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.
- "The music of the markets" explaining the bias-variance tradeoff, when modelling a trading strategy
- "First steps of building quant" steps discretionary investors can do, to begin using quant, starting with training their team
- "New Year 2020 quant resolution" some of my resolutions including teach more Python (& learning!)
- "Hundreds of quant papers from #QuantLinkADay in 2019" I've collected together all the papers/libraries I've tweeted this year about financial markets, Python etc. in a single article
- "Putting quant into your market analysis" how to go about learning quant/Python & applying to how you look at the market