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
In our drive to extract alphas from a variety of non-price data, we came across this old-fashioned source: Commitments of Traders (COT) on futures. This indicator is well-known to futures traders since 1923 (see www.cmegroup.com/education/files/COT_FBD_Update_2012-4-26.pdf), but there are often persistent patterns (risk factors?) in the markets that refuse to be arbitraged away. It is worth another look, especially since the data has become richer over the years. First, some facts about COT: 1) CFTC collects the reports of the number of long and short futures and options contracts ("open interest") held by different types of firms by Tuesdays, and reports them every Friday by 4:30 CT. 2) Options positions are added to COT as if they were futures but adjusted by their deltas. 3) COT are then broken down into contracts held by different types of firms. The most familiar types are "Commercial" (e.g. an ethanol plant) and "Non-Commercial" (i.e. speculators). 4