Showing 4 open source projects for "python framework"

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  • 1
    Jesse

    Jesse

    An advanced crypto trading bot written in Python

    Jesse is an open-source AI agent framework designed to help developers build and orchestrate intelligent workflows that combine large language models (LLMs) with external tools, automation logic, and real-world actions. It acts as an agent manager where you can define tasks, contexts, and tool integrations so that AI reasoning is reliably connected to deterministic procedures like API calls, data retrieval, and task execution. Jesse emphasizes structured outputs, safety boundaries, and...
    Downloads: 2 This Week
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  • 2
    JesseAi

    JesseAi

    Advanced AI-Powered Python Crypto Trading Bot 2026 - Free Backtesting

    Jesse Bot: Advanced AI-Powered Python Best Crypto Trading Bot & Framework (2026) Jesse Bot — free open-source Python crypto trading bot & robust framework for cryptocurrency markets. Build, backtest, AI-optimize, and execute precise automated strategies with zero look-ahead bias. Use JesseGPT — built-in AI assistant — to write, debug, and refine strategies effortlessly, even as a beginner.
    Downloads: 0 This Week
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  • 3
    All-in-one-Ton-Trading-Bot
    This project provides a framework for scanning and executing various trading strategies on the TON blockchain ecosystem. It retrieves market data from multiple decentralized exchanges (DEXs) and applies a range of trading strategies to identify potential opportunities. This open-source, AI-powered bot is a versatile tool designed to run seamlessly on both Windows and macOS platforms.
    Downloads: 4 This Week
    Last Update:
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  • 4
    Catalyst

    Catalyst

    An Algorithmic Trading Library for Crypto-Assets in Python

    Catalyst is an algorithmic trading library for crypto-assets written in Python, originally developed to let quants and developers design, backtest, and deploy trading strategies in a unified environment. It builds on top of Zipline, extending that ecosystem to support crypto exchanges and high-resolution historical data (daily and minute bars). Users can express strategies in Python, run backtests against historical price data, and analyze performance through built-in metrics and analytics to evaluate profitability, risk, and behavior under different market conditions. ...
    Downloads: 0 This Week
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