lastexport

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

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},
}