webclinic017 / stocktradingbotcloud

Alpaca Cloud Algo gcloud

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgements

About The Project

Python program that learns stock movement based on past and current data and buy/sells stocks accordingly. Implemented with the Alpaca Trading API to import data and create buy/sell orders, the AI software can predict future movements of stocks and place orders automatically. It's also deployed to the Google Cloud Storage to run infinitely on Google Cloud.

Built With

• Python
• Keras
• scikit-learn
• Numpy
• Pandas
• Google Cloud Platform

Getting Started

Prerequisites

• Python

  https://www.python.org/downloads/

Installation

1. Get a free key_id and secret_key at https://alpaca.markets/algotrading
2. Clone the repo

   git clone https://github.com/rexliu3/StockTradingBotCloud.git

3. Install requirement.txt

   pip3 install -r requirements.txt

4. Enter your key_id and secret_key in Stock_Trading_Bot.py

   super().__init__(key_id='ENTER YOUR KEY_ID',
                                                secret_key='ENTER YOUR SECRET_KEY',
                                                base_url='https://paper-api.alpaca.markets')

Usage

Simply run main.py: python3 main.py

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Rex Liu - [email protected]

Project Link: https://github.com/rexliu3/StockTradingBotCloud

Acknowledgements

Built with reference to article