With the rise of artificial intelligence, more and more powerful open source AI tools are emerging to help investors make better decisions. As a beginner, it can be difficult to find the best free resources to get started with AI-powered investing. GitHub has become a treasure trove for open source finance and trading tools based on cutting-edge machine learning algorithms. By leveraging these freely available codes and frameworks, retail investors can backtest rule-based strategies, build neural network models for stock prediction, and even train deep reinforcement learning agents for automated trading. This article explores the most beginner-friendly AI investing tools on GitHub to empower individual investors with data-driven insights and superior analytics.
FinRL automates trading strategies with deep reinforcement learning
FinRL is an open source Python library designed to help developers quickly build and optimize stock trading strategies with deep reinforcement learning. It includes implementations of latest DRL algorithms like DQN, DDPG, PPO for financial markets and comes fully equipped with backtesting capabilities. With easy customization options, even coding novices can train intelligent agents that beat conventional baselines. The interactive tutorials make this one of the best entry points to hands-on AI for trading.
ElegantRL speeds up research with flexible frameworks
While FinRL focuses on trading, ElegantRL offers high performance deep reinforcement learning primitives that can be adapted to any finance or investing use case. Modular design makes it simple to experiment with the built-in DRL optimizers like SAC, DQN, PPO. The efficiency comes from optimal GPU utilization and better hyperparameter tuning. With just a few lines of code, the elegant frameworks can run large scale training jobs seamlessly on the cloud.
VectorBT streamlines backtesting and analytics
VectorBT eliminates the hardest part for aspiring algo traders – backtesting strategy ideas on historical data. Without writing any custom code, this Python library lets you backtest rule-based strategies by composing vectors of indicators. Analyze performance matrices, visualize equity curves, tweak parameters – all conveniently through Jupyter Notebook. Built on NumPy and Numba, VectorBT can efficiently run analytics on millions of datapoints.
Streamlit builds interactive web apps sans coding
While libraries like VectorBT are great for exploration in Python, Streamlit makes it trivial to create web apps for interactive strategy analysis without writing any JavaScript. Just with Python scripts, you can build beautiful GUIs and dashboards that give insights into trading signals, portfolio risks etc. Open source templates are available to quickly develop apps for analyzing order flows, visualizing price charts or even to deploy algorithmic trading systems with minimum coding.
GitHub offers powerful free AI tools for analyzing financial markets and automating quantitative trading strategies. Frameworks like FinRL, ElegantRL, VectorBT and apps from Streamlit provide ample ammunition for beginners to get hands-on with data-driven investing.