statarb

An implementation of a statistical arbitrage model, done on jupyter notebook. Based on Avellaneda, Lee 2008.

Notes

• Only SP500 Stocks are used (with stocks that have less than 1 year of data exluded)
• 1 year of backtest data is used (August 3rd, 2020 - August 3rd, 2021)
• Backtests start at day 60 in the data, as the estimation window is 60 days to account for 2 earnings cycles
• The model is given $300,000 to simulate in backtest.
• This model assumes frictionless transactions, and no slippage.

An Overview of Statistical Arbitrage

As described by Avellaneda 2008, we assume that every stock in the trading universe has returns that follow the regression model: $$R_i = \beta_i F + \widetilde{R}$$

Where:

• $\widetilde{R}$ is a stock's individual/idiosyncratic return
• $\beta_i F$ are returns from a market portfolio $F$ projected by some market factors $\beta_i$. Note that each stock has differing projections from these market factors.

Assuming that market returns and a stock's idiosyncratic return are uncorrelated, we can isolate the idiosyncratic return of each stock by taking a opposing position in the market portfolio, such that the overall portfolio remains both dollar neutral and market neutral. Because we use only SP500 stocks as a trading universe, in practice the market portfolio is either $SPY or a corresponding industry ETF. This is referred to by generalized pairs trading, where instead of entering pairs trades between individual stocks, we enter positions against market portfolios.

Results and Findings

• Two of the worst performing stocks, DISCK and DISCA, had unforeseen volatility due to the collapse of Archegos Capital Management in March 2021. VIAC, which was also part of Archegos's portfolio, had positive returns in our model.
• Total Return: $25,359.38
• Percentage Return: 8.45%
• Sharpe Ratio: 1.5125

• Best and worst performers out of all 501 Stocks

• Mean reversion strength distribution, where 1/k gives mean reversion time

• Signal Evolution for DISCA

• Signal Evolution for ENPH

TODO

• Add portfolio weighting optimization, such that faster mean reverting stocks are chosen over slower reverting stocks.
• Possibly connect to IBKR to test on live data
• Fix bugs relating to negative stock beta, which shouldn't be possible