Thread by @quantscience_

Statistical arbitrage (stat arb) is the strategy Ken Griffen used to grow his net worth to $43,900,000,000.

Here's how to get started with Stat Arb in Python:

1. Select a Basket of Assets

Choose a group of related assets (e.g., stocks in the same sector like tech giants) that are likely to co-move over time. Gather their historical price data.

2. Model the Portfolio Relationship

Use a statistical method like Principal Component Analysis (PCA) or a simple index (e.g., weighted average) to estimate the "fair value" of the portfolio. Here, we’ll use a rolling mean of normalized prices.

3. Identify Mispricings

Calculate the deviation of each asset from the portfolio’s fair value. Large deviations signal potential arbitrage opportunities.

4. Generate Trading Signals

Trade individual assets: go long on underpriced stocks (deviation < lower threshold) and short overpriced stocks (deviation > upper threshold), betting on convergence to the portfolio mean.

5. Backtest and Deploy

Calculate returns across the basket, assess profitability, and deploy the strategy with real-time data feeds.

6. Want to learn how to get started with algorithmic trading with Python?

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That's a wrap! Over the next 24 days, I'm sharing my top 24 algorithmic trading concepts to help you get started.

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