This project aims at predicting stock price based on high frequency stock data. There is a big difference between high frequency data and others, thus certain preprocessing methods are necessary in mining useful information. LSTM is again proved effective in this problem. As a contrast, we also tested some other classical machine learning model such as XGBoost and random forest.
Prediction of next tick's price:
We use LSTM to predict stock price, mid-price of next tick. Random Forest and XGBoost are used to classify the following price trend.
- label: next price delta
- label: next mid price delta
Prediction of future mean price:
- label: 2.5 min mean price delta
The size of circle indicates its feature importance.
- model: Random Forest, label: next price delta
- model: XGBoost, label: next price delta