CloudShiftCloudShift + Bollinger Bands
This version of CloudShift now includes fully optimized Bollinger Bands with all three dynamic lines:
Upper Band: Highlights expansion during volatility spikes.
Lower Band: Identifies compression and accumulation zones.
Centerline (Basis): A smooth reference of the moving average, providing better visual balance and directional context.
The bands are drawn with thin, clean lime lines, designed to integrate perfectly with the cloud logic — keeping your chart minimalist yet powerful.
This update enhances the CloudShift indicator by providing a clear visual framework of market volatility and structure without altering its original logic.
Recommended for use on: NASDAQ, S&P 500, and other high-volatility futures.
Recommended timeframe: 5–15 minutes.
Tìm kiếm tập lệnh với "bands"
✨ Astonishing Smooth Double Keltner Channels ✨This indicator brings a fresh, ultra-smooth take on the classic Keltner Channels, designed to help you better visualize volatility and trend with minimal lag and maximum clarity.
Key Features:
Hull Moving Average (HMA) as the middle line for superior smoothness and responsiveness compared to traditional EMA.
Double Keltner Channels with customizable multipliers (default 4 and 8) to capture different volatility zones.
Adaptive ATR smoothing using RMA for stable yet responsive channel width.
Dynamic coloring of channel bands and background based on price position relative to the middle line — green for bullish, red for bearish.
Visual fills between channel bands for easy zone identification.
Price crossover signals plotted as arrows to highlight potential breakout or breakdown points.
Why use this indicator?
Keltner Channels are a powerful tool for identifying trend direction, volatility expansion, and potential reversal zones. This version enhances the classic formula by applying advanced smoothing techniques and visual cues, making it easier to interpret price action and make informed trading decisions.
How to use:
Adjust the length and multipliers to fit your trading style and the instrument’s volatility.
Watch the smooth middle line (HMA) to identify trend direction.
Use the channel bands as dynamic support/resistance and volatility boundaries.
Pay attention to the crossover signals for potential entry or exit points.
The background color helps quickly gauge market bias at a glance.
Feel free to customize the inputs and experiment with different settings to suit your strategy. This indicator works well on all timeframes and instruments.
If you find it useful, please leave a like and comment! Happy trading! 🚀📈
BB + Keltner Squeeze (con SL)BB + Keltner Squeeze with Dynamic SL
This indicator combines Bollinger Bands (2σ and optional 3σ) with Keltner Channels to detect phases of volatility compression (squeeze) and their release (expansion).
Squeeze ON (orange dot): Bollinger Bands are inside the Keltner Channel → low volatility / market compression.
Release (green triangle): Bollinger Bands break outside the Keltner Channel → volatility expansion.
Orange background: visually highlights squeeze phases.
Dynamic Stop Loss options:
KC Mode: stop at the opposite Keltner band (wider, good for trend following).
ATRlike Mode: stop based on a multiple of the range (tighter, good for scalping or short swings).
Intended use:
Identify moments when the market is “building energy” and trade breakouts after a release.
Adjust stop losses dynamically according to volatility.
Note: This is not a standalone trading system. It works best when combined with trend confirmation tools (EMA, MACD, market structure, etc.).
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
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🔍 Overview
The Squeeze Momentum Regression Clouds (SMRC) indicator is a powerful visual tool for identifying price compression , trend strength , and slope momentum using multiple layers of linear regression Clouds. Designed to extend the classic squeeze framework, this indicator captures the behavior of price through dynamic slope detection, percentile-based spread analytics, and an optional UI for trend inspection — across up to four customizable regression Clouds .
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⚙️ Core Features
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Up to 4 Regression Clouds – Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine – Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap – Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring – Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles – Optional candle coloring reflects the real-time composite score.
UI Table – A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling – Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options – Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
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🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
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Each Cloud changes color based on its current slope:
Green indicates a rising trend.
Red indicates a falling trend.
Gray indicates a flat slope — often seen during chop or transitions.
Cloud 1 typically reflects short-term structure, while Cloud 4 represents long-term directional bias. Watch for multi-Cloud alignment — when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
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2. Use Compression Heat to Anticipate Breakouts
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The space between each Cloud’s top and bottom regression lines is measured, normalized, and analyzed over time. When this spread tightens relative to its history, the script highlights the band with a yellow compression glow .
This visual cue helps identify squeeze zones before volatility expands. If you see compression paired with a changing slope color (e.g., gray to green), this may indicate an impending breakout.
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3. Leverage the Optional Table UI
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The indicator includes a dynamic, floating table that displays real-time metrics per Cloud. These include:
Slope direction and value , with historical Min/Max reference.
Top and Bottom percentile ranks , showing how price sits within the Cloud range.
Current spread width , compared to its historical norms.
Composite score , which blends trend, slope, and compression for that Cloud.
You can customize the table’s position, theme, transparency, and whether to show a combined summary score in the header.
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4. Analyze Candle Color for Composite Signals
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When enabled, the indicator colors candles based on a weighted composite score. This score factors in:
The signed slope of each Cloud (up, down, or flat)
The percentile pressure from the top and bottom bands
The degree of spread compression
Expect green candles in bullish trend phases, red candles during bearish regimes, and gray candles in mixed or low-conviction zones.
Candle coloring provides a visual shorthand for market conditions , useful for intraday scanning or historical backtesting.
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🧰 Configuration Guidance
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To tailor the indicator to your strategy:
Use Cloud lengths like 21, 34, 55, and 89 for a balanced multi-timeframe view.
Adjust the slope threshold (default 0.05) to control how sensitive the trend coloring is.
Set the spread floor (e.g., 0.15) to tune when compression is detected and visualized.
Choose your weighting style : Inverse Length (favor faster bands), Equal, or Max Pooling (most aggressive).
Set composite weights to emphasize trend slope, percentile bias, or compression—depending on your market edge.
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✅ Best Practices
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Use aligned Cloud colors across all bands to confirm trend conviction.
Combine slope direction with compression glow for early breakout entry setups.
In choppy markets, watch for Clouds 1 and 2 turning flat while Clouds 3 and 4 remain directional — a sign of potential trend exhaustion or consolidation.
Keep the table enabled during backtesting to manually evaluate how each Cloud behaved during price turns and consolidations.
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📌 License & Usage Terms
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This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
✅ You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
❌ You are not allowed to:
Resell or redistribute the script without permission
Use it inside any paid product or service
Republish without giving clear attribution to the original author
For commercial licensing , private customization, or collaborations, please contact Joshua Danford directly.
Volatility-Adjusted Momentum Score (VAMS) [QuantAlgo]🟢 Overview
The Volatility-Adjusted Momentum Score (VAMS) measures price momentum relative to current volatility conditions, creating a normalized indicator that identifies significant directional moves while filtering out market noise. It divides annualized momentum by annualized volatility to produce scores that remain comparable across different market environments and asset classes.
The indicator displays a smoothed VAMS Z-Score line with adaptive standard deviation bands and an information table showing real-time metrics. This dual-purpose design enables traders and investors to identify strong trend continuation signals when momentum persistently exceeds normal levels, while also spotting potential mean reversion opportunities when readings reach statistical extremes.
🟢 How It Works
The indicator calculates annualized momentum using a simple moving average of logarithmic returns over a specified period, then measures annualized volatility through the standard deviation of those same returns over a longer timeframe. The raw VAMS score divides momentum by volatility, creating a risk-adjusted measure where high volatility reduces scores and low volatility amplifies them.
This raw VAMS value undergoes Z-Score normalization using rolling statistical parameters, converting absolute readings into standardized deviations that show how current conditions compare to recent history. The normalized Z-Score receives exponential moving average smoothing to create the final VAMS line, reducing false signals while preserving sensitivity to meaningful momentum changes.
The visualization includes dynamically calculated standard deviation bands that adjust to recent VAMS behavior, creating statistical reference zones. The information table provides real-time numerical values for VAMS Z-Score, underlying momentum percentages, and current volatility readings with trend indicators.
🟢 How to Use
1. VAMS Z-Score Bands and Signal Interpretation
Above Mean Line: Momentum exceeds historical averages adjusted for volatility, indicating bullish conditions suitable for trend following
Below Mean Line: Momentum falls below statistical norms, suggesting bearish conditions or downward pressure
Mean Line Crossovers: Primary transition signals between bullish and bearish momentum regimes
1 Standard Deviation Breaks: Strong momentum conditions indicating statistically significant directional moves worth following
2 Standard Deviation Extremes: Rare momentum readings that often signal either powerful breakouts or exhaustion points
2. Information Table and Market Context
Z-Score Values: Current VAMS reading displayed in standard deviations (σ), showing how far momentum deviates from its statistical norm
Momentum Percentage: Underlying annualized momentum displayed as percentage return, quantifying the directional strength
Volatility Context: Current annualized volatility levels help interpret whether VAMS readings occur in high or low volatility environments
Trend Indicators: Directional arrows and change values provide immediate feedback on momentum shifts and market transitions
3. Strategy Applications and Alert System
Trend Following: Use sustained readings beyond the mean line and 1σ band penetrations for directional trades, especially when VAMS maintains position in upper or lower statistical zones
Mean Reversion: Focus on 2σ extreme readings for contrarian opportunities, particularly effective in sideways markets where momentum tends to revert to statistical norms
Alert Notifications: Built-in alerts for mean crossovers (regime changes), 1σ breaks (strong signals), and 2σ touches (extreme conditions) help monitor multiple instruments for both continuation and reversal setups
Quantum Reversal# 🧠 Quantum Reversal
## **Quantitative Mean Reversion Framework**
This algorithmic trading system employs **statistical mean reversion theory** combined with **adaptive volatility modeling** to capitalize on Bitcoin's inherent price oscillations around its statistical mean. The strategy integrates multiple technical indicators through a **multi-layered signal processing architecture**.
---
## ⚡ **Core Technical Architecture**
### 📊 **Statistical Foundation**
- **Bollinger Band Mean Reversion Model**: Utilizes 20-period moving average with 2.2 standard deviation bands for volatility-adjusted entry signals
- **Adaptive Volatility Threshold**: Dynamic standard deviation multiplier accounts for Bitcoin's heteroscedastic volatility patterns
- **Price Action Confluence**: Entry triggered when price breaches lower volatility band, indicating statistical oversold conditions
### 🔬 **Momentum Analysis Layer**
- **RSI Oscillator Integration**: 14-period Relative Strength Index with modified oversold threshold at 45
- **Signal Smoothing Algorithm**: 5-period simple moving average applied to RSI reduces noise and false signals
- **Momentum Divergence Detection**: Captures mean reversion opportunities when momentum indicators show oversold readings
### ⚙️ **Entry Logic Architecture**
```
Entry Condition = (Price ≤ Lower_BB) OR (Smoothed_RSI < 45)
```
- **Dual-Condition Framework**: Either statistical price deviation OR momentum oversold condition triggers entry
- **Boolean Logic Gate**: OR-based entry system increases signal frequency while maintaining statistical validity
- **Position Sizing**: Fixed 10% equity allocation per trade for consistent risk exposure
### 🎯 **Exit Strategy Optimization**
- **Profit-Lock Mechanism**: Positions only closed when showing positive unrealized P&L
- **Trend Continuation Logic**: Allows winning trades to run until momentum exhaustion
- **Dynamic Exit Timing**: No fixed profit targets - exits based on profitability state rather than arbitrary levels
---
## 📈 **Statistical Properties**
### **Risk Management Framework**
- **Long-Only Exposure**: Eliminates short-squeeze risk inherent in cryptocurrency markets
- **Mean Reversion Bias**: Exploits Bitcoin's tendency to revert to statistical mean after extreme moves
- **Position Management**: Single position limit prevents over-leveraging
### **Signal Processing Characteristics**
- **Noise Reduction**: SMA smoothing on RSI eliminates high-frequency oscillations
- **Volatility Adaptation**: Bollinger Bands automatically adjust to changing market volatility
- **Multi-Timeframe Coherence**: Indicators operate on consistent timeframe for signal alignment
---
## 🔧 **Parameter Configuration**
| Technical Parameter | Value | Statistical Significance |
|-------------------|-------|-------------------------|
| Bollinger Period | 20 | Standard statistical lookback for volatility calculation |
| Std Dev Multiplier | 2.2 | Optimized for Bitcoin's volatility distribution (95.4% confidence interval) |
| RSI Period | 14 | Traditional momentum oscillator period |
| RSI Threshold | 45 | Modified oversold level accounting for Bitcoin's momentum characteristics |
| Smoothing Period | 5 | Noise reduction filter for momentum signals |
---
## 📊 **Algorithmic Advantages**
✅ **Statistical Edge**: Exploits documented mean reversion tendency in Bitcoin markets
✅ **Volatility Adaptation**: Dynamic bands adjust to changing market conditions
✅ **Signal Confluence**: Multiple indicator confirmation reduces false positives
✅ **Momentum Integration**: RSI smoothing improves signal quality and timing
✅ **Risk-Controlled Exposure**: Systematic position sizing and long-only bias
---
## 🔬 **Mathematical Foundation**
The strategy leverages **Bollinger Band theory** (developed by John Bollinger) which assumes that prices tend to revert to the mean after extreme deviations. The RSI component adds **momentum confirmation** to the statistical price deviation signal.
**Statistical Basis:**
- Mean reversion follows the principle that extreme price deviations from the moving average are temporary
- The 2.2 standard deviation multiplier captures approximately 97.2% of price movements under normal distribution
- RSI momentum smoothing reduces noise inherent in oscillator calculations
---
## ⚠️ **Risk Considerations**
This algorithm is designed for traders with understanding of **quantitative finance principles** and **cryptocurrency market dynamics**. The strategy assumes mean-reverting behavior which may not persist during trending market phases. Proper risk management and position sizing are essential.
---
## 🎯 **Implementation Notes**
- **Market Regime Awareness**: Most effective in ranging/consolidating markets
- **Volatility Sensitivity**: Performance may vary during extreme volatility events
- **Backtesting Recommended**: Historical performance analysis advised before live implementation
- **Capital Allocation**: 10% per trade sizing assumes diversified portfolio approach
---
**Engineered for quantitative traders seeking systematic mean reversion exposure in Bitcoin markets through statistically-grounded technical analysis.**
Dynamic Flow Ribbons [BigBeluga]🔵 OVERVIEW
A dynamic multi-band trend visualization system that adapts to market volatility and reveals trend momentum with layered ribbon channels.
Dynamic Flow Ribbons transforms price action into flowing trend bands that expand and contract with volatility. It not only shows the active directional bias but also visualizes how strong or weak the trend is through layered ribbons, making it easier to assess trend quality and structure.
🔵 CONCEPTS
Uses an adaptive trend detection system built on a volatility envelope derived from an EMA of the average price (HLC3).
Measures volatility using a long-period average of the high-low range, which scales the envelope width dynamically.
Trend direction flips when the average price crosses above or below these envelopes.
Ribbons form around the trend line to show how far price is stretching or compressing relative to the mean.
🔵 FEATURES
Volatility-Based Trend Line:
A thick, color-coded line tracks the current trend with smoother transitions between phases.
Multi-Layered Flow Ribbons:
Up to 10 bands (5 above and 5 below) radiate outward from the upper and lower envelopes, reflecting volatility strength and direction.
Trend Coloring & Transitions:
Ribbons and candles are dynamically colored based on trend direction— green for bullish , orange for bearish . Transparency fades with distance from the core trend band.
Real-Time Responsiveness:
Ribbon structure and trend shifts update in real time, adapting instantly to fast market changes.
🔵 HOW TO USE
Use the color and thickness of the core trend line to follow directional bias.
When ribbons widen symmetrically, it signals strong trend momentum .
Narrowing or overlapping ribbons can suggest consolidation or transition zones .
Combine with breakout systems or volume tools to confirm impulsive or corrective phases .
Adjust the “Length” (factor) input to tune sensitivity—higher values smooth trends more.
🔵 CONCLUSION
Dynamic Flow Ribbons offers a sleek and insightful view into trend strength and structure. By visualizing volatility expansion with directional flow, it becomes a powerful overlay for momentum traders, swing strategists, and trend followers who want to stay ahead of evolving market flows
Trend Impulse Channels (Zeiierman)█ Overview
Trend Impulse Channels (Zeiierman) is a precision-engineered trend-following system that visualizes discrete trend progression using volatility-scaled step logic. It replaces traditional slope-based tracking with clearly defined “trend steps,” capturing directional momentum only when price action decisively confirms a shift through an ATR-based trigger.
This tool is ideal for traders who prefer structured, stair-step progression over fluid curves, and value the clarity of momentum-based bands that reveal breakout conviction, pullback retests, and consolidation zones. The channel width adapts automatically to market volatility, while the step logic filters out noise and false flips.
⚪ The Structural Assumption
This indicator is built on a core market structure observation:
After each strong trend impulse, the market typically enters a “cooling-off” phase as profit-taking occurs and counter-trend participants enter. This often results in a shallow pullback or stall, creating a slight negative slope in an uptrend (or a positive slope in a downtrend).
These “cooling-off” phases don’t reverse the trend — they signal temporary pressure before the next leg continues. By tracking trend steps discretely and filtering for this behavior, Trend Impulse Channels helps traders align with the rhythm of impulse → pause → impulse.
█ How It Works
⚪ Step-Based Trend Engine
At the heart of this tool is a dynamic step engine that progresses only when price crosses a predefined ATR-scaled trigger level:
Trigger Threshold (× ATR) – Defines how far price must break beyond the current trend state to register a new trend step.
Step Size (Volatility-Guided) – Each trend continuation moves the trend line in discrete units, scaling with ATR and trend persistence.
Trend Direction State – Maintains a +1/-1 internal bias to support directional filters and step tracking.
⚪ Volatility-Adaptive Channel
Each step is wrapped inside a dynamic envelope scaled to current volatility:
Upper and Lower Bands – Derived from ATR and band multipliers to expand/contract as volatility changes.
⚪ Retest Signal System
Optional signal markers show when price re-tests the upper or lower band:
Upper Retest → Pullback into resistance during a bearish trend.
Lower Retest → Pullback into support during a bullish trend.
⚪ Trend Step Signals
Circular markers can be shown to mark each time the trend steps forward, making it easy to identify structurally significant moments of continuation within a larger trend.
█ How to Use
⚪ Trend Alignment
Use the Trend Line and Step Markers to visually confirm the direction of momentum. If multiple trend steps occur in sequence without reversal, this typically signals strong conviction and trend persistence.
⚪ Retest-Based Entries
Wait for pullbacks into the channel and monitor for triangle retest signals. When used in confluence with trend direction, these offer high-quality continuation setups.
⚪ Breakouts
Look for breakouts beyond the upper or lower band after a longer period of pause. For higher likelihood of success, look for breakouts in the direction of the trend.
█ Settings
Trigger Threshold (× ATR) - Defines how far price must move to register a new trend step. Controls sensitivity to trend flips.
Max Step Size (× ATR) - Caps how far each trend step can extend. Prevents runaway step expansion in high volatility.
Band Multiplier (× ATR) - Expands the upper and lower channels. Controls how much breathing room the bands allow.
Trend Hold (bars) - Minimum number of bars the trend must remain active before allowing a flip. Helps reduce noise.
Filter by Trend - Restrict retest signals to those aligned with the current trend direction.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Bollinger Free BarsIdentify Extreme Price Actions with Non-Overlay Visualization
Core Functionality
This indicator detects two types of Bollinger Band breakout patterns without cluttering your chart:
1 ️⃣ Half Breakout Bar (Blue Triangles)
- Triggers when both open & close prices are outside the Bollinger Bands
- Suggests strong directional momentum continuation
2 ️⃣ Complete Breakout Bar (Red Flags)
- Activates when entire price action (including wicks) stays outside the bands
- Signals extreme volatility exhaustion points
Feature Highlights
◾ Smart Band Display
Translucent bands (#2196F3 & #FF9800 with 70% transparency) maintain chart clarity while showing dynamic volatility ranges
◾ Parameter Customization
- Adjustable period (default 20) & deviation multiplier (default 2.0)
- Selectable price source (close/open/high/low)
◾ Statistical Validation
Based on Bollinger Band's 95.4% price containment principle, signals filter out 4.6% extreme market conditions for high-probability scenarios.
Recommended Usage
1. Combine with volume analysis (significant breakout with high volume increases signal reliability)
2. Confirm with trend lines or RSI divergence
3. Adjust transparency via "Style" tab for multi-indicator layouts
Code Safety
- No repainting: All calculations use historical price data only
- No external network requests
- Open-source logic compliant with Pine Script v6 standards
Disclaimer
This tool is for technical analysis education only. Past performance doesn't guarantee future results. Always validate signals with fundamental analysis and proper risk management.
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
United HUN CityPurpose and Usage
The purpose of this strategy is to create a composite indicator that combines the signals from the MFI, Fisher Transform, and Bollinger Bands %b indicators. By normalizing and averaging these indicators, the script aims to provide a smoother and more comprehensive signal that can be used to make trading decisions.
MFI (Money Flow Index): Measures buying and selling pressure based on price and volume.
Fisher Transform: Highlights potential reversal points by transforming price data to a Gaussian normal distribution.
Bollinger Bands %b: Indicates where the price is relative to the Bollinger Bands, helping to identify overbought or oversold conditions.
The combined indicator can be used to identify potential buy or sell signals based on the smoothed composite value. For instance, a high combined indicator value might indicate overbought conditions, while a low value might indicate oversold conditions.
CME Gap Oscillator [CryptoSea]Introducing the CME Gap Oscillator , a pioneering tool designed to illuminate the significance of market gaps through the lens of the Chicago Mercantile Exchange (CME). By leveraging gap sizes in relation to the Average True Range (ATR), this indicator offers a unique perspective on market dynamics, particularly around the critical weekly close periods.
Key Features
Gap Measurement : At its core, the CME Oscillator quantifies the size of weekend gaps in the context of the market's volatility, using the ATR to standardize this measurement.
Dynamic Levels : Incorporating a dynamic extreme level calculation, the tool adapts to current market conditions, providing real-time insights into significant gap sizes and their implications.
Band Analysis : Through the introduction of upper and lower bands, based on standard deviations, traders can visually assess the oscillator's position relative to typical market ranges.
Enhanced Insights : A built-in table tracks the frequency of the oscillator's breaches beyond these bands within the latest CME week, offering a snapshot of recent market extremities.
Settings & Customisation
ATR-Based Measurement : Choose to measure gap sizes directly or in terms of ATR for a volatility-adjusted view.
Band Period Adjustability : Tailor the oscillator's sensitivity by modifying the band calculation period.
Dynamic Level Multipliers : Adjust the multiplier for dynamic levels to suit your analysis needs.
Visual Preferences : Customise the oscillator, bands, and table visuals, including color schemes and line styles.
In the example below, it demonstrates that the CME will want to return to the 0 value, this would be considered a reset or gap fill.
Application & Strategy
Deploy the CME Oscillator to enhance your market analysis
Market Sentiment : Gauge weekend market sentiment shifts through gap analysis, refining your strategy for the week ahead.
Volatility Insights : Use the oscillator's ATR-based measurements to understand the volatility context of gaps, aiding in risk management.
Trend Identification : Identify potential trend continuations or reversals based on the frequency and magnitude of gaps exceeding dynamic levels.
The CME Oscillator stands out as a strategic tool for traders focusing on gap analysis and volatility assessment. By offering a detailed breakdown of market gaps in relation to volatility, it empowers users with actionable insights, enabling more informed trading decisions across a range of markets and timeframes.
Trending RSI [ChartPrime]Trending RSI takes a new approach to RSI intended to provide all of the missing information that traditional RSI lacks. Questions such as "why does the price continue to decline even during an oversold period?" can be aided using the Trending RSI.
These types of movements are due to the market still trending and traditional RSI can not tell traders this. Trending RSI fixes this by introducing trend information back into the oscillator. By reverse engineering RSI we have been able to make a new indicator that is no longer bound between 0 and 100. Instead it provides the traditional 70 and 30 zones as bands, and 50 as a center line that still represent these zones perfectly. This transforms RSI into a centered oscillator instead of a normalized oscillator. When the market is trending our indicator represents this as the center line being below or above 0. Just like MACD the center line is colored to represent the market phases. This helps in identifying reversals more clearly by adding a layer of confluence to the already renowned RSI. We have also included a novel filtering technique that has a low lag to smoothing ratio. This is primarily used to smooth the bands by default but you can also utilize this on the RSI. Several alerts have been included to provide users with easy to configure signals.
You can use the center line as a directional filter for your trades by only picking trades in the direction of the center line. When the center line is above 0, the market is trending up. Conversely, when the center line is below 0 the market is trending down trend. Use the polarity of the center line to estimate the strength of retracements from the oversold and overbought zones. We have also included a special moving average to help you find the momentum of a move. The Binomial MA filter approximates a normal curve making it similar to a gaussian filter. We have also included standard divergences which are fully configurable in the settings. Finally, we have built this indicator to be compatible with the built in multi time frame option to allow users to freely pick the time frame they wish to use. It is worth noting that due to the limitations of the standard MTF implementation divergences will not plot as expected when using time frames outside of the charts time frame. This is standard and also affects the built in RSI.
All of the colors are fully adjustable with the option to enable or disable the glow effect. We have also designed this indicator to only display the information for plots that are enabled to reduce clutter and provide a cleaner charting experience. All alerts are built to work with the standard alert builder and do not have to be enabled or disabled inside of the indicator.
Included Alerts:
RSI Cross Over Center
RSI Cross Under Center
RSI Cross Under Upper Range
RSI Cross Over Upper Range
RSI Cross Over Lower Range
RSI Cross Under Lower Range
RSI Cross Over MA
RSI Cross Under MA
RSI Cross Over 0
RSI Cross Under 0
Center Cross Over 0
Center Cross Under 0
Center Bullish
Center Bearish
Bullish Divergence
Bearish Divergence
In wrapping up, the Trending RSI aims to enhance the conventional RSI by adding trend insights directly into the oscillator, addressing the gap that traditional RSI leaves regarding market trends. This version of RSI breaks away from the 0 to 100 range, offering bands and a center line that better represent market conditions. It includes a set of features like the Binomial MA for momentum analysis, configurable settings for divergence detection, and compatibility with multi-time frame analysis. The color customization and glow effects aim to improve visual clarity, and the inclusion of alerts is designed to streamline alert configuration. Overall, this indicator is designed to provide a more view of the markets, suitable for traders looking to incorporate trend analysis into their RSI-based strategies.
Enjoy
Bollinger and Stochastic with Trailing Stop - D.M.P.This trading strategy combines Bollinger Bands and the Stochastic indicator to identify entry opportunities in oversold and overbought conditions in the market. The aim is to capitalize on price rebounds from the extremes defined by the Bollinger Bands, with the confirmation of the Stochastic to maximize the probability of success of the operations.
Indicators Used
- Bollinger Bands Used to measure volatility and define oversold and overbought levels. When the price touches or breaks through the lower band, it indicates a possible oversold condition. Similarly, when it touches or breaks through the upper band, it indicates a possible overbought condition.
- Stochastic: A momentum oscillator that compares the closing price of an asset with its price range over a certain period. Values below 20 indicate oversold, while values above 80 indicate overbought.
Strategy Logic
- Long Entry (Buy): A purchase operation is executed when the price closes below the lower Bollinger band (indicating oversold) and the Stochastic is also in the oversold zone.
- Short Entry (Sell): A sell operation is executed when the price closes above the upper Bollinger band (indicating overbought) and the Stochastic is in the overbought zone.
Confluence Buy-Sell Indicator with Fibonacci The script is a "Confluence Indicator with Fibonacci" designed to work on the TradingView platform. This indicator combines multiple technical analysis strategies to generate buy and sell signals based on user-defined confluence criteria. Here's a breakdown of its features:
Confluence Criteria: Users can enable or disable various strategies like MACD, RSI, Bollinger Bands, Divergence, Fibonacci, and Moving Average. The number of strategies that need to align for a signal to be generated can be set by the user.
Strategies Included:
MACD Strategy: Uses the Moving Average Convergence Divergence method to identify buy/sell opportunities.
RSI Strategy: Utilizes the Relative Strength Index to detect overbought or oversold conditions.
Bollinger Bands Strategy: Incorporates Bollinger Bands to identify volatility and potential buy/sell signals.
Divergence Strategy: A basic implementation that detects bullish and bearish divergences using the RSI.
Fibonacci Strategy: Uses Fibonacci retracement levels to determine potential support and resistance levels.
Moving Average Strategy: Employs a crossover system between the 50-period and 200-period simple moving averages.
Additional Features:
Support & Resistance: Identifies major support and resistance levels from the last 50 bars.
Pivot Points: Calculates pivot points to determine potential turning points.
Stop Loss Levels: Automatically calculates and plots stop-loss levels for buy and sell signals.
NYC Midnight Level: Option to display the New York City midnight price level.
Visualization: Plots buy and sell signals on the chart with green and red markers respectively.
Adequate Category:
"Technical Analysis Indicators & Overlays" or "Strategy & Scripting Tools".
BB Support & ResistanceChoosing support and resistance can be time consuming and subjective. Personally, I don't want to spend too much time manually marking up charts. Credit to video I saw, forget the producer, that demonstrated how multi-time frame Bollinger Bands can act as support and resistance. I suggest reading up on Bollinger Bands (en.wikipedia.org) and how to trade them. This indicator draws support and resistance lines based on Bollinger Bands on three time frames. You can select 1 or all three time frames to display on your chart. Enjoy.
Trend Gaussian Channels [DeltaAlgo]This Script Introduces The Use Of The Gaussian Channel Concepts
This indicator consists of three lines: a central line that represents the moving average, and an upper and lower band that represent the volatility of the price movements.
The Gaussian channels is a concept consists of an upper & lower bands along with the basis; the mid band. The use of the Gaussian bands are simple, as described below.👇
Use Case:
There are many ways the Gaussian channel indicator can be used!
1. Look for the price to touch or cross the upper/lower bands of the Gaussian Channel Indicator. This indicates that the price has reached an high level of volatility. a reversal or correction may be imminent.
2. Wait for confirmation of the potential reversal or correction. This can be in the form of a bearish or bullish candlestick pattern, or a signal from another technical indicator.
a. For this reason I have implemented some signals that indicate trend shifts & candle colors to clearly display the switching between a bullish sentiment or bearish.
3. Enter a trade in the direction of the reversal or correction. For example, if the price touches the upper band and a bearish candlestick pattern occurs or if you get a bearish signal, enter a short trade. If the price touches the lower band and indicates bullish candlestick pattern or bullish signal, enter a long trade.
Sometimes this band can act as a support & resistance, THIS is not always the case as it is not meant to be used as support & resistance.
[dharmatech] KBDR Mean ReversionBased on the criteria described in the book "Mean Revision Trading" by Nishant Pant.
Bullish signal criteria:
Bollinger Bands must be outside Keltner Channel
Price near bottom bband
DI+ increasing
DI- decreasing
RSI near bottom and increasing
Bearish signal criteria:
Bollinger Bands must be outside Keltner Channel
Price near upper bband
DI+ decreasing
DI- increasing
RSI near upper and decreasing
A single triangle indicates that all 4 criteria are met.
If letters appear with the triangle, this indicates that there was a partial criteria match.
K : bbands outside Keltner
B : bbands criteria met
D : DI criteria met
R : RSI criteria met
You can use the settings to turn off partial signals. For example:
"Partial 3" means show signals where 3 of the criteria are met.
If you want more insight into the underlying criteria, load these indicators as well:
Bollinger Bands (built-in to TradingView)
Keltner Channels (built-in to TradingView)
RSI (built-in to TradingView)
ADX and DI
Warning:
Not meant to be used as a stand-alone buy/sell signal.
It regularly provides signals which would not be profitable.
It's meant to be used in conjunction with other analysis.
Think of this as a time-saving tool. Instead of manually checking RSI, DI+/DI-, bbands, distance, etc. this does all of that for you on the fly.
Nadaraya-Watson Envelope: Modified by YosietRange Filter indicator based on the LuxAlgo Nadaraya-Watson Envelope () indicator adding the SMA 30 high and SMA 7 low to predict the changes of the trends lines price.
WARNING: This indicator, as the same as the original, repaints the chart and could affect the exact values of the prices.
SMA Low 7 was identified using tensorflowJS years ago as accurate and abstract rsi indicator
SMA High 30 was identified using tensorflowJS years ago as accurate and strong trend line
This two SMAs were added to the original indicator Nadaraya-Watson to predict the exact points where the price will change direction or will re-test the trend to continue on.
The signals will act as the Williams Fractals, replacing the original signals of the indicator.
For those ICT/SMC traders, the bands and SMAs can toggle off in the settings of this indicator.
SETTINGS
Can set the source of the UPPER band indivuadilly
Can set the source of the LOWER band indivuadilly
Can toggle the visibility of the bands, this will not affect the calculations
Can toggle the visibility of SMAs
ALERTS AND SIGNALS
When the SMA LOW 7 cross under or over the bands, will trigger a signal orange
When the SMA 30 High cross over the upper band, will trigger a short signal purpple
HOW TO USE IT
If the both signals appears (sma 7 low and sma 30 high) crossing the upper band at the same point, this means that the price will drop strongly.
If the sma 7 low cross signal (orange triangle) appears under the price and lower band, means that the price will go up.
The separation of the signals from the chart will suggest the force of the movement. While more distance be, strongest reaction of the price.
DISCLAIMER : This indicator or script does not imply or constitute financial advice, investment advice, trading advice or any other type of advice or recommendation by and for TradingView. Use it at your own risk and your own decision.
Bitcoin wave modelBitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log (BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in prices ranging between 200,000 and 240,000 USD.
Enjoy the mathematical insights!
Smoothing ATR bandThere are two bands calculated with the ATR and I added "Smoothing" into the script.
Smoothing ATR with multiplier can display two bands above and below the price.
We can ONLY find some ATR bands in Community Scripts with "Basic" setting which is used to set Stop Loss.
And yet , Smoothing ATR with multiplier is capable of making traders manifestly recognize OverBought & OverSold.
FurtherMore, I added a condition with "plotshape", which is "Stop Hunt"
Stop Hunt is an absolutely usual strategy to clean the leverage and it always makes high volatility moves.
When high> above band and close< above band , long signal, it means it had been abundantly bought but the larger traders weren't satisfied; therefore, they quickly sold out to lower the price. The sell condition is on the contrary.
The signals mainly make traders manifestly recognize OverBought & OverSold.
Greedy DCA█ OVERVIEW
Detect price crashes in volatile conditions. This is an indicator for a greedy dollar cost average (DCA) strategy. That is, for people who want to repeatedly buy an asset over time when its price is crashing.
█ CONCEPTS
Price crashes are indicated if the price falls below one or more of the 4 lower Bollinger Bands which are calculated with increasing multipliers for the standard deviation.
In these conditions, the price is far below the average. Therefore they are considered good buying opportunities.
No buy signals are emitted if the Bollinger Bands are tight, i.e. if the bandwidth (upper -lower band) is below the value of the moving average multiplied with a threshold factor. This ensures that signals are only emitted if the conditions are highly volatile.
The Bollinger Bands are calculated based on the daily candles, irrespective the chart time frame. This allows to check the strategy on lower time frames
Nadaraya-Watson: Envelope (Non-Repainting)Due to popular request, this is an envelope implementation of my non-repainting Nadaraya-Watson indicator using the Rational Quadratic Kernel. For more information on this implementation, please refer to the original indicator located here:
What is an Envelope?
In technical analysis, an "envelope" typically refers to a pair of upper and lower bounds that surrounds price action to help characterize extreme overbought and oversold conditions. Envelopes are often derived from a simple moving average (SMA) and are placed at a predefined distance above and below the SMA from which they were generated. However, envelopes do not necessarily need to be derived from a moving average; they can be derived from any estimator, including a kernel estimator such as Nadaraya-Watson.
How to use this indicator?
Overall, this indicator offers a high degree of flexibility, and the location of the envelope's bands can be adjusted by (1) tweaking the parameters for the Rational Quadratic Kernel and (2) adjusting the lookback window for the custom ATR calculation. In a trending market, it is often helpful to use the Nadaraya-Watson estimate line as a floating SR and/or reversal zone. In a ranging market, it is often more convenient to use the two Upper Bands and two Lower Bands as reversal zones.
How are the Upper and Lower bounds calculated?
In this indicator, the Rational Quadratic (RQ) Kernel estimates the price value at each bar in a user-defined lookback window. From this estimation, the upper and lower bounds of the envelope are calculated based on a custom ATR calculated from the kernel estimations for the high, low, and close series, respectively. These calculations are then scaled against a user-defined multiplier, which can be used to further customize the Upper and Lower bounds for a given chart.
How to use Kernel Estimations like this for other indicators?
Kernel Functions are highly underrated, and when calibrated correctly, they have the potential to provide more value than any mundane moving average. For those interested in using non-repainting Kernel Estimations for technical analysis, I have written a Kernel Functions library that makes it easy to access various well-known kernel functions quickly. The Rational Quadratic Kernel is used in this implementation, but one can conveniently swap out other kernels from the library by modifying only a single line of code. For more details and usage examples, please refer to the Kernel Functions library located here:
