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

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.

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

Deep Learning Statistical Arbitrage

High-frequency statistical arbitrage

Pairs Trading using Statistical Arbitrage

statistical-arbitrage-for-sports-betting

QuantInsti EPAT: Final Project on Statistical Arbitrage

High Frequency Pairs Trading Based on Statistical Arbitrage (Python) :moneybag:

Proprietary trading solution for high-frequency trading (HFT) and statistical arbitrage algorithms

A bot coded for an algorithmic trading competition using market making, statistical arbitrage, and delta and vega hedging

A statistical arbitrage strategy on treasury futures using mean-reversion property and meanwhile insensitive to the yield change

This repository stores the implementation of the paper "DETECTING DATA-DRIVEN ROBUST STATISTICAL ARBITRAGE STRATEGIES WITH DEEP NEURAL NETWORKS"

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

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

A framework of statistical arbitrage mining in performance-driven display advertising.

This repository includes an introduction to statistical arbitrage pairs trading. Specifically, I discuss some of the research methods required in order to find a successful pair as well as the code implementation for a backtest.

Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM

Statistical arbitrage simulation, modeling and backtesting with Python.

This course, taught by Prof.Jerzy in NYU, applies the R programming language to momentum trading, statistical arbitrage (pairs trading), and other active portfolio management strategies. The course implements volatility and price forecasting models, asset pricing and factor models, and portfolio optimization. The course applies machine learning techniques, such as backtesting (cross-validation) and parameter regularization (shrinkage).

No description available

This project implements an advanced pairs trading strategy using statistical arbitrage techniques. It leverages Bayesian optimization to fine-tune Kappa and Half-life parameters, enhancing the mean-reversion trading approach. The system includes comprehensive backtesting, risk management, and performance analysis tools.

Using Particle Markov Chain Monte Carlo

Implementation for "Statistical arbitrage in the US equities market" by Marco Avellaneda and Jeong-hyun Lee

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.

Web GUI for backtesting pair trading statistical arbitrage portfolio strategies

quantitative trading strategies including VIX Calculator, Pattern Recognition, Monte Carlo, Heikin-Ashi, Pair Trading

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

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