In today’s data-driven world, predicting stock market trends can give investors a significant edge. This is where Bulbea, a deep learning-based Python library, comes into play. In this article, we will guide you through the installation, usage, and troubleshooting tips to help you effectively utilize Bulbea for stock market prediction and modeling.

Table of Contents

• Installation
• Usage
• Documentation
• Dependencies
• License

Installation

To get started with Bulbea, you first need to clone the git repository and install the necessary dependencies:

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

$ cd bulbea

$ pip install -r requirements.txt

Next, install Bulbea:

$ python setup.py install

If you haven’t installed TensorFlow yet, do so based on your requirements:

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

Usage

Once you have Bulbea installed, you are ready to start predicting stock market trends.

1. Prediction

a. Loading Data

First, you need to create a share object to load data:

import bulbea as bb
share = bb.Share(YAHOO, GOOGL)
share.data

This command opens the data for the specified stocks, displaying their historical prices including open, high, low, close, and volumes.

b. Preprocessing

Next, split your data into training and testing sets:

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

c. Modeling

Transform your training data for the RNN model and fit the model:

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)

d. Testing

Finally, test the model’s performance:

from sklearn.metrics import mean_squared_error
p = rnn.predict(Xtest)
mean_squared_error(ytest, p)

Visualize the results using:

import matplotlib.pyplot as plt
plt.plot(ytest)
plt.plot(p)
plt.show()

2. Sentiment Analysis

To add sentiment analysis component, set your Twitter credentials:

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

Then run the sentiment analysis command:

bb.sentiment(share)

This will provide sentiment scores that can be beneficial in making informed predictions.

Documentation

For detailed documentation on Bulbea, visit: here.

Dependencies

• quandl
• keras
• tweepy
• textblob

License

This code is released under the Apache 2.0 License.

Troubleshooting Tips

Should you run into any issues while using Bulbea, here are some troubleshooting ideas:

• Ensure that all dependencies are properly installed.
• Double-check your TensorFlow installation, especially if using GPU.
• Review your Twitter API credentials for sentiment analysis to avoid errors.
• Make sure your data formats are compatible (e.g., ensure you have historical price data).

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