Applied Cycle Analysis

The objective of MarketScalpel's cycle analysis is to allow clients to dynamically tailor their holding periods to capture maximum performance.

We use cycle analysis for sector forecasting in conjunction with various proprietary sector internals data and analysis, including our Volume Confirmation signals.  For more background information please see a basic introduction to the principals of cycle analysis and terminology.

Spectral Analysis Based Cycle Analytics

The MarketScalpel Navigator research platform uses a spectral analysis algorithm with a fractal filter to identify frequency response and, by extension, the presence of cyclicality in our sector price indices.

This technique is derived from mathematical analysis originally developed for digital signal processing to filter noisy frequency data. In the Traders' Edition Newsletter, the interplay of individual cycles is additionally analyzed manually for a selected set of sectors.

The analysis seeks to identify cycles (and associated frequencies) that provide the greatest amount of regular, periodic price movement at the sector level.

Our spectral analysis algorithm has the advantage of working well with small data samples making it highly responsive to changes in the dominant cyclicality.

Interpreting Sector Based Cycle Charts

The Market Navigator sector charts provide two dominant cycle estimates; a long cycle and a short cycle.

These are graphically represented in the default sector chart (see A3 in the controls overview) by oscillators with the long cycle colored green and the shorter cycle orange.

Additional text-based information necessary for evaluation of the cycle estimates is presented in the legend to the right of the chart, as well in the Cycles Sector Overview table (see Market Navigator).

The key evaluation factors are:

Cycle Length:

  The length is the number of trading days comprising a full cycle (e.g. measured from a bottom to the next bottom on the oscillator). The longer the cycle, the easier it tends to be to trade and the greater the price movement attributable

Cycle Fit:

  Our spectral analysis algorithm will always seek to fit the best cycle estimate given the parameters supplied.  The cycle fit score provides an independent ranking of the fit after it has been estimated as a cross-check

A score of greater than 75% is generally a very good fit. A score of less than 50% is generally a poor fit

Days To/From Top/Bottom:

  An estimate of the number of trading days from the current position to the next projected cycle high and low respectively.

Identifying Cyclical Highs

The  “Days To Top” estimate is supplied as a very rough guide that should be treated with some caution.  Cycles often exhibit left or right translation meaning that the top occurs late or early relative to the cycle midpoint in time, even though the cycle is well-defined in terms of the bottom-to-bottom symmetry, which is the more stable measure of cyclicality.

It is instead strongly recommended to monitor for cyclical highs via:

• Unsmoothed Cycle Oscillator:

  The leading oscillator is precisely tuned to the estimated dominant cycle length and should roll over along with the price index if the fit is good, unless there is extreme right translation

• Sector Internals:

  Weakness in sector sentiment/internals data is invariably apparent prior to a downside reversal in price, as sector leadership becomes increasingly narrow prior to the exhaustion of an advance.  In particular the percent of sector components above key moving averages is a critical indicator to be monitored - at timeframes appropriate to clients' investment horizon - and contains significant cyclical information with zero lag.