PINE LIBRARY
FunctionPatternDecomposition
Library "FunctionPatternDecomposition"
Methods for decomposing price into common grid/matrix patterns.
series_to_array(source, length) Helper for converting series to array.
Parameters:
source: float, data series.
length: int, size.
Returns: float array.
smooth_data_2d(data, rate) Smooth data sample into 2d points.
Parameters:
data: float array, source data.
rate: float, default=0.25, the rate of smoothness to apply.
Returns: tuple with 2 float arrays.
thin_points(data_x, data_y, rate) Thin the number of points.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
rate: float, default=2.0, minimum threshold rate of sample stdev to accept points.
Returns: tuple with 2 float arrays.
extract_point_direction(data_x, data_y) Extract the direction each point faces.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
Returns: float array.
find_corners(data_x, data_y, rate) ...
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
rate: float, minimum threshold rate of data y stdev.
Returns: tuple with 2 float arrays.
grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
m_size: int, default=10, size of the matrix.
Returns: flat 2d pseudo matrix.
Methods for decomposing price into common grid/matrix patterns.
series_to_array(source, length) Helper for converting series to array.
Parameters:
source: float, data series.
length: int, size.
Returns: float array.
smooth_data_2d(data, rate) Smooth data sample into 2d points.
Parameters:
data: float array, source data.
rate: float, default=0.25, the rate of smoothness to apply.
Returns: tuple with 2 float arrays.
thin_points(data_x, data_y, rate) Thin the number of points.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
rate: float, default=2.0, minimum threshold rate of sample stdev to accept points.
Returns: tuple with 2 float arrays.
extract_point_direction(data_x, data_y) Extract the direction each point faces.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
Returns: float array.
find_corners(data_x, data_y, rate) ...
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
rate: float, minimum threshold rate of data y stdev.
Returns: tuple with 2 float arrays.
grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.
Parameters:
data_x: float array, points x value.
data_y: float array, points y value.
m_size: int, default=10, size of the matrix.
Returns: flat 2d pseudo matrix.
Pine library
In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can reuse it. Cheers to the author! You may use this library privately or in other open-source publications, but reuse of this code in a publication is governed by House rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.