getting it ready for publishing

Cargo.toml

@@ -4,9 +4,9 @@ version = "0.1.0"
 edition = "2024"
 
 [lib]
-name = "backtestingfx"
+name = "_backtestingfx"
 crate-type = ["cdylib", "rlib"]
 
 [dependencies]
 chrono = "0.4"
-pyo3 = { version = "0.28", features = ["extension-module"]}
+pyo3 = { version = "0.28", features = ["extension-module"]}

backtestingfx/__init__.py

@@ -0,0 +1,2 @@
+from backtestingfx._backtestingfx import Bar, Engine, Stats, Broker  # type: ignore
+from backtestingfx.backtest import Strategy, Backtest

backtestingfx/backtest.py

@@ -0,0 +1,100 @@
+from typing import Any
+
+from backtestingfx import _backtestingfx as _rust  # type: ignore
+import pandas as pd
+
+
+class Strategy:
+    def __init__(self):
+        self._bars: Any = None
+        self._bar: Any = None
+        self._broker: Any = None
+
+    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,
+        contract_size=100000.0,
+        quote_to_account=1.0,
+    ):
+        self._df = df
+        self._strategy_class = strategy_class
+        self._cash = cash
+        self._commission = commission
+        self._spread = spread
+        self._contract_size = contract_size
+        self._quote_to_account = quote_to_account
+
+    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())  # type: ignore
+
+            bars.append(
+                _rust.Bar(  # type: ignore
+                    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(  # type: ignore
+            bars,
+            self._cash,
+            self._commission,
+            self._spread,
+            self._contract_size,
+            self._quote_to_account,
+        )
+        strategy = self._strategy_class()
+        return engine.run(_Adapter(strategy))

pyproject.toml

@@ -0,0 +1,14 @@
+[build-system]
+requires = ["maturin>=1.0,<2.0"]
+build-backend = "maturin"
+
+[project]
+name = "backtestingfx"
+version = "0.1.0"
+description = "FX backtesting library built in Rust"
+requires-python = ">=3.9"
+dependencies = ["pandas"]
+
+[tool.maturin]
+module-name = "backtestingfx._backtestingfx"
+features = ["pyo3/extension-module"]

python/simple_strategy.py

@@ -1,7 +1,7 @@
 import os
 
 import pandas as pd
-from backtest import Backtest, Strategy
+from backtestingfx import Backtest, Strategy
 from dotenv import load_dotenv
 from lse import LSE
 
@@ -17,7 +17,7 @@ df.to_csv("data/EURUSD_1H.csv", index=False)
 class BuyEveryBar(Strategy):
     def next(self):
         self.close_all()
-        self.buy(1.0)
+        self.buy(0.1)
 
 
 df = pd.read_csv("data/EURUSD_1H.csv")
@@ -25,11 +25,4 @@ 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(stats)
-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}")

src/lib.rs

@@ -8,6 +8,7 @@ pub mod types;
 use pyo3::prelude::*;
 
 #[pymodule]
+#[pyo3(name = "_backtestingfx")]
 fn backtestingfx(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<types::Bar>()?;
     m.add_class::<stats::Stats>()?;