✅ Part 1: VectorBT Tutorial
Backtest a Strategy in 30 Minutes
Your first vectorized backtest, parameter heatmaps, and honest cost defaults. Adds backtest.py.
✅ Part 2: Walk-Forward Analysis
Stop Fooling Yourself with In-Sample Results
Why single backtests lie, how walk-forward validation works, and honest out-of-sample numbers. Adds validation.py.
✅ Part 3: Data Cleaning & Alignment
Survivorship bias, corporate actions, timezone hell. Adds data.py.
✅ Part 4: Backtest Overfitting
The statistics of not fooling yourself when testing hundreds of variants. Adds overfitting.py.
✅ Part 5: Performance Metrics
Beyond Sharpe: The Metrics That Actually Matter
Beyond Sharpe: Sortino, Calmar, tail ratios, and when each lies. Adds metrics.py.
✅ Part 6: Slippage & Commissions
Honest cost modeling that kills most “profitable” strategies. Adds costs.py.
✅ Part 7: Position Sizing
Size Matters: Position Sizing That Survives
Kelly, volatility targeting, and why sizing matters more than signals. Adds sizing.py.
✅ Part 8: Multi-Strategy Portfolios
Don’t Put All Your Alpha in One Basket
Combining strategies, correlation, and portfolio-level backtesting.
✅ Part 9: Parameter Robustness
Stability across parameters, regimes, and markets. When to trust a parameter set.
✅ Part 10: Assembling the Full System
Wiring all modules into a complete research pipeline. Capstone.
Each tutorial is tested with real data and runnable code. ✅ = published, 🔲 = upcoming.