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KhizarImran
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# backtestingfx
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A Rust library for backtesting FX trading strategies on OHLCV data.
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A Rust-powered FX backtesting library for Python. Write your strategy in Python, let Rust handle the heavy lifting.
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Inspired by [backtesting.py](https://kernc.github.io/backtesting.py/), built specifically for forex — lot sizes, long/short positions, stop loss, take profit, and pip-based PnL.
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## Overview
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Implement the `Strategy` trait, point it at your OHLCV data, and the engine handles the rest. The library is event-driven — your strategy receives one bar at a time and interacts with a simulated broker to open and close positions.
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```rust
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struct SmaCross {
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period: usize,
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prices: Vec<f64>,
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}
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impl Strategy for SmaCross {
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fn next(&mut self, bar: &Bar, broker: &mut Broker) {
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self.prices.push(bar.close);
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if self.prices.len() < self.period {
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return;
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}
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let sma: f64 = self.prices.iter().rev().take(self.period).sum::<f64>() / self.period as f64;
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if bar.close > sma {
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broker.buy(bar.close, 1.0, bar.timestamp, None, None);
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} else {
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broker.close_all(bar.close, bar.timestamp);
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}
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}
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}
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```
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## Features
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- Event-driven backtesting on OHLCV bar data
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- Long and short positions
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- Per-position stop loss and take profit
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- Full trade history with PnL per trade
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- CSV data loading
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- Designed to be imported into Python via PyO3 (coming soon)
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## Getting Started
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Add to your `Cargo.toml`:
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```toml
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[dependencies]
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backtestingfx = { git = "https://github.com/KhizarImran/backtestingfx" }
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```
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## Project Structure
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Inspired by [backtesting.py](https://kernc.github.io/backtesting.py/) but built specifically for forex — lot sizes, pip-based PnL, stop loss, take profit, and realistic account currency conversion.
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```
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src/
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├── lib.rs # crate root
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├── types.rs # Bar, Position, Trade
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├── strategy.rs # Strategy trait
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├── broker.rs # simulated broker
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├── engine.rs # backtest event loop
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└── data.rs # CSV data loader
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pip install backtestingfx
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```
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## Status
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## Quick Start
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Early development. Core types and broker are implemented. Data loading and engine are in progress.
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```python
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import pandas as pd
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from backtestingfx import Backtest, Strategy
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class MyCrossStrategy(Strategy):
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def next(self):
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self.close_all()
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self.buy(lot_size=0.1)
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df = pd.read_csv("EURUSD_1H.csv")
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bt = Backtest(df, MyCrossStrategy, cash=10000.0, spread=0.0001)
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stats = bt.run()
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print(stats)
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```
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```
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--- Backtest Results ---
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Initial Cash: 10000.00
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Final Cash: 9823.50
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Total Return: -1.77%
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Trades: 248
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Win Rate: 52.4%
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Avg PnL: -0.71380
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Best Trade: 84.20000
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Worst Trade: -61.30000
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Profit Factor: 0.94
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Max Drawdown: 3.21%
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```
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## Installation
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```
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pip install backtestingfx
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```
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Requires Python 3.9+.
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## Writing a Strategy
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Inherit from `Strategy` and implement `next()`. It is called once per bar.
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```python
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from backtestingfx import Backtest, Strategy
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class MyStrategy(Strategy):
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def init(self):
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# called once before the loop starts
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# self._bars contains all Bar objects if you need to pre-compute
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pass
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def next(self):
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# self._bar — current bar (open, high, low, close, volume, timestamp)
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# self._broker — the broker instance (advanced use)
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if self._bar.close > 1.1000:
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self.buy(lot_size=0.1, stop_loss=1.0950, take_profit=1.1100)
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else:
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self.close_all()
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```
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### Strategy methods
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| Method | Description |
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|--------|-------------|
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| `self.buy(lot_size, stop_loss=None, take_profit=None)` | Open a long position |
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| `self.sell(lot_size, stop_loss=None, take_profit=None)` | Open a short position |
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| `self.close_all()` | Close all open positions |
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| `self.close_position(id)` | Close a specific position by ID |
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### Bar fields
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```python
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self._bar.open
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self._bar.high
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self._bar.low
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self._bar.close
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self._bar.volume
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self._bar.timestamp # unix timestamp (int)
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```
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## Backtest Parameters
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```python
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Backtest(
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df, # pandas DataFrame with OHLCV columns
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StrategyClass,
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cash=10000.0, # starting account balance in USD
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commission=0.0, # commission per lot (e.g. 7.0 = $7/lot)
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spread=0.0, # spread in price units (e.g. 0.0001 = 1 pip)
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contract_size=100000.0, # standard FX lot size, don't change this
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quote_to_account=1.0, # conversion rate from quote currency to USD
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)
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```
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### Trading non-USD pairs
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By default `quote_to_account=1.0` which is correct for USD-quoted pairs (EURUSD, GBPUSD).
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For other pairs, pass the rate that converts the quote currency to USD:
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| Pair | quote_to_account |
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|------|-----------------|
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| EURUSD, GBPUSD | `1.0` (default) |
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| EURGBP | GBPUSD rate (e.g. `1.27`) |
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| USDCAD, GBPCAD | CADUSD rate (e.g. `0.74`) |
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| USDJPY | JPYUSD rate (e.g. `0.0067`) |
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```python
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bt = Backtest(df, MyStrategy, cash=10000.0, spread=0.00015, quote_to_account=1.27)
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```
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## Stats
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| Field | Description |
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|-------|-------------|
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| `initial_cash` | Starting balance |
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| `final_cash` | Ending balance |
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| `total_return_pct` | Total return as a percentage |
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| `num_trades` | Number of completed trades |
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| `num_wins` | Number of winning trades |
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| `win_rate_pct` | Win rate as a percentage |
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| `avg_pnl` | Average PnL per trade in USD |
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| `best_trade` | Best single trade PnL in USD |
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| `worst_trade` | Worst single trade PnL in USD |
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| `profit_factor` | Gross profit / gross loss |
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| `max_drawdown_pct` | Maximum drawdown as a percentage |
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## Data Format
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Pass a pandas DataFrame with these columns:
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```
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open, high, low, close, volume
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```
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The index should be a `DatetimeIndex`, or include a `timestamp` column. Volume is optional (defaults to 0).
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## Why Rust?
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The backtesting engine is written in Rust and compiled as a native Python extension via [PyO3](https://pyo3.rs). This means the event loop, broker simulation, and stats computation run at native speed while your strategy stays in plain Python.
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## License
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@@ -8,7 +8,12 @@ version = "0.1.0"
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description = "FX backtesting library built in Rust"
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requires-python = ">=3.9"
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dependencies = ["pandas"]
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license = { text = "MIT" }
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readme = "README.md"
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[tool.maturin]
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module-name = "backtestingfx._backtestingfx"
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features = ["pyo3/extension-module"]
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[project.urls]
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Repository = "https://github.com/KhizarImran/backtestingfx"
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