Statistical-Arbitrage

In this project we provide a backtesting pipeline for intraday statistical arbitrage. Both traditional spread models (i.e. pairs trading with cointegration tests, time series analysis) and continuous time trading models (i.e. Ornstein-Uhlenbeck process) are used to model the spread portfolios.

Scripts

data: intraday data files, including stocks, options, and dual listing stocks
utils: arbitrage tool functions including cointegration tests and regression analysis
models: simulations and parameter estimations for stochastic models and option greeks
- BM.py: brownian motion related functions
- Vasicek.py: OU-process related functions
- BS_model.py: Black-Scholes model and option greeks
statistical_arbitrage: notebook for realizing pair trading based on limit orderbook stock data
res: results for positions, thresholds, and PnLs

Backtesting

Spread Portfolios

Threshold Analysis

Position Analysis

PnL Visualization

Note

Higher the transaction costs, larger the optimal entry points for arbitrage, lower the trading frequency.
Sensitivity analysis needs to be conducted with respect to the level of transaction costs (price impact, bid-ask spread, and commission fees).

duggar/Statistical-Arbitrage2