|
PureBytes Links
Trading Reference Links
|
Over the last few weeks I have spent a lot of time, & received a lot of
advice form the list about walk forward optimization - After weeks of
testing and reading I have arrived at the following conclusions:
The 1st principal of optimization is that if you are going to do it - it
should be done over as much historical data as possible.
Rolling Walk forward optimization, (shifting the in-sample window, say two
years, forward to encapsulate the most recent out of sample period, say six
months, and re-opimazing the new shifted two year window) although sounds
good - and mimics a lot of the currently popular adaptive techniques
(adaptive moving averages etc) is unstable as far as i have investigated.
Furthermore how does one know how much in-sample data to use versus how
much out-of sample data to trade on etc etc (too many variables)
Anchored Walk forward optimization seems very stable - it has been
described in several books on optimization as is based on the following
principal:
A contract is selected - I am using the DAX future:
The initial starting point is selected as a function of the contract
characteristics - I have chosen 1/4/99 as this was the time the contract
was denominated in euros and traded on the eurex platform. (if using the
S&P for example a good place to start would be when the tick size changed
from $500 to $250)
The contract is optimized for the best parameter (my system has one , just
length)
The market is traded over the next month of unseen data
At the end of the month the new optimization period is performed from the
original point again 1/4/99 up to the end of the most recently traded
month, the new parameter is recorded, the next month traded with the new
parameter, before repeating the whole process at the end of the month.
I have performed the this testing on two contracts over 32 unseen monthly
periods and the results are good. (total 64 months each month having an
average of 3 trades in it)
I suspect the following problem will arise and am very interested in
knowing any potential work arounds:
The stability of the parameter increases dramatically as a function of how
much data I use - at the beginning of the 32 month period the parameter
changed quite frequently and towards the end it seems to have stabilised
into the same value month after month.
This is retrospectively predictable, and am now however expecting for the
system to start breaking down in the near future as the sensitivity of the
optimization process decreases as more data is used. I do not know how
sensitive the optimization process will become in order to adjust for
recent changes in the market
What I figured I need is the following:
A type of weighted anchored forward optimization process that pays more
attention to the profitability of the more recent trades yet still takes
all the data into account.
Does anyone have any ideas on how to do this - I feel that the process has
to involve anchored walk forward optimization and the more common rolling
window methods are really not that reliable;
The system is based changes in slope in a type of regression line - it has
1 parameter (length).
Thank you very much in advance
Mark
---------------------------------------------------------------------------
This message (including any attachments) is confidential and may be
privileged. If you have received it by mistake please notify the sender by
return e-mail and delete this message from your system. Any unauthorised
use or dissemination of this message in whole or in part is strictly
prohibited. Please note that e-mails are susceptible to change.
ABN AMRO Bank N.V. (including its group companies) shall not be liable for
the improper or incomplete transmission of the information contained in
this communication nor for any delay in its receipt or damage to your
system. ABN AMRO Bank N.V. (or its group companies) does not guarantee that
the integrity of this communication has been maintained nor that this
communication is free of viruses, interceptions or interference.
---------------------------------------------------------------------------
|