As somebedy asked it this for SP500 (I will do the spoos and TNX this we):
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Why some people cry about their stops :)
- Thread starter harrytrader
- Start date
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A little remark: the daily number could be overestimated since there is a theorical relation assuming independancy that the std of scale 2 = squareroot of number of periods of subscale * std of subscale. For example you can check that:
std of 1 min scale multiplied by SQRT(5) give roughly the number on 5 minutes scales
4*SQRT(5) = 8.94 near 9
etc...
If you do on hourly scale 35*SQRT(7.5) it gives 96 so it is rather far from 126 of daily scale: +30 points if you multiply by 2 for roughly 95% probability it goes up to 60 !
Nevertheless since I want to evaluate extreme zone, I prefer to use 126. By experience I know that the market can go as far as that number in extreme condition (for example the day we made the last low at 7628 it was precisely the number I have on my daily extreme probability zone on that day although the projection could target lower it was somehow stopped by the probability zone). You can use 96 instead for other purpose.
To better feel the numbers it is some kind of intra-volatilities taking out intervolatility (so detrended), that what is meant by "intrinsic". So it can even replace ATR with the advantage of being rather constant for example for detecting congestion zone or NR7-like day without even calculating NR7.
PS.: I didn't give 30 minutes because it is not in proportion on x scale that would deform the chart. But you can use the formula above to get the estimation 35 / SQRT(2) = 24.7 near the 26 value if it was estimated directly by real market datas.
A little remark: the daily number could be overestimated since there is a theorical relation assuming independancy that the std of scale 2 = squareroot of number of periods of subscale * std of subscale. For example you can check that:
std of 1 min scale multiplied by SQRT(5) give roughly the number on 5 minutes scales
4*SQRT(5) = 8.94 near 9
etc...
If you do on hourly scale 35*SQRT(7.5) it gives 96 so it is rather far from 126 of daily scale: +30 points if you multiply by 2 for roughly 95% probability it goes up to 60 !
Nevertheless since I want to evaluate extreme zone, I prefer to use 126. By experience I know that the market can go as far as that number in extreme condition (for example the day we made the last low at 7628 it was precisely the number I have on my daily extreme probability zone on that day although the projection could target lower it was somehow stopped by the probability zone). You can use 96 instead for other purpose.
To better feel the numbers it is some kind of intra-volatilities taking out intervolatility (so detrended), that what is meant by "intrinsic". So it can even replace ATR with the advantage of being rather constant for example for detecting congestion zone or NR7-like day without even calculating NR7.
PS.: I didn't give 30 minutes because it is not in proportion on x scale that would deform the chart. But you can use the formula above to get the estimation 35 / SQRT(2) = 24.7 near the 26 value if it was estimated directly by real market datas.
Yes I know that this is the problem for many people that's why I posted that thread. It depends on the framework. generally trading system based on stochastics need "room" to perform. It is different approach than chartism. So you can't use a trading system that need room with the framework of chartists you will get killed and after that you will say: "this trading system doesn't work" it is rather you that don't take this system requirement into account 
. This is often related to capital requirements. People buy some systems and they don't have the capital to use the system so they use too tight stop or they don't take some trade etc.
. This is often related to capital requirements. People buy some systems and they don't have the capital to use the system so they use too tight stop or they don't take some trade etc.I just posted in this
http://www.elitetrader.com/vb/showthread.php?s=&threadid=33135&perpage=6&pagenumber=9
since it is related to what I said about intrinsec standard deviation I post it here too:
"The way novice traders react to variation is no more or less than what I have seen when workers are reacting in front of machine variation (when I was statistical process engineer in food and pharmaceutical industries): they tend to do TAMPERING :
From Quality Encyclopedia
http://www.qa-inc.com/knowledgecente/knowctrTampering.htm
Tampering
Tampering with a process occurs when we respond to variation In the process (such as by "adjusting" the process) when the process has not shifted. In other words, it is when we treat variation due to common causes as variation due to special causes. This is also called "responding to a false alarm," since a false alarm is when we think that the process has shifted when it really hasn't.
In practice, tampering generally occurs when we attempt to control the process to limits that are within the natural control limits defined by common cause variation. Some causes of this include:
1. We try to control the process to specifications, or goals. These limits are defined externally to the process, rather than being based on the statistics of the process.
2. Rather than using the suggested control limits defined at 3 standard deviations from the center line, we instead choose to use limits that are tighter (or narrower) than these (sometimes called Warning Limits). We might do this based on the faulty notion that this will improve the performance of the chart, since it is more likely that subgroups will plot outside of these limits.
[...]
Deming showed how tampering actually increases variation. It can easily be seen that when we react to these false alarms, we take action on the process by shifting its location. Over time, this results in process output that varies much more than if the process had just been left alone.
[...]
deming's funnel experience
http://www.qa-inc.com/knowledgecente/articles/CQEIVH3f.html
Tampering effects and diagnosis
Tampering occurs when adjustments are made to a process that is in statistical control. Adjusting a controlled process will always increase process variability, an obviously undesirable result. The best means of diagnosing tampering is to conduct a process capability study (see IV.H.4) and to use a control chart to provide guidelines for adjusting the process.
Perhaps the best analysis of the effects of tampering is from Deming (1986). Deming describes four common types of tampering by drawing the analogy of aiming a funnel to hit a desired target. These "funnel rules" are described by Deming (1986, p. 328):
1. âLeave the funnel fixed, aimed at the target, no adjustment.
2. âAt drop k (k = 1, 2, 3, ...) the marble will come to rest at point zk, measured from the target. (In other words, zk is the error at drop k.) Move the funnel the distance -zk from the last position. Memory 1.
3. âSet the funnel at each drop right over the spot zk, measured from the
target. No memory.
4. âSet the funnel at each drop right over the spot (zk) where it last came
to rest. No memory.â
Rule #1 is the best rule for stable processes. By following this rule, the process average will remain stable and the variance will be minimized. Rule #2 produces a stable output but one with twice the variance of rule #1. Rule #3 results in a system that "explodes", i.e., a symmetrical pattern will appear with a variance that increases without bound. Rule #4 creates a pattern that steadily moves away from the target, without limit.
At first glance, one might wonder about the relevance of such apparently abstract rules. However, upon more careful consideration, one finds many practical situations where these rules apply.
Rule #1 is the ideal situation and it can be approximated by using control charts to guide decision-making. If process adjustments are made only when special causes are indicated and identified, a pattern similar to that produced by rule #1 will result.
Rule #2 has intuitive appeal for many people. It is commonly encountered in such activities as gage calibration (check the standard once and adjust the gage accordingly) or in some automated equipment (using an automatic gage, check the size of the last feature produced and make a compensating adjustment). Since the system produces a stable result, this situation can go unnoticed indefinitely. However, as shown by Taguchi, increased variance translates to poorer quality and higher cost.
The rationale that leads to rule #3 goes something like this: "A measurement was taken and it was found to be 10 units above the desired target. This happened because the process was set 10 units too high. I want the average to equal the target. To accomplish this I must try to get the next unit to be 10 units too low." This might be used, for example, in preparing a chemical solution. While reasonable on its face, the result of this approach is a wildly oscillating system.
A common example of rule #4 is the "train-the-trainer" method. A master spends a short time training a group of "experts," who then train others, who train others, et cetera. An example is on-the-job training. Another is creating a setup by using a piece from the last job. Yet another is a gage calibration system where standards are used to create other standards, which are used to create still others, and so on. Just how far the final result will be from the ideal depends on how many levels deep the scheme has progressed.
http://www.elitetrader.com/vb/showthread.php?s=&threadid=33135&perpage=6&pagenumber=9
since it is related to what I said about intrinsec standard deviation I post it here too:
"The way novice traders react to variation is no more or less than what I have seen when workers are reacting in front of machine variation (when I was statistical process engineer in food and pharmaceutical industries): they tend to do TAMPERING :
From Quality Encyclopedia
http://www.qa-inc.com/knowledgecente/knowctrTampering.htm
Tampering
Tampering with a process occurs when we respond to variation In the process (such as by "adjusting" the process) when the process has not shifted. In other words, it is when we treat variation due to common causes as variation due to special causes. This is also called "responding to a false alarm," since a false alarm is when we think that the process has shifted when it really hasn't.
In practice, tampering generally occurs when we attempt to control the process to limits that are within the natural control limits defined by common cause variation. Some causes of this include:
1. We try to control the process to specifications, or goals. These limits are defined externally to the process, rather than being based on the statistics of the process.
2. Rather than using the suggested control limits defined at 3 standard deviations from the center line, we instead choose to use limits that are tighter (or narrower) than these (sometimes called Warning Limits). We might do this based on the faulty notion that this will improve the performance of the chart, since it is more likely that subgroups will plot outside of these limits.
[...]
Deming showed how tampering actually increases variation. It can easily be seen that when we react to these false alarms, we take action on the process by shifting its location. Over time, this results in process output that varies much more than if the process had just been left alone.
[...]
deming's funnel experience
http://www.qa-inc.com/knowledgecente/articles/CQEIVH3f.html
Tampering effects and diagnosis
Tampering occurs when adjustments are made to a process that is in statistical control. Adjusting a controlled process will always increase process variability, an obviously undesirable result. The best means of diagnosing tampering is to conduct a process capability study (see IV.H.4) and to use a control chart to provide guidelines for adjusting the process.
Perhaps the best analysis of the effects of tampering is from Deming (1986). Deming describes four common types of tampering by drawing the analogy of aiming a funnel to hit a desired target. These "funnel rules" are described by Deming (1986, p. 328):
1. âLeave the funnel fixed, aimed at the target, no adjustment.
2. âAt drop k (k = 1, 2, 3, ...) the marble will come to rest at point zk, measured from the target. (In other words, zk is the error at drop k.) Move the funnel the distance -zk from the last position. Memory 1.
3. âSet the funnel at each drop right over the spot zk, measured from the
target. No memory.
4. âSet the funnel at each drop right over the spot (zk) where it last came
to rest. No memory.â
Rule #1 is the best rule for stable processes. By following this rule, the process average will remain stable and the variance will be minimized. Rule #2 produces a stable output but one with twice the variance of rule #1. Rule #3 results in a system that "explodes", i.e., a symmetrical pattern will appear with a variance that increases without bound. Rule #4 creates a pattern that steadily moves away from the target, without limit.
At first glance, one might wonder about the relevance of such apparently abstract rules. However, upon more careful consideration, one finds many practical situations where these rules apply.
Rule #1 is the ideal situation and it can be approximated by using control charts to guide decision-making. If process adjustments are made only when special causes are indicated and identified, a pattern similar to that produced by rule #1 will result.
Rule #2 has intuitive appeal for many people. It is commonly encountered in such activities as gage calibration (check the standard once and adjust the gage accordingly) or in some automated equipment (using an automatic gage, check the size of the last feature produced and make a compensating adjustment). Since the system produces a stable result, this situation can go unnoticed indefinitely. However, as shown by Taguchi, increased variance translates to poorer quality and higher cost.
The rationale that leads to rule #3 goes something like this: "A measurement was taken and it was found to be 10 units above the desired target. This happened because the process was set 10 units too high. I want the average to equal the target. To accomplish this I must try to get the next unit to be 10 units too low." This might be used, for example, in preparing a chemical solution. While reasonable on its face, the result of this approach is a wildly oscillating system.
A common example of rule #4 is the "train-the-trainer" method. A master spends a short time training a group of "experts," who then train others, who train others, et cetera. An example is on-the-job training. Another is creating a setup by using a piece from the last job. Yet another is a gage calibration system where standards are used to create other standards, which are used to create still others, and so on. Just how far the final result will be from the ideal depends on how many levels deep the scheme has progressed.
Quote from harrytrader:
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This is an estimation of Dow Jones "intrinsic" standard deviations for each scale intervention
I use the term intrinsic in the sens used in quality statistical control: intrinsic means the minimal expected standard deviation. This can be useful to realise that some stops are just unrealistic: if you take signal on daily scale and put a stop that is too tight well don't cry since there is a huge probability that it will be executed