[STUDYPIE] Predicting Financial Time Series using Deep Learning

This is a repository for the [STUDYPIE] Predicting Financial Time Series using Deep Learning

Introduction

• Instructor: Jongho Kim, Quantitative Researcher at NICE Pricing and Information (quantic.jh@gmail.com)
• This session DOES NOT aim to learn automatic trading systems, rather DOES focus on stock / coin price prediction based on deep learning, pursuing the most essential algorithms
• This session is an intermediate level course for those who have basic knowledge (Python, Numpy, Pandas, pip) of Python and machine learning

Goal of this study

• Implement three neural network models from the simple model to advanced models with cryptocurrency data
• Understand the problems with financial time series predictions and advantages/disadvantages of machine learning
• Learn how to implement FNN, CNN, RNN models using Tensorflow Keras API on Google Colaboratory
• Learn which metrics could be important for robustness of time series prediction algorithms

Modules

• This session is desinged with four modules
  • Module 1. Deep Learning Revisiting
  • Module 2. RNN Model for Price Prediction
  • Module 3. CNN Encoder + RNN Model for Price Prediction
  • Module 4. Wavelet Denoising + Auto Encoder Model + RNN Prediction Model for Price Prediction

Datasets

• This session use three datasets:
  • Public dataset: Prices of multiple crypotocurrecies from pythonprogramming.net [Download]
  • Self Crawled dataset: Prices and orderbook data of multiple crypotocurrecies from CoinOne [Downloadable on Request]

Q&A

• General Machine Learning Questions
• Module 1. Deep Learning Revisiting
• Module 2. RNN Model for Price Prediction
• Module 3. CNN Encoder + RNN Model for Price Prediction
• Module 4. Wavelet Denoising + Auto Encoder Model + RNN Prediction Model for Price Prediction

For Citation

@misc{jonghkim,
  author = {Jongho Kim},
  title = {financial-time-series-prediction-v2},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/jonghkim/financial-time-series-prediction-v2}},
}