https://github.com/marinaredamekhael/financialadvisor
A personalized financial advisory web application that leverages machine learning, sentiment analysis, and real-time market data to recommend smart investment strategies. Built with Flask, PostgreSQL, and integrated with financial APIs like Alpha Vantage, Yahoo Finance, and News API.
https://github.com/marinaredamekhael/financialadvisor
ai alpha-vantage dashboard finance financial-data flask hybrid investment-recommendation lstm machine-learning newsapi portfolio-management postgresql python real-time-data sentiment-analysis stock-market
Last synced: about 2 months ago
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A personalized financial advisory web application that leverages machine learning, sentiment analysis, and real-time market data to recommend smart investment strategies. Built with Flask, PostgreSQL, and integrated with financial APIs like Alpha Vantage, Yahoo Finance, and News API.
- Host: GitHub
- URL: https://github.com/marinaredamekhael/financialadvisor
- Owner: marinaredamekhael
- Created: 2025-06-12T09:21:19.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-12T09:53:33.000Z (4 months ago)
- Last Synced: 2025-06-19T19:10:56.992Z (4 months ago)
- Topics: ai, alpha-vantage, dashboard, finance, financial-data, flask, hybrid, investment-recommendation, lstm, machine-learning, newsapi, portfolio-management, postgresql, python, real-time-data, sentiment-analysis, stock-market
- Language: HTML
- Homepage:
- Size: 3.73 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: news_service.py
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README
# 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
```bash
git clone https://github.com/marinaredamekhael/FinancialAdvisor.git
cd FinancialAdvisor
```
### 2. Create a virtual environment
#### For Windows:
```bash
python -m venv venv
venv\Scripts\activate
```
#### For macOS/Linux:
```bash
python3 -m venv venv
source venv/bin/activate
```
### 3. Install dependencies
```bash
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)
```bash
# 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
```bash
# Run the application once to create the database tables only
python main.py
```
## Running the Application
```bash
python main.py
```
Or with gunicorn (for production):
```bash
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