github.com/lilianweng/stock-rnn @main sqlite

Predict stock market prices using RNN

Check my blog post "Predict Stock Prices Using RNN": Part 1 and Part 2 for the tutorial associated.

One thing I would like to emphasize that because my motivation is more on demonstrating how to build and train an RNN model in Tensorflow and less on solve the stock prediction problem, I didn't try too hard on improving the prediction outcomes. You are more than welcome to take this repo as a reference point and add more stock prediction related ideas to improve it. Enjoy.

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.
3. Run python data_fetcher.py to download the prices of individual stocks in S & P 500, each saved to data/{{stock_abbreviation}}.csv. (NOTE: Google Finance API returns the prices for 4000 days maximum. If you are curious about the data in even early times, try modify data_fetcher.py code to send multiple queries for one stock. Here is the data archive (stock-data-lilianweng.tar.gz) of stock prices I crawled up to Jul, 2017. Please untar this file to replace the "data" folder in the repo for test runs.)
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=1 --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=1 --lstm_size=128 --max_epoch=50 --embed_size=8

• Start your Tensorboard

cd stock-rnn
mkdir logs
tensorboard --logdir ./logs --port 1234 --debug

My python environment: Python version == 2.7

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