https://github.com/ishtufinquant/portfolio-optimizer
Optimizes stock portfolios using simulation and real-world constraints (SLSQP).
https://github.com/ishtufinquant/portfolio-optimizer
backtesting finance numpy pandas portfolio-optimization python quant scipy yfinance
Last synced: 2 months ago
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Optimizes stock portfolios using simulation and real-world constraints (SLSQP).
- Host: GitHub
- URL: https://github.com/ishtufinquant/portfolio-optimizer
- Owner: ishtufinquant
- Created: 2025-06-11T20:15:30.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-06-13T20:56:08.000Z (about 1 year ago)
- Last Synced: 2025-06-13T21:29:35.545Z (about 1 year ago)
- Topics: backtesting, finance, numpy, pandas, portfolio-optimization, python, quant, scipy, yfinance
- Language: Python
- Homepage:
- Size: 708 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
# 📈 Portfolio Optimization with YFinance and Python
This project demonstrates how to use Python to perform **portfolio optimization** on a set of stocks using historical price data. It includes:
- Downloading stock price data with `yfinance`
- Calculating daily returns, mean returns, and the covariance matrix
- Simulating 1,000 random portfolios to visualize the risk/return trade-off
- Optimizing the portfolio using **two methods**:
- **Method 1**: Simulation-based (brute-force)
- **Method 2**: SLSQP-based (constrained optimization using `scipy.optimize.minimize`)
---
## 🛠️ Technologies Used
- Python
- `yfinance` – for fetching historical stock data
- `pandas` and `numpy` – for data handling
- `matplotlib` – for plotting the results
- `scipy.optimize` – for portfolio optimization
---
## 📊 Stocks Used
This analysis includes the following tickers:
- Johnson & Johnson (`JNJ`)
- Amazon (`AMZN`)
- JPMorgan Chase (`JPM`)
- Apple (`AAPL`)
- Exxon Mobil (`XOM`)
Historical data is downloaded for the period from **2020-01-01** to **2024-12-31**.
---
# ⚙️ Method 1: Portfolio Simulation (Random Portfolios)
This approach:
- Randomly generates 1,000 portfolio weight combinations
- Calculates expected return, volatility, and Sharpe ratio for each
- Identifies:
- 📍 **Max Sharpe Ratio Portfolio**
- 📍 **Minimum Volatility Portfolio**
---
## 🔄 Workflow Overview
1. **Download Price Data**
Use `yfinance` to fetch historical closing prices.
2. **Calculate Daily Returns**
Compute daily percentage change.
3. **Estimate Statistics**
- Mean returns
- Covariance matrix
4. **Simulate 1,000 Portfolios**
- Random weight combinations
- Portfolio return, volatility, Sharpe ratio
5. **Visualize Efficient Frontier**
- Risk vs return plot
- Highlight max Sharpe and min volatility
---
## ✅ Sample Output (Method 1 – Simulation)
📌 Output from **1,000 simulated portfolios**:
```
Optimized Portfolio Weights: [0.20, 0.15, 0.30, 0.25, 0.10]
Optimized Portfolio Return: 0.00091
Optimized Portfolio Volatility: 0.01345
```
---
## 📊 Visualization
This plot shows:
- 1,000 simulated portfolios, colored by Sharpe ratio
- 🔴 Red star = Portfolio with **max Sharpe ratio**
- 🔵 Blue star = Portfolio with **min volatility**
---
# ⚙️ Method 2: Portfolio Optimization with SLSQP
This approach:
- Uses `scipy.optimize.minimize` (Sequential Least Squares Programming)
- Solves for **maximum Sharpe ratio** under real-world constraints
### Constraints:
- Portfolio weights must sum to 1
- No stock may exceed 40% of the portfolio
---
## ✅ Sample Output (Method 2 – SLSQP)
📌 Output from `scipy.optimize.minimize`:
```
Optimized Portfolio Weights:
JNJ : 0.0000
AMZN : 0.4000
JPM : 0.2000
AAPL : 0.4000
XOM : 0.0000
Expected Portfolio Return : 0.000997
Expected Portfolio Volatility: 0.017179
```
---
## 👤 Real-World Use Case (Client-Style)
A client wants to allocate capital across 5 stocks (JNJ, AMZN, JPM, AAPL, XOM) with these goals:
- **Maximize risk-adjusted return (Sharpe ratio)**
- **Limit any single stock to ≤ 40% of the portfolio**
- **Keep the total investment sum to 100%**
This project uses `scipy.optimize.minimize` (SLSQP method) to:
- Apply these real-world constraints
- Find the optimal allocation
- Return exact weights, expected return, and volatility
📊 This approach is highly practical for asset managers and analysts building production-grade portfolios.
---
## 🚀 How to Run and Customize
### 1. Clone the repository
```bash
git clone https://github.com/ishtufinquant/portfolio-optimizer.git
cd portfolio-optimizer
```
### 2. Install dependencies
```bash
pip install yfinance pandas numpy matplotlib scipy
```
### 3. Run the script
```bash
python portfolio_optimization.py
```
📌 The script runs both the simulation and optimization methods in a straightforward top-down flow.
Future versions will modularize the code and may include CLI support or a `main()` wrapper.
---
## 🔧 TODO (Planned Improvements)
- Refactor into separate modules
- Add input flexibility via CLI or config
- Include unit tests
- Add export to CSV/PDF
---
## 📁 File Overview
```
portfolio-optimizer/
├── portfolio_optimization.py # Main script (initial version)
├── README.md # Project documentation
├── requirements.txt # Dependencies
├── assets/
│ └── efficient_frontier.png # Output plot (automatically generated)
```
---
## 📄 License
This project is licensed under the **MIT License**. You are free to use, modify, and distribute it.