Trig-Log Scaled Momentum OscillatorTaylor Series Approximations for Trigonometry:
1. The indicator starts by calculating sine and cosine values of the close price using Taylor Series approximations. These approximations use polynomial terms to estimate the values of these trigonometric functions.
Mathematical Component Formation:
2. The calculated sine and cosine values are then multiplied together. This gives us the primary mathematical component, termed as the 'trigComponent'.
Smoothing Process:
3. To ensure that our indicator is less susceptible to market noise and more reactive to genuine price movements, this 'trigComponent' undergoes a smoothing process using a simple moving average (SMA). The length of this SMA is defined by the user.
Logarithmic Transformation:
4. With our smoothed value, we apply a natural logarithm approximation. Again, this approximation is based on the Taylor expansion. This step ensures that all resultant values are positive and offers a different scale to interpret the smoothed component.
Dynamic Scaling:
5. To make our indicator more readable and comparable over different periods, the logarithmically transformed values are scaled between a range. This range is determined by the highest and lowest values of the transformed component over the user-defined 'lookback' period.
ROC (Rate of Change) Direction:
6. The direction of change in our scaled value is determined. This offers a quick insight into whether our mathematical component is increasing or decreasing compared to the previous value.
Visualization:
7. Finally, the indicator plots the dynamically scaled and smoothed mathematical component on the chart. The color of the plotted line depends on its direction (increasing or decreasing) and its boundary values.
Scaling
Feature ScalingLibrary "Feature_Scaling"
FS: This library helps you scale your data to certain ranges or standarize, normalize, unit scale or min-max scale your data in your prefered way. Mostly used for normalization purposes.
minmaxscale(source, min, max, length)
minmaxscale: Min-max normalization scales your data to set minimum and maximum range
Parameters:
source
min
max
length
Returns: res: Data scaled to the set minimum and maximum range
meanscale(source, length)
meanscale: Mean normalization of your data
Parameters:
source
length
Returns: res: Mean normalization result of the source
standarize(source, length, biased)
standarize: Standarization of your data
Parameters:
source
length
biased
Returns: res: Standarized data
unitlength(source, length)
unitlength: Scales your data into overall unit length
Parameters:
source
length
Returns: res: Your data scaled to the unit length
Feature scalerFeature scaler | Pine Utilities series, ready to be used in "study-on-study" fashion |
Includes min-max, normalization, standardization and unit length scaling.
One and only source: en.wikipedia.org
Endpoint inputs allow to set an interval of interest for min-max scaler.
Can be (and should be) applied to other studies, or to the chart itself. In this example, I applied min-max scaling to weighted linear regression's slope values.
Unfortunately, "All data" is still "experimental" and works only on charts where less than 5000 bars are available. max_bars_back() didn't help.
Sup TV
Squeeze Momentum Indicator ModThis is a scaled version of LazyBear's Squeeze Momentum Indicator. Also added are fibo-based periods for BB and KC.
Forecast Oscillator (ps4)This is a scaled version of a Forecast Oscillator, which may be used as a standalone indicator or as a filter. Scaling allows to reduce data to a standard interval, say, 0..1 or -1..1. Oftentimes, it also makes data more contrastive.
Logistic Difference (ps4)This is an PS4 update to the Logistic Difference Indicator. It uses logistic function (sigmoid), which stabilizes the variance of data. The logistic function resembles the inverse Fisher transform. This version has a repaint/non-repaint switch and a scaling feature.
