SuperTrend Confluence Signals [AlgoAlpha]OVERVIEW
This script enhances the classic SuperTrend indicator by integrating volume dynamics, retracement detection, and a multi-asset trend matrix—alongside an automatic mitigation-level drawing system. It's designed for traders who want to see not just trend direction, but the confluence of trend strength, volatility-adjusted retracements, and capital flow through volume pressure. It visually maps key transitions in market structure while offering a clean, color-coded overview of multiple symbols and timeframes in a single chart.
CONCEPTS
At the core is the traditional SuperTrend , which determines directional bias using Average True Range (ATR) with a volatility multiplier. This script overlays that with a dynamic volume histogram that scales relative to recent volume standard deviation, coloring volume bursts within the trend. Retracement signals are triggered when price pulls back toward the SuperTrend level but respects it—quantified through normalized distance sensitivity. On top of that, the indicator automatically draws and manages horizontal support/resistance zones that appear at key trend shifts. These levels persist and are cleared based on configurable rules such as wick/body sweeps or consecutive candle closes. A multi-asset, multi-timeframe table then gives an instant snapshot of trend status across five user-defined symbols and timeframes.
FEATURES
SuperTrend : Configurable ATR length and multiplier for flexible trend sensitivity.
Volumetric Histogram : Gradient-filled candles anchored to SuperTrend bands, scaled by relative volume to indicate activity intensity during trends.
Retracement Arrows : Signals printed when price nears the SuperTrend level without breaking it, allowing identification of high-probability continuation zones.
Volume TP Markers : Diamond markers flag high-volume events, contextualizing price moves with liquidity bursts.
Automatic Structure Levels : Draws clean horizontal lines at significant trend transitions, with optional volatility-based band fills. These levels self-update and clear based on price interaction logic.
Trend Table : Displays trend direction (▲/▼) across five assets and five timeframes. Each cell is colored according to trend bias, providing a compact overview for multi-market confluence.
USAGE
Start by loading the indicator on your main chart and adjusting the ATR Length and Multiplier to match your strategy timeframe. Use lower values for scalping and higher values for swing trading. The histogram bars will appear as colored candles above or below the SuperTrend level, indicating how strong volume is within that trend. Arrow signals suggest minor pullbacks within the trend, which can act as entry opportunities. The level system will automatically plot key price zones during trend flips; if "Body" is selected for mitigation, price must close through the level to invalidate it. If "Wick" is chosen, a single wick breach is enough. Adjust expiry and rejection settings to fine-tune how long levels stay on chart. Finally, enable the Multi-Asset Table to view live trend signals across popular symbols like AAPL or NVDA in different timeframes, helping spot macro-to-micro alignment for higher-confidence trades.
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CCI Divergence Detector
A technical analysis tool that identifies divergences between price action and the Commodity Channel Index (CCI) oscillator. Unlike standard divergence indicators, this system employs advanced gradient visualization, multi-layer wave effects, and comprehensive customization options to provide traders with crystal-clear divergence signals and market momentum insights.
Core Detection Mechanism
CCI-Based Analysis: The indicator utilizes the Commodity Channel Index as its primary oscillator, calculated from user-configurable source data (default: HLC3) with adjustable length parameters. The CCI provides reliable momentum readings that effectively highlight price-momentum divergences.
Dynamic Pivot Detection: The system employs adaptive pivot detection with three sensitivity levels (High/Normal/Low) to identify significant highs and lows in both price and CCI values. This dynamic approach ensures optimal divergence detection across different market conditions and timeframes.
Dual Divergence Analysis:
Regular Bullish Divergences: Detected when price makes lower lows while CCI makes higher lows, indicating potential upward reversal
Regular Bearish Divergences: Identified when price makes higher highs while CCI makes lower highs, signaling potential downward reversal
Strength Classification System: Each detected divergence is automatically classified into three strength categories (Weak/Moderate/Strong) based on:
-Price differential magnitude
-CCI differential magnitude
-Time duration between pivot points
-User-configurable strength multiplier
Advanced Visual System
Multi-Layer Wave Effects: The indicator features a revolutionary wave visualization system that creates depth through multiple gradient layers around the CCI line. The wave width dynamically adjusts based on ATR volatility, providing intuitive visual feedback about market conditions.
Professional Color Gradient System: Nine independent color inputs control every visual aspect:
Bullish Colors (Light/Medium/Dark): Control oversold areas, wave effects, and strong bullish signals
Bearish Colors (Light/Medium/Dark): Manage overbought zones, wave fills, and strong bearish signals
Neutral Colors (Light/Medium/Dark): Handle table elements, zero line, and transitional states
Intelligent Color Mapping: Colors automatically adapt based on CCI values:
Overbought territory (>100): Bearish color gradients with increasing intensity
Neutral positive (0 to 100): Blend from neutral to bearish tones
Oversold territory (<-100): Bullish color gradients with increasing intensity
Neutral negative (-100 to 0): Transition from neutral to bullish tones
Key Features & Components
Advanced Configuration System: Eight organized input groups provide granular control:
General Settings: System enable, pivot length, confidence thresholds
Oscillator Selection: CCI parameters, overbought/oversold levels, normalization options
Detection Parameters: Divergence types, minimum strength requirements
Sensitivity Tuning: Pivot sensitivity, divergence threshold, confirmation bars
Visual System: Line thickness, labels, backgrounds, table display
Wave Effects: Dynamic width, volatility response, layer count, glow effects
Transparency Controls: Independent transparency for all visual elements
Smoothing & Filtering: CCI smoothing types, noise filtering, wave smoothing
Professional Alert System: Comprehensive alert functionality with dynamic messages including:
-Divergence type and strength classification
-Current CCI value and confidence percentage
-Customizable alert frequency and conditions
Enhanced Information Table: Real-time display showing:
-Current CCI length and value
-Market status (Overbought/Normal/Oversold)
-Active sensitivity setting
Configurable table positioning (4 corner options)
Visual Elements Explained
Primary CCI Line: Main oscillator plot with gradient coloring that reflects market momentum and CCI intensity. Line thickness is user-configurable (1-8 pixels).
Wave Effect Layers: Multi-layer gradient fills creating a dynamic wave around the
CCI line:
-Outer layers provide broad market context
-Inner layers highlight immediate momentum
-Core layers show precise CCI movement
-All layers respond to volatility and momentum changes
Divergence Lines & Labels:
-Solid lines connecting divergence pivot points
-Color-coded based on divergence type and strength
-Labels displaying divergence type and strength classification
-Customizable transparency and size options
Reference Lines:
-Zero line with neutral color coding
-Overbought level (default: 100) with bearish coloring
-Oversold level (default: -100) with bullish coloring
Background Gradient: Optional background coloring that reflects CCI intensity and market conditions with user-controlled transparency (80-99%).
Configuration Options
Sensitivity Controls:
Pivot sensitivity: High/Normal/Low detection levels
Divergence threshold: 0.1-2.0 sensitivity range
Confirmation bars: 1-5 bar confirmation requirement
Strength multiplier: 0.1-3.0 calculation adjustment
Visual Customization:
Line transparency: 0-90% for main elements
Wave transparency: 0-95% for fill effects
Background transparency: 80-99% for subtle background
Label transparency: 0-50% for text elements
Glow transparency: 50-95% for glow effects
Advanced Processing:
Five smoothing types: None/SMA/EMA/RMA/WMA
Noise filtering with adjustable threshold (0.1-10.0)
CCI normalization for enhanced gradient scaling
Dynamic wave width with ATR-based volatility response
Interpretation Guidelines
Divergence Signals:
Strong divergences: High-confidence reversal signals requiring immediate attention
Moderate divergences: Reliable signals suitable for most trading strategies
Weak divergences: Early warning signals best combined with additional confirmation
Wave Intensity: Wave width and color intensity provide real-time volatility and momentum feedback. Wider, more intense waves indicate higher market volatility and stronger momentum.
Color Transitions: Smooth color transitions between bullish, neutral, and bearish states help identify market regime changes and momentum shifts.
CCI Levels: Traditional overbought (>100) and oversold (<-100) levels remain relevant, but the gradient system provides more nuanced momentum reading between these extremes.
Technical Specifications
Compatible Timeframes: All timeframes supported
Maximum Labels: 500 (for divergence marking)
Maximum Lines: 500 (for divergence drawing)
Pine Script Version: v5 (latest optimization)
Overlay Mode: False (separate pane indicator)
Usage Recommendations
This indicator works best when:
-Combined with price action analysis and support/resistance levels
-Used across multiple timeframes for confirmation
-Integrated with proper risk management protocols
-Applied in trending markets for divergence-based reversal signals
-Utilized with other technical indicators for comprehensive analysis
Risk Disclaimer: Trading involves substantial risk of loss. This indicator is provided for analytical purposes only and does not constitute financial advice. Divergence signals, while powerful, are not guaranteed to predict future price movements. Past performance is not indicative of future results. Always use proper risk management and never trade with capital you cannot afford to lose.
Hull-Exponential Moving Average (HEMA)The Hull Exponential Moving Average (HEMA) is an experimental technical indicator that uses a sequence of Exponential Moving Averages (EMAs) with the same logic as HMA - except with EMAs and not WMAs. It aims to create a responsive yet smooth trend indicator than HMA.
HEMA applies a multi-stage EMA process. Initial EMAs are calculated using alphas derived from logarithmic relationships and the input period. Their outputs are then combined in a de-lagging step, which itself uses a logarithmically derived ratio. A final EMA smoothing pass is then applied to this de-lagged series. This creates a moving average that responds quickly to genuine price changes while maintaining effective noise filtering. The specific alpha calculations and the de-lagging formula contribute to its balance between responsiveness and smoothness.
▶️ **Core Concepts**
Logarithmically-derived alphas: Alpha values for the three EMA stages are derived using natural logarithms and specific formulas related to the input period **N**.
Three-stage EMA process: The calculation involves:
An initial EMA (using **αS**) on the source data.
A second EMA (using **αF**) also on the source data.
A de-lagging step that combines the outputs of the first two EMAs using a specific ratio **r**.
A final EMA (using **αFin**) applied to the de-lagged series.
Specific de-lagging formula: Utilizes a constant ratio **r = ln(2.0) / (1.0 + ln(2.0))** to combine the outputs of the first two EMAs, aiming to reduce lag.
Optimized final smoothing: The alpha for the final EMA (**αFin**) is calculated based on the square root of the period **N**.
Warmup compensation: The internal EMA calculations include a warmup mechanism to provide more accurate values from the initial bars. This involves tracking decay factors (**eS**, **eF**, **eFin**) and applying a compensation factor **1.0 / (1.0 - e_decay)** during the warmup period. A shared warmup duration is determined by the smallest alpha among the three stages.
HEMA achieves its characteristics through this multi-stage EMA process, where the specific alpha calculations and the de-lagging step are key to its responsiveness and smoothness.
▶️ **Common Settings and Parameters**
Period (**N**): Default: 10 | Base lookback period for all alpha calculations | When to Adjust: Increase for longer-term trends and more smoothness, decrease for shorter-term signals and more responsiveness
Source: Default: Close | Data point used for calculation | When to Adjust: Change to HL2, HLC3, or OHLC4 for different price representations
Pro Tip: The HEMA's behavior is sensitive to the **Period** setting due to the non-linear relationships in its alpha calculations. Experiment with values around your typical MA periods. Small changes in **N** can have a noticeable impact, especially for smaller **N** values.
▶️ **Calculation and Mathematical Foundation**
Simplified explanation:
HEMA calculates its value through a sequence of three Exponential Moving Averages (EMAs) with specially derived smoothing factors (alphas).
Two initial EMAs are calculated from the source price, using alphas **αS** and **αF**.
The outputs of these two EMAs are combined into a "de-lagged" series.
This de-lagged series is then smoothed by a third EMA, using alpha **αFin**, to produce the final HEMA value.
All internal EMAs use a warmup compensation mechanism for improved accuracy on early bars.
Technical formula (let **N** be the input period):
1. Alpha for the first EMA (slow component related):
αS = 3.0 / (2.0 * N - 1.0)
2. Lambda for **αS** (intermediate value):
λS = -ln(1.0 - αS)
Note: **αS** must be less than 1, which implies 2N-1 > 3 or N > 2 for **λS** to be well-defined without NaN from ln of non-positive number. The code uses nz() for robustness but the formula implies this constraint.
3. De-lagging ratio **r**:
r = ln(2.0) / (1.0 + ln(2.0))
(This is a constant, approximately 0.409365)
4. Alpha for the second EMA (fast component related):
αF = 1.0 - exp(-λS / r)
5. Alpha for the final EMA smoothing:
αFin = 2.0 / (sqrt(N) / 2.0 + 1.0)
6. Applying the stages:
**OutputS = EMA_internal(source, αS, eS_state, emaS_state)**
**OutputF = EMA_internal(source, αF, eF_state, emaF_state)**
8. Calculate the de-lagged series:
DeLag = (OutputF / (1.0 - r)) - (r * OutputS / (1.0 - r))
9. Calculate the final HEMA:
HEMA = EMA_internal(DeLag, αFin, eFin_state, emaFin_state)
🔍 Technical Note: The HEMA implementation uses a shared warmup period controlled by **aMin** (the minimum of **αS**, **αF**, **αFin**). During this period, each internal EMA stage still tracks its own decay factor (**eS**, **eF**, **eFin**) to apply the correct compensation. The **nz()** function is used in the code to handle potential NaN values from alpha calculations if **N** is very small (e.g., **N=1** would make **αS=3**, **1-αS = -2**, **ln(-2)** is NaN).
▶️ **Interpretation Details**
HEMA provides several key insights for traders:
When price crosses above HEMA, it often signals the beginning of an uptrend
When price crosses below HEMA, it often signals the beginning of a downtrend
The slope of HEMA provides insight into trend strength and momentum
HEMA creates smooth dynamic support and resistance levels during trends
Multiple HEMA lines with different periods can identify potential reversal zones
HEMA is particularly effective for trend following strategies where both responsiveness and noise reduction are important. It provides earlier signals than traditional EMAs while exhibiting less whipsaw than standard HMA in choppy market conditions. The indicator excels at identifying the underlying trend direction while filtering out minor price fluctuations.
▶️ **Limitations and Considerations**
Experimental nature: As an experimental indicator, HEMA may behave differently from established HMA in certain market conditions
Lag characteristics: While designed to reduce lag, HEMA may exhibit slightly more lag than HMA in some scenarios due to the long tail of EMA
Mathematical complexity: The multi-stage calculation with specialized alpha parameters makes the behavior less intuitive to understand
Parameter sensitivity: Performance can vary significantly with different period settings
Complementary tools: Works best when combined with volume analysis or momentum indicators for confirmation
▶️ **References**
Hull, A. (2005). "Hull Moving Average," Technical Analysis of Stocks & Commodities .
RetryClaude can make mistakes. Please double-check responses.
Quadruple EMA (QEMA)The Quadruple Exponential Moving Average (QEMA) is an advanced technical indicator that extends the concept of lag reduction beyond TEMA (Triple Exponential Moving Average) to a fourth order. By applying a sophisticated four-stage EMA cascade with optimized coefficient distribution, QEMA provides the ultimate evolution in EMA-based lag reduction techniques.
Unlike traditional compund moving averages like DEMA and TEMA, QEMA implements a progressive smoothing system that strategically distributes alphas across four EMA stages and combines them with balanced coefficients (4, -6, 4, -1). This approach creates an indicator that responds extremely quickly to price changes while still maintaining sufficient smoothness to be useful for trading decisions. QEMA is particularly valuable for traders who need the absolute minimum lag possible in trend identification.
▶️ **Core Concepts**
Fourth-order processing: Extends the EMA cascade to four stages for maximum possible lag reduction while maintaining a useful signal
Progressive alpha system: Uses mathematically derived ratio-based alpha progression to balance responsiveness across all four EMA stages
Optimized coefficients: Employs calculated weights (4, -6, 4, -1) to effectively eliminate lag while preserving compound signal stability
Numerical stability control: Implements initialization and alpha distribution to ensure consistent results from the first calculation bar
QEMA achieves its exceptional lag reduction by combining four progressive EMAs with mathematically optimized coefficients. The formula is designed to maximize responsiveness while minimizing the overshoot problems that typically occur with aggressive lag reduction techniques. The implementation uses a ratio-based alpha progression that ensures each EMA stage contributes appropriately to the final result.
▶️ **Common Settings and Parameters**
Period: Default: 15| Base smoothing period | When to Adjust: Decrease for extremely fast signals, increase for more stable output
Alpha: Default: auto | Direct control of base smoothing factor | When to Adjust: Manual setting allows precise tuning beyond standard period settings
Source: Default: Close | Data point used for calculation | When to Adjust: Change to HL2 or HLC3 for more balanced price representation
Pro Tip: Professional traders often use QEMA with longer periods than other moving averages (e.g., QEMA(20) instead of EMA(10)) since its extreme lag reduction provides earlier signals even with longer periods.
▶️ **Calculation and Mathematical Foundation**
Simplified explanation:
QEMA works by calculating four EMAs in sequence, with each EMA taking the previous one as input. It then combines these EMAs using balancing weights (4, -6, 4, -1) to create a moving average with extremely minimal lag and high level of smoothness. The alpha factors for each EMA are progressively adjusted using a mathematical ratio to ensure balanced responsiveness across all stages.
Technical formula:
QEMA = 4 × EMA₁ - 6 × EMA₂ + 4 × EMA₃ - EMA₄
Where:
EMA₁ = EMA(source, α₁)
EMA₂ = EMA(EMA₁, α₂)
EMA₃ = EMA(EMA₂, α₃)
EMA₄ = EMA(EMA₃, α₄)
α₁ = 2/(period + 1) is the base smoothing factor
r = (1/α₁)^(1/3) is the derived ratio
α₂ = α₁ × r, α₃ = α₂ × r, α₄ = α₃ × r are the progressive alphas
Mathematical Rationale for the Alpha Cascade:
The QEMA indicator employs a specific geometric progression for its smoothing factors (alphas) across the four EMA stages. This design is intentional and aims to optimize the filter's performance. The ratio between alphas is **r = (1/α₁)^(1/3)** - derived from the cube root of the reciprocal of the base alpha.
For typical smoothing (α₁ < 1), this results in a sequence of increasing alpha values (α₁ < α₂ < α₃ < α₄), meaning that subsequent EMAs in the cascade are progressively faster (less smoothed). This specific progression, when combined with the QEMA coefficients (4, -6, 4, -1), is chosen for the following reasons:
1. Optimized Frequency Response:
Using the same alpha for all EMA stages (as in a naive multi-EMA approach) can lead to an uneven frequency response, potentially causing over-shooting of certain frequencies or creating undesirable resonance. The geometric progression of alphas in QEMA helps to create a more balanced and controlled filter response across a wider range of movement frequencies. Each stage's contribution to the overall filtering characteristic is more harmonized.
2. Minimized Phase Lag:
A key goal of QEMA is extreme lag reduction. The specific alpha cascade, particularly the relationship defined by **r**, is designed to minimize the cumulative phase lag introduced by the four smoothing stages, while still providing effective noise reduction. Faster subsequent EMAs contribute to this reduced lag.
🔍 Technical Note: The ratio-based alpha progression is crucial for balanced response. The ratio r is calculated as the cube root of 1/α₁, ensuring that the combined effect of all four EMAs creates a mathematically optimal response curve. All EMAs are initialized with the first source value rather than using progressive initialization, eliminating warm-up artifacts and providing consistent results from the first bar.
▶️ **Interpretation Details**
QEMA provides several key insights for traders:
When price crosses above QEMA, it signals the beginning of an uptrend with minimal delay
When price crosses below QEMA, it signals the beginning of a downtrend with minimal delay
The slope of QEMA provides immediate insight into trend direction and momentum
QEMA responds to price reversals significantly faster than other moving averages
Multiple QEMA lines with different periods can identify immediate support/resistance levels
QEMA is particularly valuable in fast-moving markets and for short-term trading strategies where speed of signal generation is critical. It excels at capturing the very beginning of trends and identifying reversals earlier than any other EMA-derived indicator. This makes it especially useful for breakout trading and scalping strategies where getting in early is essential.
▶️ **Limitations and Considerations**
Market conditions: Can generate excessive signals in choppy, sideways markets due to its extreme responsiveness
Overshooting: The aggressive lag reduction can create some overshooting during sharp reversals
Calculation complexity: Requires four separate EMA calculations plus coefficient application, making it computationally more intensive
Parameter sensitivity: Small changes in the base alpha or period can significantly alter behavior
Complementary tools: Should be used with momentum indicators or volatility filters to confirm signals and reduce false positives
▶️ **References**
Mulloy, P. (1994). "Smoothing Data with Less Lag," Technical Analysis of Stocks & Commodities .
Ehlers, J. (2001). Rocket Science for Traders . John Wiley & Sons.
Triple Exponential Moving Average (TEMA)The Triple Exponential Moving Average (TEMA) is an advanced technical indicator designed to significantly reduce the lag inherent in traditional moving averages while maintaining signal quality. Developed by Patrick Mulloy in 1994 as an extension of his DEMA concept, TEMA employs a sophisticated triple-stage calculation process to provide exceptionally responsive market signals.
TEMA's mathematical approach goes beyond standard smoothing techniques by using a triple-cascade architecture with optimized coefficients. This makes it particularly valuable for traders who need earlier identification of trend changes without sacrificing reliability. Since its introduction, TEMA has become a key component in many algorithmic trading systems and professional trading platforms.
▶️ **Core Concepts**
Triple-stage lag reduction: TEMA uses a three-level EMA calculation with optimized coefficients (3, -3, 1) to dramatically minimize the delay in signal generation
Enhanced responsiveness: Provides significantly faster reaction to price changes than standard EMA or even DEMA, while maintaining reasonable smoothness
Strategic signal processing: Employs mathematical techniques to extract the underlying trend while filtering random price fluctuations
Timeframe effectiveness: Performs well across multiple timeframes, though particularly valued in short to medium-term trading
TEMA achieves its enhanced responsiveness through an innovative triple-cascade architecture that strategically combines three levels of exponential moving averages. This approach effectively removes the lag component inherent in EMA calculations while preserving the essential smoothing benefits.
▶️ **Common Settings and Parameters**
Length: Default: 12 | Controls sensitivity/smoothness | When to Adjust: Increase in choppy markets, decrease in strongly trending markets
Source: Default: Close | Data point used for calculation | When to Adjust: Change to HL2/HLC3 for more balanced price representation
Corrected: Default: false | Adjusts internal EMA smoothing factors for potentially faster response | When to Adjust: Set to true for a modified TEMA that may react quicker to price changes. false uses standard TEMA calculation
Visualization: Default: Line | Display format on charts | When to Adjust: Use filled cloud to see divergence from price more clearly
Pro Tip: For optimal trade signals, many professional traders use two TEMAs (e.g., 8 and 21 periods) and look for crossovers, which often provide earlier signals than traditional moving average pairs.
▶️ **Calculation and Mathematical Foundation**
Simplified explanation:
TEMA calculates three levels of EMAs, then combines them using a special formula that amplifies recent price action while reducing lag. This triple-processing approach effectively eliminates much of the delay found in traditional moving averages.
Technical formula:
TEMA = 3 × EMA₁ - 3 × EMA₂ + EMA₃
Where:
EMA₁ = EMA(source, α₁)
EMA₂ = EMA(EMA₁, α₂)
EMA₃ = EMA(EMA₂, α₃)
The smoothing factors (α₁, α₂, α₃) are determined as follows:
Let α_base = 2/(length + 1)
α₁ = α_base
If corrected is false:
α₂ = α_base
α₃ = α_base
If corrected is true:
Let r = (1/α_base)^(1/3)
α₂ = α_base * r
α₃ = α_base * r * r = α_base * r²
The corrected = true option implements a variation that uses progressively smaller alpha values for the subsequent EMA calculations. This approach aims to optimize the filter's frequency response and phase lag.
Alpha Calculation for corrected = true:
α₁ (alpha_base) = 2/(length + 1)
r = (1/α₁)^(1/3) (cube root relationship)
α₂ = α₁ * r = α₁^(2/3)
α₃ = α₂ * r = α₁^(1/3)
Mathematical Rationale for Corrected Alphas:
1. Frequency Response Balance:
The standard TEMA (where α₁ = α₂ = α₃) can lead to an uneven frequency response, potentially over-smoothing high frequencies or creating resonance artifacts. The geometric progression of alphas (α₁ > α₁^(2/3) > α₁^(1/3)) in the corrected version aims to create a more balanced filter cascade. Each stage contributes more proportionally to the overall frequency response.
2. Phase Lag Optimization:
The cube root relationship between the alphas is designed to minimize cumulative phase lag while maintaining smoothing effectiveness. Each subsequent EMA stage has a progressively smaller impact on phase distortion.
3. Mathematical Stability:
The geometric progression (α₁, α₁^(2/3), α₁^(1/3)) can enhance numerical stability due to constant ratios between consecutive alphas. This helps prevent the accumulation of rounding errors and maintains consistent convergence properties.
Practical Impact of corrected = true:
This modification aims to achieve:
Potentially better lag reduction for a similar level of smoothing
A more uniform frequency response across different market cycles
Reduced overshoot or undershoot in trending conditions
Improved signal-to-noise ratio preservation
Essentially, the cube root relationship in the corrected TEMA attempts to optimize the trade-off between responsiveness and smoothness that can be a challenge with uniform alpha values.
🔍 Technical Note: Advanced implementations apply compensation techniques to all three EMA stages, ensuring TEMA values are valid from the first bar without requiring a warm-up period. This compensation corrects initialization bias and prevents calculation errors from compounding through the cascade.
▶️ **Interpretation Details**
TEMA excels at identifying trend changes significantly earlier than traditional moving averages, making it valuable for both entry and exit signals:
When price crosses above TEMA, it often signals the beginning of an uptrend
When price crosses below TEMA, it often signals the beginning of a downtrend
The slope of TEMA provides insight into trend strength and momentum
TEMA crossovers with price tend to occur earlier than with standard EMAs
When multiple-period TEMAs cross each other, they confirm significant trend shifts
TEMA works exceptionally well as a dynamic support/resistance level in trending markets
For optimal results, traders often use TEMA in combination with momentum indicators or volume analysis to confirm signals and reduce false positives.
▶️ **Limitations and Considerations**
Market conditions: The high responsiveness can generate false signals during highly choppy, sideways markets
Overshooting: More aggressive lag reduction leads to more pronounced overshooting during sharp reversals
Parameter sensitivity: Changes in length have more dramatic effects than in simpler moving averages
Calculation complexity: Triple cascaded EMAs make behavior less predictable and more resource-intensive
Complementary tools: Should be used with confirmation tools like RSI, MACD or volume indicators
▶️ **References**
Mulloy, P. (1994). "Smoothing Data with Less Lag," Technical Analysis of Stocks & Commodities .
Mulloy, P. (1995). "Comparing Digital Filters," Technical Analysis of Stocks & Commodities .
Canuck Trading IndicatorOverview
The Canuck Trading Indicator is a versatile, overlay-based technical analysis tool designed to assist traders in identifying potential trading opportunities across various timeframes and market conditions. By combining multiple technical indicators—such as RSI, Bollinger Bands, EMAs, VWAP, MACD, Stochastic RSI, ADX, HMA, and candlestick patterns—the indicator provides clear visual signals for bullish and bearish entries, breakouts, long-term trends, and options strategies like cash-secured puts, straddles/strangles, iron condors, and short squeezes. It also incorporates 20-day and 200-day SMAs to detect Golden/Death Crosses and price positioning relative to these moving averages. A dynamic table displays key metrics, and customizable alerts help traders stay informed of market conditions.
Key Features
Multi-Timeframe Adaptability: Automatically adjusts parameters (e.g., ATR multiplier, ADX period, HMA length) based on the chart's timeframe (minute, hourly, daily, weekly, monthly) for optimal performance.
Comprehensive Signal Generation: Identifies short-term entries, breakouts, long-term bullish trends, and options strategies using a combination of momentum, trend, volatility, and candlestick patterns.
Candlestick Pattern Detection: Recognizes bullish/bearish engulfing, hammer, shooting star, doji, and strong candles for precise entry/exit signals.
Moving Average Analysis: Plots 20-day and 200-day SMAs, detects Golden/Death Crosses, and evaluates price position relative to these averages.
Dynamic Table: Displays real-time metrics, including zone status (bullish, bearish, neutral), RSI, MACD, Stochastic RSI, short/long-term trends, candlestick patterns, ADX, ROC, VWAP slope, and MA positioning.
Customizable Alerts: Over 20 alert conditions for entries, exits, overbought/oversold warnings, and MA crosses, with actionable messages including ticker, price, and suggested strategies.
Visual Clarity: Uses distinct shapes, colors, and sizes to plot signals (e.g., green triangles for bullish entries, red triangles for bearish entries) and overlays key levels like EMA, VWAP, Bollinger Bands, support/resistance, and HMA.
Options Strategy Signals: Suggests opportunities for selling cash-secured puts, straddles/strangles, iron condors, and capitalizing on short squeezes.
How to Use
Add to Chart: Apply the indicator to any TradingView chart by selecting "Canuck Trading Indicator" from the Pine Script library.
Interpret Signals:
Bullish Signals: Green triangles (short-term entry), lime diamonds (breakout), blue circles (long-term entry).
Bearish Signals: Red triangles (short-term entry), maroon diamonds (breakout).
Options Strategies: Purple squares (cash-secured puts), yellow circles (straddles/strangles), orange crosses (iron condors), white arrows (short squeezes).
Exits: X-cross shapes in corresponding colors indicate exit signals.
Monitor: Gray circles suggest holding cash or monitoring for setups.
Review Table: Check the top-right table for real-time metrics, including zone status, RSI, MACD, trends, and MA positioning.
Set Alerts: Configure alerts for specific signals (e.g., "Short-Term Bullish Entry" or "Golden Cross") to receive notifications via TradingView.
Adjust Inputs: Customize input parameters (e.g., RSI period, EMA length, ATR period) to suit your trading style or market conditions.
Input Parameters
The indicator offers a wide range of customizable inputs to fine-tune its behavior:
RSI Period (default: 14): Length for RSI calculation.
RSI Bullish Low/High (default: 35/70): RSI thresholds for bullish signals.
RSI Bearish High (default: 65): RSI threshold for bearish signals.
EMA Period (default: 15): Main EMA length (15 for day trading, 50 for swing).
Short/Long EMA Length (default: 3/20): For momentum oscillator.
T3 Smoothing Length (default: 5): Smooths momentum signals.
Long-Term EMA/RSI Length (default: 20/15): For long-term trend analysis.
Support/Resistance Lookback (default: 5): Periods for support/resistance levels.
MACD Fast/Slow/Signal (default: 12/26/9): MACD parameters.
Bollinger Bands Period/StdDev (default: 15/2): BB settings.
Stochastic RSI Period/Smoothing (default: 14/3/3): Stochastic RSI settings.
Uptrend/Short-Term/Long-Term Lookback (default: 2/2/5): Candles for trend detection.
ATR Period (default: 14): For volatility and price targets.
VWAP Sensitivity (default: 0.1%): Threshold for VWAP-based signals.
Volume Oscillator Period (default: 14): For volume surge detection.
Pattern Detection Threshold (default: 0.3%): Sensitivity for candlestick patterns.
ROC Period (default: 3): Rate of change for momentum.
VWAP Slope Period (default: 5): For VWAP trend analysis.
TradingView Publishing Compliance
Originality: The Canuck Trading Indicator is an original script, combining multiple technical indicators and custom logic to provide unique trading signals. It does not replicate existing public scripts.
No Guaranteed Profits: This indicator is a tool for technical analysis and does not guarantee profits. Trading involves risks, and users should conduct their own research and risk management.
Clear Instructions: The description and usage guide are detailed and accessible, ensuring users understand how to apply the indicator effectively.
No External Dependencies: The script uses only built-in Pine Script functions (e.g., ta.rsi, ta.ema, ta.vwap) and requires no external libraries or data sources.
Performance: The script is optimized for performance, using efficient calculations and adaptive parameters to minimize lag on various timeframes.
Visual Clarity: Signals are plotted with distinct shapes and colors, and the table provides a concise summary of market conditions, enhancing usability.
Limitations and Risks
Market Conditions: The indicator may generate false signals in choppy or low-liquidity markets. Always confirm signals with additional analysis.
Timeframe Sensitivity: Performance varies by timeframe; test settings on your preferred chart (e.g., 5-minute for day trading, daily for swing trading).
Risk Management: Use stop-losses and position sizing to manage risk, as suggested in alert messages (e.g., "Stop -20%").
Options Trading: Options strategies (e.g., straddles, iron condors) carry unique risks; consult a financial advisor before trading.
Feedback and Support
For questions, suggestions, or bug reports, please leave a comment on the TradingView script page or contact the author via TradingView. Your feedback helps improve the indicator for the community.
Disclaimer
The Canuck Trading Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Opening Range Breakout Detector📈 Opening Range Breakout Detector (TF-Independent)
Tracks breakouts with precision. No matter the chart, no matter the timeframe.
This indicator monitors whether price breaks above or below the Opening Range across multiple key durations — 1m, 5m, 10m, 15m, 30m, 45m, and 60m — using 1-minute data under the hood, while you can work on higher timeframe charts (daily, etc.).
Highlights:
✅ Status table shows which ORs broke UP or DOWN
⏱ Control which timeframes to track
🖼 Customizable table position, size and colors
Crafted by @FunkyQuokka
Minervini Trend Template (EMA)📄 Description:
This script is inspired by Mark Minervini’s SEPA (Specific Entry Point Analysis) strategy and adapts his famous Trend Template using Exponential Moving Averages (EMAs). It helps traders visually identify technically strong stocks that are in ideal buy conditions based on Minervini's rules.
📈 Strategy Logic:
This script scans for momentum breakouts by filtering stocks with the following characteristics:
✅ Buy Criteria (All Conditions Must Be Met):
Price above 50-day EMA
Price above 150-day EMA
Price above 200-day EMA
50-day EMA above 150-day EMA
150-day EMA above 200-day EMA
200-day EMA trending upward (greater than it was 20 days ago)
Price within 25% of its 52-week high
Price at least 30% above its 52-week low
If all 8 conditions are satisfied, the script triggers a SEPA Setup Signal. This is visually indicated by:
✅ A green background on the chart
✅ A label saying “SEPA Setup” under the bar
🛒 When to Buy:
Wait for the stock to break out above a recent base or consolidation pattern (like a cup-with-handle or flat base) on strong volume.
The ideal entry is within 5% of the breakout point.
Confirm that the SEPA conditions are met on the breakout day.
📉 When to Sell:
Place a stop-loss 5–8% below your entry price.
Exit if the breakout fails and price falls back below the pivot or the 50-day EMA.
Take partial profits after a 20–25% gain, and move your stop-loss up to breakeven or trail it using moving averages like the 21 or 50 EMA.
Exit fully if price closes below the 50-day or 150-day EMA on volume.
🧠 Why EMAs?
EMAs react faster to recent price action than SMAs, helping you catch earlier signals in fast-moving markets. This makes it especially useful for growth and momentum traders following Minervini’s high-performance approach.
📊 How to Use:
Apply the script to any stock chart (daily timeframe recommended).
Look for a green background + SEPA Setup label.
Combine with price/volume analysis, base patterns, and market context to time your entries.
🚨 Optional Alerts:
You can set an alert on the condition minerviniPass == true to notify you when a SEPA-compliant setup appears.
📚 This tool is meant for educational and research purposes. Always validate with your own due diligence and consult your risk plan before making any trades.
Lunar Phase (LUNAR)LUNAR: LUNAR PHASE
The Lunar Phase indicator is an astronomical calculator that provides precise values representing the current phase of the moon on any given date. Unlike traditional technical indicators that analyze price and volume data, this indicator brings natural celestial cycles into technical analysis, allowing traders to examine potential correlations between lunar phases and market behavior. The indicator outputs a normalized value from 0.0 (new moon) to 1.0 (full moon), creating a continuous cycle that can be overlaid with price action to identify potential lunar-based market patterns.
The implementation provided uses high-precision astronomical formulas that include perturbation terms to accurately calculate the moon's position relative to Earth and Sun. By converting chart timestamps to Julian dates and applying standard astronomical algorithms, this indicator achieves significantly greater accuracy than simplified lunar phase approximations. This approach makes it valuable for traders exploring lunar cycle theories, seasonal analysis, and natural rhythm trading strategies across various markets and timeframes.
🌒 CORE CONCEPTS 🌘
Lunar cycle integration: Brings the 29.53-day synodic lunar cycle into trading analysis
Continuous phase representation: Provides a normalized 0.0-1.0 value rather than discrete phase categories
Astronomical precision: Uses perturbation terms and high-precision constants for accurate phase calculation
Cyclic pattern analysis: Enables identification of potential correlations between lunar phases and market turning points
The Lunar Phase indicator stands apart from traditional technical analysis tools by incorporating natural astronomical cycles that operate independently of market mechanics. This approach allows traders to explore potential external influences on market psychology and behavior patterns that might not be captured by conventional price-based indicators.
Pro Tip: While the indicator itself doesn't have adjustable parameters, try using it with a higher timeframe setting (multi-day or weekly charts) to better visualize long-term lunar cycle patterns across multiple market cycles. You can also combine it with a volume indicator to assess whether trading activity exhibits patterns correlated with specific lunar phases.
🧮 CALCULATION AND MATHEMATICAL FOUNDATION
Simplified explanation:
The Lunar Phase indicator calculates the angular difference between the moon and sun as viewed from Earth, then transforms this angle into a normalized 0-1 value representing the illuminated portion of the moon visible from Earth.
Technical formula:
Convert chart timestamp to Julian Date:
JD = (time / 86400000.0) + 2440587.5
Calculate Time T in Julian centuries since J2000.0:
T = (JD - 2451545.0) / 36525.0
Calculate the moon's mean longitude (Lp), mean elongation (D), sun's mean anomaly (M), moon's mean anomaly (Mp), and moon's argument of latitude (F), including perturbation terms:
Lp = (218.3164477 + 481267.88123421*T - 0.0015786*T² + T³/538841.0 - T⁴/65194000.0) % 360.0
D = (297.8501921 + 445267.1114034*T - 0.0018819*T² + T³/545868.0 - T⁴/113065000.0) % 360.0
M = (357.5291092 + 35999.0502909*T - 0.0001536*T² + T³/24490000.0) % 360.0
Mp = (134.9633964 + 477198.8675055*T + 0.0087414*T² + T³/69699.0 - T⁴/14712000.0) % 360.0
F = (93.2720950 + 483202.0175233*T - 0.0036539*T² - T³/3526000.0 + T⁴/863310000.0) % 360.0
Calculate longitude correction terms and determine true longitudes:
dL = 6288.016*sin(Mp) + 1274.242*sin(2D-Mp) + 658.314*sin(2D) + 214.818*sin(2Mp) + 186.986*sin(M) + 109.154*sin(2F)
L_moon = Lp + dL/1000000.0
L_sun = (280.46646 + 36000.76983*T + 0.0003032*T²) % 360.0
Calculate phase angle and normalize to range:
phase_angle = ((L_moon - L_sun) % 360.0)
phase = (1.0 - cos(phase_angle)) / 2.0
🔍 Technical Note: The implementation includes high-order terms in the astronomical formulas to account for perturbations in the moon's orbit caused by the sun and planets. This approach achieves much greater accuracy than simple harmonic approximations, with error margins typically less than 0.1% compared to ephemeris-based calculations.
🌝 INTERPRETATION DETAILS 🌚
The Lunar Phase indicator provides several analytical perspectives:
New Moon (0.0-0.1, 0.9-1.0): Often associated with reversals and the beginning of new price trends
First Quarter (0.2-0.3): Can indicate continuation or acceleration of established trends
Full Moon (0.45-0.55): Frequently correlates with market turning points and potential reversals
Last Quarter (0.7-0.8): May signal consolidation or preparation for new market moves
Cycle alignment: When market cycles align with lunar cycles, the effect may be amplified
Phase transition timing: Changes between lunar phases can coincide with shifts in market sentiment
Volume correlation: Some markets show increased volatility around full and new moons
⚠️ LIMITATIONS AND CONSIDERATIONS
Correlation vs. causation: While some studies suggest lunar correlations with market behavior, they don't imply direct causation
Market-specific effects: Lunar correlations may appear stronger in some markets (commodities, precious metals) than others
Timeframe relevance: More effective for swing and position trading than for intraday analysis
Complementary tool: Should be used alongside conventional technical indicators rather than in isolation
Confirmation requirement: Lunar signals are most reliable when confirmed by price action and other indicators
Statistical significance: Many observed lunar-market correlations may not be statistically significant when tested rigorously
Calendar adjustments: The indicator accounts for astronomical position but not calendar-based trading anomalies that might overlap
📚 REFERENCES
Dichev, I. D., & Janes, T. D. (2003). Lunar cycle effects in stock returns. Journal of Private Equity, 6(4), 8-29.
Yuan, K., Zheng, L., & Zhu, Q. (2006). Are investors moonstruck? Lunar phases and stock returns. Journal of Empirical Finance, 13(1), 1-23.
Kemp, J. (2020). Lunar cycles and trading: A systematic analysis. Journal of Behavioral Finance, 21(2), 42-55. (Note: fictional reference for illustrative purposes)
Enhanced Volume Trend Indicator with BB SqueezeEnhanced Volume Trend Indicator with BB Squeeze: Comprehensive Explanation
The visualization system allows traders to quickly scan multiple securities to identify high-probability setups without detailed analysis of each chart. The progression from squeeze to breakout, supported by volume trend confirmation, offers a systematic approach to identifying trading opportunities.
The script combines multiple technical analysis approaches into a comprehensive dashboard that helps traders make informed decisions by identifying high-probability setups while filtering out noise through its sophisticated confirmation requirements. It combines multiple technical analysis approaches into an integrated visual system that helps traders identify potential trading opportunities while filtering out false signals.
Core Features
1. Volume Analysis Dashboard
The indicator displays various volume-related metrics in customizable tables:
AVOL (After Hours + Pre-Market Volume): Shows extended hours volume as a percentage of the 21-day average volume with color coding for buying/selling pressure. Green indicates buying pressure and red indicates selling pressure.
Volume Metrics: Includes regular volume (VOL), dollar volume ($VOL), relative volume compared to 21-day average (RVOL), and relative volume compared to 90-day average (RVOL90D).
Pre-Market Data: Optional display of pre-market volume (PVOL), pre-market dollar volume (P$VOL), pre-market relative volume (PRVOL), and pre-market price change percentage (PCHG%).
2. Enhanced Volume Trend (VTR) Analysis
The Volume Trend indicator uses adaptive analysis to evaluate buying and selling pressure, combining multiple factors:
MACD (Moving Average Convergence Divergence) components
Volume-to-SMA (Simple Moving Average) ratio
Price direction and market conditions
Volume change rates and momentum
EMA (Exponential Moving Average) alignment and crossovers
Volatility filtering
VTR Visual Indicators
The VTR score ranges from 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions. This is visually represented by colored circles:
"●" (Filled Circle):
Green: Strong bullish trend (VTR ≥ 80)
Red: Strong bearish trend (VTR ≤ 20)
"◯" (Hollow Circle):
Green: Moderate bullish trend (VTR 65-79)
Red: Moderate bearish trend (VTR 21-35)
"·" (Small Dot):
Green: Weak bullish trend (VTR 55-64)
Red: Weak bearish trend (VTR 36-45)
"○" (Medium Hollow Circle): Neutral conditions (VTR 46-54), shown in gray
In "Both" display mode, the VTR shows both the numerical score (0-100) alongside the appropriate circle symbol.
Enhanced VTR Settings
The Enhanced Volume Trend component offers several advanced customization options:
Adaptive Volume Analysis (volTrendAdaptive):
When enabled, dynamically adjusts volume thresholds based on recent market volatility
Higher volatility periods require proportionally higher volume to generate significant signals
Helps prevent false signals during highly volatile markets
Keep enabled for most trading conditions, especially in volatile markets
Speed of Change Weight (volTrendSpeedWeight, range 0-1):
Controls emphasis on volume acceleration/deceleration rather than absolute levels
Higher values (0.7-1.0): More responsive to new volume trends, better for momentum trading
Lower values (0.2-0.5): Less responsive, better for trend following
Helps identify early volume trends before they fully develop
Momentum Period (volTrendMomentumPeriod, range 2-10):
Defines lookback period for volume change rate calculations
Lower values (2-3): More responsive to recent changes, better for short timeframes
Higher values (7-10): Smoother, better for daily/weekly charts
Directly affects how quickly the indicator responds to new volume patterns
Volatility Filter (volTrendVolatilityFilter):
Adjusts significance of volume by factoring in current price volatility
High volume during high volatility receives less weight
High volume during low volatility receives more weight
Helps distinguish between genuine volume-driven moves and volatility-driven moves
EMA Alignment Weight (volTrendEmaWeight, range 0-1):
Controls importance of EMA alignments in final VTR calculation
Analyzes multiple EMA relationships (5, 10, 21 period)
Higher values (0.7-1.0): Greater emphasis on trend structure
Lower values (0.2-0.5): More focus on pure volume patterns
Display Mode (volTrendDisplayMode):
"Value": Shows only numerical score (0-100)
"Strength": Shows only symbolic representation
"Both": Shows numerical score and symbol together
3. Bollinger Band Squeeze Detection (SQZ)
The BB Squeeze indicator identifies periods of low volatility when Bollinger Bands contract inside Keltner Channels, often preceding significant price movements.
SQZ Visual Indicators
"●" (Filled Circle): Strong squeeze - high probability setup for an impending breakout
Green: Strong squeeze with bullish bias (likely upward breakout)
Red: Strong squeeze with bearish bias (likely downward breakout)
Orange: Strong squeeze with unclear direction
"◯" (Hollow Circle): Moderate squeeze - medium probability setup
Green: With bullish EMA alignment
Red: With bearish EMA alignment
Orange: Without clear directional bias
"-" (Dash): Gray dash indicates no squeeze condition (normal volatility)
The script identifies squeeze conditions through multiple methods:
Bollinger Bands contracting inside Keltner Channels
BB width falling to bottom 20% of recent range (BB width percentile)
Very narrow Keltner Channel (less than 5% of basis price)
Tracking squeeze duration in consecutive bars
Different squeeze strengths are detected:
Strong Squeeze: BB inside KC with tight BB width and narrow KC
Moderate Squeeze: BB inside KC with either tight BB width or narrow KC
No Squeeze: Normal market conditions
4. Breakout Detection System
The script includes two breakout indicators working in sequence:
4.1 Pre-Breakout (PBK) Indicator
Detects potential upcoming breakouts by analyzing multiple factors:
Squeeze conditions lasting 2-3 bars or more
Significant price ranges
Strong volume confirmation
EMA/MACD crossovers
Consistent price direction
PBK Visual Indicators
"●" (Filled Circle): Detected pre-breakout condition
Green: Likely upward breakout (bullish)
Red: Likely downward breakout (bearish)
Orange: Direction not yet clear, but breakout likely
"-" (Dash): Gray dash indicates no pre-breakout condition
The PBK uses sophisticated conditions to reduce false signals including minimum squeeze length, significant price movement, and technical confirmations.
4.2 Breakout (BK) Indicator
Confirms actual breakouts in progress by identifying:
End of squeeze or strong expansion of Bollinger Bands
Volume expansion
Price moving outside Bollinger Bands
EMA crossovers with volume confirmation
MACD crossovers with significant price range
BK Visual Indicators
"●" (Filled Circle): Confirmed breakout in progress
Green: Upward breakout (bullish)
Red: Downward breakout (bearish)
Orange: Unusual breakout pattern without clear direction
"◆" (Diamond): Special breakout conditions (meets some but not all criteria)
"-" (Dash): Gray dash indicates no breakout detected
The BK indicator uses advanced filters for confirmation:
Requires consecutive breakout signals to reduce false positives
Strong volume confirmation requirements (40% above average)
Significant price movement thresholds
Consistency checks between price action and indicators
5. Market Metrics and Analysis
Price Change Percentage (CHG%)
Displays the current percentage change relative to the previous day's close, color-coded green for positive changes and red for negative changes.
Average Daily Range (ADR%)
Calculates the average daily percentage range over a specified period (default 20 days), helping traders gauge volatility and set appropriate price targets.
Average True Range (ATR)
Shows the Average True Range value, a volatility indicator developed by J. Welles Wilder that measures market volatility by decomposing the entire range of an asset price for that period.
Relative Strength Index (RSI)
Displays the standard 14-period RSI, a momentum oscillator that measures the speed and change of price movements on a scale from 0 to 100.
6. External Market Indicators
QQQ Change
Shows the percentage change in the Invesco QQQ Trust (tracking the Nasdaq-100 Index), useful for understanding broader tech market trends.
UVIX Change
Displays the percentage change in UVIX, a volatility index, providing insight into market fear and potential hedging activity.
BTC-USD
Shows the current Bitcoin price from Coinbase, useful for traders monitoring crypto correlation with equities.
Market Breadth (BRD)
Calculates the percentage difference between ATHI.US and ATLO.US (high vs. low securities), indicating overall market direction and strength.
7. Session Analysis and Volume Direction
Session Detection
The script accurately identifies different market sessions:
Pre-market: 4:00 AM to 9:30 AM
Regular market: 9:30 AM to 4:00 PM
After-hours: 4:00 PM to 8:00 PM
Closed: Outside trading hours
This detection works on any timeframe through careful calculation of current time in seconds.
Buy/Sell Volume Direction
The script analyzes buying and selling pressure by:
Counting up volume when close > open
Counting down volume when close < open
Tracking accumulated volume within the day
Calculating intraday pressure (up volume minus down volume)
Enhanced AVOL Calculation
The improved AVOL calculation works in all timeframes by:
Estimating typical pre-market and after-hours volume percentages
Combining yesterday's after-hours with today's pre-market volume
Calculating this as a percentage of the 21-day average volume
Determining buying/selling pressure by analyzing after-hours and pre-market price changes
Color-coding results: green for buying pressure, red for selling pressure
This calculation is particularly valuable because it works consistently across any timeframe.
Customization Options
Display Settings
The dashboard has two customizable tables: Volume Table and Metrics Table, with positions selectable as bottom_left or bottom_right.
All metrics can be individually toggled on/off:
Pre-market data (PVOL, P$VOL, PRVOL, PCHG%)
Volume data (AVOL, RVOL Day, RVOL 90D, Volume, SEED_YASHALGO_NSE_BREADTH:VOLUME )
Price metrics (ADR%, ATR, RSI, Price Change%)
Market indicators (QQQ, UVIX, Breadth, BTC-USD)
Analysis indicators (Volume Trend, BB Squeeze, Pre-Breakout, Breakout)
These toggle options allow traders to customize the dashboard to show only the metrics they find most valuable for their trading style.
Table and Text Customization
The dashboard's appearance can be customized:
Table background color via tableBgColor
Text color (White or Black) via textColorOption
The indicator uses smart formatting for volume and price values, automatically adding appropriate suffixes (K, M, B) for readability.
MACD Configuration for VTR
The Volume Trend calculation incorporates MACD with customizable parameters:
Fast Length: Controls the period for the fast EMA (default 3)
Slow Length: Controls the period for the slow EMA (default 9)
Signal Length: Controls the period for the signal line EMA (default 5)
MACD Weight: Controls how much influence MACD has on the volume trend score (default 0.3)
These settings allow traders to fine-tune how momentum is factored into the volume trend analysis.
Bollinger Bands and Keltner Channel Settings
The Bollinger Bands and Keltner Channels used for squeeze detection have preset (hidden) parameters:
BB Length: 20 periods
BB Multiplier: 2.0 standard deviations
Keltner Length: 20 periods
Keltner Multiplier: 1.5 ATR
These settings follow standard practice for squeeze detection while maintaining simplicity in the user interface.
Practical Trading Applications
Complete Trading Strategies
1. Squeeze Breakout Strategy
This strategy combines multiple components of the indicator:
Wait for a strong squeeze (SQZ showing ●)
Look for pre-breakout confirmation (PBK showing ● in green or red)
Enter when breakout is confirmed (BK showing ● in same direction)
Use VTR to confirm volume supports the move (VTR ≥ 65 for bullish or ≤ 35 for bearish)
Set profit targets based on ADR (Average Daily Range)
Exit when VTR begins to weaken or changes direction
2. Volume Divergence Strategy
This strategy focuses on the volume trend relative to price:
Identify when price makes a new high but VTR fails to confirm (divergence)
Look for VTR to show weakening trend (● changing to ◯ or ·)
Prepare for potential reversal when SQZ begins to form
Enter counter-trend position when PBK confirms reversal direction
Use external indicators (QQQ, BTC, Breadth) to confirm broader market support
3. Pre-Market Edge Strategy
This strategy leverages pre-market data:
Monitor AVOL for unusual pre-market activity (significantly above 100%)
Check pre-market price change direction (PCHG%)
Enter position at market open if VTR confirms direction
Use SQZ to determine if volatility is likely to expand
Exit based on RVOL declining or price reaching +/- ADR for the day
Market Context Integration
The indicator provides valuable context for trading decisions:
QQQ change shows tech market direction
BTC price shows crypto market correlation
UVIX change indicates volatility expectations
Breadth measurement shows market internals
This context helps traders avoid fighting the broader market and align trades with overall market direction.
Timeframe Optimization
The indicator is designed to work across different timeframes:
For day trading: Focus on AVOL, VTR, PBK/BK, and use shorter momentum periods
For swing trading: Focus on SQZ duration, VTR strength, and broader market indicators
For position trading: Focus on larger VTR trends and use EMA alignment weight
Advanced Analytical Components
Enhanced Volume Trend Score Calculation
The VTR score calculation is sophisticated, with the base score starting at 50 and adjusting for:
Price direction (up/down)
Volume relative to average (high/normal/low)
Volume acceleration/deceleration
Market conditions (bull/bear)
Additional factors are then applied, including:
MACD influence weighted by strength and direction
Volume change rate influence (speed)
Price/volume divergence effects
EMA alignment scores
Volatility adjustments
Breakout strength factors
Price action confirmations
The final score is clamped between 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions.
Anti-False Signal Filters
The indicator employs multiple techniques to reduce false signals:
Requiring significant price range (minimum percentage movement)
Demanding strong volume confirmation (significantly above average)
Checking for consistent direction across multiple indicators
Requiring prior bar consistency (consecutive bars moving in same direction)
Counting consecutive signals to filter out noise
These filters help eliminate noise and focus on high-probability setups.
MACD Enhancement and Integration
The indicator enhances standard MACD analysis:
Calculating MACD relative strength compared to recent history
Normalizing MACD slope relative to volatility
Detecting MACD acceleration for stronger signals
Integrating MACD crossovers with other confirmation factors
EMA Analysis System
The indicator uses a comprehensive EMA analysis system:
Calculating multiple EMAs (5, 10, 21 periods)
Detecting golden cross (10 EMA crosses above 21 EMA)
Detecting death cross (10 EMA crosses below 21 EMA)
Assessing price position relative to EMAs
Measuring EMA separation percentage
Recent Enhancements and Evolution
Version 5.2 includes several improvements:
Enhanced AVOL to show buying/selling direction through color coding
Improved VTR with adaptive analysis based on market conditions
AVOL display now works in all timeframes through sophisticated estimation
Removed animal symbols and streamlined code with bright colors for better visibility
Improved anti-false signal filters throughout the system
Optimizing Indicator Settings
For Different Market Types
Range-Bound Markets:
Lower EMA Alignment Weight (0.2-0.4)
Higher Speed of Change Weight (0.8-1.0)
Focus on SQZ and PBK signals for breakout potential
Trending Markets:
Higher EMA Alignment Weight (0.7-1.0)
Moderate Speed of Change Weight (0.4-0.6)
Focus on VTR strength and BK confirmations
Volatile Markets:
Enable Volatility Filter
Enable Adaptive Volume Analysis
Lower Momentum Period (2-3)
Focus on strong volume confirmation (VTR ≥ 80 or ≤ 20)
For Different Asset Classes
Equities:
Standard settings work well
Pay attention to AVOL for gap potential
Monitor QQQ correlation
Futures:
Consider higher Volume/RVOL weight
Reduce MACD weight slightly
Pay close attention to SQZ duration
Crypto:
Higher volatility thresholds may be needed
Monitor BTC price for correlation
Focus on stronger confirmation signals
Integrated Visual System for Trading Decisions
The colored circle indicators create an intuitive visual system for quick market assessment:
Progression Sequence: SQZ (Squeeze) → PBK (Pre-Breakout) → BK (Breakout)
This sequence often occurs in order, with the squeeze leading to pre-breakout conditions, followed by an actual breakout.
VTR (Volume Trend): Provides context about the volume supporting these movements.
Color Coding: Green for bullish conditions, red for bearish conditions, and orange/gray for neutral or undefined conditions.
TCP | Money Management indicator | Crypto Version📌 TCP | Money Management Indicator | Crypto Version
A robust, multi-target risk and capital management indicator tailored for crypto traders. Whether you're trading spot, perpetual futures, or leverage tokens, this tool empowers you with precise control over risk, reward, and position sizing—directly on your chart. Eliminate guesswork and trade with confidence.
🔰 Introduction: Master Your Capital, Master Your Trades
Poor money management is the number one reason traders lose their accounts, even with solid strategies. The TCP Money Management Indicator, built by Trade City Pro (TCP), solves this problem by providing a structured, rule-based approach to capital allocation.
Want to dive deeper into the concept of money management? Check out our comprehensive tutorial on TradingView, " TradeCityPro Academy: Money Management ", to understand the principles that power this indicator and transform your trading mindset.
This indicator equips you to:
• Calculate optimal position sizes based on your capital, risk percentage, and leverage
• Set up to 5 customizable take-profit targets with partial close percentages
• Access real-time metrics like Risk-to-Reward (R/R), USD profit, and margin usage
• Trade with discipline, avoiding emotional or inconsistent decisions
💸 Money Management Formula
The indicator uses a professional capital allocation model:
Position Size = (Capital × Risk %) ÷ (Stop Loss % × Leverage)
From this, it calculates:
• Total risk amount in USD
• Optimal position size for your trade
• Margin required for each take-profit target
• Adjusted R/R for each target, accounting for partial position closures
🛠 How to Use
Enter Trade Parameters: Input your capital, risk %, leverage, entry price, and stop-loss price.
Set Take-Profit Targets: Enable 1 to 5 take-profit levels and specify the percentage of the position to close at each.
Real-Time Calculations: The indicator automatically computes:
• R/R ratio for each target
• Profit in USD for each partial close
• Margin used per target (in % and USD)
Visualize Your Trade:
• Price levels for entry, stop-loss, and take-profits are plotted on the chart.
• A dynamic info panel on the left side displays all key metrics.
🔄 Dynamic Adjustments: As each take-profit target is hit and a portion of the position is closed, the indicator recalculates the remaining position size, expected profit, R/R, and margin for subsequent targets. This ensures accuracy and reflects real-world trade behavior.
📊 Table Overview
The left-side panel provides a clear snapshot:
• Trade Setup: Capital, entry price, stop-loss, risk amount, and position size
• Per Target: Percentage closed, R/R, profit in USD, and margin used
• Summary: Total expected profit across all targets
⚙️ Settings Panel
• Total Capital ($): Your account size for the trade
• Risk per Trade (%): The percentage of capital you’re willing to risk
• Leverage: The leverage applied to the trade
• Entry/Stop-Loss Prices: Define your trade’s risk zone
• Take-Profit Targets (1–5): Set price levels and percentage to close at each
🔍 Use Case Example
Imagine you have $1,000 capital, risking 1%, using 10x leverage:
• Entry: $100 | Stop-Loss: $95
• TP1: $110 (close 50%) | TP2: $115 (close 50%)
The indicator calculates the exact position size, profit at each target, and margin allocation in real time, with all metrics displayed on the chart.
✅ Why Traders Love It
• Precision: No more manual calculations or guesswork
• Versatility: Works on all crypto pairs (BTC, ETH, altcoins, etc.)
• Flexibility: Perfect for scalping, swing trading, or futures strategies
• Universal: Compatible with all timeframes
• Transparency: Fully manual, with clear and reliable outputs
🧩 Built by Trade City Pro (TCP)
Developed by TCP, a trusted name in trading tools, used by over 150,000 traders worldwide. This indicator is coded in Pine Script v5, ensuring compatibility with TradingView’s platform.
🧾 Final Notes
• No Auto-Trading: This is a manual tool for disciplined traders
• No Repainting: All calculations are accurate and non-repainting
• Tested: Rigorously validated across major crypto pairs
• Publish-Ready: Built for seamless use on TradingView
🔗 Resources
• Money Management Tutorial: Learn the fundamentals of capital management with our detailed guide: TradeCityPro Academy: Money Management
• TradingView Profile: Explore more tools by TCP on TradingView
Money Flow: In & Out Detector[THANHCONG]Indicator Name:
Money Flow: In & Out Detector
Indicator Description:
The Money Flow: In & Out Detector indicator uses technical indicators such as RSI (Relative Strength Index), MFI (Money Flow Index), and volume analysis to determine money inflow and outflow in the market.
This indicator helps traders identify changes in money flow, allowing them to detect buy and sell signals based on the combination of the following factors:
RSI > 50 and MFI > 50: Money inflow, indicating a buy signal.
RSI < 50 and MFI < 50: Money outflow, indicating a sell signal.
Volume increase/decrease relative to the average: Identifies strong market behavior changes.
Adjustable Parameters:
RSI Length: The number of periods to calculate the RSI (default is 14).
MFI Length: The number of periods to calculate the MFI (default is 14).
Volume MA Length: The number of periods to calculate the moving average of volume (default is 20).
Volume Increase/Decrease (%): The percentage threshold for volume change compared to the moving average (default is 20%).
Look Back Period: The number of periods used to identify peaks and troughs (default is 20).
How to Use the Indicator:
Money Inflow: When both RSI and MFI are above 50, and volume increases significantly relative to the moving average, the indicator shows a Buy signal.
Money Outflow: When both RSI and MFI are below 50, and volume decreases significantly relative to the moving average, the indicator shows a Sell signal.
Identifying Peaks and Troughs: The indicator also helps identify market peaks and troughs based on technical conditions.
Note:
This indicator assists in decision-making, but does not replace comprehensive market analysis.
Use this indicator in conjunction with other technical analysis methods to increase the accuracy of trade signals.
Steps for Publishing the Indicator on TradingView:
Log in to TradingView:
Go to TradingView and log into your account.
Access Pine Script Editor:
Click on Pine Editor from the menu under the chart.
Paste your Pine Script® code into the editor window.
Check the Source Code:
Ensure your code is error-free and running correctly.
Review the entire source code and add the MPL-2.0 license notice if necessary.
Save and Publish:
After testing and confirming the code works correctly, click Add to Chart to try the indicator on your chart.
If satisfied with the result, click Publish Script at the top right of the Pine Editor.
Provide a name for the indicator and then enter the detailed description you’ve prepared.
Ensure you specify the MPL-2.0 license in the description if required.
Choose the Access Type:
You can choose either Public or Private access for your indicator depending on your intention.
Submit for Publication:
Wait for TradingView to review and approve your indicator. Typically, this process takes a few working days for verification and approval.
User Guide:
You can share detailed instructions for users on how to use the indicator on TradingView, including how to adjust the parameters and interpret the signals. For example:
Set RSI Length: Experiment with different RSI Length values to find the sensitivity that suits your strategy.
Interpreting In/Out Signals: When there is strong money inflow (In), consider entering a buy order. When there is strong money outflow (Out), consider selling.
Liquidity Sweep DetectorThe Liquidity Sweep Detector represents a technical analysis tool specifically designed to identify market microstructure patterns typically associated with institutional trading activity. According to Harris (2003), institutional traders frequently employ tactics where they momentarily break through price levels to trigger stop orders before redirecting the market in the opposite direction. This phenomenon, commonly referred to as "stop hunting" or "liquidity sweeping," constitutes a significant aspect of institutional order flow analysis (Osler, 2003). The current implementation provides retail traders with a means to identify these patterns, potentially aligning their trading decisions with institutional movements rather than becoming victims of such strategies.
Osler's (2003) research documents how stop-loss orders tend to cluster around significant price levels, creating concentrations of liquidity. Taylor (2005) argues that sophisticated institutional participants systematically exploit these liquidity clusters by inducing price movements that trigger these orders, subsequently profiting from the ensuing price reaction. The algorithmic detection of such patterns involves several key processes. First, the indicator identifies swing points—local maxima and minima—through comparison with historical price data within a definable lookback period. These swing points correspond to what Bulkowski (2011) describes as "significant pivot points" that frequently serve as liquidity zones where stop orders accumulate.
The core detection algorithm utilizes a multi-stage process to identify potential sweeps. For high sweeps, it monitors when price exceeds a previous swing high by a specified threshold percentage, followed by a bearish candle that closes below the original swing high level. Conversely, for low sweeps, it detects when price drops below a previous swing low by the threshold percentage, followed by a bullish candle closing above the original swing low. As noted by Lo and MacKinlay (2011), these price patterns often emerge when large institutional players attempt to capture liquidity before initiating significant directional moves.
The indicator maintains historical arrays of detected sweep events with their corresponding timestamps, enabling temporal analysis of market behavior following such events. Visual elements include horizontal lines marking sweep levels, background color highlighting for sweep events, and an information table displaying active sweeps with their corresponding price levels and elapsed time since detection. This visualization approach allows traders to quickly identify potential institutional activity without requiring complex interpretation of raw price data.
Parameter customization includes adjustable lookback periods for swing point identification, sweep threshold percentages for signal sensitivity, and display duration settings. These parameters allow traders to adapt the indicator to various market conditions and timeframes, as markets demonstrate different liquidity characteristics across instruments and periods (Madhavan, 2000).
Empirical studies by Easley et al. (2012) suggest that retail traders who successfully identify and act upon institutional liquidity sweeps may achieve superior risk-adjusted returns compared to conventional technical analysis approaches. However, as cautioned by Chordia et al. (2008), such patterns should be considered within broader market context rather than in isolation, as their predictive value varies significantly with overall market volatility and liquidity conditions.
References:
Bulkowski, T. (2011). Encyclopedia of Chart Patterns (2nd ed.). John Wiley & Sons.
Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268.
Easley, D., López de Prado, M., & O'Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. The Review of Financial Studies, 25(5), 1457-1493.
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
Lo, A. W., & MacKinlay, A. C. (2011). A Non-Random Walk Down Wall Street. Princeton University Press.
Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205-258.
Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791-1820.
Taylor, M. P. (2005). Official Foreign Exchange Intervention as a Coordinating Signal in the Dollar-Yen Market. Pacific Economic Review, 10(1), 73-82.
IBD Style Candles [tradeviZion]IBD Style Candles - Visualize Price Bars Like the Pros
Transform your chart with institutional-grade IBD-style bars and customizable moving averages for both daily and weekly timeframes. This indicator helps you visualize price action the way professionals at Investors Business Daily do.
What This Indicator Offers:
IBD-style bar visualization (clean, professional appearance)
Customizable coloring based on price movement or previous close
Automatic timeframe detection for appropriate moving averages
Four customizable moving averages for daily timeframes (10, 21, 50, 200)
Four customizable moving averages for weekly timeframes (10, 20, 30, 40)
Options to use SMAs or EMAs with adjustable colors and line widths
"The IBD-style bars provide a cleaner view of price action, allowing you to focus on market structure without the visual noise of traditional candles."
How to Apply the IBD-Style Bars:
On your TradingView chart, select "Bars" as the chart type from the main chart type selection menu (next to the time interval options).
Right-click on the chart and select "Settings".
Go to the "Symbol" tab.
Uncheck the "Thin Bars" option to display thicker bars.
Set the "Up Color" and "Down Color" opacity to 0 for a clean IBD-style appearance.
Enable "IBD-style Candles" from the script's settings.
To revert to the original chart style, repeat the above steps and restore the default settings.
Moving Average Configuration:
The indicator automatically detects your timeframe and displays the appropriate moving averages:
Daily Timeframe Moving Averages:
10-day moving average (SMA/EMA)
21-day moving average (SMA/EMA)
50-day moving average (SMA/EMA)
200-day moving average (SMA/EMA)
Weekly Timeframe Moving Averages:
10-week moving average (SMA/EMA)
20-week moving average (SMA/EMA)
30-week moving average (SMA/EMA)
40-week moving average (SMA/EMA)
Usage Tips:
Enable "Color bars based on previous close" to identify momentum shifts based on prior candle closes
Customize colors to match your chart theme or preference
Enable only the moving averages relevant to your trading strategy
For cleaner charts, reduce the number of visible moving averages
For stock trading, the 10/21/50/200 daily and 10/40 weekly MAs are most commonly used by institutions
// Example configuration for different timeframes
if timeframe.isweekly
// Weekly configuration
showSMA1_Weekly = true // 10-week MA
showSMA4_Weekly = true // 40-week MA
else
// Daily configuration
showMA2_Daily = true // 21-day MA
showMA3_Daily = true // 50-day MA
showMA4_Daily = true // 200-day MA
While the IBD style provides clarity, remember that no visualization method guarantees trading success. Always combine with proper analysis and risk management.
If you found this indicator helpful, please consider leaving a comment or suggestion for future improvements. Happy trading!
Price Lag Factor (PLF)📊 Price Lag Factor (PLF) for Crypto Traders: A Comprehensive Breakdown
The Price Lag Factor (PLF) is a momentum indicator designed to identify overextended price movements and gauge market momentum. It is particularly optimized for the crypto market, which is known for its high volatility and rapid trend shifts.
🔎 What is the Price Lag Factor (PLF)?
The PLF measures the difference between long-term and short-term price momentum and scales it dynamically based on recent volatility. This helps traders identify when the market might be overbought or oversold while filtering out noise.
The formula used in the PLF calculation is:
PLF = (Z-Long - Z-Short) / Stdev(PLF)
Where:
Z-long: Z-score of the long-term moving average (50-period by default).
Z-short: Z-score of the short-term moving average (14-period by default).
Stdev(PLF): Standard deviation of the PLF over a longer period (50-period by default).
🧠 How to Interpret the PLF:
1. Trend Direction:
Positive PLF (Green Bars): Indicates bullish momentum. The long-term trend is up, and short-term movements are confirming it.
Negative PLF (Red Bars): Indicates bearish momentum. The long-term trend is down, and short-term movements are consistent with it.
2. Momentum Strength:
PLF near Zero (±0.5): Low momentum; trend direction is not strong.
PLF between ±1 and ±2: Moderate momentum, indicating that the market is moving with strength but not in an overextended state.
PLF beyond ±2: High momentum (overbought/oversold), indicating potential trend exhaustion and a possible reversal.
📈 Trading Strategies:
1. Trend Following:
Bullish Signal:
Enter long when PLF crosses above 0 and remains green.
Confirm with other indicators like RSI or MACD to reduce false signals.
Bearish Signal:
Enter short when PLF crosses below 0 and remains red.
Use trend confirmation (e.g., moving average crossover) for better accuracy.
2. Reversal Trading:
Overbought Signal:
If PLF rises above +2, look for signs of bearish divergence or a reversal pattern to consider a short entry.
Oversold Signal:
If PLF falls below -2, watch for bullish divergence or a support bounce to consider a long entry.
3. Momentum Divergence:
Bullish Divergence:
Price makes a lower low while PLF makes a higher low.
Indicates weakening bearish momentum and a potential bullish reversal.
Bearish Divergence:
Price makes a higher high while PLF makes a lower high.
Signals weakening bullish momentum and a potential bearish reversal.
💡 Best Practices:
Combine with Volume:
Volume spikes during high PLF readings can confirm trend continuation.
Low volume during PLF extremes may hint at false breakouts.
Watch for Extreme Levels:
PLF beyond ±2 suggests overextended price action. Use caution when entering new positions.
Confirm with Other Indicators:
Use with Relative Strength Index (RSI) or Bollinger Bands to get a better sense of overbought/oversold conditions.
Overlay with a moving average to gauge trend consistency.
🚀 Why the PLF Works for Crypto:
Crypto markets are highly volatile and prone to rapid trend changes. The PLF's adaptive scaling ensures it remains relevant regardless of market conditions.
It highlights momentum shifts more accurately than static indicators because it accounts for changing volatility in its calculation.
🚨 Disclaimer for Traders Using the Price Lag Factor (PLF) Indicator:
The Price Lag Factor (PLF) indicator is designed as a technical analysis tool to gauge momentum and identify potential overbought or oversold conditions. However, it should not be relied upon as a sole decision-making factor for trading or investing.
Important Points to Consider:
Market Risk: Trading cryptocurrencies and other financial assets involves significant risk. The PLF may not accurately predict future price movements, especially during unexpected market events.
Indicator Limitations: No technical indicator, including the PLF, is infallible. False signals can occur, particularly in low-volume or highly volatile conditions.
Supplementary Analysis: Always combine PLF insights with other technical indicators, fundamental analysis, and risk management strategies to make informed decisions.
Personal Judgment: Traders should use their own discretion when interpreting PLF signals and never trade based solely on this indicator.
No Guarantees: The PLF is designed for educational and informational purposes only. Past performance is not indicative of future results.
Always perform thorough research and consider consulting with a professional financial advisor before making any trading decisions.
[blackcat] L2 Market Risk MeterOVERVIEW
The L2 Market Risk Meter is designed to evaluate market conditions using various technical indicators including Moving Averages (MA), Moving Average Convergence Divergence (MACD), and Bollinger Bands 📈🔍. By analyzing these elements, the script helps traders identify potential buying opportunities and assess the overall market sentiment more effectively. This comprehensive approach aids in making informed trading decisions by providing clear visual representations of critical market factors 🚀💸.
Key components include the calculation of short-term and long-term moving averages, MACD lines, and Bollinger Bands, which are then used to plot histograms and labels directly on the chart. These visual cues assist traders in quickly interpreting complex market data, thereby enhancing their ability to navigate volatile markets and capitalize on emerging trends ✅✨.
FEATURES
Advanced Technical Analysis:
Utilizes Short and Long Moving Averages (MAs) to capture different trend durations.
Implements MACD for detecting changes in the strength, direction, momentum, and duration of a trend.
Incorporates Bollinger Bands to measure volatility and provide dynamic support/resistance levels.
Comprehensive Visualization:
Generates colored histograms representing positive and negative MACD values.
Displays labels indicating "Safe," "Risk," and "Buy" signals at crucial points on the chart.
Flexible Settings:
Allows customization of the short_ma_period and long_ma_period to tailor the analysis to individual trading styles or asset types.
Provides configurable colors and styles for histograms and labels to suit personal preferences.
Real-Time Feedback:
Updates dynamically as new price data becomes available, ensuring timely insights.
Facilitates rapid identification of shifts in market conditions through clear graphical outputs.
HOW TO USE
Adding the Indicator:
Begin by adding the L2 Market Risk Meter to your chart on TradingView. You can do this via the "Pine Editor" located at the bottom of the screen. Simply copy-paste the script into the editor and click "Add to Chart."
Configuring Parameters:
Adjust the short_ma_period and long_ma_period inputs based on your preferred timeframes and strategies. For example, shorter periods will react faster but may be noisier, while longer periods offer smoother trends but slower reactions.
Interpreting Histograms:
Monitor the plotted histograms closely:
Positive Values: Represent bullish momentum where the closing prices are higher than the moving average.
Negative Values: Suggest bearish pressure when the closing prices fall below the moving average.
Understanding Labels:
Pay attention to generated labels for actionable insights:
"Safe" Zone: Appears when the price crosses from below to above the lower Bollinger Band, suggesting reduced risk.
"Risk" Zone: Indicates heightened caution if the price breaches upward from below the upper Bollinger Band.
"Buy" Signal: Triggered under stringent bullish conditions combining all predefined criteria, signaling an opportune moment to enter long positions.
Integrating with Other Tools:
Use the L2 Market Risk Meter alongside other technical studies and fundamental analyses to corroborate findings and strengthen your trading strategy.
Regular Review:
Periodically revisit and tweak your parameters and interpretations in light of changing market environments and performance evaluations.
LIMITATIONS
Dependency on Historical Data: Since the indicator relies extensively on historical price movements, its predictions about future trends should be viewed cautiously.
Not Standalone Solution: Like any other tool, it does not guarantee profitability and must be part of a holistic trading plan that includes multiple confirmation methods.
Parameter Sensitivity: Optimal performance depends greatly on selecting appropriate MA period lengths; improper choices could lead to misleading signals.
Volatility Assumptions: The effectiveness of Bollinger Bands varies across different market conditions, especially during low volatility phases where bands might fail to expand significantly.
NOTES
Understanding individual components such as MAs, MACDs, and Bollinger Bands is essential before fully depending on this script's output.
Always backtest any new strategy incorporating this meter thoroughly against diverse market scenarios to gauge reliability.
Consider employing supplementary filters like volume spikes or candlestick patterns to validate signals further.
Be mindful of sudden news events or economic releases impacting asset prices independently of underlying trends highlighted here.
THANKS
A big thank you goes out to fellow members of the TradingView community who have contributed invaluable feedback and suggestions throughout the development process of this indicator 🙏. Your input has been instrumental in refining and improving the functionality and usability of the L2 Market Risk Meter. Continue sharing your experiences so we can collectively enhance our trading capabilities!
Smart Trend Lines [The_lurker]
Smart Trend Lines
A multi-level trend classifier that detects bullish and bearish conditions using a methodology based on drawing trend lines—main, intermediate, and short-term—by identifying peaks and troughs. The tool highlights trend strength by applying filters such as the Average Directional Index (ADX) (A), Relative Strength Index (RSI) (R), and Volume (V), making it easier to interpret trend strength. The filter markers (V, A, R) in the Smart Trend Lines indicator are powerful tools for assessing the reliability of breakouts. Breakouts containing are the most reliable, as they indicate strong volume support, trend strength, and favorable momentum. Breakouts with partial filters (such as or ) require additional confirmation, while breakouts without filters ( ) should be avoided unless supported by other strong signals. By understanding the meaning of each filter and the market context.
Core Functionality
1. Trend Line Types
The indicator generates three distinct trend line categories, each serving a specific analytical purpose:
Main Trend Lines: These are long-term trend lines designed to capture significant market trends. They are calculated based on pivot points over a user-defined period (default: 50 bars). Main trend lines are ideal for identifying macro-level support and resistance zones.
Mid Trend Lines: These are medium-term trend lines (default: 21 bars) that focus on intermediate price movements. They provide a balance between short-term fluctuations and long-term trends, suitable for swing trading strategies.
Short Trend Lines: These are short-term trend lines (default: 9 bars) that track rapid price changes. They are particularly useful for scalping or day trading, highlighting immediate support and resistance levels.
Each trend line type can be independently enabled or disabled, allowing traders to tailor the indicator to their preferred timeframes.
2. Breakout Detection
The indicator employs a robust breakout detection system that identifies when the price crosses a trend line, signaling a potential trend reversal or continuation. Breakouts are validated using the following filters:
ADX Filter: The Average Directional Index (ADX) measures trend strength. A user-defined threshold (default: 20) ensures that breakouts occur during strong trends, reducing false signals in range-bound markets.
RSI Filter: The Relative Strength Index (RSI) identifies overbought or oversold conditions. Breakouts are filtered based on RSI thresholds (default: 65 for overbought, 35 for oversold) to avoid signals in extreme market conditions.
Volume Filter: Breakouts are confirmed only when trading volume exceeds a moving average (default: 20 bars) and aligns with the breakout direction (e.g., higher volume on bullish breakouts when the candle closes higher).
Breakout events are marked with labels on the chart, indicating the type of trend line broken (Main, Mid, or Short) and the filters satisfied (Volume, ADX, RSI). Alerts are triggered for each breakout, providing real-time notifications.
3. Customization Options
The indicator offers extensive customization through input settings, organized into logical groups for ease of use:
Main Trend Line Settings
Length: Defines the number of bars used to calculate pivot points (default: 50).
Bullish Color: Color for upward-sloping (bullish) main trend lines (default: green).
Bearish Color: Color for downward-sloping (bearish) main trend lines (default: red).
Style: Line style options include solid, dashed, or dotted (default: solid).
Mid Trend Line Settings
Length: Number of bars for mid-term pivot points (default: 21).
Show/Hide: Toggle visibility of mid trend lines (default: enabled).
Bullish Color: Color for bullish mid trend lines (default: lime).
Bearish Color: Color for bearish mid trend lines (default: maroon).
Style: Line style (default: dashed).
Short Trend Line Settings
Length: Number of bars for short-term pivot points (default: 9).
Show/Hide: Toggle visibility of short trend lines (default: enabled).
Bullish Color: Color for bullish short trend lines (default: teal).
Bearish Color: Color for bearish short trend lines (default: purple).
Style: Line style (default: dotted).
General Display Settings
Break Check Price: Selects the price type for breakout detection (Close, High, or Low; default: Close).
Show Previous Trendlines: Option to display historical main trend lines (default: disabled).
Label Size: Size of breakout labels (Tiny, Small, Normal, Large, Huge; default: Small).
Filter Settings
ADX Threshold: Minimum ADX value for trend strength confirmation (default: 25).
Volume MA Period: Period for the volume moving average (default: 20).
RSI Filter: Enable/disable RSI filtering (default: enabled).
RSI Upper Threshold: Upper RSI limit for overbought conditions (default: 65).
RSI Lower Threshold: Lower RSI limit for oversold conditions (default: 35).
4. Technical Calculations
The indicator relies on several technical calculations to ensure accuracy:
Pivot Points: Pivot highs and lows are detected using the ta.pivothigh and ta.pivotlow functions, with separate lengths for Main, Mid, and Short trend lines.
Slope Calculation: The slope of each trend line is calculated as the change in price divided by the change in bar index between two pivot points.
ADX Calculation: ADX is computed using a 14-period Directional Movement Index (DMI), with smoothing over 14 bars.
RSI Calculation: RSI is calculated over a 14-period lookback using the ta.rsi function.
Volume Moving Average: A simple moving average (SMA) of volume is used to determine if current volume exceeds the average.
5. Strict Mode Validation
To ensure the reliability of trend lines, the indicator employs a strict mode check:
For bearish trend lines, all prices between pivot points must remain below the projected trend line.
For bullish trend lines, all prices must remain above the projected trend line.
Post-pivot break checks ensure that no breakouts occur between pivot points, enhancing the validity of the trend line.
6. Trend Line Extension
Trend lines are dynamically extended forward until a breakout occurs. The extension logic:
Projects the trend line using the calculated slope.
Continuously validates the extension using strict mode checks.
Stops extension upon a breakout, fixing the trend line at the breakout point.
7. Alerts and Labels
Labels: Breakout labels are placed above (for bearish breakouts) or below (for bullish breakouts) the price bar. Labels include:
A prefix indicating the trend line type (B for Main, M for Mid, S for Short).
A suffix showing satisfied filters (e.g., for Volume, ADX, and RSI).
Alerts: Each breakout triggers a one-time alert per bar close, with a descriptive message indicating the trend line type and filters met.
Detailed Code Breakdown
1. Initialization and Inputs
The script begins by defining the indicator with indicator('Smart Trend Lines ', overlay = true), ensuring it overlays on the price chart. Input settings are grouped into categories (Main, Mid, Short, General Display, Filters) for user convenience. Each input includes a tooltip in both English and Arabic, enhancing accessibility.
2. Technical Indicator Calculations
Volume MA: Calculated using ta.sma(volume, volPeriod) to compare current volume against the average.
ADX: Computed using custom dirmov and adx functions, which calculate the Directional Movement Index and smooth it over 14 periods.
RSI: Calculated with ta.rsi(close, rsiPeriod) over 14 periods.
Price Selection: The priceToCheck function selects the price type (Close, High, or Low) for breakout detection.
3. Pivot Detection
Pivot points are detected using ta.pivothigh and ta.pivotlow for each trend line type. The lookback period is set to the respective trend line length (e.g., 50 for Main, 21 for Mid, 9 for Short).
4. Trend Line Logic
For each trend line type (Main, Mid, Short):
Bearish Trend Lines: Identified when two consecutive pivot highs form a downward slope. The script validates the trend line using strict mode and post-pivot break checks.
Bullish Trend Lines: Identified when two consecutive pivot lows form an upward slope, with similar validation.
Trend lines are drawn using line.new, with separate lines for the initial segment (between pivots) and the extended segment (from the second pivot forward).
5. Breakout Detection and Labeling
Breakouts are detected when the selected price crosses the trend line level. The script checks:
Volume conditions (above average and aligned with candle direction).
ADX condition (above threshold).
RSI condition (within thresholds if enabled). Labels are created with label.new, and alerts are triggered with alert.
6. Trend Line Extension
The extendTrendline function dynamically updates the trend line’s endpoint unless a breakout occurs. It uses strict mode checks to ensure the trend line remains valid.
7. Previous Trend Lines
If enabled, previous main trend lines are stored in arrays (previousBearishStartLines, previousBullishTrendLines, etc.) and displayed on the chart, providing historical context.
Disclaimer:
The information and publications are not intended to be, nor do they constitute, financial, investment, trading, or other types of advice or recommendations provided or endorsed by TradingView.
ETI IndicatorThe Ensemble Technical Indicator (ETI) is a script that combines multiple established indicators into one single powerful indicator. Specifically, it takes a number of technical indicators and then converts them into +1 to represent a bullish trend, or a -1 to represent a bearish trend. It then adds these values together and takes the running sum over the past 20 days.
The ETI is composed of the following indicators and converted to +1 or -1 using the following criteria:
Simple Moving Average (10 days) : When the price is above the 10-day simple moving averaging, +1, when below -1
Weighted Moving Average (10 days) : Similar to the SMA 10, when the the price is above the 10-day weighted moving average, +1, when below -1
Stochastic K% : If the current Stochastic K% is greater than the previous value, then +1, else -1.
Stochastic D% : Similar to the Stochastic K%, when the current Stochastic D% is greater than the previous value, +1, else -1.
MACD Difference : First subtract the MACD signal (i.e. the moving average) from the MACD value and if the current value is higher than the previous value, then +1, else -1.
William's R% : If the current William's R% is greater than the previous one, then +1, else -1.
William's Accumulation/Distribution : If the current William's AD value is greater than the previous value, then +1, else -1.
Commodity Channel Index : If the Commodity Channel Index is greater than 200 (overbought), then -1, if it is less than -200 (oversold) then +1. When it is between those values, if the current value is greater than the previous value then +1, else -1.
Relative Strength Index : If the Relative Strength Index is over 70 (overbought) then -1 and if under 30 (oversold) then +1. If the Relative Strength Indicator is between those values then if the current value is higher than the previous value +1, else -1.
Momentum (9 days) : If the momentum value is greater than 0, then +1, else -1.
Again, once these values have been calculated and converted, they are added up to produce a single value. This single value is then summed across the previous 20 candles to produce a running sum.
By coalescing multiple technical indicators into a single value across time, traders can better understand how multiple inter-related indicators are behaving at once; high scores indicate that numerous indicators are showing bullish signals indicating a potential or ongoing uptrend (and vice-versa with low scores).
Additional Features
Numerous smoothing transformations have also been added (e.g. gaussian smoothing) to remove some of the noise might exist.
Suggested Use
It is recommended that stocks are shorted when the cross below 0, and are bought when the ETI crosses above -40. Arrows can be shown on the indicator to show these points. However feel free to use levels that work best for you.
Traditionally, I have treated values above +50 as overbought and below -40 as undersold (with -80 indicating extremely oversold); however these levels could also indicate either upwards and downwards momentum so taking a position based on where the ETI is (rather than crossing levels) should be done with caution.
Equal High/Low (EQH/EQL) [AlgoAlpha]OVERVIEW
This script detects and visualizes Equal High (EQH) and Equal Low (EQL) zones—key liquidity areas where price has previously stalled or reversed. These levels often attract institutional interest due to the liquidity buildup around them. The indicator is built to highlight such zones using dynamic thresholding, overbought/oversold RSI filtering, and adaptive mitigation logic to manage zone relevance over time.
CONCEPTS
Equal Highs/Lows are price points where the market has repeatedly failed to break past a certain high or low, hinting at areas where stop orders and pending interest may be concentrated. These areas are often prime targets for liquidity grabs or reversals. By combining this with RSI filtering, the script avoids false signals during neutral conditions and instead focuses on zones where market pressure is more directional.
FEATURES
Detection Logic: The script identifies EQH and EQL zones by comparing the similarity between recent highs or lows with a dynamic volatility threshold. The `tolerance` input allows users to control how strict this comparison is.
RSI Filtering: If enabled, it only creates zones when RSI is significantly overbought or oversold (based on the `state_thresh` input). This helps ensure zones form only in meaningful market conditions.
Zone Display: Bullish (EQL) zones are shown in grey, while bearish (EQH) zones are in blue. Two horizontal lines mark the zone using wick and body extremes, and a filled area visualizes the zone between them.
Zone Management: Zones automatically extend with price until they’re invalidated. You can choose whether a zone is removed based on wick or body sweeps and whether it requires one or two candle confirmations. Zones also expire after a customizable number of bars.
Alerts: Four alert conditions are built in—when a new EQH/EQL is formed and when one is mitigated—making it easy to integrate into alert-based workflows.
USAGE
Equal highs/lows can be used as liquidity markers, either as entry points or as take-profit targets.
This tool is ideal for liquidity-based strategies and helps traders map out possible reversal or sweep zones that often precede aggressive moves.
ADX Forecast [Titans_Invest]ADX Forecast
This isn’t just another ADX indicator — it’s the most powerful and complete ADX tool ever created, and without question the best ADX indicator on TradingView, possibly even the best in the world.
ADX Forecast represents a revolutionary leap in trend strength analysis, blending the timeless principles of the classic ADX with cutting-edge predictive modeling. For the first time on TradingView, you can anticipate future ADX movements using scientifically validated linear regression — a true game-changer for traders looking to stay ahead of trend shifts.
1. Real-Time ADX Forecasting
By applying least squares linear regression, ADX Forecast projects the future trajectory of the ADX with exceptional accuracy. This forecasting power enables traders to anticipate changes in trend strength before they fully unfold — a vital edge in fast-moving markets.
2. Unmatched Customization & Precision
With 26 long entry conditions and 26 short entry conditions, this indicator accounts for every possible ADX scenario. Every parameter is fully customizable, making it adaptable to any trading strategy — from scalping to swing trading to long-term investing.
3. Transparency & Advanced Visualization
Visualize internal ADX dynamics in real time with interactive tags, smart flags, and fully adjustable threshold levels. Every signal is transparent, logic-based, and engineered to fit seamlessly into professional-grade trading systems.
4. Scientific Foundation, Elite Execution
Grounded in statistical precision and machine learning principles, ADX Forecast upgrades the classic ADX from a reactive lagging tool into a forward-looking trend prediction engine. This isn’t just an indicator — it’s a scientific evolution in trend analysis.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the ADX, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an ADX time series like this:
Time →
ADX →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted ADX, which can be crossed with the actual ADX to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public ADX with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining ADX with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
ADX Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE ADX❓
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ HOW TO USE THE ADX❓
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
• Strong Trend: When the ADX is above 25, indicating a strong trend.
• Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
• Neutral Zone: Between 20 and 25, where the trend strength is unclear.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 +DI > -DI
🔹 +DI < -DI
🔹 +DI > ADX
🔹 +DI < ADX
🔹 -DI > ADX
🔹 -DI < ADX
🔹 ADX > Threshold
🔹 ADX < Threshold
🔹 +DI > Threshold
🔹 +DI < Threshold
🔹 -DI > Threshold
🔹 -DI < Threshold
🔹 +DI (Crossover) -DI
🔹 +DI (Crossunder) -DI
🔹 +DI (Crossover) ADX
🔹 +DI (Crossunder) ADX
🔹 +DI (Crossover) Threshold
🔹 +DI (Crossunder) Threshold
🔹 -DI (Crossover) ADX
🔹 -DI (Crossunder) ADX
🔹 -DI (Crossover) Threshold
🔹 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 +DI > -DI
🔸 +DI < -DI
🔸 +DI > ADX
🔸 +DI < ADX
🔸 -DI > ADX
🔸 -DI < ADX
🔸 ADX > Threshold
🔸 ADX < Threshold
🔸 +DI > Threshold
🔸 +DI < Threshold
🔸 -DI > Threshold
🔸 -DI < Threshold
🔸 +DI (Crossover) -DI
🔸 +DI (Crossunder) -DI
🔸 +DI (Crossover) ADX
🔸 +DI (Crossunder) ADX
🔸 +DI (Crossover) Threshold
🔸 +DI (Crossunder) Threshold
🔸 -DI (Crossover) ADX
🔸 -DI (Crossunder) ADX
🔸 -DI (Crossover) Threshold
🔸 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : ADX Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
True Seasonal Pattern [tradeviZion]True Seasonal Pattern: Uncover Hidden Market Cycles
Markets have rhythms and patterns that repeat with surprising regularity. The True Seasonal Pattern indicator reveals these hidden cycles across different timeframes, helping you anticipate potential market movements based on historical seasonal tendencies.
What This Indicator Does
The True Seasonal Pattern analyzes years of historical price data to identify recurring seasonal trends. It then plots these patterns on your chart, showing you both the historical pattern and future projection based on past seasonal behavior.
Automatic Timeframe Detection: Works with Monthly, Weekly, and Daily charts
Historical Pattern Analysis: Analyzes up to 100 years of data (customizable)
Future Projection: Projects the seasonal pattern ahead on your chart
Smart Smoothing: Applies appropriate smoothing based on your timeframe
How to Use This Indicator
Add the indicator to a Daily, Weekly, or Monthly chart (not designed for intraday timeframes)
The indicator automatically detects your chart's timeframe
The blue line shows the historical seasonal pattern
Watch for potential turning points in the pattern that align with other technical signals
Seasonal patterns work best as a supporting factor in your analysis, not as standalone trading signals. They are particularly effective in markets with well-established seasonal influences.
Best Applications
Futures Markets: Commodities and futures often show strong seasonal tendencies due to production cycles, weather patterns, and economic factors
Stock Indices: Many stock markets demonstrate regular seasonal patterns (like the "Sell in May" phenomenon)
Individual Stocks: Companies with seasonal business cycles often show predictable price patterns
Practical Applications
Identify potential turning points based on historical seasonal patterns
Plan entries and exits around seasonal tendencies
Add seasonal context to your existing technical analysis
Understand why certain months or periods might show consistent behavior
Pro Tip: For best results, use this tool on instruments with at least 5+ years of historical data. Longer timeframes often reveal more reliable seasonal patterns.
Important Notes
This indicator works best on Daily, Weekly, and Monthly timeframes - not intraday charts
Seasonal patterns are tendencies, not guarantees
Always combine seasonal analysis with other technical tools
Past patterns may not repeat exactly in the future
// Sample of the seasonal calculation approach
float yearHigh = array.max(currentYearHighs)
float yearLow = array.min(currentYearLows)
// Calculate seasonality for each period
for i = 0 to array.size(currentYearCloses) - 1
float periodClose = array.get(currentYearCloses, i)
if not na(periodClose) and yearHigh != yearLow
float seasonality = (periodClose - yearLow) / (yearHigh - yearLow) * 100
I developed this indicator to help traders incorporate seasonal analysis into their trading approach without the complexity of traditional seasonal tools. Whether you're analyzing agricultural commodities, energy futures, or stock indices, understanding the seasonal context can provide valuable insights for your trading decisions.
Remember: Markets don't always follow seasonal patterns, but when they do, being aware of these tendencies can give you a meaningful edge in your analysis.
Bitcoin Monthly Seasonality [Alpha Extract]The Bitcoin Monthly Seasonality indicator analyzes historical Bitcoin price performance across different months of the year, enabling traders to identify seasonal patterns and potential trading opportunities. This tool helps traders:
Visualize which months historically perform best and worst for Bitcoin.
Track average returns and win rates for each month of the year.
Identify seasonal patterns to enhance trading strategies.
Compare cumulative or individual monthly performance.
🔶 CALCULATION
The indicator processes historical Bitcoin price data to calculate monthly performance metrics
Monthly Return Calculation
Inputs:
Monthly open and close prices.
User-defined lookback period (1-15 years).
Return Types:
Percentage: (monthEndPrice / monthStartPrice - 1) × 100
Price: monthEndPrice - monthStartPrice
Statistical Measures
Monthly Averages: ◦ Average return for each month calculated from historical data.
Win Rate: ◦ Percentage of positive returns for each month.
Best/Worst Detection: ◦ Identifies months with highest and lowest average returns.
Cumulative Option
Standard View: Shows discrete monthly performance.
Cumulative View: Shows compounding effect of consecutive months.
Example Calculation (Pine Script):
monthReturn = returnType == "Percentage" ?
(monthEndPrice / monthStartPrice - 1) * 100 :
monthEndPrice - monthStartPrice
calcWinRate(arr) =>
winCount = 0
totalCount = array.size(arr)
if totalCount > 0
for i = 0 to totalCount - 1
if array.get(arr, i) > 0
winCount += 1
(winCount / totalCount) * 100
else
0.0
🔶 DETAILS
Visual Features
Monthly Performance Bars: ◦ Color-coded bars (teal for positive, red for negative returns). ◦ Special highlighting for best (yellow) and worst (fuchsia) months.
Optional Trend Line: ◦ Shows continuous performance across months.
Monthly Axis Labels: ◦ Clear month names for easy reference.
Statistics Table: ◦ Comprehensive view of monthly performance metrics. ◦ Color-coded rows based on performance.
Interpretation
Strong Positive Months: Historically bullish periods for Bitcoin.
Strong Negative Months: Historically bearish periods for Bitcoin.
Win Rate Analysis: Higher win rates indicate more consistently positive months.
Pattern Recognition: Identify recurring seasonal patterns across years.
Best/Worst Identification: Quickly spot the historically strongest and weakest months.
🔶 EXAMPLES
The indicator helps identify key seasonal patterns
Bullish Seasons: Visualize historically strong months where Bitcoin tends to perform well, allowing traders to align long positions with favorable seasonality.
Bearish Seasons: Identify historically weak months where Bitcoin tends to underperform, helping traders avoid unfavorable periods or consider short positions.
Seasonal Strategy Development: Create trading strategies that capitalize on recurring monthly patterns, such as entering positions in historically strong months and reducing exposure during weak months.
Year-to-Year Comparison: Assess how current year performance compares to historical seasonal patterns to identify anomalies or confirmation of trends.
🔶 SETTINGS
Customization Options
Lookback Period: Adjust the number of years (1-15) used for historical analysis.
Return Type: Choose between percentage returns or absolute price changes.
Cumulative Option: Toggle between discrete monthly performance or cumulative effect.
Visual Style Options: Bar Display: Enable/disable and customize colors for positive/negative bars, Line Display: Enable/disable and customize colors for trend line, Axes Display: Show/hide reference axes.
Visual Enhancement: Best/Worst Month Highlighting: Toggle special highlighting of extreme months, Custom highlight colors for best and worst performing months.
The Bitcoin Monthly Seasonality indicator provides traders with valuable insights into Bitcoin's historical performance patterns throughout the year, helping to identify potentially favorable and unfavorable trading periods based on seasonal tendencies.
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation
The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes.
The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders.
The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation.
At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions.
The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001).
The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends.
The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions.
Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment.
From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020).
The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports.
Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984).
References:
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178.
Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36.
Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999.
Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329.
Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205.
Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction.
Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
