ideaplexus / stock-prediction

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

Stock Prediction with Recurrent Neural Network

Stock price prediction with RNN. The data we used is from the Chinese stock.

Requirements

• Python 3.5
• TuShare 0.7.4
• Pandas 0.19.2
• Keras 1.2.2
• Numpy 1.12.0
• scikit-learn 0.18.1
• TensorFlow 1.0 (GPU version recommended)

I personally recommend you to use Anaconda to build your virtual environment. And the program probably cost a significant time if you are not using the GPU version Tensorflow.

Get Data

You can run fetch_data.py to get a piece of test data. Without changing the script, you can get two seperated csv file named:

• 000002-from-1995-01-01.csv =====> Contains general data for stock 000002 from 1995-01-01 to today.
• 000002-3-year.csv =====> Contains candlestick chart data for stock 000002 (万科A) for the most recent 3 years.

You are expected to see results look like (the first DataFrame contains general data where the the second contains detailed candlestick chart data):

$ python3 fetch_data.py
[Getting data:]#########################################################################################
Saving DataFrame:
     open   high    low      volume        amount  close
0  20.64  20.64  20.37  16362363.0  3.350027e+08  20.56
1  20.92  20.92  20.60  21850597.0  4.520071e+08  20.64
2  21.00  21.15  20.72  26910139.0  5.628396e+08  20.94
3  20.70  21.57  20.70  64585536.0  1.363421e+09  21.02
4  20.60  20.70  20.20  45886018.0  9.382043e+08  20.70

Saving DataFrame:
     open   high    low     volume  price_change  p_change     ma5    ma10  \
0  20.64  20.64  20.37  163623.62         -0.08     -0.39  20.772  20.721
1  20.92  20.92  20.60  218505.95         -0.30     -1.43  20.780  20.718
2  21.00  21.15  20.72  269101.41         -0.08     -0.38  20.812  20.755
3  20.70  21.57  20.70  645855.38          0.32      1.55  20.782  20.788
4  20.60  20.70  20.20  458860.16          0.10      0.48  20.694  20.806

     ma20      v_ma5     v_ma10     v_ma20  close
0  20.954  351189.30  388345.91  394078.37  20.56
1  20.990  373384.46  403747.59  411728.38  20.64
2  21.022  392464.55  405000.55  426124.42  20.94
3  21.054  445386.85  403945.59  473166.37  21.02
4  21.038  486615.13  378825.52  461835.35  20.70

Demo

Reference

• Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
• Understanding LSTM Networks by Christopher Olah