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Tutorial for Stock Prediction in tf.keras

  • Final update: 2018 Oct
  • All right reserved @ Chanhee Jeong and Jaewook Kang 2018

About

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This repository is to provide a toy stock price prediction in tf.keras. For the dataset, we have used the 5 years (1255 days) S&P500 data, where

  • 1004 days data is used for the model training, and
  • the remaining 251 data is for the model testing.

We consider single layer RNN-like model for the problem. Various options are available to compose the layer from tf.keras:

  • Simple RNN model: tf.keras.layers.SimpleRNN
  • LSTM model: tf.keras.layers.LSTM
  • GRU model: tf.keras.layers.GRU

where the model shape in the training as

Input X: 
- A sequence of the `close value` in past 3-days 
- X: [X_0:X_1003] has its shape as [1004 x 3 ] where the input of each  cell  is X_t = [1 x 3]

Output Y: 
- A prediction of the `close value` of the next day
- Y has its shape as [1004 x 1] where the output of each  cell is Y_t = [1]

Hidden state of each cell H:
- H_t denote the hidden state of each RNN cell which has its shape [64 x 1]

How to Run

Training

python ./tfmodule/trainer.py

Inference

python ./tfmodule/eval.py

Components

./tfmodule/
├── data
│   └──  all_stocks_5yr.csv
├── data_loader.py
├── eval.py
├── model_builder.py
├── model_config.py
├── train_config.py
└── trainer.py

Compiler/Interface Dependencies

  • Tensorflow >=1.9
  • Python2 <= 2.7.12
  • Python3 <= 3.6.0
  • pandas >= 0.23.0
  • numpy >= 1.14.5
  • matplotlib >= 3.0.0

Related Materials

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License

  • Apach License 2.0

Authors information

  • Jaewook Kang Ph.D.

  • Chanhee Jeong M.S.

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An tf-keras example for RNN time prediction

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