quantadesh 0.2.1

A comprehensive, beginner-friendly quantitative finance toolkit by Adesh Patel.

quantadesh

A comprehensive, beginner-friendly quantitative finance toolkit by Adesh Patel.

quantadesh covers the majority of what a quant practitioner computes day-to-day — options pricing, Greeks, exotics, fixed income, short-rate models, portfolio optimization, risk measures, and time-series diagnostics — behind a clean, consistent API designed so AI assistants (and humans) can write code against it correctly the first time.

Why quantadesh

• Consistent signatures everywhere: every pricer is f(S, K, T, r, sigma, ..., option_type="call") — option_type is always last.
• Structured outputs: Monte Carlo returns MCResult(price, se, paths, ci95), Greeks return GreeksResult(...), never bare tuples.
• One-call analyzers: qa.analyze_option(...) or qa.analyze_bond(...) give you everything in one go.
• No surprise dependencies: only numpy, pandas, scipy. yfinance is optional.
• Deeply tested: 30+ pytest cases covering parity, convergence, and edge cases.

Installation

pip install quantadesh
# optional extras:
pip install quantadesh[market]   # adds yfinance market-data wrappers
pip install quantadesh[plot]     # adds matplotlib for examples
pip install quantadesh[dev]      # everything (tests, docs, market, plots)

Quick start

import quantadesh as qa

# 1) Plain Black-Scholes — returns a float
qa.bs_price(S=100, K=100, T=1, r=0.05, sigma=0.2, option_type="call")
# 10.4506

# 2) Monte Carlo — returns a structured MCResult, never a bare tuple
mc = qa.mc_price(S=100, K=100, T=1, r=0.05, sigma=0.2, paths=200_000, option_type="call", seed=42)
print(mc)
# MCResult(price=10.4485, se=0.034170, paths=200000, ci95=(10.3815, 10.5155))

# 3) Greeks — full bundle (incl. higher-order vanna/volga/charm)
g = qa.bs_greeks(100, 100, 1, 0.05, 0.2, option_type="call")
print(g.delta, g.gamma, g.vega, g.theta, g.rho)

# 4) One-call full analysis
report = qa.analyze_option(S=100, K=100, T=1, r=0.05, sigma=0.2)
print(report["Black-Scholes"], report["Binomial (American)"], report["Greeks"])

What's inside

Area	Functions
European pricing	bs_price, bachelier_price, black76_price, binomial_price, mc_price, heston_price
American options	binomial_price(..., american=True)
Implied vol	bs_implied_vol (Brent solver)
Exotics	asian_price, barrier_price (4 types), lookback_price, digital_price
Greeks	bs_greeks (Δ, Γ, Vega, Θ, ρ + vanna, volga, charm), numerical_greeks
Fixed income	bond_price, ytm, duration, modified_duration, convexity, YieldCurve
Short-rate models	vasicek_simulate/vasicek_zcb_price, cir_simulate/cir_zcb_price, hull_white_simulate
Portfolio	efficient_frontier, min_variance, max_sharpe, capm_beta, black_litterman
Risk	historical_var, parametric_var, monte_carlo_var, historical_cvar, parametric_cvar, max_drawdown
Time series	simple_returns, log_returns, realized_vol, ewma_vol, garch11_fit, adf_test, hurst_exponent, ljung_box
Performance	sharpe_ratio, sortino_ratio, calmar_ratio, information_ratio, omega_ratio, treynor_ratio
Market data	qa.market.get_prices, qa.market.get_returns (optional, requires yfinance)
Result types	MCResult, GreeksResult, PricingResult, BondResult, PortfolioResult, RiskResult, FrontierResult
Analyzers	analyze_option, analyze_bond, analyze_portfolio

API design rules (what makes this library AI-friendly)

1. Same signature everywhere: f(S, K, T, r, sigma, ..., option_type="call").
2. Structured returns: every non-trivial function returns a dataclass with __repr__, to_dict(), and float() cast.
3. Validation up front: option_type is normalized; positives are checked; clear errors.
4. No silent magic: optional behaviour is opt-in via keyword (return_result=True, american=True, antithetic=True).

License

MIT © Adesh Patel