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
Order flow is signed trade size, and it has long been known to be predictive of future price changes. (See Lyons, 2001 , or Chan, 2017 .) The problem, however, is that it is often quite difficult or expensive to obtain such data, whether historical or live. This is especially true for foreign exchange transactions which occur over-the-counter. Recognizing the profit potential of such data, most FX market operators guard them as their crown jewels, never to be revealed to customers. But recently FXCM, a FX broker, has kindly provided me with their proprietary data , and I have made use of that to test a simple trading strategy using order flow on EURUSD. First, let us examine some general characteristics of the data. It captures all trades transacted on FXCM occurring in 2017, time stamped in milliseconds, and with their trade prices and signed trade sizes. The sign of a trade is positive if it is the result of a buy market order, and negative if it is the result of a sell. If we take t