Portfolio-Optimizer

Overview

This project provides a modular and extensible Python API class called PriceHistory for retrieving historical financial data from the Alpha Vantage API. Additionally, the project integrates portfolio optimization techniques such as Monte Carlo simulation and scipy.optimize using SLSQP to derive optimal asset allocations based on user-defined metrics like Sharpe Ratio, volatility, and expected returns.

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Features

PriceHistory Class

• Data Fetching:

  • Pulls historical stock/ETF data from Alpha Vantage.
  • Handles rate limits and API exceptions.

• Symbol Management:

  • Accepts flat lists or nested dictionaries of ticker symbols.

• Data Cleaning:

  • Standardizes column names, formats dates, converts data types.
  • Adds features like daily returns, price ranges, and average prices.

• Convenience Methods:

  • build_URL: Builds the API URL for a given symbol.
  • symbols: Returns the stored symbols by category or subcategory.
  • fetch_prices: Fetches and processes historical prices.
  • build_df: Constructs a unified DataFrame.
  • get_data_summary: Summarizes collected data.

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Portfolio Optimization

Monte Carlo Simulation

• Generates thousands of random portfolio weight combinations.

• Calculates:

  • Portfolio return
  • Portfolio volatility
  • Sharpe Ratio

• Visualizes results with a scatter plot colored by Sharpe Ratio.

• Identifies:

  • Portfolio with maximum Sharpe Ratio
  • Portfolio with minimum volatility

SLSQP Optimization (Scipy)

• Uses Sequential Least Squares Programming (SLSQP) to:

  • Maximize Sharpe Ratio
  • Minimize volatility

• Subject to constraints:

  • Weights must sum to 1
  • No short selling (weights between 0 and 1)

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Usage Example

from operations.fetch_data import PriceHistory

api_key = 'YOUR_API_KEY'
symbols = ['AAPL', 'MSFT', 'GOOGL', 'TSLA']

ph = PriceHistory(api_key, symbols=symbols)
data = ph.build_df()
summary = ph.get_data_summary()

Optimization Example

from optimizations import monte_carlo_simulation, scipy_SLSQP_optimization

# Run Monte Carlo simulation
simulation_df = monte_carlo_simulation(data)

# Perform SLSQP optimization
optimal_weights = scipy_SLSQP_optimization(data)

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Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• seaborn
• requests
• scipy

Install via pip:

pip install -r requirements.txt

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Visualization Outputs

• Efficient Frontier
• Capital Allocation Line (CAL)
• Optimal Portfolios Highlighted

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Project Structure

.
├── data
│   └── stocks.csv
├── keys
│   └── all_keys.py
├── operations
│   ├── __init__.py
│   └── fetch_data.py
├── optimizations
│   ├── monte_carlo_simulation.ipynb
│   └── scipy_SLSQP_optimization.ipynb
├── venv
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

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License

MIT License

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Author

Abdul Basit Tonmoy

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Acknowledgments

• Alpha Vantage for financial market data.
• NumPy, SciPy, and Matplotlib for core scientific computing and visualization.
• sigmacoding