Ultimate Algorithmic Trading System

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0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44
46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80
FIGURE 2.16 Bollinger Band optimization.
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STOCHASTICS AND AVERAGES AND RSI! OH, MY!
length parameter has on the number of generated trades in the different-width bins.
Also notice initially how congruent the bars are in each bin. It seems like the drop-off
of trades as we move from one bin to another occurs in a linear fashion—that is,
up until bins with width values greater than 2.0. An inversion occurs in these bins
(shorter lengths begin to create less trades). In addition, the number of trades start
demonstrating a parabolic theme. Let’s do the same exact analysis with the Keltner
Channel algorithm utilizing the same data and optimization ranges. Figure 2.17
utilizes the exact same chart properties but with the newly derived results.
This graph starts out looking like the Bollinger Band graph; initially, the length
parameter has a large impact in the 0.25 bin, just like the Bollinger chart. However,
as we span across the different-width bins, the length parameter has less and less
impact. By the time we reach the 1.50 bin, an inversion occurs, just like the Bollinger,
where the lower-length parameters start to generate less trades. The two algorithms
are quite similar but these graphs illustrate the difference between utilizing ATR and
standard deviation.
Armed with this information, can a normalized parameter set for both the
Bollinger and Keltner algorithms be derived? The 60-day two-standard-deviation
algorithm generated 3,580 trades. Let’s see which parameters generated nearly the
same number for the Keltner algorithm. If you refer back to Figure 2.17, you will
see several instances where a set of parameters generated nearly the same number
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