MarkovAlgorithmLibrary "MarkovAlgorithm"
Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of
symbols. Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a
general model of computation and can represent any mathematical expression from its simple notation.
~ wikipedia
.
reference:
en.wikipedia.org
rosettacode.org
parse(rules, separator)
Parameters:
rules (string)
separator (string)
Returns: - `array _rules`: List of rules.
---
Usage:
- `parse("|0 -> 0|| 1 -> 0| 0 -> ")`
apply(expression, rules)
Aplies rules to a expression.
Parameters:
expression (string) : `string`: Text expression to be formated by the rules.
rules (rule ) : `string`: Rules to apply to expression on a string format to be parsed.
Returns: - `string _result`: Formated expression.
---
Usage:
- `apply("101", parse("|0 -> 0|| 1 -> 0| 0 -> "))`
apply(expression, rules)
Parameters:
expression (string)
rules (string)
Returns: - `string _result`: Formated expression.
---
Usage:
- `apply("101", parse("|0 -> 0|| 1 -> 0| 0 -> "))`
rule
String pair that represents `pattern -> replace`, each rule may be ordinary or terminating.
Fields:
pattern (series string) : Pattern to replace.
replacement (series string) : Replacement patterns.
termination (series bool) : Termination rule.
Indikator dan strategi
new_line_dot_3Library "new_line"
TODO: plot line based on 3 points.
new_line(x_1, x_2, x_3, y_1, y_2, y_3)
TODO: plot line based on 3 points. (each different)
Parameters:
x_1 (int)
x_2 (int)
x_3 (int)
y_1 (float)
y_2 (float)
y_3 (float)
Returns: TODO: new line based on each different 3 values.
OHLC📕 LIBRARY OHLC
🔷 Introduction
This library is a custom library designed to work with real-time bars. It allows to easily calculate OHLC values for any source.
Personally, I use this library to accurately display the highest and lowest values on visual indicators such as my progress bars.
🔷 How to Use
◼ 1. Import the OHLC library into your TradingView script:
import cryptolinx/OHLC/1
- or -
Instead of the library namespace, you can define a custom namespace as alias.
import cryptolinx/OHLC/1 as src
◼ 2. Create a new OHLC source using the `new()` function.
varip mySrc = OHLC.new() // It is required to use the `varip` keyword to init your ``
- or -
If you has set up an alias before.
varip mySrc = src.new()
===
In that case, your `` needs to be `na`, define your object like that
varip mySrc = na
◼ 3. Call the `hydrateOHLC()` method on your OHLC source to update its values:
Basic
float rsi = ta.rsi(close, 14)
mySrc.hydrateOHLC(rsi)
- or -
Inline
rsi = ta.rsi(close, 14).hydrateOHLC(mySrc)
◼ 4. The data is accessible under their corresponding names.
mySrc.open
mySrc.high
mySrc.low
mySrc.close
🔷 Note: This library only works with real-time bars and will not work with historical bars.
Lex_3CR_Functions_Library2Library "Lex_3CR_Functions_Library2"
This is a source code for a technical analysis library in Pine Script language,
designed to identify and mark Bullish and Bearish Three Candle Reversal (3CR) chart patterns.
The library provides three functions to be used in a trading algorithm.
The first function, Bull_3crMarker, adds a dashed line and label to a Bullish 3CR chart pattern, indicating the 3CR point.
The second function, Bear_3crMarker, adds a dashed line and label to a Bearish 3CR chart pattern.
The third function, Bull_3CRlogicals, checks for a Bullish 3CR pattern where the first candle's low is greater than the second candle's low and the second candle's low is less than the third candle's low.
If found, creates a line at the breakout point and a label at the fail point,
if specified. All functions take parameters such as the chart pattern's characteristics and output colors, labels, and markers.
Bull_3crMarker(bulllinearray, barnum, breakpoint, failpointB, failpoint, linecolorbull, bulllabelarray, labelcolor, textcolor, labelon)
Bull_3crMarker Adds a 3CR marker to a Bullish 3CR chart pattern
@description Adds a dashed line and label to a 3CR up chart pattern, indicating the 3CR (3 Candle Reversal) point.
Parameters:
bulllinearray (line )
barnum (int)
breakpoint (float)
failpointB (float )
failpoint (float)
linecolorbull (color)
bulllabelarray (label )
labelcolor (color)
textcolor (color)
labelon (bool)
Bear_3crMarker(bearlinearray, barnum, breakpoint, failpointB, failpoint, linecolorbear, bearlabelarray, labelcolor, textcolor, labelon)
Bear_3crMarker Adds a 3CR marker to a Bearish 3CR chart pattern
@description Adds a dashed line and label to a 3CR down chart pattern, indicating the 3CR (3 Candle Reversal) point.
Parameters:
bearlinearray (line )
barnum (int)
breakpoint (float)
failpointB (float )
failpoint (float)
linecolorbear (color)
bearlabelarray (label )
labelcolor (color)
textcolor (color)
labelon (bool)
Bull_3CRlogicals(low1, low2, low3, bulllinearray, bulllabelarray, failpointB, linecolorbull, labelcolor, textcolor, labelon)
Checks for a bullish three candle reversal pattern and creates a line and label at the breakout point if found
@description Checks for a bullish three candle reversal pattern where the first candle's low is greater than the second candle's low and the second candle's low is less than the third candle's low. If found, creates a line at the breakout point and a label at the fail point, if specified.
Parameters:
low1 (float)
low2 (float)
low3 (float)
bulllinearray (line )
bulllabelarray (label )
failpointB (float )
linecolorbull (color)
labelcolor (color)
textcolor (color)
labelon (bool)
Bear_3CRlogicals(high1, high2, high3, bearlinearray, bearlabelarray, failpointB, linecolorbear, labelcolor, textcolor, labelon)
Checks for a Bearish 3CR pattern and draws a bearish marker on the chart at the appropriate location
@description This function checks for a Bearish 3CR (Three-Candle Reversal) pattern, which is defined as the second candle having a higher high than the first and third candles, and the third candle having a lower high than the first candle. If the pattern is detected, a bearish marker is drawn on the chart at the appropriate location, and an optional label can be added to the marker.
Parameters:
high1 (float)
high2 (float)
high3 (float)
bearlinearray (line )
bearlabelarray (label )
failpointB (float )
linecolorbear (color)
labelcolor (color)
textcolor (color)
labelon (bool)
bullLineDelete(i, bulllinearray, failarray, bulllabelarray, labelon)
Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bulllinearray (line )
failarray (float )
bulllabelarray (label )
labelon (bool)
bearLineDelete(i, bearlinearray, failarray, bearlabelarray, labelon)
Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bearlinearray (line )
failarray (float )
bearlabelarray (label )
labelon (bool)
bulloffsetdelete(i, bulllinearray, failarray, bulllabelarray, labelon)
Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bulllinearray (line )
failarray (float )
bulllabelarray (label )
labelon (bool)
bearoffsetdelete(i, bearlinearray, failarray, bearlabelarray, labelon)
Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bearlinearray (line )
failarray (float )
bearlabelarray (label )
labelon (bool)
BullEntry_setter(i, bulllinearray, failpointB, entrystopB, entryB, entryboolB)
Checks if the specified value is greater than the break point of any bullish line in an array, and removes that line if true
@description Checks if the s pecified value is greater than the break point of any bullish line in an array, and removes that line if true.
Parameters:
i (int)
bulllinearray (line )
failpointB (float )
entrystopB (float )
entryB (float )
entryboolB (bool )
Bull3CRchecker(close1, bulllinearray, FailpointB, rsiB, bulllabelarray, labelt, bullcolored, directionarray, rsi, secondbullline, entrystopB, entryB, entryboolB)
Parameters:
close1 (float)
bulllinearray (line )
FailpointB (float )
rsiB (float )
bulllabelarray (label )
labelt (bool)
bullcolored (color)
directionarray (label )
rsi (float)
secondbullline (line )
entrystopB (float )
entryB (float )
entryboolB (bool )
Bear3CRchecker(close1, bearlinearray, FailpointB, bearlabelarray, labelt, bearcolored, directionarray, rsi, secondbearline, rsiB)
Checks if the specified value is less than the break point of any bearish line in an array, and removes that line if true
@description Checks if the specified value is less than the break point of any bearish line in an array, and removes that line if true.
Parameters:
close1 (float)
bearlinearray (line )
FailpointB (float )
bearlabelarray (label )
labelt (bool)
bearcolored (color)
directionarray (label )
rsi (float)
secondbearline (line )
rsiB (float )
Bulloffsetcheck(FailpointB, bulllabelarray, linearray, labelt, offset)
Checks the offset of bullish lines and deletes them if they are beyond a certain offset from the current bar index
@description Checks the offset of bullish lines and deletes them if they are beyond a certain offset from the current bar index
Parameters:
FailpointB (float )
bulllabelarray (label )
linearray (line )
labelt (bool)
offset (int)
Bearoffsetcheck(FailpointB, bearlabelarray, linearray, labelt, offset)
Checks the offset of bearish lines and deletes them if they are beyond a certain offset from the current bar index
@description Checks the offset of bearish lines and deletes them if they are beyond a certain offset from the current bar index
Parameters:
FailpointB (float )
bearlabelarray (label )
linearray (line )
labelt (bool)
offset (int)
Bullfailchecker(close1, FailpointB, bulllabelarray, linearray, labelt)
Checks if the current price has crossed above a bullish fail point and deletes the corresponding line and label
@description Checks if the current price has crossed above a bullish fail point and deletes the corresponding line and label
Parameters:
close1 (float)
FailpointB (float )
bulllabelarray (label )
linearray (line )
labelt (bool)
Bearfailchecker(close1, FailpointB, bearlabelarray, linearray, labelt)
Checks for bearish lines that have failed to trigger and removes them from the chart
@description This function checks for bearish lines that have failed to trigger (i.e., where the current price is above the fail point) and removes them from the chart along with any associated label.
Parameters:
close1 (float)
FailpointB (float )
bearlabelarray (label )
linearray (line )
labelt (bool)
rsibullchecker(rsiinput, rsiBull, secondbullline)
Checks for bullish RSI lines that have failed to trigger and removes them from the chart
@description This function checks for bullish RSI lines that have failed to trigger (i.e., where the current RSI value is below the line's trigger level) and removes them from the chart along with any associated line.
Parameters:
rsiinput (float)
rsiBull (float )
secondbullline (line )
rsibearchecker(rsiinput, rsiBear, secondbearline)
Checks for bearish RSI lines that have failed to trigger and removes them from the chart
@description This function checks for bearish RSI lines that have failed to trigger (i.e., where the current RSI value is above the line's trigger level) and removes them from the chart along with any associated line.
Parameters:
rsiinput (float)
rsiBear (float )
secondbearline (line )
MarkovChainLibrary "MarkovChain"
Generic Markov Chain type functions.
---
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the
probability of each event depends only on the state attained in the previous event.
---
reference:
Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas Privault.
en.wikipedia.org
www.geeksforgeeks.org
towardsdatascience.com
github.com
stats.stackexchange.com
timeseriesreasoning.com
www.ris-ai.com
github.com
gist.github.com
github.com
gist.github.com
writings.stephenwolfram.com
kevingal.com
towardsdatascience.com
spedygiorgio.github.io
github.com
www.projectrhea.org
method to_string(this)
Translate a Markov Chain object to a string format.
Namespace types: MC
Parameters:
this (MC) : `MC` . Markov Chain object.
Returns: string
method to_table(this, position, text_color, text_size)
Namespace types: MC
Parameters:
this (MC)
position (string)
text_color (color)
text_size (string)
method create_transition_matrix(this)
Namespace types: MC
Parameters:
this (MC)
method generate_transition_matrix(this)
Namespace types: MC
Parameters:
this (MC)
new_chain(states, name)
Parameters:
states (state )
name (string)
from_data(data, name)
Parameters:
data (string )
name (string)
method probability_at_step(this, target_step)
Namespace types: MC
Parameters:
this (MC)
target_step (int)
method state_at_step(this, start_state, target_state, target_step)
Namespace types: MC
Parameters:
this (MC)
start_state (int)
target_state (int)
target_step (int)
method forward(this, obs)
Namespace types: HMC
Parameters:
this (HMC)
obs (int )
method backward(this, obs)
Namespace types: HMC
Parameters:
this (HMC)
obs (int )
method viterbi(this, observations)
Namespace types: HMC
Parameters:
this (HMC)
observations (int )
method baumwelch(this, observations)
Namespace types: HMC
Parameters:
this (HMC)
observations (int )
Node
Target node.
Fields:
index (series int) : . Key index of the node.
probability (series float) : . Probability rate of activation.
state
State reference.
Fields:
name (series string) : . Name of the state.
index (series int) : . Key index of the state.
target_nodes (Node ) : . List of index references and probabilities to target states.
MC
Markov Chain reference object.
Fields:
name (series string) : . Name of the chain.
states (state ) : . List of state nodes and its name, index, targets and transition probabilities.
size (series int) : . Number of unique states
transitions (matrix) : . Transition matrix
HMC
Hidden Markov Chain reference object.
Fields:
name (series string) : . Name of thehidden chain.
states_hidden (state ) : . List of state nodes and its name, index, targets and transition probabilities.
states_obs (state ) : . List of state nodes and its name, index, targets and transition probabilities.
transitions (matrix) : . Transition matrix
emissions (matrix) : . Emission matrix
initial_distribution (float )
FunctionProbabilityViterbiLibrary "FunctionProbabilityViterbi"
The Viterbi Algorithm calculates the most likely sequence of hidden states *(called Viterbi path)*
that results in a sequence of observed events.
viterbi(observations, transitions, emissions, initial_distribution)
Calculate most probable path in a Markov model.
Parameters:
observations (int ) : array . Observation states data.
transitions (matrix) : matrix . Transition probability table, (HxH, H:Hidden states).
emissions (matrix) : matrix . Emission probability table, (OxH, O:Observed states).
initial_distribution (float ) : array . Initial probability distribution for the hidden states.
Returns: array. Most probable path.
FunctionBaumWelchLibrary "FunctionBaumWelch"
Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm
to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed
feature vectors.
---
### Function List:
> `forward (array pi, matrix a, matrix b, array obs)`
> `forward (array pi, matrix a, matrix b, array obs, bool scaling)`
> `backward (matrix a, matrix b, array obs)`
> `backward (matrix a, matrix b, array obs, array c)`
> `baumwelch (array observations, int nstates)`
> `baumwelch (array observations, array pi, matrix a, matrix b)`
---
### Reference:
> en.wikipedia.org
> github.com
> en.wikipedia.org
> www.rdocumentation.org
> www.rdocumentation.org
forward(pi, a, b, obs)
Computes forward probabilities for state `X` up to observation at time `k`, is defined as the
probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.
Parameters:
pi (float ) : Initial probabilities.
a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing
states given a state matrix is size (M x M) where M is number of states.
b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given
state matrix is size (M x O) where M is number of states and O is number of different
possible observations.
obs (int ) : List with actual state observation data.
Returns: - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first
dimension refers to the state and the second dimension to time.
forward(pi, a, b, obs, scaling)
Computes forward probabilities for state `X` up to observation at time `k`, is defined as the
probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.
Parameters:
pi (float ) : Initial probabilities.
a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing
states given a state matrix is size (M x M) where M is number of states.
b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given
state matrix is size (M x O) where M is number of states and O is number of different
possible observations.
obs (int ) : List with actual state observation data.
scaling (bool) : Normalize `alpha` scale.
Returns: - #### Tuple with:
> - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first
dimension refers to the state and the second dimension to time.
> - `array _c`: Array with normalization scale.
backward(a, b, obs)
Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.
Parameters:
a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states
given a state matrix is size (M x M) where M is number of states
b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state
matrix is size (M x O) where M is number of states and O is number of different possible observations
obs (int ) : Array with actual state observation data.
Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time.
backward(a, b, obs, c)
Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.
Parameters:
a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states
given a state matrix is size (M x M) where M is number of states
b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state
matrix is size (M x O) where M is number of states and O is number of different possible observations
obs (int ) : Array with actual state observation data.
c (float ) : Array with Normalization scaling coefficients.
Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time.
baumwelch(observations, nstates)
**(Random Initialization)** Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the
unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm
to compute the statistics for the expectation step.
Parameters:
observations (int ) : List of observed states.
nstates (int)
Returns: - #### Tuple with:
> - `array _pi`: Initial probability distribution.
> - `matrix _a`: Transition probability matrix.
> - `matrix _b`: Emission probability matrix.
---
requires: `import RicardoSantos/WIPTensor/2 as Tensor`
baumwelch(observations, pi, a, b)
Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the
unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm
to compute the statistics for the expectation step.
Parameters:
observations (int ) : List of observed states.
pi (float ) : Initial probaility distribution.
a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states
given a state matrix is size (M x M) where M is number of states
b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state
matrix is size (M x O) where M is number of states and O is number of different possible observations
Returns: - #### Tuple with:
> - `array _pi`: Initial probability distribution.
> - `matrix _a`: Transition probability matrix.
> - `matrix _b`: Emission probability matrix.
---
requires: `import RicardoSantos/WIPTensor/2 as Tensor`
MyLibraryLibrary "MyLibrary"
TODO: add library description here
fun(x)
TODO: add function description here
Parameters:
x (float) : TODO: add parameter x description here
Returns: TODO: add what function returns
TrendIndicatorsLibrary "TrendIndicators"
This is a library of 'Trend Indicators'.
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the source, not being restricted to just the closing price.
Indicators (this is a work in progress):
1. Absolute DI (Directional Moviment Index) (Difference between DI+ and DI-).
Used in 'DMI Stochastic Extreme' by Barbara Star.
2. DMI
DI_Abs(lengthDI, smoothDI, typeMA, lengthMA)
@description Absolute DI (Directional Moviment Index).
Used in 'DMI Stochastic Extreme' by Barbara Star.
Difference between DI+ and DI-
Parameters:
lengthDI : (int) Length of DI+/DI-
smoothDI : (bool) Sets whether absolute DI should be smoothed
typeMA : (int) Type of moving average of smoothing
lengthMA : (int) Length for moving average of smoothing
Returns: (float) Absolute value of DI
dmi(diLength, adxSmoothing)
@description DMI (Directional Movement Index)
Same as ta.dmi()
Parameters:
diLength : (int) Length of DI+/DI-
adxSmoothing : (int) ADX Smoothing
Returns: Tuple of three DMI series: Positive Directional
Movement (+DI), Negative Directional Movement (-DI) and Average Directional Movement Index (ADX).
dmi(source, diLength, adxSmoothing)
@description DMI (Directional Movement Index)
Customized version of ta.dmi(), with custom source
Parameters:
source : (float) Source for DI+/DI-
diLength : (int) Length of DI+/DI-
adxSmoothing : (int) ADX Smoothing
Returns: Tuple of three DMI series: Positive Directional
Movement (+DI), Negative Directional Movement (-DI) and Average Directional Movement Index (ADX).
Scaled Order Sizing and Take Profit Target ArraysWOAH Order Scaling!
This Provides a user with methods to create a list of profit targets and order sizes which grow or shrink. For size, the will add up to specific sum. for Targets they will include the first and last, and can lean towards either, to scale the order grid.
And thanks to @Hoanghetti for the markdown, i've included a basic usage example within the hover , o you don't need to search for the usage example, simply import, and when writing, the code hint contains a full example.
scaled_sizes(total_size, count, weight, min_size, as_percent)
create an array of sizes which grow or shrink from first to last
which add up to 1.0 if set the as_percent flag , or a total value / sum.
Parameters:
total_size : (float) total size to divide ito split
count : (int ) desired number of splits to create
weight : (float) a weight to apply to grow or shrink the split either towards the last being most, or the first being most, or 1.0 being each is equally sized as 1/n count
min_size : (float) a minimum size for the smallest value (in value of ttotal_size units)
as_percent : (float) a minimum size for the smallest value (in value of total_size units)
Returns: Array of Sizes for each split
scaled_targets(count, weight, minimum, maximum)
create a list of take profitt targets from the smallest to larget distance
Parameters:
count : (int ) number of targets
weight : (float) weight to apply to growing or shrinking
minimum : (float) first value of the output
maximum : (float) last value of the output
Returns: Array of percentage targets
DiddlyUtilityLibrary "DiddlyUtility"
TODO: add library description here
getStringTimeMinus1Minute(london_ssth, london_sstm)
Parameters:
london_ssth
london_sstm
getLadderStepIncrement(_price)
Parameters:
_price
getLadderIndexForPrice(_price, _ladderRange)
Parameters:
_price
_ladderRange
getLadderStartPriceRange(_price, _ladderRange)
Parameters:
_price
_ladderRange
get_volume_string(_volume)
Parameters:
_volume
floorDown(number, decimals)
Parameters:
number
decimals
countDigitsBeforeDecimal(n)
Parameters:
n
countDigitsAfterDecimal(n)
Parameters:
n
getChartTimePeriodAsSeconds(_chartPeriod)
Parameters:
_chartPeriod
debug(_txt)
Parameters:
_txt
MomentumIndicatorsLibrary "MomentumIndicators"
This is a library of 'Momentum Indicators', also denominated as oscillators.
The purpose of this library is to organize momentum indicators in just one place, making it easy to access.
In addition, it aims to allow customized versions, not being restricted to just the price value.
An example of this use case is the popular Stochastic RSI.
# Indicators:
1. Relative Strength Index (RSI):
Measures the relative strength of recent price gains to recent price losses of an asset.
2. Rate of Change (ROC):
Measures the percentage change in price of an asset over a specified time period.
3. Stochastic Oscillator (Stoch):
Compares the current price of an asset to its price range over a specified time period.
4. True Strength Index (TSI):
Measures the price change, calculating the ratio of the price change (positive or negative) in relation to the
absolute price change.
The values of both are smoothed twice to reduce noise, and the final result is normalized
in a range between 100 and -100.
5. Stochastic Momentum Index (SMI):
Combination of the True Strength Index with a signal line to help identify turning points in the market.
6. Williams Percent Range (Williams %R):
Compares the current price of an asset to its highest high and lowest low over a specified time period.
7. Commodity Channel Index (CCI):
Measures the relationship between an asset's current price and its moving average.
8. Ultimate Oscillator (UO):
Combines three different time periods to help identify possible reversal points.
9. Moving Average Convergence/Divergence (MACD):
Shows the difference between short-term and long-term exponential moving averages.
10. Fisher Transform (FT):
Normalize prices into a Gaussian normal distribution.
11. Inverse Fisher Transform (IFT):
Transform the values of the Fisher Transform into a smaller and more easily interpretable scale is through the
application of an inverse transformation to the hyperbolic tangent function.
This transformation takes the values of the FT, which range from -infinity to +infinity, to a scale limited
between -1 and +1, allowing them to be more easily visualized and compared.
12. Premier Stochastic Oscillator (PSO):
Normalizes the standard stochastic oscillator by applying a five-period double exponential smoothing average of
the %K value, resulting in a symmetric scale of 1 to -1
# Indicators of indicators:
## Stochastic:
1. Stochastic of RSI (Relative Strengh Index)
2. Stochastic of ROC (Rate of Change)
3. Stochastic of UO (Ultimate Oscillator)
4. Stochastic of TSI (True Strengh Index)
5. Stochastic of Williams R%
6. Stochastic of CCI (Commodity Channel Index).
7. Stochastic of MACD (Moving Average Convergence/Divergence)
8. Stochastic of FT (Fisher Transform)
9. Stochastic of Volume
10. Stochastic of MFI (Money Flow Index)
11. Stochastic of On OBV (Balance Volume)
12. Stochastic of PVI (Positive Volume Index)
13. Stochastic of NVI (Negative Volume Index)
14. Stochastic of PVT (Price-Volume Trend)
15. Stochastic of VO (Volume Oscillator)
16. Stochastic of VROC (Volume Rate of Change)
## Inverse Fisher Transform:
1.Inverse Fisher Transform on RSI (Relative Strengh Index)
2.Inverse Fisher Transform on ROC (Rate of Change)
3.Inverse Fisher Transform on UO (Ultimate Oscillator)
4.Inverse Fisher Transform on Stochastic
5.Inverse Fisher Transform on TSI (True Strength Index)
6.Inverse Fisher Transform on CCI (Commodity Channel Index)
7.Inverse Fisher Transform on Fisher Transform (FT)
8.Inverse Fisher Transform on MACD (Moving Average Convergence/Divergence)
9.Inverse Fisher Transfor on Williams R% (Williams Percent Range)
10.Inverse Fisher Transfor on CMF (Chaikin Money Flow)
11.Inverse Fisher Transform on VO (Volume Oscillator)
12.Inverse Fisher Transform on VROC (Volume Rate of Change)
## Stochastic Momentum Index:
1.Stochastic Momentum Index of RSI (Relative Strength Index)
2.Stochastic Momentum Index of ROC (Rate of Change)
3.Stochastic Momentum Index of VROC (Volume Rate of Change)
4.Stochastic Momentum Index of Williams R% (Williams Percent Range)
5.Stochastic Momentum Index of FT (Fisher Transform)
6.Stochastic Momentum Index of CCI (Commodity Channel Index)
7.Stochastic Momentum Index of UO (Ultimate Oscillator)
8.Stochastic Momentum Index of MACD (Moving Average Convergence/Divergence)
9.Stochastic Momentum Index of Volume
10.Stochastic Momentum Index of MFI (Money Flow Index)
11.Stochastic Momentum Index of CMF (Chaikin Money Flow)
12.Stochastic Momentum Index of On Balance Volume (OBV)
13.Stochastic Momentum Index of Price-Volume Trend (PVT)
14.Stochastic Momentum Index of Volume Oscillator (VO)
15.Stochastic Momentum Index of Positive Volume Index (PVI)
16.Stochastic Momentum Index of Negative Volume Index (NVI)
## Relative Strength Index:
1. RSI for Volume
2. RSI for Moving Average
rsi(source, length)
RSI (Relative Strengh Index). Measures the relative strength of recent price gains to recent price losses of an asset.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of RSI
roc(source, length)
ROC (Rate of Change). Measures the percentage change in price of an asset over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of ROC
stoch(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Stochastic Oscillator. Compares the current price of an asset to its price range over a specified time period.
Parameters:
kLength
kSmoothing : (int) Period for smoothig stochastic
dSmoothing : (int) Period for signal (moving average of stochastic)
maTypeK : (int) Type of Moving Average for Stochastic Oscillator
maTypeD : (int) Type of Moving Average for Stochastic Oscillator Signal
almaOffsetKD : (float) Offset for Arnaud Legoux Moving Average for Oscillator and Signal
almaSigmaKD : (float) Sigma for Arnaud Legoux Moving Average for Oscillator and Signal
lsmaOffSetKD : (int) Offset for Least Squares Moving Average for Oscillator and Signal
Returns: A tuple of Stochastic Oscillator and Moving Average of Stochastic Oscillator
stoch(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Stochastic Oscillator. Customized source. Compares the current price of an asset to its price range over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
kLength : (int) Period of loopback to calculate the stochastic
kSmoothing : (int) Period for smoothig stochastic
dSmoothing : (int) Period for signal (moving average of stochastic)
maTypeK : (int) Type of Moving Average for Stochastic Oscillator
maTypeD : (int) Type of Moving Average for Stochastic Oscillator Signal
almaOffsetKD : (float) Offset for Arnaud Legoux Moving Average for Stoch and Signal
almaSigmaKD : (float) Sigma for Arnaud Legoux Moving Average for Stoch and Signal
lsmaOffSetKD : (int) Offset for Least Squares Moving Average for Stoch and Signal
Returns: A tuple of Stochastic Oscillator and Moving Average of Stochastic Oscillator
tsi(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet)
TSI (True Strengh Index). Measures the price change, calculating the ratio of the price change (positive or negative) in relation to the absolute price change.
The values of both are smoothed twice to reduce noise, and the final result is normalized in a range between 100 and -100.
Parameters:
source : (float) Source of series (close, high, low, etc.)
shortLength : (int) Short length
longLength : (int) Long length
maType : (int) Type of Moving Average for TSI
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: (float) TSI
smi(sourceTSI, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
SMI (Stochastic Momentum Index). A TSI (True Strengh Index) plus a signal line.
Parameters:
sourceTSI : (float) Source of series for TSI (close, high, low, etc.)
shortLengthTSI : (int) Short length for TSI
longLengthTSI : (int) Long length for TSI
maTypeTSI : (int) Type of Moving Average for Signal of TSI
almaOffsetTSI : (float) Offset for Arnaud Legoux Moving Average
almaSigmaTSI : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSetTSI : (int) Offset for Least Squares Moving Average
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
Returns: A tuple with TSI, signal of TSI and histogram of difference
wpr(source, length)
Williams R% (Williams Percent Range). Compares the current price of an asset to its highest high and lowest low over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of Williams R%
cci(source, length, maType, almaOffset, almaSigma, lsmaOffSet)
CCI (Commodity Channel Index). Measures the relationship between an asset's current price and its moving average.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
maType : (int) Type of Moving Average
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: (float) Series of CCI
ultimateOscillator(fastLength, middleLength, slowLength)
UO (Ultimate Oscilator). Combines three different time periods to help identify possible reversal points.
Parameters:
fastLength : (int) Fast period of loopback
middleLength : (int) Middle period of loopback
slowLength : (int) Slow period of loopback
Returns: (float) Series of Ultimate Oscilator
ultimateOscillator(source, fastLength, middleLength, slowLength)
UO (Ultimate Oscilator). Customized source. Combines three different time periods to help identify possible reversal points.
Parameters:
source : (float) Source of series (close, high, low, etc.)
fastLength : (int) Fast period of loopback
middleLength : (int) Middle period of loopback
slowLength : (int) Slow period of loopback
Returns: (float) Series of Ultimate Oscilator
macd(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet)
MACD (Moving Average Convergence/Divergence). Shows the difference between short-term and long-term exponential moving averages.
Parameters:
source : (float) Source of series (close, high, low, etc.)
fastLength : (int) Period for fast moving average
slowLength : (int) Period for slow moving average
signalLength : (int) Signal length
maTypeFast : (int) Type of fast moving average
maTypeSlow : (int) Type of slow moving average
maTypeMACD : (int) Type of MACD moving average
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: A tuple with MACD, Signal, and Histgram
fisher(length)
Fisher Transform. Normalize prices into a Gaussian normal distribution.
Parameters:
length
Returns: A tuple with Fisher Transform and signal
fisher(source, length)
Fisher Transform. Customized source. Normalize prices into a Gaussian normal distribution.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length
Returns: A tuple with Fisher Transform and signal
inverseFisher(source, length, subtrahend, denominator)
Inverse Fisher Transform.
Transform the values of the Fisher Transform into a smaller and more easily interpretable scale is
through the application of an inverse transformation to the hyperbolic tangent function.
This transformation takes the values of the FT, which range from -infinity to +infinity,
to a scale limited between -1 and +1, allowing them to be more easily visualized and compared.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period for loopback
subtrahend : (int) Denominator. Useful in unbounded indicators. For example, in CCI.
denominator
Returns: (float) Series of Inverse Fisher Transform
premierStoch(length, smoothlen)
Premier Stochastic Oscillator (PSO).
Normalizes the standard stochastic oscillator by applying a five-period double exponential smoothing
average of the %K value, resulting in a symmetric scale of 1 to -1.
Parameters:
length : (int) Period for loopback
smoothlen : (int) Period for smoothing
Returns: (float) Series of PSO
premierStoch(source, smoothlen, subtrahend, denominator)
Premier Stochastic Oscillator (PSO) of custom source.
Normalizes the source by applying a five-period double exponential smoothing average.
Parameters:
source : (float) Source of series (close, high, low, etc.)
smoothlen : (int) Period for smoothing
subtrahend : (int) Denominator. Useful in unbounded indicators. For example, in CCI.
denominator
Returns: (float) Series of PSO
stochRsi(sourceRSI, lengthRSI, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
sourceRSI
lengthRSI
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochRoc(sourceROC, lengthROC, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
sourceROC
lengthROC
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochUO(fastLength, middleLength, slowLength, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
fastLength
middleLength
slowLength
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochTSI(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
shortLength
longLength
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochWPR(source, length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochCCI(source, length, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochFT(length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVolume(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochMFI(source, length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochOBV(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochPVI(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochNVI(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochPVT(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVROC(length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
iftRSI(sourceRSI, lengthRSI, lengthIFT)
Parameters:
sourceRSI
lengthRSI
lengthIFT
iftROC(sourceROC, lengthROC, lengthIFT)
Parameters:
sourceROC
lengthROC
lengthIFT
iftUO(fastLength, middleLength, slowLength, lengthIFT)
Parameters:
fastLength
middleLength
slowLength
lengthIFT
iftStoch(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD, lengthIFT)
Parameters:
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
lengthIFT
iftTSI(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
shortLength
longLength
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftCCI(source, length, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
length
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftFisher(length, lengthIFT)
Parameters:
length
lengthIFT
iftMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftWPR(source, length, lengthIFT)
Parameters:
source
length
lengthIFT
iftMFI(source, length, lengthIFT)
Parameters:
source
length
lengthIFT
iftCMF(length, lengthIFT)
Parameters:
length
lengthIFT
iftVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftVROC(length, lengthIFT)
Parameters:
length
lengthIFT
smiRSI(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiROC(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVROC(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiWPR(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiFT(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiFT(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiCCI(source, length, maTypeCCI, almaOffsetCCI, almaSigmaCCI, lsmaOffSetCCI, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
maTypeCCI
almaOffsetCCI
almaSigmaCCI
lsmaOffSetCCI
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiUO(fastLength, middleLength, slowLength, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
fastLength
middleLength
slowLength
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVol(shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiMFI(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiCMF(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiOBV(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiPVT(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiPVI(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiNVI(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
rsiVolume(length)
Parameters:
length
rsiMA(sourceMA, lengthMA, maType, almaOffset, almaSigma, lsmaOffSet, lengthRSI)
Parameters:
sourceMA
lengthMA
maType
almaOffset
almaSigma
lsmaOffSet
lengthRSI
UtilsLibrary "Utils"
Utility functions. Mathematics, colors, and auxiliary algorithms.
setTheme(vc, theme)
Set theme for levels (predefined colors).
Parameters:
vc : (valueColorSpectrum) Object to associate a color with a value, taking into account the previous value and its levels.
theme : (int) Theme (predefined colors).
0 = 'User defined'
1 = 'Spectrum Blue-Green-Red'
2 = 'Monokai'
3 = 'Green'
4 = 'Purple'
5 = 'Blue'
6 = 'Red'
Returns: (void)
setTheme(vc, colorLevel_Lv1, colorLevel_Lv1_Lv2, colorLevel_Lv2_Lv3, colorLevel_Lv3_Lv4, colorLevel_Lv4_Lv5, colorLevel_Lv5)
Set theme for levels (customized colors).
Parameters:
vc : (valueColorSpectrum) Object to associate a color with a value, taking into account the previous value and its levels
colorLevel_Lv1 : (color) Color associeted with value when below Level 1.
colorLevel_Lv1_Lv2 : (color) Color associeted with value when between Level 1 and 2.
colorLevel_Lv2_Lv3 : (color) Color associeted with value when between Level 2 and 3.
colorLevel_Lv3_Lv4 : (color) Color associeted with value when between Level 3 and 4.
colorLevel_Lv4_Lv5 : (color) Color associeted with value when between Level 4 and 5.
colorLevel_Lv5 : (color) Color associeted with value when above Level 5.
Returns: (void)
setCurrentColorValue(vc)
Set color to a current value, taking into account the previous value and its levels
Parameters:
vc : (valueColorSpectrum) Object to associate a color with a value, taking into account the previous value and its levels
Returns: (void)
setCurrentColorValue(vc, gradient)
Set color to a current value, taking into account the previous value.
Parameters:
vc : (valueColor) Object to associate a color with a value, taking into account the previous value
gradient
Returns: (void)
setCustomLevels(vc, level1, level2, level3, level4, level5)
Set boundaries for custom levels.
Parameters:
vc : (valueColorSpectrum) Object to associate a color with a value, taking into account the previous value and its levels
level1 : (float) Boundary for level 1
level2 : (float) Boundary for level 2
level3 : (float) Boundary for level 3
level4 : (float) Boundary for level 4
level5 : (float) Boundary for level 5
Returns: (void)
getPeriodicColor(originalColor, density)
Returns a periodic color. Useful for creating dotted lines for example.
Parameters:
originalColor : (color) Original color.
density : (float) Density of color. Expression used in modulo to obtain the integer remainder.
If the remainder equals zero, the color appears, otherwise it remains hidden.
Returns: (color) Periodic color.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source : (float) Source
sampleLength : (int) Sample Length
pcntAbove : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
valueColorSpectrum
# Object to associate a color with a value, taking into account the previous value and its levels.
Fields:
currentValue
previousValue
level1
level2
level3
level4
level5
currentColorValue
colorLevel_Lv1
colorLevel_Lv1_Lv2
colorLevel_Lv2_Lv3
colorLevel_Lv3_Lv4
colorLevel_Lv4_Lv5
colorLevel_Lv5
theme
valueColor
# Object to associate a color with a value, taking into account the previous value
Fields:
currentValue
previousValue
currentColorValue
colorUp
colorDown
Commission-aware Trade LabelsCommission-aware Trade Labels
Description:
This library provides an easy way to visualize take-profit and stop-loss levels on your chart, taking into account trading commissions. The library calculates and displays the net profit or loss, along with other useful information such as risk/reward ratio, shares, and position size.
Features:
Configurable take-profit and stop-loss prices or percentages.
Set entry amount or shares.
Calculates and displays the risk/reward ratio.
Shows net profit or loss, considering trading commissions.
Customizable label appearance.
Usage:
Add the script to your chart.
Create an Order object for take-profit and stop-loss with desired configurations.
Call target_label() and stop_label() methods for each order object.
Example:
target_order = Order.new(take_profit_price=27483, stop_loss_price=28000, shares=0.2)
stop_order = Order.new(stop_loss_price=29000, shares=1)
target_order.target_label()
stop_order.stop_label()
This script is a powerful tool for visualizing your trading strategy's performance and helps you make better-informed decisions by considering trading commissions in your profit and loss calculations.
Library "tradelabels"
entry_price(this)
Parameters:
this : Order object
@return entry_price
take_profit_price(this)
Parameters:
this : Order object
@return take_profit_price
stop_loss_price(this)
Parameters:
this : Order object
@return stop_loss_price
is_long(this)
Parameters:
this : Order object
@return entry_price
is_short(this)
Parameters:
this : Order object
@return entry_price
percent_to_target(this, target)
Parameters:
this : Order object
target : Target price
@return percent
risk_reward(this)
Parameters:
this : Order object
@return risk_reward_ratio
shares(this)
Parameters:
this : Order object
@return shares
position_size(this)
Parameters:
this : Order object
@return position_size
commission_cost(this, target_price)
Parameters:
this : Order object
@return commission_cost
target_price
net_result(this, target_price)
Parameters:
this : Order object
target_price : The target price to calculate net result for (either take_profit_price or stop_loss_price)
@return net_result
create_take_profit_label(this, prefix, size, offset_x, bg_color, text_color)
Parameters:
this
prefix
size
offset_x
bg_color
text_color
create_stop_loss_label(this, prefix, size, offset_x, bg_color, text_color)
Parameters:
this
prefix
size
offset_x
bg_color
text_color
create_entry_label(this, prefix, size, offset_x, bg_color, text_color)
Parameters:
this
prefix
size
offset_x
bg_color
text_color
create_line(this, target_price, line_color, offset_x, line_style, line_width, draw_entry_line)
Parameters:
this
target_price
line_color
offset_x
line_style
line_width
draw_entry_line
Order
Order
Fields:
entry_price : Entry price
stop_loss_price : Stop loss price
stop_loss_percent : Stop loss percent, default 2%
take_profit_price : Take profit price
take_profit_percent : Take profit percent, default 6%
entry_amount : Entry amount, default 5000$
shares : Shares
commission : Commission, default 0.04%
WIPTensorLibrary "WIPTensor"
A Tensor or 3 dimensional array structure and interface.
---
Note: im just highjacking the name to use it as a 3d array on a project..
there is no optimization attempts or tensor specific functionality within.
to_string(this)
Convert `Tensor` to a string format.
Parameters:
this : Tensor data.
Returns: string.
to_vector(this)
Convert `Tensor` to a one dimension array.
Parameters:
this : Tensor data.
Returns: New array with flattened `Tensor` data.
new(x, y, z, initial_value)
Create a new `Tensor` with provided shape.
Parameters:
x : Dimension `X` size.
y : Dimension `Y` size.
z : Dimension `Z` size.
initial_value : Value to fill the `Tensor`.
Returns: New `Tensor`.
new(shape, initial_value)
Create a new `Tensor` with provided shape.
Parameters:
shape : Shape of dimensions size.
initial_value : Value to fill the `Tensor`.
Returns: New `Tensor`.
from(expression, sepx, sepy, sepz)
Create a `Tensor` from provided array and shape.
Parameters:
expression
sepx
sepy
sepz
Returns: New `Tensor`.
from(vector, x, y, z)
Create a `Tensor` from provided array and shape.
Parameters:
vector : Data with flattened dimensions.
x
y
z
Returns: New `Tensor`.
from(vector, shape)
Parameters:
vector
shape
get(this, x, y, z)
Get the value at position.
Parameters:
this : `Tensor` data.
x
y
z
Returns: Value at position.
get(this, position)
Parameters:
this
position
set(this, x, y, z, value)
Set the value at position.
Parameters:
this : `Tensor` data.
x
y
z
value : New Value.
set(this, position, value)
Parameters:
this
position
value
Vector
Helper type for 3d structure.
Fields:
v : Vector of the 3rd dimension.
Tensor
A Tensor is a three dimensional array were the 3rd dimension accounts for time.
Fields:
m : Matrix that holds the vectors.
Bitwise, Encode, DecodeLibrary "Bitwise, Encode, Decode"
Bitwise, Encode, Decode, and more Library
docs()
Hover-Over Documentation for inside Text Editor
bAnd(a, b)
Returns the bitwise AND of two integers
Parameters:
a : `int` - The first integer
b : `int` - The second integer
Returns: `int` - The bitwise AND of the two integers
bOr(a, b)
Performs a bitwise OR operation on two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The result of the bitwise OR operation.
bXor(a, b)
Performs a bitwise Xor operation on two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The result of the bitwise Xor operation.
bNot(n)
Performs a bitwise NOT operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise NOT operation on.
Returns: `int` - The result of the bitwise NOT operation.
bShiftLeft(n, step)
Performs a bitwise left shift operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise left shift operation on.
step : `int` - The number of positions to shift the bits to the left.
Returns: `int` - The result of the bitwise left shift operation.
bShiftRight(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise right shift operation on.
step : `int` - The number of bits to shift by.
Returns: `int` - The result of the bitwise right shift operation.
bRotateLeft(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The int to perform the bitwise Left rotation on the bits.
step : `int` - The number of bits to shift by.
Returns: `int`- The result of the bitwise right shift operation.
bRotateRight(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The int to perform the bitwise Right rotation on the bits.
step : `int` - The number of bits to shift by.
Returns: `int` - The result of the bitwise right shift operation.
bSetCheck(n, pos)
Checks if the bit at the given position is set to 1.
Parameters:
n : `int` - The integer to check.
pos : `int` - The position of the bit to check.
Returns: `bool` - True if the bit is set to 1, False otherwise.
bClear(n, pos)
Clears a particular bit of an integer (changes from 1 to 0) passes if bit at pos is 0.
Parameters:
n : `int` - The integer to clear a bit from.
pos : `int` - The zero-based index of the bit to clear.
Returns: `int` - The result of clearing the specified bit.
bFlip0s(n)
Flips all 0 bits in the number to 1.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all 0 bits in the number.
bFlip1s(n)
Flips all 1 bits in the number to 0.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all 1 bits in the number.
bFlipAll(n)
Flips all bits in the number.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all bits in the number.
bSet(n, pos, newBit)
Changes the value of the bit at the given position.
Parameters:
n : `int` - The integer to modify.
pos : `int` - The position of the bit to change.
newBit : `int` - na = flips bit at pos reguardless 1 or 0 | The new value of the bit (0 or 1).
Returns: `int` - The modified integer.
changeDigit(n, pos, newDigit)
Changes the value of the digit at the given position.
Parameters:
n : `int` - The integer to modify.
pos : `int` - The position of the digit to change.
newDigit : `int` - The new value of the digit (0-9).
Returns: `int` - The modified integer.
bSwap(n, i, j)
Switch the position of 2 bits of an int
Parameters:
n : `int` - int to manipulate
i : `int` - bit pos to switch with j
j : `int` - bit pos to switch with i
Returns: `int` - new int with bits switched
bPalindrome(n)
Checks to see if the binary form is a Palindrome (reads the same left to right and vice versa)
Parameters:
n : `int` - int to check
Returns: `bool` - result of check
bEven(n)
Checks if n is Even
Parameters:
n : `int` - The integer to check.
Returns: `bool` - result.
bOdd(n)
checks if n is Even if not even Odd
Parameters:
n : `int` - The integer to check.
Returns: `bool` - result.
bPowerOfTwo(n)
Checks if n is a Power of 2.
Parameters:
n : `int` - number to check.
Returns: `bool` - result.
bCount(n, to_count)
Counts the number of bits that are equal to 1 in an integer.
Parameters:
n : `int` - The integer to count the bits in.
to_count `string` - the bits to count
Returns: `int` - The number of bits that are equal to 1 in n.
GCD(a, b)
Finds the greatest common divisor (GCD) of two numbers.
Parameters:
a : `int` - The first number.
b : `int` - The second number.
Returns: `int` - The GCD of a and b.
LCM(a, b)
Finds the least common multiple (LCM) of two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The LCM of a and b.
aLCM(nums)
Finds the LCM of an array of integers.
Parameters:
nums : `int ` - The list of integers.
Returns: `int` - The LCM of the integers in nums.
adjustedLCM(nums, LCM)
adjust an array of integers to Least Common Multiple (LCM)
Parameters:
nums : `int ` - The first integer
LCM : `int` - The second integer
Returns: `int ` - array of ints with LCM
charAt(str, pos)
gets a Char at a given position.
Parameters:
str : `string` - string to pull char from.
pos : `int` - pos to get char from string (left to right index).
Returns: `string` - char from pos of string or "" if pos is not within index range
decimalToBinary(num)
Converts a decimal number to binary
Parameters:
num : `int` - The decimal number to convert to binary
Returns: `string` - The binary representation of the decimal number
decimalToBinary(num, to_binary_int)
Converts a decimal number to binary
Parameters:
num : `int` - The decimal number to convert to binary
to_binary_int : `bool` - bool to convert to int or to string (true for int, false for string)
Returns: `string` - The binary representation of the decimal number
binaryToDecimal(binary)
Converts a binary number to decimal
Parameters:
binary : `string` - The binary number to convert to decimal
Returns: `int` - The decimal representation of the binary number
decimal_len(n)
way of finding decimal length using arithmetic
Parameters:
n `float` - floating decimal point to get length of.
Returns: `int` - number of decimal places
int_len(n)
way of finding number length using arithmetic
Parameters:
n : `int`- value to find length of number
Returns: `int` - lenth of nunber i.e. 23 == 2
float_decimal_to_whole(n)
Converts a float decimal number to an integer `0.365 to 365`.
Parameters:
n : `string` - The decimal number represented as a string.
Returns: `int` - The integer obtained by removing the decimal point and leading zeroes from s.
fractional_part(x)
Returns the fractional part of a float.
Parameters:
x : `float` - The float to get the fractional part of.
Returns: `float` - The fractional part of the float.
form_decimal(a, b, zero_fix)
helper to form 2 ints into 1 float seperated by the decimal
Parameters:
a : `int` - a int
b : `int` - b int
zero_fix : `bool` - fix for trailing zeros being truncated when converting to float
Returns: ` ` - float = float decimal of ints | string = string version of b for future use to ref length
bEncode(n1, n2)
Encodes two numbers into one using bit OR. (fastest)
Parameters:
n1 : `int` - The first number to Encodes.
n2 : `int` - The second number to Encodes.
Returns: `int` - The result of combining the two numbers using bit OR.
bDecode(n)
Decodes an integer created by the bCombine function.(fastest)
Parameters:
n : `int` - The integer to decode.
Returns: ` ` - A tuple containing the two decoded components of the integer.
Encode(a, b)
Encodes by seperating ints into left and right of decimal float
Parameters:
a : `int` - a int
b : `int` - b int
Returns: `float` - new float of encoded ints one on left of decimal point one on right
Decode(encoded)
Decodes float of 2 ints seperated by decimal point
Parameters:
encoded : `float` - the encoded float value
Returns: ` ` - tuple of the 2 ints from encoded float
encode_heavy(a, b)
Encodes by combining numbers and tracking size in the
decimal of a floating number (slowest)
Parameters:
a : `int` - a int
b : `int` - b int
Returns: `float` - new decimal of encoded ints
decode_heavy(encoded)
Decodes encoded float that tracks size of ints in float decimal
Parameters:
encoded : `float` - encoded float
Returns: ` ` - tuple of decoded ints
decimal of float (slowest)
Parameters:
encoded : `float` - the encoded float value
Returns: ` ` - tuple of the 2 ints from encoded float
Bitwise, Encode, Decode Docs
In the documentation you may notice the word decimal
not used as normal this is because when referring to
binary a decimal number is a number that
can be represented with base 10 numbers 0-9
(the wiki below explains better)
A rule of thumb for the two integers being
encoded it to keep both numbers
less than 65535 this is because anything lower uses 16 bits or less
this will maintain 100% accuracy when decoding
although it is possible to do numbers up to 2147483645 with
this library doesnt seem useful enough
to explain or demonstrate.
The functions provided work within this 32-bit range,
where the highest number is all 1s and
the lowest number is all 0s. These functions were created
to overcome the lack of built-in bitwise functions in Pinescript.
By combining two integers into a single number,
the code can access both values i.e when
indexing only one array index
for a matrices row/column, thus improving execution time.
This technique can be applied to various coding
scenarios to enhance performance.
Bitwise functions are a way to use integers in binary form
that can be used to speed up several different processes
most languages have operators to perform these function such as
`<<, >>, &, ^, |, ~`
en.wikipedia.org
Simple Trendlines📈 Trendlines, made easy.
Simple Trendlines is a carefully made library that provides an easy and accessible way to draw trendlines on the chart.
Containing only 10 properties and 2 methods, the implementation is designed to be understandable through an object-oriented structure and provides developers the opportunity to expand without having to deal with slope calculation while also ensuring that there's no leakage between the trendlines before they're drawn.
Developers only need to provide 5 expressions to get everything up in running. This includes the following but is not limited to
The x-axis
Point A (Y1 Value)
Point B (Y2 Value)
A condition to draw the line
A condition to keep the trendline under continuation
Automatic x-axis calculation is not a built-in feature due to the inconsistency it could bring.
📕 Quick Example
import HoanGhetti/SimpleTrendlines/1 as tl
input_len = input.int(defval = 10)
pivotLow = fixnan(ta.pivotlow(input_len, input_len))
xAxis = ta.valuewhen(ta.change(pivotLow), bar_index, 0) - ta.valuewhen(ta.change(pivotLow), bar_index, 1)
prevPivot = ta.valuewhen(ta.change(pivotLow), pivotLow, 1)
pivotCondition = ta.change(pivotLow) and pivotLow > prevPivot
plData = tl.new(x_axis = xAxis, offset = input_len)
plData.drawLine(pivotCondition, prevPivot, pivotLow)
plData.drawTrendline(close > 0)
plData.lines.trendline.set_style(line.style_dashed)
plData.lines.trendline.set_width(2)
plData.lines.startline.set_width(2)
Excluding the styling at the bottom, that was only 8 lines of code which yields the following result.
⏳ Before continuing
The library does not support block-scoped execution. Conditions must be declared before and integrated as a parameter. This doesn't limit any capabilities and only involves thinking logically about precedence. It was made this way for code readability and to keep things organized.
The offset value inside the TrendlineSettings object can potentially affect performance (although very minimal) if you're using strict mode. When using strict mode, it loops through historical values to then do backend calculations.
🔽 Getting Started 🔽
Creating trendlines without a library isn't a hard task. However, the library features a built-in system called strict mode. We'll dive further into this below.
Creating an Instance
You can create an instance of the library by calling the new() function. Passing an identifier is conventionally mandatory in this case so you can reference properties and methods.
import HoanGhetti/SimpleTrendlines/2 as tl
lineData = tl.new(int x_axis, int offset, bool strictMode, int strictType)
___
int x_axis (Required) The distance between point A and point B provided by the user.
int offset (Optional) The offset from x2 and the current bar_index. Used in situations where conditions execute ahead of where the x2 location is such as pivót events.
bool strictMode (Optional) Strict mode works in the backend of things to ensure that the price hasn't closed below the trendline before the trendline is drawn.
int strictType (Optional) Only accepts 0 and 1, 0 ensures that the price during slope calculation is above the line, and 1 ensures that the price during slope calculation is below the line.
The Initial Line
After instantiating the library, we can go ahead use the identifer we made above and create an instance of our initial line by calling the drawLine() method.
lineData.drawLine(bool condition, float y1, float y2, float src)
___
bool condition (Required) The condition in order to draw a new line.
float y1 (Required) The y-value of point A.
float y2 (Required) The y-value of point B.
float src (Optional) Determines which value strict mode will actively check for leakage before a trendline is drawn.
Typically used if you're not referencing OHLC values for your y-values, or you want to check for another value to exceed the line besides using the close value.
The Trendline
The trendline that gets drawn solely uses the values of the initial line and can be called using the drawTrendline() method. The library enforces a condition as a parameter in order to maintain simplicity.
lineData.drawTrendline(bool condition)
___
bool condition (Required) The condition in order to maintain and continue drawing the trendline.
⚙️ Features
🔹 Automatic Slope Calculation
In the background, the library calculates the next Y2 and X2 values on every tick for the trendline. Preventing the developer from having to do such a process themself.
🔹 Object-Oriented
Each object contains manipulative properties that allow the developer to debug and have the freedom they want.
🔹 Enforced Error Checking
Runtime errors have been put in place to ensure you're doing things correctly.
🔹 Strict Mode & Offset
Strict mode can only be used when the offset value is over 0. It's a feature that's only meant to function under scenarios where a condition executes further than where the X2 is relative to the current bar_index value.
Let's think about pivot systems. As you're aware, pivot events are detected based on historical factors. If a swing low occurred nth bars ago, then the pivot condition will execute at the current bar_index instead of executing nth bars back.
Now because of this, what if you wanted to draw a trendline when the pivot event is executed? The offset value takes care of this just as you would when developing your other scripts, basically how we always do bar_index - n. However, what does this mean for strict mode?
The photo below represents the logic behind the execution.
When looking at this image, imagine this just happened, the event just executed and the trendline is now drawn. Pay attention to all the values inside the surrounding box. As you can see there are some candles that closed below the trendline before the trendline was drawn.
From what I can see 5-6 candles closed below the trendline during slope calculation. The goal of strict mode is to be a provisional system that prevents such occurrences from happening.
Here's a photo with strict mode on.
🔹 Strict Type
A parameter used in the new() function that acts as a representation of what strict mode should calculate for. It accepts only two values, 0 and 1.
0 - Ensures that all candles have closed above the trendline before the trendline is drawn.
1 - Ensures that all candles have closed below the trendline before the trendline is drawn.
In the most recent photo above, I used 0 for strict type, since I was wanting to have a clean trendline and ensure that not a single candlestick closed below.
If you want to reference something else besides the close value during strict mode calculation, you can change it in the drawLine() method.
If it's still difficult to understand, think 0 for pivot lows, and 1 for pivot highs.
📕 Methods and Property Inheritance
The library isn't crazy, but hopefully, it helps.
That is all.👍
LineWrapperLibrary "LineWrapper"
Wrapper Type for Line. Useful when you want to store the line details without drawing them. Can also be used in scnearios where you collect lines to be drawn and draw together towards the end.
draw(this)
draws line as per the wrapper object contents
Parameters:
this : (series Line) Line object.
Returns: current Line object
draw(this)
draws lines as per the wrapper object array
Parameters:
this : (series array) Array of Line object.
Returns: current Array of Line objects
update(this)
updates or redraws line as per the wrapper object contents
Parameters:
this : (series Line) Line object.
Returns: current Line object
update(this)
updates or redraws lines as per the wrapper object array
Parameters:
this : (series array) Array of Line object.
Returns: current Array of Line objects
get_price(this, bar)
get line price based on bar
Parameters:
this : (series Line) Line object.
bar : (series/int) bar at which line price need to be calculated
Returns: line price at given bar.
get_x1(this)
Returns UNIX time or bar index (depending on the last xloc value set) of the first point of the line.
Parameters:
this : (series Line) Line object.
Returns: UNIX timestamp (in milliseconds) or bar index.
get_x2(this)
Returns UNIX time or bar index (depending on the last xloc value set) of the second point of the line.
Parameters:
this : (series Line) Line object.
Returns: UNIX timestamp (in milliseconds) or bar index.
get_y1(this)
Returns price of the first point of the line.
Parameters:
this : (series Line) Line object.
Returns: Price value.
get_y2(this)
Returns price of the second point of the line.
Parameters:
this : (series Line) Line object.
Returns: Price value.
set_x1(this, x, draw, update)
Sets bar index or bar time (depending on the xloc) of the first point.
Parameters:
this : (series Line) Line object.
x : (series int) Bar index or bar time. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_x2(this, x, draw, update)
Sets bar index or bar time (depending on the xloc) of the second point.
Parameters:
this : (series Line) Line object.
x : (series int) Bar index or bar time. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_y1(this, y, draw, update)
Sets price of the first point
Parameters:
this : (series Line) Line object.
y : (series int/float) Price.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_y2(this, y, draw, update)
Sets price of the second point
Parameters:
this : (series Line) Line object.
y : (series int/float) Price.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_color(this, color, draw, update)
Sets the line color
Parameters:
this : (series Line) Line object.
color : (series color) New line color
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_extend(this, extend, draw, update)
Sets extending type of this line object. If extend=extend.none, draws segment starting at point (x1, y1) and ending at point (x2, y2). If extend is equal to extend.right or extend.left, draws a ray starting at point (x1, y1) or (x2, y2), respectively. If extend=extend.both, draws a straight line that goes through these points.
Parameters:
this : (series Line) Line object.
extend : (series string) New extending type.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_style(this, style, draw, update)
Sets the line style
Parameters:
this : (series Line) Line object.
style : (series string) New line style.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_width(this, width, draw, update)
Sets the line width.
Parameters:
this : (series Line) Line object.
width : (series int) New line width in pixels.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_xloc(this, x1, x2, xloc, draw, update)
Sets x-location and new bar index/time values.
Parameters:
this : (series Line) Line object.
x1 : (series int) Bar index or bar time of the first point.
x2 : (series int) Bar index or bar time of the second point.
xloc : (series string) New x-location value.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_xy1(this, x, y, draw, update)
Sets bar index/time and price of the first point.
Parameters:
this : (series Line) Line object.
x : (series int) Bar index or bar time. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
y : (series int/float) Price.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
set_xy2(this, x, y, draw, update)
Sets bar index/time and price of the second point
Parameters:
this : (series Line) Line object.
x : (series int) Bar index or bar time. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
y : (series int/float) Price.
draw : (series bool) draw line after setting attribute
update : (series bool) update line instead of redraw. Only valid if draw is set.
Returns: Current Line object
delete(this)
Deletes the underlying line drawing object
Parameters:
this : (series Line) Line object.
Returns: Current Line object
Line
Line Wrapper object
Fields:
x1 : (series int) Bar index (if xloc = xloc.bar_index) or bar UNIX time (if xloc = xloc.bar_time) of the first point of the line. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
y1 : (series int/float) Price of the first point of the line.
x2 : (series int) Bar index (if xloc = xloc.bar_index) or bar UNIX time (if xloc = xloc.bar_time) of the second point of the line. Note that objects positioned using xloc.bar_index cannot be drawn further than 500 bars into the future.
y2 : (series int/float) Price of the second point of the line.
xloc : (series string) See description of x1 argument. Possible values: xloc.bar_index and xloc.bar_time. Default is xloc.bar_index.
extend : (series string) If extend=extend.none, draws segment starting at point (x1, y1) and ending at point (x2, y2). If extend is equal to extend.right or extend.left, draws a ray starting at point (x1, y1) or (x2, y2), respectively. If extend=extend.both, draws a straight line that goes through these points. Default value is extend.none.
color : (series color) Line color.
style : (series string) Line style. Possible values: line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_left, line.style_arrow_right, line.style_arrow_both.
width : (series int) Line width in pixels.
obj : line object
L_Trade_BoundariesLibrary "L_Trade_Boundaries"
Trade Boundaries suggest a strength of the security with respect to previous lows. The "L" implies library, and the trade boundaries implies it could be utilized for price strengths. Though, this should not be used as a single parameter to trade wildly. This library can be imported to a custom indicator to utilized the custom functions. There are moving averages attached at the bottom right of the canvas (overlay) to benchmark the closing price with respect to Moving Averages: 20, 28, and 200 (i.e., "D" if timeframe == "D") respectively. The Volume Indicator located at the top of the canvas is a default function (function already made by the trading view) this shows the volume with respect to the selected time frame. All of the indicators tell a story with regard to the security price (in strength terms).
What is available in this Library?
Litmus Color
> This is a function will change color of two numbers, if the first number is less than the second, the color will be red; otherwise, the color will be green.
Lister
> This is simply using an array by revisiting previous lows and plotting to the current time frame (i.e., "D"). There is a custom frequency input for the function, it will go back as much as the implied/specified length. Note: I am still learning how to use array, use this function with discretion. I would also appreciate if there are suggestions commented below.
Moving Average
> This function invokes three moving average metrics: 20, 28, and 200 respectively. The values are displayed at the bottom right of the canvas.
Timeframe Highlight
> This function checks for the input timeframe (i.e., "D", "W", "M") and if the time frame happens to be the same, it will give a "true" result. This result can be utilized for highlighting the positive results on the canvas (the red lines).
litmus_color(value1, value2)
Parameters:
value1
value2
lister(length)
Parameters:
length
moving_averages()
timeframe_highlight(timeframe)
Parameters:
timeframe
[VWMA] Net Volume LibraryLibrary " Net Volume Library"
TODO: The underlying logic and function that calculates the net volume for the Net Volume indicator. Exposes the nv function and nvPoint fields for use.
nv(src, length, useVwma, offset, sigma, multHigh, multMed, multLow)
Parameters:
src : (float) The source price value
length : (int) The lookback length
useVwma : (bool) To use VWMA in the calculation or not
offset : (float) The ALMA offset value
sigma : (int) The ALMA sigma value
multHigh : (float) The multiplier high band
multMed : (float) The multiplier medium band
multLow : (float) The multiplier low band
Returns: Returns the calculated net volume for each band in an nvPoint object
nvPoint
Fields:
h2
h1
h
n
l
l1
l2
ThemeLibraryLibrary "ThemeLibrary"
TODO: add library description here
theme(_theme)
: a library of themed colors
Parameters:
_theme : : the theme color to fetch
Returns: : an array of colors
f_maSelectLibrary "f_maSelect"
Easy to use drop-in facade function to lots of different moving average calculations, including some that are not natively available in PineScript v5 such as Zero-Lag EMA. Simply call f_maSelect(series float serie, simple string ma_type="sma", ma_length=14) instead of a ta.*ma() call and you get access to all MAs offered by PineScript and more.
zema(src, len)
Zero-lag EMA (ZLMA)
Parameters:
src : Input series
len : Lookback period
Returns: Series smoothed with ZLMA
approximate_sma(x, ma_length)
Approximate Standard Moving Average, which substracts the average instead of popping the oldest element, hence losing the base frequency and is why it is approximative. For some reason, this appears to give the same results as a standard RMA
Parameters:
x : Input series.
ma_length : Lookback period.
Returns: Approximate SMA series.
f_maSelect(serie, ma_type, ma_length)
Generalized moving average selector
Parameters:
serie : Input series
ma_type : String describing which moving average to use
ma_length : Lookback period
Returns: Serie smoothed with the selected moving average.
generalized_dev(src, length, avg, lmode)
Generalized deviation calculation: Whereas other Bollinger Bands often just change the basis but not the stdev calculation, the correct way to change the basis is to also change it inside the stdev calculation.
Parameters:
src : Series to use (default: close)
length : Lookback period
avg : Average basis to use to calculate the standard deviation
lmode : L1 or L2 regularization? (ie, lmode=1 uses abs() to cutoff negative values hence it calculates the Mean Absolute Deviation as does the ta.dev(), lmode=2 uses sum of squares hence it calculates the true Standard Deviation as the ta.stdev() function does). See also the research works of everget:
Returns: stdev Standard deviation series
generalized_dev_discount(src, length, avg, lmode, temporal_discount)
Standard deviation calculation but with different probabilities assigned to each bar, with newer bars having more weights en.wikipedia.org
Parameters:
src : Series to use (default: close)
length : Lookback period
avg : Average basis to use to calculate the standard deviation
lmode : L1 or L2 regularization? (ie, lmode=1 uses abs() to cutoff negative values hence it calculates the Mean Absolute Deviation as does the ta.dev(), lmode=2 uses sum of squares hence it calculates the true Standard Deviation as the ta.stdev() function does). See also the research works of everget:
temporal_discount : Probabilistic gamma factor to discount old values in favor of new ones, higher value = more weight to newer bars
Returns: stdev Standard deviation series
median_absdev(src, length, median)
Median Absolute Deviation
Parameters:
src : Input series
length : Lookback period
median : Median already calculated on the input series
Returns: mad, the median absolute deviation value
composite_ticker_cleanerLibrary "composite_ticker_cleaner"
Extract a clean symbol from a composite ticker. E.g., (BINANCE:BTCUSD+KRAKEN:BTCUSD)/2 as input will return BTCUSD or BINANCE:BTCUSD
composite_ticker_cleaner_extract_first(symbol, keepexchange)
Extract the first symbol out of the supplied string (usually ticker.standard(syminfo.tickerid) )
Parameters:
symbol : string input string to search in
keepexchange : bool (optional) Keep exchange in the returned ticker? By default, we only return the symbol without the exchange.
Returns: first occurrence of a symbol
composite_ticker_cleaner_extract_first(keepexchange)
Extract the first symbol out of the current tickerid (as provided by ticker.standard(syminfo.tickerid) )
Parameters:
keepexchange : bool (optional) Keep exchange in the returned ticker? By default, we only return the symbol without the exchange.
Returns: first occurrence of a symbol in the current tickerid
This is inspired by the work I did on this indicator:
I needed a similar functionality in another script, so instead of duplicating code, I thought generalizing the process in a library could be helpful for me and others, and will be easier to maintain and upgrade with new features if I need to.
