moen0
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
from data.loader import load_candles, resample_candles
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from engine.backtester import run_backtest
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from strategies.ict_strategy import ICTStrategy
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import time
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from tqdm import tqdm
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from joblib import Parallel, delayed
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print("Loading data...")
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candles_1m = load_candles("data/data1.csv")
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candles_5m = resample_candles(candles_1m, period=5)
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print(f"Loaded {len(candles_5m):,} 5-minute candles.\n")
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param_combos = []
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for session in ["london", "new_york"]:
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for lookback in [3, 5, 7, 10]:
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for ob_age in [20, 50, 80]:
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for atr in [1.0, 1.5, 2.0, 2.5]:
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for sweep in [True, False]:
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sweep_lbs = [5, 10, 15] if sweep else [0]
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for sweep_lb in sweep_lbs:
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param_combos.append({
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"session": session,
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"lookback": lookback,
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"ob_age": ob_age,
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"atr": atr,
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"sweep": sweep,
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"sweep_lb": sweep_lb
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})
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print(f"Starting parallel optimization of {len(param_combos)} combinations...\n")
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def run_one_combo(params):
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strategy = ICTStrategy(
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session=params["session"],
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lookback=params["lookback"],
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ob_max_age=params["ob_age"],
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atr_mult=params["atr"],
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use_liquidity_sweep=params["sweep"],
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sweep_lookback=params["sweep_lb"],
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)
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t0 = time.perf_counter()
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trades = run_backtest(candles_5m, strategy, 10000)
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elapsed = time.perf_counter() - t0
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if len(trades) < 5:
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return None
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total_pnl = sum(t.pnl for t in trades)
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wr = len([t for t in trades if t.pnl > 0]) / len(trades) * 100 if trades else 0.0
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return {
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"params": params,
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"pnl": total_pnl,
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"trades": len(trades),
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"wr": round(wr, 2),
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"time": round(elapsed, 4)
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}
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total_start = time.perf_counter()
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results = Parallel(n_jobs=-1, verbose=10)(
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delayed(run_one_combo)(params) for params in param_combos
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)
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valid_results = [r for r in results if r is not None]
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if not valid_results:
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print("No valid strategies found with at least 5 trades.")
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exit()
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best_result = max(valid_results, key=lambda x: x["pnl"])
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total_time = time.perf_counter() - total_start
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print("\n" + "="*70)
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print("PARALLEL OPTIMIZATION FINISHED!")
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print(f"Total time on M4 Mac: {total_time:.1f} seconds ({total_time/60:.1f} minutes)")
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print(f"Processed {len(param_combos)} combinations at ~{len(param_combos)/total_time:.2f} combos/second")
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print(f"Best PnL: {best_result['pnl']:.2f}")
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print(f"Best Params: {best_result['params']}")
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print(f"Trades: {best_result['trades']} | Win Rate: {best_result['wr']}%")
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print("="*70)
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print("\nTop 5 results:")
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for res in sorted(valid_results, key=lambda x: x["pnl"], reverse=True)[:5]:
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print(f"PnL: {res['pnl']:.2f} | Trades: {res['trades']} | WR: {res['wr']}% | {res['params']}") |