reorganized data files and enhance backtesting structure, monte carlo sim

backend/run.py

@@ -3,42 +3,38 @@ from engine.backtester import run_backtest
 from strategies.ict_strategy import ICTStrategy
 import time
 
-# Load data
-candles_1m = load_candles("data/data1.csv")
+candles_1m = load_candles("data/2023gj.csv")
 candles_5m = resample_candles(candles_1m, period=5)
 
-print("Testing different Risk-Reward ratios with optimized ICTStrategy...\n")
-
-# Best params from optimization (you can tweak session/lookback etc. if you want)
 strategy = ICTStrategy(
-    session="new_york",          # Best was New York
+    session="london",
     lookback=7,
-    ob_max_age=20,               # Best was 20
+    ob_max_age=50,
     atr_mult=2.5,
-    use_liquidity_sweep=False,   # Best was False
+    use_liquidity_sweep=True,
     sweep_lookback=5,
 )
 
+total_start = time.perf_counter()
+
 for rr in [1.0, 1.5, 2.0, 2.5, 3.0]:
     t0 = time.perf_counter()
-
     trades = run_backtest(candles_5m, strategy, 10000, risk_reward=rr)
-
     elapsed = time.perf_counter() - t0
-
     if not trades:
-        print(f"RR={rr}: No trades")
+        print(f"RR={rr}: No trades ({elapsed:.2f}s)")
         continue
-
     total_pnl = sum(t.pnl for t in trades)
     winners = [t for t in trades if t.pnl > 0]
     losers = [t for t in trades if t.pnl <= 0]
-
-    wr = len(winners) / len(trades) * 100 if trades else 0
+    wr = len(winners) / len(trades) * 100
     avg_win = sum(t.pnl for t in winners) / len(winners) if winners else 0
     avg_loss = sum(t.pnl for t in losers) / len(losers) if losers else 0
-    profit_factor = abs(sum(t.pnl for t in winners) / sum(t.pnl for t in losers)) if losers else float('inf')
 
-    print(f"RR={rr:4.1f} | Trades={len(trades):4d} | WR={wr:5.1f}% | "
-          f"PnL={total_pnl:8.2f} | AvgWin={avg_win:6.3f} | AvgLoss={avg_loss:6.3f} | "
-          f"PF={profit_factor:5.2f} | Time={elapsed:.3f}s")
+    print(
+        f"RR={rr}: Trades={len(trades)}, WR={wr:.1f}%, PnL={total_pnl:.2f}, "
+        f"AvgW={avg_win:.3f}, AvgL={avg_loss:.3f}, Time={elapsed:.2f}s"
+    )
+
+total_elapsed = time.perf_counter() - total_start
+print(f"Total run time: {total_elapsed:.2f}s")