About

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Seeking the cues in macro markets

What are the signals we can use to trade macro markets? Cuemacro is a company focused on understanding macro markets from a quantitative perspective, in particular currency markets. Our goal is understand how data can be used to deepen understanding of macro markets. We use both existing and innovative data sources to create systematic trading strategies, analytics and data indices. We build our analytics using Python and our open source libraries chartpy, findatapy and finmarketpy. We offer several services for clients which include:

  

  • Data Products / Creating exciting new datasets for clients to improve their own trading decisions and understand financial markets better
  • Research Consulting / Writing bespoke quant research papers and developing bespoke models for clients
  • Monetising Data / Helping data companies and corporate institutions monetise their datasets through research and marketing services and aiding financial institutions to get into the data age
  • Software / Developing bespoke market analytics to be deployed on clients systems, building on our open source Python frameworks, including for backtesting, visualisation and TCA.

Why the name Cuemacro?

Cue is defined as “a thing said or done that serves as a signal to an actor or other performer to enter or to begin their speech or performance.” In a trading context, market participants seek to understand the cues to enter into a trade. We seek to find these signals. Given our focus on macro markets, it was natural to put the two ideas to name our company Cuemacro.

Team

Saeed Amen is the founder of Cuemacro. Over the past decade, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan). Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians.

  

  

  

Client Portfolio

Below we give examples of some of the client projects we have done at Cuemacro. Our clients have ranged from data vendors, to asset managers to quant hedge funds over the years, and have been based in both the US and also Europe. Our bespoke projects can range from delivering innovative new quant research for clients to developing analytics platforms for clients to run (such as TCA/transaction cost analysis).

 

  

  • Bloomberg / We were commissioned by Bloomberg to write a research paper to show how their machine readable news could be used to trade FX. The project involved using a large dataset consisting of text, which we processed to construct sentiment scores and FX based trading signals. We also discussed how the dataset could be used to understand FX volatility around ECB and FOMC meetings. The paper was published on Bloomberg’s website and we also discussed the paper on Bloomberg TV.
  • Investopedia / Investopedia commissioned us to conduct research to examine how web search data to their site could be used by investors. Their investor anxiety index is based on searches around subjects such as “short selling” which are consistent with investors concerns. We showed how the index could be used to trade equities. We talked about the project on Bloomberg TV.
  • Freepoint Commodities / We were commissioned to examine how to apply a quant approach to commodities trading.
  • A large European asset manager / We were commissioned by the firm to develop a Python based FX TCA library. Over nearly 2 years, we wrote the specifications with our client, and later implemented the framework, both a web based front end and also a back end for computation. Through the course of the project, we solved some crucial issues associated with the computation of large datasets. In particular, we worked on functionality to allow the computation to be distributed efficiently, to ensure the library was fast and could scale to the hardware. Elements of the project grew into our tcapy software product. Enterprise licences are available to purchase for tcapy.
  • A Chicago proprietary trading firm / We were commissioned to develop an intraday FX trading strategy for the firm, which was later run profitably with real capital.

  

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