Showing 3 open source projects for "python accounting source code"

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    Employees get more done with Rippling

    Streamline your business with an all-in-one platform for HR, IT, payroll, and spend management.

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    See what everyone is allocated to. Projects, clients, meetings - all in one tool.

    The fast, simple way to schedule people, equipment and other resources online.

    Designed to replace clunky, old scheduling spreadsheets, Resource Guru helps managers get organized fast. The platform covers resource planning, resource scheduling, resource management, staff leave management, reporting, and more.
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  • 1
    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. ...
    Downloads: 33 This Week
    Last Update:
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  • 2
    Optopsy

    Optopsy

    A nimble options backtesting library for Python

    Optopsy is a Python-based, nimble backtesting and statistics library focused on evaluating options trading strategies like calls, puts, straddles, spreads, and more, using pandas-driven analysis. The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads...
    Downloads: 5 This Week
    Last Update:
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  • 3
    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: 3 This Week
    Last Update:
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