A Long-Short Term Memory approach for Stock Market Movements
The MSFT stock (Microssoft) was used for fit and evaluated the movements.
About LSTM
LSTM are a special kind of Recurrent Neural Network, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following works [Sundermeyer et al. (2012), Zhou, Chunting, et al. (2015), Gensler, André, et al. (2016)].
All recurrent neural networks have the form of a chain of repeating modules of neural network. LSTM also have this chain like structure, bellow follow the module in an LSTM.
The repeating module in an LSTM containg four interacting layers. To know more about lstm neural networks, please click here.
Getting Started
Install
Bellow, follow some of packages you have to be install for run this experiment:
- The first step is create run virtual environment (this step is optional, but I recommend):
python3 -m pip install --user virtualenv
source .virtualenv/bin/activate
- The second step is install Sklearn
pip install -U scikit-learn
- The third step is install Pandas
pip install pandas
- After install Pandas, you need to install TensorFlow:
pip install tensorflow
- When TensorFlow installation has finished, please install the Keras library:
pip install keras
- And the last library was MatPlotLib for you plot some graphics to analisy the results:
python -m pip install -U matplotlib
Run the Experiment
After the instalations, you can run now the experiment type the following command in your terminal:
sh run.sh
For evaluat your model, open "evaluating_experiment.ipynb" (Jupyter Notebook File) and run all the cells:
jupyter-notebook evaluating_experiment.ipynb
Note: you have to specify the correct path for your load_model cell in Jupyter Notebook.
Result
Stock price movement for 30 days prediction.
Sincerely: Neemias B. da Silva