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2 repositorios

Awesome GitHub RepositoriesStrategy State Persistence

Saves and restores the full trading strategy state including positions, orders, and cash balances across sessions.

Distinct from Execution State Persistence: Distinct from Execution State Persistence: focuses on saving and resuming algorithmic trading strategy state rather than database query execution checkpoints.

Explore 2 awesome GitHub repositories matching data & databases · Strategy State Persistence. Refine with filters or upvote what's useful.

Awesome Strategy State Persistence GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • ricequant/rqalphaAvatar de ricequant

    ricequant/rqalpha

    6,166Ver en GitHub↗

    RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

    Saves and restores the full strategy state so execution can be paused and resumed across sessions.

    Pythonbacktestfinancefutures
    Ver en GitHub↗6,166
  • freqtrade/freqtrade-strategiesAvatar de freqtrade

    freqtrade/freqtrade-strategies

    4,861Ver en GitHub↗

    This is a library of cryptocurrency trading algorithms and technical analysis strategies designed for use with the Freqtrade trading bot. The project provides a collection of pre-defined rules and mathematical indicators used to automate the buying and selling of digital assets. The repository focuses on algorithmic trading strategies and bot-driven asset management to remove manual execution from cryptocurrency trades. It enables quantitative trading analysis by allowing the development and testing of rule-based logic against historical market data. The system utilizes class-based strategy

    Saves and restores the full trading strategy state including positions across sessions to ensure continuous trade management.

    Pythonbitcoincryptocurrencyfreqtrade-strategies
    Ver en GitHub↗4,861
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