Forex Forecast API

Overview

The Forex Forecast API is a FastAPI application designed to provide accurate forex forecasting using machine learning models. The API leverages a trained LSTM (Long Short-Term Memory) model to predict future forex rates based on historical data. It is deployed on Heroku using Docker for containerization.

• 🌐 Live Deployment: Forex Forecast API

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Features

• 📈 Forex Rate Prediction: Predict forex rates using an advanced LSTM model.
• 🔍 Interactive API Documentation: Explore and test the API endpoints using Swagger UI at /docs and ReDoc at /redoc.
• ⚡ Fast and Scalable: Built with FastAPI, ensuring high performance and scalability.
• 🐳 Containerized Deployment: Deployed as a Docker container on Heroku for ease of scaling and portability.

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API Endpoints

Method	Endpoint	Description
POST	/predict	Predict future forex rates.
GET	/docs	Swagger UI for interactive API docs.
GET	/redoc	ReDoc for API documentation.

Request Example for /predict

{
  "data": [
    [0.1, 0.2, 0.3, ..., 0.21],  // 1st time step
    [0.3, 0.4, 0.5, ..., 0.21],  // 2nd time step
    // ... 10 time steps in total
  ]
}

Response Example

{
  "forecast": 1.2345
}

Project Structure

project/
├── app/
│   ├── main.py            # Entry point for the FastAPI app
│   ├── utils/
│   │   └── preprocess.py  # Preprocessing logic for input data
│   ├── models/
│   │   └── lstm_model.h5  # Pretrained LSTM model
├── Dockerfile             # Docker configuration
├── requirements.txt       # Python dependencies
└── Procfile               # Heroku process definition

Installation and Usage

Prerequisites Python 3.9+ Docker Heroku CLI

Local development

1. Clone the repository

git clone https://github.com/your-username/forex-forecast-api.git
cd forex-forecast-api

2. Create virtual Environment and Install Dependencies

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Run the FastAPI app:

uvicorn app.main:app --reload

4. Access the API:

Swagger UI: http://127.0.0.1:8000/docs
ReDoc: http://127.0.0.1:8000/redoc

Docker Deployment

1. Build the Docker image:

docker build -t forex-forecast .

2. Run the Docker container:

docker run -p 8000:8000 forex-forecast

Deploy to Heroku

1. Log in to Heroku and set up the app:

heroku login
heroku create forex-forecast --stack=container

2. Push the Docker image to Heroku:

heroku container:push web -a forex-forecast
heroku container:release web -a forex-forecast

3. Open the app:

heroku open -a forex-forecast

Deployed URL: https://forex-forecast-fde2872e4c4d.herokuapp.com/

Technologies Used

• Python: Core programming language.
• FastAPI: Framework for building the API.
• TensorFlow: Machine learning library for LSTM model.
• Docker: Containerization for deployment.
• Heroku: Cloud platform for hosting.

Contributing

1. Fork the repository.
2. Create a new branch:

git checkout -b feature-name

3. Commit changes:

git commit -m "Add new feature"

4. Push to the branch:

git push origin feature-name

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.