stockpy-learn 0.1.16

Machine Learning library to make stock market prediction

Table of contents

• Description
• Documentation
• Data Downloader
• License

Description

stockpy is a Python Machine Learning library designed to facilitate stock market data analysis and predictions. It currently supports the following algorithms:

• Long Short Term Memory (LSTM)
• Bidirectional Long Short Term Memory (BiLSTM)
• Gated Recurrent Unit (GRU)
• Bidirectional Gated Recurrent Unit (BiGRU)
• Multilayer Perceptron (MLP)
• Gaussian Hidden Markov Models (GaussianHMM)
• Bayesian Neural Networks (BayesianNN)
• Deep Markov Model (DeepMarkovModel)

stockpy can be used to perform a range of tasks such as detecting relevant trading patterns, making predictions and generating trading signals.

Usage

To use stockpy and perform predictions on stock market data, start by importing the relevant models from the stockpy.neural_network and stockpy.probabilistic modules. The library can be used with various types of input data, such as CSV files, pandas dataframes and numpy arrays.

To demonstrate the usage of stockpy, we can perform the following code to read a CSV file containing stock market data for Apple (AAPL), split the data into training and testing sets, fit an LSTM model to the training data, and use the model to make predictions on the test data:

from sklearn.model_selection import train_test_split
import pandas as pd
from stockpy.probabilistic import DeepMarkovModel, BayesianNN, GaussianHMM
from stockpy.neural_network import LSTM, GRU, MLP, BiGRU, BiLSTM

# read CSV file and drop missing values
df = pd.read_csv('AAPL.csv', parse_dates=True, index_col='Date').dropna(how="any")

# split data into training and testing sets
X_train, X_test = train_test_split(df, test_size=0.1, shuffle=False)

# create model instance and fit to training data
predictor = DeepMarkovModel()
predictor.fit(X_train, batch_size=24, epochs=10)

# predictions on test data
y_pred = predictor.predict(X_test)

The above code can be applied to all models in the library, just make sure to import from the correct location, either stockpy.neural_network or stockpy.probabilistic.

Dependencies and installation

stockpy requires the modules numpy, torch, pyro-ppl. The code is tested for Python 3. It can be installed using pip or directly from the source cod.

Installing via pip

To install the package:

> pip install stockpy-learn

To uninstall the package:

> pip uninstall stockpy-learn

Installing from source

You can clone this repository on your local machines using:

> git clone https://github.com/SilvioBaratto/stockpy

To install the package:

> cd stockpy
> ./install.sh

Data downloader

The data downloader is a command-line application located named data.py, which can be used to download and update stock market data. The downloader has been tested and verified using Ubuntu 22.04 LTS.

Parameter	Explanation
--download	Download all the S&P 500 stocks. If no start and end dates are specified, the default range is between "2017-01-01" and today's date.
--stock	Download a specific stock specified by the user. If no start and end dates are specified, the default range is between "2017-01-01" and today's date.
--update	Update all the stocks present in the folder containing the files. It is possible to update the files to any range of dates. If a stock wasn't listed before a specific date, it will be downloaded from the day it enters the public market.
--update.stock	Update a specific stock specified by the user. It is possible to update the files to any range of dates by specifying the start and end dates.
--start	Specify the start date for downloading or updating data.
--end	Specify the end date for downloading or updating data.
--delete	Delete all files present in the files folder.
--delete-stock	Delete a specific stock present in the files folder.
--folder	Choose the folder where to read or download all the files.

Usage example

Below are some examples of how to use the downloader:

# Download all the data between "2017-01-01" and "2018-01-01"
python3 data.py --download --start="2017-01-01" --end="2018-01-01"

# Download data for Apple (AAPL) from "2017-01-01" to today's date
python3 data.py --stock="AAPL" --end="today"

# Update all the data between "2014-01-01" and "2020-01-01"
python3 data.py --update --start="2014-01-01" --end="2020-01-01"

# Update a specific stock from "2014-01-01" until the last day present in the stock file
python3 data.py --update-stock --stock="AAPL" --start="2014-01-01"

# Download all the data between "2017-01-01" and today's date, 
# choosing the folder where to download the files
python3 data.py --download --folder="../../example"

Authors and acknowledgements

stockpy is currently developed and mantained by Silvio Baratto. You can contact me at:

• silvio.baratto22 at gmail.com

Reporting a bug

The best way to report a bug is using the Issues section. Please, be clear, and give detailed examples on how to reproduce the bug (the best option would be the graph which triggered the error you are reporting).

How to contribute

We are more than happy to receive contributions on tests, documentation and new features. Our Issues section is always full of things to do.

Here are the guidelines to submit a patch:

1. Start by opening a new issue describing the bug you want to fix, or the feature you want to introduce. This lets us keep track of what is being done at the moment, and possibly avoid writing different solutions for the same problem.

2. Fork the project, and setup a new branch to work in (fix-issue-22, for instance). If you do not separate your work in different branches you may have a bad time when trying to push a pull request to fix a particular issue.

3. Run black before pushing your code for review.

4. Provide menaningful commit messages to help us keeping a good git history.

5. Finally you can submbit your pull request!

License

See the LICENSE file for license rights and limitations (MIT).