Supported Data Types
This article describes the types of data you can bring into Model Reef using Google Finance and Yahoo style APIs, and how that data is intended to be used inside your models.
You will learn:
Which data types are supported.
How those data types are represented in the Data Library.
Typical use cases for each type.
These imports are designed to augment your models with external series, not to replace core financial statement imports such as PDFs, Xero, QuickBooks or fundamentals feeds.
Overview of supported data types
Model Reef supports importing the following data types through Google Finance, Yahoo style or similar public APIs, subject to what is available for a given symbol:
Price series
Daily, weekly or monthly close prices.
Optionally open, high and low values.
Adjusted prices where available.
Volume series
Traded volume per period.
Index levels
Equity indices.
Sector or thematic indices.
FX rates
Spot or average exchange rates.
Typically represented as currency pairs, for example
GBPUSD.
Yield or rate curves (where provided)
Policy rates or benchmark yields.
Short end and long end indicators.
These series are usually imported as drivers or modifiers rather than as full fundamentals that directly hit P&L or the Balance Sheet.
Tickers and symbols
For each import you specify one or more symbols, which could be:
Equity tickers.
Index tickers.
Currency pairs.
Rate or bond tickers supported by the API.
The exact symbol syntax depends on the underlying data source definition, but in Model Reef you simply store the resulting series as named Data Library entries.
Frequency and granularity
API based data typically supports several frequencies, such as:
Daily.
Weekly.
Monthly.
When you import, you choose the frequency that best matches how you intend to use the data. Model Reef then:
Stores the series at that frequency in the Data Library.
Aligns them with your model's timeline when used in formulas and drivers.
If you later change the model's periodicity, Model Reef will resample or aggregate the imported series as needed.
Use cases for API based data
Common use cases include:
Pricing and FX drivers
Use FX rates as multipliers in revenue or cost formulas.
Use commodity or index prices as drivers for pricing power or input costs.
Macro and market drivers
Use index levels or yields as explanatory variables in regression based forecasts.
Use rate curves for interest cost sensitivities.
Scenario conditioning
Create different market condition scenarios by scaling API derived series.
Combine external data with internal drivers to form scenarios.
API sourced series give you flexible, continuously updatable drivers that complement your own internal data.
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