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https://github.com/achillesrasquinha/bulbea
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
https://github.com/achillesrasquinha/bulbea
deep-learning finance machine-learning prediction python-library quantitative-finance quantitative-trading sentiment-analysis stock-market stock-market-prediction stock-prediction
Last synced: 4 days ago
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:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
- Host: GitHub
- URL: https://github.com/achillesrasquinha/bulbea
- Owner: achillesrasquinha
- License: other
- Created: 2017-03-09T06:11:06.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2021-01-17T09:07:36.000Z (about 4 years ago)
- Last Synced: 2025-02-08T21:04:31.960Z (11 days ago)
- Topics: deep-learning, finance, machine-learning, prediction, python-library, quantitative-finance, quantitative-trading, sentiment-analysis, stock-market, stock-market-prediction, stock-prediction
- Language: Python
- Homepage: http://bulbea.readthedocs.io
- Size: 2.64 MB
- Stars: 2,081
- Watchers: 130
- Forks: 478
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# bulbea
> *"Deep Learning based Python Library for Stock Market Prediction and Modelling."*
[](https://gitter.im/bulbea/bulbea) [](http://bulbea.readthedocs.io/en/latest/?badge=latest)
### Table of Contents
* [Installation](#installation)
* [Usage](#usage)
* [Documentation](#documentation)
* [Dependencies](#dependencies)
* [License](#license)
### Installation
Clone the git repository:
```console
$ git clone https://github.com/achillesrasquinha/bulbea.git && cd bulbea
```
Install necessary dependencies
```console
$ pip install -r requirements.txt
```
Go ahead and install as follows:
```console
$ python setup.py install
```
You may have to install TensorFlow:
```console
$ pip install tensorflow # CPU
$ pip install tensorflow-gpu # GPU - Requires CUDA, CuDNN
```
### Usage
#### 1. Prediction
##### a. Loading
Create a share object.
```python
>>> import bulbea as bb
>>> share = bb.Share('YAHOO', 'GOOGL')
>>> share.data
# Open High Low Close Volume \
# Date
# 2004-08-19 99.999999 104.059999 95.959998 100.339998 44659000.0
# 2004-08-20 101.010005 109.079998 100.500002 108.310002 22834300.0
# 2004-08-23 110.750003 113.479998 109.049999 109.399998 18256100.0
# 2004-08-24 111.239999 111.599998 103.570003 104.870002 15247300.0
# 2004-08-25 104.960000 108.000002 103.880003 106.000005 9188600.0
...
```
##### b. Preprocessing
Split your data set into training and testing sets.
```python
>>> from bulbea.learn.evaluation import split
>>> Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize = True)
```
##### c. Modelling
```python
>>> import numpy as np
>>> Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
>>> Xtest = np.reshape( Xtest, ( Xtest.shape[0], Xtest.shape[1], 1))
>>> from bulbea.learn.models import RNN
>>> rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
>>> rnn.fit(Xtrain, ytrain)
# Epoch 1/10
# 1877/1877 [==============================] - 6s - loss: 0.0039
# Epoch 2/10
# 1877/1877 [==============================] - 6s - loss: 0.0019
...
```
##### d. Testing
```python
>>> from sklearn.metrics import mean_squared_error
>>> p = rnn.predict(Xtest)
>>> mean_squared_error(ytest, p)
0.00042927869370525931
>>> import matplotlib.pyplot as pplt
>>> pplt.plot(ytest)
>>> pplt.plot(p)
>>> pplt.show()
```
#### 2. Sentiment Analysis
Add your Twitter credentials to your environment variables.
```bash
export BULBEA_TWITTER_API_KEY=""
export BULBEA_TWITTER_API_SECRET=""
export BULBEA_TWITTER_ACCESS_TOKEN=""
export BULBEA_TWITTER_ACCESS_TOKEN_SECRET=""
```
And then,
```python
>>> bb.sentiment(share)
0.07580128205128206
```
### Documentation
Detailed documentation is available [here](http://bulbea.readthedocs.io/en/latest/).
### Dependencies
1. quandl
2. keras
3. tweepy
4. textblob
### License
This code has been released under the [Apache 2.0 License](LICENSE).