FunctionPatternFrequencyLibrary "FunctionPatternFrequency"
Counts the word or integer number pattern frequency on a array.
reference:
rosettacode.org
count(pattern)
counts the number a pattern is repeated.
Parameters:
pattern : : array : array with patterns to be counted.
Returns:
array : list of unique patterns.
array : list of counters per pattern.
usage:
count(array.from('a','b','c','a','b','a'))
count(pattern)
counts the number a pattern is repeated.
Parameters:
pattern : : array : array with patterns to be counted.
Returns:
array : list of unique patterns.
array : list of counters per pattern.
usage:
count(array.from(1,2,3,1,2,1)) Library

FunctionDynamicTimeWarpingLibrary "FunctionDynamicTimeWarping"
"In time series analysis, dynamic time warping (DTW) is an algorithm for
measuring similarity between two temporal sequences, which may vary in
speed. For instance, similarities in walking could be detected using DTW,
even if one person was walking faster than the other, or if there were
accelerations and decelerations during the course of an observation.
DTW has been applied to temporal sequences of video, audio, and graphics
data — indeed, any data that can be turned into a linear sequence can be
analyzed with DTW. A well-known application has been automatic speech
recognition, to cope with different speaking speeds. Other applications
include speaker recognition and online signature recognition.
It can also be used in partial shape matching applications."
"Dynamic time warping is used in finance and econometrics to assess the
quality of the prediction versus real-world data."
~~ wikipedia
reference:
en.wikipedia.org
towardsdatascience.com
github.com
cost_matrix(a, b, w)
Dynamic Time Warping procedure.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: matrix optimum match matrix.
traceback(M)
perform a backtrace on the cost matrix and retrieve optimal paths and cost between arrays.
Parameters:
M : matrix, cost matrix.
Returns: tuple:
array aligned 1st array of indices.
array aligned 2nd array of indices.
float final cost.
reference:
github.com
report(a, b, w)
report ordered arrays, cost and cost matrix.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: string report. Library

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Candilator RSI [AstrideUnicorn]OVERVIEW
The name Candilator comes from blending the words "candlestick" and "oscillator". And as the name suggests, this indicator is a good old RSI plotted as a candlestick chart. To produce a candlestick chart, Candilator RSI calculates four RSI's based on the open, high, low, and close time series. It also has a candlestick patterns detection feature.
HOW TO USE
You can use Candilator RSI as a normal RSI to analyze momentum, detect overbought and oversold markets, and find the oscillator's divergences with the price. You can also get creative and apply all sorts of technical analysis to the RSI candlestick chart, including candlestick patterns analysis.
Candilator RSI can automatically scan the price for some candlestick patterns in the overbought and oversold zones. This feature can help detect price reversals early.
SETTINGS
The indicator settings are divided into two groups: Main Settings and Pattern Detection. In the Main Settings, you can find standard RSI settings. In the Pattern Detection part, you can turn on and off the automatic search for a particular candlestick pattern. Indicator

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FunctionPatternDecompositionLibrary "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. Library

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Doji Hunter█ OVERVIEW
This script is built to search for 8 different Doji candlestick patterns in markets and makes them appear on screen with bar coloring and creating color-coded labels/shapes. It will identify the following variants based upon user input for various rules to abide by:
Gapping Up
Gapping Down
Gravestone
Dragonfly
Long-Legged
Rickshaw Man
Northern (Doji in uptrend)
Southern (Doji in downtrend)
Note: for the remainder of this description, the types for inputs will be marked by italic text.
█ OPTIONS
This script features a wide range of options available to the user to modify how it functions. The first set of inputs dictate how the trend analysis is done with moving averages. The second and third sets of inputs dictate specific rules for how Doji candles are analyzed and the colors used for when they appear.
█ INPUTS (short)
1 — Moving Average Rules:
The Northern and Southern Doji variants require some trend analysis which will be done by Moving Averages. The inputs in this section change various things about the moving average(s) to be used. In the second section of inputs, there is one boolean option that will nullify the need for trend detection and consolidates the Northern and Southern Doji variants into one.
2/3 — Doji Rules and Colors:
The next two sections of inputs correspond to the various rules that dictate how various doji variants will be analyzed, as well as the colors that correspond to each variant. The colors will also apply to each of the labels/shapes used.
4 — Diagnostics:
The last boolean will allow the user to see extra detail with regards to how and when dojis are detected. Note: This is not a part of any prior section and is simply included as a last functional item to the list of all inputs.
An example of multiple labels being shown on screen for various types of Dojis (DJI 1D chart):
█ INPUTS (extended)
1 — Moving Average Rules:
This section consists of 10 different inputs specific to the rules on how the moving average functions for trend analysis.
"Trend Rule" ( string list) determines which Moving Average will be used for trend detection. It has 3 options: "MA 1", "MA 2", or "BOTH". The second input "Trend Source" determines which OHLC (or combination) value to use in comparison to either MA 1 or MA 2 (EX: Trend Rule -> "MA 1" and Trend Source -> "close": if close > MA 1 -> uptrend, downtrend otherwise). If "BOTH" is selected then "Trend Source" is ignored and added nuance in the script ensures that the shorter MA being above the longer MA yields an uptrend (downtrend otherwise).
The next 8 inputs focus on 4 different parts of both MA 1 and 2.
Length ( integer(s) )
Color
Switch between SMA/EMA ( boolean(s) )
Source for MA
Note: Additional attention to detail has been made here as trend direction is ignored if "BOTH" is selected for the MA Rules and the lengths of both Moving Averages are set to be the same.
2/3 — Doji Rules and Colors:
The next two sections include 19 inputs that are related to how this script will analyze and identify the different variants of Doji candles.
"Identify Pattern On Close" ( boolean ) modifies which candles are to be used for determining when Doji candles are recognized. This changes an offset used for historical reference on some global variables which will force the script to only identify patterns after the current candle has closed.
"Doji Body Tolerance" ( float ) tells the script the maximum % the candle body may be of the high-low range to be considered a Doji candle.
"Doji Wick Sample" ( integer ) defines how many prior candles to sample from in calculating the current average upper and lower wick sizes.
"Simplify Northern/Southern Dojis" ( boolean ) makes this script ignore trend direction for Doji detection and consolidates Northern and Southern Dojis into being recognized as the same. This has an added effect of removing the plotted moving averages from the screen.
"Northern/Southern Display" ( string list ) that has multiple options for how Northern and Southern Dojis will be displayed on screen. Because of how labels may be extremely taxing on PulseWire's servers to display, the default setting is "shapes" where Northern and Southern (N/S) Dojis will be marked with a colored triangle at the top of the candle. If "Simplify Northern/Southern Dojis" is true, all N/S Dojis will be marked with an x-cross instead. Other options include "labels" which enables the use of labels accompanied by their respective tooltip and color, or "none" where N/S Dojis will be only noticeable by their changed barcolor.
"Allow Gravestone/Dragonfly Shadows" ( boolean ) allows a bit of additional nuance to the definition of Gravestone or Dragonfly Dojis with small shadows.
"Gravestone/Dragonfly Shadow Tolerance" ( float ) defines the maximum % that the lower wick/upper wick (respectively) may be relative to the high-low range for Gravestone or Dragonfly Dojis to still be considered valid.
"Doji Long Wick Setting" ( string list) is a list of settings for three different ways of confirming if a Doji is Long-Legged. The settings are "one", "two", and "average". These define how many wick lengths of a candle need to exceed the calculated average wick lengths (EX: "both" -> upper wick length > upper wick average and lower wick length > lower wick average). The "average" setting will combine the lengths of both wicks and both prior wick averages, divide both of these sums by 2 and compare them instead.
"Doji Long Wick Tolerance" ( float ) defines how large compared to the averages that wick lengths need to be in order for them to be considered "Long-Legged" (EX: 1.50 -> upper/lower wick needs to exceed 150% the average of previous upper/lower wicks).
"Rickshaw Man Body Placement Tolerance" ( float ) defines how close to the high-low range's midpoint the candle body's midpoint needs to be in order for it to be considered a Rickshaw Man Doji candle instead.
The remaining 9 inputs define the colors to use for differentiating between all Doji variants this script will recognize.
█ USAGE
My hope for this script is that users find this easy to use/understand and will tinker with the input values to better identify Doji candlesticks across a wide range of markets.
Suggestions for changes in the future are welcome. Indicator

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