Showing 7 open source projects for "linux ai"

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  • 1
    AIQuant

    AIQuant

    AI-powered platform for quantitative trading

    ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models,...
    Downloads: 4 This Week
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  • 2
    Qbot

    Qbot

    AI-powered Quantitative Investment Research Platform

    Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies —...
    Downloads: 38 This Week
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  • 3
    Kalshi Trading Bot CLI

    Kalshi Trading Bot CLI

    AI-native CLI for trading Kalshi prediction markets

    Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a...
    Downloads: 3 This Week
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  • 4
    TradingGoose Studio

    TradingGoose Studio

    Technical analysis + LLM powered trading workflows

    TradingGoose Studio is an open-source AI workflow platform designed to enable advanced financial trading analysis and automation through a visual, modular interface powered by large language models. It combines traditional technical analysis with modern AI-driven decision-making by allowing users to build workflows where multiple specialized agents collaborate to interpret market signals and execute actions. The platform supports end-to-end trading pipelines, starting from ingesting...
    Downloads: 0 This Week
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    NautilusTrader

    NautilusTrader

    A high-performance algorithmic trading platform

    NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the...
    Downloads: 23 This Week
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  • 6
    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
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  • 7
    A suite of libraries and applications using genetic algorithms and AI for financial analysis and simulation. Currently the focus is to route FIX messages to an exchange simulator and use genetic algorithms to explore algorithmic trading strategies.
    Downloads: 0 This Week
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