Quantitative Finance
Showing 18 of 54 projects
A curated list of resources, tools, websites, and books for stock trading and investing research.
A reinforcement learning framework for portfolio management that learns optimal trading strategies through online training.
Code repository for the second edition of Mastering Python for Finance, implementing advanced financial statistical applications using Python.
A comprehensive Rust library for technical analysis, providing common indicators, methods, and an interface for custom indicators.
An open-source algorithmic trading execution engine for deploying and managing multi-language trading bots with real-time monitoring.
A curated, categorized collection of books about the R programming language for data science, statistics, and visualization.
A high-performance, type-safe DataFrame library for the JVM enabling large-scale data analysis with parallel processing capabilities.
A high-performance Rust library for simulating stochastic processes, with applications in quantitative finance, statistical modeling, and synthetic data generation.
A trading environment for reinforcement learning agents, supporting backtesting, live trading, and multiple RL algorithms.
A development environment for managing algorithmic trading operations across a distributed Elixir cluster.
A TensorBoard dashboard for visualizing and comparing Zipline algorithmic trading backtests in real-time.
A Python SDK for building, backtesting, and deploying AI-powered algorithmic trading bots for crypto markets across 100+ exchanges.
A toolset for parallel parameter optimization of QuantConnect Lean trading algorithms using various optimization methods.
An open-source quantitative crypto trading bot framework for backtesting and live trading with multi-coin, multi-timeframe strategies.
Automatically evolves profitable trading strategies using genetic algorithms across 484 factors with walk-forward validation for crypto, A-shares, and US stocks.
A backtesting framework for a simple MACD crossover trading strategy applied to Bitcoin.
Generates random numbers with realistic financial market properties like fat tails and volatility clustering for simulations and research.
A backtesting project comparing bt and Quantopian Zipline frameworks for cryptocurrency trading strategies.
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