With the rise of artificial intelligence (ai) and machine learning, there has been increasing interest in using ai to make better investing decisions. For investing beginners looking to leverage ai, open source ai investing apps available on github provide a free and customizable option. By reviewing code and experimenting with different models, beginners can gain valuable hands-on experience in ai for finance. This article will introduce 4 of the top open source ai investing apps on github that are suitable for beginners.
FinRL – Implement deep reinforcement learning for automated trading
FinRL (https://github.com/AI4Finance-Foundation/FinRL) is an open source deep reinforcement learning (DRL) library focused on automated stock trading. It provides implementations of state-of-the-art DRL algorithms like DQN, DDPG, PPO that are optimized for trading. The Modularized structure makes it easy to customize different components like reward functions and neural network models. FinRL also includes a complete set of baselines and backtesting analysis to evaluate trading performance. This hands-on framework allows beginners to fully utilize DRL for ai investing app development.
Starter agent – Zero to Hero DRL in Quantitative Finance
The starter agent (https://github.com/AI4Finance-LLC/Starter-Agent-Zero-to-Hero-DRL-in-Quantitative-Finance) demonstrates step-by-step tutorials for DRL development in finance, from environment setup, neural network configuration, DRL algorithm implementation, to backtesting and analysis. The tutorials cover both high frequency trading using limit order book data and long term trading on daily stock prices. By following examples on a single stock and ETF portfolio, beginners can quickly get up and running in applying DRL for ai investing.
Trading Gym – Flexible framework for training trading agents
Trading Gym (https://github.com/AI4Finance-LLC/Trading-Gym) provides a flexible framework for training and evaluating trading agents using reinforcement learning and other ai techniques. It includes configurable environments with different action spaces, reward functions, and data sources. The modular design allows users to easily incorporate custom components like new gym environments and neural network models. Trading Gym helps beginners rapidly prototype and iterate on trading strategies for ai investing apps.
Alphalens – Algorithmic trading strategy analysis
Alphalens (https://github.com/quantopian/alphalens) focuses on analyzing and visualizing algorithmic trading strategies by calculating various performance metrics and generating plots. It integrates seamlessly with Pandas for data wrangling and visualization packages like Plotly for interactive charts. Alphalens enables beginners to conduct comprehensive backtesting analysis when developing ai investing strategies. The detailed performance breakdowns help users diagnose issues and improve strategy profitability.
Open source ai investing apps on github like FinRL, Starter Agent, Trading Gym, Alphalens provide great starting points for beginners to learn applying ai in finance. By building on top of these projects, one can rapidly prototype and validate algorithmic trading strategies.