Common Definitions#

Like any niche field, the world of quantamental data and its analysis has its own terminology and definitions. This section will cover the most common terms and definitions used within the Macrosynergy package.

The Quantamental Data Format (QDF)#

The Quantamental Data Format, alternatively a Quantamental DataFrame, is a tabular schema for storing and organizing quantamental data. It is a simple, yet flexible format that allows for ease of use and extensibility. The Macrosynergy package is built around the QDF, and all data is stored in this format.

An example of the QDF is shown below:

real_date	cid	xcat	value	grading	mop_lag
2023-01-02	AUD	FXXR_NSA	3.8	1	10
2023-01-02	AUD	RIR_NSA	2.0	1	10
2023-01-02	AUD	EQXR_NSA	-0.1	1	10
2023-01-02	CAD	FXXR_NSA	0.1	2	10
2023-01-02	CAD	RIR_NSA	1.3	2	10
2023-01-02	CAD	EQXR_NSA	-0.2	2	10

To get a better understanding of the QDF, please see Ralph’s Research posts:

How to build a quantamental system for investment management

and

Quantitative methods for macro information efficiency

on the Macrosynergy Research website.

Cross-Section Identifiers#

This would be a 3-letter string that identifies a cross-section/currency area. In the package, these are commonly referred to as cid or cids.

Examples:

USD - United States Dollar
FRA - France
EUR - Euro Area
GBP - Great Britain Pound

Extended Category Codes#

These are strings identifying the category of a data series. In the package, these are commonly referred to as xcat or xcats.

Extended category codes denote base category tickers and their transformations. . For example CPI_NSA would be a base category for seasonally adjusted headline CPI and P1M1ML12 would mean % change of the latest month versus one year ago.

An extended category code will contain the following parts:

Base category code (e.g. CPI)
Adjustment (e.g. NSA)
Transformation (e.g. P1M1ML12)

Combining these parts with underscores (_) will give us the extended category code. In the example above, the extended category code would be CPI_NSA_P1M1ML12.

Grading#

This is an integer between 1 and 3 (inclusive) that specifies the quality of the data series. Grade 1 is the highest quality, and Grade 3 is the lowest quality. Here, “quality” means estimated proximity to the actual value of an indicator to what was seen by the market at the related point in time.

End-of-Period (EOP) lags#

These are integers that specify for each real-time date the number of business days that passed since the end of the concurrent observation period.

For example, if the series reports monthly data, and the last day of the month would be the end of the period, then the EOP lag would be 0. When data is published for the next date in the series, the EOP lag would be 1.

Median-of-Period (MOP) lags#

These are integers that specify for each real-time date the number of business days that passed since the median date of the concurrent observation period.

For example, if the series reports monthly data, and the middle day of the month would be the median of the period, and for a value published on the first of the next month - the MOP lag would be approximated to 11 or 12.