stock-movement-prediction

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stock-movement-prediction

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Prediction of stock market using high-frequency data

stock-movement-prediction 최신버젼 다운로드

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# Machine Learning Engineer Nanodegree
# Capstone Project
## Project: Prediction of Stock Moving Direction

### Install

This project requires **Python 3.6** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)
- [Pandas](http://pandas.pydata.org)
- [matplotlib](http://matplotlib.org/)
- [pyti](https://github.com/kylejusticemagnuson/pyti)
- [brew](http://brew.readthedocs.io/en/latest/)
- [scikit-learn](http://scikit-learn.org/stable/)
- [imbalanced-learn](http://contrib.scikit-learn.org/imbalanced-learn/stable/)
- [keras](https://keras.io)

You will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html)

We recommend installing [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains most of the necessary libraries and software for this project.

### Code

The code is provided in the `Notebooks` folder. The CSV files and `Clfs` folder (pool of trained classifiers) are not included due to the size limitation.

### Run

In a terminal or command window, navigate to the top-level project directory `Notebooks/` (that contains this README) and run one of the following commands:

```bash
ipython notebook {name of notebook file}.ipynb
```  
or
```bash
jupyter notebook {name of notebook file}.ipynb
```

This will open the iPython Notebook software and project file in your browser.

### Data

This dataset consists of 1-min stock data from a [post](https://github.com/Finance-And-ML/US-Stock-Intraday-Dataset) at Github for a time period from 06/06/2017 to 07/14/2017 (approximately 10,000 data points). A more recent dataset (from 2017-07-18 to 2017-07-21) from the same Github post (approximately 1,500 data points) was downloaded for back testing.

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