HTF Descending TriangleHTF Descending Triangle aims at detecting descending triangles using higher time frame data, without repainting nor misalignment issues.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected descending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a low factor detection criteria to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
Low Factor checkbox: Turns on/off low factor detection criteria.
Low Factor field: Sets low factor to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
High must be lower than previous bar.
Open/close min value must be higher than reference bar low.
When low factor criteria is turned on, low must be lower than reference bar open/close min value minus low factor proportion of the distance between reference bar low and open/close min value.
Cerca negli script per "bar"
HTF Ascending TriangleHTF Ascending Triangle aims at detecting ascending triangles using higher time frame data, without repainting nor misalignment issues.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high factor detection criteria to apply on higher time frame bars high as a proportion of the distance between the reference bar high and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
High Factor checkbox: Turns on/off high factor detection criteria.
High Factor field: Sets high factor to apply on higher time frame bars high as a proportion of the distance between the reference bar high and close/open.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Low must be higher than previous bar.
Open/close max value must be lower than reference bar high.
When high factor criteria is turned on, high must be higher than reference bar open/close max value plus high factor proportion of the distance between reference bar high and open/close max value.
regressionsLibrary "regressions"
This library computes least square regression models for polynomials of any form for a given data set of x and y values.
fit(X, y, reg_type, degrees)
Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.
Parameters:
X (array) : (float ) X inputs for regression fit.
y (array) : (float ) y outputs for regression fit.
reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom
degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.
Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial
regress(reg, x)
Regress one x input.
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
x (float) : (float) The input value cooresponding to the y_pred.
Returns: (float) The best fit y value for the given x input and regression.
predict(reg, X)
Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
X (array)
Returns: (float ) The best fit y values for the given x input and regression.
generate_points(reg, x, y, left_index, right_index)
Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array) : (float ) x values which coorispond to passed y values
y (array) : (float ) y values which coorispond to passed x values
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
Returns: (chart.point ) Returns an array of chart points
plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)
Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array)
y (array)
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
curved (bool) : (bool) If the polyline is curved or not.
close (bool) : (bool) If true the polyline will be closed.
line_color (color) : (color) The color of the line.
line_width (int) : (int) The width of the line.
Returns: (polyline) The polyline for the regression.
series_to_list(src, left_index, right_index)
Convert a series to a list. Creates a list of all the cooresponding source values
from left_index to right_index. This should be called at the highest scope for consistency.
Parameters:
src (float) : (float ) The source the list will be comprised of.
left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.
right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.
Returns: (float ) An array of size left_index-right_index
range_list(start, stop, step)
Creates an from the start value to the stop value.
Parameters:
start (int) : (float ) The true y values.
stop (int) : (float ) The predicted y values.
step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:
Returns: (float ) An array of size stop-start
regression
Fields:
coeffs (array__float)
degrees (array__float)
type_linear (series__string)
type_quadratic (series__string)
type_cubic (series__string)
type_custom (series__string)
_squared_error (series__float)
X (array__float)
Volume Delta Candles [LuxAlgo]Volume Delta Candles provides insights about Intrabar trading activity in an easy-to-interpret manner. Lower timeframe or real-time data is used for displaying Volume Delta percentage against the total volume as a coloured bar part.
The script also highlights the intrabar price with the maximum trading activity, as well as complementary information.
🔶 USAGE
The tool focuses on intrabar volume to provide more information about the trading activity associated with a candle, without having to use an external volume indicator.
Each indicator components is further explained below:
🔹 Volume Delta
The volume delta is obtained by the difference between buy volume and sell volume, where buy volume is the volume associated with a bullish intrabar candle, and sell volume with a bearish intrabar candle.
Positive volume delta is displayed with a green candle area, while negative delta is displayed with a red candle area.
🔹 Bar Coloring
The script displays VD as a percentage of the whole, or from the candle half, depending on the setting ' Display '.
Bars can be coloured as follows:
Full (100%) when Display is set at ' Full Bar '
Half (50% or 100% of half a bar) when Display is set at ' Half Bar '
A negative VD (more bearish than bullish volume) will fill the bar from the top (or centre) of the bar towards the bottom, and a positive VD will fill a bar from the bottom (or centre) of the bar towards the top.
A negative VD on a green candle will show a red-coloured VD against a green-bordered candle. On the other hand, a positive VD on a red candle will show a green-coloured VD against a red-bordered candle.
Colours for VD sentiment opposite to the candle sentiment can be set differently if desired.
🔹 Highest Volume Price Level
The script displays a white (black on light mode) line highlighting the intrabar price level with the highest volume.
When ' Show Previous Max Volume Price ' is checked, a white (black on light mode) dot is displayed 1 bar to the right.
🔶 DETAILS
🔹 Tick/LTF data
The above example used Lower TimeFrame (LTF) data.
The following example uses real-time tick data ( Settings -> Data From )
Both options, LTF or tick data, will show a vertical dotted line where the data starts.
🔹 LTF settings
When ' Data from ' LTF is chosen and ' Auto ' enabled, the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔹 Notes
Different LTFs give different data, which means different results; this doesn't mean it isn't correct; they are just different data sets.
(LTF is displayed at the top right corner)
To ensure maximum visibility of values, we recommend using Bars from the Bar's style menu.
🔶 SETTINGS
Data from: Lower TimeFrame or real-time Tick data
Resolution: LTF setting
Auto + multiple: Adjusts the initial set resolution
Premium: Enable when your TradingView plan is Premium or higher
🔹 Intrabar Data
Colours
Display: Full/Half bar
Show previous max volume price: White/black dot, showing previous highest volume price level
🔹 Table
Show TF: Show LTF at the top right corner
Colour + table text size
🔹 Details
Show details: label with 'Volume', 'Delta' (VD) and '%'
See USAGE for more information
Statistics • Chi Square • P-value • SignificanceThe Statistics • Chi Square • P-value • Significance publication aims to provide a tool for combining different conditions and checking whether the outcome is significant using the Chi-Square Test and P-value.
🔶 USAGE
The basic principle is to compare two or more groups and check the results of a query test, such as asking men and women whether they want to see a romantic or non-romantic movie.
–––––––––––––––––––––––––––––––––––––––––––––
| | ROMANTIC | NON-ROMANTIC | ⬅︎ MOVIE |
–––––––––––––––––––––––––––––––––––––––––––––
| MEN | 2 | 8 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
| WOMEN | 7 | 3 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
|⬆︎ SEX | 10 | 10 | 20 |
–––––––––––––––––––––––––––––––––––––––––––––
We calculate the Chi-Square Formula, which is:
Χ² = Σ ( (Observed Value − Expected Value)² / Expected Value )
In this publication, this is:
chiSquare = 0.
for i = 0 to rows -1
for j = 0 to colums -1
observedValue = aBin.get(i).aFloat.get(j)
expectedValue = math.max(1e-12, aBin.get(i).aFloat.get(colums) * aBin.get(rows).aFloat.get(j) / sumT) //Division by 0 protection
chiSquare += math.pow(observedValue - expectedValue, 2) / expectedValue
Together with the 'Degree of Freedom', which is (rows − 1) × (columns − 1) , the P-value can be calculated.
In this case it is P-value: 0.02462
A P-value lower than 0.05 is considered to be significant. Statistically, women tend to choose a romantic movie more, while men prefer a non-romantic one.
Users have the option to choose a P-value, calculated from a standard table or through a math.ucla.edu - Javascript-based function (see references below).
Note that the population (10 men + 10 women = 20) is small, something to consider.
Either way, this principle is applied in the script, where conditions can be chosen like rsi, close, high, ...
🔹 CONDITION
Conditions are added to the left column ('CONDITION')
For example, previous rsi values (rsi ) between 0-100, divided in separate groups
🔹 CLOSE
Then, the movement of the last close is evaluated
UP when close is higher then previous close (close )
DOWN when close is lower then previous close
EQUAL when close is equal then previous close
It is also possible to use only 2 columns by adding EQUAL to UP or DOWN
UP
DOWN/EQUAL
or
UP/EQUAL
DOWN
In other words, when previous rsi value was between 80 and 90, this resulted in:
19 times a current close higher than previous close
14 times a current close lower than previous close
0 times a current close equal than previous close
However, the P-value tells us it is not statistical significant.
NOTE: Always keep in mind that past behaviour gives no certainty about future behaviour.
A vertical line is drawn at the beginning of the chosen population (max 4990)
Here, the results seem significant.
🔹 GROUPS
It is important to ensure that the groups are formed correctly. All possibilities should be present, and conditions should only be part of 1 group.
In the example above, the two top situations are acceptable; close against close can only be higher, lower or equal.
The two examples at the bottom, however, are very poorly constructed.
Several conditions can be placed in more than 1 group, and some conditions are not integrated into a group. Even if the results are significant, they are useless because of the group formation.
A population count is added as an aid to spot errors in group formation.
In this example, there is a discrepancy between the population and total count due to the absence of a condition.
The results when rsi was between 5-25 are not included, resulting in unreliable results.
🔹 PRACTICAL EXAMPLES
In this example, we have specific groups where the condition only applies to that group.
For example, the condition rsi > 55 and rsi <= 65 isn't true in another group.
Also, every possible rsi value (0 - 100) is present in 1 of the groups.
rsi > 15 and rsi <= 25 28 times UP, 19 times DOWN and 2 times EQUAL. P-value: 0.01171
When looking in detail and examining the area 15-25 RSI, we see this:
The population is now not representative (only checking for RSI between 15-25; all other RSI values are not included), so we can ignore the P-value in this case. It is merely to check in detail. In this case, the RSI values 23 and 24 seem promising.
NOTE: We should check what the close price did without any condition.
If, for example, the close price had risen 100 times out of 100, this would make things very relative.
In this case (at least two conditions need to be present), we set 1 condition at 'always true' and another at 'always false' so we'll get only the close values without any condition:
Changing the population or the conditions will change the P-value.
In the following example, the outcome is evaluated when:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is lower/equal than the close value from 2 bars back
Or:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is equal than the close value from 2 bars back
close value from 1 bar back is lower than the close value from 2 bars back
In both examples, all possibilities of close against close are included in the calculations. close can only by higher, equal or lower than close
Both examples have the results without a condition included (5 = 5 and 5 < 5) so one can compare the direction of current close.
🔶 NOTES
• Always keep in mind that:
Past behaviour gives no certainty about future behaviour.
Everything depends on time, cycles, events, fundamentals, technicals, ...
• This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc., but not numerical data such as height or weight. One might argue that such tests shouldn't use rsi, close, ... values.
• Consider what you're measuring
For example rsi of the current bar will always lead to a close higher than the previous close, since this is inherent to the rsi calculations.
• Be careful; often, there are na -values at the beginning of the series, which are not included in the calculations!
• Always keep in mind considering what the close price did without any condition
• The numbers must be large enough. Each entry must be five or more. In other words, it is vital to make the 'population' large enough.
• The code can be developed further, for example, by splitting UP, DOWN in close UP 1-2%, close UP 2-3%, close UP 3-4%, ...
• rsi can be supplemented with stochRSI, MFI, sma, ema, ...
🔶 SETTINGS
🔹 Population
• Choose the population size; in other words, how many bars you want to go back to. If fewer bars are available than set, this will be automatically adjusted.
🔹 Inputs
At least two conditions need to be chosen.
• Users can add up to 11 conditions, where each condition can contain two different conditions.
🔹 RSI
• Length
🔹 Levels
• Set the used levels as desired.
🔹 Levels
• P-value: P-value retrieved using a standard table method or a function.
• Used function, derived from Chi-Square Distribution Function; JavaScript
LogGamma(Z) =>
S = 1
+ 76.18009173 / Z
- 86.50532033 / (Z+1)
+ 24.01409822 / (Z+2)
- 1.231739516 / (Z+3)
+ 0.00120858003 / (Z+4)
- 0.00000536382 / (Z+5)
(Z-.5) * math.log(Z+4.5) - (Z+4.5) + math.log(S * 2.50662827465)
Gcf(float X, A) => // Good for X > A +1
A0=0., B0=1., A1=1., B1=X, AOLD=0., N=0
while (math.abs((A1-AOLD)/A1) > .00001)
AOLD := A1
N += 1
A0 := A1+(N-A)*A0
B0 := B1+(N-A)*B0
A1 := X*A0+N*A1
B1 := X*B0+N*B1
A0 := A0/B1
B0 := B0/B1
A1 := A1/B1
B1 := 1
Prob = math.exp(A * math.log(X) - X - LogGamma(A)) * A1
1 - Prob
Gser(X, A) => // Good for X < A +1
T9 = 1. / A
G = T9
I = 1
while (T9 > G* 0.00001)
T9 := T9 * X / (A + I)
G := G + T9
I += 1
G *= math.exp(A * math.log(X) - X - LogGamma(A))
Gammacdf(x, a) =>
GI = 0.
if (x<=0)
GI := 0
else if (x
Chisqcdf = Gammacdf(Z/2, DF/2)
Chisqcdf := math.round(Chisqcdf * 100000) / 100000
pValue = 1 - Chisqcdf
🔶 REFERENCES
mathsisfun.com, Chi-Square Test
Chi-Square Distribution Function
Xen's Flag Pattern Scalper1. Input Parameters:
FlagLength: Determines the length of the flag pattern.
TakeProfit1Ratio, takeProfit2Ratio, takeProfit3Ratio: Define the ratios for calculating
the take-profit levels relative to the entry price.
RiskRewardRatio: Specifies the risk-reward ratio for calculating the stop-loss level
relative to the entry price.
2 Flag Conditions:
BullishFlag: Checks if the current bar meets the conditions for a bullish flag pattern. It
evaluates to true if the low of the current bar is lower than the low flagLength bars
ago, and the close of the current bar is higher than the high flagLength bars ago.
BearishFlag: Checks if the current bar meets the conditions for a bearish flag pattern. It evaluates to true if the high of the current bar is higher than the high flagLength bars
ago, and the close of the current bar is lower than the low flagLength bars ago.
3. Entry Price:
EntryPrice: Calculates the entry price based on whether a bullish or bearish flag
pattern is identified. For a bullish flag, the entry price is set to the low of the current bar.
For a bearish flag, the entry price is set to the high of the current bar.
4. Stop Loss:
StopLoss: Determines the stop-loss level based on the entry price and the specified
riskRewardRatio . For a bullish flag, the stop-loss level is calculated by subtracting the
difference between the high and low of the current bar multiplied by the riskRewardRatio from the low of the current bar. For a bearish flag, the stop-loss level
is calculated similarly but added to the high of the current bar.
5. Take Profit Levels:
Three take-profit levels ( takeProfit1, takeProfit2, takeProfit3 ) are calculated based on
the entry price, stop-loss level, and specified take-profit ratios ( takeProfit1Ratio,
takeProfit2Ratio, takeProfit3Ratio ).
6. Plotting Signals and Levels:
Bullish and bearish flag patterns are plotted using triangle shapes ( shape.triangleup for
bullish and shape.triangledown for bearish) above or below the bars, respectively.
Entry, stop-loss, and take-profit levels are plotted using horizontal lines ( line.new )
with different colors and styles. Entry and stop-loss levels are labeled with "Entry" and "SL",
respectively, while take-profit levels are labeled with "TP 1", "TP 2", and "TP 3".
The colors for bullish flags are white for entry, red for stop-loss, and green for take-profit levels. For bearish flags, the colors are the same, but the labels are plotted above the bars.
7. Label Placement:
Labels for entry, stop-loss, and take-profit levels are placed a distance of 4 bars to the right
of the entry price using bar_index + 4 .
This indicator is intended to help traders identify flag patterns on price charts and visualize potential entry, stop-loss, and take-profit levels associated with these patterns.
Please use risk management and when TP1 is hit, move stoploss to breakeven .
ottlibLibrary "ottlib"
█ OVERVIEW
This library contains functions for the calculation of the OTT (Optimized Trend Tracker) and its variants, originally created by Anıl Özekşi (Anil_Ozeksi). Special thanks to him for the concept and to Kıvanç Özbilgiç (KivancOzbilgic) and dg_factor (dg_factor) for adapting them to Pine Script.
█ WHAT IS "OTT"
The OTT (Optimized Trend Tracker) is a highly customizable and very effective trend-following indicator that relies on moving averages and a trailing stop at its core. Moving averages help reduce noise by smoothing out sudden price movements in the markets, while trailing stops assist in detecting trend reversals with precision. Initially developed as a noise-free trailing stop, the current variants of OTT range from rapid trend reversal detection to long-term trend confirmation, thanks to its extensive customizability.
It's well-known variants are:
OTT (Optimized Trend Tracker).
TOTT (Twin OTT).
OTT Channels.
RISOTTO (RSI OTT).
SOTT (Stochastic OTT).
HOTT & LOTT (Highest & Lowest OTT)
ROTT (Relative OTT)
FT (Original name is Fırsatçı Trend in Turkish which translates to Opportunist Trend)
█ LIBRARY FEATURES
This library has been prepared in accordance with the style, coding, and annotation standards of Pine Script version 5. As a result, explanations and examples will appear when users hover over functions or enter function parameters in the editor.
█ USAGE
Usage of this library is very simple. Just import it to your script with the code below and use its functions.
import ismailcarlik/ottlib/1 as ottlib
█ FUNCTIONS
• f_vidya(source, length, cmoLength)
Short Definition: Chande's Variable Index Dynamic Average (VIDYA).
Details: This function computes Chande's Variable Index Dynamic Average (VIDYA), which serves as the original moving average for OTT. The 'length' parameter determines the number of bars used to calculate the average of the given source. Lower values result in less smoothing of prices, while higher values lead to greater smoothing. While primarily used internally in this library, it has been made available for users who wish to utilize it as a moving average or use in custom OTT implementations.
Parameters:
source (float) : (series float) Series of values to process.
length (simple int) : (simple int) Number of bars to lookback.
cmoLength (simple int) : (simple int) Number of bars to lookback for calculating CMO. Default value is `9`.
Returns: (float) Calculated average of `source` for `length` bars back.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
plot(vidyaValue, color = color.blue)
• f_mostTrail(source, multiplier)
Short Definition: Calculates trailing stop value.
Details: This function calculates the trailing stop value for a given source and the percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although only used once internally in this library, it has been made available for users who wish to utilize it as a traditional trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of trailing stop.
Example:
emaValue = ta.ema(source = close, length = 14)
mostValue = ottlib.f_mostTrail(source = emaValue, multiplier = 2.0)
plot(mostValue, color = emaValue >= mostValue ? color.green : color.red)
• f_ottTrail(source, multiplier)
Short Definition: Calculates OTT-specific trailing stop value.
Details: This function calculates the trailing stop value for a given source in the manner used in OTT. Unlike a traditional trailing stop, this function modifies the traditional trailing stop value from two bars prior by adjusting it further with half the specified percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although primarily used internally in this library, it has been made available for users who wish to utilize it as a trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of OTT-specific trailing stop.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
ottValue = ottlib.f_ottTrail(source = vidyaValue, multiplier = 1.5)
plot(ottValue, color = vidyaValue >= ottValue ? color.green : color.red)
• ott(source, length, multiplier)
Short Definition: Calculates OTT (Optimized Trend Tracker).
Details: The OTT consists of two lines. The first, known as the "Support Line", is the VIDYA of the given source. The second, called the "OTT Line", is the trailing stop based on the Support Line. The market is considered to be in an uptrend when the Support Line is above the OTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ottLine`.
Example:
= ottlib.ott(source = close, length = 2, multiplier = 1.4)
longCondition = ta.crossover(supportLine, ottLine)
shortCondition = ta.crossunder(supportLine, ottLine)
• tott(source, length, multiplier, bandsMultiplier)
Short Definition: Calculates TOTT (Twin OTT).
Details: TOTT consists of three lines: the "Support Line," which is the VIDYA of the given source; the "Upper Line," a trailing stop of the Support Line adjusted with an added multiplier; and the "Lower Line," another trailing stop of the Support Line, adjusted with a reduced multiplier. The market is considered in an uptrend if the Support Line is above the Upper Line and in a downtrend if it is below the Lower Line.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `40`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
bandsMultiplier (simple float) : Multiplier for bands. Default value is `0.0006`.
Returns: ( [ float, float, float ]) Tuple of `supportLine`, `upperLine` and `lowerLine`.
Example:
= ottlib.tott(source = close, length = 40, multiplier = 0.6, bandsMultiplier = 0.0006)
longCondition = ta.crossover(supportLine, upperLine)
shortCondition = ta.crossunder(supportLine, lowerLine)
• ott_channel(source, length, multiplier, ulMultiplier, llMultiplier)
Short Definition: Calculates OTT Channels.
Details: OTT Channels comprise nine lines. The central line, known as the "Mid Line," is the OTT of the given source's VIDYA. The remaining lines are positioned above and below the Mid Line, shifted by specified multipliers.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`
ulMultiplier (simple float) : (simple float) Multiplier for upper line. Default value is `0.01`
llMultiplier (simple float) : (simple float) Multiplier for lower line. Default value is `0.01`
Returns: ( [ float, float, float, float, float, float, float, float, float ]) Tuple of `ul4`, `ul3`, `ul2`, `ul1`, `midLine`, `ll1`, `ll2`, `ll3`, `ll4`.
Example:
= ottlib.ott_channel(source = close, length = 2, multiplier = 1.4, ulMultiplier = 0.01, llMultiplier = 0.01)
• risotto(source, length, rsiLength, multiplier)
Short Definition: Calculates RISOTTO (RSI OTT).
Details: RISOTTO comprised of two lines: the "Support Line," which is the VIDYA of the given source's RSI value, calculated based on the length parameter, and the "RISOTTO Line," a trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the RISOTTO Line, and in a downtrend if it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `50`.
rsiLength (simple int) : (simple int) Number of bars used for RSI calculation. Default value is `100`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.2`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `risottoLine`.
Example:
= ottlib.risotto(source = close, length = 50, rsiLength = 100, multiplier = 0.2)
longCondition = ta.crossover(supportLine, risottoLine)
shortCondition = ta.crossunder(supportLine, risottoLine)
• sott(source, kLength, dLength, multiplier)
Short Definition: Calculates SOTT (Stochastic OTT).
Details: SOTT is comprised of two lines: the "Support Line," which is the VIDYA of the given source's Stochastic value, based on the %K and %D lengths, and the "SOTT Line," serving as the trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the SOTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
kLength (simple int) : (simple int) Stochastic %K length. Default value is `500`.
dLength (simple int) : (simple int) Stochastic %D length. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.5`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `sottLine`.
Example:
= ottlib.sott(source = close, kLength = 500, dLength = 200, multiplier = 0.5)
longCondition = ta.crossover(supportLine, sottLine)
shortCondition = ta.crossunder(supportLine, sottLine)
• hottlott(length, multiplier)
Short Definition: Calculates HOTT & LOTT (Highest & Lowest OTT).
Details: HOTT & LOTT are composed of two lines: the "HOTT Line", which is the OTT of the highest price's VIDYA, and the "LOTT Line", the OTT of the lowest price's VIDYA. A high price surpassing the HOTT Line can be considered a long signal, while a low price dropping below the LOTT Line may indicate a short signal.
Parameters:
length (simple int) : (simple int) Number of bars to lookback. Default value is `20`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
Returns: ( [ float, float ]) Tuple of `hottLine` and `lottLine`.
Example:
= ottlib.hottlott(length = 20, multiplier = 0.6)
longCondition = ta.crossover(high, hottLine)
shortCondition = ta.crossunder(low, lottLine)
• rott(source, length, multiplier)
Short Definition: Calculates ROTT (Relative OTT).
Details: ROTT comprises two lines: the "Support Line", which is the VIDYA of the given source, and the "ROTT Line", the OTT of the Support Line's VIDYA. The market is considered in an uptrend if the Support Line is above the ROTT Line, and in a downtrend if it is below. ROTT is similar to OTT, but the key difference is that the ROTT Line is derived from the VIDYA of two bars of Support Line, not directly from it.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.1`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `rottLine`.
Example:
= ottlib.rott(source = close, length = 200, multiplier = 0.1)
isUpTrend = supportLine > rottLine
isDownTrend = supportLine < rottLine
• ft(source, length, majorMultiplier, minorMultiplier)
Short Definition: Calculates Fırsatçı Trend (Opportunist Trend).
Details: FT is comprised of two lines: the "Support Line", which is the VIDYA of the given source, and the "FT Line", a trailing stop of the Support Line calculated using both minor and major trend values. The market is considered in an uptrend when the Support Line is above the FT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `30`.
majorMultiplier (simple float) : (simple float) Percent of major trend. Default value is `3.6`.
minorMultiplier (simple float) : (simple float) Percent of minor trend. Default value is `1.8`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ftLine`.
Example:
= ottlib.ft(source = close, length = 30, majorMultiplier = 3.6, minorMultiplier = 1.8)
longCondition = ta.crossover(supportLine, ftLine)
shortCondition = ta.crossunder(supportLine, ftLine)
█ CUSTOM OTT CREATION
Users can create custom OTT implementations using f_ottTrail function in this library. The example code which uses EMA of 7 period as moving average and calculates OTT based of it is below.
Source Code:
//@version=5
indicator("Custom OTT", shorttitle = "COTT", overlay = true)
import ismailcarlik/ottlib/1 as ottlib
src = input.source(close, title = "Source")
length = input.int(7, title = "Length", minval = 1)
multiplier = input.float(2.0, title = "Multiplier", minval = 0.1)
support = ta.ema(source = src, length = length)
ott = ottlib.f_ottTrail(source = support, multiplier = multiplier)
pSupport = plot(support, title = "Moving Average Line (Support)", color = color.blue)
pOtt = plot(ott, title = "Custom OTT Line", color = color.orange)
fillColor = support >= ott ? color.new(color.green, 60) : color.new(color.red, 60)
fill(pSupport, pOtt, color = fillColor, title = "Direction")
Result:
█ DISCLAIMER
Trading is risky and most of the day traders lose money eventually. This library and its functions are only for educational purposes and should not be construed as financial advice. Past performances does not guarantee future results.
A_Traders_Edge__LibraryLibrary "A_Traders_Edge__Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing Market Overview Scanners (MOS)
LabelLocation(_firstLocation)
This function is used when there's a desire to print an assets ALERT LABELS at a set location on the scale that will
NOT change throughout the progression of the script. This is created so that if a lot of alerts are triggered, they
will stay relatively visible and not overlap each other. Ex. If you set your '_firstLocation' parameter as 1, since
there are a max of 40 assets that can be scanned, the 1st asset's location is assigned the value in the '_firstLocation' parameter,
the 2nd asset's location is the (1st asset's location+1)...and so on. If your first location is set to 81 then
the 1st asset is 81 and 2nd is 82 and so on until the 40th location = 120(in this particular example).
Parameters:
_firstLocation (simple int) : (simple int)
Optional(starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables each being a different location to print the labels so that an asset is asssigned to
a particular location on the scale. Regardless of if you have the maximum amount of assets being screened (40 max), this
function will output 40 locations… So there needs to be 40 variables assigned in the tuple in this function. What I
mean by that is you need to have 40 output location variables within your tuple (ie. between the ' ') regarless of
if your scanning 40 assets or not. If you only have 20 assets in your scripts input settings, then only the first 20
variables within the ' ' Will be assigned to a value location and the other 20 will be assigned 'NA', but their
variables still need to be present in the tuple.
SeparateTickerids(_string)
You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your MO scanner.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my MOS Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
Returns: Returns 40 output variables of the different strings of TickerID's (ie. you need to output 40 variables within the
tuple ' ' regardless of if you were scanning using all possible (40) assets or not.
If your scanning for less than 40 assets, then once the variables are assigned to all of the tickerIDs, the rest
of the 40 variables in the tuple will be assigned "NA".
TickeridForLabelsAndSecurity(_includeExchange, _ticker)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_includeExchange (simple bool) : (bool)
Optional(if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your MOS as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the MOS's input.
InvalidTID(_tablePosition, _stackVertical, _close, _securityTickerid, _invalidArray)
This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
Parameters:
_tablePosition (simple string) : (string)
Optional(if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional(if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here.
_close (float) : (float)
If you want them printed as a single row then input 'false' here.
This should be the closing value of each of the assets being tested to determine in the TickerID is valid or not.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never regestered then the logic counts the asset as INVALID.
This will be the 1st TickerID varible (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (string ) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
Returns: (na)
Returns a table with the screened assets Invalid TickerID's. Table draws automatically if any are Invalid, thus,
no output variable to deal with.
LabelSizes(_barCnt, _lblSzRfrnce)
This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your MOS includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the 'PrintedBarCount' function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') outputted by this function.
_lblSzRfrnce (string) : (string)
Optional(if parameter not included, it defaults to size.small). This will be the size of the 1st variable outputted
by this function ('assetNameLabel') BUT also affects the 2nd variable outputted by this function.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
AssetColor()
This function is used to assign 40 different colors to 40 variables to be used for the different labels/plots.
Returns: Returns 40 output variables each with a different color assigned to them to be used in your plots & labels.
Regardless of if you have the maximum amount of assets your scanning(40 max) or less,
this function will assign 40 colors to 40 variables that you have between the ' '.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
RCI(_rciLength, _source, _interval)
You will see me using this a lot. DEFINITELY my favorite oscillator to utilize for SO many different things from
timing entries/exits to determining trends.Calculation of this indicator based on Spearmans Correlation.
Parameters:
_rciLength (int) : (int)
Amount of bars back to use in RCI calculations.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). RCI calculation groups bars by this amount and then will.
rank these groups of bars.
Returns: (float)
Returns a single RCI value that will oscillates between -100 and +100.
RCIAVG(firstLength, _amtBtLengths, _rciSMAlen, _source, _interval)
20 RCI's are averaged together to get this RCI Avg (Rank Correlation Index Average). Each RCI (of the 20 total RCI)
has a progressively LARGER Lookback Length. Though the RCI Lengths are not individually adjustable,
there are 2 factors that ARE:
(1) the Lookback Length of the 1st RCI and
(2) the amount of values between one RCI's Lookback Length and the next.
*** If you set 'firstLength' to it's default of 200 and '_amtBtLengths' to it's default of 120 (aka AMOUNT BETWEEN LENGTHS=120)...
then RCI_2 Length=320, RCI_3 Length=440, RCI_4 Length=560, and so on.
Parameters:
firstLength (int) : (int)
Optional(if parameter is not included when the function is called, then it defaults to 200).
This parameter is the Lookback Length for the 1st RCI used in the RCI Avg.
_amtBtLengths (int) : (int)
Optional(if parameter not included when the function is called, then it defaults to 120).
This parameter is the value amount between each of the progressively larger lengths used for the 20 RCI's that
are averaged in the RCI Avg.
***** BEWARE ***** Too large of a value here will cause the calc to look back too far, causing an error(thus the value must be lowered)
_rciSMAlen (int) : (int)
Unlike the Single RCI Function, this function smooths out the end result using an SMA with a length value that is this parameter.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). Within the RCI calculation, bars next to each other are grouped together
and then these groups are Ranked against each other. This parameter is the number of adjacent bars that are grouped together.
Returns: (float)
Returns a single RCI value that is the Avg of many RCI values that will oscillate between -100 and +100.
PercentChange(_startingValue, _endingValue)
This is a quick function to calculate how much % change has occurred between the '_startingValue' and the '_endingValue'
that you input into the function.
Parameters:
_startingValue (float) : (float)
The source value to START the % change calculation from.
_endingValue (float) : (float)
The source value to END the % change caluclation from.
Returns: Returns a single output being the % value between 0-100 (with trailing numbers behind a decimal). If you want only
a certain amount of numbers behind the decimal, this function needs to be put within a formatting function to do so.
Rescale(_source, _oldMin, _oldMax, _newMin, _newMax)
Rescales series with a known '_oldMin' & '_oldMax'. Use this when the scale of the '_source' to
rescale is known (bounded).
Parameters:
_source (float) : (float)
Source to be normalized.
_oldMin (int) : (float)
The known minimum of the '_source'.
_oldMax (int) : (float)
The known maximum of the '_source'.
_newMin (int) : (float)
What you want the NEW minimum of the '_source' to be.
_newMax (int) : (float)
What you want the NEW maximum of the '_source' to be.
Returns: Outputs your previously bounded '_source', but now the value will only move between the '_newMin' and '_newMax'
values you set in the variables.
Normalize_Historical(_source, _minimumLvl, _maximumLvl)
Normalizes '_source' that has a previously unknown min/max(unbounded) determining the max & min of the '_source'
FROM THE ENTIRE CHARTS HISTORY. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns your same '_source', but now the value will MOSTLY stay between the minimum and maximum values you set in the
'_minimumLvl' and '_maximumLvl' variables (ie. if the source you input is an RSI...the output is the same RSI value but
instead of moving between 0-100 it will move between the maxand min you set).
Normailize_Local(_source, _length, _minimumLvl, _maximumLvl)
Normalizes series with previously unknown min/max(unbounded). Much like the Normalize_Historical function above this one,
but rather than using the Highest/Lowest Values within the ENTIRE charts history, this on looks for the Highest/Lowest
values of '_source' within the last ___ bars (set by user as/in the '_length' parameter. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_length (int) : (float)
The amount of bars to look back to determine the highest/lowest '_source' value.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns a single output variable being the previously unbounded '_source' that is now normalized and bound between
the values used for '_minimumLvl'/'_maximumLvl' of the '_source' within the user defined lookback period.
ATR GOD Strategy by TradeSmart (PineConnector-compatible)This is a highly-customizable trading strategy made by TradeSmart, focusing mainly on ATR-based indicators and filters. The strategy is mainly intended for trading forex , and has been optimized using the Deep Backtest feature on the 2018.01.01 - 2023.06.01 interval on the EUR/USD (FXCM) 15M chart, with a Slippage value of 3, and a Commission set to 0.00004 USD per contract. The strategy is also made compatible with PineConnector , to provide an easy option to automate the strategy using a connection to MetaTrader. See tooltips for details on how to set up the bot, and check out our website for a detailed guide with images on how to automate the strategy.
The strategy was implemented using the following logic:
Entry strategy:
A total of 4 Supertrend values can be used to determine the entry logic. There is option to set up all 4 Supertrend parameters individually, as well as their potential to be used as an entry signal/or a trend filter. Long/Short entry signals will be determined based on the selected potential Supertrend entry signals, and filtered based on them being in an uptrend/downtrend (also available for setup). Please use the provided tooltips for each setup to see every detail.
Exit strategy:
4 different types of Stop Losses are available: ATR-based/Candle Low/High Based/Percentage Based/Pip Based. Additionally, Force exiting can also be applied, where there is option to set up 4 custom sessions, and exits will happen after the session has closed.
Parameters of every indicator used in the strategy can be tuned in the strategy settings as follows:
Plot settings:
Plot Signals: true by default, Show all Long and Short signals on the signal candle
Plot SL/TP lines: false by default, Checking this option will result in the TP and SL lines to be plotted on the chart.
Supertrend 1-4:
All the parameters of the Supertrends can be set up here, as well as their individual role in the entry logic.
Exit Strategy:
ATR Based Stop Loss: true by default
ATR Length (of the SL): 100 by default
ATR Smoothing (of the SL): RMA/SMMA by default
Candle Low/High Based Stop Loss: false by default, recent lowest or highest point (depending on long/short position) will be used to calculate stop loss value. Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Candle Lookback (of the SL): 50 by default
Percentage Based Stop Loss: false by default, Set the stop loss to current price - % of current price (long) or price + % of current price (short).
Percentage (of the SL): 0.3 by default
Pip Based Stop Loss: Set the stop loss to current price - x pips (long) or price + x pips (short). Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Pip (of the SL): 10 by default
Base Risk Multiplier: 4.5 by default, the stop loss will be placed at this risk level (meaning in case of ATR SL that the ATR value will be multiplied by this factor and the SL will be placed that value away from the entry level)
Risk to Reward Ratio: 1.5 by default, the take profit level will be placed such as this Risk/Reward ratio is met
Force Exiting:
4 total Force exit on custom session close options: none applied by default. If enabled, trades will close automatically after the set session is closed (on next candle's open).
Base Setups:
Allow Long Entries: true by default
Allow Short Entries: true by default
Order Size: 10 by default
Order Type: Capital Percentage by default, allows adjustment on how the position size is calculated: Cash: only the set cash amount will be used for each trade Contract(s): the adjusted number of contracts will be used for each trade Capital Percentage: a % of the current available capital will be used for each trade
ATR Limiter:
Use ATR Limiter: true by default, Only enter into any position (long/short) if ATR value is higher than the Low Boundary and lower than the High Boundary.
ATR Limiter Length: 50 by default
ATR Limiter Smoothing: RMA/SMMA by default
High Boundary: 1000 by default
Low Boundary: 0.0003 by default
MA based calculation: ATR value under MA by default, If not Unspecified, an MA is calculated with the ATR value as source. Only enter into position (long/short) if ATR value is higher/lower than the MA.
MA Type: RMA/SMMA by default
MA Length: 400 by default
Waddah Attar Filter:
Explosion/Deadzone relation: Not specified by default, Explosion over Deadzone: trades will only happen if the explosion line is over the deadzone line; Explosion under Deadzone: trades will only happen if the explosion line is under the deadzone line; Not specified: the opening of trades will not be based on the relation between the explosion and deadzone lines.
Limit trades based on trends: Not specified by default, Strong Trends: only enter long if the WA bar is colored green (there is an uptrend and the current bar is higher then the previous); only enter short if the WA bar is colored red (there is a downtrend and the current bar is higher then the previous); Soft Trends: only enter long if the WA bar is colored lime (there is an uptrend and the current bar is lower then the previous); only enter short if the WA bar is colored orange (there is a downtrend and the current bar is lower then the previous); All Trends: only enter long if the WA bar is colored green or lime (there is an uptrend); only enter short if the WA bar is colored red or orange (there is a downtrend); Not specified: the color of the WA bar (trend) is not relevant when considering entries.
WA bar value: Not specified by default, Over Explosion and Deadzone: only enter trades when the WA bar value is over the Explosion and Deadzone lines; Not specified: the relation between the explosion/deadzone lines to the value of the WA bar will not be used to filter opening trades.
Sensitivity: 150 by default
Fast MA Type: SMA by default
Fast MA Length: 10 by default
Slow MA Type: SMA
Slow MA Length: 20 by default
Channel MA Type: EMA by default
BB Channel Length: 20 by default
BB Stdev Multiplier: 2 by default
Trend Filter:
Use long trend filter 1: false by default, Only enter long if price is above Long MA.
Show long trend filter 1: false by default, Plot the selected MA on the chart.
TF1 - MA Type: EMA by default
TF1 - MA Length: 120 by default
TF1 - MA Source: close by default
Use short trend filter 1: false by default, Only enter long if price is above Long MA.
Show short trend filter 1: false by default, Plot the selected MA on the chart.
TF2 - MA Type: EMA by default
TF2 - MA Length: 120 by default
TF2 - MA Source: close by default
Volume Filter:
Only enter trades where volume is higher then the volume-based MA: true by default, a set type of MA will be calculated with the volume as source, and set length
MA Type: RMA/SMMA by default
MA Length: 200 by default
Date Range Limiter:
Limit Between Dates: false by default
Start Date: Jan 01 2023 00:00:00 by default
End Date: Jun 24 2023 00:00:00 by default
Session Limiter:
Show session plots: false by default, show market sessions on chart: Sidney (red), Tokyo (orange), London (yellow), New York (green)
Use session limiter: false by default, if enabled, trades will only happen in the ticked sessions below.
Sidney session: false by default, session between: 15:00 - 00:00 (EST)
Tokyo session: false by default, session between: 19:00 - 04:00 (EST)
London session: false by default, session between: 03:00 - 11:00 (EST)
New York session: false by default, session between: 08:00 - 17:00 (EST)
Trading Time:
Limit Trading Time: true by default, tick this together with the options below to enable limiting based on day and time
Valid Trading Days Global: 123567 by default, if the Limit Trading Time is on, trades will only happen on days that are present in this field. If any of the not global Valid Trading Days is used, this field will be neglected. Values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) To trade on all days use: 123457
(1) Valid Trading Days: false, 123456 by default, values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) The script will trade on days that are present in this field. Please make sure that this field and also (1) Valid Trading Hours Between is checked
(1) Valid Trading Hours Between: false, 1800-2000 by default, hours between which the trades can happen. The time is always in the exchange's timezone
All other options are also disabled by default
PineConnector Automation:
Use PineConnector Automation: false by default, In order for the connection to MetaTrader to work, you will need do perform prerequisite steps, you can follow our full guide at our website, or refer to the official PineConnector Documentation. To set up PineConnector Automation on the TradingView side, you will need to do the following:
1. Fill out the License ID field with your PineConnector License ID;
2. Fill out the Risk (trading volume) with the desired volume to be traded in each trade (the meaning of this value depends on the EA settings in Metatrader. Follow the detailed guide for additional information);
3. After filling out the fields, you need to enable the 'Use PineConnector Automation' option (check the box in the strategy settings);
4. Check if the chart has updated and you can see the appropriate order comments on your chart;
5. Create an alert with the strategy selected as Condition, and the Message as {{strategy.order.comment}} (should be there by default);
6. Enable the Webhook URL in the Notifications section, set it as the official PineConnector webhook address and enjoy your connection with MetaTrader.
License ID: 60123456789 by default
Risk (trading volume): 1 by default
NOTE! Fine-tuning/re-optimization is highly recommended when using other asset/timeframe combinations.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (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%)
Examples:
Sniffer
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Overview
A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results.
This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space.
Background
In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer .
Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user.
How It Works
A brief overview of how the tool is implemented:
- Every bar is processed, and a set of features are mapped to it.
- Bars selected by the user are captured, and saved for later use.
- Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars).
- Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc.
- Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors.
A brief overview of the features space for bars:
- Range: Difference between high & low
- Body: Difference between close & open
- Volume: Traded volume for that candle
- Head: Upper wick for green candles & lower wick for red candles
- Tail: Lower wick for green candles & upper wick for red candles
- BTR: Body to Range ratio
- HTR: Head to Range ratio
- TTR: Tail to Range ratio
- HTB: Head to Body ratio
- TTB: Tail to Body ratio
- ROC: Rate of change for HL2 for four different periods
- RSI: Relative Strength Index
- CCI: Commodity Channel Index
- Stochastic: Stochastic Index
- ADX: DMI+, DMI- & ADX
A brief overview of how degree of similarity is calculated:
- Each bar set is compared to the inout bar set within the selected feature space
- Features are represented as vectors, and distance between the vectors is calculated
- Shorter the distance, greater the similarity
- Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson
- Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case
How To Use It
- Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically
- Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations
- Boxes will be draw around the clusters that fit the matching criteria
- Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input
- Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period
User Settings
Users can configure the following options:
- Select the time-range to set input bars
- Select the look-back period, number of candles to backtrack for similarity search
- Select the number of top-n matches to show on the chart
- Select the method for similarity calculation
- Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc
- Toggle verbosity, shows degree of similarity as a percentage value inside the box
Top Features
- Pattern Agnostic: Designed to work with variable number of candles & complex patterns
- Customisable Feature Space: Users get to add custom features to each bar
- Comprehensive Comparison: Generates a degree of similarity for all possible combinations
Final Note
- Similarity matches will be shown only within last 4500 bars.
- In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results.
- As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range.
- At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome.
Credits
- @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier.
Oliver Velez IndicatorOliver Velez is a well-known trader and educator who has developed multiple trading strategies. One of them is the 20-200sma strategy, which is a basic moving average crossover strategy. The strategy involves using two simple moving averages (SMAs) - a short-term SMA with a period of 20 and a long-term SMA with a period of 200 - on a 2-minute timeframe chart.
When the short-term SMA crosses above the long-term SMA, it signals a potential bullish trend and traders may look for opportunities to enter a long position. Conversely, when the short-term SMA crosses below the long-term SMA, it signals a potential bearish trend and traders may look for opportunities to enter a short position.
Traders using this strategy may also look for additional confirmations, such as price action signals or other technical indicators, before entering or exiting a trade. It is important to note that no trading strategy can guarantee profits, and traders should always use risk management techniques to limit potential losses.
This script is an implementation of the 2 SMA's (can also choose other types of MA's), with Elephant Bar Indicator (EBI) and the Tail Bars Indicator in TradingView.
The Elephant Bar Indicator is a technical indicator used in trading to identify potential trend reversals in the market. It is named after the large size of the bullish or bearish candlestick that it represents. The Tail Bars Indicator is a pattern recognition technique that identifies candlestick patterns with long tails or wicks.
The script starts by defining the input parameters for both indicators. For the Elephant Bar Indicator, the user inputs the lookback period and the size multiplier. For the Tail Bars Indicator, the user inputs the tail ratio and opposite wick ratio.
Next, the script calculates the moving averages of the closing price over the defined short and long periods using the Moving Average function. The script then calculates the average candle size and volume over the lookback period.
The script then identifies the Elephant Bars and Tail Bars using the input parameters and additional conditions. For Elephant Bars, the script identifies bullish and bearish bars that meet certain criteria, such as a size greater than the average candle size and volume greater than the average volume.
For Tail Bars, the script identifies bullish and bearish bars that have long tails or wicks and meet certain criteria such as opposite wick size less than or equal to the tail size multiplied by the input opposite wick ratio.
Finally, the script plots the Elephant Bar and Tail Bar signals on the chart using different colors and shapes. The script also plots the moving averages and Keltner Channels to help traders identify potential trend reversals.
It is still under development, so please, if someone has ideas to add, more than welcome
True Trend Average BandsThis is the indicator I am most proud of. After reading Glenn Neely's book "Mastering Eliott Waves" / "Neowave" and chatting with @timwest who got acknowledged by Neely, we came up with the idea of an moving average which does calculate the real average price since a trend started. Addionally I adapted a method from Neely Neowave and Tim Wests TimeAtMode to not force a timeframe on a chart but instead let the charts data decide which timeframe to use, to then calculate the real average price since the trend started.
It took me a while to get this right and coded, so take a moment and dive deeper and you might learn something new.
We assume that the price is in multiple trends on multiple timeframes, this is caused by short term traders, long term traders and investors who trade on different timeframes. To find out in which timeframe the important trends are, we have to look out for significant lows and highs. Then we change the timeframe in the chart to a value so that we have 10 to 20 bars since the significant low/high. While new bars are printed, and we reach more than 20 bars, we have to switch to a higher timeframe so we have 10 to 20 bars again. In the chart you see two significant trends: a downtrend on the 3 week timeframe and an uptrend from the 2 month timeframe. Based on the logic I have described, these are the two important timeframes to watch right now for the spx (there is another uptrend in the yearly chart, which is not shown here).
Now that we understand how to find the important timeframes, let's look what the magic in this script is that tells us the real average price since a trend started.
I developed a new type of moving average, which includes only the prices since a trend started. The difference to the regular sma is that it will not include prices which happened before the significant low or high happened. For example, if a top happened in a market 10 days ago, the regular sma20 would be calculated by 10 bars which happened before the top and 10 bars which happened after the top. If we want to know the average price of the last 10 bars we manually have to change the ma20 to the ma10 which is annoying manual work, additionally even if we use the ma10 in this case, and we look at yesterday's bar the ma10 will include 9 bars from after the top and one bar before the top, so the ma10 would only show the real average price for the current bar which is not what we want.
To come up with a solution to this problem, the True Trend Average searches for the lowest/highest bar in a given period (20 bars). Then starts to calculate the average value since the low/high. For example: if the price reaches a new 20 day high and then trades below it, the day of the high will be the sma1, the day after it's the sma2, ... up to the maximum look back length.
This way, we always know what the average price would have been if someone sold/bought a little bit every bar of his investment since the high/low.
Why is this even important? Let's assume we missed selling the top or buying the low, and think it would have been at least better to buy/sell a little bit since the new trend started. Once the price reaches the true trend average again, we can buy/sell, and it would be as good as selling/buying a little bit every day. We find prices to buy the dip and sell the bounce, which are as good as scaling in/out.
There is a lot more we can learn from these price levels but I think it is better to let you figure out yourself what you can learn from the information given by this indicator. Think about how market participants who accumulate or distribute feel when prices are above or below certain levels.
Now that we understand this new type of moving average, let's look into the lines we see in the chart:
The upper red band line shows the true trend average high price since the last significant top within 20 bars.
The lower red band line shows the true trend average hl2 price since the last significant top within 20 bars.
The lower green band line shows the true trend average low price since the last significant low within 20 bars.
The upper green band line shows the true trend average hl2 price since the last significant low within 20 bars.
The centerline is the average between the upper red band and the lower green band.
The teal lines show 1 standard deviation from the outer bands.
Before today only a few people had access to this indicator, now that it is public and open source, I am curious if you will find it useful and what you will do with it. Please share your findings.
/edit: The chart only shows the 3week timeframe so here are the other two trends from the 2month and 1year timeframe
Traders_Reality_LibLibrary "Traders_Reality_Lib"
This library contains common elements used in Traders Reality scripts
calcPvsra(pvsraVolume, pvsraHigh, pvsraLow, pvsraClose, pvsraOpen, redVectorColor, greenVectorColor, violetVectorColor, blueVectorColor, darkGreyCandleColor, lightGrayCandleColor)
calculate the pvsra candle color and return the color as well as an alert if a vector candle has apperared.
Situation "Climax"
Bars with volume >= 200% of the average volume of the 10 previous chart TFs, or bars
where the product of candle spread x candle volume is >= the highest for the 10 previous
chart time TFs.
Default Colors: Bull bars are green and bear bars are red.
Situation "Volume Rising Above Average"
Bars with volume >= 150% of the average volume of the 10 previous chart TFs.
Default Colors: Bull bars are blue and bear are violet.
Parameters:
pvsraVolume : the instrument volume series (obtained from request.sequrity)
pvsraHigh : the instrument high series (obtained from request.sequrity)
pvsraLow : the instrument low series (obtained from request.sequrity)
pvsraClose : the instrument close series (obtained from request.sequrity)
pvsraOpen : the instrument open series (obtained from request.sequrity)
redVectorColor : red vector candle color
greenVectorColor : green vector candle color
violetVectorColor : violet/pink vector candle color
blueVectorColor : blue vector candle color
darkGreyCandleColor : regular volume candle down candle color - not a vector
lightGrayCandleColor : regular volume candle up candle color - not a vector
@return
adr(length, barsBack)
Parameters:
length : how many elements of the series to calculate on
barsBack : starting possition for the length calculation - current bar or some other value eg last bar
@return adr the adr for the specified lenght
adrHigh(adr, fromDo)
Calculate the ADR high given an ADR
Parameters:
adr : the adr
fromDo : boolean flag, if false calculate traditional adr from high low of today, if true calcualte from exchange midnight
@return adrHigh the position of the adr high in price
adrLow(adr, fromDo)
Parameters:
adr : the adr
fromDo : boolean flag, if false calculate traditional adr from high low of today, if true calcualte from exchange midnight
@return adrLow the position of the adr low in price
splitSessionString(sessXTime)
given a session in the format 0000-0100:23456 split out the hours and minutes
Parameters:
sessXTime : the session time string usually in the format 0000-0100:23456
@return
calcSessionStartEnd(sessXTime, gmt)
calculate the start and end timestamps of the session
Parameters:
sessXTime : the session time string usually in the format 0000-0100:23456
gmt : the gmt offset string usually in the format GMT+1 or GMT+2 etc
@return
drawOpenRange(sessXTime, sessXcol, showOrX, gmt)
draw open range for a session
Parameters:
sessXTime : session string in the format 0000-0100:23456
sessXcol : the color to be used for the opening range box shading
showOrX : boolean flag to toggle displaying the opening range
gmt : the gmt offset string usually in the format GMT+1 or GMT+2 etc
@return void
drawSessionHiLo(sessXTime, show_rectangleX, show_labelX, sessXcolLabel, sessXLabel, gmt, sessionLineStyle)
Parameters:
sessXTime : session string in the format 0000-0100:23456
show_rectangleX : show the session high and low lines
show_labelX : show the session label
sessXcolLabel : the color to be used for the hi/low lines and label
sessXLabel : the session label text
gmt : the gmt offset string usually in the format GMT+1 or GMT+2 etc
sessionLineStyle : the line stile for the session high low lines
@return void
calcDst()
calculate market session dst on/off flags
@return indicating if DST is on or off for a particular region
timestampPreviousDayOfWeek(previousDayOfWeek, hourOfDay, gmtOffset, oneWeekMillis)
Timestamp any of the 6 previous days in the week (such as last Wednesday at 21 hours GMT)
Parameters:
previousDayOfWeek : Monday or Satruday
hourOfDay : the hour of the day when psy calc is to start
gmtOffset : the gmt offset string usually in the format GMT+1 or GMT+2 etc
oneWeekMillis : the amount if time for a week in milliseconds
@return the timestamp of the psy level calculation start time
getdayOpen()
get the daily open - basically exchange midnight
@return the daily open value which is float price
newBar(res)
new_bar: check if we're on a new bar within the session in a given resolution
Parameters:
res : the desired resolution
@return true/false is a new bar for the session has started
toPips(val)
to_pips Convert value to pips
Parameters:
val : the value to convert to pips
@return the value in pips
rLabel(ry, rtext, rstyle, rcolor, valid, labelXOffset)
a function that draws a right aligned lable for a series during the current bar
Parameters:
ry : series float the y coordinate of the lable
rtext : the text of the label
rstyle : the style for the lable
rcolor : the color for the label
valid : a boolean flag that allows for turning on or off a lable
labelXOffset : how much to offset the label from the current position
rLabelOffset(ry, rtext, rstyle, rcolor, valid, labelXOffset)
a function that draws a right aligned lable for a series during the current bar
Parameters:
ry : series float the y coordinate of the lable
rtext : the text of the label
rstyle : the style for the lable
rcolor : the color for the label
valid : a boolean flag that allows for turning on or off a lable
labelXOffset : how much to offset the label from the current position
rLabelLastBar(ry, rtext, rstyle, rcolor, valid, labelXOffset)
a function that draws a right aligned lable for a series only on the last bar
Parameters:
ry : series float the y coordinate of the lable
rtext : the text of the label
rstyle : the style for the lable
rcolor : the color for the label
valid : a boolean flag that allows for turning on or off a lable
labelXOffset : how much to offset the label from the current position
drawLine(xSeries, res, tag, xColor, xStyle, xWidth, xExtend, isLabelValid, labelXOffset, validTimeFrame)
a function that draws a line and a label for a series
Parameters:
xSeries : series float the y coordinate of the line/label
res : the desired resolution controlling when a new line will start
tag : the text for the lable
xColor : the color for the label
xStyle : the style for the line
xWidth : the width of the line
xExtend : extend the line
isLabelValid : a boolean flag that allows for turning on or off a label
labelXOffset : how much to offset the label from the current position
validTimeFrame : a boolean flag that allows for turning on or off a line drawn
drawLineDO(xSeries, res, tag, xColor, xStyle, xWidth, xExtend, isLabelValid, labelXOffset, validTimeFrame)
a function that draws a line and a label for the daily open series
Parameters:
xSeries : series float the y coordinate of the line/label
res : the desired resolution controlling when a new line will start
tag : the text for the lable
xColor : the color for the label
xStyle : the style for the line
xWidth : the width of the line
xExtend : extend the line
isLabelValid : a boolean flag that allows for turning on or off a label
labelXOffset : how much to offset the label from the current position
validTimeFrame : a boolean flag that allows for turning on or off a line drawn
drawPivot(pivotLevel, res, tag, pivotColor, pivotLabelColor, pivotStyle, pivotWidth, pivotExtend, isLabelValid, validTimeFrame, levelStart, pivotLabelXOffset)
draw a pivot line - the line starts one day into the past
Parameters:
pivotLevel : series of the pivot point
res : the desired resolution
tag : the text to appear
pivotColor : the color of the line
pivotLabelColor : the color of the label
pivotStyle : the line style
pivotWidth : the line width
pivotExtend : extend the line
isLabelValid : boolean param allows to turn label on and off
validTimeFrame : only draw the line and label at a valid timeframe
levelStart : basically when to start drawing the levels
pivotLabelXOffset : how much to offset the label from its current postion
@return the pivot line series
getPvsraFlagByColor(pvsraColor, redVectorColor, greenVectorColor, violetVectorColor, blueVectorColor, lightGrayCandleColor)
convert the pvsra color to an internal code
Parameters:
pvsraColor : the calculated pvsra color
redVectorColor : the user defined red vector color
greenVectorColor : the user defined green vector color
violetVectorColor : the user defined violet vector color
blueVectorColor : the user defined blue vector color
lightGrayCandleColor : the user defined regular up candle color
@return pvsra internal code
updateZones(pvsra, direction, boxArr, maxlevels, pvsraHigh, pvsraLow, pvsraOpen, pvsraClose, transperancy, zoneupdatetype, zonecolor, zonetype, borderwidth, coloroverride, redVectorColor, greenVectorColor, violetVectorColor, blueVectorColor, lightGrayCandleColor)
a function that draws the unrecovered vector candle zones
Parameters:
pvsra : internal code
direction : above or below the current pa
boxArr : the array containing the boxes that need to be updated
maxlevels : the maximum number of boxes to draw
pvsraHigh : the pvsra high value series
pvsraLow : the pvsra low value series
pvsraOpen : the pvsra open value series
pvsraClose : the pvsra close value series
transperancy : the transparencfy of the vecor candle zones
zoneupdatetype : the zone update type
zonecolor : the zone color if overriden
zonetype : the zone type
borderwidth : the width of the border
coloroverride : if the color overriden
redVectorColor : the user defined red vector color
greenVectorColor : the user defined green vector color
violetVectorColor : the user defined violet vector color
blueVectorColor : the user defined blue vector color
lightGrayCandleColor : the user defined regular up candle color
cleanarr(arr)
clean an array from na values
Parameters:
arr : the array to clean
@return if the array was cleaned
calcPsyLevels(oneWeekMillis, showPsylevels, psyType, sydDST)
calculate the psy levels
4 hour res based on how mt4 does it
mt4 code
int Li_4 = iBarShift(NULL, PERIOD_H4, iTime(NULL, PERIOD_W1, Li_0)) - 2 - Offset;
ObjectCreate("PsychHi", OBJ_TREND, 0, Time , iHigh(NULL, PERIOD_H4, iHighest(NULL, PERIOD_H4, MODE_HIGH, 2, Li_4)), iTime(NULL, PERIOD_W1, 0), iHigh(NULL, PERIOD_H4,
iHighest(NULL, PERIOD_H4, MODE_HIGH, 2, Li_4)));
so basically because the session is 8 hours and we are looking at a 4 hour resolution we only need to take the highest high an lowest low of 2 bars
we use the gmt offset to adjust the 0000-0800 session to Sydney open which is at 2100 during dst and at 2200 otherwize. (dst - spring foward, fall back)
keep in mind sydney is in the souther hemisphere so dst is oposite of when london and new york go into dst
Parameters:
oneWeekMillis : a constant value
showPsylevels : should psy levels be calculated
psyType : the type of Psylevels - crypto or forex
sydDST : is Sydney in DST
@return
Volume Buoyancy [LucF]█ OVERVIEW
This indicator uses simple analysis of past volume to determine how well it supports recent market activity. What I call Volume buoyancy measures the strength and direction of that support.
█ CONCEPTS
Buoyancy
In physics, buoyancy is the force described in Archemedes' principle :
Any object, wholly or partially immersed in a fluid, is buoyed up by a force equal to the weight of the fluid displaced by the object.
I use the term loosely in this indicator's context, as "Volume buoyancy" here can be directed either up or down, indicating that past volume displays a bullish or bearish bias.
The calculation of buoyancy begins from a target quantity of volume summed over n bars. We then search chart bars backward, adding the volume of up and down bars in two different slots until each slot reaches the target. We then calculate two average distances: one each for the up and down bars whose volume was summed to reach the target. These average distances are then subtracted and the difference is divided by the farthest distance we had to go to find the target in either up or down bars. The last part of the calculations looks like this:
(avgDistanceDn - avgDistanceUp) / barsAnalyzed
When the average distance of down bars is greater than that of up bars, buoyancy will be positive, indicating that past activity favors the upside and vice versa. The force's strength, which in the case of actual buoyancy is the weight of the displaced fluid, in our case is measured by the size of the gap between the average distance of up vs down bars in relation to the farthest distance we had to go in the past. Buoyancy is always between +1 and -1, with values higher/lower than 0.3/-0.3 typically being unsustainable.
█ HOW TO USE THE INDICATOR
Load the indicator on an active chart (see here if you don't know how).
The default configuration displays:
• Buoyancy as a monochrome gray line.
• A channel between buoyancy and its MA, colored in one of four colors. The MA is not plotted by default, but you can see where it is with the channel.
The default settings use an Arnaud Legoux moving average over 20 bars.
• A fill between the MA and the centerline, which can be one of two colors.
• A high level at 0.30, a low level at -0.30 and the centerline at zero.
The default target is the sum of volume in the last 20 bars.
█ FEATURES
The indicator's settings allow you to define:
• A higher timeframe you want the calculations to be made on. Note that you should then ensure your chart's timeframe is always lower than the higher timeframe you specified,
as calculating on a timeframe lower than the chart's does not make much sense because the indicator is then displaying only the value of the last intrabar in the chart bar.
• The number of bars for which to add volume to obtain the target value that will be searched for in past up and down bars.
• The display of the buoyancy and MA lines, the channel between them and the fill between the MA line and the centerline.
• The type and length of the MA.
Using the "Style" tab of the indicator's settings, you can change the type and width of the lines, and the level values.
█ INTERPRETATION
Buoyancy shares the properties and shortcomings of many oscillators:
• It tends to be noisy, which is why the MA line can be helpful.
• The safest way to use it may be as a rough sentiment indicator, i.e., by paying more attention to its bull/bear state above/below the centerline.
• The more intrepid traders will want to use the channel between the main line and the MA, as it will provide earlier information than main line crosses of the centerline.
Decreasing the number of bars for which the source is added to calculate the target value will increase the noise level, somewhat like decreasing an MA's length would, but keep in mind that the number of bars is not the length of an MA.
█ LIMITATIONS
Under some circumstances, the indicator will display zero values because it cannot find the target in past bars. This will happen at the beginning of the dataset when not enough past bars have elapsed, or in the rarer cases anywhere in the dataset, when the target cannot be found in the `MAX_BARS_BACK` number of bars defined in the first line of the indicator's code (the default is 1000).
The calculations use a very primitive interpretation of volume similar to that of OBV , where all the volume of a bar is attributed to either the up or down slot. The indicator nonetheless produces results I think can be useful because we are not so much calculating precise buying/selling pressure as trying to build a big picture of where past activity over many bars appears to be taking price.
Volume data is notoriously high-variance; large values that come into or exit the calculations' scope can produce sudden variations in results, somewhat like the drop-off effect in moving averages.
█ NOTES
• The script can be used with any chart timeframe, including seconds.
• Historical values will always produce the same results. In real time, values will change until the bar closes.
Stochastic of Two-Pole SuperSmoother [Loxx]Stochastic of Two-Pole SuperSmoother is a Stochastic Indicator that takes as input Two-Pole SuperSmoother of price. Includes gradient coloring and Discontinued Signal Lines signals with alerts.
What is Ehlers ; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Adaptive Two-Pole Super Smoother Entropy MACD [Loxx]Adaptive Two-Pole Super Smoother Entropy (Math) MACD is an Ehlers Two-Pole Super Smoother that is transformed into an MACD oscillator using entropy mathematics. Signals are generated using Discontinued Signal Lines.
What is Ehlers; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Close v Open Moving Averages Strategy (Variable) [divonn1994]This is a simple moving average based strategy that works well with a few different coin pairings. It takes the moving average 'opening' price and plots it, then takes the moving average 'closing' price and plots it, and then decides to enter a 'long' position or exit it based on whether the two lines have crossed each other. The reasoning is that it 'enters' a position when the average closing price is increasing. This could indicate upwards momentum in prices in the future. It then exits the position when the average closing price is decreasing. This could indicate downwards momentum in prices in the future. This is only speculative, though, but sometimes it can be a very good indicator/strategy to predict future action.
What I've found is that there are a lot of coins that respond very well when the appropriate combination of: 1) type of moving average is chosen (EMA, SMA, RMA, WMA or VWMA) & 2) number of previous bars averaged (typically 10 - 250 bars) are chosen.
Depending on the coin.. each combination of MA and Number of Bars averaged can have completely different levels of success.
Example of Usage:
An example would be that the VWMA works well for BTCUSD (BitStamp), but it has different successfulness based on the time frame. For the 12 hour bar timeframe, with the 66 bar average with the VWMA I found the most success. The next best successful combo I've found is for the 1 Day bar timeframe with the 35 bar average with the VWMA.. They both have a moving average that records about a month, but each have a different successfulness. Below are a few pair combos I think are noticeable because of the net profit, but there are also have a lot of potential coins with different combos:
It's interesting to see the strategy tester change as you change the settings. The below pairs are just some of the most interesting examples I've found, but there might be other combos I haven't even tried on different coin pairs..
Some strategy settings:
BTCUSD (BitStamp) 12 Hr Timeframe : 66 bars, VWMA=> 10,387x net profit
BTCUSD (BitStamp) 1 Day Timeframe : 35 bars, VWMA=> 7,805x net profit
BNBUSD (Binance) 12 Hr Timeframe : 27 bars, VWMA => 15,484x net profit
ETHUSD (BitStamp) 16 Hr Timeframe : 60 bars, SMA => 5,498x net profit
XRPUSD (BitStamp) 16 Hr Timeframe : 33 bars, SMA => 10,178x net profit
I only chose these coin/combos because of their insane net profit factors. There are far more coins with lower net profits but more reliable trade histories.
Also, usually when I want to see which of these strategies might work for a coin pairing I will check between the different Moving Average types, for example the EMA or the SMA, then I also check between the moving average lengths (the number of bars calculated) to see which is most profitable over time.
Features:
-You can choose your preferred moving average: SMA, EMA, WMA, RMA & VWMA.
-You can also adjust the previous number of calculated bars for each moving average.
-I made the background color Green when you're currently in a long position and Red when not. I made it so you can see when you'd be actively in a trade or not. The Red and Green background colors can be toggled on/off in order to see other indicators more clearly overlayed in the chart, or if you prefer a cleaner look on your charts.
-I also have a plot of the Open moving average and Close moving average together. The Opening moving average is Purple, the Closing moving average is White. White on top is a sign of a potential upswing and purple on top is a sign of a potential downswing. I've made this also able to be toggled on/off.
Please, comment interesting pairs below that you've found for everyone :) thank you!
I will post more pairs with my favorite settings as well. I'll also be considering the quality of the trades.. for example: net profit, total trades, percent profitable, profit factor, trade window and max drawdown.
*if anyone can figure out how to change the date range, I woul really appreciate the help. It confuses me -_- *
SUPERTREND MIXED ICHI-DMI-DONCHIAN-VOL-GAP-HLBox@RLSUPERTREND MIXED ICHI-DMI-VOL-GAP-HLBox@RL
by RegisL76
This script is based on several trend indicators.
* ICHIMOKU (KINKO HYO)
* DMI (Directional Movement Index)
* SUPERTREND ICHIMOKU + SUPERTREND DMI
* DONCHIAN CANAL Optimized with Colored Bars
* HMA Hull
* Fair Value GAP
* VOLUME/ MA Volume
* PRICE / MA Price
* HHLL BOXES
All these indications are visible simultaneously on a single graph. A data table summarizes all the important information to make a good trade decision.
ICHIMOKU Indicator:
The ICHIMOKU indicator is visualized in the traditional way.
ICHIMOKU standard setting values are respected but modifiable. (Traditional defaults = .
An oriented visual symbol, near the last value, indicates the progression (Ascending, Descending or neutral) of the TENKAN-SEN and the KIJUN-SEN as well as the period used.
The CLOUD (KUMO) and the CHIKOU-SPAN are present and are essential for the complete analysis of the ICHIMOKU.
At the top of the graph are visually represented the crossings of the TENKAN and the KIJUN.
Vertical lines, accompanied by labels, make it possible to quickly visualize the particularities of the ICHIMOKU.
A line displays the current bar.
A line visualizes the end of the CLOUD (KUMO) which is shifted 25 bars into the future.
A line visualizes the end of the chikou-span, which is shifted 25 bars in the past.
DIRECTIONAL MOVEMENT INDEX (DMI) : Treated conventionally : DI+, DI-, ADX and associated with a SUPERTREND DMI.
A visual symbol at the bottom of the graph indicates DI+ and DI- crossings
A line of oriented and colored symbols (DMI Line) at the top of the chart indicates the direction and strength of the trend.
SUPERTREND ICHIMOKU + SUPERTREND DMI :
Trend following by SUPERTREND calculation.
DONCHIAN CHANNEL: Treated conventionally. (And optimized by colored bars when overshooting either up or down.
The lines, high and low of the last values of the channel are represented to quickly visualize the level of the RANGE.
SUPERTREND HMA (HULL) Treated conventionally.
The HMA line visually indicates, according to color and direction, the market trend.
A visual symbol at the bottom of the chart indicates opportunities to sell and buy.
VOLUME:
Calculation of the MOBILE AVERAGE of the volume with comparison of the volume compared to the moving average of the volume.
The indications are colored and commented according to the comparison.
PRICE: Calculation of the MOBILE AVERAGE of the price with comparison of the price compared to the moving average of the price.
The indications are colored and commented according to the comparison.
HHLL BOXES:
Visualizes in the form of a box, for a given period, the max high and min low values of the price.
The configuration allows taking into account the high and low wicks of the price or the opening and closing values.
FAIR VALUE GAP :
This indicator displays 'GAP' levels over the current time period and an optional higher time period.
The script takes into account the high/low values of the current bar and compares with the 2 previous bars.
The "gap" is generated from the lack of overlap between these bars. Bearish or bullish gaps are determined by whether the gap is above or below HmaPrice, as they tend to fill, and can be used as targets.
NOTE: FAIR VALUE GAP has no values displayed in the table and/or label.
Important information (DATA) relating to each indicator is displayed in real time in a table and/or a label.
Each information is commented and colored according to direction, value, comparison etc.
Each piece of information indicates the values of the current bar and the previous value (in "FULL" mode).
The other possible modes for viewing the table and/or the label allow a more synthetic view of the information ("CONDENSED" and "MINIMAL" modes).
In order not to overload the vision of the chart too much, the visualization box of the RANGE DONCHIAN, the vertical lines of the shifted marks of the ICHIMOKU, as well as the boxes of the HHLL Boxes indicator are only visualized intermittently (managed by an adjustable time delay ).
The "HISTORICAL INFO READING" configuration parameter set to zero (by default) makes it possible to read all the information of the current bar in progress (Bar #0). All other values allow to read the information of a historical bar. The value 1 reads the information of the bar preceding the current bar (-1). The value 10 makes it possible to read the information of the tenth bar behind (-10) compared to the current bar, etc.
At the bottom of the DATAS table and label, lights, red, green or white indicate quickly summarize the trend from the various indicators.
Each light represents the number of indicators with the same trend at a given time.
Green for a bullish trend, red for a bearish trend and white for a neutral trend.
The conditions for determining a trend are for each indicator:
SUPERTREND ICHIMOHU + DMI: the 2 Super trends together are either bullish or bearish.
Otherwise the signal is neutral.
DMI: 2 main conditions:
BULLISH if DI+ >= DI- and ADX >25.
BEARISH if DI+ < DI- and ADX >25.
NEUTRAL if the 2 conditions are not met.
ICHIMOKU: 3 main conditions:
BULLISH if PRICE above the cloud and TENKAN > KIJUN and GREEN CLOUD AHEAD.
BEARISH if PRICE below the cloud and TENKAN < KIJUN and RED CLOUD AHEAD.
The other additional conditions (Data) complete the analysis and are present for informational purposes of the trend and depend on the context.
DONCHIAN CHANNEL: 1 main condition:
BULLISH: the price has crossed above the HIGH DC line.
BEARISH: the price has gone below the LOW DC line.
NEUTRAL if the price is between the HIGH DC and LOW DC lines
The 2 other complementary conditions (Datas) complete the analysis:
HIGH DC and LOW DC are increasing, falling or stable.
SUPERTREND HMA HULL: The script determines several trend levels:
STRONG BUY, BUY, STRONG SELL, SELL AND NEUTRAL.
VOLUME: 3 trend levels:
VOLUME > MOVING AVERAGE,
VOLUME < MOVING AVERAGE,
VOLUME = MOVING AVERAGE.
PRICE: 3 trend levels:
PRICE > MOVING AVERAGE,
PRICE < MOVING AVERAGE,
PRICE = MOVING AVERAGE.
If you are using this indicator/strategy and you are satisfied with the results, you can possibly make a donation (a coffee, a pizza or more...) via paypal to: lebourg.regis@free.fr.
Thanks in advance !!!
Have good winning Trades.
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SUPERTREND MIXED ICHI-DMI-VOL-GAP-HLBox@RL
by RegisL76
Ce script est basé sur plusieurs indicateurs de tendance.
* ICHIMOKU (KINKO HYO)
* DMI (Directional Movement Index)
* SUPERTREND ICHIMOKU + SUPERTREND DMI
* DONCHIAN CANAL Optimized with Colored Bars
* HMA Hull
* Fair Value GAP
* VOLUME/ MA Volume
* PRIX / MA Prix
* HHLL BOXES
Toutes ces indications sont visibles simultanément sur un seul et même graphique.
Un tableau de données récapitule toutes les informations importantes pour prendre une bonne décision de Trade.
I- Indicateur ICHIMOKU :
L’indicateur ICHIMOKU est visualisé de manière traditionnelle
Les valeurs de réglage standard ICHIMOKU sont respectées mais modifiables. (Valeurs traditionnelles par défaut =
Un symbole visuel orienté, à proximité de la dernière valeur, indique la progression (Montant, Descendant ou neutre) de la TENKAN-SEN et de la KIJUN-SEN ainsi que la période utilisée.
Le NUAGE (KUMO) et la CHIKOU-SPAN sont bien présents et sont primordiaux pour l'analyse complète de l'ICHIMOKU.
En haut du graphique sont représentés visuellement les croisements de la TENKAN et de la KIJUN.
Des lignes verticales, accompagnées d'étiquettes, permettent de visualiser rapidement les particularités de l'ICHIMOKU.
Une ligne visualise la barre en cours.
Une ligne visualise l'extrémité du NUAGE (KUMO) qui est décalé de 25 barres dans le futur.
Une ligne visualise l'extrémité de la chikou-span, qui est décalée de 25 barres dans le passé.
II-DIRECTIONAL MOVEMENT INDEX (DMI)
Traité de manière conventionnelle : DI+, DI-, ADX et associé à un SUPERTREND DMI
Un symbole visuel en bas du graphique indique les croisements DI+ et DI-
Une ligne de symboles orientés et colorés (DMI Line) en haut du graphique, indique la direction et la puissance de la tendance.
III SUPERTREND ICHIMOKU + SUPERTREND DMI
Suivi de tendance par calcul SUPERTREND
IV- DONCHIAN CANAL :
Traité de manière conventionnelle.
(Et optimisé par des barres colorées en cas de dépassement soit vers le haut, soit vers le bas.
Les lignes, haute et basse des dernières valeurs du canal sont représentées pour visualiser rapidement la fourchette du RANGE.
V- SUPERTREND HMA (HULL)
Traité de manière conventionnelle.
La ligne HMA indique visuellement, selon la couleur et l'orientation, la tendance du marché.
Un symbole visuel en bas du graphique indique les opportunités de vente et d'achat.
*VI VOLUME :
Calcul de la MOYENNE MOBILE du volume avec comparaison du volume par rapport à la moyenne mobile du volume.
Les indications sont colorées et commentées en fonction de la comparaison.
*VII PRIX :
Calcul de la MOYENNE MOBILE du prix avec comparaison du prix par rapport à la moyenne mobile du prix.
Les indications sont colorées et commentées en fonction de la comparaison.
*VIII HHLL BOXES :
Visualise sous forme de boite, pour une période donnée, les valeurs max hautes et min basses du prix.
La configuration permet de prendre en compte les mèches hautes et basses du prix ou bien les valeurs d'ouverture et de fermeture.
IX - FAIR VALUE GAP
Cet indicateur affiche les niveaux de 'GAP' sur la période temporelle actuelle ET une période temporelle facultative supérieure.
Le script prend en compte les valeurs haut/bas de la barre actuelle et compare avec les 2 barres précédentes.
Le "gap" est généré à partir du manque de recouvrement entre ces barres.
Les écarts baissiers ou haussiers sont déterminés selon que l'écart est supérieurs ou inférieur à HmaPrice, car ils ont tendance à être comblés, et peuvent être utilisés comme cibles.
NOTA : FAIR VALUE GAP n'a pas de valeurs affichées dans la table et/ou l'étiquette.
Les informations importantes (DATAS) relatives à chaque indicateur sont visualisées en temps réel dans une table et/ou une étiquette.
Chaque information est commentée et colorée en fonction de la direction, de la valeur, de la comparaison etc.
Chaque information indique la valeurs de la barre en cours et la valeur précédente ( en mode "COMPLET").
Les autres modes possibles pour visualiser la table et/ou l'étiquette, permettent une vue plus synthétique des informations (modes "CONDENSÉ" et "MINIMAL").
Afin de ne pas trop surcharger la vision du graphique, la boite de visualisation du RANGE DONCHIAN, les lignes verticales des marques décalées de l'ICHIMOKU, ainsi que les boites de l'indicateur HHLL Boxes ne sont visualisées que de manière intermittente (géré par une temporisation réglable ).
Le paramètre de configuration "HISTORICAL INFO READING" réglé sur zéro (par défaut) permet de lire toutes les informations de la barre actuelle en cours (Barre #0).
Toutes autres valeurs permet de lire les informations d'une barre historique. La valeur 1 permet de lire les informations de la barre précédant la barre en cours (-1).
La valeur 10 permet de lire les information de la dixième barre en arrière (-10) par rapport à la barre en cours, etc.
Dans le bas de la table et de l'étiquette de DATAS, des voyants, rouge, vert ou blanc indique de manière rapide la synthèse de la tendance issue des différents indicateurs.
Chaque voyant représente le nombre d'indicateur ayant la même tendance à un instant donné. Vert pour une tendance Bullish, rouge pour une tendance Bearish et blanc pour une tendance neutre.
Les conditions pour déterminer une tendance sont pour chaque indicateur :
SUPERTREND ICHIMOHU + DMI : les 2 Super trends sont ensemble soit bullish soit Bearish. Sinon le signal est neutre.
DMI : 2 conditions principales :
BULLISH si DI+ >= DI- et ADX >25.
BEARISH si DI+ < DI- et ADX >25.
NEUTRE si les 2 conditions ne sont pas remplies.
ICHIMOKU : 3 conditions principales :
BULLISH si PRIX au dessus du nuage et TENKAN > KIJUN et NUAGE VERT DEVANT.
BEARISH si PRIX en dessous du nuage et TENKAN < KIJUN et NUAGE ROUGE DEVANT.
Les autres conditions complémentaires (Datas) complètent l'analyse et sont présents à titre informatif de la tendance et dépendent du contexte.
CANAL DONCHIAN : 1 condition principale :
BULLISH : le prix est passé au dessus de la ligne HIGH DC.
BEARISH : le prix est passé au dessous de la ligne LOW DC.
NEUTRE si le prix se situe entre les lignes HIGH DC et LOW DC
Les 2 autres conditions complémentaires (Datas) complètent l'analyse : HIGH DC et LOW DC sont croissants, descendants ou stables.
SUPERTREND HMA HULL :
Le script détermine plusieurs niveaux de tendance :
STRONG BUY, BUY, STRONG SELL, SELL ET NEUTRE.
VOLUME : 3 niveaux de tendance :
VOLUME > MOYENNE MOBILE, VOLUME < MOYENNE MOBILE, VOLUME = MOYENNE MOBILE.
PRIX : 3 niveaux de tendance :
PRIX > MOYENNE MOBILE, PRIX < MOYENNE MOBILE, PRIX = MOYENNE MOBILE.
Si vous utilisez cet indicateur/ stratégie et que vous êtes satisfait des résultats,
vous pouvez éventuellement me faire un don (un café, une pizza ou plus ...) via paypal à : lebourg.regis@free.fr.
Merci d'avance !!!
Ayez de bons Trades gagnants.
Price Displacement - Candlestick (OHLC) CalculationsA Magical little helper friend for Candle Math.
When composing scripts, it is often necessary to manipulate the math around the OHLC. At times, you want a scalar (absolute) value others you want a vector (+/-). Sometimes you want the open - close and sometimes you want just the positive number of the body size. You might want it in ticks or you might want it in points or you might want in percentages. And every time you try to put it together you waste precious time and brain power trying to think about how to properly structure what you're looking for. Not to mention it's normally not that aesthetically pleasing to look at in the code.
So, this fixes all of that.
Using this library. A function like 'pd.pt(_exp)' can call any kind of candlestick math you need. The function returns the candlestick math you define using particular expressions.
Candle Math Functions Include:
Points:
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Ticks:
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Percentages:
pct(_exp, _prec) Absolute Percent Displacement. (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Expressions You Can Use with Formulas:
The expressions are simple (simple strings that is) and I did my best to make them sensible, generally using just the ohlc abreviations. I also included uw, lw, bd, and rg for when you're just trying to pull a candle component out. That way you don't have to think about which of the ohlc you're trying to get just use pd.tick("uw") and now the variable is assigned the length of the upper wick, absolute value, in ticks. If you wanted the vector in pts its pd.vpt("uw"). It also makes changing things easy too as I write it out.
Expression List:
Combinations
"oh" = open - high
"ol" = open - low
"oc" = open - close
"ho" = high - open
"hl" = high - low
"hc" = high - close
"lo" = low - open
"lh" = low - high
"lc" = low - close
"co" = close - open
"ch" = close - high
"cl" = close - low
Candle Components
"uw" = Upper Wick
"bd" = Body
"lw" = Lower Wick
"rg" = Range
Pct() Only
"scp" = Scalar Close Position
"sop" = Scalar Open Position
"vcp" = Vector Close Position
"vop" = Vector Open Position
The attributes are going to be available in the pop up dialogue when you mouse over the function, so you don't really have to remember them. I tried to make that look as efficient as possible. You'll notice it follows the OHLC pattern. Thus, "oh" precedes "ho" (heyo) because "O" would be first in the OHLC. Its a way to help find the expression you're looking for quickly. Like looking through an alphabetized list for traders.
There is a copy/paste console friendly helper list in the script itself.
Additional Notes on the Pct() Only functions:
This is the original reason I started writing this. These concepts place a rating/value on the bar based on candle attributes in one number. These formulas put a open or close value in a percentile of the bar relative to another aspect of the bar.
Scalar - Non-directional. Absolute Value.
Scalar Position: The position of the price attribute relative to the scale of the bar range (high - low)
Example: high = 100. low = 0. close = 25.
(A) Measure price distance C-L. How high above the low did the candle close (e.g. close - low = 25)
(B) Divide by bar range (high - low). 25 / (100 - 0) = .25
Explaination: The candle closed at the 25th percentile of the bar range given the bar range low = 0 and bar range high = 100.
Formula: scp = (close - low) / (high - low)
Vector = Directional.
Vector Position: The position of the price attribute relative to the scale of the bar midpoint (Vector Position at hl2 = 0)
Example: high = 100. low = 0. close = 25.
(A) Measure Price distance C-L: How high above the low did the candle close (e.g. close - low = 25)
(B) Measure Price distance H-C: How far below the high did the candle close (e.g. high - close = 75)
(C) Take Difference: A - B = C = -50
(D) Divide by bar range (high - low). -50 / (100 - 0) = -0.50
Explaination: Candle close at the midpoint between hl2 and the low.
Formula: vcp = { / (high - low) }
Thank you for checking this out. I hope no one else has already done this (because it took half the day) and I hope you find value in it. Be well. Trade well.
Library "PD"
Price Displacement
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as an absolute value.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as a vector.
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as an absolute value.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as a vector.
pct(_exp, _prec) Absolute Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
ConditionalAverages█ OVERVIEW
This library is a Pine Script™ programmer’s tool containing functions that average values selectively.
█ CONCEPTS
Averaging can be useful to smooth out unstable readings in the data set, provide a benchmark to see the underlying trend of the data, or to provide a general expectancy of values in establishing a central tendency. Conventional averaging techniques tend to apply indiscriminately to all values in a fixed window, but it can sometimes be useful to average values only when a specific condition is met. As conditional averaging works on specific elements of a dataset, it can help us derive more context-specific conclusions. This library offers a collection of averaging methods that not only accomplish these tasks, but also exploit the efficiencies of the Pine Script™ runtime by foregoing unnecessary and resource-intensive for loops.
█ NOTES
To Loop or Not to Loop
Though for and while loops are essential programming tools, they are often unnecessary in Pine Script™. This is because the Pine Script™ runtime already runs your scripts in a loop where it executes your code on each bar of the dataset. Pine Script™ programmers who understand how their code executes on charts can use this to their advantage by designing loop-less code that will run orders of magnitude faster than functionally identical code using loops. Most of this library's function illustrate how you can achieve loop-less code to process past values. See the User Manual page on loops for more information. If you are looking for ways to measure execution time for you scripts, have a look at our LibraryStopwatch library .
Our `avgForTimeWhen()` and `totalForTimeWhen()` are exceptions in the library, as they use a while structure. Only a few iterations of the loop are executed on each bar, however, as its only job is to remove the few elements in the array that are outside the moving window defined by a time boundary.
Cumulating and Summing Conditionally
The ta.cum() or math.sum() built-in functions can be used with ternaries that select only certain values. In our `avgWhen(src, cond)` function, for example, we use this technique to cumulate only the occurrences of `src` when `cond` is true:
float cumTotal = ta.cum(cond ? src : 0) We then use:
float cumCount = ta.cum(cond ? 1 : 0) to calculate the number of occurrences where `cond` is true, which corresponds to the quantity of values cumulated in `cumTotal`.
Building Custom Series With Arrays
The advent of arrays in Pine has enabled us to build our custom data series. Many of this library's functions use arrays for this purpose, saving newer values that come in when a condition is met, and discarding the older ones, implementing a queue .
`avgForTimeWhen()` and `totalForTimeWhen()`
These two functions warrant a few explanations. They operate on a number of values included in a moving window defined by a timeframe expressed in milliseconds. We use a 1D timeframe in our example code. The number of bars included in the moving window is unknown to the programmer, who only specifies the period of time defining the moving window. You can thus use `avgForTimeWhen()` to calculate a rolling moving average for the last 24 hours, for example, that will work whether the chart is using a 1min or 1H timeframe. A 24-hour moving window will typically contain many more values on a 1min chart that on a 1H chart, but their calculated average will be very close.
Problems will arise on non-24x7 markets when large time gaps occur between chart bars, as will be the case across holidays or trading sessions. For example, if you were using a 24H timeframe and there is a two-day gap between two bars, then no chart bars would fit in the moving window after the gap. The `minBars` parameter mitigates this by guaranteeing that a minimum number of bars are always included in the calculation, even if including those bars requires reaching outside the prescribed timeframe. We use a minimum value of 10 bars in the example code.
Using var in Constant Declarations
In the past, we have been using var when initializing so-called constants in our scripts, which as per the Style Guide 's recommendations, we identify using UPPER_SNAKE_CASE. It turns out that var variables incur slightly superior maintenance overhead in the Pine Script™ runtime, when compared to variables initialized on each bar. We thus no longer use var to declare our "int/float/bool" constants, but still use it when an initialization on each bar would require too much time, such as when initializing a string or with a heavy function call.
Look first. Then leap.
█ FUNCTIONS
avgWhen(src, cond)
Gathers values of the source when a condition is true and averages them over the total number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
Returns: (float) A cumulative average of values when a condition is met.
avgWhenLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over a defined number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of last occurrences of the condition for which to average values.
Returns: (float) The average of `src` for the last `x` occurrences where `cond` is true.
avgWhenInLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over the total number of occurrences during a defined number of bars back.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of bars back to evaluate.
Returns: (float) The average of `src` in last `cnt` bars, but only when `cond` is true.
avgSince(src, cond)
Averages values of the source since a condition was true.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when the average is reset.
Returns: (float) The average of `src` since `cond` was true.
avgForTimeWhen(src, ms, cond, minBars)
Averages values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be averaged.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The average of `src` when `cond` is true in the moving window.
totalForTimeWhen(src, ms, cond, minBars)
Sums values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be summed.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The sum of `src` when `cond` is true in the moving window.
Overnight Gap AnalysisThere is a wide range of opinion on holding positions overnight due to gap risk. So, out of curiosity, I coded this analysis as a strategy to see what the result of only holding a position overnight on an asset would be. The results really surprised me. The script backtests 10+ years, and here are the findings:
Holding a position for 1 hour bar overnight on QQQ since January 2010 results in a 545% return. QQQ's entire return holding through the same period is 643%
The max equity drawdown on holding that position overnight is lower then the buy/hold drawdown on the underlying asset.
It doesn't matter if the last bar of the day is green or red, the results are similar.
It doesn't matter if it is a bull or bear market. Filtering the script to only trade when the price is above the 200-day moving average actually reduces its return from 545% to 301%, though it does also reduce drawdown.
I see similar patterns when applying the script to other index ETFs. Applying it to leveraged index ETFs can end up beating buy/hold of the underlying index.
Since this script holds through the 1st bar of the day, this could also speak to a day-opening price pattern
The default inputs are for the script to be applied to 1 hour charts only that have 7 bars on the chart per day. You can apply it to other chart types, but must follow the instructions below for it to work properly.
What the script is doing :
This script is buying the close of the last bar of the day and closing the trade at the close of the next bar. So, all trades are being held for 1 bar. By default, the script is setup for use on a 1hr chart that has 7 bars per day. If you try to apply it to a different timeframe, you will need to adjust the count of the last bar of the day with the script input. I.e. There are 7 bars per day on an hour chart on US Stocks/ETFs, so the input is set to 7 by default.
Other ways this script can be used :
This script can also test the result of holding a position over any 1 bar in the day using that same input. For instance, on an hour chart you can input 6 on the script input, and it will model buying the close of the 6th bar of the day while selling on the close of the next bar. I used this out of curiosity to model what only holding the last bar of the day would result in. On average, you lose money on the last bar every day.
The irony here is that the root cause of this last bar of the day losing may be people selling their positions at the end of day so that they aren't exposed to overnight gap risk.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
[BCT] Can BTC be predicted or is it purely random?Variance Ratio**This indicator can be applied to the ticker of your choice (not just BTC)**
Markets are said to be "efficient". An efficient market is by definition unpredictable - no matter the amount of ML, computation, or indicators thrown at it. In particular, in an efficient market, TA will not be of help.
An illustration of efficient markets is the WSJ's longstanding monkey vs. human contest:Blindfolded Monkey Beats Humans With Stock Picks, granted there are several flaws to it.
BTC is a relatively new market. New markets are typically highly inefficient (easier to make money) and become more and more efficient over time (harder to make money). How much more efficient is BTC becoming?
We apply the Variance Ratio method and apply it to BTC.
BACKGROUND ON THE VARIANCE RATIO METHOD
Based on 1988 MacKinlay's seminal paper "Stock Market Prices do not Follow a Random Walk", the idea is to exploit a phenomenon called "variance scaling".
For those keen on looking into the math, the short version of it is under the assumption of iid (random walk) we have the following:
H0: Var(Sum(returns over K bars))=Sum(Var(returns over 1 bar))=k*Var(return over 1 bar)
We look to reject or not H0 depending on the observations.
In this script, we compare the variance of the (log) returns for the chart selected between:
(1) The (average) variance over k bars (call this Vk)
(2) The (average) variance over 1 bar (call this V1)
H0 simply says that Vk=k*V1 if the stock follows a random walk.
We compute the Variance Ratio VR(k)=Variance(returns over k bar)/(Sum(Var(returns over 1 bar)))-1
We then compute the associated Z-score which we chart out for a configurable k number of bars.
HOW TO INTERPRET THE CHART
The line drawn is the Z-Score for VR(k). It represents the number of standard deviations of VR(k) from 0 - the further out, the less random.
- If the line is close / hovers around 0, the ticker appears to follow a random walk (i.e. may not be predictable)
- If the line is consistently > 2 or <-2, the ticker likely does not follow a random walk (i.e. may have predictable features)
- If the line is positive, it means that the Variance on the k bars is larger than the variance on 1 bar (more variance on longer timeframes)
- If the line is negative, it means that the Variance on the k bars is smaller than the variance on 1 bar (more variance on smaller timeframes)
USE CASES
- Identify timeframes where you won't be able to make money
- Identify whether a stock cannot be predicted (forget about TA, indicators etc. -- a random walk is not predictable)
- Identify whether a stock is becoming less and less predictable (Z-score amplitude will decrease over time)
FEATURES
- select the number of K bar to compare vs. 1 bar (default = 16) - ideally a power of 2 but any other number will work. The chart is based off this selection
- select the lookback period for the analysis (500 bars by default)
- select the source to analyze (default = close, but you may select other inputs to calculate the returns from)
- results form the statistical tests on different K's in the table on the right/bottom side of the chart (H0 rejected = not random walk; H0 not rejected = it essentially looks rather random and we can't conclude that it's not a random walk)
COMMENTARY ON BTC
- It appears BTC's absolute value of the ZScore on the Variance Ratio is declining year after year - corroborating an increasingly efficient market as new participants join.
- However, we can still detect a fair amount of potential inefficiency using this simple test.
As usual, this is not investment advice. DYOR.
With love,
🐵BCT🐵
