Predict stock market prices using RNN

Check my blog post "Predict Stock Prices Using RNN: Part 1" for the tutorial associated.

1. Make sure tensorflow has been installed.
2. First download the full S&P 500 data from Yahoo! Finance ^GSPC (click the "Historical Data" tab and select the max time period). And save the .csv file to data/SP500.csv. (NOTE: Unfortunately, the startdate in the Google finance historical prices url does not seem to work any more. Each stock only gets one year's data, which is too short for training. I will update data_fetcher once I find other better alternative.)
3. Run python data_fetcher.py to download the prices of individual stocks in S & P 500, each saved to data/{{stock_abbreviation}}.csv.
4. Run python main.py --help to check the available command line args.
5. Run python main.py to train the model.

For examples,

• Train a model only on SP500.csv; no embedding

python main.py --stock_symbol=SP500 --train --input_size=5 --lstm_size=128 --max_epoch=50

• Train a model on 100 stocks; with embedding of size 8

python main.py --stock_count=100 --train --input_size=5 --lstm_size=128 --max_epoch=50 --embed_size=8

My python environment:

BeautifulSoup==3.2.1
numpy==1.13.1
pandas==0.16.2
scikit-learn==0.16.1
scipy==0.19.1
tensorflow==1.2.1
urllib3==1.8