bulbea

Table of Contents

Installation

$ git clone https://github.com/achillesrasquinha/bulbea.git && cd bulbea

$ pip install -r requirements.txt

$ python setup.py install

$ pip install tensorflow # CPU
$ pip install tensorflow-gpu # GPU - Requires CUDA, CuDNN

Usage

1. Prediction

a. Loading

>>> import bulbea as bb
>>> share = bb.Share('YAHOO', 'GOOGL')
>>> share.data
# Open High Low Close Volume \
# Date 
# 2004年08月19日 99.999999 104.059999 95.959998 100.339998 44659000.0 
# 2004年08月20日 101.010005 109.079998 100.500002 108.310002 22834300.0 
# 2004年08月23日 110.750003 113.479998 109.049999 109.399998 18256100.0 
# 2004年08月24日 111.239999 111.599998 103.570003 104.870002 15247300.0 
# 2004年08月25日 104.960000 108.000002 103.880003 106.000005 9188600.0
...

b. Preprocessing

>>> from bulbea.learn.evaluation import split
>>> Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize = True)

c. Modelling

>>> 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

>>> 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

export BULBEA_TWITTER_API_KEY="<YOUR_TWITTER_API_KEY>"
export BULBEA_TWITTER_API_SECRET="<YOUR_TWITTER_API_SECRET>"
export BULBEA_TWITTER_ACCESS_TOKEN="<YOUR_TWITTER_ACCESS_TOKEN>"
export BULBEA_TWITTER_ACCESS_TOKEN_SECRET="<YOUR_TWITTER_ACCESS_TOKEN_SECRET>"

>>> bb.sentiment(share)
0.07580128205128206

Documentation

Dependencies

License