Model discussion

[TraderD]

While at it, a couple of general thoughts:
- Whipsaws: If the losses on whipsaws are small, best to look at those just as the cost of doing business. Many systems whipsawed in the huge volatility of last year much more than in the last twenty years, simply bringing out the reality of the market... politicians and central banks flip-flopping.

AFAICT, EV as a follower of large money can only be as good as they are. 2011 performance of large funds have been rather dismal (an understatement), so it's likely to expect EV to deteriorate in its predictive ability. Paul in a recent GGT post shows a bunch of examples where contrary to LEV divergence, price keeps going in the other direction, which begs the use of this technique with other indicator(s) as you've suggested, or at least a much more accurate understanding where EV does work.

- We are very interested in the Maximum Drawdown of a backtest (and more than that, of a system we trade live). It is also important to understand that the MDD does not represent well the statistics of a trading system output (it is the outcome of a specific path in time, out of many that could have happened). Therefore, MDD is not a good predictor of a system’s future drawdown and is not a good measure for a system’s risk. The saying “your worst drawdown did not happen yet” has indeed a theoretical basis. When comparing different versions of a system in development - it is much better to use measures with more statistical contents, like a rolling period downward deviation.

Fully agree with MDD comment. Do you have a link to RPDD stat calculations?

[Pascal]

AFAICT, EV as a follower of large money can only be as good as they are. 2011 performance of large funds have been rather dismal (an understatement), so it's likely to expect EV to deteriorate in its predictive ability. Paul in a recent GGT post shows a bunch of examples where contrary to LEV divergence, price keeps going in the other direction, which begs the use of this technique with other indicator(s) as you've suggested, or at least a much more accurate understanding where EV does work.

Fully agree with MDD comment. Do you have a link to RPDD stat calculations?

EV detects an equilibrium and not a force. It statistically detects when money comes in/out. For a single stock, the movements of money will often be opposite to the price moves, because large players use available liquidity to buy/sell. However, when the price is in a trading range or at a turning point, EV will often show what is happening below the surface and what the next move will be. This is why I always use AB/LER as a combination.

The money flow is however more predictive when it is collected by sectors or industry group.

Also, large money does not necessarily mean that these are large funds. It could be some artificial FED liquidity injection. Therefore, I believe that your statement that EV's predictive ability will deteriorate might or might not be true. I however believe that EV still gives early warnings of what large money is doing. A few years ago, I noted that it could give up to one day advance warning. Today, because the level field is pretty high and everybody is running fast computers, the warning time is probably less than a day.

Anyway, I still prefer to know where the money is going than not to know it.


Pascal

[Pascal]

I am attaching here a general figure of the different models that are in use, but also of a back-test campaign made last week using these models.

We can see that the one level that cannot be traded is the sectors level, simply because these are sectors that I have defined myself and for which there is no instrument. These sectors are mainly used either for the 20DMF or for the stock filters.



Below are the yearly returns of the S&P and the 20DMF. These are returns compounded within one year. If a 20DMF trade is overlapping two years, I separated taking one part in one year and the second in the following year. This way, we can have a better comparison.



The objective of this back-test work was to measure the relevance to trade sector information.
Is it good to buy a sector when the sector issues a buy signal and short it when it issues a short signal?

The results are below. We can see that indeed, sectors trading is better than B/H on the S&P 500.
However, sectors trading in sync with the 20DMF is still better. Unfortunately, "in Sync" does not give better results than the 20DMF itself.

These results are not surprising: they are "in line" with what I had when I did a similar back-test two years ago.
My conclusion at that time was that even when a sector is flashing a buy signal, it still needs to be as close as possible from a 20DMF buy signal. The later we are from that signal, the worst the returns.



Therefore, the next idea was to select only five sectors when the 20DMF issued a signal and then from each of these sectors, get all the stocks AB/LER data and select the five stocks that were showing the best AB/LER combination. The results of such a test are shown below. These are also in line with the results I had two years ago. The table below show us that whatever efforts we can do to select specific stocks, it will be hard to beat a two time leveraged ETF that trades the 20DMF signals. Of course, specific stock trading might lead to lower DD (I did not calculate such DD.)



I however did another test that gives interesting results.
I selected one date in the past and decided to trade either long or short by intervals of 20 days from that date.
this means at day one, I buy the five best sectors and I sell them at day 20.
The five best sectors are those that show the weakest price RS.
On day 20, I again select the five best sectors and buy them.

On exactly the same day, I also short the five sectors that are the most overbought and I sell these positions 20 days later.

The results are shown in the table below.

We can see that this dumb strategy worked well for longs in 2009 and for shorts in 2008.
In 2010 and 2011, it did not work that well.

However, this strategy shows something important: the rotational aspect of the market. It shows that it makes sense to rotate money. This is of course obvious! I still prefer to see it in the data than not seeing it, because this means that it will probably make sense to develop a set of ETFs MF by industry groups and rotate between them.




I will now start working on these industry group MF models.


Pascal

[TraderD]

...
I will now start working on these industry group MF models.
Pascal

Pascal, how do you explain the (admittedly anecdotal) observation that test results of models that use 20DMF invariably show 2010/2011 performance to be lower (typically much lower) than 2008/2009 performance? Is there a reason to suspect the market is becoming more "efficient" in arbitraging away the edge attributed to money flow rotation?

Trader D

[Pascal]

Pascal, how do you explain the (admittedly anecdotal) observation that test results of models that use 20DMF invariably show 2010/2011 performance to be lower (typically much lower) than 2008/2009 performance? Is there a reason to suspect the market is becoming more "efficient" in arbitraging away the edge attributed to money flow rotation?

Trader D

Simple: 2008 and 2009 were trending markets. Crash and reversal from a deep bottom.


Pascal

[mingpan.lam]

Hi Pascal,

Which model GDX robot using in the above table?
Cheers,

Ellis

[Pascal]

Hi Pascal,

Which model GDX robot using in the above table?
Cheers,

Ellis

The second level: Industry group

pascal

[senco]

Therefore, the next idea was to select only five sectors when the 20DMF issued a signal and then from each of these sectors, get all the stocks AB/LER data and select the five stocks that were showing the best AB/LER combination. The results of such a test are shown below. These are also in line with the results I had two years ago. The table below show us that whatever efforts we can do to select specific stocks, it will be hard to beat a two time leveraged ETF that trades the 20DMF signals. Of course, specific stock trading might lead to lower DD (I did not calculate such DD.)

- Pascal, could you please clarify: Was it five stocks per sector, or total of five from all sectors? Was it buying when a 20DMF signal is issued, and holding until next short, or something else?

- on a first blush I am not sure I would dismiss that based on comparison to trading a leveraged ETFs. Since we can tailor the amount of leverage we take, It is all a matter of risk-reward; so depending on the downward volatility, the results could be just ho-hum, very good, or spectacular.

In 2011 many sector rotation systems did not work that great, and numbers like in the table are not to sneeze at (especially if it were 25 stocks total). Also seeing better relative performance in 2011 than in 2010 is intriguing. If it were me I would check further whether it is just a matter of beta of stocks - or maybe there is a significant edge here. If you indeed do check the risk (e.g. downward volatility) and it is not higher than the market - it might be worthwhile looking at hedged results, and also at results obtained with a different timing signal gating entry and exit. For diversification, it would be great to identify added value that is not fully correlated to the 20DMF.

.... The five best sectors are those that show the weakest price RS.
... I also short the five sectors that are the most overbought and I sell these positions 20 days later.

- Could you please clarify the specific selection criteria: For longs, is it weak RS only, or you look at money flow as well? The timeframe for RS - is it 20 days? For shorts, what is the definition of 'overbought' in this context? ... I am trying to understand how EV is used here, and whether we are looking at simple mean reversion at the sector level.

I have encountered in the past added value for mean reversion of individual stocks within a strong sector, and for longer timeframe sector momentum; this seems to be quite different and intriguing.

[Pascal]

- Pascal, could you please clarify: Was it five stocks per sector, or total of five from all sectors? Was it buying when a 20DMF signal is issued, and holding until next short, or something else?

In synch with the 20DMF means that you buy when the 20DMF issues a buy signal and keep the position until the 20DMF signal change. The selection process is to first select the five weakest sectors in terms of 20D price RS, take all the stocks in these five sectors and sort them by AB/LER (Take the five closest to LB that show a strong accumulation pattern in terms of LER.) The idea here is to ride the shorts covering phase.

On the short side, the selection was also to short the weakest sectors in terms of price RS. And in these sectors, select the stocks whose AB is closest to UB with the weakest LER. This is also basically because shorts will go after the weakest stocks first that have bounced to their resistance level.

This is very different from a CANSLIM approach on stocks selection, but the "weakest sectors" selection process is rather risky, because it all depends on the MDM. If the MDM is wrong, you will be wrong footed in a big way. Also, I believe that after the initial shorts covering phase of the first 5 to 10 days after a buy signal, the advantage of targeting the weakest sector might disappear. Therefore, execution timing is rather important, which I do not believe is a strong point of a human trader. A human trader will enter slowly, consider risk/profits, etc. A large fund will be even more prudent I believe. However, the market is now mostly traded by machines. These trade momentum, volatility and liquidity machines tend to forget fundamentals (especially for the past few years.)

-

- Could you please clarify the specific selection criteria: For longs, is it weak RS only, or you look at money flow as well? The timeframe for RS - is it 20 days? For shorts, what is the definition of 'overbought' in this context? ... I am trying to understand how EV is used here, and whether we are looking at simple mean reversion at the sector level.

I have encountered in the past added value for mean reversion of individual stocks within a strong sector, and for longer timeframe sector momentum; this seems to be quite different and intriguing.

The Overbought selection criterium was used only in the context of trading sectors not in synch with the market.
Hence I showed that buying every 20 days the weakest sectors was a strategy that produced good results in a strong uptrend (2009) and selling the most overbought sectors every 20 days was a strategy that worked well in a continuously down market (2008). Both strategies worked miserably in 2010 and 2011. This means that we need to work in synch with the market.

This is also the reason why the stock filters must be used also in synch with the market direction.



Pascal

[adam ali]

Pascal,

Does the RT 20DMF signal information go back to 2007, i.e., inception? If so, how many instances were there of the 20DMF exceeding the -70 mark intra-day but closing above it?