OrangeCandle 4EMA 55 + Fib Bands + SignalsThe script is a TradingView indicator that combines three popular technical analysis tools: Exponential Moving Averages (EMAs), Fibonacci bands, and buy/sell signals based on these indicators. Here’s a breakdown of its features:
1. EMA Settings and Calculation:
The script calculates and plots several Exponential Moving Averages (EMAs) on the chart with different lengths:
Short-term EMAs: EMA 9, EMA 13, EMA 21, and EMA 55 (used for tracking short-term price trends).
Long-term EMAs: EMA 100 and EMA 200 (used to analyze longer-term trends).
These EMAs are plotted with different colors to visually distinguish between the short-term and long-term trends.
2. Fibonacci Bands:
The script calculates Fibonacci Bands based on the Average True Range (ATR) and a Simple Moving Average (SMA).
Fibonacci factors (1.618, 2.618, 4.236, 6.854, and 11.090) are used to determine the upper and lower bounds of five Fibonacci bands.
Upper Fibonacci Bands (e.g., fib1u, fib2u) represent resistance levels.
Lower Fibonacci Bands (e.g., fib1l, fib2l) represent support levels.
These bands are plotted with different colors for each level, helping traders identify potential price reversal zones.
3. Buy and Sell Signals:
Long Condition: A buy signal occurs when the price crosses above the EMA 55 (long-term trend indicator) and is above the lower Fibonacci band (support zone).
Short Condition: A sell signal occurs when the price crosses below the EMA 55 and is below the upper Fibonacci band (resistance zone).
These conditions trigger visual signals on the chart (green arrow for long, red arrow for short).
4. Alerts:
The script includes alert conditions to notify the trader when a long or short signal is triggered based on the crossover of price and EMA 55 near the Fibonacci support or resistance levels.
Long Entry Alert: Triggers when the price crosses above the EMA 55 and is near a Fibonacci support level.
Short Entry Alert: Triggers when the price crosses below the EMA 55 and is near a Fibonacci resistance level.
5. Visualization:
EMAs are plotted with distinct colors:
EMA 9 is aqua,
EMA 13 is purple,
EMA 21 is orange,
EMA 55 is blue (with thicker line width for emphasis),
EMA 100 is gray,
EMA 200 is black.
Fibonacci bands are plotted with different colors for each level:
Fib Band 1 (upper and lower) in white,
Fib Band 2 in green (upper) and red (lower),
Fib Band 3 in green (upper) and red (lower),
Fib Band 4 in blue (upper) and orange (lower),
Fib Band 5 in purple (upper) and yellow (lower).
Summary:
This script provides a comprehensive strategy for analyzing the market with multiple EMAs for trend detection, Fibonacci bands for support/resistance, and signals based on price action in relation to these indicators. The combination of these tools can assist traders in making more informed decisions by providing potential entry and exit points on the chart.
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Fibonacci Bands [BigBeluga]The Fibonacci Band indicator is a powerful tool for identifying potential support, resistance, and mean reversion zones based on Fibonacci ratios. It overlays three sets of Fibonacci ratio bands (38.2%, 61.8%, and 100%) around a central trend line, dynamically adapting to price movements. This structure enables traders to track trends, visualize potential liquidity sweep areas, and spot reversal points for strategic entries and exits.
🔵 KEY FEATURES & USAGE
Fibonacci Bands for Support & Resistance:
The Fibonacci Band indicator applies three key Fibonacci ratios (38.2%, 61.8%, and 100%) to construct dynamic bands around a smoothed price. These levels often act as critical support and resistance areas, marked with labels displaying the percentage and corresponding price. The 100% band level is especially crucial, signaling potential liquidity sweep zones and reversal points.
Mean Reversion Signals at 100% Bands:
When price moves above or below the 100% band, the indicator generates mean reversion signals.
Trend Detection with Midline:
The central line acts as a trend-following tool: when solid, it indicates an uptrend, while a dashed line signals a downtrend. This adaptive midline helps traders assess the prevailing market direction while keeping the chart clean and intuitive.
Extended Price Projections:
All Fibonacci bands extend to future bars (default 30) to project potential price levels, providing a forward-looking perspective on where price may encounter support or resistance. This feature helps traders anticipate market structure in advance and set targets accordingly.
Liquidity Sweep:
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-Liquidity Sweep at Previous Lows:
The price action moves below a previous low, capturing sell-side liquidity (stop-losses from long positions or entries for breakout traders).
The wick suggests that the price quickly reversed, leaving a failed breakout below support.
This is a classic liquidity grab, often indicating a bullish reversal .
-Liquidity Sweep at Previous Highs:
The price spikes above a prior high, sweeping buy-side liquidity (stop-losses from short positions or breakout entries).
The wick signifies rejection, suggesting a failed breakout above resistance.
This is a bearish liquidity sweep , often followed by a mean reversion or a downward move.
Display Customization:
To declutter the chart, traders can choose to hide Fibonacci levels and only display overbought/oversold zones along with the trend-following midline and mean reversion signals. This option enables a clearer focus on key reversal areas without additional distractions.
🔵 CUSTOMIZATION
Period Length: Adjust the length of the smoothed moving average for more reactive or smoother bands.
Channel Width: Customize the width of the Fibonacci channel.
Fibonacci Ratios: Customize the Fibonacci ratios to reflect personal preference or unique market behaviors.
Future Projection Extension: Set the number of bars to extend Fibonacci bands, allowing flexibility in projecting price levels.
Hide Fibonacci Levels: Toggle the visibility of Fibonacci levels for a cleaner chart focused on overbought/oversold regions and midline trend signals.
Liquidity Sweep: Toggle the visibility of Liquidity Sweep points
The Fibonacci Band indicator provides traders with an advanced framework for analyzing market structure, liquidity sweeps, and trend reversals. By integrating Fibonacci-based levels with trend detection and mean reversion signals, this tool offers a robust approach to navigating dynamic price action and finding high-probability trading opportunities.
Holt-Winters Forecast BandsDescription:
The Holt-Winters Adaptive Bands indicator combines seasonal trend forecasting with adaptive volatility bands. It uses the Holt-Winters triple exponential smoothing model to project future price trends, while Nadaraya-Watson smoothed bands highlight dynamic support and resistance zones.
This indicator is ideal for traders seeking to predict future price movements and visualize potential market turning points. By focusing on broader seasonal and trend data, it provides insight into both short- and long-term market directions. It’s particularly effective for swing trading and medium-to-long-term trend analysis on timeframes like daily and 4-hour charts, although it can be adjusted for other timeframes.
Key Features:
Holt-Winters Forecast Line: The core of this indicator is the Holt-Winters model, which uses three components — level, trend, and seasonality — to project future prices. This model is widely used for time-series forecasting, and in this script, it provides a dynamic forecast line that predicts where price might move based on historical patterns.
Adaptive Volatility Bands: The shaded areas around the forecast line are based on Nadaraya-Watson smoothing of historical price data. These bands provide a visual representation of potential support and resistance levels, adapting to recent volatility in the market. The bands' fill colors (red for upper and green for lower) allow traders to identify potential reversal zones without cluttering the chart.
Dynamic Confidence Levels: The indicator adapts its forecast based on market volatility, using inputs such as average true range (ATR) and price deviations. This means that in high-volatility conditions, the bands may widen to account for increased price movements, helping traders gauge the current market environment.
How to Use:
Forecasting: Use the forecast line to gain insight into potential future price direction. This line provides a directional bias, helping traders anticipate whether the price may continue along a trend or reverse.
Support and Resistance Zones: The shaded bands act as dynamic support and resistance zones. When price enters the upper (red) band, it may be in an overbought area, while the lower (green) band may indicate oversold conditions. These bands adjust with volatility, so they reflect the current market conditions rather than fixed levels.
Timeframe Recommendations:
This indicator performs best on daily and 4-hour charts due to its reliance on trend and seasonality. It can be used on lower timeframes, but accuracy may vary due to increased price noise.
For traders looking to capture swing trades, the daily and 4-hour timeframes provide a balance of trend stability and signal reliability.
Adjustable Settings:
Alpha, Beta, and Gamma: These settings control the level, trend, and seasonality components of the forecast. Alpha is generally the most sensitive setting for adjusting responsiveness to recent price movements, while Beta and Gamma help fine-tune the trend and seasonal adjustments.
Band Smoothing and Deviation: These settings control the lookback period and width of the volatility bands, allowing users to customize how closely the bands follow price action.
Parameters:
Prediction Length: Sets the length of the forecast, determining how far into the future the prediction line extends.
Season Length: Defines the seasonality cycle. A setting of 14 is typical for bi-weekly cycles, but this can be adjusted based on observed market cycles.
Alpha, Beta, Gamma: These parameters adjust the Holt-Winters model's sensitivity to recent prices, trends, and seasonal patterns.
Band Smoothing: Determines the smoothing applied to the bands, making them either more reactive or smoother.
Ideal Use Cases:
Swing Trading and Trend Following: The Holt-Winters model is particularly suited for capturing larger market trends. Use the forecast line to determine trend direction and the bands to gauge support/resistance levels for potential entries or exits.
Identifying Reversal Zones: The adaptive bands act as dynamic overbought and oversold zones, giving traders potential reversal areas when price reaches these levels.
Important Notes:
No Buy/Sell Signals: This indicator does not produce direct buy or sell signals. It’s intended for visual trend analysis and support/resistance identification, leaving trade decisions to the user.
Not for High-Frequency Trading: Due to the nature of the Holt-Winters model, this indicator is optimized for higher timeframes like the daily and 4-hour charts. It may not be suitable for high-frequency or scalping strategies on very short timeframes.
Adjust for Volatility: If using the indicator on lower timeframes or more volatile assets, consider adjusting the band smoothing and prediction length settings for better responsiveness.
Volatility Trend Bands [UAlgo]The Volatility Trend Bands is a trend-following indicator that combines the concepts of volatility and trend detection. Built using the Average True Range (ATR) to measure volatility, this indicator dynamically adjusts upper and lower bands around price movements. The bands act as dynamic support and resistance levels, making it easier to identify trend shifts and potential entry and exit points.
With the ATR multiplier, this indicator effectively captures volatility-based shifts in the market. The use of midline values allows for accurate trend detection, which is displayed through color-coded signals on the chart. Additionally, this tool provides clear buy and sell signals, accompanied by intuitive graphical markers for ease of use.
The Volatility Trend Bands is ideal for traders seeking an adaptive trend-following method that responds to changing market conditions while maintaining robust volatility control.
🔶 Key Features
Dynamic Support and Resistance: The indicator utilizes volatility to create dynamic bands. The upper band acts as resistance, and the lower band acts as support for the price. Wider bands indicate higher volatility, while narrower bands indicate lower volatility.
Customizable Inputs
You can tailor the indicator to your strategy by adjusting the:
Price Source: Select the price data (e.g., closing price) used for calculations.
ATR Length: Define the lookback period for the Average True Range (ATR) volatility measure.
ATR Multiplier: This factor controls the width of the volatility bands relative to the ATR value.
Color Options: Choose colors for the bands and signal arrows for better visualization.
Visual Signals: Arrows ("▲" for buy, "▼" for sell) appear on the chart when the trend changes, providing clear entry point indications.
Alerts: Integrated alerts for both buy and sell conditions, allowing you to receive notifications for potential trade opportunities.
🔶 Interpreting Indicator
Upper and Lower Bands: The upper and lower bands are dynamic, adjusting based on market volatility using the ATR. These bands serve as adaptive support and resistance levels. When price breaks above the upper band, it indicates a potential bullish breakout, signaling a strong uptrend. Conversely, a break below the lower band signals a bearish breakout, indicating a downtrend.
Buy/Sell Signals: The indicator provides clear buy and sell signals at breakout points. A buy signal ("▲") is generated when the price breaks above the upper band, suggesting the start of a bullish trend. A sell signal ("▼") is triggered when the price breaks below the lower band, indicating the beginning of a bearish trend. These signals help traders identify potential entry and exit points at key breakout levels.
Color-Coded Bars: The bars on the chart change color based on the trend direction. Teal bars represent bullish momentum, while purple bars signify bearish momentum. This color coding provides a quick visual cue about the market's current direction.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Moving Average Bands with Signals [UAlgo]The "Moving Average Bands with Signals combines various moving average types with ATR-based bands to help traders identify potential support and resistance levels.
It plots moving average bands with upper and lower support/resistance levels based on the Average True Range (ATR) and user-defined settings.Additionally, the script generates buy/sell signals based on price crossing above or below the bands.
🔶 Key Features
Multiple Moving Average Types:
Supports various moving average calculations including Arnaud Legoux Moving Average (ALMA), Exponential Moving Average (EMA), Double Exponential Moving Average (DEMA), Triple Exponential Moving Average (TEMA), Kaufman Adaptive Moving Average (KAMA), Hull Moving Average (HMA), Least Squares Moving Average (LSMA), Simple Moving Average (SMA), Triangular Moving Average (TMA), Volume-Weighted Moving Average (VWMA), Weighted Moving Average (WMA), and Zero-Lag Moving Average (ZLMA).
Customizable ATR Bands:
Integrates the Average True Range (ATR) to calculate dynamic support and resistance bands around the moving average. The multiplier for the bands is user-adjustable, allowing for finer control over the sensitivity and width of the bands.
Signal Generation:
Provides visual signals on the chart when the price interacts with the support or resistance bands. Users can choose between using the wick or the close price to generate these signals, adding an extra layer of customization based on their trading style.
Flexible Input Parameters:
Allows users to input parameters for moving average length, ATR length, band multiplier, and signal type. Additional settings are available for specific moving average types, such as ALMA's offset and sigma, KAMA's fast and slow periods, and LSMA's offset.
🔶 Disclaimer
This script is provided for educational purposes only and should not be considered financial advice.
Trading financial instruments involves substantial risk and can result in significant financial losses.
The script’s performance in the past is not indicative of future results, and no guarantees are made regarding its accuracy, reliability, or performance.
Dynamic Adaptive Regression BandsThis script provides a dynamic adaptive regression band indicator that adjusts based on recent market volatility. The regression bands are calculated using a length parameter adapted to the ATR (Average True Range) to ensure responsiveness to market conditions.
Key Features:
Dynamic Length Adjustment: The length of the regression calculation is adjusted based on the ATR to reflect current market volatility.
Multiple Bands: The script plots upper and lower bands at different ratios (1.618, 2.618, and 4.236) to provide comprehensive support and resistance levels.
Detailed Fillings: The areas between bands are filled with different colors to visualize different levels of volatility and trend strength.
Usage:
Regression Line: The main regression line follows the general trend of the price.
Upper/Lower Bands: These bands represent volatility-adjusted support and resistance levels.
Extended Bands: Additional bands at different ratios provide extended support and resistance zones for further trend analysis.
Original Script Credit:
This script is inspired by the original "Regr Linear Bands" script by MarcoValente, published on Jan 15, 2017. The original script starts from a linear regression and uses Fibonacci parameters to add bands above and below. The original work incorporates range and volatility, making the price move between bands of the same color. The middle line (linear regression) serves as a good signal; after a break occurs, the price typically moves to the last or second last band.
[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.
[blackcat] L3 Fibonacci Bands With ATRToday, what I'm going to introduce is a technical indicator that I think is quite in line with the indicator displayed by Tang - Fibonacci Bands with ATR. This indicator combines Bollinger Bands and Average True Range (ATR) to provide insights into market volatility and potential price reversals. Sounds complicated, right? Don't worry, I will explain it to you in the simplest way.
First, let's take a look at how Fibonacci Bands are constructed. They are similar to Bollinger Bands and consist of three lines: upper band, middle band (usually a 20-period simple moving average), and lower band. The difference is that Fibonacci Bands use ATR to calculate the distance between the upper and lower bands and the middle band.
Next is a key factor - ATR multiplier. We need to smooth the ATR using Welles Wilder's method. Then, by multiplying the ATR by a Fibonacci multiplier (e.g., 1.618), we get the upper band, called the upper Fibonacci channel. Similarly, multiplying the ATR by another Fibonacci multiplier (e.g., 0.618 or 1.0) gives us the lower band, called the lower Fibonacci channel.
Now, let's see how Fibonacci Bands can help us assess market volatility. When the channel widens, it means that market volatility is high, while a narrow channel indicates low market volatility. This way, we can determine the market's activity level based on the width of the channel.
In addition, when the price touches or crosses the Fibonacci channel, it may indicate a potential price reversal, similar to Bollinger Bands. Therefore, using Fibonacci Bands in trading can help us capture potential buy or sell signals.
In summary, Fibonacci Bands with ATR is an interesting and practical technical indicator that provides information about market volatility and potential price reversals by combining Bollinger Bands and ATR. Remember, make good use of these indicators and apply them flexibly in trading!
This code is a TradingView indicator script used to plot L3 Fibonacci Bands With ATR.
First, the indicator function is used to define the title and short title of the indicator, and whether it should be overlaid on the main chart.
Then, the input function is used to define three input parameters: MA type (maType), MA length (maLength), and data source (src). There are four options for MA type: SMA, EMA, WMA, and HMA. The default values are SMA, 55, and hl2 respectively.
Next, the moving average line is calculated based on the user's selected MA type. If maType is 'SMA', the ta.sma function is called to calculate the simple moving average; if maType is 'EMA', the ta.ema function is called to calculate the exponential moving average; if maType is 'WMA', the ta.wma function is called to calculate the weighted moving average; if maType is 'HMA', the ta.hma function is called to calculate the Hull moving average. The result is then assigned to the variable ma.
Then, the _atr variable is used to calculate the ATR (Average True Range) value using ta.atr, and multiplied by different coefficients to obtain four Fibonacci bias values: fibo_bias4, fibo_bias3, fibo_bias2, and fibo_bias1.
Finally, the prices of the upper and lower four Fibonacci bands are calculated by adding or subtracting the corresponding Fibonacci bias values from the current price, and plotted on the chart using the plot function.
Bollinger Band Width Oscillator %🧠 Bollinger Band Width Oscillator %
The Bollinger Band Width Oscillator % is a volatility-focused tool that measures the relative width of Bollinger Bands and transforms it into an oscillator format. It helps traders visualize volatility expansions and contractions directly in an indicator pane — a powerful way to anticipate breakout or consolidation phases.
🔍 How It Works
Band Width %: Calculates the percentage distance between the upper and lower Bollinger Bands relative to the basis (SMA).
Smoothed Output: The raw bandwidth is smoothed using a moving average for cleaner, more stable signals.
Dynamic Volatility Zones: The script automatically computes average, high, and low volatility thresholds — each dynamically adapting to market conditions.
User-Adjustable Multipliers: Control how sensitive your high/low zones are with the High Zone Multiplier and Low Zone Multiplier inputs.
⚙️ Key Features
📊 Oscillator Format – Easy-to-read visualization of volatility compression and expansion.
🔥 High/Low Volatility Detection – Automatic labeling and color-coded alerts for shifts in volatility.
🧩 Dynamic Thresholds – Zones adjust automatically with market activity for adaptive sensitivity.
🧠 Hysteresis Logic – Prevents rapid signal flipping, improving clarity and reliability.
🎨 Custom Visuals – Adjustable smoothing and background highlights for quick interpretation.
📈 Trading Applications
Identify Breakouts: Rising bandwidth often precedes price breakouts.
Spot Consolidations: Low bandwidth indicates tightening volatility and potential range trades.
Volatility Regime Analysis: Understand market rhythm and adapt strategies accordingly.
⚡ Inputs
Parameter Description
Band Length Period for Bollinger Band calculation
Band Multiplier Standard deviation multiplier for the bands
Source Price source (default: close)
Smoothing Period for smoothing the oscillator line
High Zone Multiplier Adjusts the high-volatility threshold
Low Zone Multiplier Adjusts the low-volatility threshold
Highlight Volatility Zones Optional background color overlay
🧊 Usage Tip
Combine this indicator with momentum tools or price action analysis to confirm trade setups. Watch for transitions from low to high volatility zones — these often signal the beginning of major market moves.
PulseRPO Zero-Lag BandsPulseRPO is a momentum and volatility timing suite built on a zero-lag Relative Price Oscillator. It pairs an RPO (fast vs slow MA spread, in %) with adaptive volatility envelopes that tighten or widen as conditions change, so you can spot true momentum bursts, exhaustion and “quiet-before-the-move” squeezes—without the usual MA lag.
What it shows
Zero-Lag RPO: Choose EMA, SMA, WMA, RMA, HMA or ZLEMA for the base, then apply ZLEMA/DEMA/TEMA/HMA zero-lag smoothing to cut delay.
Adaptive Bands: StdDev, ATR, Range or Hybrid volatility; bands auto-tighten in high vol and widen in quiet regimes.
Dynamic OB/OS: Levels scale with current regime so extremes mean something even as volatility shifts.
Signal & Histogram: Classic signal cross plus histogram for quick read of acceleration vs deceleration.
Squeeze Paint: Subtle background highlight when band width compresses below its average.
Divergences & Triggers: Optional bullish/bearish divergence tags, plus band-cross and signal-cross alerts out of the box.
How to use it (general guide)
Momentum entries: Look for RPO crossing up its signal from below or snapping out of a squeeze; extra weight if it also re-enters from below the lower band.
Trend continuation: RPO riding outside the upper (or lower) band with rising histogram = power move; trail risk on pullbacks to the signal line.
Exhaustion / fades: Taps beyond dynamic OB/OS or band re-entries can mark mean-revert windows—confirm with price/volume.
Risk filter: During squeeze, size down and prepare for expansion; after expansion, respect extremes.
Tweak the MA type, band method and zero-lag strength to match your timeframe. PulseRPO is designed to be a self-contained read: regime → setup → trigger → alert.
Ethereum Logarithmic Regression BandsOverview
This indicator displays logarithmic regression bands for Ethereum. Logarithmic regression is a statistical method used to model data where growth slows down over time. I initially created these bands in 2021 using a spreadsheet, and later coded them in TradingView in 2022. Over time, the bands proved effective at capturing bull market peaks and bear market lows. In 2025, I decided to share this indicator because I believe these logarithmic regression bands offer the best fit for the Ethereum chart.
How It Works
The logarithmic regression lines are fitted to the Ethereum (ETHUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). The formula for logarithmic regression is 10^((a * ln) - b).
How to Use the Logarithmic Regression Bands
1. Lower Band:
The lower (blue) band forms a potential support area for Ethereum’s price. Historically, Ethereum has found its lows within this band during past market cycles. When the price is within the lower band, it suggests that Ethereum is undervalued.
2. Upper Band:
The upper (red) band forms a potential resistance area for Ethereum’s price. The logarithmic band is fitted to the past two market cycle peaks; therefore, there is not enough historical data to be sure it will reach the upper band again. However, the chance is certainly there! If the price is within the upper band, it indicates that Ethereum is overvalued and that a potential price correction may be imminent.
Standard Deviation BandsStandard Deviation Bands
คำอธิบายอินดิเคเตอร์:
อินดิเคเตอร์ SD Bands (Standard Deviation Bands) เป็นเครื่องมือวิเคราะห์ทางเทคนิคที่ออกแบบมาเพื่อวัดความผันผวนของราคาและระบุโอกาสในการเทรดที่อาจเกิดขึ้น อินดิเคเตอร์นี้จะแสดงผลเป็นเส้นขอบ 2 เส้นบนกราฟราคาโดยตรง โดยอ้างอิงจากค่าเฉลี่ยเคลื่อนที่ (Moving Average) และค่าส่วนเบี่ยงเบนมาตรฐาน (Standard Deviation)
* เส้นบน (Upper Band): แสดงระดับที่ราคาเคลื่อนไหวสูงกว่าค่าเฉลี่ย
* เส้นล่าง (Lower Band): แสดงระดับที่ราคาเคลื่อนไหวต่ำกว่าค่าเฉลี่ย
ความกว้างของช่องระหว่างเส้นทั้งสองบ่งบอกถึงระดับความผันผวนของตลาดในปัจจุบัน
วิธีการใช้งานอย่างละเอียด:
คุณสามารถนำอินดิเคเตอร์ SD Bands ไปประยุกต์ใช้ได้หลายวิธีเพื่อประกอบการตัดสินใจ ดังนี้:
1. การใช้เป็นแนวรับ-แนวต้านแบบไดนามิก (Dynamic Support & Resistance)
* แนวรับ: เมื่อราคาวิ่งลงมาแตะหรือเข้าใกล้เส้นล่าง (เส้นสีน้ำเงิน) เส้นนี้อาจทำหน้าที่เป็นแนวรับชั่วคราวและมีโอกาสที่ราคาจะเด้งกลับขึ้นไปหาเส้นกลาง
* แนวต้าน: เมื่อราคาวิ่งขึ้นไปแตะหรือเข้าใกล้เส้นบน (เส้นสีแดง) เส้นนี้อาจทำหน้าที่เป็นแนวต้านชั่วคราวและมีโอกาสที่ราคาจะย่อตัวลงมา
2. การวัดความผันผวนและสัญญาณ Breakout
* ช่วงตลาดสงบ (Low Volatility): เมื่อเส้น SD ทั้งสองเส้นบีบตัวเข้าหากันเป็นช่องที่แคบมาก (คล้ายกับ Bollinger Squeeze) แสดงว่าตลาดมีความผันผวนต่ำมาก ซึ่งมักจะเป็นสัญญาณว่ากำลังจะเกิดการเคลื่อนไหวครั้งใหญ่ (Breakout)
* ช่วงตลาดเป็นเทรนด์ (High Volatility): เมื่อเส้น SD ขยายตัวกว้างออกอย่างรวดเร็ว พร้อมกับที่ราคาวิ่งอยู่นอกขอบ แสดงว่าตลาดเข้าสู่ช่วงเทรนด์ที่แข็งแกร่งและมีโมเมนตัมสูง
3. สัญญาณการกลับตัว (Reversal Signals)
* เมื่อราคาปิดแท่งเทียน นอกเส้น SD Bands อย่างชัดเจน (โดยเฉพาะหลังจากที่เทรนด์นั้นดำเนินมานาน) อาจเป็นสัญญาณว่าแรงซื้อ/แรงขายเริ่มอ่อนกำลังลง และมีโอกาสที่จะเกิดการกลับตัวของราคาในไม่ช้า
การตั้งค่าอินพุต (Input Parameters):
* ระยะเวลา (Length): กำหนดจำนวนแท่งเทียนที่ใช้ในการคำนวณค่าเฉลี่ยและ SD
* 20: สำหรับการวิเคราะห์ระยะสั้นถึงกลาง
* 50 หรือ 100: สำหรับการวิเคราะห์ระยะยาว
* ตัวคูณ (Multiplier): กำหนดระยะห่างของเส้น SD จากค่าเฉลี่ย
* 1.0 - 2.0: เส้นจะอยู่ใกล้ราคามากขึ้น ทำให้เกิดสัญญาณบ่อยขึ้น
* 2.0 - 3.0: เส้นจะอยู่ห่างจากราคามากขึ้น ทำให้เกิดสัญญาณที่น่าเชื่อถือมากขึ้น แต่จะเกิดไม่บ่อย
ข้อควรระวังและคำเตือน:
* อินดิเคเตอร์นี้เป็นเพียง เครื่องมือวิเคราะห์ เพื่อช่วยในการตัดสินใจ ไม่ใช่สัญญาณการซื้อขายที่ถูกต้อง 100%
* ควรใช้ร่วมกับเครื่องมืออื่นๆ เช่น RSI, MACD, หรือ Volume เพื่อยืนยันสัญญาณ
* การเทรดมีความเสี่ยงสูง ควรบริหารจัดการความเสี่ยงและตั้งจุด Stop Loss ทุกครั้ง
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English
Standard Deviation Bands
Indicator Description:
The SD Bands (Standard Deviation Bands) indicator is a powerful technical analysis tool designed to measure price volatility and identify potential trading opportunities. The indicator displays two dynamic bands directly on the price chart, based on a moving average and a customizable standard deviation multiplier.
* Upper Band: Indicates price levels above the moving average.
* Lower Band: Indicates price levels below the moving average.
The width of the channel between these two bands provides a clear picture of current market volatility.
Detailed User Guide:
You can use SD Bands in several ways to enhance your trading decisions:
1. Dynamic Support and Resistance:
These bands can act as dynamic support and resistance levels.
* Support: When the price moves down and touches or approaches the lower band, it can act as support, offering the possibility of a rebound to the average.
* Resistance: When the price moves up and touches or approaches the upper band, it can act as resistance, offering the possibility of a rebound.
2. Volatility Measurement and Breakout Signals:
* Low Volatility (Squeeze): When the two bands converge and form a narrow channel. Indicates very low market volatility. This condition often occurs before significant price movements or breakouts.
* High Volatility (Expansion): When the bands expand and widen rapidly, it indicates that the market is entering a period of strong trending momentum with high momentum.
3. Reversal Signals:
* When the price closes significantly outside the SD Bands (especially after a long-term trend), it may signal that the current momentum has expired and a reversal may be imminent.
Input Parameters:
The indicator's parameters are fully customizable to suit your trading style:
* Length: Defines the number of bars used to calculate the moving average and standard deviation.
* 20: Suitable for short- to medium-term analysis.
* 50 or 100: Suitable for long-term trend analysis.
* Multiplier: Adjusts the sensitivity of the signal bars.
* 1.0 - 2.0: Creates narrower signal bars, leading to more frequent signals.
* 2.0 - 3.0: Creates wider signal bars, providing fewer but potentially more significant signals.
Important Warning:
* This indicator is an analytical tool only. It does not provide guaranteed buy or sell signals.
* Always use it in conjunction with other indicators (such as RSI, MACD, and Volume) for confirmation.
* Trading involves high risk. Proper risk management, including the use of stop-loss orders, is recommended.
You can use this structure for your posts on TradingView. Good luck with your indicators!
K Bands v2.2K Bands v2 - Settings Breakdown (Timeframe Agnostic)
K Bands v2 is an adaptive volatility envelope tool designed for flexibility across different trading
styles and timeframes.
The settings below allow complete control over how the bands are constructed, smoothed, and how
they respond to market volatility.
1. Upstream MA Type
Controls the core smoothing applied to price before calculating the bands.
Options:
- EMA: Fast, responsive, reacts quickly to price changes.
- SMA: Classic moving average, slower but provides stability.
- Hull: Ultra smooth, reduces noise significantly but may react differently to choppy conditions.
- GeoMean: Geometric mean smoothing, creates a unique, slightly smoother line.
- SMMA: Wilder-style smoothing, balances noise reduction and responsiveness.
- WMA: Weighted Moving Average, emphasizes recent price action for sharper responsiveness.
2. Smoothing Length
Lookback period for the upstream moving average.
- Lower values: Faster reaction, captures short-term shifts.
- Higher values: Smoother trend depiction, filters out noise.
3. Multiplier
Determines the width of the bands relative to calculated volatility.
- Lower multiplier: Tighter bands, more signals, but increased false breakouts.
- Higher multiplier: Wider bands, fewer false signals, more conservative.
4. Downstream MA Type
Applies final smoothing to the band plots after initial calculation.
Same options as Upstream MA.
5. Downstream Smoothing Length
Lookback period for downstream smoothing.
- Lower: More responsive bands.
- Higher: Smoother, visually cleaner bands.
6. Band Width Source
Selects the method used to calculate band width based on market volatility.
Options:
- ATR (Average True Range): Smooth, stable bands based on price range expansion.
- Stdev (Standard Deviation): More reactive bands highlighting short-term volatility spikes.
7. ATR Smoothing Type
Controls how the ATR or Stdev value is smoothed before applying to band width.
Options:
- Wilder: Classic, stable smoothing.
- SMA: Simple moving average smoothing.
- EMA: Faster, more reactive smoothing.
- Hull: Ultra-smooth, noise-reducing smoothing.
- GeoMean: Geometric mean smoothing.
8. ATR Length
Lookback period for smoothing the volatility measurement (ATR or Stdev).
- Lower: More reactive bands, captures quick shifts.
- Higher: Smoother, more stable bands.
9. Dynamic Multiplier Based on Volatility
Allows the band multiplier to adapt automatically to changes in market volatility.
- ON: Bands expand during high volatility and contract during low volatility.
- OFF: Bands remain fixed based on the set multiplier.
10. Dynamic Multiplier Sensitivity
Controls how aggressively the dynamic multiplier responds to volatility changes.
- Lower values: Subtle adjustments.
- Higher values: More aggressive band expansion/contraction.
K Bands v2 is designed to be adaptable across any market or timeframe, helping visualize price
structure, trend, and volatility behavior.
Gamma + Fibonacci EMA Bands# Gamma + Fibonacci EMA Bands
## Overview
The Gamma + Fibonacci EMA Bands indicator combines two powerful analytical approaches: Gamma-weighted Exponential Moving Averages and Fibonacci sequence-based standard EMAs. This dual system creates a comprehensive "band" structure that helps identify trend direction, strength, and potential reversal zones with greater precision than single moving average systems.
## Features
- **Gamma-weighted EMAs**: Three customizable Gamma EMAs (fast-responding) with adjustable gamma parameters
- **Fibonacci Sequence EMAs**: Six standard EMAs based on the Fibonacci sequence (34, 55, 89, 144, 233, 377)
- **Visual Band Structure**: Color-coded for instant visual analysis
- **Trend Confirmation**: Multiple timeframe validation through varied moving average periods
- **Support/Resistance Identification**: Natural price reaction zones highlighted by EMA confluences
## How It Works
The indicator uses two complementary EMA systems:
1. **Gamma EMAs** (γ-EMAs) - These responsive moving averages use a direct gamma weighting factor (between 0-1) rather than a period length. Lower gamma values create smoother lines, while higher values create more responsive ones. These react quickly to price changes and serve as short-term trend indicators.
2. **Fibonacci EMAs** - These traditional EMAs use period lengths based on the Fibonacci sequence (34, 55, 89, 144, 233, 377). They provide longer-term trend context and naturally identify key support/resistance levels that align with market psychology.
## Interpretation
### Trend Direction
- When price is above all bands: Strong bullish trend
- When price is below all bands: Strong bearish trend
- When price is between bands: Consolidation or trend transition
### Support/Resistance
- Gamma EMAs (purple shades): Short-term dynamic support/resistance
- Fibonacci EMAs (orange/red shades): Stronger, longer-term support/resistance
### Trend Strength
- Wider band separation: Stronger trend momentum
- Compressed bands: Consolidation or trend weakness
### Reversal Signals
- Price breaking through multiple bands: Potential trend reversal
- Gamma EMAs crossing Fibonacci EMAs: Changing momentum
## Settings
- **Source**: Price data source (default: close)
- **Gamma 1**: Fast γ-EMA value (default: 0.2)
- **Gamma 2**: Medium γ-EMA value (default: 0.5)
- **Gamma 3**: Slow γ-EMA value (default: 0.8)
## Notes
This indicator works best on higher timeframes (1H+) and liquid markets. The Gamma-weighted EMAs provide faster signals while the Fibonacci sequence EMAs provide reliable support/resistance levels that often align with key market turning points.
For optimal use, watch for price interaction with these bands and how the bands interact with each other to confirm trend changes before they become obvious to the majority of market participants.
Linear Regression with StdDev BandsLinear Regression with Standard Deviation Bands Indicator
This indicator plots a linear regression line along with upper and lower bands based on standard deviation. It helps identify potential overbought and oversold conditions, as well as trend direction and strength.
Key Components:
Linear Regression Line: Represents the average price over a specified period.
Upper and Lower Bands: Calculated by adding and subtracting the standard deviation (multiplied by a user-defined factor) from the linear regression line. These bands act as dynamic support and resistance levels.
How to Use:
Trend Identification: The direction of the linear regression line indicates the prevailing trend.
Overbought/Oversold Signals: Prices approaching or crossing the upper band may suggest overbought conditions, while prices near the lower band may indicate oversold conditions.
Dynamic Support/Resistance: The bands can act as potential support and resistance levels.
Alerts: Option to enable alerts when the price crosses above the upper band or below the lower band.
Customization:
Regression Length: Adjust the period over which the linear regression is calculated.
StdDev Multiplier: Modify the width of the bands by changing the standard deviation multiplier.
Price Source: Choose which price data to use for calculations (e.g., close, open, high, low).
Alerts: Enable or disable alerts for band crossings.
This indicator is a versatile tool for understanding price trends and potential reversal points.
Adaptive Bollinger BandsAdaptive Bollinger Bands
This indicator displays Bollinger Bands with parameters that dynamically adjust based on market volatility. Unlike standard Bollinger Bands with fixed parameters, this version adaptively modifies both the period and standard deviation multiplier in real-time based on measured market conditions.
Key Features
Dynamic adjustment of period and standard deviation based on normalized volatility
Color-coded visualization of current volatility regime (expanding, normal, contracting)
Integration with Keltner Channels for band refinement
Bandwidth analysis for volatility regime identification
Optional on-chart parameter labels showing current settings
Band cross alerts and visual markers
Volatility Visualization
The indicator uses color-coding to display different volatility regimes:
Red: Expanding volatility regime (higher measured volatility)
Blue: Normal volatility regime (average measurements)
Green: Contracting volatility regime (lower measured volatility)
Technical Information
The indicator calculates volatility by analyzing price returns over a configurable lookback period (default 50 bars). The standard deviation of returns is normalized against historical extremes to create an adaptive scaling factor.
Band adaptation occurs through two primary mechanisms:
1. Period adjustment: Higher volatility uses shorter periods (more responsive), while lower volatility uses longer periods (more stable)
2. Standard deviation multiplier adjustment: Higher volatility increases the multiplier (wider bands), while lower volatility decreases it (tighter bands)
The middle band uses a simple moving average with the adaptive period. Additional refinement occurs through Keltner Channel integration, which can tighten bands when contained within Keltner boundaries.
Volatility regimes are determined by analyzing Bollinger Bandwidth relative to its recent history, providing contextual information about the current market state.
Settings Customization
The indicator provides extensive customization options:
- Base parameters (period and standard deviation)
- Adaptive range limits (min/max period and standard deviation)
- Keltner Channel parameters for band refinement
- Bandwidth analysis settings
- Display options for visual elements
Limitations and Considerations
All technical indicators have inherent limitations and should not be used in isolation
Past performance does not guarantee future results
The indicator requires sufficient historical data for proper volatility normalization
Smaller timeframes may produce more noise in the adaptive calculations
Parameters may require adjustment for different markets and trading styles
Band crosses are not trading signals on their own and should be evaluated with other factors
This indicator is designed to provide objective information about market volatility conditions and potential support/resistance zones. Always combine with other analysis methods within a comprehensive trading approach.
Elliptic bands
Why Elliptic?
Unlike traditional indicators (e.g., Bollinger Bands with constant standard deviation multiples), the elliptic model introduces a cyclical, non-linear variation in band width. This reflects the idea that price movements often follow rhythmic patterns, widening and narrowing in a predictable yet dynamic way, akin to natural market cycles.
Buy: When the price enters from below (green triangle).
Sell: When the price enters from above (red triangle).
Inputs
MA Length: 50 (This is the period for the central Simple Moving Average (SMA).)
Cycle Period: 50 (This is the elliptic cycle length.)
Volatility Multiplier: 2.0 (This value scales the band width.)
Mathematical Foundation
The indicator is based on the ellipse equation. The basic formula is:
Ellipse Equation:
(x^2) / (a^2) + (y^2) / (b^2) = 1
Solving for y:
y = b * sqrt(1 - (x^2) / (a^2))
Parameters Explained:
a: Set to 1 (normalized).
x: Varies from -1 to 1 over the period.
b: Calculated as:
ta.stdev(close, MA Length) * Volatility Multiplier
(This represents the standard deviation of the close prices over the MA period, scaled by the volatility multiplier.)
y (offset): Represents the band distance from the moving average, forming the elliptic cycle.
Behavior
Bands:
The bands are narrow at the cycle edges (when the offset is 0) and become widest at the midpoint (when the offset equals b).
Trend:
The central moving average (MA) shows the overall trend direction, while the bands adjust according to the volatility.
Signals:
Standard buy and sell signals are generated when the price interacts with the bands.
Practical Use
Trend Identification:
If the price is above the MA, it indicates an uptrend; if below, a downtrend.
Support and Resistance:
The elliptic bands act as dynamic support and resistance levels.
Narrowing bands may signal potential trend reversals.
Breakouts:
Multi-Band Comparison (Uptrend)Multi-Band Comparison
Overview:
The Multi-Band Comparison indicator is engineered to reveal critical levels of support and resistance in strong uptrends. In a healthy upward market, the price action will adhere closely to the 95th percentile line (the Upper Quantile Band), effectively “riding” it. This indicator combines a modified Bollinger Band (set at one standard deviation), quantile analysis (95% and 5% levels), and power‑law math to display a dynamic picture of market structure—highlighting a “golden channel” and robust support areas.
Key Components & Calculations:
The Golden Channel: Upper Bollinger Band & Upper Std Dev Band of the Upper Quantile
Upper Bollinger Band:
Calculation:
boll_upper=SMA(close,length)+(boll_mult×stdev)
boll_upper=SMA(close,length)+(boll_mult×stdev) Here, the 20-period SMA is used along with one standard deviation of the close, where the multiplier (boll_mult) is 1.0.
Role in an Uptrend:
In a healthy uptrend, price rides near the 95th percentile line. When price crosses above this Upper Bollinger Band, it confirms strong bullish momentum.
Upper Std Dev Band of the Upper Quantile (95th Percentile) Band:
Calculation:
quant_upper_std_up=quant_upper+stdev
quant_upper_std_up=quant_upper+stdev The Upper Quantile Band, quant_upperquant_upper, is calculated as the 95th percentile of recent price data. Adding one standard deviation creates an extension that accounts for normal volatility around this extreme level.
The Golden Channel:
When the price crosses above the Upper Bollinger Band, the Upper Std Dev Band of the Upper Quantile immediately shifts to gold (yellow) and remains gold until price falls below the Bollinger level. Together, these two lines form the “golden channel”—a visual hallmark of a healthy uptrend where the price reliably hugs the 95th percentile level.
Upper Power‑Law Band
Calculation:
The Upper Power‑Law Band is derived in two steps:
Determine the Extreme Return Factor:
power_upper=Percentile(returns,95%)
power_upper=Percentile(returns,95%) where returns are computed as:
returns=closeclose −1.
returns=close close−1.
Scale the Current Price:
power_upper_band=close×(1+power_upper)
power_upper_band=close×(1+power_upper)
Rationale and Correlation:
By focusing on the upper 5% of returns (reflecting “fat tails”), the Upper Power‑Law Band captures extreme but statistically expected movements. In an uptrend, its value often converges with the Upper Std Dev Band of the Upper Quantile because both measures reflect heightened volatility and extreme price levels. When the Upper Power‑Law Band exceeds the Upper Std Dev Band, it can signal a temporary overextension.
Upper Quantile Band (95% Percentile)
Calculation:
quant_upper=Percentile(price,95%)
quant_upper=Percentile(price,95%) This level represents where 95% of past price data falls below, and in a robust uptrend the price action practically rides this line.
Color Logic:
Its color shifts from a neutral (blackish) tone to a vibrant, bullish hue when the Upper Power‑Law Band crosses above it—signaling extra strength in the trend.
Lower Quantile and Its Support
Lower Quantile Band (5% Percentile):
Calculation:
quant_lower=Percentile(price,5%)
quant_lower=Percentile(price,5%)
Behavior:
In a healthy uptrend, price remains well above the Lower Quantile Band. It turns red only when price touches or crosses it, serving as a warning signal. Under normal conditions it remains bright green, indicating the market is not nearing these extreme lows.
Lower Std Dev Band of the Lower Quantile:
This line is calculated by subtracting one standard deviation from quant_lowerquant_lower and typically serves as absolute support in nearly all conditions (except during gap or near-gap moves). Its consistent role as support provides traders with a robust level to monitor.
How to Use the Indicator:
Golden Channel and Trend Confirmation:
As price rides the Upper Quantile (95th percentile) perfectly in a healthy uptrend, the Upper Bollinger Band (1 stdev above SMA) and the Upper Std Dev Band of the Upper Quantile form a “golden channel” once price crosses above the Bollinger level. When this occurs, the Upper Std Dev Band remains gold until price dips back below the Bollinger Band. This visual cue reinforces trend strength.
Power‑Law Insights:
The Upper Power‑Law Band, which is based on extreme (95th percentile) returns, tends to align with the Upper Std Dev Band. This convergence reinforces that extreme, yet statistically expected, price moves are occurring—indicating that even though the price rides the 95th percentile, it can only stretch so far before a correction or consolidation.
Support Indicators:
Primary and Secondary Support in Uptrends:
The Upper Bollinger Band and the Lower Std Dev Band of the Upper Quantile act as support zones for minor retracements in the uptrend.
Absolute Support:
The Lower Std Dev Band of the Lower Quantile serves as an almost invariable support area under most market conditions.
Conclusion:
The Multi-Band Comparison indicator unifies advanced statistical techniques to offer a clear view of uptrend structure. In a healthy bull market, price action rides the 95th percentile line with precision, and when the Upper Bollinger Band is breached, the corresponding Upper Std Dev Band turns gold to form a “golden channel.” This, combined with the Power‑Law analysis that captures extreme moves, and the robust lower support levels, provides traders with powerful, multi-dimensional insights for managing entries, exits, and risk.
Disclaimer:
Trading involves risk. This indicator is for educational purposes only and does not constitute financial advice. Always perform your own analysis before making trading decisions.
Multi-Period % Change Bands (Extreme Dots)Multiple Period Percentage Change Extreme Dots
This indicator visualizes percentage changes across three different timeframes (8, 13, and 21 days), highlighting extreme movements that break out of a user-defined band. It's designed to identify which timeframe is showing the most significant percentage change when prices make notable moves.
Features:
- Tracks percentage changes for 8-day, 13-day, and 21-day periods
- Customizable upper and lower bands to define significant moves
- Shows dots only for the most extreme moves (highest above band or lowest below band)
- Color-coded for easy identification:
- Blue: 8-day changes
- Green: 13-day changes
- Red: 21-day changes
- Includes current values display for all timeframes
Usage Tips:
- Shorter timeframes (8-day) are more sensitive to price changes and should use narrower bands (e.g., ±3%)
- Medium timeframes (13-day) work well with moderate bands (e.g., ±5%)
- Longer timeframes (21-day) can use wider bands (e.g., ±8%)
- Dots appear only when a timeframe shows the most extreme move above/below bands
- Use the gray zone between bands to identify normal price action ranges
The indicator helps identify which lookback period is showing the strongest momentum in either direction, while filtering out normal market noise within the bands.
Note: This is particularly useful for:
- Identifying trend strength across different timeframes
- Spotting which duration is showing the most extreme moves
- Filtering out minor fluctuations through the band system
- Comparing relative strength of moves across different periods
Bollinger Bands with RSI Buy/Sell Signals (15 min) Bollinger Bands with RSI Buy/Sell Signals (15 Min)
Description:
The Bollinger Bands with RSI Buy/Sell Signals (15 Min) indicator is designed to help traders identify potential reversal points in the market using two popular technical indicators: Bollinger Bands and the Relative Strength Index (RSI).
How It Works:
Bollinger Bands:
Bollinger Bands consist of an upper band, lower band, and a middle line (Simple Moving Average). These bands adapt to market volatility, expanding during high volatility and contracting during low volatility.
This indicator monitors the 15-minute Bollinger Bands. If the price moves completely outside the bands, it signals that the market is potentially overextended.
Relative Strength Index (RSI):
RSI is a momentum indicator that measures the strength of price movements. RSI readings above 70 indicate an overbought condition, while readings below 30 suggest an oversold condition.
This indicator uses the RSI on the 15-minute time frame to further confirm overbought and oversold conditions.
Buy/Sell Signal Generation:
Buy Signal:
A buy signal is triggered when the market price crosses above the lower Bollinger Band on the 15-minute time frame, indicating that the market may be oversold.
Additionally, the RSI must be below 30, confirming an oversold condition.
A "Buy" label appears below the price when this condition is met.
Sell Signal:
A sell signal is triggered when the market price crosses below the upper Bollinger Band on the 15-minute time frame, indicating that the market may be overbought.
The RSI must be above 70, confirming an overbought condition.
A "Sell" label appears above the price when this condition is met.
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.
Dynamic Bollinger Bands with Momentum and Volume (DBBMV)Overview
The Dynamic Bollinger Bands with Momentum and Volume (DBBMV) indicator enhances the traditional Bollinger Bands by dynamically adjusting their width and position based on momentum and volume. This provides a more responsive and context-aware indication of price volatility and potential reversals.
Key Features
Momentum Adjusted Bands: Adjusts the bands' width based on the momentum indicator, reflecting the rate of change in price.
Volume Weighted Bands: Further adjusts the bands based on trading volume to reflect market activity and price volatility.
Signal Alerts: Provides buy and sell signals based on price action relative to the dynamic bands, helping traders identify entry and exit points.
Customizable Parameters: Allows users to adjust the lookback period, momentum sensitivity, and volume weighting for personalized analysis.
How It Works
The DBBMV indicator starts with the traditional Bollinger Bands, which are calculated using a moving average and standard deviation of the selected price source. The width of these bands is then adjusted based on the momentum of the price, making them more sensitive to price changes. Further adjustments are made based on trading volume, which ensures that the bands accurately reflect current market conditions. This results in a set of dynamic Bollinger Bands that provide more nuanced insights into price volatility and potential reversals.
Usage Instructions
Identify Volatile Periods: Use the dynamically adjusted bands to identify periods of high and low volatility in the market.
Spot Reversals: Look for buy signals when the price crosses above the lower band and sell signals when the price crosses below the upper band.
Adjust Sensitivity: Customize the lookback period, momentum sensitivity, and volume weighting to fine-tune the indicator to your specific trading strategy and market conditions.
Enhance Analysis: Combine the DBBMV indicator with other technical analysis tools for a more comprehensive market analysis.
Volume Confirmation: Use the volume-weighted adjustments to confirm the strength of price movements and potential breakouts.
The Dynamic Bollinger Bands with Momentum and Volume (DBBMV) indicator provides traders with a powerful tool to understand market dynamics better and make informed trading decisions based on adjusted volatility and market activity.
EMA 9/13/18/25 + Bollinger BandThe indicator combines two components: Exponential Moving Averages (EMAs) and Bollinger Bands.
Exponential Moving Averages (EMAs): The indicator calculates four EMAs with different periods: 9, 13, 18, and 25. An Exponential Moving Average is a type of moving average that places a greater weight and significance on the most recent data points. As the name suggests, it's an average of the asset's price over a certain period, with recent prices given more weight in the calculation, making it more responsive to recent price changes.
Bollinger Bands: Bollinger Bands consist of a simple moving average (the basis) and two standard deviations plotted away from it. The standard deviations are multiplied by a factor (usually 2) to determine the distance from the basis. These bands dynamically adjust themselves based on recent price movements. The upper band represents the highest price level reached in the given period, while the lower band represents the lowest price level.
Combining these components provides traders with insights into both trend direction and volatility. The EMAs help identify trends by smoothing out price data, while the Bollinger Bands offer insights into volatility and potential price reversal points. Traders often use the crossovers of EMAs and interactions with Bollinger Bands to make trading decisions. For example, when the price touches the upper Bollinger Band, it may indicate overbought conditions, while touching the lower band may suggest oversold conditions. Additionally, crossovers of EMAs (such as the shorter-term EMA crossing above or below the longer-term EMA) may signal changes in trend direction.
