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rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Jupyter Notebook

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

high-frequency-tradinglimit-order-bookpythonmachine-learningfeature-selectionfeature-engineeringbacktesting-trading-strategiesmodel-selectiontrading-strategiesquantitative-tradingalgorithmic-tradinginvestment
스타 성장
스타
2.3k
포크
696
주간 성장
이슈
5
1k2k
2016년 12월2020년 2월2023년 5월2026년 7월
README

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning

  • Framework to capture the dynamics of high-frequency limit order books.

Overview

In this project I used machine learning methods to capture the high-frequency limit order book dynamics and simple trading strategy to get the P&L outcomes.

  • Feature Extractor

    • Rise Ratio

    • Depth Ratio

      [Note] : [Feature_Selection] (Feature_Selection)

  • Learning Model Trainer

    • RandomForestClassifier
    • ExtraTreesClassifier
    • AdaBoostClassifier
    • GradientBoostingClassifier
    • SVM
  • Use best model to predict next 10 seconds

  • Prediction outcome

  • Profit & Loss

    [Note] : [Model_Selection] (Model_Selection)

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