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The efficiency
and real-world success of a trading model is usually dependent on two
variables. The simplicity of the algorithm, the amount of trades produced by
the algorithm and the amount of human adjustment. The aim is to generate the
highest IRR with the simplest method, the least amount of trades and without
the need to interfere and adjust for different trading environments. The simplicity is
a good predictor of future success because the more complicated an algorithm
the more likely that it is retrofitted to describe the past data and that it
will fail in future trials. The amount of trades generated is also important
due to the transaction costs. It is
probably possible to generate enormous theoretical returns with a complex
algorithm that executes 150 trades a day but after transaction costs are
factored in, the end result would be a disaster. Any backward testing of a
trading algorithm should also be without any human interference (such as
assertions that in reality an intelligent human would interfere in October
2008 and then give back the control to the algorithm in mid
2009) to test the long-term predictive power of the algorithm. Model Based Value
Fund aims to execute one of three trading models developed by the site owner.
This model is designed to exploit contrarian movements to take advantage of
value dislocations resulting from extreme market movements. The performance
of the model in the last 6years is presented in the graph to the left as well
as the performances of DJIA and the long term Treasuries (as represented by iShares Barclays 7- 10 Year Treasury Fund) for
comparison. The algorithm is back-tested with no human interference or
adjustment and on that basis has produced an annual return of 13,1% with a standard deviation of 22,48% compared to an
annual return of 2,0% and 22,6% annual
standard deviation for the DJIA. In executing the model for the Fund the model might be suppressed if there is an expected market movement that would interfere with the model. So while the back-testing results are completely without interference the performance of the Fund will be a combination of the algorithm’s success and the site administrator’s occasional choices to use the model or not. If the model is suppressed it will be noted in the comments section. |
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Since Inception Relative Performance Graph vs
DJIA and 7 -10 Year Bond Index Latest Trades
Comments Jan 26th’ 12
13:35 Just to give
an update, the model is still suppressed. The market has proven extremely
resilient and it doesn't give the market correction that would make me
comfortable before I start trading this Fund. Also the model is rather
inefficient when the market is very steady as it has been recently. I will
try to activate the model when the conditions are accommodative to a value
trading strategy. However so far the market is just overbought on an absence
of news and valuation does not seem to matter. The fluctuations are also not
enough for medium term trading models. Check back for updates. Dec 8th' 11
11:52 The model is
still suppressed. However to generate some income until the market corrects
itself I have initiated some long-term Treasury positions for the Fund
similar to other Funds. This way the Fund can also generate some gains from a
decrease in bond yields too, until I activate the model. Dec 2nd' 11
9:43 The model
that underlies the Quant Valuation Fund is a medium term strategy that
triggers trade signals roughly twice a month. However, it is my opinion that
the market is rather overbought and needs a correction. In executing a
relative value model, even if it is a quant model, the initial entrance is
quite important. Therefore I will suppress the model until a relatively
modest market correction happens. When the model is activated and traded
there will most likely be a comment in this section. Since Inception Performance Chart vs Equity and Bond Benchmarks
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Latest Trades
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