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).

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.

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

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.

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

This is the final project of Statistical Arbitrage course and it aims to apply pairs trading in high frequency data to realize auto-trading

The course, authored 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 will apply machine learning techniques, such as backtesting (cross-validation) and parameter regularization (shrinkage).

Replication of "Reinforcement Learning Pair Trading: A Dynamic Scaling Approach" by Yang and Malik (2024). This project implements pair trading with reinforcement learning for cryptocurrency markets, comparing traditional statistical arbitrage with RL-enhanced approaches.

generalized pairs trading and statistical arbitrage in python.

The purpose of this project is to measure how much of the performance of a diversified quantitative investment portfolio is significantly impacted by random market behavior, if at all. If successful, the results of this analysis will lay the groundwork for a broader analysis pertaining to the separation of alpha and beta across the investment portfolio. If the "luck" portion of the portfolio can be measured dynamically (accounting for lags etc) then a hedging tool could potentially eliminate random market risk without eroding portfolio returns in times of erratic market behavior. The methodology is to obtain historical performance data from 11 different trading models (mean reversion, pairs, market making, momentum, statistical arbitrage, etc) that together form a diversified investment portfolio over a particularly volatile trading period. I will explore the data by analyzing the distribution of performance across symbols and across time periods to reveal the structure of the performance data and how it relates to and is impacted by market behavior. I will then model the data to measure how much of the performance is explained by the market and market volatility, its clustering tendencies and its correlation to the predictor variables. Finally, I will interpret the results and reconcile the results with my original hypothesis to determine if it makes sense to continue work to create a hedging instrument for the portfolio.

Statistical Arbitrage trading algorithm for QuantConnect. Main ideas taken from "Pairs Trading Evaluation of profitability and risks on the Swedish stock market" (http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=7370318&fileOId=7370368).

Inspired Copula-Based Trading of Cointegrated Cryptocurrency Pairs by Masood Tadi, Jiří Witzany, I developed a dynamic spread trading strategy using Gumbel Copulas in Python. The purpose was to create a statistical arbitrage strategy which identifies the pairs assets from FX and commodities’ markets, to trade based on their dependence structure.

Statistical arbitrage pairs trading project that scans the Dow Jones Global Titans 50 for cointegrated stock pairs and tests a mean-reversion trading strategy using Engle–Granger cointegration tests.

A quantitative pairs trading strategy using dynamic hedge ratios estimated via Kalman filtering and cointegration techniques. Designed for statistical arbitrage and mean reversion in equities.

Implements statistical arbitrage trading using time series analysis models and a simple pair-wise trading strategy.

This script is a quantitative trading strategy that focuses on statistical arbitrage, particularly mean reversion and copula-based dependency modeling for pairs trading.

A comprehensive statistical arbitrage (pairs trading) bot that downloads stock data from Yahoo Finance using yfinance, finds cointegrated pairs via the Engle–Granger test, trades the spread using z-score entry and exit rules, and backtests the strategy with Backtrader.

Pairs trading is a statistical arbitrage strategy. The strategy is to simultaneously take a long position and a short position on a pair of stocks that are cointegrated in the past but have sufficient spread between them on the day of trading.This is based on the assumption that the spread between them is mean-reverting. The weights for each leg of the pair (hedge ratio) is estimated using a statistical method.

This repo is show Statistical Arbitrage Pairs trading strategy by using optimal pairs and robust system

This repo examines a statistical arbitrage trading strategy (pairs trading) developed using Machine Learning and NLP.

Quantitative Finance Project: Statistical Arbitrage & Pairs Trading Strategy Analyzing 20 Tech Stocks from the S&P 500 using Cointegration, Mean Reversion, and Backtesting.

This project implements a statistical arbitrage (pairs trading) strategy using historical stock price data (from yfinance). It demonstrates knowledge in quantitative modelling, financial data handling, Python programming, and statistical testing.

An end to end python based quantitative pairs trading framework , built to replicate institutional style statistical arbitrage workflows from pair discovery and structural validation to signal calibration, execution logic, and performance visualization.

A high-performance C++17 statistical arbitrage backtesting engine for pairs trading strategies. Processes tick-level market data using Z-score mean reversion signals to identify and execute profitable trading opportunities with sub-microsecond latency.

An autonomous DyDx trading bot built using Python, and deployed on AWS EC2. This bot interacts with the DYDX Layer 2 Ethereum Trading Exchange, exploiting statistical arbitrage in pairs trading with exceptional precision. It operates unsupervised, consistently, and sends real-time trade performance updates via Telegram for continuous user insights.

An exposition of a simple pairs trading strategy on two stocks (Bajaj Finserv and Indian Bank) in the Nifty500, at the one-minute time frequency, in order to demonstrate some of the core ideas of statistical arbitrage strategies.

Using Copulas model to capture non-linear relationship between stock pairs and conduct statistical arbitrage by pairs trading strategies.

Leveraging statistical arbitrage strategy in the China stock market Mathematical Framework: - Correlation Analysis using Pearson correlation coefficient - Hierarchical clustering for stock grouping - Cointegration testing for pair identification - Z-score based mean reversion signals - Monte Carlo simulation for strategy validation

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A basic trading strategy using pairs trading and statistical arbitrage