https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy

algorithmic-trading backtesting-trading-strategies feature-engineering feature-selection high-frequency-trading investment limit-order-book machine-learning market-maker market-making market-microstructure model-selection orderbook orderbook-tick-data python quant quantitative-trading trading trading-strategies

Last synced: 7 days ago
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Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

• Host: GitHub
• URL: https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy
• Owner: rorysroes
• Created: 2016-07-21T05:14:14.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-08-27T20:48:46.000Z (about 2 years ago)
• Last Synced: 2024-10-23T01:04:53.159Z (13 days ago)
• Topics: algorithmic-trading, backtesting-trading-strategies, feature-engineering, feature-selection, high-frequency-trading, investment, limit-order-book, machine-learning, market-maker, market-making, market-microstructure, model-selection, orderbook, orderbook-tick-data, python, quant, quantitative-trading, trading, trading-strategies
• Language: Jupyter Notebook
• Homepage:
• Size: 83.6 MB
• Stars: 1,935
• Watchers: 99
• Forks: 664
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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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)