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Bias Finder
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Can we look at what happened yesterday in the market to predict what happens today?
Not really, but we can use simple historical statistics to help form a bias for todays market direction.
This indicator attempts to do just that. It is broken into 3 parts, none of which are related to each other, but taken together can help a trader improve the quality of their daily bias.
The first table simply groups all the daily win and loss streaks together and looks at the change of the next day after each streak.
The second table groups each day of the week then returns what percentage of those days were up, down and flat.
The bottom graphic compares the range of the last session with the current ATR.
The code then groups each session into either being larger or smaller than the current ATR.
Once that is determined, the close is classified into which quartile of the range it closed in. This means there are 8 different groups of closes. Once a close has been grouped, it is compared with all the other closes of the same group to return the historical change of the next day. So for example, a day might have a range inside the current ATR and have closed in the 3rd quartile of the range. From this we can determine the historical percentage of days that were up days after such a close.
The only important thing here is to decide if you want to use a close to close change IE an up day is when the close is higher than the previous close, or an open to close where, as the name suggests, the close is higher than the open for an up day.
The start date and start month can be used to eliminate any bad data and it is advisable to check the start of your dataset to make sure any data included in the statistics is stable and legitimate.
Hope you find this useful!
Not really, but we can use simple historical statistics to help form a bias for todays market direction.
This indicator attempts to do just that. It is broken into 3 parts, none of which are related to each other, but taken together can help a trader improve the quality of their daily bias.
The first table simply groups all the daily win and loss streaks together and looks at the change of the next day after each streak.
The second table groups each day of the week then returns what percentage of those days were up, down and flat.
The bottom graphic compares the range of the last session with the current ATR.
The code then groups each session into either being larger or smaller than the current ATR.
Once that is determined, the close is classified into which quartile of the range it closed in. This means there are 8 different groups of closes. Once a close has been grouped, it is compared with all the other closes of the same group to return the historical change of the next day. So for example, a day might have a range inside the current ATR and have closed in the 3rd quartile of the range. From this we can determine the historical percentage of days that were up days after such a close.
The only important thing here is to decide if you want to use a close to close change IE an up day is when the close is higher than the previous close, or an open to close where, as the name suggests, the close is higher than the open for an up day.
The start date and start month can be used to eliminate any bad data and it is advisable to check the start of your dataset to make sure any data included in the statistics is stable and legitimate.
Hope you find this useful!
Информация о релизе
Bug fix - moved the streak counter back one bar to show the streak as of the last sessions close.Fixed the counting bug for when the close is equal to the open
Информация о релизе
Further bug fixes with streak counter and tableИнформация о релизе
Additional table colour fixes
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