testing out python library integration

python/backtest.py

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+import backtestingfx as _rust
+import pandas as pd
+
+
+class Strategy:
+    def init(self):
+        pass
+
+    def next(self):
+        pass
+
+    def buy(self, lot_size, stop_loss=None, take_profit=None):
+        self._broker.buy(self._bar.close, lot_size, self._bar.timestamp, stop_loss, take_profit)
+
+    def sell(self, lot_size, stop_loss=None, take_profit=None):
+        self._broker.sell(self._bar.close, lot_size, self._bar.timestamp, stop_loss, take_profit)
+
+    def close_all(self):
+        self._broker.close_all(self._bar.close, self._bar.timestamp)
+
+    def close_position(self, id):
+        self._broker.close_position(id, self._bar.close, self._bar.timestamp)
+
+
+class _Adapter:
+    def __init__(self, strategy):
+        self._strategy = strategy
+
+    def init(self, bars):
+        self._strategy.bars = bars
+        self._strategy.init()
+
+    def next(self, bar, broker):
+        self._strategy._bar = bar
+        self._strategy._broker = broker
+        self._strategy.next()
+
+
+class Backtest:
+    def __init__(self, df, strategy_class, cash=10000.0, commission=0.0, spread=0.0):
+        self._df = df
+        self._strategy_class = strategy_class
+        self._cash = cash
+        self._commission = commission
+        self._spread = spread
+
+    def _to_bars(self):
+        bars = []
+        for idx, row in self._df.iterrows():
+            if isinstance(idx, pd.Timestamp):
+                ts = int(idx.timestamp())
+            else:
+                ts = int(pd.Timestamp(row["timestamp"]).timestamp())
+
+            bars.append(_rust.Bar(
+                timestamp=ts,
+                open=float(row["open"]),
+                high=float(row["high"]),
+                low=float(row["low"]),
+                close=float(row["close"]),
+                volume=float(row.get("volume", 0.0))
+            ))
+        return bars
+
+    def run(self):
+        bars = self._to_bars()
+        engine = _rust.Engine(bars, self._cash, self._commission, self._spread)
+        strategy = self._strategy_class()
+        return engine.run(_Adapter(strategy))

python/simple_strategy.py

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+import os
+
+import pandas as pd
+from backtest import Backtest, Strategy
+from dotenv import load_dotenv
+from lse import LSE
+
+load_dotenv()
+
+client = LSE(api_key=os.environ["LSE_API_KEY"])
+
+rows = client.candles("EUR/USD", "1h", limit=2000)
+df = pd.DataFrame(rows)
+df.to_csv("data/EURUSD_1H.csv", index=False)
+
+
+class BuyEveryBar(Strategy):
+    def next(self):
+        self.close_all()
+        self.buy(1.0)
+
+
+df = pd.read_csv("data/EURUSD_1H.csv")
+
+bt = Backtest(df, BuyEveryBar, cash=10000.0, commission=0.0, spread=0.0001)
+stats = bt.run()
+
+print(f"Return:       {stats.total_return_pct:.2f}%")
+print(f"Trades:       {stats.num_trades}")
+print(f"Win Rate:     {stats.win_rate_pct:.1f}%")
+print(f"Avg PnL:      {stats.avg_pnl:.5f}")
+print(f"Best Trade:   {stats.best_trade:.5f}")
+print(f"Worst Trade:  {stats.worst_trade:.5f}")
+print(f"Max Drawdown: {stats.max_drawdown_pct:.2f}%")
+print(f"Profit Factor:{stats.profit_factor:.2f}")