optimized backtesting effiency

2026-04-11 17:23:26 +02:00
parent 766009b11a
commit 373e589297
10 changed files with 371780 additions and 330 deletions
@@ -0,0 +1,84 @@
+from data.loader import load_candles, resample_candles
+from engine.backtester import run_backtest
+from strategies.ict_strategy import ICTStrategy
+import time
+from tqdm import tqdm
+
+# Load data once
+candles_1m = load_candles("data/data1.csv")
+candles_5m = resample_candles(candles_1m, period=5)
+
+# Initialize best results
+best_pnl = float("-inf")
+best_params = None
+
+# Generate all parameter combinations
+param_combos = []
+for session in ["london", "new_york"]:
+    for lookback in [3, 5, 7, 10]:
+        for ob_age in [20, 50, 80]:
+            for atr in [1.0, 1.5, 2.0, 2.5]:
+                for sweep in [True, False]:
+                    sweep_lbs = [5, 10, 15] if sweep else [0]
+                    for sweep_lb in sweep_lbs:
+                        param_combos.append({
+                            "session": session,
+                            "lookback": lookback,
+                            "ob_age": ob_age,
+                            "atr": atr,
+                            "sweep": sweep,
+                            "sweep_lb": sweep_lb
+                        })
+
+print(f"Starting optimization of {len(param_combos)} combinations on your M4 Mac...\n")
+
+total_start = time.perf_counter()
+
+# Main loop with progress bar
+for params in tqdm(param_combos, desc="Optimizing ICT Strategy", unit="backtest"):
+    strategy = ICTStrategy(
+        session=params["session"],
+        lookback=params["lookback"],
+        ob_max_age=params["ob_age"],
+        atr_mult=params["atr"],
+        use_liquidity_sweep=params["sweep"],
+        sweep_lookback=params["sweep_lb"],
+    )
+
+    # Accurate timing
+    t0 = time.perf_counter()
+    trades = run_backtest(candles_5m, strategy, 10000)
+    elapsed = time.perf_counter() - t0
+
+    # Optional: print every backtest (can be noisy, comment out if you want cleaner output)
+    # print(f"Backtest took {elapsed:.4f}s | Trades: {len(trades)}")
+
+    if len(trades) < 5:
+        continue
+
+    total_pnl = sum(t.pnl for t in trades)
+    wr = len([t for t in trades if t.pnl > 0]) / len(trades) * 100 if trades else 0.0
+
+    if total_pnl > best_pnl:
+        best_pnl = total_pnl
+        best_params = {
+            "session": params["session"],
+            "lookback": params["lookback"],
+            "ob_age": params["ob_age"],
+            "atr": params["atr"],
+            "sweep": params["sweep"],
+            "sweep_lb": params["sweep_lb"],
+            "trades": len(trades),
+            "wr": round(wr, 2)
+        }
+        tqdm.write(f"New best! PnL = {total_pnl:.2f} | Params: {best_params}")
+
+# Final results
+total_time = time.perf_counter() - total_start
+
+print("\n" + "="*60)
+print("Optimization finished!")
+print(f"Total time on your M4: {total_time:.1f} seconds ({total_time/60:.1f} minutes)")
+print(f"Best params: {best_params}")
+print(f"Best PnL: {best_pnl:.2f}")
+print("="*60)