This project proposed a machine learning (Logistic Regreesion and SVM) framework to predict price movement in a high frequency environment and build trading strategy based on this prediction.
###The project has three main parts:
#####Part 1: Data processing and visualization
- Data for this project is the limit order book of E-mini S&P500 future. A sample data is in sample_data folder.
- Training set is created at every trade entry with attributes calculate from previous book entry.
- Target value is the directional movement of next trade price.
- 'ggplot' package is used for visualization
#####Part 2: Model training and testing
- Logistic Regression with nearly 78% accuracy
- Support Vector Machines with nearly 87% accuracy
#####Part 3: Iceberg Detection
- Introduce the basic idea of iceberg order detection algorithm
- Found more than 11,000 iceberg orders within one day
MSFE candidate at University of Illinois at Urbana-Champaign
My Resume：[Shuyue Fu](https://github.com/fushuyue/Financial_Computing/raw/master/MyResume/MyResume.pdf)
My Linkedln：[Linkedln](https://www.linkedin.com/in/shuyuefu)
My Email：sfu11@illinois.edu