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rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Jupyter NotebookProviding 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
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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)
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