How to do statistical arbitrage?

<p>Statistical arbitrage involves finding patterns in how prices of related things, like stocks, change over time. It's like spotting opportunities to buy low and sell high. To do this, you pick two related things, collect their price data, and calculate the difference between their prices (called the spread). You use math to see if this spread behaves predictably. When the spread moves too much from its usual pattern, you might decide to make a trade. But, you have to be careful and not risk too much money. You keep an eye on the trade, and when the spread goes back to its usual level, you might finish the trade to make a profit. Some people use computers to do all this faster. Just remember, while statistical arbitrage can make money, it's not a sure thing, and you need to be smart about it.</p>

<p id="isPasted">Statistical arbitrage is a quantitative trading strategy that involves using mathematical models to identify and profit from temporary price discrepancies between related financial instruments. The core principle is mean reversion: the assumption that prices will eventually return to their historical average relationship.&nbsp;</p><p>Core Concepts</p><p>Market Neutrality: Positions are balanced (long one asset, short the other) so profits are derived from the relative price movement, not the overall market direction.</p><p>Cointegration: This statistical property is crucial for pairs trading, indicating a long-term, stable equilibrium relationship between two or more non-stationary assets. When their price spread is stationary, it tends to revert to the mean.</p><p>Z-score: This metric measures how far the current price spread is from its historical mean in standard deviations. A high or low z-score often generates the signal to enter a trade.&nbsp;</p><p>Step-by-Step Implementation</p><p>Data Collection: Gather extensive historical price data (e.g., closing prices, volume) for a wide universe of potential assets (stocks, ETFs, cryptocurrencies) using tools like the CoinGecko API or Python libraries such as yfinance.</p><p>Model Development and Pair Selection:</p><p>Identify pairs or groups of assets that are historically correlated.</p><p>Apply statistical tests, such as the Augmented Dickey-Fuller (ADF) test or the Engle-Granger test, to confirm that the spread between the assets is stationary (cointegrated).</p><p>Calculate the optimal ratio (hedge ratio) to balance positions, often using linear regression.</p><p><strong>Signal Generation:</strong></p><p>Calculate the z-score of the current spread in real-time.</p><p>Define entry and exit thresholds (e.g., enter a trade when the spread deviates by 2 standard deviations from the mean, and exit when it returns to the mean).</p><ul><li>Backtesting: Simulate the strategy using historical data to evaluate its profitability, risks (max drawdown), and performance under various market conditions. Account for transaction costs and slippage to ensure realistic results.</li><li>Automated Execution: Since opportunities can be fleeting (lasting seconds to days), use automated trading systems or bots to execute trades quickly and efficiently based on generated signals.</li></ul><p><strong>Risk Management and Monitoring:</strong></p><ul><li>Implement strict risk controls like stop-loss orders, position sizing rules, and portfolio diversification.</li><li>Continuously monitor the model's performance, as historical relationships can break down due to market shifts or new information. </li></ul><p><strong>Common Strategies</strong></p><ul><li>Pairs Trading: The most common form, involving two correlated assets like Coca-Cola and Pepsi. When one outperforms the other, the underperformer is bought (long) and the outperformer is sold (short).</li><li>Basket Trading: An extension of pairs trading that uses a portfolio of many correlated stocks (hundreds in institutional cases) to diversify risk and reduce exposure to specific stock movements.</li><li>Triangular Arbitrage (in crypto): Exploiting price discrepancies among three different cryptocurrency pairs on the same exchange (e.g., BTC/ETH, ETH/USD, and BTC/USD) to profit from the implied exchange rate difference. </li></ul>