Statistical-Arbitrage
Unverified Arbitrage strategy on Options by bradleyboyuyang. BotFinder score 18 out of 100.
High-frequency statistical arbitrage
Source: github
BotFinder analysis pending.
Statistical-Arbitrage
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 - BSmodel.py: Black-Scholes model and option greeks - statisticalarbitrage: 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 c
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