LN
Type:
ln• Category:indicators
Description
Vector Log Natural
Parameters
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
dataExp | string | Input data | no | |
real | string | Data column to apply the calculation to (e.g., closing price) | no |
Help
LN (Vector Log Natural)
Description
The LN worker is a financial analysis indicator that calculates the natural logarithm of a specified data column. This indicator is commonly used in technical analysis to normalize data and make it more manageable.
What does this worker do?
The LN worker takes an input data set (dataExp) and applies the natural logarithm calculation to a specified column (real). The result is a new data set with the logarithmic values.
How to interpret the results
The natural logarithm calculation compresses the scale of the data, making it easier to analyze and visualize. This can be particularly useful when dealing with large price movements or exponential growth.
Usage
To use the LN worker, simply provide the input data (dataExp) and specify the column to apply the calculation to (real). The worker will return a new data set with the logarithmic values.
Example Usage
For example, if you have a data set with a column for closing prices, you can use the LN worker to calculate the natural logarithm of these prices.
Indicator Visuals
For a visual representation of how to use the LN worker, please see the following GIFs:
Full GIF
[](https://pub-6c7cc7f707d94ca98153d59a039b9a3d.r2.dev/indicator_full.gif)
Short GIF
[](https://pub-6c7cc7f707d94ca98153d59a039b9a3d.r2.dev/indicator_short.gif)
Additional Information
The natural logarithm is a fundamental concept in mathematics and is widely used in finance and economics. It has several useful properties, including:
- The natural logarithm is a monotonic increasing function, meaning that it preserves the ordering of the data.
- The natural logarithm is a continuous function, making it suitable for use in mathematical models.
By applying the natural logarithm to financial data, analysts can:
- Reduce the effect of extreme values
- Improve the stability of statistical models
- Enhance the interpretability of results
Parameters
dataExp: Input datareal: Data column to apply the calculation to (e.g., closing price)