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

Related tags

Deep Learningpythonmachine-learningtradingfeature-selectionmodel-selectionquanttrading-strategiesinvestmentmarket-makerfeature-engineeringalgorithmic-tradingbacktesting-trading-strategieslimit-order-bo
Overview

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)

Owner
Chang-Shu Chung
Chang-Shu Chung
GitHub Repository
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