1 Attachment(s) Lately I'm playing around with Recursive Bayesian estimators (http://en.wikipedia.org/wiki/Recursi...ian_estimation) for which the Kalman filter is a special case. I won't enter into the gory details; visit Bayesian Forecasting and Dynamic Models by West and Harrison.
Initially I had been trying to gauge the trend and the volatility of this market... But that's not what this article is all about. It is about why you would better be long on the Dragon today.
The market return isn't Normally distributed but someplace between Cauchy and Student distributed. Kalman filter performances degrade for inventions. That's why I use another Kalman filter that estimates the error of the initial one and I feedback this information into the first model as a modulation of the permitted variance of this state. Lt; smoothness is lagged by A sort of cursor.
This first model is a second order polynomial local estimation. I picked a 2nd order polynomial since it can approximate (Taylor) any smooth function just like a sine wave for a market using a cyclic component (range or valatile fad ) or an exponential trend (such as index and stocks). The accelaration factor helps ching up using the price in case of a movement. A model that is constant is just used by the second filter. I utilize H4 to evaluate the daily fad.
Here is a screenshot of EUR/USD H4. The blue line is the perfect, but non-causal, low pass filter sinc (http://en.wikipedia.org/wiki/Sinc_filter) used with 41 samples. It gives you an idea of this lag. The filter is green when the trend is probably red and up otherwise. The dashboard envelope is the 95% confidence interval of the price estimate. Below are the error between the price and the estimate (black) and this worth filtered using the 2nd filter (red). It doesn't adhere to the mistake too much to not make the most important filter over-react.
https://www.cliqforex.com/trading-sy...t-trading.html