Using convolutional neural network to predict the stock market
##Overview Python script for the Stock Price Prediction. The code uses the scikit-learn machine learning library to train a support vector regression on a stock price dataset from Google Finance to estimate a buy, hold and sell based on settings.
This project is an attempt to predict the stock volatility using an python finance stock tools. In the data set I will explore different observation and normalization schemes. At the end I will compare the model performance with the benchmarks and discuss issues.
##Dependencies
- numpy (http://www.numpy.org/)
- pandas (http://pandas.pydata.org/)
- csv (https://pypi.python.org/pypi/csv)
- keras (https://keras.io)
- quandl (https://www.quandl.com/tools/python)
##Get started Once you have your dependencies installed via pip, run the demo script in terminal via cd src python sklearn_main.py
After running the sklearn_main.py you should see the following graph:
##Reference These links are a good way to learn about some of the dependencies used in this proj