numfolio 0.0.3

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numfolio ⚡

Portfolio performance accelerated by Numba

A lightweight, flexible Python package for analyzing portfolio returns, risk metrics, and correlations using modern statistical and machine learning methods.

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🚀 Features

✅ Bootstrapped metric estimation (e.g., Sharpe Ratio, Sortino Ratio)

✅ Automatic Scorecard Generation with time-based aggregation (Yearly, Quarterly, Monthly)

✅ Covariance and Correlation estimation with robust shrinkage methods

✅ Parallel computation for scalability

✅ Clean, consistent API inspired by scikit-learn & pandas

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🔧 Installation

To install the package the simplest procedure is:

pip install numfolio

Now you can test the installation... In a python shell:

import numfolio as nf

nf.__version__

Optional dependencies are docs for documentation and build for development. To install optional dependencies pip install numfolio[docs,build].

📚 Example Usage

1. Compute Scorecard from PnL or Returns:

import pandas as pd
from numfolio import get_scorecard

# Sample PnL data
dates = pd.date_range("2025-01-01", periods=60, freq="D")
pnl = pd.Series(range(100, 160), index=dates)

df = pd.DataFrame({"pnl": pnl})

scorecard = get_scorecard(df, freq="M")
print(scorecard)

2. Estimate Bootstrapped Sharpe Ratio:

import numpy as np
from numfolio import bootstrap_metric

# Generate fake returns
returns = np.random.default_rng().normal(0, 1, 100)

bootstrapped = bootstrap_metric(returns, metric="sharpe_ratio", n_bootstraps=500)
print("Bootstrapped Sharpe Ratios:", bootstrapped[:5])

3. Estimate Correlation Matrix:

import pandas as pd
import numpy as np
from numfolio import estimate_correlation

dates = pd.date_range("2025-01-01", periods=100, freq="D")
returns = pd.DataFrame(np.random.default_rng().normal(0, 1, (100, 3)), columns=["A", "B", "C"], index=dates)

correlation = estimate_correlation(returns, method="ledoit_wolf", n_bootstraps=200)
print(correlation)