python-option-calculator

Vanilla Option Calculator

Black-Scholes-Merton pricing and greeks for vanilla options, written in pure Python — no NumPy, no SciPy, no other numerical dependencies. The only external dependency is matplotlib, and only for the optional plotting layer.

Available on PyPI: option-calculator.

• Theoretical price and greeks: Theo, Delta, Theta, Vega, Gamma
• Portfolio aggregation and mark-to-market P&L across many positions
• Price-grid visualisation (P&L / Delta / Theta / Vega / Gamma)
• Delta-neutral price search
• Optional risk-free rate r (defaults to 0, the rates-free crypto convention)

Installation

From PyPI:

pip install option-calculator            # core only (standard library)
pip install "option-calculator[plot]"    # + matplotlib for the plotting layer

From source:

pip install .            # core only (standard library)
pip install ".[plot]"    # + matplotlib for the plotting layer
pip install ".[dev]"     # + pytest and ruff for development

Or, for development, install in editable mode:

pip install -e ".[plot,dev]"

Quick start

Price and greeks of a single option

from option_calculator import BSM

calc = BSM()

S = 19500          # underlying price
K = 19000          # strike
V = 0.45           # implied volatility as a fraction (45%)
T = 30 / 365       # time to expiry in years
dType = "P"        # 'C' call, 'P' put, 'F' future

print("Theo: ", round(calc.theo(S, K, V, T, dType), 2))
print("Delta:", round(calc.delta(S, K, V, T, dType), 2))
print("Theta:", round(calc.theta(S, K, V, T, dType), 2))
print("Vega: ", round(calc.vega(S, K, V, T), 2))
print("Gamma:", round(calc.gamma(S, K, V, T), 2))

See examples/example_greeks.py.

Portfolio aggregation and plotting

from option_calculator import Plot

plot = Plot()
exp = 30 / 365
params = [
    {"dType": "F", "price": 20000, "quant": 1,  "strike": 0,     "vola": 0,    "exp": exp},
    {"dType": "C", "price": 500,   "quant": 1,  "strike": 25000, "vola": 0.75, "exp": exp},
    {"dType": "P", "price": 100,   "quant": 1,  "strike": 15000, "vola": 0.75, "exp": exp},
]

plot.plotPL(13500, 28500, params, exp, step=10)

print("Delta:", plot.deltaFull(21000, params, exp))
print("P&L:  ", plot.p_l(21000, params, exp))

See examples/example_plot.py.

Position format

A position is either an OptionPosition dataclass or a plain dictionary with the same fields:

Field	Meaning
dType	'C' call, 'P' put, 'F' linear future
strike	strike price (> 0 for options; use 0 for futures)
vola	implied volatility as a fraction (e.g. 0.45)
exp	time to expiry in years
quant	signed quantity (positive = long, negative = short)
price	entry premium / price, used for P&L

from option_calculator import OptionPosition, Pricing

pricing = Pricing()
positions = [OptionPosition("C", 25000, 0.75, 30 / 365, quant=1, price=500)]
print(pricing.deltaFull(21000, positions))

Passing a positional exp to the aggregators overrides every position’s expiry (and exp=0 is honoured). Inputs are never mutated.

Conventions and limitations

• Rate: r is a continuously compounded risk-free rate and defaults to 0.0. With r = 0 the formulas reduce to the classic zero-rate Black-Scholes, the convention used for coin-margined / futures-style crypto options. Pass r= to use the discounted model.
• Vega is scaled per 1 percentage point of volatility (VEGA_SCALE = 100).
• Theta is expressed per calendar day (DAYS_PER_YEAR = 365).
• Degenerate inputs: T = 0 (expired) and V = 0 are valid and yield the discounted intrinsic value with deterministic greeks; other invalid inputs (S ≤ 0, K ≤ 0, V < 0, T < 0, unknown dType) raise ValueError.
• Only European vanilla options are supported. No dividends, no American exercise, no volatility smile.

Project layout

src/option_calculator/
├── bsm.py         # Black-Scholes-Merton core (pure stdlib)
├── position.py    # OptionPosition dataclass
├── pricing.py     # portfolio aggregation (greeks, P&L, price grid)
├── plot.py        # optional matplotlib layer
└── searching.py   # delta-neutral price search
examples/          # runnable demo scripts
tests/             # pytest suite

Development

pip install -e ".[plot,dev]"
ruff check src tests examples   # lint
ruff format --check src tests examples # formatting
pytest                          # tests

Changelog and releases

See CHANGELOG.md. Releases follow Semantic Versioning; see CONTRIBUTING.md for the release checklist.

License

MIT © 2017 yzoz