Improving on the Hilbert Sine Wave

Improving on the Hilbert Sine Wave

Improving on the Hilbert Sine Wave

In addition to the cyclical trend turns, a new version of the Hilbert Sine Wave plots these on the price chart. Support and resistance levels are generated with the new sine wave plots. Cyclical periods are characterized by price bouncing off support or resistance levels and failed breakouts. Trending periods are characterized by new highs or new lows and pull backs that continue until exhausted.

The beauty of the Better Sine Wave indicator is that it can be used to define market structure in any time frame, whether tick or monthly charts, and for any instrument or market, making it a truly versatile product.

High Probability Turning Point

A key skill when trading with the Hilbert Sine Wave is determining if price will respect the support or resistance level, or overshoot it and maybe even develop into a trend move. Two things used to identify high probability turning points:coincident cycle turning points and volume confirmation patterns. The same sequence of volume patterns are repeated time and again at market turning points. When these volume patterns occur with coincident turning points in at least two different time frames, the probability of a valid turning point is greatly increased!

Multiple Time Frames

A key principle of using the Hilbert Sine Wave with multiple time frames is that trend moves equate to cyclical moves. Since the Better Sine Wave indicator can be used to predict cyclical turning points and the end of trending periods, it is uniquely capable of being used in multiple time frame analysis, a definite improvement over the original Hilbert Sine Wave. Trend moves in a lower time frame are cycle moves in a higher time frame.

There are four versions of the Sine Wave indicator: original code using weighted moving average; original code using moving average; original code using Hull moving average; and updated code. The version that works best is the original code using weighted moving average.

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