Smart Investment Advisor

A comprehensive investment advisory system that uses artificial intelligence techniques to help users make informed investment decisions.

Features

• Risk Assessment using Fuzzy Logic: Evaluates the user's risk profile based on age, income, risk tolerance, and market volatility.
• Portfolio Optimization using Genetic Algorithms: Selects the optimal investment portfolio that balances risk and return.
• Automated Recommendations using an Intelligent Agent: Provides personalized investment advice based on portfolio health and market conditions.
• Interactive Dashboard: Displays risk assessment, portfolio optimization, and investment recommendations.

Technical Implementation

This project integrates four artificial intelligence concepts:

1. Fuzzy Logic: Used to determine the user's risk level based on multiple factors.
2. Genetic Algorithm: Used to optimize portfolio allocation across different asset classes.
3. Intelligent Agent: Monitors market conditions and provides automated investment advice.
4. Web-based Visualization: Presents recommendations via an interactive Streamlit dashboard.

Installation

1. Ensure you have Python 3.8+ installed.
2. Clone this repository.
3. Install the required dependencies:

pip install -r requirements.txt

Usage

1. Navigate to the project directory:

cd smart_investment_advisor

2. Run the application:

streamlit run main.py

3. Access the application in your web browser at http://localhost:8501.

How to Use

1. Enter your profile information in the sidebar:

   • Age
   • Monthly income
   • Risk tolerance

2. Configure advanced settings (optional):

   • Market volatility
   • Asset classes to include

3. Click "Analyze Profile" to generate:

   • Risk assessment
   • Optimized portfolio allocation
   • Personalized investment advice

Sample Scenarios

User Age	Income	Risk Tolerance	System Recommendation
22	$2000	High	Invest 70% in stocks
50	$5000	Low	Invest 40% in bonds
35	$3000	Medium	Invest 50% in stocks, 30% in gold

Limitations and Disclaimer

• This system is for educational purposes only and does not constitute financial advice.
• Historical performance is not indicative of future results.
• The system uses publicly available market data which may have limitations.
• Always consult with a qualified financial advisor before making investment decisions.

Technologies Used

• Python 3.8+
• Scikit-Fuzzy for fuzzy logic implementation
• DEAP for genetic algorithm implementation
• YFinance for market data
• Streamlit for web interface
• Pandas and NumPy for data manipulation
• Matplotlib for visualization =======