## Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning * Framework to capture the dynamics of high-frequency limit order books. <img src="./Graph/pipline.png" width="650"> #### 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 <img src="./Graph/Price_B1A1.png" width="650"> * Depth Ratio <img src="./Graph/depth.png" width="650"> [Note] : [Feature_Selection] (Feature_Selection) * Learning Model Trainer * RandomForestClassifier * ExtraTreesClassifier * AdaBoostClassifier * GradientBoostingClassifier * SVM * Use best model to predict next 10 seconds <img src="./Graph/CV_Best_Model.png" width="650"> * Prediction outcome <img src="./Graph/prediction.png" width="650"> * Profit & Loss <img src="./Graph/P_L.png" width="650"> [Note] : [Model_Selection] (Model_Selection)