Slope VortexI stumbled upon creating this and thought it was cool and worth sharing. If anyone has ever dabbled with the Aroon or Vortex Indicators, there is some similarity to how it can be used visually.
The indicator basically takes two slopes from the trailing time period you indicate- one from the highest point in that time period, and one from the lowest point. It then plots both.
Here are some of the ways I think this indicator can be used--
Crossover This is the most obvious way, visually. If the blue line crosses under the red, an uptrend has ended or is testing support here. The opposite can be used as well. When the blue line crosses above the red an uptrend is starting or it is testing resistance.
Trending or Ranging If the two bands are moving away from each other/increasing the width between them, there is definite trending action going on. If the two are very narrow or keep crossing each other, whatever symbol you're looking at is likely ranging or consolidating. The black "midpoint" line can also be used to help identify-- if this line is moving up, regardless of if the blue or red band is above, the trend has momentum; if it is going down or flat the previous trend is either slowing greatly or we are ranging.
Support & Resistance Crosses can identify meaningful support and resistance. Having both lines kiss or get very close to the black midpoint line but not cross can also indicate S/R or confirm trend strength.
Here are some snippets of examples I outlined-
I purposely kept this indicator as clean and simple as possible for publication. But I already have tinkered around with taking the output and putting it through the likes of RSI, Stoch, etc. and I think the outcomes are pretty intriguing as well for something so simple. I think dropping the length too much makes it too noisy, so 20, 50, 100 look most useful.
"ha溢价率" için komut dosyalarını ara
Black-Scholes Options Pricing ModelThis is an updated version of my "Black-Scholes Model and Greeks for European Options" indicator, that i previously published. I decided to make this updated version open-source, so people can tweak and improve it.
The Black-Scholes model is a mathematical model used for pricing options. From this model you can derive the theoretical fair value of an options contract. Additionally, you can derive various risk parameters called Greeks. This indicator includes three types of data: Theoretical Option Price (blue), the Greeks (green), and implied volatility (red); their values are presented in that order.
1) Theoretical Option Price:
This first value gives only the theoretical fair value of an option with a given strike based on the Black-Scholes framework. Remember this is a model and does not reflect actual option prices, just the theoretical price based on the Black-Scholes model and its parameters and assumptions.
2)Greeks (all of the Greeks included in this indicator are listed below):
a)Delta is the rate of change of the theoretical option price with respect to the change in the underlying's price. This can also be used to approximate the probability of your option expiring in the money. For example, if you have an option with a delta of 0.62, then it has about a 62% chance of expiring in-the-money. This number runs from 0 to 1 for Calls, and 0 to -1 for Puts.
b)Gamma is the rate of change of delta with respect to the change in the underlying's price.
c)Theta, aka "time decay", is the rate of change in the theoretical option price with respect to the change in time. Theta tells you how much an option will lose its value day by day.
d) Vega is the rate of change in the theoretical option price with respect to change in implied volatility .
e)Rho is the rate of change in the theoretical option price with respect to change in the risk-free rate. Rho is rarely used because it is the parameter that options are least effected by, it is more useful for longer term options, like LEAPs.
f)Vanna is the sensitivity of delta to changes in implied volatility . Vanna is useful for checking the effectiveness of delta-hedged and vega-hedged portfolios.
g)Charm, aka "delta decay", is the instantaneous rate of change of delta over time. Charm is useful for monitoring delta-hedged positions.
h)Vomma measures the sensitivity of vega to changes in implied volatility .
i)Veta measures the rate of change in vega with respect to time.
j)Vera measures the rate of change of rho with respect to implied volatility .
k)Speed measures the rate of change in gamma with respect to changes in the underlying's price. Speed can be used when evaluating delta-hedged and gamma hedged portfolios.
l)Zomma measures the rate of change in gamma with respect to changes in implied volatility . Zomma can be used to evaluate the effectiveness of a gamma-hedged portfolio.
m)Color, aka "gamma decay", measures the rate of change of gamma over time. This can also be used to evaluate the effectiveness of a gamma-hedged portfolio.
n)Ultima measures the rate of change in vomma with respect to implied volatility .
o)Probability of Touch, is not a Greek, but a metric that I included, which tells you the probability of price touching your strike price before expiry.
3) Implied Volatility:
This is the market's forecast of future volatility . Implied volatility is directionless, it cannot be used to forecast future direction. All it tells you is the forecast for future volatility.
How to use this indicator:
1st. Input the strike price of your option. If you input a strike that is more than 3 standard deviations away from the current price, the model will return a value of n/a.
2nd. Input the current risk-free rate.(Including this is optional, because the risk-free rate is so small, you can just leave this number at zero.)
3rd. Input the time until expiry. You can enter this in terms of days, hours, and minutes.
4th.Input the chart time frame you are using in terms of minutes. For example if you're using the 1min time frame input 1, 4 hr time frame input 480, daily time frame input 1440, etc.
5th. Pick what style of option you want data for, European Vanilla or Binary.
6th. Pick what type of option you want data for, Long Call or Long Put.
7th . Finally, pick which Greek you want displayed from the drop-down list.
*Remember the Option price presented, and the Greeks presented, are theoretical in nature, and not based upon actual option prices. Also, remember the Black-Scholes model is just a model based upon various parameters, it is not an actual representation of reality, only a theoretical one.
*Note 1. If you choose binary, only data for Long Binary Calls will be presented. All of the Greeks for Long Binary Calls are available, except for rho and vera because they are negligible.
*Note 2. Unlike vanilla european options, the delta of a binary option cannot be used to approximate the probability of the option expiring in-the-money. For binary options, if you want to approximate the probability of the binary option expiring in-the-money, use the price. The price of a binary option can be used to approximate its probability of expiring in-the-money. So if a binary option has a price of $40, then it has approximately a 40% chance of expiring in-the-money.
*Note 3. As time goes on you will have to update the expiry, this model does not do that automatically. So for example, if you originally have an option with 30 days to expiry, tomorrow you would have to manually update that to 29 days, then the next day manually update the expiry to 28, and so on and so forth.
There are various formulas that you can use to calculate the Greeks. I specifically chose the formulations included in this indicator because the Greeks that it presents are the closest to actual options data. I compared the Greeks given by this indicator to brokerage option data on a variety of asset classes from equity index future options to FX options and more. Because the indicator does not use actual option prices, its Greeks do not match the brokerage data exactly, but are close enough.
I may try to make future updates that include data for Long Binary Puts, American Options, Asian Options, etc.
ATR _NormalizedThis script is good to use with Williams %R indicator, to find out when price has bottomed out.
ATR has to be over 90 and Williams %R ( lenght 52 ) has to be over 95 to find out level around which one is good to buy.
You can check back, to see that this worked very well over history. Best way to use this 2 indicators is with DCA ( dollar cost average ), as area where to buy can go a little bit down and up for as long as few months. So dont just jump in, use DCA .
Short-Term Ichimoku Kinko-hyo+This Ichimoku Kinko-Hyo is an indicator which has been changed for short-term trading and, It has a “target price theory(one of three theory of Ichimoku Kinko-Hyo) function.”
Also, In this indicator, It can be plotting the “Span model”, “Super Bollinger Bands” which has Invented by a Japanese currency dealer Toshihiko Masaki, And Moving Average.
In addition, you can select setting only “clouds” and “Lagging span” or displaying Default Ichimoku Kinko-Hyo.
This indicator is modified original Ichimoku Kinko-Hyo, but It made based on the true usage of Ichimoku Kinko-Hyo.
For the evidence, I referred to the book supervised by Ichimoku-Sanjin the third generation.
Describe below about features↓↓↓.
- 2nd Cloud to check relation two Lead Lines and Lagging span.
- Background-color for discovering “Three Roles Improvement (In Japanese: 三役好転)” and “Three Roles Reversal (In Japanese: 三役逆転)”.
- Signal of Crossing Base Line and Conversion Line.
- mode selection of Ichimoku Kinko Hyo.
- Calculation feature for Target Price theory.
- A switch to replace Base Line and Conversion Line with 3 Moving Average lines.
- And others...
Rate Of Change - Weekly SignalsRate of Change - Weekly Signals
This indicator gives a potential "buy signal" using Rate of Change of SPX and VIX together,
using the following criteria:
SPX Weekly ROC(10) has been BELOW -9 and now rises ABOVE -5
*PLUS*
VIX Weekly ROC(10) has been ABOVE +80 and now falls BELOW +10
The background will turn RED when ROC(SPX) is below -9 and ROC(VIX) is above +80.
The background will turn GREEN when ROC(SPX) is above -5 and ROC(VIX) is below +10.
So the potential "buy signal" is when you start to get GREEN BARS AFTER RED - usually with
some white/empty bars in between...but wait for the green. This indicates that the volatility
has settled down, and the market is starting to turn up.
This indicator gives excellent entry points, but be careful of the occasional false signals.
See Nov. 2001 and Nov. 2008, in both cases the market dropped another 25-30% before the final
bottom was formed. Always have an exit strategy, especially when buying in after a downtrend.
How I use this indicator, pretty much as shown in the preview. Weekly SPX as the main chart with
some medium/long moving averages to identify the trend, VIX added as a "Compare Symbol" in red,
and then the Weekly ROC signals below.
For the ROC graphs, you can show SPX+VIX together, SPX alone, or VIX alone. I prefer to display
them separately because they don't scale well together (VIX crowds out the SPX when it spikes).
Background color is still based on both SPX/VIX together, regardless of which graph is shown.
Note that there is no VIX data available on Trading View prior to 1990, so for those dates the
formula is using only ROC(SPX) and the assigned thresholds (-9 and -5, or whatever you choose).
(JS) Squeeze Pro OverlaysSo this was something I planned on doing in the future, I knew it would take some time to put together but here it is, the Squeeze Pro 2 Overlays.
On my original Squeeze Pro, I had made several overlay indicators to go along with it, this time my goal was to combine all that stuff into a single indicator and allow the user to turn on and off the specific features they'd prefer to use. The version illustrated in the preview has everything turned on. What is "everything"? Here's the breakdown...
First of all - the color schemes in the Squeeze Pro match the color schemes in the Overlays indicator, so you can match them up (Color Scheme 3 in example). There are 6 schemes, option 1 is the original Squeeze colors.
There's also an option to make the light squeeze black, rather than white. This is for people who aren't using Dark Mode. It will flip all white to black, to make your charts better to read!
So there are 4 main overlays that can be switched on and off with this indicator, they include;
1. Early Signal Candles
2. BBMA Basis Line
3. Bollinger Bands/Keltner Channel Breaches
4. Signal Arrows
Early Signal Candles
The Early Signal Candles have two parameters, the entry smoothing period and the exit smoothing period.
There is a different type of early entry signal for each type of squeeze.
Low Squeeze generates white dots on the highs of the candles.
Mid Squeeze generates a lime green candle (or purple candle in color scheme 3).
High Squeeze generates a bigger purple circle on the high of the candle.
These three signals are made to mimic the original Early In/Out Candles from John Carter and represent the same thing (they work the same way).
As for the early exit, that would be determined by the color of the candle vs the color of the squeeze, works the same way as the original as well.
BBMA Basis Line
The BBMA (Bollinger Bands Momentum Average) was a moving average I had made to use with the squeeze on the previous version.
It is the basis line of the BB and KC used to make up the Squeeze (a 20 SMA). There are 4 different colors to it on this version.
1. Orange - This means no squeeze.
2. White/Black - Low Squeeze
3. Red - Mid Squeeze
4. Yellow - High Squeeze
You'll also notice these colors are light and dark in different spots - this is a representation of whether the Bollinger Bands are expanding or contracting. Dark means expanding, light means contracting.
Bollinger Bands/Keltner Channel Breaches
This is a pretty simple feature. If there is an ongoing squeeze, and a candle closes above or below the Bollinger Bands or Keltner Channels, a circle appears at the top or the bottom of the chart telling you which way the channel has been breached.
Signal Arrows
This is what makes up most of the overlay indicator. If you turn it on, the default is set to work just like the original. There are lots of options with this though.
First, you can turn each type of Squeeze Arrow on or off by checking/unchecking the boxes for them.
Now allow me to explain the "Signal Length", as there are several options.
The default is "6 Dots", this generates a signal when a particular type of Squeeze reaches the 6th dot ("12 Dots" works the same way).
"End of Squeeze" generates a signal once a type of Squeeze has concluded.
"End of Early Signal" generates a signal when the early dots (or candle) finishes.
"Custom" allows you to select your own dot duration to produce a signal, you select that number in the field below.
The other portion of this is the "Signal Type", this is where you select how each signal is generated once the selected amount of time takes place.
The default is the same as the original "+/-", this generates a signal based on whether Squeeze momentum is positive or negative.
"Rising/Falling" will only generate a signal if the Squeeze momentum maintains consistently over the last 6 bars.
"Crossed Zero" only generates a signal if the Squeeze momentum crosses above or below the zero line.
"Basis Line Momentum" is based on the BBMA. A signal is generated based on whether the current candle closes above or below the basis line.
"Divergence" only generates a signal if there is a divergence signal present at the time of the signal.
"Current Momentum" generates a signal based simply on the current direction of Squeeze momentum.
"Sum of Change" generates a signal based on the sum of the change in the Squeeze momentum being positive (long) or negative (short) over the length of time you select in the "Sum of Change Length" field.
Then "Combo" tries to take a look at everything and generates a score based on these parameters. Positive score = long, negative = short.
I hope I gave a detailed enough explanation on how everything works, let me know if you have any questions! Hope you like it!
True Accumulation/DistributionAccumulation/Distribution is developed by Marc Chaikin to provide insight into strength of a trend by measuring flow of buy and sell volume.
The fact that A/D only factors current period's range for calculating the volume multiplier causes problem with price gaps. They are ignored or even misinterpreted.
True Accumulation/Distribution solves the problem by using True Range instead of only relying on current period's high and low.
In this example you can see when a gap has occurred in Amazon Inc.'s daily chart True A/D has handled it better than Accumulation/Distribution which a bearish close in period's range has caused it to misinterpret the strong buy pressure as sell volume.
ATR Percentage of PriceThis indicator takes the standard ATR and expresses it as a percentage of the OHLC4 price. This has the advantage of normalising the ATR value across the history of an asset. For example, an ATR of value 20 when the price is 2000 actually has a very different meaning when the price rises to 4000. The ATR may be the same value but actually the volatility it represents has halved.
I also add an SMA to the value and a histogram which shows the difference between the two. Positive values mean that volatility is expanding while negative values mean volatility is contracting.
SD-Break
The supply trend line and the demand trend line are used to judge the main trend trend, and the supply and demand trend line is used to judge the local supply and demand intensity. When the supply and demand channel is located under the supply and demand trend channel, it is a strong downward trend; when the supply and demand channel is located above the supply and demand trend channel, it is a strong upward trend.In an uptrend, when the candle chart shows a significant uprush and the closing price has not been able to break through the supply line since then, we think the uptrend will present a red flag.In a downtrend, when the candle chart has a significant downtrend rebound and the closing price has not been able to break the demand line since then, we believe that the downtrend will be reversed.
Uhl MA Crossover SystemToday proposed indicator is based on the corrected moving average, an indicator originally proposed by Andreas Uhl professor at Salzburg University. This moving average is not the most well known, which is a pity since its design is extremely elegant.
The corrected moving average (CMA) is an adaptive moving average based on exponential averaging and aim to correct common problems of classical moving averages such as crosses occurring during sideway markets, more details will be introduced in the calculation section. The CMA aim to act as a slow moving average in a moving average crossover system.
Here a new fast adaptive moving average named corrected trend step (CTS) based on the CMA is introduced in order to provide a full moving average crossover system based on A. Uhl design.
To Andreas Uhl
Calculation And Understanding The CTS
Even if the code is quite compact, the original idea behind the CMA can be blurry for some users, however it is actually relatively simple to understand. The CMA is based on exponential averaging and a smoothing variable is therefore required, in the CMA the calculation of the smoothing variable is based on the squared distance between the precedent CMA output and a simple moving average, and the rolling variance, where the rolling variance act as threshold.
The CTS work the same way but instead of using the squared error between a simple moving average and the previous CMA output, we use the squared error between the closing price and the previous CTS output, this allow the CTS to better fit with the closing price. As said before the rolling variance act as threshold, if the squared error is lower than the rolling variance this mean that the CTS is close to the price, which can indicate a sideway market, therefore we should filter the entirety of the current price, therefore on sideways market the CTS is equal to the precedent value of the CTS.
In trending/volatile markets we expect the price to go away from the CTS, thus having an high squared error, if the squared error is greater than the rolling variance, the smoothing variable is equal to 1 - variance/squared error , here variance/squared error < 1 since the squared error is greater than the rolling variance ( remember that the smoothing variable need to be in a (0,1) range ), however if the squared error is way higher than variance this ratio will be small, which would return a non reactive output, but thats not what we want ! This is why we subtract 1 by this ratio in order to make the CTS more reactive instead of less reactive.
In case the squared error is greater than the rolling variance during sideway markets we would not expect a huge difference anyway, that is squared error ≈ variance and therefore:
1 - variance/squared error ≈ 1 - 1/1 ≈ 1 - 1 ≈ 0
This is a beautiful way to make an adaptive moving average, the CMA is not a flashy indicator, but when we look at the details behind the design we can only get amazed, or maybe that its just me, truly a great adaptive moving average.
The System
length control the filtering amount of both moving averages, with higher values of length returning larger filtering amount. Mult multiply the rolling variance by an user selected value, this also allow a greater amount of filtering.
The CTS act as a fast moving average while the CMA act as a slow moving average.
Here the indicator with length = 200, we can see how a sideway market who could have generated a large amount of signals don't affect our system.
Unlike classical crossovers systems where the slow moving average will rarely produce a cross with the fast moving average and price at the same time, the Uhl system can actually do that:
Conclusion
A moving average crossover system based on the corrected moving average proposed by Andreas Uhl has been presented, a new moving average that aim to produce good fits with the price has been created especially for this system. The logic behind the CMA has also been explained. A possible strategy analysis could be presented in the future.
In conclusion i would say the CMA is a bit underrated, in a field where arrows, signals, alerts are the only things appreciated by peoples, original content is slowly dying, this actually make today technical indicators have a pretty bad academic reputations. I'am afraid that today haiku master is Uhl rather than me, i hope to see more indicators from him in the future.
Thanks for reading !
Original paper: www.buero-uhl.de
Shapeshifting Moving Average - Switching From Low-Lag To SmoothThe term "shapeshifting" is more appropriate when used with something with a shape that isn't supposed to change, this is not the case of a moving average whose shape can be altered by the length setting or even by an external factor in the case of adaptive moving averages, but i'll stick with it since it describe the purpose of the proposed moving average pretty well.
In the case of moving averages based on convolution, their properties are fully described by the moving average kernel ( set of weights ), smooth moving averages tend to have a symmetrical bell shaped kernel, while low lag moving averages have negative weights. One of the few moving averages that would let the user alter the shape of its kernel is the Arnaud Legoux moving average, which convolve the input signal with a parametric gaussian function in which the center and width can be changed by the user, however this moving average is not a low-lagging one, as the weights don't include negative values.
Other moving averages where the user can change the kernel from user settings where already presented, i posted a lot of them, but they only focused on letting the user decrease or increase the lag of the moving average, and didn't included specific parameters controlling its smoothness. This is why the shapeshifting moving average is proposed, this parametric moving average will let the user switch from a smooth moving average to a low-lagging one while controlling the amount of lag of the moving average.
Settings/Kernel Interaction
Note that it could be possible to design a specific kernel function in order to provide a more efficient approach to today goal, but the original indicator was a simple low-lag moving average based on a modification of the second derivative of the arc tangent function and because i judged the indicator a bit boring i decided to include this parametric particularity.
As said the moving average "kernel", who refer to the set of weights used by the moving average, is based on a modification of the second derivative of the arc tangent function, the arc tangent function has a "S" shaped curve, "S" shaped functions are called sigmoid functions, the first derivative of a sigmoid function is bell shaped, which is extremely nice in order to design smooth moving averages, the second derivative of a sigmoid function produce a "sinusoid" like shape ( i don't have english words to describe such shape, let me know if you have an idea ) and is great to design bandpass filters.
We modify this 2nd derivative in order to have a decreasing function with negative values near the end, and we end up with:
The function is parametric, and the user can change it ( thus changing the properties of the moving average ) by using the settings, for example an higher power value would reduce the lag of the moving average while increasing overshoots. When power < 3 the moving average can act as a slow moving average in a moving average crossover system, as weights would not include negative values.
Here power = 0 and length = 50. The shapeshifting moving average can approximate a simple moving average with very low power values, as this would make the kernel approximate a rectangular function, however this is only a curiosity and not something you should do.
As A Smooth Moving Average
“So smooth, and so tranquil. It doesn't get any quieter than this”
A smooth moving average kernel should be : symmetrical, not to width and not to sharp, bell shaped curve are often appropriates, the proposed moving average kernel can be symmetrical and can return extremely smooth results. I will use the Blackman filter as comparison.
The smooth version of the moving average can be used when the "smooth" setting is selected. Here power can only be an even number, if power is odd, power will be equal to the nearest lowest even number. When power = 0, the kernel is simply a parabola:
More smoothness can be achieved by using power = 2
In red the shapeshifting moving average, in green a Blackman filter of both length = 100. Higher values of power will create lower negative values near the border of the kernel shape, this often allow to retain information about the peaks and valleys in the input signal. Power = 6 approximate the Blackman filter pretty well.
Conclusion
A moving average using a modification of the 2nd derivative of the arc tangent function as kernel has been presented, the kernel is parametric and allow the user to switch from a low-lag moving average where the lag can be increased/decreased to a really smooth moving average.
As you can see once you get familiar with a function shape, you can know what would be the characteristics of a moving average using it as kernel, this is where you start getting intimate with moving averages.
On a side note, have you noticed that the views counter in posted ideas/indicators has been removed ? This is truly a marvelous idea don't you think ?
Thanks for reading !
Sequentially Filtered Moving AverageThe previously proposed sequential filter aimed to filter variations lower than a certain period, this allowed to remove noisy variations and retain only the closing price values that occurred after a consecutive up/down, however because of the noisy nature of the closing price large filtering was impossible, in order to tackle to this problem the same indicator using a simple moving average as input is proposed, this allow for smoother results.
We will see that the proposed indicator can provide an alternative moving average that could be used as slow moving average in crossover systems.
The Indicator
The length parameter as the same function as the one described in the sequential filter post, however here length also control the period of the moving average used input, in short larger values of length will return a smoother but less reactive output.
In blue the moving average with length = 200, and in red the moving average with length = 50.
It is interesting to see how the moving average remain flat during ranging/flat market periods
Unfortunately like the sequential filter the sequentially filtered moving average (SFMA) is not affected by large short term variations such as gaps or short term volatile events. This is because of the nature of the sequential filter to ignore movements amplitude and only focus on the variation period.
Moving Average Crossover System
The SFMA is equal to a simple moving average of period length when a consecutive up/down sequence of size length has occurred, else the SFMA is equal to its precedent value, therefore we could expect less crosses between a fast moving average and the SFMA as slow moving average.
We can see on the figure above that the fast moving average of period 50 (in green) cross more with the slow moving average of period 200 (in red) than with the SFMA of period 200 (in blue).
Crosses can occur at the same time as with the classical slow moving average (in red) or a bit later.
Conclusion
A new moving average based on the recently proposed sequential filter has been proposed, it can be seen that under a moving average crossover system the proposed moving average seems to be more effective at producing less crosses without necessarily doing it with an excessive lag, in fact the moving average has either lag (length-1)/2 or lag length .
In the future it could be interesting to provide an hybrid alternative that take into account volatility as well as variations period.
Thanks for reading !
London Breakout with MDX Trailing StopThis indicator aims to aid in using the regular London Breakout strategy, as well as improve on it by adding a trailing stop based on the Mean Deviation Index.
The London Breakout strategy (according to my personal understanding) basically sees the morning before London open as the accumulation or distribution range for large buyers or sellers, and assumes the market will break either above that mornings high or below that mornings low when they start to move price. It is mostly used to trade stock indices and forex.
This indicator plots the morning high and low for each day. The green line is the morning high, and the red line is the morning low. If price moves above the green line (the morning high) it fills that area with a green color. If price moves below the green line (the morning low) it fills that area with a red color. This makes the breakouts easy to spot.
The background color of the chart turns green when the MDX is above 0 (price is more than X times ATR above the mean) and a breakout above the morning high has occurred, and stays green until the opposite happens.
The background color of the chart turns red when the MDX is below 0 (price is more than X times ATR below the mean) and a breakout above the morning high has occurred, and stays green until the opposite happens.
The default for X above is 1.0, but this can be changed in the settings by changing "ATR Multiplier".
The background is always neutral during the morning session since the morning high and morning low are not established yet.
A trailing stop is shown when price is more than X times away from the mean and a breakout has occured. The distance is set using the MDX. The trailing stop uses a separate ATR multiplier though, to make the signal and trailing stop MDX values different, if one likes. The default ATR multiplier for the trailing stop is 1.25, but this can be changed is the settings by changing "ATR multiplier for trailing stop".
When the high or low of a candle breaks the trailing stop, it is moved further away, indicating you have been stopped out, but gives opportunity to use it if you enter again (so it doesn't just disappear).
As an added bonus, take profit levels have been added based on the mornnig range. The take profit distance is set by multiplying the range with a factor. The levels are then plotted that distance from the morning high and morning low.
MDX:
Grover Llorens Cycle Oscillator [alexgrover & Lucía Llorens]Cycles represent relatively smooth fluctuations with mean 0 and of varying period and amplitude, their estimation using technical indicators has always been a major task. In the additive model of price, the cycle is a component :
Price = Trend + Cycle + Noise
Based on this model we can deduce that :
Cycle = Price - Trend - Noise
The indicators specialized on the estimation of cycles are oscillators, some like bandpass filters aim to return a correct estimate of the cycles, while others might only show a deformation of them, this can be done in order to maximize the visualization of the cycles.
Today an oscillator who aim to maximize the visualization of the cycles is presented, the oscillator is based on the difference between the price and the previously proposed Grover Llorens activator indicator. A relative strength index is then applied to this difference in order to minimize the change of amplitude in the cycles.
The Indicator
The indicator include the length and mult settings used by the Grover Llorens activator. Length control the rate of convergence of the activator, lower values of length will output cycles of a faster period.
here length = 50
Mult is responsible for maximizing the visualization of the cycles, low values of mult will return a less cyclical output.
Here mult = 1
Finally you can smooth the indicator output if you want (smooth by default), you can uncheck the option if you want a noisy output.
The smoothing amount is also linked with the period of the rsi.
Here the smoothing amount = 100.
Conclusion
An oscillator based on the recently posted Grover Llorens activator has been proposed. The oscillator aim to maximize the visualization of cycles.
Maximizing the visualization of cycles don't comes with no cost, the indicator output can be uncorrelated with the actual cycles or can return cycles that are not present in the price. Other problems arises from the indicator settings, because cycles are of a time-varying periods it isn't optimal to use fixed length oscillators for their estimation.
Thanks for reading !
If my work has ever been of use to you you can donate, addresses on my signature :)
Volatility Stop MTFThis is a multi-timeframe version of our Volatility Stop , an ATR-based trend detector that can be used as a stop.
► Timeframe selection
The higher timeframe can be selected using 3 different ways:
• By steps (60 min., 1D, 3D, 1W, 1M, 1Y).
• As a multiple of the current chart's resolution, which can be fractional, so 3.5 will work.
• Fixed.
Note that you can also use this indicator without the higher timeframe functionality. It will then behave as our normal Volatility Stop would.
► Stop breaches
Two modes of stop-breaching logic can be selected.
• In the default, Early Breach mode, the stop is considered breached when a bar at the chart's current resolution breaches the higher timeframe stop.
• You may also choose to calculate breaches on the higher timeframe information only.
Choosing the Early Breach mode has the advantage of generating faster exits. It will create a state of limbo where the stop has been breached but the Volatility Stop trend has not yet reversed. The impact of detecting earlier exits to minimize losses comes, as is usually the case, at the cost of a compromise: if the stop is breached early in a long trend, the indicator will then spend most of that trend in limbo. Sizeable portions of a trend can thus be missed.
A few options are provided when you use Early Breach mode:
• A red triangle can identify early breaches (default).
• You can color bars or the background to identify limbo states.
When in limbo, the color used to plot the indicator's line or shapes will always be darker.
► Alerts
Five pre-defined alerts are supplied:
• #1: On any trend change.
• #2: On changes into an uptrend.
• #3: On changes into a downtrend.
• #4: Only on breaches of the uptrend by the chart's bars (Early Breach mode). Will not trigger on a trend change.
• #5: Only on breaches of the downtrend by the chart's bars (Early Breach mode). Will not trigger on a trend change.
As usual, alerts should be configured to trigger Once Per Bar Close . When creating alerts, you will see a warning to the effect that potentially repainting code is used, even if the indicator's default non-repainting mode is active. The warning is normal.
► Other features
• You can color bars using the indicator's up/down state. When bars are colored, up bars are more brightly colored.
• The HTF line is non-repainting by default, but you can allow it to repaint.
• You can confirm the higher timeframe used by displaying it at a selectable distance from the last bar on the chart.
• Choice of 2 color themes.
• Choice of display as a line, circles, diamonds or arrows. The line can be used with the other shapes. If no line is required, set its thickness to zero.
Enjoy!
Look first. Then leap.
UT Bot Strategy with Backtesting Range [QuantNomad]UT Bot indicator was inially developer by @Yo_adriiiiaan
Idea of original code belongs @HPotter
I can't update my original UT Bot Strategy so I publishing new strategy with backtesting range included.
I just took code of Yo_adriiiiaan, cleaned it, deleted all useless pieces of code, transformet to v4 and created a strategy from it.
Also I added an input that allows you to swich to signals from Heiking Ashi. I saw that author uses HA for the indicator and on HA it look much nices then on real candles.
Do not add this strategy to HA candles, use usual candles and this checkbox.
Original script:
UT Bot
Windowed Volume Weighted Moving AverageIntroduction
The concept of windowing was briefly introduced in the Blackman filter post, however windowing is more than just some window functions, and isn't exclusively used in filter design.
Today we will use windowing with the volume weighted moving average, a moving average that weight the price with volume in order to be more reactive when volume is high, that is the moving average is more reactive when the market is more active. The use of windowing in the vwma allow to enhance its performance in the frequency domain which result in a smoother output.
Note that i made a similar indicator long ago, but at that time I was not great at all with math and pinescript in general and the indicator was therefore wrong, i want to remind to the community that i'am not a professional, only an enthusiast, I never claimed to be a master coder and i'am totally open to receive criticism, if I sounded like bragging in the past I apologize, at 20 years old it is still easy to act like a kid, the information contained in my posts is only shared in order to help others but also myself, since sharing is also a way to learn more effectively. That said lets go with the indicator.
Windowing
Windowing consist on applying a window function to a signal, by applying i mostly talk about multiplying, this process is mostly used with windowed sinc filters in order to reduce ripples in the pass/stop band, but can be used with any kind of filters in order to have better frequency domain performance, the only thing we need to do is to multiply the filter weights by a window function.
In order to understand windowing it is useful to visualize this process and understand spectral leakage. Remember that we can describe a signal as the sum of sine/cosine waves of different frequencies, amplitude and phase, leakage is an effect that appear with signals having discontinuities, that is when a signal non periodic.
This figure show a non periodic sine wave of frequency 0.1, a non periodic signal will have is last sample value different from its first sample value, if we where to do its fourier transform we wouldn't end up with a single bin at 0.1 but with more bins, this is spectral leakage, the discontinuities in the signal create additional frequency components. In order to reduce leakage we must make the signal approximately periodic, this is done by making use of window functions.
A window function is symmetric and relatively smooth, all we have to do is to multiply our first non periodic signal with the window function.
We end up with the following windowed signal :
The signal is approximately periodic and leakage has been reduced. Now that we have seen that, it might be useful to see why it is useful in filters.
Remember that the Fourier transform of the filter weights gives us its frequency response, if our weights introduce leakage we end up with ripples, so windowing the filter weights might help reduce the ripples in the frequency response, which result in a smoother filter output.
Volume Weighted Moving Average
A volume weighted moving average is a FIR filter who use volume as filter kernel, therefore the frequency response of this filter always change, it is therefore not wrong to qualify the vwma as an adaptive moving average. Higher volume mean higher weighting of the current closing price value, which therefore produce a more reactive output.
However the smoothness of the moving average is relatively poor.
Windowed Volume Weighted Moving Average
The proposed moving average has a length setting who control the moving average period, and various options that we will describe below. The first option is the type of window, there are many windows, certains more complex than others, here 3 windows are proposed, the famous Blackman window, the Bartlett, and finally the Hanning window, they provide each different level of smoothness. lets compare our moving average with period 100 with a vwma of the same period.
Our moving average in red, and the vwma in blue. As you can see the results are smoother.
The power parameter is used in order to give an even higher weighting to closing prices with high volume, this create a more boxy output. Below is a comparison with a vwma in blue and a powered vwma in red with power = 2 without windowing :
We can then apply a window, here i will choose the Blackman window :
Conclusion
A new moving average based on windowed volume weighting has been proposed. The result are smoother which might therefore reduce whipsaw trades. I wish i could have explained things better, unfortunately windowing isn't something i use much, i wanted to post this moving average earlier this year.
I will be off in France for 1 week, my flight is tomorrow in the morning, therefore i don't think i'll have the possibility to make other posts this year. I want to profit from this occasion to review my year in tradingview.
Many indicators have been posted, some being extremely bad and others really interesting, this year introduced my attempts on estimating the lsma efficiently, the linear channels, an attempt on making lines and remain the first indicator from the v4 i posted if i'am right. Then came the efficient auto-line, who gained some popularity quite fast. Then finally the %G oscillator and the recursive bands where posted, and remain some of the favorites indicators i made. I also wanted to leave this year due to studies, that i totally abandoned, i'am thankful that i chosen to stay.
I also want to express my apologies to any member that i could have offended, i think that i'am not a mean person but i certainly not contest the fact that i'am clumsy, even in my work, however my clumsiness is far greater when it comes to interact with other peoples or a group of peoples, i don't want to hurt anyone, if i made anything that made you feel bad then i'am sincerely sorry, and hope we can start this new year from 0.
Finally i thank the tradingview community for their interest and curiosity, i thank all the great coders who work on making pinescript a better scripting language, i also thank the tradingview staff for their work this year. I wish you all a merry christmas, and an happy new year.
Thanks for reading.
Elephant Bar by Oliver VelezThis script detects an event created by Oliver Velez, basically it is a wide-range candle, its range is noticeably larger than the previous candles, this event indicates a possible continuation of the movement, or the beginning of an extended movement. The candle has to be of good body, as a rule it can be taken that the body must be more than 70%. The stop goes below the minimum of the candle and the signal is given when the next candle followed by the elephant candle exceeds its body, this condition is not programmed so that the alert indicates that an elephant candle was generated and the trader has some time to visualize the graph and wait for the signal. Example below:
NOTE: IT IS VERY IMPORTANT THAT THE TRADER ANALYZE THE CONTEXT OF THE MARKET WHERE THE ELEPHANT BAR IS GENERATED AND DETERMINE ACCORDING TO ITS EXPERIENCE IF THE EVENT HAS A GOOD PROBABILITY OF PROJECTION, YOU MUST NOT TAKE AN ENTRY ONLY BY THIS EVENT, IF YOU DO YOU WILL LOSE ALL YOUR MONEY
.
One of the problems of the elephant bar is that it generates a fairly wide risk unit with respect to other narrow range events, so the risk / benefit ratio is not very large, but it is an event that deserves attention when it occurs in a good location since it generally generates continuation.
If you want to have a lower risk unit and improve the risk / benefit ratio, you can play the “Gift Zone”, when detecting an elephant bar you can wait for a step back inside the elephant bar area and take a position, this will give you a less distance to the stop, but this can lead to the event escaping if there is no recoil.
- The size of the candle is determined by comparing a range of previous candles (you can set the amount at your discretion)
- Search factor: by default 1.3, this means that all bars that have a range greater than the average range of previous candles + 30%, are considered elephant candles (can be configured at your discretion)
- Possibility to configure the percentage of the body that the elephant candle must have.
- Possibility of filtering up to 2 means with direction detection and color change (fully configurable)
- Possibility of filtering by mobile averages
- Alerts
- Additional features
Thumb up if you liked me ..
Damped Sine Wave Weighted FilterIntroduction
Remember that we can make filters by using convolution, that is summing the product between the input and the filter coefficients, the set of filter coefficients is sometime denoted "kernel", those coefficients can be a same value (simple moving average), a linear function (linearly weighted moving average), a gaussian function (gaussian filter), a polynomial function (lsma of degree p with p = order of the polynomial), you can make many types of kernels, note however that it is easy to fall into the redundancy trap.
Today a low-lag filter who weight the price with a damped sine wave is proposed, the filter characteristics are discussed below.
A Damped Sine Wave
A damped sine wave is a like a sine wave with the difference that the sine wave peak amplitude decay over time.
A damped sine wave
Used Kernel
We use a damped sine wave of period length as kernel.
The coefficients underweight older values which allow the filter to reduce lag.
Step Response
Because the filter has overshoot in the step response we can conclude that there are frequencies amplified in the passband, we could have reached to this conclusion by simply seeing the negative values in the kernel or the "zero-lag" effect on the closing price.
Enough ! We Want To See The Filter !
I should indeed stop bothering you with transient responses but its always good to see how the filter act on simpler signals before seeing it on the closing price. The filter has low-lag and can be used as input for other indicators
Filter with length = 100 as input for the rsi.
The bands trailing stop utility using rolling squared mean average error with length 500 using the filter of length 500 as input.
Approximating A Least Squares Moving Average
A least squares moving average has a linear kernel with certain values under 0, a lsma of length k can be approximated using the proposed filter using period p where p = k + k/4 .
Proposed filter (red) with length = 250 and lsma (blue) with length = 200.
Conclusions
The use of damping in filter design can provide extremely useful filters, in fact the ideal kernel, the sinc function, is also a damped sine wave.
UT Bot StrategyUT Bot indicator was inially developer by @Yo_adriiiiaan
Idea of original code belongs @HPotter
I just took code of Yo_adriiiiaan, cleaned it, deleted all useless pieces of code, transformet to v4 and created a strategy from it.
Also I added an input that allows you to swich to signals from Heiking Ashi. I saw that author uses HA for the indicator and on HA it look much nices then on real candles.
Do not add this strategy to HA candles, use usual candles and this checkbox.
Original script:
MACD Leader [ChuckBanger]MACD makes use of moving averages and therefor usually lags behind the price. It is possible to eliminate lag completely but the work around of this is usually by adding a component of the price/MA difference back to MA. This technique is called Zero-lag. It is not zero lag but it is close enough. "MACD Leader" makes use of this to form a leading signal to MACD.
First proposed by Giorgos E. Siligardos, "Leader" leads normal MACD , especially when significant trend changes are about to take place. This has the following features:
- It is similar to MACD in smoothness.
- It can be plotted along with MACD in the same window using the same scaling.
- It has the ability to lead MACD at critical situations
For detailed discussion on the various divergence patterns, refer to the PDF here: drive.google.com
This script provide an option to plot MACD and MACD leader signal on the same pane. You can enable/disable them how you want via options page. It also has the option to change to different MA types.
Visual RSI [LucF]Visual RSI offers a different way of looking at RSI by providing a composite representation of 9 different RSI-generated components. Instead of focusing on one line only, this approach blends multiple sources to provide the viewer with a larger context RSI-based picture.
For those who don’t want to read
• Green in bullish (>50) zone is the most bullish.
• Red in bullish zone doesn’t necessarily mean bearish—it just means bullish strength is weakening. It may be just a pause before a reprise or exhaustion signalling a reversal—impossible to tell.
• The same in inverse applies to the bearish zone (<50).
For those who want to understand
The nine components making up Visual RSI are:
• a current timeframe RSI
• a higher timeframe RSI
• the delta between these two RSI lines
• for each of these three basic components, two independent Bollinger band: one calculated for the bullish section of the scale (>50) and a separate one calculated for the lower bearish region.
Dual BBs
In my view, RSI’s position with regards to the centerline is much more important than its position in extreme areas. Why? Because the building block of RSI is the ratio of the averages of up/down moves during the RSI period. When the average of ups is greater, RSI is > 50. So while a rising signal starting from 20 let’s say, indicates that the rate of change is increasing, only when it crosses 50 can we say that sentiment balance has truly become bullish, and this information is more reliable than the signal being at a level corresponding to whatever estimate we make of what constitutes an extreme value. In my landscape, the general balance of a ratio provides more valuable information than the ratio’s exact value.
The idea behind the dual BBs is to provide independent tracking information for both halves of the indicator’s space, which I find more useful than the normal method of simply adding a multiple of the standard deviation on both sides of the mean. With dual BBs, the upper BB will never go lower than the indicator’s centerline, and the lower BB will never go higher. The upper BB focuses on upper-bound volatility when the signal is bearish, and the lower BB focuses on downside volatility when the signal is bearish.
The functions used to calculate the independent BBs are reusable on other signals if a centerline can be defined for them. A clamping percentage is implemented, so that when a BB line is hugging the centerline it clamps to it. This helps in providing earlier signals when they use the BB line states.
Providing context to RSI
What RSI measures indirectly is the balance in the rate of change—or the speed of price movement, but not its instant value, otherwise RSI would be even noisier. More precisely, RSI represents the relative strength of the up/down movement in the last n bars of RSI’s length, with 14 often used because that’s what Wilder proposed (Visual RSI’s defaults are 20 for the current timeframe and 40 for the higher timeframe). At every bar, a new value is added to the equation and an old value carrying equal weight is dropped, so a large dropped off value will have more impact on RSI’s value if the new bar’s move is small. This accounts for some of RSI’s speed in identifying exhaustion after important moves, but almost for some of its noise.
Visual RSI is the result of trying to drown RSI’s noise in the context of other informational streams, while simultaneously providing even faster information than RSI alone, by giving more visual weight to the delta between the current and higher timeframe RSI’s.
How to read Visual RSI
The default settings show all 9 basic components as green/red areas of intensities varying with their importance. The most intense colors are reserved for the delta RSI and the BBs have the lightest intensities. The individual lines of components are intentionally difficult to distinguish so that focus is first on the general picture, including the all-important six-state background, and then on the delta RSI.
One entry setup could be reversals in a larger trend context, so low pivots of the delta in a fully bullish context (a green background in the upper section of the indicator), and inversely, high pivots in a fully bearish context (a red background in the lower section of the indicator).
Please resist the common misconception, when interpreting RSI, that a reversal in the signal will necessarily lead to a reversal in price. Each trend has its rhythm. Only machine-generated price action can progress regularly. It’s normal for trends to take a breather for some time before they continue or reverse, as traders driving the trend experience emotional fatigue and gradual fear. RSI reversals merely signify that such a breather has occurred—nothing more. Only the larger context can provide information that can situate that pause and put more meaningful odds on it having more probability of continuing in one direction or the other. This is the reasoning behind the setup just described.
Features
• All components can be hidden, displayed as a simple line, a uniformly colored fill, or a green/red fill (the default).
• The background can be colored using 9 different methods, including 3 six-state methods using the rising/falling BB lines of the 3 basic components. These six states allow for bullish/bearish/neutral sentiment in both the upper and lower regions of the indicator. A bearish (dark red) background in the bullish (>50) section of the indicator represents decreasing bullishness. A bearish (slightly brighter red) in the bearish (<50) section of the indicator means incresingly bearish sentiment. The six-state backgrounds allow for neutral (no color) sentiment when no compelling signs can be found to conclude anything with meaningful odds. The default background uses the six-state method on the higher timeframe RSI’s BBs because I find it the most useful, as it represents the largest—and slowest—context sentiment among all the indicator’s components.
• A thin status bar in the top part of the indicator also allows selection of the same 9 methods to color it. The default is a triple-state system using the rising/falling characteristics of the current timeframe RSI’s BBs to provide a short-term counterbalance to the long-term background.
• Three different markers can be configured using approximately 70 permutations each, each filtered by 20 different filter permutations. When modification of the relevant parameters in the script’s Settings/Settings/Parameters section is added, possibilities are almost endless. If the generated signals are then fed into the PineCoders Engine and combined with the Engine’s own options, the permutations go up another order of magnitude, and changes to any setting can be instantly evaluated using the Engine’s backtesting results.
• Five simple filters can be combined. They are additive. They include volume-related conditions and a chandelier, which I find useful because both volume and volatility (the chandelier using highs/lows and ATR) are sensible complementary sources to RSI’s momentum information. The filter’s state can be shown as a thin line at the bottom of the indicator.
• Alerts can be configured using any of the marker/filter combinations mentioned. As usual, once your markers/filters are set up the way you want, create your alert from the chart/timeframe you want the alert to run on and be sure to use the “Once Per Bar Close” triggering condition. Use an alert message that will remind you of which combination of markers were used when creating the alert.
• A plot providing entry signals for the PineCoders Backtesting & Trading Engine is supplied. It will use whichever marker/filter configuration is active to generate signals.
• All higher timeframe information is non-repainting. Higher timeframe lines can be smoothed (the default). The selection of the higher timeframe can be made using 3 different methods:
1. By steps (if current timeframe <= 1 minute: 60 min, <= 60 min: 1D, <= 6H: 3D, <= 1D: 1W, <=1W: 1M, >1W: 12M)
2. By a user-defined multiple of the current timeframe
3. Using a fixed timeframe
Thanks to:
• Alex Orekhov aka @everget for the chandelier code.
• @RicardoSantos who through a small remark early on, unknowingly put me on the track of eliminating noise through visual crowding.
• The brilliant guys in the PineCoders Pro room for your knowledge, limitless creativity and constant companionship.
Delta Volume Columns [LucF]Displays delta volume columns using intrabar volume information. Each volume column is divided into three sections: buying, selling and neutral volume. Volume for each section is determined from the volume and price movement of each intrabar at a user-selected lower resolution.
Features include:
- Choice of color themes for either dark or light chart backgrounds
- Delta volume columns
- Volume Balance displayed as the difference between the MAs of buying and selling volume
- Display of divergences between a bar’s volume balance and the bar’s price movement (example: buying volume > selling volume but close < open). Divergences can be shown in 2 different color schemes (including green/red showing a tentative direction), on volume columns and/or on chart bars
- Display of bar by bar volume balance with highlighting of above average volume
- Display of the usual total volume MA
- Choice of the lower resolution used to retrieve intrabar information
- Alerts configurable on any combination of the markers, with control over long/short direction
- Choice of 3 different markers:
1. Double bumps: two consecutive bars where buying or selling volume is in the same direction and where volume > volume MA
2. Divergence confirmations: direction of the price bar following a price/volume balance divergence
3. Volume balance shifts: zero level crossings of the volume balance MA delta
The chart shows the two main modes of display:
- Top pane : shows the stacked volume columns with divergences in orange and the flattened volume balance MAs delta at the bottom of the volume columns. This volume balance is the same shown in the bottom pane. The top pane also shows the instant volume balance strip above the volume columns. The strip’s colors show which of the buying or selling volume was greater, and colors are brighter if the total volume was above the total volume MA.
- Bottom pane : shows the volume balance MAs delta with markers 1 and 2. Given that this graphic has no price momentum component, I find quite eerie how it often looks like a momentum-based signal.
The default 5 minute intrabar resolution is used in combination with the weekly chart, which is excessive.
This script uses a special characteristic of the security() function’s behavior when it is sent to a resolution lower than the chart’s resolution. Details are given in the script’s comments. This method has the advantage of working under more circumstances than some of the other loop-based methods, but it also has its limits.
IMPORTANT
This is what you need to know:
- The method used does not work on the realtime bar—only on historical bars. Consequently, the volume column shown on the realtime bar is a normal volume column plotted in green or red, following price movement. The column will only show delta volume information after it closes and becomes a historical bar.
- The indicator only works on some chart resolutions: 5, 10, 15 and 30 minutes, 1, 2, 4, 6, and 12 hours, 1 day, 1 week and 1 month. The script’s code can be modified to run on other resolutions, but chart resolutions must be divisible by the lower resolution used for intrabars.
- Intrabar resolutions can be selected from 1, 5, 15, 30, 45 minutes, 1, 2, 3, 4 hours, 1 day, 1 week and 1 month. The intrabar resolution must of course be smaller than the chart’s resolution.
- Contrary to my other indicators where alerts must be configured to trigger “Once Per Bar Close” in order to avoid false triggers (or repainting), all this indicator’s alerts are designed to trigger using previous bar information since the indicator’s calculations in the realtime bar are not exact. Markers are not plotted with a negative offset; they appear at the beginning of the realtime bar following confirmation of the marker’s condition on the previous bar. Alerts for this indicator should thus be configured to trigger “Once Per Bar” so they trigger at the beginning of the realtime bar. Note that the penalty is not that great, as it is simply the instant between the close of the previous realtime bar and the opening of the next. The advantage of using this technique is that the indicator does not repaint; a marker that appears at the beginning of the realtime bar will never disappear.
- The script only plots information that is reliable in the realtime bar, i.e., total volume and markers. All other plots are set to n/a to prevent misleading traders.
- When the difference between the chart’s resolution and the lower resolution is too important, volume columns will not calculate for all bars in the dataset.
On Delta Volume
Buying or selling volume are misnomers, as every unit of volume transacted is both bought and sold by 2 different traders. There is no such thing as “buy only” or “sell only” volume, but trader lingo is riddled with original fabulations.
Without access to order book information, traders work with the assumption that when price moves up during a bar, there was more buying pressure than selling pressure. The built-in volume indicator available on TradingView uses this logic to color the volume columns green or red. While this script’s numbers are more precise because it analyses a number of intrabars to calculate its information, it uses the exact same imperfect logic to calculate its buying/selling/neutral sections.
Until Pine scripts can have access to how much volume was transacted at the bid/ask prices, our so-called buying/selling volume information will always be a mere proxy.
Divergences
You may wonder how there can be divergences between buying/selling volume information and price movement. This will sometimes be due to the methodology’s shortcomings we have just discussed, but divergences may also occur in instances where because of order book structure, it takes less volume to increase the price of an asset than it takes to decrease it.
As usual, divergences are points of interest because they reveal imbalances, which may or may not become turning points. I do not share the overwhelming enthusiasm traders have for divergences. To your pattern-hungry brain, the orange bars this indicator shows on chart will—as divergences on other indicators do–appear to often indicate turnarounds. My opinion is that reality is generally quite sobering, as many who have tried building automated rules based on divergences will tell you. I do not have hard numbers on the lack of performance of divergences—only many failed attempts to make them perform, which a few experienced strategy modelers I know share with me. Please don’t try to read too much into them. While they look great on past data, I find they are often difficult to use in realtime to make bets with good odds.
Thanks to:
- A guy called Kuan who commented on a Backtest Rookies presentation of an intrabar delta volume indicator using a for loop. The heart of “my” indicator is code borrowed from Kuan; I just built a hopefully useful wrapper around it.
- @theheirophant, my partner in the exploration of the sometimes weird abysses of security() ’s behavior at lower resolutions.
