windowing_taAll Signals Are the Sum of Sines. When looking at real-world signals, you usually view them as a price changing over time. This is referred to as the time domain. Fourier’s theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines. For example, take two sine waves, where one is three times as fast as the other–or the frequency is 1/3 the first signal. When you add them, you can see you get a different signal.
Although performing an FFT on a signal can provide great insight, it is important to know the limitations of the FFT and how to improve the signal clarity using windowing. When you use the FFT to measure the frequency component of a signal, you are basing the analysis on a finite set of data. The actual FFT transform assumes that it is a finite data set, a continuous spectrum that is one period of a periodic signal. For the FFT, both the time domain and the frequency domain are circular topologies, so the two endpoints of the time waveform are interpreted as though they were connected together. When the measured signal is periodic and an integer number of periods fill the acquisition time interval, the FFT turns out fine as it matches this assumption. However, many times, the measured signal isn’t an integer number of periods. Therefore, the finiteness of the measured signal may result in a truncated waveform with different characteristics from the original continuous-time signal, and the finiteness can introduce sharp transition changes into the measured signal. The sharp transitions are discontinuities.
When the number of periods in the acquisition is not an integer, the endpoints are discontinuous. These artificial discontinuities show up in the FFT as high-frequency components not present in the original signal. These frequencies can be much higher than the Nyquist frequency and are aliased between 0 and half of your sampling rate. The spectrum you get by using a FFT, therefore, is not the actual spectrum of the original signal, but a smeared version. It appears as if energy at one frequency leaks into other frequencies. This phenomenon is known as spectral leakage, which causes the fine spectral lines to spread into wider signals.
You can minimize the effects of performing an FFT over a noninteger number of cycles by using a technique called windowing. Windowing reduces the amplitude of the discontinuities at the boundaries of each finite sequence acquired by the digitizer. Windowing consists of multiplying the time record by a finite-length window with an amplitude that varies smoothly and gradually toward zero at the edges. This makes the endpoints of the waveform meet and, therefore, results in a continuous waveform without sharp transitions. This technique is also referred to as applying a window.
Here is a windowing_ta library with J.F Ehlers Windowing functions proposed on Sep, 2021.
Library "windowing_ta"
hann()
hamm()
fir_sma()
fir_triangle()
指標和策略
harmonicpatterns1Library "harmonicpatterns1"
harmonicpatterns: methods required for calculation of harmonic patterns. Correction for library (missing export in line 303)
isGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isGartleyPattern: Checks for harmonic pattern Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Gartley. False otherwise.
isBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isBatPattern: Checks for harmonic pattern Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Bat. False otherwise.
isButterflyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isButterflyPattern: Checks for harmonic pattern Butterfly
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Butterfly. False otherwise.
isCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isCrabPattern: Checks for harmonic pattern Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Crab. False otherwise.
isDeepCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isDeepCrabPattern: Checks for harmonic pattern DeepCrab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is DeepCrab. False otherwise.
isCypherPattern(xabRatio, axcRatio, xadRatio, err_min, err_max) isCypherPattern: Checks for harmonic pattern Cypher
Parameters:
xabRatio : AB/XA
axcRatio : XC/AX
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Cypher. False otherwise.
isSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isSharkPattern: Checks for harmonic pattern Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Shark. False otherwise.
isNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNenStarPattern: Checks for harmonic pattern Nenstar
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Nenstar. False otherwise.
isAntiNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiNenStarPattern: Checks for harmonic pattern Anti NenStar
Parameters:
xabRatio : - AB/XA
abcRatio : - BC/AB
bcdRatio : - CD/BC
xadRatio : - AD/XA
err_min : - Minumum error threshold
err_max : - Maximum error threshold
Returns: True if the pattern is Anti NenStar. False otherwise.
isAntiSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiSharkPattern: Checks for harmonic pattern Anti Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Shark. False otherwise.
isAntiCypherPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCypherPattern: Checks for harmonic pattern Anti Cypher
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Cypher. False otherwise.
isAntiCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCrabPattern: Checks for harmonic pattern Anti Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Crab. False otherwise.
isAntiButterflyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiButterflyPattern: Checks for harmonic pattern Anti Butterfly
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Butterfly. False otherwise.
isAntiBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiBatPattern: Checks for harmonic pattern Anti Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Bat. False otherwise.
isAntiGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiGartleyPattern: Checks for harmonic pattern Anti Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Gartley. False otherwise.
isNavarro200Pattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNavarro200Pattern: Checks for harmonic pattern Navarro200
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Navarro200. False otherwise.
isHarmonicPattern(x, a, c, c, d, flags, errorPercent) isHarmonicPattern: 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
TS_FFALibrary "TS_FFA"
Splits the ticker and generates best configs for FP and PP
splitter(x) Splits the ticker and found the configuration regarding to name.
Parameters:
_x: ticker
Returns: Fib and Profit percent values
- Splitter had been added.
- USDTPERP coins on Binance had been added
- timeFrameMultiplier() timeframe multiplier had been added to the library
- timeframe period value fixed
- Changed timezone multiplier
- Changed VWMA Percent values
bytimeLibrary "bytime"
TODO: to do something at the specified time.
////Return =>> ht = hour , mt = minute , st = second ,Dt = Day, Mt = month, Yt = year , dateTime = full time format./////////////
Note : Remember to always add import when you call our library and change Gtime() to Timeset.Gtime() is used to access internal data.
import hapharmonic/bytime/1 as Timeset
=Timeset.Gtime()
/////////////Set a time to trigger an alert./////////////
ck = false
///hour : minute : second
if ht == TH and mt == TM and st == TS
//some action
//...
//.
ck := true
Gtime()
[e2] Drawing Library :: Horizontal Ray█ OVERVIEW
Library "e2hray"
A drawing library that contains the hray() function, which draws a horizontal ray/s with an initial point determined by a specified condition. It plots a ray until it reached the price. The function let you control the visibility of historical levels and setup the alerts.
█ HORIZONTAL RAY FUNCTION
hray(condition, level, color, extend, hist_lines, alert_message, alert_delay, style, hist_style, width, hist_width)
Parameters:
condition : Boolean condition that defines the initial point of a ray
level : Ray price level.
color : Ray color.
extend : (optional) Default value true, current ray levels extend to the right, if false - up to the current bar.
hist_lines : (optional) Default value true, shows historical ray levels that were revisited, default is dashed lines. To avoid alert problems set to 'false' before creating alerts.
alert_message : (optional) Default value string(na), if declared, enables alerts that fire when price revisits a line, using the text specified
alert_delay : (optional) Default value int(0), number of bars to validate the level. Alerts won't trigger if the ray is broken during the 'delay'.
style : (optional) Default value 'line.style_solid'. Ray line style.
hist_style : (optional) Default value 'line.style_dashed'. Historical ray line style.
width : (optional) Default value int(1), ray width in pixels.
hist_width : (optional) Default value int(1), historical ray width in pixels.
Returns: void
█ EXAMPLES
• Example 1. Single horizontal ray from the dynamic input.
//@version=5
indicator("hray() example :: Dynamic input ray", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
inputTime = input.time(timestamp("20 Jul 2021 00:00 +0300"), "Date", confirm = true)
inputPrice = input.price(54, 'Price Level', confirm = true)
e2draw.hray(time == inputTime, inputPrice, color.blue, alert_message = 'Ray level re-test!')
var label mark = label.new(inputTime, inputPrice, 'Selected point to start the ray', xloc.bar_time)
• Example 2. Multiple horizontal rays on the moving averages cross.
//@version=5
indicator("hray() example :: MA Cross", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
float sma1 = ta.sma(close, 20)
float sma2 = ta.sma(close, 50)
bullishCross = ta.crossover( sma1, sma2)
bearishCross = ta.crossunder(sma1, sma2)
plot(sma1, 'sma1', color.purple)
plot(sma2, 'sma2', color.blue)
// 1a. We can use 2 function calls to distinguish long and short sides.
e2draw.hray(bullishCross, sma1, color.green, alert_message = 'Bullish Cross Level Broken!', alert_delay = 10)
e2draw.hray(bearishCross, sma2, color.red, alert_message = 'Bearish Cross Level Broken!', alert_delay = 10)
// 1b. Or a single call for both.
// e2draw.hray(bullishCross or bearishCross, sma1, bullishCross ? color.green : color.red)
• Example 3. Horizontal ray at the all time highs with an alert.
//@version=5
indicator("hray() example :: ATH", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
var float ath = 0, ath := math.max(high, ath)
bool newAth = ta.change(ath)
e2draw.hray(nz(newAth ), high , color.orange, alert_message = 'All Time Highs Tested!', alert_delay = 10)
TAExtLibrary "TAExt"
Indicator functions can be used in other indicators and strategies. This will be extended by time with indicators I use in my strategies and studies.
atrwo(length, stdev_length, stdev_mult) ATR without outliers
Parameters:
length : The length of the ATR
stdev_length : The length of the standard deviation, used for detecting outliers
stdev_mult : The multiplier of the standard deviation, used for detecting outliers
Returns: The ATR value
atrwma(src, period, type, atr_length, stdev_length, stdev_mult) ATR without outlier weighted moving average
Parameters:
src : The source of the moving average
period : The period of the moving average
type : The type of the moving average, possible values: SMA, EMA, RMA
atr_length : The length of the ATR
stdev_length : The length of the standard deviation, used for detecting outliers
stdev_mult : The multiplier of the standard deviation, used for detecting outliers
Returns: The moving average value
jma(src, period, phase, power) Jurik Moving Average
Parameters:
src : The source of the moving average
period : The period of the moving average calculation
phase : The phase of jurik MA calculation (-100..100)
power : The power of jurik MA calculation
Returns: The Jurik MA series
anyma(src, period, type, offset, sigma, phase, power) Moving Average by type
Parameters:
src : The source of the moving average
period : The period of the moving average calculation
type : The type of the moving average
offset : Used only by ALMA, it is the ALMA offset
sigma : Used only by ALMA, it is the ALMA sigma
phase : The phase of jurik MA calculation (-100..100)
power : The power of jurik MA calculation
Returns: The moving average series
wae(macd_src, macd_fast_length, macd_slow_length, macd_sensitivity, bb_base_src, bb_upper_src, bb_lower_src, bb_length, bb_mult, dead_zone_length, dead_zone_mult) Waddah Attar Explosion (WAE)
Parameters:
macd_src : The source series used by MACD
macd_fast_length : The fast MA length of the MACD
macd_slow_length : The slow MA length of the MACD
macd_sensitivity : The MACD diff multiplier
bb_base_src : The source used by stdev
bb_upper_src : The source used by the upper Bollinger Band
bb_lower_src : The source used by the lower Bollinger Band
bb_length : The lenth for Bollinger Bands
bb_mult : The multiplier for Bollinger Bands
dead_zone_length : The ATR length for dead zone calculation
dead_zone_mult : The ATR multiplier for dead zone
Returns:
ssl(length, high_src, low_src) Semaphore Signal Level channel (SSL)
Parameters:
length : The length of the moving average
high_src : Source of the high moving average
low_src : Source of the low moving average
Returns:
adx(atr_length, di_length, adx_length, high_src, low_src, atr_ma_type, di_ma_type, adx_ma_type) Average Directional Index + Direction Movement Index (ADX + DMI)
Parameters:
atr_length : The length of ATR
di_length : DI plus and minus smoothing length
adx_length : ADX smoothing length
high_src : Source of the high moving average
low_src : Source of the low moving average
atr_ma_type : MA type of the ATR calculation
di_ma_type : MA type of the DI calculation
adx_ma_type : MA type of the ADX calculation
Returns:
FunctionCosineSimilarityLibrary "FunctionCosineSimilarity"
Cosine Similarity method.
function(sample_a, sample_b) Measure the similarity of 2 vectors.
Parameters:
sample_a : float array, values.
sample_b : float array, values.
Returns: float.
diss(cosim) Dissimilarity helper function.
Parameters:
cosim : float, cosine similarity value (0 > 1)
Returns: float
historicalrangeLibrary "historicalrange"
Library provices a method to calculate historical percentile range of series.
hpercentrank(source) calculates historical percentrank of the source
Parameters:
source : Source for which historical percentrank needs to be calculated. Source should be ranging between 0-100. If using a source which can beyond 0-100, use short term percentrank to baseline them.
Returns: pArray - percentrank array which contains how many instances of source occurred at different levels.
upperPercentile - percentile based on higher value
lowerPercentile - percentile based on lower value
median - median value of the source
max - max value of the source
distancefromath(source) returns stats on historical distance from ath in terms of percentage
Parameters:
source : for which stats are calculated
Returns: percentile and related historical stats regarding distance from ath
distancefromma(maType, length, source) returns stats on historical distance from moving average in terms of percentage
Parameters:
maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median
length : Moving Average Length
source : for which stats are calculated
Returns: percentile and related historical stats regarding distance from ath
bpercentb(source, maType, length, multiplier, sticky) returns percentrank and stats on historical bpercentb levels
Parameters:
source : Moving Average Source
maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median
length : Moving Average Length
multiplier : Standard Deviation multiplier
sticky : - sticky boundaries which will only change when value is outside boundary.
Returns: percentile and related historical stats regarding Bollinger Percent B
kpercentk(source, maType, length, multiplier, useTrueRange, sticky) returns percentrank and stats on historical kpercentk levels
Parameters:
source : Moving Average Source
maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median
length : Moving Average Length
multiplier : Standard Deviation multiplier
useTrueRange : - if set to false, uses high-low.
sticky : - sticky boundaries which will only change when value is outside boundary.
Returns: percentile and related historical stats regarding Keltener Percent K
dpercentd(useAlternateSource, alternateSource, length, sticky) returns percentrank and stats on historical dpercentd levels
Parameters:
useAlternateSource : - Custom source is used only if useAlternateSource is set to true
alternateSource : - Custom source
length : - donchian channel length
sticky : - sticky boundaries which will only change when value is outside boundary.
Returns: percentile and related historical stats regarding Donchian Percent D
oscillator(type, length, shortLength, longLength, source, highSource, lowSource, method, highlowLength, sticky) oscillator - returns Choice of oscillator with custom overbought/oversold range
Parameters:
type : - oscillator type. Valid values : cci, cmo, cog, mfi, roc, rsi, stoch, tsi, wpr
length : - Oscillator length - not used for TSI
shortLength : - shortLength only used for TSI
longLength : - longLength only used for TSI
source : - custom source if required
highSource : - custom high source for stochastic oscillator
lowSource : - custom low source for stochastic oscillator
method : - Valid values for method are : sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median
highlowLength : - length on which highlow of the oscillator is calculated
sticky : - overbought, oversold levels won't change unless crossed
Returns: percentile and related historical stats regarding oscillator
ZxLibraryLibrary "ZxLibrary"
ZxLibrary is an easy with more than 130 Indicators and more than 60 Candlestick Patterns.
dc_taAdaptive technical indicators are importants in a non stationary market, the ability to adapt to a situation can boost the efficiency of your strategy. A lot of methods have been proposed to make technical indicators "smarters", the dominant cycle tuned indicators are one of them which are based on J.F.Ehlers theory. Here is a collections of algorithms to calculate dominant cycles. ENJOY!
Library "dc_ta"
bton()
EhlersHoDyDC()
EhlersPhAcDC()
EhlersDuDiDC()
EhlersCycPer()
EhlersCycPer2()
EhlersBPZC()
EhlersAutoPer()
EhlersHoDyDCE()
EhlersPhAcDCE()
EhlersDuDiDCE()
EhlersDFTDC()
EhlersDFTDC2()
ArrayOperationsLibrary "ArrayOperations"
Array element wise basic operations.
add(sample_a, sample_b) Adds sample_b to sample_a and returns a new array.
Parameters:
sample_a : values to be added to.
sample_b : values to add.
Returns: array with added results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
subtract(sample_a, sample_b) Subtracts sample_b from sample_a and returns a new array.
sample_a : values to be subtracted from.
sample_b : values to subtract.
Returns: array with subtracted results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
multiply(sample_a, sample_b) multiply sample_a by sample_b and returns a new array.
sample_a : values to multiply.
sample_b : values to multiply with.
Returns: array with multiplied results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
divide(sample_a, sample_b) Divide sample_a by sample_b and returns a new array.
sample_a : values to divide.
sample_b : values to divide with.
Returns: array with divided results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
power(sample_a, sample_b) power sample_a by sample_b and returns a new array.
sample_a : values to power.
sample_b : values to power with.
Returns: float array with power results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
remainder(sample_a, sample_b) Remainder sample_a by sample_b and returns a new array.
sample_a : values to remainder.
sample_b : values to remainder with.
Returns: array with remainder results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
equal(sample_a, sample_b) Check element wise sample_a equals sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
not_equal(sample_a, sample_b) Check element wise sample_a not equals sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
over_or_equal(sample_a, sample_b) Check element wise sample_a over or equals sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
under_or_equal(sample_a, sample_b) Check element wise sample_a under or equals sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
over(sample_a, sample_b) Check element wise sample_a over sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
under(sample_a, sample_b) Check element wise sample_a under sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
and_(sample_a, sample_b) Check element wise sample_a and sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
or_(sample_a, sample_b) Check element wise sample_a or sample_b and returns a new array.
sample_a : values to check.
sample_b : values to check.
Returns: int array with results.
- sample_a provides type format for output.
- arrays do not need to be symmetric.
- sample_a must have same or more elements than sample_b
all(sample) Check element wise if all numeric samples are true (!= 0).
sample : values to check.
Returns: int.
any(sample) Check element wise if any numeric samples are true (!= 0).
sample : values to check.
Returns: int.
Logger Library For Pinescript (Logging and Debugging)Library "LoggerLib"
This is a logging library for Pinescript. It is aimed to help developers testing and debugging scripts with a simple to use logger function.
Pinescript lacks a native logging implementation. This library would be helpful to mitigate this insufficiency.
This library uses table to print outputs into its view. It is simple, customizable and robust.
You can start using it's .log() method just like any other logging method in other languages.
//////////////////
USAGE
//////////////////
-- Recommended: Please Read The Documentation From Source Code Below. It Is Much More Readable There And Will Be Updated Along With Newer Versions. --
Importing the Library
---------------------
import paragjyoti2012/LoggerLib/ as Logger
.init() : Initializes the library and returns the logger pointer. (Later will be used as a function parameter)
.initTable: Initializes the Table View for the Logger and returns the table id. (Later will be used as a function parameter)
parameters:
logger: The logger pointer got from .init()
max_rows_count: Number of Rows to display in the Logger Table (default is 10)
offset: The offset value for the rows (Used for scrolling the view)
position: Position of the Table View
Values could be:
left
right
top-right
(default is left)
size: Font Size of content
Values could be:
small
normal
large
(default is small)
hide_date: Whether to hide the Date/Time column in the Logger (default is false)
returns: Table
example usage of .initTable()
import paragjyoti2012/LoggerLib/1 as Logger
var logger=Logger.init()
var logTable=Logger.initTable(logger, max_rows_count=20, offset=0, position="top-right")
-------------------
LOGGING
-------------------
.log() : Logging Method
params: (string message, |string| logger, table table_id, string type="message")
logger: pass the logger pointer from .init()
table_id: pass the table pointer from .initTable()
message: The message to log
type: Type of the log message
Values could be:
message
warning
error
info
success
(default is message)
returns: void
///////////////////////////////////////
Full Boilerplate For Using In Indicator
///////////////////////////////////////
P.S: Change the | (pipe) character into square brackets while using in script (or copy it from the source code instead)
offset=input.int(0,"Offset",minval=0)
size=input.string("small","Font Size",options=|"normal","small","large"|)
rows=input.int(15,"No Of Rows")
position=input.string("left","Position",options=|"left","right","top-right"|)
hide_date=input.bool(false,"Hide Time")
import paragjyoti2012/LoggerLib/1 as Logger
var logger=Logger.init()
var logTable=Logger.initTable(logger,rows,offset,position,size,hide_date)
rsi=ta.rsi(close,14)
|macd,signal,hist|=ta.macd(close,12,26,9)
if(ta.crossunder(close,34000))
Logger.log("Dropped Below 34000",logger,logTable,"warning")
if(ta.crossunder(close,35000))
Logger.log("Dropped Below 35000",logger,logTable)
if(ta.crossover(close,38000))
Logger.log("Crossed 38000",logger,logTable,"info")
if(ta.crossunder(rsi,20))
Logger.log("RSI Below 20",logger,logTable,"error")
if(ta.crossover(macd,signal))
Logger.log("Macd Crossed Over Signal",logger,logTable)
if(ta.crossover(rsi,80))
Logger.log("RSI Above 80",logger,logTable,"success")
////////////////////////////
// For Scrolling the Table View
////////////////////////////
There is a subtle way of achieving nice scrolling behaviour for the Table view. Open the input properties panel for the table/indicator. Focus on the input field for "Offset", once it's focused, you could use your mouse scroll wheel to increment/decrement the offset values; It will smoothly scroll the Logger Table Rows as well.
/////////////////////
For any assistance using this library or reporting issues, please write in the comment section below.
I will try my best to guide you and update the library. Thanks :)
/////////////////////
FunctionWeekofmonthLibrary "FunctionWeekofmonth"
Week of Month function.
weekofmonth(utime) Week of month for provided unix time.
Parameters:
utime : int, unix timestamp.
Returns: int
interval_taA pine V5 library with several functions to handle time and sessions in trading.
Library "interval_ta"
bton()
tir()
nbs()
ismarket()
isclose()
dow()
tp1_timestamp()
datetime()
noldo_taI'm a follower of Noldo, and I've learned almost all of his published scripts. I like some of the basic functions he wrote so much that I decided to collect them as a noldo_ta library file to share. Most of these functions are the same as Noldo's version, and there are some interesting algorithmic processing, which I also encapsulated into functions. Enjoy.
COURTESY OF NOLDO for these intersting functions!
Library "noldo_ta"
bton()
f_ema()
f_highest()
f_lowest()
f_rma()
f_rsi()
f_stoch()
f_kdj()
f_sum()
f_sma()
f_stdev()
f_bb()
f_pearson_corr()
f_multiple_corr()
f_adjusted_r_squared()
f_mfi()
dow()
pivothl()
f_adjusted_r_squared2()
linreg()
f_roc()
f_macd()
f_mom()
f_wma()
f_hull()
f_vwma()
f_obv()
f_sar()
f_stochastic()
f_stochrsi()
f_stochmfi()
f_kst()
f_smahist()
f_emahist()
f_fisher()
f_ao()
f_accdist()
f_highestbars()
f_lowestbars()
WIPNNetworkLibrary "WIPNNetwork"
this is a work in progress (WIP) and prone to have some errors, so use at your own risk...
let me know if you find any issues..
Method for a generalized Neural Network.
network(x) Generalized Neural Network Method.
Parameters:
x : TODO: add parameter x description here
Returns: TODO: add what function returns
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.
ObjectStackLibrary "ObjectStack"
init()
push()
push()
push()
push()
push()
nextIndex()
nextIndex()
nextIndex()
nextIndex()
nextIndex()
delete()
delete()
delete()
delete()
delete()
cleanOldest()
cleanOldest()
cleanOldest()
cleanOldest()
cleanOldest()
BjCandlePatternsLibrary "BjCandlePatterns"
Patterns is a Japanese candlestick pattern recognition Library for developers. Functions here within detect viable setups in a variety of popular patterns. Please note some patterns are without filters such as comparisons to average candle sizing, or trend detection to allow the author more freedom.
doji(dojiSize, dojiWickSize) Detects "Doji" candle patterns
Parameters:
dojiSize : (float) The relationship of body to candle size (ie. body is 5% of total candle size). Default is 5.0 (5%)
dojiWickSize : (float) Maximum wick size comparative to the opposite wick. (eg. 2 = bottom wick must be less than or equal to 2x the top wick). Default is 2
Returns: (series bool) True when pattern detected
dLab(showLabel, labelColor, textColor) Produces "Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bullEngulf(maxRejectWick, mustEngulfWick) Detects "Bullish Engulfing" candle patterns
Parameters:
maxRejectWick : (float) Maximum rejection wick size.
The maximum wick size as a percentge of body size allowable for a top wick on the resolution candle of the pattern. 0.0 disables the filter.
eg. 50 allows a top wick half the size of the body. Default is 0% (Disables wick detection).
mustEngulfWick : (bool) input to only detect setups that close above the high prior effectively engulfing the candle in its entirety. Default is false
Returns: (series bool) True when pattern detected
bewLab(showLabel, labelColor, textColor) Produces "Bullish Engulfing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bearEngulf(maxRejectWick, mustEngulfWick) Detects "Bearish Engulfing" candle patterns
Parameters:
maxRejectWick : (float) Maximum rejection wick size.
The maximum wick size as a percentge of body size allowable for a bottom wick on the resolution candle of the pattern. 0.0 disables the filter.
eg. 50 allows a botom wick half the size of the body. Default is 0% (Disables wick detection).
mustEngulfWick : (bool) Input to only detect setups that close below the low prior effectively engulfing the candle in its entirety. Default is false
Returns: (series bool) True when pattern detected
bebLab(showLabel, labelColor, textColor) Produces "Bearish Engulfing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
hammer(ratio, shadowPercent) Detects "Hammer" candle patterns
Parameters:
ratio : (float) The relationship of body to candle size (ie. body is 33% of total candle size). Default is 33%.
shadowPercent : (float) The maximum allowable top wick size as a percentage of body size. Default is 5%.
Returns: (series bool) True when pattern detected
hLab(showLabel, labelColor, textColor) Produces "Hammer" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
star(ratio, shadowPercent) Detects "Star" candle patterns
Parameters:
ratio : (float) The relationship of body to candle size (ie. body is 33% of total candle size). Default is 33%.
shadowPercent : (float) The maximum allowable bottom wick size as a percentage of body size. Default is 5%.
Returns: (series bool) True when pattern detected
ssLab(showLabel, labelColor, textColor) Produces "Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
dragonflyDoji() Detects "Dragonfly Doji" candle patterns
Returns: (series bool) True when pattern detected
ddLab(showLabel, labelColor) Produces "Dragonfly Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
Returns: (series label) A label visible at the chart level intended for the title pattern
gravestoneDoji() Detects "Gravestone Doji" candle patterns
Returns: (series bool) True when pattern detected
gdLab(showLabel, labelColor, textColor) Produces "Gravestone Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tweezerBottom(closeUpperHalf) Detects "Tweezer Bottom" candle patterns
Parameters:
closeUpperHalf : (bool) input to only detect setups that close above the mid-point of the candle prior increasing its bullish tendancy. Default is false
Returns: (series bool) True when pattern detected
tbLab(showLabel, labelColor, textColor) Produces "Tweezer Bottom" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tweezerTop(closeLowerHalf) Detects "TweezerTop" candle patterns
Parameters:
closeLowerHalf : (bool) input to only detect setups that close below the mid-point of the candle prior increasing its bearish tendancy. Default is false
Returns: (series bool) True when pattern detected
ttLab(showLabel, labelColor, textColor) Produces "TweezerTop" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTopBull(wickSize) Detects "Bullish Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stwLab(showLabel, labelColor, textColor) Produces "Bullish Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTopBear(wickSize) Detects "Bearish Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stbLab(showLabel, labelColor, textColor) Produces "Bearish Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTop(wickSize) Detects "Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stLab(showLabel, labelColor, textColor) Produces "Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
morningStar() Detects "Bullish Morning Star" candle patterns
Returns: (series bool) True when pattern detected
msLab(showLabel, labelColor, textColor) Produces "Bullish Morning Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
eveningStar() Detects "Bearish Evening Star" candle patterns
Returns: (series bool) True when pattern detected
esLab(showLabel, labelColor, textColor) Produces "Bearish Evening Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBull() Detects "Bullish Harami" candle patterns
Returns: (series bool) True when pattern detected
hwLab(showLabel, labelColor, textColor) Produces "Bullish Harami" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBear() Detects "Bearish Harami" candle patterns
Returns: (series bool) True when pattern detected
hbLab(showLabel, labelColor, textColor) Produces "Bearish Harami" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBullCross() Detects "Bullish Harami Cross" candle patterns
Returns: (series bool) True when pattern detected
hcwLab(showLabel, labelColor, textColor) Produces "Bullish Harami Cross" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBearCross() Detects "Bearish Harami Cross" candle patterns
Returns: (series bool) True when pattern detected
hcbLab(showLabel, labelColor) Produces "Bearish Harami Cross" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
Returns: (series label) A label visible at the chart level intended for the title pattern
marubullzu() Detects "Bullish Marubozu" candle patterns
Returns: (series bool) True when pattern detected
mwLab(showLabel, labelColor, textColor) Produces "Bullish Marubozu" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
marubearzu() Detects "Bearish Marubozu" candle patterns
Returns: (series bool) True when pattern detected
mbLab(showLabel, labelColor, textColor) Produces "Bearish Marubozu" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
abandonedBull() Detects "Bullish Abandoned Baby" candle patterns
Returns: (series bool) True when pattern detected
abwLab(showLabel, labelColor, textColor) Produces "Bullish Abandoned Baby" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
abandonedBear() Detects "Bearish Abandoned Baby" candle patterns
Returns: (series bool) True when pattern detected
abbLab(showLabel, labelColor, textColor) Produces "Bearish Abandoned Baby" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
piercing() Detects "Piercing" candle patterns
Returns: (series bool) True when pattern detected
pLab(showLabel, labelColor, textColor) Produces "Piercing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
darkCloudCover() Detects "Dark Cloud Cover" candle patterns
Returns: (series bool) True when pattern detected
dccLab(showLabel, labelColor, textColor) Produces "Dark Cloud Cover" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tasukiBull() Detects "Upside Tasuki Gap" candle patterns
Returns: (series bool) True when pattern detected
utgLab(showLabel, labelColor, textColor) Produces "Upside Tasuki Gap" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tasukiBear() Detects "Downside Tasuki Gap" candle patterns
Returns: (series bool) True when pattern detected
dtgLab(showLabel, labelColor, textColor) Produces "Downside Tasuki Gap" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
risingThree() Detects "Rising Three Methods" candle patterns
Returns: (series bool) True when pattern detected
rtmLab(showLabel, labelColor, textColor) Produces "Rising Three Methods" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
fallingThree() Detects "Falling Three Methods" candle patterns
Returns: (series bool) True when pattern detected
ftmLab(showLabel, labelColor, textColor) Produces "Falling Three Methods" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
risingWindow() Detects "Rising Window" candle patterns
Returns: (series bool) True when pattern detected
rwLab(showLabel, labelColor, textColor) Produces "Rising Window" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
fallingWindow() Detects "Falling Window" candle patterns
Returns: (series bool) True when pattern detected
fwLab(showLabel, labelColor, textColor) Produces "Falling Window" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
kickingBull() Detects "Bullish Kicking" candle patterns
Returns: (series bool) True when pattern detected
kwLab(showLabel, labelColor, textColor) Produces "Bullish Kicking" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
kickingBear() Detects "Bearish Kicking" candle patterns
Returns: (series bool) True when pattern detected
kbLab(showLabel, labelColor, textColor) Produces "Bearish Kicking" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
lls(ratio) Detects "Long Lower Shadow" candle patterns
Parameters:
ratio : (float) A relationship of the lower wick to the overall candle size expressed as a percent. Default is 75%
Returns: (series bool) True when pattern detected
llsLab(showLabel, labelColor, textColor) Produces "Long Lower Shadow" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
lus(ratio) Detects "Long Upper Shadow" candle patterns
Parameters:
ratio : (float) A relationship of the upper wick to the overall candle size expressed as a percent. Default is 75%
Returns: (series bool) True when pattern detected
lusLab(showLabel, labelColor, textColor) Produces "Long Upper Shadow" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bullNeck() Detects "Bullish On Neck" candle patterns
Returns: (series bool) True when pattern detected
nwLab(showLabel, labelColor, textColor) Produces "Bullish On Neck" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bearNeck() Detects "Bearish On Neck" candle patterns
Returns: (series bool) True when pattern detected
nbLab(showLabel, labelColor, textColor) Produces "Bearish On Neck" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
soldiers(wickSize) Detects "Three White Soldiers" candle patterns
Parameters:
wickSize : (float) Maximum allowable top wick size throughout pattern expressed as a percent of total candle height. Default is 5%
Returns: (series bool) True when pattern detected
wsLab(showLabel, labelColor, textColor) Produces "Three White Soldiers" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
crows(wickSize) Detects "Three Black Crows" candle patterns
Parameters:
wickSize : (float) Maximum allowable bottom wick size throughout pattern expressed as a percent of total candle height. Default is 5%
Returns: (series bool) True when pattern detected
bcLab(showLabel, labelColor, textColor) Produces "Three Black Crows" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
triStarBull() Detects "Bullish Tri-Star" candle patterns
Returns: (series bool) True when pattern detected
tswLab(showLabel, labelColor, textColor) Produces "Bullish Tri-Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
triStarBear() Detects "Bearish Tri-Star" candle patterns
Returns: (series bool) True when pattern detected
tsbLab(showLabel, labelColor, textColor) Produces "Bearish Tri-Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
wrap(cond, barsBack, borderColor, bgcolor) Produces a box wrapping the highs and lows over the look back.
Parameters:
cond : (series bool) Condition under which to draw the box.
barsBack : (series int) the number of bars back to begin drawing the box.
borderColor : (series color) Color of the four borders. Optional. The default is color.gray.
bgcolor : (series color) Background color of the box. Optional. The default is color.gray.
Returns: (series box) A box who's top and bottom are above and below the highest and lowest points over the lookback
topWick() returns the top wick size of the current candle
Returns: (series float) A value equivelent to the distance from the top of the candle body to its high
bottomWick() returns the bottom wick size of the current candle
Returns: (series float) A value equivelent to the distance from the bottom of the candle body to its low
body() returns the body size of the current candle
Returns: (series float) A value equivelent to the distance between the top and the bottom of the candle body
highestBody() returns the highest body of the current candle
Returns: (series float) A value equivelent to the highest body, whether it is the open or the close
lowestBody() returns the lowest body of the current candle
Returns: (series float) A value equivelent to the highest body, whether it is the open or the close
barRange() returns the height of the current candle
Returns: (series float) A value equivelent to the distance between the high and the low of the candle
bodyPct() returns the body size as a percent
Returns: (series float) A value equivelent to the percentage of body size to the overall candle size
midBody() returns the price of the mid-point of the candle body
Returns: (series float) A value equivelent to the center point of the distance bewteen the body low and the body high
bodyupGap() returns true if there is a gap up between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap up and no overlap in the real bodies of the current candle and the preceding candle
bodydwnGap() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
gapUp() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
gapDwn() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
dojiBody() returns true if the candle body is a doji
Returns: (series bool) true if the candle body is a doji. Defined by a body that is 5% of total candle size
statisticsLibrary "statistics"
General statistics library.
erf(x) The "error function" encountered in integrating the normal
distribution (which is a normalized form of the Gaussian function).
Parameters:
x : The input series.
Returns: The Error Function evaluated for each element of x.
erfc(x)
Parameters:
x : The input series
Returns: The Complementary Error Function evaluated for each alement of x.
sumOfReciprocals(src, len) Calculates the sum of the reciprocals of the series.
For each element 'elem' in the series:
sum += 1/elem
Should the element be 0, the reciprocal value of 0 is used instead
of NA.
Parameters:
src : The input series.
len : The length for the sum.
Returns: The sum of the resciprocals of 'src' for 'len' bars back.
mean(src, len) The mean of the series.
(wrapper around ta.sma).
Parameters:
src : The input series.
len : The length for the mean.
Returns: The mean of 'src' for 'len' bars back.
average(src, len) The mean of the series.
(wrapper around ta.sma).
Parameters:
src : The input series.
len : The length for the average.
Returns: The average of 'src' for 'len' bars back.
geometricMean(src, len) The Geometric Mean of the series.
The geometric mean is most important when using data representing
percentages, ratios, or rates of change. It cannot be used for
negative numbers
Since the pure mathematical implementation generates a very large
intermediate result, we performed the calculation in log space.
Parameters:
src : The input series.
len : The length for the geometricMean.
Returns: The geometric mean of 'src' for 'len' bars back.
harmonicMean(src, len) The Harmonic Mean of the series.
The harmonic mean is most applicable to time changes and, along
with the geometric mean, has been used in economics for price
analysis. It is more difficult to calculate; therefore, it is less
popular than eiter of the other averages.
0 values are ignored in the calculation.
Parameters:
src : The input series.
len : The length for the harmonicMean.
Returns: The harmonic mean of 'src' for 'len' bars back.
median(src, len) The median of the series.
(a wrapper around ta.median)
Parameters:
src : The input series.
len : The length for the median.
Returns: The median of 'src' for 'len' bars back.
variance(src, len, biased) The variance of the series.
Parameters:
src : The input series.
len : The length for the variance.
biased : Wether to use the biased calculation (for a population), or the
unbiased calculation (for a sample set). .
Returns: The variance of 'src' for 'len' bars back.
stdev(src, len, biased) The standard deviation of the series.
Parameters:
src : The input series.
len : The length for the stdev.
biased : Wether to use the biased calculation (for a population), or the
unbiased calculation (for a sample set). .
Returns: The standard deviation of 'src' for 'len' bars back.
skewness(src, len) The skew of the series.
Skewness measures the amount of distortion from a symmetric
distribution, making the curve appear to be short on the left
(lower prices) and extended to the right (higher prices). The
extended side, either left or right is called the tail, and a
longer tail to the right is called positive skewness. Negative
skewness has the tail extending towards the left.
Parameters:
src : The input series.
len : The length for the skewness.
Returns: The skewness of 'src' for 'len' bars back.
kurtosis(src, len) The kurtosis of the series.
Kurtosis describes the peakedness or flatness of a distribution.
This can be used as an unbiased assessment of whether prices are
trending or moving sideways. Trending prices will ocver a wider
range and thus a flatter distribution (kurtosis < 3; negative
kurtosis). If prices are range-bound, there will be a clustering
around the mean and we have positive kurtosis (kurtosis > 3)
Parameters:
src : The input series.
len : The length for the kurtosis.
Returns: The kurtosis of 'src' for 'len' bars back.
excessKurtosis(src, len) The normalized kurtosis of the series.
kurtosis > 0 --> positive kurtosis --> trending
kurtosis < 0 --> negative krutosis --> range-bound
Parameters:
src : The input series.
len : The length for the excessKurtosis.
Returns: The excessKurtosis of 'src' for 'len' bars back.
normDist(src, len, value) Calculates the probability mass for the value according to the
src and length. It calculates the probability for value to be
present in the normal distribution calculated for src and length.
Parameters:
src : The input series.
len : The length for the normDist.
value : The series of values to calculate the normal distance for
Returns: The normal distance of 'value' to 'src' for 'len' bars back.
normDistCumulative(src, len, value) Calculates the cumulative probability mass for the value according
to the src and length. It calculates the cumulative probability for
value to be present in the normal distribution calculated for src
and length.
Parameters:
src : The input series.
len : The length for the normDistCumulative.
value : The series of values to calculate the cumulative normal distance
for
Returns: The cumulative normal distance of 'value' to 'src' for 'len' bars
back.
zScore(src, len, value) Returns then z-score of objective to the series src.
It returns the number of stdev's the objective is away from the
mean(src, len)
Parameters:
src : The input series.
len : The length for the zScore.
value : The series of values to calculate the cumulative normal distance
for
Returns: The z-score of objectiv with respect to src and len.
er(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
efficiencyRatio(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
fractalEfficiency(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
mse(src, len) Calculates the Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the mean squared error.
Returns: The mean squared error of 'src' for 'len' bars back.
meanSquaredError(src, len) Calculates the Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the mean squared error.
Returns: The mean squared error of 'src' for 'len' bars back.
rmse(src, len) Calculates the Root Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the root mean squared error.
Returns: The root mean squared error of 'src' for 'len' bars back.
rootMeanSquaredError(src, len) Calculates the Root Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the root mean squared error.
Returns: The root mean squared error of 'src' for 'len' bars back.
mae(src, len) Calculates the Mean Absolute Error of the series.
Parameters:
src : The input series.
len : The length for the mean absolute error.
Returns: The mean absolute error of 'src' for 'len' bars back.
meanAbsoluteError(src, len) Calculates the Mean Absolute Error of the series.
Parameters:
src : The input series.
len : The length for the mean absolute error.
Returns: The mean absolute error of 'src' for 'len' bars back.
CRCHud - HUD Library (Heads Up Display)Library "CRCHud"
Library of functions which will contain functions that allow reusable HUD (Heads up Display) components to used from within other scripts
add_cell_change() - Adds a new cell to designated table which displays the data source value, the line color, data title, and automatically calculated %percent change stats based on lookback value supplied (default - previous bar)
LibraryCOT█ OVERVIEW
This library is a Pine programmer's tool that provides functions to access Commitment of Traders (COT) data for futures. Four of our scripts use it:
• Commitment of Traders: Legacy Metrics
• Commitment of Traders: Disaggregated Metrics
• Commitment of Traders: Financial Metrics
• Commitment of Traders: Total
If you do not program in Pine and want to use COT data, please see the indicators linked above.
█ CONCEPTS
Commitment of Traders (COT) data is tallied by the Commodity Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so COT data is available for assets that can be traded on CBOT, CME, NYMEX, COMEX, and ICEUS.
Accessing COT data from a Pine script requires the generation of a ticker ID string for use with request.security() . The ticker string must be encoded in a special format that includes both CFTC and TradingView-specific content. The format of the ticker IDs is somewhat complex; this library's functions make their generation easier. Note that if you know the COT ticker ID string for specific data, you can enter it from the chart's "Symbol Search" dialog box.
A ticker for COT data in Pine has the following structure:
COT:__<_metricDirection><_metricType>
where an underscore prefixing a component name inside <> is only included if the component is not a null string, and:
Is a digit representing the type of the COT report the data comes from: "" for legacy COT data, "2" for disaggregated data and "3" for financial data.
Is a six digit code that represents a commodity. Example: wheat futures (root "ZW") have the code "001602".
Is either "F" if the report data should exclude Options data, or "FO" if such data is included.
Is the TradingView code of the metric. This library's `metricNameAndDirectionToTicker()` function creates both
the and components of a COT ticker from the metric names and directions listed in the above chart.
The different metrics are explained in the CFTC's Explanatory Notes .
Is the direction of the metric: "Long", "Short", "Spreading" or "No direction".
Not all directions are applicable to all metrics. The valid ones are listed next to each metric in the above chart.
Is the type of the metric, possible values are "All", "Old" and "Other".
The difference between the types is explained in the "Old and Other Futures" section of the CFTC's Explanatory Notes .
As an example, the Legacy report Open Interest data for ZW futures (options included) in the old standard has the ticker "COT:001602_FO_OI_OLD". The same data using the current standard without futures has the ticker "COT:001602_F_OI".
█ USING THE LIBRARY
The first functions in the library are helper functions that generate components of a COT ticker ID. The last function, `COTTickerid()`, is the one that generates the full ticker ID string by calling some of the helper functions. We use it like this in our example:
exampleTicker = COTTickerid(
COTType = "Legacy",
CFTCCode = convertRootToCOTCode("Auto"),
includeOptions = false,
metricName = "Open Interest",
metricDirection = "No direction",
metricType = "All")
This library's chart displays the valid values for the `metricName` and `metricDirection` arguments. They vary for each of the three types of COT data (the `COTType` argument). The chart also displays the COT ticker ID string in the `exampleTicker` variable.
Look first. Then leap.
The library's functions are:
rootToCFTCCode(root)
Accepts a futures root and returns the relevant CFTC code.
Parameters:
root : Root prefix of the future's symbol, e.g. "ZC" for "ZC1!"" or "ZCU2021".
Returns: The part of a COT ticker corresponding to `root`, or "" if no CFTC code exists for the `root`.
currencyToCFTCCode(curr)
Converts a currency string to its corresponding CFTC code.
Parameters:
curr : Currency code, e.g., "USD" for US Dollar.
Returns: The corresponding to the currency, if one exists.
optionsToTicker(includeOptions)
Returns the part of a COT ticker using the `includeOptions` value supplied, which determines whether options data is to be included.
Parameters:
includeOptions : A "bool" value: 'true' if the symbol should include options and 'false' otherwise.
Returns: The part of a COT ticker: "FO" for data that includes options and "F" for data that doesn't.
metricNameAndDirectionToTicker(metricName, metricDirection)
Returns a string corresponding to a metric name and direction, which is one component required to build a valid COT ticker ID.
Parameters:
metricName : One of the metric names listed in this library's chart. Invalid values will cause a runtime error.
metricDirection : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction".
Valid values vary with metrics. Invalid values will cause a runtime error.
Returns: The part of a COT ticker ID string, e.g., "OI_OLD" for "Open Interest" and "No direction",
or "TC_L" for "Traders Commercial" and "Long".
typeToTicker(metricType)
Converts a metric type into one component required to build a valid COT ticker ID.
See the "Old and Other Futures" section of the CFTC's Explanatory Notes for details on types.
Parameters:
metricType : Metric type. Accepted values are: "All", "Old", "Other".
Returns: The part of a COT ticker.
convertRootToCOTCode(mode, convertToCOT)
Depending on the `mode`, returns a CFTC code using the chart's symbol or its currency information when `convertToCOT = true`.
Otherwise, returns the symbol's root or currency information. If no COT data exists, a runtime error is generated.
Parameters:
mode : A string determining how the function will work. Valid values are:
"Root": the function extracts the futures symbol root (e.g. "ES" in "ESH2020") and looks for its CFTC code.
"Base currency": the function extracts the first currency in a pair (e.g. "EUR" in "EURUSD") and looks for its CFTC code.
"Currency": the function extracts the quote currency ("JPY" for "TSE:9984" or "USDJPY") and looks for its CFTC code.
"Auto": the function tries the first three modes (Root -> Base Currency -> Currency) until a match is found.
convertToCOT : "bool" value that, when `true`, causes the function to return a CFTC code.
Otherwise, the root or currency information is returned. Optional. The default is `true`.
Returns: If `convertToCOT` is `true`, the part of a COT ticker ID string.
If `convertToCOT` is `false`, the root or currency extracted from the current symbol.
COTTickerid(COTType, CTFCCode, includeOptions, metricName, metricDirection, metricType)
Returns a valid TradingView ticker for the COT symbol with specified parameters.
Parameters:
COTType : A string with the type of the report requested with the ticker, one of the following: "Legacy", "Disaggregated", "Financial".
CTFCCode : The for the asset, e.g., wheat futures (root "ZW") have the code "001602".
includeOptions : A boolean value. 'true' if the symbol should include options and 'false' otherwise.
metricName : One of the metric names listed in this library's chart.
metricDirection : Direction of the metric, one of the following: "Long", "Short", "Spreading", "No direction".
metricType : Type of the metric. Possible values: "All", "Old", and "Other".
Returns: A ticker ID string usable with `request.security()` to fetch the specified Commitment of Traders data.
█ AVAILABLE METRICS
Different COT types provide different metrics. The table of all metrics available for each of the types can be found below.
+------------------------------+------------------------+
| Legacy (COT) Metric Names | Directions |
+------------------------------+------------------------+
| Open Interest | No direction |
| Noncommercial Positions | Long, Short, Spreading |
| Commercial Positions | Long, Short |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No direction |
| Traders Noncommercial | Long, Short, Spreading |
| Traders Commercial | Long, Short |
| Traders Total Reportable | Long, Short |
| Concentration Gross LT 4 TDR | Long, Short |
| Concentration Gross LT 8 TDR | Long, Short |
| Concentration Net LT 4 TDR | Long, Short |
| Concentration Net LT 8 TDR | Long, Short |
+------------------------------+------------------------+
+-----------------------------------+------------------------+
| Disaggregated (COT2) Metric Names | Directions |
+-----------------------------------+------------------------+
| Open Interest | No Direction |
| Producer Merchant Positions | Long, Short |
| Swap Positions | Long, Short, Spreading |
| Managed Money Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Producer Merchant | Long, Short |
| Traders Swap | Long, Short, Spreading |
| Traders Managed Money | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-----------------------------------+------------------------+
+-------------------------------+------------------------+
| Financial (COT3) Metric Names | Directions |
+-------------------------------+------------------------+
| Open Interest | No Direction |
| Dealer Positions | Long, Short, Spreading |
| Asset Manager Positions | Long, Short, Spreading |
| Leveraged Funds Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Dealer | Long, Short, Spreading |
| Traders Asset Manager | Long, Short, Spreading |
| Traders Leveraged Funds | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-------------------------------+------------------------+
FunctionBlackScholesLibrary "FunctionBlackScholes"
Some methods for the Black Scholes Options Model, which demonstrates several approaches to the valuation of a European call.
// reference:
// people.math.sc.edu
// people.math.sc.edu
asset_path(s0, mu, sigma, t1, n) Simulates the behavior of an asset price over time.
Parameters:
s0 : float, asset price at time 0.
mu : float, growth rate.
sigma : float, volatility.
t1 : float, time to expiry date.
n : int, time steps to expiry date.
Returns: option values at each equal timed step (0 -> t1)
binomial(s0, e, r, sigma, t1, m) Uses the binomial method for a European call.
Parameters:
s0 : float, asset price at time 0.
e : float, exercise price.
r : float, interest rate.
sigma : float, volatility.
t1 : float, time to expiry date.
m : int, time steps to expiry date.
Returns: option value at time 0.
bsf(s0, t0, e, r, sigma, t1) Evaluates the Black-Scholes formula for a European call.
Parameters:
s0 : float, asset price at time 0.
t0 : float, time at which the price is known.
e : float, exercise price.
r : float, interest rate.
sigma : float, volatility.
t1 : float, time to expiry date.
Returns: option value at time 0.
forward(e, r, sigma, t1, nx, nt, smax) Forward difference method to value a European call option.
Parameters:
e : float, exercise price.
r : float, interest rate.
sigma : float, volatility.
t1 : float, time to expiry date.
nx : int, number of space steps in interval (0, L).
nt : int, number of time steps.
smax : float, maximum value of S to consider.
Returns: option values for the european call, float array of size ((nx-1) * (nt+1)).
mc(s0, e, r, sigma, t1, m) Uses Monte Carlo valuation on a European call.
Parameters:
s0 : float, asset price at time 0.
e : float, exercise price.
r : float, interest rate.
sigma : float, volatility.
t1 : float, time to expiry date.
m : int, time steps to expiry date.
Returns: confidence interval for the estimated range of valuation.
