Ultimate Algorithmic Trading System

TABLE 3.17
Results for the Donchian Algorithm when Walked Forward across an
Out-of-Sample (OOS) Time Period
Name
Net
Profit
CAR
Max.
Sys
DD
Profit
Factor
Ulcer
Index
#
Trades
Avg
P/L
Avg
Bars
Held
%
Winners
<Portfolio> −31803 −0.58 −222494.3 0.91 11.93 158 −201.28 56.15 30
AD0_E0B −2570 −0.05 −26410 0.91 1.64 16 −160.62 64.56 38
C20_E0B −8500 −0.15 −26325 0.71 1.73 20 −425 53.85 30
CL20_E0B −14060 −0.25 −58490 0.78 3.36 21 −669.52 40.95 29
CU0_E0B 16888 0.3 −39763 1.62 2.17 14 1206.25 67.57 36
HG20_E0B −25763 −0.46 −38750 0.56 2.47 19 −1355.92 45.26 26
KW20_E0B 8525 0.15 −15063 1.4 0.59 17 501.47 65.71 41
RB0_E0B −29267 −0.53 −79688 0.7 4.51 24 −1219.48 36.63 13
SB20_E0B 15185 0.27 −19315 2.07 1.28 11 1380.44 94.27 36
TY0_E0B 7760 0.14 −9334 1.46 0.49 16 484.99 65.38 38
small diversified portfolio. We did everything humanly possible to make these trendfollowing
systems work: algorithm selection, optimization, and portfolio selection.
What if we performed a more frequent periodic reoptimization? We saw the best
parameter set shifting from just five years ago. This shift may be attributable to the
disappearance of the pit session and the adoption of purely electronic markets. Who
knows why, but the fact remains that the commodity markets have had a fundamental
shift. Indicators such as the ones we have experimented with are adaptive by nature;
they change based on market activity and/or volatility. Theoretically, they should
be able to keep up with the evolution of the markets. In practice, this was not
the case.
AmiBroker has a walk-forward backtesting capability—meaning we can periodically
reoptimize parameters throughout a historic backtest. I discuss this optimization
method in great detail in Chapter 8. After this chapter, if you like, you can skip to
Chapter 8 to help you understand exactly what is going on here. Figure 3.4 illustrates
the basic concept of walk-forward testing. Basically it involves two primary steps:
1. Backtest and derive—backtest and extract the best parameter set based on some
criteria. It could be profit, drawdown, profit-to-drawdown ratio, etc.
2. Walk forward—take the parameter sets and apply them forward in time on data
not used in the backtest. Do this on a periodic basis.
In this example, the Bollinger Band algorithm will be reoptimized annually based
on the best-performing parameters for the prior four years. Keep in mind that we
are not cheating, as only the OOS (out-of-sample) data will be used to calculate
performance metrics. Let’s see if this method can turn around the trend of this
trend-following method. Figure 3.4 shows the process of walking forward.
105
COMPLETE TRADING ALGORITHMS
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