This project is a blazing-fast, modular back-testing engine for financial strategies, built with Rust and exposed to Python via pyo3. It enables:

• High-performance tick-level backtesting
• Walk-forward evaluation
• Parameter grid search
• Beautiful plots with Matplotlib and Pandas

• Pure Rust core for fast execution and memory safety
• SMA Crossover strategy implemented as a modular strategy.rs
• Supports CSV tick data (timestamp, price, volume)
• Equity tracking with commission & PnL calculations
• Export equity curve as a Pandas DataFrame using Polars or native CSV
• Compatible with Python 3.8+ via maturin

pip install maturin

cd rust_core
maturin develop --release
cd ..

Run the demo notebook to:

• Run a single backtest
• View equity curve
• Perform walk-forward testing

import json, rust_core as rc, pandas as pd, matplotlib.pyplot as plt

cfg = json.dumps({"fast": 50, "slow": 200, "initial_capital": 100_000})
rows = rc.backtest("data/es_tick_sample.csv", cfg)

df = pd.DataFrame(rows, columns=["ts_ms", "equity", "sharpe"])
df["ts"] = pd.to_datetime(df.ts_ms, unit="ms")
df.plot(x="ts", y="equity", figsize=(12, 4), title="Equity Curve")
plt.show()

BackTester/
├── rust_core/          # Rust backend
│   ├── src/
│   │   ├── engine.rs   # Core event loop and execution
│   │   ├── strategy.rs # SMA crossover strategy
│   │   ├── events.rs   # Event queue system
│   │   ├── data.rs     # CSV reader and Tick struct
│   │   └── lib.rs      # PyO3 entrypoint
├── data/
│   └── es_tick_sample.csv
├── notebooks/
│   └── demo.ipynb
├── readme.md
└── equity_curve_preview.png

Built by Yuvraj Malik – combining low-level systems power with Python flexibility.