Financial Investment Recommendation System

An advanced financial investment recommendation platform that leverages machine learning to generate personalized investment strategies. The system integrates real-time market data, historical stock data, user preferences, and predictive analytics to provide intelligent investment guidance.

Features

• Personalized stock recommendations based on user preferences
• Portfolio management and performance tracking
• Real-time market data integration
• Historical stock price data and visualization
• News sentiment analysis
• Interactive dashboard with charts and analytics

Database Options

This project uses PostgreSQL as the database.

Prerequisites

• Python 3.8 or higher
• PostgreSQL
• Git (for cloning the repository)

Installation

1. Clone the repository

git clone https://github.com/marinaredamekhael/FinancialAdvisor.git
cd FinancialAdvisor

2. Create a virtual environment

For Windows:

python -m venv venv
venv\Scripts\activate

For macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r project_requirements.txt

4. Set up the PostgreSQL database

• Create a PostgreSQL database
• Note down your database credentials (host, database name, username, password, port)

5. Create a .env file

Create a .env file in the root directory with the following variables:

# Database configuration
DATABASE_URL=postgresql://username:password@host:port/database_name

# API Keys
# Optional for enhanced functionality
ALPHAVANTAGE_API_KEY=your_alphavantage_api_key
NEWS_API_KEY=your_newsapi_key

# Flask configuration
FLASK_SECRET_KEY=your_secret_key

6. Initialize the database

You have two options to set up the database:

Option 1: Initialize with sample data (recommended for testing)

# Run the setup script to create tables and add sample data
python setup_local.py

This will:

• Create all database tables
• Add a demo user (username: demo_user, password: password123)
• Add sample stocks, portfolio items, news, and recommendations

Option 2: Initialize empty database

# Run the application once to create the database tables only
python main.py

Running the Application

python main.py

Or with gunicorn (for production):

gunicorn --bind 0.0.0.0:5000 main:app

The application will be available at http://localhost:5000

Initial Setup Steps

1. Register a new user account
2. Set up your investment preferences
3. Navigate to /test/generate-recommendations to populate sample stocks and recommendations
4. Add stocks to your portfolio
5. Explore the dashboard, recommendations, and news features

Project Structure

• main.py: Entry point for the application
• app.py: Flask app configuration
• models.py: Database models
• routes.py: Application routes and views
• data_fetcher.py: Functions to fetch stock and news data
• recommendation.py: Recommendation engine
• sentiment_analysis.py: News sentiment analysis
• templates/: HTML templates
• static/: CSS, JavaScript, and other static files

Technologies Used

• Backend: Python, Flask, SQLAlchemy
• Database: PostgreSQL
• Data Processing: Pandas, NumPy, scikit-learn
• Natural Language Processing: NLTK
• Frontend: HTML, CSS, Bootstrap, Chart.js
• APIs: Yahoo Finance, News API, Alpha Vantage