strategy-development

There are 10 repositories under strategy-development topic.

  • dsinyakov/quant

    Codera Quant is a Java framework for algorithmic trading strategies development, execution and backtesting via Interactive Brokers TWS API or other brokers API

    Language:Java17020158

  • dysonance/Strategems.jl

    Quantitative systematic trading strategy development and backtesting in Julia

    Language:Julia162242138

  • jialuechen/openlpa

    Liquidity Provision Analytics Python Library Beyond TCA

    Language:Python10214

  • GameRocket/RTS

    RTS - real-time strategy sample game project. Here are all the necessary scripts and prepared settings for controlling the camera, selecting and destroying objects, units movements, building placement and much more.

    Language:C#7001

  • midassystems/midastrader

    A robust backtesting and live trading engine designed for seamless strategy development and deployment. It supports user-defined strategies, multi-threaded execution, and integrations with brokers and data sources.

    Language:Python3101

  • silvia-maier/causable

    Causable offers training, sparring, and strategy consulting for executive boards and leaders

    Language:HTML3101

  • emskiphoto/Alpaca-Strategy-Development

    Query and process Alpaca OHLCV data. Add features, identify principal components and develop trade algorithm

    Language:Jupyter Notebook1111

  • gokulghate/telcom-churn-analysis-using-SVM-algorithm

    With the enormous increase in the number of customers using telephone services, the marketing division for a telcom company wants to attract more new customers and avoid contract termination from existing customers. This churn prediction model would be able to provide clarity to the telcom company on how well it is retaining its existing customers and understand what are the underlying reasons that are causing existing customers to terminate their contract (high churn rate).

    Language:Jupyter Notebook0100

  • Peppershaker/turning_around_polish_grocer

    🍇🍉🦞Revenue optimization through recommender system. Recommended trategic reposition into a specialty retailer .🥬🥦🌶️

    Language:Jupyter Notebook0000

  • liz-song/kpoms-hyundai-case-competition

    An award-winning solution proposal for optimizing vertiport site selection in Urban Air Mobility (UAM) using multi-source data analysis and platform logic design. Recognized at the 9th Hyundai Motors University Case Competition.