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Menampilkan postingan dari Maret, 2012

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

High-frequency trading in the foreign exchange market

This is the title of a report published by the Bank of International Settlements (which serves central banks around the world) in September 2011. As a Forex trader myself, I of course peruse it with great interest hoping to glimpse whatever is the state-of-the-art. Here are a few interesting nuggets, together with my commentary: 1) FX HFT operate with a latency of less than 1 ms, while most of us mere algorithmic traders typically suffer a latency of at least 10ms.  For example, Interactive Brokers does not yet provide collocation facilities for its customers, so the best we can do is to place our trading servers on the internet backbone close to its Stamford, CT, location. The best round-trip ping time is 10ms. Those who trade with FXCM may have a better chance for lower latency, as they provide free collocation to their clients. Those who trade on the ECN FXall can collocate at their Equinix data center , while FCM360 provides collocation service to EBS traders. I cannot find any c

Hidden Markov model applied to FX prediction

I read with interest an older paper " Can Markov Switching Models Predict Excess Foreign Exchange Returns? " by Dueker and Neely of the Federal Reserve Bank of St. Louis. I have a fondness for hidden Markov models because of its great success in speech recognition applications, but I confess that I have never been able to create a HMM model that outperforms simple technical indicators. I blame that both on my own lack of creativity as well as the fact that HMM tend to have too many parameters that need to be fitted to historical data, which makes it vulnerable to data snooping bias. Hence I approached this paper with the great hope that experts can teach me how to apply HMM properly to finance. The objective of the model is simple: to predict the excess return of an exchange rate over an 8-day period. (Excess return in this context is measured by the % change in the exchange rate minus the interest rate differential  between the base and quote currencies of the currency pair.