TechnicalRating█ OVERVIEW
This library is a Pine Script™ programmer’s tool for incorporating TradingView's well-known technical ratings within their scripts. The ratings produced by this library are the same as those from the speedometers in the technical analysis summary and the "Rating" indicator in the Screener , which use the aggregate biases of 26 technical indicators to calculate their results.
█ CONCEPTS
Ensemble analysis
Ensemble analysis uses multiple weaker models to produce a potentially stronger one. A common form of ensemble analysis in technical analysis is the usage of aggregate indicators together in hopes of gaining further market insight and reinforcing trading decisions.
Technical ratings
Technical ratings provide a simplified way to analyze financial markets by combining signals from an ensemble of indicators into a singular value, allowing traders to assess market sentiment more quickly and conveniently than analyzing each constituent separately. By consolidating the signals from multiple indicators into a single rating, traders can more intuitively and easily interpret the "technical health" of the market.
Calculating the rating value
Using a variety of built-in TA functions and functions from our ta library, this script calculates technical ratings for moving averages, oscillators, and their overall result within the `calcRatingAll()` function.
The function uses the script's `calcRatingMA()` function to calculate the moving average technical rating from an ensemble of 15 moving averages and filters:
• Six Simple Moving Averages and six Exponential Moving Averages with periods of 10, 20, 30, 50, 100, and 200
• A Hull Moving Average with a period of 9
• A Volume-Weighted Moving Average with a period of 20
• An Ichimoku Cloud with a conversion line length of 9, base length of 26, and leading span B length of 52
The function uses the script's `calcRating()` function to calculate the oscillator technical rating from an ensemble of 11 oscillators:
• RSI with a period of 14
• Stochastic with a %K period of 14, a smoothing period of 3, and a %D period of 3
• CCI with a period of 20
• ADX with a DI length of 14 and an ADX smoothing period of 14
• Awesome Oscillator
• Momentum with a period of 10
• MACD with fast, slow, and signal periods of 12, 26, and 9
• Stochastic RSI with an RSI period of 14, a %K period of 14, a smoothing period of 3, and a %D period of 3
• Williams %R with a period of 14
• Bull Bear Power with a period of 50
• Ultimate Oscillator with fast, middle, and slow lengths of 7, 14, and 28
Each indicator is assigned a value of +1, 0, or -1, representing a bullish, neutral, or bearish rating. The moving average rating is the mean of all ratings that use the `calcRatingMA()` function, and the oscillator rating is the mean of all ratings that use the `calcRating()` function. The overall rating is the mean of the moving average and oscillator ratings, which ranges between +1 and -1. This overall rating, along with the separate MA and oscillator ratings, can be used to gain insight into the technical strength of the market. For a more detailed breakdown of the signals and conditions used to calculate the indicators' ratings, consult our Help Center explanation.
Determining rating status
The `ratingStatus()` function produces a string representing the status of a series of ratings. The `strongBound` and `weakBound` parameters, with respective default values of 0.5 and 0.1, define the bounds for "strong" and "weak" ratings.
The rating status is determined as follows:
Rating Value Rating Status
< -strongBound Strong Sell
< -weakBound Sell
-weakBound to weakBound Neutral
> weakBound Buy
> strongBound Strong Buy
By customizing the `strongBound` and `weakBound` values, traders can tailor the `ratingStatus()` function to fit their trading style or strategy, leading to a more personalized approach to evaluating ratings.
Look first. Then leap.
█ FUNCTIONS
This library contains the following functions:
calcRatingAll()
Calculates 3 ratings (ratings total, MA ratings, indicator ratings) using the aggregate biases of 26 different technical indicators.
Returns: A 3-element tuple: ( [(float) ratingTotal, (float) ratingOther, (float) ratingMA ].
countRising(plot)
Calculates the number of times the values in the given series increase in value up to a maximum count of 5.
Parameters:
plot : (series float) The series of values to check for rising values.
Returns: (int) The number of times the values in the series increased in value.
ratingStatus(ratingValue, strongBound, weakBound)
Determines the rating status of a given series based on its values and defined bounds.
Parameters:
ratingValue : (series float) The series of values to determine the rating status for.
strongBound : (series float) The upper bound for a "strong" rating.
weakBound : (series float) The upper bound for a "weak" rating.
Returns: (string) The rating status of the given series ("Strong Buy", "Buy", "Neutral", "Sell", or "Strong Sell").
Cerca negli script per "Exponential Moving Average"
TASC 2022.09 LRAdj EMA█ OVERVIEW
TASC's September 2022 edition of Traders' Tips includes an article by Vitali Apirine titled "The Linear Regression-Adjusted Exponential Moving Average". This script implements the titular indicator presented in this article.
█ CONCEPT
The Linear Regression-Adjusted Exponential Moving Average (LRAdj EMA) is a new tool that combines a linear regression indicator with exponential moving averages . First, the indicator accounts for the linear regression deviation, that is, the distance between the price and the linear regression indicator. Subsequently, an exponential moving average (EMA) smooths the price data and and provides an indication of the current direction.
As part of a trading system, LRAdj EMA can be used in conjunction with an exponential moving average of the same length to identify the overall trend. Alternatively, using LRAdj EMAs of different lengths together can help identify turning points.
█ CALCULATION
The script uses the following input parameters:
EMA Length
LR Lookback Period
Multiplier
The calculation of LRAdj EMA is carried out as follows:
Current LRAdj EMA = Prior LRAdj EMA + MLTP × (1+ LRAdj × Multiplier ) × ( Price − Prior LRAdj EMA ),
where MLTP is a weighting multiplier defined as MLTP = 2 ⁄ ( EMA Length + 1), and LRAdj is the linear regression adjustment (LRAdj) multiplier:
LRAdj = (Abs( Current LR Dist )−Abs( Minimum LR Dist )) ⁄ (Abs( Maximum LR Dist )−Abs( Minimum LR Dist ))
When calculating the LRAdj multiplier, the absolute values of the following quantities are used:
Current LR Dist is the distance between the current close and the linear regression indicator with a length determined by the LR Lookback Period parameter,
Minimum LR Dist is the minimum distance between the close and the linear regression indicator for the LR lookback period ,
Maximum LR Dist is the maximum distance between the close and the linear regression indicator for the LR lookback period .
Squeeze M + ADX + TTM (Trading Latino & John Carter) by [Rolgui]About this indicator:
This indicator aims to combine two good performing strategies, which can be used separately or together, mainly for investment positions, although it can also be used for intraday trading.
Strategy 1) Squeeze Oscillator and Average Directional Index:
This strategy is taught by Jaime Aibsai, which determines market entries based on reading the direction of the price movement (Directionality of the Oscillator) along with the strength of the Oscillator (Slope of the ADX).
Both tools are configured according to Jaime Abisai's strategy, by default (note that point 23 of the ADX is represented by point 0 on the panel, to make reading easier, its interpretation is not affected). Anyway you can adjust the input data according to your interest.
*You can see this setting in the first panel.
Strategy 2) Squeeze Momentum and Trade The Market Waves:
This strategy can be consulted either in John F. Carter's books or on his website.
This market reading is based on Price Volatility (Bollinger Bands and Keltner Channels interaction) and its Trend (Exponential Moving Averages), showing entries at times when price volatility is low and taking filtering active trend using T.T.M. Waves.
To configure the indicator in the same way that Carter does, it would be enough to turn off the ADX, turn on the Squeeze Momentum signals along with the T.T.M. Waves, and importantly, change the Linear Momentum value to 12 (this configuration can be found in his book).
*You can see this setting in the second panel.
Why this indicator?
I've added and removed the above flags as I needed to query them (which became tedious for me). The main objective of having merged them into one is to make their reading more agile and comfortable and thus improve the decision-making capacity of the trader who wishes to use them.
Credits and Acknowledgments:
I would like to give credits to other authors, for the sections of code that I have used to make this technical indicator. Thanks to @LazyBear, @matetaronna, @jombie and @joren for contributing to the community and keeping their code open. It is priceless!
Feel free to combine and practice your trading with both strategies, personally, they improved my profitability and this is why I recommend researching more about them. I've been using it for crypto investing, let me know if it's worth for you on stock market!
If you have any questions or suggestions you can leave it in the comments!
Greetings!
EMA/Session/ATR/LotSizeSeveral indicators combined
1. 6 Exponential Moving Averages - Identifying the trend direction or using EMAs as a dynamic support/resistance.
2. Session on Chart - Highlighting session for day trading. London, New York, Tokyo, and Sydney.
3. Average True Range - display the Average True Range on recent price to calculate the volatility.
4. Lot Size Calculator - to calculate lot size based on account balance, risk per trade, atr stop-loss, and art multiplier.
5. ATRX - ATRX is an indicator that gives the value of the (close price - EMA 27)/ATR (14)
It tells how strong the trend is compared to its volatility
According to AJ. BANK FTMO Trader Thailand, if the value of ATRX is between 2X-3X or -2X-3X, you should consider trading using climax zone on timeframe H1.
If the ATRX is more than 3X or less than -3X but does not exceed 4X or -4X, you should consider trading using timeframe M15 in the climax zone.
However, if the ATRX exceeds 4X or -4X, use M5 instead.
Kelt + BBand Combination (kingthies) █ Overview
The Kelt-BBand Combo is a trading approach that I've used for multiple years now, and works on any timeframe, chart possible. There are various versions of this approach published by myself and others who find value in measuring the deviations of price and strategize market entries and exits. For an entry-level description of each component, I'll type them up below.
█ Using This Indicator
While there are various strategies to use this tool, I'll share the one that has yielded me the most success across traditional and cryptocurrency markets - first understand the different appearances of both....
IF the bbands are inside the kelts, the squeeze is on. In 90% of cases this is often a bullish leaning event
IF the bbands are pinching (regardless of slope or kelt behavior),these are your primary support and resistances, respectively
When trending up, HA candles will touch between the upper kelt and upper bband on every candle, across all timeframes
When trending down, HA candles will touch between the lower kelt and lower bband on every candle, across all timeframes
If one timeframe is not giving clear indicator of trend direction or s/r to follow, zoom out. the higher timeframe will always win and show you the true direction
█ Intro to Bollinger Bands
Bollinger Bands consists of a center line representing the moving average of a security’s price over a certain period, and two additional parallel lines (called the trading bands) one of which is just the moving average plus k-times the standard deviation over the selected time frame, and the other being the moving average minus k-times the standard deviation over that same timeframe. This technique has been developed in the 1980’s by John Bollinger, who lately registered the terms “Bollinger Bands” as a U.S. trademark in 2011. Technical analysts typically use 20 periods and k = 2 as default settings to build Bollinger Bands, while they can choose a simple or exponential moving average. Bollinger Bands provide a relative definition of high and low prices of a security. When the security is trading within the upper band, the price is considered high, while it is considered low when the security is trading within the lower band.
There is no general consensus on the use of Bollinger Bands among traders. Some traders see a buy signal when the price hits the lower Bollinger Band and close their position when the price hits the moving average. Some others buy when the price crosses over the upper band and sell when the price crosses below the lower band. We can see here two opposing interpretations based on different rationales, depending whether we are in a reversal or continuation pattern. Another interesting feature of the Bollinger Bands is that they give an indication of the volatility levels; a widening gap between the upper and lower bands indicates an increasing volatility, while a narrowing band indicates a decreasing volatility. Moreover, when the bands have an almost flat slope (parallel to the x-axis) the price will generally oscillate between the bands as if trading through a channel.
█ Intro to Keltner Channels
Keltner Channels aka Kelts were first described by a Chicago grain trader called Chester W. Keltner in his 1960 book How to Make Money in Commodities. Though Keltner claimed no ownership of the original idea and simply called it the ten-day moving average trading rule, his name was applied by those who heard of this concept through his books.
Similarly to the Bollinger Bands, Keltner channel is a technical analysis tool based on three parallel lines. In fact, the Keltner indicator consists of a central moving average in addition to channel lines spread above and below it. The central line represents a 10-day simple moving average of what Chester W. Keltner called typical price. The typical price is defined as the average of the high, low and close. The distance between the central line and the upper, or lower line, is equivalent to the simple moving average of the preceding 10 days' trading ranges.
One way to interpret the Keltner Channel would be to consider the price breakouts outside of the channel. A trader would track price movement and consider any close above the upper line as a strong buy signal. Equivalently, any close below the lower line would be considered a strong sell signal. The trader would follow the trend emphasized by the indicator while complementing his analysis with the use of other indicators as well. However, the breakout method only works well when the market moves from a range-bound setting to an established trend. In a trend-less configuration, the Keltner Channel is better used as an overbought/oversold indicator. Thus, as the price breaks out below the lower band, a trader waits for the next close inside the Keltner Channel and considers this price behavior as an oversold situation indicating a potential buy signal. Similarly, as the price breaks out above the upper band, the trader waits for the next close inside the Keltner Channel and considers this price movement as an overbought situation indicating a potential sell signal. By waiting for the price to close within the Channel, the trader avoids getting caught in a real upside or downside breakout.
Happy Trading!
DEMA / EMA moving averagesSince a moving average averages values, they lag recent price action. So when there’s a sudden price spike or when a new trend begins, the moving average is a bit late to the party. Over the years several people have developed moving averages that reduce price lag as much as possible.
One of those tweaked moving averages is the Double Exponential Moving Average (DEMA). This indicator was introduced by Patrick Mulloy in a 1994 article named “Smoothing Data With Faster Moving Averages” (Mitchell, 2019).
You can easily switch DEMA or EMA averages and show crossover signals
Adjustable Moving AveragesAdjustable Moving Averages
This script has fixed simple moving averages and fixed exponential moving averages function.
And script has 2 lines
1. Simpe Moving Average Line
2. Exponential Moving Average Line
You can change this 2 lines length and also you can change periods aswell.
With this; you can use any length of sma and ema with different periods without changing period.
For example this chart on 1 day period
And you can see 2 lines
Red Line: SMA100 and 4H perioıd
Yellow Line : EMA100 and 4H period
[blackcat] L2 Ehlers Cyber Cycle Trading StrategyLevel: 2
Background
John F. Ehlers introuced Cyber Cycle Trading Strategy in his "Cybernetic Analysis for Stocks and Futures" chapter 4 on 2004.
Function
With cyber cycle alone, the Trigger lags the Cycle by one bar, so that their crossing introduces at least another bar of lag. Finally, Dr Ehler concluded that we can’t execute the trade until the bar after the signal is observed. In total, that means our trade execution will be at least four bars late. If we are working with an eight-bar cycle, that means the signal will be exactly wrong. We could do better to buy when the signal says sell, and vice versa.
The difficulties arising from the lag suggest a way to build an automatic trading strategy. Suppose we choose to use the trading signal in the opposite direction of the signal. That will work if we can introduce lag so the correct signal will be given in the more general case, not just the case of an eight-bar cycle. Therefore, the Cyber Cycle trading strategy was introduced by Dr. Ehlers. It starts exactly the same as the Cyber Cycle Indicator. Dr. Ehlers then introduce the variable Signal, which is an exponential moving average of the Cycle variable. The exponential moving average generates the desired lag in the trading signal. The relationship between the alpha of an exponential moving average and lag is alpha2 = 1/ (Lag+1). This relationship is used to create the variable alpha2 in the code and the variable Signal using the exponential moving average. The trading signals using the variable Signal crossing itself delayed by one bar are exactly the opposite of the trading signals I would have used if there were no delay. But, since the variable Signal is delayed such that the net delay is less than half a cycle, the trading signals are correct to catch the next cyclic reversal. The idea of betting against the correct direction by waiting for the next cycle reversal can be pretty scary because that reversal may “never” happen because the market takes off in a trend. For this reason Dr. Ehlers included two lines of code that are escape mechanisms if we were wrong in our entry signal. These last two Signal lines of code reverse the trading position if we have been in the trade for more than eight bars and the trade has an open position loss.
Key Signal
Cycle ---> Cyber Cycle fast line
Cycle (2) ---> Cyber Cycle slow line
Signal ---> Trading signal fast line
Signal(1) ---> Trading signal slow line
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 25th 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.
@theCF StochasticStochastic with Bullish , Bearish and Neutral Ranges as well as a modification to check the %D line with a Exponential Moving Average .
Since Exponential Moving Averages put more weight onto recent values, they react differently than the Standard Simple Moving Average .
On Stochastic I have observed, that there can often occur false crossover signals. While this also happens with an Exponential Moving Average as %D indeed, the amount is more limited, therefore giving less false signals.
I prefer to display both at the same time, for more information.
The ranges act as general support and resistance zones on Stochastic . Whenever Stochastic oscillates within one of the ranges, one can interpret it as either bullish , neutral or bearish for price.
A Stochastic that oscillates mainly within the bullish range can be interpreted rather bullish , the opposite goes for the bearish range which is interpreted bearish .
I have coded these ranges into the Standard Stochastic Script, as well as the option to display the %D line as a Exponential Moving Average .
For more information about the indicator, feel free to leave a comment and ask.
RV-Scalping 34EAV ChannelWorks well with 1/3/5/15 min & above
//34 Exponential Moving Average of the Close
//34 Exponential Moving Average of the High
//34 Exponential Moving Average of the Low
//https://www.forexstrategiesresources.com/scalping-forex-strategies/106-1-min-scalping-with-34-exponential-moving-average-channel/
// When price is above the MAs (Moving Averages) we are only looking to buy as price comes back to the MAs.
// And when price is below the MAs, we are only looking to sell when price comes back to the MAs
// What we’re looking for when price pulls back to the MAs is for it to hold and then show that it is going to continue.
// We look for this continuation signal in terms of a strong, momentumdriven bar.
// 1) – Wait for pullback
// 2) – Enter when momentum comes into market
// 3) – Exit when momentum slows
// When the market has already moved a significant amount that day – Lets not enter in the same direct expecting a further move.
MASelect Crossover StratBasic Crossover Strategy for backtesting purposes, to use with the study+alert script.
Use "Format" to change your settings. Both Moving Averages can be changed individually to swap between EMA (Exponential Moving Average), SMA (Simple Moving Average), WMA (Weighted Moving Average), DEMA (Double Exponential Moving Average) and VWMA (Volume Weighted Moving Average).
"Active Length" should be shorter than "Base Length". As usual with crossover strategies, candle resolution will affect results greatly. Longer=better.
Strategy options are "Long+Short" or "Long Only".
Entries/Exits are based on crossovers/crossunders only, but I encourage you to add further exit conditions and play around with the code.
I made this for beginners on Autoview discord to have something to play with, and added some unnecessary visual changes just to give code examples (changing things like background color, candle color, line color, plotting shapes, different plot styles).
Play around combining different types of Moving Averages of different lengths.
Money Flow Indicator (Chaikin Oscillator) Strategy Indicator plots Money Flow Indicator (Chaikin). This indicator looks
to improve on Larry William's Accumulation Distribution formula that
compared the closing price with the opening price. In the early 1970's,
opening prices for stocks stopped being transmitted by the exchanges.
This made it difficult to calculate Williams' formula. The Chaikin
Oscillator uses the average price of the bar calculated as follows
(High + Low) /2 instead of the Open.
The indicator subtracts a 10 period exponential moving average of the
AccumDist function from a 3 period exponential moving average of the
AccumDist function.
WARNING:
This script to change bars colors.
Moving Average Cross Alert, Multi-Timeframe (MTF) (by ChartArt)See when two moving averages cross. With the option to choose between four moving average calculations:
SMA = simple moving average
EMA = exponential moving average (default)
WMA = weighted moving average
Linear = linear regression
The moving averages can be plotted from different time-frames, like e.g. the weekly or 4 hour time-frame using HL2, HLC3 or OHLC4 as price source for the calculation. In addition there is a background color alert and arrows when the moving averages cross each other when the price also rises or falls. And the moving averages are colored depending on their trend direction (if they are trending up or down).
Beyond Insights IndicatorThe Beyond Insights Indicator is a multi-timeframe overlay tool designed for TradingView that displays key Exponential Moving Averages (EMAs) and volatility insights to support various trading strategies. It intelligently adapts its display based on the current chart timeframe to reduce clutter and enhance relevance. Specifically, it shows EMA 6, 18, and 50 when viewing intraday timeframes.
In addition to EMAs, the indicator includes an ATR (Average True Range) calculation with a default length of 14, offering insight into market volatility.
Ultimate Scalping Tool[BullByte]Overview
The Ultimate Scalping Tool is an open-source TradingView indicator built for scalpers and short-term traders released under the Mozilla Public License 2.0. It uses a custom Quantum Flux Candle (QFC) oscillator to combine multiple market forces into one visual signal. In plain terms, the script reads momentum, trend strength, volatility, and volume together and plots a special “candlestick” each bar (the QFC) that reflects the overall market bias. This unified view makes it easier to spot entries and exits: the tool labels signals as Strong Buy/Sell, Pullback (a brief retracement in a trend), Early Entry, or Exit Warning . It also provides color-coded alerts and a small dashboard of metrics. In practice, traders see green/red oscillator bars and symbols on the chart when conditions align, helping them scalp or trend-follow without reading multiple separate indicators.
Core Components
Quantum Flux Candle (QFC) Construction
The QFC is the heart of the indicator. Rather than using raw price, it creates a candlestick-like bar from the underlying oscillator values. Each QFC bar has an “open,” “high/low,” and “close” derived from calculated momentum and volatility inputs for that period . In effect, this turns the oscillator into intuitive candle patterns so traders can recognize momentum shifts visually. (For comparison, note that Heikin-Ashi candles “have a smoother look because take an average of the movement”. The QFC instead represents exact oscillator readings, so it reflects true momentum changes without hiding price action.) Colors of QFC bars change dynamically (e.g. green for bullish momentum, red for bearish) to highlight shifts. This is the first open-source QFC oscillator that dynamically weights four non-correlated indicators with moving thresholds, which makes it a unique indicator on its own.
Oscillator Normalization & Adaptive Weights
The script normalizes its oscillator to a fixed scale (for example, a 0–100 range much like the RSI) so that various inputs can be compared fairly. It then applies adaptive weighting: the relative influence of trend, momentum, volatility or volume signals is automatically adjusted based on current market conditions. For instance, in very volatile markets the script might weight volatility more heavily, or in a strong trend it might give extra weight to trend direction. Normalizing data and adjusting weights helps keep the QFC sensitive but stable (normalization ensures all inputs fit a common scale).
Trend/Momentum/Volume/Volatility Fusion
Unlike a typical single-factor oscillator, the QFC oscillator fuses four aspects at once. It may compute, for example, a trend indicator (such as an ADX or moving average slope), a momentum measure (like RSI or Rate-of-Change), a volume-based pressure (similar to MFI/OBV), and a volatility measure (like ATR) . These different values are combined into one composite oscillator. This “multi-dimensional” approach follows best practices of using non-correlated indicators (trend, momentum, volume, volatility) for confirmation. By encoding all these signals in one line, a high QFC reading means that trend, momentum, and volume are all aligned, whereas a neutral reading might mean mixed conditions. This gives traders a comprehensive picture of market strength.
Signal Classification
The script interprets the QFC oscillator to label trades. For example:
• Strong Buy/Sell : Triggered when the oscillator crosses a high-confidence threshold (e.g. breaks clearly above zero with strong slope), indicating a well-confirmed move. This is like seeing a big green/red QFC candle aligned with the trend.
• Pullbacks : Identified when the trend is up but momentum dips briefly. A Pullback Buy appears if the overall trend is bullish but the oscillator has a short retracement – a typical buying opportunity in an uptrend. (A pullback is “a brief decline or pause in a generally upward price trend”.)
• Early Buy/Sell : Marks an initial swing in the oscillator suggesting a possible new trend, before it is fully confirmed. It’s a hint of momentum building (an early-warning signal), not as strong as the confirmed “Strong” signal.
• Exit Warnings : Issued when momentum peaks or reverses. For instance, if the QFC bars reach a high and start turning red/green opposite, the indicator warns that the move may be ending. In other words, a Momentum Peak is the point of maximum strength after which weakness may follow.
These categories correspond to typical trading concepts: Pullback (temporary reversal in an uptrend), Early Buy (an initial bullish cross), Strong Buy (confirmed bullish momentum), and Momentum Peak (peak oscillator value suggesting exhaustion).
Filters (DI Reversal, Dynamic Thresholds, HTF EMA/ADX)
Extra filters help avoid bad trades. A DI Reversal filter uses the +DI/–DI lines (from the ADX system) to require that the trend direction confirms the signal . For example, it might ignore a buy signal if the +DI is still below –DI. Dynamic Thresholds adjust signal levels on-the-fly: rather than fixed “overbought” lines, they move with volatility so signals happen under appropriate market stress. An optional High-Timeframe EMA or ADX filter adds a check against a larger timeframe trend: for instance, only taking a trade if price is above the weekly EMA or if weekly ADX shows a strong trend. (Notably, the ADX is “a technical indicator used by traders to determine the strength of a price trend”, so requiring a high-timeframe ADX avoids trading against the bigger trend.)
Dashboard Metrics & Color Logic
The Dashboard in the Ultimate Scalping Tool (UST) serves as a centralized information hub, providing traders with real-time insights into market conditions, trend strength, momentum, volume pressure, and trade signals. It is highly customizable, allowing users to adjust its appearance and content based on their preferences.
1. Dashboard Layout & Customization
Short vs. Extended Mode : Users can toggle between a compact view (9 rows) and an extended view (13 rows) via the `Short Dashboard` input.
Text Size Options : The dashboard supports three text sizes— Tiny, Small, and Normal —adjustable via the `Dashboard Text Size` input.
Positioning : The dashboard is positioned in the top-right corner by default but can be moved if modified in the script.
2. Key Metrics Displayed
The dashboard presents critical trading metrics in a structured table format:
Trend (TF) : Indicates the current trend direction (Strong Bullish, Moderate Bullish, Sideways, Moderate Bearish, Strong Bearish) based on normalized trend strength (normTrend) .
Momentum (TF) : Displays momentum status (Strong Bullish/Bearish or Neutral) derived from the oscillator's position relative to dynamic thresholds.
Volume (CMF) : Shows buying/selling pressure levels (Very High Buying, High Selling, Neutral, etc.) based on the Chaikin Money Flow (CMF) indicator.
Basic & Advanced Signals:
Basic Signal : Provides simple trade signals (Strong Buy, Strong Sell, Pullback Buy, Pullback Sell, No Trade).
Advanced Signal : Offers nuanced signals (Early Buy/Sell, Momentum Peak, Weakening Momentum, etc.) with color-coded alerts.
RSI : Displays the Relative Strength Index (RSI) value, colored based on overbought (>70), oversold (<30), or neutral conditions.
HTF Filter : Indicates the higher timeframe trend status (Bullish, Bearish, Neutral) when using the Leading HTF Filter.
VWAP : Shows the V olume-Weighted Average Price and whether the current price is above (bullish) or below (bearish) it.
ADX : Displays the Average Directional Index (ADX) value, with color highlighting whether it is rising (green) or falling (red).
Market Mode : Shows the selected market type (Crypto, Stocks, Options, Forex, Custom).
Regime : Indicates volatility conditions (High, Low, Moderate) based on the **ATR ratio**.
3. Filters Status Panel
A secondary panel displays the status of active filters, helping traders quickly assess which conditions are influencing signals:
- DI Reversal Filter: On/Off (confirms reversals before generating signals).
- Dynamic Thresholds: On/Off (adjusts buy/sell thresholds based on volatility).
- Adaptive Weighting: On/Off (auto-adjusts oscillator weights for trend/momentum/volatility).
- Early Signal: On/Off (enables early momentum-based signals).
- Leading HTF Filter: On/Off (applies higher timeframe trend confirmation).
4. Visual Enhancements
Color-Coded Cells : Each metric is color-coded (green for bullish, red for bearish, gray for neutral) for quick interpretation.
Dynamic Background : The dashboard background adapts to market conditions (bullish/bearish/neutral) based on ADX and DI trends.
Customizable Reference Lines : Users can enable/disable fixed reference lines for the oscillator.
How It(QFC) Differs from Traditional Indicators
Quantum Flux Candle (QFC) Versus Heikin-Ashi
Heikin-Ashi candles smooth price by averaging (HA’s open/close use averages) so they show trend clearly but hide true price (the current HA bar’s close is not the real price). QFC candles are different: they are oscillator values, not price averages . A Heikin-Ashi chart “has a smoother look because it is essentially taking an average of the movement”, which can cause lag. The QFC instead shows the raw combined momentum each bar, allowing faster recognition of shifts. In short, HA is a smoothed price chart; QFC is a momentum-based chart.
Versus Standard Oscillators
Common oscillators like RSI or MACD use fixed formulas on price (or price+volume). For example, RSI “compares gains and losses and normalizes this value on a scale from 0 to 100”, reflecting pure price momentum. MFI is similar but adds volume. These indicators each show one dimension: momentum or volume. The Ultimate Scalping Tool’s QFC goes further by integrating trend strength and volatility too. In practice, this means a move that looks strong on RSI might be downplayed by low volume or weak trend in QFC. As one source notes, using multiple non-correlated indicators (trend, momentum, volume, volatility) provides a more complete market picture. The QFC’s multi-factor fusion is unique – it is effectively a multi-dimensional oscillator rather than a traditional single-input one.
Signal Style
Traditional oscillators often use crossovers (RSI crossing 50) or fixed zones (MACD above zero) for signals. The Ultimate Scalping Tool’s signals are custom-classified: it explicitly labels pullbacks, early entries, and strong moves. These terms go beyond a typical indicator’s generic “buy”/“sell.” In other words, it packages a strategy around the oscillator, which traders can backtest or observe without reading code.
Key Term Definitions
• Pullback : A short-term dip or consolidation in an uptrend. In this script, a Pullback Buy appears when price is generally rising but shows a brief retracement. (As defined by Investopedia, a pullback is “a brief decline or pause in a generally upward price trend”.)
• Early Buy/Sell : An initial or tentative entry signal. It means the oscillator first starts turning positive (or negative) before a full trend has developed. It’s an early indication that a trend might be starting.
• Strong Buy/Sell : A confident entry signal when multiple conditions align. This label is used when momentum is already strong and confirmed by trend/volume filters, offering a higher-probability trade.
• Momentum Peak : The point where bullish (or bearish) momentum reaches its maximum before weakening. When the oscillator value stops rising (or falling) and begins to reverse, the script flags it as a peak – signaling that the current move could be overextended.
What is the Flux MA?
The Flux MA (Moving Average) is an Exponential Moving Average (EMA) applied to a normalized oscillator, referred to as FM . Its purpose is to smooth out the fluctuations of the oscillator, providing a clearer picture of the underlying trend direction and strength. Think of it as a dynamic baseline that the oscillator moves above or below, helping you determine whether the market is trending bullish or bearish.
How it’s calculated (Flux MA):
1.The oscillator is normalized (scaled to a range, typically between 0 and 1, using a default scale factor of 100.0).
2.An EMA is applied to this normalized value (FM) over a user-defined period (default is 10 periods).
3.The result is rescaled back to the oscillator’s original range for plotting.
Why it matters : The Flux MA acts like a support or resistance level for the oscillator, making it easier to spot trend shifts.
Color of the Flux Candle
The Quantum Flux Candle visualizes the normalized oscillator (FM) as candlesticks, with colors that indicate specific market conditions based on the relationship between the FM and the Flux MA. Here’s what each color means:
• Green : The FM is above the Flux MA, signaling bullish momentum. This suggests the market is trending upward.
• Red : The FM is below the Flux MA, signaling bearish momentum. This suggests the market is trending downward.
• Yellow : Indicates strong buy conditions (e.g., a "Strong Buy" signal combined with a positive trend). This is a high-confidence signal to go long.
• Purple : Indicates strong sell conditions (e.g., a "Strong Sell" signal combined with a negative trend). This is a high-confidence signal to go short.
The candle mode shows the oscillator’s open, high, low, and close values for each period, similar to price candlesticks, but it’s the color that provides the quick visual cue for trading decisions.
How to Trade the Flux MA with Respect to the Candle
Trading with the Flux MA and Quantum Flux Candle involves using the MA as a trend indicator and the candle colors as entry and exit signals. Here’s a step-by-step guide:
1. Identify the Trend Direction
• Bullish Trend : The Flux Candle is green and positioned above the Flux MA. This indicates upward momentum.
• Bearish Trend : The Flux Candle is red and positioned below the Flux MA. This indicates downward momentum.
The Flux MA serves as the reference line—candles above it suggest buying pressure, while candles below it suggest selling pressure.
2. Interpret Candle Colors for Trade Signals
• Green Candle : General bullish momentum. Consider entering or holding a long position.
• Red Candle : General bearish momentum. Consider entering or holding a short position.
• Yellow Candle : A strong buy signal. This is an ideal time to enter a long trade.
• Purple Candle : A strong sell signal. This is an ideal time to enter a short trade.
3. Enter Trades Based on Crossovers and Colors
• Long Entry : Enter a buy position when the Flux Candle turns green and crosses above the Flux MA. If it turns yellow, this is an even stronger signal to go long.
• Short Entry : Enter a sell position when the Flux Candle turns red and crosses below the Flux MA. If it turns purple, this is an even stronger signal to go short.
4. Exit Trades
• Exit Long : Close your buy position when the Flux Candle turns red or crosses below the Flux MA, indicating the bullish trend may be reversing.
• Exit Short : Close your sell position when the Flux Candle turns green or crosses above the Flux MA, indicating the bearish trend may be reversing.
•You might also exit a long trade if the candle changes from yellow to green (weakening strong buy signal) or a short trade from purple to red (weakening strong sell signal).
5. Use Additional Confirmation
To avoid false signals, combine the Flux MA and candle signals with other indicators or dashboard metrics (e.g., trend strength, momentum, or volume pressure). For example:
•A yellow candle with a " Strong Bullish " trend and high buying volume is a robust long signal.
•A red candle with a " Moderate Bearish " trend and neutral momentum might need more confirmation before shorting.
Practical Example
Imagine you’re scalping a cryptocurrency:
• Long Trade : The Flux Candle turns yellow and is above the Flux MA, with the dashboard showing "Strong Buy" and high buying volume. You enter a long position. You exit when the candle turns red and dips below the Flux MA.
• Short Trade : The Flux Candle turns purple and crosses below the Flux MA, with a "Strong Sell" signal on the dashboard. You enter a short position. You exit when the candle turns green and crosses above the Flux MA.
Market Presets and Adaptation
This indicator is designed to work on any market with candlestick price data (stocks, crypto, forex, indices, etc.). To handle different behavior, it provides presets for major asset classes. Selecting a “Stocks,” “Crypto,” “Forex,” or “Options” preset automatically loads a set of parameter values optimized for that market . For example, a crypto preset might use a shorter lookback or higher sensitivity to account for crypto’s high volatility, while a stocks preset might use slightly longer smoothing since stocks often trend more slowly. In practice, this means the same core QFC logic applies across markets, but the thresholds and smoothing adjust so signals remain relevant for each asset type.
Usage Guidelines
• Recommended Timeframes : Optimized for 1 minute to 15 minute intraday charts. Can also be used on higher timeframes for short term swings.
• Market Types : Select “Crypto,” “Stocks,” “Forex,” or “Options” to auto tune periods, thresholds and weights. Use “Custom” to manually adjust all inputs.
• Interpreting Signals : Always confirm a signal by checking that trend, volume, and VWAP agree on the dashboard. A green “Strong Buy” arrow with green trend, green volume, and price > VWAP is highest probability.
• Adjusting Sensitivity : To reduce false signals in fast markets, enable DI Reversal Confirmation and Dynamic Thresholds. For more frequent entries in trending environments, enable Early Entry Trigger.
• Risk Management : This tool does not plot stop loss or take profit levels. Users should define their own risk parameters based on support/resistance or volatility bands.
Background Shading
To give you an at-a-glance sense of market regime without reading numbers, the indicator automatically tints the chart background in three modes—neutral, bullish and bearish—with two levels of intensity (light vs. dark):
Neutral (Gray)
When ADX is below 20 the market is considered “no trend” or too weak to trade. The background fills with a light gray (high transparency) so you know to sit on your hands.
Bullish (Green)
As soon as ADX rises above 20 and +DI exceeds –DI, the background turns a semi-transparent green, signaling an emerging uptrend. When ADX climbs above 30 (strong trend), the green becomes more opaque—reminding you that trend-following signals (Strong Buy, Pullback) carry extra weight.
Bearish (Red)
Similarly, if –DI exceeds +DI with ADX >20, you get a light red tint for a developing downtrend, and a darker, more solid red once ADX surpasses 30.
By dynamically varying both hue (green vs. red vs. gray) and opacity (light vs. dark), the background instantly communicates trend strength and direction—so you always know whether to favor breakout-style entries (in a strong trend) or stay flat during choppy, low-ADX conditions.
The setup shown in the above chart snapshot is BTCUSD 15 min chart : Binance for reference.
Disclaimer
No indicator guarantees profits. Backtest or paper trade this tool to understand its behavior in your market. Always use proper position sizing and stop loss orders.
Good luck!
- BullByte
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it.
EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been.
EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac.
If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context.
▶️ What is period in EMA used for?
What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it.
Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name.
For example:
Period 10 → alpha = 0.1818
Period 11 → alpha = 0.1667
What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels.
Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading!
▶️ Calculating EMAs Efficiently
The standard textbook formula for EMA is:
EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha)
But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra:
EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA
The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction.
That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants.
Relative CPU cost of float operations :
Addition/Subtraction: ~1 cycle
Multiplication: ~5 cycles (depending on precision and architecture)
Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator.
▶️ The Warmup Problem: how to start the EMA sequence right
How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history:
Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months.
Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward.
Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug.
Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile.
These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record.
▶️ A Better Way to start EMA: Decaying compensation
Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday.
But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point.
Here's how phantom data compensation works:
We start our normal EMA calculation:
EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday)
But we add a correction factor that adjusts for the missing history:
Correction = 1 at the start
Correction = Correction × (1-α) after each calculation
We then apply this correction:
True_EMA = Raw_EMA / (1-Correction)
This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant.
Let's see how this works in practice:
For the first price bar:
Raw_EMA = 0
Correction = 1
True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price)
For the second price bar:
Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2
Correction = 1 × (1-α) = (1-α)
True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2
For the third price bar:
Raw_EMA updates using the standard formula
Correction = (1-α) × (1-α) = (1-α)²
True_EMA = Raw_EMA ÷ (1-(1-α)²)
With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data.
This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase.
Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible.
// Enhanced EMA function with proper initialization and efficient calculation
ema(series float source, simple int period=0, simple float alpha=0)=>
// Input validation - one of alpha or period must be provided
if alpha<=0 and period<=0
runtime.error("Alpha or period must be provided")
// Calculate alpha from period if alpha not directly specified
float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1)
// Initialize variables for EMA calculation
var float ema = na // Stores raw EMA value
var float result = na // Stores final corrected EMA
var float e = 1.0 // Decay compensation factor
var bool warmup = true // Flag for warmup phase
if not na(source)
if na(ema)
// First value case - initialize EMA to zero
// (we'll correct this immediately with the compensation)
ema := 0
result := source
else
// Standard EMA calculation (optimized formula)
ema := a * (source - ema) + ema
if warmup
// During warmup phase, apply decay compensation
e *= (1-a) // Update decay factor
float c = 1.0 / (1.0 - e) // Calculate correction multiplier
result := c * ema // Apply correction
// Stop warmup phase when correction becomes negligible
if e <= 1e-10
warmup := false
else
// After warmup, EMA operates without correction
result := ema
result // Return the properly compensated EMA value
▶️ CONCLUSION
EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques.
By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O.
The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions.
EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
ATR Strength Index~~~~~~~ATRRSI~~~~~~~~~
Understanding the ATR Strength IndexThe "ATR Strength Index" (ATR SI) is a custom technical indicator derived by applying the calculation methodology of the Relative Strength Index (RSI) to the values of the Average True Range (ATR).
While the standard RSI measures the momentum of price changes, the ATR SI measures the momentum of volatility itself, as represented by the ATR.It is important to note that this is not a standard, widely recognised indicator like the traditional RSI or ATR.
It's a custom construction designed to provide a different perspective on market dynamics – specifically, the speed and magnitude of changes in volatility.
How it is Calculated
The calculation of the ATR Strength Index follows the same steps as the standard RSI, but the input data is the ATR value for each period, rather than the price.Let ATRi be the Average True Range value for the current period i.Let ATRi−1 be the Average True Range value for the previous period i−1.Calculate the period-over-period change in ATR:ΔATRi=ATRi−ATRi−1Separate ATR Gains and ATR Losses:If ΔATRi>0, then ATR,Gaini=ΔATRi and ATR,Lossi=0.If ΔATRi<0, then ATR,Gaini=0 and ATR,Lossi=∣ΔATRi∣.If ΔATRi=0, then ATR,Gaini=0 and ATR,Lossi=0.Calculate the Smoothed Average ATR Gain and Average ATR Loss over a specified lookback period (let's call this the "RSI Length" or n).
This typically uses a smoothing method similar to Wilder's original RSI calculation (a modified moving average or exponential moving average).Average,ATR,Gainn=Smoothed Average of ATR,Gain over n periodsAverage,ATR,Lossn=Smoothed Average of ATR,Loss over n periodsCalculate the ATR Relative Strength (ATR RS):ATR,RSn=Average,ATR,LossnAverage,ATR,GainnCalculate the ATR Strength Index:ATR,SIn=100−1+ATR,RSn100The resulting index oscillates between 0 and 100, just like the standard RSI.
How to Use It
Interpreting the ATR Strength Index focuses on the momentum of volatility rather than price momentum:High Values (e.g., above 70): Indicate that volatility (as measured by ATR) has been increasing rapidly over the chosen period.
This could suggest a market transitioning from a period of low volatility to high volatility, potentially preceding or accompanying strong directional price moves or increased choppiness.Low Values (e.g., below 30): Indicate that volatility has been decreasing rapidly.
This could suggest a market transitioning from high volatility to low volatility, potentially entering a period of consolidation or ranging price action.Midline (50): Represents a balance between increasing and decreasing volatility momentum.Divergence: You could potentially look for divergence between the ATR value itself and the ATR Strength Index. For example, if ATR is making higher highs but the ATR SI is making lower highs, it might suggest that while volatility is still increasing, the speed of that increase is slowing down. The interpretation and reliability of such divergence would need careful testing.
This indicator is best used as a supplementary tool to gain insight into the underlying volatility dynamics of the market, rather than as a primary signal generator for price direction.
It can help in understanding the current market environment – whether volatility is picking up or dying down – which can inform the suitability of different trading strategies (e.g., trend-following strategies might be more effective when volatility momentum is high, while range-bound strategies might suit periods of low volatility momentum).
Uniqueness
The ATR Strength Index is unique because it applies a momentum oscillator's logic (RSI) to a volatility indicator's output (ATR).Standard RSI: Focuses on the directional force of price movements.Standard ATR: Measures the amount of volatility, regardless of direction.ATR Strength Index: Measures the speed and direction of change in volatility.
It provides a perspective that neither the standard RSI nor ATR offers on their own – a quantified measure of how quickly the market's choppiness or range is expanding or contracting. This can be valuable for traders who incorporate volatility analysis into their decision-making process.In summary, the ATR Strength Index is a custom indicator that adapts the RSI calculation to measure the momentum of volatility, offering a unique view on market dynamics by showing how rapidly volatility is increasing or decreasing.
Cointegration Buy and Sell Signals [EdgeTerminal]The Cointegration Buy And Sell Signals is a sophisticated technical analysis tool to spot high-probability market turning points — before they fully develop on price charts.
Most reversal indicators rely on raw price action, visual patterns, or basic and common indicator logic — which often suffer in noisy or trending markets. In most cases, they lag behind the actual change in trend and provide useless and late signals.
This indicator is rooted in advanced concepts from statistical arbitrage, mean reversion theory, and quantitative finance, and it packages these ideas in a user-friendly visual format that works on any timeframe and asset class.
It does this by analyzing how the short-term and long-term EMAs behave relative to each other — and uses statistical filters like Z-score, correlation, volatility normalization, and stationarity tests to issue highly selective Buy and Sell signals.
This tool provides statistical confirmation of trend exhaustion, allowing you to trade mean-reverting setups. It fades overextended moves and uses signal stacking to reduce false entries. The entire indicator is based on a very interesting mathematically grounded model which I will get into down below.
Here’s how the indicator works at a high level:
EMAs as Anchors: It starts with two Exponential Moving Averages (EMAs) — one short-term and one long-term — to track market direction.
Statistical Spread (Regression Residuals): It performs a rolling linear regression between the short and long EMA. Instead of using the raw difference (short - long), it calculates the regression residual, which better models their natural relationship.
Normalize the Spread: The spread is divided by historical price volatility (ATR) to make it scale-invariant. This ensures the indicator works on low-priced stocks, high-priced indices, and crypto alike.
Z-Score: It computes a Z-score of the normalized spread to measure how “extreme” the current deviation is from its historical average.
Dynamic Thresholds: Unlike most tools that use fixed thresholds (like Z = ±2), this one calculates dynamic thresholds using historical percentiles (e.g., top 10% and bottom 10%) so that it adapts to the asset's current behavior to reduce false signals based on market’s extreme volatility at a certain time.
Z-Score Momentum: It tracks the direction of the Z-score — if Z is extreme but still moving away from zero, it's too early. It waits for reversion to start (Z momentum flips).
Correlation Check: Uses a rolling Pearson correlation to confirm the two EMAs are still statistically related. If they diverge (low correlation), no signal is shown.
Stationarity Filter (ADF-like): Uses the volatility of the regression residual to determine if the spread is stationary (mean-reverting) — a key concept in cointegration and statistical arbitrage. It’s not possible to build an exact ADF filter in Pine Script so we used the next best thing.
Signal Control: Prevents noisy charts and overtrading by ensuring no back-to-back buy or sell signals. Each signal must alternate and respect a cooldown period so you won’t be overwhelmed and won’t get a messy chart.
Important Notes to Remember:
The whole idea behind this indicator is to try to use some stat arb models to detect shifting patterns faster than they appear on common indicators, so in some cases, some assumptions are made based on historic values.
This means that in some cases, the indicator can “jump” into the conclusion too quickly. Although we try to eliminate this by using stationary filters, correlation checks, and Z-score momentum detection, there is still a chance some signals that are generated can be too early, in the stock market, that's the same as being incorrect. So make sure to use this with other indicators to confirm the movement.
How To Use The Indicator:
You can use the indicator as a standalone reversal system, as a filter for overbought and oversold setups, in combination with other trend indicators and as a part of a signal stack with other common indicators for divergence spotting and fade trades.
The indicator produces simple buy and sell signals when all criteria is met. Based on our own testing, we recommend treating these signals as standalone and independent from each other . Meaning that if you take position after a buy signal, don’t wait for a sell signal to appear to exit the trade and vice versa.
This is why we recommend using this indicator with other advanced or even simple indicators as an early confirmation tool.
The Display Table:
The floating diagnostic table in the top-right corner of the chart is a key part of this indicator. It's a live statistical dashboard that helps you understand why a signal is (or isn’t) being triggered, and whether the market conditions are lining up for a potential reversal.
1. Z-Score
What it shows: The current Z-score value of the volatility-normalized spread between the short EMA and the regression line of the long EMA.
Why it matters: Z-score tells you how statistically extreme the current relationship is. A Z-score of:
0 = perfectly average
> +2 = very overbought
< -2 = very oversold
How to use it: Look for Z-score reaching extreme highs or lows (beyond dynamic thresholds). Watch for it to start reversing direction, especially when paired with green table rows (see below)
2. Z-Score Momentum
What it shows: The rate of change (ROC) of the Z-score:
Zmomentum=Zt − Zt − 1
Why it matters: This tells you if the Z-score is still stretching out (e.g., getting more overbought/oversold), or reverting back toward the mean.
How to use it: A positive Z-momentum after a very low Z-score = potential bullish reversal A negative Z-momentum after a very high Z-score = potential bearish reversal. Avoid signals when momentum is still pushing deeper into extremes
3. Correlation
What it shows: The rolling Pearson correlation coefficient between the short EMA and long EMA.
Why it matters: High correlation (closer to +1) means the EMAs are still statistically connected — a key requirement for cointegration or mean reversion to be valid.
How to use it: Look for correlation > 0.7 for reliable signals. If correlation drops below 0.5, ignore the Z-score — the EMAs aren’t moving together anymore
4. Stationary
What it shows: A simplified "Yes" or "No" answer to the question:
“Is the spread statistically stable (stationary) and mean-reverting right now?”
Why it matters: Mean reversion strategies only work when the spread is stationary — that is, when the distance between EMAs behaves like a rubber band, not a drifting cloud.
How to use it: A "Yes" means the indicator sees a consistent, stable spread — good for trading. "No" means the market is too volatile, disjointed, or chaotic for reliable mean reversion. Wait for this to flip to "Yes" before trusting signals
5. Last Signal
What it shows: The last signal issued by the system — either "Buy", "Sell", or "None"
Why it matters: Helps avoid confusion and repeated entries. Signals only alternate — you won’t get another Buy until a Sell happens, and vice versa.
How to use it: If the last signal was a "Buy", and you’re watching for a Sell, don’t act on more bullish signals. Great for systems where you only want one position open at a time
6. Bars Since Signal
What it shows: How many bars (candles) have passed since the last Buy or Sell signal.
Why it matters: Gives you context for how long the current condition has persisted
How to use it: If it says 1 or 2, a signal just happened — avoid jumping in late. If it’s been 10+ bars, a new opportunity might be brewing soon. You can use this to time exits if you want to fade a recent signal manually
Indicator Settings:
Short EMA: Sets the short-term EMA period. The smaller the number, the more reactive and more signals you get.
Long EMA: Sets the slow EMA period. The larger this number is, the smoother baseline, and more reliable trend bases are generated.
Z-Score Lookback: The period or bars used for mean & std deviation of spread between short and long EMAs. Larger values result in smoother signals with fewer false positives.
Volatility Window: This value normalizes the spread by historical volatility. This allows you to prevent scale distortion, showing you a cleaner and better chart.
Correlation Lookback: How many periods or how far back to test correlation between slow and long EMAs. This filters out false positives when EMAs lose alignment.
Hurst Lookback: The multiplier to approximate stationarity. Lower leads to more sensitivity to regime change, higher produces a more stricter filtering.
Z Threshold Percentile: This value sets how extreme Z-score must be to trigger a signal. For example, 90 equals only top/bottom 10% of extremes, 80 = more frequent.
Min Bars Between Signals: This hard stop prevents back-to-back signals. The idea is to avoid over-trading or whipsaws in volatile markets even when Hurst lookback and volatility window values are not enough to filter signals.
Some More Recommendations:
We recommend trying different EMA pairs (10/50, 21/100, 5/20) for different asset behaviors. You can set percentile to 85 or 80 if you want more frequent but looser signals. You can also use the Z-score reversion monitor for powerful confirmation.
Combined EMA Technical AnalysisThis script is written in Pine Script (version 5) for TradingView and creates a comprehensive technical analysis indicator called "Combined EMA Technical Analysis." It overlays multiple technical indicators on a price chart, including Exponential Moving Averages (EMAs), VWAP, MACD, PSAR, RSI, Bollinger Bands, ADX, and external data from the S&P 500 (SPX) and VIX indices. The script also provides visual cues through colors, shapes, and a customizable table to help traders interpret market conditions.
Here’s a breakdown of the script:
---
### **1. Purpose**
- The script combines several popular technical indicators to analyze price trends, momentum, volatility, and market sentiment.
- It uses color coding (green for bullish, red for bearish, gray/white for neutral) and a table to display key information.
---
### **2. Custom Colors**
- Defines custom RGB colors for bullish (`customGreen`), bearish (`customRed`), and neutral (`neutralGray`) signals to enhance visual clarity.
---
### **3. User Inputs**
- **EMA Colors**: Users can customize the colors of five EMAs (8, 20, 9, 21, 50 periods).
- **MACD Settings**: Adjustable short length (12), long length (26), and signal length (9).
- **RSI Settings**: Adjustable length (14).
- **Bollinger Bands Settings**: Length (20), multiplier (2), and proximity threshold (0.1% of band width).
- **ADX Settings**: Adjustable length (14).
- **Table Settings**: Position (e.g., "Bottom Right") and text size (e.g., "Small").
---
### **4. Indicator Calculations**
#### **Exponential Moving Averages (EMAs)**
- Calculates five EMAs: 8, 20, 9, 21, and 50 periods based on the closing price.
- Used to identify short-term and long-term trends.
#### **Volume Weighted Average Price (VWAP)**
- Resets daily and calculates the average price weighted by volume.
- Color-coded: green if price > VWAP (bullish), red if price < VWAP (bearish), white if neutral.
#### **MACD (Moving Average Convergence Divergence)**
- Uses short (12) and long (26) EMAs to compute the MACD line, with a 9-period signal line.
- Displays "Bullish" (green) if MACD > signal, "Bearish" (red) if MACD < signal.
#### **Parabolic SAR (PSAR)**
- Calculated with acceleration factors (start: 0.02, increment: 0.02, max: 0.2).
- Indicates trend direction: green if price > PSAR (bullish), red if price < PSAR (bearish).
#### **Relative Strength Index (RSI)**
- Measures momentum over 14 periods.
- Highlighted in green if > 70 (overbought), red if < 30 (oversold), white otherwise.
#### **Bollinger Bands (BB)**
- Uses a 20-period SMA with a 2-standard-deviation multiplier.
- Color-coded based on price position:
- Green: Above upper band or close to it.
- Red: Below lower band or close to it.
- Gray: Neutral (within bands).
#### **Average Directional Index (ADX)**
- Manually calculates ADX to measure trend strength:
- Strong trend: ADX > 25.
- Very strong trend: ADX > 50.
- Direction: Bullish if +DI > -DI, bearish if -DI > +DI.
#### **EMA Crosses**
- Detects bullish (crossover) and bearish (crossunder) events for:
- EMA 9 vs. EMA 21.
- EMA 8 vs. EMA 20.
- Visualized with green (bullish) or red (bearish) circles.
#### **SPX and VIX Data**
- Fetches daily closing prices for the S&P 500 (SPX) and VIX (volatility index).
- SPX trend: Bullish if EMA 9 > EMA 21, bearish if EMA 9 < EMA 21.
- VIX levels: High (> 25, fear), Low (< 15, stability).
- VIX color: Green if SPX bullish and VIX low, red if SPX bearish and VIX high, white otherwise.
---
### **5. Visual Outputs**
#### **Plots**
- EMAs, VWAP, and PSAR are plotted on the chart with their respective colors.
- EMA crosses are marked with circles (green for bullish, red for bearish).
#### **Table**
- Displays a summary of indicators in a customizable position and size.
- Indicators shown (if enabled):
- EMA 8/20, 9/21, 50: Green dot if bullish, red if bearish.
- VWAP: Green if price > VWAP, red if price < VWAP.
- MACD: Green if bullish, red if bearish.
- MACD Zero: Green if MACD > 0, red if MACD < 0.
- PSAR: Green if price > PSAR, red if price < PSAR.
- ADX: Arrows for very strong trends (↑/↓), dots for weaker trends, colored by direction.
- Bollinger Bands: Arrows (↑/↓) or dots based on price position.
- RSI: Numeric value, colored by overbought/oversold levels.
- VIX: Numeric value, colored based on SPX trend and VIX level.
---
### **6. Alerts**
- Triggers alerts for EMA 8/20 crosses:
- Bullish: "EMA 8/20 Bullish Cross on Candle Close!"
- Bearish: "EMA 8/20 Bearish Cross on Candle Close!"
---
### **7. Key Features**
- **Flexibility**: Users can toggle indicators on/off in the table and adjust parameters.
- **Visual Clarity**: Consistent use of green (bullish), red (bearish), and neutral colors.
- **Comprehensive**: Combines trend, momentum, volatility, and market sentiment indicators.
---
### **How to Use**
1. Add the script to TradingView.
2. Customize inputs (colors, lengths, table position) as needed.
3. Interpret the chart and table:
- Green signals suggest bullish conditions.
- Red signals suggest bearish conditions.
- Neutral signals indicate indecision or consolidation.
4. Set up alerts for EMA crosses to catch trend changes.
This script is ideal for traders who want a multi-indicator dashboard to monitor price action and market conditions efficiently.
MTF Moving Averages (only EMA)MTF Moving Averages (only EMA)
This script provides a Multi-Timeframe (MTF) Exponential Moving Average (EMA) indicator for traders to visualize multiple EMAs across different timeframes directly on a single chart.
The indicator dynamically calculates and plots up to four EMAs per timeframe (15-minute, 30-minute, 1-hour, and Daily) with user-defined lengths, offering valuable insight into price trends and potential entry or exit points.
Key Features:
Multiple Timeframe Support: The script allows you to view EMAs from different timeframes simultaneously. This is especially useful for traders who follow trends across different timeframes to make more informed decisions.
Customizable Lengths: For each timeframe, the lengths of the EMAs are fully customizable. You can adjust the length of up to four EMAs per timeframe to suit your strategy.
EMA Calculation: The Exponential Moving Average (EMA) is used, which gives more weight to recent prices and reacts faster to price changes compared to the simple moving average (SMA).
Timeframe Flexibility: The indicator supports the following timeframes:
15-minute: Ideal for short-term traders and scalpers.
30-minute: For intraday trading with a slightly longer perspective.
1-hour: Suitable for swing traders and those who prefer a more medium-term view.
Daily: Great for longer-term trend-following strategies.
Interactive and User-Friendly: You can toggle the visibility of each EMA on each timeframe, allowing you to choose exactly which EMAs you wish to display, depending on your trading strategy.
Color-Coded for Clarity: The script uses distinct colors for each EMA on the chart:
Blue: EMA1
Green: EMA2
Red: EMA3
Purple: EMA4
Line Width Customization: Each plotted EMA line has a customizable width for better visual clarity.
Use Case:
Traders who use multiple timeframes for analysis (e.g., those using the "multi-timeframe analysis" technique) will find this script particularly useful. For example, a trader may look at the 15-minute chart to catch short-term movements, the 30-minute chart for intraday trends, the 1-hour chart for swing positions, and the Daily chart for identifying the overarching market trend. The script enables them to view the EMAs for all these timeframes in one glance without having to manually switch between them.
By observing the relationships between EMAs across multiple timeframes, traders can gain valuable insights into market conditions such as:
Crossovers: When a shorter-term EMA crosses above or below a longer-term EMA, it can signal a potential trend reversal or continuation.
Trend Strength: Multiple EMAs in alignment across different timeframes can indicate strong trend strength.
Support and Resistance: EMAs can act as dynamic support and resistance levels, guiding traders on price action levels to watch for potential price reversals.
Instructions:
Enable/Disable EMAs: Toggle on or off the EMAs for each timeframe (15-min, 30-min, 1-hour, Daily) using the script’s settings.
Adjust EMA Lengths: Change the default lengths for each EMA to match your preferred settings for different timeframes.
Monitor Key Levels: Watch how price interacts with the plotted EMAs to spot potential trading signals based on your strategy.
This indicator is designed to enhance your multi-timeframe analysis and help make more informed, data-driven trading decisions.
Jumbalika BandsThis indicator is designed using several common technical analysis tools: Bollinger Bands, Exponential Moving Averages (EMAs), and the Parabolic SAR. I'll walk you through each section to explain how it works and how you can use it:
1. Bollinger Bands
Bollinger Bands are used to measure volatility and overbought/oversold conditions. It consists of three lines:
Basis (Middle Line): A simple moving average (SMA) of the price over a defined period (in this case, 20 periods).
Upper Band: The basis plus a certain number of standard deviations. It represents the upper boundary of expected price movement.
Lower Band: The basis minus the same number of standard deviations. It represents the lower boundary of expected price movement.
Interpretation:
Overbought: If the price moves above the upper band, it could signal that the asset is overbought.
Oversold: If the price moves below the lower band, it could signal that the asset is oversold.
Volatility: A wider band indicates higher volatility, and a narrower band indicates lower volatility.
2. Exponential Moving Averages (EMAs)
The indicator plots four different EMAs:
9-period EMA: This is a short-term trend indicator.
20-period EMA on Close: This is another medium-term trend indicator, based on the close price.
20-period EMA on High: A variation of the 20-period EMA, but based on the high prices.
20-period EMA on Low: A variation of the 20-period EMA, but based on the low prices.
Interpretation:
9 EMA: A faster-moving average that responds quicker to price changes. It can be used to identify short-term trends.
20 EMA: A slower-moving average that reacts more gradually to price changes. It helps identify the broader trend.
High/Low EMAs: These give additional insights into the extremes of price action, which can help identify possible support or resistance levels.
Trading signals (common usage):
Crossover: When a shorter EMA (like the 9 EMA) crosses above a longer EMA (like the 20 EMA), it could be a bullish signal. When it crosses below, it could be a bearish signal.
3. Parabolic SAR
The Parabolic SAR is a trend-following indicator that is used to identify potential price reversals. The Parabolic SAR is plotted as a series of dots either above or below the price, depending on the trend:
Below the price: The trend is up (bullish).
Above the price: The trend is down (bearish)
4. Background Coloring (Optional)
The background will change color when the price crosses the Bollinger Bands:
Green background when the price is above the upper Bollinger Band.
Red background when the price is below the lower Bollinger Band.
Adjust the values for Bollinger Bands, EMAs, and Parabolic SAR directly in the indicator settings to suit your trading preferences.
Bollinger Bands: If the price is above the upper band, it might indicate an overbought condition, while if it's below the lower band, it might indicate an oversold condition.
EMAs: The 9 EMA is often used to track short-term trends, while the 20-period EMAs (on the close, high, and low) help analyze the broader market trend.
Parabolic SAR: The Parabolic SAR is often used to identify trend reversals. If the SAR is below the price, the trend is up, and if it's above the price, the trend is down.
Background Color: The background coloring helps visually highlight potential market conditions when the price breaks out of the Bollinger Bands.
Example Use Case:
Decide the trend based on the parabolic SAR, when the bar touches the upper or lower Bollinger take a short or long position based on the price action using EMAs.
BK Multiple MA, RMA, SMA, HMA, VWAP, Rolling VWAP **Indicator Description**
I’m incredibly proud to introduce my third indicator to the TradingView community: **BK Multiple MA with HMA, VWAP, and Rolling VWAP**! This tool has been a game-changer in my trading strategy, and I’m excited to share it with others who are navigating the markets.
This indicator holds a special place in my heart because it represents the first technical analysis concept introduced to me by my mentor when I began apprenticing under him. His wisdom, guidance, and passion for trading—and for life—left an indelible mark on my journey. I dedicate this work, and every indicator I introduce, to the foundation he helped me build, while giving glory first and foremost to God.
**Moving Averages (MAs)** are one of the most widely used tools in technical analysis, and this indicator takes them to the next level. It allows you to plot **six fully customizable moving averages simultaneously**, with options including:
- **Exponential Moving Average (EMA)**
- **Simple Moving Average (SMA)**
- **Relative Moving Average (RMA)**
- **Hull Moving Average (HMA)**
- **Volume Weighted Average Price (VWAP)**
- **Rolling VWAP**
This flexibility makes the indicator highly versatile, whether you’re a day trader, swing trader, or long-term investor. By customizing periods, colors, and line widths for each MA, you can tailor the indicator to perfectly suit your trading style.
**Key Features**
1. **Six Fully Customizable MAs**:
- Adjust periods, line colors, and widths to match your preferences.
- Select from EMA, SMA, RMA, HMA, VWAP, or Rolling VWAP for each line.
2. **Unique Rolling VWAP Option**:
- Rolling VWAP calculates the volume-weighted average price over a user-defined period, such as 200 candles.
- This feature is ideal for traders seeking volume-weighted levels that don’t reset with each session, making it invaluable for trend-following and swing trading.
3. **HMA for Smoother Trends**:
- The Hull Moving Average (HMA) is designed to reduce lag and provide a responsive, noise-free view of price trends.
- It’s a powerful tool for spotting reversals and confirming directional momentum.
4. **Session VWAP**:
- Traditional VWAP resets with each trading session, making it a reliable benchmark for intraday support and resistance levels.
**How It Works**
- **VWAP**: Reflects the average price weighted by volume for the current trading session, commonly used by institutional traders to identify key price levels.
- **Rolling VWAP**: Extends VWAP functionality by calculating over a user-defined period, allowing for flexible multi-timeframe analysis.
- **HMA**: A fast, smooth moving average that reacts quickly to price changes while filtering out noise.
The combination of these options provides traders with a comprehensive view of market dynamics, enabling better decision-making.
**Final Thoughts**
This indicator is deeply meaningful to me because it represents the first concept my mentor introduced when I began apprenticing under him. His wisdom, guidance, and passion for trading—and for life—left an indelible mark on my journey. I dedicate this work, and every indicator I introduce, to the foundation he helped me build, while giving glory first and foremost to God.
If this indicator helps you succeed, I humbly ask that you honor the blessings in your life by giving back—whether through acts of kindness, philanthropy, or helping others in need.
May the Almighty guide us all toward wisdom and success in our endeavors. All glory belongs to God!
Buy Signal Forex & Crypto v0 ImprovedPurpose of the Script:
This script is designed to generate buy and sell signals for trading Forex and cryptocurrencies by analyzing price trends using exponential moving averages (EMAs), volatility, and volume filters. The signals are displayed as arrows on the chart.
What the Script Does
Input Settings:
The script allows the user to configure various settings, such as the lengths of EMAs, a higher timeframe for trend confirmation, and thresholds for volume and volatility (ATR - Average True Range).
Key settings:
5 EMA Length – Length of the short-term EMA.
13 EMA Length – Length of the medium-term EMA.
26 EMA Length – Length of the long-term EMA.
21 EMA Length – Used for trend confirmation on a higher timeframe.
Higher Timeframe – Lets you select a timeframe (e.g., daily) for confirming the overall trend.
ATR Threshold – Filters out signals when the market's volatility is too low.
Volume Filter – Ensures sufficient trading activity before generating signals.
Calculating EMAs (Exponential Moving Averages):
Four EMAs are calculated:
ema5 (short-term), ema13 (medium-term), ema26 (long-term), and ema21 (higher timeframe confirmation).
These EMAs help determine price trends and crossovers, which are critical for identifying buy and sell opportunities.
Trend Confirmation Using a Higher Timeframe:
The 21 EMA on the higher timeframe (e.g., daily) is used to confirm the overall direction of the market.
Defining Signal Conditions:
Buy Signal:
A buy signal is generated when:
ema5 crosses above ema13 (indicating a bullish trend).
ema5 crosses above ema26 (stronger bullish confirmation).
The closing price is above ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Sell Signal:
A sell signal is generated when:
ema5 crosses below ema13 (indicating a bearish trend).
ema5 crosses below ema26 (stronger bearish confirmation).
The closing price is below ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Volume Filtering:
Ensures there’s enough trading activity by comparing the current volume to a 20-period moving average of volume.
Persistent Variables:
These variables (crossed13 and crossed13Sell) help track whether the short-term EMA (ema5) has crossed the medium-term EMA (ema13). This prevents false or repeated signals.
Displaying Signals on the Chart:
Buy signals are displayed as green upward arrows below the price.
Sell signals are displayed as red downward arrows above the price.
How It Helps Traders:
This script provides visual cues for potential entry and exit points by combining moving average crossovers, volatility, volume, and higher timeframe trend confirmation. It works well for trending markets and ensures signals are filtered for stronger conditions to reduce noise.
