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Menampilkan postingan dari Mei, 2009

Applying Corrective AI to Daily Seasonal Forex Trading

  By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal     ABSTRACT We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012)   observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from September 2021 to January 2023 and then applied Corrective AI to this trading strategy to achieve a significant increase in performance. Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European working hours (3 AM ET to 9 AM ET, where ET denotes the local time in New York, accounting for daylight savings) and bought EURUSD during US working hours (11 AM ET to 3 PM ET). The rationale is that large-scale institutional buying of the US dollar takes place during European working hours to pa

MATLAB as an Automated Execution System

I just published an article " MATLAB as an Automated Execution System ". (It is available to readers of my book and subscribers to my Premium Content website.) It comes with ex ample MATLAB codes executing a simple Bollinger-band high-frequency E-mini trading strategy. As I mentioned before, I now find MATLAB to be a good platform not just for backtesting, but for automated execution as well. Of course, not all brokerages have API's that connect to MATLAB. My example codes are for submitting orders automatically to an Interactive Brokers account. In general, I find that writing execution programs in MATLAB is a breeze compared to C++, Java or even C#. It takes about 1/5 the development time of a C++ program. Any performance limitations will probably not be due to MATLAB, but to the latency of your brokerage in updating positions and order status.