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.

struct SmaCross {
    period: usize,
    prices: Vec<f64>,
}

impl Strategy for SmaCross {
    fn next(&mut self, bar: &Bar, broker: &mut Broker) {
        self.prices.push(bar.close);

        if self.prices.len() < self.period {
            return;
        }

        let sma: f64 = self.prices.iter().rev().take(self.period).sum::<f64>() / self.period as f64;

        if bar.close > sma {
            broker.buy(bar.close, 1.0, bar.timestamp, None, None);
        } else {
            broker.close_all(bar.close, bar.timestamp);
        }
    }
}

Features

Event-driven backtesting on OHLCV bar data
Long and short positions
Per-position stop loss and take profit
Full trade history with PnL per trade
CSV data loading
Designed to be imported into Python via PyO3 (coming soon)

Getting Started

Add to your Cargo.toml:

[dependencies]
backtestingfx = { git = "https://github.com/KhizarImran/backtestingfx" }

Project Structure

src/
├── lib.rs        # crate root
├── types.rs      # Bar, Position, Trade
├── strategy.rs   # Strategy trait
├── broker.rs     # simulated broker
├── engine.rs     # backtest event loop
└── data.rs       # CSV data loader

Status

Early development. Core types and broker are implemented. Data loading and engine are in progress.

License

MIT — see LICENSE