Deep learning LSTM prediction model for stock returns

Python implementation for "Deep learning with long short-term memory networks for financial market predictions" working paper by Thomas Fischer and Christopher Krauss. https://www.econstor.eu/bitstream/10419/157808/1/886576210.pdf

Simply collect and load three years of data for the S&P 500 constituents. You can do this easily using https://github.com/nicolasvianavega/stock_price_time_series.

The output consists of:

• Trained Keras model (json)
• Model weights (h5)
• Pandas data frame consisting of normalized stock returns and up/down probabilities for each stock at any given day for a test dataset

Example

# Collect S&P 500 Constituents data using stock_price_time_series
symbol_list = ['AAPL','GOOGL','TSLA']
since = '2015-01-01'
until = '2018-01-05'
value = 'adj_close' #recommended

results = data_collect(symbol_list, since, until, value)
results

date	AAPL	GOOGL	TSLA
2015-01-02	170.9074	1073.2100	320.5300
2015-01-03	170.8776	1091.5200	317.2500
...	...	...	...
2018-01-05	173.6259	1110.2900	316.5800

After collecting, just load the data into the script