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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

A few announcements

First, an iPad version of this blog has been launched, so if you are reading this on an iPad, the look will be different. If you want to go back to the old look, just hit Page Turn in the bottom left corner and choose the option there. Any comments or suggestions on this new look are most welcome! Second, and this is probably irrelevant to most of you reading this blog, a Chinese translation of my book Quantitative Trading is now available . Third, and most interesting, Larry Connors will be hosting a webinar on "How to Trade High Probability Stock Gaps" on  Tuesday, May 1, 2:00pm ET .  (Click on link to register.) It is sheer coincidence that I was just writing about stock gaps in my previous post ! I have always found Larry's strategies to be clear, concise, and simple - exactly the ingredients for out-of-sample as opposed to in-sample returns!

The life and death of a strategy

Sometimes it is instructive to look back at some strategies that used to thrive, and then quite suddenly contracted a chronic illness that ultimately led to its demise. It gives us a sense of the unreliability of backtests and curb our over-confidence, which is always useful when dealing with the financial markets. One good example is a well-known strategy that we called "buy-on-gap". In its simplest version, just buy at the open 100 stocks within the S&P500 which have the lowest returns from their previous day's lows to the current day's open, provided that these returns are lower than one standard deviation. (The standard deviation is computed as the 90-day moving standard deviation of close-to-close returns of a stock.) Exit such  long positions at the day's close. Many traders know of variants of this strategy, and I started trading it around the beginning of 2007, and in fact, it formed part of my first fund's portfolio of strategies. You can see the