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"""
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Adaptive 500 Match Backtest
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=============================
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Skips NO match unless NO odds exist.
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Evaluates ALL available markets (MS, OU, BTTS) and picks the BEST value bet.
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"""
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import os
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import sys
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import json
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import time
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import psycopg2
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from psycopg2.extras import RealDictCursor
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AI_DIR = os.path.dirname(os.path.abspath(__file__))
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ROOT_DIR = os.path.dirname(AI_DIR)
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sys.path.insert(0, ROOT_DIR)
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if "scripts" in os.path.basename(AI_DIR):
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ROOT_DIR = os.path.dirname(ROOT_DIR)
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from services.single_match_orchestrator import get_single_match_orchestrator
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def get_clean_dsn() -> str:
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return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
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def run_adaptive_backtest():
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print("🔄 ADAPTIVE 500 MATCH BACKTEST")
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print("="*60)
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# 1. Load Top Leagues
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leagues_path = os.path.join(ROOT_DIR, "top_leagues.json")
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with open(leagues_path, 'r') as f:
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top_leagues = json.load(f)
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league_ids = tuple(str(lid) for lid in top_leagues)
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dsn = get_clean_dsn()
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conn = psycopg2.connect(dsn)
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cur = conn.cursor(cursor_factory=RealDictCursor)
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# 2. Fetch 500 Finished Matches with Odds
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cur.execute("""
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SELECT m.id, m.match_name, m.home_team_id, m.away_team_id,
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m.score_home, m.score_away, m.league_id,
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t1.name as home_team, t2.name as away_team
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FROM matches m
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LEFT JOIN teams t1 ON m.home_team_id = t1.id
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LEFT JOIN teams t2 ON m.away_team_id = t2.id
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WHERE m.league_id IN %s
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AND m.status = 'FT'
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AND m.score_home IS NOT NULL
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AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
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ORDER BY m.mst_utc DESC
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LIMIT 500
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""", (league_ids,))
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rows = cur.fetchall()
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print(f"📊 Found {len(rows)} matches. Analyzing...\n")
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if not rows:
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print("⚠️ No matches found.")
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return
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try: orchestrator = get_single_match_orchestrator()
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except Exception as e:
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print(f"❌ AI Error: {e}")
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return
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# Stats
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total_evaluated = 0
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total_bet = 0
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total_won = 0
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total_profit = 0.0
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skipped_count = 0
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for i, row in enumerate(rows):
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match_id = str(row['id'])
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home = row['home_team'] or "?"
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away = row['away_team'] or "?"
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h_score = row['score_home'] or 0
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a_score = row['score_away'] or 0
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total_evaluated += 1
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# print(f"[{i+1}] {home} vs {away} ... ", end="", flush=True)
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try:
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pred = orchestrator.analyze_match(match_id)
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if not pred:
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# print("⚠️ No Data")
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continue
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# ─── ADAPTIVE PICKING ───
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# Check ALL recommendations (Expert or Standard) to find the BEST option
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candidates = []
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# Add main picks
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if pred.get("expert_recommendation"):
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rec = pred["expert_recommendation"]
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if rec.get("main_pick"): candidates.append(rec["main_pick"])
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if rec.get("safe_alternative"): candidates.append(rec["safe_alternative"])
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if rec.get("value_picks"): candidates.extend(rec["value_picks"])
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elif pred.get("main_pick"):
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candidates.append(pred["main_pick"])
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best_bet = None
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for c in candidates:
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if not c: continue
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conf = c.get("confidence", 0)
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odds = c.get("odds", 0)
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pick = c.get("pick")
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# Flexible Criteria:
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# 1. Confidence > 60%
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# 2. Odds > 1.10 (Not "free" odds like 1.00)
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# 3. Edge > -2% (Slightly tolerant)
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if conf >= 60 and odds > 1.10:
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implied = 1.0 / odds
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edge = ((conf/100) - implied) * 100
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# Prioritize positive edge, but accept small negative if confidence is high
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if edge > -2.0:
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if best_bet is None or (conf > best_bet.get("confidence", 0)):
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best_bet = c
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if best_bet:
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pick = str(best_bet.get("pick")).upper()
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conf = best_bet.get("confidence")
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odds = best_bet.get("odds")
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# Resolution Logic
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won = False
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if pick in ["1", "MS 1", "İY 1"] and h_score > a_score: won = True
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elif pick in ["X", "MS X", "İY X"] and h_score == a_score: won = True
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elif pick in ["2", "MS 2", "İY 2"] and a_score > h_score: won = True
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elif pick in ["1X", "X2"]:
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if "1X" in pick and h_score >= a_score: won = True
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elif "X2" in pick and a_score >= h_score: won = True
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elif pick == "12" and h_score != a_score: won = True
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elif "ÜST" in pick or "OVER" in pick:
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line = 2.5
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if "1.5" in pick: line = 1.5
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elif "3.5" in pick: line = 3.5
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if (h_score + a_score) > line: won = True
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elif "ALT" in pick or "UNDER" in pick:
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line = 2.5
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if "1.5" in pick: line = 1.5
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elif "3.5" in pick: line = 3.5
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if (h_score + a_score) < line: won = True
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elif "VAR" in pick and h_score > 0 and a_score > 0: won = True
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elif "YOK" in pick and (h_score == 0 or a_score == 0): won = True
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total_bet += 1
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if won:
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total_won += 1
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profit = odds - 1.0
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total_profit += profit
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# print(f"✅ WON (+{profit:.2f}) | {pick}")
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else:
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total_profit -= 1.0
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# print(f"❌ LOST ({pick} @ {odds:.2f})")
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else:
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skipped_count += 1
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# print(f"🚫 SKIP (No Value)")
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except Exception as e:
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# print(f"💥 Error: {e}")
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pass
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print("\n" + "="*60)
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print("🔄 ADAPTIVE BACKTEST RESULTS (500 Matches)")
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print("="*60)
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print(f"📊 Evaluated: {total_evaluated}")
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print(f"🎲 Played: {total_bet}")
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print(f"🚫 Skipped: {skipped_count}")
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print(f"✅ Won: {total_won}")
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if total_bet > 0:
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win_rate = (total_won / total_bet) * 100
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roi = (total_profit / total_bet) * 100
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print(f"📈 Win Rate: {win_rate:.2f}%")
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print(f"💰 Total Profit: {total_profit:.2f} Units")
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print(f"📊 ROI: {roi:.2f}%")
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if total_profit > 0: print("🟢 KARLI STRATEJİ")
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else: print("🔴 ZARARDA")
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else:
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print("⚠️ Hiç bahis oynanmadı. Veri kalitesi çok düşük.")
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cur.close()
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conn.close()
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if __name__ == "__main__":
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run_adaptive_backtest()
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