IdleFinance 0.2.0

A Python library for quantitative finance and financial modeling

IdleFinance

IdleFinance reimagines quantitative finance in Python by embedding portfolio theory, risk modeling, and asset pricing tools directly into the pandas ecosystem.

Rather than acting as a standalone optimizer, IdleFinance extends financial data structures with institutional-grade analytical capabilities through a seamless .finance accessor — transforming raw market data into a research-ready quantitative framework.

🚀 Key Pillars

IdleFinance is built on four core modules:

1. Portfolio Optimization:

   • Black-Litterman: Full J. Walters & T. Idzorek models with constraint handling.
   • Efficient Frontier: Min-Variance, Max-Sharpe (Tangency), Target-Return portfolios.
   • Automated market-implied risk aversion and prior equilibrium returns.

2. Risk Metrics:

   • Covariance Estimation: Sample, EMA/EWMA, and Denoised (Marchenko-Pastur eigenvalue clipping).
   • Risk Decomposition: Marginal Contribution to Risk (MCTR), Component VaR.

3. Fixed Income:

   • Unified Bond Pricing: Automatic detection of zero-coupon vs. coupon-bearing bonds.
   • Risk Metrics: Macaulay/Modified Duration, Convexity, and Effective Duration/Convexity (for bonds with embedded options).
   • Yield Analysis: Brent-method optimized YTM, Yield to Call (YTC), and current yield.
   • Arbitrary Cashflows: Duration and NPV analysis for any stream of payments.

4. Core Utilities:

   • Standard financial math: NPV, IRR, Payback Period, Loan Payments (PMT), and more.
   • Seamless conversion between price data and return series.

📦 Installation

pip install IdleFinance

🛠️ Quick Start

1. Pandas Objects accessor

IdleFinance extends pandas DataFrames and Series with the .finance accessor.

import pandas as pd
import IdleFinance as idf

prices = pd.Series([100, 102, 101, 105, 107])

print(f"Annualized Return: {prices.finance.annualized_return():.2%}")
print(f"Sharpe Ratio: {prices.finance.sharpe_ratio():.2f}")
print(f"Max Drawdown: {prices.finance.max_drawdown():.2%}")

2. Portfolio Optimization with Views

Combine market priors with investor views using the Black-Litterman model.

df_prices = pd.read_csv("portfolio_prices.csv", index_col=0)

post_ret, post_cov, weights = df_prices.finance.black_litterman(
    views={'AAPL': 0.12, 'MSFT': 0.08},
    view_confidences=[0.8, 0.6],
    bounds=(0, 0.30)
)

3. Risk Decomposition

prices = pd.Series([100, 102, 101, 105, 107])

print(f"Annualized Return: {prices.finance.annualized_return():.2%}")
print(f"Sharpe Ratio: {prices.finance.sharpe_ratio():.2f}")
print(f"Max Drawdown: {prices.finance.max_drawdown():.2%}")

4. Comprehensive Asset Valuation

ytm = idf.fixed_income.bond_ytm(price=925.61, face_value=1000, coupon_rate=0.05, years_to_maturity=10)