Deep Learning Framework for Financial Time Series Prediction in Python Keras

• Randomly partitions time series segments into train, development, and test sets

• Trains multiple models optimizing parameters for a development set, and performs a final validation on the test set

• Calculates a model’s annualized return, improvement over a buy & hold strategy, % of trades that are profitable, profit factor, and max drawdown

train_stock_mdl.py

This script imports a csv file containing predictors and an outcome 
variable (last column). It was designed with stock/ETF prediction in mind, 
but could potentially be adapted for other data. The default neural network
architecture is very simple (2 hidden layers with 200 units each and 
L2 regularization). However, this can easily be changed. A number of models
are trained (specified by nModels) on the Training data, and each is 
evaluated relative to the out-of-sample development ("Dev") data. An 
optimal model can then be tested on the final "Test" data, avoiding 
issues with multiple comparisons. (Note that the model training is very 
non-deterministic: some models may end up using very different "strategies"
that are more or less effective at predicting the data. Some will 
cross-validate better than others, hence why training multiple models on 
and testing for cross-validation to the Dev data can be helpful. 
Note that training multiple models in this way, however, necessitates the 
use of Test data in addition to Dev data, in order to fairly evaluate 
the final model.)

stock_mdl_new_ticker.py

A script that loads a previously optimized neural network and predicts 
data from a new stock/ETF ticker. After evaluating performance, there is
the option to train the model further on the new ticker.

stock_mdl_tomorrow.py

Script to load previously trained model, provide stats on full dataset, and 
predict % gain/loss for tomorrow