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

Last update: Jan 07, 2023

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

GitHub Repository

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

Related tags

Overview

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning

Overview

Owner

Chang-Shu Chung

Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation

This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

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