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Hi,
I found following post regarding MCS for amibroker during my googling:
In "Casino Trading," Phil Abel presents methods of evaluating trading
system robustness using Monte Carlo simulation. To perform a Monte
Carlo simulation, we would need a "decision tree" -- a set of data
showing probabilities of profits in a given range.
As the author suggests, we could derive such a decision tree from an
individual trade list produced by the backtester, but we will show a
slightly more automatic method that involves using AmiBroker's
exploration mode.
Listing 1 shows the exploration formula to be applied in the
Automatic Analysis window. In our example, we have used a very simple
EMA crossover system, but the user can replace it with any other
system. The code produces a table that shows the number of
winning/losing trades, total number of trades, and percentage
probabilities of profits in user-defined ranges. In the sample code
below, we have used 25 ranges for winning trades (0..100,
100..200, .., 2400...infinity), and a similar 25 ranges for losing
trades. The user can modify both the number of ranges evaluated and
the size of the range by modifying appropriate variables in the code.
Once the exploration is complete, the results can be exported to a
CSV file for further analysis in Excel.
LISTING 1
///// Trading system to be evaluated
Buy = Cross( Close, EMA(Close, 15) );
Sell = Cross( EMA(Close, 15), Close );
///////////////////////////////////
e = Equity(1);
tradeend = Sell;
profit = e - ValueWhen( Buy, e );
endprofit = IIf( tradeend , profit, 0 );
LosingTrades = LastValue( Cum( endprofit < 0 ) );
WiningTrades = LastValue( Cum( endprofit > 0 ) );
TotalTrades = LastValue( Cum( tradeend ) );
AddColumn( LosingTrades, "Losing trades", 1 );
AddColumn( WiningTrades, "Wining trades", 1 );
AddColumn( TotalTrades, "Total trades", 1 );
Filter = Status("lastbarinrange");
// now we will count the number of
// count trades winning in given $ ranges
NumRanges = 25; // number of ranges to generate
RangeSize = 100; // controls $ size of the range
for( i = 0; i < NumRanges; i++ )
{
rangestart = i * RangeSize;
rangeend = (i+1)*RangeSize;
if( i == NumRanges - 1 ) rangeend = 999999;
AddColumn(
LastValue( 100* Cum( endprofit > rangestart AND endprofit <
rangeend ) /TotalTrades ),
"% with profit " + rangestart + ".." + rangeend );
}
for( i = 0; i < NumRanges; i++ )
{
rangestart = (-i - 1 ) * RangeSize;
rangeend = -i*RangeSize;
if( i == NumRanges - 1 ) rangestart = -999999;
AddColumn(
LastValue( 100* Cum( endprofit > rangestart AND endprofit <
rangeend ) /TotalTrades ),
" % with loss " + rangeend + ".." + rangestart );
}
A sample Monte Carlo spreadsheet for AmiBroker can be found in the
files area of AmiBroker's mailing list at
http://groups.yahoo.com/group/amibroker/files/.
--Tomasz Janeczko, AmiBroker.com
www.amibroker.com
Song
--- In amibroker@xxxxxxxxxxxxxxx, "brian.z123" <brian.z123@xxx> wrote:
>
> Hello Whitne,
>
> Thanks for your post.
> Two of your leads provided new material for me.
>
> I am not using MCP but I have it under consideration.
> Conceptually, if not practically, I have rejected the walk forward
> method, which means I am bound to consider all methods that
validate
> single sample tests.
>
> So far I am considering three approaches and measurements for each:
> error, significance and symptoms of overfitting.
> Obviously something from amongst those groups will have to do
better
> than the walk forward hypothesis for me to accept it.
>
> It might end up that none of them will provide a conclusive answer
> but *in the land of the blind, the one eyed person is King*.
>
> At the least study, in this area will improve my depth of knowledge
> of evaluation techniques.
>
> BrianB2.
>
> --- In amibroker@xxxxxxxxxxxxxxx, "whitneybroach" <WhitneyBroach@>
> wrote:
> >
> > While reading David Aronson's book _Evidence-based Technical
> > Analysis_, I stumbled across a modified Monte Carlo permutation
> > (MCP) procedure that compensates for data mining bias, assuming
that
> > the "best" permutation of rules was not selected with a directed
> search.
> >
> > From Aronson's perspective, this is good news. He views data
mining
> > as a useful procedure in the discovery phase of research. Plus,
MCP
> > does not require out-of-sample data. Thus it is possible to use
> more
> > data for mining and still minimize data mining bias in test
> results.
> > The likely result: fewer false positives for systems that are
> > worthless, and fewer false negatives for systems that are
valuable.
> >
> > The paper with discussion and C# code is here:
> > <http://www.evidencebasedta.com/MonteDoc12.15.06.pdf>.
> >
> > Aronson's book site, including a link to Amazon, is:
> > <http://www.evidencebasedta.com>. Separately, I'm looking
forward
> to
> > the imminent books from Howard
> > <http://www.quantitativetradingsystems.com/> and Ralph Vince
> > <http://tinyurl.com/2os2p7>.
> >
> > Not being a user of IO (or other AB add-ons), I have no idea if
this
> > MCP approach is already being used in the AB community. It looks
> > interesting to me. MCP appears to require market data and trade
> data
> > from every run, not simply the trade data. That suggests to me
that
> > an AB add-on, rather than a completely external program, would be
a
> > more straightforward implementation.
> >
> > Aronson also refers to a patented boostrap procedure that
> accomplishes
> > much the same thing, White's Reality Check, named for Halbert
White,
> > the patent holder. Apparently WRC is not available commercially.
> >
> > Best,
> >
>
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