Model discussion

[asomani]

A few ideas I'll throw out there to encourage more brainstorming (I apologize if they are impractical or don't seem reasonable, but, they are at least worth thinking about - I hope):

-Instead of taking -70 as the 20DMF oversold level, normalize the 20DMF historical values. In other words, take all the 20DMF historical values and split them into 100 buckets or percentiles. This can be done in Excel using the PercentRank function. Then, test what percentile of 20DMF values works best as an oversold level (could be the 15th percentile of values arranged in descending order, for example). This way, you're not looking at 20DMF values on an absolute basis, but, instead on a normalized / relative basis - which self-adapts to the market as more data is collected and the computer puts the data into the buckets.

-Consider employing regime-switching. In other words, categorize the market environment into one of six categories, for example:
-low volatility uptrend
-high volatility uptrend
-low volatility downtrend
-high volatility downtrend
-low volatility sideways or not trending
-high volatility sideways or not trending

Detecting when the market is in one of the above environments is the hard part, but, this can be thought about. For example, to assess the volatility profile of the market, one could use a PercentRank of 21-day historical volatility values for the past year (based on IWM or SPY, for instance). To assess whether the market is trending, one could use something like the ADX indicator or Trend Strength Index (search online for the latter). And so on...

Once you're able to detect the regime of the market effectively, determine which settings for the 20DMF and Robot are best in each regime - so as to optimize risk-adjusted returns within each regime. An obvious problem here will be the lack of a historical data set to work with for each regime (and in general), as I think 20DMF values have only been available since sometime in 2007 (?).

-A nice complementary or potentially confirming indicator to the 20DMF in terms of detecting oversold levels may be the S&P Oscillator. I keep track of the S&P Oscillator for the S&P 500 via MarketTells and have found, at least in my experience, that a reading of -6.5 or below (yes, this is an absolute rather than normalized level...I know) normally coincides with an oversold condition in the 20DMF. The last -6.5 or below reading was on Dec. 19, 2011, when the Oscillator just barely triggered oversold by hitting -6.6 (I believe this was the oversold period that the 20DMF just barely missed seeing as "oversold"). A spreadsheet of historical values can be downloaded from the MarketTells website should you wish to look into this further. Also, the S&P Oscillator can be calculated for other indices (NYSE common-stock-only, Nasdaq Composite, etc....even Russell 2000, providing one has the requisite advance/decline and up/down volume data for that index). MarketTells has it calculated for the S&P 500 and NYSE-common stock-only, I believe. I'm most comfortable using the S&P 500 version, as I find the -6.5 threshold on it particularly useful for detecting oversold conditions.

-Consider keeping track of POMO operations or using Bob's liquidity indicator, so that the 20DMF and/or Robot is able to get an idea if there is a Fed-supported put underneath the market, and thereby perhaps modify how it operates (it may operate more conservatively on the short side and more aggressively on the long side when liquidity is thought to be more than ample, for example). I know this idea has already perhaps been suggested, along with incorporating the $TICK indicator into the Robot somehow. But, I'm repeating it here nonetheless.

-Consider incorporating seasonality into the 20DMF model and/or Robot. I know seasonality is not thought to be a strong indicator, but, it has stood the test of time in some cases - like the end-of-month / beginning-of-month window dressing (last 4 trading days of month ending and first 2-3 days of month beginning) along with the Oct-Apr or Nov-Apr seasonally strong period - for example. An oversold condition that occurs in the early part of the window dressing period or right before the window dressing period often turns out to be a good at least short-term buying opportunity, for instance. Meanwhile, the biggest drops in the market tend to happen between May-Oct/Nov, I believe. Selloffs that start during these months should typically be taken more seriously than selloffs that start in the remainder of the year.

-One would think that the top and bottom of the month can happen at anytime in the month, close to equally. But, I think Michael Stokes at MarketSci did some research showing that the top or bottom of the month happens in the first 7 trading days of the month about 80% of the time. Perhaps this fact (although it needs to be confirmed) would be useful to keep in mind in programming the 20DMF and/or Robot. Maybe there is some good way to take advantage of it.

There is lots more that could be said, but, I must stop here due to time constraints. I hope many more will join in sincerely contributing to this thread.

[Pascal]

-Instead of taking -70 as the 20DMF oversold level, normalize the 20DMF historical values. In other words, take all the 20DMF historical values and split them into 100 buckets or percentiles. This can be done in Excel using the PercentRank function. Then, test what percentile of 20DMF values works best as an oversold level (could be the 15th percentile of values arranged in descending order, for example). This way, you're not looking at 20DMF values on an absolute basis, but, instead on a normalized / relative basis - which self-adapts to the market as more data is collected and the computer puts the data into the buckets.

The lower panel of the 20DMF is normalized as of now between -100 and +100.
Your suggestion is however something that I already use to evaluate the OB/OS levels of the GDX_MF. One issue that I have though is that during 2008/2009, the OB/OS levels were much more extended than the OB/OS levels of 2010/2011. Something to study related to the evolution of long-term volatility.

-Consider employing regime-switching. In other words, categorize the market environment into one of six categories, for example:
-low volatility uptrend
-high volatility uptrend
-low volatility downtrend
-high volatility downtrend
-low volatility sideways or not trending
-high volatility sideways or not trending

Detecting when the market is in one of the above environments is the hard part, but, this can be thought about. For example, to assess the volatility profile of the market, one could use a PercentRank of 21-day historical volatility values for the past year (based on IWM or SPY, for instance). To assess whether the market is trending, one could use something like the ADX indicator or Trend Strength Index (search online for the latter). And so on...

Once you're able to detect the regime of the market effectively, determine which settings for the 20DMF and Robot are best in each regime - so as to optimize risk-adjusted returns within each regime. An obvious problem here will be the lack of a historical data set to work with for each regime (and in general), as I think 20DMF values have only been available since sometime in 2007 (?).

I do have 20DMF data prior to 2007, but the uptick rules for shorts was abolished in July 2007 I believe. before that date, MF data was skewed to the long side.

-A nice complementary or potentially confirming indicator to the 20DMF in terms of detecting oversold levels may be the S&P Oscillator. I keep track of the S&P Oscillator for the S&P 500 via MarketTells and have found, at least in my experience, that a reading of -6.5 or below (yes, this is an absolute rather than normalized level...I know) normally coincides with an oversold condition in the 20DMF. The last -6.5 or below reading was on Dec. 19, 2011, when the Oscillator just barely triggered oversold by hitting -6.6 (I believe this was the oversold period that the 20DMF just barely missed seeing as "oversold"). A spreadsheet of historical values can be downloaded from the MarketTells website should you wish to look into this further. Also, the S&P Oscillator can be calculated for other indices (NYSE common-stock-only, Nasdaq Composite, etc....even Russell 2000, providing one has the requisite advance/decline and up/down volume data for that index). MarketTells has it calculated for the S&P 500 and NYSE-common stock-only, I believe. I'm most comfortable using the S&P 500 version, as I find the -6.5 threshold on it particularly useful for detecting oversold conditions.

-Consider keeping track of POMO operations or using Bob's liquidity indicator, so that the 20DMF and/or Robot is able to get an idea if there is a Fed-supported put underneath the market, and thereby perhaps modify how it operates (it may operate more conservatively on the short side and more aggressively on the long side when liquidity is thought to be more than ample, for example). I know this idea has already perhaps been suggested, along with incorporating the $TICK indicator into the Robot somehow. But, I'm repeating it here nonetheless.

-Consider incorporating seasonality into the 20DMF model and/or Robot. I know seasonality is not thought to be a strong indicator, but, it has stood the test of time in some cases - like the end-of-month / beginning-of-month window dressing (last 4 trading days of month ending and first 2-3 days of month beginning) along with the Oct-Apr or Nov-Apr seasonally strong period - for example. An oversold condition that occurs in the early part of the window dressing period or right before the window dressing period often turns out to be a good at least short-term buying opportunity, for instance. Meanwhile, the biggest drops in the market tend to happen between May-Oct/Nov, I believe. Selloffs that start during these months should typically be taken more seriously than selloffs that start in the remainder of the year.

-One would think that the top and bottom of the month can happen at anytime in the month, close to equally. But, I think Michael Stokes at MarketSci did some research showing that the top or bottom of the month happens in the first 7 trading days of the month about 80% of the time. Perhaps this fact (although it needs to be confirmed) would be useful to keep in mind in programming the 20DMF and/or Robot. Maybe there is some good way to take advantage of it.

There is lots more that could be said, but, I must stop here due to time constraints. I hope many more will join in sincerely contributing to this thread.

Thank you for all these ideas. I am now working on the ETFs linked MF for deployment still within February with their related RT figures.

Once this is done, I'll come back to reworking the 20DMF.


Pascal

[pdp-brugge]

Following the discussion in this thread I have the feeling that I personally would like to follow the track of selecting a number of stocks from the best sectors that show the best AB/LER combination.

I would like to backtest that idea.

I only have the EV data since I joined this forum (October2011).
For my back test to be a bit reliable I would like to get more data then these 4 months.

Is it possible to obtain a file with all the stocks of the PascalA_List with “AB Buy signal”, “Extension Tot EV”,“LER” & “Rating” and this for the longest period possible?

PdP

[Rembert]

Following the discussion in this thread I have the feeling that I personally would like to follow the track of selecting a number of stocks from the best sectors that show the best AB/LER combination.

Just my personal oppinion but I wouldn't complicate the robot by selecting individual stocks. Index ETF's are nice and easy. No worries about liquidity, earnings, diversification etc.

[pdp-brugge]

Hi Rembert,

It is not my intention to suggest that the robot would use individualstocks.
I am considering to use, beside trading the robots, alsotrading a discretional system.
This discretional system would use stock picking according theEV data.

Regards

PdP

[Rembert]

Ah ok, I understand. Besides a couple of ETF robots I also trade individual stocks discretionary but I don't use any EV concepts for those.