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VolCurve fits an implied volatility smile for a single option expiry. It follows a scikit-learn style workflow: configure an estimator, call fit, then evaluate the fitted object. The resulting smile lets you price options, compute Greeks, export IV data, and convert directly into a risk-neutral probability distribution. The steps below walk through the complete workflow.
1

Initialize

Create a VolCurve with your chosen calibration algorithm and pricing engine. The default configuration uses SVI for the smile and Black-76 for IV inversion.
from oipd import MarketInputs, VolCurve, sources

vol = VolCurve(method="svi", pricing_engine="black76")
By default, use pricing_engine="black76". OIPD infers the forward price from put-call parity, so the fitted forward already reflects the market’s expected carry, including dividends. You do not need to enter dividends.
2

Fetch data

Download a single-expiry chain and populate MarketInputs from the vendor snapshot.This example uses the built-in yfinance fetcher. For research or production work, your own vendor, broker, or exchange data will usually be cleaner. See Data sources to load a CSV or DataFrame instead.
ticker = "PLTR"
expiries = sources.list_expiry_dates(ticker)
single_expiry = expiries[1]

chain, snapshot = sources.fetch_chain(ticker, expiries=single_expiry)

market = MarketInputs(
    valuation_date=snapshot.asof,
    underlying_price=snapshot.underlying_price,
    risk_free_rate=0.04,
)
3

Fit

Call vol.fit(chain, market). The method validates the chain, inverts IVs, calibrates the SVI parameters, and stores results on the instance.
vol.fit(chain, market)
4

Query IV

Query IVs for any strike or array of strikes using either implied_vol or the callable shorthand.
import numpy as np

strikes = np.array([80, 90, 100, 110, 120])

ivs = vol.implied_vol(strikes)   # returns np.ndarray
ivs = vol(strikes)               # identical shorthand

atm = vol.atm_vol                # IV where strike == forward price (float)
fwd = vol.forward_price          # parity-implied forward price (float)
5

Diagnostics

After fitting, diagnostics returns a dictionary of calibration metrics such as RMSE. params returns the raw SVI parameter set {a, b, rho, m, sigma}.
print(vol.diagnostics)   # {"rmse": 0.0014, "status": "converged", ...}
print(vol.params)        # {"a": ..., "b": ..., "rho": ..., "m": ..., "sigma": ...}
6

Prices and Greeks

vol.price uses the fitted smile to value options at arbitrary strikes. vol.greeks returns a DataFrame with columns strike, delta, gamma, vega, theta, and rho.
prices = vol.price(strikes, call_or_put="call")

greeks_df = vol.greeks(strikes, call_or_put="call")
print(greeks_df)
#    strike   delta   gamma    vega   theta     rho
# 0    80.0   0.923   0.012   3.210  -0.041   0.172
# ...
7

Export and plot

iv_results returns a DataFrame with the fitted smile curve alongside observed market bid/ask/mid IVs for quality-checking the calibration.
import matplotlib.pyplot as plt

smile_df = vol.iv_results(include_observed=True)
# columns: strike, fitted_iv, market_bid_iv, market_ask_iv, ...

fig = vol.plot(x_axis="strike", y_axis="iv")
plt.show()
Example output:Example VolCurve implied volatility smile plot
8

Probability distribution

Call implied_distribution() to derive a ProbCurve from the fitted smile. The returned object supports all probability queries described in the single-expiry probability guide.
prob = vol.implied_distribution()

print(prob.prob_below(100))
print(prob.quantile(0.50))
VolCurve.fit requires that the input chain contain exactly one expiry. If the DataFrame has multiple expiry dates, OIPD raises a ValueError. Use VolSurface.fit for multi-expiry chains.