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Smart-Portfolio-Optimizer

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최종 버전 다운로드 (.zip)

- optimizer.cpython-311.pyc
LICENSE
main.py
optimizer.py
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/tubakhxn/Smart-Portfolio-Optimizer

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Smart-Portfolio-Optimizer

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python main.py

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Smart Portfolio Optimizer

**What is this project about?**

This project is a Smart Portfolio Optimizer that helps investors find the best mix of stocks using Modern Portfolio Theory (MPT). It fetches historical stock data, simulates thousands of portfolios, and identifies the ones with the best risk-return tradeoff. The tool visualizes the efficient frontier and highlights optimal portfolios, making it easy to see how to maximize returns for a given level of risk.

**Developer/Creator:** tubakhxn

## Features
- Fetches historical stock data using yfinance
- Calculates expected returns, covariance matrix, portfolio volatility, Sharpe ratio
- Generates 10,000+ random portfolios
- Identifies maximum Sharpe ratio and minimum volatility portfolios
- Visualizes efficient frontier and portfolio scatter plot
- Highlights optimal portfolios

## Structure
- main.py: Entry point for running the optimizer and visualizations
- optimizer.py: Contains portfolio optimization logic and calculations
- requirements.txt: Lists required Python packages
- README.md: Explains MPT math and project usage

## Modern Portfolio Theory (MPT) Math

Modern Portfolio Theory aims to maximize portfolio return for a given level of risk, or minimize risk for a given level of expected return. Key concepts:

- **Expected Returns ($\mu$):**
  $$\mu = \frac{1}{N} \sum_{i=1}^{N} r_i$$
  Where $r_i$ is the return for period $i$.

- **Covariance Matrix ($\Sigma$):**
  $$\Sigma_{ij} = \text{Cov}(r_i, r_j)$$
  Measures how returns of assets move together.

- **Portfolio Volatility ($\sigma_p$):**
  $$\sigma_p = \sqrt{w^T \Sigma w}$$
  Where $w$ is the vector of asset weights.

- **Sharpe Ratio ($S$):**
  $$S = \frac{\mu_p - r_f}{\sigma_p}$$
  Where $\mu_p$ is portfolio expected return, $r_f$ is risk-free rate, $\sigma_p$ is portfolio volatility.

- **Efficient Frontier:**
  The set of portfolios offering the highest expected return for a given risk.

## Usage
1. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
2. Run the optimizer:
   ```bash
   python main.py
   ```

## Clean Finance-Style Visualization
- Efficient frontier plotted
- Portfolios colored by Sharpe ratio
- Optimal portfolios highlighted

---

For more details on MPT, see [Wikipedia](https://en.wikipedia.org/wiki/Modern_portfolio_theory).