Python quantitative trading strategies including Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

Scalable, event-driven, deep-learning-friendly backtesting library

A Project to identify statistical arbitrage opportunities between cointegrated pairs. This is referred to as 'Pairs Trading' which is a bet on the mean reversion property of the spread.

A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python

Quantitative analysis, strategies and backtests

This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.

Identify and trade statistical arbitrage opportunities between cointegrated pairs using Bitfinex API

A walk through the frameworks of Python in Finance. The repository is currently in the development phase. The finalized version will include a full-fledged integration and utilization of Quantopian, GS-Quant, WRDS API and their relevant datasets and analytics.

statistic arbitrage strategy research tools

pair trading(stat arb), July 2017