BTC Active1Y holders: OnchainUse this Indicator in The Weekly timeframe
This indicator is based on "Percent of Supply Last Active 1+ Years Ago".
This is so important indicator that shows " The percent of circulating supply that has not moved in at least 1 year."
It can show the situation of the holders who have been holding their coins for more than a year. When this indicator starts to decline, it means that the price has risen so much that the holders are selling their coins. When this indicator starts to increase, it means that the number of coins held has been increasing for more than a year. This is because the price is too low for investors.
This indicator can be used to indicate accumulation and distribution areas. When the indicator enters the overlow area (red) it means that the distribution is happening
When the indicator enters the overhigh range (blue), it means that accumulation is taking place by the holders
Cari dalam skrip untuk "accumulation"
Intraday Power 3 VisualDescription
This indicator draws a dynamic "Open High Low Close" type visual on intraday charts so the trader can easily keep track of the daily/weekly movement. This indicator was inspired by the Inner Circle Trader’s (ICT) “Power 3” concept, which is Accumulation, Manipulation, and Distribution of price on a daily timeframe.
Visual
This indicator plots the chosen timeframes opening price along with a live line for the current price. This makes it very easy to identify the daily/weekly range along it’s open. And the user can combine this indicator with my other indicator “Futures Exchange Sessions” to plot the midnight EST & 8:30 AM EST lines to get a great summation of over night price action.
Inputs and Style
In the Input section the user can dynamically switch between Daily and Weekly timeframes. Built in ability to move the entire Visual to the right makes preventing indicator overlap a breeze. All of the lines can be configured: color, style, and width. Independently toggle ON/OFF the Power 3 labels (Accumulation, Manipulation, Range Extension, Distribution) and can change labels color. The labels dynamically move and switch positions based upon bear or bull daily/weekly range.
Special Notes
The Futures market is open 23/5. It is closed everyday for 1-hour at 5pm EST and closed over the weekends. Because this Intraday Power 3 Visual is drawing in the 'future' on the users TradingView chart, when the visual is close or in a time when the market is closed, the visual doesn't behave properly. This is because TradingView doesn't display times when the Market is closed, thus the drawings cannot be displayed during those times. There is nothing wrong with the script. Please wait until the Market is open and the visual will be drawn normally.
This indicator is intended for use in the Futures Market
Adaptive Average Vortex Index [lastguru]As a longtime fan of ADX, looking at Vortex Indicator I often wondered, where is the third line. I have rarely seen that anybody is calculating it. So, here it is: Average Vortex Index - an ADX calculated from Vortex Indicator. I interpret it similarly to the ADX indicator: higher values show stronger trend. If you discover other interpretation or have suggestions, comments are welcome.
Both VI+ and VI- lines are also drawn. As I use adaptive length calculation in my other scripts (based on the libraries I've developed and published), I have also included the possibility to have an adaptive length here, so if you hate the idea of calculating ADX from VI, you can disable that line and just look at the adaptive Vortex Indicator.
Note that as with all my oscillators, all the lines here are renormalized to -1..1 range unlike the original Vortex Indicator computation. To do that for VI+ and VI- lines, I subtract 1 from their values. It does not change the shape or the amplitude of the lines.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
If no Adaptation is selected ( None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
The oscillator also has the option to configure the internal smoothing function with Window setting. By default, RMA is used (like in ADX calculation). Fast Default option is using half the length for smoothing. Triangle , Hamming and Hann Window algorithms are some better smoothers suggested by John F. Ehlers.
After the oscillator a Moving Average can be applied. The following Moving Averages are included: SMA , RMA, EMA , HMA , VWMA , 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS.
Postfilter options are applied last:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic ) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Momentum - momentum (derivative)
Except for Inverse Fisher Transform , all Postfilter algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Slow MA Length is used. If Filter/MA Length is less than 2 or Postfilter Length is less than 1, they are calculated as a multiplier of the calculated oscillator length.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Adaptive Oscillator constructor [lastguru]Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Oscillators section offer you a choice of Oscillator algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
CMO - Chande Momentum Oscillator
RSI - Relative Strength Index
Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume
Momentum RSI - RSI of price momentum
Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation)
MFI - Money Flow Index
LRSI - Laguerre RSI by John F. Ehlers
LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma
Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss
Efficiency ratio - based on Kaufman Adaptive Moving Average calculation
DMI - Directional Movement Index (only ADX is drawn)
Fast DMI - same as DMI, but without secondary smoothing
If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator:
The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource, so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Moving Averages.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers, but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers. I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power (exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration.
SMA - Simple Moving Average
RMA - Running Moving Average
EMA - Exponential Moving Average
HMA - Hull Moving Average
VWMA - Volume Weighted Moving Average
2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers
3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers
Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers
Triangle Window - Triangle Window Filter by John F. Ehlers
Hamming Window - Hamming Window Filter by John F. Ehlers
Hann Window - Hann Window Filter by John F. Ehlers
Lowpass - removes cyclic components shorter than length (Price - Highpass)
DSSS - Derivation Scaled Super Smoother by John F. Ehlers
There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
DominantCycleCollection of Dominant Cycle estimators. Length adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). This collection may become encyclopaedic, so if you have any working cycle estimator, drop me a line in the comments below. Suggestions are welcome. Currently included estimators are based on the work of John F. Ehlers
mamaPeriod(src, dynLow, dynHigh) MESA Adaptation - MAMA Cycle
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on MESA Adaptive Moving Average by John F. Ehlers
Performs Hilbert Transform Homodyne Discriminator cycle measurement
Unlike MAMA Alpha function (in LengthAdaptation library), this does not compute phase rate of change
Introduced in the September 2001 issue of Stocks and Commodities
Inspired by the @everget implementation:
Inspired by the @anoojpatel implementation:
paPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Pearson Autocorrelation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Pearson Autocorrelation Periodogram by John F. Ehlers
Introduced in the September 2016 issue of Stocks and Commodities
Inspired by the @blackcat1402 implementation:
Inspired by the @rumpypumpydumpy implementation:
Corrected many errors, and made small speed optimizations, so this could be the best implementation to date (still slow, though, so may revisit in future)
High Pass and Super Smoother prefilters are used in the original implementation
dftPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Discrete Fourier Transform
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Spectrum from Discrete Fourier Transform by John F. Ehlers
Inspired by the @blackcat1402 implementation:
High Pass, Super Smoother and Hann Windowing prefilters are used in the original implementation
phasePeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Phase Accumulation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hamm Windowing prefilter
Returns: Calculated period
Based on Dominant Cycle from Phase Accumulation by John F. Ehlers
High Pass and Super Smoother prefilters are used in the original implementation
doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower, preHP, preSS, preHP) Execute a particular Length Adaptation or Dominant Cycle Estimator from the list
Parameters:
type : Length Adaptation or Dominant Cycle Estimator type to use
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length
chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length
chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
doEstimate(type, src, dynLow, dynHigh, preHP, preSS, preHP) Execute a particular Dominant Cycle Estimator from the list
Parameters:
type : Dominant Cycle Estimator type to use
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
Jake Bernstein - Moving Average ChannelWe all know that moving averages, in particular, moving averages of closing prices tend to be highly inaccurate indicators and frequently miss major tops and bottoms. In backtesting, they tend to be accurate some 30 to 40% of the time which is to my way of thinking unacceptable. On the contrary moving averages of opens versus closes for highs versus lows, when used properly avoid the drawbacks of closing moving averages, particularly when combined with a trigger. Shown above is my moving average channel method which uses the 57 SMA of Williams accumulation distribution as a setup or trigger. As shown by the arrows two consecutive price bars completely below the MA channel low and triggered by Williams below SMA constitutes a sell signal. Conversely, two consecutive price bars or more above the moving average channel high accompanied by Williams above its moving average constitutes a sell trigger. The moving average channel high, the red line is a 10 period Moving average of highs. The Moving average channel low, the green line is an 8 period Moving average of the low. There are at least a dozen applications of this methodology including its ability to spot trend changes, support, resistance, swing trades, market strength, market weakness, and more. I will post some of these additional uses of the moving average channel as they present themselves. Do note that in this chart there were two instances above the moving average channel high but these were not triggered by Williams AD and therefore the trend remains down for the duration of this chart. The methodology associated with my MAC is completely rules-based and works in any timeframe. Thank you my friend Larry Williams for developing your excellent version of accumulation-distribution
Baekdoo multi OverSold OverBuy colored CandleHi forks,
I'm trader Baekdoosan who trading Equity from South Korea. This Baekdoo multi OverSold OverBuy colored candle will give you the idea of
multiple indicators in one shot with colored candle. Those indicators tell us that oversold or overbuy statistically. For the color, you can freely change
based on your comfort. For me, in Korea white candle has red color and black candle has blue color. So somewhat confusing for you. Anyway you can
easily modify color in the script. Please refer this line.
barcolor(open<close and result_pos == 4 ? color.new(color.red, 0) : open<close and result_pos == 3 ? color.new(color.red, 25) : open<close and result_pos == 2 ? color.new(color.red, 50) : open<close and result_pos == 1? color.new(color.red, 75) : na)
you can see I put different transparency at color.new() function with color code. Let me divide and conquer to explain for up candle
white candle and black candle.
1. White candle
with 4 oversold signal case with white candle tells us it is almost reached real bottom and try to rebound. In this case, I put vivid color (no transparency) on the candle. And all 4 signal case, I put text on "OverSold". It will not happen frequently. Then 2 approaches can be made.
(a) short term approach
You can buy on this time. and you set stop loss with open price. This is mainly aimed for technical rebound.
(b) long term approach
You can accumulate based on your budget with 5 times dividing. At that day might not be the very bottom but those period will most probably real bottom. You can put more weight on latter buy. Let say, 1 : 1.25 : 1.5 : 1.75 : 2.5. So for example, if you have $8,000 to investigate then, buy $1,000 and then $1,250, $1,500, accordingly. If price rebound then don't adding weight on accumulation but with the first amount that you buy(i.e., $1,000 with above example). With this approach, you will not have much stress and you will get profit well. If this is grand bottom case, then you can HODL this long term. What you needs is stick to the plan. :)
with 3 signals the color is less vivid, 2 signals is much less vivid, accordingly.
2. Black candle
The approaches are opposite to above. The signal will tells us for 4 overBuy signals, then vivid blue candle will be shown. Our strategy is distribute to sell. Please do not sell in one shot. As Newton said, "I can calculate the motions of the heavenly bodies, but not the madness of the people". Strong buy phase, we don't know how far will it go. But indicators will tell us it is quite overSold situation. So what I can suggest you is sell it 10% to 20% on resistance price, and put 50% of lower than certain support price. Remember, accumulation and distribution will always better than one shot trading if you want to survive long time on this war field.
Hope this will help your trading on equity as well as crypto. I didn't try it on futures. Best of luck all of you. Gazua~!
CMT's ProGo indicatorThis is an experiment. I've never traded with it and won't tell you to. The nuances of how effective this is have yet to be seen.
Shoutout to @BillionaireLau, who very recently posted Larry William's original ProGo indicator. I hypothesized that a few minor changes to values and operations would allow for greater utility and responsiveness. I believe this has been achieved. What we're looking at here appears to offer a new means of spotting divergences. Have fun. To quote BillionaireLau regarding the nature of this indicator:
"ProGo, created by Larry William, (earlier than 2002), is a 2 line graph using daily data.
1. Professional Line (color orange) is a professional Accumulation/Distribution line is constructed by using the change from today's open to today's close.
2. The Public Line (color blue) is done by creating a public accumulation/distribution line that shows the change from yesterdays close to today's open.
The graph is an index of the previous close to open +/- values (public) and then taking a 14 day average which is plotted against a 14 day average of the +/- values of the open to close(pro).
Background color:
Green colored area is where "pro" line crossover line, and the "pro" line is also positive."
William's ProGo indicatorProGo, created by Larry William, (earlier than 2002), is a 2 line graph using daily data.
1. Professional Line (color orange) is a professional Accumulation/Distribution line is constructed by using the change from today's open to today's close.
2. The Public Line (color blue) is done by creating a public accumulation/distribution line that shows the change from yesterdays close to today's open.
The graph is an index of the previous close to open +/- values (public) and then taking a 14 day average which is plotted against a 14 day average of the +/- values of the open to close(pro).
Background color:
Green colored area is where "pro" line crossover "amatuers" line, and the "pro" line is also positive.
Created this for literature review.
Combo Backtest 123 Reversal & Smoothed Williams ADThis is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
Accumulation is a term used to describe a market controlled by buyers;
whereas distribution is defined by a market controlled by sellers.
Williams recommends trading this indicator based on divergences:
Distribution of the security is indicated when the security is making
a new high and the A/D indicator is failing to make a new high. Sell.
Accumulation of the security is indicated when the security is making
a new low and the A/D indicator is failing to make a new low. Buy.
WARNING:
- For purpose educate only
- This script to change bars colors.
Function Square WaveThis is a script to draw a square wave on the chart, with an indicator for current price.
Markets undergoing Dow Jones or Wyckoff Accumulation/Distribution cycles tend to move in such waves, and if the period of the cycles are detected, a signal for accumulation/distribution phases can be created as an early warning.
Useful inputs:
- Average True Range as the wave height.
- Assumed Wave period as the wave duration.
I divided the current price wave by 2 to make the indicator more visually friendly.
GLHF
- DPT
Combo Backtest 123 Reversal & Klinger Volume Oscillator This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Klinger Oscillator (KO) was developed by Stephen J. Klinger. Learning
from prior research on volume by such well-known technicians as Joseph Granville,
Larry Williams, and Marc Chaikin, Mr. Klinger set out to develop a volume-based
indicator to help in both short- and long-term analysis.
The KO was developed with two seemingly opposite goals in mind: to be sensitive
enough to signal short-term tops and bottoms, yet accurate enough to reflect the
long-term flow of money into and out of a security.
The KO is based on the following tenets:
Price range (i.e. High - Low) is a measure of movement and volume is the force behind
the movement. The sum of High + Low + Close defines a trend. Accumulation occurs when
today's sum is greater than the previous day's. Conversely, distribution occurs when
today's sum is less than the previous day's. When the sums are equal, the existing trend
is maintained.
Volume produces continuous intra-day changes in price reflecting buying and selling pressure.
The KO quantifies the difference between the number of shares being accumulated and distributed
each day as "volume force". A strong, rising volume force should accompany an uptrend and then
gradually contract over time during the latter stages of the uptrend and the early stages of
the following downtrend. This should be followed by a rising volume force reflecting some
accumulation before a bottom develops.
WARNING:
- For purpose educate only
- This script to change bars colors.
[blackcat] L2 Ehlers Phase Accumulator Cycle Period MeasurerLevel: 2
Background
John F. Ehlers introuced Phase Accumulation technique of cycle period measurement in his "Rocket Science for Traders" chapter 7. It is perhaps the easiest to comprehend. In this technique, John Ehlers measures the phase at each sample by taking the arctangent of the ratio of the Quadrature component to the In-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample Dr. Ehlers then looks backward, adding up the delta phases. When the sum of the delta phases reaches 360 degrees (2*pi in tradingview), we must have passed through one full cycle, on average. The process is repeated for each new sample.
Function
blackcat L2 Ehlers Phase Accumulator Cycle Period Measurer is used to measure Dominant Cycle (DC). This is one of John Ehlers three major methods to measure DC. The Phase Accumulation method of cycle measurement always uses one full cycle’s worth of historical data. This is both an advantage and disadvantage. The advantage is the lag in obtaining the answer scales directly with the cycle period. That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. Longer averaging reduces the noise level compared to the signal. Therefore, shorter cycle periods necessarily have a higher output Signal-to-Noise Ratio (SNR).
Key Signal
Smooth --> 4 bar WMA w/ 1 bar lag
Detrender --> The amplitude response of a minimum-length HT can be improved by adjusting the filter coefficients by
trial and error. HT does not allow DC component at zero frequency for transformation. So, Detrender is used to remove DC component/ trend component.
Q1 --> Quadrature phase signal
I1 --> In-phase signal
Period --> Dominant Cycle in bars
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 2nd script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
MACD-X, More Than MACD by DGTMoving Average Convergence Divergence – MACD
The most popular indicator used in technical analysis, the moving average convergence divergence (MACD), created by Gerald Appel. MACD is a trend-following momentum indicator, designed to reveal changes in the strength, direction, momentum, and duration of a trend in a financial instrument’s price
Historical evolution of MACD,
- Gerald Appel created the MACD line,
- Thomas Aspray added the histogram feature to MACD
- Giorgos E. Siligardos created a leader of MACD
MACD employs two Moving Averages of varying lengths (which are lagging indicators) to identify trend direction and duration. Then, MACD takes the difference in values between those two Moving Averages (MACD Line) and an EMA of those Moving Averages (Signal Line) and plots that difference between the two lines as a histogram which oscillates above and below a center Zero Line. The histogram is used as a good indication of a security's momentum.
Mathematically expressed as;
macd = ma(source, fast_length) – ma(source, slow_length)
signal = ma(macd, signal_length)
histogram = macd – signal
where exponential moving average (ema) is in common use as a moving average (ma)
fast_length = 12
slow_length = 26
signal_length = 9
The MACD indicator is typically good for identifying three types of basic signals ;
Signal Line Crossovers
A Signal Line Crossover is the most common signal produced by the MACD. On the occasions where the MACD Line crosses above or below the Signal Line, that can signify a potentially strong move. The standard interpretation of such an event is a recommendation to buy if the MACD line crosses up through the Signal Line (a "bullish" crossover), or to sell if it crosses down through the Signal Line (a "bearish" crossover). These events are taken as indications that the trend in the financial instrument is about to accelerate in the direction of the crossover.
Zero Line Crossovers
Zero Line Crossovers occur when the MACD Line crossed the Zero Line and either becomes positive (above 0) or negative (below 0). A change from positive to negative MACD is interpreted as "bearish", and from negative to positive as "bullish". Zero crossovers provide evidence of a change in the direction of a trend but less confirmation of its momentum than a signal line crossover
Divergence
Divergence is another signal created by the MACD. Simply, divergence occurs when the MACD and actual price are not in agreement. A "positive divergence" or "bullish divergence" occurs when the price makes a new low but the MACD does not confirm with a new low of its own. A "negative divergence" or "bearish divergence" occurs when the price makes a new high but the MACD does not confirm with a new high of its own. A divergence with respect to price may occur on the MACD line and/or the MACD Histogram
Moving Average Crossovers , another hidden signal that MACD Indicator identifies
Many traders will watch for a short-term moving average to cross above a longer-term moving average and use this to signal increasing upward momentum. This bullish crossover suggests that the price has recently been rising at a faster rate than it has in the past, so it is a common technical buy sign. Conversely, a short-term moving average crossing below a longer-term average is used to illustrate that the asset's price has been moving downward at a faster rate and that it may be a good time to sell.
Moving Average Crossovers in reality is Zero Line Crossovers, the value of the MACD indicator is equal to zero each time the two moving averages cross over each other. For easy interpretation by trades, Zero Line Crossovers are simply described as positive or negative MACD
False signals
Like any forecasting algorithm, the MACD can generate false signals. A false positive, for example, would be a bullish crossover followed by a sudden decline in a financial instrument. A false negative would be a situation where there is bearish crossover, yet the financial instrument accelerated suddenly upwards
What is “MACD-X” and Why it is “More Than MACD”
In its simples form, MACD-X implements variety of different calculation techniques applied to obtain MACD Line, ability to use of variety of different sources , including Volume related sources, and can be plotted along with MACD in the same window and all those features are available and presented within a single indicator, MACD-X
Different calculation techniques lead to different values for MACD Line, as will further discuss below, and as a consequence the signal line and the histogram values will differentiate accordingly. Mathematical calculation of both signal line and the histogram remain the same.
Main features of MACD-X ;
1- Introduces different proven techniques applied on MACD calculation , such as MACD-Histogram, MACD-Leader and MACD-Source, besides the traditional MACD (MACD-TRADITIONAL)
• MACD-Traditional , by Gerald Appel
It is the MACD that we know, stated as traditional just to avoid confusion with other techniques used with this study
• MACD-Histogram , by Thomas Aspray
The MACD-Histogram measures the distance between MACD and its signal line (the 9-day EMA of MACD). Aspray developed the MACD-Histogram to anticipate signal line crossovers in MACD. Because MACD uses moving averages and moving averages lag price, signal line crossovers can come late and affect the reward-to-risk ratio of a trade. Bullish or bearish divergences in the MACD-Histogram can alert chartists to an imminent signal line crossover in MACD
The MACD-Histogram represents the difference between MACD and its 9-day EMA, the signal line. Mathematically,
macdx = macd - ma(macd, signal_length)
Aspray's contribution served as a way to anticipate (and therefore cut down on lag) possible MACD crossovers which are a fundamental part of the indicator.
Here come a question, what if repeat the same calculations once more (macdh2 = macdh - ma(macdh, signal_length), will it be even better, this question will remain to be tested
• MACD-Leader , by Giorgos E. Siligardos, PhD
MACD Leader has the ability to lead MACD at critical situations. Almost all smoothing methods encounter in technical analysis are based on a relative-weighted sum of past prices, and the Leader is no exception. The concealed weights of MACD Leader are such that more relative weight is used in the more recent prices than the respective weights used by the components of MACD. In effect, the Leader expresses more changes in average price dynamics for the recent price movement than MACD, thus eventually leading MACD, especially when significant trend changes are about to take place.
Siligardos creates two less-laggard moving averages indicators in its formula using the same periods as follows
Indicator1 = ma(source, fast_length) + ma(source - ma(source, fast_length), fast_length)
Indicator2 = ma(source, slow_length) + ma(source - ma(source, slow_length), slow_length)
and then take the difference:
Indicator1 - Indicator2
The result is a new MACD Leader indicator
macdx = macd + ma(source - fast_ma, fast_length) - ma(source - slow_ma, slow_length)
• MACD-Source , a custom experimental interpretation of mine ,
MACD Source, presents an application of MACD that evaluates Source/MA Ratio, relatively with less lag, as a basis for MACD Line, also can be expressed as source convergence/divergence to its moving average. Among the various techniques for removing the lag between price and moving average (MA) of the price, one in particular stands out: the addition to the moving average of a portion of the difference between the price and MA. MACD Source, is based on signal length mean of the difference between Source and average value of shot length and long length moving average of the source (Source/MA Ratio), where the source is actual value and hence no lag and relatively less lag with the average value of moving average of the source . Mathematically expressed as,
macdx = ma(source - avg( ma(source, fast_length), ma(source, slow_length) ), signal_length)
MACD Source provides relatively early crossovers comparing to MACD and better momentum direction indications, assuming the lengths are set to same values
For further details, you are invited to check the following two studies, where the first seeds were sown of the MACD-Source idea
Price Distance to its Moving Averages study, adapts the idea of “Prices high above the moving average (MA) or low below it are likely to be remedied in the future by a reverse price movement", presented in an article by Denis Alajbeg, Zoran Bubas and Dina Vasic published in International Journal of Economics, Commerce and Management
First MACD like interpretation comes with the second study named as “ P-MACD ”, where P stands for price, P-MACD study attempts to display relationship between Price and its 20 and 200-period moving average. Calculations with P-MACD were based on price distance (convergence/divergence) to its 200-period moving average, and moving average convergence/divergence of 20-period moving average to 200-period moving average of price.
Now as explained above, MACD Source is a one adapted with traditional MACD, where Source stands for Price, Volume Indicator etc, any source applicable with MACD concept
2- Allows usage of variety of different sources, including Volume related indicators
The most common usage of Source for MACD calculation is close value of the financial instruments price. As an experimental approach, this study will allow source to be selected as one of the following series;
• Current Close Price (close)
• Average of High, Low, and Close Price (hlc3)
• On Balance Volume (obv)
• Accumulation Distribution (accdist)
• Price Volume Trend (pvt)
Where,
-Current Close Price and Average of High, Low, and Close Price are price actions of the financial instrument
- Accumulation Distribution is a volume based indicator designed to measure underlying supply and demand
- On Balance Volume (OBV) , is a momentum indicator that measures positive and negative volume flow
- Price Volume Trend (PVT) is a momentum based indicator used to measure money flow
3- Can be plotted along with MACD in the same window using the same scaling
Default setting of MACD-X will display MACD-Source with Current Close Price as a source and traditional MACD can be plotted eighter as a companion of MACD-X or can be selected to be plotted alone.
Applying both will add ability to compare, or use as a confirmation of one other
In case, traditional MACD Is plotted along with MACD-X to avoid misinterpreting, the lines plotted, the area between MACD-X Line and Signal-X Line is highlighted automatically, even if the highlight option not selected. Otherwise highlight will be applied only if that option selected
4- 4C Histogram
Histogram is plotted with four colors to emphasize the momentum and direction
5- Customizable
Additional to ability of selecting Calculation Method, Source, plotting along with MACD, there are few other option that allows users to customize the MACD-X indicator
Lengths are configurable, default values are set as 12, 26, 9 respectively for fast, slow and smoothing length. Setting lengths to 8,21,5 respectively Is worth checking, slower length moving averages will lead to less lag and earlier reaction to price actions but yet requires a caution and back testing before applying
Highlight the area between MACD-X Line and Signal-X Line, with colors emphasising the direction
Label can be added to display Calculation Method, Source and Length settings, the aim of this label is to server only as a reminder to trades to be aware of settings while they are occupied with charts, analysis etc.
Here comes another question, which is of more importance having the reminder or having the indicators with multi timeframe feature? Build-in Multi Time Frame features of Pine is not supported when labels and lines introduced in the script, there are other methods but brings complexity. To be studied further, this version will be with labels for time being.
Epilogue
MACD-X is an alternative variant of MACD, the insight/signals provided by MACD are also applicable to MACD-X with early and clear warnings for the changes in the trend.
If MACD is essential to your analysis, then it is my guess that after using the MACD-X for a while and familiarizing yourself with its unique character and personality, you will make it an inseparable companion to other indicators in your charts.
The various signals generated by MACD/MACD-X are easily interpreted and very few indicators in technical analysis have proved to be more reliable than the MACD, and this relatively simple indicator can quickly be incorporated into any short-term trading strategy
Disclaimer : Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
RedK_Supply/Demand Volume Viewer v1Background
============
VolumeViewer is a volume indicator, that offers a simple way to estimate the movement and balance (or lack of) of supply & demand volume based on the shape of the price bar. i put this together few years ago and i have a version of this published for another platform under different names (Directional Volume, BetterVolume) in case you come across them
what is V.Viewer
=====================
The idea here is to find a "simple proxy" for estimating the demand or supply portions of a volume bar - these 2 forces have the potential to affect the current price trend so we want an easy way to track them - or to understand if a stock is in accumulation or distribution - we want to do this without having access to Level II or bid/ask data, and without having to get into the complexity of exploring the lower timeframe price & volume data
- to achieve that, we depend on a simple assumption, that the volume associated with an up move is "demand" and the volume associated with a down move is "Supply". so we basically extrapolate these supply and demand values based on how the bar looks like - a full "green" price bar / candle will be considered 100% demand, and a full "red" price bar will be considered 100% supply - a bar that opens and closes at the same level will be 50/50 split between supply & demand.
- you may say this is a "too simple" of an assumption to make, but believe me, it works :) at least at the basic scenario we need here: i'm just exploring the volume movement and finding key levels - and it provides a good improvement compared to the classic way we see volume on a chart - which is still available here in VolumeViewer.
in all cases, i consider this to be work in progress, so i'd welcome any ideas to improve (without getting too complicated) - there's already a host of great volume-based indicators that will do the multi timeframe drill down, but that's not my scope here.
Technical Jargon & calculation
===========================
1. first we calculate a score % for the volume portion that is considered demand based on the bar shape
skip this part if it sounds too technical => if you're into coding indicators, you would probably know there are couple of different concepts for that algorithm - for example, the one used in Balance Of Power formula - which i'm a big fan of - but the one i use here is different. (how?) this is my own, ant it simply applies double weight for the "wick" parts of a price bar compared to the "body of the bar" -- i did some side-by-side comparison in past and decided this one works better. you can change it in the code if you like
2. after calculating the Bull vs Bears portion of volume, we take a moving average of both for the length you set, to come up with what we consider to be the Demand vs Supply - as usual, i use a weighted moving average (WMA) here.
3. the balance or net volume between these 2 lines is calculated, then we apply a final smoothing and that's the main plot we will get
4. being a very visual person, i did my best to build up the visuals in the correct order - then also to ensure the "study title" bar is properly organized and is simple and useful (Full Volume, Supply, Demand, Net Volume).
- i wish there was a way in Pine to hide a value that i still need to visually plot but don't want it showing its value on the study title bar, but couldn't find it. so the last plot value is repeated twice.
How to use
===========
- V.Viewer is set up to show the simplified view by default for simplicity. so when you first add it to a chart, you will get only the supply vs demand view you can see in the middle pane in the above chart
- Optional / detailed mode: go into the settings, and expose all other plots, you will be able to add the classic volume histogram, and the Supply / Demand lines - note these 2 lines will be overlay-ed on top of each other - this provides an easy way to see who is in control - especially if you change the display of these 2 lines into "area" style. This is what is showing in the lower pane in the above chart.
** Exploring Key Price Levels
- the premise is, at spots where there's big lack of balance, that's where to expect to find key price levels (support / resistance) and these price levels will come into play in future so can be used to set entry / exit targets for our trades - see the example in the AAPL chart where you can easily locate these "balance or reversal levels" using the tops/bottoms/zero-crossings from the Net Volume line
** Use for longer-term Price Analysis
- we can also use this simple indicator to gain more insights (at a high level) of the price in terms of accumulation vs distribution and if the sellers or buyers are in control - for example, in the above AAPL chart, V.Viewer tells us that buyers have been in control since October 19 - even during the recent drop, demand continued to be in play - compare that to DIS chart below for the same period, where it shows that the market was dumping DIS thru the weakness. DIS was bleeding red most of the time
Final thoughts
=============
- V.Viewer is an attempt to enhance the way we see and use Volume by leveraging the shape of the price bar to estimate volume supply & demand - and the Net between the 2
- it will work for stocks and other instruments as long as there's volume data
- note that V.Viewer does not track trend. each bar is taken in isolation of prior bars - the price may be going down and V.Viewer is showing supply going up (absorption scenario?) - so i suggest you do not use it to make decisions without consulting other trend / momentum indicators - of course this is a possible improvement idea, or can be implemented in another indicator, add in trend somehow, or maybe think of making this a +100 / -100 Oscillator .. feel free to play with these thoughts
- all thoughts welcome - if this is useful to you in your trading, please share with other trades here to learn from each other
- the code is commented - please feel free to use it as you like, or build things on top of it - but please continue to credit the author of this code :)
good luck!
-
stock gain% vs index gain %This shows the relative strength or weakness of a stock vs an index on any given candle price movement.
Negative stock candle and relative strength shows accumulation
Positive stock candle and relative weakness shows distribution
accumulation will plot an 'A'
distribution will plot a 'D'
MCI and VCI - Modified CCI FormulasFor private peeps only
- Takes a modified version of the CCI formula into 2 parts
VCI - Volume Channel Index (Yellow Histogram)
- Measures accurate accumulation and distribution levels and times
MCI - Modified Channel Index
- Measures (when compared to VCI) levels where clearly buys are interested vs not interested.
Example:
If VCI > MCI
- Shows buyer's are more than interested in buying, you've either hit a bottom or heavy resistance
if MCI > VCI
- Show's buyer's aren't interested and will most likely result in a dump/lower price
Great for monitoring accumulation and distribution, these auto buy and sells look for the transition points over 0, works on EVERY commodity/stock/FOREX/Crypto
Results are from trading 1 BTC x25 leveraging. Not all trades will get in if put in at limit, but it does survive with profits after the massive 0.075 fee (results shown are after fees)
Chaikin MF% (CMFP) w. Alerts, Bells & Whistles [LucF]This is Chaikin’s Money Flow indicator on a 0-100 scale with buy/sell signals, alerts and other bells & whistles.
It includes:
- a fast EMA (16 periods by default),
- a slow MA (64 periods by default),
- histograms,
- 3 different sorts of crosses,
- big swings identification,
- buy/sell signals on CMFP crossing back from outside user-defined levels,
- buy/sell signals on the slow MA pivots above/below user-defined levels,
- alerts on big swings and buy/sells.
This indicator started with @LazyBear code (VAPI) at:
@cI8DH then changed the scale to 0-100, which I find very useful:
I then added the rest.
The chart above shows both clean and busy versions of the indicator.
Note that the default length is 10 rather than the commonly used 20. I use CMFP in conjunction with VFI and like the fact that it is faster than VFI. The default inputs show the way I normally use this indicator, with the slow MA shown in histogram mode. I find it gives good context to the signal line. Crosses between the two are often useful.
The buy/sell signals aren’t the main attraction of this indicator, and nothing to write home about. Like the big swing markers, I think it’s more realistic to view them as pointers to potentially interesting areas on charts. Their nature makes them more suited to identifying reversals. They certainly aren’t reliable enough to turn this study into a strategy and I normally don’t use them. The levels pre-defined for the buy/sell signals on CMFP are most useful on short intervals. The buy/sell signals on the slow MA pivots work on a more complete range of intervals. Optimization for your specific instruments and intervals will improve their reliability.
As usual when defining alerts, be sure you already have defined proper inputs and that you are on the intended interval, as they will be used when triggering alerts.
Klinger Volume Oscillator (KVO) Backtest The Klinger Oscillator (KO) was developed by Stephen J. Klinger. Learning
from prior research on volume by such well-known technicians as Joseph Granville,
Larry Williams, and Marc Chaikin, Mr. Klinger set out to develop a volume-based
indicator to help in both short- and long-term analysis.
The KO was developed with two seemingly opposite goals in mind: to be sensitive
enough to signal short-term tops and bottoms, yet accurate enough to reflect the
long-term flow of money into and out of a security.
The KO is based on the following tenets:
Price range (i.e. High - Low) is a measure of movement and volume is the force behind
the movement. The sum of High + Low + Close defines a trend. Accumulation occurs when
today's sum is greater than the previous day's. Conversely, distribution occurs when
today's sum is less than the previous day's. When the sums are equal, the existing trend
is maintained.
Volume produces continuous intra-day changes in price reflecting buying and selling pressure.
The KO quantifies the difference between the number of shares being accumulated and distributed
each day as "volume force". A strong, rising volume force should accompany an uptrend and then
gradually contract over time during the latter stages of the uptrend and the early stages of
the following downtrend. This should be followed by a rising volume force reflecting some
accumulation before a bottom develops.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading.
Klinger Volume Oscillator (KVO) Strategy The Klinger Oscillator (KO) was developed by Stephen J. Klinger. Learning
from prior research on volume by such well-known technicians as Joseph Granville,
Larry Williams, and Marc Chaikin, Mr. Klinger set out to develop a volume-based
indicator to help in both short- and long-term analysis.
The KO was developed with two seemingly opposite goals in mind: to be sensitive
enough to signal short-term tops and bottoms, yet accurate enough to reflect the
long-term flow of money into and out of a security.
The KO is based on the following tenets:
Price range (i.e. High - Low) is a measure of movement and volume is the force behind
the movement. The sum of High + Low + Close defines a trend. Accumulation occurs when
today's sum is greater than the previous day's. Conversely, distribution occurs when
today's sum is less than the previous day's. When the sums are equal, the existing trend
is maintained.
Volume produces continuous intra-day changes in price reflecting buying and selling pressure.
The KO quantifies the difference between the number of shares being accumulated and distributed
each day as "volume force". A strong, rising volume force should accompany an uptrend and then
gradually contract over time during the latter stages of the uptrend and the early stages of
the following downtrend. This should be followed by a rising volume force reflecting some
accumulation before a bottom develops.
WARNING:
This script to change bars colors.
FutureObitz Official Bank Levels)The FutureObitz Bank Levels indicator, by @FutureObitz is a powerful tool designed to help traders identify key daily structural price levels. It automatically identifies and plots the Daily High (D. High) and Daily Low (D. Low), ensuring these crucial levels are visible and consistent across various chart timeframes (e.g., 15m, 30m, 1h, 4h, and Daily), providing a reliable higher-timeframe perspective.
What This Indicator Provides:
Dynamic Daily Levels: Automatically calculates and plots daily High, Low, Middle, Middle High, and Middle Low of the current trading day. These lines provide clear reference points for price action.
Defined Buy & Sell Zones: Clearly visualizes potential accumulation (Buy Zone) and distribution (Sell Zone) areas. These zones are calculated as a customizable percentage (via inputs) above/below the 'Middle' of the daily range, helping to identify potential entry or exit points.
Multi-Timeframe Compatibility: Displays consistent daily levels regardless of the chart's active timeframe, offering a consistent higher-timeframe perspective for intraday traders.
Visual Clarity: Includes filled zones for enhanced visibility of key areas.
Optional Labels: Provides clear labels for the latest daily levels (D. High, D. Low, D. Middle, D. Buy Zone, D. Sell Zone) on the last bar of your chart for quick reference.
This indicator serves as a robust framework for understanding daily price action and identifying potential areas of support, resistance, and trading opportunities. It's an excellent tool for traders who rely on structured levels derived from higher timeframes to inform their decisions.
🦄 Unicorn Entry Checklist🦄 *Unicorn Entry Checklist* is a visual decision-making tool for SMC/ICT traders who want to validate confluence before entering a trade.
It provides a structured approach based on Smart Money Concepts including:
✅ Liquidity Grab
✅ MSS with Displacement
✅ BB + FVG/IFVG
✅ Killzone Timing
✅ SMT / 3Drive / StopHunt
✅ Accumulation / Reaccumulation Zones
🎯 Use it to confirm high-probability entries and avoid weak setups.
Built for discretionary traders who want clarity and consistency on their charts.
Clean visual table with live checklist and auto-scoring.
Developed by *@dragosburdulea*
