Ultimate Algorithmic Trading System

TABLE 8.4
Out-of-Sample Testing
Le Se Lx Sx Begin Date End Date Bars Profit Max DD
20 20 12 10 2005/06/15–2006/07/07 244 4, 031.25 −2, 562.50
20 20 12 10 2006/07/07–2007/06/27 244 4, 437.50 −3, 093.75
20 20 16 10 2007/06/27–2008/06/25 244 −3, 437.50 −10, 343.75
20 24 12 12 2008/06/26–2009/04/07 244 8, 968.75 −7, 343.75
20 20 12 14 2009/04/07–2010/04/16 244 −6, 437.50 −13, 187.50
20 36 10 14 2010/06/04–2011/03/29 244 8, 250.00 −2, 093.75
20 20 12 14 2011/04/18–2012/04/06 244 −2, 500.00 −17, 562.50
20 24 12 14 2012/04/06–2013/03/27 244 −1, 937.50 −11, 562.50
20 56 10 14 2013/03/27–2014/04/04 244 −4, 062.50 −13, 187.50
24 24 12 14 2014/04/04–2015/03/18 244 1, 718.75 −5, 156.25
9, 031.25
252
GENETIC OPTIMIZATION, WALK FORWARD
years, it should overhang or carry over into the subsequent year. A practitioner of
this school of thought also believes the market is changing and a trading algorithm
must change or adapt as well. Table 8.4 shows the WFO of this system on the
30-year bond going back to 2001. Notice how the results start in 2005. The system
backed up to 2001 and derived the best parameter set for those four years and then
traded that set starting in June 2005 and ending in July 2006.
When July 2006 arrived, the system then switched to the second parameter set.
These parameters were derived using data from 2002 through 2005 (refer back to
table 3—second row). The WFO swapped parameters on an annual basis. Overall,
the system was profitable, but the walk-forward efficiency was only 19.03 percent,
and it lost 5 out of 10 years. Why did this happen? The only answer is the lack of
optimization overhang in the out-of-sample periods.
System TA2 used the default WFO settings:
■
■
Walk-forward runs = 10; in this test, the computer switched parameters 10 times
over the history of the test. This works out to be about once a year. If the test
period had been 20 years, then the WFO would have switched every 2 years.
OOS% = 20%; this percentage informs the computer to utilize 80 percent of
each interval’s data (in-sample) to determine the best parameter set and then
carry that parameter set forward 20 percent of the interval (out-of-sample).
If these settings are changed, then the number of bars used for optimization and
the number of bars those optimizations are carried forward will change as well.
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