🤖💹 algorithmic trading strategy built backtested using backtrader and python, optimizing risk-adjusted returns with a bollinger mean-reversion strategy

OpenFintech is a financial analysis library designed for Python developers and financial analysts. It provides powerful tools for conducting both trend following and mean reversion analyses, utilizing financial market data. This project aims to make complex financial algorithms accessible and easy to use.

This repository contains python code to create, backtest and automate intraday-trading algorithms in financial markets using Machine Learning (Regression, Classification) and Statistical (Mean-Reversion, Moving Averages, Momentum) trading strategies

Python algorithm for trading the EUR/USD forex pair using a mean reversion strategy. The algorithm retrieves price data from OANDA's API, calculates the z-score of the closing prices, and executes a trade if the z-score is above a certain threshold (indicating an overbought condition) or below a certain threshold (indicating an oversold condition

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.

A simple mean-reversion based trading algorithm implemented in python

A Python-based algorithmic trading system supporting 4 modular strategies for Mean reversion and Momentum.

Algorithmic trading platform with backtesting engine, FastAPI REST API, and multiple strategy types (momentum, mean reversion, breakout). Built with Python, PostgreSQL, and Redis.

A Python application that retrieves and analyzes cryptocurrency data. It includes trading strategy backtesting with moving averages, momentum, and mean reversion algorithms, perfect for crypto analysis and experimentation.

Participated in Prosperity 3, an algorithmic trading challenge with 12,000+ teams worldwide. Built Python trading bots using mean reversion, EMA, and basket arbitrage strategies. Achieved rank 1271 globally and 223rd in India through adaptive risk management.

QuantCLI is a research-focused Python CLI toolkit for learning algorithmic trading, featuring real-time price fetching via CCXT, three built-in strategies (RSI mean reversion, EMA trend following, and breakout), and a backtesting engine with realistic fees, slippage, Sharpe ratio, and drawdown metrics. It defaults to paper trading with a simulated

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A systematic mean-reversion trading algorithm implemented in Python

A basic mean reversion, pairs trading algorithm implemented with Python.

Python-based algorithmic trading backtester — tests momentum, mean-reversion, and custom strategies against historical market data with performance analytics.

An algorithmic trading project in Python that switches between mean-reversion and trend-following strategies based on ADX regime detection.

A modular algorithmic trading backtesting framework in Python, featuring moving average and mean reversion strategies, parameter optimisation and walk-forward testing.

Python algorithmic trading bot that analyzes live market data using Mean Reversion, Moving Average Crossover, and Momentum strategies and executes trades via the Alpaca API.

A Python-based algorithmic trading system that backtests a mean-reversion strategy, uses an XGBoost model for price prediction, and automates trade logging to Google Sheets and Telegram.

Developed a mean-reversion algorithmic trading strategy with a custom Python backtesting engine. Evaluates performance using Sharpe ratio, drawdown, volatility, and Monte Carlo risk analysis on historical market data.

Part of the Quant 2025 Challenge, this repository explores mean-reversion strategies and beginner-friendly quant trading techniques. Includes Python implementations, backtesting, and detailed explanations of concepts for algorithmic trading enthusiasts.

A professional-grade Algorithmic Trading Bot built in Python. Features multi-coin scanning (BTC, ETH, SOL, BNB), RSI Mean Reversion strategy, real-time Discord alerts, and automated risk management (Stop Loss/Take Profit).

Mean-reversion algorithmic trading EA for MetaTrader 5, based on EWM band strategy. Includes full development history from Python backtest to MT5 Expert Advisor, parameter optimization with Optuna, ML training data, and backtest results.

A collection of quantitative finance and algorithmic trading strategies, including statistical arbitrage, momentum, mean reversion, volatility forecasting, and more. Built with Python, pandas, NumPy, and statsmodels, with a focus on backtesting, risk management, and performance analytics.

A Python tool for learning and backtesting trading strategies using Yahoo Finance data. Implements Moving Average, Mean Reversion, and custom RSI strategies with interactive parameters, visualizations, and performance metrics to help beginners understand algorithmic trading principles.

A Python-based algorithmic trading system that identifies and capitalizes on mean reversion opportunities in equities using z-score, RSI, and fundamental filtering. Includes automated data collection, signal generation, position sizing, and rule-based entry/exit logic.

rypto Auto-Trading Demo – VWAP & Z-Score Strategy This project is a beginner-level automated trading system built in Python. It demonstrates the core logic behind algorithmic trading using **VWAP (Volume Weighted Average Price)** and **Z-Score mean reversion** strategy.

This repository showcases algorithmic trading strategies I’m learning and building. It includes Python code for momentum, mean reversion, and indicator-based models, along with backtesting and performance evaluation. A personal journey into quant trading, open to feedback and improvements.

Implementation of advanced algorithmic trading and market making strategies developed for the IMC Prosperity Trading Challenge 2023, including fair value estimation, Bollinger Bands, RSI, moving averages, and mean reversion for cointegrated pairs. Features empirical distribution analysis and real-world scenario simulation with Python

This repository contains the implementation of a mean reversion trading strategy using historical stock price data. The strategy is developed and backtested using Python and the Backtrader library. This project aims to demonstrate the process of building, testing, and optimizing an algorithmic trading strategy.