You mean if the market is trending or braketing ?
Auction Market Value Theory & Analytics
- #1,102
- Apr 21, 2017 12:13pm Apr 21, 2017 12:13pm
- Joined May 2009 | Status: Trader | 1,879 Posts
Markets are not efficient, rather they are effective - Jones
- #1,104
- Edited 1:20pm Apr 21, 2017 1:03pm | Edited 1:20pm
- Joined May 2009 | Status: Trader | 1,879 Posts
{quote} By observing the number of distribution, one unbroken distribution = braketing. Of course if the longer term dist (let's say the 20d) is braketing, it doesen't mean that the smaller one's (let's say the 3 and the 5D) are doing the same. Only observing the market condition (bracketing /trending) in relation to 1)a specific set of overaly days (3,5,10,15, etc) 2) the behave of the ref. points we could get a better picture of price behaviour, aka if the 3D s trending into his braket range[assuming is braketing], or there are some early sign...
Only observing the market condition (bracketing /trending) in relation to 1)a specific set of overaly days (3,5,10,15, etc) 2) the behave of the ref. points we could get a better picture of price behaviour
You're trying to give examples of "how" overlays are use.....my question is "why". What is the purpose of using overlays?
#1 Is this overlay bracketing or trending? If it is..... were are the bracket limits?
#2 Is this overlay bracketing or trending? If it is..... were are the bracket limits?
#3 Is this overlay bracketing or trending? If it is..... were are the bracket limits?
Markets are not efficient, rather they are effective - Jones
- #1,105
- Apr 21, 2017 1:17pm Apr 21, 2017 1:17pm
The "Why"
Overlays of multiple timeframes are used to find the market condition.
Only when the market condition is known can an informed trading decision be made.
Finding UL and LL.
Using three TPO's as UL and LL cutoff - This is because i'm assuming this to be a Overlay from a timeframe less than 10 days. If using a timeframe of 10 days or above, then the cutoff should be five TPO's.
Overlay #1 - Bracketing
Overlay #2 - Bracketing
Overlay #3 - Bracketing
Overlays of multiple timeframes are used to find the market condition.
Only when the market condition is known can an informed trading decision be made.
Finding UL and LL.
Using three TPO's as UL and LL cutoff - This is because i'm assuming this to be a Overlay from a timeframe less than 10 days. If using a timeframe of 10 days or above, then the cutoff should be five TPO's.
Overlay #1 - Bracketing
Overlay #2 - Bracketing
Overlay #3 - Bracketing
- #1,106
- Apr 21, 2017 1:36pm Apr 21, 2017 1:36pm
- Joined May 2009 | Status: Trader | 1,879 Posts
The "Why" Overlays of multiple timeframes are used to find the market condition. Only when the market condition is known can an informed trading decision be made. Finding UL and LL. Using three TPO's as UL and LL cutoff - This is because i'm assuming this to be a Overlay from a timeframe less than 10 days. If using a timeframe of 10 days or above, then the cutoff should be five TPO's. Overlay #1 - Bracketing {image} Overlay #2 - Bracketing {image} Overlay #3 - Bracketing {image}
The "Why" Overlays of multiple timeframes are used to find the market condition. Only when the market condition is known can an informed trading decision be made. Finding UL and LL. Using three TPO's as UL and LL cutoff - This is because i'm assuming this to be a Overlay from a timeframe less than 10 days. If using a timeframe of 10 days or above, then the cutoff should be five TPO's.
Overlay #1 - Bracketing
correctOverlay #2 - Bracketing
Overlay #3 - Bracketing
try again........see if you understand why #2 & #3 are not correct
Markets are not efficient, rather they are effective - Jones
- #1,110
- Edited 5:55pm Apr 21, 2017 5:26pm | Edited 5:55pm
- Joined May 2009 | Status: Trader | 1,879 Posts
Markets are not efficient, rather they are effective - Jones
Hey its me again. Can someone please help me with this question. And please please dont tell me im missing
the whole picture and that this is just the **** of the tree or what someone told me. The overlay quant analysis
in here is just one way to trade, its not the only. Im not looking to copy Mzvega 100% like most people seem to do.
I have my own way of trading and try to pick the raisin of the cake which i would recomend others to do too, you can
never understand the strategi or spreadsheet as well as the person who made it. Just my 2 cents. Okey anyway.
In this example we dont have any attemped direction but we can see that value is lower. What would you asume out of this
if volume was higher lower or unchanged?
clearly only 1 distribution (more or less) which significe balance
but please tell me if you know this i would be glad
for all inputs, thanks
Dave.
the whole picture and that this is just the **** of the tree or what someone told me. The overlay quant analysis
in here is just one way to trade, its not the only. Im not looking to copy Mzvega 100% like most people seem to do.
I have my own way of trading and try to pick the raisin of the cake which i would recomend others to do too, you can
never understand the strategi or spreadsheet as well as the person who made it. Just my 2 cents. Okey anyway.
In this example we dont have any attemped direction but we can see that value is lower. What would you asume out of this
if volume was higher lower or unchanged?
clearly only 1 distribution (more or less) which significe balance
but please tell me if you know this i would be glad
for all inputs, thanks
Dave.
- #1,112
- Apr 25, 2017 10:07am Apr 25, 2017 10:07am
- Joined May 2009 | Status: Trader | 1,879 Posts
Markets are not efficient, rather they are effective - Jones
- #1,113
- Apr 25, 2017 10:30am Apr 25, 2017 10:30am
- Joined May 2009 | Status: Trader | 1,879 Posts
Markets are not efficient, rather they are effective - Jones
- #1,117
- Apr 27, 2017 3:30pm Apr 27, 2017 3:30pm
- Joined May 2009 | Status: Trader | 1,879 Posts
Markets are not efficient, rather they are effective - Jones
- #1,120
- May 3, 2017 5:13am May 3, 2017 5:13am
- Joined May 2009 | Status: Trader | 1,879 Posts
Serial Correlation........
The lack of day-to-day serial correlation in auction markets is often the starting point for random market theory. Studies as early as Labys and Granger (1970) confirm the day-to-day randomness. Even in trending markets, the expectation of up price or down price for the next day is still about 50-50. Even odds in non-trending markets is easy to understand; in trending ones it is not so obvious. A market that is trending up or down does have an enhanced probability in the trend direction; it is just pretty well masked by the random component that is always present. For example, a trend of one cent per day (on average) is virtually lost in the average daily trading range of, say, ten cents. By grouping, however, the random component can be averaged out and the underlying price movement can be seen. We will illustrate this point with a trend in soybeans.
Comparing one day's data to the previous days data (or comparing one candlestick to the next candlestick, or comparing one price to the next price) following the market in this way, are all examples of "linear measures".
Now if you follow the data, linearly, "day to day" in sequential/chronological order, the market appears to move randomly. There is no "day to day" evidence of a underlying trend.
This is where the random theory conspiracist's see this as some type of evidence that the markets are a random walk. When you look at the data in a linear, chronological , sequential, series (time series) it only appears to be random What this proves is that even in a trend there is no "day to day" serial correlation. Even in trending markets, the expectation of up price or down price for the next day is still about 50-50
Rather than "linearly" follow the data price to price. Lets start measuring the same exact data, using "non linear" measures used in auction market analysis. Here we will use a 2 day sample of data. Rather than following the data "day to day" in sequential order. The results look just as random as following the market day to day. There is still no evidence of the underlying downtrend Even with a 2 day sample of data.
Let's look at a 3 day sample of data......... Now things start to get interesting..Same exact data.. still using "non linear" measures.Now you start seeing some "evidence" of the underlying downtrend. By using a 3 day sample of data you began to filter out the "day to day" random fluctuations ("random component")
It also becomes more apparent with the 4 & 5 day sample of data.
So what does this prove?.......... do the math
Testing a Trend for Serial Correlation
There is one long downward trend from March 6, 1991, through July 10, 1991. This run covered 88 trading days (127 calendar days) or just over four months. The total price drop is 121 cents or 1.375 cents per trading day. The daily trading-range average over the 88 trading days is 8.17 cents, ranging between a high of 29.25 and a low of 3.25 cents. Table 1-5 has the prices and a list of higher/lower prices on the basis of 1 day, 2 days,. . . through 5 days.
Since a downtrend was Pre-selected, we have inserted a bias to the downside.
Therefore ties those cases where the compared prices were the same-will be awarded to the H, or higher count. That results in Table 1-6
One of the basic fundamentals in AMVT is an understanding that the markets are not linear. Using linear measures to describe & analyze a non linear market is like using a tape measure to measure how many gallons it takes to fill a bucket
If you group your data, group prices over the correct sample of time (price over time) rather than look at the data "linearly", day to day through a series of candlesticks, you see the markets are in no way a random walk.
"A market that is trending up or down does have an enhanced probability in the trend direction"
To test that statement in a downtrend using the data starting with the 1 day sample of data.
If you traded in the direction of the previous day's trend (following price) with the probability of trading with the underlying trend, you would have correctly traded in the direction of the underlying trend 52% of the time and you would have been wrong 48% of the time. Correctly trading in the direction of the underlying trend following price "day to day" is still pretty much a coin toss.
Let's look at the 2 day sample.had you traded in the direction of the underlying trend following the 2 day samples of data rather than following price, you would have correctly traded in the direction of the underlying trend 62% of the time and you would have been wrong 38% of the time.
Now things get interesting..
Let's look at the 3 day sample.had you traded in the direction of the underlying trend using the 3 day samples of data rather than following price, You would have correctly traded in the direction of the underlying trend, 70% of the time and wrong 30% of the time.
4 day sample you would have been correct 73% of the time and wrong 27% of the time.
5 day sample you would have been correct 76% of the time and wrong 24% of the time.etc.
This is what you call "proof of principle"...
The lack of day-to-day serial correlation in auction markets is often the starting point for random market theory. Studies as early as Labys and Granger (1970) confirm the day-to-day randomness. Even in trending markets, the expectation of up price or down price for the next day is still about 50-50. Even odds in non-trending markets is easy to understand; in trending ones it is not so obvious. A market that is trending up or down does have an enhanced probability in the trend direction; it is just pretty well masked by the random component that is always present. For example, a trend of one cent per day (on average) is virtually lost in the average daily trading range of, say, ten cents. By grouping, however, the random component can be averaged out and the underlying price movement can be seen. We will illustrate this point with a trend in soybeans.
Comparing one day's data to the previous days data (or comparing one candlestick to the next candlestick, or comparing one price to the next price) following the market in this way, are all examples of "linear measures".
Now if you follow the data, linearly, "day to day" in sequential/chronological order, the market appears to move randomly. There is no "day to day" evidence of a underlying trend.
This is where the random theory conspiracist's see this as some type of evidence that the markets are a random walk. When you look at the data in a linear, chronological , sequential, series (time series) it only appears to be random What this proves is that even in a trend there is no "day to day" serial correlation. Even in trending markets, the expectation of up price or down price for the next day is still about 50-50
Rather than "linearly" follow the data price to price. Lets start measuring the same exact data, using "non linear" measures used in auction market analysis. Here we will use a 2 day sample of data. Rather than following the data "day to day" in sequential order. The results look just as random as following the market day to day. There is still no evidence of the underlying downtrend Even with a 2 day sample of data.
Let's look at a 3 day sample of data......... Now things start to get interesting..Same exact data.. still using "non linear" measures.Now you start seeing some "evidence" of the underlying downtrend. By using a 3 day sample of data you began to filter out the "day to day" random fluctuations ("random component")
It also becomes more apparent with the 4 & 5 day sample of data.
So what does this prove?.......... do the math
Testing a Trend for Serial Correlation
There is one long downward trend from March 6, 1991, through July 10, 1991. This run covered 88 trading days (127 calendar days) or just over four months. The total price drop is 121 cents or 1.375 cents per trading day. The daily trading-range average over the 88 trading days is 8.17 cents, ranging between a high of 29.25 and a low of 3.25 cents. Table 1-5 has the prices and a list of higher/lower prices on the basis of 1 day, 2 days,. . . through 5 days.
Since a downtrend was Pre-selected, we have inserted a bias to the downside.
Therefore ties those cases where the compared prices were the same-will be awarded to the H, or higher count. That results in Table 1-6
One of the basic fundamentals in AMVT is an understanding that the markets are not linear. Using linear measures to describe & analyze a non linear market is like using a tape measure to measure how many gallons it takes to fill a bucket
If you group your data, group prices over the correct sample of time (price over time) rather than look at the data "linearly", day to day through a series of candlesticks, you see the markets are in no way a random walk.
"A market that is trending up or down does have an enhanced probability in the trend direction"
To test that statement in a downtrend using the data starting with the 1 day sample of data.
If you traded in the direction of the previous day's trend (following price) with the probability of trading with the underlying trend, you would have correctly traded in the direction of the underlying trend 52% of the time and you would have been wrong 48% of the time. Correctly trading in the direction of the underlying trend following price "day to day" is still pretty much a coin toss.
Let's look at the 2 day sample.had you traded in the direction of the underlying trend following the 2 day samples of data rather than following price, you would have correctly traded in the direction of the underlying trend 62% of the time and you would have been wrong 38% of the time.
Now things get interesting..
Let's look at the 3 day sample.had you traded in the direction of the underlying trend using the 3 day samples of data rather than following price, You would have correctly traded in the direction of the underlying trend, 70% of the time and wrong 30% of the time.
4 day sample you would have been correct 73% of the time and wrong 27% of the time.
5 day sample you would have been correct 76% of the time and wrong 24% of the time.etc.
This is what you call "proof of principle"...
Markets are not efficient, rather they are effective - Jones