Quantitative analysis, strategies and backtests

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

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.

A curated list of awesome algorithmic trading frameworks, libraries, software and resources

📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

This project involves using a combination of statistics along with financial thoery to demonstrate a popular trading strategy used in equity markets: Pairs Trading.

A stock backtesting engine written in Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model

We tested 3 approaches for Pair Trading: distance, cointegration and reinforcement learning approach.

High-frequency statistical arbitrage

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

😲🤑Method for Investors and Traders to make Buying and Selling Decisions. 😄Fundamental hare Market Analysis is about using Real data to evaluate a Stock's Value📊 📈 📉

Pair Trading View - .NET application for visual analysis of synthetic financial instruments based on statistical models.

Find trading pairs with Machine Learning

stock-pairs-trading is a python library for backtest with stock pairs trading using kalman filter on Python 3.8 and above.

Design your own Trading Strategy

A pairs trade is a market neutral trading strategy enabling traders to profit from virtually any market conditions. This strategy is categorized as a statistical arbitrage and convergence trading strategy.

Pairs Trading with Alpaca - created on behalf of AlgoTrading101.com for alpaca.markets/learn

RESTful API for trading stocks (single or pairs), deployed on Heroku

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