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Dashboard Service#

This file contains the core service layer that drives the dashboard application.

Orchestrates all logic for the Streamlit dashboard.

This class acts as a stateful service to manage data loading, model calibration, and result generation for a given ticker and date.

Parameters:

Name Type Description Default
ticker str

The stock ticker to analyze.

 required
snapshot_date str

The market data snapshot date ('YYYY-MM-DD') or 'Live Data'.

 required
model_configs dict[str, Any]

A dictionary of model configurations to be used for calibration.

 required
Source code in src/quantfin/dashboard/service.py 
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class DashboardService:
    """
    Orchestrates all logic for the Streamlit dashboard.

    This class acts as a stateful service to manage data loading,
    model calibration, and result generation for a given ticker and date.

    Parameters
    ----------
    ticker : str
        The stock ticker to analyze.
    snapshot_date : str
        The market data snapshot date ('YYYY-MM-DD') or 'Live Data'.
    model_configs : dict[str, Any]
        A dictionary of model configurations to be used for calibration.
    """

    def __init__(
        self,
        ticker: str,
        snapshot_date: str,
        model_configs: dict[str, Any],
    ):
        self.ticker = ticker
        self.snapshot_date = snapshot_date
        self.model_configs = model_configs
        self._market_data: pd.DataFrame | None = None
        self._stock: Stock | None = None
        self._rate: Rate | None = None
        self.calibrated_models: dict[str, Any] = {}
        self.summary_df: pd.DataFrame | None = None

    @property
    def market_data(self) -> pd.DataFrame:
        """
        Lazily loads the market data for the selected ticker and date.

        Returns
        -------
        pd.DataFrame
            A DataFrame containing the option chain market data.
        """
        if self._market_data is None:
            if self.snapshot_date == "Live Data":
                self._market_data = get_live_option_chain(self.ticker)

            else:
                self._market_data = load_market_snapshot(
                    self.ticker, self.snapshot_date
                )
        return self._market_data

    @property
    def stock(self) -> Stock:
        """
        Lazily computes and caches the underlying Stock object.

        The dividend yield is implied from put-call parity.

        Returns
        -------
        Stock
            A Stock instance representing the underlying asset.
        """
        if self._stock is None:
            spot = self.market_data["spot_price"].iloc[0]
            _, div = fit_rate_and_dividend(
                self.market_data[self.market_data["optionType"] == "call"],
                self.market_data[self.market_data["optionType"] == "put"],
                spot,
            )
            self._stock = Stock(spot=spot, dividend=div)

        return self._stock

    @property
    def rate(self) -> Rate:
        """
        Lazily computes and caches the risk-free Rate object.

        The rate is implied from put-call parity.

        Returns
        -------
        Rate
            A Rate instance representing the risk-free rate.
        """
        if self._rate is None:
            spot = self.market_data["spot_price"].iloc[0]
            r, _ = fit_rate_and_dividend(
                self.market_data[self.market_data["optionType"] == "call"],
                self.market_data[self.market_data["optionType"] == "put"],
                spot,
            )
            self._rate = Rate(rate=r)

        return self._rate

    def run_calibrations(self):
        """
        Runs the daily calibration workflow for all selected models.

        This method iterates through the model configurations, runs the
        `DailyWorkflow` for each, and stores the calibrated models and
        a summary of the results.
        """
        all_results = []
        for model_name, config in self.model_configs.items():
            workflow = DailyWorkflow(market_data=self.market_data, model_config=config)
            workflow.run()
            all_results.append(workflow.results)
            if workflow.results.get("Status") == "Success":
                params = workflow.results.get("Calibrated Params")
                self.calibrated_models[model_name] = config["model_class"](
                    params=params
                )
        self.summary_df = pd.DataFrame(all_results)

    def get_iv_plots(self):
        """
        Generates and returns the volatility smile and 3D surface plots.

        It first computes the market IV surface, then computes the IV surface
        for each successfully calibrated model, and finally generates the plots.

        Returns
        -------
        tuple[matplotlib.figure.Figure, plotly.graph_objects.Figure]
            A tuple containing the volatility smile figure and the 3D surface figure.
        """
        market_surface = (
            VolatilitySurface(self.market_data)
            .calculate_market_iv(self.stock, self.rate)
            .surface
        )

        model_surfaces = {}
        for name, model in self.calibrated_models.items():
            technique = select_fastest_technique(model)
            model_surfaces[name] = (
                VolatilitySurface(self.market_data)
                .calculate_model_iv(self.stock, self.rate, model, technique)
                .surface
            )

        smile_fig = plot_smiles_by_expiry(
            market_surface,
            model_surfaces,
            self.ticker,
            self.snapshot_date,
        )
        surface_fig = plot_iv_surface_3d(market_surface, model_surfaces, self.ticker)

        return smile_fig, surface_fig

market_data: pd.DataFrame property #

Lazily loads the market data for the selected ticker and date.

Returns:

Type Description
DataFrame

A DataFrame containing the option chain market data.

rate: Rate property #

Lazily computes and caches the risk-free Rate object.

The rate is implied from put-call parity.

Returns:

Type Description
Rate

A Rate instance representing the risk-free rate.

stock: Stock property #

Lazily computes and caches the underlying Stock object.

The dividend yield is implied from put-call parity.

Returns:

Type Description
Stock

A Stock instance representing the underlying asset.

get_iv_plots() #

Generates and returns the volatility smile and 3D surface plots.

It first computes the market IV surface, then computes the IV surface for each successfully calibrated model, and finally generates the plots.

Returns:

Type Description
tuple[Figure, Figure]

A tuple containing the volatility smile figure and the 3D surface figure.
Source code in src/quantfin/dashboard/service.py 
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def get_iv_plots(self):
    """
    Generates and returns the volatility smile and 3D surface plots.

    It first computes the market IV surface, then computes the IV surface
    for each successfully calibrated model, and finally generates the plots.

    Returns
    -------
    tuple[matplotlib.figure.Figure, plotly.graph_objects.Figure]
        A tuple containing the volatility smile figure and the 3D surface figure.
    """
    market_surface = (
        VolatilitySurface(self.market_data)
        .calculate_market_iv(self.stock, self.rate)
        .surface
    )

    model_surfaces = {}
    for name, model in self.calibrated_models.items():
        technique = select_fastest_technique(model)
        model_surfaces[name] = (
            VolatilitySurface(self.market_data)
            .calculate_model_iv(self.stock, self.rate, model, technique)
            .surface
        )

    smile_fig = plot_smiles_by_expiry(
        market_surface,
        model_surfaces,
        self.ticker,
        self.snapshot_date,
    )
    surface_fig = plot_iv_surface_3d(market_surface, model_surfaces, self.ticker)

    return smile_fig, surface_fig

run_calibrations() #

Runs the daily calibration workflow for all selected models.

This method iterates through the model configurations, runs the DailyWorkflow for each, and stores the calibrated models and a summary of the results.

Source code in src/quantfin/dashboard/service.py 
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def run_calibrations(self):
    """
    Runs the daily calibration workflow for all selected models.

    This method iterates through the model configurations, runs the
    `DailyWorkflow` for each, and stores the calibrated models and
    a summary of the results.
    """
    all_results = []
    for model_name, config in self.model_configs.items():
        workflow = DailyWorkflow(market_data=self.market_data, model_config=config)
        workflow.run()
        all_results.append(workflow.results)
        if workflow.results.get("Status") == "Success":
            params = workflow.results.get("Calibrated Params")
            self.calibrated_models[model_name] = config["model_class"](
                params=params
            )
    self.summary_df = pd.DataFrame(all_results)