Going Deeper with Convolutional Neural Network for Stock Market Prediction
Repository for Going Deeper with Convolutional Neural Network for Stock Market Prediction
Introduction
Predict the stock market price will go up or not in the near future.
Data Collection
- Using Yahoo! Finance for time series data source
- 50 Taiwan Companies from 0050.TW index.
- Top 10 Indonesia Stock exchange companies.
Methodology
- Using candlestick chart for input model
- DeepCNN
- ResNet 50
- VGG16
- VGG19
- Randomforest
- KNN
Usage
Prepare Environment
Recommended using virtual environment
python3 -m venv .env
Running on Python3.5
pip install -U -r requirements.txt
Prepare Dataset
- Convert OHLCV stock market data to Candlestickchart
python run_binary_preprocessing.py <ticker> <tradingdays> <windows>
example
python run_binary_preprocessing.py 2880.TW 20 50
- Generate dataset
python generatedata.py <pathdir> <origindir> <destinationdir>
example
python generatedata.py dataset 20_50/2880.TW dataset_2880TW_20_50
- Remove alpha channel
cd /dataset/dataset_2880TW_20_50
find . -name "*.png" -exec convert "{}" -alpha off "{}" \;
Training
- DeepCNN
python myDeepCNN.py -i <datasetdir> -e <numberofepoch> -d <dimensionsize> -b <batchsize> -o <outputresultreport>
example
python myDeepCNN.py -i dataset/dataset_2880TW_20_50 -e 50 -d 50 -b 8 -o outputresult.txt
Performance Evaluation
- Accuracy
- Specitivity
- Sensitivity
- MCC
- F1
Citation
@misc{1903.12258,
Author = {Rosdyana Mangir Irawan Kusuma and Trang-Thi Ho and Wei-Chun Kao and Yu-Yen Ou and Kai-Lung Hua},
Title = {Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market},
Year = {2019},
Eprint = {arXiv:1903.12258},
}