Pairs Trading Strategy with Python In this notebook, we implement a pairs trading strategy using Python. Pairs trading is a market-neutral strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement. The strategy involves identifying two assets that have historically moved together, finding divergences in their prices, and placing trades to bet on the convergence of their prices.
Step-by-Step Explanation:
Import Libraries: We start by importing the necessary libraries: yfinance for downloading historical market data, pandas and numpy for data manipulation, matplotlib and seaborn for plotting, and statsmodels for statistical tests. Define Constants and Universe of Assets:
Start and End Date: The period over which we will analyze and backtest the strategy.
P-value Threshold: For the cointegration test, to identify pairs of assets that are statistically correlated.
Transaction Costs: A small percentage representing the cost of trading.
Universe of Tickers: A list of assets including cryptocurrencies, bank stocks, global indexes, tech stocks, consumer goods stocks, and bond ETFs. Download Historical Data: Using yfinance, we download the adjusted closing prices for each ticker in our universe from the start date to the end date.
Data Sanitization: Ensure the data is complete and interpolate missing values. Align all time series to the same date range. Filter out incomplete time series.
Find Cointegrated Pairs: Using the Engle-Granger cointegration test, we identify pairs of assets that are statistically cointegrated, meaning they have a stable, long-term relationship.
Generate Trading Signals: For each cointegrated pair, we calculate the spread between their prices, normalize it to a z-score, and generate buy/sell signals based on entry and exit thresholds.
Backtest the Strategy: Simulate the trading strategy to evaluate its performance. Calculate daily returns, account for transaction costs, and compute cumulative returns and the Sharpe ratio.
Optimize Parameters: Test different entry thresholds to find the optimal parameters that maximize the Sharpe ratio.