Ultimate Algorithmic Trading System

superfast processors, searching a large search space is extremely time sensitive.
Imagine a trading algorithm (TA1) that has five different parameters that can be
optimized across 50 different values. Assuming a brute force optimization and three
seconds to complete each iteration, it would take the computer almost 30 years
to complete or span the entire search space (search space and optimization range are
also interchangeable terms). Here is the math behind the time needed to complete
this task:
P1(50) ×P2(50) ×P3(50) ×P4(50) ×P5(50) ×3 seconds =
937,500,500 seconds
937,500,500 seconds
= 10,850 days, or 29 years
86,400 seconds∕day
This example is an exaggeration, but it shows multiple parameter optimizations
can be very time consuming. Figure 8.1 shows how quickly the search space grows
as the number of iterations increase for each of the five parameters. This is where
artificial intelligence (AI) comes into play; AI uses brains over brawn. In the above
example, AI can shrink the overall search space by eliminating some values of each
parameter that do not lead to a favorable outcome. In doing so, it cuts down the
number of total iterations, and less iteration means less time.
The first step in the process of incorporating these tools into a trader’s algorithm
development is to understand the basic foundations of GA. Don’t let the words
genetic or algorithms scare you away. They are simply terms to explain how a
solution to a problem can be quickly and solidly uncovered. As we already know,
probably too well, an algorithm is just a set of rules one follows to find a solution to a
problem. Genetic refers to the biological process of evolution through reproduction,
mutation, and survival of the fittest. Remember time is of the essence but so is a good
answer and this type of algorithm provides quick yet robust solutions. Computer
software and biology may seem like strange bedfellows but their synthesis makes up
a large portion in the study of AI.
1200000
1000000
800000
229
GENETIC OPTIMIZATION, WALK FORWARD
600000
400000
200000
0
3 4 5 6 7 8 9 10 11 12 13 14 15 16
FIGURE 8.1
Iteration growth rate of five parameters.
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