Ultimate Algorithmic Trading System

FIGURE 8.8
The Cluster WFO results matrix.
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GENETIC OPTIMIZATION, WALK FORWARD
WFO Cluster Analysis The optimization using the genetic method took only a
few moments. This time, instead of running a single WFO, the cluster WFO
option was chosen. In this example, the cluster analysis ran 30 different walkforward
optimizations. Each optimization changed the values for OOS% and
walk-forward runs. In the first single WFA, 10 runs utilizing 20 percent OOS% were
evaluated to determine WFO fitness. Figure 8.8 shows the different values of WF
runs and OOS% levels in a matrix format.
Similar to the single WFA on the Turtle-like system, this system failed as well,
using the 10 WFA runs with 20 percent as the OOS%. However, this system passed
the test on several different OOS% / Runs values. Please note that in an attempt
to increase the likelihood of a passing grade, the criterion for the walk-forward
efficiency was lowered from 50 to 30 percent in the test criteria setup. This criterion
seems to be the one that rejects a larger portion of optimizations. To expect a
50 percent WFE is perhaps setting the bar a bit too high. The TS WFO selected
a WFO using 15 percent for OOS% and 25 runs. It is the dark gray selection
from Figure 8.8. This value is the center cell where a majority of its neighboring
cells showed promise. This translates to using only 15 percent out-of-sample data
and changing the parameters 25 times during the test period. The optimization
produced over $52K in profits with a WFE of 49.9 percent. Table 8.6 shows the
OOS report.
Table 8.7 provides the report card on this system using the optimal optimization
values.
The cluster WFA is a very powerful tool because it allows the user to quickly
look at different WFOs utilizing different OOS% values and walk-forward periods.
A good trading algorithm may be rejected by simply using a single WFA. The
e-mini SP system failed on the default 20 OOS% with 10 runs but passed on several
different values.
The TS WFO software provides all the detailed information given in the cluster
optimization matrix. A user can select any of the different optimizations and see a
plethora of details. The ultimate question is then answered as to what parameter
set should be used and how many days that set should be carried forward. If the
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