Wall Street Bots: Building an Automatic Stock Trading Platform based on Artificial Intelligence From Scratch

1. Background

Trading stocks using an automatic stock trading platform is nothing new. In fact, banks, hedge funds, and trading firms have been using similar algorithmic trading methods for ages. Ever since the advent of digitalized market order flow, the idea of algorithmic trading has been becoming more and more relevant. Since these trading entities must constantly execute large quantities of orders accurately, it seems far more reasonable and efficient to leave it to a machine algorithm as opposed to humans. However, when algorithmic trading is used by these mega-corporations in practice, it creates many potential issues. For example, the 1987 stock market crash and the 2010 flash crash are widely speculated to be caused by large-scale orders placed by algorithmic trading machines. In addition, these machine learning algorithms are often not as accessible to the public. If the consumer chooses to put their money in one of these banks or hedge funds, a portion of their profit would often be taken away from them. Moreover, the investor would often not have control over what kind of trading strategy the algorithm employs. One might ask, “why would anyone choose their own stock trading strategy when they can just leave it to the Wall Street analysts?” The answer to this question is simple. As more and more regular people enter the stock market in the post-Covid-19 era, retail investors now have more power to influence the market than ever before. The influx of investors, and the tension between global powers, compounded with the high growth of tech companies during the pandemic, have created one of the most volatile markets in the past 100 years. Because of this, we believe that an individualized, open-source, automatic trading platform like WallStreetBots could potentially be another tool for common investors and traders to exert their influence in the market and generate profit. In events like the GME short squeeze, these investors and traders have already proved they can punish bad trading decisions made by institutional trading firms. With a platform like WallStreetBots, these traders and investors will have the same tool that institutional firms use to facilitate trading while also making it more consistent and accurate.

2. The Platform

2.1. Alpaca

Top-level file tree for the Wall Street Bots web app and trading model deployment.
Trading strategy pipeline structure for the Wall Street Bots.

3. Data Collection

Data is an essential part of any analysis task. The Wall Street Bots project collects market data, including stock prices, indices prices, stock news, investor comments, and fundamentals from a variety of sources. These sources and methods used are listed below.

4. The Strategies

4.1. Stock Closing Price Prediction with NLP

Train loss and test loss for trend prediction MLP.
Train and Test Loss (left) and Ground Truth and Predicted Price (Right) for LSTM Distilled.
Flow chart for models tested.
Hidden Markov model structure.
  • Z_(1) …, Z_(t+1) represents the hidden state, which is a representation of hidden stock market conditions.
  • Y_(1), …, Y_(t) represents the current day’s closing price on a minute basis, and Y_(t+1) represents the next day’s opening price.
  • Arrow represents dependency.
Opening price prediction by HMM on MSFT from 2019 to 2021.
Example graph for Monte Carlo analysis from [5]

5. Demonstration

This section demonstrates all components in Wall Street Bots working together as a whole. It is also a step-by-step guide on how to use the web app to try out the strategies.

  1. First, you will need to create a free account at https://alpaca.markets/
  2. After you create your free account, navigate to the paper trade dashboard at https://app.alpaca.markets/paper/dashboard/overview
  3. Beside Your API Keys, click the View button and then click Regenerate Key.
  4. Copy the Key ID and Secret Key.
  5. Now navigate to http://wallstreetbots.org/. Follow the prompt and create an account. Login in and you will be directed to the dashboard page.
  6. Paste your Key ID and Secret Key in the Alpaca ID and Alpaca Key fields respectively, then click UPDATE CREDENTIAL. (Note: by doing this step, you agree to share your Alpaca API credential to Wall Street Bots database)
  7. You are all set! Now you can place orders, view stock information/portfolio history, and build your own portfolio on the Position page.

6. Closing Remarks

In conclusion, predicting stock movement is hard, and building a platform that integrates various models and strategies and executes orders on behalf of the users is even harder. In this project, we went through all the steps to build a “mini hedgefund” — from selecting and verifying strategies to software development and deployment. We hope this article inspires you to do something similar or even consider contributing to this open source project. Wall Street Bots is a continuous effort and new features and strategies will constantly be developed in the future. Stay tuned!

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University of Toronto Machine Intelligence Team

University of Toronto Machine Intelligence Team

UTMIST’s Technical Writing Team produces articles on the topics within machine learning for beginner to advanced level audiences. https://utmist.gitlab.io/team/