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cs229project-lim-lundgren

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# CS 229 (Machine Learning, Spring 2019) Final Project.

This is the repository for my final project for the class. It is joint with Justin Lundgren.

Title: Algorithmic Trading using LSTM-Models for Intraday Stock Predictions

# Paper: 
Our final project is available at http://cs229.stanford.edu/proj2019/report/31.pdf.
If you do not have access to Stanford papers, it can be found at https://www.dropbox.com/s/77ynq4ymd93o3al/CS229_final_version.pdf?dl=0

# Code:
The src directory contains the pipelines for a VAR-model, a VARMAX-model, an LSTM-model and an R2N2-model which is the combination of the LSTM model and the residuals of the VARMAX-model.
The src directory also contins a directory with the raw data, the output, and the evaluation methods, which are based on correlations with actual returns, a simple long/short investment strategy and mean squared error.

# Setup
Make sure that you have Tensorflow 1.13 installed instead of Tensorflow 2.0. We use Keras for our code with a tensorflow backend. The current version of Keras (which I believe is 2.2.4, as of June 2019) is not compatible with Tensorflow 2.0.

# Hyperparameter tuning
Here's something to play around with, namely hyperparameter tuning via a random grid search. First clone the repository, then go to ```src/LSTM```
and run

```
python hyp_tuning.py. 
```

Make sure that in the python file, the boolean ```rand_tuning``` is set to ```True```. Now when you run ```hyp_tuning.py```, it randomly selects the hyperparameters

* Number of LSTM units. 
* Lookback period
* Learning rate

and saves the MSE value obtained on the validation set of our data into a CSV file. In the file ```hyp_tuning.py```, on about line 140, the quantity ```num_trials``` is the number of times we want python to generate a random choice of these parameters. At the moment, ```num_trials``` is 4. We save the CSV file in  ```src/output/LSTM_tuning/random_samples```. To distinguish the  different files each time we execute ```python hyp_tuning.py```, the CSV files contain a random number in their name.

To obtain Fig. 2 in our CS 229 project linked above, go to ```src/output/LSTM_training/``` and run the jupyter notebook ```Tuning_plot.ipynb```. Run all the code in there, and the figure generated will be a plot of hyperparameters against MSE value.

Finally, note that the plot is made out of *all* the CSV files in the folder ```src/output/LSTM_tuning/random_samples```.

cs229project-lim-lundgren

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