71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
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from __future__ import annotations
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import pandas as pd
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from pathlib import Path
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from trade import TradeState, enter_long, exit_long, maybe_trailing_stop
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from indicators import add_supertrends, compute_meta_trend
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from metrics import compute_metrics
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from logging_utils import write_trade_log
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DEFAULT_ST_SETTINGS = [(12, 3.0), (10, 1.0), (11, 2.0)]
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def backtest(
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df: pd.DataFrame,
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df_1min: pd.DataFrame,
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timeframe_minutes: int,
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stop_loss: float,
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exit_on_bearish_flip: bool,
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fee_bps: float,
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slippage_bps: float,
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log_path: Path | None = None,
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):
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df = add_supertrends(df, DEFAULT_ST_SETTINGS)
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df["meta_bull"] = compute_meta_trend(df, DEFAULT_ST_SETTINGS)
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state = TradeState(stop_loss_frac=stop_loss, fee_bps=fee_bps, slippage_bps=slippage_bps)
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equity, trades = [], []
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for i, row in df.iterrows():
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price = float(row["Close"])
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ts = pd.Timestamp(row["Timestamp"])
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if state.qty <= 0 and row["meta_bull"] == 1:
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evt = enter_long(state, price)
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if evt:
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evt.update({"t": ts.isoformat(), "reason": "bull_flip"})
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trades.append(evt)
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start = ts
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end = df["Timestamp"].iat[i + 1] if i + 1 < len(df) else ts + pd.Timedelta(minutes=timeframe_minutes)
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if state.qty > 0:
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win = df_1min[(df_1min["Timestamp"] >= start) & (df_1min["Timestamp"] < end)]
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for _, m in win.iterrows():
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hi = float(m["High"])
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lo = float(m["Low"])
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state.max_px = max(state.max_px or hi, hi)
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trail = state.max_px * (1.0 - state.stop_loss_frac)
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if lo <= trail:
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evt = exit_long(state, trail)
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if evt:
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prev = trades[-1]
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pnl = (evt["price"] - (prev.get("price") or evt["price"])) * (prev.get("qty") or 0.0)
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evt.update({"t": pd.Timestamp(m["Timestamp"]).isoformat(), "reason": "stop", "pnl": pnl})
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trades.append(evt)
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break
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if state.qty > 0 and exit_on_bearish_flip and row["meta_bull"] == 0:
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evt = exit_long(state, price)
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if evt:
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prev = trades[-1]
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pnl = (evt["price"] - (prev.get("price") or evt["price"])) * (prev.get("qty") or 0.0)
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evt.update({"t": ts.isoformat(), "reason": "bearish_flip", "pnl": pnl})
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trades.append(evt)
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equity.append(state.cash + state.qty * price)
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equity_curve = pd.Series(equity, index=df["Timestamp"])
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if log_path:
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write_trade_log(trades, log_path)
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perf = compute_metrics(equity_curve, trades)
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return perf, equity_curve, trades
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