This project implements an investment strategy system that utilizes machine learning techniques for effective portfolio management. The system consists of a Django backend and a React frontend, providing a seamless integration between the server-side and client-side components. The models are build in Google Colab and trained on GCP. Exported into the backend Django Framework where it is used for testing.
- Utilizes machine learning techniques to identify financial trends and optimize portfolio allocation.
- Navigate through the application to see investment portfolio options that aid in decision making.
- Predict the subsequent time series data given the past data.
- Monitor the performance of your stocks through visualizations.
- Django: A powerful Python web framework for building the backend server.
- Django REST Framework: Enables the creation of RESTful APIs for communication between the backend and frontend.
- React: A popular JavaScript library for building interactive user interfaces.
- Chart.js: A versatile charting library for visualizing financial trends and performance.
- Clone the repository:
git clone <repository_url> - Set up the Django backend:
- Create a virtual environment:
python -m venv venv - Activate the virtual environment:
- On Windows:
venv\Scripts\activate - On macOS/Linux:
source venv/bin/activate
- On Windows:
- Install the Python dependencies:
pip install -r requirements.txt - Apply database migrations:
python manage.py migrate - Run the backend server:
python manage.py runserver
- Create a virtual environment:
- Set up the React frontend:
- Navigate to the
frontenddirectory:cd advbigdata_frontend - Install the Node.js dependencies:
npm install - Start the frontend development server:
npm start
- Navigate to the
- Access the application: Open your web browser and visit
http://localhost:3000to use the investment strategy system. - Machine Learning Trained models present in the root directory Adv_Big_Data_Project.ipynb.