Incorporating seasonality into Heidi. A concept of a better forecasting component for an intraday trading system. - Intraday Season 4 Optimization: 5-7am ET, 11-13 CET

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Written by Forex Automaton   
Monday, 07 March 2011 17:21
Article Index
Incorporating seasonality into Heidi. A concept of a better forecasting component for an intraday trading system.
Intraday Season 0 Optimization: 5-7pm ET, 23-1 CET
Intraday Season 1 Optimization: 8-10pm ET, 2-4 CET
Intraday Season 2 Optimization: 11pm-1am ET, 5-7 CET
Intraday Season 3 Optimization: 2-4am ET, 8-10 CET
Intraday Season 4 Optimization: 5-7am ET, 11-13 CET
Intraday Season 5 Optimization: 8-10am ET, 14-16 CET
Intraday Season 6 Optimization: 11am-1pm ET, 17-19 CET
Intraday Season 7 Optimization: 2-4pm ET, 20-22 CET
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Season 4 is the most difficult one to optimize. Focusing on hourly close, some currency pairs are systematically above zero (positive correlation) and some are systematically below zero (negative correlation). No single Fred would work. A solution may be not to trade or to pick an independent strategy for each currency pair, thus increasing the number of free parameters more than seems optimal for the dataset at large.

Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly HIGH on the optimization parameter Fred. Season 4. 4.1 Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly LOW on the optimization parameter Fred. Season 4. 4.2 Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly LOW on the optimization parameter Fred. Season 4. 4.3

Fig. 4.1-4.3 Dependence of Pearson correlation coefficients between predicted and actual logarithmic differences (returns) in the three components of the hour candle (high, low and close, in that order) the optimization parameter nicknamed Fred. 4.1: hourly high, 4.2: hourly low, 4.3: hourly close. Data are for the Intraday Season 3.

Dependence of normalized 4-point cumulant among predicted and actual logarithmic differences in hourly HIGH and hourly LOW on the optimization parameter Fred. Season 4. 4.4

Fig. 4.4 Dependence of the normalized 4th order cumulant among predicted and actual logarithmic differences (returns) in hourly high and low on the optimization parameter nicknamed Fred. Data are for the Intraday Season 4.



Last Updated ( Saturday, 07 July 2012 10:44 )