MTF Signal XpertMTF Signal Xpert – Detailed Description
Overview:
MTF Signal Xpert is a proprietary, open‑source trading signal indicator that fuses multiple technical analysis methods into one cohesive strategy. Developed after rigorous backtesting and extensive research, this advanced tool is designed to deliver clear BUY and SELL signals by analyzing trend, momentum, and volatility across various timeframes. Its integrated approach not only enhances signal reliability but also incorporates dynamic risk management, helping traders protect their capital while navigating complex market conditions.
Detailed Explanation of How It Works:
Trend Detection via Moving Averages
Dual Moving Averages:
MTF Signal Xpert computes two moving averages—a fast MA and a slow MA—with the flexibility to choose from Simple (SMA), Exponential (EMA), or Hull (HMA) methods. This dual-MA system helps identify the prevailing market trend by contrasting short-term momentum with longer-term trends.
Crossover Logic:
A BUY signal is initiated when the fast MA crosses above the slow MA, coupled with the condition that the current price is above the lower Bollinger Band. This suggests that the market may be emerging from a lower price region. Conversely, a SELL signal is generated when the fast MA crosses below the slow MA and the price is below the upper Bollinger Band, indicating potential bearish pressure.
Recent Crossover Confirmation:
To ensure that signals reflect current market dynamics, the script tracks the number of bars since the moving average crossover event. Only crossovers that occur within a user-defined “candle confirmation” period are considered, which helps filter out outdated signals and improves overall signal accuracy.
Volatility and Price Extremes with Bollinger Bands
Calculation of Bands:
Bollinger Bands are calculated using a 20‑period simple moving average as the central basis, with the upper and lower bands derived from a standard deviation multiplier. This creates dynamic boundaries that adjust according to recent market volatility.
Signal Reinforcement:
For BUY signals, the condition that the price is above the lower Bollinger Band suggests an undervalued market condition, while for SELL signals, the price falling below the upper Bollinger Band reinforces the bearish bias. This volatility context adds depth to the moving average crossover signals.
Momentum Confirmation Using Multiple Oscillators
RSI (Relative Strength Index):
The RSI is computed over 14 periods to determine if the market is in an overbought or oversold state. Only readings within an optimal range (defined by user inputs) validate the signal, ensuring that entries are made during balanced conditions.
MACD (Moving Average Convergence Divergence):
The MACD line is compared with its signal line to assess momentum. A bullish scenario is confirmed when the MACD line is above the signal line, while a bearish scenario is indicated when it is below, thus adding another layer of confirmation.
Awesome Oscillator (AO):
The AO measures the difference between short-term and long-term simple moving averages of the median price. Positive AO values support BUY signals, while negative values back SELL signals, offering additional momentum insight.
ADX (Average Directional Index):
The ADX quantifies trend strength. MTF Signal Xpert only considers signals when the ADX value exceeds a specified threshold, ensuring that trades are taken in strongly trending markets.
Optional Stochastic Oscillator:
An optional stochastic oscillator filter can be enabled to further refine signals. It checks for overbought conditions (supporting SELL signals) or oversold conditions (supporting BUY signals), thus reducing ambiguity.
Multi-Timeframe Verification
Higher Timeframe Filter:
To align short-term signals with broader market trends, the script calculates an EMA on a higher timeframe as specified by the user. This multi-timeframe approach helps ensure that signals on the primary chart are consistent with the overall trend, thereby reducing false signals.
Dynamic Risk Management with ATR
ATR-Based Calculations:
The Average True Range (ATR) is used to measure current market volatility. This value is multiplied by a user-defined factor to dynamically determine stop loss (SL) and take profit (TP) levels, adapting to changing market conditions.
Visual SL/TP Markers:
The calculated SL and TP levels are plotted on the chart as distinct colored dots, enabling traders to quickly identify recommended exit points.
Optional Trailing Stop:
An optional trailing stop feature is available, which adjusts the stop loss as the trade moves favorably, helping to lock in profits while protecting against sudden reversals.
Risk/Reward Ratio Calculation:
MTF Signal Xpert computes a risk/reward ratio based on the dynamic SL and TP levels. This quantitative measure allows traders to assess whether the potential reward justifies the risk associated with a trade.
Condition Weighting and Signal Scoring
Binary Condition Checks:
Each technical condition—ranging from moving average crossovers, Bollinger Band positioning, and RSI range to MACD, AO, ADX, and volume filters—is assigned a binary score (1 if met, 0 if not).
Cumulative Scoring:
These individual scores are summed to generate cumulative bullish and bearish scores, quantifying the overall strength of the signal and providing traders with an objective measure of its viability.
Detailed Signal Explanation:
A comprehensive explanation string is generated, outlining which conditions contributed to the current BUY or SELL signal. This explanation is displayed on an on‑chart dashboard, offering transparency and clarity into the signal generation process.
On-Chart Visualizations and Debug Information
Chart Elements:
The indicator plots all key components—moving averages, Bollinger Bands, SL and TP markers—directly on the chart, providing a clear visual framework for understanding market conditions.
Combined Dashboard:
A dedicated dashboard displays key metrics such as RSI, ADX, and the bullish/bearish scores, alongside a detailed explanation of the current signal. This consolidated view allows traders to quickly grasp the underlying logic.
Debug Table (Optional):
For advanced users, an optional debug table is available. This table breaks down each individual condition, indicating which criteria were met or not met, thus aiding in further analysis and strategy refinement.
Mashup Justification and Originality
MTF Signal Xpert is more than just an aggregation of existing indicators—it is an original synthesis designed to address real-world trading complexities. Here’s how its components work together:
Integrated Trend, Volatility, and Momentum Analysis:
By combining moving averages, Bollinger Bands, and multiple oscillators (RSI, MACD, AO, ADX, and an optional stochastic), the indicator captures diverse market dynamics. Each component reinforces the others, reducing noise and filtering out false signals.
Multi-Timeframe Analysis:
The inclusion of a higher timeframe filter aligns short-term signals with longer-term trends, enhancing overall reliability and reducing the potential for contradictory signals.
Adaptive Risk Management:
Dynamic stop loss and take profit levels, determined using ATR, ensure that the risk management strategy adapts to current market conditions. The optional trailing stop further refines this approach, protecting profits as the market evolves.
Quantitative Signal Scoring:
The condition weighting system provides an objective measure of signal strength, giving traders clear insight into how each technical component contributes to the final decision.
How to Use MTF Signal Xpert:
Input Customization:
Adjust the moving average type and period settings, ATR multipliers, and oscillator thresholds to align with your trading style and the specific market conditions.
Enable or disable the optional stochastic oscillator and trailing stop based on your preference.
Interpreting the Signals:
When a BUY or SELL signal appears, refer to the on‑chart dashboard, which displays key metrics (e.g., RSI, ADX, bullish/bearish scores) along with a detailed breakdown of the conditions that triggered the signal.
Review the SL and TP markers on the chart to understand the associated risk/reward setup.
Risk Management:
Use the dynamically calculated stop loss and take profit levels as guidelines for setting your exit points.
Evaluate the provided risk/reward ratio to ensure that the potential reward justifies the risk before entering a trade.
Debugging and Verification:
Advanced users can enable the debug table to see a condition-by-condition breakdown of the signal generation process, helping refine the strategy and deepen understanding of market dynamics.
Disclaimer:
MTF Signal Xpert is intended for educational and analytical purposes only. Although it is based on robust technical analysis methods and has undergone extensive backtesting, past performance is not indicative of future results. Traders should employ proper risk management and adjust the settings to suit their financial circumstances and risk tolerance.
MTF Signal Xpert represents a comprehensive, original approach to trading signal generation. By blending trend detection, volatility assessment, momentum analysis, multi-timeframe alignment, and adaptive risk management into one integrated system, it provides traders with actionable signals and the transparency needed to understand the logic behind them.
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Enhanced Bollinger Bands Strategy with SL/TP// Title: Enhanced Bollinger Bands Strategy with SL/TP
// Description:
// This strategy is based on the classic Bollinger Bands indicator and incorporates Stop Loss (SL) and Take Profit (TP) levels for automated trading. It identifies potential long and short entry points based on price crossing the lower and upper Bollinger Bands, respectively. The strategy allows users to customize several parameters to suit different market conditions and risk tolerances.
// Key Features:
// * **Bollinger Bands:** Uses Simple Moving Average (SMA) as the basis and calculates upper and lower bands based on a user-defined standard deviation multiplier.
// * **Customizable Parameters:** Offers extensive customization, including SMA length, standard deviation multiplier, Stop Loss (SL) in pips, and Take Profit (TP) in pips.
// * **Long/Short Position Control:** Allows users to independently enable or disable long and short positions.
// * **Stop Loss and Take Profit:** Implements Stop Loss and Take Profit levels based on pip values to manage risk and secure profits. Entry prices are set to the band levels on signals.
// * **Visualizations:** Provides options to display Bollinger Bands and entry signals on the chart for easy analysis.
// Strategy Logic:
// 1. **Bollinger Bands Calculation:** The strategy calculates the Bollinger Bands using the specified SMA length and standard deviation multiplier.
// 2. **Entry Conditions:**
// * **Long Entry:** Enters a long position when the closing price crosses above the lower Bollinger Band and the `Enable Long Positions` setting is enabled.
// * **Short Entry:** Enters a short position when the closing price crosses below the upper Bollinger Band and the `Enable Short Positions` setting is enabled.
// 3. **Exit Conditions:**
// * **Stop Loss:** Exits the position if the price reaches the Stop Loss level, calculated based on the input `Stop Loss (Pips)`.
// * **Take Profit:** Exits the position if the price reaches the Take Profit level, calculated based on the input `Take Profit (Pips)`.
// Input Parameters:
// * **SMA Length (length):** The length of the Simple Moving Average used to calculate the Bollinger Bands (default: 20).
// * **Standard Deviation Multiplier (mult):** The multiplier applied to the standard deviation to determine the width of the Bollinger Bands (default: 2.0).
// * **Enable Long Positions (enableLong):** A boolean value to enable or disable long positions (default: true).
// * **Enable Short Positions (enableShort):** A boolean value to enable or disable short positions (default: true).
// * **Pip Value (pipValue):** The value of a pip for the traded instrument. This is crucial for accurate Stop Loss and Take Profit calculations (default: 0.0001 for most currency pairs). **Important: Adjust this value to match the specific instrument you are trading.**
// * **Stop Loss (Pips) (slPips):** The Stop Loss level in pips (default: 10).
// * **Take Profit (Pips) (tpPips):** The Take Profit level in pips (default: 20).
// * **Show Bollinger Bands (showBands):** A boolean value to show or hide the Bollinger Bands on the chart (default: true).
// * **Show Entry Signals (showSignals):** A boolean value to show or hide entry signals on the chart (default: true).
// How to Use:
// 1. Add the strategy to your TradingView chart.
// 2. Adjust the input parameters to optimize the strategy for your chosen instrument and timeframe. Pay close attention to the `Pip Value`.
// 3. Backtest the strategy over different periods to evaluate its performance.
// 4. Use the `Enable Long Positions` and `Enable Short Positions` settings to customize the strategy for specific market conditions (e.g., only long positions in an uptrend).
// Important Notes and Disclaimers:
// * **Backtesting Results:** Past performance is not indicative of future results. Backtesting results can be affected by various factors, including market volatility, slippage, and transaction costs.
// * **Risk Management:** This strategy is provided for informational and educational purposes only and should not be considered financial advice. Always use proper risk management techniques when trading. Adjust Stop Loss and Take Profit levels according to your risk tolerance.
// * **Slippage:** The strategy takes into account slippage by specifying a slippage parameter on the `strategy` declaration. However, real-world slippage may vary.
// * **Market Conditions:** The performance of this strategy can vary significantly depending on market conditions. It may perform well in trending markets but poorly in ranging or choppy markets.
// * **Pip Value Accuracy:** **Ensure the `Pip Value` is correctly set for the specific instrument you are trading. Incorrect pip value will result in incorrect stop loss and take profit placement.** This is critical.
// * **Broker Compatibility:** The strategy's performance may vary depending on your broker's execution policies and fees.
// * **Disclaimer:** I am not a financial advisor, and this script is not financial advice. Use this strategy at your own risk. I am not responsible for any losses incurred while using this strategy.
Dynamic RSI Bollinger Bands with Waldo Cloud
TradingView Indicator Description: Dynamic RSI Bollinger Bands with Waldo Cloud
Title: Dynamic RSI Bollinger Bands with Waldo Cloud
Short Title: Dynamic RSI BB Waldo
Overview:
Introducing an experimental indicator, the Dynamic RSI Bollinger Bands with Waldo Cloud, designed for adventurous traders looking to explore new dimensions in technical analysis. This indicator overlays on your chart, providing a unique perspective by integrating the Relative Strength Index (RSI) with Bollinger Bands, creating a dynamic trading tool that adapts to market conditions through the lens of momentum and volatility.
What is it?
This innovative indicator combines the traditional Bollinger Bands with the RSI in a way that hasn't been commonly explored. Here's a breakdown:
RSI Integration: The RSI is calculated with customizable length settings, and its values are used not just for momentum analysis but as the basis for the Bollinger Bands. This means the position and width of the bands are directly influenced by the RSI, offering a visual representation of momentum within the context of price volatility.
Dynamic Bollinger Bands: Instead of using price directly, the Bollinger Bands are calculated using a scaled version of the RSI. This scaling is done to fit the RSI values into the price range, ensuring the bands are relevant to the actual price movement. The standard deviation for these bands is also scaled accordingly, providing a unique volatility measure that's momentum-driven.
Waldo Cloud: Named after a visual representation concept, the 'Waldo Cloud' refers to the colored area between the Bollinger Bands, which changes based on various conditions:
Purple when RSI is overbought.
Blue when RSI is oversold.
Green for bullish conditions, defined by the fast-moving average crossing above the slow one, RSI is bullish, and the price is above the slow MA.
Red for bearish conditions, when the fast MA crosses below the slow MA, the RSI is bearish, and the price is below the slow MA.
Gray for neutral market conditions.
Moving Averages: Two simple moving averages (Fast MA and Slow MA) are included, which can be toggled on or off, offering additional trend analysis through crossovers.
How to Use It:
Given its experimental nature, this indicator should be used with caution and in conjunction with other analysis methods:
Identifying Market Conditions: Use the color of the Waldo Cloud to gauge market sentiment. A green cloud might suggest a good time to consider long positions, while a red cloud could indicate potential shorting opportunities. Purple and blue clouds highlight extreme conditions that might precede reversals.
Volatility and Momentum: The dynamic nature of the Bollinger Bands based on RSI provides insight into how momentum is affecting price volatility. When the bands are wide, it might indicate high momentum and potential trend continuation or reversal, depending on the RSI's position relative to its overbought/oversold levels.
Trend Confirmation: The moving average crossovers can act as confirmation signals. For instance, a bullish crossover (fast MA over slow MA) within a green cloud might strengthen a buy signal, whereas a bearish crossover in a red cloud might reinforce a sell decision.
Customization: Adjust the RSI length, overbought/oversold levels, and moving average lengths to suit different trading styles or market conditions. Experiment with these settings to find what works best for your strategy.
Combining with Other Indicators: Since this is an experimental tool, it's advisable to use it alongside established indicators like traditional Bollinger Bands, MACD, or trend lines to validate signals.
Conclusion:
The Dynamic RSI Bollinger Bands with Waldo Cloud is an experimental venture into combining momentum with volatility visually and interactively. It's designed for traders who are open to exploring new methods of market analysis.
Remember, due to its experimental status, this indicator should be part of a broader trading strategy, and backtesting or paper trading is recommended before applying it in live trading scenarios. Keep an eye on how the market reacts to the signals provided by this indicator and always consider risk management practices.
Waldo Cloud Bollinger Bands
Waldo Cloud Bollinger Bands Indicator Description for TradingView
Title: Waldo Cloud Bollinger Bands
Short Title: Waldo Cloud BB
Overview:
The Waldo Cloud Bollinger Bands indicator is a sophisticated tool designed for traders looking to combine the volatility analysis of Bollinger Bands with the momentum insights of the Relative Strength Index (RSI) and moving average crossovers. This indicator overlays on your chart, providing a visual representation that helps in identifying potential trading opportunities based on price action, momentum, and trend direction.
Concept:
This indicator merges three key technical analysis concepts:
Bollinger Bands: These are used to measure market volatility. The bands consist of a central moving average (basis) with an upper and lower band that are standard deviations away from this average. In this indicator, you can customize the type of moving average used for the basis (SMA, EMA, SMMA, WMA, VWMA), the length of the period, the source price, and the standard deviation multiplier, offering flexibility to adapt to different market conditions.
Relative Strength Index (RSI): The RSI is incorporated to provide insight into the momentum of price movements. Users can adjust the RSI length and overbought/oversold levels and even choose the price source for RSI calculation, allowing for tailored momentum analysis. The RSI values influence the cloud color between the Bollinger Bands, signaling market conditions.
Moving Average Crossovers: Two moving averages with customizable lengths and types are used to identify trend direction through crossovers. A fast MA (default 20 periods) and a slow MA (default 50 periods) are plotted when enabled, helping to signal potential bullish or bearish market conditions when they cross over each other.
Functionality:
Bollinger Bands Calculation: The basis of the Bollinger Bands is calculated using a user-defined moving average type, with a customizable length, source, and standard deviation multiplier. The upper and lower bands are then plotted around this basis.
RSI Calculation: The RSI is computed using a user-specified source, length, and overbought/oversold levels. This RSI value is used to determine the color of the cloud between the Bollinger Bands, which visually represents market sentiment:
Purple when RSI is overbought.
Blue when RSI is oversold.
Green for bullish conditions (when the fast MA crosses above the slow MA, RSI is bullish, and the price is above the slow MA).
Red for bearish conditions (when the fast MA crosses below the slow MA, RSI is bearish, and the price is below the slow MA).
Gray for neutral conditions.
Trend Analysis: The indicator uses two moving averages to help determine the trend direction.
When the fast MA crosses over the slow MA, it suggests a potential change in trend direction, which, combined with RSI conditions, provides a more comprehensive trading signal.
Customization:
Users can select the type of moving average for all calculations through the "Global MA Type" setting, ensuring consistency in how trends and volatility are interpreted.
The Bollinger Bands settings allow for adjustments in length, source, standard deviation, and offset, giving traders control over how volatility is measured.
RSI settings include the ability to change the RSI source, length, and overbought/oversold thresholds, which can be fine-tuned to match trading strategies.
The option to show or hide moving averages provides clarity on the chart, focusing on either the Bollinger Bands or including the MA crossovers for trend analysis.
Usage:
This indicator is ideal for traders who incorporate both volatility and momentum in their trading decisions.
By observing the color changes in the cloud, along with the position of the price relative to the moving averages, traders can gauge potential entry and exit points.
For instance, a green cloud with a price above the slow MA might suggest a strong buying opportunity, while a red cloud with a price below might indicate selling pressure.
Conclusion:
The Waldo Cloud Bollinger Bands indicator offers a unique blend of volatility, momentum, and trend analysis, providing traders with a multi-faceted view of market conditions. Its customization options make it adaptable to various trading styles and market environments, making it a valuable addition to any trader's toolkit on Trading View.
EMA & Bollinger BandsThis indicator combines three main functionalities into a single script:
1. Exponential Moving Average (EMA):
- Purpose: Calculates and plots the EMA of a chosen price source.
- Inputs:
- EMA Length: The period for the EMA calculation.
- EMA Source: The price series (such as close) used for the EMA.
- EMA Offset: Allows shifting the EMA line left or right on the chart.
- Output: A blue-colored EMA line plotted on the chart.
2. Smoothing MA on EMA:
- Purpose: Applies a secondary moving average (MA) on the previously calculated EMA. There is also an option to overlay Bollinger Bands on this smoothed MA.
- Inputs:
- Smoothing MA Type: Options include "None", "SMA", "SMA + Bollinger Bands", "EMA", "SMMA (RMA)", "WMA", and "VWMA".
- Selecting "None" disables this feature.
- Choosing "SMA + Bollinger Bands" will additionally plot Bollinger Bands around the smoothed MA.
- Smoothing MA Length: The period used to calculate the smoothing MA.
- BB StdDev for Smoothing MA: The standard deviation multiplier for the Bollinger Bands (applies only when "SMA + Bollinger Bands" is selected).
- Calculation Details:
- The chosen MA type is applied to the EMA value.
- If Bollinger Bands are enabled, the script computes the standard deviation of the EMA over the smoothing period, multiplies it by the specified multiplier, and then plots an upper and lower band around the smoothing MA.
- Output:
- A yellow-colored smoothing MA line.
- Optionally, green-colored upper and lower Bollinger Bands with a filled background if the "SMA + Bollinger Bands" option is selected.
3. Bollinger Bands on Price:
- Purpose: Independently calculates and plots traditional Bollinger Bands based on a moving average of a selected price source.
- Inputs:
- BB Length: The period for calculating the moving average that serves as the basis of the Bollinger Bands.
- BB Basis MA Type: The type of moving average to use (options include SMA, EMA, SMMA (RMA), WMA, and VWMA).
- BB Source: The price series (such as close) used for the Bollinger Bands calculation.
- BB StdDev: The multiplier for the standard deviation used to calculate the upper and lower bands.
- BB Offset: Allows shifting the Bollinger Bands left or right on the chart.
- Calculation Details:
- The script computes a basis line using the selected MA type on the chosen price source.
- The standard deviation of the price over the specified period is then multiplied by the provided multiplier to determine the distance for the upper and lower bands.
- Output:
- A basis line (typically drawn in a blue tone), an upper band (red), and a lower band (teal).
- The area between the upper and lower bands is filled with a semi-transparent blue background for easier visualization.
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How It Works Together
- Integration:
The script is divided into clearly labeled sections for each functionality. All parts are drawn on the same chart (overlay mode enabled), providing a comprehensive view of market trends.
- Customization:
Users can adjust parameters for the EMA, the smoothing MA (and its optional Bollinger Bands), as well as the traditional Bollinger Bands independently. This allows for flexible customization depending on the trader's strategy or visual preference.
- Utility:
Combining these three analyses into one indicator enables traders to view:
- The immediate trend via the EMA.
- A secondary smoothed trend that might help reduce noise.
- A volatility measure through Bollinger Bands on both the price and the smoothed EMA.
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This combined indicator is useful for technical analysis by providing both trend-following (EMA and smoothing MA) and volatility indicators (Bollinger Bands) in one streamlined tool.
Volume Delta with Bollinger Bands [EMA]TL;DR
This indicator displays a “Volume Delta” candle chart based on a lower timeframe approximation of up vs. down volume. Bollinger Bands (using an EMA and a configurable standard deviation multiplier) highlight when Volume Delta exceeds typical volatility thresholds. Green bars will darken when Volume Delta is above the upper Bollinger band, and red bars will darken when Volume Delta is below the lower Bollinger band. You can optionally include wicks in the Bollinger calculations. Note : TradingView uses tick-based volume data, so these values may not precisely match true market orders.
What Is Volume Delta ?
• Volume Delta is a metric that identifies buying vs. selling activity in a market by distinguishing between orders transacting at the ask (buy volume) and orders transacting at the bid (sell volume).
• A positive Volume Delta indicates more buy volume during a bar, while a negative Volume Delta indicates more sell volume.
How TradingView Calculates Volume Delta
• TradingView relies on tick data to approximate up/down volume. This may not perfectly capture true order-flow distribution, particularly on higher timeframes or illiquid symbols.
• While it can provide useful insights into volume flow, keep in mind the underlying data’s limitations.
Key Features of This Indicator
1. Automatic or Custom Lower Timeframe Data
• The script can automatically select a lower timeframe for Volume Delta, or you can manually specify one in the settings.
2. Bollinger Bands on Volume Delta
• Uses an EMA of the Volume Delta (or a wick-based average) and calculates a standard deviation.
• The upper and lower bands highlight when activity deviates from typical volatility.
3. Configurable Wick Inclusion
• Decide whether to use only the “close” (lastVolume) of the Volume Delta bar or the average of its wicks ((maxVolume + minVolume) / 2) for Bollinger calculations.
4. Dynamic Bar Colors
• Positive Volume Delta bars turn dark green if they exceed the upper Bollinger band, otherwise lighter green .
• Negative Volume Delta bars turn dark red if they fall below the lower Bollinger band, otherwise lighter red .
How To Use
1. Add the Indicator to Your Chart
• Apply it to any symbol and timeframe in TradingView.
• Configure the lower timeframe for Volume Delta if desired.
2. Adjust Bollinger Settings
• Bollinger Length defines the EMA and standard deviation period.
• Bollinger Multiplier sets how far the bands lie from the EMA.
3. Choose Whether To Use Wicks
• Toggle to use the average of high/low for a potentially more volatile reading.
• Turn it off to rely solely on the Volume Delta “close.”
4. Interpret the Signals
• Dark Green Above the Upper Band : Suggests strong buying pressure above normal.
• Lighter Green : Positive but within typical volatility bounds.
• Dark Red Below the Lower Band : Suggests strong selling pressure below normal.
• Lighter Red : Negative but within typical volatility.
Important Caveats
• TradingView Volume Data : Tick-based and aggregated data may not reflect actual order-flow precisely.
• Context Matters : Combine Volume Delta with other forms of analysis (price action, support/resistance, etc.) to form a more comprehensive strategy.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
.
---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
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.
Fibonacci & Bollinger Bands StrategyThis strategy combines Bollinger Bands and Fibonacci retracement/extension levels to identify potential entry and exit points in the market. Here’s a breakdown of each component and how the strategy works:
1. Bollinger Bands:
Bollinger Bands consist of a simple moving average (SMA) and two standard deviations (upper and lower bands) plotted above and below the SMA. The bands expand and contract based on market volatility.
Purpose in Strategy:
The lower band represents an area where the market might be oversold.
The upper band represents an area where the market might be overbought.
The price crossing these bands suggests overextended market conditions, which can be used to identify potential reversals.
2. Fibonacci Retracement and Extension Levels:
Fibonacci retracement levels are horizontal lines that indicate where price might find support or resistance as it retraces some of its previous movement. Common retracement levels are 61.8% and 78.6%.
Fibonacci extension levels are used to project areas where the price might extend after completing a retracement. These levels can help determine potential targets after a significant price movement.
Purpose in Strategy:
The strategy calculates the most recent swing high (fibHigh) and swing low (fibLow) over a lookback period. It then plots Fibonacci retracement and extension levels based on this range.
The Fibonacci levels are used as key support and resistance areas. The price approaching or touching these levels signals potential turning points in the market.
3. Entry Criteria:
A long position (buy) is triggered when:
The price crosses below the lower Bollinger Band, indicating an oversold condition.
The price is near or above a Fibonacci extension level (calculated based on the most recent price swing).
This suggests that the price is potentially reaching a strong support area, where a reversal is likely.
4. Exit Criteria:
The long position is closed (exit trade) when either:
The price touches or crosses the upper Bollinger Band, signaling an overbought condition.
The price reaches a Fibonacci retracement level or exceeds the recent swing high (fibHigh), indicating a potential exhaustion point or a reversal area.
5. General Strategy Logic:
The strategy takes advantage of market volatility (captured by the Bollinger Bands) and key support/resistance levels (determined by Fibonacci retracement and extension levels).
By combining these two techniques, the strategy identifies potential entry points at oversold levels with the expectation that the market will retrace or reverse upward, especially when near key Fibonacci extension levels.
Exit points are identified by potential overbought levels (Bollinger upper band) or key Fibonacci retracement levels, where the price might reverse downward.
6. Conditions to Execute the Strategy:
The Fibonacci levels are only calculated once the price has made a significant movement, establishing a recent high and low over a 50-bar period (which you can adjust). This ensures the Fibonacci levels are based on meaningful swings.
The entry and exit signals are filtered using both Bollinger Bands and Fibonacci levels to ensure that trades are not taken solely based on one indicator, thus reducing false signals.
Key Features of the Strategy:
Trend-following with reversal: It tries to catch reversals when the price hits extreme levels (Bollinger Bands) while respecting important Fibonacci levels.
Dynamic market adaptation: The strategy adapts to market conditions as it recalculates Fibonacci levels based on recent price swings and adjusts the Bollinger Bands for market volatility.
Confirmation through multiple indicators: It uses both the volatility-based signals from Bollinger Bands and the price structure from Fibonacci levels to confirm trade entries and exits.
Summary of the Strategy:
The strategy looks to buy low and sell high based on oversold/overbought signals from Bollinger Bands and Fibonacci levels that indicate key support and resistance zones.
By combining these two technical indicators, the strategy aims to reduce risk and increase accuracy by only entering trades when both indicators suggest favorable conditions.
Sinc Bollinger BandsKaiser Windowed Sinc Bollinger Bands Indicator
The Kaiser Windowed Sinc Bollinger Bands indicator combines the advanced filtering capabilities of the Kaiser Windowed Sinc Moving Average with the volatility measurement of Bollinger Bands. This indicator represents a sophisticated approach to trend identification and volatility analysis in financial markets.
Core Components
At the heart of this indicator is the Kaiser Windowed Sinc Moving Average, which utilizes the sinc function as an ideal low-pass filter, windowed by the Kaiser function. This combination allows for precise control over the frequency response of the moving average, effectively separating trend from noise in price data.
The sinc function, representing an ideal low-pass filter, provides the foundation for the moving average calculation. By using the sinc function, analysts can independently control two critical parameters: the cutoff frequency and the number of samples used. The cutoff frequency determines which price movements are considered significant (low frequency) and which are treated as noise (high frequency). The number of samples influences the filter's accuracy and steepness, allowing for a more precise approximation of the ideal low-pass filter without altering its fundamental frequency response characteristics.
The Kaiser window is applied to the sinc function to create a practical, finite-length filter while minimizing unwanted oscillations in the frequency domain. The alpha parameter of the Kaiser window allows users to fine-tune the trade-off between the main-lobe width and side-lobe levels in the frequency response.
Bollinger Bands Implementation
Building upon the Kaiser Windowed Sinc Moving Average, this indicator adds Bollinger Bands to provide a measure of price volatility. The bands are calculated by adding and subtracting a multiple of the standard deviation from the moving average.
Advanced Centered Standard Deviation Calculation
A unique feature of this indicator is its specialized standard deviation calculation for the centered mode. This method employs the Kaiser window to create a smooth deviation that serves as an highly effective envelope, even though it's always based on past data.
The centered standard deviation calculation works as follows:
It determines the effective sample size of the Kaiser window.
The window size is then adjusted to reflect the target sample size.
The source data is offset in the calculation to allow for proper centering.
This approach results in a highly accurate and smooth volatility estimation. The centered standard deviation provides a more refined and responsive measure of price volatility compared to traditional methods, particularly useful for historical analysis and backtesting.
Operational Modes
The indicator offers two operational modes:
Non-Centered (Real-time) Mode: Uses half of the windowed sinc function and a traditional standard deviation calculation. This mode is suitable for real-time analysis and current market conditions.
Centered Mode: Utilizes the full windowed sinc function and the specialized Kaiser window-based standard deviation calculation. While this mode introduces a delay, it offers the most accurate trend and volatility identification for historical analysis.
Customizable Parameters
The Kaiser Windowed Sinc Bollinger Bands indicator provides several key parameters for customization:
Cutoff: Controls the filter's cutoff frequency, determining the divide between trends and noise.
Number of Samples: Sets the number of samples used in the FIR filter calculation, affecting the filter's accuracy and computational complexity.
Alpha: Influences the shape of the Kaiser window, allowing for fine-tuning of the filter's frequency response characteristics.
Standard Deviation Length: Determines the period over which volatility is calculated.
Multiplier: Sets the number of standard deviations used for the Bollinger Bands.
Centered Alpha: Specific to the centered mode, this parameter affects the Kaiser window used in the specialized standard deviation calculation.
Visualization Features
To enhance the analytical value of the indicator, several visualization options are included:
Gradient Coloring: Offers a range of color schemes to represent trend direction and strength for the moving average line.
Glow Effect: An optional visual enhancement for improved line visibility.
Background Fill: Highlights the area between the Bollinger Bands, aiding in volatility visualization.
Applications in Technical Analysis
The Kaiser Windowed Sinc Bollinger Bands indicator is particularly useful for:
Precise trend identification with reduced noise influence
Advanced volatility analysis, especially in the centered mode
Identifying potential overbought and oversold conditions
Recognizing periods of price consolidation and potential breakouts
Compared to traditional Bollinger Bands, this indicator offers superior frequency response characteristics in its moving average and a more refined volatility measurement, especially in centered mode. These features allow for a more nuanced analysis of price trends and volatility patterns across various market conditions and timeframes.
Conclusion
The Kaiser Windowed Sinc Bollinger Bands indicator represents a significant advancement in technical analysis tools. By combining the ideal low-pass filter characteristics of the sinc function, the practical benefits of Kaiser windowing, and an innovative approach to volatility measurement, this indicator provides traders and analysts with a sophisticated instrument for examining price trends and market volatility.
Its implementation in Pine Script contributes to the TradingView community by making advanced signal processing and statistical techniques accessible for experimentation and further development in technical analysis. This indicator serves not only as a practical tool for market analysis but also as an educational resource for those interested in the intersection of signal processing, statistics, and financial markets.
Related:
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.
Big Candle Touches Bollinger BandWhat It Does:
This indicator helps you spot important trading signals by combining Bollinger Bands with big candles.
Key Features:
Bollinger Bands: These bands show the average price (middle band) and the range of price movement (upper and lower bands) over a set period. The bands widen when prices are more volatile and narrow when they are less volatile.
Big Candle Detection: A "big candle" is a candle that has a larger body compared to the average price movement over a period. This is determined using the Average True Range (ATR), which measures market volatility.
How It Works:
Detects Big Candles: It checks if a candle’s body (the difference between its open and close prices) is bigger than usual, based on a multiplier of the ATR.
Touching Bollinger Bands: It looks for candles that touch or cross the upper or lower Bollinger Bands.
Highlights Important Signals:
Sell Signal: When a big candle touches the upper Bollinger Band, it marks it as a "Sell" signal with a red label.
Buy Signal: When a big candle touches the lower Bollinger Band, it marks it as a "Buy" signal with a green label.
Alerts:
You'll get alerts when a big candle touches the upper or lower Bollinger Bands, so you don’t miss these potential trading opportunities.
Visuals:
Bollinger Bands: Shown as three lines on the chart — the upper band (red), the lower band (green), and the middle band (blue).
Labels: Red labels for sell signals and green labels for buy signals when a big candle touches the bands.
This indicator helps you identify potential trading opportunities by focusing on significant price movements and how they interact with the Bollinger Bands.
Adaptive Bollinger-RSI Trend Signal [CHE]Adaptive Bollinger-RSI Trend Signal
Indicator Overview:
The "Adaptive Bollinger-RSI Trend Signal " (ABRT Signal ) is a sophisticated trading tool designed to provide clear and actionable buy and sell signals by combining the power of Bollinger Bands and the Relative Strength Index (RSI). This indicator aims to help traders identify potential trend reversals and confirm entry and exit points with greater accuracy.
Key Features:
1. Bollinger Bands Integration:
- Utilizes Bollinger Bands to detect price volatility and identify overbought or oversold conditions.
- Configurable parameters: Length, Source, and Multiplier for precise adjustments based on trading preferences.
- Color customization: Change the colors of the basis line, upper band, lower band, and the fill color between bands.
2. RSI Integration:
- Incorporates the Relative Strength Index (RSI) to validate potential buy and sell signals.
- Configurable parameters: Length, Source, Upper Threshold, and Lower Threshold for customized signal generation.
3. Signal Generation:
- Buy Signal: Generated when the price crosses below the lower Bollinger Band and the RSI crosses above the lower threshold, indicating a potential upward trend.
- Sell Signal: Generated when the price crosses above the upper Bollinger Band and the RSI crosses below the upper threshold, indicating a potential downward trend.
- Color customization: Change the colors of the buy and sell signal labels.
4. State Tracking:
- Tracks and records crossover and crossunder states of the price and RSI to ensure signals are only generated under the right conditions.
- Monitors the basis trend (SMA of the Bollinger Bands) to provide context for signal validation.
5. Counters and Labels:
- Labels each buy and sell signal with a counter to indicate the number of consecutive signals.
- Counters reset upon the generation of an opposite signal, ensuring clarity and preventing signal clutter.
6. DCA (Dollar-Cost Averaging) Calculation:
- Stores the close price at each signal and calculates the average entry price (DCA) for both buy and sell signals.
- Displays the number of positions and DCA values in a label on the chart.
7. Customizable Inputs:
- Easily adjustable parameters for Bollinger Bands, RSI, and colors to suit various trading strategies and timeframes.
- Boolean input to show or hide the table label displaying position counts and DCA values.
- Intuitive and user-friendly configuration options for traders of all experience levels.
How to Use:
1. Setup:
- Add the "Adaptive Bollinger-RSI Trend Signal " to your TradingView chart.
- Customize the input parameters to match your trading style and preferred timeframe.
- Adjust the colors of the indicator elements to your preference for better visibility and clarity.
2. Interpreting Signals:
- Buy Signal: Look for a "Buy" label on the chart, indicating a potential entry point when the price is oversold and RSI signals upward momentum.
- Sell Signal: Look for a "Sell" label on the chart, indicating a potential exit point when the price is overbought and RSI signals downward momentum.
3. Trade Execution:
- Use the buy and sell signals to guide your trade entries and exits, aligning them with your overall trading strategy.
- Monitor the counter labels to understand the strength and frequency of signals, helping you make informed decisions.
4. Adjust and Optimize:
- Regularly review and adjust the indicator parameters based on market conditions and backtesting results.
- Combine this indicator with other technical analysis tools to enhance your trading accuracy and performance.
5. Monitor DCA Values:
- Enable the table label to display the number of positions and average entry prices (DCA) for both buy and sell signals.
- Use this information to assess the cost basis of your trades and make strategic adjustments as needed.
Conclusion:
The Adaptive Bollinger-RSI Trend Signal is a powerful and versatile trading tool designed to help traders identify and capitalize on trend reversals with confidence. By combining the strengths of Bollinger Bands and RSI, this indicator provides clear and reliable signals, making it an essential addition to any trader's toolkit. Customize the settings, interpret the signals, and execute your trades with precision using this comprehensive indicator.
Averaged Moving Average Ribbon with Bollinger BandsThis indicator provides a visual representation of an averaged weighted moving average (WMA) ribbon (default setting) along with Bollinger Bands on a price chart. Pay attention to how the moving average and band expand and contract, as well as where price crosses the Bollinger bands (Green and red) or the basis line (blue). Look for patterns, and exploit them to your advantage to give you another edge in trading.
>> Feel free to suggest changes or other additions in the comments :)
Here's a brief explanation of how this indicator works:
1. **Moving Average Type:** You can select the type of moving average (MA) to use from the dropdown menu. The available options are Weighted Moving Average (WMA), Simple Moving Average (SMA), and Exponential Moving Average (EMA).
2. **Bollinger Bands Deviation:** This input allows you to adjust the deviation for the Bollinger Bands. Higher values increase the width of the bands, while lower values decrease it.
3. **Moving Average Lengths:** The script calculates various moving averages (WMA, SMA, or EMA) with different lengths, ranging from 5 to 100, in increments of 5. These moving averages are used to create the ribbon.
4. **Ribbon Calculation:** The indicator calculates the selected moving average (WMA, SMA, or EMA) for each of the specified lengths. It then averages these moving averages to create a ribbon of MAs. This ribbon represents a smoother and more encompassing view of the underlying price action.
5. **Bollinger Bands:** The script also calculates and plots Bollinger Bands based on the ribbon's average. The upper Bollinger Band (green) and lower Bollinger Band (red) are plotted around the ribbon average. These bands provide insights into potential overbought and oversold conditions.
In summary, this indicator allows traders and analysts to visualize a weighted moving average ribbon with Bollinger Bands to gain a better understanding of price trends, volatility, and potential reversal points in the market. The combination of different moving average lengths and Bollinger Bands can help in making informed trading decisions.
Adaptive MA-Bollinger HistogramVisualize two of your favorite moving averages in a fun new way.
This script calculates the distance (or difference) between the price and two moving averages of your choosing and then creates two histograms.
The two histograms are plotted inversely, so if the price is over both moving averages, one will be positive above the centerline while the other still positive will be below the centerline.
(In a future update you will have the option to have them both positive at the same time)
Next, what it does is apply Bollinger Bands (optional) to each of the histograms.
This creates a very interesting effect that can highlight areas of interest you may miss with other indicators.
You have plenty of options for coloring, the type of moving average, Bollinger Band length, and toggling features on and off.
Give it a few minutes of your time to study, and see what information you can learn from watching this indicator by comparing it with the chart.
Here is a full user guide:
Adaptive MA-Bollinger Histogram Indicator User Guide
Welcome to the user guide for the **Adaptive MA-Bollinger Histogram** indicator. This custom indicator is designed to help traders analyze trends and potential reversals in a financial instrument's price movements. The indicator combines two Moving Averages (MA) and Bollinger Bands to provide valuable insights into market conditions.
### Indicator Overview
The Adaptive MA-Bollinger Histogram indicator comprises the following components:
1. **Moving Averages (MA1 and MA2):** The indicator uses two moving averages, namely MA1 and MA2, to track different time periods. MA1 has a user-defined length (default: 50) and MA2 has a longer user-defined length (default: 100). These moving averages can be calculated using different methods such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Volume Weighted Moving Average (VWMA), or Smoothed Moving Average (RMA).
2. **Histograms:** The indicator displays histograms based on the differences between the price source and the respective moving averages. Positive values of the histogram for MA1 are plotted in one color (default: green), while negative values are plotted in another color (default: red). Similarly, positive values of the histogram for MA2 are plotted in one color (default: blue), while negative values are plotted in another color (default: yellow). It's important to note that the histogram for MA1 is plotted positively, while the histogram for MA2 is plotted inversely.
3. **Bollinger Bands:** The indicator also features Bollinger Bands calculated based on the differences between the price source and the respective moving averages (dist1 and dist2). Bollinger Bands consist of three lines: the middle band, upper band, and lower band. These bands help visualize the potential volatility and overbought/oversold levels of the instrument's price.
### Understanding the Indicator
- **Histograms:** The histograms highlight the divergence between the price and the two moving averages. When the histogram for MA1 is positive, it indicates that the price is above the MA1. Conversely, when the histogram for MA1 is negative, it suggests that the price is below the MA1. Similarly, the histogram for MA2 is plotted inversely.
- **Bollinger Bands:** The Bollinger Bands consist of three lines. The middle band represents the moving average (MA1 or MA2), while the upper and lower bands are calculated based on the standard deviation of the differences between the price source and the moving average. The bands expand during periods of higher volatility and contract during periods of lower volatility.
### Possible Trading Ideas
1. **Trend Confirmation:** When the histograms for both MA1 and MA2 are consistently positive, it may indicate a strong bullish trend. Conversely, when both histograms are consistently negative, it may suggest a strong bearish trend.
2. **Divergence:** Divergence between price and the histograms could signal potential reversals. For example, if the price is making new highs while the histogram is declining, it might indicate a bearish divergence and a possible upcoming trend reversal.
3. **Bollinger Bands Squeeze:** A narrowing of the Bollinger Bands indicates lower volatility and often precedes a significant price movement. Traders might consider a potential breakout trade when the bands start to expand again.
4. **Overbought/Oversold Levels:** Prices touching or exceeding the upper Bollinger Band could suggest overbought conditions, while prices touching or falling below the lower Bollinger Band could indicate oversold conditions. Traders might look for reversals or corrections in such scenarios.
### Customization
- You can adjust the parameters such as MA lengths, Bollinger Bands length, width, and colors to suit your preferences and trading strategy.
### Conclusion
The **Adaptive MA-Bollinger Histogram** indicator provides a comprehensive view of price trends, divergences, and potential reversal points. Traders can use the information from this indicator to make informed decisions in their trading strategies. However, like any technical tool, it's recommended to combine this indicator with other forms of analysis and risk management techniques for optimal results.
Logarithmic Bollinger BandsLogarithmic Bollinger Bands
Published by Eric Thies on January 14, 2022
Summary
In this script I have taken the standard Bollinger band pinescript and made efforts to eliminate the behavior experienced in periods of high volatility in which we see the bands disappear completely off the chart by adding exponential plotting and logarithmic sourcing to the tool.
This tool will also show periods of Bearish and Bullish Expansion for users to see when volatility is running high in the market.
More On Bollinger Bands
Bollinger Bands consist of a center line representing the moving average of a security’s price over a certain period, and two additional parallel lines (called the upper and lower trading bands) one of which is just the moving average plus k-times the standard deviation over the selected time frame, and the other being the moving average minus k-times the standard deviation over that same timeframe. This technique has been developed in the 1980’s by John Bollinger, who lately registered the terms “Bollinger Bands” as a U.S. trademark in 2011. Technical analysts typically use 20 periods and k = 2 as default settings to build Bollinger Bands, while they can choose a simple or exponential moving average. Bollinger Bands provide a relative definition of high and low prices of a security. When the security is trading within the upper band, the price is considered high, while it is considered low when the security is trading within the lower band.
There is no general consensus on the use of Bollinger Bands among traders. Some traders see a buy signal when the price hits the lower Bollinger Band and close their position when the price hits the moving average. Some others buy when the price crosses over the upper band and sell when the price crosses below the lower band. We can see here two opposing interpretations based on different rationales, depending whether we are in a reversal or continuation pattern. Another interesting feature of the Bollinger Bands is that they give an indication of the volatility levels; a widening gap between the upper and lower bands indicates an increasing volatility, while a narrowing band indicates a decreasing volatility. Moreover, when the bands have an almost flat slope (parallel to the x-axis) the price will generally oscillate between the bands as if trading through a channel.
// © 2022 KINGTHIES THIS SOURCE CODE IS SUBJECT TO TERMS OF MOZILLA PUBLIC LICENSE 2.0 (MOZILLA.ORG/MPL/2.0)
//@version=5
//## !<---------------- © KINGTHIES --------------------->
indicator('Logarithmic Bollinger Bands (kingthies)',shorttitle='LogBands_KT',overlay=true)
// { BBANDS
src = math.log(input(close,title="Source"))
lenX = input(20,title='lenX')
highlights = input(false,title="Highlight Bear and Bull Expansions?")
mult = 2
bbandBasis = ta.sma(src,lenX)
dev = 2 * ta.stdev(src, 20)
upperBB = bbandBasis + dev
lowerBB = bbandBasis - dev
bbw = (upperBB-lowerBB)/bbandBasis
bbr = (src - lowerBB)/(upperBB - lowerBB)
// }
// { BBAND EXPANSIONS
bullExp= ta.rising(upperBB,1) and ta.falling(lowerBB,1) and ta.rising(bbandBasis,1) and ta.rising(bbw,1) and ta.rising(bbr,1)
bearExp= ta.rising(upperBB,1) and ta.falling(lowerBB,1) and ta.falling(bbandBasis,1) and ta.rising(bbw,1) and ta.falling(bbr,1)
// }
// { COLORS
greenBG = color.rgb(9,121,105,75), redBG = color.rgb(136,8,8,75)
bullCol = highlights and bullExp ? greenBG : na, bearCol = highlights and bearExp ? redBG : na
// }
// { INDICATOR PLOTTING
lowBB=plot(math.exp(lowerBB),title='Low Band',color=color.aqua),plot(math.exp(bbandBasis),title='BBand Basis',color=color.red),
highBB=plot(math.exp(upperBB),title='High Band',color=color.aqua),fill(lowBB,highBB,title='Band Fill Color',color=color.rgb(0,128,128,75))
bgcolor(bullCol,title='Bullish Expansion Highlights'),bgcolor(bearCol,title='Bearish Expansion Highlights')
// }
Volume Flow with Bollinger Bands and EMA Cross SignalsThe Volume Flow with Bollinger Bands and EMA Cross Signals indicator is a custom technical analysis tool designed to identify potential buy and sell signals based on several key components:
Volume Flow: This component combines price movement and trading volume to create a signal that indicates the strength or weakness of price movements. When the price is rising with increasing volume, it suggests strong buying activity, whereas falling prices with increasing volume indicate strong selling pressure.
Bollinger Bands: Bollinger Bands consist of three lines:
The Basis (middle line), which is a Simple Moving Average (SMA) of the price over a set period.
The Upper Band, which is the Basis plus a multiple of the standard deviation (typically 2).
The Lower Band, which is the Basis minus a multiple of the standard deviation. Bollinger Bands help identify periods of high volatility and potential overbought/oversold conditions. When the price touches the upper band, it might indicate that the market is overbought, while touching the lower band might indicate oversold conditions.
EMA Crossovers: The script includes two Exponential Moving Averages (EMAs):
Fast EMA: A shorter-term EMA, typically more sensitive to price changes.
Slow EMA: A longer-term EMA, responding slower to price changes. The crossover of the Fast EMA crossing above the Slow EMA (bullish crossover) signals a potential buy opportunity, while the Fast EMA crossing below the Slow EMA (bearish crossover) signals a potential sell opportunity.
Background Color and Candle Color: The indicator highlights the chart's background with specific colors based on the signals:
Green background for buy signals.
Yellow background for sell signals. Additionally, the candles are colored green for buy signals and yellow for sell signals to visually reinforce the trade opportunities.
Buy/Sell Labels: Small labels are placed on the chart:
"BUY" label in green is placed below the bar when a buy signal is generated.
"SELL" label in yellow is placed above the bar when a sell signal is generated.
Working of the Indicator:
Volume Flow Calculation: The Volume Flow is calculated by multiplying the price change (current close minus the previous close) with the volume. This product is then smoothed with a Simple Moving Average (SMA) over a user-defined period (length). The result is then multiplied by a multiplier to adjust its sensitivity.
Price Change = close - close
Volume Flow = Price Change * Volume
Smoothed Volume Flow = SMA(Volume Flow, length)
The Volume Flow Signal is then: Smooth Volume Flow * Multiplier
This calculation represents the buying or selling pressure in the market.
Bollinger Bands: Bollinger Bands are calculated using the Simple Moving Average (SMA) of the closing price (basis) and the Standard Deviation (stdev) of the price over a period defined by the user (bb_length).
Basis (Middle Band) = SMA(close, bb_length)
Upper Band = Basis + (bb_std_dev * Stdev)
Lower Band = Basis - (bb_std_dev * Stdev)
The upper and lower bands are plotted alongside the price to identify the price's volatility. When the price is near the upper band, it could be overbought, and near the lower band, it could be oversold.
EMA Crossovers: The Fast EMA and Slow EMA are calculated using the Exponential Moving Average (EMA) function. The crossovers are detected by checking:
Buy Signal (Bullish Crossover): When the Fast EMA crosses above the Slow EMA.
Sell Signal (Bearish Crossover): When the Fast EMA crosses below the Slow EMA.
The long_condition variable checks if the Fast EMA crosses above the Slow EMA, and the short_condition checks if it crosses below.
Visual Signals:
Background Color: The background is colored green for a buy signal and yellow for a sell signal. This gives an immediate visual cue to the trader.
Bar Color: The candles are colored green for buy signals and yellow for sell signals.
Labels:
A "BUY" label in green appears below the bar when the Fast EMA crosses above the Slow EMA.
A "SELL" label in yellow appears above the bar when the Fast EMA crosses below the Slow EMA.
Summary of Buy/Sell Logic:
Buy Signal:
The Fast EMA crosses above the Slow EMA (bullish crossover).
Volume flow is positive, indicating buying pressure.
Background turns green and candles are colored green.
A "BUY" label appears below the bar.
Sell Signal:
The Fast EMA crosses below the Slow EMA (bearish crossover).
Volume flow is negative, indicating selling pressure.
Background turns yellow and candles are colored yellow.
A "SELL" label appears above the bar.
Usage of the Indicator:
This indicator is designed to help traders identify potential entry (buy) and exit (sell) points based on:
The interaction of Exponential Moving Averages (EMAs).
The strength and direction of Volume Flow.
Price volatility using Bollinger Bands.
By combining these components, the indicator provides a comprehensive view of market conditions, helping traders make informed decisions on when to enter and exit trades.
Abhi's Bollinger Band Reversal SignalThis Pine Script indicator is designed to detect reversal trade opportunities using Bollinger Band breakouts. It identifies both buy and sell setups with clearly defined entry, stop-loss (SL), and target (TP) conditions. It also manages trades visually with real-time signal plotting, and limits entries per trading day.
⚙️ How It Works
🔽 Sell Signal Conditions
- The previous candle must close above the upper Bollinger Band, and its entire body must be above the band
- The current candle must fail to break the previous high, and must break below the previous low
- Entry is taken at the previous candle’s low, with SL at its high
- Target is calculated based on a configurable Risk:Reward ratio
🔼 Buy Signal Conditions
- The previous candle must close below the lower Bollinger Band, and its entire body must be below the band
- The current candle must fail to break the previous low, and must break above the previous high
- Entry is at the previous candle’s high, with SL at its low
- Target is calculated using the same Risk:Reward ratio
⏰ Time-Based Exit
- If a trade is still active by a user-defined exit time (e.g. 15:15), the trade is closed
- Labels are plotted to show whether this exit was a profit or loss
🧩 User Inputs
- Start Time for signals
- Exit Time for open trades
- Bollinger Band Settings: Period and Std Dev
- Max Entries Per Day
- Risk:Reward Ratio: Dropdown for 1:1, 1:1.5, ..., 1:3
🎨 Visual Features
✅ BUY and SELL signals are plotted when valid conditions are detected
🟢 TP and 🔴 SL labels show trade outcome
🕒 TIME EXIT labels appear at user-set exit time with green/red coloring based on profitability
📉 Bollinger Bands plotted for visual context
📌 Notes:
- Designed for intraday trading, resets entry counter daily
- Uses bar_index > tradeBarIndex to avoid SL/TP being triggered on the same candle as entry
- Tracks only one trade at a time (tradeActive) — ensures clear, non-overlapping logic
Fibonacci & Bollinger Bands StrategyTrading System: Fibonacci & Bollinger Bands Strategy
1. Session Timing
Trade only from 1 PM onwards.
Identify the first candle on the 1 PM vertical line to set the market direction.
If it's a bullish candle, look for buy opportunities.
If it's a bearish candle, look for sell opportunities.
2. Fibonacci Retracement as a Measuring Tool
Identify the recent swing high and swing low before the 1 PM session.
Draw Fibonacci retracement levels from low to high (for buys) or high to low (for sells).
Key retracement levels to watch: 0.0%, 50.0%, and 100.0%.
Entries can be placed at 0.0% or 50.0%, aiming for a move toward 100.0% retracement.
3. Bollinger Bands Confirmation
If the Bollinger Bands are above price, expect a downward move (sell).
If the Bollinger Bands are below price, expect an upward move (buy).
Use this as additional confirmation for your Fibonacci-based trade.
4. Entry & Exit Rules
Entry:
If the 1 PM candle confirms a bullish bias, enter long near Fibonacci 0.0% or 50.0%.
If the 1 PM candle confirms a bearish bias, enter short near Fibonacci 0.0% or 50.0%.
Stop Loss: Below (for buys) or above (for sells) the swing low/high used for Fibonacci.
Take Profit: Target 100.0% retracement level or next key resistance/support.
5. Risk Management
Risk 1-2% per trade.
Avoid trading if price is too far from Fibonacci levels.
Confirm setup with Bollinger Bands alignment.
Double Bollinger Bands Strategy with Signals (By Rolwin)Double Bollinger Bands Strategy with Signals 1.0 (By Rolwin)
📌 Overview
The Double Bollinger Bands Strategy is a trend-following system that utilizes two sets of Bollinger Bands (2 standard deviations and 3 standard deviations) to identify high-probability entry and exit points. This strategy helps traders capitalize on strong price movements and potential reversals by detecting overbought and oversold conditions more effectively.
📊 How It Works
• Bollinger Bands Setup:
o Middle Band: 20-period Simple Moving Average (SMA)
o Upper & Lower Bands (2 SD): Standard Bollinger Bands (±2 standard deviations)
o Extreme Bands (3 SD): Additional Bollinger Bands (±3 standard deviations) for extreme price moves
• Entry Signals:
✅ Buy (Long Entry): When the price crosses above the lower 3SD band (oversold zone)
❌ Sell (Short Entry): When the price crosses below the upper 3SD band (overbought zone)
• Exit Signals:
🔼 Exit Long: When the price reaches the upper 2SD band
🔽 Exit Short: When the price reaches the lower 2SD band
• Additional Features:
✅ Buy & Sell Signals plotted directly on the chart
🎨 Candles turn white when price touches the extreme 3SD band
🔥 Why Use This Strategy?
✔️ Clear Entry & Exit Points: Based on strong statistical levels
✔️ Effective in Trending & Reversal Markets: Captures both momentum & mean reversion setups
✔️ Easy-to-Use Visualization: Signals & bands make it beginner-friendly
✔️ Customizable: Adjust Bollinger Band length and multipliers to fit different assets & timeframes
⚠️ Risk Management Tip
While this strategy provides high-probability trade signals, it is essential to use stop-loss orders (e.g., ATR-based) and proper position sizing to manage risk effectively.
📈 Try it out and optimize the settings for your favorite markets! 🚀
Dual Bollinger Bands (20 & 200)Dual Bollinger Bands (20 & 200) - Enhanced Trading Strategy
Overview
The Dual Bollinger Bands (20 & 200) indicator is an enhanced version of the Double Bollinger Bands by Alixnet. This advanced tool integrates two sets of Bollinger Bands with 20-period (short-term) and 200-period (long-term) moving averages, helping traders identify market trends, volatility, and potential trade setups more effectively.
Key Features
✅ Two Bollinger Band Sets – Short-term (20-period) and Long-term (200-period).
✅ Enable/Disable Each BB – Customize visibility for better analysis.
✅ Multiple Standard Deviations – Identify different levels of volatility.
✅ Background Fill for Clarity – Highlights volatility zones.
How to Use This Indicator Effectively
1. Understanding the Two Bollinger Bands
BB1 (20-Period): Measures short-term price movements and volatility.
BB2 (200-Period): Acts as a long-term trend filter to determine the dominant trend.
2. Trade Entries & Exits
Bullish Trade Setup (Long Entry)
🔹 Price Above 200 MA Basis Line (BB2) – Confirms an uptrend.
🔹 Price Pulls Back to the Lower Band of BB1 (20 MA) – Ideal buy opportunity.
🔹 Confirmation: If price bounces off the lower BB1 band and moves back toward the midline or upper band, enter a long position.
🔹 Exit: When price touches or exceeds the upper BB1 band.
Bearish Trade Setup (Short Entry)
🔹 Price Below 200 MA Basis Line (BB2) – Confirms a downtrend.
🔹 Price Pulls Back to the Upper Band of BB1 (20 MA) – Ideal short opportunity.
🔹 Confirmation: If price gets rejected at the upper BB1 band and moves downward, enter a short position.
🔹 Exit: When price reaches or drops below the lower BB1 band.
3. Avoiding Sideways Markets
❌ Avoid trading when price stays between the two bands of BB1 without breaking out.
❌ Flat 200 MA Line (BB2 Basis) indicates a ranging market – best to wait for a breakout.
✅ Wait for Price to Cross the 200 MA Basis Line to confirm trend direction before entering trades.
4. Catching Trending Moves
✅ Strong Trend Confirmation: When price stays above or below the 20-period BB bands and also above/below the 200-period MA.
✅ Trend Continuation: If price consolidates near the upper or lower bands without breaking opposite levels.
✅ Breakout Confirmation: Look for a candle close outside BB1 bands with momentum to confirm strong moves.
Final Thoughts
The Dual Bollinger Bands (20 & 200) indicator is a powerful tool for both short-term traders and long-term investors. By combining the short-term volatility of the 20-period BB with the long-term trend of the 200-period BB, traders can make more informed trading decisions, filter out noise, and capture high-probability trade setups.
Normalized Bollinger Band DistanceThis TradingView script calculates and visualizes the Normalized Bollinger Band Distance to analyze the relative spread of Bollinger Bands as a percentage of the moving average. It also determines thresholds based on global statistics to highlight unusual market conditions. Here's a detailed description:
Indicator Overview
Purpose: The indicator measures the normalized distance between the upper and lower Bollinger Bands relative to the Simple Moving Average (SMA). It helps identify periods of high or low volatility.
Visualization: Displays the normalized distance along with dynamic thresholds based on global statistical calculations (mean and standard deviation).
Inputs
Length (length): Defines the period for the SMA and Bollinger Bands calculation. Default is 200.
Standard Deviations (stdDev): Number of standard deviations for the Bollinger Bands. Default is 2.
Calculation
Bollinger Bands:
Upper Band:
SMA
+
(
Standard Deviation
×
stdDev
)
SMA+(Standard Deviation×stdDev)
Lower Band:
SMA
−
(
Standard Deviation
×
stdDev
)
SMA−(Standard Deviation×stdDev)
Normalized Distance:
Normalized Distance
=
Upper Band
−
Lower Band
SMA
Normalized Distance=
SMA
Upper Band−Lower Band
Global Statistics:
Global Mean (
𝜇
μ): Average of all normalized distances up to the current bar.
Global Standard Deviation (
𝜎
σ): Standard deviation of all normalized distances up to the current bar.
High Threshold:
𝜇
+
1.5
×
𝜎
μ+1.5×σ
Low Threshold:
𝜇
−
1.5
×
𝜎
μ−1.5×σ
Visualization
Normalized Distance Plot:
The normalized distance is plotted in blue as a percentage for easy interpretation.
Threshold Lines:
High Threshold: Red line to signal unusually high volatility.
Low Threshold: Green line to signal unusually low volatility.
Mean Line: White line indicating the average normalized distance.
Zero Line: Horizontal white line for reference.
Use Case
High Threshold Breach: Indicates an unusual increase in Bollinger Band width relative to the SMA, signaling potential high market volatility.
Low Threshold Breach: Indicates an unusual narrowing of Bollinger Band width, suggesting low volatility and potential consolidation.
Trend Analysis: Observe how the normalized distance evolves over time to anticipate market conditions.
Enhanced Kaufman Adaptive Moving Average (KAMA) with Bollinger B# Enhanced Kaufman Adaptive Moving Average (KAMA) with Bollinger Bands
## Overview
This indicator combines the Kaufman Adaptive Moving Average (KAMA) with Bollinger Bands to create a comprehensive trading system. It provides adaptive trend following capabilities while measuring market volatility and potential reversal points.
## Key Features
- Adaptive moving average that adjusts to market conditions
- Dynamic Bollinger Bands for volatility measurement
- Color-coded KAMA line indicating trend direction
- Integrated buy/sell signals based on multiple confirmations
- Customizable parameters for both KAMA and Bollinger Bands
- Optional bar confirmation wait feature
- Built-in alert conditions for trade signals
## Main Components
### 1. Kaufman Adaptive Moving Average (KAMA)
- Adapts to market volatility using an efficiency ratio
- Changes color based on trend direction (green for uptrend, red for downtrend)
- Adjustable parameters for fine-tuning:
- Base Length: Controls the main calculation period (default: 10)
- Fast EMA Length: For rapid market response (default: 2)
- Slow EMA Length: For stable market conditions (default: 30)
### 2. Bollinger Bands
- Standard deviation-based volatility bands
- Customizable length and standard deviation multiplier
- Includes expansion threshold for volatility measurement
- Components:
- Upper Band: Upper volatility threshold
- Middle Band: Simple moving average
- Lower Band: Lower volatility threshold
## Signal Generation
### Buy Signals
Generated when:
1. KAMA color changes from red to green
2. Price closes above KAMA
3. Price closes above the middle Bollinger Band
4. Signals are marked with:
- Green triangles below the candles
- "B" labels for easy identification
### Sell Signals
Generated when:
1. KAMA color changes from green to red
2. Price closes below KAMA
3. Price closes below the middle Bollinger Band
4. Signals are marked with:
- Red triangles above the candles
- "S" labels for easy identification
## Customizable Parameters
### KAMA Settings
- Base Length (1-50)
- Fast EMA Length (1-10)
- Slow EMA Length (10-50)
- Source Price Selection
- Direction Highlight Toggle
- Bar Confirmation Option
### Bollinger Bands Settings
- Length (default: 20)
- Standard Deviation Multiplier (default: 2.0)
- Expansion Threshold (0.1-3.0)
## Alert Functionality
Built-in alerts for:
- Buy signals with customizable messages
- Sell signals with customizable messages
## Best Practices
### Timeframe Selection
- Works well on multiple timeframes
- Recommended for 15m to 4h charts for optimal signal generation
- Higher timeframes provide more reliable trend signals
### Parameter Optimization
- Adjust KAMA lengths based on trading style:
- Shorter lengths for day trading
- Longer lengths for swing trading
- Fine-tune BB multiplier based on market volatility
- Consider waiting for bar confirmation in volatile markets
### Risk Management
- Use in conjunction with other indicators for confirmation
- Consider market conditions and volatility when trading signals
- Implement proper position sizing and stop-loss levels
## Technical Notes
- Written in Pine Script™ v6
- Overlay indicator (displays on price chart)
- Compatible with all TradingView-supported markets
- Resource-efficient implementation for smooth performance
## Disclaimer
This indicator is provided under the Mozilla Public License 2.0. While it can be a valuable tool for technical analysis, it should not be used as the sole basis for trading decisions. Always combine with proper risk management and additional analysis methods.
