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

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Written by Forex Automaton   
Monday, 07 March 2011 17:21
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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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For Season 0, the Pearson prediction quality statistic for hourly extremes is somewhat lower than it is for the rest of seasons. The value 32 (or 33 as determined using more precise studies before) is probably the best to maximize Pearson for hourly extremes. For hourly close, we see with good confidence that one needs to flip sign of the predicted return to obtain positively correlated forecast, valuable for most pairs except for EUR/USD where prediction has zero correlation with reality.

For cumulants, as Fig.0.4 shows, one needs to avoid the very low values of Fred. The value which works best for the second order correlation of daily extremes with their forecasts will likely be fine for their forth-order cumulant.

Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly HIGH on the optimization parameter Fred. Season 0. 0.1 Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly LOW on the optimization parameter Fred. Season 0. 0.2 Dependence of Pearson correlation coefficient between predicted and actual logarithmic differences in hourly LOW on the optimization parameter Fred. Season 0. 0.3

Fig. 0.1-0.3 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. 0.1: hourly high, 0.2: hourly low, 0.3: hourly close. Data are for the Intraday Season 0.

Dependence of normalized 4-point cumulant among predicted and actual logarithmic differences in hourly HIGH and hourly LOW on the optimization parameter Fred. Season 0. 0.4

Fig. 0.4 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 0.



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