📊 Finance Project

This project is a Django web application for financial backtesting and stock price predictions using a linear regression model. It includes functionalities like backtesting strategies and generating performance reports with both graphical and tabular data.

✨ Features

• 🔄 Financial backtesting with historical stock data.
• 📈 Prediction of stock prices using machine learning.
• 📊 Report generation including key financial metrics and visual comparisons.
• 🐳 Dockerized setup for easy development and deployment.

✅ Prerequisites

Before running the project locally, ensure you have the following installed:

• 🐳 Docker and Docker Compose
• 🐍 Python 3.10+
• 🛠 Git (for version control)

🛠 Project Setup

1. Clone the repository

git clone https://github.com/DeepLeau/finance
cd finance_project

2. Create and configure environment variables 📝

Create a .env file at the root of your project to store environment variables, including your database credentials and API keys. Here's an example of what the .env file should look like:

POSTGRES_DB=finance_db
POSTGRES_USER=your_db_user
POSTGRES_PASSWORD=your_db_password
POSTGRES_HOST=db
POSTGRES_PORT=5432

API_KEY=your_api_key_here
DEBUG=True
SECRET_KEY=your_secret_key_here

🔑 Make sure to replace your_db_user, your_db_password, and your_api_key_here with your actual credentials and API key. You can find your API key on alphavantage

3. Install Docker and Docker Compose 🐳

Make sure Docker and Docker Compose are installed on your machine. If not, install them:

• 📦 Docker Installation Guide
• 📦 Docker Compose Installation Guide

4. Build and run the project 🏗️

Run the following commands to start the application in a Dockerized environment:

docker-compose up --build

This will build the Docker images and start the services defined in the docker-compose.yml file.

5. Apply migrations and create superuser 🗂️

Once the containers are up and running, you need to apply the database migrations:

docker-compose exec web python manage.py migrate

To create a superuser (for Django admin access), run:

docker-compose exec web python manage.py createsuperuser

6. Access the application 🌐

You can access the Django application in your browser at:

http://localhost:8000

7. Running Tests 🧪

To run the tests included in the project, use the following command:

docker-compose exec web python manage.py test

📡 API Endpoints

The application provides several API endpoints for backtesting and predictions:

• 🔄 GET /backtesting?symbol=<SYMBOL>: Runs the backtest for the given stock symbol.
• 📈 GET /predict-stock/<SYMBOL>/: Predicts stock prices for the next 30 days for the given symbol.
• 📄 GET /report/<SYMBOL>/?format=pdf: Generates a performance report in PDF format.

🖥️ Local Development :

If you want to run the project without Docker, follow these steps:

1. Install dependencies 📦

pip install -r requirements.txt

2. Configure PostgreSQL locally 🗄️

Ensure that PostgreSQL is running on your machine, and update the DATABASES section in settings.py to point to your local PostgreSQL database.

3.Run the application ▶️

python manage.py runserver

Deployment 🚀

This project is Dockerized, which makes deployment straightforward on most platforms. For cloud deployment or CI/CD automation, adjust the deployment settings in the Docker and settings.py configuration files to meet your specific needs.

Additional Setup for Cloud Deployment ☁️

To deploy the application on AWS or any other cloud platform, follow these steps:

• Set up an RDS PostgreSQL instance on AWS and modify the environment variables to point to the remote database.
• Ensure proper security groups and inbound rules are set for your EC2 instance to allow traffic on port 8000.
• Set up GitHub Actions or another CI/CD pipeline for automated deployment if required.