options-analysis-suite 1.0.0

Python SDK for the Options Analysis Suite API: 17 pricing models, auto-calibration, GEX/DEX exposure, IV surfaces, and pre-computed market data.

Options Analysis Suite: Python SDK

Type-safe Python client for the Options Analysis Suite API.

Status: stable (1.0). Full coverage of every typed /v1/* operationId, plus a Calibration domain helper. Drift-checked against the deployed OpenAPI spec. Follows semantic versioning: the public surface will not change incompatibly without a major version bump.

Install

pip install options-analysis-suite

Quickstart

from oas import OASClient, TradierCredentials

with OASClient(api_key="oas_live_...") as client:
    # Data: cached EOD analytics
    snap = client.snapshot("SPY")
    print(snap.atmIv, snap.netGex, snap.maxPain)

    if snap.maxPainCurve:
        for row in snap.maxPainCurve:
            print(row.strike, row.totalPain)

    # Compute: 17 pricing models, full Greeks, exposure, expected move...
    price = client.price(model="bs", is_call=True, S=650, K=650, r=0.05,
                         q=0.012, sigma=0.15, t=0.25)
    greeks = client.greeks(model="heston", is_call=True, S=650, K=650, r=0.05,
                           q=0.012, sigma=0.15, t=0.25)

    # Calibrate once, persist, reuse: never re-touches the calibrationId TTL.
    cal = client.calibrate(
        "SPY", model="heston",
        broker=TradierCredentials(token="..."),
    )
    cal.save("spy_heston.json")

    # Evaluate the calibrated model anywhere across the chain.
    fair = cal.price(is_call=True, K=655, expiry="2026-06-19")

    # Stream batched metrics without manually paging.
    for m in client.iter_metrics(["SPY", "QQQ", "IWM", "DIA"], batch_size=50):
        print(m.symbol, m.ivRank)

Monte Carlo distributions

When model="mc", pass detail="distribution" to receive the full terminal-price distribution (percentiles + histogram) alongside the scalar price, or detail="full" to additionally receive the (subsampled) raw paths. detail="summary" is the default and matches the byte-identical shape any older caller already sees, plus an mcStats block (stdError + 95% CI + effective path count).

res = client.price(
    model="mc", detail="distribution",
    is_call=True, S=650, K=650, r=0.05, q=0.012, sigma=0.15, t=0.25,
)
print(res.price, res.mcStats.stdError)
print(res.distribution.percentiles.p50, res.distribution.percentiles.p95)

Sensitivity sweeps under Heston

client.sensitivity(...) returns the full 17-Greek set per point under Black-Scholes by default. Pass model="heston" together with the fitted Heston parameters (typically from a recent client.calibrate(...) call) to swap the per-point price to the Heston Fourier value and add a modelGreeks block with derivatives w.r.t. the five Heston parameters.

cal = client.calibrate("SPY", model="heston",
                       broker=TradierCredentials(token="..."))
sweep = client.sensitivity(
    is_call=True, S=650, K=650, r=0.05, sigma=0.15, t=0.25,
    axis="spot", model="heston", model_params=cal.params,
)
for row in sweep.data:
    print(row.x, row.delta, row.modelGreeks.dV0, row.modelGreeks.dRho)

Calibration round-trip

A Calibration is the durable wrapper around a /v1/compute/calibrate result. The fitted params dict survives a JSON round-trip; the 30-second-only calibrationId is intentionally not surfaced.

# Load a saved calibration in another process / hours later.
from oas import Calibration, OASClient

cal = Calibration.from_json("spy_heston.json")
with OASClient(api_key="oas_live_...") as client:
    cal.bind(client)  # attach so cal.price() / cal.greeks() can fire HTTP
    price = cal.price(is_call=True, K=650, S=650, r=0.05, q=0.012,
                      sigma=0.15, t=0.25)

Errors

Every error subclass carries the HTTP status, the server's structured code field (when present), and any extra fields the server returned.

from oas.errors import NotFoundError, RateLimitError, CalibrationQuotaError

try:
    snap = client.snapshot("UNKNOWN")
except NotFoundError as e:
    print(f"warehouse miss: {e}")
except RateLimitError as e:
    print(f"slow down, retry in {e.retry_after}s (bucket: {e.bucket})")
except CalibrationQuotaError as e:
    print(f"calibration quota exhausted; resets at {e.resets_at}")

The full hierarchy: OASError → AuthenticationError, ValidationError, PermissionDeniedError (with .required_scope), NotFoundError, RateLimitError (with .retry_after, .bucket), CalibrationQuotaError (with .resets_at), ConcurrencyLimitError (with .current, .max), ServerError.

Models

Response objects are typed Pydantic v2 models. Import them from oas._generated.models for type hints. The classes use extra='ignore' so additive server fields (e.g., a new metric in MetricsResponse) don't break older SDK versions; older SDKs simply omit unknown fields.

License

MIT licensed.