A Reinforcement Learning (PPO) based Statistical Arbitrage bot for Pairs Trading. Utilizes Engle-Granger cointegration tests and custom Gymnasium environments to optimize for risk-adjusted returns (Sharpe Ratio).

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Statistical arbitrage bot using cointegration-based pairs trading. Screens 2000+ U.S. stock pairs and backtests trades on NXPI–AMAT using z-score signals. ~35% CAGR, Sharpe 1.8, 24% YTD paper returns. Built with Python, yfinance, statsmodels, and matplotlib.

Python-based algorithmic trading bot implementing a pairs trading statistical arbitrage strategy using cointegration, Z-score signals, backtesting, and paper trading.

A Statistical Arbitrage trading bot that leverages historical cointegration and correlation data between asset pairs to execute mean-reversion strategies.

Statistical Arbitrage Trading Bot is a Python-based quant trading system that applies pairs trading using cointegration, mean reversion, and Z-score signals to generate market-neutral trading strategies.

Python project using Engle-Granger cointegration and 1:1 beta hedging to identify stable crypto pairs. Includes rolling cointegration scanning, a simple spread backtester, and an async WebSocket trading bot to automate trades on detected pairs