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Stocks

machine learning web app game where the user competes against the AI in picking stocks

Stocks

machine learning web app game where the user competes against the AI in picking stocks

Github Stars Github Stars: 134
Last Commit Last Commit: Jan 12, 2019 -
First Commit Created: Aug 27, 2024 -
Stocks screenshot

Overview

StockIT is an innovative machine learning web app that transforms the stock trading experience into a competitive game against artificial intelligence. Users can engage in a unique challenge where they buy and sell randomly selected stocks while the price trends span a full year. This engaging concept not only entertains but also educates users on stock trading dynamics and the influence of historical trends on stock prices.

The app is crafted with a blend of advanced technologies, making it accessible on both desktop and mobile platforms. With an intuitive interface powered by D3 and React, it not only updates stock data in real time but also maintains a leaderboard to track user performance against the AI.

Features

  • Competitive Gameplay: Engage in thrilling matches against AI, testing your stock trading skills in real time.
  • Historical Stock Data: Random stock selections combined with price trending from a randomly chosen past date provide a unique trading challenge.
  • Machine Learning Model: Built using Pandas and Scikit Learn, the app employs a linear regression model for stock price prediction, enhancing the strategic aspect of trading decisions.
  • Dynamic Visualizations: D3 combined with React displays real-time stock trends and updates to the leaderboard, ensuring users stay informed and engaged.
  • Cross-Platform Compatibility: Optimized for both desktop and mobile devices, allowing users to trade and compete anytime, anywhere.
  • Simple Setup: The app provides an easy-to-follow setup guide for creating a local development server, making it accessible for developers wanting to explore its inner workings.
  • Open Source Project: As an MIT licensed project, it’s welcoming for contributions from other developers passionate about machine learning and web app development.