Agatha

Agatha is a tool to help you predict future prices (open, close) or daily volume for any given stock ticker.

<p align="center"> <img src="https://github.com/driemworks/agatha/blob/master/resources/images/full.PNG?raw=true" alt="full" width="400" height="400">|<img src="https://github.com/driemworks/agatha/blob/master/resources/images/prediction.PNG?raw=true" alt="full" width="400" height="400"> </p>

Should I have faith in the predictions?

Probably not.

How it works

Agatha uses an LSTM network to predict close prices for a user-specified number of days in the future. The training data is downloaded via Alpha Vantage.

Requirements

• python 3.5 or higher

Installation

There are two ways to install agatha.

Install using pip

The easiest way to install agatha is via pip:

pip install agatha

Note: keep in mind that this requires python 3.5 or higher. Another Note: If you want the latest version build from sources.

Build form sources

Clone this repository. Inside the Agatha folder, create the agatha package using

python setup.py sdist

Then install using pip.

pip install dist/*

If you use anaconda, you can load the conda environment using the environment.yml file in resources/conda and running conda env create -f environment.yml

Usage

First, import agatha's functions

from agatha import getOrTrainModel, predictFuture

Then get an API key from Alpha Vantage. To train a model for a particular ticker, use

model = getOrTrainModel(alpha_vantage_api_key, ticker, attribute, alphavantage_data,
						model_data, weights_data, epochs=epochs, look_back=look_back)

where

• ticker is the stock ticker
• attribute is the stock attribute to predict (open, close, volume),
• alphavantage data is downloaded as a csv and then pickled (saved as .pkl)
• the model_data is saved as json
• the weights file is saved as .h5

Predictions for future close prices for a stock can have output type as json or plot (pyplot, as shown in graphs above)

prediction_output = predictFuture(model, num_days_to_predict, ouptut_type)

Example:

model = getOrTrainModel('adsfadsfasdf', 'GE', 'GE.pkl', 'open', 'model.json', 'weights.h5')
prediction_output = predictFuture(model, 2, 'json')

Example output JSON from predictFuture:

{
   "ticker":"GE",
   "column":"open",
   "predictions":[
      {
         "day":"1",
         "price":"8.009521"
      },
      {
         "day":"2",
         "price":"8.117293"
      }
}

Refer to app.py, for a working example.

Future Enhancements

• Allow other sources of historical data (including cryptocurrencies)
• Any suggestions?