quantadesh 0.1.0

A unified, developer-friendly quantitative finance toolkit for Python — options pricing, Greeks, Monte Carlo, portfolio risk, and clean market data.

quantadesh

A unified, developer-friendly quantitative finance toolkit for Python — options pricing, Greeks, Monte Carlo, portfolio risk, and clean market data, all behind one consistent API.

Built by Adesh Patel (github).

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Why quantadesh?

Every quant project starts the same way: rewriting Black-Scholes, hand-rolling Monte Carlo, wrestling with yfinance, building yet another portfolio class. quantadesh ends that loop with one consistent, batteries-included API.

from quantadesh import Option

opt = Option("call", S=100, K=110, T=1, r=0.05, sigma=0.20)

opt.price()                                # Black-Scholes
opt.price(method="binomial", steps=500, american=True)
opt.price(method="monte_carlo", paths=50_000, seed=42)
opt.greeks()                               # {'delta': ..., 'gamma': ..., ...}

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Install

pip install quantadesh
pip install "quantadesh[market]"      # add yfinance for market data
pip install "quantadesh[all]"         # everything: market + tests + docs

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Features

Module	What it gives you
models	Black-Scholes, CRR Binomial Trees (American/European), Monte Carlo with antithetic variates
greeks	Analytical Δ, Γ, Vega, Θ, ρ + numerical finite-difference Greeks
Option	Object-oriented wrapper combining pricing + Greeks
market	Clean Market.download() wrapper around yfinance
Portfolio	Multi-asset returns, vol, Sharpe, VaR, CVaR, max drawdown
timeseries	Returns, log returns, rolling vol, Sharpe, Sortino, max drawdown

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Examples

Options & Greeks

from quantadesh import Option

opt = Option("put", S=100, K=95, T=0.5, r=0.04, sigma=0.25)
print(opt.price())                          # Black-Scholes price
print(opt.greeks())                         # All Greeks
print(opt.price(method="monte_carlo", paths=100_000, seed=1))

Portfolio risk

from quantadesh import Market, Portfolio

prices = Market.download(["AAPL", "MSFT", "GOOG"], period="2y")
pf = Portfolio(prices, weights={"AAPL": 0.4, "MSFT": 0.4, "GOOG": 0.2})

print(pf.summary())
# {'expected_return': 0.18, 'volatility': 0.21, 'sharpe': 0.86,
#  'var_95': 0.024, 'cvar_95': 0.036, 'max_drawdown': -0.27, ...}

Functional API (NumPy-style)

from quantadesh import bs_price, mc_price, all_greeks

bs_price("call", S=100, K=110, T=1, r=0.05, sigma=0.2)
all_greeks("put", S=100, K=110, T=1, r=0.05, sigma=0.2)
mc_price("call", S=100, K=110, T=1, r=0.05, sigma=0.2, paths=20_000, seed=0)

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Roadmap

• v0.2 — Implied volatility solver, vol surface, exotic options (Asian, barrier)
• v0.3 — Markowitz mean-variance optimizer, efficient frontier
• v0.4 — Fama-French factor models, CAPM, beta utilities
• v1.0 — Stable API, full docs site, 95%+ test coverage

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License

MIT © Adesh Patel