# Going-Deeper-with-Convolutional-Neural-Network-for-Stock-Market-Prediction

**Repository Path**: MyAnonymousSubmission/Going-Deeper-with-Convolutional-Neural-Network-for-Stock-Market-Prediction

## Basic Information

- **Project Name**: Going-Deeper-with-Convolutional-Neural-Network-for-Stock-Market-Prediction
- **Description**: stock prediction
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2022-11-09
- **Last Updated**: 2024-10-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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},
}
```