Orion:SagittaSagitta
Sagitta is an indicator the works to assist in the validation of potential long entries and to place stop-loss orders. Sagitta is not a "golden indicator" but more of a confirmation indicator of what prices might be suggesting.
The concept is that while stocks can turn in one bar, it usually takes two bars or more to signal a turn. So, using a measurement of two bars help determine the potential turning of prices.
Behind the scenes, Sagitta is nothing more than a 2 period stochastic which has had its values divided into five specific zones.
Dividing the range of the two bars in five sections, the High is equal to 100 and the Low is equal to 0.
The zones are:
20 = bearish (red) – This is when the close is the lower 20% of the two bars
40 = bearish (orange) – This is when the close is between the lower 20% and 40% of the two bars.
60 = neutral (yellow) – This is when the close is between the middle 40% - 60% of the two bars.
80 = bullish (blue) – This is when the close is between the upper 60% - 80% of the two bars.
100 = bullish (green) – This is when the close is above the upper 80% of the bar.
The general confirmation concept works as such:
When the following bar is of a higher value than the previous bar, there is potential for further upward price movement. Conversely when the following bar is lower than the previous bar, there is potential for further downward movement.
Going from a red bar to orange bar Might be an indication of a positive turn in direction of prices.
Going from a green bar to an orange bar would also be considered a negative directional turn of prices.
When the follow on bar decreases (ie, green to blue, blue to yellow, etc) placing a stop-loss would be prudent.
Maroon lines in the middle of a bar is an indication that prices are currently caught in consolidation.
Silver/Gray bars indicate that a high potential exists for a strong upward turn in prices exists.
Consolidation is calculated by determining if the close of one bar is between the high and low of another bar. This then establishes the range high and low. As long as closes continue with this range, the high and low of the range can expand. When the close is outside of the range, the consolidation is reset.
Signals in areas of consolidation (maroon center bar) should be looked upon as if the prices are going to challenge the high of the consolidation range and not necessarily break through.
The entry technique used is:
The greater of the following two calculations:
High of signal bar * 1.002 or High of signal bar + .03
The stop-loss technique used is:
The lesser of the following two calculations:
Low of signal bar * .998 or Low of signal bar - .03
IF an entry signal is generated and the price doesn’t reach the entry calculation. It is considered a failed entry and is not considered a negative or that you missed out on something. This has saved you from losing money since the prices are not ready to commit to the direction.
When placing a stop-loss, it is never suggested that you lower the value of a stop-loss. Always move your stop-losses higher in order to lock in profit in case of a negative turn.
Cerca negli script per "bar"
Strat Dashboard [TFO]The Strat Dashboard tracks up to 10 signals while highlighting common strat reversal patterns, the SSS 50% rule, timeframe continuity, and some additional criteria with VWAP and moving averages.
With the strat, all price action bars/candles are simplified into 3 total possibilities: 1 (inside bar), 2 (a bar that takes the previous bar's high OR low), and 3 (outside bar). The first table column for Last X Candles shows the most recent candles according to this notation, for example, 1 - 2D - 2U. This would mean we had an inside bar, followed by a bar that took the previous bar's low, followed then by a bar that took the previous bar's high. Note that the colors in this column are set according to whether the current bar's close exceeds the previous bar's high/low. By default, these colors are green if above the previous bar's highs, or red if below the previous bar's lows. If the current close is in between the previous candle's high and low (even after already taking the prior high or low), no color will be applied.
The SSS 50% column shows a yes or no value for whether the current bar aligns with the SSS 50% rule, where a bar has taken either the previous high or low, and has since reversed to at least the midway point of the previous bar's height - essentially anticipating a 2 that may become a 3 (outside bar).
Timeframe continuity (TFC) shows a yes or no value for when the current candle on multiple timeframes are all green or red (above the open price or below the open price, respectively). For example, if you were looking at the current 15m, 1h, and 1D bars, and they were all above the open price, you could say there's TFC between all three timeframes. As of the initial release, you can select up to 3 different timeframes. The table values will only be true when all selected timeframes are in alignment. When setting alerts, first deselect the timeframes if you don't want TFC logic to impact alerts.
The "Last" column shows the last strat reversal pattern that was confirmed (after the last bar closes). Waiting for a candle close is the safer option since a 2 can turn into a 3; however for higher timeframes, it may be beneficial to make an update to this indicator in which you can have live alerts as well (not waiting for a candle close). You can select which strat reversals you want to be shown from the settings. Various strat reversals may be selected for alerts of type "Any"; for example, if setting up an alert for "Any" strat reversal on Symbol 1, then this alert will go off when any of the *selected* strat reversals occur for that specific symbol. Deselect any strat reversals that you don't want to be included in these alerts.
Lastly, the EMA and VWAP columns simply show whether price is above or below said value. This tracks the current candle close, and may repaint/change several times if the current bar is oscillating above and below these values.
Swing Levels and Liquidity - By LeviathanThis script will plot pivot points (swing highs and lows) in the form of lines, boxes or labels to help you identify market structure, “liquidity” areas, swing failure patterns, etc. You are also able to see the volume traded at each pivot point, which will help you compare their significance.
Bars Left-Right
A pivot high (swing high) is a bar in a series of bars that has a higher value than the bars around it and a pivot low (swing low) is a bar in a series of bars that has a lower value than the bars surrounding it. The Bars Left and Bars Right parameters are used to define the number of bars on the left and right sides of a pivot point that the function should consider when identifying pivot highs and lows in a time series. For example, if Bars Left is set to 5 and Bars Right is set to 6, the function will look for a pivot point by comparing the value of the current bar with the values of the 5 bars to its left and the 6 bars to its right. If the value of the current bar is higher than all of these bars, it is considered a pivot high point. These parameter can be used to adjust the sensitivity of the script (lowering the Bars Left and Bars Right parameters will give you more swing points and increasing the Bars Left and Bars Right parameters will give you fewer swing points).
”Show Boxes” - This will draw a box above the swing high and a box below the swing low to help you visualise a large area of interest around swing points. Additional box types and the width of the box can be adjusted in Appearance settings below.
”Show Lines” - This will draw a horizontal line at the level of each swing high and swing low.
”Show Labels” - This will plot a circle at the high point of each swing high and at the low point of each swing low.
”Show Volume” - This will display the amount of volume traded in a given swing point candle. It can help you identify the significance of a given swing point by comparing it to the volumes of other swing points.
”Extend Until Filled” - This will extend the swing point levels until they are mitigated by the price. Turning it off will continue plotting the levels just a few more bars after a swing point occurs.
”Appearance” - You can show/hide swing points, choose the colors of labels, lines and boxes, choose the size and positioning of the text, choose line and box appearance (adjust the Box Width when switching between timeframes!) and more.
More updates coming soon (MTF, more data…)
Simple STRAT Tool by nnamWhat this Indicator Does
This indicator is a very simple tool created specifically for experienced Straters. It was created for those Straters who fully understand the 1-2-3 Strat Scenarios, are in need of an easy to use tool, and do not want or need a lot of messy markings on their chart.
The indicator simply allows the user to color code the Strat 1, 2 ,3 (Inside /Outside /Up / Down) Bars as desired and by default extends lines to the right of the chart from the Highs and Lows of the previous 2 Bars giving the user a simple reference for Strat scenario structure breaks.
As shown above, the bars are color coded, but the original bar color is maintained via the border and wick.
If a bar is an Outside Bar or an Inside Bar, it is still easy to identify whether or not the bar was a Bullish or Bearish 1 or 3.
The same goes for 2UP and 2Down Bars - It is easy to identify Bullish or Bearish UP or DOWN Bars.
Optionally, as show in the screenshot below, the user can extend the lines in both directions to get an "at a glance" better understanding of where price is currently vs previous support and resistance areas.
For Straters that prefer to trade only INSIDE BAR BREAKOUTS there is an optional input setting labeled "Trade Inside Bars ONLY".
This setting turns OFF the lines that extend from the 2nd previous bar back and only displays and extend lines from the previous bar IF and ONLY IF the current bar is an INSIDE (one) bar. .
The User Input settings allow for the following customizations:
1. Custom Outside Bar Color
2. Custom Inside Bar Color
3. Custom 2 Up Bar Color
4. Custom 2 Down Bar Color
5. Turn ON or OFF color coded bars
6. Trade only INSIDE Bar Breakouts
7. Extend Lines Both Directions
8. Hide all Lines
The customizable settings above allow the user to hide all lines and turn OFF color coding without having to fully remove the indicator from the chart. This is convenient when the user has another indicator that uses color coded bars or the lines conflict with another indicator and they need to be temporarily disabled.
If you have any questions regarding this indicator please let me know. If you have any suggestions for minor tweaks to the indicator do not hesitate to ask for them.
I hope you enjoy this indicator and get some usefulness from it... HAPPY TRADING!!
Signs of the Times [LucF]█ OVERVIEW
This oscillator calculates the directional strength of bars using a primitive weighing mechanism based on a small number of what I consider to be fundamental properties of a bar. It does not consider the amplitude of price movements, so can be used as a complement to momentum-based oscillators. It thus belongs to the same family of indicators as my Bar Balance , Volume Ticks , Efficient work , Volume Buoyancy or my Delta Volume indicators.
█ CONCEPTS
The calculations underlying Signs of the Times (SOTT) use a simple, oft-explored concept: measure bar attributes, assign a weight to them, and aggregate results to provide an evaluation of a bar's directional strength. Bull and bear weights are added independently, then subtracted and divided by the maximum possible weight, so the final calculation looks like this:
(up - dn) / weightRange
SOTT has a zero centerline and oscillates between +1 and -1. Ten elementary properties are evaluated. Most carry a weight of one, a few are doubly weighted. All properties are evaluated using only the current bar's values or by comparing its values to those of the preceding bar. The bull conditions follow; their inverse applies to bear conditions:
Weight of 1
• Bar's close is greater than the bar's open (bar is considered to be of "up" polarity)
• Rising open
• Rising high
• Rising low
• Rising close
• Bar is up and its body size is greater than that of the previous bar
• Bar is up and its body size is greater than the combined size of wicks
Weight of 2
• Gap to the upside
• Efficient Work when it is positive
• Bar is up and volume is greater than that of the previous bar (this only kicks in if volume is actually available on the chart's data feed)
Except for the Efficient Work weight, which is a +1 to -1 float value multiplied by 2, all weights are discrete; either zero or the full weight of 1 or 2 is generated. This will cause any gap, for example, to generate a weight of +2 or -2, regardless of the gap's size. That is the reason why the oscillator is oblivious to the amplitude of price movements.
You can see the code used to calculate SOTT in my ta library 's `sott()` function.
█ HOW TO USE THE INDICATOR
No videos explain this indicator and none are planned; reading this description or the script's code is the only way to understand what Signs of the Times does.
Load the indicator on an active chart (see here if you don't know how).
The default configuration displays:
• An Arnaud-Legoux moving average of length 20 of the instant SOTT value. This is the signal line.
• A fill between the MA and the centerline.
• Levels at arbitrary values of +0.3 and -0.3.
• A channel between the signal line and its MA (a simple MA of length 20), which can be one of four colors:
• Bull (green): The signal line is above its MA.
• Strong bull (lime): The bull condition is fulfilled and the signal line is above the centerline.
• Bear (red): The signal line is below its MA.
• Strong bear (pink): The bear condition is fulfilled and the signal line is below the centerline.
The script's "Inputs" tab allows you to:
• Choose a higher timeframe to calculate the indicator's values. This can be useful to get a wider perspective of the indicator's values.
If you elect to use a higher timeframe, make sure that 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.
• Specify the type of MA used to produce the signal line. Use a length of 1 or the Data Window to see the instant value of SOTT. It is quite noisy, thus the need to average it.
• Specify the type of MA applied to the signal line. The idea here is to provide context to the signal.
• Control the display and colors of the lines and fills.
The first pane of this publication's chart shows the default setup. The second one shows only a monochrome signal line.
Using the "Style" tab of the indicator's settings, you can change the type and width of the lines, and the level values.
█ INTERPRETATION
Remember that Signs of the Times evaluates directional bar strength — not price movement. Its highs and lows do not reflect price, but the strength of chart bars. The fact that SOTT knows nothing of how far price moves or of trends is easy to forget. As such, I think SOTT is best used as a confirmation tool. Chart movements may appear to be easy to read when looking at historical bars, but when you have to make go-no-go decisions on the last bar, the landscape often becomes murkier. By providing a quantitative evaluation of the strength of the last few bars, which is not always easily discernible by simply looking at them, SOTT aims to help you decide if the short-term past favors the bets you are considering. Can SOTT predict the future? Of course not.
While SOTT uses completely different calculations than classical momentum oscillators, its profile shares many of their characteristics. This could lead one to infer that directional bar strength correlates with price movement, which could in turn lead one to conclude that indicators such as this one are useless, or that they can be useful tools to confirm momentum oscillators or other models of price movement. The call is, of course, up to you. You can try, for example, to compare a Wilder MA of SOTT to an RSI of the same length.
One key difference with momentum oscillators is that SOTT is much less sensitive to large price movements. The default Arnaud-Legoux MA used for the signal line makes it quite active; you can use a more quiet SMA or EMA if you prefer to tone it down.
In systems where it can be useful to only enter or exit on short-term strength, an average of SOTT values over the last 3 to 5 bars can be used as a more quiet filter than a momentum oscillator would.
█ NOTES
My publications often go through a long gestation period where I use them on my charts or in systems before deciding if they are worth a publication. With an incubation period of more than three years, Signs of the Times holds the record. The properties SOTT currently evaluates result from the systematic elimination of contaminants over that lengthy period of time. It was long because of my usual, slow gear, but also because I had to try countless combinations of conditions before realizing that, contrary to my intuition, best results were achieved by:
• Keeping the number of evaluated properties to the absolute minimum.
• Limiting the evaluation's scope to the current and preceding bar.
• Choosing properties that, in my view, were unmistakably indicative of bullish/bearish conditions.
Repainting
As most oscillators, the indicator provides live realtime values that will recalculate with chart updates. It will thus repaint in real time, but not on historical values. To learn more about repainting, see the Pine Script™ User Manual's page on the subject .
Poly Cycle [Loxx]This is an example of what can be done by combining Legendre polynomials and analytic signals. I get a way of determining a smooth period and relative adaptive strength indicator without adding time lag.
This indicator displays the following:
The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
The normalized analytic signal of the cycle (signal and quadrature).
The Phase shift of the analytic signal per bar.
The Period and HalfPeriod lengths, in bars of the current cycle.
A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
The Relative Strength Indicator, is adaptive to the time series, and it can be smoothed by increasing the length of decreasing the number of degrees of freedom.
Other adaptive indicators based upon the period and can be similarly constructed.
There is some new math here, so I have broken the story up into 5 Parts:
Part 1:
Any time series can be decomposed into a orthogonal set of polynomials .
This is just math and here are some good references:
Legendre polynomials - Wikipedia, the free encyclopedia
Peter Seffen, "On Digital Smoothing Filters: A Brief Review of Closed Form Solutions and Two New Filter Approaches", Circuits Systems Signal Process, Vol. 5, No 2, 1986
I gave some thought to what should be done with this and came to the conclusion that they can be used for basic smoothing of time series. For the analysis below, I decompose a time series into a low number of degrees of freedom and discard the zero mode to introduce smoothing.
That is:
time series => c_1 t + c_2 t^2 ... c_Max t^Max
This is the cycle. By construction, the cycle does not have a zero mode and more physically, I am defining the "Trend" to be the zero mode.
The data for the cycle and the fit of the cycle can be viewed by setting
ShowDataAndFit = TRUE;
There, you will see the fit of the last bar as well as the time series of the leading edge of the fits. If you don't know what I mean by the "leading edge", please see some of the postings in . The leading edges are in grayscale, and the fit of the last bar is in color.
I have chosen Length = 17 and Degree = 4 as the default. I am simply making sure by eye that the fit is reasonably good and degree 4 is the lowest polynomial that can represent a sine-like wave, and 17 is the smallest length that lets me calculate the Phase Shift (Part 3 below) using the Hilbert Transform of width=7 (Part 2 below).
Depending upon the fit you make, you will capture different cycles in the data. A fit that is too "smooth" will not see the smaller cycles, and a fit that is too "choppy" will not see the longer ones. The idea is to use the fit to try to suppress the smaller noise cycles while keeping larger signal cycles.
Part 2:
Every time series has an Analytic Signal, defined by applying the Hilbert Transform to it. You can think of the original time series as amplitude * cosine(theta) and the transformed series, called the quadrature, can be thought of as amplitude * sine(theta). By taking the ratio, you can get the angle theta, and this is exactly what was done by John Ehlers in . It lets you get a frequency out of the time series under consideration.
Amazon.com: Rocket Science for Traders: Digital Signal Processing Applications (9780471405672): John F. Ehlers: Books
It helps to have more references to understand this. There is a nice article on Wikipedia on it.
Read the part about the discrete Hilbert Transform:
en.wikipedia.org
If you really want to understand how to go from continuous to discrete, look up this article written by Richard Lyons:
www.dspguru.com
In the indicator below, I am calculating the normalized analytic signal, which can be written as:
s + i h where i is the imagery number, and s^2 + h^2 = 1;
s= signal = cosine(theta)
h = Hilbert transformed signal = quadrature = sine(theta)
The angle is therefore given by theta = arctan(h/s);
The analytic signal leading edge and the fit of the last bar of the cycle can be viewed by setting
ShowAnalyticSignal = TRUE;
The leading edges are in grayscale fit to the last bar is in color. Light (yellow) is the s term, and Dark (orange) is the quadrature (hilbert transform). Note that for every bar, s^2 + h^2 = 1 , by construction.
I am using a width = 7 Hilbert transform, just like Ehlers. (But you can adjust it if you want.) This transform has a 7 bar lag. I have put the lag into the plot statements, so the cycle info should be quite good at displaying minima and maxima (extrema).
Part 3:
The Phase shift is the amount of phase change from bar to bar.
It is a discrete unitary transformation that takes s + i h to s + i h
explicitly, T = (s+ih)*(s -ih ) , since s *s + h *h = 1.
writing it out, we find that T = T1 + iT2
where T1 = s*s + h*h and T2 = s*h -h*s
and the phase shift is given by PhaseShift = arctan(T2/T1);
Alas, I have no reference for this, all I doing is finding the rotation what takes the analytic signal at bar to the analytic signal at bar . T is the transfer matrix.
Of interest is the PhaseShift from the closest two bars to the present, given by the bar and bar since I am using a width=7 Hilbert transform, bar is the earliest bar with an analytic signal.
I store the phase shift from bar to bar as a time series called PhaseShift. It basically gives you the (7-bar delayed) leading edge the amount of phase angle change in the series.
You can see it by setting
ShowPhaseShift=TRUE
The green points are positive phase shifts and red points are negative phase shifts.
On most charts, I have looked at, the indicator is mostly green, but occasionally, the stock "retrogrades" and red appears. This happens when the cycle is "broken" and the cycle length starts to expand as a trend occurs.
Part 4:
The Period:
The Period is the number of bars required to generate a sum of PhaseShifts equal to 360 degrees.
The Half-period is the number of bars required to generate a sum of phase shifts equal to 180 degrees. It is usually not equal to 1/2 of the period.
You can see the Period and Half-period by setting
ShowPeriod=TRUE
The code is very simple here:
Value1=0;
Value2=0;
while Value1 < bar_index and math.abs(Value2) < 360 begin
Value2 = Value2 + PhaseShift ;
Value1 = Value1 + 1;
end;
Period = Value1;
The period is sensitive to the input length and degree values but not overly so. Any insight on this would be appreciated.
Part 5:
The Relative Strength indicator:
The Relative Strength is just the current value of the series minus the minimum over the last cycle divided by the maximum - minimum over the last cycle, normalized between +1 and -1.
RelativeStrength = -1 + 2*(Series-Min)/(Max-Min);
It therefore tells you where the current bar is relative to the cycle. If you want to smooth the indicator, then extend the period and/or reduce the polynomial degree.
In code:
NewLength = floor(Period + HilbertWidth+1);
Max = highest(Series,NewLength);
Min = lowest(Series,NewLength);
if Max>Min then
Note that the variable NewLength includes the lag that comes from the Hilbert transform, (HilbertWidth=7 by default).
Conclusion:
This is an example of what can be done by combining Legendre polynomials and analytic signals to determine a smooth period without adding time lag.
________________________________
Changes in this one : instead of using true/false options for every single way to display, use Type parameter as following :
1. The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
2. The normalized analytic signal of the cycle (signal and quadrature).
3. The Phase shift of the analytic signal per bar.
4. The Period and HalfPeriod lengths, in bars of the current cycle.
5. A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
statisticsLibrary "statistics"
General statistics library.
erf(x) The "error function" encountered in integrating the normal
distribution (which is a normalized form of the Gaussian function).
Parameters:
x : The input series.
Returns: The Error Function evaluated for each element of x.
erfc(x)
Parameters:
x : The input series
Returns: The Complementary Error Function evaluated for each alement of x.
sumOfReciprocals(src, len) Calculates the sum of the reciprocals of the series.
For each element 'elem' in the series:
sum += 1/elem
Should the element be 0, the reciprocal value of 0 is used instead
of NA.
Parameters:
src : The input series.
len : The length for the sum.
Returns: The sum of the resciprocals of 'src' for 'len' bars back.
mean(src, len) The mean of the series.
(wrapper around ta.sma).
Parameters:
src : The input series.
len : The length for the mean.
Returns: The mean of 'src' for 'len' bars back.
average(src, len) The mean of the series.
(wrapper around ta.sma).
Parameters:
src : The input series.
len : The length for the average.
Returns: The average of 'src' for 'len' bars back.
geometricMean(src, len) The Geometric Mean of the series.
The geometric mean is most important when using data representing
percentages, ratios, or rates of change. It cannot be used for
negative numbers
Since the pure mathematical implementation generates a very large
intermediate result, we performed the calculation in log space.
Parameters:
src : The input series.
len : The length for the geometricMean.
Returns: The geometric mean of 'src' for 'len' bars back.
harmonicMean(src, len) The Harmonic Mean of the series.
The harmonic mean is most applicable to time changes and, along
with the geometric mean, has been used in economics for price
analysis. It is more difficult to calculate; therefore, it is less
popular than eiter of the other averages.
0 values are ignored in the calculation.
Parameters:
src : The input series.
len : The length for the harmonicMean.
Returns: The harmonic mean of 'src' for 'len' bars back.
median(src, len) The median of the series.
(a wrapper around ta.median)
Parameters:
src : The input series.
len : The length for the median.
Returns: The median of 'src' for 'len' bars back.
variance(src, len, biased) The variance of the series.
Parameters:
src : The input series.
len : The length for the variance.
biased : Wether to use the biased calculation (for a population), or the
unbiased calculation (for a sample set). .
Returns: The variance of 'src' for 'len' bars back.
stdev(src, len, biased) The standard deviation of the series.
Parameters:
src : The input series.
len : The length for the stdev.
biased : Wether to use the biased calculation (for a population), or the
unbiased calculation (for a sample set). .
Returns: The standard deviation of 'src' for 'len' bars back.
skewness(src, len) The skew of the series.
Skewness measures the amount of distortion from a symmetric
distribution, making the curve appear to be short on the left
(lower prices) and extended to the right (higher prices). The
extended side, either left or right is called the tail, and a
longer tail to the right is called positive skewness. Negative
skewness has the tail extending towards the left.
Parameters:
src : The input series.
len : The length for the skewness.
Returns: The skewness of 'src' for 'len' bars back.
kurtosis(src, len) The kurtosis of the series.
Kurtosis describes the peakedness or flatness of a distribution.
This can be used as an unbiased assessment of whether prices are
trending or moving sideways. Trending prices will ocver a wider
range and thus a flatter distribution (kurtosis < 3; negative
kurtosis). If prices are range-bound, there will be a clustering
around the mean and we have positive kurtosis (kurtosis > 3)
Parameters:
src : The input series.
len : The length for the kurtosis.
Returns: The kurtosis of 'src' for 'len' bars back.
excessKurtosis(src, len) The normalized kurtosis of the series.
kurtosis > 0 --> positive kurtosis --> trending
kurtosis < 0 --> negative krutosis --> range-bound
Parameters:
src : The input series.
len : The length for the excessKurtosis.
Returns: The excessKurtosis of 'src' for 'len' bars back.
normDist(src, len, value) Calculates the probability mass for the value according to the
src and length. It calculates the probability for value to be
present in the normal distribution calculated for src and length.
Parameters:
src : The input series.
len : The length for the normDist.
value : The series of values to calculate the normal distance for
Returns: The normal distance of 'value' to 'src' for 'len' bars back.
normDistCumulative(src, len, value) Calculates the cumulative probability mass for the value according
to the src and length. It calculates the cumulative probability for
value to be present in the normal distribution calculated for src
and length.
Parameters:
src : The input series.
len : The length for the normDistCumulative.
value : The series of values to calculate the cumulative normal distance
for
Returns: The cumulative normal distance of 'value' to 'src' for 'len' bars
back.
zScore(src, len, value) Returns then z-score of objective to the series src.
It returns the number of stdev's the objective is away from the
mean(src, len)
Parameters:
src : The input series.
len : The length for the zScore.
value : The series of values to calculate the cumulative normal distance
for
Returns: The z-score of objectiv with respect to src and len.
er(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
efficiencyRatio(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
fractalEfficiency(src, len) Calculates the efficiency ratio of the series.
It measures the noise of the series. The lower the number, the
higher the noise.
Parameters:
src : The input series.
len : The length for the efficiency ratio.
Returns: The efficiency ratio of 'src' for 'len' bars back.
mse(src, len) Calculates the Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the mean squared error.
Returns: The mean squared error of 'src' for 'len' bars back.
meanSquaredError(src, len) Calculates the Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the mean squared error.
Returns: The mean squared error of 'src' for 'len' bars back.
rmse(src, len) Calculates the Root Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the root mean squared error.
Returns: The root mean squared error of 'src' for 'len' bars back.
rootMeanSquaredError(src, len) Calculates the Root Mean Squared Error of the series.
Parameters:
src : The input series.
len : The length for the root mean squared error.
Returns: The root mean squared error of 'src' for 'len' bars back.
mae(src, len) Calculates the Mean Absolute Error of the series.
Parameters:
src : The input series.
len : The length for the mean absolute error.
Returns: The mean absolute error of 'src' for 'len' bars back.
meanAbsoluteError(src, len) Calculates the Mean Absolute Error of the series.
Parameters:
src : The input series.
len : The length for the mean absolute error.
Returns: The mean absolute error of 'src' for 'len' bars back.
BE_CustomFx_LibraryLibrary "BE_CustomFx_Library"
A handful collection of regular functions, Custom Tools & Utility Functions could be used in regular Scripts. hope these functions can be understood by a non programmer like me too.
G_TextValOfNumber(ValueToConvert, RequiredDecimalPlaces, BeginingChar, EndChar) Function to return the String Value of Number with decimal precision with the prefix and suffix characters provided
Parameters:
ValueToConvert : = Number to Convert
RequiredDecimalPlaces : = No of Decimal values Required. supports to a max of 5 decimals else defaults to 2
BeginingChar : = Prefix character which is needed.
EndChar : = Suffix character which is needed.
Returns: Returns Out put with formated value of Given Number for the specified deicimal values with Prefix and suffix string
G_TradableValue(ValueToConvert, NeedCustomization, RequiredDecimalPlaces) Function to return the Tradable Value of Number
Parameters:
ValueToConvert : = Number to Convert
NeedCustomization : = set to 1 if you want to customize the decimal percision values. default is No customization needed, which provides output equalent to round_to_mintick
RequiredDecimalPlaces : = if NeedCustomization is set to 1 mention the decimal percision value required. max supported decimal is 5 else defaults to 2
Returns: Returns Out put with formated value of Given Number
G_TxtSizeForLables(SizeValue) Function to Get size Value for text values used in Lables
Parameters:
SizeValue : = auto, tiny, small, normal, large, huge. specify either of these values or default value Normal will be displayed as output
Returns: Returns Respective Text size
G_Reg_LineType(LineType) Function to Get Line Style Value for text values used in Lines
Parameters:
LineType : = 'solid (─)', 'dotted (┈)', 'dashed (╌)', 'arrow left (←)', 'arrow right (→)', 'arrows both (↔)' or default line style 'dotted (┈)' will be the output
Returns: Returns Respective Line style
G_ShapeTypeForLables(ShapeType) Function to Get Shape Style Value for text values used in plot shapes
Parameters:
ShapeType : = 'XCross', 'Cross', 'Triangle Up', 'Triangle Down', 'Flag', 'Circle','Arrow Up', 'Arrow Down','Lable Up', 'Lable Down' or default shpae style Triangle Up will be the output
Returns: Returns Respective Shape style
G_Indicator_Val(string, float, int, int) Gets Output of the technical analyis indicator which has length Parameter. RSI, ATR, EMA, SMA, HMA, WMA, VWMA, 'CMO', 'MOM', 'ROC','VWAP'
Parameters:
string : IndicatorName to be specified
float : SrcVal for the TA indicator default is close
int : Length for the TA indicator
int : DecimalValue optional to specify if required formatted output with decimal percision
Returns: Value with the given parameters
G_CandleInfo(string, bool, float, bool) function to get Candle Informarion such as both wicksize, top wick size , bottom wick size, full candle size and body size in default points
Parameters:
string : WhatCandleInfo, string input with either of these options "Wick" , "TWick" , "BWick" , "Candle", "Body" , "BearfbVal", "BullfbVal" , "CandleOpen" ,"CandleClose", "CandleHigh" , "CandleLow", "BodyPct"
bool : RepaintingVersion, set to true if required data on the realtime bar else default is set to false
float : FibValueOfCandle, set the fibo value to extract fibvalue of the candle else default is set to 38.2%
bool : AccountforGaps, set to true if required data on considering the gap between previous and current bar else default is set to false
Returns: Returns Respective values for the candles
G_BullBearBarCount(int, int) Counts how many green & red bars have printed recently (ie. pullback count)
Parameters:
int : HowManyCandlesToCheck The lookback period to look back over
int : BullBear The color of the bar to count (1 = Bull, -1 = Bear), Open = close candles are ignored
Returns: The bar count of how many candles have retraced over the given lookback with specific candles
BarToStartYourCalculation(Int) function to get candle co-ordinate in order to use it further for calculating your analysis work . "Heart full Thanks to 3 Pine motivators (LonesomeTheBlue, Myank & Sriki) who helped me cracking this logic"
Parameters:
Int : SelectedCandleNumber (default=450) How many candles you would need to anlysie in your script from the right.
Returns: A boolean - output is returned to say the starting point and continue to diplay true for the future candles
isHammer(float, bool, bool) Checks if the current bar is a hammer candle based on the given parameters
Parameters:
float : fib (default=0.382) The fib to base candle body on
bool : colorMatch (default=false) Does the candle need to be green? (true/false)
bool : NeedRepainting (default=false) Specify True if you need them to calculate on the realtime bars
Returns: A boolean - true if the current bar matches the requirements of a hammer candle
isStar(float, bool, bool) Checks if the current bar is a shooting star candle based on the given parameters
Parameters:
float : fib (default=0.382) The fib to base candle body on
bool : colorMatch (default=false) Does the candle need to be red? (true/false)
bool : NeedRepainting (default=false) Specify True if you need them to calculate on the realtime bars
Returns: A boolean - true if the current bar matches the requirements of a shooting star candle
isDoji(float, float, bool) Checks if the current bar is a doji candle based on the given parameters
Parameters:
float : _wickSize (default=1.5 times) The maximum allowed times can be top wick size compared to the bottom (and vice versa)
float : _bodySize (default= 5 percent to be mentioned as 0.05) The maximum body size as a percentage compared to the entire candle size
bool : NeedRepainting (default=false) Specify true if you need them to calculate on the realtime bars
Returns: A boolean - true if the current bar matches the requirements of a doji candle
isBullishEC(float, float, bool, bool) Checks if the current bar is a bullish engulfing candle
Parameters:
float : _allowance (default=0) How many POINTS to allow the open to be off by (useful for markets with micro gaps)
float : _rejectionWickSize (default=disabled) The maximum rejection wick size compared to the body as a percentage
bool : _engulfWick (default=false) Does the engulfing candle require the wick to be engulfed as well?
bool : NeedRepainting (default=false) Specify True if you need them to calculate on the realtime bars
Returns: A boolean - true if the current bar matches the requirements of a bullish engulfing candle
isBearishEC(float, float, bool, bool) Checks if the current bar is a bearish engulfing candle
Parameters:
float : _allowance (default=0) How many POINTS to allow the open to be off by (useful for markets with micro gaps)
float : _rejectionWickSize (default=disabled) The maximum rejection wick size compared to the body as a percentage
bool : _engulfWick (default=false) Does the engulfing candle require the wick to be engulfed as well?
bool : NeedRepainting (default=false) Specify True if you need them to calculate on the realtime bars
Returns: A boolean - true if the current bar matches the requirements of a bearish engulfing candle
Plot_TrendLineAtDegree(float, float, int, string, bool) helps you to plot the Trendlines based on the specified angle at the defined price to bar ratio
Parameters:
float : Degree (default=14) angle at which Trendline to be plot
float : price2bar_ratio (default=1e-10) The maximum rejection wick size compared to the body as a percentage
int : Bars2Plot (default=6) Does the engulfing candle require the wick to be engulfed as well?
string : LineStyle = 'solid (─)', 'dotted (┈)', 'dashed (╌)', 'arrow left (←)', 'arrow right (→)', 'arrows both (↔)' or default line style 'dotted (┈)' will be the output
bool : PlotOnOpen_Close (default=false) Specify True if you need them to calculate on the Open\Close Values
Returns: plot the Trendlines based on the specified angle at the defined price to bar ratio
Donchian DipThe Donchian Dip
This strategy is designed to look for good "Buy the Dip" entries on stocks that are clearly in a strong 1-year upward trend. If you do not know how to identify those stocks on your own please do not use this system or continue your education until you do. The Donchian Dip strategy was designed on the daily time frame but works amazingly well on both daily and weekly timeframes. It does still work on intraday charts also if the current trend on the daily chart is in a strong uptrend.
Chart Setup:
3-period Donchian Channel with a 1-period offset (hide basis)
Bollinger Bands with the default settings of 20/2 (display basis)
Entry Signals:
There are 3 different entry signals that will be printed on the chart that have similar underlying criteria but are ranked based on skill level just like ski slope skill levels! I recommend only taking green entries until you are familiar with the system and the stocks you are trading.
Green Easy Entry:
This is the safest buy the dip entry that is normally found at or near a large retracement bottom. You might get one or two bad entries but be persistent and eventually, a great entry will present itself!
These are the specifics for the conditions that trigger a Green entry if you want to know what they are:
1. The current bar is an up bar (green or white bar) and closed above the lower Donchian channel
2. Previous bar or 2 bars back closed below the lower Donchian channel
3. Previous bar or 2 bars back closed below the Bollinger Band Basis (20 SMA )
4. The low of the previous bar or 2 bars back was below the lower Bollinger Band
Blue Intermediate Entry:
This is a decent entry if you missed the green entry, want to add to an existing position, or are not sure it will pull back far enough to even give a green entry. I would suggest only trade these entries to add to an existing pyramid position or get back into a trade that you were recently stopped out of. However, on high-flying stocks like TSLA these signals and the Black Diamond entry signals might be the only ones you get for a long time. Also, on the weekly chart, Blue or Black entries are sometimes all you will get for a year or more.
These are the specifics for the conditions that trigger a Blue entry if you want to know what they are:
1. The current bar is an up bar (green or white bar) and closed above the lower Donchian channel
2. Previous bar or 2 bars back closed below the lower Donchian channel
3. Previous bar or 2 bars back closed below the Bollinger Band Basis (20 SMA )
Black Diamond Advanced Rule:
This is normally just a small pullback re-entry signal on a strong trending stock like TSLA ...trade with extreme caution!!! You have been warned but daredevils feel free to give it a shot. I sometimes do trade these entries if the market and sector of the stock I am trading are extremely bullish or if I am looking to add to a position but I use a conservative stop.
These are the specifics for the conditions that trigger a Black entry if you want to know what they are:
1. The current bar is an up bar (green or white bar) and closed above the lower Donchian channel
2. Previous bar or 2 bars back closed below the lower Donchian channel
3. Previous bar or 2 bars back closed above the Bollinger Band Basis (20 SMA )
Exit Criteria:
The goal of this strategy is to buy the dip and hold as long as possible...let's practice some Paytience and exercise those holding muscles! RLT!!!
So, we don't want to exit early but we also want to protect our profits somehow. We do this by using the built-in trailing stops that are defined by dots of three different shades of purple on the chart (feel free to change these in the settings). Simply move your trailing stop to the highest current dot price level. Do not move the trailing stop down ever even if a lower dot is printed later. These are simply the suggested trailing stops and definitely use your own judgment for exits but if you backtest this strategy enough you will most likely discover that in the long run, these trailing stops work really well.
I hope this strategy helps you to identify good "Buy the Dip" entries on stocks you love as well as trains you to hold your winners longer for bigger gains.
***HOW TO ADD TO YOUR CHARTS***
1) Click the "Add to Favorite Scripts" button
2) Go to a stock chart and click the "Indicators" icon at the top
3) Next, on the left, click the "Favorites" and then click the "Naked Put - Growth Indicator v2"
4) It should appear on your charts, and you can click the "gear" icon on the study to edit a few settings.
5) Read the release notes above so you understand how it works.
How to avoid repainting when using security() - PineCoders FAQNOTE
The non-repainting technique in this publication that relies on bar states is now deprecated, as we have identified inconsistencies that undermine its credibility as a universal solution. The outputs that use the technique are still available for reference in this publication. However, we do not endorse its usage. See this publication for more information about the current best practices for requesting HTF data and why they work.
This indicator shows how to avoid repainting when using the security() function to retrieve information from higher timeframes.
What do we mean by repainting?
Repainting is used to describe three different things, in what we’ve seen in TV members comments on indicators:
1. An indicator showing results that change during the realtime bar, whether the script is using the security() function or not, e.g., a Buy signal that goes on and then off, or a plot that changes values.
2. An indicator that uses future data not yet available on historical bars.
3. An indicator that uses a negative offset= parameter when plotting in order to plot information on past bars.
The repainting types we will be discussing here are the first two types, as the third one is intentional—sometimes even intentionally misleading when unscrupulous script writers want their strategy to look better than it is.
Let’s be clear about one thing: repainting is not caused by a bug ; it is caused by the different context between historical bars and the realtime bar, and script coders or users not taking the necessary precautions to prevent it.
Why should repainting be avoided?
Repainting matters because it affects the behavior of Pine scripts in the realtime bar, where the action happens and counts, because that is when traders (or our systems) take decisions where odds must be in our favor.
Repainting also matters because if you test a strategy on historical bars using only OHLC values, and then run that same code on the realtime bar with more than OHLC information, scripts not properly written or misconfigured alerts will alter the strategy’s behavior. At that point, you will not be running the same strategy you tested, and this invalidates your test results , which were run while not having the additional price information that is available in the realtime bar.
The realtime bar on your charts is only one bar, but it is a very important bar. Coding proper strategies and indicators on TV requires that you understand the variations in script behavior and how information available to the script varies between when the script is running on historical and realtime bars.
How does repainting occur?
Repainting happens because of something all traders instinctively crave: more information. Contrary to trader lure, more information is not always better. In the realtime bar, all TV indicators (a.k.a. studies ) execute every time price changes (i.e. every tick ). TV strategies will also behave the same way if they use the calc_on_every_tick = true parameter in their strategy() declaration statement (the parameter’s default value is false ). Pine coders must decide if they want their code to use the realtime price information as it comes in, or wait for the realtime bar to close before using the same OHLC values for that bar that would be used on historical bars.
Strategy modelers often assume that using realtime price information as it comes in the realtime bar will always improve their results. This is incorrect. More information does not necessarily improve performance because it almost always entails more noise. The extra information may or may not improve results; one cannot know until the code is run in realtime for enough time to provide data that can be analyzed and from which somewhat reliable conclusions can be derived. In any case, as was stated before, it is critical to understand that if your strategy is taking decisions on realtime tick data, you are NOT running the same strategy you tested on historical bars with OHLC values only.
How do we avoid repainting?
It comes down to using reliable information and properly configuring alerts, if you use them. Here are the main considerations:
1. If your code is using security() calls, use the syntax we propose to obtain reliable data from higher timeframes.
2. If your script is a strategy, do not use the calc_on_every_tick = true parameter unless your strategy uses previous bar information to calculate.
3. If your script is a study and is using current timeframe information that is compared to values obtained from a higher timeframe, even if you can rely on reliable higher timeframe information because you are correctly using the security() function, you still need to ensure the realtime bar’s information you use (a cross of current close over a higher timeframe MA, for example) is consistent with your backtest methodology, i.e. that your script calculates on the close of the realtime bar. If your system is using alerts, the simplest solution is to configure alerts to trigger Once Per Bar Close . If you are not using alerts, the best solution is to use information from the preceding bar. When using previous bar information, alerts can be configured to trigger Once Per Bar safely.
What does this indicator do?
It shows results for 9 different ways of using the security() function and illustrates the simplest and most effective way to avoid repainting, i.e. using security() as in the example above. To show the indicator’s lines the most clearly, price on the chart is shown with a black line rather than candlesticks. This indicator also shows how misusing security() produces repainting. All combinations of using a 0 or 1 offset to reference the series used in the security() , as well as all combinations of values for the gaps= and lookahead= parameters are shown.
The close in the call labeled “BEST” means that once security has reached the upper timeframe (1 day in our case), it will fetch the previous day’s value.
The gaps= parameter is not specified as it is off by default and that is what we need. This ensures that the value returned by security() will not contain na values on any of our chart’s bars.
The lookahead security() to use the last available value for the higher timeframe bar we are using (the previous day, in our case). This ensures that security() will return the value at the end of the higher timeframe, even if it has not occurred yet. In our case, this has no negative impact since we are requesting the previous day’s value, with has already closed.
The indicator’s Settings/Inputs allow you to set:
- The higher timeframe security() calls will use
- The source security() calls will use
- If you want identifying labels printed on the lines that have no gaps (the lines containing gaps are plotted using very thick lines that appear as horizontal blocks of one bar in length)
For the lines to be plotted, you need to be on a smaller timeframe than the one used for the security() calls.
Comments in the code explain what’s going on.
Look first. Then leap.
Trend Gazer: Unified ICT Trading System with Signals# Trend Gazer User Guide (English)
## 📖 Table of Contents
1. (#about-this-indicator)
2. (#quick-start-guide-3-steps)
3. (#detailed-usage)
4. (#settings-customization)
5. (#why-combine-multiple-features)
6. (#faq)
---
## About This Indicator
**Trend Gazer** is an integrated trading system designed to read institutional order flow like professional traders.
### 🎯 3 Problems This Indicator Solves
#### ❌ Problem 1: Too Many Indicators = Information Overload
```
Normal: RSI + MACD + Moving Average + Bollinger Bands... → Cluttered chart
Solution: All integrated into ONE indicator → Clean & Clear
```
#### ❌ Problem 2: Single Indicators Give False Signals
```
Normal: Enter based on RSI alone → Frequent stop-outs
Solution: Structure × Zone × Momentum multi-angle confirmation → Higher win rate
```
#### ❌ Problem 3: Unclear Entry Timing
```
Normal: Know the trend but don't know WHERE to enter
Solution: LS Bounce Signal shows EXACT entry points
```
---
## Quick Start Guide (3 Steps)
### 🚀 STEP 1: Confirm Trend Direction
**Look for CHoCH (Change of Character)**
```
📍 (1.CHoCH) label = Uptrend starting
📍 (a.CHoCH) label = Downtrend starting
```
**Important**: Wait for CHoCH! No direction without it.
---
### 🎯 STEP 2: Find Entry Points
**Wait for LS Bounce Signal (green/red labels)**
```
🟢 "Long@ HL only" label → LONG (buy) candidate
🔴 "Short@ LH only" label → SHORT (sell) candidate
```
**Label text color meaning**:
- **White text**: Clean trend (high confidence)
- **Yellow text**: Trend transition (moderate caution)
---
### 🛡️ STEP 3: Final Confirmation with Bar Color
**Bar color shows market state**
```
🔴 Red bar: BUY zone (buying is favored)
🟢 Green bar: SELL zone (selling is favored)
⚪ White bar: Neutral (wait and see)
```
---
## Detailed Usage
### 📊 Understanding the Chart
#### 1. Labels (Market Structure Changes)
```
(1.CHoCH) / (a.CHoCH) : Trend reversal
(2.SiMS) / (b.SiMS) : Momentum confirmation
(3.BoMS) / (c.BoMS) : Trend continuation
```
#### 2. Boxes (Institutional Order Zones)
```
📦 Blue boxes: Bullish OB (buy orders accumulated)
📦 Red boxes: Bearish OB (sell orders accumulated)
📦 Black transparent boxes: Liquidity Sweep
```
**How to use Order Blocks**:
- Function as support/resistance
- Signals within OB have higher reliability
- Use for stop-loss placement
#### 3. Lines (Trends and Support/Resistance)
```
━━━ Red lines: EMA20, EMA50, EMA100 (short to mid-term trends)
━━━ Blue lines: 60min NPR/BB bands (support/resistance)
```
#### 4. Bar Colors (Filter 6)
```
Bar color = Real-time market state
🔴 Red: Buying is favored
🟢 Green: Selling is favored
⚪ White: Neutral
```
---
### 🎯 Practical Trading Flow
#### 📍 Preparation Phase
```
1. Open chart (recommended: 5min or 15min)
2. Add Trend Gazer to chart
3. Start in observation mode (don't enter yet)
```
#### 📍 Entry Decision
```
✅ CHoCH confirms direction → Uptrend starting
✅ LS Bounce Signal "Long@ HL only" appears
→ Entry point candidate
✅ Bar turns red → Market supports buying
→ Entry decision 🎯
✅ Place stop below nearest Order Block (blue box)
```
#### 📍 Exit Decision
```
🔴 Opposite LS Bounce Signal "Short@ LH only" appears
→ Consider taking profit
🔴 Bar turns green
→ Potential trend reversal, review position
🔴 Stop loss hit
→ Exit with loss
```
---
### 💡 Tips for Higher Win Rate
#### ✅ DO's
```
1. Enter AFTER CHoCH appears
2. Prioritize white-text LS Bounce Signals
3. Check higher timeframe (1H or Daily) trend
4. Emphasize signals within Order Blocks
5. Use bar color as final confirmation
```
#### ❌ DON'Ts
```
1. Enter before CHoCH → No clear direction
2. Enter only on yellow text → Unstable transition period
3. Ignore bar color → Trading against market state
4. Don't check Order Blocks → Unclear support/resistance
5. Enter same direction consecutively → Overtrading
```
---
## Settings Customization
### 🔧 How to Open Settings
```
1. Right-click on indicator name on chart
2. Select "Settings..."
3. Settings panel opens
```
---
### 📋 Recommended Setting Profiles
#### 🔰 Beginner Settings (Simple)
**Goal**: Reduce noise, show only important signals
```
【FILTERS】
✅ Bonus Filter: ON
✅ Filter 6 (OB/BB/NPR Zone Filter): ON
❌ Direction Filter: OFF
❌ Liquidation Reversal Filter: OFF
❌ ICT Market Structure Filter: OFF
❌ EMA Trend Filter: OFF
❌ OB/FVG Filter 1: OFF
❌ OB/FVG Filter 2: OFF
【SIGNALS】
✅ Signal 0 (Bonus): ON
✅ Signal 1 (VWC Change): ON
✅ Signal 2 (Liq Rev): ON
❌ Signal 3 (LS): OFF (complex alone)
❌ Signal 4 (LS Break): OFF
❌ Signal 5 (OB+LS NPR): OFF
❌ Signal 6 (OB+LS EMA): OFF
【LS BOUNCE SIGNAL】
✅ Exclude EMA50 from touch detection: OFF
❌ Only show when EMA fills are mixed: OFF
```
**What happens with this setup**:
- Only Bonus (black background) signals display
- LS Bounce Signals clearly visible
- Noisy signals filtered out
---
#### 💪 Intermediate Settings (Balanced)
**Goal**: Enable key filters for better accuracy
```
【FILTERS】
✅ Bonus Filter: ON
✅ Filter 6 (OB/BB/NPR Zone Filter): ON
✅ ICT Market Structure Filter: ON
❌ Direction Filter: OFF
❌ Liquidation Reversal Filter: OFF
❌ EMA Trend Filter: OFF
❌ OB/FVG Filter 1: OFF
❌ OB/FVG Filter 2: OFF
【SIGNALS】
✅ Signal 0 (Bonus): ON
✅ Signal 1 (VWC Change): ON
✅ Signal 2 (Liq Rev): ON
✅ Signal 3 (LS): ON
❌ Signal 4 (LS Break): OFF
❌ Signal 5 (OB+LS NPR): OFF
❌ Signal 6 (OB+LS EMA): OFF
【LS BOUNCE SIGNAL】
✅ Exclude EMA50 from touch detection: OFF
❌ Only show when EMA fills are mixed: OFF
```
**What happens with this setup**:
- Signals only after CHoCH (trend confirmed)
- Filter 6 changes bar colors
- Liquidity Sweeps also displayed
---
#### 🚀 Advanced Settings (Full Utilization)
**Goal**: Master all features
```
【FILTERS】
✅ Bonus Filter: ON
✅ Filter 6 (OB/BB/NPR Zone Filter): ON
✅ ICT Market Structure Filter: ON
✅ Direction Filter: ON
✅ EMA Trend Filter: ON
❌ Liquidation Reversal Filter: OFF (optional)
✅ OB/FVG Filter 1: ON
✅ OB/FVG Filter 2: ON
【SIGNALS】
✅ All ON
【LS BOUNCE SIGNAL】
✅ Exclude EMA50 from touch detection: ON (reduce EMA50 noise)
✅ Only show when EMA fills are mixed: ON (show only transition zones)
```
**What happens with this setup**:
- Fewer signals (precision-focused)
- Multiple confirmations greatly reduce false signals
- Only signals confirmed by trend, momentum, and zones
---
### 🎨 Display Customization
#### Change Label Size
```
【BUY/SELL SIGNAL APPEARANCE】
→ "BUY/SELL Label Size"
→ Choose from: tiny / small / normal / large / huge
Recommended: small (default)
```
#### Order Block Display Settings
```
【ORDER BLOCK (OB) SETTINGS】
✅ Show Current TF OB: Current timeframe OB
✅ Show 1min OB: 1-minute OB
✅ Show 5min OB: 5-minute OB
✅ Show 15min OB: 15-minute OB
Recommended: Only 15min OB ON (simple)
```
#### Liquidity Sweep Display
```
【LIQUIDITY SWEEPS SETTINGS】
→ "Sweep Length": Sensitivity (small=frequent, large=selective)
→ "Sweep Option": Standard / Maximum
Recommended: Length=40, Option=Standard
```
#### NPR/BB Bands Display
```
【NPR (NON-REPAINT STDEV) SETTINGS】
✅ Display 60min NPR Bands: 60-minute support/resistance
❌ Display Current TF NPR Bands: Current timeframe (optional)
Recommended: Only 60min ON
```
---
### ⚙️ Advanced Settings
#### Fine-tune Filter 6
```
【FINAL FILTERS】
→ "Enable Filter 6 (OB/BB/NPR Zone Filter)"
When ON:
- Bars color-coded red/green/white
- Behavior at OB, NPR/BB touches controlled
```
#### LS Bounce Signal Adjustments
```
【LS BOUNCE SIGNAL】
→ "Exclude EMA50 from touch detection"
OFF: Detect NPR/BB/EMA50 (all 3)
ON: Detect NPR/BB only (exclude EMA50)
→ "Only show when EMA fills are mixed"
OFF: Show all LS Bounce Signals
ON: Show only transition zone signals (yellow text)
```
#### MTF (Multi-Timeframe) Control
```
【ORDER BLOCK (OB) SETTINGS】
→ "Disable MTF on 1hr+ Charts"
ON: Disable MTF on 1H+ (save memory)
OFF: MTF enabled on all timeframes
Recommended: ON (unnecessary on larger timeframes)
```
---
### 🎯 Purpose-Based Configuration Guide
#### 🔍 Goal 1: Reduce Signal Count
```
✅ Bonus Filter: ON
✅ ICT Market Structure Filter: ON
✅ Filter 6: ON
✅ All Signals OFF, only Signal 0 ON
```
#### 🔍 Goal 2: Get More Signals
```
❌ All Filters OFF
✅ All Signals ON
```
#### 🔍 Goal 3: Trend Following Only
```
✅ ICT Market Structure Filter: ON
✅ Direction Filter: ON
✅ EMA Trend Filter: ON
```
#### 🔍 Goal 4: Counter-Trend Trading
```
✅ LS Bounce Signal: ON
✅ Filter 6: ON
❌ ICT Market Structure Filter: OFF
```
#### 🔍 Goal 5: Day Trading (5-15min charts)
```
✅ Show 15min OB: ON
✅ Display 60min NPR Bands: ON
✅ LS Bounce Signal: ON
❌ Show 1min/5min OB: OFF
```
#### 🔍 Goal 6: Scalping (1-5min charts)
```
✅ Show 5min OB: ON
✅ Show 15min OB: ON
✅ Display 60min NPR Bands: ON
✅ All Signals: ON
```
---
### 💾 Saving and Loading Settings
#### Save Settings
```
1. Click "..." in top-right of Settings screen
2. Select "Save as default"
→ Same settings auto-applied next time
```
#### Reset Settings
```
1. Click "..." in top-right of Settings screen
2. Select "Reset settings"
→ Return to default settings
```
---
## Why Combine Multiple Features?
### 🎯 Problem: Single Indicator Limitations
Common trader problems:
```
❌ RSI alone → Trade against trend, lose
❌ Moving Average alone → Late entry timing
❌ Support/Resistance alone → Caught by false breakouts
```
**Markets are complex**. One angle isn't enough.
---
### 💡 Solution: Multi-Angle Integrated Approach
#### 1️⃣ Structure × Zone × Momentum
```
📐 Structure (ICT CHoCH)
→ "Which direction is likely?"
📦 Zone (OB/NPR/BB)
→ "Where will price react?"
💨 Momentum (EMA/VWC)
→ "Is there momentum now?"
```
**When all 3 align = Highest win-rate timing**
---
#### 2️⃣ Multi-Timeframe Analysis
```
Big picture: Confirm Daily direction
Medium-term: Check 1H Order Blocks
Short-term: Time entry on 5min
```
**Short-term entries aligned with higher timeframes = Better win rate**
---
#### 3️⃣ Understanding Liquidity
```
🎣 Institutional strategy:
1. Intentionally move price opposite to stop out retail
2. Then, move in real direction
💡 Liquidity Sweep = Visualize this "trap"
→ Read institutional order flow
```
---
### 🧠 Integration Examples
#### Case 1: RSI Alone vs Integrated System
**Scenario**: RSI at 30 (oversold)
```
❌ RSI-only decision:
→ "Buy!"
→ But downtrend continues, loss 😢
✅ Trend Gazer:
CHoCH check → Still downtrend ❌
Order Block → In Bearish OB ❌
LS Bounce → SHORT signal only ❌
→ Skip or SHORT
→ Avoid loss ✅
```
**Result**: Multiple filters block wrong entry
---
#### Case 2: LS Bounce Signal 2-Stage Logic
**Scenario**: Price touches 60min NPR lower band
```
🔍 Traditional method:
Touched → Buy!
→ But price continues down 😢
✅ Trend Gazer:
Stage 1: NPR touch + red bar → Flag ON
Stage 2: EMA20 crosses above EMA50 → Confirm bounce
→ Now "Long@ HL only" displays
→ Entry → Success ✅
```
**Result**: Not just "touch" but "touch + bounce confirmation" improves accuracy
---
### 🎓 Progressive Learning Design
This indicator is designed for **beginners to advanced**:
```
📖 Beginner (Month 1):
Use only CHoCH + LS Bounce Signal
→ Learn trend and entry points
📖 Intermediate (Months 2-3):
Add Order Block + Bar Color
→ Learn support/resistance and filtering
📖 Advanced (Month 6+):
Master all features
→ Read institutional order flow
```
**Ultimate goal**: Indicator becomes confirmation tool. Your market sense becomes primary.
---
### 🔬 Technical Advantages
#### 1. Non-Repaint STDEV (NPR)
```
Normal Bollinger Bands:
→ Past data changes (repaints)
→ Inaccurate backtesting
NPR:
→ Past data doesn't change (non-repaint)
→ Reliable verification possible
```
#### 2. 2-Stage Signal Logic
```
Traditional: Condition met → Immediate signal
→ Many false signals
Trend Gazer: Condition1 → Flag ON → Condition2 → Signal
→ Confirmation step improves accuracy
```
#### 3. Alternating Filter
```
Problem: Same-direction signals spam
→ Overtrading
Solution: LONG → SHORT → LONG alternating only
→ Prevent unnecessary entries
```
---
### 💎 Conclusion: Why Integration?
```
Single indicator = "Partial truth"
Integrated system = "3D market perspective"
```
**Markets are multifaceted**. One angle isn't enough.
Trend Gazer **integrates multiple screens pros watch simultaneously into ONE**,
allowing beginners to read charts with institutional perspective.
---
## FAQ
### ❓ Q1: Which timeframe is best?
**A**: Depends on trading style
```
Scalping: 1min ~ 5min
Day Trading: 5min ~ 15min
Swing: 1H ~ 4H
```
**Important**: LS Bounce Signal only works on 30min and below.
---
### ❓ Q2: Too many signals, confused
**A**: Enable filters
```
【Recommended Settings】
✅ Bonus Filter: ON
✅ Filter 6: ON
✅ ICT Market Structure Filter: ON
→ Show only Signal 0
```
This significantly reduces signal count.
---
### ❓ Q3: No CHoCH appearing, what to do?
**A**: Wait or check higher timeframe
```
Method 1: Wait for CHoCH (recommended)
Method 2: Check higher timeframe (e.g., Daily) for trend
Method 3: Disable ICT Filter (not recommended)
```
**When trend is unclear, sitting out is also strategy**.
---
### ❓ Q4: LS Bounce Signal not appearing
**A**: Checkpoints
```
1. Are you on 30min or below chart?
→ Doesn't show on 1H+
2. Are NPR/BB bands displayed?
→ Check Settings "Display 60min NPR Bands"
3. Is EMA50 excluded?
→ If "Exclude EMA50" is ON, EMA50 signals won't show
```
---
### ❓ Q5: Bar color not changing?
**A**: Check Filter 6
```
Settings → FINAL FILTERS
→ Confirm "Enable Filter 6 (OB/BB/NPR Zone Filter)" is ON
If ON but still not changing:
→ Current price may be outside OB/NPR/BB zones
```
---
### ❓ Q6: Too many Order Blocks, hard to see
**A**: Narrow down displayed OBs
```
Settings → ORDER BLOCK (OB) SETTINGS
Recommended:
❌ Show Current TF OB: OFF
❌ Show 1min OB: OFF
❌ Show 5min OB: OFF
✅ Show 15min OB: ON (only this)
```
---
### ❓ Q7: How to improve win rate?
**A**: Thorough multiple confirmations
```
Checklist:
✅ CHoCH appeared
✅ LS Bounce Signal (white text)
✅ Bar color matches (red bar=LONG, green bar=SHORT)
✅ Signal within Order Block
✅ Aligns with higher timeframe trend
Enter ONLY when all align
```
---
### ❓ Q8: Want to practice on demo
**A**: Recommended practice method
```
Week 1: Observation only
→ Watch signals and chart movement
→ Resist entering
Weeks 2-3: Keep records
→ Screenshot when signal appears
→ Record subsequent movement
Week 4+: Start demo trading
→ Start with small amounts
→ Continue keeping records
```
---
### ❓ Q9: Are there alert features?
**A**: Yes, multiple alerts available
```
Setup method:
1. Right-click indicator on chart
2. Select "Add Alert..."
3. Choose from:
- ANY ALERT: BUY/SELL Signals
- BUY ONLY ALERT
- SELL ONLY ALERT
- MS UP / MS DOWN
- BAR COLOR: RED / LIME
- LS BOUNCE: LONG / SHORT Signal
```
---
### ❓ Q10: Works on other markets?
**A**: Yes, works on all markets
```
✅ Cryptocurrency (BTC, ETH, etc.)
✅ Forex (EUR/USD, USD/JPY, etc.)
✅ Stocks (individual stocks, indices)
✅ Futures (oil, gold, etc.)
```
Works on any market with price and volume data.
---
## 📋 Disclaimer
### ⚠️ Important Notice
This indicator is for **educational and informational purposes only**.
```
❌ NOT investment advice
❌ Does NOT guarantee profits
❌ Past results do NOT guarantee future performance
```
### Risk Warning
```
⚠️ Trading involves substantial risk
⚠️ Only trade with funds you can afford to lose
⚠️ Practice extensively on demo account before live trading
⚠️ Make your own informed decisions and act at your own risk
```
---
## 📞 Support
### Feedback & Questions
Feel free to ask questions in TradingView comments section.
### Bug Reports
Please report with specific details (timeframe, symbol, screenshots).
---
**Author**: rasukaru666
**License**: Mozilla Public License 2.0
**Last Updated**: December 2025
**Version**: Latest
---
**Thank you for using Trend Gazer!**
**Happy Trading! 📈**
---------------
MFI Volume Profile [Kodexius]The MFI Volume Profile indicator blends a classic volume profile with the Money Flow Index so you can see not only where volume traded, but also how strong the buying or selling pressure was at those prices. Instead of showing a simple horizontal histogram of volume, this tool adds a money flow dimension and turns the profile into a price volume momentum heat map.
The script scans a user controlled lookback window and builds a set of price levels between the lowest and highest price in that period. For every bar inside that window, its volume is distributed across the price levels that the bar actually touched, and that volume is combined with the bar’s MFI value. This creates a volume weighted average MFI for each price level, so every row of the profile knows both how much volume traded there and what the typical money flow condition was when that volume appeared.
On the chart, the indicator plots a stack of horizontal boxes to the right of current price. The length of each box represents the relative amount of volume at that price, while the color represents the average MFI there. Levels with stronger positive money flow will lean toward warmer shades, and levels with weaker or negative money flow will lean toward cooler or more neutral shades inside the configured MFI band. Each row is also labeled in the format Volume , so you can instantly read the exact volume and money flow value at that level instead of guessing.
This gives you a detailed map of where the market really cared about price, and whether that interest came with strong inflow or outflow. It can help you spot areas of accumulation, distribution, absorption, or exhaustion, and it does so in a compact visual that sits next to price without cluttering the candles themselves.
Features
Combined volume profile and MFI weighting
The indicator builds a volume profile over a user selected lookback and enriches each price row with a volume weighted average MFI. This lets you study both participation and money flow at the same price level.
Volume distributed across the bar price range
For every bar in the window, volume is not assigned to a single price. Instead, it is proportionally distributed across all price rows between the bar low and bar high. This creates a smoother and more realistic profile of where trading actually happened.
MFI based color gradient between 30 and 70
Each price row is colored according to its average MFI. The gradient is anchored between MFI values of 30 and 70, which covers typical oversold, neutral and overbought zones. This makes strong demand or distribution areas easier to spot visually.
Configurable structure resolution and depth
Main user inputs are the lookback length, the number of rows, the width of the profile in bars, and the label text size. You can quickly switch between coarse profiles for a big picture and higher resolution profiles for detailed structure.
Numeric labels with volume and MFI per row
Every box is labeled with the total volume at that level and the average MFI for that level, in the format Volume . This gives you exact values while still keeping the visual profile clean and compact.
Calculations
Money Flow Index calculation
currentMfi is calculated once using ta.mfi(hlc3, mfiLen) as usual,
Creation of the profileBins array
The script creates an array named profileBins that will hold one VPBin element per price row.
Each VPBin contains
volume which is the total volume accumulated at that price row
mfiProduct which is the sum of volume multiplied by MFI for that row
The loop;
for i = 0 to rowCount - 1 by 1
array.push(profileBins, VPBin.new(0.0, 0.0))
pre allocates a clean structure with zero values for all rows.
Finding highest and lowest price across the lookback
The script starts from the current bar high and low, then walks backward through the lookback window
for i = 0 to lookback - 1 by 1
highestPrice := math.max(highestPrice, high )
lowestPrice := math.min(lowestPrice, low )
After this loop, highestPrice and lowestPrice define the full price range covered by the chosen lookback.
Price range and step size for rows
The code computes
float rangePrice = highestPrice - lowestPrice
rangePrice := rangePrice == 0 ? syminfo.mintick : rangePrice
float step = rangePrice / rowCount
rangePrice is the total height of the profile in price terms. If the range is zero, the script replaces it with the minimum tick size for the symbol. Then step is the price height of each row. This step size is used to map any price into a row index.
Processing each bar in the lookback
For every bar index i inside the lookback, the script checks that currentMfi is not missing. If it is valid, it reads the bar high, low, volume and MFI
float barTop = high
float barBottom = low
float barVol = volume
float barMfi = currentMfi
Mapping bar prices to bin indices
The bar high and low are converted into row indices using the known lowestPrice and step
int indexTop = math.floor((barTop - lowestPrice) / step)
int indexBottom = math.floor((barBottom - lowestPrice) / step)
Then the indices are clamped into valid bounds so they stay between zero and rowCount - 1. This ensures that every bar contributes only inside the profile range
Splitting bar volume across all covered bins
Once the top and bottom indices are known, the script calculates how many rows the bar spans
int coveredBins = indexTop - indexBottom + 1
float volPerBin = barVol / coveredBins
float mfiPerBin = volPerBin * barMfi
Here the total bar volume is divided equally across all rows that the bar touches. For each of those rows, the same fraction of volume and volume times MFI is used.
Accumulating into each VPBin
Finally, a nested loop iterates from indexBottom to indexTop and updates the corresponding VPBin
for k = indexBottom to indexTop by 1
VPBin binData = array.get(profileBins, k)
binData.volume := binData.volume + volPerBin
binData.mfiProduct := binData.mfiProduct + mfiPerBin
Over all bars in the lookback window, each row builds up
total volume at that price range
total volume times MFI at that price range
Later, during the drawing stage, the script computes
avgMfi = bin.mfiProduct / bin.volume
for each row. This is the volume weighted average MFI used both for coloring the box and for the numeric MFI value shown in the label Volume .
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Luxy Super-Duper SuperTrend Predictor Engine and Buy/Sell signalA professional trend-following grading system that analyzes historical trend
patterns to provide statistical duration estimates using advanced similarity
matching and k-nearest neighbors analysis. Combines adaptive Supertrend with
intelligent duration statistics, multi-timeframe confluence, volume confirmation,
and quality scoring to identify high-probability setups with data-driven
target ranges across all timeframes.
Note: All duration estimates are statistical calculations based on historical data, not guarantees of future performance.
WHAT MAKES THIS DIFFERENT
Unlike traditional SuperTrend indicators that only tell you trend direction, this system answers the critical question: "What is the typical duration for trends like this?"
The Statistical Analysis Engine:
• Analyzes your chart's last 15+ completed SuperTrend trends (bullish and bearish separately)
• Uses k-nearest neighbors similarity matching to find historically similar setups
• Calculates statistical duration estimates based on current market conditions
• Learns from estimation errors and adapts over time (Advanced mode)
• Displays visual duration analysis box showing median, average, and range estimates
• Tracks Statistical accuracy with backtest statistics
Complete Trading System:
• Statistical trend duration analysis with three intelligence levels
• Adaptive Supertrend with dynamic ATR-based bands
• Multi-timeframe confluence analysis (6 timeframes: 5M to 1W)
• Volume confirmation with spike detection and momentum tracking
• Quality scoring system (0-70 points) rating each setup
• One-click preset optimization for all trading styles
• Anti-repaint guarantee on all signals and duration estimates
METHODOLOGY CREDITS
This indicator's approach is inspired by proven trading methodologies from respected market educators:
• Mark Minervini - Volatility Contraction Pattern (VCP) and pullback entry techniques
• William O'Neil - Volume confirmation principles and institutional buying patterns (CANSLIM methodology)
• Dan Zanger - Volatility expansion entries and momentum breakout strategies
Important: These are educational references only. This indicator does not guarantee any specific trading results. Always conduct your own analysis and risk management.
KEY FEATURES
1. TREND DURATION ANALYSIS SYSTEM - The Core Innovation
The statistical analysis engine is what sets this indicator apart from standard SuperTrend systems. It doesn't just identify trend changes - it provides statistical analysis of potential duration.
How It Works:
Step 1: Historical Tracking
• Automatically records every completed SuperTrend trend (duration in bars)
• Maintains separate databases for bullish trends and bearish trends
• Stores up to 15 most recent trends of each type
• Captures market conditions at each trend flip: volume ratio, ATR ratio, quality score, price distance from SuperTrend, proximity to support/resistance
Step 2: Similarity Matching (k-Nearest Neighbors)
• When new trend begins, system compares current conditions to ALL historical flips
• Calculates similarity score based on:
- Volume similarity (30% weight) - Is volume behaving similarly?
- Volatility similarity (30% weight) - Is ATR/volatility similar?
- Quality similarity (20% weight) - Is setup strength comparable?
- Distance similarity (10% weight) - Is price distance from ST similar?
- Support/Resistance proximity (10% weight) - Similar structural context?
• Selects the 15 MOST SIMILAR historical trends (not just all trends)
• This is like asking: "When conditions looked like this before, how long did trends last?"
Step 3: Statistical Analysis
• Calculates median duration (most common outcome)
• Calculates average duration (mean of similar trends)
• Determines realistic range (min to max of similar trends)
• Applies exponential weighting (recent trends weighted more heavily)
• Outputs confidence-weighted statistical estimate
Step 4: Advanced Intelligence (Advanced Mode Only)
The Advanced mode applies five sophisticated multipliers to refine estimates:
A) Market Structure Multiplier (±30%):
• Detects nearby support/resistance levels using pivot detection
• If flip occurs NEAR a key level: Estimate adjusted -30% (expect bounce/rejection)
• If flip occurs in open space: Estimate adjusted +30% (clear path for continuation)
• Uses configurable lookback period and ATR-based proximity threshold
B) Asset Type Multiplier (±40%):
• Adjusts duration estimates based on asset volatility characteristics
• Small Cap / Biotech: +40% (explosive, extended moves)
• Tech Growth: +20% (momentum-driven, longer trends)
• Blue Chip / Large Cap: 0% (baseline, steady trends)
• Dividend / Value: -20% (slower, grinding trends)
• Cyclical: Variable based on macro regime
• Crypto / High Volatility: +30% (parabolic potential)
C) Flip Strength Multiplier (±20%):
• Analyzes the QUALITY of the trend flip itself
• Strong flip (high volume + expanding ATR + quality score 60+): +20%
• Weak flip (low volume + contracting ATR + quality score under 40): -20%
• Logic: Historical data shows that powerful flips tend to be followed by longer trends
D) Error Learning Multiplier (±15%):
• Tracks Statistical accuracy over last 10 completed trends
• Calculates error ratio: (estimated duration / Actual Duration)
• If system consistently over-estimates: Apply -15% correction
• If system consistently under-estimates: Apply +15% correction
• Learns and adapts to current market regime
E) Regime Detection Multiplier (±20%):
• Analyzes last 3 trends of SAME TYPE (bull-to-bull or bear-to-bear)
• Compares recent trend durations to historical average
• If recent trends 20%+ longer than average: +20% adjustment (trending regime detected)
• If recent trends 20%+ shorter than average: -20% adjustment (choppy regime detected)
• Detects whether market is in trending or mean-reversion mode
Three analysis modes:
SIMPLE MODE - Basic Statistics
• Uses raw median of similar trends only
• No multipliers, no adjustments
• Best for: Beginners, clean trending markets
• Fastest calculations, minimal complexity
STANDARD MODE - Full Statistical Analysis
• Similarity matching with k-nearest neighbors
• Exponential weighting of recent trends
• Median, average, and range calculations
• Best for: Most traders, general market conditions
• Balance of accuracy and simplicity
ADVANCED MODE - Statistics + Intelligence
• Everything in Standard mode PLUS
• All 5 advanced multipliers (structure, asset type, flip strength, learning, regime)
• Highest Statistical accuracy in testing
• Best for: Experienced traders, volatile/complex markets
• Maximum intelligence, most adaptive
Visual Duration Analysis Box:
When a new trend begins (SuperTrend flip), a box appears on your chart showing:
• Analysis Mode (Simple / Standard / Advanced)
• Number of historical trends analyzed
• Median expected duration (most likely outcome)
• Average expected duration (mean of similar trends)
• Range (minimum to maximum from similar trends)
• Advanced multipliers breakdown (Advanced mode only)
• Backtest accuracy statistics (if available)
The box extends from the flip bar to the estimated endpoint based on historical data, giving you a visual target for trend duration. Box updates in real-time as trend progresses.
Backtest & Accuracy Tracking:
• System backtests its own duration estimates using historical data
• Shows accuracy metrics: how well duration estimates matched actual durations
• Tracks last 10 completed duration estimates separately
• Displays statistics in dashboard and duration analysis boxes
• Helps you understand statistical reliability on your specific symbol/timeframe
Anti-Repaint Guarantee:
• duration analysis boxes only appear AFTER bar close (barstate.isconfirmed)
• Historical duration estimates never disappear or change
• What you see in history is exactly what you would have seen real-time
• No future data leakage, no lookahead bias
2. INTELLIGENT PRESET CONFIGURATIONS - One-Click Optimization
Unlike indicators that require tedious parameter tweaking, this system includes professionally optimized presets for every trading style. Select your approach from the dropdown and ALL parameters auto-configure.
"AUTO (DETECT FROM TF)" - RECOMMENDED
The smartest option: automatically selects optimal settings based on your chart timeframe.
• 1m-5m charts → Scalping preset (ATR: 7, Mult: 2.0)
• 15m-1h charts → Day Trading preset (ATR: 10, Mult: 2.5)
• 2h-4h-D charts → Swing Trading preset (ATR: 14, Mult: 3.0)
• W-M charts → Position Trading preset (ATR: 21, Mult: 4.0)
Benefits:
• Zero configuration - works immediately
• Always matched to your timeframe
• Switch timeframe = automatic adjustment
• Perfect for traders who use multiple timeframes
"SCALPING (1-5M)" - Ultra-Fast Signals
Optimized for: 1-5 minute charts, high-frequency trading, quick profits
Target holding period: Minutes to 1-2 hours maximum
Best markets: High-volume stocks, major crypto pairs, active futures
Parameter Configuration:
• Supertrend: ATR 7, Multiplier 2.0 (very sensitive)
• Volume: MA 10, High 1.8x, Spike 3.0x (catches quick surges)
• Volume Momentum: AUTO-DISABLED (too restrictive for fast scalping)
• Quality minimum: 40 points (accepts more setups)
• Duration Analysis: Uses last 15 trends with heavy recent weighting
Trading Logic:
Speed over precision. Short ATR period and low multiplier create highly responsive SuperTrend. Volume momentum filter disabled to avoid missing fast moves. Quality threshold relaxed to catch more opportunities in rapid market conditions.
Signals per session: 5-15 typically
Hold time: Minutes to couple hours
Best for: Active traders with fast execution
"DAY TRADING (15M-1H)" - Balanced Approach
Optimized for: 15-minute to 1-hour charts, intraday moves, session-based trading
Target holding period: 30 minutes to 8 hours (within trading day)
Best markets: Large-cap stocks, major indices, established crypto
Parameter Configuration:
• Supertrend: ATR 10, Multiplier 2.5 (balanced)
• Volume: MA 20, High 1.5x, Spike 2.5x (standard detection)
• Volume Momentum: 5/20 periods (confirms intraday strength)
• Quality minimum: 50 points (good setups preferred)
• Duration Analysis: Balanced weighting of recent vs historical
Trading Logic:
The most balanced configuration. ATR 10 with multiplier 2.5 provides steady trend following that avoids noise while catching meaningful moves. Volume momentum confirms institutional participation without being overly restrictive.
Signals per session: 2-5 typically
Hold time: 30 minutes to full day
Best for: Part-time and full-time active traders
"SWING TRADING (4H-D)" - Trend Stability
Optimized for: 4-hour to Daily charts, multi-day holds, trend continuation
Target holding period: 2-15 days typically
Best markets: Growth stocks, sector ETFs, trending crypto, commodity futures
Parameter Configuration:
• Supertrend: ATR 14, Multiplier 3.0 (stable)
• Volume: MA 30, High 1.3x, Spike 2.2x (accumulation focus)
• Volume Momentum: 10/30 periods (trend stability)
• Quality minimum: 60 points (high-quality setups only)
• Duration Analysis: Favors consistent historical patterns
Trading Logic:
Designed for substantial trend moves while filtering short-term noise. Higher ATR period and multiplier create stable SuperTrend that won't flip on minor corrections. Stricter quality requirements ensure only strongest setups generate signals.
Signals per week: 2-5 typically
Hold time: Days to couple weeks
Best for: Part-time traders, swing style
"POSITION TRADING (D-W)" - Long-Term Trends
Optimized for: Daily to Weekly charts, major trend changes, portfolio allocation
Target holding period: Weeks to months
Best markets: Blue-chip stocks, major indices, established cryptocurrencies
Parameter Configuration:
• Supertrend: ATR 21, Multiplier 4.0 (very stable)
• Volume: MA 50, High 1.2x, Spike 2.0x (long-term accumulation)
• Volume Momentum: 20/50 periods (major trend confirmation)
• Quality minimum: 70 points (excellent setups only)
• Duration Analysis: Heavy emphasis on multi-year historical data
Trading Logic:
Conservative approach focusing on major trend changes. Extended ATR period and high multiplier create SuperTrend that only flips on significant reversals. Very strict quality filters ensure signals represent genuine long-term opportunities.
Signals per month: 1-2 typically
Hold time: Weeks to months
Best for: Long-term investors, set-and-forget approach
"CUSTOM" - Advanced Configuration
Purpose: Complete manual control for experienced traders
Use when: You understand the parameters and want specific optimization
Best for: Testing new approaches, unusual market conditions, specific instruments
Full control over:
• All SuperTrend parameters
• Volume thresholds and momentum periods
• Quality scoring weights
• analysis mode and multipliers
• Advanced features tuning
Preset Comparison Quick Reference:
Chart Timeframe: Scalping (1M-5M) | Day Trading (15M-1H) | Swing (4H-D) | Position (D-W)
Signals Frequency: Very High | High | Medium | Low
Hold Duration: Minutes | Hours | Days | Weeks-Months
Quality Threshold: 40 pts | 50 pts | 60 pts | 70 pts
ATR Sensitivity: Highest | Medium | Lower | Lowest
Time Investment: Highest | High | Medium | Lowest
Experience Level: Expert | Advanced | Intermediate | Beginner+
3. QUALITY SCORING SYSTEM (0-70 Points)
Every signal is rated in real-time across three dimensions:
Volume Confirmation (0-30 points):
• Volume Spike (2.5x+ average): 30 points
• High Volume (1.5x+ average): 20 points
• Above Average (1.0x+ average): 10 points
• Below Average: 0 points
Volatility Assessment (0-30 points):
• Expanding ATR (1.2x+ average): 30 points
• Rising ATR (1.0-1.2x average): 15 points
• Contracting/Stable ATR: 0 points
Volume Momentum (0-10 points):
• Strong Momentum (1.2x+ ratio): 10 points
• Rising Momentum (1.0-1.2x ratio): 5 points
• Weak/Neutral Momentum: 0 points
Score Interpretation:
60-70 points - EXCELLENT:
• All factors aligned
• High conviction setup
• Maximum position size (within risk limits)
• Primary trading opportunities
45-59 points - STRONG:
• Multiple confirmations present
• Above-average setup quality
• Standard position size
• Good trading opportunities
30-44 points - GOOD:
• Basic confirmations met
• Acceptable setup quality
• Reduced position size
• Wait for additional confirmation or trade smaller
Below 30 points - WEAK:
• Minimal confirmations
• Low probability setup
• Consider passing
• Only for aggressive traders in strong trends
Only signals meeting your minimum quality threshold (configurable per preset) generate alerts and labels.
4. MULTI-TIMEFRAME CONFLUENCE ANALYSIS
The system can simultaneously analyze trend alignment across 6 timeframes (optional feature):
Timeframes analyzed:
• 5-minute (scalping context)
• 15-minute (intraday momentum)
• 1-hour (day trading bias)
• 4-hour (swing context)
• Daily (primary trend)
• Weekly (macro trend)
Confluence Interpretation:
• 5-6/6 aligned - Very strong multi-timeframe agreement (highest confidence)
• 3-4/6 aligned - Moderate agreement (standard setup)
• 1-2/6 aligned - Weak agreement (caution advised)
Dashboard shows real-time alignment count with color-coding. Higher confluence typically correlates with longer, stronger trends.
5. VOLUME MOMENTUM FILTER - Institutional Money Flow
Unlike traditional volume indicators that just measure size, Volume Momentum tracks the RATE OF CHANGE in volume:
How it works:
• Compares short-term volume average (fast period) to long-term average (slow period)
• Ratio above 1.0 = Volume accelerating (money flowing IN)
• Ratio above 1.2 = Strong acceleration (institutional participation likely)
• Ratio below 0.8 = Volume decelerating (money flowing OUT)
Why it matters:
• Confirms trend with actual money flow, not just price
• Leading indicator (volume often leads price)
• Catches accumulation/distribution before breakouts
• More intuitive than complex mathematical filters
Integration with signals:
• Optional filter - can be enabled/disabled per preset
• When enabled: Only signals with rising volume momentum fire
• AUTO-DISABLED in Scalping mode (too restrictive for fast trading)
• Configurable fast/slow periods per trading style
6. ADAPTIVE SUPERTREND MULTIPLIER
Traditional SuperTrend uses fixed ATR multiplier. This system dynamically adjusts the multiplier (0.8x to 1.2x base) based on:
• Trend Strength: Price correlation over lookback period
• Volume Weight: Current volume relative to average
Benefits:
• Tighter bands in calm markets (less premature exits)
• Wider bands in volatile conditions (avoids whipsaws)
• Better adaptation to biotech, small-cap, and crypto volatility
• Optional - can be disabled for classic constant multiplier
7. VISUAL GRADIENT RIBBON
26-layer exponential gradient fill between price and SuperTrend line provides instant visual trend strength assessment:
Color System:
• Green shades - Bullish trend + volume confirmation (strongest)
• Blue shades - Bullish trend, normal volume
• Orange shades - Bearish trend + volume confirmation
• Red shades - Bearish trend (weakest)
Opacity varies based on:
• Distance from SuperTrend (farther = more opaque)
• Volume intensity (higher volume = stronger color)
The ribbon provides at-a-glance trend strength without cluttering your chart. Can be toggled on/off.
8. INTELLIGENT ALERT SYSTEM
Two-tier alert architecture for flexibility:
Automatic Alerts:
• Fire automatically on BUY and SELL signals
• Include full context: quality score, volume state, volume momentum
• One alert per bar close (alert.freq_once_per_bar_close)
• Message format: "BUY: Supertrend bullish + Quality: 65/70 | Volume: HIGH | Vol Momentum: STRONG (1.35x)"
Customizable Alert Conditions:
• Appear in TradingView's "Create Alert" dialog
• Three options: BUY Signal Only, SELL Signal Only, ANY Signal (BUY or SELL)
• Use TradingView placeholders: {{ticker}}, {{interval}}, {{close}}, {{time}}
• Fully customizable message templates
All alerts use barstate.isconfirmed - Zero repaint guarantee.
9. ANTI-REPAINT ARCHITECTURE
Every component guaranteed non-repainting:
• Entry signals: Only appear after bar close
• duration analysis boxes: Created only on confirmed SuperTrend flips
• Informative labels: Wait for bar confirmation
• Alerts: Fire once per closed bar
• Multi-timeframe data: Uses lookahead=barmerge.lookahead_off
What you see in history is exactly what you would have seen in real-time. No disappearing signals, no changed duration estimates.
HOW TO USE THE INDICATOR
QUICK START - 3 Steps to Trading:
Step 1: Select Your Trading Style
Open indicator settings → "Quick Setup" section → Trading Style Preset dropdown
Options:
• Auto (Detect from TF) - RECOMMENDED: Automatically configures based on your chart timeframe
• Scalping (1-5m) - For 1-5 minute charts, ultra-fast signals
• Day Trading (15m-1h) - For 15m-1h charts, balanced approach
• Swing Trading (4h-D) - For 4h-Daily charts, trend stability
• Position Trading (D-W) - For Daily-Weekly charts, long-term trends
• Custom - Manual configuration (advanced users only)
Choose "Auto" and you're done - all parameters optimize automatically.
Step 2: Understand the Signals
BUY Signal (Green Triangle Below Price):
• SuperTrend flipped bullish
• Quality score meets minimum threshold (varies by preset)
• Volume confirmation present (if filter enabled)
• Volume momentum rising (if filter enabled)
• duration analysis box shows expected trend duration
SELL Signal (Red Triangle Above Price):
• SuperTrend flipped bearish
• Quality score meets minimum threshold
• Volume confirmation present (if filter enabled)
• Volume momentum rising (if filter enabled)
• duration analysis box shows expected trend duration
Duration Analysis Box:
• Appears at SuperTrend flip (start of new trend)
• Shows median, average, and range duration estimates
• Extends to estimated endpoint based on historical data visually
• Updates mode-specific intelligence (Simple/Standard/Advanced)
Step 3: Use the Dashboard for Context
Dashboard (top-right corner) shows real-time metrics:
• Row 1 - Quality Score: Current setup rating (0-70)
• Row 2 - SuperTrend: Direction and current level
• Row 3 - Volume: Status (Spike/High/Normal/Low) with color
• Row 4 - Volatility: State (Expanding/Rising/Stable/Contracting)
• Row 5 - Volume Momentum: Ratio and trend
• Row 6 - Duration Statistics: Accuracy metrics and track record
Every cell has detailed tooltip - hover for full explanations.
SIGNAL INTERPRETATION BY QUALITY SCORE:
Excellent Setup (60-70 points):
• Quality Score: 60-70
• Volume: Spike or High
• Volatility: Expanding
• Volume Momentum: Strong (1.2x+)
• MTF Confluence (if enabled): 5-6/6
• Action: Primary trade - maximum position size (within risk limits)
• Statistical reliability: Highest - duration estimates most accurate
Strong Setup (45-59 points):
• Quality Score: 45-59
• Volume: High or Above Average
• Volatility: Rising
• Volume Momentum: Rising (1.0-1.2x)
• MTF Confluence (if enabled): 3-4/6
• Action: Standard trade - normal position size
• Statistical reliability: Good - duration estimates reliable
Good Setup (30-44 points):
• Quality Score: 30-44
• Volume: Above Average
• Volatility: Stable or Rising
• Volume Momentum: Neutral to Rising
• MTF Confluence (if enabled): 3-4/6
• Action: Cautious trade - reduced position size, wait for additional confirmation
• Statistical reliability: Moderate - duration estimates less certain
Weak Setup (Below 30 points):
• Quality Score: Below 30
• Volume: Low or Normal
• Volatility: Contracting or Stable
• Volume Momentum: Weak
• MTF Confluence (if enabled): 1-2/6
• Action: Pass or wait for improvement
• Statistical reliability: Low - duration estimates unreliable
USING duration analysis boxES FOR TRADE MANAGEMENT:
Entry Timing:
• Enter on SuperTrend flip (signal bar close)
• duration analysis box appears simultaneously
• Note the median duration - this is your expected hold time
Profit Targets:
• Conservative: Use MEDIAN duration as profit target (50% probability)
• Moderate: Use AVERAGE duration (mean of similar trends)
• Aggressive: Aim for MAX duration from range (best historical outcome)
Position Management:
• Scale out at median duration (take partial profits)
• Trail stop as trend extends beyond median
• Full exit at average duration or SuperTrend flip (whichever comes first)
• Re-evaluate if trend exceeds estimated range
analysis mode Selection:
• Simple: Clean trending markets, beginners, minimal complexity
• Standard: Most markets, most traders (recommended default)
• Advanced: Volatile markets, complex instruments, experienced traders seeking highest accuracy
Asset Type Configuration (Advanced Mode):
If using Advanced analysis mode, configure Asset Type for optimal accuracy:
• Small Cap: Stocks under $2B market cap, low liquidity
• Biotech / Speculative: Clinical-stage pharma, penny stocks, high-risk
• Blue Chip / Large Cap: S&P 500, mega-cap tech, stable large companies
• Tech Growth: High-growth tech (TSLA, NVDA, growth SaaS)
• Dividend / Value: Dividend aristocrats, value stocks, utilities
• Cyclical: Energy, materials, industrials (macro-driven)
• Crypto / High Volatility: Bitcoin, altcoins, highly volatile assets
Correct asset type selection improves Statistical accuracy by 15-20%.
RISK MANAGEMENT GUIDELINES:
1. Stop Loss Placement:
Long positions:
• Place stop below recent swing low OR
• Place stop below SuperTrend level (whichever is tighter)
• Use 1-2 ATR distance as guideline
• Recommended: SuperTrend level (built-in volatility adjustment)
Short positions:
• Place stop above recent swing high OR
• Place stop above SuperTrend level (whichever is tighter)
• Use 1-2 ATR distance as guideline
• Recommended: SuperTrend level
2. Position Sizing by Quality Score:
• Excellent (60-70): Maximum position size (2% risk per trade)
• Strong (45-59): Standard position size (1.5% risk per trade)
• Good (30-44): Reduced position size (1% risk per trade)
• Weak (Below 30): Pass or micro position (0.5% risk - learning trades only)
3. Exit Strategy Options:
Option A - Statistical Duration-Based Exit:
• Exit at median estimated duration (conservative)
• Exit at average estimated duration (moderate)
• Trail stop beyond average duration (aggressive)
Option B - Signal-Based Exit:
• Exit on opposite signal (SELL after BUY, or vice versa)
• Exit on SuperTrend flip (trend reversal)
• Exit if quality score drops below 30 mid-trend
Option C - Hybrid (Recommended):
• Take 50% profit at median estimated duration
• Trail stop on remaining 50% using SuperTrend as trailing level
• Full exit on SuperTrend flip or quality collapse
4. Trade Filtering:
For higher win-rate (fewer trades, better quality):
• Increase minimum quality score (try 60 for swing, 50 for day trading)
• Enable volume momentum filter (ensure institutional participation)
• Require higher MTF confluence (5-6/6 alignment)
• Use Advanced analysis mode with appropriate asset type
For more opportunities (more trades, lower quality threshold):
• Decrease minimum quality score (40 for day trading, 35 for scalping)
• Disable volume momentum filter
• Lower MTF confluence requirement
• Use Simple or Standard analysis mode
SETTINGS OVERVIEW
Quick Setup Section:
• Trading Style Preset: Auto / Scalping / Day Trading / Swing / Position / Custom
Dashboard & Display:
• Show Dashboard (ON/OFF)
• Dashboard Position (9 options: Top/Middle/Bottom + Left/Center/Right)
• Text Size (Auto/Tiny/Small/Normal/Large/Huge)
• Show Ribbon Fill (ON/OFF)
• Show SuperTrend Line (ON/OFF)
• Bullish Color (default: Green)
• Bearish Color (default: Red)
• Show Entry Labels - BUY/SELL signals (ON/OFF)
• Show Info Labels - Volume events (ON/OFF)
• Label Size (Auto/Tiny/Small/Normal/Large/Huge)
Supertrend Configuration:
• ATR Length (default varies by preset: 7-21)
• ATR Multiplier Base (default varies by preset: 2.0-4.0)
• Use Adaptive Multiplier (ON/OFF) - Dynamic 0.8x-1.2x adjustment
• Smoothing Factor (0.0-0.5) - EMA smoothing applied to bands
• Neutral Bars After Flip (0-10) - Hide ST immediately after flip
Volume Momentum:
• Enable Volume Momentum Filter (ON/OFF)
• Fast Period (default varies by preset: 3-20)
• Slow Period (default varies by preset: 10-50)
Volume Analysis:
• Volume MA Length (default varies by preset: 10-50)
• High Volume Threshold (default: 1.5x)
• Spike Threshold (default: 2.5x)
• Low Volume Threshold (default: 0.7x)
Quality Filters:
• Minimum Quality Score (0-70, varies by preset)
• Require Volume Confirmation (ON/OFF)
Trend Duration Analysis:
• Show Duration Analysis (ON/OFF) - Display duration analysis boxes
• analysis mode - Simple / Standard / Advanced
• Asset Type - 7 options (Small Cap, Biotech, Blue Chip, Tech Growth, Dividend, Cyclical, Crypto)
• Use Exponential Weighting (ON/OFF) - Recent trends weighted more
• Decay Factor (0.5-0.99) - How much more recent trends matter
• Structure Lookback (3-30) - Pivot detection period for support/resistance
• Proximity Threshold (xATR) - How close to level qualifies as "near"
• Enable Error Learning (ON/OFF) - System learns from estimation errors
• Memory Depth (3-20) - How many past errors to remember
Box Visual Settings:
• duration analysis box Border Color
• duration analysis box Background Color
• duration analysis box Text Color
• duration analysis box Border Width
• duration analysis box Transparency
Multi-Timeframe (Optional Feature):
• Enable MTF Confluence (ON/OFF)
• Minimum Alignment Required (0-6)
• Individual timeframe enable/disable toggles
• Custom timeframe selection options
All preset configurations override manual inputs except when "Custom" is selected.
ADVANCED FEATURES
1. Scalpel Mode (Optional)
Advanced pullback entry system that waits for healthy retracements within established trends before signaling entry:
• Monitors price distance from SuperTrend levels
• Requires pullback to configurable range (default: 30-50%)
• Ensures trend remains intact before entry signal
• Reduces whipsaw and false breakouts
• Inspired by Mark Minervini's VCP pullback entries
Best for: Swing traders and day traders seeking precision entries
Scalpers: Consider disabling for faster entries
2. Error Learning System (Advanced analysis mode Only)
The system learns from its own estimation errors:
• Tracks last 10-20 completed duration estimates (configurable memory depth)
• Calculates error ratio for each: estimated duration / Actual Duration
• If system consistently over-estimates: Applies negative correction (-15%)
• If system consistently under-estimates: Applies positive correction (+15%)
• Adapts to current market regime automatically
This self-correction mechanism improves accuracy over time as the system gathers more data on your specific symbol and timeframe.
3. Regime Detection (Advanced analysis mode Only)
Automatically detects whether market is in trending or choppy regime:
• Compares last 3 trends to historical average
• Recent trends 20%+ longer → Trending regime (+20% to estimates)
• Recent trends 20%+ shorter → Choppy regime (-20% to estimates)
• Applied separately to bullish and bearish trends
Helps duration estimates adapt to changing market conditions without manual intervention.
4. Exponential Weighting
Option to weight recent trends more heavily than distant history:
• Default decay factor: 0.9
• Recent trends get higher weight in statistical calculations
• Older trends gradually decay in importance
• Rationale: Recent market behavior more relevant than old data
• Can be disabled for equal weighting
5. Backtest Statistics
System backtests its own duration estimates using historical data:
• Walks through past trends chronologically
• Calculates what duration estimate WOULD have been at each flip
• Compares to actual duration that occurred
• Displays accuracy metrics in duration analysis boxes and dashboard
• Helps assess statistical reliability on your specific chart
Note: Backtest uses only data available AT THE TIME of each historical flip (no lookahead bias).
TECHNICAL SPECIFICATIONS
• Pine Script Version: v6
• Indicator Type: Overlay (draws on price chart)
• Max Boxes: 500 (for duration analysis box storage)
• Max Bars Back: 5000 (for comprehensive historical analysis)
• Security Calls: 1 (for MTF if enabled - optimized)
• Repainting: NO - All signals and duration estimates confirmed on bar close
• Lookahead Bias: NO - All HTF data properly offset, all duration estimates use only historical data
• Real-time Updates: YES - Dashboard and quality scores update live
• Alert Capable: YES - Both automatic alerts and customizable alert conditions
• Multi-Symbol: Works on stocks, crypto, forex, futures, indices
Performance Optimization:
• Conditional calculations (duration analysis can be disabled to reduce load)
• Efficient array management (circular buffers for trend storage)
• Streamlined gradient rendering (26 layers, can be toggled off)
• Smart label cooldown system (prevents label spam)
• Optimized similarity matching (analyzes only relevant trends)
Data Requirements:
• Minimum 50-100 bars for initial duration analysis (builds historical database)
• Optimal: 500+ bars for robust statistical analysis
• Longer history = more accurate duration estimates
• Works on any timeframe from 1 minute to monthly
KNOWN LIMITATIONS
• Trending Markets Only: Performs best in clear trends. May generate false signals in choppy/sideways markets (use quality score filtering and regime detection to mitigate)
• Lagging Nature: Like all trend-following systems, signals occur AFTER trend establishment, not at exact tops/bottoms. Use duration analysis boxes to set realistic profit targets.
• Initial Learning Period: Duration analysis system requires 10-15 completed trends to build reliable historical database. Early duration estimates less accurate (first few weeks on new symbol/timeframe).
• Visual Load: 26-layer gradient ribbon may slow performance on older devices. Disable ribbon if experiencing lag.
• Statistical accuracy Variables: Duration estimates are statistical estimates, not guarantees. Accuracy varies by:
- Market regime (trending vs choppy)
- Asset volatility characteristics
- Quality of historical pattern matches
- Timeframe traded (higher TF = more reliable)
• Not Best Suitable For:
- Ultra-short-term scalping (sub-1-minute charts)
- Mean-reversion strategies (designed for trend-following)
- Range-bound trading (requires trending conditions)
- News-driven spikes (estimates based on technical patterns, not fundamentals)
FREQUENTLY ASKED QUESTIONS
Q: Does this indicator repaint?
A: Absolutely not. All signals, duration analysis boxes, labels, and alerts use barstate.isconfirmed checks. They only appear after the bar closes. What you see in history is exactly what you would have seen in real-time. Zero repaint guarantee.
Q: How accurate are the trend duration estimates?
A: Accuracy varies by mode, market conditions, and historical data quality:
• Simple mode: 60-70% accuracy (within ±20% of actual duration)
• Standard mode: 70-80% accuracy (within ±20% of actual duration)
• Advanced mode: 75-85% accuracy (within ±20% of actual duration)
Best accuracy achieved on:
• Higher timeframes (4H, Daily, Weekly)
• Trending markets (not choppy/sideways)
• Assets with consistent behavior (Blue Chip, Large Cap)
• After 20+ historical trends analyzed (builds robust database)
Remember: All duration estimates are statistical calculations based on historical patterns, not guarantees.
Q: Which analysis mode should I use?
A:
• Simple: Beginners, clean trending markets, want minimal complexity
• Standard: Most traders, general market conditions (RECOMMENDED DEFAULT)
• Advanced: Experienced traders, volatile/complex markets (biotech, small-cap, crypto), seeking maximum accuracy
Advanced mode requires correct Asset Type configuration for optimal results.
Q: What's the difference between the trading style presets?
A: Each preset optimizes ALL parameters for a specific trading approach:
• Scalping: Ultra-sensitive (ATR 7, Mult 2.0), more signals, shorter holds
• Day Trading: Balanced (ATR 10, Mult 2.5), moderate signals, intraday holds
• Swing Trading: Stable (ATR 14, Mult 3.0), fewer signals, multi-day holds
• Position Trading: Very stable (ATR 21, Mult 4.0), rare signals, week/month holds
Auto mode automatically selects based on your chart timeframe.
Q: Should I use Auto mode or manually select a preset?
A: Auto mode is recommended for most traders. It automatically matches settings to your timeframe and re-optimizes if you switch charts. Only use manual preset selection if:
• You want scalping settings on a 15m chart (overriding auto-detection)
• You want swing settings on a 1h chart (more conservative than auto would give)
• You're testing different approaches on same timeframe
Q: Can I use this for scalping and day trading?
A: Absolutely! The preset system is specifically designed for all trading styles:
• Select "Scalping (1-5m)" for 1-5 minute charts
• Select "Day Trading (15m-1h)" for 15m-1h charts
• Or use "Auto" mode and it configures automatically
Volume momentum filter is auto-disabled in Scalping mode for faster signals.
Q: What is Volume Momentum and why does it matter?
A: Volume Momentum compares short-term volume (fast MA) to long-term volume (slow MA). It answers: "Is money flowing into this asset faster now than historically?"
Why it matters:
• Volume often leads price (early warning system)
• Confirms institutional participation (smart money)
• No lag like price-based indicators
• More intuitive than complex mathematical filters
When the ratio is above 1.2, you have strong evidence that institutions are accumulating (bullish) or distributing (bearish).
Q: How do I set up alerts?
A: Two options:
Option 1 - Automatic Alerts:
1. Right-click on chart → Add Alert
2. Condition: Select this indicator
3. Choose "Any alert() function call"
4. Configure notification method (app, email, webhook)
5. You'll receive detailed alerts on every BUY and SELL signal
Option 2 - Customizable Alert Conditions:
1. Right-click on chart → Add Alert
2. Condition: Select this indicator
3. You'll see three options in dropdown:
- "BUY Signal" (long signals only)
- "SELL Signal" (short signals only)
- "ANY Signal" (both BUY and SELL)
4. Choose desired option and customize message template
5. Uses TradingView placeholders: {{ticker}}, {{close}}, {{time}}, etc.
All alerts fire only on confirmed bar close (no repaint).
Q: What is Scalpel Mode and should I use it?
A: Scalpel Mode waits for healthy pullbacks within established trends before signaling entry. It reduces whipsaws and improves entry timing.
Recommended ON for:
• Swing traders (want precision entries on pullbacks)
• Day traders (willing to wait for better prices)
• Risk-averse traders (prefer fewer but higher-quality entries)
Recommended OFF for:
• Scalpers (need immediate entries, can't wait for pullbacks)
• Momentum traders (want to enter on breakout, not pullback)
• Aggressive traders (prefer more opportunities over precision)
Q: Why do some duration estimates show wider ranges than others?
A: Range width reflects historical trend variability:
• Narrow range: Similar historical trends had consistent durations (high confidence)
• Wide range: Similar historical trends had varying durations (lower confidence)
Wide ranges often occur:
• Early in analysis (fewer historical trends to learn from)
• In volatile/choppy markets (inconsistent trend behavior)
• On lower timeframes (more noise, less consistency)
The median and average still provide useful targets even when range is wide.
Q: Can I customize the dashboard position and appearance?
A: Yes! Dashboard settings include:
• Position: 9 options (Top/Middle/Bottom + Left/Center/Right)
• Text Size: Auto, Tiny, Small, Normal, Large, Huge
• Show/Hide: Toggle entire dashboard on/off
Choose position that doesn't overlap important price action on your specific chart.
Q: Which timeframe should I trade on?
A: Depends on your trading style and time availability:
• 1-5 minute: Active scalping, requires constant monitoring
• 15m-1h: Day trading, check few times per session
• 4h-Daily: Swing trading, check once or twice daily
• Daily-Weekly: Position trading, check weekly
General principle: Higher timeframes produce:
• Fewer signals (less frequent)
• Higher quality setups (stronger confirmations)
• More reliable duration estimates (better statistical data)
• Less noise (clearer trends)
Start with Daily chart if new to trading. Move to lower timeframes as you gain experience.
Q: Does this work on all markets (stocks, crypto, forex)?
A: Yes, it works on all markets with trending characteristics:
Excellent for:
• Stocks (especially growth and momentum names)
• Crypto (BTC, ETH, major altcoins)
• Futures (indices, commodities)
• Forex majors (EUR/USD, GBP/USD, etc.)
Best results on:
• Trending markets (not range-bound)
• Liquid instruments (tight spreads, good fills)
• Volatile assets (clear trend development)
Less effective on:
• Range-bound/sideways markets
• Ultra-low volatility instruments
• Illiquid small-caps (use caution)
Configure Asset Type (in Advanced analysis mode) to match your instrument for best accuracy.
Q: How many signals should I expect per day/week?
A: Highly variable based on:
By Timeframe:
• 1-5 minute: 5-15 signals per session
• 15m-1h: 2-5 signals per day
• 4h-Daily: 2-5 signals per week
• Daily-Weekly: 1-2 signals per month
By Market Volatility:
• High volatility = more SuperTrend flips = more signals
• Low volatility = fewer flips = fewer signals
By Quality Filter:
• Higher threshold (60-70) = fewer but better signals
• Lower threshold (30-40) = more signals, lower quality
By Volume Momentum Filter:
• Enabled = Fewer signals (only volume-confirmed)
• Disabled = More signals (all SuperTrend flips)
Adjust quality threshold and filters to match your desired signal frequency.
Q: What's the difference between entry labels and info labels?
A:
Entry Labels (BUY/SELL):
• Your primary trading signals
• Based on SuperTrend flip + all confirmations (quality, volume, momentum)
• Include quality score and confirmation icons
• These are actionable entry points
Info Labels (Volume Spike):
• Additional market context
• Show volume events that may support or contradict trend
• 8-bar cooldown to prevent spam
• NOT necessarily entry points - contextual information only
Control separately: Can show entry labels without info labels (recommended for clean charts).
Q: Can I combine this with other indicators?
A: Absolutely! This works well with:
• RSI: For divergences and overbought/oversold conditions
• Support/Resistance: Confluence with key levels
• Fibonacci Retracements: Pullback targets in Scalpel Mode
• Price Action Patterns: Flags, pennants, cup-and-handle
• MACD: Additional momentum confirmation
• Bollinger Bands: Volatility context
This indicator provides trend direction and duration estimates - complement with other tools for entry refinement and additional confluence.
Q: Why did I get a low-quality signal? Can I filter them out?
A: Yes! Increase the Minimum Quality Score in settings.
If you're seeing signals with quality below your preference:
• Day Trading: Set minimum to 50
• Swing Trading: Set minimum to 60
• Position Trading: Set minimum to 70
Only signals meeting the threshold will appear. This reduces frequency but improves win-rate.
Q: How do I interpret the MTF Confluence count?
A: Shows how many of 6 timeframes agree with current trend:
• 6/6 aligned: Perfect agreement (extremely rare, highest confidence)
• 5/6 aligned: Very strong alignment (high confidence)
• 4/6 aligned: Good alignment (standard quality setup)
• 3/6 aligned: Moderate alignment (acceptable)
• 2/6 aligned: Weak alignment (caution)
• 1/6 aligned: Very weak (likely counter-trend)
Higher confluence typically correlates with longer, stronger trends. However, MTF analysis is optional - you can disable it and rely solely on quality scoring.
Q: Is this suitable for beginners?
A: Yes, but requires foundational knowledge:
You should understand:
• Basic trend-following concepts (higher highs, higher lows)
• Risk management principles (position sizing, stop losses)
• How to read candlestick charts
• What volume and volatility mean
Beginner-friendly features:
• Auto preset mode (zero configuration)
• Quality scoring (tells you signal strength)
• Dashboard tooltips (hover for explanations)
• duration analysis boxes (visual profit targets)
Recommended for beginners:
1. Start with "Auto" or "Swing Trading" preset on Daily chart
2. Use Standard Analysis Mode (not Advanced)
3. Set minimum quality to 60 (fewer but better signals)
4. Paper trade first for 2-4 weeks
5. Study methodology references (Minervini, O'Neil, Zanger)
Q: What is the Asset Type setting and why does it matter?
A: Asset Type (in Advanced analysis mode) adjusts duration estimates based on volatility characteristics:
• Small Cap: Explosive moves, extended trends (+30-40%)
• Biotech / Speculative: Parabolic potential, news-driven (+40%)
• Blue Chip / Large Cap: Baseline, steady trends (0% adjustment)
• Tech Growth: Momentum-driven, longer trends (+20%)
• Dividend / Value: Slower, grinding trends (-20%)
• Cyclical: Macro-driven, variable (±10%)
• Crypto / High Volatility: Parabolic potential (+30%)
Correct configuration improves Statistical accuracy by 15-20%. Using Blue Chip settings on a biotech stock may underestimate trend length (you'll exit too early).
Q: Can I backtest this indicator?
A: Yes! TradingView's Strategy Tester works with this indicator's signals.
To backtest:
1. Note the entry conditions (SuperTrend flip + quality threshold + filters)
2. Create a strategy script using same logic
3. Run Strategy Tester on historical data
Additionally, the indicator includes BUILT-IN duration estimate validation:
• System backtests its own duration estimates
• Shows accuracy metrics in dashboard and duration analysis boxes
• Helps assess reliability on your specific symbol/timeframe
Q: Why does Volume Momentum auto-disable in Scalping mode?
A: Scalping requires ultra-fast entries to catch quick moves. Volume Momentum filter adds friction by requiring volume confirmation before signaling, which can cause missed opportunities in rapid scalping.
Scalping preset is optimized for speed and frequency - the filter is counterproductive for that style. It remains enabled for Day Trading, Swing Trading, and Position Trading presets where patience improves results.
You can manually enable it in Custom mode if desired.
Q: How much historical data do I need for accurate duration estimates?
A:
Minimum: 50-100 bars (indicator will function but duration estimates less reliable)
Recommended: 500+ bars (robust statistical database)
Optimal: 1000+ bars (maximum Statistical accuracy)
More history = more completed trends = better pattern matching = more accurate duration estimates.
New symbols or newly-switched timeframes will have lower Statistical accuracy initially. Allow 2-4 weeks for the system to build historical database.
IMPORTANT DISCLAIMERS
No Guarantee of Profit:
This indicator is an educational tool and does not guarantee any specific trading results. All trading involves substantial risk of loss. Duration estimates are statistical calculations based on historical patterns and are not guarantees of future performance.
Past Performance:
Historical backtest results and Statistical accuracy statistics do not guarantee future performance. Market conditions change constantly. What worked historically may not work in current or future markets.
Not Financial Advice:
This indicator provides technical analysis signals and statistical duration estimates only. It is not financial, investment, or trading advice. Always consult with a qualified financial advisor before making investment decisions.
Risk Warning:
Trading stocks, options, futures, forex, and cryptocurrencies involves significant risk. You can lose all of your invested capital. Never trade with money you cannot afford to lose. Only risk capital you can lose without affecting your lifestyle.
Testing Required:
Always test this indicator on a demo account or with paper trading before risking real capital. Understand how it works in different market conditions. Verify Statistical accuracy on your specific instruments and timeframes before trusting it with real money.
User Responsibility:
You are solely responsible for your trading decisions. The developer assumes no liability for trading losses, incorrect duration estimates, software errors, or any other damages incurred while using this indicator.
Statistical Estimation Limitations:
Trend Duration estimates are statistical estimates based on historical pattern matching. They are NOT guarantees. Actual trend durations may differ significantly from duration estimates due to unforeseen news events, market regime changes, or lack of historical precedent for current conditions.
CREDITS & ACKNOWLEDGMENTS
Methodology Inspiration:
• Mark Minervini - Volatility Contraction Pattern (VCP) concepts and pullback entry techniques
• William O'Neil - Volume analysis principles and CANSLIM institutional buying patterns
• Dan Zanger - Momentum breakout strategies and volatility expansion entries
Technical Components:
• SuperTrend calculation - Classic ATR-based trend indicator (public domain)
• Statistical analysis - Standard median, average, range calculations
• k-Nearest Neighbors - Classic machine learning similarity matching concept
• Multi-timeframe analysis - Standard request.security implementation in Pine Script
For questions, feedback, or support, please comment below or send a private message.
Happy Trading!
ATH대비 지정하락률에 도착 시 매수 - 장기홀딩 선물 전략(ATH Drawdown Re-Buy Long Only)본 스크립트는 과거 하락 데이터를 이용하여, 정해진 하락 %가 발생하는 경우 자기 자본의 정해진 %만큼을 진입하게 설계되어진 스트레티지입니다.
레버리지를 사용할 수 있으며 기본적으로 셋팅해둔 값이 내장되어있습니다.(자유롭게 바꿔서 쓰시면 됩니다.) 추가적으로 2번의 진입 외에도 다른 진입 기준, 진입 %를 설정하실 수 있으며 - ChatGPT에게 요청하면 수정해줄 것입니다.
실제 사용용도로는 KillSwitch 기능을 꺼주세요. 바 돋보기 기능을 켜주세요.
ATH Drawdown Re-Buy Long Only 전략 설명
1. 전략 개요
ATH Drawdown Re-Buy Long Only 전략은 자산의 역대 최고가(ATH, All-Time High)를 기준으로 한 하락폭(드로우다운)을 활용하여,
특정 구간마다 단계적으로 롱 포지션을 구축하는 자동 재매수(Long Only) 전략입니다.
본 전략은 다음과 같은 목적을 가지고 설계되었습니다.
급격한 조정 구간에서 체계적인 분할 매수 및 레버리지 활용
ATH를 기준으로 한 명확한 진입 규칙 제공
실시간으로
평단가
레버리지
청산가 추정
계좌 MDD
수익률
등을 시각적으로 제공하여 리스크와 포지션 상태를 직관적으로 확인할 수 있도록 지원
※ 본 전략은 교육·연구·백테스트 용도로 제공되며,
어떠한 형태의 투자 권유 또는 수익을 보장하지 않습니다.
2. 전략의 핵심 개념
2-1. ATH(역대 최고가) 기준 드로우다운
전략은 차트 상에서 항상 가장 높은 고가(High)를 ATH로 기록합니다.
새로운 고점이 형성될 때마다 ATH를 갱신하고, 해당 ATH를 기준으로 다음을 계산합니다.
현재 바의 저가(Low)가 ATH에서 몇 % 하락했는지
현재 바의 종가(Close)가 ATH에서 몇 % 하락했는지
그리고 사전에 설정한 두 개의 드로우다운 구간에서 매수를 수행합니다.
1차 진입 구간: ATH 대비 X% 하락 시
2차 진입 구간: ATH 대비 Y% 하락 시
각 구간은 ATH가 새로 갱신될 때마다 한 번씩만 작동하며,
새로운 ATH가 생성되면 다시 “1차 / 2차 진입 가능 상태”로 초기화됩니다.
2-2. 첫 포지션 100% / 300% 특수 규칙
이 전략의 중요한 특징은 **“첫 포지션 진입 시의 예외 규칙”**입니다.
전략이 현재 어떠한 포지션도 들고 있지 않은 상태에서
최초로 롱 포지션을 진입하는 시점(첫 포지션)에 대해:
기본적으로는 **자산의 100%**를 기준으로 포지션을 구축하지만,
만약 그 순간의 가격이 ATH 대비 설정값 이상(예: 약 –72.5% 이상 하락한 상황) 이라면
→ 자산의 300% 규모로 첫 포지션을 진입하도록 설계되어 있습니다.
이 규칙은 다음과 같이 동작합니다.
첫 진입이 1차 드로우다운 구간에서 발생하든,
첫 진입이 2차 드로우다운 구간에서 발생하든,
현재 하락폭이 설정된 기준 이상(예: –72.5% 이상) 이라면
→ “이 정도 하락이면 첫 진입부터 더 공격적으로 들어간다”는 의미로 300% 규모로 진입
그 이하의 하락폭이라면
→ 첫 진입은 100% 규모로 제한
즉, 전략은 다음 두 가지 모드로 동작합니다.
일반적인 상황의 첫 진입: 자산의 100%
심각한 드로우다운 구간에서의 첫 진입: 자산의 300%
이 특수 규칙은 깊은 하락에서는 공격적으로, 평소에는 상대적으로 보수적으로 진입하도록 설계된 것입니다.
3. 전략 동작 구조
3-1. 매수 조건
차트 상 High 기준으로 ATH를 추적합니다.
각 바마다 해당 ATH에서의 하락률을 계산합니다.
사용자가 설정한 두 개의 드로우다운 구간(예시):
1차 구간: 예를 들어 ATH – 50%
2차 구간: 예를 들어 ATH – 72.5%
각 구간에 대해 다음과 같은 조건을 확인합니다.
“이번 ATH 구간에서 아직 해당 구간 매수를 한 적이 없는 상태”이고,
현재 바의 저가(Low)가 해당 구간 가격 이하를 찍는 순간
→ 해당 바에서 매수 조건 충족으로 간주
실제 주문은:
해당 구간 가격에 맞춰 롱 포지션 진입(리밋/시장가 기반 시뮬레이션) 으로 처리됩니다.
3-2. ATH 갱신과 진입 기회 리셋
차트 상에서 새로운 고점(High)이 기존 ATH를 넘어서는 순간,
ATH가 갱신되고,
1차 / 2차 진입 여부를 나타내는 내부 플래그가 초기화됩니다.
이를 통해, 시장이 새로운 고점을 돌파해 나갈 때마다,
해당 구간에서 다시 한 번씩 1차·2차 드로우다운 진입 기회를 갖게 됩니다.
4. 포지션 사이징 및 레버리지
4-1. 계좌 자산(Equity) 기준 포지션 크기 결정
전략은 현재 계좌 자산을 다음과 같이 정의하여 사용합니다.
현재 자산 = 초기 자본 + 실현 손익 + 미실현 손익
각 진입 구간에서의 포지션 가치는 다음과 같이 결정됩니다.
1차 진입 구간:
“자산의 몇 %를 사용할지”를 설정값으로 입력
설정된 퍼센트를 계좌 자산에 곱한 뒤,
다시 전략 내 레버리지 배수(Leverage) 를 곱하여 실제 포지션 가치를 계산
2차 진입 구간:
동일한 방식으로, 독립된 퍼센트 설정값을 사용
즉, 포지션 가치는 다음과 같이 계산됩니다.
포지션 가치 = 현재 자산 × (해당 구간 설정 % / 100) × 레버리지 배수
그리고 이를 해당 구간의 진입 가격으로 나누어 실제 수량(토큰 단위) 를 산출합니다.
4-2. 첫 포지션의 예외 처리 (100% / 300%)
첫 포지션에 대해서는 위의 일반적인 퍼센트 설정 대신,
다음과 같은 고정 비율이 사용됩니다.
기본: 자산의 100% 규모로 첫 포지션 진입
단, 진입 시점의 ATH 대비 하락률이 설정값 이상(예: –72.5% 이상) 일 경우
→ 자산의 300% 규모로 첫 포지션 진입
이때 역시 다음 공식을 사용합니다.
포지션 가치 = 현재 자산 × (100% 또는 300%) × 레버리지
그리고 이를 가격으로 나누어 실제 진입 수량을 계산합니다.
이 규칙은:
첫 진입이 1차 구간이든 2차 구간이든 동일하게 적용되며,
“충분히 깊은 하락 구간에서는 첫 진입부터 더 크게,
평소에는 비교적 보수적으로” 라는 운용 철학을 반영합니다.
4-3. 실레버리지(Real Leverage)의 추적
전략은 각 바 단위로 다음을 추적합니다.
바가 시작할 때의 기존 포지션 크기
해당 바에서 새로 진입한 수량
이를 바탕으로, 진입이 발생한 시점에 다음을 계산합니다.
실제 레버리지 = (포지션 가치 / 현재 자산)
그리고 차트 상에 예를 들어:
Lev 2.53x 와 같은 형식의 레이블로 표시합니다.
이를 통해, 매수 시점마다 실제 계좌 레버리지가 어느 정도였는지를 직관적으로 확인할 수 있습니다.
5. 시각화 및 모니터링 요소
5-1. 차트 상 시각 요소
전략은 차트 위에 다음과 같은 정보를 직접 표시합니다.
ATH 라인
High 기준으로 계산된 역대 최고가를 주황색 선으로 표시
평단가(평균 진입가) 라인
현재 보유 포지션이 있을 때,
해당 포지션의 평균 진입가를 노란색 선으로 표시
추정 청산가(고정형 청산가) 라인
포지션 수량이 변화하는 시점을 감지하여,
당시의 평단가와 실제 레버리지를 이용해 근사적인 청산가를 계산
이를 빨간색 선으로 차트에 고정 표시
포지션이 없거나 레버리지가 1배 이하인 경우에는 청산가 라인을 제거
매수 마커 및 레이블
1차/2차 매수 조건이 충족될 때마다 해당 지점에 매수 마커를 표시
"Buy XX% @ 가격", "Lev XXx" 형태의 라벨로
진입 비율과 당시 레버리지를 함께 시각화
레이블의 위치는 설정에서 선택 가능:
바 아래 (Below Bar)
바 위 (Above Bar)
실제 가격 위치 (At Price)
5-2. 우측 상단 정보 테이블
차트 우측 상단에는 현재 계좌·포지션 상태를 요약한 정보 테이블이 표시됩니다.
대표적으로 다음 항목들이 포함됩니다.
Pos Qty (Token)
현재 보유 중인 포지션 수량(토큰 기준, 절대값 기준)
Pos Value (USDT)
현재 포지션의 시장 가치 (수량 × 현재 가격)
Leverage (Now)
현재 실레버리지 (포지션 가치 / 현재 자산)
DD from ATH (%)
현재 가격 기준, 최근 ATH에서의 하락률(%)
Avg Entry
현재 포지션의 평균 진입 가격
PnL (%)
현재 포지션 기준 미실현 손익률(%)
Max DD (Equity %)
전략 전체 기간 동안 기록된 계좌 기준 최대 손실(MDD, Max Drawdown)
Last Entry Price
가장 최근에 포지션을 추가로 진입한 직후의 평균 진입 가격
Last Entry Lev
위 “Last Entry Price” 시점에서의 실레버리지
Liq Price (Fixed)
위에서 설명한 고정형 추정 청산가
Return from Start (%)
전략 시작 시점(초기 자본) 대비 현재 계좌 자산의 총 수익률(%)
이 테이블을 통해 사용자는:
현재 계좌와 포지션의 상태
리스크 수준
누적 성과
를 직관적으로 파악할 수 있습니다.
6. 시간 필터 및 라벨 옵션
6-1. 전략 동작 기간 설정
전략은 옵션으로 특정 기간에만 전략을 동작시키는 시간 필터를 제공합니다.
“Use Date Range” 옵션을 활성화하면:
시작 시각과 종료 시각을 지정하여
해당 구간에 한해서만 매매가 발생하도록 제한
옵션을 비활성화하면:
전략은 전체 차트 구간에서 자유롭게 동작
6-2. 진입 라벨 위치 설정
사용자는 매수/레버리지 라벨의 위치를 선택할 수 있습니다.
바 아래 (Below Bar)
바 위 (Above Bar)
실제 가격 위치 (At Price)
이를 통해 개인 취향 및 차트 가독성에 맞추어
시각화 방식을 유연하게 조정할 수 있습니다.
7. 활용 대상 및 사용 예시
본 전략은 다음과 같은 목적에 적합합니다.
현물 또는 선물 롱 포지션 기준 장기·스윙 관점 추매 전략 백테스트
“고점 대비 하락률”을 기준으로 한 규칙 기반 운용 아이디어 검증
레버리지 사용 시
계좌 레버리지·청산가·MDD를 동시에 모니터링하고자 하는 경우
특정 자산에 대해
“새로운 고점이 형성될 때마다
일정한 규칙으로 깊은 조정 구간에서만 분할 진입하고자 할 때”
실거래에 그대로 적용하기보다는,
전략 아이디어 검증 및 리스크 프로파일 분석,
자신의 성향에 맞는 파라미터 탐색 용도로 사용하는 것을 권장합니다.
8. 한계 및 유의사항
백테스트 결과는 미래 성과를 보장하지 않습니다.
과거 데이터에 기반한 시뮬레이션일 뿐이며,
실제 시장에서는
유동성
슬리피지
수수료 체계
강제청산 규칙
등 다양한 변수가 존재합니다.
청산가는 단순화된 공식에 따른 추정치입니다.
거래소별 실제 청산 규칙, 유지 증거금, 수수료, 펀딩비 등은
본 전략의 계산과 다를 수 있으며,
청산가 추정 라인은 참고용 지표일 뿐입니다.
레버리지 및 진입 비율 설정에 따라 손실 폭이 매우 커질 수 있습니다.
특히 **“첫 포지션 300% 진입”**과 같이 매우 공격적인 설정은
시장 급락 시 계좌 손실과 청산 리스크를 크게 증가시킬 수 있으므로
신중한 검토가 필요합니다.
실거래 연동 시에는 별도의 리스크 관리가 필수입니다.
개별 손절 기준
포지션 상한선
전체 포트폴리오 내 비중 관리 등
본 전략 외부에서 추가적인 안전장치가 필요합니다.
9. 결론
ATH Drawdown Re-Buy Long Only 전략은 단순한 “저가 매수”를 넘어서,
ATH 기준으로 드로우다운을 구조적으로 활용하고,
첫 포지션에 대한 **특수 규칙(100% / 300%)**을 적용하며,
레버리지·청산가·MDD·수익률을 통합적으로 시각화함으로써,
하락 구간에서의 규칙 기반 롱 포지션 구축과
리스크 모니터링을 동시에 지원하는 전략입니다.
사용자는 본 전략을 통해:
자신의 시장 관점과 리스크 허용 범위에 맞는
드로우다운 구간
진입 비율
레버리지 설정
다양한 시나리오에 대한 백테스트와 분석
을 수행할 수 있습니다.
다시 한 번 강조하지만,
본 전략은 연구·학습·백테스트를 위한 도구이며,
실제 투자 판단과 책임은 전적으로 사용자 본인에게 있습니다.
/ENG Version.
This script is designed to use historical drawdown data and automatically enter positions when a predefined percentage drop from the all-time high occurs, using a predefined percentage of your account equity.
You can use leverage, and default parameter values are provided out of the box (you can freely change them to suit your style).
In addition to the two main entry levels, you can add more entry conditions and custom entry percentages – just ask ChatGPT to modify the script.
For actual/live usage, please turn OFF the KillSwitch function and turn ON the Bar Magnifier feature.
ATH Drawdown Re-Buy Long Only Strategy
1. Strategy Overview
The ATH Drawdown Re-Buy Long Only strategy is an automatic re-buy (Long Only) system that builds long positions step-by-step at specific drawdown levels, based on the asset’s all-time high (ATH) and its subsequent drawdown.
This strategy is designed with the following goals:
Systematic scaled buying and leverage usage during sharp correction periods
Clear, rule-based entry logic using drawdowns from ATH
Real-time visualization of:
Average entry price
Leverage
Estimated liquidation price
Account MDD (Max Drawdown)
Return / performance
This allows traders to intuitively monitor both risk and position status.
※ This strategy is provided for educational, research, and backtesting purposes only.
It does not constitute investment advice and does not guarantee any profits.
2. Core Concepts
2-1. Drawdown from ATH (All-Time High)
On the chart, the strategy always tracks the highest high as the ATH.
Whenever a new high is made, ATH is updated, and based on that ATH the following are calculated:
How many percent the current bar’s Low is below the ATH
How many percent the current bar’s Close is below the ATH
Using these, the strategy executes buys at two predefined drawdown zones:
1st entry zone: When price drops X% from ATH
2nd entry zone: When price drops Y% from ATH
Each zone is allowed to trigger only once per ATH cycle.
When a new ATH is created, the “1st / 2nd entry possible” flags are reset, and new opportunities open up for that ATH leg.
2-2. Special Rule for the First Position (100% / 300%)
A key feature of this strategy is the special rule for the very first position.
When the strategy currently holds no position and is about to open the first long position:
Under normal conditions, it builds the position using 100% of account equity.
However, if at that moment the price has dropped by at least a predefined threshold from ATH (e.g. around –72.5% or more),
→ the strategy will open the first position using 300% of account equity.
This rule works as follows:
Whether the first entry happens at the 1st drawdown zone or at the 2nd drawdown zone,
If the current drawdown from ATH is at or below the threshold (e.g. –72.5% or worse),
→ the strategy interprets this as “a sufficiently deep crash” and opens the initial position with 300% of equity.
If the drawdown is less severe than the threshold,
→ the first entry is capped at 100% of equity.
So the strategy has two modes for the first entry:
Normal market conditions: 100% of equity
Deep drawdown conditions: 300% of equity
This special rule is intended to be aggressive in extremely deep crashes while staying more conservative in normal corrections.
3. Strategy Logic & Execution
3-1. Entry Conditions
The strategy tracks the ATH using the High price.
For each bar, it calculates the drawdown from ATH.
The user defines two drawdown zones, for example:
1st zone: ATH – 50%
2nd zone: ATH – 72.5%
For each zone, the strategy checks:
If no buy has been executed yet for that zone in the current ATH leg, and
If the current bar’s Low touches or falls below that zone’s price level,
→ That bar is considered to have triggered a buy condition.
Order simulation:
The strategy simulates entering a long position at that zone’s price level
(using a limit/market-like approximation for backtesting).
3-2. ATH Reset & Entry Opportunity Reset
When a new High goes above the previous ATH:
The ATH is updated to this new high.
Internal flags that track whether the 1st and 2nd entries have been used are reset.
This means:
Each time the market makes a new ATH,
The strategy once again has a fresh opportunity to execute 1st and 2nd drawdown entries for that new ATH leg.
4. Position Sizing & Leverage
4-1. Position Size Based on Account Equity
The strategy defines current equity as:
Current Equity = Initial Capital + Realized PnL + Unrealized PnL
For each entry zone, the position value is calculated as follows:
The user inputs:
“What % of equity to use at this zone”
The strategy:
Multiplies current equity by that percentage
Then multiplies by the strategy’s leverage factor
Thus:
Position Value = Current Equity × (Zone % / 100) × Leverage
Finally, this position value is divided by the entry price to determine the actual position size in tokens.
4-2. Exception for the First Position (100% / 300%)
For the very first position (when there is no open position),
the strategy does not use the zone % parameters. Instead, it uses fixed ratios:
Default: Enter the first position with 100% of equity.
If the drawdown from ATH at that moment is greater than or equal to a predefined threshold (e.g. –72.5% or more)
→ Enter the first position with 300% of equity.
The position value is computed as:
Position Value = Current Equity × (100% or 300%) × Leverage
Then it is divided by the entry price to obtain the token quantity.
This rule:
Applies regardless of whether the first entry occurs at the 1st zone or 2nd zone.
Embeds the philosophy:
“In very deep crashes, go much larger on the first entry; otherwise, stay more conservative.”
4-3. Tracking Real Leverage
On each bar, the strategy tracks:
The existing position size at the start of the bar
The newly added size (if any) on that bar
When a new entry occurs, it calculates the real leverage at that moment:
Real Leverage = (Position Value / Current Equity)
This is then displayed on the chart as a label, for example:
Lev 2.53x
This makes it easy to see the actual leverage level at each entry point.
5. Visualization & Monitoring
5-1. On-Chart Visual Elements
The strategy plots the following directly on the chart:
ATH Line
The all-time high (based on High) is plotted as an orange line.
Average Entry Price Line
When a position is open, the average entry price of that position is plotted as a yellow line.
Estimated Liquidation Price (Fixed) Line
The strategy detects when the position size changes.
At each size change, it uses the current average entry price and real leverage to compute an approximate liquidation price.
This “fixed liquidation price” is then plotted as a red line on the chart.
If there is no position, or if leverage is 1x or lower, the liquidation line is removed.
Entry Markers & Labels
When 1st/2nd entry conditions are met, the strategy:
Marks the entry point on the chart.
Displays labels such as "Buy XX% @ Price" and "Lev XXx",
showing both entry percentage and real leverage at that time.
The label placement is configurable:
Below Bar
Above Bar
At Price
5-2. Information Table (Top-Right Panel)
In the top-right corner of the chart, the strategy displays a summary table of the current account and position status. It typically includes:
Pos Qty (Token)
Absolute size of the current position (in tokens)
Pos Value (USDT)
Market value of the current position (qty × current price)
Leverage (Now)
Current real leverage (position value / current equity)
DD from ATH (%)
Current drawdown (%) from the latest ATH, based on current price
Avg Entry
Average entry price of the current position
PnL (%)
Unrealized profit/loss (%) of the current position
Max DD (Equity %)
The maximum equity drawdown (MDD) recorded over the entire backtest period
Last Entry Price
Average entry price immediately after the most recent add-on entry
Last Entry Lev
Real leverage at the time of the most recent entry
Liq Price (Fixed)
The fixed estimated liquidation price described above
Return from Start (%)
Total return (%) of equity compared to the initial capital
Through this table, users can quickly grasp:
Current account and position status
Current risk level
Cumulative performance
6. Time Filters & Label Options
6-1. Strategy Date Range Filter
The strategy provides an option to restrict trading to a specific time range.
When “Use Date Range” is enabled:
You can specify start and end timestamps.
The strategy will only execute trades within that range.
When this option is disabled:
The strategy operates over the entire chart history.
6-2. Entry Label Placement
Users can customize where entry/leverage labels are drawn:
Below Bar (Below Bar)
Above Bar (Above Bar)
At the actual price level (At Price)
This allows you to adjust visualization according to personal preference and chart readability.
7. Use Cases & Applications
This strategy is suitable for the following purposes:
Long-term / swing-style re-buy strategies for spot or futures long positions
Testing rule-based strategies that rely on “drawdown from ATH” as a main signal
Monitoring account leverage, liquidation price, and MDD when using leverage
Handling situations where, for a given asset:
“Every time a new ATH is formed,
you want to wait for deep corrections and enter only at specific drawdown zones”
It is generally recommended to use this strategy not as a direct plug-and-play live system, but as a tool for:
Strategy idea validation
Risk profile analysis
Parameter exploration to match your personal risk tolerance and style
8. Limitations & Warnings
Backtest results do not guarantee future performance.
They are based on historical data only.
In live markets, additional factors exist:
Liquidity
Slippage
Fee structures
Exchange-specific liquidation rules
Funding fees, etc.
The liquidation price is only an approximate estimate, derived from a simplified formula.
Actual liquidation rules, maintenance margin requirements, fees, and other details differ by exchange.
The liquidation line should be treated as a reference indicator, not an exact guarantee.
Depending on the configured leverage and entry percentages, losses can be very large.
In particular, extremely aggressive settings such as “first position 300% of equity” can greatly increase the risk of large account drawdowns and liquidation during sharp market crashes.
Use such settings with extreme caution.
For live trading, additional risk management is essential:
Your own stop-loss rules
Maximum position size limits
Portfolio-level exposure controls
And other external safety mechanisms beyond this strategy
9. Conclusion
The ATH Drawdown Re-Buy Long Only strategy goes beyond simple “buy the dip” logic. It:
Systematically utilizes drawdowns from ATH as a structural signal
Applies a special first-position rule (100% / 300%)
Integrates visualization of leverage, liquidation price, MDD, and returns
All of this supports rule-based long position building in drawdown phases and comprehensive risk monitoring.
With this strategy, users can:
Explore different:
Drawdown zones
Entry percentages
Leverage levels
Run various backtests and scenario analyses
Better understand the risk/return profile that fits their own market view and risk tolerance
Once again, this strategy is intended for research, learning, and backtesting only.
All real trading decisions and their consequences are solely the responsibility of the user.
Dobrusky Pressure CoreWhat it does & who it’s for
Dobrusky Pressure Core is a volume by time replacement for traders who care about which side actually controls each bar. Instead of just plotting total volume, it splits each bar into estimated buy vs sell pressure and overlays a custom, session-aware volume baseline. It’s built for discretionary traders who want more nuanced volume context for entries, breakouts, and pullbacks.
Core ideas
Buy/sell pressure split: Each bar’s volume is broken into estimated buying and selling pressure.
Dominant side highlighting: The dominant side (buy or sell) is always displayed starting from the bottom of the bar, so you can quickly see who “owned” that bar.
Median-based baseline: Uses the median of the last N bars (50 by default) to build a robust volume baseline that’s less sensitive to one-off spikes.
Session-aware behavior: Baseline is calculated from Regular Trading Hours (RTH) by default, with an option to include Extended Hours (ETH) and a control to force Regular data on higher timeframes.
Volume regimes: Three multipliers (1x, 1.5x, 2x by default) show normal, high, and extreme volume regions.
Flexible display: Baseline can be shown as lines or as columns behind the volume, with full color customization.
How the pressure logic works
For each bar, the script:
Adjusts the range for gaps relative to the prior close so the “true” traded range is more consistent.
Computes buy pressure as a proportion of the adjusted range from low to close.
Defines sell pressure as: total volume minus buy pressure.
Marks the bar as buy-dominant if buy pressure ≥ sell pressure, otherwise sell-dominant, and colors the dominant side from the bottom to at least the midpoint using the selected buy/sell colors.
In practice, this turns basic volume columns into bars where the internal split and dominant side are clearly visible, helping you judge whether aggressive buyers or sellers truly controlled the bar instead of just looking at the price action.
Volume baseline & session logic
The script builds a session-aware baseline from recent volume:
Baseline length: A rolling window (default 50 bars) is used to compute a median volume value instead of a simple moving average.
RTH-only by default: By default, the baseline is built from Regular Trading Hours bars only. During extended hours, the baseline effectively “freezes” at the last RTH-derived value unless you choose to include extended session data.
Extended mode: If you select Extended mode, the script builds separate rolling baselines for RTH and ETH trading, using the appropriate one depending on the current session.
Force Regular Above Timeframe: On timeframes equal to or higher than your chosen threshold, the baseline automatically uses Regular session data, even if Extended is selected.
Multipliers: Three adjustable multipliers (1x, 1.5x, 2x by default) create normal, high, and extreme volume bands for quick identification.
This lets you choose whether you want a pure RTH reference or a baseline that adapts to extended-session activity.
Example ways to use it
1. Replace standard volume bars
Add Dobrusky Pressure Core to your volume pane and hide the default volume if you prefer a clean look.
Use the colors and split to see at a glance whether buyers or sellers were dominant on each bar.
2. Pressure confirmation for entries
For longs (example concept; adapt to your own rules):
Require that the entry bar’s buy pressure is greater than the previous bar’s sell pressure , or
If the entry and prior bar are both buy-dominant, require that the entry bar has more buy pressure than the prior bar.
This helps avoid taking a long when buying pressure is clearly fading relative to what sellers recently showed. A mirrored idea can be used for short setups with sell pressure.
3. Context from baseline multipliers
Use ~1x baseline as “normal” volume.
Watch for bars at or above 1.5x baseline when you want to see increased participation.
Treat 2x baseline and above as “extreme” volume zones that may mark climactic or especially important bars.
In practice, the baseline and multipliers are best used as context and filters, not as rigid rules.
Settings overview
Display
- Show Volume Baseline: toggle the baseline and its levels on or off.
- Baseline Display: choose between Line or Bars for the baseline visualization.
Baseline Calculation
- Length: lookback for the median baseline (default 50, configurable).
- Baseline Session Data: choose Regular or Extended to control which session data feeds the baseline.
Session Controls
- Regular Session (Local to TZ): define your RTH window (e.g., 0930-1600).
- Session Time Zone: choose the time zone used for that window.
- Force Regular Above Timeframe: on higher timeframes, force the baseline to use Regular session data only.
Baseline Levels
- Show Level x Multiplier 1/2/3: toggle each volume regime level.
- Multiplier 1/2/3: define what you consider normal, high, and extreme volume (defaults: 1.0, 1.5, 2.0).
Colors
- Buy Volume / Sell Volume: choose colors for buy and sell pressure.
- Baseline Bars (Base / x2 / x3): colors when the baseline is drawn as columns.
- Baseline Line (Base / x2 / x3): colors when the baseline is drawn as lines.
Limitations & best practices
This is a decision-support and visualization tool, not a buy/sell signal generator.
Best suited to markets where volume data is meaningful (e.g., index futures, liquid equities, liquid crypto).
The usefulness of any volume-based metric depends on the underlying data feed and instrument structure.
Always combine pressure and baseline context with your own strategy, risk management, and testing.
Originality
Most volume tools either show total volume only or compare it to a simple moving average. Dobrusky Pressure Core combines:
An intrabar buy/sell pressure split based on a gap-adjusted price range.
A median-based, configurable baseline built from session-specific data.
Session-aware behavior that keeps the baseline focused on Regular hours by default, with the option to incorporate Extended hours and force Regular data on higher timeframes.
The goal is to give traders a richer, session-aware view of participation and pressure that standard volume bars and simple SMA overlays don’t provide, while keeping everything transparent and open-source so users can review and adapt the logic.
MA SMART Angle
### 📊 WHAT IS MA SMART ANGLE?
**MA SMART Angle** is an advanced momentum and trend detection indicator that analyzes the angles (slopes) of multiple moving averages to generate clear, non-repainting BUY and SELL signals.
**Original Concept Credit:** This indicator builds upon the "MA Angles" concept originally created by **JD** (also known as Duyck). The core angle calculation methodology and Jurik Moving Average (JMA) implementation by **Everget** are preserved from the original open-source work. The angle calculation formula was contributed by **KyJ**. This enhanced version is published with respect to the open-source nature of the original indicator.
Original indicator reference: "ma angles - JD" by Duyck
---
## 🎯 ORIGINALITY & VALUE PROPOSITION
### **What Makes This Different from the Original:**
While the original "MA Angles" by **JD** provided excellent angle visualization, it lacked actionable entry signals. **MA SMART Angle** addresses this by adding:
**1. Clear Entry/Exit Signals**
- Explicit BUY/SELL arrows based on angle crossovers, momentum confirmation, and MA alignment
- No guessing when to enter trades - the indicator tells you exactly when conditions align
**2. Non-Repainting Logic**
- All signals use confirmed historical data (shifted by 2 bars minimum)
- Critical for backtesting reliability and live trading confidence
- Original indicator could repaint signals on current bar
**3. Dual Signal System**
- **Simple Mode:** More frequent signals based on angle crossovers + momentum (for active traders)
- **Strict Mode:** Requires full multi-MA alignment + momentum confirmation (for conservative traders)
- Adaptable to different trading styles and risk tolerances
**4. Smart Signal Filtering**
- **Anti-spam cooldown:** Prevents duplicate signals within configurable bar count
- **No-trade zone detection:** Filters out low-conviction sideways markets automatically
- **Multi-timeframe MA alignment:** Ensures all moving averages agree on direction before signaling
**5. Enhanced Visualization**
- Large, clear BUY/SELL arrows with descriptive labels
- Color-coded backgrounds for market states (trending vs. ranging)
- Momentum histogram showing acceleration/deceleration in real-time
- Live status table displaying trend strength, angle value, momentum, and MA alignment
**6. Professional Alert System**
- Four distinct alert conditions: BUY Signal, SELL Signal, Strong BUY, Strong SELL
- Enables automated trade notifications and strategy integration
**7. Modified MA Periods**
- Original used EMA(27), EMA(83), EMA(278)
- Enhanced version uses faster EMA(3), EMA(8), EMA(13) for more responsive signals
- Better suited for modern volatile markets and shorter timeframes
---
## 📐 HOW IT WORKS - TECHNICAL EXPLANATION
### **Core Methodology:**
The indicator calculates angles (slopes) for five key moving averages:
- **JMA (Jurik Moving Average)** - Smooth, lag-reduced trend line (original implementation by **Everget**)
- **JMA Fast** - Responsive momentum indicator with higher power parameter
- **MA27 (EMA 3)** - Primary fast-moving average for signal generation
- **MA83 (EMA 8)** - Medium-term trend confirmation
- **MA278 (EMA 13)** - Slower trend filter
### **Angle Calculation Formula (by KyJ):**
```
angle = arctan((MA - MA ) / ATR(14)) × (180 / π)
```
**Why ATR normalization?**
- Makes angles comparable across different instruments (forex, stocks, crypto)
- Makes angles comparable across different timeframes
- Accounts for volatility - a 10-point move in different assets has different significance
**Angle Interpretation:**
- **> 15°** = Strong trend (momentum accelerating)
- **0° to 15°** = Weak trend (momentum present but moderate)
- **-2° to +2°** = No-trade zone (sideways/choppy market)
- **< -15°** = Strong downtrend
### **Signal Generation Logic:**
#### **BUY Signal Conditions:**
1. MA27 angle crosses above 0° (upward momentum initiates)
2. All three EMAs (3, 8, 13) pointing upward (trend alignment confirmed)
3. Momentum is positive for 2+ bars (acceleration, not deceleration)
4. Angle exceeds minimum threshold (not in no-trade zone)
5. Cooldown period passed (prevents signal spam)
#### **SELL Signal Conditions:**
1. MA27 angle crosses below 0° (downward momentum initiates)
2. All three EMAs pointing downward (downtrend alignment)
3. Momentum is negative for 2+ bars
4. Angle below negative threshold (not in no-trade zone)
5. Cooldown period passed
#### **Strong BUY+ / SELL+ Signals:**
Additional entry opportunities when JMA Fast crosses JMA Slow while maintaining strong directional angle - indicates momentum acceleration within established trend.
---
## 🔧 HOW TO USE
### **Recommended Settings by Trading Style:**
**Scalpers / Day Traders:**
- Signal Type: **Simple**
- Minimum Angle: **3-5°**
- Cooldown Bars: **3-5 bars**
- Timeframes: 1m, 5m, 15m
**Swing Traders:**
- Signal Type: **Strict**
- Minimum Angle: **7-10°**
- Cooldown Bars: **8-12 bars**
- Timeframes: 1H, 4H, Daily
**Position Traders:**
- Signal Type: **Strict**
- Minimum Angle: **10-15°**
- Cooldown Bars: **15-20 bars**
- Timeframes: Daily, Weekly
### **Parameter Descriptions:**
**1. Source** (default: OHLC4)
- Price data used for MA calculations
- OHLC4 provides smoothest angles
- Close is more responsive but noisier
**2. Threshold for No-Trade Zones** (default: 2°)
- Angles below this are considered sideways/ranging
- Increase for stricter filtering of choppy markets
- Decrease to allow signals in quieter trending periods
**3. Signal Type** (Simple vs. Strict)
- **Simple:** Angle crossover OR (trend + momentum)
- **Strict:** Angle crossover AND all MAs aligned AND momentum confirmed
- Start with Simple, switch to Strict if too many false signals
**4. Minimum Angle for Signal** (default: 5°)
- Only generate signals when angle exceeds this threshold
- Higher values = stronger trends required
- Lower values = more sensitive to momentum changes
**5. Cooldown Bars** (default: 5)
- Minimum bars between consecutive signals
- Prevents spam during volatile chop
- Scale with your timeframe (higher TF = more bars)
**6. Color Bars** (default: true)
- Colors chart bars based on signal state
- Green = bullish conditions, Red = bearish conditions
- Can disable if you prefer clean price bars
**7. Background Colors**
- **Yellow background** = No-trade zone (low angle, ranging market)
- **Green flash** = BUY signal generated
- **Red flash** = SELL signal generated
- All customizable or can be disabled
---
## 📊 INTERPRETING THE INDICATOR
### **Visual Elements:**
**Main Chart Window:**
- **Thick Lime/Fuchsia Line** = MA27 angle (primary signal line)
- **Medium Green/Red Line** = MA83 angle (trend confirmation)
- **Thin Green/Red Line** = MA278 angle (slow trend filter)
- **Aqua/Orange Line** = JMA Fast (momentum detector)
- **Green/Red Area** = JMA slope (overall trend context)
- **Blue/Purple Histogram** = Momentum (angle acceleration/deceleration)
**Signal Arrows:**
- **Large Green ▲ "BUY"** = Primary buy signal (all conditions met)
- **Small Green ▲ "BUY+"** = Strong momentum buy (JMA fast cross)
- **Large Red ▼ "SELL"** = Primary sell signal (all conditions met)
- **Small Red ▼ "SELL+"** = Strong momentum sell (JMA fast cross)
**Status Table (Top Right):**
- **Angle:** Current MA27 angle in degrees
- **Trend:** Classification (STRONG UP/DOWN, UP/DOWN, FLAT)
- **Momentum:** Acceleration state (ACCEL UP/DN, Up/Down)
- **MAs:** Alignment status (ALL UP/DOWN, Mixed)
- **Zone:** Trading zone status (ACTIVE vs. NO TRADE)
- **Last:** Bars since last signal
### **Trading Strategies:**
**Strategy 1: Pure Signal Following**
- Enter LONG on BUY signal
- Exit on SELL signal
- Use stop-loss at recent swing low/high
- Works best on trending instruments
**Strategy 2: Confirmation with Price Action**
- Wait for BUY signal + bullish candlestick pattern
- Wait for SELL signal + bearish candlestick pattern
- Increases win rate by filtering premature signals
- Recommended for beginners
**Strategy 3: Momentum Acceleration**
- Use BUY+/SELL+ signals for adding to positions
- Only take these in direction of primary signal
- Scalp quick moves during momentum spikes
- For experienced traders
**Strategy 4: Mean Reversion in No-Trade Zones**
- When status shows "NO TRADE", fade extremes
- Wait for angle to exit no-trade zone for reversal
- Contrarian approach for range-bound markets
- Requires tight stops
---
## ⚠️ LIMITATIONS & DISCLAIMERS
**What This Indicator DOES:**
✅ Measures momentum direction and strength via angle analysis
✅ Generates signals when multiple conditions align
✅ Filters out low-conviction sideways markets
✅ Provides visual clarity on trend state
**What This Indicator DOES NOT:**
❌ Predict future price movements with certainty
❌ Guarantee profitable trades (no indicator can)
❌ Work equally well on all instruments/timeframes
❌ Replace proper risk management and position sizing
**Known Limitations:**
- **Lagging Nature:** Like all moving averages, signals occur after momentum begins
- **Whipsaw Risk:** Can generate false signals in volatile, directionless markets
- **Optimization Required:** Parameters need adjustment for different assets
- **Not a Complete System:** Should be combined with risk management, position sizing, and other analysis
**Best Performance Conditions:**
- Strong trending markets (crypto bull runs, stock breakouts)
- Liquid instruments (major forex pairs, large-cap stocks)
- Appropriate timeframe selection (match to trading style)
- Used alongside support/resistance and volume analysis
---
## 🔔 ALERT SETUP
The indicator includes four alert conditions:
**1. BUY SIGNAL**
- Message: "MA SMART Angle: BUY SIGNAL! Angle crossed up with momentum"
- Use for: Primary long entries
**2. SELL SIGNAL**
- Message: "MA SMART Angle: SELL SIGNAL! Angle crossed down with momentum"
- Use for: Primary short entries or long exits
**3. Strong BUY**
- Message: "MA SMART Angle: Strong BUY momentum - JMA fast crossed up"
- Use for: Adding to longs or aggressive entries
**4. Strong SELL**
- Message: "MA SMART Angle: Strong SELL momentum - JMA fast crossed down"
- Use for: Adding to shorts or aggressive exits
**Setting Up Alerts:**
1. Right-click indicator → "Add Alert on MA SMART Angle"
2. Select desired condition from dropdown
3. Choose notification method (popup, email, webhook)
4. Set alert expiration (typically "Once Per Bar Close")
---
## 📚 EDUCATIONAL VALUE
This indicator serves as an excellent learning tool for understanding:
**1. Angle-Based Momentum Analysis**
- Traditional indicators show MA crossovers
- This shows the *rate of change* (velocity) of MAs
- Teaches traders to think in terms of momentum acceleration
**2. Multi-Timeframe Confirmation**
- Shows how fast, medium, and slow MAs interact
- Demonstrates importance of trend alignment
- Helps develop patience for high-probability setups
**3. Signal Quality vs. Quantity Tradeoff**
- Simple mode = more signals, more noise
- Strict mode = fewer signals, higher quality
- Teaches discretionary filtering skills
**4. Market State Recognition**
- Visual distinction between trending and ranging markets
- Helps traders avoid trading choppy conditions
- Develops "market context" awareness
---
## 🔄 DIFFERENCES FROM OTHER MA INDICATORS
**vs. Traditional MA Crossovers:**
- Measures momentum (angle) rather than just price crossing MA
- Provides earlier signals as angles change before price crosses
- Filters better for sideways markets using no-trade zones
**vs. MACD:**
- Uses multiple MAs instead of just two
- ATR normalization makes it universal across instruments
- Visual angle representation more intuitive than histogram
**vs. Supertrend:**
- Not based on ATR bands but on MA slope analysis
- Provides graduated strength indication (not just binary trend)
- Less prone to whipsaw in low volatility
**vs. Original "MA Angles" by JD:**
- Adds explicit entry/exit signals (original had none)
- Implements no-repaint logic for reliability
- Includes signal filtering and quality controls
- Provides dual signal systems (Simple/Strict)
- Enhanced visualization and status monitoring
- Uses faster MA periods (3/8/13 vs 27/83/278) for modern markets
---
## 📖 CODE STRUCTURE (for Pine Script learners)
This indicator demonstrates:
**Advanced Pine Script Techniques:**
- Custom function implementation (JMA, angle calculation)
- Var declarations for stateful tracking
- Table creation for HUD display
- Multi-condition signal logic
- Alert system integration
- Proper use of historical references for no-repaint
**Code Organization:**
- Modular function definitions (JMA, angle)
- Clear separation of concerns (inputs, calculations, plotting, alerts)
- Extensive commenting for maintainability
- Best practices for Pine Script v5
**Learning Resources:**
- Study the JMA function to understand adaptive smoothing
- Examine angle calculation for ATR normalization technique
- Review signal logic for multi-condition confirmation patterns
- Analyze anti-spam filtering for state management
The code is open-source - feel free to study, modify, and improve upon it!
---
## 🙏 CREDITS & ATTRIBUTION
**Original Concepts:**
- **"ma angles - JD" by JD (Duyck)** - Core angle calculation methodology and indicator concept
Original open-source indicator on TradingView Community Scripts
- **JMA (Jurik Moving Average) implementation by Everget** - Smooth, low-lag moving average function
Acknowledged in original JD indicator code
- **Angle Calculation formula by KyJ** - Mathematical formula for converting MA slope to degrees using ATR normalization
Acknowledged in original JD indicator code comments
**Enhancements in This Version:**
- Signal generation logic - Original implementation for this indicator
- No-repaint confirmation system - Original implementation
- Dual signal modes (Simple/Strict) - Original implementation
- Visual enhancements and status table - Original implementation
- Alert system and signal filtering - Original implementation
- Modified MA periods (3/8/13 instead of 27/83/278) - Optimization for modern markets
**Open Source Philosophy:**
This indicator follows the open-source spirit of TradingView and the Pine Script community. The original "ma angles - JD" by JD (Duyck) was published as open-source, enabling this enhanced version. Similarly, this code is published as open-source to allow further community improvements.
---
## ⚡ QUICK START GUIDE
**For New Users:**
1. Add indicator to chart
2. Start with default settings (Simple mode)
3. Wait for BUY signal (green arrow)
4. Observe how price behaves after signal
5. Check status table to understand market state
6. Adjust parameters based on your instrument/timeframe
**For Experienced Traders:**
1. Switch to Strict mode for higher quality signals
2. Increase cooldown bars to reduce frequency
3. Raise minimum angle threshold for stronger trends
4. Combine with your existing strategy for confirmation
5. Set up alerts for desired signal types
6. Backtest on your preferred instruments
---
## 🎓 RECOMMENDED COMBINATIONS
**Works Well With:**
- **Volume Analysis:** Confirm signals with volume spikes
- **Support/Resistance:** Take signals near key levels
- **RSI/Stochastic:** Avoid overbought/oversold extremes
- **ATR:** Size positions based on volatility
- **Price Action:** Wait for candlestick confirmation
**Complementary Indicators:**
- Order Flow / Footprint (for institutional confirmation)
- Volume Profile (for identifying value areas)
- VWAP (for intraday mean reversion reference)
- Fibonacci Retracements (for target setting)
---
## 📈 PERFORMANCE EXPECTATIONS
**Realistic Win Rates:**
- Simple Mode: 45-55% (higher frequency, moderate accuracy)
- Strict Mode: 55-65% (lower frequency, higher accuracy)
- Combined with price action: 60-70%
**Best Asset Classes:**
1. **Cryptocurrencies** (strong trends, clear signals)
2. **Forex Major Pairs** (smooth price action, good angles)
3. **Large-Cap Stocks** (trending behavior, liquid)
4. **Index Futures** (trending instruments)
**Challenging Conditions:**
- Low volatility consolidation periods
- News-driven erratic movements
- Thin/illiquid instruments
- Counter-trending markets
---
## 🛡️ RISK DISCLAIMER
**IMPORTANT LEGAL NOTICE:**
This indicator is for **educational and informational purposes only**. It is **NOT financial advice** and does not constitute a recommendation to buy or sell any financial instrument.
**Trading Risks:**
- Trading carries substantial risk of loss
- Past performance does not guarantee future results
- No indicator can predict market movements with certainty
- You can lose more than your initial investment (especially with leverage)
**User Responsibilities:**
- Conduct your own research and due diligence
- Understand the instruments you trade
- Never risk more than you can afford to lose
- Use proper position sizing and risk management
- Consider consulting a licensed financial advisor
**Indicator Limitations:**
- Signals are based on historical data only
- No guarantee of accuracy or profitability
- Parameters must be optimized for your specific use case
- Results vary significantly by market conditions
By using this indicator, you acknowledge and accept all trading risks. The author is not responsible for any financial losses incurred through use of this indicator.
---
## 📧 SUPPORT & FEEDBACK
**Found a bug?** Please report it in the comments with:
- Chart symbol and timeframe
- Parameter settings used
- Description of unexpected behavior
- Screenshot if possible
**Have suggestions?** Share your ideas for improvements!
**Enjoying the indicator?** Leave a like and follow for updates!
Smart VWAP FVG SystemSmart VWAP FVG System - Professional Multi-Filter Trading Indicator
📊 OVERVIEW
The Smart VWAP FVG System is an advanced multi-layered trading indicator that combines institutional volume analysis, multi-timeframe VWAP trend confirmation, and Fair Value Gap detection to identify high-probability trade entries. This indicator uses a sophisticated filtering mechanism where signals appear only when multiple independent confirmation criteria align simultaneously.
Recommended Timeframe: 5-minute (M5) or higher. The indicator works best on M5, M15, and M30 charts for intraday trading.
🎯 ORIGINALITY & PURPOSE
This indicator is original because it combines three distinct analytical methods into a unified decision-making system:
Market Profile Volume Analysis - Identifies institutional accumulation/distribution zones
Dual VWAP Filtering - Confirms trend direction using two independent VWAP calculations
Fair Value Gap Detection - Validates institutional interest through price inefficiency zones
The key innovation is the directional filter system: the primary Market Profile generates BUY-ONLY or SELL-ONLY states based on higher timeframe value area reversals, which then controls which signals from the main system are displayed. This creates a multi-timeframe confluence that significantly reduces false signals.
Unlike simple indicator mashups, each component serves a specific purpose:
Market Profile → Direction bias (trend filter)
Primary VWAP (Session) → Short-term trend confirmation
Secondary VWAP (Week) → Medium-term trend confirmation
FVG Detection → Institutional activity validation
🔧 HOW IT WORKS
1. Primary Market Profile Filter (Higher Timeframe)
The indicator calculates Market Profile on a higher timeframe (default: 1 hour) to determine the overall market structure:
Value Area High (VAH): Top 70% of volume distribution
Value Area Low (VAL): Bottom 70% of volume distribution
Point of Control (POC): Price level with highest volume
When price reaches VAH and reverses down → SELL-ONLY mode activated
When price reaches VAL and reverses up → BUY-ONLY mode activated
This higher timeframe filter ensures you're trading in the direction of institutional flow.
2. Dual VWAP System
Two independent VWAP calculations provide multi-timeframe trend confirmation:
Primary VWAP (Session-based): Resets daily, tracks intraday momentum
Secondary VWAP (Week-based): Resets weekly, confirms longer-term trend
Filter Logic:
BUY signals require: Price > Primary VWAP AND Price > Secondary VWAP
SELL signals require: Price < Primary VWAP AND Price < Secondary VWAP
This dual confirmation prevents counter-trend trades during ranging conditions.
3. Fair Value Gap (FVG) Detection
FVG zones identify price inefficiencies where institutional orders were executed rapidly:
Bullish FVG: Gap between candle .high and candle .low (upward imbalance)
Bearish FVG: Gap between candle .high and candle .low (downward imbalance)
The indicator monitors recent FVG formation (lookback: 50 bars) and requires:
Bullish FVG present for BUY signals
Bearish FVG present for SELL signals
FVG zones are displayed as colored boxes and automatically marked as "mitigated" when price fills the gap.
4. Main Trading Signal Logic
The secondary Market Profile (default: 1 hour) generates the actual trading signals:
BUY Signal Conditions:
Price reaches Value Area Low
Reversal pattern confirmed (minimum 1 bar)
Price > Primary VWAP
Price > Secondary VWAP (if filter enabled)
Recent Bullish FVG detected (if filter enabled)
Primary MP Filter = BUY-ONLY or NEUTRAL
SELL Signal Conditions:
Price reaches Value Area High
Reversal pattern confirmed (minimum 1 bar)
Price < Primary VWAP
Price < Secondary VWAP (if filter enabled)
Recent Bearish FVG detected (if filter enabled)
Primary MP Filter = SELL-ONLY or NEUTRAL
All conditions must be TRUE simultaneously for a signal to appear.
📈 VISUAL ELEMENTS
On Chart:
🟢 Green Triangle (▲) = BUY Signal
🔴 Red Triangle (▼) = SELL Signal
🟦 Blue horizontal lines = Value Area zones
🟡 Yellow line = Point of Control (POC)
🟩 Green boxes = Bullish FVG zones
🟥 Red boxes = Bearish FVG zones
🔵 Blue line = Primary VWAP (Session)
⚪ White line = Secondary VWAP (Week)
Info Panel (Top Right):
Real-time status display showing:
Filter Direction (BUY ONLY / SELL ONLY / NEUTRAL)
Active timeframes for both MP filters
FVG filter status and count
VWAP positions (ABOVE/BELOW)
Signal enablement status
Alert status
⚙️ KEY SETTINGS
MP/TPO Filter Settings (Primary Indicator)
MP Filter Time Frame: 60 minutes (controls directional bias)
Filter Value Area %: 70% (standard Market Profile calculation)
Filter Alert Distance: 1 bar
Filter Min Bars for Reversal: 1 bar
Filter Alert Zone Margin: 0.01 (1%)
FVG Filter Settings
Use FVG Filter: Enabled (toggle on/off)
FVG Timeframe: 60 minutes (1 hour)
FVG Filter Mode: Both (require bullish FVG for BUY, bearish for SELL)
FVG Lookback Period: 50 bars (how far back to search)
Show FVG Formation Signals: Optional visual markers
Max FVG on Chart: 50 zones
Show Mitigated FVG: Display filled gaps
Market Profile Settings
Higher Time Frame: 60 minutes (for main signals)
Percent for Value Area: 70%
Show POC Line: Enabled
Keep Old MPs: Enabled (maintain historical profiles)
Primary VWAP Filter
Use Primary VWAP Filter: Enabled
Primary VWAP Anchor Period: Session (resets daily)
Primary VWAP Source: HLC3 (typical price)
Secondary VWAP Filter
Use Secondary VWAP Filter: Enabled
Secondary VWAP Anchor Period: Week (resets weekly)
Secondary VWAP Filter Mode: Both
Secondary VWAP Line Color: White
Trading Signals
Show Trading Signals on Chart: Enabled
Show SELL Signals: Enabled
Show BUY Signals: Enabled
Alert Distance: 1 bar
Min Bars for Reversal: 1 bar
Alert Zone Margin: 0.01 (1%)
Retest Search Period: 20 bars
Min Bars Between Retests: 5 bars
Show Only Retests: Disabled
Alert Settings
Enable Trading Notifications: Enabled
VAH Reversal Alert: Enabled (SELL signals)
VAL Reversal Alert: Enabled (BUY signals)
Time Filter Settings
Filter Alerts By Time: Optional (exclude specific hours)
⚠️ IMPORTANT WARNINGS & LIMITATIONS
1. Repainting Behavior
CRITICAL: This indicator uses lookahead=barmerge.lookahead_on to access higher timeframe data immediately for FVG detection. This is necessary to provide real-time FVG zone visualization but has the following implications:
FVG zones may shift slightly until the higher timeframe candle closes
FVG detection signals are preliminary until HTF bar confirmation
The main trading signals (triangles) appear on confirmed bars and do not repaint
Best Practice: Always wait for the current timeframe bar to close before acting on signals. The filter status and FVG zones are informational but may adjust as new data arrives.
2. Minimum Timeframe
Do NOT use on timeframes below 5 minutes (M5)
Recommended: M5, M15, M30 for intraday trading
Higher timeframes (H1, H4) can also be used but will generate fewer signals
3. Multiple Filters Can Block Signals
By design, this indicator is conservative. When all filters are enabled:
Signals appear ONLY when all conditions align
You may see extended periods with no signals
This is intentional to reduce false positives
If you see no signals:
Check the Info Panel to see which filters are failing
Consider adjusting FVG lookback period
Temporarily disable FVG filter to test
Verify VWAP filters match current market trend
4. Market Profile Limitations
Market Profile requires sufficient volume data
Low-volume instruments may produce unreliable profiles
Value Areas update only on higher timeframe bar close
Works best on liquid markets (major forex pairs, indices, crypto)
📖 HOW TO USE
Step 1: Add to Chart
Apply indicator to M5 or higher timeframe chart
Ensure chart shows volume data
Use standard candles (NOT Heikin Ashi, Renko, etc.)
Step 2: Configure Settings
Primary MP Filter TF: Set to 60 (1 hour) minimum, or 240 (4 hour) for swing trading
Main MP TF: Set to 60 (1 hour) for intraday signals
FVG Timeframe: Match or exceed main MP timeframe
Leave other settings at default initially
Step 3: Understand the Info Panel
Monitor the top-right panel:
FILTER STATUS: Shows current directional bias
NEUTRAL = Both signals allowed
BUY ONLY = Only green triangles will appear
SELL ONLY = Only red triangles will appear
FVG Filter: Shows if bullish/bearish gaps detected recently
VWAP positions: Confirms trend alignment
Step 4: Take Signals
For BUY Signal (Green Triangle ▲):
Wait for green triangle to appear
Check Info Panel shows ✓ for BUY signals
Confirm current bar has closed
Enter long position
Stop loss: Below recent VAL or swing low
Target: Previous Value Area High or 1.5-2× risk
For SELL Signal (Red Triangle ▼):
Wait for red triangle to appear
Check Info Panel shows ✓ for SELL signals
Confirm current bar has closed
Enter short position
Stop loss: Above recent VAH or swing high
Target: Previous Value Area Low or 1.5-2× risk
Step 5: Risk Management
Risk per trade: Maximum 1-2% of account equity
Position sizing: Adjust based on stop loss distance
Avoid trading: During major news events or time filter periods
Multiple confirmations: Look for confluence with price action (support/resistance, trendlines)
🎓 UNDERLYING CONCEPTS
Market Profile Theory
Developed by J. Peter Steidlmayer in the 1980s, Market Profile organizes price and volume data to identify:
Value Areas: Where 70% of trading activity occurred
POC: Price level with highest acceptance (most volume)
Imbalances: When price moves away from value quickly
This indicator uses TPO (Time Price Opportunity) calculation method to build the volume profile distribution.
VWAP (Volume Weighted Average Price)
VWAP represents the average price weighted by volume, showing where institutional traders are positioned:
Price above VWAP = Bullish (institutions accumulated lower)
Price below VWAP = Bearish (institutions distributed higher)
Using dual VWAP (Session + Week) creates multi-timeframe trend alignment.
Fair Value Gaps (FVG)
Also known as "imbalance" or "inefficiency," FVG occurs when:
Price moves so rapidly that a gap forms in the candlestick structure
Indicates institutional order flow (large market orders)
Price often returns to "fill" these gaps (rebalance)
The 3-candle FVG pattern (gap between candle and candle ) is widely used in ICT (Inner Circle Trader) methodology and Smart Money Concepts.
🔍 CREDITS & CODE ATTRIBUTION
This indicator builds upon established technical analysis concepts and combines multiple methodologies:
1. Market Profile / TPO Calculation
Concept Origin: J. Peter Steidlmayer (Chicago Board of Trade, 1980s)
Code Inspiration: TradingView's public domain Market Profile examples
Modifications: Custom filtering logic for directional bias, dual timeframe implementation
2. VWAP Calculation
Concept Origin: Standard financial instrument (widely used since 1980s)
Code Base: TradingView built-in ta.vwap() function (public domain)
Modifications: Dual VWAP system with independent anchor periods, custom filtering modes
3. Fair Value Gap Detection
Concept Origin: Inner Circle Trader (ICT) / Smart Money Concepts methodology
Code Implementation: Original implementation based on 3-candle gap pattern
Features: Multi-timeframe detection, automatic mitigation tracking, visual zone display
4. Pine Script Framework
Language: Pine Script v6 (TradingView)
Built-in Functions Used:
ta.vwap() - Volume weighted average price
request.security() - Higher timeframe data access
ta.change() - Period detection
ta.cum() - Cumulative volume
time() - Timestamp functions
Note: All code is original implementation. While concepts are based on established trading methodologies, the combination, filtering logic, and execution are unique to this indicator.
📊 RECOMMENDED INSTRUMENTS
Best Performance:
Major Forex Pairs (EURUSD, GBPUSD, USDJPY)
Stock Indices (ES, NQ, SPX, DAX)
Major Cryptocurrencies (BTCUSD, ETHUSD)
Liquid Stocks (high daily volume)
Avoid:
Low-volume altcoins
Illiquid stocks
Exotic forex pairs with wide spreads
⚡ PERFORMANCE TIPS
Start Conservative: Enable all filters initially
Reduce Filters Gradually: If too few signals, disable Secondary VWAP filter first
Match Timeframes: Keep MP Filter TF and FVG TF at same value
Backtest First: Review historical performance on your preferred instrument/timeframe
Combine with Price Action: Look for support/resistance confluence
Use Time Filter: Avoid low-liquidity hours (optional setting)
🚫 WHAT THIS INDICATOR DOES NOT DO
Does not guarantee profits - No trading system is 100% accurate
Does not predict the future - Based on historical patterns
Does not replace risk management - Always use stop losses
Does not work on all instruments - Requires volume data and liquidity
Does not provide exact entry/exit prices - Signals are zones, not precise levels
Does not account for fundamentals - Purely technical analysis
📜 DISCLAIMER
This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results.
Trading Risk Warning:
All trading involves risk of loss
You can lose more than your initial investment (leverage products)
Only trade with capital you can afford to lose
Always use appropriate position sizing and risk management
Consider seeking advice from a licensed financial advisor
Technical Limitations:
Indicator may repaint FVG zones until HTF bar closes
Signals are based on historical patterns that may not repeat
Market conditions change and no system works in all environments
Volume data quality varies by exchange/broker
By using this indicator, you acknowledge these risks and agree that the author bears no responsibility for trading losses.
📞 SUPPORT & UPDATES
Questions? Comment on this publication
Issues? Describe the problem with chart screenshot
Feature Requests? Suggest improvements in comments
Updates: Will be published as new versions using TradingView's update feature
📝 VERSION HISTORY
Version 1.0 (Current)
Initial public release
Multi-filter system: MP + Dual VWAP + FVG
Directional bias filter
Real-time info panel
Comprehensive alert system
Time-based filtering
Thank you for using Smart VWAP FVG System!
Happy Trading! 📈
Range Trading StrategyOVERVIEW
The Range Trading Strategy is a systematic trading approach that identifies price ranges
from higher timeframe candles or trading sessions, tracks pivot points, and generates
trading signals when range extremes are mitigated and confirmed by pivot levels.
CORE CONCEPT
The strategy is based on the principle that when a candle (or session) closes within the
range of the previous candle (or session), that previous candle becomes a "range" with
identifiable high and low extremes. When price breaks through these extremes, it creates
trading opportunities that are confirmed by pivot levels.
RANGE DETECTION MODES
1. HTF (Higher Timeframe) Mode:
Automatically selects a higher timeframe based on the current chart timeframe
Uses request.security() to fetch HTF candle data
Range is created when an HTF candle closes within the previous HTF candle's range
The previous HTF candle's high and low become the range extremes
2. Sessions Mode:
- Divides the trading day into 4 sessions (UTC):
* Session 1: 00:00 - 06:00 (6 hours)
* Session 2: 06:00 - 12:00 (6 hours)
* Session 3: 12:00 - 20:00 (8 hours)
* Session 4: 20:00 - 00:00 (4 hours, spans midnight)
- Tracks high, low, and close for each session
- Range is created when a session closes within the previous session's range
- The previous session's high and low become the range extremes
PIVOT DETECTION
Pivots are detected based on candle color changes (bullish/bearish transitions):
1. Pivot Low:
Created when a bullish candle appears after a bearish candle
Pivot low = minimum of the current candle's low and previous candle's low
The pivot bar is the actual bar where the low was formed (current or previous bar)
2. Pivot High:
Created when a bearish candle appears after a bullish candle
Pivot high = maximum of the current candle's high and previous candle's high
The pivot bar is the actual bar where the high was formed (current or previous bar)
IMPORTANT: There is always only ONE active pivot high and ONE active pivot low at any
given time. When a new pivot is created, it replaces the previous one.
RANGE CREATION
A range is created when:
(HTF Mode) An HTF candle closes within the previous HTF candle's range AND a new HTF
candle has just started
(Sessions Mode) A session closes within the previous session's range AND a new session
has just started
Or Range Can Be Created when the Extreme of Another Range Gets Mitigated and We Have a Pivot low Just Above the Range Low or Pivot High just Below the Range High
Range Properties:
rangeHigh: The high extreme of the range
rangeLow: The low extreme of the range
highStartTime: The timestamp when the range high was actually formed (found by looping
backwards through bars)
lowStartTime: The timestamp when the range low was actually formed (found by looping
backwards through bars)
highMitigated / lowMitigated: Flags tracking whether each extreme has been broken
isSpecial: Flag indicating if this is a "special range" (see Special Ranges section)
RANGE MITIGATION
A range extreme is considered "mitigated" when price interacts with it:
High is mitigated when: high >= rangeHigh (any interaction at or above the level)
Low is mitigated when: low <= rangeLow (any interaction at or below the level)
Mitigation can happen:
At the moment of range creation (if price is already beyond the extreme)
At any point after range creation when price touches the extreme
SIGNAL GENERATION
1. Pending Signals:
When a range extreme is mitigated, a pending signal is created:
a) BEARISH Pending Signal:
- Triggered when: rangeHigh is mitigated
- Confirmation Level: Current pivotLow
- Signal is confirmed when: close < pivotLow
- Stop Loss: Current pivotHigh (at time of confirmation)
- Entry: Short position
Signal Confirmation
b) BULLISH Pending Signal:
- Triggered when: rangeLow is mitigated
- Confirmation Level: Current pivotHigh
- Signal is confirmed when: close > pivotHigh
- Stop Loss: Current pivotLow (at time of confirmation)
- Entry: Long position
IMPORTANT: There is only ever ONE pending bearish signal and ONE pending bullish signal
at any given time. When a new pending signal is created, it replaces the previous one
of the same type.
2. Signal Confirmation:
- Bearish: Confirmed when price closes below the pivot low (confirmation level)
- Bullish: Confirmed when price closes above the pivot high (confirmation level)
- Upon confirmation, a trade is entered immediately
- The confirmation line is drawn from the pivot bar to the confirmation bar
TRADE EXECUTION
When a signal is confirmed:
1. Position Management:
- Any existing position in the opposite direction is closed first
- Then the new position is entered
2. Stop Loss:
- Bearish (Short): Stop at pivotHigh
- Bullish (Long): Stop at pivotLow
3. Take Profit:
- Calculated using Risk:Reward Ratio (default 2:1)
- Risk = Distance from entry to stop loss
- Target = Entry ± (Risk × R:R Ratio)
- Can be disabled with "Stop Loss Only" toggle
4. Trade Comments:
- "Range Bear" for short trades
- "Range Bull" for long trades
SPECIAL RANGES
Special ranges are created when:
- A range high is mitigated AND the current pivotHigh is below the range high
- A range low is mitigated AND the current pivotLow is above the range low
In these cases:
- The pivot value is stored in an array (storedPivotHighs or storedPivotLows)
- A "special range" is created with only ONE extreme:
* If pivotHigh < rangeHigh: Creates a range with rangeHigh = pivotLow, rangeLow = na
* If pivotLow > rangeLow: Creates a range with rangeLow = pivotHigh, rangeHigh = na
- Special ranges can generate signals just like normal ranges
- If a special range is mitigated on the creation bar or the next bar, it is removed
entirely without generating signals (prevents false signals)
Special Ranges
REVERSE ON STOP LOSS
When enabled, if a stop loss is hit, the strategy automatically opens a trade in the
opposite direction:
1. Long Stop Loss Hit:
- Detects when: position_size > 0 AND position_size <= 0 AND low <= longStopLoss
- Action: Opens a SHORT position
- Stop Loss: Current pivotHigh
- Trade Comment: "Reverse on Stop"
2. Short Stop Loss Hit:
- Detects when: position_size < 0 AND position_size >= 0 AND high >= shortStopLoss
- Action: Opens a LONG position
- Stop Loss: Current pivotLow
- Trade Comment: "Reverse on Stop"
The reverse trade uses the same R:R ratio and respects the "Stop Loss Only" setting.
VISUAL ELEMENTS
1. Range Lines:
- Drawn from the time when the extreme was formed to the mitigation point (or current
time if not mitigated)
- High lines: Blue (or mitigated color if mitigated)
- Low lines: Red (or mitigated color if mitigated)
- Style: SOLID
- Width: 1
2. Confirmation Lines:
- Drawn when a signal is confirmed
- Extends from the pivot bar to the confirmation bar
- Bearish: Red, solid line
- Bullish: Green, solid line
- Width: 1
- Can be toggled on/off
STRATEGY SETTINGS
1. Range Detection Mode:
- HTF: Uses higher timeframe candles
- Sessions: Uses trading session boundaries
2. Auto HTF:
- Automatically selects HTF based on current chart timeframe
- Can be disabled to use manual HTF selection
3. Risk:Reward Ratio:
- Default: 2.0 (2:1)
- Minimum: 0.5
- Step: 0.5
4. Stop Loss Only:
- When enabled: Trades only have stop loss (no take profit)
- Trades close on stop loss or when opposite signal confirms
5. Reverse on Stop Loss:
- When enabled: Hitting a stop loss opens opposite trade with stop at opposing pivot
6. Max Ranges to Display:
- Limits the number of ranges kept in memory
- Oldest ranges are purged when limit is exceeded
KEY FEATURES
1. Dynamic Pivot Tracking:
- Pivots update on every candle color change
- Always maintains one high and one low pivot
2. Range Lifecycle:
- Ranges are created when price closes within previous range
- Ranges are tracked until mitigated
- Mitigation creates pending signals
- Signals are confirmed by pivot levels
3. Signal Priority:
- Only one pending signal of each type at a time
- New signals replace old ones
- Confirmation happens on close of bar
4. Position Management:
- Closes opposite positions before entering new trades
- Tracks stop loss levels for reverse functionality
- Respects pyramiding = 1 (only one position per direction)
5. Time-Based Drawing:
- Uses time coordinates instead of bar indices for line drawing
- Prevents "too far from current bar" errors
- Lines can extend to any historical point
USAGE NOTES
- Best suited for trending and ranging markets
- Works on any timeframe, but HTF mode adapts automatically
- Sessions mode is ideal for intraday trading
- Pivot detection requires clear candle color changes
- Range detection requires price to close within previous range
- Signals are generated on bar close, not intra-bar
The strategy combines range identification, pivot tracking, and signal confirmation to
create a systematic approach to trading breakouts and reversals based on price structure, past performance does not in any way predict future performance
TheStrat: Failed 2 + 2 ConfirmationTheStrat: Failed-2 + 2 Confirmation (2-2 option)
This indicator spots a classic Strat “failed 2 → 2” sequence on consecutive bars, and fires alerts only when both conditions are met:
Failed-2 bar on the prior candle
Failed 2-Down (F2D): Took the prior low but closed green
Failed 2-Up (F2U): Took the prior high but closed red
By default this is strict (excludes outside bars/3’s). You can toggle to allow 3’s if you prefer a looser definition.
Current bar is a clean “2”
2U = broke prior high only; 2D = broke prior low only (excludes 3’s).
Optional: Require flip so the second bar must reverse direction (F2D→2U for longs, F2U→2D for shorts). Turn this off if you want any 2 after a Failed-2.
What gets plotted
Tiny markers on each Failed-2 as they occur (F2U/F2D).
A label when the combo confirms on the next bar:
Bullish: F2D→2U (or F2D→2 if flip is off) below the bar
Bearish: F2U→2D (or F2U→2) above the bar
Alerts (set from the “Add Alert” dialog)
Failed2 + 2 (Bull): Prior bar was F2D; current bar is a 2 (flip optional).
Failed2 + 2 (Bear): Prior bar was F2U; current bar is a 2 (flip optional).
Inputs
Colors for Failed-2 markers and combo labels
Require flip (on by default) → focuses on true 2-2 reversals
Count outside bars (3) as Failed-2? (off by default) → stricter = fewer, cleaner signals
How to trade it (typical Strat style)
Bullish combo (F2D→2U): Entry on break of the combo bar’s high; risk under its low (or under the prior bar).
Bearish combo (F2U→2D): Entry on break of the combo bar’s low; risk above its high.
First target = prior pivot (“magnitude”), then nearby pivots/BF levels. Improve odds by aligning with time-frame continuity (e.g., day/week in your direction).
Notes / Best practices
Uses barstate.isconfirmed → signals/labels are based on closed bars to avoid repaint.
If you see too many Failed-2 tags, keep outside-bar counting OFF (strict mode).
Multi-Timeframe SFP (Swing Failure Pattern)How to Use
1. Set Pivot Timeframe: Choose the timeframe for identifying major swing points (e.g., 'D' for Daily pivots).
2. Set SFP Timeframe: Choose the timeframe to find the SFP candle (e.g., '240' for the 4-Hour chart).
3. Set Confirmation Bars: Set how many SFP Timeframe bars must pass without invalidating the level. A value of '0' confirms immediately on the SFP bar's close. A value of '1' waits for one more bar to close.
4. Adjust Filters (Optional): Enable the 'Wick % Filter' to add a quality check for strong rejections.
5. Watch & Wait: The indicator will draw lines and labels and fire alerts for fully confirmed signals.
In-Depth Explanation
1. Overview
The Dynamic Pivot SFP Engine is a multi-timeframe tool designed to identify and validate Swing Failure Patterns (SFPs) at significant price levels.
An SFP is a common price action pattern where price briefly trades beyond a previous swing high or low (sweeping liquidity) but then fails to hold those new prices, closing back inside the previous range. This "failure" often signals a reversal.
This indicator enhances SFP detection by separating the Pivot (Liquidity) from the SFP (Rejection), allowing you to monitor them on different timeframes.
2. The Core Multi-Timeframe Logic
The indicator's power comes from two key inputs:
• Pivot Timeframe (Pivot Timeframe)
This is the "high timeframe" used to establish significant support and resistance levels. The script finds standard pivots (swing highs and lows) on this timeframe based on the Pivot Left Strength and Pivot Right Strength inputs. These pivots are the "liquidity" levels the SFP will target. The Pivot Lookback input controls how long (in Pivot Timeframe bars) a pivot remains active and monitored.
• SFP Timeframe (SFP Timeframe)
This is the "execution timeframe" where the script looks for the actual SFP. On every new bar of this timeframe, the script checks if price has swept and rejected any of the active pivots.
Example Setup:
You might set Pivot Timeframe to 'D' (Daily) to find major daily swing points. You then set SFP Timeframe to '240' (4-Hour) to find a 4-hour candle that sweeps a daily pivot and closes back below/above it.
3. The SFP Confirmation Process
An SFP is not confirmed instantly. It must pass a rigorous, multi-step validation process.
Step 1: The SFP Candle (The Sweep)
A potential SFP is identified when an SFP Timeframe bar does the following:
• Bearish SFP: The bar's high trades above an active pivot high, but the bar closes below that same pivot high.
• Bullish SFP: The bar's low trades below an active pivot low, but the bar closes above that same pivot low.
Step 2: The Wick Filter (Optional Quality Check)
If Enable Wick % Filter is checked, the SFP candle from Step 1 is also measured.
• For a bearish SFP, the upper wick (from the high to the open/close) must be at least Min. Wick % of the entire candle's range (high-to-low).
• For a bullish SFP, the lower wick (from the low to the open/close) must meet the same percentage requirement.
If the SFP candle fails this test, it is discarded, even if it met the sweep/close criteria.
Step 3: The Validation Window (The Confirmation)
This is the most critical feature, controlled by Confirmation Bars.
• If Confirmation Bars = 0: The SFP is confirmed immediately on the SFP candle's close (assuming it passed the optional wick check). The label, line, and alert are triggered at this moment.
• If Confirmation Bars > 0: The SFP enters a "pending" state. The script will wait for $N$ more SFP Timeframe bars to close.
o Invalidation: If, during this waiting period, any bar closes back across the pivot (e.g., a close above the pivot for a bearish SFP), the SFP is considered failed and invalidated. All pending plots are deleted.
o Confirmation: If the $N$ confirmation bars all complete without invalidating the level, the SFP is finally confirmed. The label, line, and alert are only triggered after this entire process is complete. This adds a significant layer of robustness, ensuring the rejection holds for a period of time.
4. Visuals & Alerts
• Lines: A horizontal line is drawn from the original pivot to the SFP bar, showing which level was targeted. Note: These lines will only be drawn on chart timeframes equal to or lower than the 'SFP Timeframe'.
• Labels: A label is placed at the SFP's extreme (the high/low of the SFP bar). The label text conveniently includes the Ticker, Pivot TF, SFP TF, and Confirmation bar settings (e.g., "Bearish SFP BTCUSD / Pivot: 1D / SFP: 4H | Conf: 1").
• MTF Boxes (Show SFP Box, Show Conf. Boxes): These boxes highlight the SFP and confirmation bars. Crucially, they are only visible when your chart timeframe is lower than the SFP Timeframe. For example, if your SFP Timeframe is '240' (4H), you will only see these boxes on the 1H, 15M, 5M, etc., charts. This allows you to see the higher-timeframe SFP unfolding on your lower-timeframe chart.
• Alerts (Enable Alerts): An alert is fired only when an SFP is fully confirmed (i.e., after the Confirmation Bars have passed successfully). For efficient, real-time monitoring, it is highly recommended to run this indicator server-side by creating an alert on TradingView set to trigger on "Any alert() function call".
ALISH WEEK LABELS THE ALISH WEEK LABELS
Overview
This indicator programmatically delineates each trading week and encapsulates its realized price range in a live-updating, filled rectangle. A week is defined in America/Toronto time from Monday 00:00 to Friday 16:00. Weekly market open to market close, For every week, the script draws:
a vertical start line at the first bar of Monday 00:00,
a vertical end line at the first bar at/after Friday 16:00, and
a white, semi-transparent box whose top tracks the highest price and whose bottom tracks the lowest price observed between those two temporal boundaries.
The drawing is timeframe-agnostic (M1 → 1D): the box expands in real time while the week is open and freezes at the close boundary.
Time Reference and Session Boundaries
All scheduling decisions are computed with time functions called using the fixed timezone string "America/Toronto", ensuring correct behavior across DST transitions without relying on chart timezone. The start condition is met at the first bar where (dayofweek == Monday && hour == 0 && minute == 0); on higher timeframes where an exact 00:00 bar may not exist, a fallback checks for the first Monday bar using ta.change(dayofweek). The close condition is met on the first bar at or after Friday 16:00 (Toronto), which guarantees deterministic closure on intraday and higher timeframes.
State Model
The indicator maintains minimal persistent state using var globals:
week_open (bool): whether the current weekly session is active.
wk_hi / wk_lo (float): rolling extrema for the active week.
wk_box (box): the graphical rectangle spanning × .
wk_start_line and a transient wk_end_line (line): vertical delimiters at the week’s start and end.
Two dynamic arrays (boxes, vlines) store object handles to support bounded history and deterministic garbage collection.
Update Cycle (Per Bar)
On each bar the script executes the following pipeline:
Start Check: If no week is open and the start condition is satisfied, instantiate wk_box anchored at the current bar_index, prime wk_hi/wk_lo with the bar’s high/low, create the start line, and push both handles to their arrays.
Accrual (while week_open): Update wk_hi/wk_lo using math.max/min with current bar extremes. Propagate those values to the active wk_box via box.set_top/bottom and slide box.set_right to the current bar_index to keep the box flush with live price.
Close Check: If at/after Friday 16:00, finalize the week by freezing the right edge (box.set_right), drawing the end line, pushing its handle, and flipping week_open false.
Retention Pruning: Enforce a hard cap on historical elements by deleting the oldest objects when counts exceed configured limits.
Drawing Semantics
The range container is a filled white rectangle (bgcolor = color.new(color.white, 100 − opacity)), with a solid white border for clear contrast on dark or light themes. Start/end boundaries are full-height vertical white lines (y1=+1e10, y2=−1e10) to guarantee visibility across auto-scaled y-axes. This approach avoids reliance on price-dependent anchors for the lines and is robust to large volatility spikes.
Multi-Timeframe Behavior
Because session logic is driven by wall-clock time in the Toronto zone, the indicator remains consistent across chart resolutions. On coarse timeframes where an exact boundary bar might not exist, the script legally approximates by triggering on the first available bar within or immediately after the boundary (e.g., Friday 16:00 occurs between two 4-hour bars). The box therefore represents the true realized high/low of the bars present in that timeframe, which is the correct visual for that resolution.
Inputs and Defaults
Weeks to keep (show_weeks_back): integer, default 40. Controls retention of historical boxes/lines to avoid UI clutter and resource overhead.
Fill opacity (fill_opacity): integer 0–100, default 88. Controls how solid the white fill appears; border color is fixed pure white for crisp edges.
Time zone is intentionally fixed to "America/Toronto" to match the strategy definition and maintain consistent historical backtesting.
Performance and Limits
Objects are reused only within a week; upon closure, handles are stored and later purged when history limits are exceeded. The script sets generous but safe caps (max_boxes_count/max_lines_count) to accommodate 40 weeks while preserving Editor constraints. Per-bar work is O(1), and pruning loops are bounded by the configured history length, keeping runtime predictable on long histories.
Edge Cases and Guarantees
DST Transitions: Using a fixed IANA time zone ensures Friday 16:00 and Monday 00:00 boundaries shift correctly when DST changes in Toronto.
Weekend Gaps/Holidays: If the market lacks bars exactly at boundaries, the nearest subsequent bar triggers the start/close logic; range statistics still reflect observed prices.
Live vs Historical: During live sessions the box edge advances every bar; when replaying history or backtesting, the same rules apply deterministically.
Scope (Intentional Simplicity)
This tool is strictly a visual framing indicator. It does not compute labels, statistics, alerts, or extended S/R projections. Its single responsibility is to clearly present the week’s realized range in the Toronto session window so you can layer your own execution or analytics on top.
🚀 Ultimate Trading Tool + Strat Method🚀 Ultimate Trading Tool + Strat Method - Complete Breakdown
Let me give you a comprehensive overview of this powerful indicator!
🎯 What This Indicator Does:
This is a professional-grade, all-in-one trading system that combines two proven methodologies:
1️⃣ Technical Analysis System (Original)
Advanced trend detection using multiple EMAs
Momentum analysis with MACD
RSI multi-timeframe analysis
Volume surge detection
Automated trendline drawing
2️⃣ Strat Method (Pattern Recognition)
Inside bars, outside bars, directional bars
Classic patterns: 2-2, 1-2-2
Advanced patterns: 3-1-2, 2-1-2, F2→3
Timeframe continuity filters
📊 How It Generates Signals:
Technical Analysis Signals (Green/Red Triangles):
Buy Signal Triggers When:
✅ Price above EMA 21 & 50 (uptrend)
✅ MACD histogram rising (momentum)
✅ RSI between 30-70 (not overbought/oversold)
✅ Volume surge above 20-period average
✅ Price breaks above resistance trendline
Scoring System:
Trend alignment: +1 point
Momentum: +1 point
RSI favorable: +1 point
Trendline breakout: +2 points
Minimum score required based on sensitivity setting
Strat Method Signals (Blue/Orange Labels):
Pattern Recognition:
2-2 Setup: Down bar → Up bar (or reverse)
1-2-2 Setup: Inside bar → Down bar → Up bar
3-1-2 Setup: Outside bar → Inside bar → Up bar
2-1-2 Setup: Down bar → Inside bar → Up bar
F2→3 Setup: Failed directional bar becomes outside bar
Confirmation Required:
Must break previous bar's high (buy) or low (sell)
Optional timeframe continuity (daily & weekly aligned)
💰 Risk Management Features:
Dynamic Stop Loss & Take Profit:
ATR-Based: Adapts to market volatility
Stop Loss: Entry - (ATR × 1.5) by default
Take Profit: Entry + (ATR × 3.0) by default
Risk:Reward: Customizable 1:2 to 1:5 ratios
Visual Risk Zones:
Colored boxes show risk/reward area
Dark, bold lines for easy identification
Clear entry, stop, and target levels
🎨 What You See On Screen:
Main Signals:
🟢 Green Triangle "BUY" - Technical analysis long signal
🔴 Red Triangle "SELL" - Technical analysis short signal
🎯 Blue Label "STRAT" - Strat method long signal
🎯 Orange Label "STRAT" - Strat method short signal
Trendlines:
Green lines - Support trendlines (bullish)
Red lines - Resistance trendlines (bearish)
Automatically drawn from pivot points
Extended forward to predict future levels
Stop/Target Levels:
Bold crosses at stop loss levels (red color)
Bold crosses at take profit levels (green color)
Line width = 3 for maximum visibility
Trade Zones:
Light green boxes - Long trade risk/reward zone
Light red boxes - Short trade risk/reward zone
Shows potential profit vs risk visually
📊 Information Dashboard (Top Right):
Shows real-time market conditions:
Main Signal: Current technical signal status
Strat Method: Active Strat pattern
Trend: Bullish/Bearish/Neutral
Momentum: Strong/Weak based on MACD
Volume: High/Normal compared to average
TF Continuity: Daily/Weekly alignment
RSI: Current RSI value with color coding
Support/Resistance: Current trendline levels
🔔 Alert System:
Entry Alerts:
Technical Signals:
🚀 BUY SIGNAL TRIGGERED!
Type: Technical Analysis
Entry: 45.23
Stop: 43.87
Target: 48.95
```
**Strat Signals:**
```
🎯 STRAT BUY TRIGGER!
Pattern: 3-1-2
Entry: 45.23
Trigger Level: 44.56
Exit Alerts:
Target hit notifications
Stop loss hit warnings
Helps maintain discipline
⚙️ Customization Options:
Signal Settings:
Sensitivity: High/Medium/Low (controls how many signals)
Volume Filter: Require volume surge or not
Momentum Filter: Require momentum confirmation
Strat Settings:
TF Continuity: Require daily/weekly alignment
Pattern Selection: Enable/disable specific patterns
Confirmation Mode: Show only confirmed triggers
Risk Settings:
ATR Multiplier: Adjust stop/target distance
Risk:Reward: Set preferred ratio
Visual Elements: Show/hide any component
Visual Settings:
Colors: Customize all signal colors
Display Options: Toggle signals, levels, zones
Trendline Length: Adjust pivot detection period
🎯 Best Use Cases:
Day Trading:
Use low sensitivity setting
Enable all Strat patterns
Watch for high volume signals
Quick in/out trades
Swing Trading:
Use medium sensitivity
Require timeframe continuity
Focus on trendline breakouts
Hold for target levels
Position Trading:
Use high sensitivity (fewer signals)
Require strong momentum
Focus on weekly/daily alignment
Larger ATR multipliers
💡 Trading Strategy Tips:
High-Probability Setups:
Double Confirmation: Technical + Strat signal together
Trend Alignment: All timeframes agree
Volume Surge: Institutional participation
Trendline Break: Clear level breakout
Risk Management:
Always use stops - System provides them
Position sizing - Risk 1-2% per trade
Don't chase - Wait for signal confirmation
Take profits - System provides targets
What Makes Signals Strong:
✅ Both technical AND Strat signals fire together
✅ Timeframe continuity (daily & weekly aligned)
✅ Volume surge confirms institutional interest
✅ Multiple indicators align (trend + momentum + RSI)
✅ Clean trendline breakout with no resistance above (or support below)
⚠️ Common Mistakes to Avoid:
Don't ignore stops - System calculates them for a reason
Don't overtrade - Wait for quality setups
Don't disable volume filter - Unless you know what you're doing
Don't use max sensitivity - You'll get too many signals
Don't ignore timeframe continuity - It filters bad trades
🚀 Why This Indicator is Powerful:
Combines Multiple Edge Sources:
Technical analysis (trend, momentum, volume)
Pattern recognition (Strat method)
Risk management (dynamic stops/targets)
Market structure (trendlines, support/resistance)
Professional Features:
No repainting - signals are final when bar closes
Clear risk/reward before entry
Multiple confirmation layers
Adaptable to any market or timeframe
Beginner Friendly:
Clear visual signals
Automatic calculations
Built-in risk management
Comprehensive dashboard
This indicator essentially gives you everything a professional trader uses - trend analysis, momentum, patterns, volume, risk management - all in one clean package!
Any specific aspect you'd like me to explain in more detail? 🎯RetryClaude can make mistakes. Please double-check responses. Sonnet 4.5
