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
I have come across many software platforms that allow traders to first specify and backtest a strategy and then, with the push of a button, turn the backtest strategy into a live trading program that can automatically submit orders to their favorite broker. (See all my articles on this topic here .) I called these platforms "Integrated Development Environment" (IDE) in my new book , and they range from the familiar and retail-oriented (e.g. MetaTrader, NinjaTrader, TradeStation), to the professional but skills-demanding (e.g. ActiveQuant, Marketcetera, TradeLink), and finally to the comprehensive and industrial-strength (e.g. Deltix, Progress Apama, QuantHouse, RTD Tango). Some of these require no programming skills at all, allowing you to construct strategies by dragging-and-dropping, others use some simple scripting languages like Python, and yet others demand full-blown programming abilities in Java, C#, or C++. But which of these allow us to backtest and execute high fr