Forex 24x7

This is a brief follow-up to the previous post on how I know my forex forecasting works. In that post I disclosed a measurement of a figure of merit I use to monitor the forecasting quality and optimize the algorithm, the figure of merit being the covariance of predicted and actual logarithmic returns on a day scale. The measurements were carried out for 16 values of the control parameter nicknamed Fred, which is currently the only "make it or break it" parameter responsible for the forecasting, and the only one being currently optimized. (As an aside note, there are other quantities which control the process like for example how big a chunk of data you look at. Those are believed to be more mundane and are currently fixed as some "reasonable" values -- which is not to say that I won't decide to take a more quantitative look at how reasonable those values are sooner or later.) The covariance of predicted and actual logarithmic returns is not the best quantity to look at when aggregating data for the different forex exchange rates: because of the somewhat different volatilities of those markets (different even despite the fact that the logarithmic returns take the absolute value of the exchange rate out of the picture), the resulting numbers for the major forex were volatility-weighted averages. Moreover, a quantity like 10-6, even if it's more than 2 standard deviations above zero, does not communicate the result to the non-expert in the intuitive way the result deserves. These are the reasons why I went over from covariances to Pearson correlation coefficients, and today I am presenting the updated measurements. Fig.1. Pearson correlation of predicted and actual day-scale logarithmic returns as a function of the forecasting parameter nicknamed Fred. The vertical bars, so called error-bars are a measure of uncertainty, are calculated as discussed in the previous post and have the same meaning. Back-testing simulations give the forecasing engine no access to the future data, direct or indirect. Significantly positive (and ideally, large) values correspond to quality forecasting.