Hi All,
Aron:
you got better results by removing your original entries,
because your original entries were not better then random and you got more time
in the market by using simple random entries (my guess).
To All:
Thanks for all the thoughts and
consideration.
To give some more hints and encourage thoughts here is a bit more
info.
My general idea is to divide a complete trading system into smaller
independently testable/optimizable pieces. I'm building a single equity,
intraday, automated trading system. To make it simple let's say it consists of a
filter (when not to trade) an entry & timing logic (generate buy and short
signals) and a trade management logic (initial stop, trailing logic, profit
taking exits, etc.)
If we accept that the price movements consists of noise
and real price movements than the trade management logic's only job is to keep
my stops (initial and trailing) out of the noise level, while minimizing initial
loss and maximizing profit. It has to accomplish this REGARDLESS OF THE QUALITY
OF THE ENTRIES AND FILTERS. If all my entries are bad it has to produce the
least amount of loss. If all my entries are excellent it has to collect the most
profit.
If I run a number of backtest runs with random entries while keeping
the settings of trade management logic constant I get a "sample" of what might
happen using the settings if my entries are not better then chance. This sample
has a distribution of profits, CARs, system drawdowns, etc. All the attributes
of a backtest runs or a series of real life trades!
If I run similar test
with each possible setting (optimization) and compare the samples of each
settings, I'm able to select settings that produce the best performance
distribution (defined by my objective).
So if my trade management logic is up
to its job (using the best settings) it has to produce the best distribution of
drawdowns and profits of backtest runs with random
entries.
Similarly, the filter's job is to keep me out of market when
trading is not profitable. It?s not profitable because there are more noise than
real price movement (so initial stop is going to be hit sooner or later) OR
because of entering the market in the wrong direction. If using random entries
(in random directions) and the filter is bad, the initial stop is hit because of
either cause. If the filter is good, the numbers of initial losses are minimized
because initial stop is hit if I try to ride the market in wrong direction but
noise is appropriately addressed.
So if I use random entries and use the same
initial loss with no trailing and add the "perfect" filter to it,the filtered
system has to provide the smallest loss and the smallest drawdown. By running a
number of random backtests for each possible filter settings, I produce a sample
of that filter settings. These samples can be somehow compared and the best
selected.
Any opinion, thoughts or experience is appreciated.
I don't
really know what the best way of comparing ?samples? is. Any
idea?
Regards,
Y