Wskaźniki i strategie
Cyclical Phases of the Market🧭 Overview
“Cyclical Phases of the Market” automatically detects major market cycles by connecting swing lows and measuring the average number of bars between them.
Once it learns the rhythm of past cycles, it projects the next expected cycle (in time and price) using a dashed orange line and a forecast label.
In simple terms:
The indicator shows where the next potential low is statistically expected to occur, based on the timing and depth of previous cycles.
⚙️ Core Logic – Step by Step
1️⃣ Pivot Detection
The script uses the built-in ta.pivotlow() and ta.pivothigh() functions to find local turning points:
pivotLow marks a local swing low, defined by pivotLeft and pivotRight bars on each side.
Only confirmed lows are used to define the major cycle points.
Each new pivot low is stored in two arrays:
cycleLows → price level of the low
cycleBars → bar index where the low occurred
2️⃣ Cycle Identification and Drawing
Every time two consecutive swing lows are found, the indicator:
Calculates the number of bars between them (cycle length).
If that distance is greater than or equal to minCycleBars, it draws a teal line connecting the two lows — visually representing one complete cycle.
These teal lines form the historical cycle structure of the market.
3️⃣ Average Cycle Length
Once there are at least three completed cycles, the script calculates the average duration (mean number of bars between lows).
This value — avgCycleLength — represents the dominant periodicity or cycle rhythm of the market.
4️⃣ Forecasting the Next Cycle
When a valid average cycle length exists, the model projects the next expected cycle:
Time projection:
Adds avgCycleLength to the last cycle’s ending bar index to find where the next low should occur.
Price projection:
Estimates the vertical amplitude by taking the difference between the last two cycle lows (priceDiff).
Adds this same difference to the last low price to forecast the next probable low level.
The result is drawn as an orange dashed line extending into the future, representing the Next Expected Cycle.
5️⃣ Forecast Label
An orange label 🔮 appears at the projected future point showing:
Text:
🔮 Upcoming Cycle Forecast
Price:
The label marks the probable area and timing of the next cyclical low.
(Note: the date/time calculation currently multiplies bar count by 7 days, so it’s designed mainly for daily charts. On other timeframes, that conversion can be adapted.)
📊 How to Read It on the Chart
Visual Element Meaning Interpretation
Teal lines Completed historical cycles (low to low) Show actual periodic rhythm of the market
Orange dashed line Projection of the next expected cycle Anticipated path toward the next cyclical low
Orange label 🔮 Upcoming Cycle Forecast Displays expected price and bar location
Average cycle length Internal variable (bars between lows) Represents the dominant cycle period
📈 Interpretation
When teal segments show consistent spacing, the market is following a stable rhythm → cycles are predictable.
When cycle spacing shortens, the market is accelerating (volatility rising).
When it widens, the market is slowing down or entering accumulation.
The orange dashed line represents the next expected low zone:
If the market drops near this line → cyclical pattern confirmed.
If the market breaks well below → cycle amplitude has increased (trend weakening).
If the market rises above and delays → a new longer cycle may be forming.
🧠 Practical Use
Combine with oscillators (e.g., RSI or TSI) to confirm momentum alignment near projected lows.
Use in conjunction with volume to identify accumulation or exhaustion near the expected turning point.
Compare across timeframes: weekly cycles confirm long-term rhythm; daily cycles refine short-term entries.
⚡ Summary
Aspect Description
Purpose Detect and forecast recurring market cycles
Cycle basis Low-to-Low pivot analysis
Visuals Teal historical cycles + Orange forecast line
Forecast Next expected low (price and time)
Ideal timeframe Daily
Main outputs Average cycle length, next projected cycle, visual cycle map
Trend Pivot Retracements [TradeEasy]▶ OVERVIEW
Trend Pivot Retracements identifies market trend direction using a Donchian-style channel and dynamically highlights retracement zones during trending conditions. It calculates the percentage pullbacks from recent highs and lows, plots labeled zones with varying intensity, and visually connects key retracement pivots. The indicator also emphasizes price proximity to trend boundaries by dynamically adjusting the thickness of plotted trend bands.
▶ TREND DETECTION & BAND STRUCTURE
The indicator determines the current trend by checking for new 50-bar extremes:
Uptrend: If a new highest high is made, the trend is considered bullish.
Downtrend: If a new lowest low is made, the trend is considered bearish.
Uptrend Band: Plots the 50-bar lowest low as a trailing support level.
Downtrend Band: Plots the 50-bar highest high as a trailing resistance level.
Thickness Variation: The thickness of the band increases the further price moves from it, indicating overextension.
▶ RETRACEMENT LABELING SYSTEM
During a trend, the indicator monitors pivot points in the opposite direction to measure retracements:
Bullish Retracement:
Triggered when a pivot low forms during an uptrend.
Measures % pullback from the most recent swing high (searched up to 20 bars back).
Plots a bold horizontal line at the low and a dashed diagonal from the previous swing high.
Adds a “-%” label above the low; intensity is based on recent 50 pullbacks.
Bearish Retracement:
Triggered when a pivot high forms during a downtrend.
Measures % pullback from the previous swing low (up to 20 bars back).
Plots a bold horizontal line at the high and a dashed diagonal from the prior swing low.
Adds a “%” label below the high with gradient color based on the past 50 extremes.
▶ PIVOT CONNECTION LINES
Each retracement includes a visual connector:
A diagonal dashed line linking the swing extreme (20 bars back) to the retracement point.
This line visually traces the path of price retreat within the trend.
Helps traders understand where the retracement originated and how steep it was.
▶ TREND SWITCH SIGNALS
When trend direction changes:
A diamond marker is plotted on the new pivot confirming the trend shift.
Green diamonds signal new bullish trends at fresh lows.
Magenta diamonds signal new bearish trends at fresh highs.
▶ COLOR INTENSITY & CONTEXTUAL AWARENESS
To help interpret the magnitude of retracements:
The % labels are color-coded using a gradient scale that references the max of the last 50 pullbacks.
Stronger pullbacks result in deeper color intensity, signaling more significant corrections.
Trend bands also use standard deviation normalization to adjust line thickness based on how far price has moved from the band.
This creates a visual cue for potential exhaustion or volatility extremes.
▶ USAGE
Trend Pivot Retracements is a powerful tool for traders who want to:
Identify trend direction and contextual pullbacks within those trends.
Spot key retracement points that may serve as entry opportunities or reversal signals.
Use visual retracement angles to understand market pressure and trend maturity.
Read dynamic band thickness as an alert for price stretch, potential mean reversion, or breakout setups.
▶ CONCLUSION
Trend Pivot Retracements gives traders a clean, visually expressive way to monitor trending markets, while capturing and labeling meaningful retracements. With adaptive color intensity, diagonal connectors, and smart trend switching, it enhances situational awareness and provides immediate clarity on trend health and pullback strength.
NWOG/NDOG + EHPDA🌐 ENGLISH DESCRIPTION
Hybrid NWOG/NDOG + EHPDA – Advanced Gaps & Event Horizon Indicator
(Enhanced with Real-Time Alerts and Info Table)
📊 Overview
This advanced indicator combines automatic detection of weekly gaps (NWOG) and daily gaps (NDOG) with the Event Horizon (EHPDA) concept, now featuring customizable alerts and a real-time info table for a more efficient trading experience. Designed for traders who operate based on institutional price structures, liquidity zones, and SMC/ICT confluences.
✨ Key Features
1. Gap Detection & Visualization
NWOG (New Week Opening Gap): Identifies and visualizes the gap between Friday’s close and Monday’s open.
NDOG (New Day Opening Gap): Detects daily gaps on intraday timeframes.
Enhanced visualization: Semi-transparent boxes, price levels (top, middle, bottom), and lines extended to the current bar.
Customizable labels: Display gap formation date and price levels (optional).
2. Event Horizon (EHPDA)
Automatically calculates the Event Horizon level between two non-overlapping gaps.
Dashed line marking the equilibrium zone between bullish and bearish gaps.
3. Advanced 5pm-6pm Mode
Special option to detect the Sunday-Monday gap using 4H bars.
4. Real-Time Alerts
New gaps (NWOG/NDOG): Immediate notification when a new gap forms.
Gap fill: Alert when price completely fills a gap.
Event Horizon active: Notification when the Event Horizon level is triggered.
5. Info Table
Real-time display: number of active gaps, Event Horizon status, time remaining until weekly/daily close.
Customizable: position, size, and style.
🎨 Customization
Configurable colors for bullish gaps, bearish gaps, and Event Horizon line.
Customizable price labels and date format.
📈 Use Cases
Reversal trading, price targets, liquidity zones, SMC/ICT confluences.
⚙️ Recommended Settings
Timeframes: Daily and intraday (15m, 1H, 4H, etc.).
NWOG: Enable on all timeframes.
NDOG: Enable only on intraday.
Max Gaps: 3-5 for clean charts, 10-15 for historical analysis.
📝 Important Notes
Works best on 24/5 markets (Forex, Crypto).
Gaps automatically close when filled.
Event Horizon only appears with at least 2 non-overlapping gaps.
Spread Trading Z-ScoreIndicator: Z-Score Spread Indicator
Description
The "Z-Score Spread Indicator" is a powerful tool for traders employing mean-reversion strategies on the spread between two financial assets (e.g., futures contracts like MNQ and MES). This indicator calculates and plots the Z-score of the price spread, indicating how far the current spread deviates from its historical mean. It features customizable entry and exit thresholds with adjustable offsets, along with an estimated p-value displayed in a table to assess statistical significance.
Key Features
Asset Selection: Allows users to select two asset symbols (e.g., CME_MINI:MNQ1! and CME_MINI:MES1!) via customizable inputs.
Z-Score Calculation: Computes the Z-score based on the spread’s simple moving average and standard deviation over a user-defined lookback period.
Customizable Thresholds with Offset: Offers adjustable base entry and exit thresholds, with an optional offset to fine-tune trading levels, plotted as horizontal lines.
P-Value Estimation: Provides an approximate p-value to evaluate the statistical significance of the Z-score, displayed in a table anchored to the top-left corner.
Visual Representation: Plots the Z-score with a zero line and threshold lines for intuitive interpretation.
Adjustable Parameters
Asset A Symbol: Symbol for Asset A (default: CME_MINI:MNQ1!).
Asset B Symbol: Symbol for Asset B (default: CME_MINI:MES1!).
Z-Score Lookback: Lookback period for Z-score calculation (default: 40, minimum 2).
Base Entry Threshold: Threshold for entry signals (default: 1.8, adjustable with a step of 0.1).
Base Exit Threshold: Threshold for exit signals (default: 0.5, adjustable with a step of 0.1).
Threshold Offset (+/-): Offset to adjust entry and exit thresholds symmetrically (default: 0.0, range -5.0 to 5.0, step 0.1).
Usage
Add the indicator to your chart via the "Indicators" tab.
Customize the parameters based on your preferred assets and trading strategy (lookback period, thresholds, offset).
Observe the Z-score plot and threshold lines (red for short entry, green for long entry, orange dotted for exits) to identify potential trade setups.
Check the p-value table in the top-left corner to assess the statistical significance of the current Z-score.
Use this data to inform mean-reversion trading decisions, ideally in conjunction with other indicators.
Notes
A Z-score above the entry threshold (positive) or below the negative entry threshold suggests a potential short or long entry, respectively. Exits are signaled when the Z-score crosses the exit thresholds.
The p-value is an approximation based on the normal distribution; a value below 0.05 typically indicates statistical significance, but further validation is recommended.
The indicator uses a simple spread (Asset A - Asset B) without volatility adjustments; consider pairing it with a lots calculator for hedging.
Limitations
The p-value is an approximation and may not reflect advanced statistical tests (e.g., ADF) due to Pine Script constraints.
No automatic trading signals are generated; it provides data for manual analysis.
Author
Developed by grogusama, October 15, 2025, 07:29 PM CEST.
HTF Candles with PVSRA Volume Coloring (PCS Series)This indicator displays higher timeframe (HTF) candles using a PVSRA-inspired color model that blends price and volume strength, allowing traders to visualize higher-timeframe activity directly on lower-timeframe charts without switching screens.
OVERVIEW
This script visualizes higher-timeframe (HTF) candles directly on lower-timeframe charts using a custom PVSRA (Price, Volume & Support/Resistance Analysis) color model.
Unlike standard HTF indicators, it aggregates real-time OHLC and volume data bar-by-bar and dynamically draws synthetic HTF candles that update as the higher-timeframe bar evolves.
This allows traders to interpret momentum, trend continuation, and volume pressure from broader market structures without switching charts.
INTEGRATION LOGIC
This script merges higher-timeframe candle projection with PVSRA volume analysis to provide a single, multi-timeframe momentum view.
The HTF structure reveals directional context, while PVSRA coloring exposes the underlying strength of buying and selling pressure.
By combining both, traders can see when a higher-timeframe candle is building with strong or weak volume, enabling more informed intraday decisions than either tool could offer alone.
HOW IT WORKS
Aggregates price data : Groups lower-timeframe bars to calculate higher-timeframe Open, High, Low, Close, and total Volume.
Applies PVSRA logic : Compares each HTF candle’s volume to the average of the last 10 bars:
• >200% of average = strong activity
• >150% of average = moderate activity
• ≤150% = normal activity
Assigns colors :
• Green/Blue = bullish high-volume
• Red/Fuchsia = bearish high-volume
• White/Gray = neutral or low-volume moves
Draws dynamic outlines : Outlines update live while the current HTF candle is forming.
Supports symbol override : Calculations can use another instrument for correlation analysis.
This multi-timeframe aggregation avoids repainting issues in request.security() and ensures accurate real-time HTF representation.
FEATURES
Dual HTF Display : Visualize two higher timeframes simultaneously (e.g., 4H and 1D).
Dynamic PVSRA Coloring : Volume-weighted candle colors reveal bullish or bearish dominance.
Customizable Layout : Adjust candle width, spacing, offset, and color schemes.
Candle Outlines : Highlight the forming HTF candle to monitor developing structure.
Symbol Override : Display HTF candles from another instrument for cross-analysis.
SETTINGS
HTF 1 & HTF 2 : enable/disable, set timeframes, choose label colors, show/hide outlines.
Number of Candles : choose how many HTF candles to plot (1–10).
Offset Position : distance to the right of the current price where HTF candles begin.
Spacing & Width : adjust separation and scaling of candle groups.
Show Wicks/Borders : toggle wick and border visibility.
PVSRA Colors : enable or disable volume-based coloring.
Symbol Override : use a secondary ticker for HTF data if desired.
USAGE TIPS
Set the indicator’s visual order to “Bring to front.”
Always choose HTFs higher than your active chart timeframe.
Use PVSRA colors to identify strong momentum and potential reversals.
Adjust candle spacing and width for your chart layout.
Outlines are not shown on chart timeframes below 5 minutes.
TRADING STRATEGY
Strategy Overview : Combine HTF structure and PVSRA volume signals to
• Identify zones of high institutional activity and potential reversals.
• Wait for confirmation through consolidation or a pullback to key levels.
• Trade in alignment with dominant higher-timeframe structure rather than chasing volatility.
Setup :
• Chart timeframe: lower (5m, 15m, 1H)
• HTF 1: 4H or 1D
• HTF 2: 1D or 1W
• PVSRA Colors: enabled
• Outlines: enabled
Entry Concept :
High-volume candles (green or red) often indicate market-maker activity , such zones often reflect liquidity absorption by larger players and are not necessarily ideal entry points.
Wait for the next consolidation or pullback toward a support or resistance level before acting.
Bullish scenario :
• After a high-volume or rejection candle near a low, price consolidates and forms a higher low.
• Enter long only when structure confirms strength above support.
Bearish scenario :
• After a high-volume or rejection candle near a top, price consolidates and forms a lower high.
• Enter short once resistance holds and momentum weakens.
Exit Guidelines :
• Exit when next HTF candle shifts in color or momentum fades.
• Exit if price structure breaks opposite to your trade direction.
• Always use stop-loss and take-profit levels.
Additional Tips :
• Never enter directly on strong green/red high-volume candles, these are usually areas of institutional absorption.
• Wait for market structure confirmation and volume normalization.
• Combine with RSI, moving averages, or support/resistance for timing.
• Avoid trading when HTF candles are mixed or low-volume (unclear bias).
• Outlines hidden below 5m charts.
Risk Management :
• Use stop-loss and take-profit on all positions.
• Limit risk to 1–2% per trade.
• Adjust position size for volatility.
FINAL NOTES
This script helps traders synchronize lower-timeframe execution with higher-timeframe momentum and volume dynamics.
Test it on demo before live use, and adjust settings to fit your trading style.
DISCLAIMER
This script is for educational purposes only and does not constitute financial advice.
SUPPORT & UPDATES
Future improvements may include alert conditions and additional visualization modes. Feedback is welcome in the comments section.
CREDITS & LICENSE
Created by @seoco — open source for community learning.
Licensed under Mozilla Public License 2.0 .
Mean Reversion Oscillator [Alpha Extract]An advanced composite oscillator system specifically designed to identify extreme market conditions and high-probability mean reversion opportunities, combining five proven oscillators into a single, powerful analytical framework.
By integrating multiple momentum and volume-based indicators with sophisticated extreme level detection, this oscillator provides precise entry signals for contrarian trading strategies while filtering out false reversals through momentum confirmation.
🔶 Multi-Oscillator Composite Framework
Utilizes a comprehensive approach that combines Bollinger %B, RSI, Stochastic, Money Flow Index, and Williams %R into a unified composite score. This multi-dimensional analysis ensures robust signal generation by capturing different aspects of market extremes and momentum shifts.
// Weighted composite (equal weights)
normalized_bb = bb_percent
normalized_rsi = rsi
normalized_stoch = stoch_d_val
normalized_mfi = mfi
normalized_williams = williams_r
composite_raw = (normalized_bb + normalized_rsi + normalized_stoch + normalized_mfi + normalized_williams) / 5
composite = ta.sma(composite_raw, composite_smooth)
🔶 Advanced Extreme Level Detection
Features a sophisticated dual-threshold system that distinguishes between moderate and extreme market conditions. This hierarchical approach allows traders to identify varying degrees of mean reversion potential, from moderate oversold/overbought conditions to extreme levels that demand immediate attention.
🔶 Momentum Confirmation System
Incorporates a specialized momentum histogram that confirms mean reversion signals by analyzing the rate of change in the composite oscillator. This prevents premature entries during strong trending conditions while highlighting genuine reversal opportunities.
// Oscillator momentum (rate of change)
osc_momentum = ta.mom(composite, 5)
histogram = osc_momentum
// Momentum confirmation
momentum_bullish = histogram > histogram
momentum_bearish = histogram < histogram
// Confirmed signals
confirmed_bullish = bullish_entry and momentum_bullish
confirmed_bearish = bearish_entry and momentum_bearish
🔶 Dynamic Visual Intelligence
The oscillator line adapts its color intensity based on proximity to extreme levels, providing instant visual feedback about market conditions. Background shading creates clear zones that highlight when markets enter moderate or extreme territories.
🔶 Intelligent Signal Generation
Generates precise entry signals only when the composite oscillator crosses extreme thresholds with momentum confirmation. This dual-confirmation approach significantly reduces false signals while maintaining sensitivity to genuine mean reversion opportunities.
How It Works
🔶 Composite Score Calculation
The indicator simultaneously tracks five different oscillators, each normalized to a 0-100 scale, then combines them into a smoothed composite score. This approach eliminates the noise inherent in single-oscillator analysis while capturing the consensus view of multiple momentum indicators.
// Mean reversion entry signals
bullish_entry = ta.crossover(composite, 100 - extreme_level) and composite < (100 - extreme_level)
bearish_entry = ta.crossunder(composite, extreme_level) and composite > extreme_level
// Bollinger %B calculation
bb_basis = ta.sma(src, bb_length)
bb_dev = bb_mult * ta.stdev(src, bb_length)
bb_percent = (src - bb_lower) / (bb_upper - bb_lower) * 100
🔶 Extreme Zone Identification
The system automatically identifies when markets reach statistically significant extreme levels, both moderate (65/35) and extreme (80/20). These zones represent areas where mean reversion has the highest probability of success based on historical market behavior.
🔶 Momentum Histogram Analysis
A specialized momentum histogram tracks the velocity of oscillator changes, helping traders distinguish between healthy corrections and potential trend reversals. The histogram's color-coded display makes momentum shifts immediately apparent.
🔶 Divergence Detection Framework
Built-in divergence analysis identifies situations where price and oscillator movements diverge, often signaling impending reversals. Diamond-shaped markers highlight these critical divergence patterns for enhanced pattern recognition.
🔶 Real-Time Information Dashboard
An integrated information table provides instant access to current oscillator readings, market status, and individual component values. This dashboard eliminates the need to manually check multiple indicators while trading.
🔶 Individual Component Display
Optional display of individual oscillator components allows traders to understand which specific indicators are driving the composite signal. This transparency enables more informed decision-making and deeper market analysis.
🔶 Adaptive Background Coloring
Intelligent background shading automatically adjusts based on market conditions, creating visual zones that correspond to different levels of mean reversion potential. The subtle color gradations make pattern recognition effortless.
1D
3D
🔶 Comprehensive Alert System
Multi-tier alert system covers confirmed entry signals, divergence patterns, and extreme level breaches. Each alert type provides specific context about the detected condition, enabling traders to respond appropriately to different signal strengths.
🔶 Customizable Threshold Management
Fully adjustable extreme and moderate levels allow traders to fine-tune the indicator's sensitivity to match different market volatilities and trading timeframes. This flexibility ensures optimal performance across various market conditions.
🔶 Why Choose AE - Mean Reversion Oscillator?
This indicator provides the most comprehensive approach to mean reversion trading by combining multiple proven oscillators with advanced confirmation mechanisms. By offering clear visual hierarchies for different extreme levels and requiring momentum confirmation for signals, it empowers traders to identify high-probability contrarian opportunities while avoiding false reversals. The sophisticated composite methodology ensures that signals are both statistically significant and practically actionable, making it an essential tool for traders focused on mean reversion strategies across all market conditions.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
Fair Value Lead-Lag Model [BackQuant]Fair Value Lead-Lag Model
A cross-asset model that estimates where price "should" be relative to a chosen reference series, then tracks the deviation as a normalized oscillator. It helps you answer two questions: 1) is the asset rich or cheap vs its driver, and 2) is the driver leading or lagging price over the next N bars.
Concept in one paragraph
Many assets co-move with a macro or sector driver. Think BTC vs DXY, gold vs real yields, a stock vs its sector ETF. This tool builds a rolling fair value of the charted asset from a reference series and shows how far price is above or below that fair value in standard deviation units. You can shift the reference forward or backward to test who leads whom, then use the deviation and its bands to structure mean-reversion or trend-following ideas.
What the model does
Reference mapping : Pulls a reference symbol at a chosen timeframe, with an optional lead or lag in bars to test causality.
Fair value engine : Converts the reference into a synthetic fair value of the chart using one of four methods:
Ratio : price/ref with a rolling average ratio. Good when the relationship is proportional.
Spread : price minus ref with a rolling average spread. Good when the relationship is additive.
Z-Score : normalizes both series, aligns on standardized units, then re-projects to price space. Good when scale drifts.
Beta-Adjusted : rolling regression style. Uses covariance and variance to compute beta, then builds a fair value = mean(price) + beta * (ref − mean(ref)).
Deviation and bands : Computes a z-scored deviation of price vs fair value and plots sigma bands (±1, ±2, ±3) around the fair value line on the chart.
Correlation context : Shows rolling correlation so you can judge if deviations are meaningful or just noise when co-movement is weak.
Visuals :
Fair value line on price chart with sigma envelopes.
Deviation as a column oscillator and optional line.
Threshold shading beyond user-set upper and lower levels.
Summary table with reference, deviation, status, correlation, and method.
Why this is useful
Mean reversion framework : When correlation is healthy and deviation stretches beyond your sigma threshold, probability favors reversion toward fair value. This is classic pairs logic adapted to a driver and a target.
Trend confirmation : If price rides the fair value line and deviation stays modest while correlation is positive, it supports trend persistence. Pullbacks to negative deviation in an uptrend can be buyable.
Lead-lag discovery : Shift the reference forward by +N bars. If correlation improves, the reference tends to lead. Shift backward for the reverse. Use the best setting for planning early entries or hedges.
Regime detection : Large persistent deviations with falling correlation hint at regime change. The relationship you relied on may be breaking down, so reduce confidence or switch methods.
How to use it step by step
Pick a sensible reference : Choose a macro, index, currency, or sector driver that logically explains the asset’s moves. Example: gold with DXY, a semiconductor stock with SOXX.
Test lead-lag : Nudge Lead/Lag Periods to small positive values like +1 to +5 to see if the reference leads. If correlation improves, keep that offset. If correlation worsens, try a small negative value or zero.
Select a method :
Start with Beta-Adjusted when the relationship is approximately linear with drift.
Use Ratio if the assets usually move in proportional terms.
Use Spread when they trade around a level difference.
Use Z-Score when scales wander or volatility regimes shift.
Tune windows :
Rolling Window controls how quickly fair value adapts. Shorter equals faster but noisier.
Normalization Period controls how deviations are standardized. Longer equals stabler sigma sizing.
Correlation Length controls how co-movement is measured. Keep it near the fair value window.
Trade the edges :
Mean reversion idea : Wait for deviation beyond your Upper or Lower Threshold with positive correlation. Fade back toward fair value. Exit at the fair value line or the next inner sigma band.
Trend idea : In an uptrend, buy pullbacks when deviation dips negative but correlation remains healthy. In a downtrend, sell bounces when deviation spikes positive.
Read the table : Deviation shows how many sigmas you are from fair value. Status tells you overvalued or undervalued. Correlation color hints confidence. Method tells you the projection style used.
Reading the display
Fair value line on price chart: the model’s estimate of where price should trade given the reference, updated each bar.
Sigma bands around fair value: a quick sense of residual volatility. Reversions often target inner bands first.
Deviation oscillator : above zero means rich vs fair value, below zero means cheap. Color bins intensify with distance.
Correlation line (optional): scale is folded to match thresholds. Higher values increase trust in deviations.
Parameter tips
Start with Rolling Window 20 to 30, Normalization Period 100, Correlation Length 50.
Upper and Lower Threshold at ±2.0 are classic. Tighten to ±1.5 for more signals or widen to ±2.5 to focus on outliers.
When correlation drifts below about 0.3, treat deviations with caution. Consider switching method or reference.
If the fair value line whipsaws, increase Rolling Window or move to Beta-Adjusted which tends to be smoother.
Playbook examples
Pairs-style reversion : Asset is +2.3 sigma rich vs reference, correlation 0.65, trend flat. Short the deviation back toward fair value. Cover near the fair value line or +1 sigma.
Pro-trend pullback : Uptrend with correlation 0.7. Deviation dips to −1.2 sigma while price sits near the −1 sigma band. Buy the dip, target the fair value line, trail if the line is rising.
Lead-lag timing : Reference leads by +3 bars with improved correlation. Use reference swings as early cues to anticipate deviation turns on the target.
Caveats
The model assumes a stable relationship over the chosen windows. Structural breaks, policy shocks, and index rebalances can invalidate recent history.
Correlation is descriptive, not causal. A strong correlation does not guarantee future convergence.
Do not force trades when the reference has low liquidity or mismatched hours. Use a reference timeframe that captures real overlap.
Bottom line
This tool turns a loose cross-asset intuition into a quantified, visual fair value map. It gives you a consistent way to find rich or cheap conditions, time mean-reversion toward a statistically grounded target, and confirm or fade trends when the driver agrees.
NY 4H Wyckoff State Machine [CHE] NY 4H Wyckoff State Machine — Full (Re-Entry, Breakout, Wick, Re-Accum/Distrib, Dynamic Table) — One-Candle Wyckoff Re-Entry (OCWR)
Summary
OCWR operationalizes a one-candle session workflow: mark the first four-hour New York candle, fix its high and low as the session range when the window closes, and drive entries through a Wyckoff-style state machine on intraday bars. The script adds an ATR-scaled buffer around the range and requires multi-bar acceptance before treating breaks or re-entries as valid. Optional wick-cluster evidence, a proximity retest, and simple volume or RSI gates increase selectivity. Background tints expose regimes, shapes mark events, a dynamic table explains the current state, and hidden plots supply alert payloads. The design reduces random flips and makes state transitions auditable without higher-timeframe calls.
Origin and name
Method name: One-Candle Wyckoff Re-Entry (OCWR)
Transcript origin: The source idea is a “stupid simple one-candle scalping” routine: mark the first New York four-hour candle (commonly between one and five in the morning New York time), drop to five minutes, observe accumulation inside, wait for a manipulation move outside, then trade the re-entry back inside. Stops go beyond the excursion extreme; targets are either a fixed reward multiple or the opposite side of the range. Preference is given to several manipulation candles. This indicator codifies that workflow with explicit states, acceptance counters, buffers, and optional quality filters. Any external performance claims are not part of the code.
Motivation: Why this design?
Session levels are widely respected, yet single-bar breaches around them are noisy. OCWR separates range discovery from trade logic. It locks the range at the end of the window, applies an ATR-scaled buffer to ignore marginal oversteps, and requires acceptance over several bars for breaks and re-entries. Wick evidence and optional retest proximity help confirm that an excursion likely cleared liquidity rather than launched a trend. This yields cleaner transitions from test to commitment.
What’s different vs. standard approaches?
Baseline: Static session lines or one-shot Wyckoff tags without process control.
Architecture: Dual long and short state machines; ATR-buffered edges; multi-bar acceptance for breaks and re-entries; optional wick dominance and cluster checks; optional retest tolerance; direct and opposite breakout paths; cooldown after fires; distribution timeout; dynamic table with highlighted row.
Practical effect: Fewer single-bar head-fakes, clearer hand-offs, and on-chart explanations of the machine’s view.
Wyckoff structure by example — OCWR on five minutes
One-candle setup:
On the four-hour chart, mark the first New York candle’s high and low, then switch to five minutes. Solid lines show the fixed range; dashed lines show ATR-buffered edges.
Long path (verbal mapping):
Phase A, Stopping Action: Price stabilizes inside the range.
Phase B, Consolidation: Sustained balance while the window is closed and after the range is fixed.
Phase C, Test (Spring): Excursion below the buffered low with preference for several outside bars and dominant lower wicks, then a return inside.
Re-entry acceptance: A required run of inside bars validates the test.
Phase D, Breakout to Markup: Long signal fires; stop beyond the excursion extreme; objective is the opposite range or a fixed reward multiple.
Phase E, Trend (Markup) and Re-Accumulation: Advance continues until target, stop, confirmation back against the box, or timeout. A pause inside trend may register as re-accumulation.
Short path mirrors the above: A UTAD-style move forms above the buffered high, then re-entry leads to Markdown and possible re-distribution.
Variant map (verbal):
Accumulation after a downtrend: with Spring and Test, or without Spring; both proceed to Markup and may pause in Re-Accumulation.
Distribution after an uptrend: with UTAD and Test, or without UTAD; both proceed to Markdown and may pause in Re-Distribution.
Note: Phases A through E occur within each variant and are not separate variants.
How it works (technical)
Session window: A configurable four-hour New York window records its high and low. At window end, the bounds are fixed for the session.
ATR buffer: A margin above and below the fixed range discourages triggers from tiny oversteps.
Inside and outside: Users choose close-based or wick-based detection. Overshoot requirements are expressed verbally as a fraction of the range with an optional absolute minimum.
Manipulation tracking: The machine counts bars spent outside and records the side extreme.
Re-entry acceptance: After a return inside, a specified number of inside bars must print before acceptance.
Direct and opposite breakouts: Direct breakouts from accumulation and opposite breakouts after manipulation are supported, subject to acceptance and optional filters.
Targets and exits: Choose the opposite boundary or a fixed reward multiple. Distribution ends on target, stop, confirmation back against the range, or timeout.
Context filters (optional): Volume above a scaled SMA, RSI thresholds, and a trend SMA for simple regime context.
Diagnostics: Background tints for regimes; arrows for re-entries; triangles for breakouts; table with row highlights; hidden plots for alert values.
Central table (Wyckoff console)
The table sits top-right and explains the machine’s stance. Columns: Structure label, plain-English description, active state pair for long and short, and human phase tags. Rows: Start and range building; accumulation branch with Spring and Test as well as direct breakout; Markup and re-accumulation; distribution branch with UTAD and Test as well as direct short breakout; Markdown and re-distribution. Only the active state cell is rewritten each last bar, for example “L_ACCUM slash S_ACCUM”. Row highlighting is context-aware: accumulation, Spring or UTAD, breakout, Markup or Markdown, and re-accumulation or re-distribution checks can highlight independently so users see simultaneous conditions. The table is created once, updated only on the last bar for efficiency, and functions as a read-only console to audit why a signal fired and where the path currently sits.
Parameter Guide
Session window and time zone: First four hours of New York by default; time zone “America/New_York”.
ATR length and buffer factor: Control buffer size; larger reduces sensitivity, smaller reacts faster.
Minimum overshoot (fraction and absolute): Demand meaningful extension beyond the buffer.
Break mode: Close-based is stricter; wick-based is more reactive.
Acceptance counts: Separate counts for break, re-entry, and opposite breakout; higher values reduce noise.
Minimum bars outside: Ensures manipulation is not a single spike.
Wick detection and clusters (optional): Dominance thresholds and cluster size within a short window.
Retest required and tolerance (optional): Gate re-entry by proximity to the buffered edge.
Volume and RSI filters (optional): Simple gates on activity and momentum.
TP mode and reward multiple: Opposite range or fixed multiple.
Cooldown and distribution timeout: Rate-limit signals and prevent endless distribution.
Visualization toggles: Background phases, labels, table, and helper lines.
Reading & Interpretation
Solid lines are the fixed session bounds; dashed lines are buffers. Backgrounds tint accumulation, manipulation, and distribution. Arrows show accepted re-entries; triangles show direct or opposite breakouts. Labels can summarize entry, stop, target, and risk. The table highlights the active row and the current state pair.
Practical Workflows & Combinations
OCWR baseline: Each morning, mark the New York four-hour candle, move to five minutes, prefer multi-bar manipulation outside, then wait for a qualified re-entry inside. Stop beyond the excursion extreme. Target the opposite range for conservative management or a fixed multiple for uniform sizing.
Trend following: Favor direct breakouts with trend alignment and no contradictory wick evidence.
Quality control: When noise rises, increase acceptance, raise the buffer factor, enable retest, and require wick clusters.
Discretionary confluences: Fair-value gaps and trend lines can be added by the user; they are not computed by this script.
Behavior, Constraints & Performance
Closed-bar confirmation is recommended when you require finality; live-bar conditions can change until close. The script does not call higher-timeframe data. It uses arrays, lines, labels, boxes, and a table; maximum bars back is five thousand; table updates are last-bar only. Known limits include compressed buffers in quiet sessions, unreliable wick evidence in thin markets, and session misalignment if the platform time zone is not New York.
Sensible Defaults & Quick Tuning
Start with ATR length fourteen, buffer factor near zero point fifteen, overshoot fraction near zero point ten, acceptance counts of two, minimum outside duration three, retest required on.
Too many flips: increase acceptance, raise buffer, enable retest, and tighten wick thresholds.
Too slow: reduce acceptance, lower buffer, switch to wick-based breaks, disable retest.
Noisy wicks: increase minimum wick ratio and cluster size, or disable wick detection.
What this indicator is—and isn’t
A session-anchored visualization and signal layer that formalizes a Wyckoff-style re-entry and breakout workflow derived from a single four-hour New York candle. It is not predictive and not a complete trading system. Use with structure analysis, risk controls, and position management.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Hyper Strength Index | QuantLapse🧠 Hyper Strength Index (HSI) | QuantLapse
Overview:
The Hyper Strength Index (HSI) is a composite momentum oscillator designed to unify multiple strength measures into a single, adaptive framework. It combines the Relative Strength Index (RSI), Chande Momentum Oscillator (CMO), Money Flow Index (MFI), and Stochastic RSI to deliver a refined, multidimensional view of market momentum and overbought/oversold conditions.
Unlike traditional oscillators that rely on a single formula, the HSI averages four distinct momentum perspectives — price velocity, directional conviction, volume participation, and stochastic behavior — offering traders a more balanced and noise-resistant reading of market strength.
⚙️ Calculation Logic:
The Hyper Strength Index is computed as the normalized average of:
📈 RSI — classic measure of relative momentum.
💪 CMO — captures directional bias and intensity of moves.
💵 MFI — integrates volume and money flow pressure.
🔄 Stochastic RSI (K-line) — identifies momentum extremes and short-term turning points.
This fusion creates a smoother, more comprehensive signal, mitigating the weaknesses of any single oscillator.
🎯 Interpretation:
Overbought Zone (Default: > 75):
Indicates potential exhaustion of bullish momentum — a cooling phase or reversal may follow.
Oversold Zone (Default: < 7):
Suggests bearish exhaustion — a rebound or accumulation phase may emerge.
Neutral Zone (Between 7 and 75):
Represents balanced market conditions or trend continuation phases.
Visual cues highlight key conditions:
🔺 Red Highlights — Overbought regions or downward inflection points.
🔻 Green Highlights — Oversold regions or upward inflection points.
Neutral zones are shaded with subtle gray backgrounds for clarity.
💡 Key Features:
🔹 Multi-factor strength analysis (RSI + CMO + MFI + StochRSI).
🔹 Adaptive overbought/oversold detection.
🔹 Visual alerts via colored backgrounds and bar markers.
🔹 Customizable smoothing and length parameters for fine-tuning sensitivity.
🔹 Intuitive visualization ideal for both short-term scalping and swing trading setups.
🧭 Usage Notes:
Works best as a momentum confirmation tool — pair with trend filters like EMA, SuperTrend, or ADX.
In trending markets, use crossovers from extreme zones as potential continuation or exhaustion signals.
In ranging markets, exploit overbought/oversold reversals for high-probability mean reversion trades.
📘 Summary:
The Hyper Strength Index | QuantLapse distills multiple dimensions of market strength into a single, cohesive oscillator. By merging price, volume, and directional momentum, it provides traders with a more robust, responsive, and context-aware perspective on market dynamics — a next-generation evolution beyond the limitations of RSI or CMO alone.
Relative Performance Tracker [QuantAlgo]🟢 Overview
The Relative Performance Tracker is a multi-asset comparison tool designed to monitor and rank up to 30 different tickers simultaneously based on their relative price performance. This indicator enables traders and investors to quickly identify market leaders and laggards across their watchlist, facilitating rotation strategies, strength-based trading decisions, and cross-asset momentum analysis.
🟢 Key Features
1. Multi-Asset Monitoring
Track up to 30 tickers across any market (stocks, crypto, forex, commodities, indices)
Individual enable/disable toggles for each ticker to customize your watchlist
Universal compatibility with any TradingView symbol format (EXCHANGE:TICKER)
2. Ranking Tables (Up to 3 Tables)
Each ticker's percentage change over your chosen lookback period, calculated as:
(Current Price - Past Price) / Past Price × 100
Automatic sorting from strongest to weakest performers
Rank: Position from 1-30 (1 = strongest performer)
Ticker: Symbol name with color-coded background (green for gains, red for losses)
% Change: Exact percentage with color intensity matching magnitude
For example, Rank #1 has the highest gain among all enabled tickers, Rank #30 has the lowest (or most negative) return.
3. Histogram Visualization
Adjustable bar count: Display anywhere from 1 to 30 top-ranked tickers (user customizable)
Bar height = magnitude of percentage change.
Bars extend upward for gains, downward for losses. Taller bars = larger moves.
Green bars for positive returns, red for negative returns.
4. Customizable Color Schemes
Classic: Traditional green/red for intuitive interpretation
Aqua: Blue/orange combination for reduced eye strain
Cosmic: Vibrant aqua/purple optimized for dark mode
Custom: Full personalization of positive and negative colors
5. Built-In Ranking Alerts
Six alert conditions detect when rankings change:
Top 1 Changed: New #1 leader emerges
Top 3/5/10/15/20 Changed: Shifts within those tiers
🟢 Practical Applications
→ Momentum Trading: Focus on top-ranked assets (Rank 1-10) that show strongest relative strength for trend-following strategies
→ Market Breadth Analysis: Monitor how many tickers are above vs. below zero on the histogram to gauge overall market health
→ Divergence Spotting: Identify when previously leading assets lose momentum (drop out of top ranks) as potential trend reversal signals
→ Multi-Timeframe Analysis: Use different lookback periods on different charts to align short-term and long-term relative strength
→ Customized Focus: Adjust histogram bars to show only top 5-10 strongest movers for concentrated analysis, or expand to 20-30 for comprehensive overview
VBE Pro - Advanced Volatility Bands with Zero Lag & PredictionVBE Pro: Zero-Lag Predictive Bands
A next-gen volatility envelope that blends zero-lag smoothing with forward-looking volatility models (EWMA/GARCH/HAR/ML) to keep bands tight in calm markets, responsive in shocks, and adaptive across regimes.
What it does
Builds volatility from multiple methods (ATR, StDev, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang).
Projects near-term vol with your choice of predictor, then blends it via a weight slider.
Applies zero-lag smoothing (ZLEMA/ZLMA/DEMA/TEMA/HMA/JMA/Ehlers/Kalman/T3) to cut delay without over-shoot.
Auto-adapts band width by regime (high/low/normal) and can expand dynamically with price acceleration.
Optional displacement to align with your execution style.
On-chart
Upper/Lower zero-lag bands with optional fill.
Middle line (ZL-smoothed source).
Regime-tinted background (High/Low).
Displacement marker (if used).
Compact top-right info table: current vs predicted vol, regime, squeeze, multiplier, methods, ZL gain, est. lag reduction.
Signals & Alerts
Break↑ / Break↓ when price crosses the bands.
Vol↑ / Vol↓ expansion/contraction sequences.
“Squeeze” when band width compresses vs its ZL average.
“ZL” marker when significant zero-lag is active.
Prediction divergence ⚠ when projected vol deviates > threshold.
Built-in alertconditions for all of the above.
Quick start
Method: ATR or Hybrid for robustness.
Smoothing: ZLEMA, length 5–8, ZL gain 2–3 (push higher only if you accept more projection).
Bands: Multiplier 2.0, Adaptive on, Dynamic off to start.
Prediction: EWMA, weight 0.25–0.35. Move to GARCH in mean-reverty tapes; HAR-RV for mixed regimes.
Regime lookback: 50.
PulseRPO Zero-Lag BandsPulseRPO is a momentum and volatility timing suite built on a zero-lag Relative Price Oscillator. It pairs an RPO (fast vs slow MA spread, in %) with adaptive volatility envelopes that tighten or widen as conditions change, so you can spot true momentum bursts, exhaustion and “quiet-before-the-move” squeezes—without the usual MA lag.
What it shows
Zero-Lag RPO: Choose EMA, SMA, WMA, RMA, HMA or ZLEMA for the base, then apply ZLEMA/DEMA/TEMA/HMA zero-lag smoothing to cut delay.
Adaptive Bands: StdDev, ATR, Range or Hybrid volatility; bands auto-tighten in high vol and widen in quiet regimes.
Dynamic OB/OS: Levels scale with current regime so extremes mean something even as volatility shifts.
Signal & Histogram: Classic signal cross plus histogram for quick read of acceleration vs deceleration.
Squeeze Paint: Subtle background highlight when band width compresses below its average.
Divergences & Triggers: Optional bullish/bearish divergence tags, plus band-cross and signal-cross alerts out of the box.
How to use it (general guide)
Momentum entries: Look for RPO crossing up its signal from below or snapping out of a squeeze; extra weight if it also re-enters from below the lower band.
Trend continuation: RPO riding outside the upper (or lower) band with rising histogram = power move; trail risk on pullbacks to the signal line.
Exhaustion / fades: Taps beyond dynamic OB/OS or band re-entries can mark mean-revert windows—confirm with price/volume.
Risk filter: During squeeze, size down and prepare for expansion; after expansion, respect extremes.
Tweak the MA type, band method and zero-lag strength to match your timeframe. PulseRPO is designed to be a self-contained read: regime → setup → trigger → alert.
Tristan's W%R StrategyOverview:
This strategy uses the raw Williams %R oscillator to automatically generate buy and sell signals based on overbought and oversold levels. It is session-aware, allowing you to restrict trades to specific market hours (New York, London, or Asia).
Key Features:
Buy Signal: Fires whenever the raw Williams %R is at or below the oversold level (default −100).
Sell Signal: Fires whenever the raw Williams %R is at or above the overbought level (default 0).
Session Filtering: Only triggers trades during the selected session (All, New York, London, or Asia).
Pyramiding: Allows multiple trades to stack, so every qualifying signal results in a new trade.
Alerts: Optional alerts for automation via webhook or broker integration.
Session Highlight: Background shading indicates when the selected session is active.
How to Use:
Set the Williams %R length, overbought, and oversold levels to match your trading style.
Select the session you want the strategy to be active in (New York, London, or Asia).
Enable alerts if you plan to automate trades with an API.
Add the strategy to your chart. It will execute trades automatically whenever the raw %R meets the configured thresholds during the selected session.
Adjust pyramiding if you want to limit the number of stacked trades per session.
Note:
This script uses the raw W%R signal vs the smoothed one because I found it to be more accurate for automated trading.
Every qualifying signal results in a trade, even if positions are already open.
CVD Divergence + Volume MarkerHere is a Pine Script concept to mark candlestick chart candles when cumulative delta is divergent to price action and volume is above average. Cumulative delta divergence typically occurs when the price forms new highs/lows while cumulative delta forms lower highs/lows (or vice versa). The script should include a marker only when this divergence occurs alongside above-average volume, increasing signal strength and filtering out weak setups.
Coding Concept
Calculate cumulative delta (approximation using price and volume if true bid/ask volume is unavailable, e.g., on spot).
Calculate moving average of volume.
Detect bullish divergence (price makes lower low, cumulative delta makes higher low) and bearish divergence (price makes higher high, cumulative delta makes lower high).
Mark candle with above-average volume when divergence is present.
Saty ATR Levels w/labelsSatys ATR Levels with labels, Allows for the user to plot ATR levels and see labels with the addition of this script
Flip to GreenPurpose:
This indicator applies a Lorentzian-distance–based machine-learning model to classify market conditions and highlight probable momentum shifts.
Where traditional indicators react to price movement, this one uses statistical pattern recognition to predict when momentum is likely to flip direction — the classic “flip to green” signal.
Concept:
Financial markets don’t move linearly; they bend and distort around major catalysts (news, FOMC meetings, earnings, etc.) in a way similar to how gravity warps space-time.
This indicator accounts for that distortion by measuring distance in Lorentzian space instead of the usual Euclidean space.
In simple terms: it adapts to volatility “warping,” allowing the model to detect structural momentum changes that normal math misses.
Core logic:
Imports two custom libraries:
MLExtensions for machine-learning utilities
KernelFunctions for advanced distance calculations
Computes relationships among multiple features (e.g., RSI, ADX, or other inputs).
Uses Lorentzian geometry to weight how recent price-time behavior influences current classification.
Outputs a visual “flip” cue when the probability of trend reversal exceeds threshold confidence.
Why it matters:
Most indicators measure what has already happened.
Lorentzian Classification attempts to capture what’s about to happen by comparing the present market state to a trained historical distribution under warped “price-time” geometry.
It’s particularly useful for spotting early accumulation or exhaustion zones before they become obvious on standard momentum tools.
Recommended use:
Run it as a background trend classifier or color overlay.
Combine it with volume-based confirmation tools (e.g., Dollar Volume Ownership Gauge) and structural analysis.
A “flip to green” suggests buyers are regaining control; a fade or flip to red implies control returning to sellers.
Dollar Volume Ownership GaugePurpose:
DVOG tracks the real money moving through a ticker by converting share volume into dollar volume (price × volume). It helps identify when institutional-sized players enter, defend, or unload positions — information that plain volume bars often hide.
How it works:
Each bar represents 4-minute aggregated dollar volume.
Green bars = moderate sponsorship ($400 K–$1 M per 4 min).
Red bars = heavy sponsorship ($1 M+ per 4 min).
Black bars = normal retail flow (under $400 K).
Optional horizontal guides mark both thresholds for quick reference.
Alerts:
Green Bar Alert: fires every time a bar exceeds $400 K, signaling fresh institutional activity.
Cross Alerts: trigger once when dollar volume crosses the $400 K or $1 M levels, perfect for automation or notifications.
Why it’s useful:
DVOG visually confirms when a breakout, knife-and-reclaim, or coil is being driven by real capital rather than low-liquidity noise.
It turns abstract volume into a direct measure of who’s actually in control.
Recommended use:
Run it in a separate pane below price. Combine with your normal structure analysis — higher lows, double bottoms, coils, etc. — and act only when structure and sponsorship line up.
VWAP H/L Break - NQVWAP crossover with fib targets
bar closing over VWAP(high) go long
bar closing under VWAP (low) go short
fib targets based on closing candle and previous candle.
Signal vs. Noise Have been working on this to get a better feel for market conditions. Am generally a pretty shit trader so just wanted to give this a go. Any feedback is appreciated.
Inside SwingsOverview
The Inside Swings indicator identifies and visualizes "inside swing" patterns in price action. These patterns occur when price creates a series of pivots that form overlapping ranges, indicating potential consolidation or reversal zones.
What are Inside Swings?
Inside swings are specific pivot patterns where:
- HLHL Pattern: High-Low-High-Low sequence where the first high is higher than the second high, and the first low is lower than the second low
- LHLH Pattern: Low-High-Low-High sequence where the first low is lower than the second low, and the first high is higher than the second high
Here an Example
These patterns create overlapping price ranges that often act as:
- Support/Resistance zones
- Consolidation areas
- Potential reversal points
- Breakout levels
Levels From the Created Range
Input Parameters
Core Settings
- Pivot Lookback Length (default: 5): Number of bars on each side to confirm a pivot high/low
- Max Boxes (default: 100): Maximum number of patterns to display on chart
Extension Settings
- Extend Lines: Enable/disable line extensions - this extends the Extremes of the Swings to where a new Swing Started or Extended Right for the Latest Inside Swings
- Show High 1 Line: Display first high/low extension line
- Show High 2 Line: Display second high/low extension line
- Show Low 1 Line: Display first low/high extension line
- Show Low 2 Line: Display second low/high extension line
Visual Customization
Box Colors
- HLHL Box Color: Color for HLHL pattern boxes (default: green)
- HLHL Border Color: Border color for HLHL boxes
- LHLH Box Color: Color for LHLH pattern boxes (default: red)
- LHLH Border Color: Border color for LHLH boxes
Line Colors
- HLHL Line Color: Extension line color for HLHL patterns
- LHLH Line Color: Extension line color for LHLH patterns
- Line Width: Thickness of extension lines (1-5)
Pattern Detection Logic
HLHL Pattern (Bullish Inside Swing)
Condition: High1 > High2 AND Low1 < Low2
Sequence: High → Low → High → Low
Visual: Two overlapping boxes with first range encompassing second
Detection Criteria:
1. Last 4 pivots form High-Low-High-Low sequence
2. Fourth pivot (first high) > Second pivot (second high)
3. Third pivot (first low) < Last pivot (second low)
LHLH Pattern (Bearish Inside Swing)
Condition: Low1 < Low2 AND High1 > High2
Sequence: Low → High → Low → High
Visual: Two overlapping boxes with first range encompassing second
Detection Criteria:
1. Last 4 pivots form Low-High-Low-High sequence
2. Fourth pivot (first low) < Second pivot (second low)
3. Third pivot (first high) > Last pivot (second high)
Visual Elements
Boxes
- Box 1: Spans from first pivot to last pivot (larger range)
- Box 2: Spans from third pivot to last pivot (smaller range)
- Overlap: The intersection of both boxes represents the inside swing zone
Extension Lines
- High 1 Line: Horizontal line at first high/low level
- High 2 Line: Horizontal line at second high/low level
- Low 1 Line: Horizontal line at first low/high level
- Low 2 Line: Horizontal line at second low/high level
Line Extension Behavior
- Historical Patterns: Lines extend until the next pattern starts
- Latest Pattern: Lines extend to the right edge of chart
- Dynamic Updates: All lines are redrawn on each bar for accuracy
Trading Applications
Support/Resistance Levels
Inside swing levels often act as:
- Dynamic support/resistance
- Breakout confirmation levels
- Reversal entry points
Pattern Interpretation
- HLHL Patterns: Potential bullish continuation or reversal
- LHLH Patterns: Potential bearish continuation or reversal
- Overlap Zone: Key area for price interaction
Entry Strategies
1. Breakout Strategy: Enter on break above/below inside swing levels
2. Reversal Strategy: Enter on bounce from inside swing levels
3. Range Trading: Trade between inside swing levels
Technical Implementation
Data Structures
type InsideSwing
int startBar // First pivot bar
int endBar // Last pivot bar
string patternType // "HLHL" or "LHLH"
float high1 // First high/low
float low1 // First low/high
float high2 // Second high/low
float low2 // Second low/high
box box1 // First box
box box2 // Second box
line high1Line // High 1 extension line
line high2Line // High 2 extension line
line low1Line // Low 1 extension line
line low2Line // Low 2 extension line
bool isLatest // Latest pattern flag
Memory Management
- Pattern Storage: Array-based storage with automatic cleanup
- Pivot Tracking: Maintains last 4 pivots for pattern detection
- Resource Cleanup: Automatically removes oldest patterns when limit exceeded
Performance Optimization
- Duplicate Prevention: Checks for existing patterns before creation
- Efficient Redraw: Only redraws lines when necessary
- Memory Limits: Configurable maximum pattern count
Usage Tips
Best Practices
1. Combine with Volume: Use volume confirmation for breakouts
2. Multiple Timeframes: Check higher timeframes for context
3. Risk Management: Set stops beyond inside swing levels
4. Pattern Validation: Wait for confirmation before entering
Common Scenarios
- Consolidation Breakouts: Inside swings often precede significant moves
- Reversal Zones: Failed breakouts at inside swing levels
- Trend Continuation: Inside swings in trending markets
Limitations
- Lagging Indicator: Patterns form after completion
- False Signals: Not all inside swings lead to significant moves
- Market Dependent: Effectiveness varies by market conditions
Customization Options
Visual Adjustments
- Modify colors for different market conditions
- Adjust line widths for visibility
- Enable/disable specific elements
Detection Sensitivity
- Increase pivot length for smoother patterns
- Decrease for more sensitive detection
- Balance between noise and signal
Display Management
- Control maximum pattern count
- Adjust cleanup frequency
- Manage memory usage
Conclusion
The Inside Swings indicator provides a systematic approach to identifying consolidation and potential reversal zones in price action. By visualizing overlapping pivot ranges
The indicator's strength lies in its ability to:
- Identify key price levels automatically
- Provide visual context for market structure
- Offer flexible customization options
- Maintain performance through efficient memory management
DayTrader Plug and Play Score Strategy HSBeen playing around with automating a strategy and to make something more flexible in updating indicators/ risk reward scenarios.
I Trade on 5 min timeframe choosing stocks from a day trading scanner I use to evaluate premarket movement.
This script take into account short term EMA crossovers, VWAP, RSI, Candlesticks, and previous day S/R lines to determine buy/sell points. It Mostly runs on a VWAP strategy and will only buy when price is above VWAP and only sell when price is below VWAP. But uses the other indicators as more confirmations.
All of these indicators come together to form a score 1-8.5 and gives buy/sell signals based on the score.
Strategy is as below:
My Stock scanner gives me anywhere from 3-5 stocks per day to trade. (Not included)
Strategy will only trade once per day per stock.
Strategy closes positions after 2 hours in the market.
Strategy closes all positions 5 min before end of day close.
Trade size is set to 1% of the account size. The risk is 2% of that trade, reward is 4%.
Score threshold for hitting the indicator threshold is set to 5.5 score
^^This is all editable in the script.
After building and testing an rebuilding for a few months this has been my most profitable strategy in PAPER TRADING so I thought id share. I enjoy this kind of tinkering and scenario testing. Enjoy!
