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Learn more about Causal Singular Spectrum Analysis  (CSSA)
Nature and its parts fluctuate in cycles. Markets are no exceptions. A quick glance at any price chart will reveal cyclical behaviors; price bobs up and down with a good degree of regularity giving evidence of rhythm. Obviously, if cycles are genuine, the presumption is that they will continue. And mostly they do, so we cannot afford to ignore them.

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What does CSSA offer that other tools don't?  
SSA (Singular Spectrum Analysis) is a powerful statistical technique that provides a complete multi-wave representation of the market. It breaks price into "modes of variability" that capture the variance at any time frame. Like other transformations, SSA is designed to enhance certain aspects of a data set; in Fourier decomposition it is the frequencies whereas in SSA it is the variance.

Viewing cycles in terms of variance represents a radical departure from spectral analysis; it allows us to perform the transform from the data itself without making any assumption whatsoever on the way to proceed.

Fourier methods assume that future data consist of a periodic repetition of past data in the form of pure sinusoids. Wavelets offer yet another way of analyzing price series. They operate with a fixed set of filters of predetermined shape. There is usually little physical evidence in markets for such assumptions.

We find it most useful to extract cycles from the data itself, in which case the transform is ideally adapted to the kind of market that is being traded. Cycles are not forced to conform to some unsubstantiated mathematical representation therefore they retain a remarkable resemblance to the original series.

Following is a brief overview of the indicators in the CSSA add-on:

Visualize the elementary patterns of behavior in price and find propagating modes. Their inherent persistence over time makes these modes especially worthy of attention.


This indicator provides a full wave-composite picture of the market. The cycles are designed to be a direct reflection of the price action so that they don't lag. In other words, you will be able to quickly anticipate turning points independently of the time-frame.


CSSA-Slope and Trendlines
These indicators are derivatives from CSSA-Cycles; they are particularly useful in identifying and confirming the direction of momentum behind a move.
CSSA-Long and Short Entries
Signals are triggered by CSSA-Cycles from its bases and peaks. A causal lead and filtering features have been added to compensate for inherent execution lags so that you can make the most of each market move

CSSA-Long and Short Entries

This indicator operates a 90 degree phase shift of CSSA-Cycles; the peaks and troughs of the cycle line become zero crossings. Signals are then triggered when the transform breaks through its center line. Because of the accuracy of the signals, tight stop losses can be kept for even better results.
In addition to generating signals, this centered oscillator can also be used to confirm developing trends:


CSSA-Turning Levels
Lines are drawn horizontally from the base before an up-move in CSSA or a peak before a down-move. The purpose of this indicator is to show where price is liable to find some local support and encounter some local resistance. The violation of these lines can generate reliable entry points.

CSSA-Turning Levels

CSSA-Percent Variance
The ratio of the variance accounted by a group of cycles to the total variance in a rolling window is given. Any sudden change in this ratio can announce that something is amiss with the current trend or that the current trend is about to change.
CSSA-Coupling Index
This indicator uses the variance accounted by individual cycles to return a degree of coupling between them. It computes the ratio of the variance accounted by two components in a rolling window. Any sudden change in this coupling can be used as a proxy for change.
CSSA-Change Point Score
The degree of change between recent and past price action (the base) is returned. It can be interpreted as the probability that some change is likely to occur due to strong causal dependencies in the data. In other words this indicator reveals hidden dependencies that may announce significant changes in trend.
Select a range, and then use the values shown in the caption to set the training range of CSSA indicators.


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