calcLibrary "calc"
Library for math functions. will expand over time.
split(_sumTotal, _divideBy, _forceMinimum, _haltOnError)
Split a large number into integer sized chunks
Parameters:
_sumTotal : (int) Total numbert of items
_divideBy : (int) Groups to make
_forceMinimum : (bool) force minimum number 1/group
_haltOnError : (bool) force error if too few groups
Returns: int array of items per group
Indicadores e estratégias
PointofControlLibrary "PointofControl"
POC_f()
The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies.
This is the indicator version of the library function.
A few things that would be unique with the built in
- it allows you to choose the kind of reset of the period, day/week or bars. This is simple enough to expand to other conditions
- it resets on bar count starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set
- A 'period' in this context is the time between resets - the start of the POC until it resets (for example at the beginning of a new day or week)
- it will calculate an increment level rather than the user specifying ticks or price brackets
- it does not allow for setting the # of rows and then calculating the implied price levels
- When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward.
- This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right
- This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs
There is a matching indicator to this library
FunctionDynamicTimeWarpingLibrary "FunctionDynamicTimeWarping"
"In time series analysis, dynamic time warping (DTW) is an algorithm for
measuring similarity between two temporal sequences, which may vary in
speed. For instance, similarities in walking could be detected using DTW,
even if one person was walking faster than the other, or if there were
accelerations and decelerations during the course of an observation.
DTW has been applied to temporal sequences of video, audio, and graphics
data — indeed, any data that can be turned into a linear sequence can be
analyzed with DTW. A well-known application has been automatic speech
recognition, to cope with different speaking speeds. Other applications
include speaker recognition and online signature recognition.
It can also be used in partial shape matching applications."
"Dynamic time warping is used in finance and econometrics to assess the
quality of the prediction versus real-world data."
~~ wikipedia
reference:
en.wikipedia.org
towardsdatascience.com
github.com
cost_matrix(a, b, w)
Dynamic Time Warping procedure.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: matrix optimum match matrix.
traceback(M)
perform a backtrace on the cost matrix and retrieve optimal paths and cost between arrays.
Parameters:
M : matrix, cost matrix.
Returns: tuple:
array aligned 1st array of indices.
array aligned 2nd array of indices.
float final cost.
reference:
github.com
report(a, b, w)
report ordered arrays, cost and cost matrix.
Parameters:
a : array, data series.
b : array, data series.
w : int , minimum window size.
Returns: string report.
loxxvarietyrsiLibrary "loxxvarietyrsi"
7 varieties of RSI used in Loxx's indicators and strategies. Default is "rsi_rsi" which is just TV's built int ta.rsi() function.
"rsi_rsi" is regular ta.rsi()
"rsi_slo" is slowed down version of regular RSI
"rsi_rap" is ta.rsi() but uses SMA instead of RMA, this is the same as Cuttlers RSI
"rsi_har" is Michael Harris RSI, but a word of "warning". I left the Harris' rsi in the choices of rsi, but be advised that, due to the way how Harris rsi is calculated, if price filtering is used (ie: some randomness is taken away from price series) Harris RSI tends to produce results that can be "surprising" at the least in some cases. Even though Harris RSI is good when it comes to natural (semi-random) price usage, keep in mind what happens if the prices are filtered and why it happens
"rsi_rsx" is Jurik's RSX
"rsi_cut" is ta.rsi() but uses SMA instead of RMA, this is the same as Rapid RSI
"rsi_ehl" is Ehles' Smoothed RSI
rsiVariety(rsiMode, src, per)
method for returning 1 of 7 different RSI calculation outputs.
Parameters:
rsiMode : string, rsi mode, can be 1 of 7 of the following: "rsi_rsi", "rsi_slo", "rsi_rap", "rsi_har", "rsi_rsx", "rsi_cut", "rsi_ehl"; defaults to "rsi_rsi"
src : float, source, either regular source type or some other caculated value.
per : int, period lookback.
Returns: float RSI.
usage:
rsiVariety("rsi_rsi", src, per)
EchoMorphicAverageLibrary "EchoMorphicAverage"
Original Self Referencing Moving Average which references
it's own output agsainst itself and the incoming source to dynamically
alter smoothness and length internally per calculation cycle.
@kaigouthro
Inputs are float length series.
Contact Me for More Dynamic Float Length Indicators.
wema(src, mod, len)
Waited Echo-Morphic Average
Parameters:
src : (float) input value
mod : (float) modifier(0-1) mix of current value
len : (float) length
Returns: output processed smoothed value
wemaStack(src, mod, len)
Stacked Multipass Waited Echo-Morphic Average
Parameters:
src : (float) input value
mod : (float) modifier(0-1) mix of current value
len : (float) length
Returns: output processed smoothed value
loxxdynamiczoneLibrary "loxxdynamiczone"
Dynamic Zones
Derives Leo Zamansky and David Stendahl's Dynamic Zone,
see "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
dZone(type, src, pval, per)
method for retrieving the dynamic zone levels from input source.
Parameters:
type : string, value of either 'buy' or 'sell'.
src : float, source, either regular source type or some other caculated value.
pval : float, probability defined by extension over/under source, a number <= 1.0.
per : int, period lookback.
Returns: float dynamic zone level.
usage:
dZone("buy", close, 0.2, 70)
FunctionKellyCriterionLibrary "FunctionKellyCriterion"
Kelly criterion methods.
the kelly criterion helps with the decision of how much one should invest in
a asset as long as you know the odds and expected return of said asset.
simplified(win_p, rr)
simplified version of the kelly criterion formula.
Parameters:
win_p : float, probability of winning.
rr : float, reward to risk rate.
Returns: float, optimal fraction to risk.
usage:
simplified(0.55, 1.0)
partial(win_p, loss_p, win_rr, loss_rr)
general form of the kelly criterion formula.
Parameters:
win_p : float, probability of the investment returns a positive outcome.
loss_p : float, probability of the investment returns a negative outcome.
win_rr : float, reward on a positive outcome.
loss_rr : float, reward on a negative outcome.
Returns: float, optimal fraction to risk.
usage:
partial(0.6, 0.4, 0.6, 0.1)
from_returns(returns)
Calculate the fraction to invest from a array of returns.
Parameters:
returns : array trade/asset/strategy returns.
Returns: float, optimal fraction to risk.
usage:
from_returns(array.from(0.1,0.2,0.1,-0.1,-0.05,0.05))
final_f(fraction, max_expected_loss)
Final fraction, eg. if fraction is 0.2 and expected max loss is 10%
then you should size your position as 0.2/0.1=2 (leverage, 200% position size).
Parameters:
fraction : float, aproximate percent fraction invested.
max_expected_loss : float, maximum expected percent on a loss (ex 10% = 0.1).
Returns: float, final fraction to invest.
usage:
final_f(0.2, 0.5)
hpr(fraction, trade, biggest_loss)
Holding Period Return function
Parameters:
fraction : float, aproximate percent fraction invested.
trade : float, profit or loss in a trade.
biggest_loss : float, value of the biggest loss on record.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
hpr(fraction=0.05, trade=0.1, biggest_loss=-0.2)
twr(returns, rr, eps)
Terminal Wealth Relative, returns a multiplier that can be applied
to the initial capital that leadds to the final balance.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, optimal fraction to invest.
usage:
twr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
ghpr(returns, rr, eps)
Geometric mean Holding Period Return, represents the average multiple made on the stake.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
ghpr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
run_coin_simulation(fraction, initial_capital, n_series, n_periods)
run multiple coin flipping (binary outcome) simulations.
Parameters:
fraction : float, fraction of capital to bet.
initial_capital : float, capital at the start of simulation.
n_series : int , number of simulation series.
n_periods : int , number of periods in each simulation series.
Returns: matrix(n_series, n_periods), matrix with simulation results per row.
usage:
run_coin_simulation(fraction=0.1)
run_asset_simulation(returns, fraction, initial_capital)
run a simulation over provided returns.
Parameters:
returns : array, trade, asset or strategy percent returns.
fraction : float , fraction of capital to bet.
initial_capital : float , capital at the start of simulation.
Returns: array, array with simulation results.
usage:
run_asset_simulation(returns=array.from(0.1,-0.2,0.-3,0.4), fraction=0.1)
strategy_win_probability()
calculate strategy() current probability of positive outcome in a trade.
strategy_avg_won()
calculate strategy() current average won on a trade with positive outcome.
strategy_avg_loss()
calculate strategy() current average lost on a trade with negative outcome.
MiteTricksLibrary "MiteTricks"
Matrix Global Registry.
Get, Set, automatic growing, universal get/set,
multi-matrix dictionaries, multi-dictionary matrixes..
add slice matrixes of any type, share one common global key registry
pull up an item from a category, and item name ie a table of info.
same cell needs a color, a size, a string, a value, etc..
all of which can be pulled up with the same group id, and key id.
just swap which matrix you pull the value from.
this has a side benefit of non-repainting and recalculating
when pulling values, changing inputs..
makes for very fast/clean usage..
benefit :
floats = value
strings = names
lines = drawn items
table =table of data items for this key
colors = color for line/table/fill,label..
all of those can be pulled with "get(_VALUES,_groupIDX,_keyIDX)" where only the values matrix needs be swapped, and the same item/coordinates remains for all the possible matrixes that item appears in.
also useful as a dictionary/registry for any given type of item,,
and goes very handy with floats/strings/colors/bools with my matrixautotable
very helpful when prototyping or doing development work as a shortcut.
initRegistry()
Registry inititalizer
Returns: registry of string matrix type
newbool(optional, optional, optional)
create bool type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is bool (na)
Returns: bool matrix of specified size and fill, or blank 2x2 for registry use
newbox(optional, optional, optional)
create box type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is box (na)
Returns: box matrix of specified size and fill, or blank 2x2 for registry use
newcolor(optional, optional, optional)
create color type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is color (na)
Returns: color matrix of specified size and fill, or blank 2x2 for registry use
newfloat(optional, optional, optional)
create float type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is float (na)
Returns: float matrix of specified size and fill, or blank 2x2 for registry use
newint(optional, optional, optional)
create int type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is int (na)
Returns: int matrix of specified size and fill, or blank 2x2 for registry use
newlabel(optional, optional, optional)
create label type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is label (na)
Returns: label matrix of specified size and fill, or blank 2x2 for registry use
newline(optional, optional, optional)
create line type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is line (na)
Returns: line matrix of specified size and fill, or blank 2x2 for registry use
newlinefill(optional, optional, optional)
create linefill type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is linefill(na)
Returns: linefill matrix of specified size and fill, or blank 2x2 for registry use
newstring(optional, optional, optional)
create string type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is string (na)
Returns: string matrix of specified size and fill, or blank 2x2 for registry use
newtable(optional, optional, optional)
create table type new matrix presized 2x2 for reg
Parameters:
optional: row size
optional: column size
optional: fill value(default is table (na)
Returns: table matrix of specified size and fill, or blank 2x2 for registry use
newfrom(INIT_FILL)
newfrom Matrix full of item input
Parameters:
INIT_FILL: item to fill (2x2) the matri and set type. a type(na) works
addrow(m, v)
addrow Add new row to matrix
Parameters:
m: matrix of type being added to
v: value of type being added to ( best leave NA on string for registry purposes)
addcolumn(matrix, value)
addcolumn
Parameters:
matrix: of type being added to
value: of type being added to ( best leave NA on string for registry purposes)
get(_VALS, _KEYREG, _GROUP, _KEY)
get Grabs value and returns single item
Parameters:
_VALS: Matrix Values slice
_KEYREG: Registry values matrix (strings)
_GROUP: name of group/category or int group key
_KEY: name of item to fetch from value registry or int key id
Returns: item
get(_VALS, _GROUP, _KEY)
get Grabs value and returns single item
Parameters:
_VALS: Matrix Values slice
_GROUP: name of group/category
_KEY: name of item to fetch from value registry
getgid(_KEYREG, _GROUP)
getgid
Parameters:
_KEYREG: Reg to pull group id from
_GROUP: group index int, or string name to get the other missing type
getkid(_KEYREG, _GROUP, _KEY)
getkid
Parameters:
_KEYREG: Reg to pull Key id from
_GROUP: group index int, or string name
_KEY: index of string key id to get it's ID int
getkey(_KEYREG, _GROUP, _KEY)
getkey
Parameters:
_KEYREG: Reg to pull Key id from
_GROUP: group index int, or string name for getting key string
_KEY: index of string key id to get it's match of other type
set(_VALS, _KEYREG, _GROUP, _KEY, _value)
set items to reg and matrix container
Parameters:
_VALS: Values matrix container
_KEYREG: Key registry
_GROUP: (string) Group/Category name
_KEY: (string) Key for item
_value: item
Returns: void
del(_VALS, _KEYREG, _GROUP, _KEY)
del grroup id
Parameters:
_VALS: Matrix Values slice
_KEYREG: Registry values matrix (strings)
_GROUP: name of group/category
_KEY: name of item to Delete from values and key
detached(_GROUP, _KEY, _VALUE)
detached make detached registry/val matrix
Parameters:
_GROUP: Name of first group
_KEY: Name of first item
_VALUE: Item of any type, sets the output type too.
pta_plotLibrary "pta_plot"
pta_plot: This library will help you to plot different value. I will keep updating with your requirement
print_array(array_id, border_color)
Display array element as a table.
Parameters:
array_id : Id of your array.
border_color : Color for border (`color.black` is used if no argument is supplied).
Returns: Display array element in bottom of the pane.
InfoTableLibrary "InfoTable"
Funzioni per tabella info
infoTable(nrRighe)
Tabella per info a n righe e 2 colonne
Parameters:
nrRighe : : num righe tabella; textColor : colore testo tabella : giallo (default), bianco, nero, blu
Returns: oggetto tabella per info
scriviCella()
McNichollBandsLibrary "McNichollBands"
This is a library which only functions to make the McNicholl's Bollinger Bands modifications. It's also my first library, so I'll probably screw some things up.
mcNichollBands(alpha, useLogScale, widthMultiplier)
Calculates the McNicholl's Bollinger Bands modifications.
Parameters:
alpha : The alpha constant to be used on the EMA calculations.
useLogScale : Whether to use the log version of the prices or not.
widthMultiplier : The number that shall be multiplied by the volatility to form the bands.
Returns: A tuple containing the lower band, the center line, and the upper band.
FunctionArrayUniqueLibrary "FunctionArrayUnique"
Method for retrieving the unique elements in a array.
for example would retrieve a array with ,
the elements retrieved will be sorted by its first seen index in
parent array.
note: float values have no precision option.
unique(source)
method for retrieving the unique elements in a array.
Parameters:
source : array source array to extract elements.
Returns: array unique elements in the source array.
unique(source)
method for retrieving the unique elements in a array.
Parameters:
source : array source array to extract elements.
Returns: array unique elements in the source array.
unique(source)
method for retrieving the unique elements in a array.
Parameters:
source : array source array to extract elements.
Returns: array unique elements in the source array.
VisibleChart█ OVERVIEW
This library is a Pine programmer’s tool containing functions that return values calculated from the range of visible bars on the chart.
This is now possible in Pine Script™ thanks to the recently-released chart.left_visible_bar_time and chart.right_visible_bar_time built-ins, which return the opening time of the leftmost and rightmost bars on the chart. These values update as traders scroll or zoom their charts, which gives way to a class of indicators that can dynamically recalculate and draw visuals on visible bars only, as users scroll or zoom their charts. We hope this library's functions help you make the most of the world of possibilities these new built-ins provide for Pine scripts.
For an example of a script using this library, have a look at the Chart VWAP indicator.
█ CONCEPTS
Chart properties
The new chart.left_visible_bar_time and chart.right_visible_bar_time variables return the opening time of the leftmost and rightmost bars on the chart. They are only two of many new built-ins in the `chart.*` namespace. See this blog post for more information, or look them up by typing "chart." in the Pine Script™ Reference Manual .
Dynamic recalculation of scripts on visible bars
Any script using chart.left_visible_bar_time or chart.right_visible_bar_time acquires a unique property, which triggers its recalculation when traders scroll or zoom their charts in such a way that the range of visible bars on the chart changes. This library's functions use the two recent built-ins to derive various values from the range of visible bars.
Designing your scripts for dynamic recalculation
For the library's functions to work correctly, they must be called on every bar. For reliable results, assign their results to global variables and then use the variables locally where needed — not the raw function calls.
Some functions like `barIsVisible()` or `open()` will return a value starting on the leftmost visible bar. Others such as `high()` or `low()` will also return a value starting on the leftmost visible bar, but their correct value can only be known on the rightmost visible bar, after all visible bars have been analyzed by the script.
You can plot values as the script executes on visible bars, but efficient code will, when possible, create resource-intensive labels, lines or tables only once in the global scope using var , and then use the setter functions to modify their properties on the last bar only. The example code included in this library uses this method.
Keep in mind that when your script uses chart.left_visible_bar_time or chart.right_visible_bar_time , your script will recalculate on all bars each time the user scrolls or zooms their chart. To provide script users with the best experience you should strive to keep calculations to a minimum and use efficient code so that traders are not always waiting for your script to recalculate every time they scroll or zoom their chart.
Another aspect to consider is the fact that the rightmost visible bar will not always be the last bar in the dataset. When script users scroll back in time, a large portion of the time series the script calculates on may be situated after the rightmost visible bar. We can never assume the rightmost visible bar is also the last bar of the time series. Use `barIsVisible()` to restrict calculations to visible bars, but also consider that your script can continue to execute past them.
Look first. Then leap.
█ FUNCTIONS
The library contains the following functions:
barIsVisible()
Condition to determine if a given bar is within the users visible time range.
Returns: (bool) True if the the calling bar is between the `chart.left_visible_bar_time` and the `chart.right_visible_bar_time`.
high()
Determines the value of the highest `high` in visible bars.
Returns: (float) The maximum high value of visible chart bars.
highBarIndex()
Determines the `bar_index` of the highest `high` in visible bars.
Returns: (int) The `bar_index` of the `high()`.
highBarTime()
Determines the bar time of the highest `high` in visible bars.
Returns: (int) The `time` of the `high()`.
low()
Determines the value of the lowest `low` in visible bars.
Returns: (float) The minimum low value of visible chart bars.
lowBarIndex()
Determines the `bar_index` of the lowest `low` in visible bars.
Returns: (int) The `bar_index` of the `low()`.
lowBarTime()
Determines the bar time of the lowest `low` in visible bars.
Returns: (int) The `time` of the `low()`.
open()
Determines the value of the opening price in the visible chart time range.
Returns: (float) The `open` of the leftmost visible chart bar.
close()
Determines the value of the closing price in the visible chart time range.
Returns: (float) The `close` of the rightmost visible chart bar.
leftBarIndex()
Determines the `bar_index` of the leftmost visible chart bar.
Returns: (int) A `bar_index`.
rightBarIndex()
Determines the `bar_index` of the rightmost visible chart bar.
Returns: (int) A `bar_index`
bars()
Determines the number of visible chart bars.
Returns: (int) The number of bars.
volume()
Determines the sum of volume of all visible chart bars.
Returns: (float) The cumulative sum of volume.
ohlcv()
Determines the open, high, low, close, and volume sum of the visible bar time range.
Returns: ( ) A tuple of the OHLCV values for the visible chart bars. Example: open is chart left, high is the highest visible high, etc.
chartYPct(pct)
Determines a price level as a percentage of the visible bar price range, which depends on the chart's top/bottom margins in "Settings/Appearance".
Parameters:
pct : (series float) Percentage of the visible price range (50 is 50%). Negative values are allowed.
Returns: (float) A price level equal to the `pct` of the price range between the high and low of visible chart bars. Example: 50 is halfway between the visible high and low.
chartXTimePct(pct)
Determines a time as a percentage of the visible bar time range.
Parameters:
pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.
Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost.
chartXIndexPct(pct)
Determines a `bar_index` as a percentage of the visible bar time range.
Parameters:
pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.
Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost.
whenVisible(src, whenCond, length)
Creates an array containing the `length` last `src` values where `whenCond` is true for visible chart bars.
Parameters:
src : (series int/float) The source of the values to be included.
whenCond : (series bool) The condition determining which values are included. Optional. The default is `true`.
length : (simple int) The number of last values to return. Optional. The default is all values.
Returns: (float ) The array ID of the accumulated `src` values.
avg(src)
Gathers values of the source over visible chart bars and averages them.
Parameters:
src : (series int/float) The source of the values to be averaged. Optional. Default is `close`.
Returns: (float) A cumulative average of values for the visible time range.
median(src)
Calculates the median of a source over visible chart bars.
Parameters:
src : (series int/float) The source of the values. Optional. Default is `close`.
Returns: (float) The median of the `src` for the visible time range.
vVwap(src)
Calculates a volume-weighted average for visible chart bars.
Parameters:
src : (series int/float) Source used for the VWAP calculation. Optional. Default is `hlc3`.
Returns: (float) The VWAP for the visible time range.
threengine_global_automation_libraryLibrary "threengine_global_automation_library"
A collection of functions used for trade automation
getBaseCurrency()
Gets the base currency for the chart's ticker. Supported trade pairs are USD, USDT, USDC, BTC, and PERP.
Returns: Base currency as a string
getChartSymbol()
Get the current chart's symbol without the base currency appended to it. Supported trade paris are USD, USDT, USDC, BTC, and PERP.
Returns: Ssymbol and base currency
getDecimals()
Calculates how many decimals are on the quote price of the current market
Returns: The current deimal places on the market quote price
checkVar()
Plot a string as a label on the chart to test variable value. Use str.tostring() for any variable that isn't a string.
Returns: Label with stringified variable
getStrategyAlertMessage()
Generates stringified JSON for a limit order that can be passed to the strategy alert_message for a long entry.
Returns: Stringifed JSON for a long entry
taGetAdx()
Calculates the Average Directional Index
Returns: The value of ADX as a float
taGetEma()
Calculates the EMA based on a type, source, and length. Supported types are EMA, SMA, RMA, and WMA.
Returns: The value of the selected EMA
isBetweenTwoTimes()
Checks to see if within a rage based on two times
@retunrs true/false boolean
getAllTradeIDs()
This gets all closed trades and open trades
@retunrs an array of all open and closed trade ID's
getOpenTradeIDs()
This gets all open trades
@retunrs an array of all open trade ID's
orderAlreadyExists()
This checks to see if a provided order id uses the getAllTradeIDs() function to check
@retunrs an array of all open and closed trade ID's
orderCurrentlyExists()
This checks to see if a provided order id uses the getAllTradeIDs() function to check
Returns: an array of all open and closed trade ID's
getContractCount()
calulates the number of contracts you can buy with a set amount of capital and a limit price
Returns: number of contracts you can buy based on amount of capital you want to use and a price
getLadderSteps()
Returns: array of ladder entry prices and amounts based on total amount you want to invest across all ladder rungs and either a range between ladderStart and LadderStop based on specificed number of ladderRungs OR ladderStart, ladderRungs, and LadderSpacingPercent
loxxrsxLibrary "loxxrsx"
loxxrsx: Jurik RSX
rsx(src, len)
rsx
Parameters:
src : float
len : int
Returns: result float
loxxpaaspecialLibrary "loxxpaaspecial"
loxxpaaspecial: Ehlers Phase Accumulation Dominant Cycle Period with multiplier and filter
paa(src, mult, filt)
(src, mult, filt)
Parameters:
src : float
mult : float
filt : float
Returns: result float
loxxfsrrdspfiltsLibrary "loxxfsrrdspfilts"
loxxfsrrdspfilts : FATL, SATL, RFTL, & RSTL Digital Signal Filters
fatl(src)
fatl
Parameters:
src : float
Returns: result float
rftl(src)
rftl
Parameters:
src : float
Returns: result float
satl(src)
satl
Parameters:
src : float
Returns: result float
rstl(src)
rstl
Parameters:
src : float
Returns: result float
loxxjuriktoolsLibrary "loxxjuriktools"
loxxjuriktools: Jurik tools used in Loxx's idicators and strategies
jcfbaux(src, len)
jcfbaux
Parameters:
src : float
len : int
Returns: result float
jcfb(src, len, len)
jcfb
Parameters:
src : float
len : int
len : int
Returns: result float
jurik_filt(src, len, phase)
jurik_filt
Parameters:
src : float
len : int
phase : float
Returns: result float
loxxexpandedsourcetypesLibrary "loxxexpandedsourcetypes"
Expanded source types used in Loxx's indicators and strategies.
rclose()
rClose: regular close
Returns: float
ropen()
rClose: regular open
Returns: float
rhigh()
rClose: regular high
Returns: float
rlow()
rClose: regular low
Returns: float
rmedian()
rClose: regular hl2
Returns: float
rtypical()
rClose: regular hlc3
Returns: float
rweighted()
rClose: regular hlcc4
Returns: float
raverage()
rClose: regular ohlc4
Returns: float
ravemedbody()
rClose: median body
Returns: float
rtrendb()
rClose: trend regular
Returns: float
rtrendbext()
rClose: trend extreme
Returns: float
haclose(haclose)
haclose: heiken-ashi close
Parameters:
haclose : float
Returns: float
haopen(haopen)
haopen: heiken-ashi open
Parameters:
haopen : float
Returns: float
hahigh(hahigh)
hahigh: heiken-ashi high
Parameters:
hahigh : float
Returns: float
halow(halow)
halow: heiken-ashi low
Parameters:
halow : float
Returns: float
hamedian(hamedian)
hamedian: heiken-ashi median
Parameters:
hamedian : float
Returns: float
hatypical(hatypical)
hatypical: heiken-ashi typical
Parameters:
hatypical : float
Returns: float
haweighted(haweighted)
haweighted: heiken-ashi weighted
Parameters:
haweighted : float
Returns: float
haaverage(haweighted)
haaverage: heiken-ashi average
Parameters:
haweighted : float
Returns: float
haavemedbody(haclose, haopen)
haavemedbody: heiken-ashi median body
Parameters:
haclose : float
haopen : float
Returns: float
hatrendb(haclose, haopen, hahigh, halow)
hatrendb: heiken-ashi trend
Parameters:
haclose : float
haopen : float
hahigh : float
halow : float
Returns: float
hatrendbext(haclose, haopen, hahigh, halow)
hatrendext: heiken-ashi trend extreme
Parameters:
haclose : float
haopen : float
hahigh : float
halow : float
Returns: float
habclose(smthtype, amafl, amasl, kfl, ksl)
habclose: heiken-ashi better open
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habopen(smthtype, amafl, amasl, kfl, ksl)
habopen: heiken-ashi better open
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habhigh(smthtype, amafl, amasl, kfl, ksl)
habhigh: heiken-ashi better high
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
hablow(smthtype, amafl, amasl, kfl, ksl)
hablow: heiken-ashi better low
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habmedian(smthtype, amafl, amasl, kfl, ksl)
habmedian: heiken-ashi better median
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habtypical(smthtype, amafl, amasl, kfl, ksl)
habtypical: heiken-ashi better typical
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habweighted(smthtype, amafl, amasl, kfl, ksl)
habweighted: heiken-ashi better weighted
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habaverage(smthtype, amafl, amasl, kfl, ksl)
habaverage: heiken-ashi better average
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habavemedbody(smthtype, amafl, amasl, kfl, ksl)
habavemedbody: heiken-ashi better median body
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habtrendb(smthtype, amafl, amasl, kfl, ksl)
habtrendb: heiken-ashi better trend
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
habtrendbext(smthtype, amafl, amasl, kfl, ksl)
habtrendbext: heiken-ashi better trend extreme
Parameters:
smthtype : string
amafl : int
amasl : int
kfl : int
ksl : int
Returns: float
loxxmas - moving averages used in Loxx's indis & stratsLibrary "loxxmas"
TODO:loxx moving averages used in indicators
kama(src, len, kamafastend, kamaslowend)
KAMA Kaufman adaptive moving average
Parameters:
src : float
len : int
kamafastend : int
kamaslowend : int
Returns: array
ama(src, len, fl, sl)
AMA, adaptive moving average
Parameters:
src : float
len : int
fl : int
sl : int
Returns: array
t3(src, len)
T3 moving average, adaptive moving average
Parameters:
src : float
len : int
Returns: array
adxvma(src, len)
ADXvma - Average Directional Volatility Moving Average
Parameters:
src : float
len : int
Returns: array
ahrma(src, len)
Ahrens Moving Average
Parameters:
src : float
len : int
Returns: array
alxma(src, len)
Alexander Moving Average - ALXMA
Parameters:
src : float
len : int
Returns: array
dema(src, len)
Double Exponential Moving Average - DEMA
Parameters:
src : float
len : int
Returns: array
dsema(src, len)
Double Smoothed Exponential Moving Average - DSEMA
Parameters:
src : float
len : int
Returns: array
ema(src, len)
Exponential Moving Average - EMA
Parameters:
src : float
len : int
Returns: array
fema(src, len)
Fast Exponential Moving Average - FEMA
Parameters:
src : float
len : int
Returns: array
hma(src, len)
Hull moving averge
Parameters:
src : float
len : int
Returns: array
ie2(src, len)
Early T3 by Tim Tilson
Parameters:
src : float
len : int
Returns: array
frama(src, len, FC, SC)
Fractal Adaptive Moving Average - FRAMA
Parameters:
src : float
len : int
FC : int
SC : int
Returns: array
instant(src, float)
Instantaneous Trendline
Parameters:
src : float
float : alpha
Returns: array
ilrs(src, int)
Integral of Linear Regression Slope - ILRS
Parameters:
src : float
int : len
Returns: array
laguerre(src, float)
Laguerre Filter
Parameters:
src : float
float : alpha
Returns: array
leader(src, int)
Leader Exponential Moving Average
Parameters:
src : float
int : len
Returns: array
lsma(src, int, int)
Linear Regression Value - LSMA (Least Squares Moving Average)
Parameters:
src : float
int : len
int : offset
Returns: array
lwma(src, int)
Linear Weighted Moving Average - LWMA
Parameters:
src : float
int : len
Returns: array
mcginley(src, int)
McGinley Dynamic
Parameters:
src : float
int : len
Returns: array
mcNicholl(src, int)
McNicholl EMA
Parameters:
src : float
int : len
Returns: array
nonlagma(src, int)
Non-lag moving average
Parameters:
src : float
int : len
Returns: array
pwma(src, int, float)
Parabolic Weighted Moving Average
Parameters:
src : float
int : len
float : pwr
Returns: array
rmta(src, int)
Recursive Moving Trendline
Parameters:
src : float
int : len
Returns: array
decycler(src, int)
Simple decycler - SDEC
Parameters:
src : float
int : len
Returns: array
sma(src, int)
Simple Moving Average
Parameters:
src : float
int : len
Returns: array
swma(src, int)
Sine Weighted Moving Average
Parameters:
src : float
int : len
Returns: array
slwma(src, int)
linear weighted moving average
Parameters:
src : float
int : len
Returns: array
smma(src, int)
Smoothed Moving Average - SMMA
Parameters:
src : float
int : len
Returns: array
super(src, int)
Ehlers super smoother
Parameters:
src : float
int : len
Returns: array
smoother(src, int)
Smoother filter
Parameters:
src : float
int : len
Returns: array
tma(src, int)
Triangular moving average - TMA
Parameters:
src : float
int : len
Returns: array
tema(src, int)
Tripple exponential moving average - TEMA
Parameters:
src : float
int : len
Returns: array
vwema(src, int)
Volume weighted ema - VEMA
Parameters:
src : float
int : len
Returns: array
vwma(src, int)
Volume weighted moving average - VWMA
Parameters:
src : float
int : len
Returns: array
zlagdema(src, int)
Zero-lag dema
Parameters:
src : float
int : len
Returns: array
zlagma(src, int)
Zero-lag moving average
Parameters:
src : float
int : len
Returns: array
zlagtema(src, int)
Zero-lag tema
Parameters:
src : float
int : len
Returns: array
threepolebuttfilt(src, int)
Three-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
threepolesss(src, int)
Three-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
twopolebutter(src, int)
Two-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
twopoless(src, int)
Two-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
UtilityFunctionsLibrary "UtilityFunctions"
Utility functions written by me
printLabelOnLastBar_string(string)
Prints string in a label on the last bar
Parameters:
string : value to print
Returns: void
printLabelOnLastBar_float(float)
Prints float in a label on the last bar
Parameters:
float : value to print
Returns: void
printSeriesInReverseOnLabels(series)
Prints a float series in labels in reverse (the first value is on the last candle, the second value is on the second to last candle, etc.)
Parameters:
series : float values to print
Returns: void
isPeriodDailyBased(string)
Returns true/false if the period is Daily based (1D, 3D, ...)
Parameters:
string : timeframe period
Returns: true/false
get_multiplier(string)
Gets the mutliplier of the timeframe passed compared to the current timeframe. If current TF is 5m and the passed timeframe period is 30m, the result will be 6
Parameters:
string : timeframe param
Returns: simple float of the multiplier
functionStringToMatrixLibrary "functionStringToMatrix"
Provides unbound methods (no error checking) to parse a string into a float or int matrix.
to_matrix_float(str, interval_sep, start_tk, end_tk)
Parse a string into a float matrix.
Parameters:
str : , string, the formated string to parse.
interval_sep : , string, cell interval separator token.
start_tk : , string, row start token.
end_tk : , string, row end token.
Returns: matrix, parsed float matrix.
to_matrix_int(str, interval_sep, start_tk, end_tk)
Parse a string into a int matrix.
Parameters:
str : , string, the formated string to parse.
interval_sep : , string, cell interval separator token.
start_tk : , string, row start token.
end_tk : , string, row end token.
Returns: matrix, parsed int matrix.
CyclicRsiLib█ OVERVIEW
This library is complementary for Cyclic RSI High Low With Noise Filter.
█ CREDITS
LoneSomeTheBlue
WhenToTrade
AlertFrequency()
: AlertFrequency
Parameters:
: : _string
Returns: : _freq
CyclicRSI()
: CyclicRSI
Parameters:
: : _source, _length, _expression
Returns: : osc
Credits to WhenToTrade
AddToZigzag()
: AddToZigzag
Parameters:
: : _id, value, max_array_size
Returns: : array.unshift, array.pop
Credits to LonesomeTheBlue
UpdateZigzag()
: UpdateZigzag
Parameters:
: : _id, value, max_array_size, dir
Returns: : AddToZigzag, array.set
Credits to LonesomeTheBlue
BoolZigzag()
: BoolZigzag
Parameters:
: : ph, pl, dirchanged, _id, dir
Returns: : AddToZigzag, UpdateZigzag
Credits to LonesomeTheBlue
NoiseSwitch()
: NoiseSwitch
Parameters:
: : _string, _id
Returns: : FilterNoise
LineGray()
: LineGray
Parameters:
: : _id
Returns: : LineGray
LabelDir()
: LabelDir
Parameters:
: : _id, _string, _color, _float
Returns: : LabelDir
TernaryLabel()
: TernaryLabel
Parameters:
: : _dir, _bool1, _bool2, _string1, _string2
Returns: : str_label
TernaryColor()
: TernaryColor
Parameters:
: : _dir, _bool1, _bool2
Returns: : col_label
