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Menampilkan postingan dari Juli, 2015

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

Time series analysis and data gaps

Most time series techniques such as the ADF test for stationarity, Johansen test for cointegration, or ARIMA model for returns prediction, assume that our data points are collected at regular intervals. In traders' parlance, it assumes bar data with fixed bar length. It is easy to see that this mundane requirement immediately presents a problem even if we were just to analyze daily bars: how are we do deal with weekends and holidays? You can see that the statistics of return bars over weekdays can differ significantly from those over weekends and holidays. Here is a table of comparison for SPY daily returns from 2005/05/04-2015/04/09: SPY daily returns Number of bars Mean Returns (bps) Mean Absolute Returns (bps) Kurtosis (3 is “normal”) Weekdays only 1,958 3.9 80.9 13.0 Weekends/holidays only 542 0.3 82.9 23.7 Though the absolute magnitude of the returns over a weekday is similar to that over a weekend, the mean returns are much more positive on