A self-contained Jupyter notebook implementing a funding rate carry strategy for crypto perpetual futures.

What's included:

• Data fetch from Binance public API (no API key required)
• Signal: 7-day rolling average funding rate, enters when market pays you to hold long
• Correlation-weighted short hedge to neutralize directional beta
• Vectorized backtest engine (no look-ahead bias by construction)
• Metrics: Sharpe, Calmar, max drawdown, win rate, monthly return heatmap
• Parameter sensitivity grid (lookback window × entry threshold)
• PDF tearsheet export via reportlab

In historical backtesting (2021-2024): Sharpe 1.2-1.8 during normal market conditions.

Requirements: Python, pandas, numpy, matplotlib, requests, reportlab. requirements.txt included.

Not financial advice. Past performance does not predict future results.

TAGS: crypto, quantitative finance, algo trading, python, jupyter, backtesting, funding rate