The J.P. Morgan Macrosynergy Quantamental System (JPMaQS) tracks macroeconomic concepts, like growth, inflation and macroeconomic balance sheets, and transforms them into macroeconomic quantamental indicators, making it easy to use quantitative-fundamental information for algorithmic trading and for the development of discretionary trading tools.

Today JPMaQS processes nearly 1 billion raw data points, cleaning and wrangling them into nearly 400 million high-quality data points to produce 12,000 macro quantamental indicators, the “building blocks” used to create trading signals and macro quantamental systematic strategies. New data and data sets are added to this process every day.

Macro quantamental indicators are real-time dated “quantitative-fundamental” information on economic financial developments that are relevant for market trends.

Using macro quantamental indicators and strategies provides for an information advantage on macro factors across global fixed income, foreign exchange, equity, credit and commodity markets. It is also a major cost saver, compared to the alternative of building clean real-time macro data series for financial markets on an ad-hoc and single institution basis.

JPMaQS is a premium data offering and a commercial/paid data service – however the license fee is a fraction compared to the significant higher resources/costs necessary to source, clean and condense this type of data in-house. Information and documentation on JPMaQS is available on J.P. Morgan Markets, and the macro quantamental indicators are available via DataQuery API (simple and fast authentication via OAuth).

Macro quantamental data refers to data that inform directly on macroeconomic activity, balance sheets and sentiment of various parts of an economy. This differentiates macro quantamental data from market data, which dominates algorithmic trading.

Key capabilities

Building strong quantamental indicators requires in-depth economic data knowledge, extensive data wrangling, logical rigor, econometrics/machine learning, careful documentation and consistency across countries.

By translating fundamental data into a real-time measures of the market's information state, JPMaQS breaks down barriers between purely quantitative and fundamental trading styles.

JPMaQS adds complementary quantamental indicators to standard algorithmic trading features.

JPMaQS delivers four key services:


Quantamental indicators associate the measurement of an event with the time at which markets have access to it – and for as long as this measurement would be the latest relevant instance of information of its kind. This means values are associated with real-time dates, and quantamental indicators always represent information status.

For most types of information the real-time date principle implies that a quantamental indicator is based on a two-dimensional data set:

  • true

    The first dimension is the timeline of real-time dates

  • true

    The second dimension is the timeline of observation dates

Macro quantamental indicators are delivered in the right format for developing and backtesting trading strategies. The system provides clear and concise explanations of its indicators from the perspective of strategy builders.


Trends are point-in-time indicators that capture actual fundamental developments. JPMaQS classifies macro quantamental indicators in six broad quantamental themes:

Indicators of changes in economic conditions designed to capture actual fundamental development as opposed to data volatility.

Indicators of the state of the economy in terms of stocks and valuations.

Indicators that track the conditions of the broader financial system and its impact on the economy.

Indicators of changes in expectations, uncertainty and risk aversion.

Stylized trading factors are generic indicators of basic trading strategy ideas based on macro quantamental indicators and, possibly, conventional trading factors.

Generic returns indicators are: Approximate daily profit and loss series of stylized derivatives positions in % of notional or risk capital. This includes returns on vol-targeted and hedged positions on all major asset classes.

How do macro quantamental indicators differ from standard economic data?

Quantamental indicators often have similar names as standard economic time series, even though their calculation and meaning are different. The main difference is that they measure market information status for any given day, not an ex-post (and often revised) actual state of the economy. Even where differences appear to be small, the distinction between a macro quantamental indicator and a standard economic time series can have a big impact on dynamic investment strategies, particularly during crises when exact real-time data can deviate drastically from simple approximations.

The JPMaQS data can be downloaded through DataQuery, either through its API or its User Interface on J.P. Morgan Markets.

Access JPMaQS via J.P. Morgan Markets

JPMaQS allows research analysts and portfolio managers to obtain the best possible information about the state of an economy at the time at which it was available. This means higher quality backtesting.

An introduction to JPMaQS:
a video with Ralph Sueppel 

Hello, my name is Ralph Sueppel. I'm Managing Director at Macrosynergy and I will give you a quick, practical introduction to the J.P. Morgan Macrosynergy Quantamental System - short form JPMaQS. So I'll explain briefly the basics of JPMaQS and of quantamental indicators. I'll show you the scope of the content of the system as it is on March 2024.  I'll show quickly how to download JPMaQS data into your environment, and then finally, I'll show a few support pages and resources for the construction of quantamental trading factors.

All right, let's start right out with the basics. What is JPMaQS?  JPMaQS stands for J.P. Morgan Macrosynergy Quantamental System. And it is a service that makes it easy to use macro quantitative fundamental information for trading, whether that's for algorithmic strategies or whether that's for discretionary trading support.

So the natural first question is what are macro quantamental indicators? That's a very, very important term and macro quantamental indicators are time series of macroeconomic information states that are designed for the development and backtesting of financial markets trading strategies. So all of these indicators are based on data vintages sort of time series that are collected then in sort of time matrices so that at every point in time, usually at the end of a reference day, we calculate macroeconomic indicators based on the time series that were available on that date.

So that's an important difference from standard economic time series in terms of the timing and in terms of the values, because standard economic time series are being revised, they usually are not being stored alongside the dates of publication and many conventions and adjustments of standard economic time series change. We always go back in history and apply the standards and the data that were available at the end of a given day.

So why is this a big deal? Why do quantamental indicators add investor value? So there's three reasons. First, quantamental data broaden the scope of easily backtestable and tradeable macro factors for investment strategies capturing critical aspects of the economic environment such as growth, inflation, profitability and so forth. Second, JPMaQS reduces the information costs of using macro quantamental information through scale effects.  So by using JPMaQS as a system, we sort of spread the investment of low level data wrangling and codifying fundamental domain knowhow across all institutions that are part of ‘the club of subscribers.’ And finally JPMaQS reduce this moral hazard in one’s production of trading factors. Because normally, if the production of indicators is a lengthy and expensive project, there's a strong incentive to salvage, even the most obviously failing propositions through data mining and flexible interpretation.

With JPMaQS, you can develop trading strategies quite quickly. So now let me show you in practice how the J.P. Morgan Macrosynergy Quantamental System works.  In order to browse through the content of JPMaQS. One of the best sources of reference is J.P. Morgan Markets and within J.P. Morgan markets in the main menu on the top left hand side, you scroll to Products and then to JPMaQS and you get to the famous JPMaQS site.

And the JPMaQS site has all the basic information on the system, such as how the system relates to strategies, the feature space of investment strategies and the data format. But also it has a section through which you can browse through the contents. It shows that the content of JPMaQS is roughly divided into six major themes, which are called Economic trends, Macroeconomic balance sheets, Financial conditions, Shocks and risk measures, Stylized trading factors and Generic returns.

Each of these themes contains a large selection of category groups. These are panels of indicators across as many countries as possible, and they are all meaningful and important for developing strategies. So, for example, the section of economic trends contains many category groups - indicators across countries - of the economic subjects that markets are usually paying particular attention.  And when you click on any of these little windows or category groups, you are being led to documentation. The main themes of economic trends that we currently have on the system include consistent and headline consumer price inflation trends, construction activity, demographic trends, external ratio trends, foreign trade trends, long term GDP growth, inflation expectations, intuitive GDP growth estimates in real-time, industrial production trends, inflation targets, labor market dynamics, labor market tightness, business confidence scores, market implied inflation expectations, private consumption data, producer price data, service confidence scores, technical GDP growth estimates, wage growth. And then we have special data sets for China and for the U.S. They are more detailed than for other countries. 

To find out what categories and take us specifically we have under each group.  You just click on any of these images and you are being passed on to a documentation book. That's just a Jupyter notebook rendered in HTML that contains the tickers, the explanations, some empirical irregularities, the availability of data and so forth.

So, for all the category groups for the categories within a group, you get a ticker, a label definition notes, you're being shown the available data panels.  When do they start? What is the average grading of the underlying data vintages and some empirical regularities of the data? This is something we produce for all the data on the JPMaQS system and that illustrates that this is not a data dump, but this is a true service where we bring in a lot of economic knowhow already in the creation of the data.  We explain everything very well and the people who produce these notebooks and the related Research, they are usually more than happy to take calls and emails from people who work with the actual JPMaQS data.

The other themes are organized similarly, Macroeconomic balance sheets contains a lot of information on banking system, public sector, external accounts and other balance sheets and the economy and go a long way in explaining the economic vulnerabilities of various countries and sectors in conjunction with measures of shocks and risk.

They together produce very good factors that trade well in economic crisis situations or in periods where the financial system is under a certain degree of shock or headwinds. Financial conditions, a very popular area of quantamental indicators as well, includes a lot of financial economic indicators like Purchasing Power Parity, exchange rates, openness adjusted real effective appreciation or terms of trade or real interest rates and so forth.

Finally, stylized trading sectors contains a couple of sectors that are nice building blocks for macro enhanced trading strategies and generic returns. Well, that's not actually quantamental indicators. These are target returns, target stylized returns across all major derivatives asset classes, which in conjunction with macro quantamental indicators, make it easy to research. Yeah, you just build out of the available categories and your own data a trading signal and you quickly check how does it relate to subsequent returns?

Then the important question is how do we get quantamental economic data into our development or research environment? Fortunately, that is it is quite easy. So all you need is a username and password and you need to know the countries (i.e., the currency ticker of the currency area or economic area that you want to research) and the category tickers.  As I've shown to you within each set of categories, we have a section that explains what categories are available and gives us a ticker.

And this ticker is really all you need in order to download the data into your environment. So, for example, here we are in the area of intervention liquidity. So measuring what central banks are doing on an ongoing basis in financial markets, you can use any of these tickers by just copying and pasting it into your environment and then passing it on to a DataQuery API.

So let me show you an example. I've opened a Jupyter notebook operating in Python, and if I want to download, say, all the major inflation rates or types of inflation rates for different countries, all I need to know is the currency tickers of these countries. I collect them here in Python lists across the developed markets and across the emerging markets.  I need to know all these category tickers that are just showing you how you can procure them and then they collect these two in lists, combine them to tickers, and then we pass them on to a DataQuery API wrapper.

In this case, it’s the API wrapper of Macrosynergy package. And then within probably less than a minute, it downloads a couple of million rows of data in a very well organized data format that you can directly use in PANDAS to analyze.

So finally, how do we get to support and help in working with JPMaQS? Here I recommend another website which is the Quantamental Academy, which you find by going to So that Quantamental Academy has many useful links, downloadable Jupyter notebooks and research notes, and it is divided into two principal parts. The one part is the thorough explanation of macro quantamental indicators together with relevant documentation.

It has a short introductory section that helps to understand quantamental indicators. It has a large array of Jupiter notebooks, one for each category group that is on JPMaQS. You can download into your environment and it has introductory tutorials, both a couple of videos, but also very importantly, two introductory notebooks that teach how to do work with JPMaQS, in particular in conjunction with a helper package, which we call the Macrosynergy package that makes it downloading and work with the data and little easier.

The second part of the Academy deals with developing macro trading factors, and it has one section which is called “Value generation based on quantamental factors”, and that links work with fundamental indicators to the academic and research world. So it contains various sections of overview articles that lead to other articles or overview articles of other articles that allows you to browse essentially through the world of ideas and theoretical and empirical Research that establishes relationships between macro quantamental data and asset returns.

This section contains various principles according to which one can build macro trading strategies. The usage of Macro trend, Indication of implicit subsidies, Detection of price distortion and the Assessment of endogenous market risk. All on a theoretical basis, but very well explained.

The Academy has also a section that links to the usage of “Statistical methods and packages with macro quantamental indicators.”

And then finally, it has a section that is particularly popular. It shows a number of examples and use cases of quantamental macro trading factors. And it shows the broad array of usages of macro quantamental information for the purpose of trading various asset classes from cross asset trading and timing to trading equity derivatives to trading bonds and interest rate swaps to trading foreign exchange forwards to trading commodity index futures and credit default swaps.

All of these pieces of research come with a research post and a downloadable Jupyter notebook that once you have access to JPMaQS, you can essentially a download into your environment and run them yourself and check whether you agree with the research results or modify them according to your needs. So that's it. That's what I wanted to show what J.P. Morgan Macrosynergy Quantamental System is how you get familiar with it and I hope I also gave you an idea that this is not merely a data service, but it is a more general service that just makes it easy for us to build quantamentally enhanced trading strategies with very little investment of time.

And I hope I also explain that it is a bit of a community or a ‘club’ because once you use the system, you are of course entitled and encouraged to get into contact with my colleagues and myself, be it at J.P. Morgan or here at Macrosynergy, all of which will be happy to help you, to guide you, and to make this as productive, as enjoyable as possible.

Thank you very much.



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