Markowitzify

Markowitzify is a lightweight Python library for portfolio optimization (portfolio) and technical-analysis helpers (stonks).

It includes:

• Portfolio analytics: Markowitz optimization, NCO optimization, Sharpe ratio, Hurst exponent, Monte Carlo simulation, trend scan.
• Stonks analytics: Fractal indicator, Bollinger bands, RSI, signal generation, and basic strategy backtesting.

Installation

pip install markowitzify

For local development:

pip install -e ".[dev]"

Optional market-data provider extras:

pip install -e ".[data]"

Quickstart (offline-safe)

import numpy as np
import pandas as pd
import markowitzify
import helper_monkey as hm

rng = np.random.default_rng(42)
returns = rng.normal(0.0005, 0.01, size=(200, 4))
prices = 100 * np.exp(np.cumsum(returns, axis=0))
df = pd.DataFrame(prices, columns=["AAA", "BBB", "CCC", "DDD"])

p = markowitzify.portfolio()
p.portfolio = df
p.cov = hm.cov_matrix(df)

p.markowitz()
print(p.optimal)

p.NCO()
print(p.nco)

API Overview

portfolio

p = markowitzify.portfolio(API_KEY=None, verbose=False)

Key methods/attributes:

• build_portfolio(TKR_list, time_delta, end_date=None, datareader=True, provider="auto")
  • provider="auto" prefers yfinance if installed, otherwise pandas_datareader.
• build_TSP() (depends on external endpoint availability).
• import_portfolio(input_path, filename="portfolio.csv", dates_kw="date")
• markowitz() → sets p.optimal
• NCO() / optimize_nco() → sets p.nco
• hurst(), sharpe_ratio(), simulate(), trend()

stonks

s = markowitzify.stonks("AAPL", provider="auto")

Key methods/attributes:

• fractal()
• bollinger()
• RSI()
• signal()
• strategize()

Testing / Contributing

Run checks locally:

ruff check .
pytest -q

Notes and limitations

• External market APIs/providers may change or break over time.
• Tests are intentionally offline and do not rely on Yahoo/MarketStack/TSP network calls.
• MarketStack-based portfolio building requires a valid API key.