Showing 4 open source projects for "research"

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  • 1
    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.
    Downloads: 30 This Week
    Last Update:
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  • 2
    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 structured multi-step risk evaluation process before executing trades. ...
    Downloads: 19 This Week
    Last Update:
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  • 3
    Awesome-Quant

    Awesome-Quant

    A curated list of insanely awesome libraries, packages and resources

    awesome-quant is a curated list (“awesome list”) of libraries, packages, articles, and resources for quantitative finance (“quants”). It includes tools, frameworks, research papers, blogs, datasets, etc. It aims to help people working in algorithmic trading, quant investing, financial engineering, etc., find useful open source or educational resources. Licensed under typical “awesome” list standards.
    Downloads: 0 This Week
    Last Update:
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  • 4
    NautilusTrader

    NautilusTrader

    A high-performance algorithmic trading platform

    ...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 parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment. NautilusTraders design, architecture and implementation philosophy holds software correctness and safety at the highest level, with the aim of supporting Python native, mission-critical, trading system backtesting and live deployment workloads.
    Downloads: 1 This Week
    Last Update:
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