Showing 3 open source projects for "artificial intelligence python"

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  • Grafana: The open and composable observability platform Icon
    Grafana: The open and composable observability platform

    Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

    Grafana is the open source analytics & monitoring solution for every database.
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  • Easy-to-use Business Software for the Waste Management Software Industry Icon
    Easy-to-use Business Software for the Waste Management Software Industry

    Increase efficiency, expedite accounts receivables, optimize routes, acquire new customers, & more!

    DOP Software’s mission is to streamline waste and recycling business’ processes by providing them with dynamic, comprehensive software and services that increase productivity and quality of performance.
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  • 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: 0 This Week
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  • 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
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  • 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: 2 This Week
    Last Update:
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