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

starsStars 662
forksForks 307
watchersWatchers 662
current-versionCurrent version
total-releasesTotal releases 0
open_issues_countOpen issues 3
dateFirst release -
dateLatest release -
updateLast update 2020-12-23

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning

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


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)