FunctionNNPerceptronLibrary "FunctionNNPerceptron"
Perceptron Function for Neural networks.
function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks.
Parameters:
inputs : float array, the inputs of the perceptron.
weights : float array, the weights for inputs.
bias : float, default=1.0, the default bias of the perceptron.
activation_function : string, default='sigmoid', activation function applied to the output.
alpha : float, default=na, if required for activation.
scale : float, default=na, if required for activation.
@outputs float
Arrays
MLActivationFunctionsLibrary "MLActivationFunctions"
Activation functions for Neural networks.
binary_step(value) Basic threshold output classifier to activate/deactivate neuron.
Parameters:
value : float, value to process.
Returns: float
linear(value) Input is the same as output.
Parameters:
value : float, value to process.
Returns: float
sigmoid(value) Sigmoid or logistic function.
Parameters:
value : float, value to process.
Returns: float
sigmoid_derivative(value) Derivative of sigmoid function.
Parameters:
value : float, value to process.
Returns: float
tanh(value) Hyperbolic tangent function.
Parameters:
value : float, value to process.
Returns: float
tanh_derivative(value) Hyperbolic tangent function derivative.
Parameters:
value : float, value to process.
Returns: float
relu(value) Rectified linear unit (RELU) function.
Parameters:
value : float, value to process.
Returns: float
relu_derivative(value) RELU function derivative.
Parameters:
value : float, value to process.
Returns: float
leaky_relu(value) Leaky RELU function.
Parameters:
value : float, value to process.
Returns: float
leaky_relu_derivative(value) Leaky RELU function derivative.
Parameters:
value : float, value to process.
Returns: float
relu6(value) RELU-6 function.
Parameters:
value : float, value to process.
Returns: float
softmax(value) Softmax function.
Parameters:
value : float array, values to process.
Returns: float
softplus(value) Softplus function.
Parameters:
value : float, value to process.
Returns: float
softsign(value) Softsign function.
Parameters:
value : float, value to process.
Returns: float
elu(value, alpha) Exponential Linear Unit (ELU) function.
Parameters:
value : float, value to process.
alpha : float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .
Returns: float
selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.
Parameters:
value : float, value to process.
alpha : float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .
scale : float, default=1.05070098, predefined constant.
Returns: float
exponential(value) Pointer to math.exp() function.
Parameters:
value : float, value to process.
Returns: float
function(name, value, alpha, scale) Activation function.
Parameters:
name : string, name of activation function.
value : float, value to process.
alpha : float, default=na, if required.
scale : float, default=na, if required.
Returns: float
derivative(name, value, alpha, scale) Derivative Activation function.
Parameters:
name : string, name of activation function.
value : float, value to process.
alpha : float, default=na, if required.
scale : float, default=na, if required.
Returns: float
MLLossFunctionsLibrary "MLLossFunctions"
Methods for Loss functions.
mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ".
Parameters:
expects : float array, expected values.
predicts : float array, prediction values.
Returns: float
binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).
Parameters:
expects : float array, expected values.
predicts : float array, prediction values.
Returns: float
PointsLibrary "Points"
Provides functions for simplifying operations with collections of x+y coordinates. Where x is typically a bar index or time (millisecond) value.
new(size) Creates two arrays. One for X (int ) and another for Y (float ).
Parameters:
size : The initial size of the arrays.
size(xA, yA) Checks the size of the arrays and if they're equal returns the size.
Parameters:
xA : The X array.
yA : The Y array.
get(xA, yA, index) Gets the X and Y values of the arrays at the index.
Parameters:
xA : The X array.
yA : The Y array.
index : The index.
Returns:
set(xA, yA, index, x, y) Sets the X and Y values of the arrays at the index.
Parameters:
xA : The X array.
yA : The Y array.
index : The index.
x : The x value.
y : The y value.
Returns:
push(xA, yA, x, y) Adds X and Y values to the end of the arrays (as the last element).
Parameters:
xA : The X array.
yA : The Y array.
x : The x value.
y : The y value.
Returns:
unshift(xA, yA, x, y) Adds X and Y values to the beginning of the arrays (as the first element).
Parameters:
xA : The X array.
yA : The Y array.
x : The x value.
y : The y value.
Returns:
insert(xA, yA, index, x, y) Inserts X and Y values to the arrays at the index.
Parameters:
xA : The X array.
yA : The Y array.
index : The index to insert at.
x : The x value.
y : The y value.
Returns:
pop(xA, yA) Removes the last element from the arrays and returns their value.
Parameters:
xA : The X array.
yA : The Y array.
Returns:
shift(xA, yA) Removes the first element from the arrays and returns their value.
Parameters:
xA : The X array.
yA : The Y array.
Returns:
remove(xA, yA) Removes the element from the arrays at the index and returns their value.
Parameters:
xA : The X array.
yA : The Y array.
Returns:
first(xA, yA) Gets the X and Y values of the first element.
Parameters:
xA : The X array.
yA : The Y array.
Returns:
last(xA, yA) Gets the X and Y values of the last element.
Parameters:
xA : The X array.
yA : The Y array.
Returns:
allIndexesBetween(xA, lo, hi, start, ordered) Gets the indexes that have values at or above the low value and below the high value.
Parameters:
xA : The X array.
lo : The inclusive low value.
hi : The excluded hi value.
start : The optional index to start the backwards search.
ordered : If true, the search ends when the first value is found that is less than the low.
lastIndexBetween(xA, lo, hi, start, ordered) Gets the first found from the end that has a value at or above the low value and below the high value.
Parameters:
xA : The X array.
lo : The inclusive low value.
hi : The excluded hi value.
start : The optional index to start the backwards search.
ordered : If true, the search ends when the first value is found that is less than the low.
lastIndexBelow(xA, hi, start) Gets the first found from the end that has a value below the high value.
Parameters:
xA : The X array.
hi : The excluded hi value.
start : The optional index to start the backwards search.
eHarmonicpatternsLibrary "eHarmonicpatterns"
Library provides an alternative method to scan harmonic patterns. This is helpful in reducing iterations
scan_xab(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern
Parameters:
bcdRatio : AB/XA ratio
err_min : minimum error threshold
err_max : maximum error threshold
patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten.
scan_abc_axc(abcRatio, axcRatio, err_min, err_max, patternArray) Checks if abc or axc ratio is in range of any harmonic pattern
Parameters:
abcRatio : BC/AB ratio
axcRatio : XC/AX ratio
err_min : minimum error threshold
err_max : maximum error threshold
patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten.
scan_bcd(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern
Parameters:
bcdRatio : CD/BC ratio
err_min : minimum error threshold
err_max : maximum error threshold
patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten.
scan_xad_xcd(xadRatio, xcdRatio, err_min, err_max, patternArray) Checks if xad or xcd ratio is in range of any harmonic pattern
Parameters:
xadRatio : AD/XA ratio
xcdRatio : CD/XC ratio
err_min : minimum error threshold
err_max : maximum error threshold
patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten.
isHarmonicPattern(x, a, c, c, d, flags, errorPercent) Checks for harmonic patterns
Parameters:
x : X coordinate value
a : A coordinate value
c : B coordinate value
c : C coordinate value
d : D coordinate value
flags : flags to check patterns. Send empty array to enable all
errorPercent : Error threshold
Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names
isHarmonicProjection(x, a, c, c, flags, errorPercent) Checks for harmonic pattern projection
Parameters:
x : X coordinate value
a : A coordinate value
c : B coordinate value
c : C coordinate value
flags : flags to check patterns. Send empty array to enable all
errorPercent : Error threshold
Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names
hashmapsA simple hashmap implementation for pinescript.
It gets your string array and transforms it into a hashmap.
Before using it you need to initialize your array with the size you need for your specific case since the size is not dynamic.
To use it, first you need to import it the following way:
> import marspumpkin/hashmaps/1
Then, initialize your array with the size needed for your specific case:
> hashmap = array.new_string(10000)
After that you can call:
> hashmaps.put() and hashmaps.get()
Passing in the array(hashmap), key and value.
I hope this helps you in your pinescript journey.
Dictionary/Object LibraryThis Library is aimed to mitigate the limitation of Pinescript having only one structured data type which is only arrays.
It lacks data types like Dictionaries(in Python) or Object (in JS) that are standard for other languages. Tuples do exist, but it hardly solves any problem.
Working only with Arrays could be overwhelming if your codebase is large. I looked for alternatives to arrays but couldn't find any library.
So I coded it myself and it's been working good for me. So I wanted to share it with you all.
What does it do:
==================
If you are familiar with Python or Javascript, this library tries to immimate Object/Dictonary like structure with Key Value Pairs.
For Example:
object= {name:"John Doe", age: 28 , org: "PineCoders"}
And then it also tries to immitate the Array of Objects (I call it Stack)
like this:
stack= Array({name:"John Doe", age: 28 , org: "PineCoders"},
{name:"Adam Smith", age: 32 , org: "PineCoders"},
{name:"Paragjyoti Deka", age: 25 , org: "PineCoders"})
So there are basically two ideas: Objects and Stacks.
But it looks whole different in Pinescript for obvious reasons.
Limitation:
The major limitation I couldn't overcome was that, for all of the values: both input and return values for properties will be of string type.
This is due to the limiation of Pinecsript that there is no way to return a value on a if-else statement dynamically with different data types.
And as the input data type must be explicitly defined when exporting the library functions, only string inputs are allowed.
Now that doesn't mean you won't be able to use integer, float or boolens, you just need to pass the string value for it using str.tostring() method.
And the output for the getter functions will be in strings as well. But I have added some type conversion methods that you could use from this library itself.
From String to Float, String To Integer and String to Boolean: these three methods are included in this library.
So basically the whole library is based on a manipulatiion of Array of strings under the hood.
///////////////
Usage
///////////////
Import the library using this statement:
import paragjyoti2012/STR_Dict_Lib/4 as DictLib
Objects
First define an object using this method:
for eample:
object1= DictLib.init("name=John,age=26,org=")
This is similar to
object1= {name:"John",age:"26", org:""} in JS or Python
Just like we did here in for "org", you can set initital value to "". But remember to pass string values, even for a numerical properties, like here in "age".
You can use "age="+str.tostring(age). If you find it tedious, you can always add properties later on using .set() method.
So it could also be initiated like this
object= DictLib.init("name=John")
and later on
DictLib.set(object1,"age", str.toString(age))
DictLib.set(object1,"org", "PineCoders")
The getter function looks like this
age= DictLib.get(object1,"age")
name=DictLib.get(object1,"name")
The first argument for all methods .get, .set, and .remove is the pointer (name of the object).
///////////////////////////
Array Of Objects (Stacks)
///////////////////////////
As I mentioned earlier, I call the array of objects as Stack.
Here's how to initialize a Stack.
stack= DictLib.initStack(object1)
The .initStack() method takes an object pointer as argument. It simply converts the array into a string and pushes it into the newly created stack.
Rest of all the methods for Stacks, takes the stack pointer as it's first arument.
For example:
DictLib.pushStack(stack,object2)
The second argument here is the object pointer. It adds the object to it's stack. Although it might feel like a two dimentional array, it's actually an one dimentional array with string values.
Under the hood, it looks like this
////////////////////
Methods
////////////////////
For Objects
-------------------
init() : Initializes the object.
params: (string) e.g
returns: The object ( )
example:
object1=DictLib.init("name=John,age=26,org=")
...................
get() : Returns the value for given property
params: (string object_pointer, string property)
returns: string
example:
age= DictLib.get(object1,"age")
.......................
set() : Adds a new property or updates an existing property
params: (string object_pointer, string property, string value)
returns: void
example:
DictLib.set(object1,"age", str.tostring(29))
........................
remove() : Removes a property from the object
params : (string object_pointer, string property)
returns: void
example:
DictLib.set(object1,"org")
........................
For Array Of Objects (Stacks)
-------------------------------
initStack() : Initializes the stack.
params: (string object_pointer) e.g
returns: The Stack
example:
stack= DictLib.initStack(object1)
...................
pushToStack() : Adds an object at at last index of the stack
params: (string stack_pointer, string object_pointer)
returns: void
example:
DictLib.pushToStack(stack,object2)
.......................
popFromStack() : Removes the last object from the stack
params: (string stack_pointer)
returns: void
example:
DictLib.popFromStack(stack)
.......................
insertToStack() : Adds an object at at the given index of the stack
params: (string stack_pointer, string object_pointer, int index)
returns: void
example:
DictLib.insertToStack(stack,object3,1)
.......................
removeFromStack() : Removes the object from the given index of the stack
params: (string stack_pointer, int index)
returns: void
example:
DictLib.removeFromStack(stack,2)
.......................
getElement () : Returns the value for given property from an object in the stack (index must be given)
params: (string stack_pointer, int index, string property)
returns: string
example:
ageFromObject1= DictLib.getElement(stack,0,"age")
.......................
setElement() : Updates an existing property of an object in the stack (index must be given)
params: (string stack_pointer, int index, string property, string value)
returns: void
example:
DictLib.setElement(stack,0,"age", str.tostring(32))
........................
includesElement() : Checks if any object exists in the stack with the given property-value pair
params : (string stack_pointer, string property, string value)
returns : Boolean
example:
doesExist= DictLib.includesElement(stack,"org","PineCoders")
........................
searchStack() : Search for a property-value pair in the stack and returns it's index
params: (stringp stack_pointer, string property, string value)
returns: int (-1 if doesn't exist)
example:
index= DictLib.searchElement(stack,"org","PineCoders")
///////////////////////
Type Conversion Methods
///////////////////////
strToFloat() : Converts String value to Float
params: (string value)
returns: float
example:
floatVal= DictLib.strToFloat("57.96")
.............................
strToInt() : Converts String value to Integer
params: (string value)
returns: int
example:
intVal= DictLib.strToFloat("45")
.............................
strToBool() : Converts String value to Boolean
params: (string value)
returns: boolean
example:
boolVal= DictLib.strToBool("true")
.............................
Points to remember
...............
1. Always pass string values as arguments.
2. The return values will be of type string, so convert them before to avoid typecasting conflict.
3. Horses can't vomit.
More Informations
====================
Yes, You can store this objects and stacks for persisting through the iterations of a script across successive bars.
You just need to set the variable using "var" keyword. Remember this objects and stacks are just arrays,
so any methods and properties an array have it pinescript, would be applicable for objects and stacks.
It can also be used in security functions without any issues for MTF Analysis.
If you have any suggestions or feedback, please comment on the thread, I would surely be happy to help.
FunctionPeakDetectionLibrary "FunctionPeakDetection"
Method used for peak detection, similar to MATLAB peakdet method
function(sample_x, sample_y, delta) Method for detecting peaks.
Parameters:
sample_x : float array, sample with indices.
sample_y : float array, sample with data.
delta : float, positive threshold value for detecting a peak.
Returns: tuple with found max/min peak indices.
AveragesLibrary "Averages"
Contains utilities for generating averages from arrays. Useful for manipulated or cleaned data.
triangular(src, startingWeight) Calculates the triangular weighted average of a set of values where the last value has the highest weight.
Parameters:
src : The array to derive the average from.
startingWeight : The weight to begin with when calculating the average. Higher numbers will decrease the bias.
weighted(src, weights, weightDefault) Calculates the weighted average of a set of values.
Parameters:
src : The array to derive the average from.
weights : The array containing the weights for the source.
weightDefault : The default value to use when a weight is NA.
triangularWeighted(src, weights, startingWeight) Calculates the weighted average of a set of values where the last value has the highest triangular multiple.
Parameters:
src : The array to derive the average from.
weights : The array containing the weights for the source.
startingWeight : The multiple to begin with when calculating the average. Higher numbers will decrease the bias.
exponential(src) Calculates the exponential average of a set of values where the last value has the highest weight.
Parameters:
src : The array to derive the average from.
arrayFrom(src, len, omitNA) Creates an array from the provided series (oldest to newest).
Parameters:
src : The array to derive from.
len : The target length of the array.
omitNA : If true, NA values will not be added to the array and the resultant array may be shorter than the target length.
arrayutilsLibrary "_arrayutils"
Library contains utility functions using arrays.
delete(arr, index)
remove an item from array at specific index. Also deletes the item
Parameters:
arr : - array from which the item needs to be deleted
index : - index of item to be deleted
Returns: void
pop(arr)
remove the last item from array. Also deletes the item
Parameters:
arr : - array from which the last item needs to be removed and deleted
Returns: void
shift(arr)
remove an item from array at index 0. Also deletes the item
Parameters:
arr : - array from which the first item needs to be removed and deleted
Returns: void
unshift(arr, val, maxItems)
add an item to the beginning of an array with max items cap
Parameters:
arr : - array to which the item needs to be added at the beginning
val : - value of item which needs to be added
maxItems : - max items array can hold. After that, items are removed from the other end
Returns: resulting array
clear(arr)
remove and delete all items in an array
Parameters:
arr : - array which needs to be cleared
Returns: void
push(arr, val, maxItems)
add an item to the end of an array with max items cap
Parameters:
arr : - array to which the item needs to be added at the beginning
val : - value of item which needs to be added
maxItems : - max items array can hold. After that, items are removed from the starting index
Returns: resulting array
check_overflow(pivots, barArray, dir)
finds difference between two timestamps
Parameters:
pivots : pivots array
barArray : pivot bar array
dir : direction for which overflow need to be checked
Returns: bool overflow
get_trend_series(pivots, length, highLow, trend)
finds series of pivots in particular trend
Parameters:
pivots : pivots array
length : length for which trend series need to be checked
highLow : filter pivot high or low
trend : Uptrend or Downtrend
Returns: int trendIndexes
get_trend_series(pivots, firstIndex, lastIndex)
finds series of pivots in particular trend
Parameters:
pivots : pivots array
firstIndex : First index of the series
lastIndex : Last index of the series
Returns: int trendIndexes
sma(source)
calculates sma for elements in array
Parameters:
source : source array
Returns: float sma
ema(source, length)
calculates ema for elements in array
Parameters:
source : source array
length : ema length
Returns: float ema
rma(source, length)
calculates rma for elements in array
Parameters:
source : source array
length : rma length
Returns: float rma
wma(source, length)
calculates wma for elements in array
Parameters:
source : source array
length : wma length
Returns: float wma
hma(source, length)
calculates hma for elements in array
Parameters:
source : source array
length : hma length
Returns: float hma
ma(source, matype, length)
wrapper for all moving averages based on array
Parameters:
source : source array
matype : moving average type. Valud values are: sma, ema, rma, wma and hma
length : moving average length length
Returns: float moving average
getFibSeries(numberOfFibs, start)
gets fib series in array
Parameters:
numberOfFibs : number of fibs
start : starting number
Returns: float fibArray
Signal_Data_2021_09_09__2021_11_18Library "Signal_Data_2021_09_09__2021_11_18"
Functions to support my timing signals system
import_start_time(harmonic) get the start time for each harmonic signal
Parameters:
harmonic : is an integer identifying the harmonic
Returns: the starting timestamp of the harmonic data
import_signal(index, harmonic) access point for pre-processed data imported here by copy paste
Parameters:
index : is the current data index, use 0 to initialize
harmonic : is the data set to index, use 0 to initialize
Returns: the data from the indicated harmonic array starting at index, and the starting timestamp of that data
FunctionArrayMaxSubKadanesAlgorithmLibrary "FunctionArrayMaxSubKadanesAlgorithm"
Implements Kadane's maximum sum sub array algorithm.
size(samples) Kadanes algorithm.
Parameters:
samples : float array, sample data values.
Returns: float.
indices(samples) Kadane's algorithm with indices.
Parameters:
samples : float array, sample data values.
Returns: tuple with format .
cacheLibrary "cache"
A simple cache library to store key value pairs.
Fed up of injecting and returning so many values all the time?
Want to separate your code and keep it clean?
Need to make an expensive calculation and use the results in numerous places?
Want to throttle calculations or persist random values across bars or ticks?
Then you've come to the right place. Or not! Up to you, I don't mind either way... ;)
Check the helpers and unit tests in the script for further detail.
Detailed Interface
init(persistant) Initialises the syncronised cache key and value arrays
Parameters:
persistant : bool, toggles data persistance between bars and ticks
Returns: [string , float ], a tuple of both arrays
set(keys, values, key, value) Sets a value into the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to create or update
value : float, the value to set
has(keys, values, key) Checks if the cache has a key
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to check
Returns: bool, true only if the key is found
get(keys, values, key) Gets a keys value from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to get
Returns: float, the stored value
remove(keys, values, key) Removes a key and value from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to remove
count() Counts how many key value pairs in the cache
Returns: int, the total number of pairs
loop(keys, values) Returns true for each value in the cache (use as the while loop expression)
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
next(keys, values) Returns each key value pair on successive calls (use in the while loop)
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
Returns: , tuple of each key value pair
clear(keys, values) Clears all key value pairs from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
unittest_cache(case) Cache module unit tests, for inclusion in parent script test suite. Usage: log.unittest_cache(__ASSERTS)
Parameters:
case : string , the current test case and array of previous unit tests (__ASSERTS)
unittest(verbose) Run the cache module unit tests as a stand alone. Usage: cache.unittest()
Parameters:
verbose : bool, optionally disable the full report to only display failures
ArrayMultipleDimensionPrototypeLibrary "ArrayMultipleDimensionPrototype"
A prototype library for Multiple Dimensional array methods
index_md_to_1d()
new_float(dimensions, initial_size) Creates a variable size multiple dimension array.
Parameters:
dimensions : int array, dimensions of array.
initial_size : float, default=na, initial value of the array.
Returns: float array
dimensions(id, value) set value of a element in a multiple dimensions array.
Parameters:
id : float array, multiple dimensions array.
value : float, new value.
Returns: float.
get(id) get value of a multiple dimensions array.
Parameters:
id : float array, multiple dimensions array.
Returns: float.
set(id) set value of a element in a multiple dimensions array.
Parameters:
id : float array, multiple dimensions array.
Returns: float.
FunctionArrayReduceLibrary "FunctionArrayReduce"
A limited method to reduce a array using a mathematical formula.
float_(formula, samples, arguments, initial_value) Method to reduce a array using a mathematical formula.
Parameters:
formula : string, the mathematical formula, accepts some default name codes (index, output, previous, current, integer index of arguments array).
samples : float array, the data to process.
arguments : float array, the extra arguments of the formula.
initial_value : float, default=0, a initial base value for the process.
Returns: float.
Notes:
** if initial value is specified as "na" it will default to the first element of samples.
FunctionProbabilityDistributionSamplingLibrary "FunctionProbabilityDistributionSampling"
Methods for probability distribution sampling selection.
sample(probabilities) Computes a random selected index from a probability distribution.
Parameters:
probabilities : float array, probabilities of sample.
Returns: int.
FunctionElementsInArrayLibrary "FunctionElementsInArray"
Methods to count number of elements in arrays
count_float(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : float array, sample data to process.
value : float value to check for equality.
Returns: int.
count_int(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : int array, sample data to process.
value : int value to check for equality.
Returns: int.
count_string(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : string array, sample data to process.
value : string value to check for equality.
Returns: int.
count_bool(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : bool array, sample data to process.
value : bool value to check for equality.
Returns: int.
count_color(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : color array, sample data to process.
value : color value to check for equality.
Returns: int.
extract_indices_float(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : float array, sample data to process.
value : float value to check for equality.
Returns: int.
extract_indices_int(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : int array, sample data to process.
value : int value to check for equality.
Returns: int.
extract_indices_string(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : string array, sample data to process.
value : string value to check for equality.
Returns: int.
extract_indices_bool(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : bool array, sample data to process.
value : bool value to check for equality.
Returns: int.
extract_indices_color(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : color array, sample data to process.
value : color value to check for equality.
Returns: int.
amibrokerLibrary "amibroker"
This library consists of functions from amibroker that doesn't exist on tradingview pinescript. The example of these are the ExRem and Flip.
In the example below, I used ExRem to remove the excessive buy and sell signals. Meanwhile, I used the Flip to highlight the bg color when there is an open position.
exrem(series1, series2) Removes excessive signals. Pinescript version of ExRem in Amibroker (www.amibroker.com)
Parameters:
series1 : boolean
series2 : boolean
Returns: boolean
flip(series1, series2) works as a flip/flop device or "latch". Pinescript version of Flip in Amibroker: (www.amibroker.com)
Parameters:
series1 : boolan
series2 : boolean
Returns: boolean.
FunctionCompoundInterestLibrary "FunctionCompoundInterest"
Method for compound interest.
simple_compound(principal, rate, duration) Computes compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rate : float, the rate of interest.
duration : float, the period of growth.
Returns: float.
variable_compound(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
simple_compound_array(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
variable_compound_array(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
FunctionSMCMCLibrary "FunctionSMCMC"
Methods to implement Markov Chain Monte Carlo Simulation (MCMC)
markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
target_path : float array, target path array.
position : int, index of the path.
last_value : float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
start_value : float, base value to start simulation.
n_iterations : integer, number of iterations to run.
Returns: float array with path.
FunctionGeometricLineDrawingsLibrary "FunctionGeometricLineDrawings"
array_delete_all_lines(lines) deletes all lines in array.
Parameters:
lines : line array, array with line objects to delete.
Returns: void.
triangle(sample_x, sample_y, xloc, extend, color, style, width) Draw a Triangle with 3 vector2D(x, y) coordinates.
Parameters:
sample_x : int array, triangle sample data X coordinate values.
sample_y : float array, triangle sample data Y coordinate values.
xloc : string, defaultoptions=xloc.bar_index, xloc.bar_time.
extend : string, default=extend.none, options=(extend.none, extend.right, extend.left, extend.both).
color : color, default=
style : options line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_left, line.style_arrow_right, line.style_arrow_both
width : width in pixels.
Returns: line array
trapezoid(sample_x, sample_y, xloc, extend, color, style, width) Draw a Trapezoid with 4 vector2D(x, y) coordinates:
Parameters:
sample_x : int array, trapezoid sample data X coordinate values.
sample_y : float array, trapezoid sample data Y coordinate values.
xloc : string, defaultoptions=xloc.bar_index, xloc.bar_time.
extend : string, default=extend.none, options=(extend.none, extend.right, extend.left, extend.both).
color : color, default=
style : options line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_left, line.style_arrow_right, line.style_arrow_both
width : width in pixels.
Returns: line array
zigzagplusThis is same as existing zigzag library with respect to functionality. But, there is a small update with respect to how arrays are used internally. This also leads to issues with backward compatibility. Hence I decided to make this as new library instead of updating the older one.
Below are the major changes:
Earlier version uses array.unshift for adding new elements and array.pop for removing old elements. But, since array.unshift is considerably slower than alternative method array.push. Hence, this library makes use of array.push method to achieve performance.
While array.push increases the performance significantly, there is also an issue with removing as we no longer will be able to remove the element using pop which is again faster than shift (which need to shit all the elements by index). Hence, have removed the logic of removing elements for zigzag pivots after certain limit. Will think further about it once I find better alternative of handling it.
These implementation change also mean that zigzag pivots received by calling method will be ordered in reverse direction. Latest pivots will be stored with higher array index whereas older pivots are stored with lower array index. This is also the reason why backward compatibility is not achievable with this code change.
Library "zigzagplus"
Library dedicated to zigzags and related indicators
zigzag(length, useAlternativeSource, source, oscillatorSource, directionBias) zigzag: Calculates zigzag pivots and generates an array
Parameters:
length : : Zigzag Length
useAlternativeSource : : If set uses the source for genrating zigzag. Default is false
source : : Alternative source used only if useAlternativeSource is set to true. Default is close
oscillatorSource : : Oscillator source for calculating divergence
directionBias : : Direction bias for calculating divergence
Returns: zigzagpivots : Array containing zigzag pivots
zigzagpivotbars : Array containing zigzag pivot bars
zigzagpivotdirs : Array containing zigzag pivot directions (Lower High : 1, Higher High : 2, Lower Low : -2 and Higher Low : -1)
zigzagpivotratios : Array containing zigzag retracement ratios for each pivot
zigzagoscillators : Array of oscillator values at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagoscillatordirs: Array of oscillator directions (HH, HL, LH, LL) at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagtrendbias : Array of trend bias at pivots. Will have valid value only if directionBias series is sent in input parameters
zigzagdivergence : Array of divergence sentiment at each pivot. Will have valid values only if oscillatorSource and directionBias inputs are provided
newPivot : Returns true if new pivot created
doublePivot : Returns true if two new pivots are created on same bar (Happens in case of candles with long wicks and shorter zigzag lengths)
drawzigzag(length, , source, linecolor, linewidth, linestyle, oscillatorSource, directionBias, showHighLow, showRatios, showDivergence) drawzigzag: Calculates and draws zigzag pivots
Parameters:
length : : Zigzag Length
: useAlternativeSource: If set uses the source for genrating zigzag. Default is false
source : : Alternative source used only if useAlternativeSource is set to true. Default is close
linecolor : : zigzag line color
linewidth : : zigzag line width
linestyle : : zigzag line style
oscillatorSource : : Oscillator source for calculating divergence
directionBias : : Direction bias for calculating divergence
showHighLow : : show highlow label
showRatios : : show retracement ratios
showDivergence : : Show divergence on label (Only works if divergence data is available - that is if we pass valid oscillatorSource and directionBias input)
Returns: zigzagpivots : Array containing zigzag pivots
zigzagpivotbars : Array containing zigzag pivot bars
zigzagpivotdirs : Array containing zigzag pivot directions (Lower High : 1, Higher High : 2, Lower Low : -2 and Higher Low : -1)
zigzagpivotratios : Array containing zigzag retracement ratios for each pivot
zigzagoscillators : Array of oscillator values at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagoscillatordirs: Array of oscillator directions (HH, HL, LH, LL) at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagtrendbias : Array of trend bias at pivots. Will have valid value only if directionBias series is sent in input parameters
zigzagdivergence : Array of divergence sentiment at each pivot. Will have valid values only if oscillatorSource and directionBias inputs are provided
zigzaglines : Returns array of zigzag lines
zigzaglabels : Returns array of zigzag labels
LibraryPrivateUsage001This is a public library that include the functions explained below. The libraries are considered public domain code and permission is not required from the author if you reuse these functions in your open-source scripts
