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Ken, here is a trading system that uses voting to determine buys and
sells. It doesn't work very well but it will give you something to
experiment with.
Longs only for a few years on a basket of stocks or funds will reveal a
few decent trades. This was originally designed to be a
visual exit signal only and I forced it to be a system.
Optimized is better than standard default values. If you want to
try default values use
RSI 14 50 xover, Stoc 14 50 xover, ma5 cross ma13, 12/26/9 macd
histogam cross 0 level and relative strength >/< its ma all within
a 4-6 day window. Take the vote and trade on 4 out of 5
values true.
Sid
***************
/*
Merwin Oscillator
written by Sid Kaiser, 27 Aug 2001
A voting scheme for early exits originally proposed by Roy Merwin with
modifications by Sid Kaiser.
Doesn't make a very good trading system...
*/
/* Set some parameters */
fastper =
5;
//Optimize("fastper",
15, 2, 15, 1);
slowper =
13;
//Optimize("slowper",
34, 8, 34, 1);
rs =
0.2;
//Optimize("rs",
0.2, 0.1, 2, 0.1);
rsper =
23;
//Optimize("rsper",
17, 8, 47, 1);
rsilvl =
50;
//Optimize("rsilvl",
56, 26, 70, 2);
stolvl =
50;
//Optimize("stolvl",
58, 26, 70, 2);
buylvl =
2.0;
//Optimize("buylvl",
2.5, 1.0, 5.0, 0.1);
selllvl =
3.8;
//Optimize("selllvl",
1.5, 1.0, 4.7, 0.2);
blb =
3;
//Optimize("blb",4,
1, 8, 1);
slb =
3;
//Optimize("slb",
4, 1, 8, 1);
fastma =
EMA(Close,
fastper);
slowma =
EMA(Close,
slowper);
relma = fastma/slowma;
/*
Relative strength vs a high fixed rate of return. */
relstr = (Close /
ValueWhen(DateNum()
==991130,Close,1))
/
exp(Cum(1)*rs/365);
relstr2 = relstr -
EMA(relstr,
rsper);
relmacd =
MACD()
-
Signal();
MO =
IIf(
relma >
1,
1,
0)
+
IIf(
relstr2 >
0,
1,
0)
+
IIf(
RSI(14)
> rsilvl,
1,
0)
+
IIf(
StochD(14)
> stolvl,
1,
0)
+
IIf(relmacd
>
0,
1,
0);
Bvote = MO > buylvl;
Svote = MO < selllvl;
/*****
system *****/
/* if MAs cross first */
Btrig =
Cross(fastma,
slowma);
Strig =
Cross(slowma,
fastma);
/* if
MACD crosses first */
Btrig2 =
Cross(MACD(),
Signal());
Strig2 =
Cross(Signal(),
MACD());
//Buy =
Cross(EMA(C,11), EMA(C,34));
Buy = Bvote;
//and
BarsSince(Btrig2) <= blb or Btrig and BarsSince(Bvote) <=
blb;
Sell =
Svote;//and
BarsSince(Strig2) <= slb or Strig and BarsSince(Svote) <=
slb;
Buy =
ExRem(Buy,Sell);
Sell =
ExRem(Sell,Buy);
/*
exploration */
Filter =
1;
NumColumns =
6;
Column0 = relma;
Column0Name =
"rel_ma";
Column1 = relstr2;
Column1Name =
"relstr2";
Column2 =
RSI(14);
Column2Name
="RSI";
Column3 = StochD(14);
Column3Name = "Stoch";
Column4 = relmacd;
Column4Name = "macd histo";
Column5 = MO;
Column5Name = "Merwin Osc";
Multiple Sub Signals: - here is a real world trading approach that I have never seen mentioned here. I would like to get some reaction. This is based on some real world trading that is happening on another platform that I am helping port over to AB.
The idea is to have signals for multiple subsystems and then take your trading signal when a majority of the subsignals line up.
For discussion purposes, visualize a mov avg cross over AND
A volume oscillator AND
An advance decline curve AND
Perhaps a VIX type signal.
If you let each one be a buy or sell, and call each S1, S2, S3, S4
then your buy statement could be
Buy = S1 AND S2 AND S3 AND S4.
This might be a little too stringent, so perhaps you code it to give a buy if 3 of the 4 signals are a buy and you do not care which ones.
In the work that I am doing with this approach, I am seeing that each S(i) has a return and a dd over a long period of time, but as you add combinations of the signals, the return increases a little bit but the dd seems to drop and drop quite a bit. An example might be returns for each one individually of say 8-10% CAR and 13-15% dd, but when 3 out of 4 are combined as I describe above, the resulting return might be 9-12% CAR and 6-9% dd. These are not barn burners, and those seeking or actually trading 50-100% CAR systems will laugh as they hit delete, but for some folks who are risk adverse, or managing retirement portfolios, or whatever, this kind of approach might have some appeal.
In the work I have done so far, there has been NO OPTIMIZATION of the parameters within the subsystems. Any In sample period compares favorably with OOS periods. The results look steady over 12 years of data, with variations due to changing market conditions. (I am not sure IS and OOSapply when no optimization has been done, but what I mean is that breaking the total time period up into sections shows no real degradation or blowupof one period relative to the other.)
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