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The column headings are self explanatory. The first row of data contains the BACKtest of the 5 native tickers using real data from the last 1000 bars.Keep in mind I used position sizing of 1% of capital (1R = $1000), so the % return figures are necessarily small because of the way AB calculates %return (on the basis of total equity). The system generated a small positive expectancy of 0.076 over the last 1000 bars and a total net profit of $14,530. The next 5 rows of data summarize the FORWARDtest results from the synthetic, scrambled data. Data in the 2nd and 3rd rows were extremely closeto the real data, giving about the same drawdowns, expectancy, net profit,avg. wins, avg. losses, no. of wins and losses, etc. However, the next 3 rows showed losses with negative expectancies and negative % returns. The drawdown numbers were very close to the previous data. The biggest differences were in the % profitable trades, i.e., the number of wins relative to thetotal no. of trades. This was the main cause of the negative expectancies and returns. Everything else seemed to be fairly uniform relative to the actual data.
When I viewed the synthetic data, I noticed that often the data were highlyvolatile, much more so than usual. Since I was expecting this behavior, I tried to control this somewhat by including in the buy and short statementsto trade only if the 20-day ATR was less than 7% of the closing price. So,all trades generated were constrained by this filter. Perhaps I should have imposed a 5- or 10-period moving average of the ATR being less than 7% ofthe close to control volatility even more. Other observations included trends, some lasting as long as several months, head-and-shoulder patterns, support and resistance behavior, and consolidation periods. So, based on thisexperiment, I see no reason why the scrambler cannot be used as out-of-sample data to test a trading system. Now, having said that, I agree with the critics who state that there is absolutely no past history to govern futureprice behavior (i.e., trading psychology, supply and demand, etc.), and for that reason use of scrambled data to test trading systems is somewhat unrealistic. There's no doubt about that. However, I still contend it can be used as a tool for testing your trading system. I welcome any comments.
Al Venosa
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<DIV>Well, folks, I conducted a test of the Scrambler today. Here is a
description of the experiment. First, I chose 5 tickers that were not correlated
as to industry and put them in a watchlist. They were AAPL (Apple Computer), AET
(Aetna Insurance), ANF (Abercrombie and Fitch), BP (British Petroleum), andBOBE
(Best Buy). I then coded a trend-following system that trades both long and
short (ADX uptick for entry with a max 2.5-ATR stoploss and a 4-ATR trailing
stop, using Stephane's RemBuyTrail and RemShortTrail calls as part of his
RemBuy.dll plugin). I tested the system on those 5 tickers for the last 1000
bars (about 4 years, starting on July 1, 1998). Then, I ran the scrambler to
generate 1000 bars of synthetic data for each ticker, creating a separate
watchlist for those synthetic tickers. I tested the system on those 1000 bars
and recorded the results. I then replicated this procedure 4 more times to
generate 5 separate replicates of these randomized synthetic tickers and
therefore 5 independent forward tests of the system. The results are summarized
in an Excel spreadsheet attached to this message. </DIV>
<DIV> </DIV>
<DIV>The column headings are self explanatory. The first row of data contains
the BACKtest of the 5 native tickers using real data from the last 1000 bars.
Keep in mind I used position sizing of 1% of capital (1R = $1000), so the%
return figures are necessarily small because of the way AB calculates % return
(on the basis of total equity). The system generated a small positive expectancy
of 0.076 over the last 1000 bars and a total net profit of $14,530. The next 5
rows of data summarize the FORWARDtest results from the synthetic,
scrambled data. Data in the 2nd and 3rd rows were extremely close to the
real data, giving about the same drawdowns, expectancy, net profit, avg. wins,
avg. losses, no. of wins and losses, etc. However, the next 3 rows showed losses
with negative expectancies and negative % returns. The drawdown numbers were
very close to the previous data. The biggest differences were in the %
profitable trades, i.e., the number of wins relative to the total no. of trades.
This was the main cause of the negative expectancies and returns. Everything
else seemed to be fairly uniform relative to the actual data. </DIV>
<DIV> </DIV>
<DIV>When I viewed the synthetic data, I noticed that often the data were highly
volatile, much more so than usual. Since I was expecting this behavior, I tried
to control this somewhat by including in the buy and short statements to trade
only if the 20-day ATR was less than 7% of the closing price. So, all trades
generated were constrained by this filter. Perhaps I should have imposed a 5- or
10-period moving average of the ATR being less than 7% of the close to control
volatility even more. Other observations included trends, some lasting as long
as several months, head-and-shoulder patterns, support and resistance behavior,
and consolidation periods. So, based on this experiment, I see no reason why the
scrambler cannot be used as out-of-sample data to test a trading system. Now,
having said that, I agree with the critics who state that there is absolutely no
past history to govern future price behavior (i.e., trading psychology, supply
and demand, etc.), and for that reason use of scrambled data to test trading
systems is somewhat unrealistic. There's no doubt about that. However, I still
contend it can be used as a tool for testing your trading system. I welcomeany
comments. </DIV>
<DIV><BR>Al Venosa</DIV></BODY></HTML>
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