Ultimate Algorithmic Trading System

TABLE 8.5
WFA Report on TA2
Test Criteria Result Comment
1 Overall Profitability Pass Total Profit > 0. System likely to perform profitably on unseen data.
2 Walk-Forward Efficiency Failed < 50%. System likely to perform in future at a rate of less than 50%
of those achieved during optimization.
3 Consistency of Profits Pass 50% of walk-forward runs were profitable.
4 Distribution of Profits Failed Run #6 contributed more than 50% of Total Net Profit.
5 Maximum Drawdown Pass No individual run had a drawdown of more than 40% of init. capital.
Overall Result
FAILED
254
GENETIC OPTIMIZATION, WALK FORWARD
The WFO process did not outperform the static parameters on this particular
algorithm. The WFA report is shown in Table 8.5.
The WFA agrees with our benchmark analysis that a WFO does not complement
this type of trading algorithm. Of the five test criteria, this system failed two.
The most important failed test was the walk-forward efficiency (WFE). This value
compares the in-sample average profit versus the out of-sample average profit. This
algorithm failed with a 19.03 percent efficiency, meaning that the average OOS
profit was one fifth the size of the in-sample profit. Now, does this mean TA2 is
not a good trading algorithm? The robustness of the system logic and results of the
static parameter analysis answers this question with an emphatic ‘‘No!’’ Just because
a WFO didn’t work well with this particular algorithm in the 30-year bonds doesn’t
mean it is not a good system. Other markets need to be tested and evaluated, as
well as different WFO settings.
WFO Example 2
Another example of a WFO on a different type of algorithm may be a beneficial
demonstration. This time, the WFO will be applied to a short-term ES (emini SP)
system that produces more trades than TA2. This system, TA3, will trade in the
direction of the long-term trend and buy pullbacks and sell rallies. The trend will be
defined by a long-term moving average and pivot points will determine pullbacks
and rallies; pivots involving the highs will determine rallies and pivots using the lows
will determine pullbacks. Different protective stops and profit objectives will be
used for long and short positions. The strength of the pivot points and the number
of days that will be used to determine the presence of the pivot points may also be
different for long and short entries. Two additional parameters will be used to limit
the number of days the system is in the market. That makes a total of 11 optimizable
parameters:
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