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Menampilkan postingan dari November, 2014

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

Rent, don’t buy, data: our experience with QuantGo (Guest Post)

By Roger Hunter I am a quant researcher and developer for QTS Partners, a commodity pool Ernie (author of this blog) founded in 2011. I help Ernie develop and implement several strategies in the pool and various separate accounts.  I wrote this article to give insights into a very important part of our strategy development process: the selection of data sources. Our main research focus is on strategies that monitor execution in milliseconds and that hold for seconds through several days. For example, a strategy that trades more than one currency pair simultaneously must ensure that several executions take place at the right price and within a very short time. Backtesting requires high quality historical intraday quote and trade, preferably tick data for testing.  Our initial focus was futures and after looking at various vendors for the tick data quality and quantity we needed, we chose Nanex data which is aggregated at 25ms. This means, for example, that aggressor flags are not availa