Adaptive Kalman Trend Filter (Zeiierman)█ Overview
The Adaptive Kalman Trend Filter indicator is an advanced trend-following tool designed to help traders accurately identify market trends. Utilizing the Kalman Filter—a statistical algorithm rooted in control theory and signal processing—this indicator adapts to changing market conditions, smoothing price data to filter out noise. By focusing on state vector-based calculations, it dynamically adjusts trend and range measurements, making it an excellent tool for both trend-following and range-based trading strategies. The indicator's adaptive nature is enhanced by options for volatility adjustment and three unique Kalman filter models, each tailored for different market conditions.
█ How It Works
The Kalman Filter works by maintaining a model of the market state through matrices that represent state variables, error covariances, and measurement uncertainties. Here’s how each component plays a role in calculating the indicator’s trend:
⚪ State Vector (X): The state vector is a two-dimensional array where each element represents a market property. The first element is an estimate of the true price, while the second element represents the rate of change or trend in that price. This vector is updated iteratively with each new price, maintaining an ongoing estimate of both price and trend direction.
⚪ Covariance Matrix (P): The covariance matrix represents the uncertainty in the state vector’s estimates. It continuously adapts to changing conditions, representing how much error we expect in our trend and price estimates. Lower covariance values suggest higher confidence in the estimates, while higher values indicate less certainty, often due to market volatility.
⚪ Process Noise (Q): The process noise matrix (Q) is used to account for uncertainties in price movements that aren’t explained by historical trends. By allowing some degree of randomness, it enables the Kalman Filter to remain responsive to new data without overreacting to minor fluctuations. This noise is particularly useful in smoothing out price movements in highly volatile markets.
⚪ Measurement Noise (R): Measurement noise is an external input representing the reliability of each new price observation. In this indicator, it is represented by the setting Measurement Noise and determines how much weight is given to each new price point. Higher measurement noise makes the indicator less reactive to recent prices, smoothing the trend further.
⚪ Update Equations:
Prediction: The state vector and covariance matrix are first projected forward using a state transition matrix (F), which includes market estimates based on past data. This gives a “predicted” state before the next actual price is known.
Kalman Gain Calculation: The Kalman gain is calculated by comparing the predicted state with the actual price, balancing between the covariance matrix and measurement noise. This gain determines how much of the observed price should influence the state vector.
Correction: The observed price is then compared to the predicted price, and the state vector is updated using this Kalman gain. The updated covariance matrix reflects any adjustment in uncertainty based on the latest data.
█ Three Kalman Filter Models
Standard Model: Assumes that market fluctuations follow a linear progression without external adjustments. It is best suited for stable markets.
Volume Adjusted Model: Adjusts the filter sensitivity based on trading volume. High-volume periods result in stronger trends, making this model suitable for volume-driven assets.
Parkinson Adjusted Model: Uses the Parkinson estimator, accounting for volatility through high-low price ranges, making it effective in markets with high intraday fluctuations.
These models enable traders to choose a filter that aligns with current market conditions, enhancing trend accuracy and responsiveness.
█ Trend Strength
The Trend Strength provides a visual representation of the current trend's strength as a percentage based on oscillator calculations from the Kalman filter. This table divides trend strength into color-coded segments, helping traders quickly assess whether the market is strongly trending or nearing a reversal point. A high trend strength percentage indicates a robust trend, while a low percentage suggests weakening momentum or consolidation.
█ Trend Range
The Trend Range section evaluates the market's directional movement over a specified lookback period, highlighting areas where price oscillations indicate a trend. This calculation assesses how prices vary within the range, offering an indication of trend stability or the likelihood of reversals. By adjusting the trend range setting, traders can fine-tune the indicator’s sensitivity to longer or shorter trends.
█ Sigma Bands
The Sigma Bands in the indicator are based on statistical standard deviations (sigma levels), which act as dynamic support and resistance zones. These bands are calculated using the Kalman Filter's trend estimates and adjusted for volatility (if enabled). The bands expand and contract according to market volatility, providing a unique visualization of price boundaries. In high-volatility periods, the bands widen, offering better protection against false breakouts. During low volatility, the bands narrow, closely tracking price movements. Traders can use these sigma bands to spot potential entry and exit points, aiming for reversion trades or trend continuation setups.
Trend Based
Volatility Based
█ How to Use
Trend Following:
When the Kalman Filter is green, it signals a bullish trend, and when it’s red, it indicates a bearish trend. The Sigma Cloud provides additional insights into trend strength. In a strong bullish trend, the cloud remains below the Kalman Filter line, while in a strong bearish trend, the cloud stays above it. Expansion and contraction of the Sigma Cloud indicate market momentum changes. Rapid expansion suggests an impulsive move, which could either signal the continuation of the trend or be an early sign of a possible trend reversal.
Mean Reversion: Watch for prices touching the upper or lower sigma bands, which often act as dynamic support and resistance.
Volatility Breakouts: Enable volatility-adjusted sigma bands. During high volatility, watch for price movements that extend beyond the bands as potential breakout signals.
Trend Continuation: When the Kalman Filter line aligns with a high trend strength, it signals a continuation in that direction.
█ Settings
Measurement Noise: Adjusts how sensitive the indicator is to price changes. Higher values smooth out fluctuations but delay reaction, while lower values increase sensitivity to short-term changes.
Kalman Filter Model: Choose between the standard, volume-adjusted, and Parkinson-adjusted models based on market conditions.
Band Sigma: Sets the standard deviation used for calculating the sigma bands, directly affecting the width of the dynamic support and resistance.
Volatility Adjusted Bands: Enables bands to dynamically adapt to volatility, increasing their effectiveness in fluctuating markets.
Trend Strength: Defines the lookback period for trend strength calculation. Shorter periods result in more responsive trend strength readings, while longer periods smooth out the calculation.
Trend Range: Specifies the lookback period for the trend range, affecting the assessment of trend stability over time.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. 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.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Cerca negli script per "bands"
DCA Strategy with Mean Reversion and Bollinger BandDCA Strategy with Mean Reversion and Bollinger Band
The Dollar-Cost Averaging (DCA) Strategy with Mean Reversion and Bollinger Bands is a sophisticated trading strategy that combines the principles of DCA, mean reversion, and technical analysis using Bollinger Bands. This strategy aims to capitalize on market corrections by systematically entering positions during periods of price pullbacks and reversion to the mean.
Key Concepts and Principles
1. Dollar-Cost Averaging (DCA)
DCA is an investment strategy that involves regularly purchasing a fixed dollar amount of an asset, regardless of its price. The idea behind DCA is that by spreading out investments over time, the impact of market volatility is reduced, and investors can avoid making large investments at inopportune times. The strategy reduces the risk of buying all at once during a market high and can smooth out the cost of purchasing assets over time.
In the context of this strategy, the Investment Amount (USD) is set by the user and represents the amount of capital to be invested in each buy order. The strategy executes buy orders whenever the price crosses below the lower Bollinger Band, which suggests a potential market correction or pullback. This is an effective way to average the entry price and avoid the emotional pitfalls of trying to time the market perfectly.
2. Mean Reversion
Mean reversion is a concept that suggests prices will tend to return to their historical average or mean over time. In this strategy, mean reversion is implemented using the Bollinger Bands, which are based on a moving average and standard deviation. The lower band is considered a potential buy signal when the price crosses below it, indicating that the asset has become oversold or underpriced relative to its historical average. This triggers the DCA buy order.
Mean reversion strategies are popular because they exploit the natural tendency of prices to revert to their mean after experiencing extreme deviations, such as during market corrections or panic selling.
3. Bollinger Bands
Bollinger Bands are a technical analysis tool that consists of three lines:
Middle Band: The moving average, usually a 200-period Exponential Moving Average (EMA) in this strategy. This serves as the "mean" or baseline.
Upper Band: The middle band plus a certain number of standard deviations (multiplier). The upper band is used to identify overbought conditions.
Lower Band: The middle band minus a certain number of standard deviations (multiplier). The lower band is used to identify oversold conditions.
In this strategy, the Bollinger Bands are used to identify potential entry points for DCA trades. When the price crosses below the lower band, this is seen as a potential opportunity for mean reversion, suggesting that the asset may be oversold and could reverse back toward the middle band (the EMA). Conversely, when the price crosses above the upper band, it indicates overbought conditions and signals potential market exhaustion.
4. Time-Based Entry and Exit
The strategy has specific entry and exit points defined by time parameters:
Open Date: The date when the strategy begins opening positions.
Close Date: The date when all positions are closed.
This time-bound approach ensures that the strategy is active only during a specified window, which can be useful for testing specific market conditions or focusing on a particular time frame.
5. Position Sizing
Position sizing is determined by the Investment Amount (USD), which is the fixed amount to be invested in each buy order. The quantity of the asset to be purchased is calculated by dividing the investment amount by the current price of the asset (investment_amount / close). This ensures that the amount invested remains constant despite fluctuations in the asset's price.
6. Closing All Positions
The strategy includes an exit rule that closes all positions once the specified close date is reached. This allows for controlled exits and limits the exposure to market fluctuations beyond the strategy's timeframe.
7. Background Color Based on Price Relative to Bollinger Bands
The script uses the background color of the chart to provide visual feedback about the price's relationship with the Bollinger Bands:
Red background indicates the price is above the upper band, signaling overbought conditions.
Green background indicates the price is below the lower band, signaling oversold conditions.
This provides an easy-to-interpret visual cue for traders to assess the current market environment.
Postscript: Configuring Initial Capital for Backtesting
To ensure the backtest results align with the actual investment scenario, users must adjust the Initial Capital in the TradingView strategy properties. This is done by calculating the Initial Capital as the product of the Total Closed Trades and the Investment Amount (USD). For instance:
If the user is investing 100 USD per trade and has 10 closed trades, the Initial Capital should be set to 1,000 USD.
Similarly, if the user is investing 200 USD per trade and has 24 closed trades, the Initial Capital should be set to 4,800 USD.
This adjustment ensures that the backtesting results reflect the actual capital deployed in the strategy and provides an accurate representation of potential gains and losses.
Conclusion
The DCA strategy with Mean Reversion and Bollinger Bands is a systematic approach to investing that leverages the power of regular investments and technical analysis to reduce market timing risks. By combining DCA with the insights offered by Bollinger Bands and mean reversion, this strategy offers a structured way to navigate volatile markets while targeting favorable entry points. The clear entry and exit rules, coupled with time-based constraints, make it a robust and disciplined approach to long-term investing.
Harmonic Rolling VWAP (Zeiierman)█ Overview
The Harmonic Rolling VWAP (Zeiierman) indicator combines the concept of the Rolling Volume Weighted Average Price (VWAP) with advanced harmonic analysis using Discrete Fourier Transform (DFT). This innovative indicator aims to provide traders with a dynamic view of price action, capturing both the volume-weighted price and underlying harmonic patterns. By leveraging this combination, traders can gain deeper insights into market trends and potential reversal points.
█ How It Works
The Harmonic Rolling VWAP calculates the rolling VWAP over a specified window of bars, giving more weight to periods with higher trading volume. This VWAP is then subjected to harmonic analysis using the Discrete Fourier Transform (DFT), which decomposes the VWAP into its frequency components.
Key Components:
Rolling VWAP (RVWAP): A moving average that gives more weight to higher volume periods, calculated over a user-defined window.
True Range (TR): Measures volatility by comparing the current high and low prices, considering the previous close price.
Discrete Fourier Transform (DFT): Analyzes the harmonic patterns within the RVWAP by decomposing it into its frequency components.
Standard Deviation Bands: These bands provide a visual representation of price volatility around the RVWAP, helping traders identify potential overbought or oversold conditions.
█ How to Use
Identify Trends: The RVWAP line helps in identifying the underlying trend by smoothing out short-term price fluctuations and focusing on volume-weighted prices.
Assess Volatility: The standard deviation bands around the RVWAP give a clear view of price volatility, helping traders identify potential breakout or breakdown points.
Find Entry and Exit Points: Traders can look for entries when the price is near the lower bands in an uptrend or near the upper bands in a downtrend. Exits can be considered when the price approaches the opposite bands or shows harmonic divergence.
█ Settings
VWAP Source: Defines the price data used for VWAP calculations. The source input defines the price data used for calculations. This setting affects the VWAP calculations and the resulting bands.
Window: Sets the number of bars used for the rolling calculations. The window input sets the number of bars used for the rolling calculations. A larger window smooths the VWAP and standard deviation bands, making the indicator less sensitive to short-term price fluctuations. A smaller window makes the indicator more responsive to recent price changes.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. 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.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Impulse Reactor RSI-SMA Trend Indicator [ApexLegion]Impulse Reactor RSI-SMA Trend Indicator
Introduction and Theoretical Background
Design Rationale
Standard indicators frequently generate binary 'BUY' or 'SELL' signals without accounting for the broader market context. This often results in erratic "Flip-Flop" behavior, where signals are triggered indiscriminately regardless of the prevailing volatility regime.
Impulse Reactor was engineered to address this limitation by unifying two critical requirements: Quantitative Rigor and Execution Flexibility.
The Solution
Composite Analytical Framework This script is not a simple visual overlay of existing indicators. It is an algorithmic synthesis designed to function as a unified decision-making engine. The primary objective was to implement rigorous quantitative analysis (Volatility Normalization, Structural Filtering) directly within an alert-enabled framework. This architecture is designed to process signals through strict, multi-factor validation protocols before generating real-time notifications, allowing users to focus on structurally validated setups without manual monitoring.
How It Works
This is not a simple visual mashup. It utilizes a cross-validation algorithm where the Trend Structure acts as a gatekeeper for Momentum signals:
Logic over Lag: Unlike simple moving average crossovers, this script uses a 15-layer Gradient Ribbon to detect "Laminar Flow." If the ribbon is knotted (Compression), the system mathematically suppresses all signals.
Volatility Normalization: The core calculation adapts to ATR (Average True Range). This means the indicator automatically expands in volatile markets and contracts in quiet ones, maintaining accuracy without constant manual tweaking.
Adaptive Signal Thresholding: It incorporates an 'Anti-Greed' algorithm (Dynamic Thresholding) that automatically adjusts entry criteria based on trend duration. This logic aims to mitigate the risk of entering positions during periods of statistical trend exhaustion.
Why Use It?
Market State Decoding: The gradient Ribbon visualizes the underlying trend phase in real-time.
◦ Cyan/Blue Flow: Strong Bullish Trend (Laminar Flow).
◦ Magenta/Pink Flow: Strong Bearish Trend.
◦ Compressed/Knotted: When the ribbon lines are tightly squeezed or overlapping, it signals Consolidation. The system filters signals here to avoid chop.
Noise Reduction: The goal is not to catch every pivot, but to isolate high-confidence setups. The logic explicitly filters out minor fluctuations to help maintain position alignment with the broader trend.
⚖️ Chapter 1: System Architecture
Introduction: Composite Analytical Framework
System Overview
Impulse Reactor serves as a comprehensive technical analysis engine designed to synthesize three distinct market dimensions—Momentum, Volatility, and Trend Structure—into a unified decision-making framework. Unlike traditional methods that analyze these metrics in isolation, this system functions as a central processing unit that integrates disparate data streams to construct a coherent model of market behavior.
Operational Objective
The primary objective is to transition from single-dimensional signal generation to a multi-factor assessment model. By fusing data from the Impulse Core (Volatility), Gradient Oscillator (Momentum), and Structural Baseline (Trend), the system aims to filter out stochastic noise and identify high-probability trade setups grounded in quantitative confluence.
Market Microstructure Analysis: Limitations of Conventional Models
Extensive backtesting and quantitative analysis have identified three critical inefficiencies in standard oscillator-based strategies:
• Bounded Oscillator Limitations (The "Oscillation Trap"): Traditional indicators such as RSI or Stochastics are mathematically constrained between fixed values (0 to 100). In strong trending environments, these metrics often saturate in "overbought" or "oversold" zones. Consequently, traders relying on static thresholds frequently exit structurally valid positions prematurely or initiate counter-trend trades against prevailing momentum, resulting in suboptimal performance.
• Quantitative Blindness to Quality: Standard moving averages and trend indicators often fail to distinguish the qualitative nature of price movement. They treat low-volume drift and high-velocity expansion identically. This inability to account for "Volatility Quality" leads to delayed responsiveness during critical market events.
• Fractal Dissonance (Timeframe Disconnect): Financial markets exhibit fractal characteristics where trends on lower timeframes may contradict higher timeframe structures. Manual integration of multi-timeframe analysis increases cognitive load and susceptibility to human error, often resulting in conflicting biases at the point of execution.
Core Design Principles
To mitigate the aforementioned systemic inefficiencies, Impulse Reactor employs a modular architecture governed by three foundational principles:
Principle A:
Volatility Precursor Analysis Market mechanics demonstrate that volatility expansion often functions as a leading indicator for directional price movement. The system is engineered to detect "Volatility Deviation" — specifically, the divergence between short-term and long-term volatility baselines—prior to its manifestation in price action. This allows for entry timing aligned with the expansion phase of market volatility.
Principle B:
Momentum Density Visualization The system replaces singular momentum lines with a "Momentum Density" model utilizing a 15-layer Simple Moving Average (SMA) Ribbon.
• Concept: This visualization represents the aggregate strength and consistency of the trend.
• Application: A fully aligned and expanded ribbon indicates a robust trend structure ("Laminar Flow") capable of withstanding minor counter-trend noise, whereas a compressed ribbon signals consolidation or structural weakness.
Principle C:
Adaptive Confluence Protocols Signal validity is strictly governed by a multi-dimensional confluence logic. The system suppresses signal generation unless there is synchronized confirmation across all three analytical vectors:
1. Volatility: Confirmed expansion via the Impulse Core.
2. Momentum: Directional alignment via the Hybrid Oscillator.
3. Structure: Trend validation via the Baseline. This strict filtering mechanism significantly reduces false positives in non-trending (choppy) environments while maintaining sensitivity to genuine breakouts.
🔍 Chapter 2: Core Modules & Algorithmic Logic
Module A: Impulse Core (Normalized Volatility Deviation)
Operational Logic The Impulse Core functions as a volatility-normalized momentum gauge rather than a standard oscillator. It is designed to identify "Volatility Contraction" (Squeeze) and "Volatility Expansion" phases by quantifying the divergence between short-term and long-term volatility states.
Volatility Z-Score Normalization
The formula implements a custom normalization algorithm. Unlike standard oscillators that rely on absolute price changes, this logic calculates the Z-Score of the Volatility Spread.
◦ Numerator: (atr_f - atr_s) captures the raw momentum of volatility expansion.
◦ Denominator: (std_f + 1e-6) standardizes this value against historical variance.
◦ Result: This allows the indicator scales consistently across assets (e.g., Bitcoin vs. Euro) without manual recalibration.
f_impulse() =>
atr_f = ta.atr(fastLen) // Fast Volatility Baseline
atr_s = ta.atr(slowLen) // Slow Volatility Baseline
std_f = ta.stdev(atr_f, devLen) // Volatility Standard Deviation
(atr_f - atr_s) / (std_f + 1e-6) // Normalized Differential Calculation
Algorithmic Framework
• Differential Calculation: The system computes the spread between a Fast Volatility Baseline (ATR-10) and a Slow Volatility Baseline (ATR-30).
• Normalization Protocol: To standardize consistency across diverse asset classes (e.g., Forex vs. Crypto), the raw differential is divided by the standard deviation of the volatility itself over a 30-period lookback.
• Signal Generation:
◦ Contraction (Squeeze): When the Fast ATR compresses below the Slow ATR, it registers a potential volatility buildup phase.
◦ Expansion (Release): A rapid divergence of the Fast ATR above the Slow ATR signals a confirmed volatility expansion, validating the strength of the move.
Module B: Gradient Oscillator (RSI-SMA Hybrid)
Design Rationale To mitigate the "noise" and "false reversal" signals common in single-line oscillators (like standard RSI), this module utilizes a 15-Layer Gradient Ribbon to visualize momentum density and persistence.
Technical Architecture
• Ribbon Array: The system generates 15 sequential Simple Moving Averages (SMA) applied to a volatility-adjusted RSI source. The length of each layer increases incrementally.
• State Analysis:
Momentum Alignment (Laminar Flow): When all 15 layers are expanded and parallel, it indicates a robust trend where buying/selling pressure is distributed evenly across multiple timeframes. This state helps filter out premature "overbought/oversold" signals.
• Consolidation (Compression): When the distance between the fastest layer (Layer 1) and the slowest layer (Layer 15) approaches zero or the layers intersect, the system identifies a "Non-Tradable Zone," preventing entries during choppy market conditions.
// Laminar Flow Validation
f_validate_trend() =>
// Calculate spread between Ribbon layers
ribbon_spread = ta.stdev(ribbon_array, 15)
// Only allow signals if Ribbon is expanded (Laminar Flow)
is_flowing = ribbon_spread > min_expansion_threshold
// If compressed (Knotted), force signal to false
is_flowing ? signal : na
Module C: Adaptive Signal Filtering (Behavioral Bias Mitigation)
This subsystem, operating as an algorithmic "Anti-Greed" Mechanism, addresses the statistical tendency for signal degradation following prolonged trends.
Dynamic Threshold Adjustment
• Win Streak Detection: The algorithm internally tracks the outcome of closed trade cycles.
• Sensitivity Multiplier: Upon detecting consecutive successful signals in the same direction, a Penalty_Factor is applied to the entry logic.
• Operational Impact: This effectively raises the Required_Slope threshold for subsequent signals. For example, after three consecutive bullish signals, the system requires a 30% steeper trend angle to validate a fourth entry. This enforces stricter discipline during extended trends to reduce the probability of entering at the point of trend exhaustion.
Anti-Greed Logic: Dynamic Threshold Calculation
f_adjust_threshold(base_slope, win_streak) =>
// Adds a 10% penalty to the difficulty for every consecutive win
penalty_factor = 0.10
risk_scaler = 1 + (win_streak * penalty_factor)
// Returns the new, harder-to-reach threshold
base_slope * risk_scaler
Module D: Trend Baseline (Triple-Smoothed Structure)
The Trend Baseline serves as the structural filter for all signals. It employs a Triple-Smoothed Hybrid Algorithm designed to balance lag reduction with noise filtration.
Smoothing Stages
1. Volatility Banding: Utilizes a SuperTrend-based calculation to establish the upper and lower boundaries of price action.
2. Weighted Filter: Applies a Weighted Moving Average (WMA) to prioritize recent price data.
3. Exponential Smoothing: A final Exponential Moving Average (EMA) pass is applied to create a seamless baseline curve.
Functionality
This "Heavy" baseline resists minor intraday volatility spikes while remaining responsive to sustained structural shifts. A signal is only considered valid if the price action maintains structural integrity relative to this baseline
🚦 Chapter 3: Risk Management & Exit Protocols
Quantitative Risk Management (TP/SL & Trailing)
Foundational Architecture: Volatility-Adjusted Geometry Unlike strategies relying on static nominal values, Impulse Reactor establishes dynamic risk boundaries derived from quantitative volatility metrics. This design aligns trade invalidation levels mathematically with the current market regime.
• ATR-Based Dynamic Bracketing:
The protocol calculates Stop-Loss and Take-Profit levels by applying Fibonacci coefficients (Default: 0.786 for SL / 1.618 for TP) to the Average True Range (ATR).
◦ High Volatility Environments: The risk bands automatically expand to accommodate wider variance, preventing premature exits caused by standard market noise.
◦ Low Volatility Environments: The bands contract to tighten risk parameters, thereby dynamically adjusting the Risk-to-Reward (R:R) geometry.
• Close-Validation Protocol ("Soft Stop"):
Institutional algorithms frequently execute liquidity sweeps—driving prices briefly below key support levels to accumulate inventory.
◦ Mechanism: When the "Soft Stop" feature is enabled, the system filters out intraday volatility spikes. The stop-loss is conditional; execution is triggered only if the candle closes beyond the invalidation threshold.
◦ Strategic Advantage: This logic distinguishes between momentary price wicks and genuine structural breakdowns, preserving positions during transient volatility.
• Step-Function Trailing Mechanism:
To protect unrealized PnL while allowing for normal price breathing, a two-phase trailing methodology is employed:
◦ Phase 1 (Activation): The trailing function remains dormant until the price advances by a pre-defined percentage threshold.
◦ Phase 2 (Dynamic Floor): Once armed, the stop level creates a moving floor, adjusting relative to price action while maintaining a volatility-based (ATR) buffer to systematically protect unrealized PnL.
• Algorithmic Exit Protocols (Dynamic Liquidity Analysis)
◦ Rationale: Inefficiencies of Static Targets Static "Take Profit" levels often result in suboptimal exits. They compel traders to close positions based on arbitrary figures rather than evolving market structure, potentially capping upside during significant trends or retaining positions while the underlying trend structure deteriorates.
◦ Solution: Structural Integrity Assessment The system utilizes a Dynamic Liquidity Engine to continuously audit the validity of the position. Instead of targeting a specific price point, the algorithm evaluates whether the trend remains statistically robust.
Multi-Factor Exit Logic (The Tri-Vector System)
The Smart Exit protocol executes only when specific algorithmic invalidation criteria are met:
• 1. Momentum Exhaustion (Confluence Decay): The system monitors a 168-hour rolling average of the Confluence Score. A significant deviation below this historical baseline indicates momentum exhaustion, signaling that the driving force behind the trend has dissipated prior to a price reversal. This enables preemptive exits before a potential drawdown.
• 2. Statistical Over-Extension (Mean Reversion): Utilizing the core volatility logic, the system identifies instances where price deviates beyond 2.0 standard deviations from the mean. While the trend may be technically bullish, this statistical anomaly suggests a high probability of mean reversion (elastic snap-back), triggering a defensive exit to capitalize on peak valuation.
• 3. Oscillator Rejection (Immediate Pivot): To manage sudden V-shaped volatility, the system monitors RSI pivots. If a sharp "Pivot High" or divergence is detected, the protocol triggers an immediate "Peak Exit," bypassing standard trend filters to secure liquidity during high-velocity reversals.
🎨 Chapter 4: Visualization Guide
Gradient Oscillator Ribbon
The 15-layer SMA ribbon visualized via plot(r1...r15) represents the "Momentum Density" of the market.
• Visuals:
◦ Cyan/Blue Ribbon: Indicates Bullish Momentum.
◦ Pink/Magenta Ribbon: Indicates Bearish Momentum.
• Interpretation:
◦ Laminar Flow: When the ribbon expands widely and flows in parallel, it signifies a robust trend where momentum is distributed evenly across timeframes. This is the ideal state for trend-following.
◦ Compression (Consolidation): If the ribbon becomes narrow, twisted, or knotted, it indicates a "Non-Tradable Zone" where the market lacks a unified direction. Traders are advised to wait for clarity.
◦ Over-Extension: If the top layer crosses the Overbought (85) or Oversold (15) lines, it visually warns of potential market overheating.
Trend Baseline
The thick, color-changing line plotted via plot(baseline) represents the Structural Backbone of the market.
• Visuals: Changes color based on the trend direction (Blue for Bullish, Pink for Bearish).
• Interpretation:
Structural Filter: Long positions are statistically favored only when price action sustains above this baseline, while short positions are favored below it.
Dynamic Support/Resistance: The baseline acts as a dynamic support level during uptrends and resistance during downtrends.
Entry Signals & Labels
Text labels ("Long Entry", "Short Entry") appear when the system detects high-probability setups grounded in quantitative confluence.
• Visuals: Labeled signals appear above/below specific candles.
• Interpretation:
These signals represent moments where Volatility (Expansion), Momentum (Alignment), and Structure (Trend) are synchronized.
Smart Exit: Labels such as "Smart Exit" or "Peak Exit" appear when the system detects momentum exhaustion or structural decay, prompting a defensive exit to preserve capital.
Dynamic TP/SL Boxes
The semi-transparent colored zones drawn via fill() represent the risk management geometry.
• Visuals: Colored boxes extending from the entry point to the Take Profit (TP) and Stop Loss (SL) levels.
• Function:
Volatility-Adjusted Geometry: Unlike static price targets, these boxes expand during high volatility (to prevent wicks from stopping you out) and contract during low volatility (to optimize Risk-to-Reward ratios).
SAR + MACD Glow
Small glowing shapes appearing above or below candles.
• Visuals: Triangle or circle glows near the price bars.
• Interpretation:
This visual indicates a secondary confirmation where Parabolic SAR and MACD align with the main trend direction. It serves as an additional confluence factor to increase confidence in the trade setup.
Support/Resistance Table
A small table located at the bottom-right of the chart.
• Function: Automatically identifies and displays recent Pivot Highs (Resistance) and Pivot Lows (Support).
• Interpretation: These levels can be used as potential targets for Take Profit or invalidation points for manual Stop Loss adjustments.
🖥️ Chapter 5: Dashboard & Operational Guide
Integrated Analytics Panel (Dashboard Overview)
To facilitate rapid decision-making without manual calculation, the system aggregates critical market dimensions into a unified "Heads-Up Display" (HUD). This panel monitors real-time metrics across multiple timeframes and analytical vectors.
A. Intermediate Structure (12H Trend)
• Function: Anchors the intraday analysis to the broader market structure using a 12-hour rolling window.
• Interpretation:
◦ Bullish (> +0.5%): Indicates a positive structural bias. Long setups align with the macro flow.
◦ Bearish (< -0.5%): Indicates structural weakness. Short setups are statistically favored.
◦ Neutral: Represents a ranging environment where the Confluence Score becomes the primary weighting factor.
B. Composite Confluence Score (Signal Confidence)
• Definition: A probability metric derived from the synchronization of Volatility (Impulse Core), Momentum (Ribbon), and Trend (Baseline).
• Grading Scale:
Strong Buy/Sell (> 7.0 / < 3.0): Indicates full alignment across all three vectors. Represents a "Prime Setup" eligible for standard position sizing.
Buy/Sell (5.0–7.0 / 3.0–5.0): Indicates a valid trend but with moderate volatility confirmation.
Neutral: Signals conflicting data (e.g., Bullish Momentum vs. Bearish Structure). Trading is not recommended ("No-Trade Zone").
C. Statistical Deviation Status (Mean Reversion)
• Logic: Utilizes Bollinger Band deviation principles to quantify how far price has stretched from the statistical mean (20 SMA).
• Alert States:
Over-Extended (> 2.0 SD): Warning that price is statistically likely to revert to the mean (Elastic Snap-back), even if the trend remains technically valid. New entries are discouraged in this zone.
Normal: Price is within standard distribution limits, suitable for trend-following entries.
D. Volatility Regime Classification
• Metric: Compares current ATR against a 100-period historical baseline to categorize the market state.
• Regimes:
Low Volatility (Lvl < 1.0): Market Compression. Often precedes volatility expansion events.
Mid Volatility (Lvl 1.0 - 1.5): Standard operating environment.
High Volatility (Lvl > 1.5): Elevated market stress. Risk parameters should be adjusted (e.g., reduced position size) to account for increased variance.
E. Performance Telemetry
• Function: Displays the historical reliability of the Trend Baseline for the current asset and timeframe.
• Operational Threshold: If the displayed Win Rate falls below 40%, it suggests the current market behavior is incoherent (choppy) and does not respect trend logic. In such cases, switching assets or timeframes is recommended.
Operational Protocols & Signal Decoding
Visual Interpretation Standards
• Laminar Flow (Trade Confirmation): A valid trend is visually confirmed when the 15-layer SMA Ribbon is fully expanded and parallel. This indicates distributed momentum across timeframes.
• Consolidation (No-Trade): If the ribbon appears twisted, knotted, or compressed, the market lacks a unified directional vector.
• Baseline Interaction: The Triple-Smoothed Baseline acts as a dynamic support/resistance filter. Long positions remain valid only while price sustains above this structure.
System Calibration (Settings)
• Adaptive Signal Filtering (Prev. Anti-Greed): Enabled by default. This logic automatically raises the required trend slope threshold following consecutive wins to mitigate behavioral bias.
• Impulse Sensitivity: Controls the reactivity of the Volatility Core. Higher settings capture faster moves but may introduce more noise.
⚙️ Chapter 6: System Configuration & Alert Guide
This section provides a complete breakdown of every adjustable setting within Impulse Reactor to assist you in tailoring the engine to your specific needs.
🌐 LANGUAGE SETTINGS (Localization)
◦ Select Language (Default: English):
Function: Instantly translates all chart labels, dashboard texts into your preferred language.
Supported: English, Korean, Chinese, Spanish
⚡ IMPULSE CORE SETTINGS (Volatility Engine)
◦ Deviation Lookback (Default: 30): The period used to calculate the standard deviation of volatility.
Role: Sets the baseline for normalizing momentum. Higher values make the core smoother but slower to react.
◦ Fast Pulse Length (Default: 10): The short-term ATR period.
Role: Detects rapid volatility expansion.
◦ Slow Pulse Length (Default: 30): The long-term ATR baseline.
Role: Establishes the background volatility level. The core signal is derived from the divergence between Fast and Slow pulses.
🎯 TP/SL SETTINGS (Risk Management)
◦ SL/TP Fibonacci (Default: 0.786 / 1.618): Selects the Fibonacci ratio used for risk calculation.
◦ SL/TP Multiplier (Default: 1.5 / 2): Applies a multiplier to the ATR-based bands.
Role: Expands or contracts the Take Profit and Stop Loss boxes. Increase these values for higher volatility assets (like Altcoins) to avoid premature stop-outs.
◦ ATR Length (Default: 14): The lookback period for calculating the Average True Range used in risk geometry.
◦ Use Soft Stop (Close Basis):
Role: If enabled, Stop Loss alerts only trigger if a candle closes beyond the invalidation level. This prevents being stopped out by wick manipulations.
🔊 RIBBON SETTINGS (Momentum Visualization)
◦ Show SMA Ribbon: Toggles the visibility of the 15-layer gradient ribbon.
◦ Ribbon Line Count (Default: 15): The number of SMA lines in the ribbon array.
◦ Ribbon Start Length (Default: 2) & Step (Default: 1): Defines the spread of the ribbon.
Role: Controls the "thickness" of the momentum density visualization. A wider step creates a broader ribbon, useful for higher timeframes.
📎 DISPLAY OPTIONS
◦ Show Entry Lines / TP/SL Box / Position Labels / S/R Levels / Dashboard: Toggles individual visual elements on the chart to reduce clutter.
◦ Show SAR+MACD Glow: Enables the secondary confirmation shapes (triangles/circles) above/below candles.
📈 TREND BASELINE (Structural Filter)
◦ Supertrend Factor (Default: 12) & ATR Period (Default: 90): Controls the sensitivity of the underlying Supertrend algorithm used for the baseline calculation.
◦ WMA Length (40) & EMA Length (14): The smoothing periods for the Triple-Smoothed Baseline.
◦ Min Trend Duration (Default: 10): The minimum number of bars the trend must be established before a signal is considered valid.
🧠 SMART EXIT (Dynamic Liquidity)
◦ Use Smart Exit: Enables the momentum exhaustion logic.
◦ Exit Threshold Score (Default: 3): The sensitivity level for triggering a Smart Exit. Lower values trigger earlier exits.
◦ Average Period (168) & Min Hold Bars (5): Defines the rolling window for momentum decay analysis and the minimum duration a trade must be held before Smart Exit logic activates.
🛡️ TRAILING STOP (Step)
◦ Use Trailing Stop: Activates the step-function trailing mechanism.
◦ Step 1 Activation % (0.5) & Offset % (0.5): The price must move 0.5% in your favor to arm the first trail level, which sets a stop 0.5% behind price.
◦ Step 2 Activation % (1) & Offset % (0.2): Once price moves 1%, the trail tightens to 0.2%, securing the position.
🌀 SAR & MACD SETTINGS (Secondary Confirmation)
◦ SAR Start/Increment/Max: Standard Parabolic SAR parameters.
◦ SAR Score Scaling (ATR): Adjusts how much weight the SAR signal has in the overall confluence score.
◦ MACD Fast/Slow/Signal: Standard MACD parameters used for the "Glow" signals.
🔄 ANTI-GREED LOGIC (Behavioral Bias)
◦ Strict Entry after Win: Enables the negative feedback loop.
◦ Strict Multiplier (Default: 1.1): Increases the entry difficulty by 10% after each win.
Role: Prevents overtrading and entering at the top of an extended trend.
🌍 HTF FILTER (Multi-Timeframe)
◦ Use Auto-Adaptive HTF Filter: Automatically selects a higher timeframe (e.g., 1H -> 4H) to filter signals.
◦ Bypass HTF on Steep Trigger: Allows an entry even against the HTF trend if the local momentum slope is exceptionally steep (catch powerful reversals).
📉 RSI PEAK & CHOPPINESS
◦ RSI Peak Exit (Instant): Triggers an immediate exit if a sharp RSI pivot (V-shape) is detected.
◦ Choppiness Filter: Suppresses signals if the Choppiness Index is above the threshold (Default: 60), indicating a flat market.
📐 SLOPE TRIGGER LOGIC
◦ Force Entry on Steep Slope: Overrides other filters if the price angle is extremely vertical (high velocity).
◦ Slope Sensitivity (1.5): The angle required to trigger this override.
⛔ FLAT MARKET FILTER (ADX & ATR)
◦ Use ADX Filter: Blocks signals if ADX is below the threshold (Default: 20), indicating no trend.
◦ Use ATR Flat Filter: Blocks signals if volatility drops below a critical level (dead market).
🔔 Alert Configuration Guide
Impulse Reactor is designed with a comprehensive suite of alert conditions, allowing you to automate your trading or receive real-time notifications for specific market events.
How to Set Up:
Click the "Alert" (Clock) icon in the TradingView toolbar.
Select "Impulse Reactor " from the Condition dropdown.
Choose one of the specific trigger conditions below:
🚀 Entry Signals (Trend Initiation)
Long Entry:
Trigger: Fires when a confirmed Bullish Setup is detected (Momentum + Volatility + Structure align).
Usage: Use this to enter new Long positions.
Short Entry:
Trigger: Fires when a confirmed Bearish Setup is detected.
Usage: Use this to enter new Short positions.
🎯 Profit Taking (Target Levels)
Long TP:
Trigger: Fires when price hits the calculated Take Profit level for a Long trade.
Usage: Automate partial or full profit taking.
Short TP:
Trigger: Fires when price hits the calculated Take Profit level for a Short trade.
Usage: Automate partial or full profit taking.
🛡️ Defensive Exits (Risk Management)
Smart Exit:
Trigger: Fires when the system detects momentum decay or statistical exhaustion (even if the trend hasn't fully reversed).
Usage: Recommended for tightening stops or closing positions early to preserve gains.
Overbought / Oversold:
Trigger: Fires when the ribbon extends into extreme zones.
Usage: Warning signal to prepare for a potential reversal or pullback.
💡 Secondary Confirmation (Confluence)
SAR+MACD Bullish:
Trigger: Fires when Parabolic SAR and MACD align bullishly with the main trend.
Usage: Ideal for Pyramiding (adding to an existing winning position).
SAR+MACD Bearish:
Trigger: Fires when Parabolic SAR and MACD align bearishly.
Usage: Ideal for adding to short positions.
⚠️ Chapter 7: Conclusion & Risk Disclosure
Methodological Synthesis
Impulse Reactor represents a shift from reactive price tracking to proactive energy analysis. By decomposing market activity into its atomic components — Volatility, Momentum, and Structure — and reconstructing them into a coherent decision model, the system aims to provide a quantitative framework for market engagement. It is designed not to predict the future, but to identify high-probability conditions where kinetic energy and trend structure align.
Disclaimer & Risk Warnings
◦ Educational Purpose Only
This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments.
◦ No Guarantee of Performance
Past performance is not indicative of future results. All metrics displayed on the dashboard (including "Win Rate" and "P&L") are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions.
◦ High-Risk Warning
Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades.
◦ Software Limitations
The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Investor Tool - Z ScoreThe Investor Tool is intended as a tool for long term investors, indicating periods where prices are likely approaching cyclical tops or bottoms. The tool uses two simple moving averages of price as the basis for under/overvalued conditions: the 2-year MA (green) and a 5x multiple of the 2-year MA (red).
Price trading below the 2-year MA has historically generated outsized returns, and signalled bear cycle lows.
Price trading above the 2-year MA x5 has been historically signalled bull cycle tops and a zone where investors de-risk.
Just like the Glassnode one, but here on TV and with StDev bands
Now with Z-SCORE calculation:
The Z-Score is calculated to be -3 Z at the bottom bands and 3 Z at the top bands
mean = (upper_sma + bottom_sma) / 2
bands_range = upper_sma - bottom_sma
stdDev = bands_range != 0 ? bands_range / 6 : 0
zScore = stdDev != 0 ? (close - mean) / stdDev : 0
Created for TRW
Daily ATR Bonanza: Expected Moves - Tr33man Daily ATR Bonanza: Expected Moves
Overview 🤷♂️
The Daily ATR Bonanza script is a powerful trading tool designed to help traders visualize and understand potential price movements using the Average True Range (ATR). It provides daily and weekly ATR levels, historical statistics, and conditional probability analysis to give traders actionable insights. The script also plots the daily Keltner channel. This script is ideal for traders who want to gauge volatility, identify key levels, and make data-driven decisions.
b]Key Features:
📈 1. Daily and Weekly ATR Levels
🔵ATR Levels: The script calculates and displays ATR-based levels for the day and week. These levels are derived from the previous day's or week's close price and are adjusted using customizable multipliers (0.5x, 1x, and 1.5x by default).
🔵You can choose the number of ATR levels (1, 2, or 3) and adjust the multipliers to suit your trading strategy.
🌐 2. ATR Bands (Keltner Channels)
🔵The script includes an option to display ATR Bands, which are volatility-based envelopes around a moving average. These bands help identify overbought and oversold conditions.
🔵You can adjust the ATR multiplier and the length of the moving average used for the bands.
🧮 3. Historical Statistics and Conditional Probability
🔵 Historical Analysis: The script analyzes historical price movements to calculate the likelihood of closing at certain ATR levels.
🔵 Conditional Probability: This feature shows the probability of the price reaching specific ATR levels given the current market conditions. The conditional matches historical data by an open in the same opening ATR bucket, as well as the current price bucket having been visited in the historical case. Conditional probabilities are just statistics, and do not predict anything.
Data Table: 📚
🔵 Historical Close Probability: The percentage of days the price closed within each ATR level.
🔵 Conditional Close Probability: The likelihood of the price closing within each ATR level today.
❓ What is Conditional Probability? ❓
Conditional probability is a statistical measure that calculates the likelihood of an event occurring given that another event has already occurred. In this script, it is used to determine the probability of the price reaching specific ATR levels based on the current opening range as well as current ATR distance from the previous close.
For example:
If the market opens near the lower end of the first ATR level, the script calculates the likelihood of the price reaching the upper end of the first, second, or third ATR level.
This analysis is based on historical data, making it a powerful tool for understanding potential price movements.
🌟 Understanding the Levels
🔵Daily Levels: These are based on the previous day's close price and ATR. They are updated at the start of each new day.
🔵Weekly Levels: These are based on the previous week's close price and ATR. They are updated at the start of each new week.
🔵ATR Bands: These are dynamic levels that adjust with market volatility.
🔬 Analyze the Statistics (Daily only for now, no weekly yet)
🔵Use the interactive table to understand historical probabilities and conditional probabilities.
🔵Focus on the current opening range and the likelihood of reaching specific levels.
🧠 Make Trading Decisions
🔵Use the ATR levels and bands to identify key support and resistance levels.
🔵Use the conditional probability table to gauge the likelihood of reaching specific targets.
🔵Adjust your strategy based on the historical performance of the market.
Example Use Cases
1. Day Trading
Use the daily ATR levels to set intraday targets and stop-loss levels.
Monitor the conditional probability table to adjust your expectations based on the opening range.
2. Swing Trading
Use the weekly ATR levels to identify longer-term support and resistance levels.
3. Scalping
Use the ATR bands to identify overbought and oversold conditions.
Use the conditional probability table to quickly assess the likelihood of price movements.
Half-Trend Channel [BigBeluga]Half Trend Channel is a powerful trend-following indicator designed to identify trend direction, fakeouts, and potential reversal points. The combination of upper/lower bands, midline coloring, and specific signals makes it ideal for spotting trend continuation and market reversals.
The base of the channel is calculated using smoothed half-trend logic.
// Initialize half trend on the first bar
if barstate.isfirst
hl_t := close
// Update half trend value based on conditions
switch
closeMA < hl_t and highestHigh < hl_t => hl_t := highestHigh
closeMA > hl_t and lowestLow > hl_t => hl_t := lowestLow
=> hl_t := hl_t
// Smooth
float s_hlt = ta.hma(hl_t, len)
🔵 Key Features:
Upper and Lower Bands:
The bands adapt dynamically to market volatility.
Price movements toward the bands help identify areas of overextension and potential reversal points.
Midline Trend Signal:
The midline changes color to reflect the current trend:
Green Midline: Indicates an uptrend.
Purple Midline: Signals a downtrend.
Fakeout Signals ("X"):
"X" markers appear when price briefly breaches the outer bands but fails to sustain the move.
Fakeouts help traders identify areas where price momentum weakens.
Reversal Signals (Triangles):
Triangles (▲ and ▼) mark potential tops and bottoms:
▲ Up Triangles: Suggest a potential bottom and a reversal to the upside.
▼ Down Triangles: Indicate a potential top and a reversal to the downside.
Dynamic Trend Labels:
At the last bar, the indicator displays labels like "Trend Up" or "Trend Dn" , reflecting the current trend direction.
🔵 Usage:
Use the colored midline to determine the overall trend direction.
Monitor "X" fakeout signals to spot failed breakouts or momentum exhaustion near the bands.
Watch for reversal triangles (▲ and ▼) to identify potential trend reversals at tops or bottoms.
Combine the bands and midline signals to confirm trade entries and exits:
Enter long trades when price bounces off the lower band with a green midline.
Consider short trades when price reverses from the upper band with a purple midline.
Use the trend label (e.g., "Trend Up" or "Trend Dn") for quick confirmation of the current market state.
The Half Trend Channel is an essential tool for traders who want to follow trends, avoid fakeouts, and identify reliable tops and bottoms to optimize their trading decisions.
Volatility Signaling 50SMAOverview of the Script:
The script implements a volatility signaling indicator using a 50-period Simple Moving Average (SMA). It incorporates Bollinger Bands and the Average True Range (ATR) to dynamically adjust the SMA's color based on volatility conditions. Here's a detailed breakdown:
Components of the Script:
1. Inputs:
The script allows the user to customize key parameters for flexibility:
Bollinger Bands Length (length): Determines the period for calculating the Bollinger Bands.
Source (src): The price data to use, defaulting to the closing price.
Standard Deviation Multiplier (mult): Scales the Bollinger Bands' width.
ATR Length (atrLength): Sets the period for calculating the ATR.
The 50-period SMA length (smaLength) is fixed at 50.
2. Bollinger Bands Calculation:
Basis: Calculated as the SMA of the selected price source over the specified length.
Upper and Lower Bands: Determined by adding/subtracting a scaled standard deviation (dev) from the basis.
3. ATR Calculation:
Computes the Average True Range over the user-defined atrLength.
4. Volatility-Based Conditions:
The script establishes thresholds for Bollinger Band width relative to ATR:
Yellow Condition: When the band width (upper - lower) is less than 1.25 times the ATR.
Orange Condition: When the band width is less than 1.5 times the ATR.
Red Condition: When the band width is less than 1.75 times the ATR.
5. Dynamic SMA Coloring:
The 50-period SMA is colored based on the above conditions:
Yellow: Indicates relatively low volatility.
Orange: Indicates moderate volatility.
Red: Indicates higher volatility.
White: Default color when no conditions are met.
6. Plotting the 50-Period SMA:
The script plots the SMA (sma50) with a dynamically assigned color, enabling visual analysis of market conditions.
Use Case:
This script is ideal for traders seeking to assess market volatility and identify changes using Bollinger Bands and ATR. The colored SMA provides an intuitive way to gauge market dynamics directly on the chart.
Example Visualization:
Yellow SMA: The market is in a low-volatility phase.
Orange SMA: Volatility is picking up but remains moderate.
Red SMA: Higher volatility, potentially signaling significant market activity.
White SMA: Neutral/default state.
Median Volume Weighted DeviationMVWD (Median Volume Weighted Deviation)
The Median Volume-Weighted Deviation is a technical trend following indicator that overlays dynamic bands on the price chart, centered around a Volume Weighted Average Price (VWAP). By incorporating volume-weighted standard deviation and its median, it identifies potential overbought and oversold conditions, generating buy and sell signals based on price interactions with the bands. The fill color between the bands visually reflects the current signal, enhancing market sentiment analysis.
How it Works
VWAP Calculation: Computes the Volume-Weighted Average Price over a specific lookback period (n), emphasizing price levels with higher volume.
Volume Weighted Standard Deviation: Measures price dispersion around the VWAP, weighted by volume, over the same period.
Median Standard Deviation: Applies a median filter over (m) periods to smooth the stand deviation, reducing noise in volatility estimates.
Bands: Constructs upper and lower bands by adding and subtracting a multiplier (k) times the median standard deviation from the VWAP
Signals:
Buy Signal: Triggers when the closing price crosses above the upper band.
Sell Signal: Triggers when the closing price crosses below the lower band.
Inputs
Lookback (n): Number of periods for the VWAP and standard deviation calculations. Default is set to 14.
Median Standard Deviation (m): Periods for the median standard deviation. Default is set to 2.
Standard Deviation Multiplier (k): Multiplier to adjust band width. Default is set to 1.7 with a step of 0.1.
Customization
Increase the Lookback (n) for a smoother VWAP and broader perspective, or decrease the value for higher sensitivity.
Adjust Median Standard Deviation (m) to control the smoothness of the standard deviation filter.
Modify the multiplier (k) to widen or narrow the bands based on the market volatility preferences.
Dynamic VWAP Levels (V1.0)The script calculates bands around the VWAP (Volume Weighted Average Price) using the Average True Range (ATR) to adjust the levels according to market reality. Buy and sell signals are generated when the price crosses these bands.
Customizable Parameters SmoothingLength (SmoothLength): The period used to smooth the levels. A higher value results in smoother bands that are less susceptible to rapid fluctuations.
Use EMA for smoothing?: Selects between using the Exponential Moving Average (EMA) or the Simple Moving Average (SMA) for smoothing.
ATR Length: The period used to calculate the ATR, which determines the frequency.
ATR Multiplier: A multiplier that adjusts the amplitude of the bands around the VWAP.
How the Script Works Calculating VWAP and Bands: The VWAP is calculated to obtain the volume weighted average price.
Bands are created around the VWAP by adding or subtracting a fraction of the ATR to account for the current market variation.
Smoothing Application: Price levels are smoothed to reduce market noise, allowing for better visualization of trends.
Signal Generation: Buy Signal: Generated when price crosses upwards the smoothed lower band (default dp7_smooth).
Sell Signal: Generated when price crosses downwards the smoothed upper band (default dp1_smooth).
PlanDeFi: Adaptive Trend Ribbons [ATR+RSI]#### **Overview**
The **Crypto Half-Trend Pro ** is a trend-following indicator designed to identify bullish and bearish market conditions using a combination of **moving averages, volatility adjustments, and dynamic ATR bands**. This enhanced version improves on the traditional Half-Trend system by incorporating **EMA smoothing, volatility-based adjustments, and additional fakeout/reversal detection mechanisms**.
#### **Key Features**
✅ **Trend Detection:**
- Uses a combination of fast and slow moving averages (EMA/SMA) to determine trend direction.
- Implements **Hull Moving Average (HMA)** smoothing for better trend visualization.
✅ **Dynamic ATR Bands:**
- Adjusts bands based on market volatility using **RSI-based ATR multipliers**.
- Helps identify potential **breakouts and trend reversals**.
✅ **Fakeout & Reversal Detection:**
- Detects potential **fake breakouts** by analyzing price action against extended ATR bands.
- Identifies **early reversal signals** using price crossovers and volume confirmation.
✅ **Customizable Alerts & Visuals:**
- Built-in **buy & sell signals** for trend confirmation.
- Color-coded bullish/bearish trend lines and **fakeout warnings**.
- **TradingView alerts** for trend shifts and reversals.
#### **How It Works**
🔹 The indicator calculates a **smoothed trend line** using a Hull Moving Average on dynamic price levels.
🔹 ATR bands expand/contract dynamically based on **market volatility** to improve signal accuracy.
🔹 Trend direction is confirmed when price crosses the trend line **with volume confirmation**.
🔹 **Fakeouts** are detected when price temporarily exceeds extended bands but fails to hold momentum.
🔹 **Reversal signals** are generated when price breaks back into the ATR zone with volume spikes.
#### **How to Use It**
- 📈 **Buy Signal:** When price breaks above the trend line, confirmed by volume and crossover signals.
- 📉 **Sell Signal:** When price breaks below the trend line with confirmed bearish conditions.
- 🚨 **Reversal Warning:** If price sharply re-enters the ATR zone with volume confirmation, expect a potential trend shift.
- 🛑 **Fakeout Alert:** If price temporarily breaks resistance but closes back inside, it may be a false move.
#### **Ideal For**
✔️ Crypto & Forex traders looking for **dynamic trend signals**
✔️ Swing traders wanting to **avoid fakeouts & catch reversals**
✔️ Traders seeking a **customizable, volatility-adjusted trend system**
🚀 **Try PlanDeFi: Adaptive Trend Ribbons today and improve your trend analysis!**
EMA 9/13/18/25 + Bollinger BandThe indicator combines two components: Exponential Moving Averages (EMAs) and Bollinger Bands.
Exponential Moving Averages (EMAs): The indicator calculates four EMAs with different periods: 9, 13, 18, and 25. An Exponential Moving Average is a type of moving average that places a greater weight and significance on the most recent data points. As the name suggests, it's an average of the asset's price over a certain period, with recent prices given more weight in the calculation, making it more responsive to recent price changes.
Bollinger Bands: Bollinger Bands consist of a simple moving average (the basis) and two standard deviations plotted away from it. The standard deviations are multiplied by a factor (usually 2) to determine the distance from the basis. These bands dynamically adjust themselves based on recent price movements. The upper band represents the highest price level reached in the given period, while the lower band represents the lowest price level.
Combining these components provides traders with insights into both trend direction and volatility. The EMAs help identify trends by smoothing out price data, while the Bollinger Bands offer insights into volatility and potential price reversal points. Traders often use the crossovers of EMAs and interactions with Bollinger Bands to make trading decisions. For example, when the price touches the upper Bollinger Band, it may indicate overbought conditions, while touching the lower band may suggest oversold conditions. Additionally, crossovers of EMAs (such as the shorter-term EMA crossing above or below the longer-term EMA) may signal changes in trend direction.
Mogwai Method with RSI and EMA - BTCUSD 15mThis is a custom TradingView indicator designed for trading Bitcoin (BTCUSD) on a 15-minute timeframe. It’s based on the Mogwai Method—a mean-reversion strategy—enhanced with the Relative Strength Index (RSI) for momentum confirmation. The indicator generates buy and sell signals, visualized as green and red triangle arrows on the chart, to help identify potential entry and exit points in the volatile cryptocurrency market.
Components
Bollinger Bands (BB):
Purpose: Identifies overextended price movements, signaling potential reversions to the mean.
Parameters:
Length: 20 periods (standard for mean-reversion).
Multiplier: 2.2 (slightly wider than the default 2.0 to suit BTCUSD’s volatility).
Role:
Buy signal when price drops below the lower band (oversold).
Sell signal when price rises above the upper band (overbought).
Relative Strength Index (RSI):
Purpose: Confirms momentum to filter out false signals from Bollinger Bands.
Parameters:
Length: 14 periods (classic setting, effective for crypto).
Overbought Level: 70 (price may be overextended upward).
Oversold Level: 30 (price may be overextended downward).
Role:
Buy signal requires RSI < 30 (oversold).
Sell signal requires RSI > 70 (overbought).
Exponential Moving Averages (EMAs) (Plotted but not currently in signal logic):
Purpose: Provides trend context (included in the script for visualization, optional for signal filtering).
Parameters:
Fast EMA: 9 periods (short-term trend).
Slow EMA: 50 periods (longer-term trend).
Role: Can be re-added to filter signals (e.g., buy only when Fast EMA > Slow EMA).
Signals (Triangles):
Buy Signal: Green upward triangle below the bar when price is below the lower Bollinger Band and RSI is below 30.
Sell Signal: Red downward triangle above the bar when price is above the upper Bollinger Band and RSI is above 70.
How It Works
The indicator combines Bollinger Bands and RSI to spot mean-reversion opportunities:
Buy Condition: Price breaks below the lower Bollinger Band (indicating oversold conditions), and RSI confirms this with a reading below 30.
Sell Condition: Price breaks above the upper Bollinger Band (indicating overbought conditions), and RSI confirms this with a reading above 70.
The strategy assumes that extreme price movements in BTCUSD will often revert to the mean, especially in choppy or ranging markets.
Visual Elements
Green Upward Triangles: Appear below the candlestick to indicate a buy signal.
Red Downward Triangles: Appear above the candlestick to indicate a sell signal.
Bollinger Bands: Gray lines (upper, middle, lower) plotted for reference.
EMAs: Blue (Fast) and Orange (Slow) lines for trend visualization.
How to Use the Indicator
Setup
Open TradingView:
Log into TradingView and select a BTCUSD chart from a supported exchange (e.g., Binance, Coinbase, Bitfinex).
Set Timeframe:
Switch the chart to a 15-minute timeframe (15m).
Add the Indicator:
Open the Pine Editor (bottom panel in TradingView).
Copy and paste the script provided.
Click “Add to Chart” to apply it.
Verify Display:
You should see Bollinger Bands (gray), Fast EMA (blue), Slow EMA (orange), and buy/sell triangles when conditions are met.
Trading Guidelines
Buy Signal (Green Triangle Below Bar):
What It Means: Price is oversold, potentially ready to bounce back toward the Bollinger Band middle line.
Action:
Enter a long position (buy BTCUSD).
Set a take-profit near the middle Bollinger Band (bb_middle) or a resistance level.
Place a stop-loss 1-2% below the entry (or based on ATR, e.g., ta.atr(14) * 2).
Best Context: Works well in ranging markets; avoid during strong downtrends.
Sell Signal (Red Triangle Above Bar):
What It Means: Price is overbought, potentially ready to drop back toward the middle line.
Action:
Enter a short position (sell BTCUSD) or exit a long position.
Set a take-profit near the middle Bollinger Band or a support level.
Place a stop-loss 1-2% above the entry.
Best Context: Effective in ranging markets; avoid during strong uptrends.
Trend Filter (Optional):
To reduce false signals in trending markets, you can modify the script:
Add and ema_fast > ema_slow to the buy condition (only buy in uptrends).
Add and ema_fast < ema_slow to the sell condition (only sell in downtrends).
Check the Fast EMA (blue) vs. Slow EMA (orange) alignment visually.
Tips for BTCUSD on 15-Minute Charts
Volatility: BTCUSD can be erratic. If signals are too frequent, increase bb_mult (e.g., to 2.5) or adjust RSI levels (e.g., 75/25).
Confirmation: Use volume spikes or candlestick patterns (e.g., doji, engulfing) to confirm signals.
Time of Day: Mean-reversion works best during low-volume periods (e.g., Asian session in crypto).
Backtesting: Use TradingView’s Strategy Tester (convert to a strategy by adding entry/exit logic) to evaluate performance with historical BTCUSD data up to March 13, 2025.
Risk Management
Position Size: Risk no more than 1-2% of your account per trade.
Stop Losses: Always use stops to protect against BTCUSD’s sudden moves.
Avoid Overtrading: Wait for clear signals; don’t force trades in choppy or unclear conditions.
Example Scenario
Chart: BTCUSD, 15-minute timeframe.
Buy Signal: Price drops to $58,000, below the lower Bollinger Band, RSI at 28. A green triangle appears.
Action: Buy at $58,000, target $59,000 (middle BB), stop at $57,500.
Sell Signal: Price rises to $60,500, above the upper Bollinger Band, RSI at 72. A red triangle appears.
Action: Sell at $60,500, target $59,500 (middle BB), stop at $61,000.
This indicator is tailored for mean-reversion trading on BTCUSD. Let me know if you’d like to tweak it further (e.g., add filters, alerts, or alternative indicators)!
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Super IndicatorOverview of the Combined Indicator
This combined indicator leverages three major technical analysis tools:
Bollinger Bands
Linear Regression Channels
Scalping Strategy Indicators (RSI, MACD, SMA)
Each of these tools provides unique insights into market conditions, and their integration offers a comprehensive view of price movements, trends, and potential trading signals.
1. Bollinger Bands
Purpose:
Bollinger Bands are used to measure market volatility and identify overbought or oversold conditions.
Components:
Basis (Middle Band): Typically a 20-period Simple Moving Average (SMA).
Upper Band: Basis + (2 * Standard Deviation).
Lower Band: Basis - (2 * Standard Deviation).
Why They Complement:
Bollinger Bands expand and contract based on market volatility. When the bands are narrow, it indicates low volatility and potential for a significant move. Wide bands indicate high volatility. This helps traders gauge the strength of market moves and potential reversals.
2. Linear Regression Channels
Purpose:
Linear Regression Channels identify the overall trend direction and measure deviation from the mean price over a specific period.
Components:
Middle Line (Linear Regression Line): The line of best fit through the price data over a specified period.
Upper and Lower Lines: Channels created by adding/subtracting a multiple of the standard deviation or another deviation measure from the regression line.
Why They Complement:
Linear Regression Channels provide a clear visual representation of the trend direction and the range within which prices typically fluctuate. This can help traders identify trend continuations and reversals, making it easier to spot entry and exit points.
3. Scalping Strategy Indicators
Purpose:
The RSI, MACD, and SMA are used to generate short-term buy and sell signals, which are essential for scalping strategies aimed at capturing quick profits from small price movements.
Components:
RSI (Relative Strength Index): Measures the speed and change of price movements, typically over 14 periods. It helps identify overbought and oversold conditions.
MACD (Moving Average Convergence Divergence): Consists of the MACD line, Signal line, and histogram. It helps identify changes in the strength, direction, momentum, and duration of a trend.
SMA (Simple Moving Average): The average price over a specified period, used to smooth out price data and identify trends.
Why They Complement:
These indicators provide short-term signals that can confirm or refute the signals given by Bollinger Bands and Linear Regression Channels. For example, a buy signal might be more reliable if the price is near the lower Bollinger Band and the MACD crosses above its signal line.
How They Work Together
Scenario 1: Confirming Trend Continuations
Bollinger Bands: Price staying near the upper band suggests a strong uptrend.
Linear Regression Channels: Price staying above the middle line confirms the uptrend.
5-Minute Scalping Strategy: RSI not in overbought territory, and MACD showing bullish momentum confirms continuation.
Scenario 2: Identifying Reversals
Bollinger Bands: Price touching or moving outside the lower band suggests oversold conditions.
Linear Regression Channels: Price at the lower channel line indicates potential support.
5-Minute Scalping Strategy: RSI in oversold territory, and MACD showing a bullish crossover indicates a reversal.
Scenario 3: Volatility Breakouts
Bollinger Bands: Bands contracting indicates low volatility and potential breakout.
Linear Regression Channels: Price moving away from the middle line signals potential breakout direction.
Scalping Strategy: MACD and RSI confirming the breakout direction for entry.
Input Parameters:
Define settings for Bollinger Bands, Linear Regression Channels, and the scalping strategy.
Allow users to customize lengths, multipliers, and colors.
Bollinger Bands Calculation:
Calculate the basis (SMA) and standard deviation.
Derive the upper and lower bands from the basis and standard deviation.
Linear Regression Channel Calculation:
Compute the slope, average, and intercept of the linear regression line.
Calculate deviations to plot upper and lower channel lines.
5-Minute Scalping Strategy:
Calculate RSI, MACD, and SMA for short-term trend analysis.
Define buy and sell conditions based on these indicators.
Plotting and Alerts:
Plot Bollinger Bands and Linear Regression Channels on the chart.
Plot buy and sell signals with shapes.
Set alerts for key conditions like exiting the regression channel bounds and trend switches.
Conclusion
By combining Bollinger Bands, Linear Regression Channels, and a 5-minute scalping strategy, this indicator offers a robust tool for traders. Bollinger Bands provide volatility insights, Linear Regression Channels highlight trend direction and potential reversals, and the scalping strategy offers precise entry and exit points. Together, these tools can enhance a trader's ability to make informed decisions in various market conditions.
No Wick Bull/Bear Candlesticks with Arrow premiumNo Wick Bull/Bear Candlesticks with Arrow premium
This script is for a custom trading indicator called "No Wick Bull/Bear Candlesticks with Arrow premium" developed by ClearTradingMind. It is designed for use with trading platforms that support scripting, such as TradingView. This indicator combines several technical analysis tools to help traders identify potential buy and sell signals in a financial market.
Key Components of the Indicator:
Moving Average (MA): The script allows users to select from various types of moving averages (SMA, EMA, HMA, etc.), which smooth out price data to identify trends. Users can set the length and type of the moving average.
Upper and Lower Bands: These bands are set at a specified deviation percentage above and below the chosen moving average. They help in identifying overbought and oversold conditions.
No Wick Bull/Bear Candlestick Identification:
Bullish Condition: A bullish candlestick is identified when the closing price is higher than the opening price, the low equals the open, and the close is above the moving average.
Bearish Condition: A bearish candlestick is identified when the closing price is lower than the opening price, the high equals the open, and the close is below the moving average.
No Wick: These conditions also imply that the candlesticks have no wicks, suggesting strong buying or selling pressure.
Arrows for Trading Signals:
No lower wick bull bar
No upper wick bear bar
When a bullish condition is met, a green upward-pointing triangle is plotted below the candlestick, indicating a potential buy signal.
When a bearish condition is met, a red downward-pointing triangle is plotted above the candlestick, indicating a potential sell signal.
EMA 20: An additional Exponential Moving Average with a length of 20 periods is plotted for further trend analysis.
Background Color Changes: The script changes the background color to blue if the EMA 20 is above the upper band, and to red if it is below the lower band, providing visual cues about the market trend.
How It Works:
Traders can input their preferences for the moving average type and length, source of the MA (like closing prices), and the deviation percentage for the bands.
The script then calculates the moving average, upper and lower bands, and checks for bullish or bearish candlestick conditions without wicks.
When such conditions are met, it plots arrows to suggest buy or sell signals.
The EMA 20 and background color changes offer additional trend information.
Usage:
This indicator is particularly useful in markets with clear trends. The no wick bull/bear candlesticks indicate strong buying or selling pressure, and the arrows provide clear visual signals for traders to consider entering or exiting positions. As with all trading indicators, it's recommended to use this tool in conjunction with other forms of analysis to confirm trading signals.
Dynamic Volatility EnvelopeDynamic Volatility Envelope: Indicator Overview
The Dynamic Volatility Envelope is an advanced, multi-faceted technical indicator designed to provide a comprehensive view of market trends, volatility, and potential future price movements. It centers around a customizable linear regression line, enveloped by dynamically adjusting volatility bands. The indicator offers rich visual feedback through gradient coloring, candle heatmaps, a background volatility pulse, and an on-chart trend strength meter.
Core Calculation Mechanism
Linear Regression Core :
-A central linear regression line is calculated based on a user-defined source (e.g., close, hl2) and lookback period.
-The regression line can be optionally smoothed using an Exponential Moving Average (EMA) to reduce noise.
-The slope of this regression line is continuously calculated to determine the current trend direction and strength.
Volatility Channel :
-Dynamic bands are plotted above and below a central basis line. This basis is typically the calculated regression line but shifts to an EMA in Keltner mode.
-The width of these bands is determined by market volatility, using one of three user-selectable modes:
ATR Mode : Bandwidth is a multiple of the Average True Range (ATR).
Standard Deviation Mode : Bandwidth is a multiple of the Standard Deviation of the source data.
Keltner Mode (EMA-based ATR) : ATR-based bands are plotted around a central Keltner EMA line, offering a smoother channel.
The channel helps identify dynamic support and resistance levels and assess market volatility.
Future Projection :
The indicator can project the current regression line and its associated volatility bands into the future for a user-defined number of bars. This provides a visual guide for potential future price pathways based on current trend and volatility characteristics.
Candle Heatmap Coloring :
-Candle bodies and/or wicks/borders can be colored based on the price's position within the upper and lower volatility bands.
-Colors transition in a gradient from bearish (when price is near the lower band) through neutral (mid-channel) to bullish (when price is near the upper band), providing an intuitive visual cue of price action relative to the dynamic envelope.
Background Volatility Pulse :
The chart background color can be set to dynamically shift based on a ratio of short-term to long-term ATR. This creates a "pulse" effect, where the background subtly changes color to indicate rising or falling market volatility.
Trend Strength Meter :
An on-chart text label displays the current trend status (e.g., "Strong Bullish", "Neutral", "Bearish") based on the calculated slope of the regression line relative to user-defined thresholds for normal and strong trends.
Key Features & Components
-Dynamic Linear Regression Line: Core trend indicator with optional smoothing and slope-based gradient coloring.
-Multi-Mode Volatility Channel: Choose between ATR, Standard Deviation, or Keltner (EMA-based ATR) calculations for band width.
-Customizable Vertical Gradient Channel Fills: Visually distinct fills for upper and lower channel segments with user-defined top/bottom colors and gradient spread.
-Future Projection: Extrapolates regression line and volatility bands to forecast potential price paths.
-Price-Action Based Candle Heatmap: Intuitive candle coloring based on position within the volatility channel, with adjustable gradient midpoint.
-Volatility-Reactive Background Gradient: Subtle background color shifts to reflect changes in market volatility.
-On-Chart Trend Strength Meter: Clear textual display of current trend direction and strength.
-Extensive Visual Customization: Fine-tune colors, line styles, widths, and gradient aggressiveness for most visual elements.
-Comprehensive Tooltips: Detailed explanations for every input setting, ensuring ease of use and understanding.
Visual Elements Explained
Regression Line : The primary trend line. Its color dynamically changes (e.g., green for uptrend, red-pink for downtrend, neutral for flat) based on its slope, with smooth gradient transitions.
Volatility Channel :
Upper & Lower Bands : These lines form the outer boundaries of the envelope, acting as dynamic support and resistance levels.
Channel Fill : The area between the band center and the outer bands is filled with a vertical gradient. For example, the upper band fill might transition from a darker green near the center to a lighter green at the upper band.
Band Borders : The lines outlining the upper and lower bands, with customizable color and width.
Future Projection Lines & Fill :
Projected Regression Line : An extension of the current regression line into the future, typically styled differently (e.g., dashed).
Projected Channel Bands : Extensions of the upper and lower volatility bands.
Projected Area Fill : A semi-transparent fill between the projected upper and lower bands.
Candle Heatmap Coloring : When enabled, candles are colored based on their closing price's relative position within the channel. Bullish colors appear when price is in the upper part of the channel, bearish in the lower, and neutral in the middle. Users can choose to color the entire candle body or just the wicks and borders.
Background Volatility Pulse : The chart's background color subtly shifts (e.g., between a calm green and an agitated red-pink) to reflect the current volatility regime.
Trend Strength Meter : A text label (e.g., "TREND: STRONG BULLISH") positioned on the chart, providing an at-a-glance summary of the trend.
Configuration Options
Users can tailor the indicator extensively via the settings panel, with options logically grouped:
Core Analysis Engine : Adjust regression source data, lookback period, and EMA smoothing for the regression line.
Regression Line Visuals : Control visibility, line width, trend-based colors (uptrend, downtrend, flat), slope thresholds for trend definition, strong slope multiplier (for Trend Meter), and color gradient sharpness.
Volatility Channel Configuration : Select band calculation mode (ATR, StdDev, Keltner), set relevant periods and multipliers. Customize colors for vertical gradient fills (upper/lower, top/bottom), border line colors, widths, and the gradient spread factor for fills.
Future Projection Configuration : Toggle visibility, set projection length (number of bars), line style, and colors for projected regression and band areas.
Appearance & Candle Theme : Set default bull/bear candle colors, enable/disable candle heatmap, choose if body color matches heatmap, and configure heatmap gradient target colors (bull, neutral, bear) and the gradient's midpoint.
Background Volatility Pulse : Enable/disable the background effect and configure short/long ATR periods for the volatility calculation.
Trend Strength Meter : Enable/disable the meter, and choose its on-chart position and text size.
Interpretation Notes
-The Regression Line is the primary indicator of trend direction. Its slope and color provide immediate insight.
-The Volatility Bands serve as dynamic support and resistance zones. Price approaching or touching these bands may indicate potential turning points or breakouts. The width of the channel itself reflects market volatility – widening suggests increasing volatility, while narrowing suggests consolidation.
Future Projections are not predictions but rather an extension of current conditions. They can help visualize potential areas where price might interact with projected support/resistance if the current trend and volatility persist.
Candle Heatmap Coloring offers a quick visual assessment of where price is trading within the dynamic envelope, highlighting strength or weakness relative to the channel.
The Background Volatility Pulse gives a contextual feel for overall market agitation or calmness.
This indicator is designed to be a comprehensive analytical tool. Its signals and visualizations are best used in conjunction with other technical analysis techniques, price action study, and robust risk management practices. It is not intended as a standalone trading system.
Risk Disclaimer
Trading and investing in financial markets involve substantial risk of loss and is not suitable for every investor. The Dynamic Volatility Envelope indicator is provided for analytical and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any security. Past performance is not indicative of future results. Always use sound risk management practices and never trade with capital you cannot afford to lose. The developers assume no liability for any financial losses incurred based on the use of this indicator.
FibonRSI / ErkOziHello,
This software is a technical analysis script written in the TradingView Pine language. The script creates a trading indicator based on Fibonacci retracement levels and the RSI indicator, providing information about price movements and asset volatility by using Bollinger Bands.
There are many different scripts in the market that draw RSI and Fibonacci retracement levels. However, this script was originally designed by me and shared publicly on TradingView.
***The indicator uses RSI (Relative Strength Index) and Bollinger Bands (BB) as the basis for the FibonRSI strategy. RSI measures the strength of a price movement, and BB measures the volatility of an asset. The FibonRSI strategy is based on the idea that the Fibonacci ratios and RSI can be used to predict a asset's price retracement levels.
***The script allows for various parameters to be adjusted. Users can specify the price source type and adjust the periods for RSI and Bollinger Bands. The standard deviation number for Bollinger Bands can also be customized.
***The script calculates the current RSI indicator position and the basic, upper, and lower levels of Bollinger Bands. It then calculates and draws the Fibonacci retracement levels. The color of the RSI line is determined by the upper and lower distribution levels of Bollinger Bands. Additionally, the color of the Fibonacci retracement levels can also be customized by the user.
***This script can be used to determine potential buy and sell signals using Fibonacci retracement levels and RSI. For example, when the RSI is oversold and the price is close to a Fibonacci retracement level, it can be interpreted as a buying opportunity. Similarly, when the RSI is overbought and the price is close to a Fibonacci retracement level, it can be interpreted as a selling opportunity.
***The script takes input parameters such as the price source used for calculation, the period for the RSI indicator, the period for the Moving Average in Bollinger Bands, and the number of standard deviations used in Bollinger Bands.
***The script's conditions include elements such as calculating the current position of the RSI indicator, calculating the upper and lower Bollinger Bands, calculating the dispersion factor, and calculating Fibonacci levels.
***The parameters in the code can be adjusted for calculation, including the price type used, the RSI period, the Moving Average period for BB, and the standard deviation count for BB. After this, the current position of the RSI, Moving Average, and standard deviation for BB are calculated. After calculating the upper and lower BB, the levels above and below the average are calculated using a specific dispersion constant.
CONDITIONS FOR THE SCRIPT
current_rsi = ta.rsi(src, for_rsi) // Current position of the RSI indicator
basis = ta.ema(current_rsi, for_ma)
dev = for_mult * ta.stdev(current_rsi, for_ma)
upper = basis + dev
lower = basis - dev
dispersion = 1
disp_up = basis + (upper - lower) * dispersion
disp_down = basis - (upper - lower) * dispersion
// Fibonacci Levels
f100 = basis + (upper - lower) * 1.0
f78 = basis + (upper - lower) * 0.78
f65 = basis + (upper - lower) * 0.65
f50 = basis
f35 = basis - (upper - lower) * 0.65
f23 = basis - (upper - lower) * 0.78
f0 = basis - (upper - lower) * 1.0
***When calculating Fibonacci levels, the distance between the average of BB and the upper and lower BB is used. These levels are 0%, 23.6%, 35%, 50%, 65%, 78.6%, and 100%. Finally, the RSI line that changes color according to a specific RSI position, Fibonacci levels, and BB are visualized. Additionally, the levels of 70, 30, and 50 are also shown.
The script then sets the color of the RSI position according to the EMA and draws Bollinger Bands, RSI, Fibonacci levels, and the 70, 30, and 50 levels.
In conclusion, this script enables traders to analyze market trends and make informed decisions. It can also be customized to suit individual trading strategies.
This script analyzes the RSI indicator using Bollinger Bands and Fibonacci levels. The default settings are 14 periods for RSI, 233 periods and 2 standard deviations for BB. The MA period inside BB is selected as the BB period and is used when calculating Fibonacci levels.
***The reason for selecting these settings is to provide enough time for BB period to confirm a possible trend. Additionally, the MA period inside BB is matched with the BB period and used when calculating Fibonacci levels.
***Fibonacci levels are calculated from the distance between the upper and lower bands of BB and show how RSI movement is related to these levels. Better results can be achieved when RSI periods are set to Fibonacci numbers such as 21, 55, and 89. Therefore, the use of Fibonacci numbers is recommended when adjusting RSI periods. Fibonacci numbers are among the technical analysis tools that can capture the reflection of naturally occurring movements in the market. Therefore, the use of Fibonacci numbers often helps to better track fluctuations in the market.
Finally, the indicator also displays the 70 and 30 levels and the middle level (50) with Fibonacci levels drawn in circles. Changing these settings can help optimize the Fibonacci levels and further improve the indicator.
Thank you in advance for your suggestions and opinions......
SuperTrendSAP1212This indicator combines Supertrend, VWAP with bands, and an optional RSI filter to generate Buy/Sell signals.
How it works
Supertrend Flip (ATR-based): Detects when trend direction changes (from bearish to bullish, or bullish to bearish).
VWAP Band Filter: Signals only trigger if the candle close is beyond the VWAP bands:
Buy = Supertrend flips up AND close > VWAP Upper Band
Sell = Supertrend flips down AND close < VWAP Lower Band
Optional RSI Filter:
Buy requires RSI < 20
Sell requires RSI > 80
Can be enabled/disabled in settings.
Features
Choice of VWAP band calculation mode: Standard Deviation or ATR.
Adjustable ATR/StDev length and multiplier for VWAP bands.
Toggle Supertrend, VWAP lines, and Buy/Sell labels.
Alerts included: add alerts on BUY or SELL conditions (use Once Per Bar Close to avoid intrabar signals).
Use
Works best on intraday or higher timeframes where VWAP is relevant.
Use the RSI filter for more selective signals.
Can be combined with your own stop-loss and risk management rules.
⚠️ Disclaimer: This script is for educational and research purposes only. It is not financial advice. Always test thoroughly and trade at your own risk.
Mean-Reversion Indicator_V2_SamleeOverview
This is the second version of my mean reversion indicator. It combines a moving average with adaptive standard deviation bands to detect when the price deviates significantly from its mean. The script provides automatic entry/exit signals, real-time PnL tracking, and shaded trade zones to make mean reversion trading more intuitive.
Core Logic
Mean benchmark: Simple Moving Average (MA).
Volatility bands: Standard deviation of the spread (close − MA) defines upper and lower bands.
Trading rules:
Price breaks below the lower band → Enter Long
Price breaks above the upper band → Enter Short
Price reverts to MA → Exit position
What’s different vs. classic Bollinger/Keltner
Bandwidth is based on the standard deviation of the price–MA spread, not raw closing prices.
Entry signals use previous-bar confirmation to reduce intrabar noise.
Exit rule is a mean-touch condition, rather than fixed profit/loss targets.
Enhanced visualization:
A shaded box dynamically shows the distance between entry and current/exit price, making it easy to see profit/loss zones over the holding period.
Instant PnL labels display current position side (Long/Short/Flat) and live profit/loss in both pips and %.
Entry and exit points are clearly marked on the chart with labels and exact prices.
These visualization tools go beyond what most indicators provide, giving traders a clearer, more practical view of trade evolution.
Key Features
Automatic detection of position status (Long / Short / Flat).
Chart labels for entries (“Entry”) and exits (“Exit”).
Real-time floating PnL calculation in both pips and %.
Info panel (top-right) showing entry price, current price, position side, and PnL.
Dynamic shading between entry and current/exit price to visualize profit/loss zones.
Usage Notes & Risk
Mean reversion may underperform in strong trending markets; parameters (len_ma, len_std, mult) should be validated per instrument and timeframe.
Works best on relatively stable, mean-reverting pairs (e.g., AUDNZD).
Risk management is essential: use independent stop-loss rules (e.g., limit risk to 1–2% of equity per trade).
This script is provided for educational purposes only and is not financial advice.
Multi Scanner Plot & Table V1Here's how to interpret each column in the table:
Price vs MAs:
What it shows: Where the current price is relative to the short-term (e.g., 20-period) and long-term (e.g., 50-period) Simple Moving Averages (SMAs) calculated on your current chart's timeframe.
Interpretation:
Above Both (Green background): Price is above both the short and long MAs. Generally considered a bullish sign for the current trend.
Below Both (Red background): Price is below both MAs. Generally considered a bearish sign.
Mixed (Gray background): Price is between the two MAs (e.g., above the short but below the long, or vice-versa). Indicates indecision or a potential trend change.
RSI Value:
What it shows: The actual numerical value of the Relative Strength Index (RSI) calculated on your current chart's timeframe.
Interpretation: Just the raw RSI number (e.g., 65.32). The background is always gray. You compare this value to standard overbought/oversold levels (like 70/30) or the levels defined in the script's inputs.
RSI Status:
What it shows: Interprets the RSI Value based on the Overbought/Oversold levels set in the script's inputs (default 70/30). Calculated on your current chart's timeframe.
Interpretation:
Overbought (Red background): RSI is above the overbought level (e.g., > 70). Suggests the asset might be due for a pullback or reversal downwards. Red indicates a potentially bearish condition.
Oversold (Green background): RSI is below the oversold level (e.g., < 30). Suggests the asset might be due for a bounce or reversal upwards. Green indicates a potentially bullish condition.
Neutral (Gray background): RSI is between the oversold and overbought levels.
Last Sig Price:
What it shows: The price level where the last "SIG NOW" Buy or Sell signal occurred on your current chart's timeframe.
Interpretation: Helps you see the entry price of the most recent short-term signal generated by this script. The background color matches the signal type: Green for the last Buy signal, Red for the last Sell signal. N/A if no signal has occurred yet.
SIG NOW:
What it shows: This is the main short-term signal generated by the script based on conditions on your current chart's timeframe. It combines the "Price vs MAs" status and specific RSI conditions (price must be above/below both MAs and RSI must be within a certain range defined in the inputs).
Interpretation:
BUY (Green background): The specific buy conditions are met right now. (Price above both MAs AND RSI is strong but not necessarily overbought).
SELL (Red background): The specific sell conditions are met right now. (Price below both MAs AND RSI is weak but not necessarily oversold).
NEUTRAL (Gray background): Neither the Buy nor the Sell conditions are currently met.
ALERT:
What it shows: Flags unusual volume activity on the current bar compared to the recent average volume (calculated on your current chart's timeframe).
Interpretation:
SPIKE (Yellow background, black text): Current volume is significantly higher than the recent average (defined by the Volume Spike Multiplier). Can indicate strong interest or a potential climax.
DUMP (Purple background): Current volume is significantly lower than the recent average (defined by the Volume Dump Multiplier). Can indicate fading interest.
NONE (Gray background): Volume is within the normal range for the lookback period.
SD$:
What it shows: The price level where the last Volume Spike or Dump occurred on your current chart's timeframe.
Interpretation: Shows the price associated with the most recent significant volume event. The background color indicates the type of the last event: Green if the last event was a Spike, Red if the last event was a Dump. N/A if no Spike/Dump has occurred yet.
BB Value (%B):
What it shows: This relates to Bollinger Bands, but specifically calculated on a Higher Timeframe (HTF) that you can set in the inputs (e.g., Daily BBs while viewing an Hourly chart). It shows the Bollinger Band Percent B (%B) value for that HTF. %B measures where the HTF closing price is relative to the HTF upper and lower bands.
Interpretation:
Value > 1: HTF price closed above the HTF upper Bollinger Band.
Value < 0: HTF price closed below the HTF lower Bollinger Band.
Value between 0 and 1: HTF price closed within the HTF Bollinger Bands (e.g., 0.5 is exactly on the middle band).
The background is always gray.
LTS (Long Term Signal):
What it shows: A signal derived only from the Higher Timeframe (HTF) Bollinger Bands.
Interpretation:
BUY (Green background): The HTF price closed above the HTF upper Bollinger Band (see BB Value > 1). Considered a strong bullish signal from the higher timeframe perspective.
SELL (Red background): The HTF price closed below the HTF lower Bollinger Band (see BB Value < 0). Considered a strong bearish signal from the higher timeframe perspective.
NEUTRAL (Gray background): The HTF price closed within the HTF Bollinger Bands.
How to Understand Bollinger Bands and Signals in this Context:
Bollinger Bands are primarily used for the Long Term Signal (LTS) column. This script calculates BBs on a higher timeframe (you choose which one, or it defaults to the chart's timeframe if left blank).
The "LTS" signal triggers:
A BUY when the price on that higher timeframe closes above its upper Bollinger Band. This often indicates strong momentum or a potential breakout.
A SELL when the price on that higher timeframe closes below its lower Bollinger Band. This often indicates strong negative momentum or a potential breakdown.
The "BB Value" column gives you the raw %B number from that same higher timeframe, showing you exactly where the price is relative to the bands (is it just barely above/below, or way outside?).
The script does not directly use Bollinger Bands from the current chart timeframe for the "SIG NOW" or other table signals. The main short-term signals ("SIG NOW") rely on Moving Averages and RSI on the current timeframe. The LTS provides a longer-term perspective using HTF Bollinger Bands.
In summary: Look at the table to quickly gauge:
Short-term trend (Price vs MAs).
Short-term momentum (RSI Status, SIG NOW).
Recent short-term entry points (Last Sig Price).
Current volume anomalies (ALERT).
Long-term strength/weakness based on HTF Bollinger Bands (LTS, BB Value).
Combine these pieces of information to get a more rounded view of the current market conditions according to this specific script's logic.
Quantum Transform - AynetQuantum Transform Trading Indicator: Explanation
This script is called a "Quantum Transform Trading Indicator" and aims to enhance market analysis by applying complex mathematical models. Written in Pine Script, the indicator includes the following elements:
1. General Structure
Quantum Parameters: Inspired by physical and mathematical concepts (Planck constant ℏ, wave function Ψ, time τ, etc.), it uses specific parameters.
Transformation Functions: Applies various mathematical operations to transform price data in different ways.
Signal Generation: Produces signals for long and short positions.
Visualization: Displays different price transformations and signals on the chart.
2. Core Parameters
The parameters allow users to control various transformations:
Planck Constant (ℏ): A scaling factor for wave modulation.
Wave (Ψ): Controls oscillation in price data.
Time (τ): The length of the lookback period for calculations.
Relativity (γ): Power factor in the Lorentz transformation.
Phase Shift (β): Manages phase shift in transformations.
Frequency (ω): Represents the frequency of price movements.
Dimensions (∇): Enables multi-dimensional field analysis.
3. Functions
a) Relativistic Transform
Inspired by the theory of relativity.
Calculates the Lorentz factor using the rate of price change.
Transforms price data to amplify the relativity effect.
b) Phase Transform
Calculates the phase of price data and applies wave modulation.
Creates phase and amplitude modulation based on the bar index.
c) Resonance Transform
Calculates resonance effects using natural frequency and oscillations.
Highlights periodic behaviors of price movements.
d) Field Transform
Applies multi-dimensional field calculations.
Combines strength, wave, and coherence aspects of price data.
e) Chaos Transform
Implements a chaos effect based on sensitivity analysis.
Simulates chaotic behaviors of price movements.
4. Main Calculations
Quantum Price: The average of all transformation functions.
Bands:
Upper Band: The highest level of quantum price.
Lower Band: The lowest level of quantum price.
Mid Band: The average of upper and lower bands.
Momentum: Calculates the rate of change in quantum price.
5. Signal Generation
Long Signal:
Triggered when the phase price crosses above the field price.
Momentum must be positive, and the price above the mid-band.
Short Signal:
Triggered when the phase price crosses below the field price.
Momentum must be negative, and the price below the mid-band.
Signal strength is calculated relative to the momentum moving average.
6. Visualization
Each transformation is displayed in a unique color.
Bands and Momentum: Visualize price behavior.
Signal Icons: Show buy/sell signals using up/down arrows on the chart.
7. Information Panel
A table in the top-right corner of the chart displays:
The current values of each transformation.
Signal strength (as a percentage).
The type of signal (⬆: Long, ⬇: Short).
Applications
Trend Following: Analyze trends with complex transformations.
Resonance and Chaos Analysis: Understand dynamic behaviors of price.
Signal Strategies: Create strong and reliable buy/sell signals.
If you have any additional questions or customization requests regarding this indicator, feel free to ask!
Extreme Entry with Mean Reversion and Trend FilterThis non-repainting indicator is an improved version of my previous work, a more versatile tool designed to provide traders with dynamic and adaptive entry signals while incorporating a mean reversion and trend filtering mechanism. By combining RSI overbought/oversold, regular divergence and confirmatory momentum oscillator such as CCI or MOM, this indicator generates more precise and timely signals for entering trades.
The indicator offers a comprehensive set of entry conditions for both Buy and Sell entries:
• For Buy entries, it checks for oversold conditions based on RSI levels, and detects bullish divergence patterns while oversold and it identifies upward crossovers in the selected entry signal source (CCI or Momentum).
• Similarly, for Sell entries, it identifies downward crossovers of the CCI or Mom, after the recent overbought conditions, and bearish divergence patterns inside the overbought RSI.
To refine the entry signals even further, the indicator utilizes a mean reversion filter. Traders can choose to display signals that occur inside or outside the upper and lower mean reversion bands:
• Range Entries are indicating potential buying opportunities near the lower band and selling opportunities near the upper band. This is based on the concept of mean reversion, which suggests that prices tend to return to the average when they reach the upper or lower bands. By focusing on these signals, traders can take advantage of price movements that have a higher probability of reversing towards the mean.
• Extreme Entries, on the other hand, represent signals that occur outside of the bands, signaling potential pullbacks during strong trends. By entering positions only at extreme highs or lows, traders can avoid getting caught in the middle of the trend. This approach helps traders capitalize more favorable trading opportunities which have a high reward-risk ratio.
Trend Filter acts as a directional bias for the entry signals. When enabled, long and short entry conditions are filtered based on the relationship between the closing price and the EMA.
Traders have the flexibility to customize, tweak the indicator filter and values in the settings according to their preferences strategies and traded assets, tailoring the signals to their specific needs. The script sets alert conditions to trigger alerts for buy, sell, or both entry signals. This indicator can be used in conjunction with price action or other technical analysis tools for confirmation and better trading decisions.
I created this indicator for my own use, and I share this for informational purposes only. It does not constitute financial advice so use at your own risk and consider your financial situation before making any trading decisions. The indicator's accuracy is not guaranteed, and past performance is not indicative of future results.
I appreciate your feedback on this indicator. As I am new to script development, I am open to comments and suggestions to improve it. If you encounter any issues while using this indicator, please let me know in the comments section. If you find it helpful, I kindly ask for your support in boosting it. Thank you for your cooperation.
