https://github.com/neemiasbsilva/stock-movement-predictions
A Long-Short Term Memory approach for Stock Market Movement Predictions.
https://github.com/neemiasbsilva/stock-movement-predictions
Last synced: 3 months ago
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A Long-Short Term Memory approach for Stock Market Movement Predictions.
- Host: GitHub
- URL: https://github.com/neemiasbsilva/stock-movement-predictions
- Owner: neemiasbsilva
- License: apache-2.0
- Created: 2020-10-02T13:21:44.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-05T08:53:56.000Z (about 5 years ago)
- Last Synced: 2025-06-29T13:45:26.487Z (4 months ago)
- Language: Jupyter Notebook
- Size: 529 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# A Long-Short Term Memory approach for Stock Market Movements
[](https://travis-ci.org/keras-team/keras) [](https://www.tensorflow.org/api_docs/)
> 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)](http://www.bioinf.jku.at/publications/older/2604.pdf), and were refined and popularized by many people in following works [[Sundermeyer et al. (2012)](https://www.isca-speech.org/archive/interspeech_2012/i12_0194.html), [Zhou, Chunting, et al. (2015)](https://arxiv.org/abs/1511.08630), [Gensler, André, et al. (2016)](https://ieeexplore.ieee.org/abstract/document/7844673)].
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
#