TMA Bands with Break Arrow @ClearTradingMind
The "TMA Bands with Break Arrow" indicator, developed by ClearTradingMind, is designed to provide traders with insights into potential trend reversals based on the movement of price within a channel defined by the Triangular Moving Average (TMA) and its bands. The TMA is a smoothed moving average, and this indicator adds upper and lower bands to visualize potential breakouts.
Key Components:
1. TMA Bands: The indicator plots the upper and lower bands of the TMA channel. These bands represent potential overbought (upper band) and oversold (lower band) conditions.
2. Break Arrows: The indicator generates buy (green triangle up) and sell (red triangle down) arrows when the closing price breaks above the upper band or below the lower band, indicating a potential trend reversal.
3. Background Color: The background color dynamically changes based on the last generated signal. A blue background suggests a recent buy signal, while a red background indicates a recent sell signal. This provides a quick visual reference for the prevailing market sentiment.
Usage:
1. Trend Reversals: Traders can use the buy and sell arrows as signals for potential trend reversals. A buy signal suggests a possible upward trend, while a sell signal suggests a potential downward trend.
2. Channel Breakouts: Watch for price breaking above the upper band (buy signal) or below the lower band (sell signal). These breakouts may indicate the start of a new trend.
3. Volatility Analysis: The width of the TMA channel represents volatility. A widening channel suggests increased volatility, while a narrowing channel suggests decreasing volatility.
4. Background Color: The background color provides additional context. A blue background indicates recent bullish sentiment, while a red background suggests recent bearish sentiment.
Parameters:
- TMA Period: The number of bars used to calculate the Triangular Moving Average.
- ATR Period: The number of bars used to calculate the Average True Range (ATR) for determining the width of the TMA channel.
- ATR Multiplier: A multiplier applied to the ATR to determine the width of the TMA channel.
Note: This indicator is a tool to assist traders in their analysis, and it is recommended to use it in conjunction with other technical and fundamental analysis methods for more comprehensive decision-making.
Disclaimer: Trading involves risk, and this indicator does not guarantee profit. Users should conduct thorough analysis and risk management before making trading decisions.
Cerca negli script per "band"
Fair value bands / quantifytools— Overview
Fair value bands, like other band tools, depict dynamic points in price where price behaviour is normal or abnormal, i.e. trading at/around mean (price at fair value) or deviating from mean (price outside fair value). Unlike constantly readjusting standard deviation based bands, fair value bands are designed to be smooth and constant, based on typical historical deviations. The script calculates pivots that take place above/below fair value basis and forms median deviation bands based on this information. These points are then multiplied up to 3, representing more extreme deviations.
By default, the script uses OHLC4 and SMA 20 as basis for the bands. Users can form their preferred fair value basis using following options:
Price source
- Standard OHLC values
- HL2 (High + low / 2)
- OHLC4 (Open + high + low + close / 4)
- HLC3 (High + low + close / 3)
- HLCC4 (High + low + close + close / 4)
Smoothing
- SMA
- EMA
- HMA
- RMA
- WMA
- VWMA
- Median
Once fair value basis is established, some additional customization options can be employed:
Trend mode
Direction based
Cross based
Trend modes affect fair value basis color that indicates trend direction. Direction based trend considers only the direction of the defined fair value basis, i.e. pointing up is considered an uptrend, vice versa for downtrend. Cross based trends activate when selected source (same options as price source) crosses fair value basis. These sources can be set individually for uptrend/downtrend cross conditions. By default, the script uses cross based trend mode with low and high as sources.
Cross based (downtrend not triggered) vs. direction based (downtrend triggered):
Threshold band
Threshold band is calculated using typical deviations when price is trading at fair value basis. In other words, a little bit of "wiggle room" is added around the mean based on expected deviation. This feature is useful for cross based trends, as it allows filtering insignificant crosses that are more likely just noise. By default, threshold band is calculated based on 1x median deviation from mean. Users can increase/decrease threshold band width via input menu for more/less noise filtering, e.g. 2x threshold band width would require price to cross wiggle room that is 2x wider than typical, 0x erases threshold band altogether.
Deviation bands
Width of deviation bands by default is based on 1x median deviations and can be increased/decreased in a similar manner to threshold bands.
Each combination of customization options produces varying behaviour in the bands. To measure the behaviour and finding fairest representation of fair and unfair value, some data is gathered.
— Fair value metrics
Space between each band is considered a lot, named +3, +2, +1, -1, -2, -3. For each lot, time spent and volume relative to volume moving average (SMA 20) is recorded each time price is trading in a given lot:
Depending on the asset, timeframe and chosen fair value basis, shape of the distributions vary. However, practically always time is distributed in a normal bell curve shape, being highest at lots +1 to -1, gradually decreasing the further price is from the mean. This is hardly surprising, but it allows accurately determining dynamic areas of normal and abnormal price behaviour (i.e. low risk area between +1 and -1, high risk area between +-2 to +-3). Volume on the other hand is typically distributed the other way around, being lowest at lots +1 to -1 and highest at +-2 to +-3. When time and volume are distributed like so, we can conclude that 1) price being outside fair value is a rare event and 2) the more price is outside fair value, the more anomaly behaviour in volume we tend to find.
Viewing metric calculations
Metric calculation highlights can be enabled from the input menu, resulting in a lot based coloring and visibility of each lot counter (time, cumulative relative volume and average relative volume) in data window:
— Alerts
Available alerts are the following:
Individual
- High crossing deviation band (bands +1 to +3 )
- Low crossing deviation band (bands -1 to -3 )
- Low at threshold band in an uptrend
- High at threshold band in a downtrend
- New uptrend
- New downtrend
Grouped
- New uptrend or downtrend
- Deviation band cross (+1 or -1)
- Deviation band cross (+2 or -2)
- Deviation band cross (+3 or -3)
— Practical guide
Example #1 : Risk on/risk off trend following
Ideal trend stays inside fair value and provides sufficient cool offs between the moves. When this is the case, fair value bands can be used for sensible entry/exit levels within the trend.
Example #2 : Mean reversions
When price shows exuberance into an extreme deviation, followed by a stall and signs of exhaustion (wicks), an opportunity for mean reversion emerges. The higher the deviation, the more volatility in the move, the more signalling of exhaustion, the better.
Example #3 : Tweaking bands for desired behaviour
The faster the length of fair value basis, the more momentum price needs to hit extreme deviation levels, as bands too are moving faster alongside price. Decreasing fair value basis length typically leads to more quick and aggressive deviations and less steady trends outside fair value.
EMA bands + leledc + bollinger bands trend following strategy v2The basics:
In its simplest form, this strategy is a positional trend following strategy which enters long when price breaks out above "middle" EMA bands and closes or flips short when price breaks down below "middle" EMA bands. The top and bottom of the middle EMA bands are calculated from the EMA of candle highs and lows, respectively.
The idea is that entering trades on breakouts of the high EMAs and low EMAs rather than the typical EMA based on candle closes gives a bit more confirmation of trend strength and minimizes getting chopped up. To further reduce getting chopped up, the strategy defaults to close on crossing the opposite EMA band (ie. long on break above high EMA middle band and close below low EMA middle band).
This strategy works on all markets on all timeframes, but as a trend following strategy it works best on markets prone to trending such as crypto and tech stocks. On lower timeframes, longer EMAs tend to work best (I've found good results on EMA lengths even has high up to 1000), while 4H charts and above tend to work better with EMA lengths 21 and below.
As an added filter to confirm the trend, a second EMA can be used. Inputting a slower EMA filter can ensure trades are entered in accordance with longer term trends, inputting a faster EMA filter can act as confirmation of breakout strength.
Bar coloring can be enabled to quickly visually identify a trend's direction for confluence with other indicators or strategies.
The goods:
Waiting for the trend to flip before closing a trade (especially when a longer base EMA is used) often leaves money on the table. This script combines a number of ways to identify when a trend is exhausted for backtesting the best early exits.
"Delayed bars inside middle bands" - When a number of candle's in a row open and close between the middle EMA bands, it could be a sign the trend is weak, or that the breakout was not the start of a new trend. Selecting this will close out positions after a number of bars has passed
"Leledc bars" - Originally introduced by glaz, this is a price action indicator that highlights a candle after a number of bars in a row close the same direction and result in greatest high/low over a period. It often triggers when a strong trend has paused before further continuation, or it marks the end of a trend. To mitigate closing on false Leledc signals, this strategy has two options: 1. Introducing requirement for increased volume on the Leledc bars can help filter out Leledc signals that happen mid trend. 2. Closing after a number of Leledc bars appear after position opens. These two options work great in isolation but don't perform well together in my testing.
"Bollinger Bands exhaustion bars" - These bars are highlighted when price closes back inside the Bollinger Bands and RSI is within specified overbought/sold zones. The idea is that a trend is overextended when price trades beyond the Bollinger Bands. When price closes back inside the bands it's likely due for mean reversion back to the base EMA in which this strategy will ideally re-enter a position. Since the added RSI requirements often make this indicator too strict to trigger a large enough sample size to backtest, I've found it best to use "non-standard" settings for both the bands and the RSI as seen in the default settings.
"Buy/Sell zones" - Similar to the idea behind using Bollinger Bands exhaustion bars as a closing signal. Instead of calculating off of standard deviations, the Buy/Sell zones are calculated off multiples of the middle EMA bands. When trading beyond these zones and subsequently failing back inside, price may be due for mean reversion back to the base EMA. No RSI filter is used for Buy/Sell zones.
If any early close conditions are selected, it's often worth enabling trade re-entry on "middle EMA band bounce". Instead of waiting for a candle to close back inside the middle EMA bands, this feature will re-enter position on only a wick back into the middle bands as will sometimes happen when the trend is strong.
Any and all of the early close conditions can be combined. Experimenting with these, I've found can result in less net profit but higher win-rates and sharpe ratios as less time is spent in trades.
The deadly:
The trend is your friend. But wouldn't it be nice to catch the trends early? In ranging markets (or when using slower base EMAs in this strategy), waiting for confirmation of a breakout of the EMA bands at best will cause you to miss half the move, at worst will result in getting consistently chopped up. Enabling "counter-trend" trades on this strategy will allow the strategy to enter positions on the opposite side of the EMA bands on either a Leledc bar or Bollinger Bands exhaustion bar. There is a filter requiring either a high/low (for Leledc) or open (for BB bars) outside the selected inner or outer Buy/Sell zone. There are also a number of different close conditions for the counter-trend trades to experiment with and backtest.
There are two ways I've found best to use counter-trend trades
1. Mean reverting scalp trades when a trend is clearly overextended. Selecting from the first 5 counter-trend closing conditions on the dropdown list will usually close the trades out quickly, with less profit but less risk.
2. Trying to catch trends early. Selecting any of the close conditions below the first 5 can cause the strategy to behave as if it's entering into a new trend (from the wrong side).
This feature can be deadly effective in profiting from every move price makes, or deadly to the strategy's PnL if not set correctly. Since counter-trend trades open opposite the middle bands, a stop-loss is recommended to reduce risk. If stop-losses for counter-trend trades are disabled, the strategy will hold a position open often until liquidation in a trending market if th trade is offsides. Note that using a slower base EMA makes counter-trend stop-losses even more necessary as it can reduce the effectiveness of the Buy/Sell zone filter for opening the trades as price can spend a long time trending outside the zones. If faster EMAs (34 and below) are used with "Inner" Buy/Zone filter selected, the first few closing conditions will often trigger almost immediately closing the trade at a loss.
The niche:
I've added a feature to default into longs or shorts. Enabling these with other features (aside from the basic long/short on EMA middle band breakout) tends to break the strategy one way or another. Enabling default long works to simulate trying to acquire more of the asset rather than the base currency. Enabling default short can have positive results for those high FDV, high inflation coins that go down-only for months at a time. Otherwise, I use default short as a hedge for coins that I hold and stake spot. I gain the utility and APR of staking while reducing the risk of holding the underlying asset by maintaining a net neutral position *most* of the time.
Disclaimer:
This script is intended for experimenting and backtesting different strategies around EMA bands. Use this script for your live trading at your own risk. I am a rookie coder, as such there may be errors in the code that cause the strategy to behave not as intended. As far as I can tell it doesn't repaint, but I cannot guarantee that it does not. That being said if there's any question, improvements, or errors you've found, drop a comment below!
VBE Pro - Advanced Volatility Bands with Zero Lag & PredictionVBE Pro: Zero-Lag Predictive Bands
A next-gen volatility envelope that blends zero-lag smoothing with forward-looking volatility models (EWMA/GARCH/HAR/ML) to keep bands tight in calm markets, responsive in shocks, and adaptive across regimes.
What it does
Builds volatility from multiple methods (ATR, StDev, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang).
Projects near-term vol with your choice of predictor, then blends it via a weight slider.
Applies zero-lag smoothing (ZLEMA/ZLMA/DEMA/TEMA/HMA/JMA/Ehlers/Kalman/T3) to cut delay without over-shoot.
Auto-adapts band width by regime (high/low/normal) and can expand dynamically with price acceleration.
Optional displacement to align with your execution style.
On-chart
Upper/Lower zero-lag bands with optional fill.
Middle line (ZL-smoothed source).
Regime-tinted background (High/Low).
Displacement marker (if used).
Compact top-right info table: current vs predicted vol, regime, squeeze, multiplier, methods, ZL gain, est. lag reduction.
Signals & Alerts
Break↑ / Break↓ when price crosses the bands.
Vol↑ / Vol↓ expansion/contraction sequences.
“Squeeze” when band width compresses vs its ZL average.
“ZL” marker when significant zero-lag is active.
Prediction divergence ⚠ when projected vol deviates > threshold.
Built-in alertconditions for all of the above.
Quick start
Method: ATR or Hybrid for robustness.
Smoothing: ZLEMA, length 5–8, ZL gain 2–3 (push higher only if you accept more projection).
Bands: Multiplier 2.0, Adaptive on, Dynamic off to start.
Prediction: EWMA, weight 0.25–0.35. Move to GARCH in mean-reverty tapes; HAR-RV for mixed regimes.
Regime lookback: 50.
CNS - Multi-Timeframe Bollinger Band OscillatorMy hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane.
I’ve been having good results setting the “Bollinger Band MA Length” in the Input tab to between 5 and 10. You can use the standard 20 period, but your results will not be as granular.
This indicator has proven very good at finding local tops and bottoms by combining data from multiple timeframes. Use BB timeframes that are lower than the timeframe you are viewing in your price pane.
The default settings work best on the weekly timeframe, but can be adjusted for most timeframes including intraday.
Be cognizant that the indicator, like other oscillators, does occasionally produce divergences at tops and bottoms.
Any feedback is appreciated.
Overview
This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness.
How It Works
Bollinger Band Calculation:
The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe.
Price Position Normalization:
For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands.
It calculates three positions based on the high, low, and close prices of the requested timeframe:
pos_high = (High - Lower Band) / (Upper Band - Lower Band)
pos_low = (Low - Lower Band) / (Upper Band - Lower Band)
pos_close = (Close - Lower Band) / (Upper Band - Lower Band)
If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline).
The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band).
Multi-Timeframe Averaging:
The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data.
It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition().
These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4
This average is the oscillator value, which is plotted and typically oscillates between 0 and 1.
Moving Averages:
Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10).
These can be potentially used for MA crossover strategies.
What Is Being Averaged?
The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe.
Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe.
This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands.
Interpretation
0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions).
0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions.
0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone).
0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions.
1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions).
The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals.
Example
If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625
This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme.
Notes
The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed.
The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
Multi-Timeframe Bollinger BandsMy hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane.
I’ve been having good results setting the “Bollinger Band MA Length” in the Input tab to between 5 and 10. You can use the standard 20 period, but your results will not be as granular.
This indicator has proven very good at finding local tops and bottoms by combining data from multiple timeframes. Use timeframes that are lower than the timeframe you are viewing in your price pane. Be cognizant that the indicator, like other oscillators, does occasionally produce divergences at tops and bottoms.
Any feedback is appreciated.
Overview
This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness.
How It Works
Bollinger Band Calculation:
The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe.
Price Position Normalization:
For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands.
It calculates three positions based on the high, low, and close prices of the requested timeframe:
pos_high = (High - Lower Band) / (Upper Band - Lower Band)
pos_low = (Low - Lower Band) / (Upper Band - Lower Band)
pos_close = (Close - Lower Band) / (Upper Band - Lower Band)
If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline).
The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band).
Multi-Timeframe Averaging:
The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data.
It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition().
These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4
This average is the oscillator value, which is plotted and typically oscillates between 0 and 1.
Moving Averages:
Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10).
These can be potentially used for MA crossover strategies.
What Is Being Averaged?
The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe.
Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe.
This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands.
Interpretation
0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions).
0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions.
0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone).
0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions.
1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions).
The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals.
Example
If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625
This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme.
Notes
The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed.
The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
Multi-Timeframe Bollinger Band PositionBeta version.
My hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane.
Any feedback is appreciated.
Overview
This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness.
How It Works
Bollinger Band Calculation:
The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe.
Price Position Normalization:
For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands.
It calculates three positions based on the high, low, and close prices of the requested timeframe:
pos_high = (High - Lower Band) / (Upper Band - Lower Band)
pos_low = (Low - Lower Band) / (Upper Band - Lower Band)
pos_close = (Close - Lower Band) / (Upper Band - Lower Band)
If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline).
The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band).
Multi-Timeframe Averaging:
The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data.
It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition().
These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4
This average is the oscillator value, which is plotted and typically oscillates between 0 and 1.
Moving Averages:
Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10).
These can be potentially used for MA crossover strategies.
What Is Being Averaged?
The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe.
Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe.
This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands.
Interpretation:
0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions).
0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions.
0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone).
0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions.
1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions).
The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals.
Example:
If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625
This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme.
Notes:
The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed.
The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
Dynamic Ray BandsAbout Dynamic Ray Bands
Dynamic Ray Bands is a volatility-adaptive envelope indicator that adjusts in real time to evolving market conditions. It uses a Double Exponential Moving Average (DEMA) as its central trend reference, with upper and lower bands scaled according to current volatility measured by the Average True Range (ATR).
This creates a dynamic structure that visually frames price action, helping traders identify areas of potential trend continuation, overextension, or mean reversion.
How It Works
🟡 Centerline (DEMA)
The central yellow line is a Double Exponential Moving Average, which offers a smoother, less laggy trend signal than traditional moving averages. It represents the market’s short- to medium-term “equilibrium.”
🔵 Outer Bands
Plotted at:
Upper Band = DEMA + (ATR × outerMultiplier)
Lower Band = DEMA - (ATR × outerMultiplier)
These bands define the extreme bounds of current volatility. When price breaks above or below them, it can signal strong directional momentum or overbought/oversold conditions, depending on context. They're often used as trend breakout zones or to time exits after extended runs.
🟣 Inner Bands
Plotted closer to the DEMA:
Inner Upper = DEMA + (ATR × innerMultiplier)
Inner Lower = DEMA - (ATR × innerMultiplier)
These are preliminary volatility thresholds, offering early cues for potential expansion or reversal. They may be used for scalping, tight stop zones, or pre-breakout positioning.
🔁 Dynamic Width (Bands are Dynamically Adjusted Per Tick)
The width of both inner and outer bands is based on ATR (Average True Range), which is recalculated in real time. This means:
During high volatility, the bands expand, allowing for wider price fluctuations.
During low volatility, the bands contract, tightening range expectations.
Unlike fixed-width channels or standard Bollinger Bands (which use standard deviation), this per-tick adjustment via ATR enables Dynamic Ray Bands to reduce false signals in choppy markets and remain more reactive during trending conditions.
⚙️ Inputs
DMA Length — Period for the central DEMA.
ATR Length — Lookback used for ATR volatility calculations.
Outer Band Multiplier — Controls sensitivity of extreme bands.
Inner Band Multiplier — Controls proximity of inner bands.
Show Inner Bands — Toggle for plotting the inner zone.
🔔 Alerts
Alert conditions are included for:
Price closing above/below the outer bands (trend momentum or overextension)
Price closing above/below the inner bands (early signs of strength/weakness)
🧭 Use Cases
Breakout detection — Catch price continuation beyond the outer bands.
Volatility filtering — Adjust trade logic based on band width.
Mean reversion — Monitor for snapbacks toward the DEMA after price stretches too far.
Trend guidance — Use band slope and price position to confirm direction.
⚠️ Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to trade any specific market or security. Always test indicators thoroughly before using them in live trading.
Faytterro Bands Breakout📌 Faytterro Bands Breakout 📌
This indicator was created as a strategy showcase for another script: Faytterro Bands
It’s meant to demonstrate a simple breakout strategy based on Faytterro Bands logic and includes performance tracking.
❓ What Is It?
This script is a visual breakout strategy based on a custom moving average and dynamic deviation bands, similar in concept to Bollinger Bands but with unique smoothing (centered regression) and performance features.
🔍 What Does It Do?
Detects breakouts above or below the Faytterro Band.
Plots visual trade entries and exits.
Labels each trade with percentage return.
Draws profit/loss lines for every trade.
Shows cumulative performance (compounded return).
Displays key metrics in the top-right corner:
Total Return
Win Rate
Total Trades
Number of Wins / Losses
🛠 How Does It Work?
Bullish Breakout: When price crosses above the upper band and stays above the midline.
Bearish Breakout: When price crosses below the lower band and stays below the midline.
Each trade is held until breakout invalidation, not a fixed TP/SL.
Trades are compounded, i.e., profits stack up realistically over time.
📈 Best Use Cases:
For traders who want to experiment with breakout strategies.
For visual learners who want to study past breakouts with performance metrics.
As a template to develop your own logic on top of Faytterro Bands.
⚠ Notes:
This is a strategy-like visual indicator, not an automated backtest.
It doesn't use strategy.* commands, so you can still use alerts and visuals.
You can tweak the logic to create your own backtest-ready strategy.
Unlike the original Faytterro Bands, this script does not repaint and is fully stable on closed candles.
MegaGas Bollinger Bands with Divergence and Circle SignalsIndicator: MegaGas Bollinger Bands with Divergence and Circle Signals
This script provides a powerful combination of Bollinger Bands, RSI Divergence detection, and signal visualization tools. Designed with flexibility and precision in mind, it aims to assist traders in identifying trend reversals, volatility zones, and divergence-based trading opportunities. The script is well-suited for swing trading, momentum trading, and even scalping when adapted to lower timeframes.
How It Works:
Bollinger Bands:
Bollinger Bands are used to detect price volatility and overbought/oversold conditions. The script calculates:
Basis Line: A 34-period Simple Moving Average (SMA) as the core trend line.
Upper Bands: Bands positioned 1x and 2x the standard deviation above the SMA.
Lower Bands: Bands positioned 1x and 2x the standard deviation below the SMA. These levels provide dynamic support and resistance zones, highlighting breakout and reversion opportunities.
RSI Divergence Detection:
The indicator detects bullish divergence (when RSI forms a higher low while price forms a lower low) and bearish divergence (when RSI forms a lower high while price forms a higher high). These divergences often precede significant reversals or momentum shifts.
Bullish divergence is displayed with blue triangles (up).
Bearish divergence is displayed with orange triangles (down).
Buy and Sell Signals:
Circle Signals are generated when price crosses key Bollinger Bands levels:
A green circle appears when the price crosses above the lower band (potential buy signal).
A red circle appears when the price crosses below the upper band (potential sell signal).
These signals help identify potential entry and exit points for trades, particularly in trend-following or mean-reversion strategies.
Trend Reference (Moving Average):
A 50-period Simple Moving Average (SMA) is included as a trend reference, helping traders gauge the overall market direction. Use this to confirm divergence signals and avoid trades against the prevailing trend.
Why This Indicator Is Unique:
This script integrates multiple tools in a meaningful way, emphasizing contextual trading signals. Unlike standalone Bollinger Bands or RSI indicators, it introduces:
Advanced Divergence Analysis: Enhancing traditional RSI with divergence-based alerts.
Dynamic Signal Filtering: Preventing repetitive signals by introducing state-based logic for circles and divergence signals.
Trend Alignment: Combining Bollinger Bands with an SMA to filter trades based on the prevailing trend.
How to Use:
Setup:
Apply the indicator to any chart and timeframe. For swing trading, higher timeframes like 4H or 1D are recommended.
Adjust the RSI, Bollinger Bands, and Moving Average lengths to match your strategy and asset.
Signals:
Look for divergence signals (triangles) as early warnings of trend reversals. Confirm these with price action or other tools.
Use circle signals (green/red) to time potential entries/exits around Bollinger Band extremes.
Confirmation:
Combine divergence and circle signals with the SMA line to avoid counter-trend trades. For example, take bullish signals when the price is above the SMA and bearish signals when it is below.
Chart Clarity:
The script is published with a clean chart for clarity. It visualizes all signals with distinct shapes (triangles and circles) and colors, ensuring they are easily recognizable. Bollinger Bands and the SMA are plotted with transparency to avoid clutter.
Originality:
This script is a thoughtful blend of Bollinger Bands and RSI divergence detection, carefully designed to provide traders with actionable insights. It introduces state-based logic to manage repetitive signals and seamlessly integrates trend filtering, making it a valuable tool for both novice and experienced traders.
Nami Bands with Future Projection [FXSMARTLAB]The Nami Bands ( Inspired by "Nami", meaning "wave" in Japanese) are two dynamic bands around price data: an upper band and a lower band. These bands are calculated based on an Asymmetric Linear Weighted Moving Average of price and a similarly asymmetric weighted standard deviation. This weighting method emphasizes recent data without overreacting to short-term price changes, thus smoothing the bands in line with prevailing market conditions.
Advantages and Benefits of Using the Indicator
* Volatility Analysis: The bands expand and contract with market volatility, helping traders assess periods of high and low volatility. Narrow bands indicate low volatility and potential consolidation, while wide bands suggest increased volatility and potential price movement.
* Dynamic Support and Resistance Levels: By adapting to recent trends, the bands serve as dynamic support (lower band) and resistance (upper band) levels, which traders can use for entry and exit signals.
* Overbought and Oversold Conditions: When prices reach or cross the bands’ outer limits, it may signal overbought (upper band) or oversold (lower band) conditions, suggesting possible reversals or trend slowdowns.
* Trend Confirmation and Continuation: The slope of the central moving average confirms trend direction. An upward slope generally indicates a bullish trend, while a downward slope suggests a bearish trend.
* Anticipating Breakouts and Reversals: The projected bands help identify where price movements may head, allowing traders to anticipate potential breakouts or reversals based on projected support and resistance.
Indicator Parameters
Source (src): The price data used for calculations, by default set to the average of high, low, and close (hlc3).
Length: The period over which calculations are made, defaulted to 50 periods.
Projection Length: The length for future band projection, defaulted to 20 periods.
StdDev Multiplier (mult): A multiplier for the standard deviation, defaulted to 2.0.
Internal Calculations
1. Asymmetric Linear Weighted Moving Average of Price
The indicator uses an Asymmetric Linear Weighted Moving Average (ALWMA) to calculate a central value for the price.
Asymmetric Weighting: This weighting technique assigns the highest weight to the most recent value, with weights decreasing linearly as the data points become older. This structure provides a nuanced focus on recent price trends, while still reflecting historical price levels.
2. Asymmetric Weighted Standard Deviation
The standard deviation in this indicator is also calculated using asymmetric weighting:
Purpose of Asymmetric Weighted Standard Deviation: Rather than aiming for high sensitivity to recent data, this standard deviation measure smooths out volatility by integrating weighted values across the length period, stabilizing the overall measurement of price variability.
This approach yields a balanced view of volatility, capturing broader market trends without being overly reactive to short-lived changes.
3. Upper and Lower Bands
The upper and lower bands are created by adding and subtracting the asymmetric weighted standard deviation from the asymmetric weighted average of price. This creates a dynamic envelope that adjusts to both recent price trends and the smoothed volatility measure:
These bands represent adaptable support and resistance levels that shift with recent market volatility.
Future Band Projection
The indicator provides a projection of the bands based on their current slope.
1. Calculating the Slope of the Bands
The slope for each band is derived from the difference between the current and previous values of each band.
2. Projecting the Bands into the Future
For each period into the future, up to the defined Projection Length, the bands are projected using the current slope.
This feature offers an anticipated view of where support and resistance levels may move, providing insight for future market behavior based on current trends.
VWAP Stdev Bands Strategy (Long Only)The VWAP Stdev Bands Strategy (Long Only) is designed to identify potential long entry points in trending markets by utilizing the Volume Weighted Average Price (VWAP) and standard deviation bands. This strategy focuses on capturing upward price movements, leveraging statistical measures to determine optimal buy conditions.
Key Features:
VWAP Calculation: The strategy calculates the VWAP, which represents the average price a security has traded at throughout the day, weighted by volume. This is an essential indicator for determining the overall market trend.
Standard Deviation Bands: Two bands are created above and below the VWAP, calculated using specified standard deviations. These bands act as dynamic support and resistance levels, providing insight into price volatility and potential reversal points.
Trading Logic:
Long Entry Condition: A long position is triggered when the price crosses below the lower standard deviation band and then closes above it, signaling a potential price reversal to the upside.
Profit Target: The strategy allows users to set a predefined profit target, closing the long position once the specified target is reached.
Time Gap Between Orders: A customizable time gap can be specified to prevent multiple orders from being placed in quick succession, allowing for a more controlled trading approach.
Visualization: The VWAP and standard deviation bands are plotted on the chart with distinct colors, enabling traders to visually assess market conditions. The strategy also provides optional plotting of the previous day's VWAP for added context.
Use Cases:
Ideal for traders looking to engage in long-only positions within trending markets.
Suitable for intraday trading strategies or longer-term approaches based on market volatility.
Customization Options:
Users can adjust the standard deviation values, profit target, and time gap to tailor the strategy to their specific trading style and market conditions.
Note: As with any trading strategy, it is important to conduct thorough backtesting and analysis before live trading. Market conditions can change, and past performance does not guarantee future results.
Bollinger Bands Mean Reversion by Kevin Davey Bollinger Bands Mean Reversion Strategy Description
The Bollinger Bands Mean Reversion Strategy is a popular trading approach based on the concept of volatility and market overreaction. The strategy leverages Bollinger Bands, which consist of an upper and lower band plotted around a central moving average, typically using standard deviations to measure volatility. When the price moves beyond these bands, it signals potential overbought or oversold conditions, and the strategy seeks to exploit a reversion back to the mean (the central band).
Strategy Components:
1. Bollinger Bands:
The bands are calculated using a 20-period Simple Moving Average (SMA) and a multiple (usually 2.0) of the standard deviation of the asset’s price over the same period. The upper band represents the SMA plus two standard deviations, while the lower band is the SMA minus two standard deviations. The distance between the bands increases with higher volatility and decreases with lower volatility.
2. Mean Reversion:
Mean reversion theory suggests that, over time, prices tend to move back toward their historical average. In this strategy, a buy signal is triggered when the price falls below the lower Bollinger Band, indicating a potential oversold condition. Conversely, the position is closed when the price rises back above the upper Bollinger Band, signaling an overbought condition.
Entry and Exit Logic:
Buy Condition: The strategy enters a long position when the price closes below the lower Bollinger Band, anticipating a mean reversion to the central band (SMA).
Sell Condition: The long position is exited when the price closes above the upper Bollinger Band, implying that the market is likely overbought and a reversal could occur.
This approach uses mean reversion principles, aiming to capitalize on short-term price extremes and volatility compression, often seen in sideways or non-trending markets. Scientific studies have shown that mean reversion strategies, particularly those based on volatility indicators like Bollinger Bands, can be effective in capturing small but frequent price reversals  .
Scientific Basis for Bollinger Bands:
Bollinger Bands, developed by John Bollinger, are widely regarded in both academic literature and practical trading as an essential tool for volatility analysis and mean reversion strategies. Research has shown that Bollinger Bands effectively identify relative price highs and lows, and can be used to forecast price volatility and detect potential breakouts . Studies in financial markets, such as those by Fernández-Rodríguez et al. (2003), highlight the efficacy of Bollinger Bands in detecting overbought or oversold conditions in various assets .
Who is Kevin Davey?
Kevin Davey is an award-winning algorithmic trader and highly regarded expert in developing and optimizing systematic trading strategies. With over 25 years of experience, Davey gained significant recognition after winning the prestigious World Cup Trading Championships multiple times, where he achieved triple-digit returns with minimal drawdown. His success has made him a key figure in algorithmic trading education, with a focus on disciplined and rule-based trading systems.
Uptrick: Logarithmic Crypto Bands
Description :
Introduction
The `Uptrick: Logarithmic Crypto Bands` indicator introduces an innovative approach to technical analysis tailored specifically for the cryptocurrency markets. By leveraging logarithmic transformations combined with dynamic exponential bands, this indicator offers a sophisticated method for identifying critical support and resistance levels, assessing market trends, and evaluating volatility. Its unique approach stands out from traditional indicators by addressing the specific challenges of high volatility and erratic price movements inherent in cryptocurrency trading.
Originality and Usefulness
** 1. Unique Logarithmic Transformation: **
- Innovation : Unlike traditional indicators that often use raw price data, the Uptrick: Logarithmic Crypto Bands applies a logarithmic transformation to the closing prices: logPrice = math.log(close). This approach is original because it reduces the impact of extreme price fluctuations, providing a smoother and more stable price series. This transformation addresses a common issue in cryptocurrency markets where large price swings can obscure true market trends.
- Advantage : The logarithmic transformation compresses the price range, which allows traders to better identify long-term trends and reduce the noise caused by outlier price movements. This results in a more reliable basis for analysis and enhances the ability to detect meaningful market patterns.
**2. Dynamic Exponential Bands :**
- Innovation : The indicator employs exponential calculations to derive dynamic support and resistance levels based on a central base line : baseLine * math.pow(multiplier, n). Unlike static bands that remain fixed regardless of market conditions, these bands adjust dynamically according to market volatility.
- Advantage : The dynamic nature of the bands provides a more responsive and adaptive tool for traders. As market volatility changes, the bands widen or narrow accordingly, offering a more accurate reflection of potential support and resistance levels. This adaptability improves the tool's effectiveness in varying market conditions compared to static or traditional bands.
Detailed Description and Substantiation
**1. Logarithmic Price Calculation :**
- Code : ` logPrice = math.log(close)
- Description : This calculation converts the closing price into its logarithmic value. By compressing the price range, it minimizes the distortion caused by extreme price movements, which can be particularly pronounced in the volatile cryptocurrency markets.
- Purpose : To provide a stabilized price series that facilitates more accurate trend analysis and reduces the influence of erratic price fluctuations.
**2. Moving Averages of Logarithmic Prices :**
- ** Long-Term Moving Average :**
- Code : maLongLogPrice = ta.sma(logPrice, longLength)
longLength = 2000
- ** Description : A simple moving average of the logarithmic price over a long period. This average helps filter out short-term noise and provides insight into the long-term market trend.
- Purpose : To offer a perspective on the overall market direction, making it easier to identify enduring trends and distinguish them from short-term price movements.
- Short-Term Moving Average :
- Code : maShortLogPrice = ta.sma(logPrice, shortLength) shortLength = 900
- Description : A simple moving average of the logarithmic price over a shorter period. This component captures more immediate price trends and potential reversal points.
- Purpose : To detect short-term trends and changes in market direction, allowing traders to make timely trading decisions based on recent price action.
**3. Base Line Calculation :**
- Code : baseLine = math.exp(maShortLogPrice)
- Description : Converts the short-term moving average of the logarithmic price back to the original price scale. This base line serves as the central reference point for calculating the surrounding bands.
- Purpose : To establish a benchmark level from which the exponential bands are calculated, providing a central reference for assessing potential support and resistance levels.
**4. Band Calculation and Plotting :**
- ** Code :**
- Band 1: plot(baseLine * math.pow(multiplier, 1), color=color.new(color.yellow, 20), linewidth=1, title="Band 1")
- Band 2: plot(baseLine * math.pow(multiplier, 2), color=color.new(color.yellow, 20), linewidth=1, title="Band 2")
- Band 3: plot(baseLine * math.pow(multiplier, 3), color=color.new(color.yellow, 20), linewidth=1, title="Band 3")
- Band 4: plot(baseLine * math.pow(multiplier, 4), color=color.new(color.yellow, 20), linewidth=1, title="Band 4")
- Band 5: plot(baseLine * math.pow(multiplier, 5), color=color.new(color.yellow, 10), linewidth=1, title="Band 5")
- Band 6: plot(baseLine * math.pow(multiplier, 6), color=color.new(color.yellow, 0), linewidth=1, title="Band 6")
- * Multiplier : Set at 1.3, adjusts the spacing between bands to accommodate varying levels of market volatility.
- Description : Bands are plotted at exponential intervals from the base line. Each band represents a potential support or resistance level, with the spacing between them increasing exponentially. The color opacity of each band indicates its level of significance, with closer bands being more relevant for immediate trading decisions.
** How to Use the Indicator :**
**1. Identifying Support and Resistance Levels :**
- Support Levels : The lower bands, closer to the base line, can act as potential support levels. When the price approaches these bands from above, they may indicate areas where the price could stabilize or reverse direction.
- Resistance Levels : The upper bands, further from the base line, serve as resistance levels. When the price nears these bands from below, they can act as barriers to price movement, potentially leading to reversals or stalls.
**2. Confirming Trends :**
- Uptrend Confirmation : When the price consistently remains above the base line and moves towards higher bands, it signals a strong bullish trend. This confirmation helps traders capitalize on upward price movements.
- Downtrend Confirmation : When the price stays below the base line and approaches lower bands, it indicates a bearish trend. This confirmation assists traders in acting on downward price movements.
3. Analyzing Volatility :
- Wide Bands : Wider spacing between bands reflects higher market volatility. This indicates a more turbulent trading environment, where price movements are less predictable. Traders may need to adjust their strategies to handle increased volatility.
- Narrow Bands : Narrower bands suggest lower volatility and a more stable market environment. This can result in more predictable price movements and clearer trading signals.
**4. Entry and Exit Points :**
- Entry Points : Consider buying when the price bounces off the base line or a band, which could signal support in an uptrend.
- Exit Points : Evaluate selling or taking profits when the price nears upper bands or shows signs of reversal at these levels. This approach helps in locking in gains or minimizing losses during a downtrend.
**Chart Example:**
Here you can see how the price reacted getting closer to this level. All green circles show a bounce-off. So just from looking at the chart we can see a potential bounce again pretty soon.
** Disclosure :**
- ** Performance Claims :** The `Uptrick: Logarithmic Crypto Bands` indicator is designed to assist traders in analyzing price levels and trends. It is important to understand that this tool provides historical data analysis and does not guarantee future performance. The features and benefits described are based on historical market behavior and should not be seen as a prediction of future results. Traders should use this indicator as part of a broader trading strategy and consider other factors before making trading decisions.
[blackcat] L1 Dynamic Multi-Layer Bollinger Bands## Overview
The L1 Dynamic Multi-Layer Bollinger Bands script is a custom indicator designed to provide a comprehensive analysis of price movements using Bollinger Bands, a widely-used technical analysis tool. The Bollinger Bands consist of a middle band being a simple moving average (SMA) of the close price, and an upper and lower band that are respectively 1.382 and 2.56 times the standard deviation of the close price over a 21-day period.
## How to Use
1. **Add the Indicator to Your Chart**: Click on the "Add to Chart" button in the Pine-Script editor. This will add the L1 Dynamic Multi-Layer Bollinger Bands to your chart.
2. **Interpreting the Bands**: The Bollinger Bands are plotted on your chart. The middle band is the 21-day SMA of the close price. The upper band is 1.382 times the standard deviation above the middle band, and the lower band is 1.382 times the standard deviation below the middle band.
3. **Analyzing the Bands**: When the price moves above the upper band, it is considered an overbought condition. When the price moves below the lower band, it is considered an oversold condition. These are potential areas of support and resistance.
4. **Multi-Layer Structure**: The script also calculates two additional bands, `up2` and `loow2`, which are respectively 2.56 times the standard deviation above and below the middle band. These bands provide a more layered perspective on the volatility of the price.
5. **Color Coding**: The area between the upper and lower bands is filled with a color that indicates the direction of the price movement. Green indicates an upward trend, and red indicates a downward trend.
## Disclaimer
The information provided by this script is for educational purposes only and should not be used as the sole basis for any trading decisions. Trading involves risk, and it is possible to lose money when trading stocks and other financial instruments. Use this script at your own risk.
Multi-Band Breakout IndicatorThe Multi-Band Breakout Indicator was created to help identify potential breakout opportunities in the market. It combines multiple bands (ATR-Based and Donchian) and moving averages to provide valuable insights into the underlying trend and potential breakouts. By understanding the calculations, interpretation, parameter adjustments, potential applications, and limitations of the indicator, traders can effectively incorporate it into their trading strategy.
Calculation:
The indicator utilizes several calculations to plot the bands and moving averages. The length parameter determines the period used for the Average True Range (ATR), which measures volatility. A higher length captures a longer-term view of price movement, while a lower length focuses on shorter-term volatility. The multiplier parameter adjusts the distance of the upper and lower bands from the ATR. A higher multiplier expands the bands, accommodating greater price volatility, while a lower multiplier tightens the bands, reflecting lower volatility. The MA Length parameter determines the period for the moving averages used to calculate the trend and trend moving average. A higher MA Length creates a smoother trend line, filtering out shorter-term fluctuations, while a lower MA Length provides a more sensitive trend line.
The Donchian calculations in the Multi-Band Breakout Indicator play a significant role in identifying potential breakout opportunities and providing additional confirmation for trading signals. In this indicator, the Donchian calculations are applied to the trend line, which represents the average of the upper and lower bands. To calculate the Donchian levels, the indicator uses the Donchian Length parameter, which determines the period over which the highest high and lowest low are calculated. A longer Donchian Length captures a broader price range, while a shorter length focuses on more recent price action. By incorporating the Donchian calculations into the Multi-Band Breakout Indicator, traders gain an additional layer of confirmation for breakout signals.
Interpretation:
The Multi-Band Breakout Indicator offers valuable interpretation for traders. The upper and lower bands represent dynamic levels of resistance and support, respectively. These bands reflect the potential price range within which the asset is expected to trade. The trend line is the average of these bands and provides a central reference point for the overall trend. When the price moves above the upper band, it suggests a potential overbought condition and a higher probability of a pullback. Conversely, when the price falls below the lower band, it indicates a potential oversold condition and an increased likelihood of a bounce. The trend moving average further smooths the trend line, making it easier to identify the prevailing direction.
The crossover of the trend line (representing the average of the upper and lower bands) and the trend moving average holds a significant benefit for traders. This crossover serves as a powerful signal for potential trend changes and breakout opportunities in the market. When the trend line crosses above the trend moving average, it suggests a shift in momentum towards the upside, indicating a potential bullish trend. This provides traders with an early indication of a possible upward movement in prices. Conversely, when the trend line crosses below the trend moving average, it indicates a shift in momentum towards the downside, signaling a potential bearish trend. This crossover acts as an early warning for potential downward price movement. By identifying these crossovers, traders can capture the initial stages of a new trend, enabling them to enter trades at favorable entry points and potentially maximize their profit potential.
Breakout Signals:
For bullish breakouts, the indicator looks for a bullish crossover between the trend line and the trend moving average. This crossover suggests a shift in momentum towards the upside. Additionally, it checks if the current price has broken above the upper band and the previous Donchian high. This confirms that the price is surpassing a previous resistance level, indicating further upward movement.
For bearish breakouts, the indicator looks for a bearish crossunder between the trend line and the trend moving average. This crossunder indicates a shift in momentum towards the downside. It also checks if the current price has broken below the lower band and the previous Donchian low. This confirms that the price is breaking through a previous support level, signaling potential downward movement.
When a bullish or bearish breakout is detected, it suggests a potential trading opportunity. Traders may consider initiating positions in the direction of the breakout, anticipating further price movement in that direction. However, it's important to remember that breakouts alone do not guarantee a successful trade. Other factors, such as market conditions, volume, and confirmation from additional indicators, should be taken into account. Risk management techniques should also be implemented to manage potential losses.
Coloration:
The coloration in the Multi-Band Breakout Indicator is used to visually represent different aspects of the indicator and provide valuable insights to traders. Let's break down the coloration components:
-- Trend/Basis Color : The tColor variable determines the color of the bars based on the relationship between the trend line (trend) and the closing price (close), as well as the relationship between the trend line and the trend moving average (trendMA). If the trend line is above the closing price and the trend moving average is also above the closing price, the bars are colored fuchsia, indicating a potential bullish trend. If the trend line is below the closing price and the trend moving average is also below the closing price, the bars are colored lime, indicating a potential bearish trend. If neither of these conditions is met, the bars are colored yellow, representing a neutral or indecisive market condition.
-- Moving Average Color : The maColor variable determines the color of the filled area between the trend line and the trend moving average. If the trend line is above the trend moving average, the area is filled with a lime color with 70% opacity, indicating a potential bullish trend. Conversely, if the trend line is below the trend moving average, the area is filled with a fuchsia color with 70% opacity, indicating a potential bearish trend. This coloration helps traders visually identify the relationship between the trend line and the trend moving average.
-- highColor and lowColor : The highColor and lowColor variables determine the colors of the high Donchian band (hhigh) and the low Donchian band (llow), respectively. These bands represent dynamic levels of resistance and support. If the highest point in the previous Donchian period (hhigh) is above the upper band, the highColor is set to olive with 90% opacity, indicating a potential resistance level. On the other hand, if the lowest point in the previous Donchian period (llow) is below the lower band, the lowColor is set to red with 90% opacity, suggesting a potential support level. These colorations help traders quickly identify important price levels and assess their significance in relation to the bands.
By incorporating coloration, the Multi-Band Breakout Indicator provides visual cues to traders, making it easier to interpret the relationships between various components and assisting in identifying potential trend changes and breakout opportunities. Traders can use these color cues to quickly assess the prevailing market conditions and make informed trading decisions.
Adjusting Parameters:
The Multi-Band Breakout Indicator offers flexibility through parameter adjustments. Traders can customize the indicator based on their preferences and trading style. The length parameter controls the sensitivity to price changes, with higher values capturing longer-term trends, while lower values focus on shorter-term price movements. By adjusting the parameters, such as the ATR length, multiplier, Donchian length, and MA length, traders can customize the indicator to suit different timeframes and trading strategies. For shorter timeframes, smaller values for these parameters may be more suitable, while longer timeframes may require larger values.
Potential Applications:
The Multi-Band Breakout Indicator can be applied in various trading strategies. It helps identify potential breakout opportunities, allowing traders to enter trades in the direction of the breakout. Traders can use the indicator to initiate trades when the price moves above the upper band or below the lower band, confirming a potential breakout and providing a signal to enter a trade. Additionally, the indicator can be combined with other technical analysis tools, such as support and resistance levels, candlestick patterns, or trend indicators, to increase the probability of successful trades. By incorporating the Multi-Band Breakout Indicator into their trading approach, traders can gain a better understanding of market trends and capture potential profit opportunities.
Limitations:
While the Multi-Band Breakout Indicator is a useful tool, it has some limitations that traders should consider. The indicator performs best in trending markets where price movements are relatively strong and sustained. During ranging or choppy market conditions, the indicator may generate false signals, leading to potential losses. It is crucial to use the indicator in conjunction with other analysis techniques and risk management strategies to enhance its effectiveness. Additionally, traders should consider external factors such as market news, economic events, and overall market sentiment when interpreting the signals generated by the indicator.
By combining multiple bands and moving averages, this indicator offers valuable insights into the underlying trend and helps traders make informed trading decisions. With customization options and careful interpretation, this indicator can be a valuable addition to any trader's toolkit, assisting in identifying potential breakouts, capturing profitable trades, and enhancing overall trading performance.
VWAP Bollinger Band Crossover Breakout with ResistanceCredit to © Jesus_Salvatierra for VWAP script
This script help you find a trend in momentum stock that is about to breakout and shows resistance point. This script utilizes Bollinger bands VWAP and is good for intra day charts.
VWAP, or Volume Weighted Average Price, is a technical analysis tool used to measure the average price a security trades at during a given time period, weighted by the trading volume for each price level. It is commonly used by traders and investors to identify the true average price of a security and to assess whether they are buying or selling at a fair price.
A Bollinger Band is a technical analysis tool that uses standard deviation to measure the volatility of a security. The Bollinger Band is typically composed of three lines: the upper band, the lower band, and the middle band. The middle band is a simple moving average of the security's price, while the upper and lower bands are calculated based on the standard deviation of the security's price.
A Bollinger Band crossover occurs when the price of a security crosses above or below one of the bands. When the price crosses above the upper band, it is considered overbought, while when it crosses below the lower band, it is considered oversold. Traders often use Bollinger Band crossovers as a signal to enter or exit a position, depending on their trading strategy.
The VWAP and Bollinger Band crossover are two separate technical analysis tools that can be used in conjunction with each other. When a security's price crosses above or below the Bollinger Band, traders may look to the VWAP to confirm whether the security is trading at a fair price. If the security is trading above the VWAP, it may be overvalued, while if it is trading below the VWAP, it may be undervalued. Similarly, traders may use the Bollinger Band crossover as a signal to enter or exit a position, while also taking into account the VWAP to assess whether the price is fair.
Bollinger Band PW PackageThis script creates a Bollinger Band Package with Bollinger Band Percent and Width displayed on chart. The indicator also prints signals when price closes above or below the Bollinger Bands using shapes (circles by default). Bollinger Bands were created by John Bollinger, and consist of three lines plotted on a chart: the basis line (typically a moving average, 20 sma by default), an upper line (equal to the basis plus a certain number of standard deviations of the price, 2), and a lower line (equal to the basis minus the same number of standard deviations, 2 by default).
The script calculates the Bollinger Bands for a given input parameters by taking the length of the moving average and the number of standard deviations to use for the upper and lower bands. It then calculates the Bollinger Bands using these input parameters.
The script defines two functions: get_bb_percent() and get_bb_width(). The get_bb_percent() function calculates the percentage distance between the closing price and the lower band, as a percentage of the total distance between the upper and lower bands. This value is commonly referred to as the "Bollinger Band Percent". The get_bb_width() function calculates the percentage distance between the upper and lower bands, as a percentage of the basis line. This value is commonly referred to as the "Bollinger Band Width".
The script creates a table at the middle right of the chart, where the Bollinger Band Percent and Width values are displayed. The table is designed with two rows, one for the Bollinger Band Percent value and the other for the Bollinger Band Width value.
The script plots the three Bollinger Bands lines on the chart. The basis line is plotted in blue, the upper band line in red, and the lower band line in green.
The script then plots the shapes that signify closures above and below the bands using plotshape. The shapes and colors are customizable in the settings menu.
This indicator is designed to have all three components and a visual aid to assist Bollinger Band technical analysis in one script which can be helpful in determining potential trading opportunities. We thank John Bollinger for his contribution to technical analysis and trading.
Please give feedback and modify to your liking.
Dual Bollinger Band Mean ReversionSimple but solid mean reversion indicator with sl and tp levels. Most of the code is based on the built in bollinger bands script. Designed for scalping 1-5 minute timeframes.
The indicator consists of two sets of bollinger bands.
Price has to close below the lower (fast) bollinger band, above the moving average of the (slower) bollinger band.
If price now closes above the lower (fast) bb, the indicator draws a label at the open of the next candle (which would be a potential entry point). Take profit becomes the upper bollinger band, stop loss the same distance below the open of the candle.
I've built in a simple backtesting function that calculates the potential win/loss-ratio. Loss and profit levels are 1:1. Exit strategy could be improved on.
Adjusting the lengths depending on the asset proves to be a good idea.
The slower bollinger bands can help to identify ranging markets and/or trends following regular bollinger bands theory.
Feel free to comment with any changes that you'd like to be made.
Percentile Rank of Bollinger BandsThis simple indicator provides you three useful information with Bollinger Bands:
How wide the current width (standard deviation) of the Bollinger Band is.
Compared to the widths in the past, is the current width relatively small or big? Value is expressed in percentile format.
What the "relative position of current price" to the current Bollinger Band is.
This indicator can be useful to identify whether the Bollinger Band has substantially "expanded" or "squeezed."
First, divide the current standard deviation by the current price, we get the current width. The current width is displayed by the columns at the bottom. When the current width becomes wider, the column becomes taller, and the color is dark green. On the contrary, if the width becomes narrower, the column becomes shorter and the color is light green.
Next, compare the current width with the previous N widths, we get the percentile rank for the current width. The percentile rank is shown by the thicker line graph. When the percentile rank grows, it is green; whereas when the rank declines, the color is red.
Lastly, calculate (close - lower)/(upper - lower) and we get an idea of the relative height of the current price, compared to the upper and lower band. This is displayed by the thinner line graph. When the relative position becomes higher, the color is in aqua. It is in blue when the relative position becomes lower. Note that since closing prices can go above the upper band or go below the lower band, the values may be greater than 100 or less than 0.
Quantile Regression Bands [BackQuant]Quantile Regression Bands
Tail-aware trend channeling built from quantiles of real errors, not just standard deviations.
What it does
This indicator fits a simple linear trend over a rolling lookback and then measures how price has actually deviated from that trend during the window. It then places two pairs of bands at user-chosen quantiles of those deviations (inner and outer). Because bands are based on empirical quantiles rather than a symmetric standard deviation, they adapt to skewed and fat-tailed behaviour and often hug price better in trending or asymmetric markets.
Why “quantile” bands instead of Bollinger-style bands?
Bollinger Bands assume a (roughly) symmetric spread around the mean; quantiles don’t—upper and lower bands can sit at different distances if the error distribution is skewed.
Quantiles are robust to outliers; a single shock won’t inflate the bands for many bars.
You can choose tails precisely (e.g., 1%/99% or 5%/95%) to match your risk appetite.
How it works (intuitive)
Center line — a rolling linear regression approximates the local trend.
Residuals — for each bar in the lookback, the indicator looks at the gap between actual price and where the line “expected” price to be.
Quantiles — those gaps are sorted; you select which percentiles become your inner/outer offsets.
Bands — the chosen quantile offsets are added to the current end of the regression line to draw parallel support/resistance rails.
Smoothing — a light EMA can be applied to reduce jitter in the line and bands.
What you see
Center (linear regression) line (optional).
Inner quantile bands (e.g., 25th/75th) with optional translucent fill.
Outer quantile bands (e.g., 1st/99th) with a multi-step gradient to visualise “tail zones.”
Optional bar coloring: bars trend-colored by whether price is rising above or falling below the center line.
Alerts when price crosses the outer bands (upper or lower).
How to read it
Trend & drift — the slope of the center line is your local trend. Persistent closes on the same side of the center line indicate directional drift.
Pullbacks — tags of the inner band often mark routine pullbacks within trend. Reaction back to the center line can be used for continuation entries/partials.
Tails & squeezes — outer-band touches highlight statistically rare excursions for the chosen window. Frequent outer-band activity can signal regime change or volatility expansion.
Asymmetry — if the upper band sits much further from the center than the lower (or vice versa), recent behaviour has been skewed. Trade management can be adjusted accordingly (e.g., wider take-profit upslope than downslope).
A simple trend interpretation can be derived from the bar colouring
Good use-cases
Volatility-aware mean reversion — fade moves into outer bands back toward the center when trend is flat.
Trend participation — buy pullbacks to the inner band above a rising center; flip logic for shorts below a falling center.
Risk framing — set dynamic stops/targets at quantile rails so position sizing respects recent tail behaviour rather than fixed ticks.
Inputs (quick guide)
Source — price input used for the fit (default: close).
Lookback Length — bars in the regression window and residual sample. Longer = smoother, slower bands; shorter = tighter, more reactive.
Inner/Outer Quantiles (τ) — choose your “typical” vs “tail” levels (e.g., 0.25/0.75 inner, 0.01/0.99 outer).
Show toggles — independently toggle center line, inner bands, outer bands, and their fills.
Colors & transparency — customize band and fill appearance; gradient shading highlights the tail zone.
Band Smoothing Length — small EMA on lines to reduce stair-step artefacts without meaningfully changing levels.
Bar Coloring — optional trend tint from the center line’s momentum.
Practical settings
Swing trading — Length 75–150; inner τ = 0.25/0.75, outer τ = 0.05/0.95.
Intraday — Length 50–100 for liquid futures/FX; consider 0.20/0.80 inner and 0.02/0.98 outer in high-vol assets.
Crypto — Because of fat tails, try slightly wider outers (0.01/0.99) and keep smoothing at 2–4 to tame weekend jumps.
Signal ideas
Continuation — in an uptrend, look for pullback into the lower inner band with a close back above the center as a timing cue.
Exhaustion probe — in ranges, first touch of an outer band followed by a rejection candle back inside the inner band often precedes mean-reversion swings.
Regime shift — repeated closes beyond an outer band or a sharp re-tilt in the center line can mark a new trend phase; adjust tactics (stop-following along the opposite inner band).
Alerts included
“Price Crosses Upper Outer Band” — potential overextension or breakout risk.
“Price Crosses Lower Outer Band” — potential capitulation or breakdown risk.
Notes
The fit and quantiles are computed on a fixed rolling window and do not repaint; bands update as the window moves forward.
Quantiles are based on the recent distribution; if conditions change abruptly, expect band widths and skew to adapt over the next few bars.
Parameter choices directly shape behaviour: longer windows favour stability, tighter inner quantiles increase touch frequency, and extreme outer quantiles highlight only the rarest moves.
Final thought
Quantile bands answer a simple question: “How unusual is this move given the current trend and the way price has been missing it lately?” By scoring that question with real, distribution-aware limits rather than one-size-fits-all volatility you get cleaner pullback zones in trends, more honest “extreme” tags in ranges, and a framework for risk that matches the market’s recent personality.
Rolling Range Bands by tvigRolling Range Bands
Plots two dynamic price envelopes that track the highest and lowest prices over a Short and Long lookback. Use them to see near-term vs. broader market structure, evolving support/resistance, and volatility changes at a glance.
What it shows
• Short Bands: recent trading range (fast, more reactive).
• Long Bands: broader range (slow, structural).
• Optional step-line style and shaded zones for clarity.
• Option to use completed bar values to avoid intrabar jitter (no repaint).
How to read
• Price pressing the short high while the long band rises → short-term momentum in a larger uptrend.
• Price riding the short low inside a falling long band → weakness with trend alignment.
• Band squeeze (narrowing) → compression; watch for breakout.
• Band expansion (widening) → rising volatility; expect larger swings.
• Repeated touches/rejections of long bands → potential areas of support/resistance.
Inputs
• Short Window, Long Window (bars)
• Use Close only (vs. High/Low)
• Use completed bar values (stability)
• Step-line style and Band shading
Tips
• Works on any symbol/timeframe; tune windows to your market.
• For consistent scaling, pin the indicator to the same right price scale as the chart.
Not financial advice; combine with trend/volume/RSI or your system for entries/exits.
Prime Bands [ChartPrime]The Prime Standard Deviation Bands indicator uses custom-calculated bands based on highest and lowest price values over specific period to analyze price volatility and trend direction. Traders can set the bands to 1, 2, or 3 standard deviations from a central base, providing a dynamic view of price behavior in relation to volatility. The indicator also includes color-coded trend signals, standard deviation labels, and mean reversion signals, offering insights into trend strength and potential reversal points.
⯁ KEY FEATURES AND HOW TO USE
⯌ Standard Deviation Bands :
The indicator plots upper and lower bands based on standard deviation settings (1, 2, or 3 SDs) from a central base, allowing traders to visualize volatility and price extremes. These bands can be used to identify overbought and oversold conditions, as well as potential trend reversals.
Example of 3-standard-deviation bands around price:
⯌ Dynamic Trend Indicator :
The midline of the bands changes color based on trend direction. If the midline is rising, it turns green, indicating an uptrend. When the midline is falling, it turns orange, suggesting a downtrend. This color coding provides a quick visual reference to the current trend.
Trend color examples for rising and falling midlines:
⯌ Standard Deviation Labels :
At the end of the bands, the indicator displays labels with price levels for each standard deviation level (+3, 0, -3, etc.), helping traders quickly reference where price is relative to its statistical boundaries.
Price labels at each standard deviation level on the chart:
⯌ Mean Reversion Signals :
When price moves beyond the upper or lower bands and then reverts back inside, the indicator plots mean reversion signals with diamond icons. These signals indicate potential reversal points where the price may return to the mean after extreme moves.
Example of mean reversion signals near bands:
⯌ Standard Deviation Scale on Chart :
A visual scale on the right side of the chart shows the current price position in relation to the bands, expressed in standard deviations. This scale provides an at-a-glance view of how far price has deviated from the mean, helping traders assess risk and volatility.
⯁ USER INPUTS
Length : Sets the number of bars used in the calculation of the bands.
Standard Deviation Level : Allows selection of 1, 2, or 3 standard deviations for upper and lower bands.
Colors : Customize colors for the uptrend and downtrend midline indicators.
⯁ CONCLUSION
The Prime Standard Deviation Bands indicator provides a comprehensive view of price volatility and trend direction. Its customizable bands, trend coloring, and mean reversion signals allow traders to effectively gauge price behavior, identify extreme conditions, and make informed trading decisions based on statistical boundaries.
