MTF MA Ribbon and Bands + BB, Gaussian F. and R. VWAP with StDev█ Multi Timeframe Moving Average Ribbon and Bands + Bollinger Bands, Gaussian Filter and Rolling Volume Weighted Average Price with Standard Deviation Bands
Up to 9 moving averages can be independently applied.
The length , type and timeframe of each moving average are configurable .
The lines, colors and background fill are customizable too.
This script can also display:
Moving Average Bands
Bollinger Bands
Gaussian Filter
Rolling VWAP and Standard Deviation Bands
Types of Moving Averages:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Smoothed Moving Average (SMMA)
Weighted Moving Average (WMA)
Volume Weighted Moving Average (VWMA)
Least Squares Moving Average (LSMA)
Hull Moving Average (HMA)
Arnaud Legoux Moving Average (ALMA)
█ Moving Average
Moving Averages are price based, lagging (or reactive) indicators that display the average price of a security over a set period of time.
A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance.
█ Bollinger Bands
Bollinger Bands consist of a band of three lines which are plotted in relation to security prices.
The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (the type of trend line and period can be changed by the trader, a 20 day moving average is by far the most popular).
The SMA then serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price.
█ Gaussian Filter
Gaussian filter can be used for smoothing.
It rejects high frequencies (fast movements) better than an EMA and has lower lag.
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve.
In the case of low-pass filters, only the upper half of the curve describes the filter.
The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
█ Rolling VWAP
The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day.
Such VWAPs cannot be used on daily, weekly or monthly charts. Instead, this rolling VWAP uses a time period that automatically adjusts to the chart's timeframe.
You can thus use the rolling VWAP on any chart that includes volume information in its data feed.
Because the rolling VWAP uses a moving window, it does not exhibit the jumpiness of VWAP plots that reset.
Made with the help from scripts of: adam24x, VishvaP, loxx and pmk07.
在脚本中搜索"averages"
Volume Oscillator RefurbishedThis is an experimental version of Volume Oscillator.
For more information about Volume Oscillator, please access the link below:
www.tradingview.com
Objective
The script presented here provides some improvements over the original indicator, namely:
Show multiple moving averages;
Color the bars according to the direction of the averages;
Color the bars when reaching predefined limits.
Below is the print comparing with the original indicator:
Thanks and credits:
Volume Oscillator: TradingView
Moving Averages: PineCoders, CrackingCryptocurrency, MightyZinger, Alex Orekhov (everget), alexgrover, paragjyoti2012, Franklin Moormann (cheatcountry)
Qullamaggie Daily with ADR% and Compression RangeQullamaggie Daily
This Indicator is a Combination of Moving Averages (Simple and Exponential) as definied from Qullamaggie and used in his TC2000 Setup
Moving Averages:
- The Moving Averages are Guidelines for the current Trend and are not decive for the Entry
- They shall be a quick view and visual assistance to find strong momentum stock that are currently in a Phase of a "Flag Pattern"
ADR% 20 Day:
- Average Daily Range in % should indicate the Momentum of the Stock. It is similar but still works different as the Volalitily indicators.
- A stock is recommend to a have a ADR% above 5-6 to be considered a Momentum Leading Stock.
Consolidation Range:
- This Indicator should help to define Ranges in which the Volumen get compressed(increase) while the price movement is minimal
- A strong breakout is to be expected. The Range should be easier to be identified with this indication.
Qullamaggie Breakout V2After publishing the Qullamaggie Breakout script and seeing that it had some decent results, I wanted to explore it a bit further. There were a few things I didn't like about that methodology that didn't really jive with the way I like to trade. So what I did was combined the Breakout Trend Follower strategy I had been using for entries with the Qullamaggie strategy for trailing stops once in profit. The results seem pretty good to me and an approach that fits my personality and something I can actually trade. Typically better profit than the Breakout Trend Follower by giving more room for your winners to run, while still protecting your entries by moving up the trailing stop until you are in profit, all while taking less trades, so that's great.
Everything is done with stop orders. So you set your buy stop at the recent swing high point and wait for a breakout. Once in a position you set your sell stop at the recent swing low point. The most recent swing high and low are shown on the chart for easy reference with the blue and orange horizontal lines. Once in a trade, trail your sell stop after a new swing low is registered (shown by the thicker orange stop line). Once you are in profit, leave that hard stop level there (the orange line will stay there helping you). Now, you wait for price to cross a Moving Average of your choosing (default is Daily 10 MA). Once the bar crosses that moving average, you move your stop to the low of that candle (shown by the blue stop line) and trail your stop along every crossing of the moving average until the trend changes and takes out your stop. So managing this trade is pretty easy...just wait for the stop lines to move and move your stop with them. It's a great way to trade when you can't be at your computer all the time because the stop orders take care of execution on both buy and sell side. If you use a daily timeframe for your moving averages (the default), you really only need to move stops around about once a day, so is a good part time trader's strategy in my opinion.
The best opportunities will come by scanning for stocks in the longer term timeframe of your moving averages. Wait for a consolidation on that timeframe so the anticipated breakout has some room to run. Once you've identified a good candidate, zoom in to your lower timeframe where the swing highs/lows will act as your entry and exit points, all while keeping the moving averages consistent between timeframes.
Hope you guys find it useful.
A few options available:
- Choose any timeframe for your moving averages, while using swing high/low points on intraday charts.
- Choose one of two moving averages shown for your trailing stops (default 10 and 20 MA).
- Choose to use the third moving average as a filter for keeping you out of trades that are below it (trading with the trend).
- Use the charts resolution candle or the moving average resolution candle for the moving average trailing stop.
- Only take trades where your buy level minus stop level is below a % of the Average Daily Range (ADR). This allows you to potentially have better risk/reward. I added a little table that shows the ADR of the stock/ticker as well as the range between the recent buy and sell levels (shown by the orange and blue horizontal lines) for easy reference.
Multi Range VWAP PivotsMulti Range VWAP Pivots turned out to be one of my most accurate pivot indicators to date!
Multi Range VWAP Pivots works by recognizing the high and low of the timeframe selected (D, W, M, 6M, and 12M) and plotting range high to VWAP averages and range low to VWAP averages.
After further examination of each completed range, I came to the conclusion that due to the nature of averages, high and low respectively would need to be completed within the current range, for the averages to actually display pivots correctly. This means that if all averages appear to be "pivoting" correctly after or during a break lower of higher, then we can only assume the most recent break higher or lower could be exhaustion and price will be reverted to the mean (VWAP). OR, this could be the most accurate hindsight indicator on the planet.
*DISCLAIMER*: This indicator repaints. DO NOT backtest or set alerts with this indicator.
{Gunzo} Heiken Ashi RibbonsHeiken Ashi Ribbons is a trend-following indicator which gives entry and exit points for short-term, medium-term and long term trading (using Exponential Moving Averages and Heiken Ashi formulas).
OVERVIEW :
The Heiken Ashi Ribbons indicator is composed of 3 moving average ribbons (slow, normal and fast) that are computed using the Heiken Ashi formulas. The 3 ribbons give a clear vision of the current trend as they use moving averages that smooth out the price and filter noise from short term fluctuations. In a simplified way, you can consider each ribbon as a moving average with a larger body size.
If the price is above the slow ribbon, we consider the asset as trending up in the short term (trending down otherwise). If the price is above the fast ribbon, we consider the asset as trending up in the long term (trending down otherwise).
CALCULATION :
First of all, to compute a ribbon for this indicator we calculate a moving average (EMA by default) for common sources (OHLC) :
EMA (open), EMA (high), EMA (low), EMA (close)
We then apply the Heiken Ashi formulas to the moving averages calculated previously.
HA (open) = HA (open) previous + HA (close) previous
HA (close) = ( EMA (open) + EMA (high) + EMA (low) + EMA (close) ) / 4
HA (high) = max( EMA (open), EMA (close), EMA (high) )
HA (low) = min ( EMA (open), EMA (close), EMA (low) )
The ribbon displayed (by default) on the chart is the area between HA (open) and HA (close).
SETTINGS :
1st Moving average length : Length of the slow moving average
2nd Moving average length : Length of the normal moving average
3rd Moving average length : Length of the fast moving average
Moving average method : Moving average calculation method (EMA : Exponential Moving Average, SMA : Simple Moving Average, WMA : Weighted Moving Average)
Ribbon type : standard ribbon uses the area between HA (open) and HA (close). Large ribbon uses the area between HA (low) and HA (high)
Display ribbon as candles : change the type of visualization between area and candles
Display short term buy/sell signals : Display short term buy/sell signals (crosses) when the fast moving average and normal moving average are crossing
Display long term buy/sell signals : Display long buy/sell signals (circles) when the fast moving average and slow moving average are crossing
Display ribbon trending up signals : Display ribbon direction change (triangle up) when the trend of the ribbon changes to trending up
Display ribbon trending down signals : Display ribbon direction change (triangle down) when the trend of the ribbon changes to trending down
VISUALIZATIONS :
This indicator has 2 possible visualizations :
Ribbons : the ribbons can be considered as enhanced moving averages for trading purposes. They represent the area between the Heiken Ashi of the moving average of the open and closing price. The color of the moving average line is green when the ribbon is trending up and red when the ribbon is trending down.
Signals : Various signals can be displayed at the bottom of the chart (Buy/Sell signals, Ribbon direction changes signals).
USAGE :
This indicator can be used in many strategies, just like when you are using multiple moving averages. You should test these strategies and use the one that best fits your trading style.
Strategy based on crossovers :
When the fast ribbon crosses above the normal ribbon, it is a short term buy signal (it is recommended to wait for a confirmation)
When the fast ribbon crosses under the normal ribbon, it is a short term sell signal (it is recommended to wait for a confirmation)
When the fast ribbon crosses above the slow ribbon, it is a long term buy signal
When the fast ribbon crosses over the slow ribbon, it is a long term buy signal
Strategy based on price position :
When the prices closes above the ribbon, it is a buy signal (long term if above slow ribbon, short term if above fast ribbon)
When the prices closes below the ribbon, it is a sell signal (long term if below slow ribbon, short term if below fast ribbon)
Strategy based on price bouncing :
When the price decreases and reaches the green long term ribbon, the price candles may not be able to cross the ribbon. If the price increases, we consider that move as a bounce on the ribbon, which is a buy signal
When the price increases and reaches a red long term ribbon, the price candles may not be able to cross the ribbon. If the price decreases, we consider that move as a bounce on the ribbon, which is a sell signal
Strategy based on ribbon direction :
When the direction of the ribbon changes, the trend of the asset is changing which may lead to a crossover to the next candles if the trend is continuing in that direction (it is recommended to validate the entry points with a second indicator as this strategy may have some false signals).
Advance AMA with Sylvain BandsMany traders believe that the moving averages are favorite tools and analysts have spent decades trying to improve moving averages partiularly the simple moving average. One way to address the disadvantages of moving averages is to multiply the weighting factor by a volatility ratio which is called Adaptive moving averages.
This indicator uses an special adaptive moving averages which is developed by John Ehlers. The model adapts to price movement “based on the rate change of phase as measured by the Hilbert Transform Discriminator”. This method of adaptation features a fast and a slow moving average so that the composite moving average swiftly responds to price changes and holds the average value until the next bars close. In addition, the smoothed Volatility Bands were created by Sylvain Vervoort is included.
Volume Indicators PackageCONTAINS 3 OF MY BEST VOLUME INDICATORS ALL FOR THE PRICE OF ONE!
CONTAINS:
Average Dollar Volume in RED
Up/Down Volume Ratio in Green
Volume Buzz/Volume Run Rate in BLUE
If you would like to get these individually, I also have scripts for that too.
Below is information about all three of these indicators, what they do, and why they are important.
---------------------------------------------------------------------------------------------AVERAGE DOLLAR VOLUME----------------------------------------------------------------------------------------
Dollar volume is simply the volume traded multiplied times the cost of the stock.
Dollar volume is an extremely important metric for finding stocks with enough liquidity for market makers to position themselves in. Market Liquidity is defined as market's feature whereby an individual or firm can quickly purchase or sell an asset without causing a drastic change in the asset's price. The key concept you want to understand is that these big instructions with billions of dollars need liquidity in a stock in order to even think about buying it, and therefore these institutions will demand a large dollar volume . A good dollar volume amount, that represents a pretty liquid name, is typically above 100 million $ average. Why are institutions important? Simple because they are the ones who make stocks move, and I mean really move. If you want to see large growth from a stock in a short amount of time, you need institutions wielding billions of dollars to be fighting one another to buy more shares. Institutions are the ones who make or break a stock, this is why we call them market makers.
My script calculates average dollar volume using four averages: the 50, the 30, the 20, and the 10 period. I use multiple averages in order to provide the accurate and up to date information to you. It then selects the minimum of these averages and divides this value by 1 million and displays this number to you.
TL;DR? If you want monster moves from your stocks, you need to pick names with average high liquidity(dollar volume >= $100 million). The number presented to you is in millions of whatever currency the name is traded in.
---------------------------------------------------------------------------------------------UP/DOWN VOLUME RATIO-----------------------------------------------------------------------------------------
Up/Down Volume Ratio is calculated by summing volume on days when it closes up and divide that total by the volume on days when the stock closed down.
High volume up days are typically a sign of accumulation(buying) by big players, while down days are signs of distribution(selling) by big market players. The Up Down volume ratio takes this assumption and turns it into a tangible number that's easier for the trader to understand. My formula is calculated using the past 50 periods, be warned it will not display a value for stocks with under 50 periods of trading history. This indicator is great for identify accumulation of growth stocks early on in their moves, most of the time you would like a growth stocks U/D value to be above 2, showing institutional sponsorship of a stock.
Up/Down Volume value interpretation:
U/D < 1 -> Bearish outlook, as sellers are in control
U/D = 1 -> Sellers and Buyers are equal
U/D > 1 -> Bullish outlook, as buyers are in control
U/D > 2 -> Bullish outlook, significant accumulation underway by market makers
U/D >= 3 -> MONSTER STOCK ALERT, market makers can not get enough of this stock and are ravenous to buy more
U/D values greater than 2 are rare and typically do not last very long, and U/D >= 3 are extremely rare one example I kind find of a stock's U/D peaking above 3 was Google back in 2005.
-----------------------------------------------------------------------------------------------------VOLUME BUZZ-----------------------------------------------------------------------------------------------
Volume Buzz/ Volume Run Rate as seen on TC2000 and MarketSmith respectively.
Basically, the volume buzz tells you what percentage over average(100 time period moving average) the volume traded was. You can use this indicator to more readily identify above-average trading volume and accumulation days on charts. The percentage will show up in the top left corner, make sure to click the settings button and uncheck the second box(left of plot) in order to get rid of the chart line.
Average Dollar VolumeDollar volume is simply the volume traded multiplied times the cost of the stock.
Dollar volume is an extremely important metric for finding stocks with enough liquidity for market makers to position themselves in. Market Liquidity is defined as market's feature whereby an individual or firm can quickly purchase or sell an asset without causing a drastic change in the asset's price. The key concept you want to understand is that these big instructions with billions of dollars need liquidity in a stock in order to even think about buying it, and therefore these institutions will demand a large dollar volume. A good dollar volume amount, that represents a pretty liquid name, is typically above 100 million $ average. Why are institutions important? Simple because they are the ones who make stocks move, and I mean really move. If you want to see large growth from a stock in a short amount of time, you need institutions wielding billions of dollars to be fighting one another to buy more shares. Institutions are the ones who make or break a stock, this is why we call them market makers.
My script calculates average dollar volume using four averages: the 50, the 30, the 20, and the 10 period. I use multiple averages in order to provide the accurate and up to date information to you. It then selects the minimum of these averages and divides this value by 1 million and displays this number to you.
TL;DR? If you want monster moves from your stocks, you need to pick names with average high liquidity(dollar volume >= $100 million). The number presented to you is in millions of whatever currency the name is traded in.
Momentum Strategy (BTC/USDT; 1h) - MACD (with source code)Good morning traders.
It's been a while from my last publication of a strategy and today I want to share with you this small piece of script that showed quite interesting result across bitcoin and other altcoins.
The macd indicator is an indicator built on the difference between a fast moving average and a slow moving average: this difference is generally plottted with a blue line while the orange line is simply a moving average computed on this difference.
Usually this indicator is used in technical analysis for getting signals of buy and sell respectively when the macd crosses above or under its moving average: it means that the distance of the fast moving average (the most responsive one) from the slower one is getting lower than what it-used-to-be in the period considered: this could anticipate a cross of the two moving averages and you want to anticipate this potential trend reversal by opening a long position
Of course the workflow is specularly the same for opening short positions (or closing long positions)
What this strategy does is simply considering the moving average computed on macd and applying a linear regression on it: in this way, even though the signal can be sligthly delayed, you reduce noise plotting a smooth curve.
Then, it simply checks the maximums and the minimums of this curve detecting whenever the changes of the values start to be negative or positive, so it opens a short position (closes long) on the maximum on this curve and it opens a long position (closes short) on the minimum.
Of course, I set an option for using this strategy in a conventional way working on the crosses between macd and its moving average. Alternatively you can use this workflow if you prefer.
In conclusion, you can use a tons of moving averages: I made a function in pine in order to allw you to use any moving average you want for the two moving averages on which the macd is based or for the moving average computed on the macd
PLEASE, BE AWARE THAT THIS TRADING STRATEGY DOES NOT GUARANTEE ANY KIND OF SUCCESS IN ADVANCE. YOU ARE THE ONE AND ONLY RESPONSIBLE OF YOUR OWN DECISIONS, I DON'T TAKE ANY RESPONSIBILITY ASSOCIATED WITH THEM. IF YOU RUN THIS STRATEGY YOU ACCEPT THE POSSIBILITY OF LOOSING MONEY, ALL OF MY PUBBLICATIONS ARE SUPPOSED TO BE JUST FOR EDUCATIONAL PURPOSES.
IT IS AT YOUR OWN RISK WHETHER TO USE IT OR NOT
But if you make money out of this, please consider to buy me a beer 😜
Happy Trading!
Trend Reversal Indicator (EMA of slopes)Good morning Traders
Inspirated by lukescream EMA-slope strategy, today I want to share with you this simple indicator whose possible use-case would be for detecting in advance possible trend reversals, specially on higher timeframes.
Once that you've chosen the desired source (RSI, EMA or Stochastic k or d), the indicator will calculate its "slope" approximating its first order derivative by the division between the last variation of the series and its last value.
You can see the slope as a white line by enabling the relative checkmark (it's disabled by default since it simply messes up the the graph)
Then, the slope itself becomes the source for two exponential moving averages: the fast one (in blue) has a period of 20 while the slow one (in red, it becomes similiar to a horizontal line actually) has a period of 500
Why the slope? Since all the sources mentioned before are directly or indirectly calculated on the price action, a more aggressiveness in the price movement would be translated into a more (positive/negative) steepness of those indicator (of course this effect would be far more evident if the indicators are calculated on low periods, but really low periods could compromise the consistency of the signals).
In this way, the slope would mirror the decisiveness of price movements and a comparison between two averages calculated from it (the first one based on more recent values, the second one that conisders also older values) could tell you in advance what direction the market is possibly about to take
The usage is simple: once that the fast moving average crosses upward the slow one, this could be a sign of potential trend reversal from bearish to bullish. On the contrary, if the fast EMA crosses downward the slow one, this could be a sign of potential trend reversal from bullish to bearish.
What I suggest you is to integrate this indicator with Exponential Moving Averages plotted on the price candles, in order to have a general bias for opening long or short positions, and with an oscillator as well such as the Stochastisc RSI in order to detect the overbought/oversold zones for opening/closing positions at the right moment.
Happy Trading!
Smoothed CandlesHello Traders,
This is " Smoothed Candles " script to get rid of noises and to get a smoothed chart to figure out breakouts and price movements easily.
There are three scaling methods: User Defined, Dynamic (ATR) and Percentage
Optionally you can add 2 Simple Moving Averages and 2 Exponential Moving Averages
Optionally you can hide the Wicks, example:
You can add moving averages:
Easily find breakouts:
Enjoy!
Noro's TrendMA StrategyThe strategy uses 2 moving averages. Fast and slow. SMA or EMA - the user can select. Moving averages are needed to identify the direction of the trend.
Trend
If both moving averages are directed upwards, it 's uptrend.
If both moving averages are pointing down, it 's downtrend.
If the moving averages are directed in different directions, the trend has not changed.
Background
Lime color is uptrend.
Red color is downtrend.
By default, background display is disabled, but you can enable it in script settings.
Trading
If uptrend (lime background) - open long position (and close short position)
If downtrend (red background) - open short position (and close long position)
Reverse trading, no stop-loss and take-profit
Short positions can be removed and only long positions can be traded.
For
- crypto/USD (XBT/USD, ETH/USD, etc)
- timeframes: 1h, 4h, 1d
TA█ TA Library
📊 OVERVIEW
TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research.
🎯 Core Features
Academic Based - Algorithms follow original papers and formulas
Performance Optimized - Pre-calculated constants for faster response
Unified Interface - Consistent function design
Research Based - Integrates technical analysis research
🎯 CONCEPTS
Library Design Philosophy
This technical analysis library focuses on providing:
Academic Foundation
Algorithms based on published research papers and academic standards
Implementations that follow original mathematical formulations
Clear documentation with research references
Developer Experience
Unified interface design for consistent usage patterns
Pre-calculated constants for optimal performance
Comprehensive function collection to reduce development time
Single import statement for immediate access to all functions
Each indicator encapsulated as a simple function call - one line of code simplifies complexity
Technical Excellence
25+ carefully implemented moving averages and filters
Support for advanced algorithms like Kalman Filter and MAMA/FAMA
Optimized code structure for maintainability and reliability
Regular updates incorporating latest research developments
🚀 USING THIS LIBRARY
Import Library
//@version=6
import DCAUT/TA/1 as dta
indicator("Advanced Technical Analysis", overlay=true)
Basic Usage Example
// Classic moving average combination
ema20 = ta.ema(close, 20)
kama20 = dta.kama(close, 20)
plot(ema20, "EMA20", color.red, 2)
plot(kama20, "KAMA20", color.green, 2)
Advanced Trading System
// Adaptive moving average system
kama = dta.kama(close, 20, 2, 30)
= dta.mamaFama(close, 0.5, 0.05)
// Trend confirmation and entry signals
bullTrend = kama > kama and mamaValue > famaValue
bearTrend = kama < kama and mamaValue < famaValue
longSignal = ta.crossover(close, kama) and bullTrend
shortSignal = ta.crossunder(close, kama) and bearTrend
plot(kama, "KAMA", color.blue, 3)
plot(mamaValue, "MAMA", color.orange, 2)
plot(famaValue, "FAMA", color.purple, 2)
plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green)
plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red)
📋 FUNCTIONS REFERENCE
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float) : Series of values to process.
alpha (series float) : The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the efficiency calculation.
fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2.
slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960).
Parameters:
source (series float) : Series of values to process.
processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05.
measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: ( ) Tuple containing values.
laguerreFilter(source, length, gamma, order)
Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8.
Returns: (float) The calculated standard Laguerre Filter value.
laguerreBinomialFilter(source, length, gamma)
Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5.
Returns: (float) The calculated Laguerre Binomial Filter value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Period for the filter calculation.
Returns: (float) The calculated Super Smoother value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the average range calculation.
multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618.
Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin.
Parameters:
source (series float) : Series of values to process.
rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14.
rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5.
qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236.
Returns: ( ) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: ( ) Tuple containing SSL Up and SSL Down lines.
ma(source, length, maType)
Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA.
Returns: (float) The calculated moving average value based on the specified type.
atr(length, maType)
Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr.
Parameters:
length (simple int) : Number of bars for the ATR calculation.
maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA.
Returns: (float) The calculated Average True Range value.
macd(source, fastLength, slowLength, signalLength, maType, signalMaType)
Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
signalLength (simple int) : Period for the signal line moving average.
maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA.
signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA.
Returns: ( ) Tuple containing MACD line, signal line, and histogram values.
dmao(source, fastLength, slowLength, maType)
Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA.
Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage.
continuationIndex(source, length, gamma, order)
Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09.
Parameters:
source (series float) : Series of values to process.
length (simple int) : The calculation length.
gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8.
Returns: (float) The calculated Continuation Index value.
📚 RELEASE NOTES
v1.0 (2025.09.24)
✅ 25+ technical analysis functions
✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA)
✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother)
✅ Performance optimized with pre-calculated constants and efficient algorithms
✅ Unified function interface design following TradingView best practices
✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.)
✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter)
✅ Continuation Index - Latest research from TASC 2025.09
✅ MACD and ATR calculations supporting multiple moving average types
✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
Adaptive Gap Bands - DolphinTradeBot1️⃣ Overview
Adaptive Gap Bands is a momentum indicator that measures the percentage difference between fast and slow moving averages. This helps identify potential overbought or oversold zones.
The goal is to analyze “gap” behaviors within a trend and generate clearer entry–exit signals.
Since the bands are anchored to the slow moving average, they are more sensitive to the trend direction, making signals stronger in line with the prevailing trend.
📌 Signals do not repaint — once confirmed, they remain fixed on the chart.
2️⃣ How It Works ?
The indicator tracks the distance between fast and slow MAs.
The indicator measures the percentage gap between the fast and slow moving averages, relative to the slow MA.
Each time the gap reaches a new extreme during a swing, that value is stored.
When the averages cross, the stored values from the last N swings (defined by Swing Count) are collected.
These gap values are then averaged to create a smoother and more adaptive reference.
The bands are built by multiplying this average gap with the % Multiplier and projecting it around the slow MA.
3️⃣ How to Use It ?
Add the script to your chart.
Green label → potential Long signal.
Red label → potential Short signal.
Signals often appear when price moves outside the adaptive bands, showing extreme momentum.
Can also be used as a reference tool in manual trades to set profit/loss expectations.
By comparing upward vs. downward gaps, it can help analyze and confirm the dominant trend direction.
4️⃣⚙️ Settings
Swing Count → Number of past swings considered.
% Multiplier → Adjusts band width (narrower or wider).
MA Lengths & Types → Choose fast and slow moving averages (EMA, SMA, RMA, etc.).
ARMA(Autoregressive Moving Average) Model -DeepALGO-📊 ARMA Model Indicator
This script is a custom indicator based on the ARMA (Autoregressive Moving Average) model, one of the fundamental and widely used models in time series analysis.
While ARMA is typically employed in statistical software, this implementation makes it accessible directly on TradingView, allowing traders to visualize and apply the dynamics of ARMA in financial markets with ease.
🧩 What is the ARMA Model?
The ARMA model explains time series data by combining two components: Autoregression (AR) and Moving Average (MA).
AR (Autoregression) component
Captures the dependence of current values on past values, modeling the inherent autocorrelation of the series.
MA (Moving Average) component
Incorporates past forecast errors (residuals), smoothing out randomness and noise while improving predictive capability.
By combining these two aspects, ARMA models can capture both the underlying structure of the data and the random fluctuations, providing a more robust description of price behavior than simple averages alone.
⚙️ Design of This Script
In classical statistics, ARMA coefficients are estimated using the ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function). However, this process is often too complex for trading environments.
This script simplifies the approach:
The coefficients theta (θ) and epsilon (ε) are fixed, automatically derived from the chosen AR and MA periods.
This eliminates the need for statistical estimation, making the indicator easy to apply with simple parameter adjustments.
The goal is not academic rigor, but practical usability for traders.
🔧 Configurable Parameters
AR Period (p): Order of the autoregressive part.
MA Period (q): Order of the moving average part. Shorter periods yield faster responsiveness, while longer periods produce smoother outputs.
Offset: Shifts the line forward or backward for easier comparison.
Smoothing Period: Additional smoothing to reduce noise.
Source: Choose from Close, HL2, HLC3, High, or Low.
🎯 Advantages Compared to Traditional Moving Averages
Commonly used moving averages such as SMA (Simple Moving Average) and EMA (Exponential Moving Average) are intuitive but have limitations:
SMA applies equal weights to past data, which makes it slow to respond to new price changes.
EMA emphasizes recent data, providing faster response but often introducing more noise and reducing smoothness.
The ARMA-based approach provides two key advantages:
Balance of Responsiveness and Smoothness
AR terms capture autocorrelation while MA terms correct residuals, resulting in a smoother line that still reacts more quickly than SMA or EMA.
Flexible Adaptation
By adjusting the MA period (q), traders can fine-tune how closely the model follows price fluctuations—ranging from rapid short-term responses to stable long-term trend recognition.
📈 Practical Use Cases
The ARMA indicator can be applied in several practical ways:
Trend Direction Estimation
The slope and position of the ARMA line can provide a straightforward read of bullish or bearish market conditions.
Trend Reversal Identification
Changes in the ARMA line’s direction may signal early signs of a reversal, often with faster reaction compared to traditional moving averages.
Confirmation with Other Indicators
Combine ARMA with oscillators such as RSI or MACD to improve the reliability of signals.
Combination with Heikin-Ashi
Heikin-Ashi candles smooth out price action and highlight trend changes. When used together with ARMA, they can significantly enhance reversal detection. For example, if Heikin-Ashi indicates a potential reversal and the ARMA line simultaneously changes direction, the confluence provides a stronger and more reliable trading signal.
⚠️ Important Notes
Risk of Overfitting
Excessive optimization of AR or MA periods may lead to overfitting, where the indicator fits historical data well but fails to generalize to future market conditions. Keep parameter choices simple and consistent.
Weakness in Sideways Markets
ARMA works best in trending environments. In range-bound conditions, signals may become noisy or less reliable. Consider combining it with range-detection tools or volume analysis.
Not a Standalone System
This indicator should not be used in isolation for trading decisions. It is best employed as part of a broader analysis framework, combining multiple indicators and fundamental insights.
💡 Summary
This script brings the theoretical foundation of ARMA into a practical, chart-based tool for traders.
It is particularly valuable for those who find SMA too lagging or EMA too noisy, offering a more nuanced balance between responsiveness and smoothness.
By capturing both autocorrelation and residual structure, ARMA provides a deeper view of market dynamics.
Combined with tools such as Heikin-Ashi or oscillators, it can significantly enhance trend reversal detection and strategy reliability.
HMA Trend Line (Croc Signal Line)HMA Trend Line (Croc Signal Line) — The Ultimate Hull Moving Average Trend Indicator
Full English description here:
What is the HMA Trend Line (Croc Signal Line)?
The HMA Trend Line (Croc Signal Line) is a powerful, adaptive trend indicator for TradingView, based on the Hull Moving Average (HMA). This indicator is designed to help traders identify real market trends with less lag and reduced noise compared to traditional moving averages like SMA (Simple Moving Average) and EMA (Exponential Moving Average).
Why use the HMA Trend Line?
+ Faster Trend Detection: The Hull Moving Average (HMA) responds more quickly to price action, giving you earlier buy and sell signals.
+ Smoother and Cleaner: It provides a visually clean trend line that avoids the choppiness of classic EMAs and SMAs.
+ Reduced Lag: The HMA Trend Line follows the market closer, helping you avoid late entries or exits and spot trend reversals sooner.
+ Dynamic Support and Resistance: Use the line as a dynamic support or resistance to manage trades and identify pullbacks or breakouts.
What does “Croc Signal Line” mean?
The “Croc” in Croc Signal Line stands for:
+ Clean
+ Responsive
+ Optimized
+ Curve
This highlights the unique advantage of this indicator: a curve that is both fast-reacting and smooth, helping traders focus on real trends and filter out market noise.
How does the Hull Moving Average (HMA) work?
The HMA was developed by Alan Hull and uses weighted moving averages and a unique calculation to deliver both responsiveness and smoothness. Unlike standard moving averages, the HMA reacts faster to new price moves and avoids false signals in ranging or volatile markets.
How to use the HMA Trend Line (Croc Signal Line) on TradingView?
+ Watch for price crossing above the trend line for potential bullish signals, and below for bearish signals.
+ Use on any timeframe: from 1-minute scalping to daily, weekly, or even monthly charts.
+ Works with all asset classes: Forex, stocks, indices, cryptocurrencies, commodities, and futures.
+ Combine with other indicators (like Stochastics, RSI, or volume) for confirmation and to build your unique trading strategy.
+ Adjust the Signal Line Period for your market and style: shorter periods for faster markets, longer for smoother trends.
Who should use this indicator?
+ Day traders, swing traders, and long-term investors looking for reliable, actionable trend signals.
+ Anyone seeking a cleaner, more responsive alternative to the classic moving averages.
+ Traders who want a simple, visually clear way to filter out market noise and see real price direction.
Disclaimer:
This indicator is for educational and study purposes only. Please perform your own backtesting and analysis before using it in live trading. This script does not constitute financial advice. Use at your own risk.
--------
CCI Orbiting-VenusIndicator Description: CCI Orbiting-Venus
This is a customized version of the Commodity Channel Index (CCI) that measures the price deviation relative to its smoothed moving average to help identify overbought or oversold market conditions.
What does it do?
Calculates the CCI based on various price sources (such as close, open, high, low, and several price averages).
Applies customizable smoothing to the CCI using different types of moving averages (SMA, EMA, WMA, Hull, JMA, and SMMA).
Visually highlights the CCI direction with different colors:
Purple when CCI is above zero (positive momentum)
Orange when CCI is below zero (negative momentum)
Shows reference lines at +100 and -100 to help identify overbought and oversold zones.
How to use this indicator?
CCI Period Setting (CCI Period):
Adjust the number of periods used to calculate the CCI. Lower values make the indicator more sensitive, while higher values smooth out fluctuations.
Price Source (CCI Price Source):
Choose which price to base the calculation on: close, open, high, low, or weighted averages. This allows you to adapt the indicator to your trading style or strategy.
Smoothing Type (CCI Smoothing Type):
Select from different smoothing methods for the CCI calculation, which affects how the indicator behaves:
SMA (Simple Moving Average) – basic and traditional.
EMA, WMA, Hull, JMA (more advanced averages) – provide different noise filtering or faster response to price movements.
Interpreting CCI values:
Values above +100 suggest the asset may be overbought and could be near a downward reversal.
Values below -100 suggest the asset may be oversold and could be near an upward reversal.
Crossing the zero line indicates a potential change in trend or momentum.
Practical usage:
Look for buy signals when CCI moves up from the oversold region (-100) and crosses above zero, turning purple (positive).
Look for sell signals when CCI moves down from the overbought region (+100) and crosses below zero, turning orange (negative).
Combine with other indicators or chart analysis to confirm signals and avoid false entries.
Advantages of this custom indicator
Flexibility in choosing the price source and smoothing method.
Intuitive visual cues with colors indicating momentum direction.
Clear reference lines for quick assessment of extreme conditions.
Multi-timeframe Moving Average Overlay w/ Sentiment Table🔍 Overview
This indicator overlays selected moving averages (MA) from multiple timeframes directly onto the chart and provides a dynamic sentiment table that summarizes the relative bullish or bearish alignment of short-, mid-, and long-term moving averages.
It supports seven moving average types — including traditional and advanced options like DEMA, TEMA, and HMA — and provides visual feedback via table highlights and alerts when strong momentum alignment is detected.
This tool is designed to support traders who rely on multi-timeframe analysis for trend confirmation, momentum filtering, and high-probability entry timing.
⚙️ Core Features
Multi-Timeframe MA Overlay:
Plot moving averages from 1-minute, 5-minute, 1-hour, 1-day, 1-week, and 1-month timeframes on the same chart for visual trend alignment.
Customizable MA Type:
Choose from:
EMA (Exponential Moving Average)
SMA (Simple Moving Average)
DEMA (Double EMA)
TEMA (Triple EMA)
WMA (Weighted MA)
VWMA (Volume-Weighted MA)
HMA (Hull MA)
Adjustable MA Length:
Change the length of all moving averages globally to suit your strategy (e.g. 9, 21, 50, etc.).
Sentiment Table:
Visually track trend sentiment across four key zones (Hourly, Daily, Weekly, Monthly). Each is based on the relative positioning of short-term and long-term MAs.
Sentiment Symbols Explained:
↑↑↑: Strong bullish momentum (short-term MAs stacked above longer-term MAs)
↑↑ / ↑: Moderate bullish bias
↓↓↓: Strong bearish momentum
↓↓ / ↓: Moderate bearish bias
Table Customization:
Choose the table’s position on the chart (bottom right, top right, bottom left, top left).
Style Customization:
Display MA lines as standard Line or Stepline format.
Color Customization:
Individual colors for each timeframe MA line for visual clarity.
Built-in Alerts:
Receive alerts when strong bullish (↑↑↑) or bearish (↓↓↓) sentiment is detected on any timeframe block.
📈 Use Cases
1. Trend Confirmation:
Use sentiment alignment across multiple timeframes to confirm the overall trend direction before entering a trade.
2. Entry Timing:
Wait for a shift from neutral to strong bullish or bearish sentiment to time entries during pullbacks or breakouts.
3. Momentum Filtering:
Only trade in the direction of the dominant multi-timeframe trend. For example, ignore long setups when all sentiment blocks show bearish alignment.
4. Swing & Intraday Scalping:
Use hourly and daily sentiment zones for swing trades, or rely on 1m/5m MAs for precise scalping decisions in fast-moving markets.
5. Strategy Layering:
Combine this overlay with support/resistance, RSI, or volume-based signals to enhance decision-making with multi-timeframe context.
⚠️ Important Notes
Lower-timeframe values (1m, 5m) may appear static on higher-timeframe charts due to resolution limits in TradingView. This is expected behavior.
The indicator uses MA stacking, not crossover events, to determine sentiment.
Power of MovingThe Power of Moving indicator is a multi-moving average indicator designed to help traders identify strong trending conditions by analyzing the alignment and separation of multiple moving averages.
This indicator allows users to select between different types of moving averages (SMA, EMA, SMMA, WMA, VWMA) and plots four configurable moving averages on the chart. The background color dynamically changes when the moving averages are correctly stacked in a bullish (green) or bearish (yellow) formation, with sufficient distance between them. This ensures that trends are not only aligned but also have strong momentum. The indicator also includes alert conditions, notifying traders when the trend direction changes, allowing them to stay ahead of market moves.
This indicator works well in trending markets and should be combined with price action analysis or other confirmation indicators like RSI or volume for optimal results.
IB & Hammer at SMA(20,50|200)IB & Hammer at SMA (20, 50, 200) Breakout/Breakdown Indicator
Overview:
The IB (Inside Bar) & Hammer at SMA Breakout/Breakdown Indicator is designed to identify breakout and breakdown opportunities using Inside Bars (IB) in combination with Simple Moving Averages (SMA 20, 50, 200) as key trend filters. This indicator is useful for traders looking to catch momentum moves after consolidation phases, confirming the trend direction with moving averages.
Indicator Logic:
Inside Bar (IB) Detection:
An Inside Bar is a candlestick that is completely within the range of the previous candle (i.e., lower high and higher low).
Inside Bars indicate consolidation, suggesting a potential breakout.
SMA Trend Confirmation:
The script uses three moving averages (SMA 20, 50, 200) to determine the trend direction.
Bullish trend: Price is above the 50 & 200 SMAs.
Bearish trend: Price is below the 50 & 200 SMAs.
The 20 SMA is used as a dynamic short-term momentum filter.
Breakout & Breakdown Conditions:
Breakout: When price breaks above the Inside Bar’s high, and the trend is bullish (above key SMAs).
Breakdown: When price breaks below the Inside Bar’s low, and the trend is bearish (below key SMAs).
Alerts can be set to notify traders of potential trade opportunities.
Features:
✅ Identifies Inside Bars (consolidation zones).
✅ Uses SMA (20, 50, 200) for trend confirmation.
✅ Breakout/Breakdown signals based on Inside Bar structure.
✅ Customizable Moving Averages & Alerts.
✅ Visual markers for easy trade identification.
How to Use:
Confirm Trend Direction:
If the price is above SMA 50 & 200, look for breakout trades.
If the price is below SMA 50 & 200, look for breakdown trades.
Watch for Inside Bars:
The script highlights Inside Bars with a specific color (configurable).
These bars indicate a low-volatility phase, preparing for a breakout.
Trade on Breakout/Breakdown:
Breakout: Enter long when the price breaks above the Inside Bar’s high (bullish trend).
Breakdown: Enter short when the price breaks below the Inside Bar’s low (bearish trend).
[blackcat] L2 Wave Base CampOVERVIEW
The L2 Wave Base Camp indicator is a technical analysis tool designed to identify trends and potential trading signals by visualizing price and volume data through moving averages and relative strength calculations. It operates in its own panel on the trading chart, providing traders with a clear and color-coded representation of market conditions.
FEATURES
Customizable Base Camp Level: Users can set a horizontal line at a specific level to mark significant price points.
Color-Coded Histograms: Different colors indicate various market conditions, such as price position relative to moving averages.
Labeled Signals: The indicator labels potential "Valley" and "Top" points, suggesting buying and selling opportunities.
Volume Analysis: Incorporates volume data to identify potential trend reversals based on volume trends.
HOW TO USE
Set the Base Camp Level: Adjust the input parameter to define a significant price level.
Interpret Histogram Colors: Use the color-coded histograms to understand the current market condition.
Look for Labeled Signals: Pay attention to "Valley" and "Top" labels for potential trading opportunities.
Analyze Volume Trends: Monitor volume data for signs of trend reversals.
LIMITATIONS
Not a Standalone Tool: Should be used in conjunction with other indicators and analysis methods.
Backtesting Required: Essential to understand historical performance before live trading.
NOTES
The indicator uses moving averages (SMA) and relative strength calculations to smooth data and identify trends.
Crossover events between different moving averages generate buy and sell signals.
THANKS
Special thanks to the original author for developing this insightful trading tool.
Volume Comparison with Buyer/Seller PressureTHIS indicator is well-structured and provides a comprehensive way to analyze volume alongside buyer and seller pressure. This indicator helps traders analyze volume dynamics in the stock or cryptocurrency market while simultaneously assessing buyer and seller pressure. Its use case revolves around identifying strong buying or selling activity, neutral conditions, and volume trends over different time periods. Below is a breakdown of how to use this indicator:
This Pine Script indicator helps traders analyze volume dynamics in the stock or cryptocurrency market while simultaneously assessing buyer and seller pressure. Its use case revolves around identifying strong buying or selling activity, neutral conditions, and volume trends over different time periods. Below is a breakdown of how to use this indicator:
Key Features and Use Case
Volume-Based Insights:
Displays daily volume and compares it to the 3-day, 5-day, 10-day, and 20-day moving averages of volume. Helps traders identify days with unusual volume spikes relative to historical averages, signaling potential reversals or breakouts.
Buyer and Seller Pressure:
Measures buyer pressure: how much the closing price dominates the trading range of the day.
Measures seller pressure: how much the opening price dominates the trading range of the day.
Highlights areas where buying or selling pressure is particularly strong (≥ 0.75).
Background Signals:
Green Background: Strong buyer pressure (indicative of potential upward momentum).
Red Background: Strong seller pressure (indicative of potential downward momentum).
Gray Background: Neutral market conditions (neither buying nor selling dominance).
Alerts:
Alerts traders when:
Strong buying signals are detected.
Strong selling signals are detected.
The market is neutral, with neither buyers nor sellers in control.
Decision-Making Aid:
Combines volume analysis with price action (buyer/seller pressure) to help traders identify:
Potential breakout opportunities.
Reversal points.
Neutral zones where a trader might avoid trading due to indecision in the market.
How to Use It in Trading:------->
Add the Indicator:
Apply this Indicator to your Trading View chart to start visualizing the buyer/seller pressure and volume averages.
Interpret Volume Trends:
Look for days when daily volume significantly exceeds the 3-day, 5-day, 10-day, or 20-day average.
These could indicate:
A breakout when aligned with strong buyer pressure.
A sell-off when aligned with strong seller pressure.
React to Background Colors:
* Green Background (Strong Buyer Pressure):
Suggests buyers are dominating the market, and upward momentum is likely.
Use this signal to consider buying opportunities, especially if volume is above average.
* Red Background (Strong Seller Pressure):
Indicates sellers are in control, and prices might fall.
Use this signal to consider selling or shorting opportunities.
* Gray Background (Neutral Market):
Reflects indecision; avoid entering trades during these periods unless other signals support a strategy.
Volume Confirmation:
Combine volume analysis with buyer/seller pressure to confirm trends.
Example: A high daily volume with strong buyer pressure signals a high-probability uptrend.
Set Alerts:
Enable alerts to receive real-time notifications when the market generates strong buy/sell signals or enters a neutral zone.
Who Can Benefit:
* Day Traders: Quickly assess intraday market dynamics and volume trends.
* Swing Traders: Identify breakout opportunities or reversal points based on strong buyer/seller pressure.
* Volume Analysts: Compare historical volume averages to current conditions for deeper insights.
Limitations:
Does not guarantee success—should be combined with other technical indicators or strategies.
In low-volume markets, signals may produce false positives or unreliable results.
Assumes traders have basic knowledge of price action and volume analysis.
By integrating this indicator into your strategy, you gain a powerful tool to analyze buyer/seller dominance alongside volume trends, improving your market timing and trade execution.
The Buyer and Seller Pressure components in this indicator provide crucial insights into the market's sentiment and momentum by analyzing the price action relative to the trading volume. Here's how they are used:
1. Buyer Pressure:
Formula:
Buyer Pressure = (Close − Open) / (High − Low )
Interpretation:
* A high buyer pressure (≥ 0.75) indicates strong bullish sentiment, where the price closes much higher than it opened, and the range (high-low) is sufficiently wide.
* It identifies periods of aggressive buying, often signaling potential bullish trends or confirming upward momentum.
2. Seller Pressure:
Formula:
Seller Pressure = (Close − Open ) / (High -Low )
Interpretation:
*A high seller pressure (≥ 0.75) suggests strong bearish sentiment, where the price closes much lower than it opened, within a wide range.
*It helps identify periods of aggressive selling, signaling potential bearish trends or downward momentum.
Purpose in the Indicator:
1. Market Sentiment Analysis:
* Buyer Pressure and Seller Pressure allow traders to gauge market sentiment—whether buyers or sellers dominate a particular time frame.
* This helps in identifying trend reversals or confirmations.
2. Decision-Making Framework:
* The indicator uses thresholds (default 0.75) to classify the market into:
* Strong Buy Signal: When buyer pressure is dominant.
* Strong Sell Signal: When seller pressure is dominant.
* Neutral Signal: When neither buyer nor seller pressure dominates.
*This classification provides a straightforward decision-making tool for traders.
Risk Management:
*By identifying periods of strong buying or selling, traders can avoid entering trades in highly volatile or one-sided markets, which helps reduce risk.
Volume Confirmation:
*Integrating volume data with buyer/seller pressure helps confirm trends. For example:
*High buyer pressure accompanied by higher-than-average volume strengthens the bullish signal.
*Similarly, high seller pressure with higher-than-average volume confirms bearish signals.
Trade Timing:
*The indicator highlights conditions of potential entry (strong buy) or exit (strong sell), allowing traders to time their trades better based on real-time market activity.
Use Case:
*Example:
*Suppose the indicator shows Buyer Pressure = 0.85 with daily volume above the 3-day average. This combination suggests strong bullish activity with momentum, signaling a buy opportunity.
*Conversely, if Seller Pressure = 0.80 with volume above the 5-day average, it signals strong bearish momentum, ideal for selling or shorting.
This indicator combines buyer/seller pressure with volume dynamics, making it valuable for short-term and intraday traders looking for precise market entries and exits.
The background color in this indicator plays an important visual role in helping traders quickly identify the market sentiment based on buyer and seller pressure. It provides a dynamic, color-coded background that changes depending on the strength of the market's buying or selling activity.
Here's how it works:
Background Color Logic:
1. Green Background (Strong Buy Signal):
*Condition: The background turns green when buyer pressure is greater than or equal to 0.75 (strong buying pressure).
*Interpretation: A green background indicates that there is significant bullish sentiment in the market, with strong buying activity. Traders can interpret this as an environment conducive to buying or holding long positions.
*Visual Effect: This helps to quickly spot bullish market conditions, reinforcing potential entry signals for buyers.
2.Red Background (Strong Sell Signal):
*Condition: The background turns red when seller pressure is greater than or equal to 0.75 (strong selling pressure).
*Interpretation: A red background indicates that the market is dominated by selling, showing strong bearish sentiment. Traders can consider this as a signal to sell or short the asset.
*Visual Effect: The red background highlights moments when the market is heavily selling, prompting traders to either exit long positions or take short positions.
Gray Background (Neutral/Indecision Zone):
Condition: The background turns gray when neither buyer nor seller pressure exceeds 0.75. This means the market is neutral, with no dominant bullish or bearish sentiment.
Interpretation: A gray background suggests market indecision or balance between buyers and sellers. It can indicate periods of consolidation or sideways movement where no strong trend is forming.
Visual Effect: The gray background helps traders avoid entering trades when the market lacks a clear direction or when the sentiment is neutral, reducing risk during indecisive times.
Practical Use:
Instant Visual Confirmation:
*Traders can use the background color as an instant confirmation of the market’s sentiment. For instance, if the background turns green, traders might feel more confident in making a long (buy) trade.
*If the background turns red, it serves as a strong visual cue to short or exit a long position.
Helps with Trade Timing:
*The background color can be used in conjunction with other indicators and volume data to time entries and exits more effectively. For example:
*A green background with strong volume indicates a strong trend that could justify a buy.
*A red background with a significant volume surge signals strong selling pressure, which could prompt a sell.
Simplifies Market Analysis:
*For traders who prefer visual cues over complex analysis, the background color simplifies market conditions. Instead of focusing on individual numbers or values, the color-coded background gives them a quick, intuitive view of the market sentiment.
Summary:
* Green background = Strong buying pressure (bullish sentiment)
* Red background = Strong selling pressure (bearish sentiment)
* Gray background = Neutral market (indecision or balance between buyers and sellers)
This background color functionality helps traders stay aware of the prevailing market sentiment at a glance, providing an intuitive way to guide trading decisions.
