[blackcat] L1 Zero-Lag EMA BandThe Zero-Lag EMA Band is a sophisticated technical analysis tool designed to provide traders with a comprehensive view of market trends. This innovative indicator merges the Zero-Lag EMA, a derivative of the traditional Exponential Moving Average, with Bollinger Bands to create a unique trend indicator that is less laggy and more responsive to market changes.
The Zero-Lag EMA Band is calculated by taking the standard deviation of the price data and adding or subtracting it from the Zero-Lag EMA to create an upper band and a lower band. This process results in a trend band that can help traders identify potential support and resistance levels, providing them with a more accurate assessment of the market's behavior.
The Zero-Lag EMA Band is particularly useful for traders who need to react quickly to market changes. It offers a more timely assessment of potential trend reversals, allowing traders to capitalize on market opportunities and mitigate risk.
The indicator's design is based on the principle of Zero-Lag, which aims to reduce the lag associated with traditional EMAs. This feature makes the Zero-Lag EMA Band a powerful tool for traders who want to stay ahead of the market and make more informed decisions.
In summary, the Zero-Lag EMA Band is a comprehensive and responsive tool for traders looking to identify and capitalize on market trends. It is a valuable addition to any trader's toolkit, offering a more accurate and timely assessment of potential trend reversals and providing a more comprehensive view of the market's behavior.
Certainly! Let's go through the Pine Script code line by line to understand its functionality:
//@version=5
This line specifies the version of Pine Script being used. In this case, it's version 5.
indicator(' L1 Zero-Lag EMA Band', shorttitle='L1 ZLEMA Band', overlay=true)
This line defines the indicator with a title and a short title. The `overlay=true` parameter means that the indicator will be plotted on top of the price data.
length = input.int(21, minval=1, title='Length')
This line creates an input field for the user to specify the length of the EMA. The default value is 21, and the minimum value is 1.
mult = input(1, title='Multiplier')
This line creates an input field for the user to specify the multiplier for the standard deviation, which is used to calculate the bands around the EMA. The default value is 1.
src = input.source(close, title="Source")
This line creates an input field for the user to specify the data source for the EMA calculation. The default value is the closing price of the asset.
// Define the smoothing factor (alpha) for the EMA
alpha = 2 / (length + 1)
This line calculates the smoothing factor alpha for the EMA. It's a common formula for EMA calculation.
// Initialize a variable to store the previous EMA value
var float prevEMA = na
This line initializes a variable to store the previous EMA value. It's initialized as `na` (not a number), which means it's not yet initialized.
// Calculate the zero-lag EMA
emaValue = na(prevEMA) ? ta.sma(src, length) : (src - prevEMA) * alpha + prevEMA
This line calculates the zero-lag EMA. If `prevEMA` is not a number (which means it's the first calculation), it uses the simple moving average (SMA) as the initial EMA. Otherwise, it uses the standard EMA formula.
// Update the previous EMA value
prevEMA := emaValue
This line updates the `prevEMA` variable with the newly calculated EMA value. The `:=` operator is used to update the variable in Pine Script.
// Calculate the upper and lower bands
dev = mult * ta.stdev(src, length)
upperBand = emaValue + dev
lowerBand = emaValue - dev
These lines calculate the upper and lower bands around the EMA. The bands are calculated by adding and subtracting the product of the multiplier and the standard deviation of the source data over the specified length.
// Plot the bands
p0 = plot(emaValue, color=color.new(color.yellow, 0))
p1 = plot(upperBand, color=color.new(color.yellow, 0))
p2 = plot(lowerBand, color=color.new(color.yellow, 0))
fill(p1, p2, color=color.new(color.fuchsia, 80))
These lines plot the EMA value, upper band, and lower band on the chart. The `fill` function is used to color the area between the upper and lower bands. The `color.new` function is used to create a new color with a specified alpha value (transparency).
In summary, this script creates an indicator that displays the zero-lag EMA and its bands on a trading chart. The user can specify the length of the EMA and the multiplier for the standard deviation. The bands are used to identify potential support and resistance levels for the asset's price.
In the context of the provided Pine Script code, `prevEMA` is a variable used to store the previous value of the Exponential Moving Average (EMA). The EMA is a type of moving average that places a greater weight on the most recent data points. Unlike a simple moving average (SMA), which is an equal-weighted average, the EMA gives more weight to the most recent data points, which can help to smooth out short-term price fluctuations and highlight the long-term trend.
The `prevEMA` variable is used to calculate the current EMA value. When the script runs for the first time, `prevEMA` will be `na` (not a number), indicating that there is no previous EMA value to use in the calculation. In such cases, the script falls back to using the simple moving average (SMA) as the initial EMA value.
Here's a breakdown of the role of `prevEMA`:
1. **Initialization**: On the first bar, `prevEMA` is `na`, so the script uses the SMA of the close price over the specified period as the initial EMA value.
2. **Calculation**: On subsequent bars, `prevEMA` holds the value of the EMA from the previous bar. This value is used in the EMA calculation to give more weight to the most recent data points.
3. **Update**: After calculating the current EMA value, `prevEMA` is updated with the new EMA value so it can be used in the next bar's calculation.
The purpose of `prevEMA` is to maintain the state of the EMA across different bars, ensuring that the EMA calculation is not reset to the SMA on each new bar. This is crucial for the EMA to function properly and to avoid the "lag" that can sometimes be associated with moving averages, especially when the length of the moving average is short.
In the provided script, `prevEMA` is used to simulate a zero-lag EMA, but as mentioned earlier, there is no such thing as a zero-lag EMA in the traditional sense. The EMA already has a very minimal lag due to its recursive nature, and any attempt to reduce the lag further would likely not be accurate or reliable for trading purposes.
Please note that the script provided is a conceptual example and may not be suitable for actual trading without further testing and validation.
Cerca negli script per "bands"
MESThe Double Bollinger Bands strategy is a trend-following strategy that aims to identify high-probability trading opportunities in trending markets. The strategy involves using two sets of Bollinger Bands with different standard deviation values to identify potential entry and exit points.
Bollinger Bands are a technical analysis tool that consists of three lines plotted on a price chart: a simple moving average (SMA) in the middle, and an upper and lower band that are each a certain number of standard deviations away from the SMA. The standard deviation value determines the width of the bands, with a larger deviation resulting in wider bands.
In this indicator, the first set of Bollinger Bands is calculated using a length of 20 bars and a standard deviation of 2, while the second set uses a length of 20 bars and a standard deviation of 3. The bands are plotted on the price chart along with the SMA for each set.
The buy signal is generated when the price falls below the lower band of the second set of Bollinger Bands (the 3-standard deviation band) and then rises above the lower band of the first set (the 2-standard deviation band). This is interpreted as a potential reversal point in a downtrend and a signal to enter a long position.
Conversely, the sell signal is generated when the price rises above the upper band of the second set of Bollinger Bands and then falls below the upper band of the first set. This is interpreted as a potential reversal point in an uptrend and a signal to enter a short position.
To make it easier to identify buy and sell signals on the price chart, the indicator plots triangles above the bars for sell signals and below the bars for buy signals.
Overall, the Double Bollinger Bands strategy can be a useful tool for traders who want to follow trends and identify potential entry and exit points. However, as with any trading strategy, it is important to backtest and thoroughly evaluate its performance before using it in live trading.
Band-Zigzag Based Trend FollowerWe defined new method to derive zigzag last month - which is called Channel-Based-Zigzag . This script is an example of one of the use case of this method.
🎲 Trend Following
Defining a trend following method is simple. Basic rule of trend following is Buy High and Sell Low (Yes, you heard it right). To explain further - methodology involve finding an established trend which is flying high and join the trend with proper risk and optimal stop. Once you get into the trade, you will not exit unless there is change in the trend. Or in other words, the parameters which you used to define trend has reversed and the trend is not valid anymore.
Few examples are:
🎯 Using bands
When price breaks out of upper bands (example, Bollinger Band, Keltener Channel, or Donchian Channel), with a pre determined length and multiplier, we can consider the trend to be bullish and similarly when price breaks down the lower band, we can consider the trend to be bearish.
Here are few examples where I have used bands for identifying trend
Band-Based-Supertrend
Donchian-Channel-Trend-Filter
🎯 Using Pivots
Simple logic using zigzag or pivot points is that when price starts making higher highs and higher lows, we can consider this as uptrend. And when price starts making lower highs and lower lows, we can consider this as downtrend. There are few supertrend implementations I have published in the past based on zigzags and pivot points.
Adoptive-Supertrend-Pivots
Zigzag-Supertrend
Drawbacks of both of these methods is that there will be too many fluctuations in both cases unless we increase the reference length. And if we increase the reference length, we will have higher drawdown.
🎲 Band Based Zigzag Method
Band Based Zigzag will help overcome these issues by combining both the methods.
Here we use bands to define our pivot high and pivot low - this makes sure that we are identifying trend only on breakouts as pivots are only formed on breakouts.
Our method also includes pivot ratio to cross over 1.0 to be able to consider it as trend. This means, we are waiting for price also to make new high high or lower low before making the decision on trend. But, this helps us ignore smaller pivot movements due to the usage of bands.
I have also implemented few tricks such as sticky bands (Bands will not contract unless there is breakout) and Adaptive Bands (Band will not expand unless price is moving in the direction of band). This makes the trend following method very robust.
To avoid fakeouts, we also use percentB of high/low in comparison with price retracement to define breakout.
🎲 The indicator
The output of indicator is simple and intuitive to understand.
🎯 Trend Criteria
Uptrend when last confirmed pivot is pivot high and has higher retracement ratio than PercentB of High. Else, considered as downtrend.
Downtrend when last confirmed pivot is pivot low and has higher retracement ratio than PercentB of High. Else, considered as uptrend.
🎯 Settings
Settings allow you to select the band type and parameters used for calculating zigzag and then trend. Also has few options to hide the display.
Cryptogrithm's Secret Momentum and Volatility IndicatorThis indicator is hard-coded for Bitcoin, but you may try it on other asset classes/coins. I have not updated this indicator in over 3 years, but it seems to still work very well for Bitcoin.
This indicator is NOT for beginners and is directed towards intermediate/advanced traders with a sensibility to agree/disagree with what this indicator is signalling (common sense).
This indicator was developed back in 2018 and I has not been maintained since, which is the reason why I am releasing it. (It still works great though! At the time of this writing of May 2022).
How to use:
Terms:
PA (Price Action): Literally the candlestick formations on your chart (and the trend formation). If you don't know how to read and understand price action, I will make a fast-track video/guide on this later (but in the meanwhile, you need to begin by learning Order-Flow Analysis, please google it first before asking).
CG Level (Cryptogrithm Level/Yellow Line): PA level above = bullish, PA level below = bearish
CG Bands (Cryptogrithm Bands): This is similar to how bollingers work, you can use this the same was as bollinger bands. The only difference is that the CG bands are more strict with the upper and lower levels as it uses different calculations to hug the price tighter allowing it to be more reactive to drastic price changes (earlier signals for oversold/overbought).
CG Upper Band (Red Upper Line): Above this upper bound line means overbought.
CG Middle Band (Light Blue Line): If PA trades above this line, the current PA trend is bullish continuing in the uptrend. If PA trades below this line, the current PA trend is bearish continuing in the downtrend. This band should only be used for short-term trends.
CG Lower Band (Green Lower Line): Below this lower bound line means oversold.
What the CG Level (yellow line) tells you:
PA is trading above CG Level = Bullish
PA is trading below CG Level = Bearish
Distance between CG Level and price = Momentum
What this means is that the further away the price is from the CG Level, the greater the momentum of the current PA trend. An increasing gap between the CG Level and PA indicates the price's strength (momentum) towards the current upward/downward trend. Basically when the PA and CG Level diverge, it means that the momentum is increasing in the current trend and when they converge, the current trend is losing momentum and the direction of the PA trend may flip towards the other direction (momentum flip).
PA+CG Level Momentum:
To use the CG Level as a momentum indicator, you need to pay attention to how the price and the CG level are moving away/closer from each other:
PA + CG Level Diverges = Momentum Increasing
PA + CG Level Converges = Momentum Decreasing
Examples (kind of common sense, but just for clarity):
Case 1: Bullish Divergence (Bullish): The PA is ABOVE and trending AWAY above from the CG Level = very bullish, this means that momentum is increasing towards the upside and larger moves will come (increasing gap between the price and CG Level)
Case 2: Bearish Convergence (Bearish): - The PA is ABOVE the CG Level and trending TOWARDS the CG Level = bearish, there is a possibility that the upward trend is ending. Look to start closing off long positions until case 1 (divergence) occurs again.
Case 3: Neutral - The PA is trading on the CG Level (no clear divergence or convergence between the PA and CG Level) = Indicates a back and forth (tug of war) between bears and bulls. Beware of choppy price patterns as the trend is undecisive until either supply/liquidity is dried out and a winner between bull/bear is chosen. This is a no trade zone, but do as you wish.
Case 4: Bearish Divergence (Bearish): The PA is BELOW and trending AWAY BELOW from the CG Level = very bearish, this means that momentum is increasing towards the downside and larger downward moves will come (increasing gap between the price and CG Level).
Case 5: Bullish Convergence (Bullish): - The PA is BELOW the CG Level and trending TOWARDS the CG Level = bullish, there is a possibility that the downward trend is ending and a trend flip is occuring. Look to start closing off short positions until case 4 (divergence) occurs again.
CG Bands + CG Level: You can use the CG bands instead of the PA candles to get a cleaner interpretation of reading the momentum. I won't go into detail as this is pretty self-explanatory. It is the same explanation as PA+CG Level Momentum, but you are replacing the PA candles with the CG Bands for interpretation. So instead of the PA converging/diverging from the CG Level, the Upper and Lower Bound levels are converging/diverging from the CG level instead.
Convergence: CG Level (yellow line) trades inside the CG bands
Divergence: CG Level (yellow line) trades outside the CG bands
Bullish/Bearish depends on whether the CG Band is trading below or above the CG level. If CG Band is above the CG Level, this is bullish. If CG Band is below the CG level, this is bearish.
Crosses (PA or CG Band crosses with CG level): This typically indicates volatility is incoming.
There are MANY MANY MANY other ways to use this indicator that is not explained here and even other undiscovered methods. Use some common sense as to how this indicator works (it is a momentum indicator and volatility predictor). You can get pretty creative and apply your own methods / knowledge to it and look for patterns that occur. Feel free to comment and share what you came up with!
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations.
To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market.
HOIW IT WORKS
At its core, this indicator fuses two ideas:
The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature.
A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity.
// Step 1: Adaptive length via RSI
rsi = ta.rsi(src, rsiLen)
rsi_scale = 1 - rsi / 100
len = math.round(minLen + (maxLen - minLen) * rsi_scale)
pine_ema(src, length) =>
alpha = 2 / (length + 1)
sum = 0.0
sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum )
sum
// Step 2: T3 with adaptive length
e1 = pine_ema(src, len)
e2 = pine_ema(e1, len)
e3 = pine_ema(e2, len)
e4 = pine_ema(e3, len)
e5 = pine_ema(e4, len)
e6 = pine_ema(e5, len)
c1 = -v * v * v
c2 = 3 * v * v + 3 * v * v * v
c3 = -6 * v * v - 3 * v - 3 * v * v * v
c4 = 1 + 3 * v + v * v * v + 3 * v * v
t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
The result: an evolving trend line that adapts to market tempo in real-time.
KEY FEATURES
⯁ RSI-Based Adaptive Smoothing
The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI.
When RSI is low → the T3 shortens, tracking reversals faster.
When RSI is high → the T3 stretches, filtering out noise during euphoria phases.
Displayed length is shown in a floating table, colored on a gradient between min/max values.
⯁ T3 Calculation (Tim Tillson Method)
The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) .
Formula:
T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA.
⯁ Visual Trend Direction & Transitions
The T3 line changes color dynamically:
Color Up (default: blue) → bullish curvature
Color Down (default: orange) → bearish curvature
Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction.
Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries.
⯁ Adaptive Volatility Bands
Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100).
Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3.
Shaded band zones help frame breakout setups or mean-reversion zones.
⯁ Dynamic Info Table
A live stats panel shows:
Current adaptive length
Maximum smoothing (▲ MaxLen)
Minimum smoothing (▼ MinLen)
All values update in real time and are color-coded to match trend direction.
HOW TO USE
Use T3 crossovers to detect trend transitions, especially during periods of volatility compression.
Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts.
The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower.
CONCLUSION
RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
True Amplitude Envelopes (TAE)The True Envelopes indicator is an adaptation of the True Amplitude Envelope (TAE) method, based on the research paper " Improved Estimation of the Amplitude Envelope of Time Domain Signals Using True Envelope Cepstral Smoothing " by Caetano and Rodet. This indicator aims to create an asymmetric price envelope with strong predictive power, closely following the methodology outlined in the paper.
Due to the inherent limitations of Pine Script, the indicator utilizes a Kernel Density Estimator (KDE) in place of the original Cepstral Smoothing technique described in the paper. While this approach was chosen out of necessity rather than superiority, the resulting method is designed to be as effective as possible within the constraints of the Pine environment.
This indicator is ideal for traders seeking an advanced tool to analyze price dynamics, offering insights into potential price movements while working within the practical constraints of Pine Script. Whether used in dynamic mode or with a static setting, the True Envelopes indicator helps in identifying key support and resistance levels, making it a valuable asset in any trading strategy.
Key Features:
Dynamic Mode: The indicator dynamically estimates the fundamental frequency of the price, optimizing the envelope generation process in real-time to capture critical price movements.
High-Pass Filtering: Uses a high-pass filtered signal to identify and smoothly interpolate price peaks, ensuring that the envelope accurately reflects significant price changes.
Kernel Density Estimation: Although implemented as a workaround, the KDE technique allows for flexible and adaptive smoothing of the envelope, aimed at achieving results comparable to the more sophisticated methods described in the original research.
Symmetric and Asymmetric Envelopes: Provides options to select between symmetric and asymmetric envelopes, accommodating various trading strategies and market conditions.
Smoothness Control: Features adjustable smoothness settings, enabling users to balance between responsiveness and the overall smoothness of the envelopes.
The True Envelopes indicator comes with a variety of input settings that allow traders to customize the behavior of the envelopes to match their specific trading needs and market conditions. Understanding each of these settings is crucial for optimizing the indicator's performance.
Main Settings
Source: This is the data series on which the indicator is applied, typically the closing price (close). You can select other price data like open, high, low, or a custom series to base the envelope calculations.
History: This setting determines how much historical data the indicator should consider when calculating the envelopes. A value of 0 will make the indicator process all available data, while a higher value restricts it to the most recent n bars. This can be useful for reducing the computational load or focusing the analysis on recent market behavior.
Iterations: This parameter controls the number of iterations used in the envelope generation algorithm. More iterations will typically result in a smoother envelope, but can also increase computation time. The optimal number of iterations depends on the desired balance between smoothness and responsiveness.
Kernel Style: The smoothing kernel used in the Kernel Density Estimator (KDE). Available options include Sinc, Gaussian, Epanechnikov, Logistic, and Triangular. Each kernel has different properties, affecting how the smoothing is applied. For example, Gaussian provides a smooth, bell-shaped curve, while Epanechnikov is more efficient computationally with a parabolic shape.
Envelope Style: This setting determines whether the envelope should be Static or Dynamic. The Static mode applies a fixed period for the envelope, while the Dynamic mode automatically adjusts the period based on the fundamental frequency of the price data. Dynamic mode is typically more responsive to changing market conditions.
High Q: This option controls the quality factor (Q) of the high-pass filter. Enabling this will increase the Q factor, leading to a sharper cutoff and more precise isolation of high-frequency components, which can help in better identifying significant price peaks.
Symmetric: This setting allows you to choose between symmetric and asymmetric envelopes. Symmetric envelopes maintain an equal distance from the central price line on both sides, while asymmetric envelopes can adjust differently above and below the price line, which might better capture market conditions where upside and downside volatility are not equal.
Smooth Envelopes: When enabled, this setting applies additional smoothing to the envelopes. While this can reduce noise and make the envelopes more visually appealing, it may also decrease their responsiveness to sudden market changes.
Dynamic Settings
Extra Detrend: This setting toggles an additional high-pass filter that can be applied when using a long filter period. The purpose is to further detrend the data, ensuring that the envelope focuses solely on the most recent price oscillations.
Filter Period Multiplier: This multiplier adjusts the period of the high-pass filter dynamically based on the detected fundamental frequency. Increasing this multiplier will lengthen the period, making the filter less sensitive to short-term price fluctuations.
Filter Period (Min) and Filter Period (Max): These settings define the minimum and maximum bounds for the high-pass filter period. They ensure that the filter period stays within a reasonable range, preventing it from becoming too short (and overly sensitive) or too long (and too sluggish).
Envelope Period Multiplier: Similar to the filter period multiplier, this adjusts the period for the envelope generation. It scales the period dynamically to match the detected price cycles, allowing for more precise envelope adjustments.
Envelope Period (Min) and Envelope Period (Max): These settings establish the minimum and maximum bounds for the envelope period, ensuring the envelopes remain adaptive without becoming too reactive or too slow.
Static Settings
Filter Period: In static mode, this setting determines the fixed period for the high-pass filter. A shorter period will make the filter more responsive to price changes, while a longer period will smooth out more of the price data.
Envelope Period: This setting specifies the fixed period used for generating the envelopes in static mode. It directly influences how tightly or loosely the envelopes follow the price action.
TAE Smoothing: This controls the degree of smoothing applied during the TAE process in static mode. Higher smoothing values result in more gradual envelope curves, which can be useful in reducing noise but may also delay the envelope’s response to rapid price movements.
Visual Settings
Top Band Color: This setting allows you to choose the color for the upper band of the envelope. This band represents the resistance level in the price action.
Bottom Band Color: Similar to the top band color, this setting controls the color of the lower band, which represents the support level.
Center Line Color: This is the color of the central price line, often referred to as the carrier. It represents the detrended price around which the envelopes are constructed.
Line Width: This determines the thickness of the plotted lines for the top band, bottom band, and center line. Thicker lines can make the envelopes more visible, especially when overlaid on price data.
Fill Alpha: This controls the transparency level of the shaded area between the top and bottom bands. A lower alpha value will make the fill more transparent, while a higher value will make it more opaque, helping to highlight the envelope more clearly.
The envelopes generated by the True Envelopes indicator are designed to provide a more precise and responsive representation of price action compared to traditional methods like Bollinger Bands or Keltner Channels. The core idea behind this indicator is to create a price envelope that smoothly interpolates the significant peaks in price action, offering a more accurate depiction of support and resistance levels.
One of the critical aspects of this approach is the use of a high-pass filtered signal to identify these peaks. The high-pass filter serves as an effective method of detrending the price data, isolating the rapid fluctuations in price that are often lost in standard trend-following indicators. By filtering out the lower frequency components (i.e., the trend), the high-pass filter reveals the underlying oscillations in the price, which correspond to significant peaks and troughs. These oscillations are crucial for accurately constructing the envelope, as they represent the most responsive elements of the price movement.
The algorithm works by first applying the high-pass filter to the source price data, effectively detrending the series and isolating the high-frequency price changes. This filtered signal is then used to estimate the fundamental frequency of the price movement, which is essential for dynamically adjusting the envelope to current market conditions. By focusing on the peaks identified in the high-pass filtered signal, the algorithm generates an envelope that is both smooth and adaptive, closely following the most significant price changes without overfitting to transient noise.
Compared to traditional envelopes and bands, such as Bollinger Bands and Keltner Channels, the True Envelopes indicator offers several advantages. Bollinger Bands, which are based on standard deviations, and Keltner Channels, which use the average true range (ATR), both tend to react to price volatility but do not necessarily follow the peaks and troughs of the price with precision. As a result, these traditional methods can sometimes lag behind or fail to capture sudden shifts in price momentum, leading to either false signals or missed opportunities.
In contrast, the True Envelopes indicator, by using a high-pass filtered signal and a dynamic period estimation, adapts more quickly to changes in price behavior. The envelopes generated by this method are less prone to the lag that often affects standard deviation or ATR-based bands, and they provide a more accurate representation of the price's immediate oscillations. This can result in better predictive power and more reliable identification of support and resistance levels, making the True Envelopes indicator a valuable tool for traders looking for a more responsive and precise approach to market analysis.
In conclusion, the True Envelopes indicator is a powerful tool that blends advanced theoretical concepts with practical implementation, offering traders a precise and responsive way to analyze price dynamics. By adapting the True Amplitude Envelope (TAE) method through the use of a Kernel Density Estimator (KDE) and high-pass filtering, this indicator effectively captures the most significant price movements, providing a more accurate depiction of support and resistance levels compared to traditional methods like Bollinger Bands and Keltner Channels. The flexible settings allow for extensive customization, ensuring the indicator can be tailored to suit various trading strategies and market conditions.
Adaptive VWAP [QuantAlgo]Introducing the Adaptive VWAP by QuantAlgo 📈🧬
Enhance your trading and investing strategies with the Adaptive VWAP , a versatile tool designed to provide dynamic insights into market trends and price behavior. This indicator offers a flexible approach to VWAP calculations by allowing users to adapt it based on lookback periods or fixed timeframes, making it suitable for a wide range of market conditions.
🌟 Key Features:
🛠 Customizable VWAP Settings: Choose between an adaptive VWAP that adjusts based on a rolling lookback period, or switch to a fixed timeframe (e.g., daily, weekly, monthly) for a more structured approach. Adjust the VWAP to suit your trading or investing style.
💫 Dynamic Bands and ATR Filter: Configurable deviation bands with multipliers allow you to visualize price movement around VWAP, while an ATR-based noise filter helps reduce false signals during periods of market fluctuation.
🎨 Trend Visualization: Color-coded trend identification helps you easily spot uptrends and downtrends based on VWAP positioning. The indicator fills the areas between the bands for clearer visual representation of price volatility and trend strength.
🔔 Custom Alerts: Set up alerts for when price crosses above or below the VWAP, signaling potential uptrend or downtrend opportunities. Stay informed without needing to monitor the charts constantly.
✍️ How to Use:
✅ Add the Indicator: Add the Adaptive VWAP to your favourites and apply to your chart. Choose between adaptive or timeframe-based VWAP calculation, adjust the lookback period, and configure the deviation bands to your preferred settings.
👀 Monitor Bands and Trends: Watch for price interaction with the VWAP and its deviation bands. The color-coded signals and band fills help identify potential trend shifts or price extremes.
🔔 Set Alerts: Configure alerts for uptrend and downtrend signals based on price crossing the VWAP, so you’re always informed of significant market movements.
⚙️ How It Works:
The Adaptive VWAP adjusts its calculation based on the user’s chosen configuration, allowing for a flexible approach to market analysis. The adaptive setting uses a rolling lookback period to continuously adjust the VWAP, while the fixed timeframe option anchors VWAP to key timeframes like daily, weekly, or monthly periods. This flexibility enables traders and investors to use the tool in various market environments.
Deviation bands, calculated with customizable multipliers, provide a clear visual of how far the price has moved from the VWAP, helping you gauge potential overbought or oversold conditions. To reduce false signals, an ATR-based filter can be applied, ensuring that only significant price movements trigger trend confirmations.
The tool also includes a fast exponential smoothing function for the VWAP, helping smooth out price fluctuations without sacrificing responsiveness. Trend confirmation is reinforced by the number of bars that price stays above or below the VWAP, ensuring a more consistent trend identification process.
Disclaimer:
The Adaptive VWAP is designed to enhance your market analysis but should not be relied upon as the sole basis for trading or investing decisions. Always combine it with other analytical tools and practices. No statements or signals from this indicator constitute financial advice. Past performance is not indicative of future results.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
Adaptive Bollinger-RSI Trend Signal [CHE]Adaptive Bollinger-RSI Trend Signal
Indicator Overview:
The "Adaptive Bollinger-RSI Trend Signal " (ABRT Signal ) is a sophisticated trading tool designed to provide clear and actionable buy and sell signals by combining the power of Bollinger Bands and the Relative Strength Index (RSI). This indicator aims to help traders identify potential trend reversals and confirm entry and exit points with greater accuracy.
Key Features:
1. Bollinger Bands Integration:
- Utilizes Bollinger Bands to detect price volatility and identify overbought or oversold conditions.
- Configurable parameters: Length, Source, and Multiplier for precise adjustments based on trading preferences.
- Color customization: Change the colors of the basis line, upper band, lower band, and the fill color between bands.
2. RSI Integration:
- Incorporates the Relative Strength Index (RSI) to validate potential buy and sell signals.
- Configurable parameters: Length, Source, Upper Threshold, and Lower Threshold for customized signal generation.
3. Signal Generation:
- Buy Signal: Generated when the price crosses below the lower Bollinger Band and the RSI crosses above the lower threshold, indicating a potential upward trend.
- Sell Signal: Generated when the price crosses above the upper Bollinger Band and the RSI crosses below the upper threshold, indicating a potential downward trend.
- Color customization: Change the colors of the buy and sell signal labels.
4. State Tracking:
- Tracks and records crossover and crossunder states of the price and RSI to ensure signals are only generated under the right conditions.
- Monitors the basis trend (SMA of the Bollinger Bands) to provide context for signal validation.
5. Counters and Labels:
- Labels each buy and sell signal with a counter to indicate the number of consecutive signals.
- Counters reset upon the generation of an opposite signal, ensuring clarity and preventing signal clutter.
6. DCA (Dollar-Cost Averaging) Calculation:
- Stores the close price at each signal and calculates the average entry price (DCA) for both buy and sell signals.
- Displays the number of positions and DCA values in a label on the chart.
7. Customizable Inputs:
- Easily adjustable parameters for Bollinger Bands, RSI, and colors to suit various trading strategies and timeframes.
- Boolean input to show or hide the table label displaying position counts and DCA values.
- Intuitive and user-friendly configuration options for traders of all experience levels.
How to Use:
1. Setup:
- Add the "Adaptive Bollinger-RSI Trend Signal " to your TradingView chart.
- Customize the input parameters to match your trading style and preferred timeframe.
- Adjust the colors of the indicator elements to your preference for better visibility and clarity.
2. Interpreting Signals:
- Buy Signal: Look for a "Buy" label on the chart, indicating a potential entry point when the price is oversold and RSI signals upward momentum.
- Sell Signal: Look for a "Sell" label on the chart, indicating a potential exit point when the price is overbought and RSI signals downward momentum.
3. Trade Execution:
- Use the buy and sell signals to guide your trade entries and exits, aligning them with your overall trading strategy.
- Monitor the counter labels to understand the strength and frequency of signals, helping you make informed decisions.
4. Adjust and Optimize:
- Regularly review and adjust the indicator parameters based on market conditions and backtesting results.
- Combine this indicator with other technical analysis tools to enhance your trading accuracy and performance.
5. Monitor DCA Values:
- Enable the table label to display the number of positions and average entry prices (DCA) for both buy and sell signals.
- Use this information to assess the cost basis of your trades and make strategic adjustments as needed.
Conclusion:
The Adaptive Bollinger-RSI Trend Signal is a powerful and versatile trading tool designed to help traders identify and capitalize on trend reversals with confidence. By combining the strengths of Bollinger Bands and RSI, this indicator provides clear and reliable signals, making it an essential addition to any trader's toolkit. Customize the settings, interpret the signals, and execute your trades with precision using this comprehensive indicator.
Matrix Momentum Expansion [IkkeOmar]The indicator consists of several features:
Candlestick chart: The indicator plots a candlestick chart based on the input parameters of the user. The candlesticks are colored blue or orange depending on whether the closing price is above or below the upper and lower bands.
Support and Resistance levels: The indicator also plots support and resistance levels based on the CCI (Commodity Channel Index) of the asset's price. These levels are dynamic and change based on the user's input parameters.
Momentum: The indicator calculates the momentum of the market based on the smoothed and standard deviation of the asset's price. It uses this momentum to calculate upper and lower bands that are plotted on the chart.
Warning signals: The indicator can also be used to identify potential warning signals. When the closing price of the asset moves above the upper band, it could indicate that the market is overbought and a potential reversal could occur. Conversely, when the closing price moves below the lower band, it could indicate that the market is oversold and a potential reversal could occur.
Contractions and expansions in the bands can provide important information to traders about potential price movements.
When the bands contract, it indicates that the market is experiencing low volatility and the price is likely to move sideways. During these periods, traders may look for other signals, such as support and resistance levels or price patterns, to determine potential entry and exit points.
On the other hand, when the bands expand, it indicates that the market is experiencing high volatility and the price is likely to move in a particular direction. Traders can use this information to identify potential trend reversals or continuation patterns. When the upper and lower bands move further apart, it indicates that the trend is becoming stronger, while when they move closer together, it indicates that the trend may be weakening.
When the price moves outside of the bands, it can also provide important information to traders. If the price moves above the upper band, it could indicate that the market is overbought and a potential reversal could occur. Conversely, if the price moves below the lower band, it could indicate that the market is oversold and a potential reversal could occur.
Very important note!
When you see contractions, please understand that it's a wonderful opportunity to pivot into position to catch a good trade because we will see an expansion after!
Band-Zigzag - TrendFollower Strategy [Trendoscope]Strategy Time!!!
Have built this on my earlier published indicator Band-Zigzag-Trend-Follower . This is just one possible implementation of strategy on Band-Based-Zigzag .
🎲 Notes
Experimental prototype. Not financial advise and strategy not guaranteed to make money despite backtest results
Not created or tested for any specific instrument or timeframe
Test and adopt with own risk
🎲 Strategy
This is trend following strategy built based on Bands and Zigzag. Traits of trend following strategies are
Lower win rate (Yes, thats right)
High risk reward (Compensates low win rate)
Higher drawdown
If market is choppy, trend following methods suffer.
The script implements few points to overcome the negatives such as lower win rate and higher drawdown by actively assessing pivots on the direction of trend along. This helps us take regular profits and exit on time during the end of trend. Most of the other concepts are defined and explained in indicator - Band-Zigzag-Trend-Follower and Band-Based-Zigzag
Defining a trend following method is simple. Basic rule of trend following is Buy High and Sell Low (Yes, you heard it right). To explain further - methodology involve finding an established trend which is flying high and join the trend with proper risk and optimal stop. Once you get into the trade, you will not exit unless there is change in the trend. Or in other words, the parameters which you used to define trend has reversed and the trend is not valid anymore.
🎯 Using bands
When price breaks out of upper bands (example, Bollinger Band , Keltener Channel, or Donchian Channel), with a pre determined length and multiplier, we can consider the trend to be bullish and similarly when price breaks down the lower band, we can consider the trend to be bearish .
🎯 Using Pivots
Simple logic using zigzag or pivot points is that when price starts making higher highs and higher lows, we can consider this as uptrend. And when price starts making lower highs and lower lows, we can consider this as downtrend. There are few supertrend implementations I have published in the past based on zigzags and pivot points .
Drawbacks of both of these methods is that there will be too many fluctuations in both cases unless we increase the reference length. And if we increase the reference length, we will have higher drawdown.
🎯 Band Based Zigzag Method
Here we use bands to define our pivot high and pivot low - this makes sure that we are identifying trend only on breakouts as pivots are only formed on breakouts
Our method also includes pivot ratio to cross over 1.0 to be able to consider it as trend. This means, we are waiting for price also to make new high high or lower low before making the decision on trend. But, this helps us ignore smaller pivot movements due to the usage of bands.
I have also implemented few tricks such as sticky bands (Bands will not contract unless there is breakout) and Adaptive Bands (Band will not expand unless price is moving in the direction of band). This makes the trend following method very robust.
To avoid fakeouts, we also use percentB of high/low in comparison with price retracement to define breakout.
🎲 Settings
Settings are fairly simpler and are explained as below. You will find most of the required information in tooltips.
The Barking Rat LiteMomentum & FVG Reversion Strategy
The Barking Rat Lite is a disciplined, short-term mean-reversion strategy that combines RSI momentum filtering, EMA bands, and Fair Value Gap (FVG) detection to identify short-term reversal points. Designed for practical use on volatile markets, it focuses on precise entries and ATR-based take profit management to balance opportunity and risk.
Core Concept
This strategy seeks potential reversals when short-term price action shows exhaustion outside an EMA band, confirmed by momentum and FVG signals:
EMA Bands:
Parameters used: A 20-period EMA (fast) and 100-period EMA (slow).
Why chosen:
- The 20 EMA is sensitive to short-term moves and reflects immediate momentum.
- The 100 EMA provides a slower, structural anchor.
When price trades outside both bands, it often signals overextension relative to both short-term and medium-term trends.
Application in strategy:
- Long entries are only considered when price dips below both EMAs, identifying potential undervaluation.
- Short entries are only considered when price rises above both EMAs, identifying potential overvaluation.
This dual-band filter avoids counter-trend signals that would occur if only a single EMA was used, making entries more selective..
Fair Value Gap Detection (FVG):
Parameters used: The script checks for dislocations using a 12-bar lookback (i.e. comparing current highs/lows with values 12 candles back).
Why chosen:
- A 12-bar displacement highlights significant inefficiencies in price structure while filtering out micro-gaps that appear every few bars in high-volatility markets.
- By aligning FVG signals with candle direction (bullish = close > open, bearish = close < open), the strategy avoids random gaps and instead targets ones that suggest exhaustion.
Application in strategy:
- Bullish FVGs form when earlier lows sit above current highs, hinting at downward over-extension.
- Bearish FVGs form when earlier highs sit below current lows, hinting at upward over-extension.
This gives the strategy a structural filter beyond simple oscillators, ensuring signals have price-dislocation context.
RSI Momentum Filter:
Parameters used: 14-period RSI with thresholds of 80 (overbought) and 20 (oversold).
Why chosen:
- RSI(14) is a widely recognized momentum measure that balances responsiveness with stability.
- The thresholds are intentionally extreme (80/20 vs. the more common 70/30), so the strategy only engages at genuine exhaustion points rather than frequent minor corrections.
Application in strategy:
- Longs trigger when RSI < 20, suggesting oversold exhaustion.
- Shorts trigger when RSI > 80, suggesting overbought exhaustion.
This ensures entries are not just technically valid but also backed by momentum extremes, raising conviction.
ATR-Based Take Profit:
Parameters used: 14-period ATR, with a default multiplier of 4.
Why chosen:
- ATR(14) reflects the prevailing volatility environment without reacting too much to outliers.
- A multiplier of 4 is a pragmatic compromise: wide enough to let trades breathe in volatile conditions, but tight enough to enforce disciplined exits before mean reversion fades.
Application in strategy:
- At entry, a fixed target is set = Entry Price ± (ATR × 4).
- This target scales automatically with volatility: narrower in calm periods, wider in explosive markets.
By avoiding discretionary exits, the system maintains rule-based discipline.
Visual Signals on Chart
Blue “▲” below candle: Potential long entry
Orange/Yellow “▼” above candle: Potential short entry
Green “✔️”: Trade closed at ATR take profit
Blue (20 EMA) & Orange (100 EMA) lines: Dynamic channel reference
⚙️Strategy report properties
Position size: 25% equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 10 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: HYPEUSDT
Backtesting range: Jul 28, 2025 — Aug 17, 2025
Note on Sample Size:
You’ll notice the report displays fewer than the ideal 100 trades in the strategy report above. This is intentional. The goal of the script is to isolate high-quality, short-term reversal opportunities while filtering out low-conviction setups. This means that the Barking Rat Lite strategy is very selective, filtering out over 90% of market noise. The brief timeframe shown in the strategy report here illustrates its filtering logic over a short window — not its full capabilities. As a result, even on lower timeframes like the 1-minute chart, signals are deliberately sparse — each one must pass all criteria before triggering.
For a larger dataset:
Once the strategy is applied to your chart, users are encouraged to expand the lookback range or apply the strategy to other volatile pairs to view a full sample.
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods.
The strategy generates a sizeable number of trades, reducing reliance on a single outcome.
Combined with conservative filters, the 25% setting offers a balance between aggression and control.
Users are strongly encouraged to customize this to suit their risk profile.
What makes Barking Rat Lite valuable
Combines multiple layers of confirmation: EMA bands + FVG + RSI
Adaptive to volatility: ATR-based exits scale with market conditions
Clear, actionable visuals: Easy to monitor and manage trades
Consolidation Range with Signals (Zeiierman)█ Overview
Consolidation Range with Signals (Zeiierman) is a precision tool for identifying and trading market consolidation zones, where price contracts into tight ranges before significant movement. It provides dynamic range detection using either ADX-based trend strength or volatility compression metrics, and offers built-in take profit and stop loss signals based on breakout dynamics.
Whether you trade breakouts, range reversals, or trend continuation setups, this indicator visualizes the balance between supply and demand with clearly defined mid-bands, breakout zones, and momentum-sensitive TP/SL placements.
█ How It Works
⚪ Multi-Method Range Detection
ADX Mode
Uses the Average Directional Index (ADX) to detect low-trend-strength environments. When ADX is below your selected threshold, price is considered to be in consolidation.
Volatility Mode
This mode detects consolidation by identifying periods of volatility compression. It evaluates whether the following metrics are simultaneously below their respective historical rolling averages:
Standard Deviation
Variance
Average True Range (ATR)
⚪ Dynamic Range Band System
Once a range is confirmed, the system builds a dynamic band structure using a volatility-based filter and price-jump logic:
Middle Line (Trend Filter): Reacts to price imbalance using adaptive jump logic.
Upper & Lower Bands: Calculated by expanding from the middle line using a configurable multiplier.
This creates a clean, visual box that reflects current consolidation conditions and adapts as price fluctuates within or escapes the zone.
⚪ SL/TP Signal Engine
On detection of a breakout from the range, the indicator generates up to 3 Take Profit levels and one Stop Loss, based on the breakout direction:
All TP/SL levels are calculated using the filtered base range and multipliers.
Cooldown logic ensures signals are not spammed bar-to-bar.
Entries are visualized with colored lines and labeled levels.
This feature is ideal for traders who want automated risk and reward reference points for range breakout plays.
█ How to Use
⚪ Breakout Traders
Use the SL/TP signals when the price breaks above or below the range bands, especially after extended sideways movement. You can customize how far TP1, TP2, and TP3 sit from the entry using your own risk/reward profile.
⚪ Mean Reversion Traders
Use the bands to locate high-probability reversion zones. These serve as reference zones for scalping or fade entries within stable consolidation phases.
█ Settings
Range Detection Method – Choose between ADX or Volatility compression to define range criteria.
Range Period – Determines how many bars are used to compute trend/volatility.
Range Multiplier – Scales the width of the consolidation zone.
SL/TP System – Optional levels that project TP1/TP2/TP3 and SL from the base price using multipliers.
Cooldown – Prevents repeated SL/TP signals from triggering too frequently.
ADX Threshold & Smoothing – Adjusts sensitivity of trend strength detection.
StdDev / Variance / ATR Multipliers – Fine-tune compression detection logic.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Moving Volume-Weighted Avg Price, % Channel, BBsThis script includes:
- Moving Volume-Weighted Average Price line.
- User-defined % band above and below, very useful for "breakout" signals, and mentally adjusting to the magnitude of price swings when viewing an automatic scale on the price axis.
- Volume-Weighted Bollinger Bands, which are more sensitive to volume.
More detail:
- This is like TV's basic VWAP in concept, except the major flaw in that is that it has reset periods that you can't override, and the volume is cumulative until the next hard reset. The 'reset' is OK for securities trading, that resets every day anyway. But not for crypto - and not if/when securities trading goes 24/7. Also, the denominator accumulating over the entire period is also *not* OK, because then what is shown means something different as the day progresses - which kind of makes it useless. In other words, it starts out very sensitive to volume, and gets progressively more numb to it as they day progresses, and starts flattening out.
- This fixes both problems, by using a user-definable moving window for the average. Essentially combining SMA with volume-weighting.
- You may also find an invaluable trading aid, in the % bands above and below.
- What can optionally be shown is standard deviation bands, aka Bollinger bands. The advantage over regular BB is that it's volume-weighted. Since it is already calculated on a moving average, the period for the standard deviation has been shortened by default, and the magnitude increased, to better approximate regular Bollinger Bands - but it's still more responsive to volume.
Adaptive Fourier Transform Supertrend [QuantAlgo]Discover a brand new way to analyze trend with Adaptive Fourier Transform Supertrend by QuantAlgo , an innovative technical indicator that combines the power of Fourier analysis with dynamic Supertrend methodology. In essence, it utilizes the frequency domain mathematics and the adaptive volatility control technique to transform complex wave patterns into clear and high probability signals—ideal for both sophisticated traders seeking mathematical precision and investors who appreciate robust trend confirmation!
🟢 Core Architecture
At its core, this indicator employs an adaptive Fourier Transform framework with dynamic volatility-controlled Supertrend bands. It utilizes multiple harmonic components that let you fine-tune how market frequencies influence trend detection. By combining wave analysis with adaptive volatility bands, the indicator creates a sophisticated yet clear framework for trend identification that dynamically adjusts to changing market conditions.
🟢 Technical Foundation
The indicator builds on three innovative components:
Fourier Wave Analysis: Decomposes price action into primary and harmonic components for precise trend detection
Adaptive Volatility Control: Dynamically adjusts Supertrend bands using combined ATR and standard deviation
Harmonic Integration: Merges multiple frequency components with decreasing weights for comprehensive trend analysis
🟢 Key Features & Signals
The Adaptive Fourier Transform Supertrend transforms complex wave calculations into clear visual signals with:
Dynamic trend bands that adapt to market volatility
Sophisticated cloud-fill visualization system
Strategic L/S markers at key trend reversals
Customizable bar coloring based on trend direction
Comprehensive alert system for trend shifts
🟢 Practical Usage Tips
Here's how you can get the most out of the Adaptive Fourier Transform Supertrend :
1/ Setup:
Add the indicator to your favorites, then apply it to your chart ⭐️
Start with close price as your base source
Use standard Fourier period (14) for balanced wave detection
Begin with default harmonic weight (0.5) for balanced sensitivity
Start with standard Supertrend multiplier (2.0) for reliable band width
2/ Signal Interpretation:
Monitor trend band crossovers for potential signals
Watch for convergence of price with Fourier trend
Use L/S markers for trade entry points
Monitor bar colors for trend confirmation
Configure alerts for significant trend reversals
🟢 Pro Tips
Fine-tune Fourier parameters for optimal sensitivity:
→ Lower Base Period (8-12) for more reactive analysis
→ Higher Base Period (15-30) to filter out noise
→ Adjust Harmonic Weight (0.3-0.7) to control shorter trend influence
Customize Supertrend settings:
→ Lower multiplier (1.5-2.0) for tighter bands
→ Higher multiplier (2.0-3.0) for wider bands
→ Adjust ATR length based on market volatility
Strategy Enhancement:
→ Compare signals across multiple timeframes
→ Combine with volume analysis
→ Use with support/resistance levels
→ Integrate with other momentum indicators
SufinBDThis TradingView script combines RSI, Stochastic RSI, MACD, and Bollinger Bands to generate Buy and Sell signals on two different timeframes: 4-hour (4H) and Daily (1D). The strategy aims to provide entry and exit points based on a multi-indicator confirmation approach, helping traders make more informed decisions.
Features:
RSI (Relative Strength Index):
Measures the speed and change of price movements.
The script looks for oversold conditions (RSI below 30) for buy signals and overbought conditions (RSI above 70) for sell signals.
Stochastic RSI:
Measures the level of RSI relative to its high-low range over a given period.
A Stochastic RSI below 0.2 indicates oversold conditions, and a value above 0.8 indicates overbought conditions.
It helps identify overbought and oversold conditions in a more precise manner than regular RSI.
MACD (Moving Average Convergence Divergence):
A trend-following momentum indicator that shows the relationship between two moving averages of a security's price.
The MACD line crossing above the Signal line generates bullish signals, and vice versa for bearish signals.
Bollinger Bands:
A volatility indicator that consists of a middle band (SMA of price), an upper band, and a lower band.
When the price is below the lower band, it signals potential buy opportunities, while prices above the upper band signal potential sell opportunities.
Timeframe Usage:
The script calculates indicators for both the 4-hour (4H) and Daily (1D) timeframes.
The combined signals from these two timeframes are used to generate Buy and Sell alerts.
Buy Signal:
A Buy signal is generated when all of the following conditions are met:
RSI on both 4H and 1D is below 30 (oversold conditions).
Stochastic RSI on both timeframes is below 0.2.
The MACD line is above the Signal line on both timeframes.
The price is below the lower Bollinger Band on both the 4H and 1D charts.
Sell Signal:
A Sell signal is generated when all of the following conditions are met:
RSI on both 4H and 1D is above 70 (overbought conditions).
Stochastic RSI on both timeframes is above 0.8.
The MACD line is below the Signal line on both timeframes.
The price is above the upper Bollinger Band on both the 4H and 1D charts.
Visuals:
Buy signals are marked with green labels below the bars.
Sell signals are marked with red labels above the bars.
Bollinger Bands are displayed on the chart with the upper and lower bands marked in blue (for 4H) and orange (for 1D).
Purpose:
This script aims to provide more reliable buy/sell signals by combining indicators across multiple timeframes. It is ideal for traders who want to use multiple confirmation points before entering or exiting a trade.
How to Use:
Apply the script to any chart on TradingView.
Look for Buy and Sell signals that meet the conditions above.
You can adjust the timeframe (e.g., 4H or 1D) based on your trading strategy.
This script can be used for intraday trading, swing trading, or position trading depending on your preferred timeframes.
Example of Signal Interpretation:
Buy Signal:
If all conditions are met (e.g., RSI is under 30, Stochastic RSI is under 0.2, MACD is bullish, and price is below the lower Bollinger Band on both the 4-hour and daily charts), the script will show a green "BUY" label below the price bar.
Sell Signal:
If all conditions are met (e.g., RSI is over 70, Stochastic RSI is over 0.8, MACD is bearish, and price is above the upper Bollinger Band on both timeframes), the script will show a red "SELL" label above the price bar.
This combination of indicators offers a multi-layered confirmation approach, which aims to reduce the risk of false signals and increase the reliability of your trading decisions.
BBSS+This Pine Script implements a custom indicator overlaying Bollinger Bands with additional features for trend analysis using Exponential Moving Averages (EMAs). Here's a breakdown of its functionality:
Bollinger Bands:
The script calculates the Bollinger Bands using a 20-period Simple Moving Average (SMA) as the basis and a multiplier of 2 for the standard deviation.
It plots the Upper Band and Lower Band in red.
EMA Calculations:
Three EMAs are calculated for the close price with periods of 5, 10, and 40.
The EMAs are plotted in green (5-period), cyan (10-period), and orange (40-period) to distinguish between them.
Trend Detection:
The script determines bullish or bearish EMA alignments:
Bullish Order: EMA 5 > EMA 10 > EMA 40.
Bearish Order: EMA 5 < EMA 10 < EMA 40.
Entry Signals:
Long Entry: Triggered when:
The close price crosses above the Upper Bollinger Band.
The Upper Band is above its 5-period SMA (indicating momentum).
The EMAs are in a bullish order.
Short Entry: Triggered when:
The close price crosses below the Lower Bollinger Band.
The Lower Band is below its 5-period SMA.
The EMAs are in a bearish order.
Trend State Tracking:
A variable tracks whether the market is in a Long or Short trend based on conditions:
A Long trend continues unless conditions for a Short Entry are met or the Upper Band dips below its average.
A Short trend continues unless conditions for a Long Entry are met or the Lower Band rises above its average.
Visual Aids:
Signal Shapes:
Triangle-up shapes indicate Long Entry points below the bar.
Triangle-down shapes indicate Short Entry points above the bar.
Bar Colors:
Green bars indicate a Long trend.
Red bars indicate a Short trend.
This script combines Bollinger Bands with EMA crossovers to generate entry signals and visualize market trends, making it a versatile tool for identifying momentum and trend reversals.
Magic Linear Regression Channel [MW]Introduction
The Magic Linear Regression Channel indicator provides users with a way to quickly include a linear regression channel ANYWHERE on their chart, in order to find channel breakouts and bounces within any time period. It uses a novel method that allows users to adjust the start and end period of the regression channel in order to quickly make adjustments faster, with fewer steps, and with more precision than with any other linear regression channel tool. It includes Fibonacci bands AND a horizontal mode in order for users to quickly define significant price levels based on the high, low, open, and close prices defined by the start period.
Settings
Start Time: This is initially MANUALLY SELECTED ON THE CHART when the indicator is first loaded.
End time: This is also initially MANUALLY SELECTED ON THE CHART when the indicator is first loaded.
Horizontal Line: This forces the baseline to be horizontal. The band distance is defined by the maximum price distance from the band.
Horizontal Line Type: This snaps the horizontal line to the close, high, low, or open price. Or, it can also use a regression calculation for the selected time period to define the y-position of the line.
Extend Line N Bars: How many bars to the left in which to extend the baseline and bands.
Show Baseline ONLY!!: Removes all lines except the baseline and it’s extension.
Add Half Band: Includes a band that is half the distance between the baseline and the top and bottom bands
Add Outer Fibonacci Band: Includes a band that is 1.618 (phi) times the default band distance
Add Inner Fibonacci Band - Upper: Includes a band that is 0.618 (1/phi) times the default band distance
Add Inner Fibonacci Band - Lower: Includes a band that is 0.382 (1 - 1/phi) times the default band distance
Calculations
This indicator uses the least squares approach for generating a straight regression line, which can be reviewed at Wikipedia’s “Simple Linear Regression” page. It sums all of the x-values, and y-values, as well as the sum of the product of corresponding x and y values, and the sum of the squares of the x-values. These values are used to calculate the slope and intercept using the following equations:
slope = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x * sum_x)
And
intercept = (sum_y - slope * sum_x) / n
The slope and intercept are then used to generate the baseline and the corresponding bands using the user-selected offsets.
How to Use
When the Magic Linear Regression Channel indicator is first added to the chart, there will be a blue prompt behind the “Indicators, Metrics & Strategies” window. Close the window, then select a START POINT by clicking at a desired location on the chart. Next, you will be prompted to select an END POINT. The end point MUST be placed after the START POINT. At this time a channel will be generated. Once you’ve selected the START POINT and END POINT, you can adjust them by dragging them anywhere on the chart. Each adjustment will generate a new channel making it easier for you to quickly visualize and recognize any channel exits and bounces.
The Magic Linear Regression Channel indicator works great at identifying wave patterns. Place the start line at a top or bottom pivot point. Place the end line at the next respective top or bottom pivot. This will give you a complete wave form to work with. When price reaches a band and rejects, it can be a strong indication that price may move back to one of the bands in the channel. If price exits the channel with volume that supports the exit, it may be an indication of a breakout.
You can also use the horizontal mode to identify key levels, then add Fibonacci bands based on regression calculations for the given time period to provide more meaningful areas of support and resistance.
Other Usage Notes and Limitations
Occasionally, off-by-1 errors appear which makes the extended lines protrude at a slightly incorrect angle. This is a known bug and will be addressed in the next release.
It's important for traders to be aware of the limitations of any indicator and to use them as part of a broader, well-rounded trading strategy that includes risk management, fundamental analysis, and other tools that can help with reducing false signals, determining trend direction, and providing additional confirmation for a trade decision. Diversifying strategies and not relying solely on one type of indicator or analysis can help mitigate some of these risks.
Uptrick: Bullish/Bearish Highlight -DEMO 1 Indicator Purpose:
• The indicator serves as a technical analysis tool for traders to identify potential bullish
and bearish trends in the market.
• It highlights periods where the closing price is above or below a 50-period simple
moving average (SMA), indicating potential bullish or bearish sentiment, respectively.
2 Moving Averages:
• The indicator calculates a 50-period SMA (sma50) to smooth out price fluctuations
and identify the overall trend direction.
• It also computes an 8-period exponential moving average (EMA), which responds
more quickly to recent price changes compared to the SMA.
3 Bollinger Bands:
• Bollinger Bands are plotted around the SMA, indicating volatility in the price
movement.
• The bands are typically set at two standard deviations above and below the SMA,
representing approximately 95% of the price data within that range.
4 Bullish and Bearish Conditions:
• The indicator defines conditions for identifying bullish and bearish market sentiments.
• When the closing price is above the SMA50, it indicates a bullish condition, and when
it's below, it suggests a bearish condition.
5 Plotting:
• The indicator visualizes the bullish and bearish conditions by changing the
background color accordingly.
• It also plots the SMA50, EMA, and Bollinger Bands to provide a graphical
representation of the market dynamics.
6 User Interface:
• The indicator is designed to be used as an overlay on price charts, allowing traders to
easily incorporate it into their analysis.
Overall, the "Uptrick: Bullish/Bearish Highlight" indicator offers traders a comprehensive view of market trends and potential reversal points, helping them make informed trading decisions.
TIP: When the white line, which is the EMA , crosses above the SMA (the orange line), it is usually a good idea to buy, but when the EMA crosses below the SMA it is a good idea to sell.
VOLD Ratio (Volume Difference Ratio) by TenozenAnother helpful indicator is here! VOLD Ratio is calculated by the net volume of a buying candle, divided by the net volume of a sell candle.
Formula:
buying net volume/selling net volume
It's a simple indicator, but don't underestimate this simplicity. It's a powerful indicator that would help you to decide whether the volume is getting interested in the direction that the market would take. So assume when the market is above the Bollinger Bands, it means that the volume is at a buying extreme, by that, we could expect the market to get back towards the mean, as there is a lot of buying demand that entered the market. How about below the Bollinger Bands? it means that the volume is at a selling extreme, we could expect that there is a lot of volume getting in toward the sellers, so we could take advantage of the opportunity to go for a long. Lastly, the Bollinger Bands would help you guys to determine the liquidity of the market, if the Bollinger Bands get smaller over time, it means there is no interest for the market to enter yet, and if the Bollinger Bands get bigger over time, it means there is interest for the market to enter in the session.
Tips & Reminder:
- We shouldn't use this indicator by itself, make sure to use an Indicator that would help you guys to determine the momentum and the liquidity of the market.
- The higher the timeframe, the slower this indicator would signal an entry, by that use a smaller timeframe... I suggest using a 15M chart for the execution.
- Always trade in the medium-longterm direction if you want to have a high probability trade.
- Be patient in your execution, it's more likely the market would go higher or lower after going in the extreme of the Bollinger Bands.
Well, that's it! Hope you guys enjoy using this indicator, let me know if there is any question or suggestion. Ciao...
Keltner Channel Width Oscillator (KingThies)Definition
The Keltner Channel Width oscillator is a technical analysis indicator derived originally from the same relationship the Bollinger Band Width indicator takes on Bollinger Bands.
Similar to the Bollinger Bands, Kelts measure volatility in relation to price, and factor in various range calculations to create three bands around the price of a given stock or digital asset. The Middle Line is typically a 20 Day Exponential Moving Average while the upper and lower bands highlight price at different range variations around its basis. Keltner Channel Width serve as a way to quantitatively measure the width between the Upper and Lower Bands and identify opportunities for entires and exits, based on the relative range price is experiencing that day.
Calculation
Kelt Channel Width = (Upper Band - Lower Band) / Middle Band
More on Keltner Channels
Keltner channel was 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 action 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.
BTC Evaluation IndicatorBTC Evaluation Indicator
The BTC Evaluation Indicator is a volatility-based tool designed to help traders evaluate Bitcoin’s price behavior relative to its moving average trend. It combines customizable moving averages with dynamic standard deviation bands to identify overbought and oversold conditions.
Key Features
Flexible Moving Averages: Choose between SMA, EMA, WMA, VWMA, HMA, or RMA for the baseline trend.
Dynamic Volatility Bands: Upper and lower bands are calculated using standard deviation, scaled by a user-defined multiplier.
Visual Clarity:
Orange line = central moving average (trend mean)
Green line = upper band (potential overbought zone)
Red line = lower band (potential oversold zone)
Shaded gray area = volatility range
Automatic Highlights: Background shading marks when price breaks above the upper band (overbought) or below the lower band (oversold).
How to Use
When price pushes above the upper band, it may indicate overextension or potential local overbought conditions.
When price falls below the lower band, it may signal undervaluation or potential oversold conditions.
The mean line acts as a dynamic equilibrium, often serving as short-term support/resistance.
This indicator is designed for Bitcoin evaluation, but it can be applied to any asset. By combining trend analysis with volatility context, it helps traders better understand when price may be stretched and when conditions are reverting to the mean.
Bitcoin Cycle Log-Curve (JDK-Analysis)Important: The standard parameters provided in the script are specifically tuned for the TradingView Bitcoin Index chart on a monthly timeframe on logarithmic scale, and will yield the most accurate visual alignment when applied to that dataset. (more below)
This very simple script visualizes Bitcoin’s long-term price behavior using a logarithmic regression model designed to reflect the cyclical nature of Bitcoin’s historical market trends. Unlike typical technical indicators that react to recent price movements, this tool is built on the assumption that Bitcoin follows an exponential growth path over time, shaped by its fixed supply structure and four-year halving cycles.
The calculation behind the curved bands:
An upper boundary, a lower boundary, and a central midline, are calculated based on logarithmic functions applied to the bar index (which serves as a proxy for time). The upper and lower bounds are defined using exponential formulas of the type y = exp(constant + coefficient * log(bar_index)), allowing the curves to evolve dynamically over time. These bands serve as a macro-level guide for identifying periods of historical overvaluation (upper red curve) and undervaluation (lower green curve), with a central black curve representing the geometric average of the two.
How to customize the parameters:
The lower1_const and upper1_const values vertically shift the respective lower and upper curves—more negative values push the curve downward, while higher values lift it.
The lower1_coef and upper1_coef control the steepness of the curves over time, with higher values resulting in faster growth relative to time.
The shift_factor allows for uniform vertical adjustment of all curves simultaneously.
Additionally, the channel_width setting determines how far the mirrored bands extend from the original curves, creating a visual “channel” that can highlight more conservative or aggressive valuation zones depending on preference.
How to use this indicator:
This indicator is not intended for short-term trading or intraday signals. Rather, it serves as a contextual framework for long-term investors to identify high-risk zones near the upper curve and potential long-term value opportunities near the lower curve. These areas historically align with cycle tops and bottoms, and the model helps to place current price action within that broader cyclical narrative. While the concept draws inspiration from Bitcoin’s halving-driven market cycles and exponential adoption curve, the implementation is original in its use of time-based logarithmic regression to define dynamic trend boundaries.
It is best used as a strategic tool for cycle analysis, macro positioning, and trend anchoring—rather than as a short-term signal provider.
