Scalable, event-driven, deep-learning-friendly backtesting library

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Markowitzify will implement a variety of portfolio and stock/cryptocurrency analysis methods to optimize portfolios or trading strategies. The two primary classes are "portfolio" and "stonks."

高性能并行、事件驱动量化回测框架 high performance backtest，factor investing, portfiolio analysis

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

freqtrade RL trading strategies for freqtrade

An advanced platform for quantitative trading strategies, including AI-driven price prediction models and user management systems. Emulating institutional-grade practices like Citadel, it facilitates the development, training, and deployment of machine learning models for precise market forecasting.

My thesis 🏅

The ModFin project aims to provide users with the necessary tools for modeling and analyzing individual assets and portfolios.

A Quantity calculator which suggests you best position size following your Risk Management.

High Quality Momentum (HQM) stock scanner - Web app for identifying consistent momentum stocks across multiple timeframes using quantitative analysis

quantitive trading platform

AI Trading Bot leveraging advanced machine learning, multi-factor regime detection, and adaptive risk management. Includes feature engineering, backtesting, and interactive dashboards for quantitative trading.

多策略投资组合优化框架

Base indicators and forecasting models for statistics and quantitive analysis

W-curve & M-curve pattern scanner + backtester for all 2,600+ NSE-listed stocks. Built in Python.

Quantitative factor research study — momentum, low volatility, and reversal signals backtested on S&P 500 constituents

High-performance TensorFlow library for quantitative finance.

Options Pricing Project

Quant Charts API reference and example scripts: indicators and strategies in Python (OHLC) and Rust (TBBO) for the quantchartsllc.com backtester. Copy-ready .py/.rs files plus llms.txt machine context.