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 markets. We use both existing and innovative data sources to create systematic trading strategies and data indices. We build our analytics using Python. 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
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.
Founder of Cuemacro
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. Independently, he is also a systematic FX trader, running a proprietary trading book trading liquid G10 FX, since 2013. 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. His clients have included major quant funds and data companies such as RavenPack and TIM Group. He is also a co-founder of the Thalesians.
- "Managing risk through planning" we can't predict the future, but we can at least investigate some possible scenarios
- "Finding the missing data factor in a trading strategy" how models are approximations & alt data can help us fill the gaps in knowledge
- "Using Python to explore FX market microstructure" how we can use Python + tcapy to crunch large amounts of tick data (in parallel!)
- "It's ok not to have a market position" a few thoughts about the risk rally & why it's ok not to understand what's going on
- "How to do TCA with tcapy in Python" how to understand your trading costs using open source tcapy Python library