With the rise of artificial intelligence and open source software, there are now many free investing bots available on platforms like GitHub that retail investors can use to automate parts of their investing process. These open source bots provide functionality like stock screening, portfolio rebalancing, trade execution, data analysis and more. By leveraging these free resources, individual investors can gain efficiencies and insights previously only accessible to hedge funds and other institutional investors. This article will introduce some of the most popular free investing bots on GitHub, explain their key capabilities, and provide guidance on how an average investor can start using them.
Types of free investing bots available on GitHub
There are a wide variety of free, open source investing bots hosted on GitHub that retail investors can leverage. Here are some of the most popular categories:
– Screening bots – These bots automate the process of scanning markets for stocks meeting certain criteria. They can screen based on fundamentals, technicals, news sentiment and more. Examples include stock-screener-bot and formula-stocks-screener.
– Algorithmic trading bots – These bots execute automated trades programmatically based on predefined strategies and signals. Some popular algorithmic trading bots are freqtrade, kelp and zenbot.
– Portfolio bots – Portfolio bots help automate portfolio management tasks like rebalancing and tax-loss harvesting. Examples are dedalus and pythingtrader.
– Data and analytics bots – Bots like stocksight and stonks provide automated data collection, financial modeling and analysis capabilities.
– Chatbots – Chatbots like stockybot allow users to query information and get alerts via natural language conversations.
Key capabilities provided by free GitHub investing bots
Here are some of the most useful capabilities provided by free open source investing bots on GitHub:
– Automated screening – Bots can automatically filter through thousands of stocks to find candidates matching custom criteria.
– Backtesting – Algorithmic trading bots support backtesting strategies against historical data to validate performance.
– Paper trading – Bots allow simulated paper trading to test strategies before risking real capital.
– Portfolio rebalancing – Portfolio bots can periodically rebalance portfolios to target allocations.
– Tax-loss harvesting – The bots can automate selling losers to generate tax losses to offset gains.
– Sentiment analysis – Some bots perform news and social media scanning for sentiment analysis.
– Technical analysis – Algorithmic trading bots have libraries of technical analysis indicators for signaling trades.
– Reporting – Bots can generate custom reports, charts and visualizations for analysis.
Steps for investors to start using free GitHub investing bots
Here is a general process investors can follow to start taking advantage of free investing bots on GitHub:
– Identify need – Assess what investing pain points could be addressed via automation.
– Find relevant bots – Search GitHub for bots meeting your needs, evaluate alternatives.
– Setup local environment – Install required software like Python, Git, IDEs, datasets.
– Run bot locally – Clone the bot repo, configure settings, run basic tests.
– Customize and extend – Modify bot to suit your specific requirements.
– Backtest strategies – Run backtests to validate strategy results before going live.
– Start small – Run bot on a small portion of your portfolio to build trust.
– Monitor performance – Keep supervising bot after deployment, gather feedback.
– Maintain and update – Manage dependencies, install updates, enhance with new features.
Free open source investing bots on GitHub provide retail investors powerful automation capabilities previously only accessible to institutions. With the right search and evaluation tactics, motivation to learn, and gradual deployment approach, average investors can start realizing major benefits from these AI-driven tools.