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

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

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

Design your own Trading Strategy

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

A quantitative trading strategy backtester with an interactive dashboard. Enables users to implement, test, and visualise trading strategies using historical market data, featuring customisable parameters and key performance metrics. Developed with Python and Polars.

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

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.