OPEN-SOURCE SCRIPT
MTF DSS (Double Smoothed Stochastic) [TH]
The Double Smoothed Stochastic indicator was created by William Blau.
The DSS ranges from 0 to 100, like the standard Stochastic Oscillator.
The same rules of interpretation apply to Stochastics can be applied to DSS, although the DSS offers a much smoother curve than the raw Stochastic.
How it works:
It applies Exponential Moving Averages (EMAs) of two different periods to a standard Stochastic %K.
The components that construct the Stochastic Oscillator are first smoothed with the two EMAs.
Then, the smoothed components are plugged into the standard Stochastic formula to calculate the indicator.
Calculation:
EMA of the ( EMA of the (Close – Lowest Low for the specified period) )
Divided by
EMA of the ( EMA of the (Highest High for the specified period – Lowest Low for the specified period) )
X 100
How to add alerts:
Check off each piece of criteria you want for the alerts, then select Okay.
Then go to 'Create Alert' and set the condition to 'MTF DSS', select create.
The DSS ranges from 0 to 100, like the standard Stochastic Oscillator.
The same rules of interpretation apply to Stochastics can be applied to DSS, although the DSS offers a much smoother curve than the raw Stochastic.
How it works:
It applies Exponential Moving Averages (EMAs) of two different periods to a standard Stochastic %K.
The components that construct the Stochastic Oscillator are first smoothed with the two EMAs.
Then, the smoothed components are plugged into the standard Stochastic formula to calculate the indicator.
Calculation:
EMA of the ( EMA of the (Close – Lowest Low for the specified period) )
Divided by
EMA of the ( EMA of the (Highest High for the specified period – Lowest Low for the specified period) )
X 100
How to add alerts:
Check off each piece of criteria you want for the alerts, then select Okay.
Then go to 'Create Alert' and set the condition to 'MTF DSS', select create.
Script open-source
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Scripts and content from TanHef are solely for information and education. Past performance does not guarantee of future results.
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