Algorithmic Backtester & Overfitting Tool

backtesting.py template · regenerated live from the rule matrix

# ============================================================# ApexTrading auto-generated strategy  ·  backtesting.py template# Asset: EUR/USD   |   Timeframe: H1# Horizon: 2024-08-07 → 2026-08-02 (2Y)# Risk: SL 1.5%  /  TP 3%# ============================================================import yfinance as yffrom backtesting import Backtest, Strategyfrom backtesting.lib import crossoverimport talib # Historical OHLCV pulled from the free yfinance asset layer.data = yf.download("EURUSD=X", start="2024-08-07", end="2026-08-02", interval="1h")   class ApexStrategy(Strategy):    stop_loss_pct = 0.0150    take_profit_pct = 0.0300    pip_size = 0.0001    trailing_stop_pct = 0.0100    breakeven_trigger_pips = 20    long_swap_pips = -0.5    short_swap_pips = -0.5     def init(self):        close = self.data.Close        self.last_rollover_date = None        self.e1a = self.I(talib.EMA, close, 21)        self.e1b = self.I(talib.SMA, close, 50)        self.e2a = self.I(talib.RSI, close, 14)     def next(self):        price = self.data.Close[-1]        current_date = self.data.index[-1].date()        if self.position and self.last_rollover_date and current_date > self.last_rollover_date:            multiplier = 3 if current_date.weekday() == 2 else 1            swap_pips = self.long_swap_pips if self.position.is_long else self.short_swap_pips            lots = abs(self.position.size) / 100_000            pip_value = 10 if "EUR/USD".endswith("/USD") else self.pip_size / price * 100_000            self._broker._cash += swap_pips * pip_value * lots * multiplier        self.last_rollover_date = current_date         if self.position:            entry = self.position.entry_price            pnl_pct = (price - entry) / entry * 1                        if pnl_pct <= -self.stop_loss_pct:                self.position.close()                return            if pnl_pct >= self.take_profit_pct:                self.position.close()                return         entry_signal = (crossover(self.e1a, self.e1b)) and (self.e2a[-1] < 70)        if entry_signal and not self.position:            sl_price = price * (1 - self.stop_loss_pct)            tp_price = price * (1 + self.take_profit_pct)            size_units = max(1, (self.equity * 0.01) / (price * self.stop_loss_pct))            self.buy(size=round(size_units), sl=sl_price, tp=tp_price)  bt = Backtest(data, ApexStrategy, cash=100_000, commission=0.000300)stats = bt.run()print(stats)