Three backtested quant strategies as Jupyter notebooks

I've spent a few years building quant strategies and most of the work isn't the math — it's the plumbing: getting clean data, structuring the backtest so it doesn't look at the future, generating a readable tearsheet. I packaged three of my working strategies as self-contained notebooks so someone can go from zero to a proper backtest in an afternoon.

The three strategies:

1. Funding Rate Carry — Goes long assets with consistently negative funding (market paying you to hold) while hedging directional exposure with a small short on a correlated index. Data from Binance public API, no account needed.

2. Momentum + Regime — Standard cross-sectional momentum with a regime filter. The regime filter uses a hidden Markov model to identify high/low volatility regimes and sizes positions down during risk-off states. This cuts most of the drawdown that raw momentum has during trend reversals.

3. Vol Mean Reversion — Trades the VIX term structure spread (VIX3M/VIX ratio) against realized vol. Core idea: implied vol mean-reverts faster than realized, so a wide spread means overpriced insurance.

Each notebook is 7 cells: data fetch, signal construction, backtest loop, metrics (Sharpe, Calmar, max drawdown, win rate), sensitivity analysis grid, and a PDF export cell.

All data is free: yfinance for equities/VIX, Binance public REST API for crypto. No Bloomberg required.

The sensitivity analysis cell is the part I always wished papers included — it runs a parameter grid so you can see the full metric landscape, not just the one published set. That's how you tell if you found a real edge or curve-fit a point.

$49 per notebook, $99 for all three: https://mattitude8861.gumroad.com/l/VolatilityMeanReversionS...

https://mattitude8861.gumroad.com/l/MomentumHMMRegimeFilterE...

https://mattitude8861.gumroad.com/l/FundingRateCarryStrategy...

https://mattitude8861.gumroad.com/l/QuantStrategyTemplatesBu...