Cuemacro’s founder, Saeed Amen, has worked on a number of different projects over the past decade at major investment banks, the Thalesians and Cuemacro. Cuemacro’s consulting services allow clients to unlock the power of data to make better trading decisions in macro markets. Saeed has published over 150 papers for clients during his career covering many exciting aspects of trading macro markets and to better understand financial markets. His expertise is primarily focused on building systematic trading strategies using both existing and unusual datasets for macro markets, notably for currencies.
Summary of Cuemacro’s quant expertise
Below is a summary of the areas which Saeed Amen has investigated over nearly a decade in researching systematic trading strategies, which form the backbone of Cuemacro’s quant expertise.
- CTA style strategies
- Comprehensive study of systematic trading with a huge array of technical indicators
- Methods of identifying trend vs. mean-reversion
- Creating proxies for popular CTA strategies
- 4pm FX fixing
- Calculated risk market makers take at the 4pm fix – work featured in Wall Street Journal, Why FX Traders Trade: A Reminder, by Katie Martin (11 March 2014)
- Transaction cost analysis
- Understanding how to optimise execution of trades
- Creating a sensitivity analysis for trade execution
- FX beta
- Methods of identifying what constitutes FX beta
- Also published chapter in Wiley book on subject
- Currency hedging
- Ways of hedging currency risk when purchasing foreign bonds and equities
- Active currency hedging strategies
- Carry
- Huge amount of work on FX carry
- Ways of filtering FX carry to reduce drawdowns
- Using news data to filter FX carry
- Central banks
- Understanding dynamics of FX spot & options markets around major central bank meetings
- Utilising Prattle data to understand text content of central bank communications to trade FX markets
- Python
- Extensive experience in using Python to backtest trading strategies and understand financial markets
- Created finmarketpy, findatapy and chartpy (which grew out of pythalesians project) open source Python financial analysis library
- Macro
- Creation of macro based indicator to trade systematically including growth surprise indices
- Understanding link between FX and rates
- Using other macro data to trade FX systematically
- Terms of trade indices
- News analysis
- One of the first researchers to publish work on trading models using Google Trends (at Lehman Brothers in 2008!)
- Since then created trading models based on Google Domestic Trends and Bloomberg News
- Carried out bespoke projects on RavenPack news analytics data (creating news based economic sentiment indicators and news FX carry filters)
- Used Twitter based data to create sentiment indicators
- Alpha capture data
- Utilising TIM Group’s alpha capture data to trade FX markets
- Positioning data
- Constructing high frequency proxies using publicly available CFTC data
- Creating systematic trading models using positioning data
- Intraday trading models
- Investigating spot behaviour around events such as NFP
- Creating trading models >1 minute holding period
- Modelling liquidity
- Using tick data to analysis intraday volume in FX markets
- Creating metrics to effectively proxy liquidity
- Vol
- Extensive work on FX systematic vol trading strategies
- Understanding impact of scheduled events on FX volatility
- Managing risk
- Helped to implement models which were run internally by major investment banks
- Trade my own cash using systematic trading models (Sharpe ratio >2 since summer 2013)
- Creation of index products
- Co-developed MarQCuS at Lehman Brothers which had $2bn AUM
- Worked on creation of G10 and EM FX total return indices