thE hAnDbook of tEchnicAL AnALySiS■ (^) Algorithmic‐based filtering
■ (^) Exogenous event‐based filtering
To further increase the potential strength and reliability of the qualifying ac-
tion, price confirmation is potentially most reliable when:
■ (^) Accompanied by at least three weakly or non‐correlated oscillators or indicators
■ (^) All three non‐correlated oscillators and indicators indicate divergence and are
in agreement with each other
■ (^) All three non‐correlated oscillators and indicators also indicate non‐conflicting
double or multiple divergences
■ (^) The expected time of breakout for price confirmation occurs in proximity to pro-
jected cycle lows for upside breakouts and cycle highs for downside breakouts.
If the three supporting data series are in some way correlated, there is a pos-
sibility that the corresponding oscillators and indicators may provide similar or
identical bullish and bearish signals. We say that the oscillators and indicators
are multicollinear. See Figure 9.37. This misleads the analyst or practitioner into
believing that the apparent agreement between the oscillators and indicators are
in some way strong and reliable signals.
In order to reduce the effects of multicollinearity within the supporting data
series, it is best to avoid introducing supporting data series that are based on the
same technical data, which can be categorized into various fields, some of which
include:
■ (^) Price
■ (^) Volume
■ (^) Open interest
■ (^) Market breadth
■ (^) Sentiment
This requires that if one of the supporting data series is a momentum indi-
cator, then the other supporting data series should be an indicator or oscillator
derived from some other technical data field like volume, open interest, market
breadth, sentiment, and so on. This will greatly mitigate the undesirable effects of
multicollinearity between the various indicators and oscillators that make up the
supporting data series, providing for potentially more independent and reliable
signals. Time‐based projections derived from standard cycle analysis, Fibonacci
time zones, Gann’s squaring of time, or triangle apex reaction analysis may also
be advantageous in pinpointing the time of breakout. See Figures 9.68 to 9.83 for
some idealized examples of price confirmation using moving averages, trendlines,
support and resistance, chart patterns, and confluence‐based breakouts on the
all four types of divergences. (Price confirmation for Bull and Bear setups is not
specifically depicted in the following examples, but they only require an extra
step, with price confirming the breakout from the expected reversal after the ini-
tial continuation of the current larger trend.)