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② One strategy in this book returned 2.3× the S&P 500 on a risk-adjusted basis over 5 years.
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Premium Members – Your Full Notebook Is Ready
The complete Google Colab notebook from today’s article (with live data, full Hidden Markov Model, interactive charts, statistics, and one-click CSV export) is waiting for you.
Preview of what you’ll get:
Inside the Strategy Lab
Data Fetching — three data sources demonstrated side by side: Yahoo Finance (yfinance), EODHD APIs, and Twelve Data, all pulling 10 years of daily OHLCV data for SPY, GLD, and BTC
OHLC Visualization — interactive candlestick charts built with Plotly and VectorBT for all three assets, including volume panels
EMA Crossover Strategy — implements a long/cash signal (fast EMA > slow EMA = 1, otherwise 0) with a 1-day shift to avoid lookahead bias, then compares equity curves, rolling Sharpe, drawdowns, monthly returns, and buy/sell signal overlays against a buy-and-hold benchmark
Grid Search Optimization — exhaustive search over fast EMA (5–50) and slow EMA (20–200) combinations to find the Sharpe-maximising pair per asset, with Sharpe surface heatmaps showing the full parameter landscape
Out-of-Sample Testing — proper walk-forward validation with an 80/20 train/test split, optimizing on train and evaluating on unseen test data to guard against overfitting
VectorBT Backtesting — full backtests with 0.1% fees and $10k starting capital, producing detailed stats (win rate, profit factor, Calmar, Sortino, trade durations) and interactive trade plots
Regime Drawdown Analysis — three-panel chart overlaying strategy vs buy-and-hold drawdowns with green/red shading for bull and bear regimes
Conclusions — asset-specific takeaways: SPY benefits defensively, GLD is the cleanest trend-following fit, BTC is convex (few massive wins tolerating large drawdowns), and a core finding that no single EMA parameterisation generalises across all asset classes
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
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