Harry,
My package 'ttrTests' is designed to do just that. However, it doesn't
support bid-ask data. It only uses a single price series for buys and
sells. In the future I might be able to modify it to support bid/ask data.
Based on my 2 years of thesis research, the testing approach in the package
is what's prescribed in the literature. You can go through my references
for more details. The package documentation should explain briefly. Even
though my package won't support your data, the methods for constructing test
statistics should all be valid.
http://cran.r-project.org/web/packages/ttrTests/ttrTests.pdfBest,
-David
Date: Thu, 5 Nov 2009 20:29:09 -0600
From: Harry Georgakopoulos <
[hidden email]>
Subject: [R-SIG-Finance] How to properly compare a trading signal to a
random strategy.
To: <
[hidden email]>
Message-ID: <
[hidden email]>
Content-Type: text/plain
Set up ^^^^
Let's say i have an evenly spaced discrete time-series of bid-ask prices.
Let's also say that the total number of such bid-ask pairs is N for a given
day. Given a signal that generates a buy opportunity on "n" such times
(where n << N), how can I reliably say that these n signals have a mean
profit that is statistically significant?
For example, assume I get 35 buy signals throughout the day where I buy the
offer, wait 5 minutes and then sell the bid. This will generate a vector
of 35 price-differences. These price differences will have a particular
distribution.
Thoughts ^^^^^ 1. I can compare the distribution of the 35
price-differences generated from the signal against the distribution of
35 randomly chosen entry points throughout the day. (maybe some kind
of t-test on the difference of the means of these distributions)
2. I can compare the distribution of the 35 price-differences to a rolling
window of all possible buys throughout the day and selling after 5 mins.
(more data-points to compare against)
3. I can compare the distribution of the 35 price-differences against an
absolute value of 0.
Any ideas on quantifying the significance of such a signal would be
appreciated. Is one method preferred over another? Am I inadvertently
introducing bias in the analysis? I realize that the distribution of the
price-differences might not be normally distributed. This might make any
analysis based on a t-test invalid.
Thank you in advance.
H.
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