A modern Python library for Markowitz portfolio optimization and analysis.

📖 Full documentation

Previously known as Diversificador. The original portfolio-analysis web app built with Dash is no longer maintained, but it is preserved on the dash-deprecated branch for reference.

Features

• Markowitz Mean-Variance Optimization — Compute the efficient frontier using scipy.optimize
• Capital Allocation Line — Mix risky portfolios with risk-free assets
• Visualization — Plotly-based charts for efficient frontier, allocation pie, CAL, correlation heatmaps, and price timelines
• Data Fetching — Built-in helpers for downloading market data via yfinance
• Web Application — FastAPI backend with a dark-themed interactive frontend

Installation

pip install markowizard

That's everything the library needs: optimization (scipy), market-data fetching (yfinance), and visualization (plotly). No optional extras.

Quick Start (Library)

from markowizard import MarkowitzOptimizer, CapitalAllocator
from markowizard.data import fetch_prices, compute_monthly_returns
from markowizard.visualization import efficiency_frontier_plot

# Fetch prices and compute monthly returns (decimal form, e.g. 0.01 = 1%)...
prices = fetch_prices(["AAPL", "MSFT", "GOOGL", "SPY"], period="5y")
returns = compute_monthly_returns(prices)
# ...or bring your own returns DataFrame (assets as columns).

# Optimize
optimizer = MarkowitzOptimizer(returns)
portfolios = optimizer.optimize()

# Compute Sharpe ratios (provide monthly risk-free rate)
risk_free_rate = 0.005  # 0.5% per month
portfolios = optimizer.compute_sharpe(risk_free_rate)

# Plot the efficient frontier
fig = efficiency_frontier_plot(portfolios, highlight_portfolio=50)
fig.show()

# Best portfolio (maximum Sharpe ratio)
best = optimizer.max_sharpe_portfolio()
print(best)

# Capital allocation line
allocator = CapitalAllocator(best, risk_free_rate)
cal_points = allocator.capital_allocation_line(steps=21)

Web Application

An interactive web UI (FastAPI + a dark-themed frontend) lives in backend/ and frontend/. It is not part of the PyPI package — run it from the container image or a clone.

Using Docker

docker run -p 8000:8000 ghcr.io/outliersanalytics/markowizard:latest

From a clone

git clone https://github.com/OutliersAnalytics/MarkoWizard
cd MarkoWizard
uv run --with-requirements backend/requirements.txt uvicorn backend.main:app --port 8000

Open http://localhost:8000 — the app auto-submits with default tickers on load.

It exposes a single endpoint, POST /api/analyze; see the Web Application docs for the request/response shape.

API Reference

Full reference for every public class and function — MarkowitzOptimizer, CapitalAllocator, the visualization chart functions, and the data fetch helpers — lives in the documentation site.

Development

See CONTRIBUTING.md for setup instructions and contribution guidelines.

License

MIT