Algorithmic trading strategies for pairs and basket trading in cross-commodity markets

Comparative study between statistical and machine learning based strategies for high frequency trading of assets

This script implements a mean reversion strategy for a given stock. It calculates the z-scores for the stock's price and generates entry and exit signals based on predefined thresholds. The script also performs a backtest on the strategy and visualizes the returns.

Repository for the Trading Team Project based on Mean Reversion for QFin Semester 1 2022. Developed by Jake Lyell

Quick calculation for profit loss of trades.

SwitchGain is a Python-based algorithmic trading project implementing Momentum and Mean Reversion strategies on stock data. It automates signal generation using technical indicators (RSI, Bollinger Bands) and provides performance analytics.

This indicator is a modified version of SteverSteves's original work, enhanced by Erika Barker. It visually represents asset price movements in terms of standard deviations from a Hull Moving Average (HMA), commonly known as a Z-Score.

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.

Mean Reversion Long Daily Strategy for VOO etf

OpenAI analysis of calculated Mean Reversion data for given [STOCK] including related news sentiment analysis

Pair trading strategy integrates multiple components, including technical analysis indicators, machine learning models, and risk management techniques.

Perform ADF-Test (stationarity test) on several forex pairs at once and rank the results from the most mean-reversion tendency to least

My Solutions to Trading Algorithms Course Practical Assignments

A systematic intraday mean reversion strategy framework built for multi-asset execution and backtesting. Implements z-score-based signal generation, position sizing, and portfolio-level risk controls. Includes complete research pipeline, performance reporting, and QuantStats analytics.

Backtesting algorithmic trading strategies on the PHI/WSOL pair (08.17-08.18) using z-score-based mean reversion signals and grid search optimization. Developed by David Yemchuk for Peanut Trade as a demonstration of motivation and commitment.