Model discussion

[Pascal]

2008 stats look particularly intriguing here. For example, assuming the model is roughly symmetric in its long/short settings, why would it perform much poorer (on most ETFs) in 2008 (down trending) than in 2009 (up trending)? I would guess a closer look at the trades is needed to answer that. Some candidates for inspection: volatility threshold (criterion for short entry), trade duration, etc.

Trader D

In fact, you are not totally correct:

First, the model is not symmetric, because the MF is not and the model follows the MF.
Then, I would say that the model performed better in 2008 than in 2009: it outperformed the market in 2008 and slightly underperformed it in 2009. Both were strongly trending market when overbought became more overbought and oversold became more oversold.

This means that in a model that has OB/OS triggers, in a strong market, the model will stay on the side-lines after having sold in OB or covered in OS, just to see the market continuing in the same direction.

Finally, as I wrote yesterday, in 2008/2009, the data was 20% different, both in term of tickers and in terms of weight. The question is: is 20% difference in data important or not? I do not know, but I know that when I switch from a fixed weight to a capitalized weight calculation, the results drastically degrade to the point that the model becomes impossible to use. The MF calculation method must follow the ETF price calculation method. But in 2008/2009, we had a 20% difference.

That being said, I'll write some code to automatically study all the trades year by year and ETF by ETF


Pascal

[TraderD]

In fact, you are not totally correct:
First, the model is not symmetric, because the MF is not and the model follows the MF.
Then, I would say that the model performed better in 2008 than in 2009: it outperformed the market in 2008 and slightly underperformed it in 2009. Both were strongly trending market when overbought became more overbought and oversold became more oversold. This means that in a model that has OB/OS triggers, in a strong market, the model will stay on the side-lines after having sold in OB or covered in OS, just to see the market continuing in the same direction.

Looks like I got it the other way around if accepting the notion that a MF-based model is to be benchmarked against the general market, since both 2008 and 2009 were one-sided in all ETFs (up or down) during the respective year. So what does explain the ability of the model to beat all ETFs in 2008 but fail to outperform in 2009? Is it the point where the OB/OS oscillator decides to give up and jump to the sidelines? Why would it do so in 2009 earlier than in 2008? Devil in the details...

Finally, as I wrote yesterday, in 2008/2009, the data was 20% different, both in term of tickers and in terms of weight. The question is: is 20% difference in data important or not? I do not know, but I know that when I switch from a fixed weight to a capitalized weight calculation, the results drastically degrade to the point that the model becomes impossible to use. The MF calculation method must follow the ETF price calculation method. But in 2008/2009, we had a 20% difference.

That being said, I'll write some code to automatically study all the trades year by year and ETF by ETF
Pascal

I can see how a composition (as well as market cap weighting, if applies) difference could be the culprit (it's a form of lookahead bias too). Overall, this looks promising, I'm sure detailed testing will unearth interesting findings.

Trader D

[TraderD]

This simple fact shows that it must be possible at any time to select the three or four best ETFs and trade in/out of a rotation of the best ETFs.

Pascal, do you mean that the MF-based indicator can be used to drive rotation in/out of the best ETFs or a more standard price-based (e.g. momentum, relative strength) indicator? Also, without a rolling time view into the ETF trade stats, I don't see how it's possible to tell at what frequency rotation would have to be applied to achieve the desired effect?

Trader D

[Pascal]

Pascal, do you mean that the MF-based indicator can be used to drive rotation in/out of the best ETFs or a more standard price-based (e.g. momentum, relative strength) indicator? Also, without a rolling time view into the ETF trade stats, I don't see how it's possible to tell at what frequency rotation would have to be applied to achieve the desired effect?

Trader D

This is work in progress as of now.
When I get results I will publish them.


Pascal

[TraderD]

This is work in progress as of now.
When I get results I will publish them.
Pascal

That's understood, looking forward to it.

Trader D

[Pascal]

In the past days, I reworked the ETF MF model in order to automatically detect the OB/OS levels and adapt the model to the changing market conditions.

The Table below does not show a notable improvement, but the model itself is now simplified as there are basically no manually adjustable "knobs" anymore. I also attach the trade data for a three ETFs portfolio. These portfolio data files have been built with the help of our fellow member Ellis. I also want to thank you for the numerous offers of assistance that I received.

We are still working on a method to select the best ETFs to trade at any time.

One interesting information: in order to avoid a continuous cash position, there should be no more than three positions to trade simultaneously. This means: each position should be no less than 1/3 of the portfolio.
This is due to the fact that the method does not generate enough signals to be fully invested in more than three positions.



Pascal



Last 2 years Equity curve for 3 ETFs portfolio



Last 2years Drawdown for 3 ETFs portfolio



Last 4 years Equity curve for 3 ETFs portfolio



Last 4 years Drawdown for 3 ETFs portfolio


4 years Summary Table for 3 ETFs portfolio

[TraderD]

One interesting information: in order to avoid a continuous cash position, there should be no more than three positions to trade simultaneously. This means: each position should be no less than 1/3 of the portfolio.
This is due to the fact that the method does not generate enough signals to be fully invested in more than three positions.

Pascal, if I understand correctly, the model average stats refer to a theoretical fully-invested portfolio of all 9 ETF stats which cannot possibly be replicated in reality and may deviate considerably from stats of a portfolio that holds 1/9th of each ETF during the test period(s).

Question: What was the selection criteria used for the 3-ETF test shown?

Thanks,

Trader D

[Pascal]

Pascal, if I understand correctly, the model average stats refer to a theoretical fully-invested portfolio of all 9 ETF stats which cannot possibly be replicated in reality and may deviate considerably from stats of a portfolio that holds 1/9th of each ETF during the test period(s).

Question: What was the selection criteria used for the 3-ETF test shown?

Thanks,

Trader D

The average return is the return as if you invested 1/9 of your portfolio in each ETF.
In reality, you can easily invest 1/9 of your portfolio in each ETF.

There was no selection criteria to take the 3 ETFs. We just took the first three available ETFs that issued a signal.
It is purely random as of now. This random selection portfolio performs better simply because there is more money being invested in the market using only 3 ETFs on a rotating base than investing 1/9 in 9 ETFs.

We are now working on a selection process of these ETFs.


Pascal

[TraderD]

The average return is the return as if you invested 1/9 of your portfolio in each ETF.
In reality, you can easily invest 1/9 of your portfolio in each ETF.

Very well, it's more realistic than I thought it was. It'd be useful to know stats regarding the 9-ETF rotation, e.g. average leverage throughout the 4-year period, longest period(s) when 5 or more (out of 9) ETFs are in cash, etc.

There was no selection criteria to take the 3 ETFs. We just took the first three available ETFs that issued a signal.
It is purely random as of now. This random selection portfolio performs better simply because there is more money being invested in the market using only 3 ETFs on a rotating base than investing 1/9 in 9 ETFs. We are now working on a selection process of these ETFs.
Pascal

A monte-carlo series of runs with random 3-ETF selection would be a good way to verify robustness.

$.02,

Trader D

[Harry]

I have always wondered as to how the +/-1000 stocks/ETF's that compromise the 20DMF model were/are selected? The reason I ask is - would have removing and replacing (say even 10 for a 1% change in the composition) impact the calculations significantly? I know we don't want to backfit, but wonder about the impacts of a difference set on the overall 20DMF model? Same question goes for the 4 inverse ETF's - why choose these 4 versus other inverse ETF's?

I am sure these questions were answered long ago when you were building the model.