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

Quantitative analysis, strategies and backtests

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

This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.

High-frequency statistical arbitrage

Pairs Trading using Statistical Arbitrage

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

👾 my onchain research, foundry boilerplates, quant bots, algorithms - rust edition

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

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.

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

Experimenting with Algo Trading using Backtrader Python Module. Specifically, statistical arbitrage using cointegration.

Equities Pair Trading/Statistical Arbitrage and Multi-Variable Index Regression

The notebook with the experiments to replicate and enhance the stock clustering proposed by Han(2022) for alogtrading, with KMeans Optimization

On-going project: I will be implementing a combination of pairs trading strategies in attempt to see which type performs best after backtesting. The main ideas involve cointegration, kalman filter, copulas, and machine learning approaches. Since it is a market-neutral strategy, we will analyse the performance on its alpha rather than sharpe ratio.

Built a pairs trading strategy in emerging markets using a rolling Kalman-filter beta and spread half-life, with z-score position sizing, and comprehensive back-testing with liquidity adjustments and transaction cost analysis for enhanced risk management

The goal of this project is to develop a statistical arbitrage strategy for cryptocurrencies using Python

statistic arbitrage strategy research tools

generalized pairs trading and statistical arbitrage in python.