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Hi, Chuck,
Thanks for the confirmation.
All my systems generate many signals during the test history. Thus my
question about 1%/99%. The system itself is not that discriminatory as
yours, hence every candidate can generate many if not hundreds of
trades during, say, 10 years history.
During the backtest, many of the potential candidates generate
negative returns over the history. I generally cut them off from the
final portfolio. [ the character shift of individual stock is always
at the back of my mind, but I just can not handle the amount of trades
coming, so I have do something about it. ]
I am firstly attracted to your posts when you mentioned about removng
the top performers in further finding optimal parameter sets, because
just started about 2 months ago, I realized there are some superstars
in some of my systems, I suspected that they bend the parameter set so
much, it is not representative any more. In my test on N100, I can
repeat the process you mentioned 2 or 3 times until there is No super
performers any more, I then put every one back to the candidate pool
and using the last parameter set to prune the pool again for the final
portfolio, then it is up to the system as well as my pruning process
to pick up or not pick up the superstars.
I force parameters shift, use student t test as well as XLSim to help
me gauge the stability of my system.
I heard about TraderSim but never really look into it, I will now.
Using XLSim requires extensive preparation because it is designed for
general use.
Thank you very much for your posts.
Thomas
--- In amibroker@xxxxxxxxxxxxxxx, "Chuck Rademacher"
<chuck_rademacher@x> wrote:
> My calculations/indicators/functions are such that they take very
few trades
> on any one stock. Over ten years of data, each stock might trade
(on
> average) about five times. Many stocks might not trade at all with
one
> system and very frequently by another system.
>
> The only subtle difference about what I am doing compared to many
system
> developers is that I don't pre-screen stocks. I don't have various
watch
> lists for different groups of stocks. No watchlist for sectors.
No
> watchlists for high dividends. No watchlists for low P/E ratios.
No high
> Zacks ratings. No low Morningstar ratings. I have all of that
> information in my data and each system looks at all of those things
every
> day for every stock. If I were to make a watch list containing low
P/E
> stocks. Exactly what timeframe would I use to make that
determination.
> Today? Last week? Ten years ago? Why not put it in the data
along
> with open, high, low, close, volume, etc. for each stock for each
day and
> let the system do the work?
>
> I do, however, have watch lists that contain a purely random
selection (out
> of the 13,500 stocks) so that I can try a new system idea without
having to
> go through all of the stocks to see if it has merit. I have two
waitlists,
> for instance, each containing exactly 50% of the stocks. Basically,
every
> other stock. I have four waitlists that each contain a random
selection
> of 25% of the total stock universe.
>
> What are your thoughts?
> -----Original Message-----
> From: tchan95014 [mailto:tchan95014@x...]
> Sent: Sunday, April 20, 2003 3:15 AM
> To: amibroker@xxxxxxxxxxxxxxx
> Subject: [amibroker] Re: Parameter selection, MCS (for Chuck)
>
>
> Hi, Chuck,
>
> Thanks again.
>
> If I read you right, your system is exploiting some fundamental
> movement of the stock universe, since the pattern does not occur
very
> often you make it up with vast amount of candidates (for validity
of
> your test results as well as trading opportunities). Sort of like
the
> pair trading you talked about. Is this correct?
>
> Then, do you have system like, for example a RSI system that
signals
> generate on every stocks in various to times? If yes, how do you
> screen for candidates then? In this case, a system might FIT only
say
> 1% of the universe, because the test results show other 99% over
10
> years are net losers.
>
> Thanks again for your insight.
>
> Thomas
>
>
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