Thange VaultLibrary "ThangeVault"
Thange Vault is a collection of utility functions required by the Thange Woodwind Playbook.
debug(msg) Print debug information
Parameters:
msg : message to be logged on console
Returns: nothing
tickFormat() Create a string template to restrict stop-loss, take-profit level precision to ticks.
Returns: A string format template
Chỉ báo và chiến lược
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.
psonPineScript Object Notation
A workaround not having objects in pinescript.
This is a Json-look-alike interpreter.
Format: "attr=value:attr1=value1:attr2=value2".
You can add new attributes, get the value in those attributes, set new values to existing attributes and check if an attribute exists.
DivergenceLibrary "Divergence"
Calculates a divergence between 2 series
bullish(_src, _low, depth) Calculates bullish divergence
Parameters:
_src : Main series
_low : Comparison series (`low` is used if no argument is supplied)
depth : Fractal Depth (`2` is used if no argument is supplied)
Returns: 2 boolean values for regular and hidden divergence
bearish(_src, _high, depth) Calculates bearish divergence
Parameters:
_src : Main series
_high : Comparison series (`high` is used if no argument is supplied)
depth : Fractal Depth (`2` is used if no argument is supplied)
Returns: 2 boolean values for regular and hidden divergence
I created this library to plug and play divergences in any code.
You can create a divergence indicator from any series you like.
Fractals are used to pinpoint the edge of the series. The higher the depth, the slower the divergence updates get.
My Plain Stochastic Divergence uses the same calculation. Watch it in action.
CRCIndicators - Common IndicatorsLibrary "CRCIndicators"
price_from_to()
price_change_from_to()
roi()
roi_from_to()
The Divergent LibraryLibrary "TheDivergentLibrary"
The Divergent Library is only useful when combined with the Pro version of The Divergent - Advanced divergence indicator . This is because the Basic (free) version of The Divergent does not expose the "Divergence Signal" value.
Usage instructions:
1. Create a new chart
2. Add The Divergent (Pro) indicator to your chart
3. Create a new strategy, import this library, add a "source" input, link it to "The Divergent: Divergence Signal", and use the library to decode the divergence signals from The Divergent (You can find example strategy code published in our profile)
4. Act on the divergences signalled by The Divergent
---
isRegularBullishEnabled(context) Returns a boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
isHiddenBullishEnabled(context) Returns a boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
isRegularBearishEnabled(context) Returns a boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
isHiddenBearishEnabled(context) Returns a boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
getPivotDetectionSource(context) Returns the 'Pivot Detection Source' setting of The Divergent. The returned value can be either "Oscillator" or "Price".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Oscillator" or "Price".
getPivotDetectionMode(context) Returns the 'Pivot Detection Mode' setting of The Divergent. The returned value can be either "Bodies" or "Wicks".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Bodies" or "Wicks".
isLinked(context) Returns a boolean value indicating the link status to The Divergent indicator.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating the link status to The Divergent indicator.
init(firstBarSignal, displayLinkStatus, debug) Initialises The Divergent Library's context with the signal produced by The Divergent on the first bar. The value returned from this function is called the "context of The Divergent Library". Some of the other functions of this library requires you to pass in this context.
Parameters:
firstBarSignal : The signal from The Divergent indicator on the first bar.
displayLinkStatus : A boolean value indicating whether the Link Status window should be displayed in the bottom left corner of the chart. Defaults to true.
debug : A boolean value indicating whether the Link Status window should display debug information. Defaults to false.
Returns: A bool array containing the context of The Divergent Library.
processSignal(signal) Processes a signal from The Divergent and returns a 5-tuple with the decoded signal: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd]. `divergenceType` can be one of the following values: na → No divergence was detected, 1 → Regular Bullish, 2 → Regular Bullish early, 3 → Hidden Bullish, 4 → Hidden Bullish early, 5 → Regular Bearish, 6 → Regular Bearish early, 7 → Hidden Bearish, 8 → Hidden Bearish early.
Parameters:
signal : The signal from The Divergent indicator.
Returns: A 5-tuple with the following values: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd].
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.
Signal_transcoder_libraryLibrary "Signal_transcoder_library"
This is my 2nd iteration for sending Signals via Plots. (first one was the 8bit Version)
Now a cleaner approach (thanks for the hints @lonesometheblue)
_16bit_encode()
Input a 16 bool Array
Outputs a Float for transmitting via Plot
_16bit_decode()
Input a Float from plot via input-mapping
Outputs a Array of 16 bools
Future Ideas:
Transmitting 2-4 Ints (-127 to 127) and Bools
InterpolationLibrary "Interpolation"
Functions for interpolating values. Can be useful in signal processing or applied as a sigmoid function.
linear(k, delta, offset, unbound) Returns the linear adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the line the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
quadIn(k, delta, offset, unbound) Returns the quadratic (easing-in) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
quadOut(k, delta, offset, unbound) Returns the quadratic (easing-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
quadInOut(k, delta, offset, unbound) Returns the quadratic (easing-in-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
cubicIn(k, delta, offset, unbound) Returns the cubic (easing-in) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
cubicOut(k, delta, offset, unbound) Returns the cubic (easing-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
cubicInOut(k, delta, offset, unbound) Returns the cubic (easing-in-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
expoIn(k, delta, offset, unbound) Returns the exponential (easing-in) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
expoOut(k, delta, offset, unbound) Returns the exponential (easing-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
expoInOut(k, delta, offset, unbound) Returns the exponential (easing-in-out) adjusted value.
Parameters:
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
using(fn, k, delta, offset, unbound) Returns the adjusted value by function name.
Parameters:
fn : The name of the function. Allowed values: linear, quadIn, quadOut, quadInOut, cubicIn, cubicOut, cubicInOut, expoIn, expoOut, expoInOut.
k : A number (float) from 0 to 1 representing where the on the curve the value is.
delta : The amount the value should change as k reaches 1.
offset : The start value.
unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
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.
CRC.lib Log FunctionsLibrary "CRCLog"
default_params() Returns default high/low intercept/slope parameter values for Bitcoin that can be adjusted and used to calculate new Regression Log lines
log_regression() Returns set of (fib) spaced lines representing log regression (default values attempt fitted to INDEX:BTCUSD genesis-2021)
Library_RICHLibrary "Library_RICH"
TODO: add library description here
sum(x) TODO: add function description here
Parameters:
x : TODO: add parameter x description here
Returns: TODO: add what function returns
checkBuyReversal() : ckeck if there are buy reversal conditions (divergences) in rsi
insure that the bar count since the last pivot low is within the specified range (min- and max range)
ckeck if there is a higher-low in rsi and lower-low in the price
(doubel) ckeck if buy reversal condition is true
Parameters:
: : rsi value, pivot left lenght, pivot right lenght, min. and max. range
Returns: : two values: bar-reversal-condition (true) and pivot low if is true
checkBuyContinuation() : ckeck if there are buy cintinuation conditions (hidden divergences) in rsi
insure that the bar count since the last pivot low is within the specified range (min- and max range)
ckeck if there is a lower-low in rsi and higher-low in the price
(doubel) ckeck if buy continuation condition is true
Parameters:
: : rsi value, pivot left lenght, pivot right lenght, min. and max. range
Returns: : two results: bar-reversal-condition (true) and pivot low, if is true
checkSellReversal() : ckeck if there are sell reversal conditions (divergences) in rsi
insure that the bar count since the last pivot high is within the specified range (min- and max range)
ckeck if there is a lower-high in rsi and higher-high in the price
(doubel) ckeck if sell reversal condition is true
Parameters:
: : rsi value, pivot left lenght, pivot right lenght, min. and max. range
Returns: : two results: sell-reversal-condition (true) and pivot high, if is true
checkSellContinuation() : ckeck if there are sell cointunuation conditions (divergences) in rsi
insure that the bar count since the last pivot high is within the specified range (min- and max range)
ckeck if there is a higher-high in rsi and lower-high in the price
(doubel) ckeck if sell continuation condition is true
Parameters:
: : rsi value, pivot left lenght, pivot right lenght, min. and max. range
Returns: : two results: sell-continuation-condition (true) and pivot high, if is true
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.
DailyDeviationLibrary "DailyDeviation"
Helps in determining the relative deviation from the open of the day compared to the high or low values.
hlcDeltaArrays(daysPrior, maxDeviation, spec, res) Retuns a set of arrays representing the daily deviation of price for a given number of days.
Parameters:
daysPrior : Number of days back to get the close from.
maxDeviation : Maximum deviation before a value is considered an outlier. A value of 0 will not filter results.
spec : session.regular (default), session.extended or other time spec.
res : The resolution (default = '1440').
Returns: Where OH = Open vs High, OL = Open vs Low, and OC = Open vs Close
fromOpen(daysPrior, maxDeviation, comparison, spec, res) Retuns a value representing the deviation from the open (to the high or low) of the current day given number of days to measure from.
Parameters:
daysPrior : Number of days back to get the close from.
maxDeviation : Maximum deviation before a value is considered an outlier. A value of 0 will not filter results.
comparison : The value use in comparison to the current open for the day.
spec : session.regular (default), session.extended or other time spec.
res : The resolution (default = '1440').
CRC.lib Bars - Bar FunctionsLibrary "CRCBars"
min_max(open, open) Get bar min (low) and max (high) price points
Parameters:
open : Open price data
open : Close price data
Returns:
is_bullish_bearish(open, open) Get bar bullish/bearish boolean signals
Parameters:
open : Open price data
open : Close price data
Returns:
sizes(open, open, open, open) Get bar sizes based on open/high/low/close data
Parameters:
open : Open price data
open : High price data
open : Low price data
open : Close price data
Returns:
CRC.lib Characters and StringsLibrary "CRCChars"
arrow_up() : ▲
arrow_down() : ▼
warning() : ⚠
checkmark() : ✅
no_entry() : 🚫
CRCPaintLibrary "CRCPaint"
black(trans)
Parameters:
trans : Transparency value (float)
Returns: color
-------------------------------------------------------------------------- //
white()
silver()
gray()
fuchsia()
maroon()
red()
orange()
yellow()
blue()
navy()
aqua()
purple()
teal()
green()
lime()
olive()
malachite()
fern()
feldgrau()
skobeloff()
viridian()
violet()
denim()
saphhire()
cyan()
auburn()
pink()
tawny()
rust()
goldenrod()
mahogany()
boysenberry()
mauve()
cosmos()
sepia()
jazzberry()
wenge()
idx_mix()
idx_mix_size()
transparent()
rgb()
shade_mint()
shade_blush()
random()
options_expiration_and_strike_price_calculatorLibrary "options_expiration_and_strike_price_calculator"
TODO: add library description here
fun()
this is a library to help calculate options strike price and expiration that you can add to a script i use it mainly for symbol calulation to place orders to buy options on TD ameritrade so it will be set up to order options on TD ameritrade using json order placer and webhook it fills in the area in the json under symbol i suggest manually adding the year it should look like this is an example of an order to buy 10 call options using json through td ameritrade api
"complexOrderStrategyType": "NONE",
"orderType": "LIMIT",
"session": "NORMAL",
"price": "6.45",
"duration": "DAY",
"orderStrategyType": "SINGLE",
"orderLegCollection":
}
ConverterTFLibrary "ConverterTF"
I have found a bug Regarding the timeframe display, on the chart I have found that the display is numeric, for example 4Hr timeframe instead of '4H', but it turns out to be '240', which I want it to be displayed in abbreviated form. And in all other timeframes it's the same. So this library was created to solve those problems. It converts a timeframe from a numeric string type to an integer type by selecting a timeframe manually and displaying it on the chart.
CTF()
str = "240"
X.GetTF( str )
Example
str = input.timeframe(title='Time frame', defval='240')
TimeF = CTF(str)
L=label.new(bar_index, high, 'Before>> Timeframe '+str+' After>> Timeframe '+TimeF,style=label.style_label_down,size=size.large)
label.delete(L )
Custom timeframes can handle this issue as well.
An example from the use. You will find it on the bottom right hand side.
Hopefully it will be helpful to the Tradingview community. :)
TradingPortfolioLibrary "TradingPortfolio"
Simple functions for portfolio management. A portfolio is essentially
a float array with 3 positions that gets passed around
into these functions that ensure it gets properly updated as trading ensues.
An example usage:
import hugodanielcom/TradingPortfolio/XXXX as portfolio
var float my_portfolio = portfolio.init(0.0, strategy.initial_capital) // Initialize the portfolio with the strategy capital
if close < 10.0
portfolio.buy(my_portfolio, 10.0, close) // Buy when the close is below 10.0
plot(portfolio.total(my_portfolio), title = "Total portfolio value")
get_balance(portfolio) Gets the number of tokens and fiat available in the supplied portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The tokens and fiat in a tuple
set_balance(portfolio, new_crypto, new_fiat) Sets the portfolio number of tokens and fiat amounts. This function overrides the current values in the portfolio and sets the provided ones as the new portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
new_crypto : The new amount of tokens in the portfolio.
new_fiat : The new amount of fiat in the portfolio
Returns: The tokens and fiat in a tuple
init(crypto, fiat) This function returns a clean portfolio. Start by calling this function and pass its return value as an argument to the other functions in this library.
Parameters:
crypto : The initial amount of tokens in the portfolio (defaults to 0.0).
fiat : The initial amount of fiat in the portfolio (defaults to 0.0).
Returns: The portfolio (a float )
crypto(portfolio) Gets the number of tokens in the portfolio
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of tokens in the portfolio
fiat(portfolio) Gets the fiat in the portfolio
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of fiat in the portfolio
retained(portfolio) Gets the amount of reatined fiat in the portfolio. Retained fiat is not considered as part of the balance when buying/selling, but it is considered as part of the total of the portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of retained fiat in the portfolio
retain(portfolio, fiat_to_retain) Sets the amount of fiat to retain. It removes the amount from the current fiat in the portfolio and marks it as retained.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
fiat_to_retain : The amount of fiat to remove and mark as retained.
Returns: void
total(portfolio, token_value) Calculates the total fiat value of the portfolio. It multiplies the amount of tokens by the supplied value and adds to the result the current fiat and retained amount.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
token_value : The fiat value of a unit (1) of token
Returns: A float that corresponds to the total fiat value of the portfolio (retained amount included)
ratio(portfolio, token_value) Calculates the ratio of tokens / fiat. The retained amount of fiat is not considered, only the active fiat being considered for trading.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
token_value : The fiat value of a unit (1) of token
Returns: A float between 1.0 and 0.0 that corresponds to the portfolio ratio of token / fiat (i.e. 0.6 corresponds to a portfolio whose value is made by 60% tokens and 40% fiat)
can_buy(portfolio, amount, token_value) Asserts that there is enough balance to buy the requested amount of tokens.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to assert that can be bought
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true if there is capacity to buy the amount of tokens provided.
can_sell(portfolio, amount) Asserts that there is enough token balance to sell the requested amount of tokens.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to assert that can be sold
Returns: A boolean value, true if there is capacity to sold the amount of tokens provided.
buy(portfolio, amount, token_value) Adjusts the portfolio state to perform the equivalent of a buy operation (as in, buy the requested amount of tokens at the provided value and set the portfolio accordingly).
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to buy
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true the requested amount of tokens was "bought" and the portfolio updated. False if nothing was changed.
sell(portfolio, amount, token_value) Adjusts the portfolio state to perform the equivalent of a sell operation (as in, sell the requested amount of tokens at the provided value and set the portfolio accordingly).
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to sell
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true the requested amount of tokens was "sold" and the portfolio updated. False if nothing was changed.
lib_Indicators_v2_DTULibrary "lib_Indicators_v2_DTU"
This library functions returns included Moving averages, indicators with factorization, functions candles, function heikinashi and more.
Created it to feed as backend of my indicator/strategy "Indicators & Combinations Framework Advanced v2 " that will be released ASAP.
This is replacement of my previous indicator (lib_indicators_DT)
I will add an indicator example which will use this indicator named as "lib_indicators_v2_DTU example" to help the usage of this library
Additionally library will be updated with more indicators in the future
NOTES:
Indicator functions returns only one series :-(
plotcandle function returns candle series
INDICATOR LIST:
hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage)
alma = 'alma(src,len,offset=0.85,sigma=6)', //Arnaud Legoux Moving Average
ama = 'ama(src,len,fast=14,slow=100)', //Adjusted Moving Average
acdst = 'accdist()', //Accumulation/distribution index.
cma = 'cma(src,len)', //Corrective Moving average
dema = 'dema(src,len)', //Double EMA (Same as EMA with 2 factor)
ema = 'ema(src,len)', //Exponential Moving Average
gmma = 'gmma(src,len)', //Geometric Mean Moving Average
hghst = 'highest(src,len)', //Highest value for a given number of bars back.
hl2ma = 'hl2ma(src,len)', //higest lowest moving average
hma = 'hma(src,len)', //Hull Moving Average.
lgAdt = 'lagAdapt(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter
lgAdV = 'lagAdaptV(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation
lguer = 'laguerre(src,len)', //Ehler's Laguerre filter
lsrcp = 'lesrcp(src,len)', //lowest exponential esrcpanding moving line
lexp = 'lexp(src,len)', //lowest exponential expanding moving line
linrg = 'linreg(src,len,loffset=1)', //Linear regression
lowst = 'lowest(src,len)', //Lovest value for a given number of bars back.
pcnl = 'percntl(src,len)', //percentile nearest rank. Calculates percentile using method of Nearest Rank.
pcnli = 'percntli(src,len)', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
rema = 'rema(src,len)', //Range EMA (REMA)
rma = 'rma(src,len)', //Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
sma = 'sma(src,len)', //Smoothed Moving Average
smma = 'smma(src,len)', //Smoothed Moving Average
supr2 = 'super2(src,len)', //Ehler's super smoother, 2 pole
supr3 = 'super3(src,len)', //Ehler's super smoother, 3 pole
strnd = 'supertrend(src,len,period=3)', //Supertrend indicator
swma = 'swma(src,len)', //Sine-Weighted Moving Average
tema = 'tema(src,len)', //Triple EMA (Same as EMA with 3 factor)
tma = 'tma(src,len)', //Triangular Moving Average
vida = 'vida(src,len)', //Variable Index Dynamic Average
vwma = 'vwma(src,len)', //Volume Weigted Moving Average
wma = 'wma(src,len)', //Weigted Moving Average
angle = 'angle(src,len)', //angle of the series (Use its Input as another indicator output)
atr = 'atr(src,len)', //average true range. RMA of true range.
bbr = 'bbr(src,len,mult=1)', //bollinger %%
bbw = 'bbw(src,len,mult=2)', //Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci = 'cci(src,len)', //commodity channel index
cctbb = 'cctbbo(src,len)', //CCT Bollinger Band Oscilator
chng = 'change(src,len)', //Difference between current value and previous, source - source .
cmo = 'cmo(src,len)', //Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog = 'cog(src,len)', //The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio.
cpcrv = 'copcurve(src,len)', //Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962).
corrl = 'correl(src,len)', //Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values.
count = 'count(src,len)', //green avg - red avg
dev = 'dev(src,len)', //ta.dev() Measure of difference between the series and it's ta.sma
fall = 'falling(src,len)', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
kcr = 'kcr(src,len,mult=2)', //Keltner Channels Range
kcw = 'kcw(src,len,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
macd = 'macd(src,len)', //macd
mfi = 'mfi(src,len)', //Money Flow Index
nvi = 'nvi()', //Negative Volume Index
obv = 'obv()', //On Balance Volume
pvi = 'pvi()', //Positive Volume Index
pvt = 'pvt()', //Price Volume Trend
rise = 'rising(src,len)', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc = 'roc(src,len)', //Rate of Change
rsi = 'rsi(src,len)', //Relative strength Index
smosc = 'smi_osc(src,len,fast=5, slow=34)', //smi Oscillator
smsig = 'smi_sig(src,len,fast=5, slow=34)', //smi Signal
stdev = 'stdev(src,len)', //Standart deviation
trix = 'trix(src,len)' , //the rate of change of a triple exponentially smoothed moving average.
tsi = 'tsi(src,len)', //True Strength Index
vari = 'variance(src,len)', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean.
wilpc = 'willprc(src,len)', //Williams %R
wad = 'wad()', //Williams Accumulation/Distribution.
wvad = 'wvad()' //Williams Variable Accumulation/Distribution.
}
f_func(string, float, simple, float, float, float, simple) f_func Return selected indicator value with different parameters. New version. Use extra parameters for available indicators
Parameters:
string : FuncType_ indicator from the indicator list
float : src_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
Returns: float Return calculated indicator value
fn_heikin(float, float, float, float) fn_heikin Return given src data (open, high,low,close) as heikin ashi candle values
Parameters:
float : o_ open value
float : h_ high value
float : l_ low value
float : c_ close value
Returns: float heikin ashi open, high,low,close vlues that will be used with plotcandle
fn_plotFunction(float, string, simple, bool) fn_plotFunction Return input src data with different plotting options
Parameters:
float : src_ indicator src_data or any other series.....
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
Returns: float
fn_funcPlotV2(string, float, simple, float, float, float, simple, string, simple, bool, bool) fn_funcPlotV2 Return selected indicator value with different parameters. New version. Use extra parameters fora available indicators
Parameters:
string : FuncType_ indicator from the indicator list
float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
Returns: float Return calculated indicator value
fn_factor(string, float, simple, float, float, float, simple, simple, string, simple, bool, bool) fn_factor Return selected indicator's factorization with given arguments
Parameters:
string : FuncType_ indicator from the indicator list
float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
simple : int fact_ Add double triple, Quatr factor to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...)
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
Returns: float Return result of the function
fn_plotCandles(string, simple, float, float, float, simple, string, simple, bool, bool, bool) fn_plotCandles Return selected indicator's candle values with different parameters also heikinashi is available
Parameters:
string : FuncType_ indicator from the indicator list
simple : int length_ indicator length
float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
bool : plotheikin_ Use Heikin Ashi on Plot
Returns: float
Last Available Bar InfoLibrary "Last_Available_Bar_Info"
getLastBarTimeStamp()
getAvailableBars()
This simple library is built with an aim of getting the last available bar information for the chart. This returns a constant value that doesn't change on bar change.
For backtesting with accurate results on non standard charts, it will be helpful. (Especially if you are using non standard charts like Renko Chart).
Methods
getLastBarTimeStamp()
: Returns Timestamp of the last available bar (Constant)
getAvailableBars()
:Returns Number of Available Bars on the chart (Constant)
Example
import paragjyoti2012/Last_Available_Bar_Info/v1 as LastBarInfo
last_bar_timestamp=LastBarInfo.getLastBarTimeStamp()
no_of_bars=LastBarInfo.getAvailableBars()
If you are using Renko Charts, for backtesting, it's necesary to filter out the historical bars that are not of this timeframe.
In Renko charts, once the available bars of the current timeframe (based on your Tradingview active plan) are exhausted,
previous bars are filled in with historical bars of higher timeframe. Which is detrimental for backtesting, and it leads to unrealistic results.
To get the actual number of bars available of that timeframe, you should use this security function to get the timestamp for the last (real) bar available.
tf=timeframe.period
real_available_bars = request.security(syminfo.ticker, tf , LastBarInfo.getAvailableBars() , lookahead = barmerge.lookahead_off)
last_available_bar_timestamp = request.security(syminfo.ticker, tf , LastBarInfo.getLastBarTimeStamp() , lookahead = barmerge.lookahead_off)
