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Menampilkan postingan dari Januari, 2010

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 method for optimizing parameters

Most trading systems have a number of parameters embedded, parameters such as the lookback period, the entry and exit thresholds, and so on. Readers of my blog (for e.g., here and here ) and my book would know my opinion on parameter optimization: I am no big fan of it. This is because I believe financial time series is too non-stationary to allow one to say what was optimal in the backtest is necessarily optimal in the future. Most traders I know would rather trade a strategy that is insensitive to small changes in parameters, or alternatively, a "parameterless" strategy that is effectively an average of models with different parameters. That being said, if you can only trade one model with one specific set of parameters, it is rational to ask how one can pick the best (optimal) set of parameters. Many trading models have a good number of parameters, and it is quite onerous to find the optimal values of all these parameters simultaneously. Recently, Ron Schoenberg publishe

Excel ADF test

Some readers have asked whether there is an Excel version of the ADF test for cointegration (mentioned in articles here or here .) You can download one such package here (Hat tip: Bruce H.). And as always, you can download the Matlab version from

Does Averaging-In Work?

Ron Schoenberg and Al Corwin recently did some interesting research on the trading technique of "averaging-in". For e.g.:  Let's say you have $4 to invest. If a future's price recently drops to $2, though you expect it to eventually revert to $3. Should you A) buy 1 contract at $2, and wait for the price to possibly drop to $1 and then buy 2 more contracts ( i.e. averaging-in); or B) buy 2 contracts at $2 each;  or C) wait to possibly buy 4 contracts at $1 each? Let's assume that the probability of the price dropping to $1 once you have reached $2 is p . It is easy to see that the average profits of the 3 options are the following: A) p *(1*$1+2*$2) + (1- p )*(1*$1)=1+4 p ; B) 2; and C) p 4*$2=8 p . Profit A is lower than C when p > 1/4, and profit A is lower than profit C when p > 1/4. Hence, whatever p is,  either option B or C is more profitable than averaging in, and thus averaging-in can never be optimal. From a backtest point of view, the Schoenber