Blackheath's AI Global Macro Strategy
is a pure Global Macro strategy; using
Artificial Intelligence to trade foreign exchange.


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Markets
Traded

Currency Markets only

Investment
Strategy

Algorithmic and Quantitative

Minimum
Investment

For Managed Accounts: $200,000 minimum

Average Margin
to Equity

8%

Fangqi (Frankie) Liu, Portfolio Manager and Head of Research

Frankie Liu, CFA, is the Portfolio Manager of the Blackheath  AI Global Macro Strategy and the architect of the Blackheath NewTrend Strategy. In addition to his Portfolio Manager role, Frankie is the Head of Research at the firm. His responsibilities include developing proprietary trading software, building quantitative pricing models as well as conducting research in asset price dynamics. 
After graduating from the University of Chicago in 2009 with Bachelor’s degrees in Mathematics and Economics, Frankie worked at Cedarcroft Capital as a trader and managed options books in commodities and foreign exchange markets. Frankie received his CFA® charter in 2013.

 


Benjamin Chung, Strategy Consultant

Ben Chung was an early champion of machine learning’s potential for application to absolute return strategies.  He created the world’s first opensource Object-Oriented Deep Learning library (Java) and released it under the GPLv2 in 2007 and continued to work on its development through 2012.
From 2009 to 2012 Ben was employed as a Portfolio Analyst, for the Canadian Pension Plan Investment Board’s Global Tactical Asset Allocation Group. There he conducted research on macroeconomic forecasting models for the Group’s emerging market currencies portfolio.
In 2013, Ben struck out on his own, launching the Neptune AI FX Fund Ltd, a neural-network, fundamental strategy. This program founds the basis of the trading method Ben is employing in the Blackheath AI Global Macro Strategy. 
Ben is a graduate from the University of Toronto in Computer Science.

How does the Model work? 

(FOR FULL PRESENTATION CLICK HERE)

 

 

 

 

 

 

 

 

 


 

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COMMODITY TRADING INVOLVES SUBSTANTIAL RISK OF LOSS.
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS.