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#	model_weight/lstm_test.h5
#	tests/test_lstm_mean.py
Latest commit cebf5f9 Oct 16, 2018




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

Feature importance:

The size of circle indicates its feature importance.

  • model: Random Forest, label: next price delta

  • model: XGBoost, label: next price delta