Showing 4 open source projects for "static code analysis"

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  • 1
    QuantDinger

    QuantDinger

    AI-driven, local-first quantitative trading platform for research

    ...QuantDinger also incorporates optional AI features via external APIs, assisting in tasks like strategy ideation or interpreting market indicators, but strategy logic remains inspectable and transparent in code.
    Downloads: 3 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 the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. ...
    Downloads: 0 This Week
    Last Update:
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  • 3

    TA-Lib.git: Technical Analysis Library

    Mirror of the TA-Lib project using a Git repository

    This project is intended to provide Git access to the code of the original project, TA-Lib, which uses Subversion. It is intended for system integrators wishing to use TA-Lib in their Git-managed project through Git submodules or subtrees. No actual development is being done here; all development happens in the original project.
    Downloads: 0 This Week
    Last Update:
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  • 4
    abu

    abu

    Abu quantitative trading system (stocks, options, futures, bitcoin)

    Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.
    Downloads: 0 This Week
    Last Update:
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