System Development Using Artificial Intelligence

The AIQ code based on Domenico D’Errico and Giovanni Trombetta’s article in August 2017 Stock & Commodities issue, “System Development Using Artificial Intelligence,” is shown here. You can also download the EDS file from here

Are humans or computers better at trading? This question has been around on many fronts since the era of punch cards, and as technology advances, you question whether machines have limits. It’s the same with trading, and here’s an algorithm that may shed some light on which performs better…

!Authors: Domenico D'Errico & Giovanni Trombetta, TASC August 2017
!Coded by: Richard Denning, 6/08/2017

O is [open].
C is [close].
H is [high].
L is [low].
exitBars is 8.
exitBarsP is 6.
enterGap is -0.08.

AvgP is (O+C+H+L)/4.
MedP is (H+L)/2.
MedB is (O+C)/2.

AvgP1 is valresult(AvgP,1).
AvgP2 is valresult(AvgP,2).
AvgP3 is valresult(AvgP,3).

MedP1 is valresult(MedP,1).
MedP2 is valresult(MedP,2).
MedP3 is valresult(MedP,3).
MedP4 is valresult(MedP,4).

MedB1 is valresult(MedB,1).
MedB2 is valresult(MedB,2).
MedB3 is valresult(MedB,3).
MedB4 is valresult(MedB,4).

Gandalf if 
 or ({position days}>=exitBars-1)
 or ({position days}>=exitBarsP-1 and (C-{position entry price}>0)).

EntryPr is min(val([low],1) + enterGap,[open]).

Buy if Gandalf and [low] <= EntryPr.

See Figure 10 for how to set up the pricing in a backtest.
Sample Chart

FIGURE 10: AIQ. This shows the EDS backtest settings for entry pricing.
—Richard Denning
for AIQ Systems

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