Three professional-grade trading strategy notebooks built from a 1.2M line quantitative trading codebase.

What's included:

• Funding Rate Carry, Crypto perpetual futures carry strategy. Pulls live data from Binance public API, builds a correlation-weighted hedge, full vectorized backtest with Sharpe/Calmar/drawdown metrics.

• Momentum + Regime Filter, Cross-sectional equity momentum with a Hidden Markov Model regime filter. The filter cuts drawdown during trend reversals without killing upside. Backtested 2005-2024 including 2008, 2020, 2022.

• Volatility Mean Reversion, VIX term structure spread vs realized vol. Systematic framework for a trade most options sellers make discretionarily.

Each notebook includes:

• 7 cells: data fetch → signal → backtest → metrics → sensitivity analysis → PDF tearsheet
• Free data only (yfinance, Binance public API, FRED), no Bloomberg, no paid feeds
• Look-ahead bias prevented by construction
• Parameter sensitivity grid so you can see if you've found a robust edge or an overfit point

Who this is for: Quant researchers who want a clean starting point, not a finished product. You should know Python and pandas. The math is commented but not dumbed down.

Not financial advice. Backtest ≠ live performance. Trade at your own risk.

TAGS: quantitative finance, trading, algo trading, python, jupyter notebook, backtesting, strategy