
-                        "type": "suspension",
-                    },
-                ],
-            },
-        )
-
-        self.assertEqual(result.total_sidelined, 2)
-        self.assertGreaterEqual(result.impact_score, 0.0)
-        self.assertTrue(len(result.player_details) >= 2)
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -1,282 +0,0 @@
-"""
-Unit tests for FeatureEnrichmentService
-========================================
-Tests all 6 enrichment methods with mocked DB cursor:
-  1. compute_team_stats
-  2. compute_h2h
-  3. compute_form_streaks
-  4. compute_referee_stats
-  5. compute_league_averages
-  6. compute_momentum
-"""
-
-import sys
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
-if str(AI_ENGINE_ROOT) not in sys.path:
-    sys.path.insert(0, str(AI_ENGINE_ROOT))
-
-from services.feature_enrichment import FeatureEnrichmentService, _safe_avg
-
-
-def _make_cursor(rows=None, side_effect=None):
-    """Create a mock RealDictCursor."""
-    cur = MagicMock()
-    if side_effect:
-        cur.execute.side_effect = side_effect
-    else:
-        cur.fetchall.return_value = rows or []
-        cur.fetchone.return_value = rows[0] if rows else None
-    return cur
-
-
-class TestSafeAvg(unittest.TestCase):
-    def test_returns_average(self):
-        self.assertAlmostEqual(_safe_avg([2.0, 4.0, 6.0], 0.0), 4.0)
-
-    def test_returns_default_on_empty(self):
-        self.assertEqual(_safe_avg([], 99.0), 99.0)
-
-    def test_single_value(self):
-        self.assertAlmostEqual(_safe_avg([7.5], 0.0), 7.5)
-
-
-class TestComputeTeamStats(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_defaults_when_no_team_id(self):
-        result = self.svc.compute_team_stats(MagicMock(), '', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
-
-    def test_returns_defaults_when_no_rows(self):
-        cur = _make_cursor(rows=[])
-        result = self.svc.compute_team_stats(cur, 'team1', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
-
-    def test_returns_defaults_on_db_error(self):
-        cur = _make_cursor(side_effect=Exception('DB down'))
-        result = self.svc.compute_team_stats(cur, 'team1', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_TEAM_STATS)
-
-    def test_calculates_averages_correctly(self):
-        rows = [
-            {'possession_percentage': 60.0, 'shots_on_target': 5, 'total_shots': 10, 'corners': 7},
-            {'possession_percentage': 40.0, 'shots_on_target': 3, 'total_shots': 12, 'corners': 3},
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_team_stats(cur, 'team1', self.ts)
-
-        self.assertAlmostEqual(result['avg_possession'], 50.0)
-        self.assertAlmostEqual(result['avg_shots_on_target'], 4.0)
-        self.assertAlmostEqual(result['shot_conversion'], (5 / 10 + 3 / 12) / 2, places=4)
-        self.assertAlmostEqual(result['avg_corners'], 5.0)
-
-    def test_handles_none_subfields_gracefully(self):
-        """Rows with None values should be skipped, not crash."""
-        rows = [
-            {'possession_percentage': 55.0, 'shots_on_target': None, 'total_shots': None, 'corners': 4},
-            {'possession_percentage': None, 'shots_on_target': 2, 'total_shots': 8, 'corners': None},
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_team_stats(cur, 'team1', self.ts)
-
-        self.assertAlmostEqual(result['avg_possession'], 55.0)
-        self.assertAlmostEqual(result['avg_shots_on_target'], 2.0)
-        self.assertAlmostEqual(result['avg_corners'], 4.0)
-
-
-class TestComputeH2H(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_defaults_when_no_ids(self):
-        result = self.svc.compute_h2h(MagicMock(), '', 'away1', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
-
-    def test_returns_defaults_when_no_rows(self):
-        cur = _make_cursor(rows=[])
-        result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
-
-    def test_calculates_h2h_stats(self):
-        rows = [
-            {'home_team_id': 'home1', 'away_team_id': 'away1', 'score_home': 2, 'score_away': 1},  # home win, btts, over25
-            {'home_team_id': 'home1', 'away_team_id': 'away1', 'score_home': 0, 'score_away': 0},  # draw, no btts, no over25
-            {'home_team_id': 'away1', 'away_team_id': 'home1', 'score_home': 1, 'score_away': 3},  # reversed: home wins again, btts, over25
-            {'home_team_id': 'away1', 'away_team_id': 'home1', 'score_home': 2, 'score_away': 0},  # reversed: away(=home1) lost
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
-
-        self.assertEqual(result['total_matches'], 4)
-        self.assertAlmostEqual(result['home_win_rate'], 2 / 4)
-        self.assertAlmostEqual(result['draw_rate'], 1 / 4)
-        self.assertAlmostEqual(result['btts_rate'], 2 / 4)
-        self.assertAlmostEqual(result['over25_rate'], 2 / 4)
-
-    def test_returns_defaults_on_db_error(self):
-        cur = _make_cursor(side_effect=Exception('connection lost'))
-        result = self.svc.compute_h2h(cur, 'home1', 'away1', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_H2H)
-
-
-class TestComputeFormStreaks(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_defaults_when_no_team_id(self):
-        result = self.svc.compute_form_streaks(MagicMock(), '', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_FORM)
-
-    def test_calculates_streaks_correctly(self):
-        """Most recent first: W, W, D, L → winning_streak=2, unbeaten_streak=3."""
-        rows = [
-            {'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 2, 'score_away': 0},  # W (clean sheet, scored)
-            {'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 1, 'score_away': 0},  # W (clean sheet, scored)
-            {'home_team_id': 'x', 'away_team_id': 'team1', 'score_home': 1, 'score_away': 1},  # D (scored, conceded)
-            {'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 2},  # L (not scored, conceded)
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_form_streaks(cur, 'team1', self.ts)
-
-        self.assertEqual(result['winning_streak'], 2)
-        self.assertEqual(result['unbeaten_streak'], 3)
-        self.assertAlmostEqual(result['clean_sheet_rate'], 2 / 4)
-        self.assertAlmostEqual(result['scoring_rate'], 3 / 4)
-
-    def test_all_losses(self):
-        rows = [
-            {'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 1},
-            {'home_team_id': 'team1', 'away_team_id': 'x', 'score_home': 0, 'score_away': 3},
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_form_streaks(cur, 'team1', self.ts)
-
-        self.assertEqual(result['winning_streak'], 0)
-        self.assertEqual(result['unbeaten_streak'], 0)
-        self.assertAlmostEqual(result['scoring_rate'], 0.0)
-
-
-class TestComputeRefereeStats(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_defaults_when_no_name(self):
-        result = self.svc.compute_referee_stats(MagicMock(), None, self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_REFEREE)
-
-    def test_calculates_referee_tendencies(self):
-        match_rows = [
-            {'home_team_id': 'h1', 'score_home': 2, 'score_away': 0, 'match_id': 'm1'},  # home win
-            {'home_team_id': 'h2', 'score_home': 1, 'score_away': 1, 'match_id': 'm2'},  # draw
-        ]
-        card_row = {'yellows': 6, 'total_cards': 8}
-
-        cur = MagicMock()
-        # First execute (match query) → match_rows
-        # Second execute (card query) → card_row
-        cur.fetchall.return_value = match_rows
-        cur.fetchone.return_value = card_row
-
-        result = self.svc.compute_referee_stats(cur, 'Ref Name', self.ts)
-
-        self.assertEqual(result['experience'], 2)
-        self.assertAlmostEqual(result['avg_goals'], (2 + 0 + 1 + 1) / 2)
-        # home_bias = (1/2) - 0.46 = 0.04
-        self.assertAlmostEqual(result['home_bias'], 0.04, places=4)
-        self.assertAlmostEqual(result['avg_yellow'], 6 / 2)
-        self.assertAlmostEqual(result['cards_total'], 8 / 2)
-
-    def test_returns_defaults_on_db_error(self):
-        cur = _make_cursor(side_effect=Exception('timeout'))
-        result = self.svc.compute_referee_stats(cur, 'Some Ref', self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_REFEREE)
-
-
-class TestComputeLeagueAverages(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_defaults_when_no_league_id(self):
-        result = self.svc.compute_league_averages(MagicMock(), None, self.ts)
-        self.assertEqual(result, FeatureEnrichmentService._DEFAULT_LEAGUE)
-
-    def test_calculates_league_averages(self):
-        rows = [
-            {'score_home': 1, 'score_away': 1},  # 2 goals
-            {'score_home': 0, 'score_away': 0},  # 0 goals (zero-goal match)
-            {'score_home': 3, 'score_away': 2},  # 5 goals
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_league_averages(cur, 'league1', self.ts)
-
-        self.assertAlmostEqual(result['avg_goals'], 7 / 3, places=4)
-        self.assertAlmostEqual(result['zero_goal_rate'], 1 / 3, places=4)
-
-
-class TestComputeMomentum(unittest.TestCase):
-    def setUp(self):
-        self.svc = FeatureEnrichmentService()
-        self.ts = 1700000000000
-
-    def test_returns_zero_when_no_team_id(self):
-        result = self.svc.compute_momentum(MagicMock(), '', self.ts)
-        self.assertEqual(result, 0.0)
-
-    def test_returns_zero_when_no_rows(self):
-        cur = _make_cursor(rows=[])
-        result = self.svc.compute_momentum(cur, 'team1', self.ts)
-        self.assertEqual(result, 0.0)
-
-    def test_all_wins_returns_one(self):
-        """All wins → momentum = 1.0 (max possible)."""
-        rows = [
-            {'home_team_id': 'team1', 'score_home': 3, 'score_away': 0},
-            {'home_team_id': 'team1', 'score_home': 2, 'score_away': 1},
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_momentum(cur, 'team1', self.ts)
-        self.assertAlmostEqual(result, 1.0, places=4)
-
-    def test_all_losses_returns_negative(self):
-        """All losses → negative momentum."""
-        rows = [
-            {'home_team_id': 'team1', 'score_home': 0, 'score_away': 2},
-            {'home_team_id': 'team1', 'score_home': 1, 'score_away': 3},
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_momentum(cur, 'team1', self.ts)
-        self.assertLess(result, 0.0)
-
-    def test_mixed_results(self):
-        """W, D, L → weighted score between -1 and 1."""
-        rows = [
-            {'home_team_id': 'team1', 'score_home': 1, 'score_away': 0},  # W (weight=3)
-            {'home_team_id': 'x', 'away_team_id': 'team1', 'score_home': 0, 'score_away': 0},  # D (weight=2)
-            {'home_team_id': 'team1', 'score_home': 0, 'score_away': 1},  # L (weight=1)
-        ]
-        cur = _make_cursor(rows)
-        result = self.svc.compute_momentum(cur, 'team1', self.ts)
-
-        # weighted = 3*3 + 1*2 + (-1)*1 = 9+2-1 = 10
-        # max_possible = 3*3 + 3*2 + 3*1 = 18
-        # normalised = 10/18 ≈ 0.5556
-        self.assertAlmostEqual(result, round(10 / 18, 4), places=4)
-
-    def test_returns_zero_on_db_error(self):
-        cur = _make_cursor(side_effect=Exception('broken pipe'))
-        result = self.svc.compute_momentum(cur, 'team1', self.ts)
-        self.assertEqual(result, 0.0)
-
-
-if __name__ == '__main__':
-    unittest.main()
@@ -1,110 +0,0 @@
-import asyncio
-import sys
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-from fastapi import HTTPException
-
-AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
-if str(AI_ENGINE_ROOT) not in sys.path:
-    sys.path.insert(0, str(AI_ENGINE_ROOT))
-
-import main as ai_main
-
-
-def _run(coro):
-    return asyncio.run(coro)
-
-
-class MainApiFunctionTests(unittest.TestCase):
-    def test_analyze_match_v20plus_returns_payload(self):
-        orchestrator = MagicMock()
-        orchestrator.analyze_match.return_value = {"match_info": {"match_id": "m1"}}
-
-        with patch("main.get_single_match_orchestrator", return_value=orchestrator):
-            result = _run(ai_main.analyze_match_v20plus("m1"))
-
-        self.assertEqual(result["match_info"]["match_id"], "m1")
-
-    def test_analyze_match_v20plus_raises_404(self):
-        orchestrator = MagicMock()
-        orchestrator.analyze_match.return_value = None
-
-        with patch("main.get_single_match_orchestrator", return_value=orchestrator):
-            with self.assertRaises(HTTPException) as ctx:
-                _run(ai_main.analyze_match_v20plus("missing"))
-
-        self.assertEqual(ctx.exception.status_code, 404)
-
-    def test_analyze_match_htms_v20plus_returns_payload(self):
-        orchestrator = MagicMock()
-        orchestrator.analyze_match_htms.return_value = {
-            "status": "ok",
-            "engine_used": "v20plus_top_htms",
-        }
-
-        with patch("main.get_single_match_orchestrator", return_value=orchestrator):
-            result = _run(ai_main.analyze_match_htms_v20plus("m1"))
-
-        self.assertEqual(result["status"], "ok")
-        self.assertEqual(result["engine_used"], "v20plus_top_htms")
-
-    def test_analyze_match_htft_timeout_validation(self):
-        with self.assertRaises(HTTPException) as ctx:
-            _run(ai_main.analyze_match_htft_v20plus("m1", timeout_sec=2))
-
-        self.assertEqual(ctx.exception.status_code, 400)
-
-    def test_generate_coupon_v20plus_forwards_payload(self):
-        orchestrator = MagicMock()
-        orchestrator.build_coupon.return_value = {"bets": []}
-
-        request = ai_main.CouponRequest(
-            match_ids=["m1", "m2"],
-            strategy="SAFE",
-            max_matches=3,
-            min_confidence=70,
-        )
-
-        with patch("main.get_single_match_orchestrator", return_value=orchestrator):
-            result = _run(ai_main.generate_coupon_v20plus(request))
-
-        self.assertEqual(result, {"bets": []})
-        orchestrator.build_coupon.assert_called_once_with(
-            match_ids=["m1", "m2"],
-            strategy="SAFE",
-            max_matches=3,
-            min_confidence=70.0,
-        )
-
-    def test_reversal_watchlist_validation(self):
-        with self.assertRaises(HTTPException) as ctx:
-            _run(ai_main.get_reversal_watchlist_v20plus(count=0))
-        self.assertEqual(ctx.exception.status_code, 400)
-
-    def test_reversal_watchlist_forwards_payload(self):
-        orchestrator = MagicMock()
-        orchestrator.get_reversal_watchlist.return_value = {"watchlist": []}
-
-        with patch("main.get_single_match_orchestrator", return_value=orchestrator):
-            result = _run(
-                ai_main.get_reversal_watchlist_v20plus(
-                    count=12,
-                    horizon_hours=48,
-                    min_score=50.5,
-                    top_leagues_only=True,
-                ),
-            )
-
-        self.assertEqual(result, {"watchlist": []})
-        orchestrator.get_reversal_watchlist.assert_called_once_with(
-            count=12,
-            horizon_hours=48,
-            min_score=50.5,
-            top_leagues_only=True,
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -1,768 +0,0 @@
-import json
-import sys
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-AI_ENGINE_ROOT = Path(__file__).resolve().parents[1]
-if str(AI_ENGINE_ROOT) not in sys.path:
-    sys.path.insert(0, str(AI_ENGINE_ROOT))
-
-from models.v20_ensemble import FullMatchPrediction
-from models.basketball_v25 import BasketballMatchPrediction
-from services.single_match_orchestrator import MatchData, SingleMatchOrchestrator
-
-
-class _CursorContext:
-    def __init__(self, cursor):
-        self._cursor = cursor
-
-    def __enter__(self):
-        return self._cursor
-
-    def __exit__(self, exc_type, exc, tb):
-        return False
-
-
-class _ConnContext:
-    def __init__(self, cursor):
-        self._cursor = cursor
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc, tb):
-        return False
-
-    def cursor(self, cursor_factory=None):
-        return _CursorContext(self._cursor)
-
-
-class _StaticFetchAllCursor:
-    def __init__(self, rows):
-        self.rows = rows
-        self.executed = []
-
-    def execute(self, query, params=None):
-        self.executed.append((query, params))
-
-    def fetchall(self):
-        return list(self.rows)
-
-
-class _RouterCursor:
-    def __init__(
-        self,
-        *,
-        live_row=None,
-        hist_row=None,
-        relational_rows=None,
-        participation_rows=None,
-        probable_rows=None,
-    ):
-        self.live_row = live_row
-        self.hist_row = hist_row
-        self.relational_rows = relational_rows or []
-        self.participation_rows = participation_rows or []
-        self.probable_rows = probable_rows or []
-        self.last_query = ""
-
-    def execute(self, query, params=None):
-        self.last_query = query
-
-    def fetchone(self):
-        if "FROM live_matches" in self.last_query:
-            return self.live_row
-        if "FROM matches m" in self.last_query:
-            return self.hist_row
-        return None
-
-    def fetchall(self):
-        if "FROM odd_categories" in self.last_query:
-            return list(self.relational_rows)
-        if "FROM match_player_participation" in self.last_query and "GROUP BY" not in self.last_query:
-            return list(self.participation_rows)
-        if "GROUP BY mpp.player_id" in self.last_query:
-            return list(self.probable_rows)
-        return []
-
-
-def _build_orchestrator() -> SingleMatchOrchestrator:
-    orchestrator = SingleMatchOrchestrator.__new__(SingleMatchOrchestrator)
-    orchestrator.v25_predictor = MagicMock()
-    orchestrator.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,
-        "DC": 0.93,
-        "OU15": 0.90,
-        "OU25": 0.85,
-        "OU35": 0.88,
-        "BTTS": 0.83,
-        "HT": 0.80,
-        "HT_OU05": 0.88,
-    }
-    orchestrator.market_min_conf = {
-        "MS": 52.0,
-        "DC": 56.0,
-        "OU15": 60.0,
-        "OU25": 58.0,
-        "OU35": 54.0,
-        "BTTS": 57.0,
-        "HT": 53.0,
-        "HT_OU05": 55.0,
-    }
-    orchestrator.market_min_play_score = {
-        "MS": 72.0,
-        "DC": 62.0,
-        "OU15": 64.0,
-        "OU25": 70.0,
-        "OU35": 76.0,
-        "BTTS": 70.0,
-        "HT": 74.0,
-        "HT_OU05": 64.0,
-    }
-    orchestrator.market_min_edge = {
-        "MS": 0.03,
-        "DC": 0.01,
-        "OU15": 0.01,
-        "OU25": 0.02,
-        "OU35": 0.04,
-        "BTTS": 0.03,
-        "HT": 0.04,
-        "HT_OU05": 0.01,
-    }
-    return orchestrator
-
-
-class SingleMatchOrchestratorTests(unittest.TestCase):
-    def setUp(self):
-        self.orchestrator = _build_orchestrator()
-
-    def test_parse_odds_json_uses_exact_market_match_and_ignores_collisions(self):
-        odds_json = {
-            "Maç Sonucu": {"1": "2.15", "X": "3.20", "2": "3.30"},
-            "İlk Yarı/Maç Sonucu": {"1/1": "4.30"},
-            "2,5 Alt/Üst": {"Üst": "1.85", "Alt": "1.95"},
-            "İY 0,5 Alt/Üst": {"Üst": "1.49", "Alt": "2.20"},
-            "1. Yarı Ev Sahibi 0,5 Alt/Üst": {"Üst": "1.99", "Alt": "1.45"},
-            "2,5 Kart Puanı Alt/Üst": {"Üst": "1.33", "Alt": "2.95"},
-            "Karşılıklı Gol": {"Var": "1.75", "Yok": "2.05"},
-            "1. Yarı Karşılıklı Gol": {"Var": "2.10", "Yok": "1.60"},
-            "Çifte Şans": {"1-X": "1.33", "X-2": "1.62", "1-2": "1.30"},
-            "1. Yarı Sonucu": {"1": "2.45", "X": "2.00", "2": "3.80"},
-        }
-
-        parsed = self.orchestrator._parse_odds_json(odds_json)
-
-        self.assertEqual(parsed["ms_h"], 2.15)
-        self.assertEqual(parsed["ms_d"], 3.20)
-        self.assertEqual(parsed["ms_a"], 3.30)
-        self.assertEqual(parsed["ou25_o"], 1.85)
-        self.assertEqual(parsed["ou25_u"], 1.95)
-        self.assertEqual(parsed["btts_y"], 1.75)
-        self.assertEqual(parsed["btts_n"], 2.05)
-        self.assertEqual(parsed["dc_1x"], 1.33)
-        self.assertEqual(parsed["dc_x2"], 1.62)
-        self.assertEqual(parsed["dc_12"], 1.30)
-        self.assertEqual(parsed["ht_h"], 2.45)
-        self.assertEqual(parsed["ht_d"], 2.00)
-        self.assertEqual(parsed["ht_a"], 3.80)
-        self.assertEqual(parsed["ht_ou05_o"], 1.49)
-        self.assertEqual(parsed["ht_ou05_u"], 2.20)
-        self.assertEqual(parsed["htft_11"], 4.30)
-
-    def test_parse_odds_json_accepts_selection_variants(self):
-        odds_json = {
-            "2,5 Alt/Üst": {"2,5 Üst": "1.91", "2,5 Alt": "1.86"},
-            "Karşılıklı Gol": {"YES": "1.82", "NO": "1.96"},
-            "Çifte Şans": {"1X": "1.28", "X2": "1.44", "12": "1.32"},
-        }
-
-        parsed = self.orchestrator._parse_odds_json(odds_json)
-
-        self.assertEqual(parsed["ou25_o"], 1.91)
-        self.assertEqual(parsed["ou25_u"], 1.86)
-        self.assertEqual(parsed["btts_y"], 1.82)
-        self.assertEqual(parsed["btts_n"], 1.96)
-        self.assertEqual(parsed["dc_1x"], 1.28)
-        self.assertEqual(parsed["dc_x2"], 1.44)
-        self.assertEqual(parsed["dc_12"], 1.32)
-
-    def test_parse_odds_json_maps_all_football_markets_with_noise(self):
-        odds_json = {
-            "Maç Sonucu": {"1": "2.31", "X": "3.22", "2": "3.05"},
-            "Çifte Şans": {"1-X": "1.34", "X-2": "1.52", "1-2": "1.28"},
-            "1,5 Alt/Üst": {"Üst": "1.29", "Alt": "3.45"},
-            "2,5 Alt/Üst": {"Üst": "1.71", "Alt": "2.05"},
-            "3,5 Alt/Üst": {"Üst": "2.62", "Alt": "1.41"},
-            "Karşılıklı Gol": {"Var": "1.66", "Yok": "2.11"},
-            "1. Yarı Sonucu": {"1": "3.10", "X": "1.95", "2": "4.60"},
-            "1. Yarı 0,5 Alt/Üst": {"Üst": "1.21", "Alt": "2.72"},
-            # noise categories that must not overwrite football main markets
-            "1. Yarı Ev Sahibi 0,5 Alt/Üst": {"Üst": "1.99", "Alt": "1.45"},
-            "1. Yarı Deplasman 0,5 Alt/Üst": {"Üst": "1.73", "Alt": "1.63"},
-            "1.Yarı 3,5 Korner Alt/Üst": {"Üst": "1.26", "Alt": "2.30"},
-            "2,5 Kart Puanı Alt/Üst": {"Üst": "1.40", "Alt": "2.60"},
-        }
-
-        parsed = self.orchestrator._parse_odds_json(odds_json)
-
-        self.assertEqual(parsed["ms_h"], 2.31)
-        self.assertEqual(parsed["ms_d"], 3.22)
-        self.assertEqual(parsed["ms_a"], 3.05)
-        self.assertEqual(parsed["dc_1x"], 1.34)
-        self.assertEqual(parsed["dc_x2"], 1.52)
-        self.assertEqual(parsed["dc_12"], 1.28)
-        self.assertEqual(parsed["ou15_o"], 1.29)
-        self.assertEqual(parsed["ou15_u"], 3.45)
-        self.assertEqual(parsed["ou25_o"], 1.71)
-        self.assertEqual(parsed["ou25_u"], 2.05)
-        self.assertEqual(parsed["ou35_o"], 2.62)
-        self.assertEqual(parsed["ou35_u"], 1.41)
-        self.assertEqual(parsed["btts_y"], 1.66)
-        self.assertEqual(parsed["btts_n"], 2.11)
-        self.assertEqual(parsed["ht_h"], 3.10)
-        self.assertEqual(parsed["ht_d"], 1.95)
-        self.assertEqual(parsed["ht_a"], 4.60)
-        self.assertEqual(parsed["ht_ou05_o"], 1.21)
-        self.assertEqual(parsed["ht_ou05_u"], 2.72)
-
-    def test_v25_market_odds_ignores_synthetic_default_when_selection_missing(self):
-        odds_json = {
-            "1,5 Alt/Üst": {"Alt": 5.70},
-            "Çifte Şans": {"1-X": 1.30, "X-2": 1.38, "1-2": 1.09},
-        }
-
-        parsed = self.orchestrator._parse_odds_json(odds_json)
-
-        self.assertEqual(parsed["ou15_o"], 0.0)
-        self.assertEqual(
-            self.orchestrator._v25_market_odds(parsed, "OU15", "Over"),
-            1.0,
-        )
-        self.assertEqual(
-            self.orchestrator._v25_market_odds(parsed, "OU15", "Under"),
-            5.7,
-        )
-        self.assertEqual(
-            self.orchestrator._v25_market_odds(parsed, "DC", "X2"),
-            1.38,
-        )
-
-    def test_parse_odds_json_extracts_basketball_ml_total_spread(self):
-        odds_json = {
-            "Maç Sonucu (Uzt. Dahil)": {"1": "1.74", "2": "2.08"},
-            "Alt/Üst (163,5)": {"Üst": "1.86", "Alt": "1.94"},
-            "1. Yarı Alt/Üst (81,5)": {"Üst": "1.89", "Alt": "1.91"},
-            "1. Yarı Alt/Üst (100,5)": {"Üst": "1.83", "Alt": "1.97"},
-            "Hnd. MS (0:5,5)": {"1": "1.91", "+5.5h": "1.87"},
-        }
-
-        parsed = self.orchestrator._parse_odds_json(odds_json)
-
-        self.assertEqual(parsed["ml_h"], 1.74)
-        self.assertEqual(parsed["ml_a"], 2.08)
-        self.assertEqual(parsed["tot_line"], 163.5)
-        self.assertEqual(parsed["tot_o"], 1.86)
-        self.assertEqual(parsed["tot_u"], 1.94)
-        self.assertEqual(parsed["spread_home_line"], -5.5)
-        self.assertEqual(parsed["spread_h"], 1.91)
-        self.assertEqual(parsed["spread_a"], 1.87)
-        self.assertNotIn("ht_ou05_o", parsed)
-        self.assertNotIn("ht_ou05_u", parsed)
-
-    def test_extract_odds_merges_relational_when_live_json_is_incomplete(self):
-        row = {
-            "match_id": "m-1",
-            "odds": {"Maç Sonucu": {"1": 2.10, "X": 3.20, "2": 3.35}},
-        }
-        relational_rows = [
-            {"category_name": "Çifte Şans", "selection_name": "1-X", "odd_value": 1.28},
-            {"category_name": "Çifte Şans", "selection_name": "X-2", "odd_value": 1.44},
-            {"category_name": "Çifte Şans", "selection_name": "1-2", "odd_value": 1.31},
-            {"category_name": "2,5 Alt/Üst", "selection_name": "Üst", "odd_value": 1.89},
-            {"category_name": "2,5 Alt/Üst", "selection_name": "Alt", "odd_value": 1.94},
-            {"category_name": "Karşılıklı Gol", "selection_name": "Var", "odd_value": 1.77},
-            {"category_name": "Karşılıklı Gol", "selection_name": "Yok", "odd_value": 2.02},
-            {"category_name": "1. Yarı Sonucu", "selection_name": "1", "odd_value": 2.55},
-            {"category_name": "1. Yarı Sonucu", "selection_name": "X", "odd_value": 1.98},
-            {"category_name": "1. Yarı Sonucu", "selection_name": "2", "odd_value": 3.40},
-        ]
-        cur = _StaticFetchAllCursor(relational_rows)
-
-        odds = self.orchestrator._extract_odds(cur, row)
-
-        self.assertEqual(odds["ms_h"], 2.10)
-        self.assertEqual(odds["ms_d"], 3.20)
-        self.assertEqual(odds["ms_a"], 3.35)
-        self.assertEqual(odds["dc_x2"], 1.44)
-        self.assertEqual(odds["ou25_o"], 1.89)
-        self.assertEqual(odds["btts_y"], 1.77)
-        self.assertEqual(odds["ht_d"], 1.98)
-        self.assertEqual(len(cur.executed), 1)
-
-    def test_extract_odds_fills_default_ms_when_no_source_available(self):
-        row = {"match_id": "m-2", "odds": None}
-        cur = _StaticFetchAllCursor([])
-
-        odds = self.orchestrator._extract_odds(cur, row)
-
-        self.assertEqual(odds["ms_h"], SingleMatchOrchestrator.DEFAULT_MS_H)
-        self.assertEqual(odds["ms_d"], SingleMatchOrchestrator.DEFAULT_MS_D)
-        self.assertEqual(odds["ms_a"], SingleMatchOrchestrator.DEFAULT_MS_A)
-
-    def test_parse_lineups_json_supports_id_playerid_personid(self):
-        lineups = {
-            "home": {
-                "xi": [
-                    {"id": "11"},
-                    {"playerId": "12"},
-                ],
-            },
-            "away": {
-                "starting": [
-                    {"personId": "21"},
-                    "22",
-                ],
-            },
-        }
-
-        home, away = self.orchestrator._parse_lineups_json(lineups)
-
-        self.assertEqual(home, ["11", "12"])
-        self.assertEqual(away, ["21", "22"])
-
-    def test_extract_lineups_uses_participation_and_probable_xi_fallbacks(self):
-        row = {
-            "match_id": "m-3",
-            "home_team_id": "h1",
-            "away_team_id": "a1",
-            "match_date_ms": 1700000000000,
-            "lineups": {
-                "home": {"xi": [{"personId": "h-live-1"}]},
-                "away": {},
-            },
-        }
-        participation = [
-            {"team_id": "a1", "player_id": "a-db-1"},
-            {"team_id": "a1", "player_id": "a-db-2"},
-        ]
-        cur = _StaticFetchAllCursor(participation)
-
-        with patch.object(
-            self.orchestrator,
-            "_build_probable_xi",
-            side_effect=[["h-prob-1"], ["a-prob-1"]],
-        ) as probable_xi:
-            home, away, source = self.orchestrator._extract_lineups(cur, row)
-
-        self.assertEqual(home, ["h-live-1"])
-        self.assertEqual(away, ["a-db-1", "a-db-2"])
-        self.assertEqual(source, "none")
-        probable_xi.assert_not_called()
-
-    def test_extract_lineups_falls_back_to_probable_xi_when_live_and_participation_missing(self):
-        row = {
-            "match_id": "m-4",
-            "home_team_id": "h2",
-            "away_team_id": "a2",
-            "match_date_ms": 1700000000000,
-            "lineups": None,
-        }
-        cur = _StaticFetchAllCursor([])
-
-        with patch.object(
-            self.orchestrator,
-            "_build_probable_xi",
-            side_effect=[["h-prob-1", "h-prob-2"], ["a-prob-1"]],
-        ) as probable_xi:
-            home, away, source = self.orchestrator._extract_lineups(cur, row)
-
-        self.assertEqual(home, ["h-prob-1", "h-prob-2"])
-        self.assertEqual(away, ["a-prob-1"])
-        self.assertEqual(source, "probable_xi")
-        self.assertEqual(probable_xi.call_count, 2)
-
-    def test_load_match_data_parses_live_row_json_and_sidelined(self):
-        odds_payload = {
-            "Maç Sonucu": {"1": 2.10, "X": 3.30, "2": 3.50},
-            "Çifte Şans": {"1-X": 1.30, "X-2": 1.52, "1-2": 1.34},
-            "1,5 Alt/Üst": {"Üst": 1.33, "Alt": 2.90},
-            "2,5 Alt/Üst": {"Üst": 1.91, "Alt": 1.85},
-            "3,5 Alt/Üst": {"Üst": 2.95, "Alt": 1.38},
-            "Karşılıklı Gol": {"Var": 1.84, "Yok": 1.92},
-            "1. Yarı Sonucu": {"1": 2.55, "X": 1.97, "2": 3.45},
-        }
-        lineups_payload = {
-            "home": {"xi": [{"personId": "101"}, {"personId": "102"}]},
-            "away": {"xi": [{"personId": "201"}, {"personId": "202"}]},
-        }
-        live_row = {
-            "match_id": "live-101",
-            "home_team_id": "h-101",
-            "away_team_id": "a-101",
-            "league_id": "l-101",
-            "sport": "FOOTBALL",
-            "match_date_ms": 1760000000000,
-            "odds": json.dumps(odds_payload),
-            "lineups": json.dumps(lineups_payload),
-            "sidelined": json.dumps(
-                {
-                    "homeTeam": {"totalSidelined": 1, "players": []},
-                    "awayTeam": {"totalSidelined": 0, "players": []},
-                }
-            ),
-            "referee_name": "John Ref",
-            "home_team_name": "Home FC",
-            "away_team_name": "Away FC",
-            "league_name": "League Name",
-        }
-        cursor = _RouterCursor(live_row=live_row)
-
-        with patch("services.single_match_orchestrator.psycopg2.connect", return_value=_ConnContext(cursor)):
-            data = self.orchestrator._load_match_data("live-101")
-
-        self.assertIsNotNone(data)
-        self.assertEqual(data.match_id, "live-101")
-        self.assertEqual(data.home_team_id, "h-101")
-        self.assertEqual(data.away_team_id, "a-101")
-        self.assertEqual(data.sport, "football")
-        self.assertEqual(data.referee_name, "John Ref")
-        self.assertEqual(data.home_lineup, ["101", "102"])
-        self.assertEqual(data.away_lineup, ["201", "202"])
-        self.assertEqual(data.lineup_source, "none")
-        self.assertEqual(data.sidelined_data["homeTeam"]["totalSidelined"], 1)
-        self.assertEqual(data.odds_data["dc_x2"], 1.52)
-        self.assertEqual(data.odds_data["ht_h"], 2.55)
-
-    def test_analyze_match_forwards_all_core_fields_to_predictor(self):
-        match_data = MatchData(
-            match_id="live-55",
-            home_team_id="home-55",
-            away_team_id="away-55",
-            home_team_name="Home 55",
-            away_team_name="Away 55",
-            match_date_ms=1760000000000,
-            sport="football",
-            league_id="league-55",
-            league_name="League 55",
-            referee_name="Ref 55",
-            odds_data={"ms_h": 2.4, "ms_d": 3.1, "ms_a": 2.9},
-            home_lineup=["h1", "h2"],
-            away_lineup=["a1", "a2"],
-            sidelined_data={
-                "homeTeam": {"totalSidelined": 2, "players": []},
-                "awayTeam": {"totalSidelined": 1, "players": []},
-            },
-            home_goals_avg=1.6,
-            home_conceded_avg=1.1,
-            away_goals_avg=1.2,
-            away_conceded_avg=1.4,
-            home_position=5,
-            away_position=8,
-            lineup_source="confirmed_live",
-        )
-        prediction = FullMatchPrediction(match_id="live-55", home_team="Home 55", away_team="Away 55")
-
-        self.orchestrator._load_match_data = MagicMock(return_value=match_data)
-        self.orchestrator.v25_predictor.predict_market_bundle = MagicMock(return_value={"MS": {"pick": "1"}})
-        self.orchestrator._build_v25_features = MagicMock(return_value={})
-        self.orchestrator._get_v25_signal = MagicMock(return_value={"MS": {"pick": "1"}})
-        self.orchestrator._build_v25_prediction = MagicMock(return_value=prediction)
-        self.orchestrator._build_prediction_package = MagicMock(return_value={"ok": True})
-
-        result = self.orchestrator.analyze_match("live-55")
-
-        self.assertEqual(result, {"ok": True})
-        self.orchestrator._build_v25_features.assert_called_once_with(match_data)
-        self.orchestrator._get_v25_signal.assert_called_once_with(match_data, {})
-        self.orchestrator._build_v25_prediction.assert_called_once_with(
-            match_data,
-            {},
-            {"MS": {"pick": "1"}},
-        )
-
-    def test_analyze_match_routes_basketball_to_basketball_predictor(self):
-        match_data = MatchData(
-            match_id="b-live-1",
-            home_team_id="bh",
-            away_team_id="ba",
-            home_team_name="Home B",
-            away_team_name="Away B",
-            match_date_ms=1760000000000,
-            sport="basketball",
-            league_id="bleague",
-            league_name="B League",
-            referee_name=None,
-            odds_data={"ml_h": 1.75, "ml_a": 2.05, "tot_line": 161.5, "tot_o": 1.88, "tot_u": 1.92},
-            home_lineup=None,
-            away_lineup=None,
-            sidelined_data={"homeTeam": {"totalSidelined": 1}, "awayTeam": {"totalSidelined": 0}},
-            home_goals_avg=85.0,
-            home_conceded_avg=79.0,
-            away_goals_avg=82.0,
-            away_conceded_avg=81.0,
-            home_position=4,
-            away_position=7,
-            lineup_source="none",
-        )
-        prediction = BasketballMatchPrediction(
-            match_id="b-live-1",
-            home_team_name="Home B",
-            away_team_name="Away B",
-            league_name="B League",
-        )
-
-        self.orchestrator._load_match_data = MagicMock(return_value=match_data)
-        self.orchestrator.basketball_predictor.predict = MagicMock(return_value=prediction)
-        self.orchestrator._build_basketball_prediction_package = MagicMock(
-            return_value={"sport": "basketball", "ok": True}
-        )
-
-        result = self.orchestrator.analyze_match("b-live-1")
-
-        self.assertEqual(result, {"sport": "basketball", "ok": True})
-        self.orchestrator.basketball_predictor.predict.assert_called_once()
-        kwargs = self.orchestrator.basketball_predictor.predict.call_args.kwargs
-        self.assertEqual(kwargs["match_id"], "b-live-1")
-        self.assertEqual(kwargs["home_team_id"], "bh")
-        self.assertEqual(kwargs["away_team_id"], "ba")
-        self.assertEqual(kwargs["league_id"], "bleague")
-        self.assertEqual(kwargs["odds_data"]["ml_h"], 1.75)
-        self.orchestrator.v25_predictor.predict_market_bundle.assert_not_called()
-
-    def test_build_market_rows_maps_odds_keys_correctly(self):
-        data = MatchData(
-            match_id="m-rows",
-            home_team_id="h",
-            away_team_id="a",
-            home_team_name="Home",
-            away_team_name="Away",
-            match_date_ms=1760000000000,
-            sport="football",
-            league_id=None,
-            league_name="",
-            referee_name=None,
-            odds_data={
-                "ms_h": 2.3,
-                "ms_d": 3.2,
-                "ms_a": 3.1,
-                "dc_x2": 1.45,
-                "ou15_o": 1.36,
-                "ou25_u": 1.92,
-                "ou35_o": 2.85,
-                "btts_y": 1.88,
-                "ht_h": 2.55,
-                "ht_ou05_o": 1.47,
-            },
-            home_lineup=None,
-            away_lineup=None,
-            sidelined_data=None,
-            home_goals_avg=1.5,
-            home_conceded_avg=1.2,
-            away_goals_avg=1.2,
-            away_conceded_avg=1.4,
-            home_position=10,
-            away_position=10,
-            lineup_source="none",
-        )
-        pred = FullMatchPrediction(
-            match_id="m-rows",
-            home_team="Home",
-            away_team="Away",
-            ms_home_prob=0.25,
-            ms_draw_prob=0.30,
-            ms_away_prob=0.45,
-            ms_pick="2",
-            ms_confidence=69.0,
-            dc_1x_prob=0.60,
-            dc_x2_prob=0.72,
-            dc_12_prob=0.68,
-            dc_pick="X2",
-            dc_confidence=67.0,
-            over_15_prob=0.74,
-            under_15_prob=0.26,
-            ou15_pick="1.5 Üst",
-            ou15_confidence=72.0,
-            over_25_prob=0.44,
-            under_25_prob=0.56,
-            ou25_pick="2.5 Alt",
-            ou25_confidence=61.0,
-            over_35_prob=0.39,
-            under_35_prob=0.61,
-            ou35_pick="3.5 Over",
-            ou35_confidence=58.0,
-            btts_yes_prob=0.57,
-            btts_no_prob=0.43,
-            btts_pick="Yes",
-            btts_confidence=63.0,
-            ht_home_prob=0.41,
-            ht_draw_prob=0.39,
-            ht_away_prob=0.20,
-            ht_pick="1",
-            ht_confidence=60.0,
-            ht_over_05_prob=0.64,
-            ht_under_05_prob=0.36,
-            ht_ou_pick="Over 0.5",
-        )
-
-        rows = self.orchestrator._build_market_rows(data, pred)
-        by_market = {row["market"]: row for row in rows}
-
-        self.assertEqual(by_market["MS"]["odds"], 3.1)
-        self.assertEqual(by_market["DC"]["odds"], 1.45)
-        self.assertEqual(by_market["OU15"]["odds"], 1.36)
-        self.assertEqual(by_market["OU25"]["odds"], 1.92)
-        self.assertEqual(by_market["OU35"]["odds"], 2.85)
-        self.assertEqual(by_market["BTTS"]["odds"], 1.88)
-        self.assertEqual(by_market["HT"]["odds"], 2.55)
-        self.assertEqual(by_market["HT_OU05"]["odds"], 1.47)
-
-    def test_build_basketball_market_rows_maps_odds_keys_correctly(self):
-        data = MatchData(
-            match_id="b-rows",
-            home_team_id="bh",
-            away_team_id="ba",
-            home_team_name="Home B",
-            away_team_name="Away B",
-            match_date_ms=1760000000000,
-            sport="basketball",
-            league_id="bl",
-            league_name="Basketball League",
-            referee_name=None,
-            odds_data={
-                "ml_h": 1.73,
-                "ml_a": 2.10,
-                "tot_line": 162.5,
-                "tot_o": 1.89,
-                "tot_u": 1.93,
-                "spread_home_line": -4.5,
-                "spread_h": 1.91,
-                "spread_a": 1.88,
-            },
-            home_lineup=None,
-            away_lineup=None,
-            sidelined_data=None,
-            home_goals_avg=84.0,
-            home_conceded_avg=80.0,
-            away_goals_avg=82.0,
-            away_conceded_avg=81.0,
-            home_position=5,
-            away_position=8,
-            lineup_source="none",
-        )
-        pred = {
-            "match_id": "b-rows",
-            "market_board": {
-                "ML": {"1": "62%", "2": "38%"},
-                "Totals": {"Under 162.5": "43%", "Over 162.5": "57%"},
-                "Spread": {"Away +4.5": "46%", "Home -4.5": "54%"}
-            }
-        }
-
-        rows = self.orchestrator._build_basketball_market_rows(data, pred)
-        by_market = {row["market"]: row for row in rows}
-
-        self.assertEqual(by_market["ML"]["odds"], 1.73)
-        self.assertEqual(by_market["TOTAL"]["odds"], 1.89)
-        self.assertEqual(by_market["SPREAD"]["odds"], 1.91)
-
-    def test_compute_data_quality_flags_missing_referee_and_lineup(self):
-        data = MatchData(
-            match_id="dq-1",
-            home_team_id="h",
-            away_team_id="a",
-            home_team_name="Home",
-            away_team_name="Away",
-            match_date_ms=1760000000000,
-            sport="football",
-            league_id=None,
-            league_name="",
-            referee_name=None,
-            odds_data={"ms_h": 2.5, "ms_d": 3.2, "ms_a": 2.9},
-            home_lineup=["h1", "h2"],
-            away_lineup=["a1"],
-            sidelined_data=None,
-            home_goals_avg=1.5,
-            home_conceded_avg=1.2,
-            away_goals_avg=1.2,
-            away_conceded_avg=1.4,
-            home_position=10,
-            away_position=10,
-            lineup_source="none",
-        )
-
-        quality = self.orchestrator._compute_data_quality(data)
-
-        self.assertIn("lineup_incomplete", quality["flags"])
-        self.assertIn("missing_referee", quality["flags"])
-        self.assertEqual(quality["label"], "MEDIUM")
-
-    def test_load_match_data_returns_none_when_team_ids_missing(self):
-        live_row = {
-            "match_id": "live-missing-ids",
-            "home_team_id": None,
-            "away_team_id": None,
-            "league_id": "l-1",
-            "sport": "football",
-            "match_date_ms": 1760000000000,
-            "odds": None,
-            "lineups": None,
-            "sidelined": None,
-            "referee_name": None,
-            "home_team_name": "Home",
-            "away_team_name": "Away",
-            "league_name": "League",
-        }
-        cursor = _RouterCursor(live_row=live_row)
-
-        with patch("services.single_match_orchestrator.psycopg2.connect", return_value=_ConnContext(cursor)):
-            data = self.orchestrator._load_match_data("live-missing-ids")
-
-        self.assertIsNone(data)
-
-    def test_decorate_market_row_blocks_required_market_when_odds_missing(self):
-        data = MatchData(
-            match_id="dq-odds",
-            home_team_id="h",
-            away_team_id="a",
-            home_team_name="Home",
-            away_team_name="Away",
-            match_date_ms=1760000000000,
-            sport="football",
-            league_id="l1",
-            league_name="League",
-            referee_name="Ref",
-            odds_data={"ms_h": 2.2, "ms_d": 3.2, "ms_a": 3.0},
-            home_lineup=["h"] * 11,
-            away_lineup=["a"] * 11,
-            sidelined_data=None,
-            home_goals_avg=1.5,
-            home_conceded_avg=1.2,
-            away_goals_avg=1.2,
-            away_conceded_avg=1.4,
-            home_position=7,
-            away_position=9,
-            lineup_source="confirmed_live",
-        )
-        prediction = FullMatchPrediction(match_id="dq-odds", home_team="Home", away_team="Away")
-        quality = self.orchestrator._compute_data_quality(data)
-        row = {
-            "market": "HT_OU05",
-            "pick": "İY 0.5 Üst",
-            "probability": 0.65,
-            "confidence": 66.0,
-            "odds": 0.0,
-        }
-
-        out = self.orchestrator._decorate_market_row(data, prediction, quality, row)
-        self.assertFalse(out["playable"])
-        self.assertIn("market_odds_missing", out["decision_reasons"])
-
-
-if __name__ == "__main__":
-    unittest.main()
@@ -1,142 +0,0 @@
-"""
-Unit Test for NEW Skip Logic in BetRecommender
-==============================================
-Run with: python ai-engine/tests/test_skip_logic.py
-"""
-
-import os
-import sys
-import unittest
-from dataclasses import dataclass
-from typing import Optional
-
-# Add paths
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
-
-from core.calculators.bet_recommender import BetRecommender, RecommendationResult, MarketPredictionDTO
-from core.calculators.risk_assessor import RiskAnalysis
-from core.calculators.match_result_calculator import MatchResultPrediction
-from core.calculators.over_under_calculator import OverUnderPrediction
-from config.config_loader import get_config
-
-@dataclass
-class DummyContext:
-    """Minimal mock for CalculationContext"""
-    odds_data: dict
-
-class TestSkipLogic(unittest.TestCase):
-
-    def setUp(self):
-        # Mock config to pass into BetRecommender
-        self.mock_config = {
-            "recommendations.market_weights": {"MS": 1.0, "ÇŞ": 0.9, "BTTS": 0.9, "2.5 Üst/Alt": 0.9},
-            "recommendations.safe_markets": ["ÇŞ", "1.5 Üst/Alt"],
-            "recommendations.market_accuracy": {"MS": 65, "ÇŞ": 75, "BTTS": 60, "2.5 Üst/Alt": 65},
-            "recommendations.baseline_accuracy": 65.0,
-            "recommendations.confidence_threshold": 60,
-            "recommendations.value_confidence_min": 45,
-            "recommendations.value_confidence_max": 60,
-            "recommendations.value_edge_margin": 0.03,
-            "recommendations.value_upgrade_edge": 5.0,
-            "recommendations.risk_safe_boost": 1.2,
-            "recommendations.risk_ms_penalty_high": 0.5,
-            "recommendations.risk_other_penalty": 0.7,
-            "recommendations.risk_ms_penalty_medium": 0.8,
-        }
-        self.recommender = BetRecommender(self.mock_config)
-
-    def _make_risk(self, level="MEDIUM", is_surprise=False):
-        return RiskAnalysis(risk_level=level, is_surprise_risk=is_surprise, risk_score=0.5)
-
-    def _make_ms_pred(self, pick, conf):
-        # pick: "1", "X", "2"
-        probs = {"1": {"ms_home_prob": 0.5, "ms_draw_prob": 0.3, "ms_away_prob": 0.2},
-                 "X": {"ms_home_prob": 0.2, "ms_draw_prob": 0.5, "ms_away_prob": 0.3},
-                 "2": {"ms_home_prob": 0.2, "ms_draw_prob": 0.3, "ms_away_prob": 0.5}}
-        p = probs.get(pick, probs["1"])
-        return MatchResultPrediction(
-            ms_pick=pick, ms_confidence=conf,
-            dc_pick="1X", dc_confidence=0,
-            dc_1x_prob=0.7, dc_x2_prob=0.7, dc_12_prob=0.7,
-            **p
-        )
-
-    def _make_ou_pred(self):
-        return OverUnderPrediction(
-            ou25_pick="2.5 Üst", ou25_confidence=50.0,
-            over_25_prob=0.55, under_25_prob=0.45,
-            
-            btts_pick="Var", btts_confidence=50.0,
-            btts_yes_prob=0.55, btts_no_prob=0.45,
-            
-            ou15_pick="1.5 Üst", ou15_confidence=60.0, over_15_prob=0.7, under_15_prob=0.3,
-            ou35_pick="3.5 Alt", ou35_confidence=50.0, over_35_prob=0.3, under_35_prob=0.7
-        )
-
-    def test_low_confidence_should_skip(self):
-        """Confidence < 45% should be SKIPPED"""
-        ms_pred = self._make_ms_pred(pick="2", conf=40.0)
-        ou_pred = self._make_ou_pred()
-        risk = self._make_risk("MEDIUM")
-        ctx = DummyContext(odds_data={"ms_2": 2.5})
-
-        res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
-        
-        # Check if MS bet is skipped
-        ms_bet = next((b for b in res.skipped_bets if b.market_type == "MS"), None)
-        self.assertIsNotNone(ms_bet, "MS bet with 40% conf should be skipped!")
-        self.assertTrue(ms_bet.is_skip)
-
-    def test_good_confidence_should_recommend(self):
-        """Confidence > 60% and Good Odds should be RECOMMENDED"""
-        ms_pred = self._make_ms_pred(pick="1", conf=70.0)
-        ou_pred = self._make_ou_pred()
-        risk = self._make_risk("MEDIUM")
-        # Odds 1.80 for 70% prob = Good Value (Need real odds for MS to pass)
-        ctx = DummyContext(odds_data={"ms_1": 1.80, "ou15_o": 1.50}) # Added ou15 odds
-
-        res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
-
-        # Check if ANY bet is recommended (doesn't have to be MS, but usually is)
-        self.assertGreater(len(res.recommended_bets), 0, "At least one bet should be recommended!")
-        # Check that MS bet is NOT skipped
-        ms_bet = next((b for b in res.recommended_bets if b.market_type == "MS"), None)
-        if ms_bet:
-            self.assertFalse(ms_bet.is_skip)
-
-    def test_negative_edge_should_skip(self):
-        """Even with high confidence, if Odds are too low (Bad Value), SKIP"""
-        ms_pred = self._make_ms_pred(pick="1", conf=70.0) # 70% prob
-        ou_pred = self._make_ou_pred()
-        risk = self._make_risk("MEDIUM")
-        # Odds 1.10 -> Implied 90%. Our prob is 70%. Edge is -20% -> SKIP
-        ctx = DummyContext(odds_data={"ms_1": 1.10})
-
-        res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
-
-        ms_bet = next((b for b in res.skipped_bets if b.market_type == "MS"), None)
-        self.assertIsNotNone(ms_bet, "MS bet with terrible odds (Negative Edge) should be skipped!")
-        self.assertTrue(ms_bet.is_skip)
-
-    def test_no_bets_recommendation(self):
-        """If all bets are low confidence, best_bet should be None"""
-        ms_pred = self._make_ms_pred(pick="1", conf=30.0) # Very low conf
-        ou_pred = self._make_ou_pred()
-        # Reset ALL OU confs to low
-        ou_pred.ou25_confidence = 30.0
-        ou_pred.btts_confidence = 30.0
-        ou_pred.ou15_confidence = 30.0 # This was 60 in setUp, causing the fail!
-        ou_pred.ou35_confidence = 30.0
-        
-        risk = self._make_risk("MEDIUM")
-        ctx = DummyContext(odds_data={"ms_1": 2.0})
-
-        res = self.recommender.calculate(ctx, ms_pred, ou_pred, risk)
-
-        self.assertIsNone(res.best_bet, "If everything is skipped, there should be no best_bet.")
-        self.assertEqual(len(res.recommended_bets), 0, "No bets should be recommended!")
-
-if __name__ == '__main__':
-    print("🧪 Running Skip Logic Unit Tests...")
-    print("="*50)
-    unittest.main(verbosity=2)
@@ -1,286 +0,0 @@
-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()
@@ -1,109 +0,0 @@
-
-import os
-import sys
-import torch
-import torch.nn.functional as F
-import pandas as pd
-import numpy as np
-
-# Path alignment
-sys.path.append(os.getcwd())
-sys.path.append(os.path.join(os.getcwd(), 'ai-engine'))
-
-from pipeline.tiered_loader import TieredDataLoader
-from pipeline.sequence_builder import SequenceBuilder
-from models.hybrid_v11 import HybridDeepModel
-from features.odds_history import OddsHistoryEngine
-from features.synthetic_xg import SyntheticXGModel
-
-DEVICE = 'cpu'
-MODEL_PATH = 'ai-engine/models/v11_hybrid_model.pth'
-TARGET_ID = 'en78ih6ec7exnpxcku3xc3das'
-
-def audit():
-    print(f"🕵️  Auditing Match: {TARGET_ID}")
-    
-    # 1. Pipeline Data
-    builder = SequenceBuilder()
-    X, y, meta = builder.build_sequences()
-    
-    # Check if target is in dataset
-    idx_list = meta.index[meta['match_id'] == TARGET_ID].tolist()
-    if not idx_list:
-        print("❌ Match not found in generated sequences. Is it too old or too new?")
-        return
-    
-    idx = idx_list[0]
-    row_meta = meta.iloc[idx]
-    
-    # 2. Features
-    loader = TieredDataLoader()
-    odds_df = loader.load_gold_data([TARGET_ID])
-    eng = OddsHistoryEngine()
-    xg_model = SyntheticXGModel()
-    
-    # Team Mapping
-    unique_teams = meta['team_id'].unique()
-    team_map = {tid: i for i, tid in enumerate(unique_teams)}
-    
-    # 3. Predict exactly like Backtest
-    state = torch.load(MODEL_PATH, map_location=DEVICE)
-    emb_key = 'entity_emb.weight' if 'entity_emb.weight' in state else 'team_embedding.weight'
-    saved_vocab_size = state[emb_key].shape[0]
-    
-    model = HybridDeepModel(num_teams=saved_vocab_size)
-    new_state = {k.replace('team_embedding', 'entity_emb'): v for k, v in state.items()}
-    model.load_state_dict(new_state, strict=False)
-    model.eval()
-    
-    # Data components
-    team_idx = team_map.get(row_meta['team_id'], 0)
-    entities = torch.LongTensor([team_idx, 0]).unsqueeze(0)
-    seq = torch.FloatTensor(X[idx]).unsqueeze(0)
-    
-    # Context (Odds + xG)
-    odds_lookup = {}
-    for _, r in odds_df.iterrows():
-        mid = r['match_id']
-        if mid not in odds_lookup: odds_lookup[mid] = {}
-        if r['category'] == 'Maç Sonucu': odds_lookup[mid][r['selection']] = r['odd_value']
-        elif r['category'] == '2,5 Alt/Üst':
-            if 'Üst' in r['selection']: odds_lookup[mid]['Over'] = r['odd_value']
-            else: odds_lookup[mid]['Under'] = r['odd_value']
-            
-    odds = odds_lookup.get(TARGET_ID, {'1': 1.0, 'X': 1.0, '2': 1.0, 'Over': 1.0, 'Under': 1.0})
-    syn_xg = 1.35 # Placeholder in trainer for xG component if used
-    hist_win_rate = eng.get_feature(row_meta['team_id'], float(odds.get('1', 1.0)))
-    
-    ctx = torch.FloatTensor([
-        float(odds.get('1', 1.0)), float(odds.get('X', 1.0)), float(odds.get('2', 1.0)),
-        float(odds.get('Over', 1.0)), float(odds.get('Under', 1.0)),
-        syn_xg, syn_xg,
-        hist_win_rate
-    ]).unsqueeze(0)
-    
-    with torch.no_grad():
-        logits_res, pred_goals, logits_btts, logits_ht_ft = model(entities, seq, ctx)
-        probs = F.softmax(logits_res, dim=1).numpy()[0]
-        prob_btts = torch.sigmoid(logits_btts).item()
-        probs_ht = F.softmax(logits_ht_ft, dim=1).numpy()[0]
-        
-    print("\n📊 INTERNAL PIPELINE PREDICTION:")
-    print(f"Target Team: {row_meta['team_id']}")
-    print(f"1X2 Probs: Home:{probs[0]:.4f} Draw:{probs[1]:.4f} Away:{probs[2]:.4f}")
-    print(f"BTTS Prob: {prob_btts:.4f}")
-    
-    ht_map = ["1/1", "1/X", "1/2", "X/1", "X/X", "X/2", "2/1", "2/X", "2/2"]
-    top3_ht = np.argsort(probs_ht)[-3:][::-1]
-    print("Top 3 HT/FT:")
-    for idx_ht in top3_ht:
-        print(f"  {ht_map[idx_ht]}: {probs_ht[idx_ht]:.4f}")
-    
-    actual_res = y[idx][0]
-    actual_ht_idx = int(y[idx][3])
-    print(f"\n✅ ACTUAL REALITY:")
-    print(f"Result (Y): {actual_res} (0.0=Away)")
-    print(f"HT/FT Class: {actual_ht_idx} ({ht_map[actual_ht_idx]})")
-
-if __name__ == "__main__":
-    audit()
@@ -1,58 +0,0 @@
-#!/usr/bin/env python3
-"""Test surprise detection on known surprise matches."""
-
-import sys
-sys.path.insert(0, 'ai-engine')
-from services.single_match_orchestrator import SingleMatchOrchestrator
-import json
-
-# Test Bayern vs Augsburg (24 Jan 2026) - 1/2 Reversal
-match_id = 'en78ih6ec7exnpxcku3xc3das'
-
-orch = SingleMatchOrchestrator()
-result = orch.analyze_match(match_id)
-
-if result:
-    print('=== Bayern Munch vs Augsburg (24 Jan 2026) ===')
-    print('Actual: HT 1-0, FT 1-2 (1/2 Reversal!)')
-    print()
-    
-    # Check risk
-    risk = result.get('risk', {})
-    print(f"Risk Level: {risk.get('level', 'N/A')}")
-    print(f"Is Surprise Risk: {risk.get('is_surprise_risk', False)}")
-    print(f"Surprise Type: {risk.get('surprise_type', 'N/A')}")
-    print(f"Risk Score: {risk.get('score', 'N/A')}")
-    print()
-    
-    # Check HT/FT probabilities from market_board
-    htft = result.get('market_board', {}).get('HTFT', {}).get('probs', {})
-    print('HT/FT Probabilities:')
-    if htft:
-        for k, v in sorted(htft.items(), key=lambda x: x[1], reverse=True):
-            print(f"  {k}: {v*100:.1f}%")
-    else:
-        print("  EMPTY!")
-    print()
-    
-    # Check main pick
-    main = result.get('main_pick', {})
-    print(f"Main Pick: {main.get('market', 'N/A')} - {main.get('pick', 'N/A')}")
-    print(f"Confidence: {main.get('calibrated_confidence', 'N/A')}%")
-    print(f"Is Guaranteed: {main.get('is_guaranteed', False)}")
-    print()
-    
-    # Check aggressive pick
-    agg = result.get('aggressive_pick', {})
-    if agg:
-        print(f"Aggressive Pick: {agg.get('market', 'N/A')} - {agg.get('pick', 'N/A')}")
-        print(f"Odds: {agg.get('odds', 'N/A')}")
-    print()
-    
-    # Check bet_summary for HTFT
-    bet_summary = result.get('bet_summary', [])
-    for bet in bet_summary:
-        if bet.get('market') == 'HTFT':
-            print(f"HTFT Bet: {bet}")
-else:
-    print('Match not found')
@@ -1,95 +0,0 @@
-#!/usr/bin/env python3
-"""Test the improved surprise detection logic"""
-
-import sys
-sys.path.insert(0, 'ai-engine')
-
-from core.calculators.risk_assessor import RiskAssessor
-from config.config_loader import get_config
-
-def test_surprise_detection():
-    config = get_config()
-    assessor = RiskAssessor(config)
-    
-    # Test cases based on real scenarios
-    test_cases = [
-        {
-            'name': 'Bayern vs Augsburg (1.30 odds, 2% 1/2 prob)',
-            'odds': {'ms_h': 1.30, 'ms_d': 5.00, 'ms_a': 8.00},
-            'ht_ft': {'1/1': 0.30, '1/X': 0.07, '1/2': 0.02, 'X/1': 0.15, 'X/X': 0.16, 'X/2': 0.09, '2/1': 0.03, '2/X': 0.04, '2/2': 0.14},
-            'expected_surprise': True,
-            'expected_type': '1/2 Potential Upset'
-        },
-        {
-            'name': 'Strong favorite (1.20 odds, 1.5% 1/2 prob)',
-            'odds': {'ms_h': 1.20, 'ms_d': 6.00, 'ms_a': 12.00},
-            'ht_ft': {'1/1': 0.35, '1/X': 0.05, '1/2': 0.015, 'X/1': 0.20, 'X/X': 0.15, 'X/2': 0.05, '2/1': 0.02, '2/X': 0.03, '2/2': 0.10},
-            'expected_surprise': True,
-            'expected_type': '1/2 Potential Upset'
-        },
-        {
-            'name': 'Moderate favorite (1.50 odds, 3% 1/2 prob)',
-            'odds': {'ms_h': 1.50, 'ms_d': 4.00, 'ms_a': 6.00},
-            'ht_ft': {'1/1': 0.28, '1/X': 0.08, '1/2': 0.03, 'X/1': 0.18, 'X/X': 0.15, 'X/2': 0.08, '2/1': 0.04, '2/X': 0.05, '2/2': 0.11},
-            'expected_surprise': True,
-            'expected_type': '1/2 Potential Upset'
-        },
-        {
-            'name': 'Even match (2.00 odds, 5% 1/2 prob)',
-            'odds': {'ms_h': 2.00, 'ms_d': 3.30, 'ms_a': 3.30},
-            'ht_ft': {'1/1': 0.20, '1/X': 0.10, '1/2': 0.05, 'X/1': 0.15, 'X/X': 0.15, 'X/2': 0.10, '2/1': 0.05, '2/X': 0.10, '2/2': 0.10},
-            'expected_surprise': False,  # No clear favorite
-            'expected_type': None
-        },
-        {
-            'name': 'Away favorite (1.40 away odds, 2% 2/1 prob)',
-            'odds': {'ms_h': 6.00, 'ms_d': 4.00, 'ms_a': 1.40},
-            'ht_ft': {'1/1': 0.10, '1/X': 0.05, '1/2': 0.04, 'X/1': 0.08, 'X/X': 0.15, 'X/2': 0.20, '2/1': 0.02, '2/X': 0.06, '2/2': 0.30},
-            'expected_surprise': True,
-            'expected_type': '2/1 Potential Upset'
-        },
-    ]
-    
-    print("=" * 70)
-    print("SURPRISE DETECTION TEST RESULTS")
-    print("=" * 70)
-    
-    passed = 0
-    failed = 0
-    
-    for tc in test_cases:
-        class MockCtx:
-            is_surprise = False
-            is_top_league = True
-            sport = 'football'
-            xgboost_preds = {'ht_ft': tc['ht_ft']}
-            odds_data = tc['odds']
-        
-        result = assessor.assess_risk(MockCtx())
-        
-        # Check if result matches expectation
-        is_correct = result.is_surprise_risk == tc['expected_surprise']
-        if tc['expected_type'] and result.surprise_type != tc['expected_type']:
-            is_correct = False
-        
-        status = "✅ PASS" if is_correct else "❌ FAIL"
-        if is_correct:
-            passed += 1
-        else:
-            failed += 1
-        
-        print(f"\n{status} - {tc['name']}")
-        print(f"  Expected: surprise={tc['expected_surprise']}, type={tc['expected_type']}")
-        print(f"  Got:      surprise={result.is_surprise_risk}, type={result.surprise_type}")
-        if result.reasons:
-            print(f"  Reasons: {result.reasons}")
-    
-    print("\n" + "=" * 70)
-    print(f"SUMMARY: {passed} passed, {failed} failed")
-    print("=" * 70)
-    
-    return failed == 0
-
-if __name__ == "__main__":
-    success = test_surprise_detection()
-    sys.exit(0 if success else 1)
@@ -1,65 +0,0 @@
-#!/usr/bin/env python3
-"""Test UpsetEngine on Bayern vs Augsburg match."""
-
-import sys
-sys.path.insert(0, 'ai-engine')
-from features.upset_engine import get_upset_engine
-from data.db import get_clean_dsn
-import psycopg2
-from psycopg2.extras import RealDictCursor
-from datetime import datetime
-
-# Get match data
-conn = psycopg2.connect(get_clean_dsn())
-cur = conn.cursor(cursor_factory=RealDictCursor)
-
-cur.execute("""
-SELECT m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away, 
-       m.ht_score_home, m.ht_score_away, m.mst_utc,
-       th.name as home_name, ta.name as away_name, l.name as league
-FROM matches m
-JOIN teams th ON m.home_team_id = th.id
-JOIN teams ta ON m.away_team_id = ta.id
-JOIN leagues l ON m.league_id = l.id
-WHERE m.id = 'en78ih6ec7exnpxcku3xc3das'
-""")
-match = cur.fetchone()
-conn.close()
-
-if match:
-    print('=== Bayern Munch vs Augsburg (24 Jan 2026) ===')
-    print(f"Actual: HT {match['ht_score_home']}-{match['ht_score_away']}, FT {match['score_home']}-{match['score_away']} (1/2 Reversal!)")
-    print()
-    
-    # Test UpsetEngine
-    engine = get_upset_engine()
-    
-    # Calculate upset potential using get_features
-    result = engine.get_features(
-        home_team_name=match['home_name'],
-        home_team_id=match['home_team_id'],
-        away_team_name=match['away_name'],
-        league_name=match['league'],
-        home_position=1,  # Bayern is typically top
-        away_position=15,  # Augsburg is typically lower
-        match_date_ms=match['mst_utc'],
-        total_teams=18,
-    )
-    
-    print('UpsetEngine Results:')
-    print(f"  Atmosphere Score: {result.get('upset_atmosphere', 0):.2f}")
-    print(f"  Motivation Score: {result.get('upset_motivation', 0):.2f}")
-    print(f"  Fatigue Score: {result.get('upset_fatigue', 0):.2f}")
-    print(f"  Historical Score: {result.get('upset_historical', 0):.2f}")
-    print(f"  TOTAL UPSET POTENTIAL: {result.get('upset_potential', 0):.2f}")
-    print()
-    
-    # Check if upset was detected
-    if result.get('upset_potential', 0) > 0.5:
-        print("🔥 HIGH UPSET POTENTIAL DETECTED!")
-    elif result.get('upset_potential', 0) > 0.3:
-        print("⚠️ MEDIUM UPSET POTENTIAL")
-    else:
-        print("❌ LOW UPSET POTENTIAL - Model did not detect this as upset")
-else:
-    print('Match not found')
@@ -1,22 +0,0 @@
-import { Test, TestingModule } from "@nestjs/testing";
-import { AppController } from "./app.controller";
-import { AppService } from "./app.service";
-
-describe("AppController", () => {
-  let appController: AppController;
-
-  beforeEach(async () => {
-    const app: TestingModule = await Test.createTestingModule({
-      controllers: [AppController],
-      providers: [AppService],
-    }).compile();
-
-    appController = app.get<AppController>(AppController);
-  });
-
-  describe("root", () => {
-    it('should return "Hello World!"', () => {
-      expect(appController.getHello()).toBe("Hello World!");
-    });
-  });
-});
@@ -51,6 +51,7 @@ async function bootstrap() {
            "https://suggestbet.bilgich.com",
            "https://iddaai.com",
            "https://www.iddaai.com",
+            "http://localhost:6195",
          ]
        : true,
    credentials: true,
@@ -1,81 +0,0 @@
-/* eslint-disable @typescript-eslint/unbound-method */
-import axios from "axios";
-import { PredictionJobType } from "./predictions.types";
-import { PredictionsProcessor } from "./predictions.processor";
-
-jest.mock("axios");
-
-const mockedAxios = axios as jest.Mocked<typeof axios>;
-
-describe("PredictionsProcessor", () => {
-  let processor: PredictionsProcessor;
-
-  beforeEach(() => {
-    jest.clearAllMocks();
-    process.env.AI_ENGINE_URL = "http://unit-ai:8000";
-    processor = new PredictionsProcessor();
-  });
-
-  afterEach(() => {
-    delete process.env.AI_ENGINE_URL;
-  });
-
-  it("posts to analyze endpoint for predict-match jobs", async () => {
-    mockedAxios.post.mockResolvedValueOnce({ data: { ok: true } } as any);
-
-    const job = {
-      id: "j1",
-      name: PredictionJobType.PREDICT_MATCH,
-      data: { matchId: "match-123" },
-    } as any;
-
-    const result = await processor.process(job);
-
-    expect(result).toEqual({ ok: true });
-    expect(mockedAxios.post).toHaveBeenCalledWith(
-      "http://unit-ai:8000/v20plus/analyze/match-123",
-      {},
-      { timeout: 30000 },
-    );
-  });
-
-  it("posts mapped payload to coupon endpoint for smart-coupon jobs", async () => {
-    mockedAxios.post.mockResolvedValueOnce({ data: { bets: [] } } as any);
-
-    const job = {
-      id: "j2",
-      name: PredictionJobType.SMART_COUPON,
-      data: {
-        matchIds: ["m1", "m2"],
-        strategy: "BALANCED",
-        options: { maxMatches: 4, minConfidence: 65 },
-      },
-    } as any;
-
-    const result = await processor.process(job);
-
-    expect(result).toEqual({ bets: [] });
-    expect(mockedAxios.post).toHaveBeenCalledWith(
-      "http://unit-ai:8000/v20plus/coupon",
-      {
-        match_ids: ["m1", "m2"],
-        strategy: "BALANCED",
-        max_matches: 4,
-        min_confidence: 65,
-      },
-      { timeout: 60000 },
-    );
-  });
-
-  it("throws for unknown job type", async () => {
-    const job = {
-      id: "j3",
-      name: "unknown-job",
-      data: {},
-    } as any;
-
-    await expect(processor.process(job)).rejects.toThrow(
-      "Unknown job type: unknown-job",
-    );
-  });
-});
@@ -1,419 +0,0 @@
-/**
- * ===================================================
- * BACKTEST ACCURACY — V30 Prediction System
- * ===================================================
- * Tests historical predictions against actual outcomes.
- * Uses the running AI Engine's /v20plus/analyze/{match_id}
- * endpoint which extracts features from DB internally.
- *
- * Usage: npx ts-node --transpile-only -r tsconfig-paths/register src/scripts/backtest-accuracy.ts
- */
-
-import { PrismaClient } from "@prisma/client";
-import axios from "axios";
-
-const prisma = new PrismaClient();
-
-// ═══════════════════════════════════════════════════════
-// Configuration
-// ═══════════════════════════════════════════════════════
-
-const AI_ENGINE_URL = process.env.AI_ENGINE_URL || "http://127.0.0.1:3005";
-const CONCURRENT_REQUESTS = 5;
-const MAX_MATCHES = 1000;
-
-// ═══════════════════════════════════════════════════════
-// Types
-// ═══════════════════════════════════════════════════════
-
-interface TestMatch {
-  id: string;
-  scoreHome: number;
-  scoreAway: number;
-  htScoreHome: number | null;
-  htScoreAway: number | null;
-}
-
-interface BacktestResult {
-  matchId: string;
-  actual: { ms: string; ou25: string; btts: string; htft: string };
-  predicted: { ms: string; ou25: string; btts: string };
-  probabilities: {
-    home: number;
-    draw: number;
-    away: number;
-    over: number;
-    under: number;
-    bttsYes: number;
-    bttsNo: number;
-  };
-  mainPickCorrect: boolean;
-}
-
-// ═══════════════════════════════════════════════════════
-// Helpers
-// ═══════════════════════════════════════════════════════
-
-function determineActualOutcome(
-  scoreHome: number,
-  scoreAway: number,
-  htScoreHome: number | null,
-  htScoreAway: number | null,
-): { ms: string; ou25: string; btts: string; htft: string } {
-  const ms = scoreHome > scoreAway ? "1" : scoreHome < scoreAway ? "2" : "X";
-  const ou25 = scoreHome + scoreAway > 2.5 ? "Over" : "Under";
-  const btts = scoreHome > 0 && scoreAway > 0 ? "Yes" : "No";
-
-  let htft = "unknown";
-  if (htScoreHome !== null && htScoreAway !== null) {
-    const htResult =
-      htScoreHome > htScoreAway ? "1" : htScoreHome < htScoreAway ? "2" : "X";
-    htft = `${htResult}/${ms}`;
-  }
-
-  return { ms, ou25, btts, htft };
-}
-
-function extractPrediction(response: unknown): {
-  ms: string;
-  ou25: string;
-  btts: string;
-  probs: BacktestResult["probabilities"];
-  mainPick: string;
-  mainMarket: string;
-} {
-  const data = response as Record<string, unknown>;
-  const predictions = data?.predictions as Record<string, unknown> | undefined;
-
-  const mainPickObj = data?.main_pick as Record<string, unknown> | undefined;
-  const mainPick =
-    typeof mainPickObj?.pick === "string" ? mainPickObj.pick : "";
-  const mainMarket =
-    typeof mainPickObj?.market === "string" ? mainPickObj.market : "";
-
-  // Extract MS from probabilities or main pick
-  const msProbs = (predictions?.ms || data?.ms || {}) as Record<
-    string,
-    unknown
-  >;
-  const homeProb =
-    typeof msProbs["1"] === "number"
-      ? msProbs["1"]
-      : typeof msProbs.home_prob === "number"
-        ? msProbs.home_prob
-        : 0;
-  const drawProb =
-    typeof msProbs["X"] === "number"
-      ? msProbs["X"]
-      : typeof msProbs.draw_prob === "number"
-        ? msProbs.draw_prob
-        : 0;
-  const awayProb =
-    typeof msProbs["2"] === "number"
-      ? msProbs["2"]
-      : typeof msProbs.away_prob === "number"
-        ? msProbs.away_prob
-        : 0;
-
-  let ms = "1";
-  if (drawProb > homeProb && drawProb > awayProb) ms = "X";
-  else if (awayProb > homeProb) ms = "2";
-
-  // Extract OU25
-  const ou25Probs = (predictions?.ou25 || data?.ou25 || {}) as Record<
-    string,
-    unknown
-  >;
-  const overProb =
-    typeof ou25Probs.Over === "number"
-      ? ou25Probs.Over
-      : typeof ou25Probs.over_prob === "number"
-        ? ou25Probs.over_prob
-        : 0;
-  const underProb =
-    typeof ou25Probs.Under === "number"
-      ? ou25Probs.Under
-      : typeof ou25Probs.under_prob === "number"
-        ? ou25Probs.under_prob
-        : 0;
-  const ou25 = overProb > underProb ? "Over" : "Under";
-
-  // Extract BTTS
-  const bttsProbs = (predictions?.btts || data?.btts || {}) as Record<
-    string,
-    unknown
-  >;
-  const bttsYes =
-    typeof bttsProbs.Yes === "number"
-      ? bttsProbs.Yes
-      : typeof bttsProbs.yes_prob === "number"
-        ? bttsProbs.yes_prob
-        : 0;
-  const bttsNo =
-    typeof bttsProbs.No === "number"
-      ? bttsProbs.No
-      : typeof bttsProbs.no_prob === "number"
-        ? bttsProbs.no_prob
-        : 0;
-  const btts = bttsYes > bttsNo ? "Yes" : "No";
-
-  return {
-    ms,
-    ou25,
-    btts,
-    probs: {
-      home: homeProb,
-      draw: drawProb,
-      away: awayProb,
-      over: overProb,
-      under: underProb,
-      bttsYes,
-      bttsNo,
-    },
-    mainPick,
-    mainMarket,
-  };
-}
-
-async function processBatch(batch: TestMatch[]): Promise<BacktestResult[]> {
-  const results: BacktestResult[] = [];
-
-  const promises = batch.map(async (match) => {
-    try {
-      const response = await axios.post(
-        `${AI_ENGINE_URL}/v20plus/analyze/${match.id}`,
-        {},
-        { timeout: 15000 },
-      );
-
-      const actual = determineActualOutcome(
-        match.scoreHome,
-        match.scoreAway,
-        match.htScoreHome,
-        match.htScoreAway,
-      );
-
-      const pred = extractPrediction(response.data);
-
-      // Check main pick
-      let mainPickCorrect = false;
-      if (pred.mainMarket === "MS") {
-        mainPickCorrect = pred.mainPick === actual.ms;
-      } else if (pred.mainMarket === "OU25") {
-        mainPickCorrect = pred.mainPick === actual.ou25;
-      } else if (pred.mainMarket === "BTTS") {
-        mainPickCorrect = pred.mainPick === actual.btts;
-      }
-
-      results.push({
-        matchId: match.id,
-        actual,
-        predicted: { ms: pred.ms, ou25: pred.ou25, btts: pred.btts },
-        probabilities: pred.probs,
-        mainPickCorrect,
-      });
-    } catch {
-      // Skip failed matches silently
-    }
-  });
-
-  await Promise.all(promises);
-  return results;
-}
-
-// ═══════════════════════════════════════════════════════
-// Main Backtest
-// ═══════════════════════════════════════════════════════
-
-async function runBacktest(): Promise<void> {
-  console.log("🎯 BACKTEST ACCURACY — V30 Betting Engine");
-  console.log("════════════════════════════════════════════════════════");
-
-  // 1. Health check
-  try {
-    const health = await axios.get(`${AI_ENGINE_URL}/health`, {
-      timeout: 5000,
-    });
-    console.log(`✅ AI Engine: ${JSON.stringify(health.data)}`);
-  } catch {
-    console.error("❌ AI Engine not reachable at", AI_ENGINE_URL);
-    process.exit(1);
-  }
-
-  // 2. Load finished matches with features
-  console.log("\n📥 Loading test matches...");
-  const matches = await prisma.$queryRaw<TestMatch[]>`
-    SELECT m.id, m.score_home AS "scoreHome", m.score_away AS "scoreAway",
-           m.ht_score_home AS "htScoreHome", m.ht_score_away AS "htScoreAway"
-    FROM matches m
-    JOIN match_ai_features maf ON maf.match_id = m.id
-    WHERE m.status = 'FT'
-      AND m.score_home IS NOT NULL
-      AND m.score_away IS NOT NULL
-      AND m.sport = 'football'
-      AND maf.home_elo != 1500
-      AND maf.implied_home != 0.33
-    ORDER BY m.mst_utc DESC
-    LIMIT ${MAX_MATCHES}
-  `;
-  console.log(`  📊 Test matches: ${matches.length}`);
-
-  // 3. Run predictions in batches
-  console.log("\n🤖 Running predictions...");
-  const allResults: BacktestResult[] = [];
-  let processed = 0;
-
-  for (let i = 0; i < matches.length; i += CONCURRENT_REQUESTS) {
-    const batch = matches.slice(i, i + CONCURRENT_REQUESTS);
-    const batchResults = await processBatch(batch);
-    allResults.push(...batchResults);
-    processed += batch.length;
-
-    if (processed % 50 === 0 || processed === matches.length) {
-      const currentMsAcc =
-        allResults.length > 0
-          ? (
-              (allResults.filter((r) => r.predicted.ms === r.actual.ms).length /
-                allResults.length) *
-              100
-            ).toFixed(1)
-          : "0";
-      console.log(
-        `  📊 ${processed}/${matches.length} — Success: ${allResults.length} — MS Acc: ${currentMsAcc}%`,
-      );
-    }
-  }
-
-  // 4. Calculate metrics
-  const total = allResults.length;
-  if (total === 0) {
-    console.error("❌ No results to analyze");
-    process.exit(1);
-  }
-
-  const msCorrect = allResults.filter(
-    (r) => r.predicted.ms === r.actual.ms,
-  ).length;
-  const ou25Correct = allResults.filter(
-    (r) => r.predicted.ou25 === r.actual.ou25,
-  ).length;
-  const bttsCorrect = allResults.filter(
-    (r) => r.predicted.btts === r.actual.btts,
-  ).length;
-  const mainPickCorrect = allResults.filter((r) => r.mainPickCorrect).length;
-
-  // Actual distribution
-  const actHome = allResults.filter((r) => r.actual.ms === "1").length;
-  const actDraw = allResults.filter((r) => r.actual.ms === "X").length;
-  const actAway = allResults.filter((r) => r.actual.ms === "2").length;
-
-  // Predicted distribution
-  const predHome = allResults.filter((r) => r.predicted.ms === "1").length;
-  const predDraw = allResults.filter((r) => r.predicted.ms === "X").length;
-  const predAway = allResults.filter((r) => r.predicted.ms === "2").length;
-
-  // Confidence calibration (based on max probability)
-  const buckets: Record<string, { correct: number; total: number }> = {
-    "33-40%": { correct: 0, total: 0 },
-    "40-50%": { correct: 0, total: 0 },
-    "50-60%": { correct: 0, total: 0 },
-    "60-70%": { correct: 0, total: 0 },
-    "70%+": { correct: 0, total: 0 },
-  };
-
-  for (const r of allResults) {
-    const maxProb = Math.max(
-      r.probabilities.home,
-      r.probabilities.draw,
-      r.probabilities.away,
-    );
-    const key =
-      maxProb >= 0.7
-        ? "70%+"
-        : maxProb >= 0.6
-          ? "60-70%"
-          : maxProb >= 0.5
-            ? "50-60%"
-            : maxProb >= 0.4
-              ? "40-50%"
-              : "33-40%";
-    buckets[key].total++;
-    if (r.predicted.ms === r.actual.ms) buckets[key].correct++;
-  }
-
-  // 5. Print Report
-  console.log("\n════════════════════════════════════════════════════════");
-  console.log("📊 BACKTEST ACCURACY REPORT");
-  console.log("════════════════════════════════════════════════════════");
-  console.log(`  Total Matches Analyzed: ${total}`);
-  console.log("");
-  console.log("  🎯 Market Accuracy:");
-  console.log(
-    `     ⚽ Match Result (MS):     ${((msCorrect / total) * 100).toFixed(2)}%  (${msCorrect}/${total})`,
-  );
-  console.log(
-    `     📈 Over/Under 2.5:        ${((ou25Correct / total) * 100).toFixed(2)}%  (${ou25Correct}/${total})`,
-  );
-  console.log(
-    `     🤝 Both Teams Score:       ${((bttsCorrect / total) * 100).toFixed(2)}%  (${bttsCorrect}/${total})`,
-  );
-  console.log(
-    `     🏆 Main Pick Success:     ${((mainPickCorrect / total) * 100).toFixed(2)}%  (${mainPickCorrect}/${total})`,
-  );
-
-  console.log("\n  📊 MS Distribution:");
-  console.log(
-    `     Actual:    1: ${actHome} (${((actHome / total) * 100).toFixed(1)}%) | X: ${actDraw} (${((actDraw / total) * 100).toFixed(1)}%) | 2: ${actAway} (${((actAway / total) * 100).toFixed(1)}%)`,
-  );
-  console.log(
-    `     Predicted: 1: ${predHome} (${((predHome / total) * 100).toFixed(1)}%) | X: ${predDraw} (${((predDraw / total) * 100).toFixed(1)}%) | 2: ${predAway} (${((predAway / total) * 100).toFixed(1)}%)`,
-  );
-
-  console.log("\n  📊 Confidence Calibration:");
-  for (const [range, bucket] of Object.entries(buckets)) {
-    if (bucket.total === 0) continue;
-    const acc = (bucket.correct / bucket.total) * 100;
-    const bar = "█".repeat(Math.round(acc / 3));
-    console.log(
-      `     ${range.padEnd(8)} : ${acc.toFixed(1)}% acc (n=${bucket.total}) ${bar}`,
-    );
-  }
-
-  // 6. Per-market deep dive
-  console.log("\n  📊 OU25 Breakdown:");
-  const actOver = allResults.filter((r) => r.actual.ou25 === "Over").length;
-  const actUnder = total - actOver;
-  const predOver = allResults.filter((r) => r.predicted.ou25 === "Over").length;
-  const predUnder = total - predOver;
-  console.log(
-    `     Actual:    Over: ${actOver} (${((actOver / total) * 100).toFixed(1)}%) | Under: ${actUnder} (${((actUnder / total) * 100).toFixed(1)}%)`,
-  );
-  console.log(
-    `     Predicted: Over: ${predOver} (${((predOver / total) * 100).toFixed(1)}%) | Under: ${predUnder} (${((predUnder / total) * 100).toFixed(1)}%)`,
-  );
-
-  console.log("\n  📊 BTTS Breakdown:");
-  const actBttsYes = allResults.filter((r) => r.actual.btts === "Yes").length;
-  const actBttsNo = total - actBttsYes;
-  const predBttsYes = allResults.filter(
-    (r) => r.predicted.btts === "Yes",
-  ).length;
-  const predBttsNo = total - predBttsYes;
-  console.log(
-    `     Actual:    Yes: ${actBttsYes} (${((actBttsYes / total) * 100).toFixed(1)}%) | No: ${actBttsNo} (${((actBttsNo / total) * 100).toFixed(1)}%)`,
-  );
-  console.log(
-    `     Predicted: Yes: ${predBttsYes} (${((predBttsYes / total) * 100).toFixed(1)}%) | No: ${predBttsNo} (${((predBttsNo / total) * 100).toFixed(1)}%)`,
-  );
-
-  console.log("════════════════════════════════════════════════════════");
-  console.log("✅ Backtest complete!");
-
-  await prisma.$disconnect();
-}
-
-runBacktest().catch((err: unknown) => {
-  console.error("❌ Backtest failed:", err);
-  void prisma.$disconnect();
-  process.exit(1);
-});
@@ -1,34 +0,0 @@
-import { FeederService } from "../modules/feeder/feeder.service";
-import { HistoricalResultsSyncTask } from "./historical-results-sync.task";
-
-describe("HistoricalResultsSyncTask", () => {
-  const runPreviousDayCompletedMatchesScan = jest.fn();
-  let task: HistoricalResultsSyncTask;
-
-  beforeEach(() => {
-    jest.clearAllMocks();
-    delete process.env.FEEDER_MODE;
-
-    task = new HistoricalResultsSyncTask({
-      runPreviousDayCompletedMatchesScan,
-    } as unknown as FeederService);
-  });
-
-  afterEach(() => {
-    delete process.env.FEEDER_MODE;
-  });
-
-  it("calls feeder service in normal mode", async () => {
-    await task.syncPreviousDayCompletedMatches();
-
-    expect(runPreviousDayCompletedMatchesScan).toHaveBeenCalledTimes(1);
-  });
-
-  it("skips execution in historical feeder mode", async () => {
-    process.env.FEEDER_MODE = "historical";
-
-    await task.syncPreviousDayCompletedMatches();
-
-    expect(runPreviousDayCompletedMatchesScan).not.toHaveBeenCalled();
-  });
-});
@@ -1,20 +0,0 @@
-import requests
-import json
-
-match_id = '7cnm7h7qbsq2bbaxngusojh90'
-url = f'http://localhost:8007/v20plus/analyze/{match_id}'
-
-print(f"🔮 Sending prediction request for: {match_id}")
-print(f"URL: {url}\n")
-
-response = requests.post(url)
-data = response.json()
-
-print("📊 DATA QUALITY:")
-print(json.dumps(data.get('data_quality', {}), indent=2))
-
-print("\n🎯 MAIN PICK:")
-print(json.dumps(data.get('main_pick', {}), indent=2))
-
-print("\n⚽ SCORE PREDICTION:")
-print(json.dumps(data.get('score_prediction', {}), indent=2))
@@ -1,25 +0,0 @@
-import { Test, TestingModule } from "@nestjs/testing";
-import { INestApplication } from "@nestjs/common";
-import request from "supertest";
-import { App } from "supertest/types";
-import { AppModule } from "./../src/app.module";
-
-describe("AppController (e2e)", () => {
-  let app: INestApplication<App>;
-
-  beforeEach(async () => {
-    const moduleFixture: TestingModule = await Test.createTestingModule({
-      imports: [AppModule],
-    }).compile();
-
-    app = moduleFixture.createNestApplication();
-    await app.init();
-  });
-
-  it("/ (GET)", () => {
-    return request(app.getHttpServer())
-      .get("/")
-      .expect(200)
-      .expect("Hello World!");
-  });
-});
@@ -1,9 +0,0 @@
-{
-  "moduleFileExtensions": ["js", "json", "ts"],
-  "rootDir": ".",
-  "testEnvironment": "node",
-  "testRegex": ".e2e-spec.ts$",
-  "transform": {
-    "^.+\\.(t|j)s$": "ts-jest"
-  }
-}