Model discussion

[Pascal]

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.

The 20DMF is a sectors based model. When I started working with it, I had about 60 sectors. The selection process was straightforward: whenever I could find at least 5 stocks in the same sector (searched through IBD, Yahoo and by reading the annual reports) I created a sector. Adding or retrieving a stock from a given sector has almost no bearing on the indicator, because each of the 96 sectors has an equal weight. Therefore, if there are let say 10 stocks in one sector, removing 10 from 10 different sectors would have much less than 1% impact on the general model. It is only if I started to completely remove or add many sectors that there would be an impact.

For example, yesterday, I added about 6 stocks in different sectors (Leisure equipment, drugs, etc). I do that only for sectors that include only five or six stocks, so that each stocks does not have a strong influence on the sector MF.

With this approach, a stock like AAPL, even though it moves its own MF sector by almost 80%, Its influence on the 20DMF is no more than 1/96

Regarding the four inversed ETFs, I just took those with the strongest volume. I selected double inversed, because traders would close position much quicker on these type of ETFs and hence, moves would be detected much quicker than on non-leveraged ETFs.


Pascal

[Harry]

Thank you for the detailed answer. Also helps explain why the indices can run away from the 20DMF model as the former can be primarily steered by just a few securities due to weighting.

[Pascal]

In the past two days, I have been working on finding out why using the same trading model, GDX is performing much better than most other ETFs.

I believe that the answers originates from the comparison of the two attached figures.

The first one shows the correlation between each ETF 20D price gain and the weighted price gain that is calculated using the latest weight/composition of each ETF. Because the weight and compositions were different in the past, we can see that the further we move back in the past, the less correlated the calculated price gain is from the ETF price gain.

You will note that there are spikes, which I believe correspond to the re-weighting of the ETFs. XLF in yellow show that the managers have been working hard to re-weight the ETF that has been hit by negative news since 2008.

You will note that for GDX, there is basically no spike, whick means almost a perfect correlation between the data and the measure. Hence, a good outcome for the model (At least, it is my explanation.)

As a reminder, the EV method calculates a fine supply/demand equilibrium. Usually, the overbought level is hit when the imbalance reaches 1.5 to 2% of the total volume. A 10% change in correlation for 2008 and 5% for 2009 shows that we are within a measurement error for the EV type of indicator.

This also shows that the EV based indicator is "fragile" as it depends on the accuracy of the measurements! So, we'll need to pay great care to the data and the weights.



Pascal

[Pascal]

In the past two days, I have been working on finding out why using the same trading model, GDX is performing much better than most other ETFs.

I believe that the answers originates from the comparison of the two attached figures.

The first one shows the correlation between each ETF 20D price gain and the weighted price gain that is calculated using the latest weight/composition of each ETF. Because the weight and compositions were different in the past, we can see that the further we move back in the past, the less correlated the calculated price gain is from the ETF price gain.

You will note that there are spikes, which I believe correspond to the re-weighting of the ETFs. XLF in yellow show that the managers have been working hard to re-weight the ETF that has been hit by negative news since 2008.

You will note that for GDX, there is basically no spike, which means almost a perfect correlation between the data and the measure. Hence, a good outcome for the model (At least, it is my explanation.)

As a reminder, the EV method calculates a fine supply/demand equilibrium. Usually, the overbought level is hit when the imbalance reaches 1.5 to 2% of the total volume. A 10% change in correlation for 2008 and 5% for 2009 shows that we are within a measurement error for the EV type of indicator.

This also shows that the EV based indicator is "fragile" as it depends on the accuracy of the measurements! So, we'll need to pay great care to the data and the weights.



Pascal


Attachment 13039
Attachment 13038

You can find below a summary table of trades for 2010/2011 executed using an identical model applied to all 9 XL(i) ETFs. The XLS file includes all the detail of these trades (except for DD). The GDX trade details are also included in teh XLS file.

It is interesting to see the following:
- There are more Buy than Short trades, because there is an ATR filter set on short trades.
- Short trade have a winning/losing days ratio lower than 1, even though the trade outcome is in general positive. This mean that short trades are more volatile than long ones. We might also expect that most of the drawdowns will occur during missed short trades.

You will note that 2008 and sometimes 2009 results are under performing, but the ETF component weighting data for these two years is not reliable.

All in all, these results point to the possibility of a mainly long ETF rotation trading strategy.
I'll need to check whether signals are much overlapping.




Pascal




Trade details.xls

[Rembert]

Pascal,

As I understand the 20DMF has a failsafe mechanism that makes it go into neutral when the 2 overbought/oversold levels are breached after a signal miss. Have you considered testing what the impact would be on the IWM robot if the 20DMF doesn't just go into neutral after the above scenario but actually switches it's signal ?

It's just an idea but maybe this could be a simple solution regarding the robot getting stuck in the wrong mode.

Regards,
Rembert

[Pascal]

Pascal,

As I understand the 20DMF has a failsafe mechanism that makes it go into neutral when the 2 overbought/oversold levels are breached after a signal miss. Have you considered testing what the impact would be on the IWM robot if the 20DMF doesn't just go into neutral after the above scenario but actually switches it's signal ?

It's just an idea but maybe this could be a simple solution regarding the robot getting stuck in the wrong mode.

Regards,
Rembert

On the long side, this fail safe mechanism on the 20DMF was actioned only once.
In hindsight, we could certainly say that switching to a Buy signal on January 4 instead of just reverting to a neutral situation, would have turned the IWM Robot in a Buy mode and it would still be in such a mode, looking to sell on a signal change or on a stop loss breach.


Pascal

[Pascal]

In the past days, our fellow member Ellis has worked on the Portfolio simulator using the 9 ETFs (XLI, XLE, XLF, etc.)
The summary table is below.

We tested a portfolio of two and three positions. We also tested six trade combinations for each possibility.
B = Buy
B_OS = Buy in Oversold
S = Short
S_OB = Short in Overbought

When for a given day there is the possibility to choose one ETF instead of another, the selection is made on the 20D Price RS. The first table shows a selection on weak price RS. The second shows a selection on strong price RS. There is not much difference, although weak ETFs produce better returns.

You will note that three positions usually produce lower system DD than two positions (this is pretty obvious.)

The annual returns of about 20% are in line with what we can expect of non-leveraged ETFs in sideways markets such as 2010-2011.

A Sharpe ratio above 1 is acceptable for a profitable trading strategy, but it is not exceptional.

The exposure is also something important, together with the return/exposure ratio, as this will give you the sort of return you might expect whenever invested. This also means that when not invested, you can use the funds for other strategies... or preferably, if you have no edge in a GDX/IWM robot, you might use your funds to trade this S&P500 ETFs based strategy. My preference would go to the two Pink/Green highlighted strategies.

Since the model is now operational at least on an EOD base, I will soon publish all the related figures and a summary table that will be daily updated. We might also publish the RT patterns for all these ETFs, but be aware that these could be heavy on the browser.


Pascal

[nickola.pazderic]

Thanks Pascal, Ellis, and all.

I find it very, very encouraging that so many people have put effort into a sector rotation model.

Since my fingers have been coated with butter for sometime now, please make clear the selection/purchase/sell rules wherever the rotation suggestions are published.

Let me know if I can help write such a section.

Best,

[Neil Stoloff]

All in all, these results point to the possibility of a mainly long ETF rotation trading strategy.

Pascal

Hi Pascal,

If the testing of different sector ETFs is not yet complete, I must ask: Would the robot's performance likely be enhanced if it traded ETFs that are expected to produce long-term excess returns in their own right? If not, then I guess you can skip the lengthy post below. But if so, I'll ask you to suspend your disbelief and consider an idea that occurred to me when I reflected on this fascinating thread. If you can backtest the idea readily, that would be the way to either refute it or validate it.

The overlay that I have in mind would add a contrarian, mean-reversion element to the robot's momentum-based approach. The combined methods would consider investor behavior across time -- from minutes to days to years -- but would require no change in the robot's design. The only change would be in the vehicles used.

Called SweetSpot, the overlay's real-time track record can be found here. SweetSpot's premise and rationale are discussed in detail elsewhere at the same site, and in a paper that was published last year (see the paper's abstract for a quick summary).

Like EV, SweetSpot looks at MF, but measures it annually instead of minute by minute. While EV's universe is constructed from the bottom up, SweetSpot's is top down, defined by the non-diversified funds that are available to retail investors. EV trades with the large players who move prices in the short term, while SweetSpot trades against all (mostly retail) investors at a time when they are likely to be making bad long-term trades.

An ideal backtest would look something like this:

1) The universe would include every sector that offers a representative, liquid ETF, and for which sufficient data are available.

2) Looking separately at EV data for 2007, 2008, 2009, 2010, and 2011, sum up each sector's calendar-year TEV, and rank the sectors for each year in ascending order (from most-negative annual MF to most-positive).

3) Beginning with the 2007 rankings, select the top five or six sectors -- the ones that investors essentially abandoned.

4) Adopt a positive long-term view of the selected sectors (defining "long term" as three years).

5) Generate robot signals for the 2007 selections in 2008, 2009, and 2010.

6) Go long on buy signals; stay long on neutral signals; go to cash on sell signals; go long on neutral signals. (Variations would be worth exploring, but don't go short under any circumstances.)

7) Repeat these steps for 2008 (the only other year when returns can be seen for the entire three-year period).

8) Repeat for 2009 (looking at returns in 2010, 2011, and YTD 2012); 2010 (looking at returns in 2011 and YTD 2012); and 2011 (looking at returns YTD 2012).

The trading strategy described in item #6 mimics one that options traders and others employ when they are long-term bullish on an investment while trading it using a short-term timing strategy that generates both buy and sell signals. They act on the buys and ignore the sells. Ernst Tanaka (among others, not including myself) can probably label this strategy and provide some insight.

If my thesis is correct:

- The backtest will show absolute and risk-adjusted robot returns that exceed those of the robot using any other vehicles you have tested. This result would be explained by the excess buy-and-hold returns of the selected sectors relative to buying and holding a broad-market-index ETF.

- The average buy-and-hold performance of the selected vehicles will become more robust over time. That is, Year Three will outperform Year Two; and Year Two will outperform Year One. This dynamic may be relevant when deciding which ETFs to trade. For example, if you wanted to limit 2012's trading vehicles to six ETFs, you would give preference to the ones added in 2010[!]. (Overlapping test periods would produce a "portfolio" of about 18 candidate ETFs at any given time. Portfolio changes would occur once a year when new sectors are added and old sectors from three years prior are dropped.)

The Short Side

If the long strategy shows promise, it would be worthwhile to test the short side as well. Short candidates would be the sectors with the strongest annual TEV, found at the bottom of each year's rankings. The strategy would be to go short on sell signals and ignore buys.

Unlike the long side, the short side hasn't been tested in real time. Previous backtesting (of short three-year SweetSpot trades) yielded negative returns, probably due to the market's long-term upward bias. That could change, however, when the short-term robot steps in.

My hope is that you can easily test these ideas using the EV universe and database. If they pass muster, I look forward to a fun thread.

Best,

Neil

Disclosure: I registered as an investment adviser in 2008 after trading SweetSpot privately for a small family office from 1998 to 2007. I don't actively market the program, but even if I did, I would not try to market a hands-off strategy like SweetSpot to this group. On the other hand, I did almost contact you about a year ago when the funny money was driving everyone batty. Do you remember posting that you would walk away from active trading if you could find a reliable yield of 5-7 percent above inflation? SweetSpot's long-term numbers are double that, and you would have heard from me except for the "lumpiness" of the returns. SweetSpot is not a coupon, but I do feel it is worth considering as a "Plan B" for any active trader who may decide that the time has come to move on.

[Pascal]

Hi Pascal,

If the testing of different sector ETFs is not yet complete, I must ask: Would the robot's performance likely be enhanced if it traded ETFs that are expected to produce long-term excess returns in their own right? If not, then I guess you can skip the lengthy post below. But if so, I'll ask you to suspend your disbelief and consider an idea that occurred to me when I reflected on this fascinating thread. If you can backtest the idea readily, that would be the way to either refute it or validate it.

The overlay that I have in mind would add a contrarian, mean-reversion element to the robot's momentum-based approach. The combined methods would consider investor behavior across time -- from minutes to days to years -- but would require no change in the robot's design. The only change would be in the vehicles used.

Called SweetSpot, the overlay's real-time track record can be found here. SweetSpot's premise and rationale are discussed in detail elsewhere at the same site, and in a paper that was published last year (see the paper's abstract for a quick summary).

Like EV, SweetSpot looks at MF, but measures it annually instead of minute by minute. While EV's universe is constructed from the bottom up, SweetSpot's is top down, defined by the non-diversified funds that are available to retail investors. EV trades with the large players who move prices in the short term, while SweetSpot trades against all (mostly retail) investors at a time when they are likely to be making bad long-term trades.

An ideal backtest would look something like this:

1) The universe would include every sector that offers a representative, liquid ETF, and for which sufficient data are available.

2) Looking separately at EV data for 2007, 2008, 2009, 2010, and 2011, sum up each sector's calendar-year TEV, and rank the sectors for each year in ascending order (from most-negative annual MF to most-positive).

3) Beginning with the 2007 rankings, select the top five or six sectors -- the ones that investors essentially abandoned.

4) Adopt a positive long-term view of the selected sectors (defining "long term" as three years).

5) Generate robot signals for the 2007 selections in 2008, 2009, and 2010.

6) Go long on buy signals; stay long on neutral signals; go to cash on sell signals; go long on neutral signals. (Variations would be worth exploring, but don't go short under any circumstances.)

7) Repeat these steps for 2008 (the only other year when returns can be seen for the entire three-year period).

8) Repeat for 2009 (looking at returns in 2010, 2011, and YTD 2012); 2010 (looking at returns in 2011 and YTD 2012); and 2011 (looking at returns YTD 2012).

The trading strategy described in item #6 mimics one that options traders and others employ when they are long-term bullish on an investment while trading it using a short-term timing strategy that generates both buy and sell signals. They act on the buys and ignore the sells. Ernst Tanaka (among others, not including myself) can probably label this strategy and provide some insight.

If my thesis is correct:

- The backtest will show absolute and risk-adjusted robot returns that exceed those of the robot using any other vehicles you have tested. This result would be explained by the excess buy-and-hold returns of the selected sectors relative to buying and holding a broad-market-index ETF.

- The average buy-and-hold performance of the selected vehicles will become more robust over time. That is, Year Three will outperform Year Two; and Year Two will outperform Year One. This dynamic may be relevant when deciding which ETFs to trade. For example, if you wanted to limit 2012's trading vehicles to six ETFs, you would give preference to the ones added in 2010[!]. (Overlapping test periods would produce a "portfolio" of about 18 candidate ETFs at any given time. Portfolio changes would occur once a year when new sectors are added and old sectors from three years prior are dropped.)

The Short Side

If the long strategy shows promise, it would be worthwhile to test the short side as well. Short candidates would be the sectors with the strongest annual TEV, found at the bottom of each year's rankings. The strategy would be to go short on sell signals and ignore buys.

Unlike the long side, the short side hasn't been tested in real time. Previous backtesting (of short three-year SweetSpot trades) yielded negative returns, probably due to the market's long-term upward bias. That could change, however, when the short-term robot steps in.

My hope is that you can easily test these ideas using the EV universe and database. If they pass muster, I look forward to a fun thread.

Best,

Neil

Disclosure: I registered as an investment adviser in 2008 after trading SweetSpot privately for a small family office from 1998 to 2007. I don't actively market the program, but even if I did, I would not try to market a hands-off strategy like SweetSpot to this group. On the other hand, I did almost contact you about a year ago when the funny money was driving everyone batty. Do you remember posting that you would walk away from active trading if you could find a reliable yield of 5-7 percent above inflation? SweetSpot's long-term numbers are double that, and you would have heard from me except for the "lumpiness" of the returns. SweetSpot is not a coupon, but I do feel it is worth considering as a "Plan B" for any active trader who may decide that the time has come to move on.

This idea might be interesting, but impossible for me to backtest, as I'd have to build models for all these ETFs, many of them are illiquid and hence unusable for teh EV method.

Pascal