moen0
44 lines
1.6 KiB
Python
44 lines
1.6 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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# Load 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("Testing different Risk-Reward ratios with optimized ICTStrategy...\n")
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# Best params from optimization (you can tweak session/lookback etc. if you want)
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strategy = ICTStrategy(
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session="new_york", # Best was New York
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lookback=7,
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ob_max_age=20, # Best was 20
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atr_mult=2.5,
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use_liquidity_sweep=False, # Best was False
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sweep_lookback=5,
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)
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for rr in [1.0, 1.5, 2.0, 2.5, 3.0]:
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t0 = time.perf_counter()
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trades = run_backtest(candles_5m, strategy, 10000, risk_reward=rr)
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elapsed = time.perf_counter() - t0
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if not trades:
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print(f"RR={rr}: No trades")
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continue
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total_pnl = sum(t.pnl for t in trades)
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winners = [t for t in trades if t.pnl > 0]
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losers = [t for t in trades if t.pnl <= 0]
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wr = len(winners) / len(trades) * 100 if trades else 0
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avg_win = sum(t.pnl for t in winners) / len(winners) if winners else 0
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avg_loss = sum(t.pnl for t in losers) / len(losers) if losers else 0
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profit_factor = abs(sum(t.pnl for t in winners) / sum(t.pnl for t in losers)) if losers else float('inf')
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print(f"RR={rr:4.1f} | Trades={len(trades):4d} | WR={wr:5.1f}% | "
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f"PnL={total_pnl:8.2f} | AvgWin={avg_win:6.3f} | AvgLoss={avg_loss:6.3f} | "
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f"PF={profit_factor:5.2f} | Time={elapsed:.3f}s") |