Genetic Optimizer: Trading System Example TS.GO.12PRO
The system is based on 2 moving average
crossover. Buy signal is generated when fast
moving average crosses over slow moving average.
Additionaly Stop-loss is included in the system.
It is very simple system and we don't recomend to
trade it but it is very useful to understand the
principles of genetic optimization. To do
optimization in usual way all parameters will be
tested.
That would include:
K = TS.GO.Chrom("Buy.Signal");
R = TS.GO.Gen("Buy.Signal.Len1",K,1,50,1); -- 1
to 50, together 50
R = TS.GO.Gen("Buy.Signal.Len2",K,1,50,1); -- 1
to 50, together 50
K = TS.GO.Chrom("Sell.Signal");
R = TS.GO.Gen("Sell.Signal.Len3",K,1,50,1); -- 1
to 50, together 50
R = TS.GO.Gen("Sell.Signal.Len4",K,1,50,1); -- 1
to 50, together 50
K = TS.GO.Chrom("StopLoss");
R = TS.GO.Gen("StopLoss.SL",K,100,1000,100); --
100 to 1000, together 11
All together:
= 50х50х50х50х11 = 68.75 million tests
TS GO finds acceptable solution with ~1000 tests.
Using our new function FresBlood it's possible to
do it even faster, with ~100 to 300 tests.
It's obvious that using new technological
possibilities it's easy to cut down time for
searching solutions. New version of Genetic
Optimizer for TradeStation v.1.2. supports up to
1000 genes (!), that allows to solve tasks that
wouldn't be posible with other methods.
Example of simple trading system to show
the possibilities of Genetic Optimizer for
TradeStation.
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{*******************************************************************
Name: TS.GO.12.PRO
Analysis Type: Strategy
Description: Example Strategy for Genetic Optimizer v.1.x
Example of simple trading system to show the possibilities of Genetic Optimizer for TradeStation.
The system is based on 2 moving average crossover.
Buy signal is generated when fast moving average crosses over slow moving average.
Additionaly Stop-loss is included in the system.
Used: TSGO12.dll
Provided By: Trade Smart Research (c) Copyright 2001 - 2004
www.tsresearchgroup.com
*******************************************************************}
Inputs:
Gen(1), {Gen - input parameter, that assigns the number of generations.
Optimize in TradeStation with "Start = 1" and "Inc = 1"}
ShowInd(1), {ShowInd - number of individual in population to show}
ModeTSGO(0),
Population(50),
FreshBlood(0),
MyReportName("MySystem1");
{ Declaration of variables }
Vars: Len1(0),Len2(0),Len3(0),Len4(0),SL(0),
Fitness(0),LastRun(0),R(0),K(0),Ind(0);
{ ---------------------------------------------------------------------- }
{ The Genetic Optimizer initialization and the definition of genes }
If CurrentBar = 1 Then Begin
{ This block runs on every run of strategy on the first bar.
The function TS.GO.Start is called having the Parameter that defines
filename for milestones.
All the tunings of an optimizer and current population are stored in the
file, that allows to continue an optimization after break, or to draw
the input/output signals after the opening the TradeStation workspace with
the strategy. It is possible to open this file in graphic interface for viewing
population.}
R = TS.GO.Start(MyReportName + "(" + GetSymbolName + ").rgo");
{ This block runs when the optimization is starting for the first bar only. }
If Gen = 1 Then Begin
{ The initializing of optimizer determination of genes and the population
regime is executed (see the description of functions).
We start optimizer with empty population in a given example. }
R = TS.GO.Mode(ModeTSGO);
R = TS.GO.Popul(Population);
R = TS.GO.FreshBlood(FreshBlood);
{ Define User variables. }
R = TS.GO.Var("NetProfit");
R = TS.GO.Var("PF");
R = TS.GO.Var("MaxIDD");
{***Sets up new chromosomes and new genes.
Chromosome Parameters: TS.GO.Chrom(Name)
Name – name of chromosome.
Gene Parameters: TS.GO.Gen(Name,Chrom,Min,Max,Incr)
Name – name of gene.
Chrom – number of chromosome that contains gene (if 0 then gene doesn’t participate in mutations, it’s fixed).
Min – minimal value of gene.
Max – maximal value of gene.
Incr – value increase (step), if = 0 then any values in set range can be used.***}
K = TS.GO.Chrom("Buy.Signal");
R = TS.GO.Gen("Buy.Signal.Len1",K,1,50,1);
R = TS.GO.Gen("Buy.Signal.Len2",K,1,50,1);
K = TS.GO.Chrom("Sell.Signal");
R = TS.GO.Gen("Sell.Signal.Len3",K,1,50,1);
R = TS.GO.Gen("Sell.Signal.Len4",K,1,50,1);
K = TS.GO.Chrom("StopLoss");
R = TS.GO.Gen("StopLoss.SL",K,100,1000,100);
End;
{ The generation of a new candidate in the population }
LastRun = TS.GO.Next(Gen);
{ If this is the last path, shows results for Ind = ShowInd;
Else get the next candidate Ind = 0; }
Ind = Iff(LastRun = 1,ShowInd,0);
{ Get values of genes for choosen candidate. }
Len1 = TS.GO.Get("Buy.Signal.Len1",Ind);
Len2 = TS.GO.Get("Buy.Signal.Len2",Ind);
Len3 = TS.GO.Get("Sell.Signal.Len3",Ind);
Len4 = TS.GO.Get("Sell.Signal.Len4",Ind);
SL = TS.GO.Get("StopLoss.SL" ,Ind);
R = TS.GO.ShowViewer;
End;
{ ---------------------------------------------------------------------- }
{ The basic strategy code. }
{ Set up the stop-loss parameter. }
SetStopPosition;
SetStopLoss(SL);
{ The Moving Averages Calculation. }
Value1 = AverageFC(C,Len1);
Value2 = AverageFC(C,Len2);
Value3 = AverageFC(C,Len3);
Value4 = AverageFC(C,Len4);
{ Generation of signals by moving averages crossover.
According to the signal, short positions are reversed to long positions and
vise versa. Besides, positions can be stopped by stop-loss and
trailing-stop orders. }
if Value1 cross over Value2 then Buy This Bar ;
if Value3 cross below Value4 then Sell This Bar ;
{ End the basic strategy code. }
{ ---------------------------------------------------------------------- }
{ Calculation an optimization criteria. The simplest
criteria is used here. }
R = TS.GO.Stat;
if LastBarOnChart Then Begin
{ Save user defined data. }
R = TS.GO.Set("NetProfit",NetProfit);
R = TS.GO.Set("PF",Iff(GrossLoss < 0,-GrossProfit/GrossLoss,0));
R = TS.GO.Set("MaxIDD",MaxIDDrawDown);
{ A fitness value is passed to the genetic optimizer on the last bar.
If the candidates are included in the current population depends on the
result of run. }
Fitness = TS.GO.Get("%T_Test",0);
R = TS.GO.Fitness(Fitness);
{ One can look at all tested variants, assigning a print of the gene
values for each generation.
In PowerEditor in debug window to the debugger.}
{print(Gen,Fitness,Len1,Len2,Len3,Len4,SL,DT,FA,PC);}
end;
{***** Copyright (c) 2001-2004 Trade Smart Research, Ltd. All rights reserved. www.tsresearchgroup.com *****
***** Trade Smart Research reserves the right to modify or overwrite this analysis technique
with each release. *****}
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