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May 7, 2020

Macrobond 1.23

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We’ve just released an updated version of the Macrobond application. This article covers the following new features that are available to all Macrobond users in this update:

1) Expanded watchlist settings

2) Chart layout customization additions

3) Bloomberg connector enhancements

Along with this update, we’re also releasing a new edition of Macrobond: the Data Scientist – which includes some exciting new API features that we also highlight at the end of this article:

1) Revision history / point in time data / vintage data available through the Macrobond APIs

2) Ability to search the Macrobond database through the APIs

If you or any of your colleagues would like to try the Data Scientist edition, you can request a free trial here.

Selection of new functionality

Watchlist Actions

The Watchlist feature, which was first introduced in 2018, allows you to flag and monitor specific series or sets of series.

We’re now introducing Watchlist Actions – a feature that lets you select among a few actions to be executed when the series used in a Watchlist item are updated, such as showing a notification on your PC or running a program. There is special support for launching Python and R. The Macrobond application has to be running for the action to be executed.

Set the “day interval” when you’re monitoring a set of series. When all monitored series have been updated within this time range, the item is alerted.

There can be a delay of up to 5 minutes before you get the alert after the data has been updated.

If you have selected “Show in application” you’ll be able to see all alerted series in the Start activity in Macrobond.

Background color gradients

We’ve added the option to specify a linear or radial gradient for background colors in charts. This applies not only to the background of the whole chart, but also to other elements where you can set a background, such as the legend.

Chart Size Units

When you pick the size of a chart for export, you can now set the unit to be either pixel, millimeter or inch.

Bloomberg Connector Enhancement

In the Bloomberg series provider, you can now paste Bloomberg Excel BDH expressions and the application will suggest a time series.

For example, if you enter or paste

=BDH(“goog us equity”,”EBIT”,”1/1/2005″,”12/31/2009″,”per=cy”)

New Features in Macrobond Data Scientist

The Data Scientist edition of Macrobond includes the following API enhancements. If they sound like something you’d like to try out, you can learn more and request a trial here.

Revision History

One significant feature of Data Scientist is that it includes access to revision history for many series in our database. This makes it possible to

  • See what data points were updated and exactly what changes were made with each revision
  • Get a vintage version of a series in order to perform an analysis using only information available at that time
  • See how a value has been revised over time

Data Coverage

The revision data we currently carry includes up to two years of history from primary sources, as well as extensions in specific cases – for example: ONS, BLS, BEA, FED and OECD data.  Our coverage focuses on forecasts, national accounts, labor market, trade, product, balance of payments, as well as foreign and public finance. Regionally, the focus is on G20 countries with a priority on larger economies initially. During 2020, we will work to expand both the geographies and categories covered.

Some Examples of Data Scientist Revision History

To find out if a series stores revision history, you can either look for the metadata attribute “StoresRevisionHistory” or use the method HasRevisions in Python/MATLAB  or getHasRevisions in R.

Here is an example in R that downloads the revision history for the series usgdp and plots the first and second release.

seriesGdp <- FetchOneTimeSeriesWithRevisions(“usgdp”)
firstRelease <- getNthRelease(seriesGdp, 0)
secondRelease <- getNthRelease(seriesGdp, 1)
x <- MakeXtsFromUnifiedResponse(c(firstRelease, secondRelease))

Here is another example that will show the UK GDP vintages for 2017-2020:

seriesGdp <- FetchOneTimeSeriesWithRevisions(“gbgdp”)
s2017 <- series2018 <- getVintage(seriesGdp, as.Date(“2017-01-01”))
s2018 <- series2018 <- getVintage(seriesGdp, as.Date(“2018-01-01”))
s2019 <- series2018 <- getVintage(seriesGdp, as.Date(“2019-01-01”))
s2020 <- series2018 <- getVintage(seriesGdp, as.Date(“2020-01-01”))
x <- MakeXtsFromUnifiedResponse(c(s2017, s2018, s2019, s2020))
rebased <- sweep(x[‘2015-01-01/’], 2, as.numeric(x[‘2015-01-01’]), “/”)


Another feature that’s now available to Data Scientist users is the ability to search the database filtering different metadata attributes through the APIs.

One common use case of API search is to use the RegionKey attribute to look for key series. The series in the “Concept & Category” tree that you can see in the Macrobond application all have this metadata. You can use the APIs to inspect the metadata of the series and learn what values this attribute has for different series. There are examples how to do that in our Macrobond API documentation.

Here is an example in Python that searches for GDP series in a set of regions:

import win32com.client
c = win32com.client.Dispatch(“Macrobond.Connection”)
d = c. Database
query = d.CreateSearchQuery()
query.AddAttributeValueFilter(“Region”, [“us”, “gb”, “cn”])
query.AddAttributeValueFilter(“RegionKey”, “gdp_total”)
result = d.Search(query).Entities

for s in result:
    text print (s.Name)

The output is:


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