Showing 3 open source projects for "artificial intelligence algorithm"

View related business solutions
  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    The database for AI-powered applications.

    MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
    Start Free
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • 1
    PyBroker

    PyBroker

    Algorithmic Trading in Python with Machine Learning

    Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    AnyTrading

    AnyTrading

    The most simple, flexible, and comprehensive OpenAI Gym trading

    gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    ML for Trading

    ML for Trading

    Code for machine learning for algorithmic trading, 2nd edition

    On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms,...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next