mirror of
https://github.com/KhizarImran/backtestingfx.git
synced 2026-07-27 20:17:44 +00:00
KhizarImran
c22153f02f
- Trade.pnl now stores net pnl (after exit commission) so per-trade stats are accurate - Position pyclass uses from_py_object to fix deprecation warning - Removed dead AttributeError swallow in engine.rs - Strategy gains self.data, self.index, self.cash, self.equity properties - Broker.cash exposed to Python via pyo3(get) - Sharpe ratio added to Stats (unannualized) - Added examples/sma_cross.py and examples/compare_bt.py - Logic verified against backtesting.py: 34 trades, 29.4% win rate match
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
"""
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Compares our SMA crossover logic against backtesting.py using the same data,
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same SMA periods, and zero commission/spread so only entry/exit logic is tested.
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Trade count and win rate should match between both libraries.
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Dollar PnL will differ because the sizing models are different:
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- backtesting.py: buys fractional units based on available cash
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- backtestingfx: fixed lot size (0.1 lots = 10,000 units)
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"""
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import pandas as pd
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from backtesting import Backtest as BtBacktest, Strategy as BtStrategy
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from backtestingfx import Backtest, Strategy
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FAST = 10
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SLOW = 50
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# ── backtesting.py ────────────────────────────────────────────────────────────
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class SmaCrossBt(BtStrategy):
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fast = FAST
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slow = SLOW
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def init(self):
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pass
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def next(self):
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if len(self.data.Close) < self.slow:
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return
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fast_sma = self.data.Close[-self.fast:].mean()
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slow_sma = self.data.Close[-self.slow:].mean()
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if not self.position:
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if fast_sma > slow_sma:
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self.buy()
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else:
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if fast_sma < slow_sma:
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self.position.close()
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# ── backtestingfx ─────────────────────────────────────────────────────────────
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class SmaCrossFx(Strategy):
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fast = FAST
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slow = SLOW
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def next(self):
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if self.index < self.slow:
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return
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closes = [b.close for b in self.data[-self.slow:]]
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fast_sma = sum(closes[-self.fast:]) / self.fast
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slow_sma = sum(closes) / self.slow
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if not self.positions:
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if fast_sma > slow_sma:
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self.buy(0.1)
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else:
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if fast_sma < slow_sma:
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self.close_all()
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# ── run both ──────────────────────────────────────────────────────────────────
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df_raw = pd.read_csv("data/EURUSD_1H.csv")
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# backtesting.py needs a DatetimeIndex and capitalised column names
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df_bt = df_raw.copy()
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df_bt["timestamp"] = pd.to_datetime(df_bt["timestamp"], utc=True)
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df_bt = df_bt.set_index("timestamp")
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df_bt.index = df_bt.index.tz_localize(None)
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df_bt = df_bt.rename(columns={"open": "Open", "high": "High", "low": "Low", "close": "Close", "volume": "Volume"})
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bt_result = BtBacktest(df_bt, SmaCrossBt, cash=10_000, commission=0).run()
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fx_result = Backtest(df_raw, SmaCrossFx, cash=10_000, commission=0, spread=0).run()
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# ── compare ───────────────────────────────────────────────────────────────────
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print("=" * 45)
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print(f"{'Metric':<20} {'backtesting.py':>12} {'backtestingfx':>12}")
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print("=" * 45)
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print(f"{'Trades':<20} {bt_result['# Trades']:>12} {fx_result.num_trades:>12}")
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print(f"{'Win Rate %':<20} {bt_result['Win Rate [%]']:>12.1f} {fx_result.win_rate_pct:>12.1f}")
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print(f"{'Return %':<20} {bt_result['Return [%]']:>12.2f} {fx_result.total_return_pct:>12.2f}")
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print(f"{'Max Drawdown %':<20} {bt_result['Max. Drawdown [%]']:>12.2f} {fx_result.max_drawdown_pct:>12.2f}")
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print("=" * 45)
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print()
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print("Note: Return % differs because backtesting.py sizes by available cash,")
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print(" backtestingfx uses fixed 0.1 lots. Trade count + win rate should match.")
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