> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-lemma.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OIPD

> OIPD is a Python library that extracts price-implied risk-neutral probability distributions from listed options data.

OIPD (Options Implied Probability Distribution) turns raw options chain data into price-implied risk-neutral probability distributions. It fits SVI volatility smiles, links maturities with total-variance interpolation, and lets you query the distribution implied by option prices under OIPD's pricing assumptions.

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Compute your first market-implied probability distribution in under five minutes using live options data.
  </Card>

  <Card title="Installation" icon="download" href="/installation">
    Install OIPD via pip and configure your Python environment for options analysis.
  </Card>

  <Card title="Core concepts" icon="book" href="/concepts/mental-model">
    Understand the four core objects — VolCurve, VolSurface, ProbCurve, ProbSurface — and how they fit together.
  </Card>

  <Card title="API reference" icon="code" href="/reference/prob-curve">
    Full reference for every public method on OIPD's classes, including parameters, return types, and examples.
  </Card>
</CardGroup>

## Capabilities

OIPD provides two tightly integrated capabilities in a single library:

* **Probability extraction** — compute the full risk-neutral PDF and CDF over future asset prices, query tail probabilities, quantiles, and distributional moments
* **Volatility modeling** — fit single-expiry SVI smiles and multi-expiry surfaces with total-variance interpolation, evaluate implied vols, price options, and compute Greeks

<Steps>
  <Step title="Install">
    ```bash theme={null}
    pip install oipd
    ```
  </Step>

  <Step title="Fetch data">
    ```python theme={null}
    from oipd import sources

    expiries = sources.list_expiry_dates("AAPL")
    chain, snapshot = sources.fetch_chain("AAPL", expiries=expiries[1])
    ```
  </Step>

  <Step title="Fit">
    ```python theme={null}
    from oipd import MarketInputs, ProbCurve

    market = MarketInputs(
        valuation_date=snapshot.asof,
        underlying_price=snapshot.underlying_price,
        risk_free_rate=0.04,
    )
    prob = ProbCurve.from_chain(chain, market)
    ```
  </Step>

  <Step title="Query">
    ```python theme={null}
    prob.prob_below(200)   # P(price < 200)
    prob.quantile(0.50)    # median implied price
    prob.plot()            # visualize the full distribution
    ```
  </Step>
</Steps>

<CardGroup cols={2}>
  <Card title="Probability" icon="chart-area" href="/guides/probability-single-expiry">
    Step-by-step walkthroughs for computing probability distributions on a single expiry or across a full time horizon.
  </Card>

  <Card title="Volatility" icon="wave-sine" href="/guides/volatility-smile">
    Learn how to fit implied volatility smiles and surfaces for pricing and risk work.
  </Card>

  <Card title="Data sources" icon="database" href="/guides/data-sources">
    Connect OIPD to live market data via yfinance, or load your own data from CSV or DataFrame.
  </Card>

  <Card title="Warnings" icon="triangle-exclamation" href="/guides/warnings-diagnostics">
    Understand how OIPD surfaces data quality issues and model risk through structured diagnostics.
  </Card>
</CardGroup>