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Richard,
In case you missed it, here is the post again (from 6/23/02), post
no. 20072).
AV
--- In amibroker@xxxx, "Avcinci" <avcinci@xxxx> wrote:
> 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), and BOBE (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.
>
> 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.
>
> 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 welcome any comments.
>
> Al Venosa
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