Screen shots

Example 1. The indicator displaying a filtered price series and a residual term of wavelet transformation:

In the Figure above one can see that the wavelet filtration deletes outliers on non-trending periods but leaves the prices on trend periods the same. Also the residual term of wavelet transformation can be used similarly to long-term moving average.

Example 2. Example 2 shows sumarized noise on all Wavelet coeficients. It can be assumed that price = signal + noise. Wavelet decomposition from price = decomposition of signal + decomposition of noise. Summarized noise on all wavelet coeficients is showen on the chart. As follows signal is price with subtracted noise. This example shows advantage of Wavelet transform from other methods as Wavelet transform eliminate different components of signal, noise and etc.

Example 3. Indicator Nowcast Trend assumes value 1 and -1 if trend is identified. On the base of this indicator strategy "Nowcast signal" is created.

Example 4. The indicator shows the family of low-frequency filters, derived by consecutive subtraction of the filtered wavelet coefficients from the filtered price (see figure):

Example 5. . The signal that opens positions if trend is identified on the given scale, and closing them if the end of trend is fond.

These elementary examples evidently show that modern computer methods can improve an arsenal of technical analytics.