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Menampilkan postingan dari April, 2019

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

Is News Sentiment Still Adding Alpha?

By Ernest Chan and Roger Hunter Nowadays it is nearly impossible to step into a quant trading conference without being bombarded with flyers from data vendors and panel discussions on news sentiment. Our team at QTS has made a vigorous effort in the past trying to extract value from such data, with indifferent results. But the central quandary of testing pre-processed alternative data is this: is the null result due to the lack of alpha in such data, or is the data pre-processing by the vendor faulty? We, like many quants, do not have the time to build a natural language processing engine ourselves to turn raw news stories into sentiment and relevance scores (though NLP was the specialty of one of us back in the day), and we rely on the data vendor to do the job for us. The fact that we couldn't extract much alpha from one such vendor does not mean news sentiment is in general useless. So it was with some excitement that we heard Two Sigma, the $42B+ hedge fund, was sponsoring a ne

The most overlooked aspect of algorithmic trading

Many algorithmic traders justifiably worship the legends of our industry, people like Jim Simons, David Shaw, or  Peter Muller , but there is one aspect of their greatness most traders have overlooked. They have built their businesses and vast wealth not just by sitting in front of their trading screens or scribbling complicated equations all day long, but by collaborating and managing other talented traders and researchers. If you read the recent interview of Simons, or the book by Lopez de Prado  (head of machine learning at AQR), you will notice that both emphasized a collaborative approach to quantitative investment management. Simons declared that total transparency within Renaissance Technologies is one reason of their success, and Lopez de Prado deemed the "production chain" (assembly line) approach the best meta-strategy for quantitative investment. One does not need to be a giant of the industry to practice team-based strategy development, but to do that well requir