Sector Performance (2x12 Grid, labeled)Sector Performance Dashboard that tracks short-term and multi-interval returns for 24 major U.S. market ETFs. It renders a clean, color-coded performance grid directly on the chart, making sector rotation and broad-market strength/weakness easy to read at a glance.
The dashboard covers t wo full rows of liquid U.S. sector and thematic ETFs, including:
Row 1 (Core Market + GICS sectors)
SPY, QQQ, IWM, XLF, XLE, XLRE, XLY, XLU, XLP, XLI, XLV, XLB
Row 2 (Extended industries / themes)
XLF, XBI, XHB, CLOU, XOP, IGV, XME, SOXX, DIA, KRE, XLK, VIX (VX1!)
Key features include:
Time-interval selector (1–60 min, 1D, 1W, 1M, 3M, 12M)
Automatic rate-of-return calculation with inside/outside-bar detection
Two-row, twelve-column grid with dynamic layout anchoring (top/middle/bottom + left/center/right)
Uniform white text for clarity, while inside/outside candles retain custom colors
Adaptive transparency rules (heavy/avg/light) based on magnitude of % change
Ticker label normalization (cleans up prefixes like “CBOE_DLY:”)
"涨幅超过60日均线的股票" için komut dosyalarını ara
Reduced-Lag Chande Momentum Oscillator [BOSWaves]Reduced-Lag Chande Momentum Oscillator – Adaptive Momentum Geometry with Reduced-Latency Reversion Logic
Overview
The Reduced-Lag Chande Momentum Oscillator represents a sophisticated extension of the classical Chande Momentum Oscillator, preserving the foundational measurement of net directional pressure while addressing inherent limitations in lag, noise, and signal clarity. The traditional CMO provides reliable snapshots of upward versus downward force but reacts slowly to rapid market accelerations and can obscure meaningful momentum inflections with delayed readings. This iteration integrates a dual-stage reduced-lag filter, optional advanced smoothing, and acceleration-based analytics, producing a real-time, multi-dimensional representation of market momentum.
The design reframes classical momentum using a layered curvature and gradient structure - main, midline, and shadow - to show trajectory, velocity, and intensity in one view. Instead of the usual ±70/30 extremes, it uses ±50 as a statistically grounded threshold where one side of the market begins exerting true dominance. This captures structural imbalance more reliably, exposing exhaustion and actionable inflection without amplifying noise.
This visualization gives traders a continuous, responsive read on market structure, revealing not just direction but rate of change, acceleration alignment, and curvature behavior. The oscillator becomes a momentum map, expressing both probability and intensity behind directional shifts.
Where conventional oscillators mislabel short-lived swings as signals, the Reduced-Lag CMO separates baseline shifts from high-conviction transitions, enabling cleaner, more decisive signal interpretation.
Theoretical Foundation
The classical Chande Momentum Oscillator, created by Tushar Chande, calculates the normalized net difference between consecutive upward and downward price changes over a defined window, generating readings from –100 to +100. While effective for capturing basic directional pressure, the unmodified CMO suffers from signal latency and sensitivity to abrupt market swings, which can obscure actionable inflection points.
The Reduced-Lag CMO augments this foundation with three key mechanisms:
Reduced-Lag Filtering : A dual-EMA structure eliminates inertial lag, aligning the oscillator curve closely with real-time market momentum without producing overshoot artifacts.
Smoothing Architecture : Optional SMA, EMA, or WMA smoothing is applied post-filter, balancing noise reduction with trajectory fidelity. A multi-layer line system (shadow → midline → main) communicates depth, curvature, and gradient dynamics.
Acceleration Integration : First and second derivatives of the smoothed curve quantify velocity and acceleration, allowing the indicator to identify not only momentum flips but the force behind each shift, forming the basis for the strong-signal overlay.
The combination of these mechanisms produces an oscillator that respects the original CMO framework while delivering real-time, context-sensitive intelligence. The ±50 boundaries are selected as the statistically validated pressure zones where directional dominance exceeds neutral oscillation. Crosses and rejections at these boundaries are not arbitrary overbought/oversold events, but measurable imbalances with actionable significance.
How It Works
The Reduced-Lag CMO is constructed through a multi-stage process:
Momentum Estimation Core : Raw CMO values are calculated and then passed through a reduced-lag filter to remove delay, creating a curve that closely tracks instantaneous directional pressure.
Smoothing & Layered Representation : The filtered curve can be smoothed and split into three layers - shadow, midline, and main - giving visual depth, trajectory clarity, and curvature instead of a single-line oscillator.
Gradient-Based Pressure Mapping : Color gradients encode momentum strength and polarity. Green-yellow transitions highlight increasing upward dominance, while red-yellow transitions indicate weakening downward force.
Pressure-Zone Anchoring (±50) : The system defines statistically significant pressure zones at ±50. Moves beyond these levels reflect dominant directional control, and rejections inside the zone signal potential exhaustion.
Signal Generation : Momentum events are evaluated through velocity and acceleration. Standard signals appear as triangle markers indicating validated momentum flips. Strong signals appear as triangles with diamonds when acceleration confirms a high-conviction transition.
A cooldown rule spaces signals apart to reduce clutter and emphasize structurally meaningful events.
Interpretation
The Reduced-Lag CMO reframes momentum as a dynamic equilibrium between directional force and structural pressure:
Positive Momentum Phases : Curves above zero with green-yellow gradients indicate sustained upward pressure. Shallow retracements or midline tests denote controlled pullbacks.
Negative Momentum Phases : Curves below zero with red-yellow gradients show downward dominance. Rejections from –50 highlight potential exhaustion and reversal readiness.
Pressure-Zone Dynamics (±50) : Crosses beyond ±50 confirm dominant directional force. Meanwhile, rejections and rotations inside the zone signal structural fatigue.
Velocity & Acceleration Analysis : Rising momentum with decelerating velocity suggests fading force; acceleration alignment amplifies signal strength and forms the basis of strong signals.
Signal Architecture
The Reduced-Lag CMO produces a single event type with two intensities: a validated momentum inflection.
Standard Signals - Triangles:
Triggered by momentum flips confirmed by velocity.
Represent moderate-intensity directional changes.
Appear at zero-line crosses or ±50 rejections with aligned velocity.
Strong Signals Triangles + Diamonds:
Triggered when acceleration confirms the directional change.
Represent high-intensity, high-conviction shifts.
Rare by design; indicate robust momentum inflections.
Cooldown mechanics prevent repeated signals in short succession, emphasizing structural reliability over noise.
Strategy Integration
Trend Confirmation : Align zero-line flips with higher-timeframe directional bias.
Reversal Detection : Strong signals from ±50 zones highlight potential inflection points.
Volatility Assessment : Gradient transitions reveal strengthening or weakening momentum.
Pullback Timing : Multi-layer curvature identifies controlled retracements vs trend exhaustion.
Confluence Mapping : Pair with structure-based indicators to filter signals in context.
Technical Implementation Details
Core Engine : Classical CMO with Ehlers reduced-lag extension
Lag Reduction : Dual EMA filtering
Smoothing : Optional SMA/EMA/WMA post-filter
Multi-Layer Curve : Shadow, midline, main
Signal System : Two-tier momentum-acceleration framework
Pressure Zones : ±50 statistically validated thresholds
Cooldown Logic : Bar-indexed suppression
Gradient Mapping : Encodes magnitude and direction
Alerts : Standard and strong signals
Optimal Application Parameters
Timeframes:
1 - 5 min : Intraday momentum tracking
15 - 60 min : Trend rotations & volatility transitions
4H - Daily : Macro momentum exhaustion & re-accumulation mapping
Suggested Ranges:
CMO Length : 7 - 12
Reduced-Lag Length : 5 - 15
Smoothing : 10 - 20
Cooldown Bars : 5 - 15
Performance Characteristics
High Effectiveness:
Markets with directional pulses & clean pressure transitions
Trending phases with measurable pullbacks
Instruments with stable volatility cycles
Reduced Edge:
Choppy consolidations
Ultra-low volatility environments
Disclaimer
The Reduced-Lag Chande Momentum Oscillator is a professional-grade analytical tool. It is not predictive and carries no guaranteed profitability. Effectiveness depends on asset class, volatility regime, parameter selection, and disciplined execution. Any suggested application timeframes or recommended ranges are guidance only - they are not universally optimal and will not deliver consistent accuracy on every asset or market condition. BOSWaves recommends using it in conjunction with structure, liquidity, and momentum context.
Volume Peak Box📄 English Description
Overview
The Volume Peak Box indicator highlights periods of unusually high volume by identifying volume spikes using Bollinger Bands on volume and drawing a price-range box around each spike window. This provides traders with a clear visual representation of supply/demand imbalances, absorption zones, and breakout/false-break areas.
All calculations come from one unified concept: detecting statistically significant volume peaks on a locked timeframe and mapping them onto the chart.
Concept & Logic
1. Locked Timeframe Volume Analysis
Instead of using the current chart timeframe, this script allows users to lock volume analysis to any timeframe (e.g., 60m, 4H, 1D).
The script retrieves from the chosen timeframe:
Volume
High price
Low price
This allows volume structure from higher timeframes to be used while trading lower timeframes.
2. Bollinger Bands on Volume
Volume volatility is analyzed using a standard Bollinger Band model:
Basis = SMA(volume, BB length)
Upper Band = Basis + (mult × standard deviation)
When:
Volume > Upper Band
→ This bar is classified as a Volume Peak.
This approach makes the peak detection statistically meaningful, instead of simply comparing raw volume to previous bars.
3. Peak Session Detection (Continuous Peaks Form One Box)
The script tracks continuous volume peaks:
When a peak starts → begin a session
While peaks continue → extend the session
When peaks end → session closes and a box is created
For each peak session, the script records:
Start bar index
End bar index
Highest high within the session
Lowest low within the session
These values determine the box boundaries.
This allows the indicator to group related peaks into a single price zone, instead of drawing a box for every bar.
4. Drawing the Volume Peak Box
When a session ends, the script draws:
A filled box covering the full price range
From startBar → endBar
Using user-defined:
Box fill color
Border color
Each box visually marks a region where strong participation entered the market, often signaling:
Breakout validation
Absorption zones
Supply/demand imbalance
High-activity trading decisions
How to Use
Use the boxes to identify high-volume reaction zones.
When price revisits a box:
Expect strong reactions (bounce, rejection, or absorption).
When price breaks out from a box:
Can signal continuation with momentum.
Lower-timeframe entry signals become more reliable when aligned with high-timeframe volume boxes.
Recommended to lock the TF to:
60m for intraday
4H or 1D for swing trading
Why This Script Is Original
It uses Bollinger Bands on volume, not price — a less common volatility-based method for detecting volume anomalies.
It groups continuous peaks into unified zones instead of treating each spike separately.
The ability to lock the volume analysis to a higher timeframe allows multi-timeframe volume interpretation without cluttering the chart.
Boxes give traders a clean and intuitive view of volume-based “decision zones”.
🇹🇭 Thai Description — คำอธิบายภาษาไทย
ภาพรวม
อินดิเคเตอร์ Volume Peak Box ใช้การตรวจจับ “Volume Peak” โดยใช้ Bollinger Band บน Volume แล้วสร้าง “กล่องช่วงราคา” ครอบช่วงที่มี Volume สูงผิดปกติ ทำให้เห็นบริเวณที่มีแรงซื้อขายเข้ามาอย่างชัดเจน เช่น จุด Breakout, จุด Absorption, หรือเขต Supply/Demand
แนวคิดและหลักการทำงาน
1. วิเคราะห์ Volume จาก Timeframe ที่ล็อกไว้
คุณสามารถเลือก TF ที่ต้องการให้ Volume ถูกนำมาคำนวณ เช่น 60 นาที, 4 ชั่วโมง, 1 วัน
แม้คุณจะเปิดกราฟ TF เล็ก เช่น 5m แต่กล่องยังอิง volume จาก TF ที่เลือกไว้ ทำให้ได้ “โซน Volume ใหญ่” ที่แม่นยำขึ้น
2. Bollinger Band บน Volume
ใช้ SMA + ส่วนเบี่ยงเบนมาตรฐานของ Volume เพื่อหา “จุดที่ Volume สูงกว่าปกติอย่างมีนัยสำคัญ”
เงื่อนไข Peak:
Volume > Upper Bollinger Band
นี่เป็นวิธีที่ดีกว่า “เทียบกับแท่งก่อนหน้า” เพราะคิดจากสถิติของทั้งช่วง
3. รวม Peak ต่อเนื่องเป็นกล่องเดียว
ถ้า Volume Peak เกิดต่อเนื่องหลายแท่ง:
จะถูกจับรวมเป็น Peak session เดียว
ใช้ High สูงสุด และ Low ต่ำสุดของทั้ง session
เมื่อ Peak จบ → วาดกล่องช่วงราคา
เหมาะกับการหาจุดที่ตลาดมีแรงเข้าซื้อ/ขายหนักในช่วงเวลาเดียวกัน
4. วาดกล่อง Volume Peak
กล่องจะครอบ:
ช่วงแท่งเริ่มต้น → แท่งสุดท้ายของ Peak
ความสูงของกล่อง = ช่วงราคาที่มี Volume สูงผิดปกติ
กล่องสามารถใช้เป็น:
โซน Breakout/Breakdown
โซน Supply/Demand
เขตที่ราคามักมี reaction
วิธีใช้งาน
ใช้กล่องเป็น “เขตการตัดสินใจ” (Decision Zone)
ราคาแตะซ้ำมักเกิดการกลับตัวหรือความผันผวนสูง
การทะลุกล่องบ่อยครั้งนำไปสู่ขาเทรนด์ใหญ่
เหมาะกับการใช้ร่วมกับ Price Action และโครงสร้างราคา
จุดเด่น / ความเป็น Original
ใช้ Bollinger Band บน Volume (น้อยอินดี้ทำ)
รวม Peak ต่อเนื่องเป็น session เดียว
วิเคราะห์ Volume ข้าม TF ได้ โดยไม่ต้องเปลี่ยน TF บนกราฟ
ได้ “โซน Volume สำคัญ” แบบชัดเจน อ่านง่าย ไม่รกจอ
MTF Checklist DashboardMTF Checklist Dashboard
Overview
The MTF Checklist Dashboard is an advanced multi-timeframe analysis tool that provides traders with a comprehensive visual dashboard to analyze market conditions across six customizable timeframes simultaneously. This indicator combines multiple technical analysis methods, including Opening Range Breakouts (ORB), VWAP, EMAs, and daily price levels, to generate high-probability confluence-based trading signals.
Unlike traditional single-timeframe indicators, this dashboard displays all critical information in one organized table, allowing traders to instantly identify when multiple timeframes align for optimal entry and exit opportunities.
Key Features
Multi-Timeframe Analysis
Analyzes up to 6 timeframes simultaneously (default: 1m, 5m, 15m, 30m, 1h, 4h)
Fully customizable timeframe selection via comma-separated input
Color-coded cells for instant visual recognition (green=bullish, red=bearish, yellow=neutral)
Technical Indicators Tracked
Current and previous candle direction
Opening Range Breakout (ORB) positioning with custom period
VWAP relationship (above/below)
200 EMA positioning
Daily and previous day high/low proximity
EMA crossovers (9 vs 21, both vs 200)
Advanced Signal Filtering System
Confluence scoring: Requires multiple timeframes to align (3-6 timeframes)
Higher timeframe confirmation: Ensures 30m/1h/4h agreement
Volume filter: Confirms signals with above-average volume (1.5x default)
ATR volatility filter: Validates sufficient market movement
Session timing: Restricts signals to optimal trading hours (EST)
Momentum confirmation: Requires recent directional strength
Range positioning: Blocks signals near daily extremes
Candle strength: Validates strong directional candles (60%+ body ratio)
Visual Signals
Optional entry arrows (above/below bars)
Background color highlighting
Organized dashboard with real-time price levels
ORB range, current day, and previous day summary rows
Alert Conditions
JSON-formatted alerts for automated trading integration
Separate alerts for long entry, short entry, long exit, and short exit
Compatible with webhook automation systems
How To Use
Dashboard Interpretation
The dashboard displays a color-coded table with the following columns:
TF: Timeframe being analyzed
C: Current candle (Green=bullish, Red=bearish)
P: Previous candle (Green=bullish, Red=bearish)
ORB: Opening Range Breakout position (A=Above, B=Below, W=Within)
VWAP: Price vs VWAP (A=Above, B=Below)
E200: Price vs 200 EMA (A=Above, B=Below)
D Hi/Lo: Proximity to current day high/low (Hi/Lo/Mid)
PD Hi/Lo: Proximity to previous day high/low (Hi/Lo/Mid)
9 vs 21: EMA 9 vs EMA 21 relationship (A=9 above 21, B=9 below 21)
9&21 v200: Both EMAs vs 200 EMA (>>=both above, <<=both below, <>=mixed)
Signal Generation
Long Entry Signal triggers when:
Minimum number of timeframes show bullish alignment (default: 5 of 6)
Higher timeframes (30m/1h/4h) confirm direction (default: 2 of 3)
Price breaks above ORB high with sufficient distance
Volume exceeds average by specified multiplier
ATR shows adequate volatility
Trade occurs during optimal session hours
Recent momentum is upward
Price not too close to daily high
Strong bullish candle forms
Short Entry Signal uses opposite conditions
Exit Signals trigger when opposing timeframe confluence reaches threshold (default: 3 timeframes)
Recommended Workflow
Select your asset and primary trading timeframe
Observe the dashboard - Look for rows showing mostly green (bullish) or red (bearish)
Wait for alignment - The indicator will show arrows when confluence requirements are met
Check the bottom rows - Review ORB levels and daily ranges for context
Set alerts - Enable TradingView alerts using the built-in alert conditions
Manage risk - Use appropriate position sizing and stop losses based on ORB range or daily ATR
Settings Guide
Basic Settings
Timeframes: Enter comma-separated values (e.g., "1,5,15,30,60,240")
Show Header: Toggle column headers on/off
ORB Minutes: Set opening range period (default: 15 minutes)
Near % for daily highs/lows: Define proximity threshold (default: 0.20%)
Use close for comparisons: Compare using close vs current price
Dashboard Position: Choose from 9 screen positions
Confluence Filters
Minimum Timeframes Aligned: Set required confluence (3-6, default: 5)
Require Higher Timeframe Confirmation: Toggle HTF requirement on/off
Min Higher Timeframes: Specify HTF agreement needed (1-3, default: 2)
Volume Filter
Volume Confirmation: Enable/disable volume filtering
Volume vs Average: Set multiplier threshold (default: 1.5x)
Volume Average Length: Period for volume average (default: 20 bars)
Volatility Filter (ATR)
Volatility Filter: Enable/disable ATR confirmation
ATR Length: Calculation period (default: 14)
Min ATR vs Average: Required ATR level (default: 0.5x = 50%)
ORB Filters
ORB Breakout Distance Required: Toggle distance requirement
Min Breakout % Beyond ORB: Additional breakout threshold (default: 0.10%)
Session Filter
Trade Only During Best Hours: Enable time-based filtering
Session 1: First trading window (default: 0930-1130 EST)
Session 2: Second trading window (default: 1400-1530 EST)
Momentum Filter
Recent Momentum Required: Enable directional momentum check
Lookback Bars: Period for momentum comparison (default: 3 bars)
Daily Range Filter
Block Signals Near Daily Extremes: Prevent entries at extremes
Distance from High/Low %: Minimum distance required (default: 2.0%)
Candle Filter
Strong Directional Candle: Require candle strength
Min Candle Body %: Body-to-range ratio threshold (default: 60%)
Visual Signals
Show Entry Signals: Master toggle for visual signals
Show Arrows: Display entry arrows on chart
Background Color: Enable background highlighting
Best Practices
Start with default settings and adjust based on your trading style and asset volatility
Higher confluence requirements (5-6 timeframes) produce fewer but higher-quality signals
Enable all filters for conservative trading; disable some for more frequent signals
Use the dashboard as confirmation alongside your existing trading strategy
Backtest on your specific instruments before live trading
Consider market conditions—trending vs ranging markets may require different settings
Alerts
This indicator includes four alert conditions with JSON formatting for webhook integration:
Long Entry Signal: Triggers when all long conditions are met
Short Entry Signal: Triggers when all short conditions are met
Long Exit Signal: Triggers when opposing confluence reaches exit threshold
Short Exit Signal: Triggers when opposing confluence reaches exit threshold
Alert messages include ticker symbol, action (buy/sell), price, and quantity for automated trading systems.
Important Notes
This indicator works best on liquid instruments with clear price action
Highly volatile markets may require adjusted ATR and ORB distance settings
Session times are in EST timezone—adjust if trading non-US markets
The ORB calculation requires sufficient price history for the day
Signals are generated in real-time but should be confirmed at candle close
Limitations
Maximum of 6 timeframes can be analyzed due to TradingView's security call limits
ORB calculations may not work correctly on instruments with gaps or irregular sessions
The indicator is most effective during regular market hours when volume and volatility are adequate
Lower timeframes (1m, 5m) may produce more false signals in choppy conditions
License
Mozilla Public License 2.0 (MPL-2.0)
This indicator is licensed under the Mozilla Public License 2.0. You are free to use, modify, and distribute this code under the terms of the MPL-2.0. The full license text is available at mozilla.org
Key license provisions:
You may use this code commercially
You may modify and distribute modified versions
Modified versions must be released under the same license
You must include the original license notice in any distributions
No trademark rights are granted
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results. Trading involves substantial risk of loss. Always:
Practice proper risk management
Test thoroughly on paper/demo accounts before live trading
Use appropriate position sizing
Never risk more than you can afford to lose
Consult with a financial advisor for personalized advice
The creator assumes no liability for trading losses incurred using this indicator.
Version: 2.0
Pine Script Version: v6
Author: © EliasVictor
Event High/Mid/LowEvent High/Mid/Low - Data Release Level Tracker
Automatically track and visualize high, low, and mid levels from major data events like FOMC announcements, CPI releases, NFP reports, and other market-moving data releases.
KEY FEATURES:
- Customizable event input - Add unlimited events using a simple text format
- Flexible time periods - Set custom duration for each event (15min, 30min, 60min, etc.)
- Visual clarity - Color-coded lines and optional background cloud between high/low
- Clean labels - Minimalist text labels without background boxes
- Fully customizable - Toggle lines, labels, and clouds on/off independently
HOW TO USE:
1. Add the indicator to your chart
2. Open settings and edit the "Event Dates" text area
3. Enter one event per line in this format: YYYY-MM-DD HH:MM Minutes Label
Example: 2025-01-29 14:00 30 Jan FOMC
Example: 2025-02-12 08:30 30 Feb CPI
4. The indicator will automatically capture and display the high, low, and mid levels
WHAT IT DISPLAYS:
- High line (teal) - Highest price during the event period
- Low line (pink) - Lowest price during the event period
- Mid line (yellow, dotted) - Midpoint between high and low
- Background cloud (optional) - Shaded area between high and low
- Event window highlighting - Orange background during active events
PERFECT FOR:
- Tracking key support/resistance levels from economic releases
- Planning entries/exits around FOMC, CPI, NFP, and other data
- Analyzing how price reacts to major announcements
- Identifying post-event trading ranges
SUPPORTED EVENTS:
Works with any scheduled economic release - FOMC, CPI, PPI, NFP, Retail Sales, GDP, and more. Simply input the date, time, duration, and a custom label.
IMPORTANT LIMITATIONS:
- Chart timeframe must be EQUAL TO OR SMALLER than event duration
- For 30-minute events: Use 30min, 15min, 5min, 1min charts (NOT 1H, 4H, Daily)
- For 60-minute events: Use 60min, 30min, 15min, 5min, 1min charts
- For 15-minute events: Use 15min, 5min, 1min charts
- If your chart timeframe is larger than the event duration, the indicator may not capture accurate high/low values
- Recommended: Use 5-minute or 1-minute charts for maximum accuracy on all event durations
NOTES:
- All times are in EST/EDT (America/New_York timezone)
- Comments starting with # are ignored, making it easy to organize and annotate your event list
- The indicator processes events only after the specified duration has elapsed
chanlun缠论 - 笔与中枢Overview
The Chanlun (缠论) Strokes & Central Zones indicator is an advanced technical analysis tool based on Chinese Chan Theory (Chanlun Theory). It automatically identifies market structure through "strokes" (笔) and "central hubs" (中枢), providing traders with a systematic framework for understanding price movements, trend structure, and potential reversal zones.
Theoretical Foundation
Chan Theory is a sophisticated price action methodology that breaks down market movements into hierarchical structures:
Local Extremes: Swing highs and lows identified through lookback periods
Strokes (笔): Valid price movements between opposite extremes that meet specific criteria
Central Hubs (中枢): Consolidation zones formed by overlapping strokes, representing key support/resistance areas
Key Components
1. Local Extreme Detection
Identifies swing highs and lows using a configurable lookback period (default: 5 bars)
Only considers extremes within the specified calculation range
Forms the foundation for stroke construction
2. Stroke (笔) Identification
The indicator applies a multi-stage filtering process to identify valid strokes:
Stage 1 - Extreme Consolidation:
Merges consecutive extremes of the same type (high or low)
Keeps only the most extreme value (highest high or lowest low)
Stage 2 - Stroke Validation:
Ensures minimum bar gap between strokes (default: 4 bars)
Alternative validation: 2+ bars with >1% price change
Eliminates noise and insignificant price movements
Color Coding:
White Lines: Regular up/down strokes
Yellow Lines: Strokes that form part of a central hub
Customizable width and colors for different stroke types
3. Central Hub (中枢) Formation
A central hub forms when at least 3 consecutive strokes have overlapping price ranges:
Formation Rules:
Stroke 1:
Stroke 2:
Stroke 3:
Hub Upper = MIN(High1, High2, High3)
Hub Lower = MAX(Low1, Low2, Low3)
Valid if: Hub Upper > Hub Lower
Hub Extension:
Subsequent strokes that overlap with the hub extend it
Hub ends when a stroke no longer overlaps
Creates rectangular zones on the chart
Visual Representation:
Green rectangular boxes: Mark the time and price range of each central hub
Dashed extension lines: Show the latest hub boundaries extending to the right
Price labels on axis: Display exact hub upper and lower boundary values
4. Extreme Point Markers (Optional)
Red markers for tops (▼)
Green markers for bottoms (▲)
Marks every validated stroke extreme point
Useful for detailed structure analysis
5. Information Table (Optional)
Displays real-time statistics:
Symbol name
Current timeframe
Lookback period setting
Minimum gap setting
Total stroke count
Parameter Settings
Performance Settings
Max Bars to Calculate (3600): Limits historical calculation to improve performance
Local Extreme Lookback Period (5): Bars used to identify swing highs/lows
Min Gap Bars (4): Minimum bars required between valid strokes
Display Settings
Show Strokes: Toggle stroke line visibility
Show Central Hub: Toggle hub box visibility
Show Hub Extension Lines: Toggle dashed boundary lines
Show Extreme Point Marks: Toggle top/bottom markers
Show Info Table: Toggle statistics table
Color Settings
Full customization of:
Up/down stroke colors and widths
Hub stroke colors and widths
Hub border and background colors
Extension line colors
Trading Applications
Trend Structure Analysis
Uptrend: Series of higher highs and higher lows connected by strokes
Downtrend: Series of lower highs and lower lows connected by strokes
Consolidation: Formation of central hubs indicating range-bound movement
Support and Resistance Identification
Central Hub Zones: Act as strong support/resistance areas
Hub Upper Boundary: Resistance level in consolidation, support after breakout
Hub Lower Boundary: Support level in consolidation, resistance after breakdown
Price tends to react at these levels due to market structure memory
Breakout Trading
Bullish Breakout: Price closes above hub upper boundary
Previous resistance becomes support
Entry on retest of upper boundary
Stop loss below hub zone
Bearish Breakdown: Price closes below hub lower boundary
Previous support becomes resistance
Entry on retest of lower boundary
Stop loss above hub zone
Reversal Detection
Hub Formation After Trend: Signals potential trend exhaustion
Multiple Hub Levels: Create probability zones for reversals
Stroke Count: Excessive strokes within hub suggest weakening momentum
Position Management
Use hub boundaries for stop loss placement
Scale out positions at hub edges
Re-enter on retests of broken hub levels
Interpretation Guide
Strong Trending Market
Long, clear strokes with minimal overlap
Few or no central hubs forming
Strokes consistently in same direction
Wide spacing between extremes
Consolidating Market
Multiple central hubs forming
Short, overlapping strokes
Yellow hub strokes dominate the chart
Narrow price range
Trend Transition
Hub formation after extended trend
Stroke direction changes frequently
Hub boundaries being tested repeatedly
Potential reversal zone
Advanced Usage Techniques
Multi-Timeframe Analysis
Higher Timeframe: Identify major hub zones for overall market structure
Lower Timeframe: Find precise entry points within larger structure
Alignment: Trade when lower timeframe strokes align with higher timeframe hub breaks
Hub Quality Assessment
Wide Hubs: Strong consolidation, higher probability support/resistance
Narrow Hubs: Weak consolidation, may break easily
Extended Hubs: More strokes = stronger zone
Isolated Hubs: Single hub = potential pivot point
Stroke Analysis
Stroke Length: Longer strokes = stronger momentum
Stroke Speed: Fewer bars per stroke = explosive moves
Stroke Clustering: Many short strokes = indecision
Best Practices
Parameter Optimization
Adjust lookback period based on timeframe and volatility
Lower periods (3-4): More strokes, more noise, faster signals
Higher periods (7-10): Fewer strokes, cleaner structure, slower signals
Confirmation Strategy
Don't trade on strokes alone
Combine with volume analysis
Use candlestick patterns at hub boundaries
Wait for breakout confirmation
Risk Management
Always place stops outside hub zones
Use hub width to size positions (wider hub = smaller position)
Exit if price re-enters broken hub from wrong direction
Avoid Common Pitfalls
Don't trade within central hubs (range-bound, unpredictable)
Don't ignore higher timeframe hub structures
Don't chase strokes after they've extended far from hub
Don't trust single-stroke hubs (need 3+ strokes for validity)
Performance Considerations
Max Bars Limit: Set to 3600 to balance detail with performance
Safe Distance Calculation: Only draws objects within 2000 bars of current price
Object Cleanup: Automatically removes old drawing objects to prevent memory issues
Efficient Arrays: Uses indexed arrays for fast lookup and processing
Ideal Market Conditions
Best Performance:
Liquid markets with clear structure (major forex pairs, indices, large-cap stocks)
Trending markets with periodic consolidations
Medium to high volatility for clear stroke formation
Less Effective:
Extremely choppy, directionless markets
Very low timeframes (< 5 minutes) with excessive noise
Illiquid instruments with erratic price action
Integration with Other Indicators
Complementary Tools:
Volume Profile: Confirm hub significance with volume nodes
Moving Averages: Use for trend bias within stroke structure
RSI/MACD: Momentum confirmation at hub boundaries
Fibonacci Retracements: Hub levels often align with Fib levels
Advantages
✓ Objective Structure: Removes subjectivity from market structure analysis
✓ Visual Clarity: Color-coded strokes and clear hub zones
✓ Multi-Timeframe Applicable: Works on all timeframes from minutes to months
✓ Complete Framework: Provides entry, exit, and risk management levels
✓ Theoretical Foundation: Based on proven Chan Theory methodology
✓ Customizable: Extensive parameter and visual customization options
Limitations
⚠ Learning Curve: Requires understanding of Chan Theory principles
⚠ Lag Factor: Strokes confirm after price movements complete
⚠ Parameter Sensitivity: Different settings produce significantly different results
⚠ Choppy Market Struggles: Can generate excessive hubs in range-bound conditions
⚠ Computation Intensive: May slow down on lower-end systems with max bars setting
Optimization Tips
Timeframe Selection
Scalping: 5-15 minute charts, lookback period 3-4
Day Trading: 15-60 minute charts, lookback period 4-5
Swing Trading: 4-hour to daily charts, lookback period 5-7
Position Trading: Daily to weekly charts, lookback period 7-10
Volatility Adjustment
High volatility: Increase minimum gap bars to reduce noise
Low volatility: Decrease lookback period to capture smaller moves
Visual Optimization
Use contrasting colors for different market conditions
Adjust line widths based on chart resolution
Toggle markers off for cleaner appearance once familiar with structure
Quick Start Guide
For Beginners:
Start with default settings (5 lookback, 4 min gap)
Enable "Show Info Table" to track stroke count
Focus on identifying clear hub formations
Practice waiting for price to break hub boundaries before trading
For Advanced Users:
Optimize lookback and gap parameters for your instrument
Use hub strokes (yellow) to identify key consolidation zones
Combine with multiple timeframes for confirmation
Develop entry rules based on hub breakout/retest patterns
This indicator provides a complete structural framework for understanding market behavior through the lens of Chan Theory, offering traders a systematic approach to identifying high-probability trading opportunities.
ENTRY CONFIRMATION V2// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Zerocapitalmx
//@version=5
indicator(title="ENTRY CONFIRMATION V2", format=format.price, timeframe="", timeframe_gaps=true)
len = input.int(title="RSI Period", minval=1, defval=50)
src = input(title="RSI Source", defval=close)
lbR = input(title="Pivot Lookback Right", defval=5)
lbL = input(title="Pivot Lookback Left", defval=5)
rangeUpper = input(title="Max of Lookback Range", defval=60)
rangeLower = input(title="Min of Lookback Range", defval=5)
plotBull = input(title="Plot Bullish", defval=true)
plotHiddenBull = input(title="Plot Hidden Bullish", defval=false)
plotBear = input(title="Plot Bearish", defval=true)
plotHiddenBear = input(title="Plot Hidden Bearish", defval=false)
bearColor = color.red
bullColor = color.green
hiddenBullColor = color.new(color.green, 80)
hiddenBearColor = color.new(color.red, 80)
textColor = color.white
noneColor = color.new(color.white, 100)
osc = ta.rsi(src, len)
rsiPeriod = input.int(50, minval = 1, title = "RSI Period")
bandLength = input.int(1, minval = 1, title = "Band Length")
lengthrsipl = input.int(1, minval = 0, title = "Fast MA on RSI")
lengthtradesl = input.int(50, minval = 1, title = "Slow MA on RSI")
r = ta.rsi(src, rsiPeriod) // RSI of Close
ma = ta.sma(r, bandLength ) // Moving Average of RSI
offs = (1.6185 * ta.stdev(r, bandLength)) // Offset
fastMA = ta.sma(r, lengthrsipl) // Moving Average of RSI 2 bars back
slowMA = ta.sma(r, lengthtradesl) // Moving Average of RSI 7 bars back
plot(slowMA, "Slow MA", color=color.black, linewidth=1) // Plot Slow MA
plot(osc, title="RSI", linewidth=2, color=color.purple)
hline(50, title="Middle Line", color=#787B86, linestyle=hline.style_dotted)
obLevel = hline(70, title="Overbought", color=#787B86, linestyle=hline.style_dotted)
osLevel = hline(30, title="Oversold", color=#787B86, linestyle=hline.style_dotted)
plFound = na(ta.pivotlow(osc, lbL, lbR)) ? false : true
phFound = na(ta.pivothigh(osc, lbL, lbR)) ? false : true
_inRange(cond) =>
bars = ta.barssince(cond == true)
rangeLower <= bars and bars <= rangeUpper
//------------------------------------------------------------------------------
// Regular Bullish
// Osc: Higher Low
oscHL = osc > ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Lower Low
priceLL = low < ta.valuewhen(plFound, low , 1)
bullCond = plotBull and priceLL and oscHL and plFound
plot(
plFound ? osc : na,
offset=-lbR,
title="Regular Bullish",
linewidth=1,
color=(bullCond ? bullColor : noneColor)
)
plotshape(
bullCond ? osc : na,
offset=-lbR,
title="Regular Bullish Label",
text=" EDM ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bullish
// Osc: Lower Low
oscLL = osc < ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Higher Low
priceHL = low > ta.valuewhen(plFound, low , 1)
hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound
plot(
plFound ? osc : na,
offset=-lbR,
title="Hidden Bullish",
linewidth=1,
color=(hiddenBullCond ? hiddenBullColor : noneColor)
)
plotshape(
hiddenBullCond ? osc : na,
offset=-lbR,
title="Hidden Bullish Label",
text=" EDM ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Regular Bearish
// Osc: Lower High
oscLH = osc < ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Higher High
priceHH = high > ta.valuewhen(phFound, high , 1)
bearCond = plotBear and priceHH and oscLH and phFound
plot(
phFound ? osc : na,
offset=-lbR,
title="Regular Bearish",
linewidth=1,
color=(bearCond ? bearColor : noneColor)
)
plotshape(
bearCond ? osc : na,
offset=-lbR,
title="Regular Bearish Label",
text=" EDM ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bearish
// Osc: Higher High
oscHH = osc > ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Lower High
priceLH = high < ta.valuewhen(phFound, high , 1)
hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound
plot(
phFound ? osc : na,
offset=-lbR,
title="Hidden Bearish",
linewidth=1,
color=(hiddenBearCond ? hiddenBearColor : noneColor)
)
plotshape(
hiddenBearCond ? osc : na,
offset=-lbR,
title="Hidden Bearish Label",
text=" EDM ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
🎯 Wyckoff Order Block Entry System🎯 Wyckoff Order Block Entry System
📝 INDICATOR DESCRIPTION
🎯 Wyckoff Order Block Entry System Short Description:
Professional institutional zone trading combined with Wyckoff methodology. Identifies high-probability entries where smart money meets classic price action patterns.
Full Description:
Wyckoff Order Block Entry System is a precision trading tool that combines two powerful concepts:
Order Blocks - Institutional zones where large players place their orders
Wyckoff Method - Classic price action patterns revealing smart money behavior
🎯 What Makes This Different?
Unlike traditional indicators that flood your chart with signals, this system only triggers entries when BOTH conditions are met:
Price enters an institutional Order Block zone (current timeframe OR higher timeframe)
A Wyckoff pattern occurs (Spring, SOS, Upthrust, or SOW)
This dual-confirmation approach ensures you're trading with institutional flow at optimal entry points.
📊 Key Features:
✅ Order Block Detection
Automatically identifies institutional buying/selling zones
Current timeframe order blocks (solid lines)
Higher timeframe order blocks (dashed lines) for stronger zones
Customizable strength and extension settings
✅ 4 Wyckoff Entry Patterns
SPRING (Bullish Reversal): Fake breakdown below support → Quick recovery
SOS (Sign of Strength): Strong bullish candle after accumulation
UPTHRUST (Bearish Reversal): Fake breakout above resistance → Quick rejection
SOW (Sign of Weakness): Strong bearish candle after distribution
✅ Clean Visual Design
Minimalist approach - only essential information
Color-coded zones (Green = Bullish, Red = Bearish, Cyan/Magenta = HTF)
Clear entry signals with pattern type labels
No chart clutter - focus on what matters
✅ Multi-Timeframe Analysis
Integrates higher timeframe order blocks
HTF signals marked with "+HTF" tag for extra confidence
Fully customizable HTF selection (H1, H4, Daily, etc.)
✅ Smart Alerts
Entry signal alerts (Long/Short)
Order block formation alerts
HTF order block alerts
Customizable alert messages
💡 How To Use:
Setup: Add indicator to your chart, configure HTF timeframe (default H1)
Wait: Let order blocks form (green/red boxes appear)
Watch: Price returns to order block zone
Entry: Signal appears when Wyckoff pattern confirms
Trade: Enter with the signal, stop below/above order block
📈 Best For:
Forex pairs (all majors and crosses)
Gold (XAUUSD)
Crypto (BTC, ETH, etc.)
Indices (SPX, NAS100, etc.)
Stocks
Commodities
⏱️ Recommended Timeframes:
M15 for scalping
M30 for day trading
H1 for swing trading
H4 for position trading
🎯 Win Rate Expectations:
Current TF signals: 60-70%
HTF signals (+HTF tag): 70-80%
Spring/Upthrust patterns: Highest probability
Works on ALL liquid markets
⚙️ Customizable Settings:
Order block detection parameters
HTF timeframe selection
Wyckoff sensitivity (swing length, volume threshold)
Zone extension duration
Color schemes
📚 Trading Strategy:
This indicator works best when:
Trading in the direction of higher timeframe trend
Using proper risk management (1-2% per trade)
Placing stops just outside order block zones
Taking profits at opposite order blocks
Focusing on HTF signals for higher quality
🔒 Risk Management:
Always use stop losses! Recommended placement:
LONG: 10-20 pips below order block
SHORT: 10-20 pips above order block
Target: Minimum 1:2 risk/reward ratio
💎 Why Traders Love This System:
"Finally, an indicator that doesn't spam my chart with useless signals!" - The quality-over-quantity approach means you only get high-probability setups.
"The HTF order blocks changed my trading!" - Multi-timeframe analysis built-in removes the need for manual higher timeframe checks.
"Wyckoff + Order Blocks = Perfect combination!" - Two proven concepts working together create powerful confluence.
📊 Universal Application:
This system works on ANY liquid market with sufficient volume:
✅ Forex (EUR/USD, GBP/USD, USD/JPY, etc.)
✅ Commodities (Gold, Silver, Oil, etc.)
✅ Indices (S&P 500, NASDAQ, DAX, etc.)
✅ Cryptocurrencies (Bitcoin, Ethereum, etc.)
✅ Stocks (Large cap with good liquidity)
🎓 Educational Value:
Beyond just signals, this indicator teaches you:
How institutional traders think
Where smart money places orders
Classic Wyckoff accumulation/distribution patterns
Multi-timeframe analysis techniques
⚡ Performance:
Lightning-fast calculations
No repainting
Real-time signal generation
Clean code, optimized for speed
🚀 Get Started:
Add to your favorite chart
Adjust HTF timeframe to match your trading style
Wait for high-quality signals
Trade with confidence
Remember: Quality beats quantity. This system prioritizes precision over frequency. You might see 2-5 signals per day on M30 - and that's exactly the point. Each signal is carefully filtered for maximum probability.
Ready to trade like institutions?
👉 Add this indicator to your chart now
👉 Configure your preferred HTF timeframe
👉 Start catching high-probability setups
👉 Trade smarter, not harder
Questions or feedback? Drop a comment below!
Found this useful? Hit that ⭐ button and share with fellow traders!
Happy Trading! 🚀📈
MA SMART Angle
### 📊 WHAT IS MA SMART ANGLE?
**MA SMART Angle** is an advanced momentum and trend detection indicator that analyzes the angles (slopes) of multiple moving averages to generate clear, non-repainting BUY and SELL signals.
**Original Concept Credit:** This indicator builds upon the "MA Angles" concept originally created by **JD** (also known as Duyck). The core angle calculation methodology and Jurik Moving Average (JMA) implementation by **Everget** are preserved from the original open-source work. The angle calculation formula was contributed by **KyJ**. This enhanced version is published with respect to the open-source nature of the original indicator.
Original indicator reference: "ma angles - JD" by Duyck
---
## 🎯 ORIGINALITY & VALUE PROPOSITION
### **What Makes This Different from the Original:**
While the original "MA Angles" by **JD** provided excellent angle visualization, it lacked actionable entry signals. **MA SMART Angle** addresses this by adding:
**1. Clear Entry/Exit Signals**
- Explicit BUY/SELL arrows based on angle crossovers, momentum confirmation, and MA alignment
- No guessing when to enter trades - the indicator tells you exactly when conditions align
**2. Non-Repainting Logic**
- All signals use confirmed historical data (shifted by 2 bars minimum)
- Critical for backtesting reliability and live trading confidence
- Original indicator could repaint signals on current bar
**3. Dual Signal System**
- **Simple Mode:** More frequent signals based on angle crossovers + momentum (for active traders)
- **Strict Mode:** Requires full multi-MA alignment + momentum confirmation (for conservative traders)
- Adaptable to different trading styles and risk tolerances
**4. Smart Signal Filtering**
- **Anti-spam cooldown:** Prevents duplicate signals within configurable bar count
- **No-trade zone detection:** Filters out low-conviction sideways markets automatically
- **Multi-timeframe MA alignment:** Ensures all moving averages agree on direction before signaling
**5. Enhanced Visualization**
- Large, clear BUY/SELL arrows with descriptive labels
- Color-coded backgrounds for market states (trending vs. ranging)
- Momentum histogram showing acceleration/deceleration in real-time
- Live status table displaying trend strength, angle value, momentum, and MA alignment
**6. Professional Alert System**
- Four distinct alert conditions: BUY Signal, SELL Signal, Strong BUY, Strong SELL
- Enables automated trade notifications and strategy integration
**7. Modified MA Periods**
- Original used EMA(27), EMA(83), EMA(278)
- Enhanced version uses faster EMA(3), EMA(8), EMA(13) for more responsive signals
- Better suited for modern volatile markets and shorter timeframes
---
## 📐 HOW IT WORKS - TECHNICAL EXPLANATION
### **Core Methodology:**
The indicator calculates angles (slopes) for five key moving averages:
- **JMA (Jurik Moving Average)** - Smooth, lag-reduced trend line (original implementation by **Everget**)
- **JMA Fast** - Responsive momentum indicator with higher power parameter
- **MA27 (EMA 3)** - Primary fast-moving average for signal generation
- **MA83 (EMA 8)** - Medium-term trend confirmation
- **MA278 (EMA 13)** - Slower trend filter
### **Angle Calculation Formula (by KyJ):**
```
angle = arctan((MA - MA ) / ATR(14)) × (180 / π)
```
**Why ATR normalization?**
- Makes angles comparable across different instruments (forex, stocks, crypto)
- Makes angles comparable across different timeframes
- Accounts for volatility - a 10-point move in different assets has different significance
**Angle Interpretation:**
- **> 15°** = Strong trend (momentum accelerating)
- **0° to 15°** = Weak trend (momentum present but moderate)
- **-2° to +2°** = No-trade zone (sideways/choppy market)
- **< -15°** = Strong downtrend
### **Signal Generation Logic:**
#### **BUY Signal Conditions:**
1. MA27 angle crosses above 0° (upward momentum initiates)
2. All three EMAs (3, 8, 13) pointing upward (trend alignment confirmed)
3. Momentum is positive for 2+ bars (acceleration, not deceleration)
4. Angle exceeds minimum threshold (not in no-trade zone)
5. Cooldown period passed (prevents signal spam)
#### **SELL Signal Conditions:**
1. MA27 angle crosses below 0° (downward momentum initiates)
2. All three EMAs pointing downward (downtrend alignment)
3. Momentum is negative for 2+ bars
4. Angle below negative threshold (not in no-trade zone)
5. Cooldown period passed
#### **Strong BUY+ / SELL+ Signals:**
Additional entry opportunities when JMA Fast crosses JMA Slow while maintaining strong directional angle - indicates momentum acceleration within established trend.
---
## 🔧 HOW TO USE
### **Recommended Settings by Trading Style:**
**Scalpers / Day Traders:**
- Signal Type: **Simple**
- Minimum Angle: **3-5°**
- Cooldown Bars: **3-5 bars**
- Timeframes: 1m, 5m, 15m
**Swing Traders:**
- Signal Type: **Strict**
- Minimum Angle: **7-10°**
- Cooldown Bars: **8-12 bars**
- Timeframes: 1H, 4H, Daily
**Position Traders:**
- Signal Type: **Strict**
- Minimum Angle: **10-15°**
- Cooldown Bars: **15-20 bars**
- Timeframes: Daily, Weekly
### **Parameter Descriptions:**
**1. Source** (default: OHLC4)
- Price data used for MA calculations
- OHLC4 provides smoothest angles
- Close is more responsive but noisier
**2. Threshold for No-Trade Zones** (default: 2°)
- Angles below this are considered sideways/ranging
- Increase for stricter filtering of choppy markets
- Decrease to allow signals in quieter trending periods
**3. Signal Type** (Simple vs. Strict)
- **Simple:** Angle crossover OR (trend + momentum)
- **Strict:** Angle crossover AND all MAs aligned AND momentum confirmed
- Start with Simple, switch to Strict if too many false signals
**4. Minimum Angle for Signal** (default: 5°)
- Only generate signals when angle exceeds this threshold
- Higher values = stronger trends required
- Lower values = more sensitive to momentum changes
**5. Cooldown Bars** (default: 5)
- Minimum bars between consecutive signals
- Prevents spam during volatile chop
- Scale with your timeframe (higher TF = more bars)
**6. Color Bars** (default: true)
- Colors chart bars based on signal state
- Green = bullish conditions, Red = bearish conditions
- Can disable if you prefer clean price bars
**7. Background Colors**
- **Yellow background** = No-trade zone (low angle, ranging market)
- **Green flash** = BUY signal generated
- **Red flash** = SELL signal generated
- All customizable or can be disabled
---
## 📊 INTERPRETING THE INDICATOR
### **Visual Elements:**
**Main Chart Window:**
- **Thick Lime/Fuchsia Line** = MA27 angle (primary signal line)
- **Medium Green/Red Line** = MA83 angle (trend confirmation)
- **Thin Green/Red Line** = MA278 angle (slow trend filter)
- **Aqua/Orange Line** = JMA Fast (momentum detector)
- **Green/Red Area** = JMA slope (overall trend context)
- **Blue/Purple Histogram** = Momentum (angle acceleration/deceleration)
**Signal Arrows:**
- **Large Green ▲ "BUY"** = Primary buy signal (all conditions met)
- **Small Green ▲ "BUY+"** = Strong momentum buy (JMA fast cross)
- **Large Red ▼ "SELL"** = Primary sell signal (all conditions met)
- **Small Red ▼ "SELL+"** = Strong momentum sell (JMA fast cross)
**Status Table (Top Right):**
- **Angle:** Current MA27 angle in degrees
- **Trend:** Classification (STRONG UP/DOWN, UP/DOWN, FLAT)
- **Momentum:** Acceleration state (ACCEL UP/DN, Up/Down)
- **MAs:** Alignment status (ALL UP/DOWN, Mixed)
- **Zone:** Trading zone status (ACTIVE vs. NO TRADE)
- **Last:** Bars since last signal
### **Trading Strategies:**
**Strategy 1: Pure Signal Following**
- Enter LONG on BUY signal
- Exit on SELL signal
- Use stop-loss at recent swing low/high
- Works best on trending instruments
**Strategy 2: Confirmation with Price Action**
- Wait for BUY signal + bullish candlestick pattern
- Wait for SELL signal + bearish candlestick pattern
- Increases win rate by filtering premature signals
- Recommended for beginners
**Strategy 3: Momentum Acceleration**
- Use BUY+/SELL+ signals for adding to positions
- Only take these in direction of primary signal
- Scalp quick moves during momentum spikes
- For experienced traders
**Strategy 4: Mean Reversion in No-Trade Zones**
- When status shows "NO TRADE", fade extremes
- Wait for angle to exit no-trade zone for reversal
- Contrarian approach for range-bound markets
- Requires tight stops
---
## ⚠️ LIMITATIONS & DISCLAIMERS
**What This Indicator DOES:**
✅ Measures momentum direction and strength via angle analysis
✅ Generates signals when multiple conditions align
✅ Filters out low-conviction sideways markets
✅ Provides visual clarity on trend state
**What This Indicator DOES NOT:**
❌ Predict future price movements with certainty
❌ Guarantee profitable trades (no indicator can)
❌ Work equally well on all instruments/timeframes
❌ Replace proper risk management and position sizing
**Known Limitations:**
- **Lagging Nature:** Like all moving averages, signals occur after momentum begins
- **Whipsaw Risk:** Can generate false signals in volatile, directionless markets
- **Optimization Required:** Parameters need adjustment for different assets
- **Not a Complete System:** Should be combined with risk management, position sizing, and other analysis
**Best Performance Conditions:**
- Strong trending markets (crypto bull runs, stock breakouts)
- Liquid instruments (major forex pairs, large-cap stocks)
- Appropriate timeframe selection (match to trading style)
- Used alongside support/resistance and volume analysis
---
## 🔔 ALERT SETUP
The indicator includes four alert conditions:
**1. BUY SIGNAL**
- Message: "MA SMART Angle: BUY SIGNAL! Angle crossed up with momentum"
- Use for: Primary long entries
**2. SELL SIGNAL**
- Message: "MA SMART Angle: SELL SIGNAL! Angle crossed down with momentum"
- Use for: Primary short entries or long exits
**3. Strong BUY**
- Message: "MA SMART Angle: Strong BUY momentum - JMA fast crossed up"
- Use for: Adding to longs or aggressive entries
**4. Strong SELL**
- Message: "MA SMART Angle: Strong SELL momentum - JMA fast crossed down"
- Use for: Adding to shorts or aggressive exits
**Setting Up Alerts:**
1. Right-click indicator → "Add Alert on MA SMART Angle"
2. Select desired condition from dropdown
3. Choose notification method (popup, email, webhook)
4. Set alert expiration (typically "Once Per Bar Close")
---
## 📚 EDUCATIONAL VALUE
This indicator serves as an excellent learning tool for understanding:
**1. Angle-Based Momentum Analysis**
- Traditional indicators show MA crossovers
- This shows the *rate of change* (velocity) of MAs
- Teaches traders to think in terms of momentum acceleration
**2. Multi-Timeframe Confirmation**
- Shows how fast, medium, and slow MAs interact
- Demonstrates importance of trend alignment
- Helps develop patience for high-probability setups
**3. Signal Quality vs. Quantity Tradeoff**
- Simple mode = more signals, more noise
- Strict mode = fewer signals, higher quality
- Teaches discretionary filtering skills
**4. Market State Recognition**
- Visual distinction between trending and ranging markets
- Helps traders avoid trading choppy conditions
- Develops "market context" awareness
---
## 🔄 DIFFERENCES FROM OTHER MA INDICATORS
**vs. Traditional MA Crossovers:**
- Measures momentum (angle) rather than just price crossing MA
- Provides earlier signals as angles change before price crosses
- Filters better for sideways markets using no-trade zones
**vs. MACD:**
- Uses multiple MAs instead of just two
- ATR normalization makes it universal across instruments
- Visual angle representation more intuitive than histogram
**vs. Supertrend:**
- Not based on ATR bands but on MA slope analysis
- Provides graduated strength indication (not just binary trend)
- Less prone to whipsaw in low volatility
**vs. Original "MA Angles" by JD:**
- Adds explicit entry/exit signals (original had none)
- Implements no-repaint logic for reliability
- Includes signal filtering and quality controls
- Provides dual signal systems (Simple/Strict)
- Enhanced visualization and status monitoring
- Uses faster MA periods (3/8/13 vs 27/83/278) for modern markets
---
## 📖 CODE STRUCTURE (for Pine Script learners)
This indicator demonstrates:
**Advanced Pine Script Techniques:**
- Custom function implementation (JMA, angle calculation)
- Var declarations for stateful tracking
- Table creation for HUD display
- Multi-condition signal logic
- Alert system integration
- Proper use of historical references for no-repaint
**Code Organization:**
- Modular function definitions (JMA, angle)
- Clear separation of concerns (inputs, calculations, plotting, alerts)
- Extensive commenting for maintainability
- Best practices for Pine Script v5
**Learning Resources:**
- Study the JMA function to understand adaptive smoothing
- Examine angle calculation for ATR normalization technique
- Review signal logic for multi-condition confirmation patterns
- Analyze anti-spam filtering for state management
The code is open-source - feel free to study, modify, and improve upon it!
---
## 🙏 CREDITS & ATTRIBUTION
**Original Concepts:**
- **"ma angles - JD" by JD (Duyck)** - Core angle calculation methodology and indicator concept
Original open-source indicator on TradingView Community Scripts
- **JMA (Jurik Moving Average) implementation by Everget** - Smooth, low-lag moving average function
Acknowledged in original JD indicator code
- **Angle Calculation formula by KyJ** - Mathematical formula for converting MA slope to degrees using ATR normalization
Acknowledged in original JD indicator code comments
**Enhancements in This Version:**
- Signal generation logic - Original implementation for this indicator
- No-repaint confirmation system - Original implementation
- Dual signal modes (Simple/Strict) - Original implementation
- Visual enhancements and status table - Original implementation
- Alert system and signal filtering - Original implementation
- Modified MA periods (3/8/13 instead of 27/83/278) - Optimization for modern markets
**Open Source Philosophy:**
This indicator follows the open-source spirit of TradingView and the Pine Script community. The original "ma angles - JD" by JD (Duyck) was published as open-source, enabling this enhanced version. Similarly, this code is published as open-source to allow further community improvements.
---
## ⚡ QUICK START GUIDE
**For New Users:**
1. Add indicator to chart
2. Start with default settings (Simple mode)
3. Wait for BUY signal (green arrow)
4. Observe how price behaves after signal
5. Check status table to understand market state
6. Adjust parameters based on your instrument/timeframe
**For Experienced Traders:**
1. Switch to Strict mode for higher quality signals
2. Increase cooldown bars to reduce frequency
3. Raise minimum angle threshold for stronger trends
4. Combine with your existing strategy for confirmation
5. Set up alerts for desired signal types
6. Backtest on your preferred instruments
---
## 🎓 RECOMMENDED COMBINATIONS
**Works Well With:**
- **Volume Analysis:** Confirm signals with volume spikes
- **Support/Resistance:** Take signals near key levels
- **RSI/Stochastic:** Avoid overbought/oversold extremes
- **ATR:** Size positions based on volatility
- **Price Action:** Wait for candlestick confirmation
**Complementary Indicators:**
- Order Flow / Footprint (for institutional confirmation)
- Volume Profile (for identifying value areas)
- VWAP (for intraday mean reversion reference)
- Fibonacci Retracements (for target setting)
---
## 📈 PERFORMANCE EXPECTATIONS
**Realistic Win Rates:**
- Simple Mode: 45-55% (higher frequency, moderate accuracy)
- Strict Mode: 55-65% (lower frequency, higher accuracy)
- Combined with price action: 60-70%
**Best Asset Classes:**
1. **Cryptocurrencies** (strong trends, clear signals)
2. **Forex Major Pairs** (smooth price action, good angles)
3. **Large-Cap Stocks** (trending behavior, liquid)
4. **Index Futures** (trending instruments)
**Challenging Conditions:**
- Low volatility consolidation periods
- News-driven erratic movements
- Thin/illiquid instruments
- Counter-trending markets
---
## 🛡️ RISK DISCLAIMER
**IMPORTANT LEGAL NOTICE:**
This indicator is for **educational and informational purposes only**. It is **NOT financial advice** and does not constitute a recommendation to buy or sell any financial instrument.
**Trading Risks:**
- Trading carries substantial risk of loss
- Past performance does not guarantee future results
- No indicator can predict market movements with certainty
- You can lose more than your initial investment (especially with leverage)
**User Responsibilities:**
- Conduct your own research and due diligence
- Understand the instruments you trade
- Never risk more than you can afford to lose
- Use proper position sizing and risk management
- Consider consulting a licensed financial advisor
**Indicator Limitations:**
- Signals are based on historical data only
- No guarantee of accuracy or profitability
- Parameters must be optimized for your specific use case
- Results vary significantly by market conditions
By using this indicator, you acknowledge and accept all trading risks. The author is not responsible for any financial losses incurred through use of this indicator.
---
## 📧 SUPPORT & FEEDBACK
**Found a bug?** Please report it in the comments with:
- Chart symbol and timeframe
- Parameter settings used
- Description of unexpected behavior
- Screenshot if possible
**Have suggestions?** Share your ideas for improvements!
**Enjoying the indicator?** Leave a like and follow for updates!
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
TrendDetectorLibLibrary "TrendDetector_Lib"
method formatTF(timeframe)
Namespace types: series string, simple string, input string, const string
Parameters:
timeframe (string) : (string) The timeframe to convert (e.g., "15", "60", "240").
Returns: (string) The formatted timeframe (e.g., "15M", "1H", "4H").
f_ma(type, src, len)
Computes a Moving Average value based on type and length.
Parameters:
type (simple string) : (string) One of: "SMA", "EMA", "RMA", "WMA", "VWMA".
src (float) : (series float) Source series for MA (e.g., close).
len (simple int) : (simple int) Length of the MA.
Returns: (float) The computed MA series.
render(tbl, trendDetectorSwitch, frameColor, frameWidth, borderColor, borderWidth, textColor, ma1ShowTrendData, ma1Timeframe, ma1Value, ma2ShowTrendData, ma2Timeframe, ma2Value, ma3ShowTrendData, ma3Timeframe, ma3Value)
Fills the provided table with Trend Detector contents.
@desc This renderer does NOT plot and does NOT create tables; call from indicator after your table exists.
Parameters:
tbl (table) : (table) Existing table to render into.
trendDetectorSwitch (bool) : (bool) Master toggle to draw the table content.
frameColor (color) : (color) Table frame color.
frameWidth (int) : (int) Table frame width (0–5).
borderColor (color) : (color) Table border color.
borderWidth (int) : (int) Table border width (0–5).
textColor (color) : (color) Table text color.
ma1ShowTrendData (bool) : (bool) Show MA #1 in table.
ma1Timeframe (simple string) : (string) MA #1 timeframe.
ma1Value (float)
ma2ShowTrendData (bool) : (bool) Show MA #2 in table.
ma2Timeframe (simple string) : (string) MA #2 timeframe.
ma2Value (float)
ma3ShowTrendData (bool) : (bool) Show MA #3 in table.
ma3Timeframe (simple string) : (string) MA #3 timeframe.
ma3Value (float)
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Moving Average Ribbon (10x, per-MA timeframe)A flexible moving‑average ribbon that plots up to 10 MAs, each with its own type, length, source, color, and independent timeframe selector for true multi‑timeframe analysis without repainting on higher‑timeframe pulls.
What it does
Plots ten moving averages with selectable types: SMA, EMA, SMMA (RMA), WMA, and VWMA.
Allows per‑line timeframe inputs (e.g., 5, 15, 60, 1D, 1W) so you can overlay higher‑ or equal‑timeframe MAs on the current chart.
Uses a non‑repainting request pattern for higher‑timeframe series to keep lines stable in realtime.
How to use
Leave a TF field blank to keep that MA on the chart’s timeframe; type a timeframe (like 15 or 1D) to fetch it from another timeframe.
Typical trend‑following setup: fast MAs (10–21) on chart TF, mid/slow MAs (34–200) from higher TFs for bias and dynamic support/resistance.
Color‑code faster vs slower lines and optionally hide lines you don’t need to reduce clutter.
Best practices
Prefer pulling equal or higher timeframes for stability; mixing lower TFs into a higher‑TF chart can create choppy visuals.
Combine with price action and volume/volatility tools (e.g., RSI, Bollinger Bands) for confirmation rather than standalone signals.
Showcase example charts in your publish post and explain default settings so users know how to interpret the ribbon.
Inputs
Show/Hide per MA, Type (SMA/EMA/SMMA/WMA/VWMA), Source, Length, Color, Timeframe.
Defaults cover common lengths (10/20/50/100/200 etc.) and can be customized to fit intraday or swing styles.
Limitations
This is an analysis overlay, not a signal generator; it doesn’t place trades or alerts by default.
Effectiveness depends on instrument liquidity and user configuration; avoid overfitting to one market or regime.
Attribution and etiquette
Provide a brief explanation of your calculation choices and note that MA formulas are standard; credit any borrowed concepts or snippets if used.
VWAP – Pivot Pairs (SECONDS‑BASED RESET)VWAP – Pivot Pairs (SECONDS-BASED RESET) is a Pine Script v6 indicator for TradingView that combines pivot-based breakout detection with resettable VWAP (Volume Weighted Average Price) calculations over user-defined rolling time periods in seconds.It identifies high and low swing pivots via breakout logic, then calculates two VWAP lines per anchor:One using high/low as the price source,
One using close as the price source.
These form "pivot pairs" that reset automatically at the start of each custom-duration period (e.g., every 300 seconds), starting from a user-defined UTC time of day (default: 09:30 UTC).Visuals include:Colored VWAP lines (high pair: red, low pair: green),
Semi-transparent fill zones between each pair,
Optional toggles to show/hide high or low pairs.
Use CasesUse Case
Description
Intraday Scalping (1–15 min charts)
Use 60–300 second resets to capture micro-trends within larger sessions. VWAP pairs act as dynamic support/resistance after breakouts.
High-Frequency / Algo Validation
Backtest strategies on tick/second charts where traditional session resets fail. Align resets with exchange micro-sessions or volatility windows.
Opening Range Breakout (ORB) Enhancement
Set period_seconds = 1800 (30 min) and start time = 09:30 UTC → VWAP builds only on first 30 mins post-open, then floats. Pairs show deviation from ORB mean.
Range-Bound Market Analysis
In choppy markets, VWAP pairs converge near fair value. Divergence signals potential breakout. Fill color intensity shows conviction.
Multi-Timeframe Confluence
Overlay on 1-second chart with 300s reset → matches 5-minute structure. Use close-based VWAP for entries, high/low-based for stops.
Key Features SummaryFeature
Function
period_seconds
Rolling window length in seconds (e.g., 300 = 5 min)
period_start_time
UTC time-of-day anchor (default: 09:30)
new_period logic
Triggers full reset of pivots + VWAP on exact second boundary
breakingHigher / breakingLower
Detects confirmed breakouts (not just close above high)
Dual VWAP per anchor
ta.vwap(high) and ta.vwap(close) for range-aware mean
Fill zones
Visual value area between high/close VWAPs
Toggle visibility
Independently show/hide high or low pivot pairs
How It Works – Step-by-StepTime Engine Converts user inputs → milliseconds
Calculates current period start time using integer division from epoch
Detects exact bar when new period begins (new_period = true)
On New Period Resets both high/low anchors to current bar’s h and l
Forces VWAP recalculation from this bar forward
Breakout Detection Only triggers on strong candles (rising/falling, non-doji)
Requires open/close beyond prior pivot → avoids wicks-only breaks
VWAP Accumulation ta.vwap(source, reset_condition) restarts when anchor resets
Two sources per side → shows where volume clustered (at highs vs closes)
Plotting Four lines + two fills
Clean, customizable, overlay-friendly
Pro TipsUse on Heikin Ashi for smoother breakout signals.
Combine with volume profile to validate VWAP clusters.
For crypto, set period_start_time = 0 (00:00 UTC) for clean 4-hour resets.
Add alerts on new_period or breakingHigher for automation.
In short: This is a precision VWAP tool for time-boxed, pivot-driven mean reversion and breakout trading, ideal for scalpers, day traders, and algo developers needing sub-session granularity.
EMA Cross + RSI + ADX - Autotrade Strategy V2Overview
A versatile trend-following strategy combining EMA 9/21 crossovers with RSI momentum filtering and optional ADX trend strength confirmation. Designed for both cryptocurrency and traditional futures/options markets with built-in stop loss management and automated position reversals.
Key Features
Multi-Market Compatibility: Works on both crypto futures (Bitcoin, Ethereum) and traditional markets (NIFTY, Bank NIFTY, S&P 500 futures, equity options)
Triple Confirmation System: EMA crossover + RSI filter + ADX strength (optional)
Automated Risk Management: 2% stop loss with wick-touch detection
Position Auto-Reversal: Opposite signals automatically close and reverse positions
Webhook Ready: Six distinct alert messages for automation (Entry Buy/Sell, Close Long/Short, SL Hit Long/Short)
Performance Metrics
NIFTY Futures (15min): 50%+ win rate with ADX filter OFF
Crypto Markets: Requires extensive backtesting before live deployment
Optimal Timeframes: 15-minute to 1-hour charts (patience required for higher timeframes)
Strategy Logic
Entry Signals:
LONG: EMA 9 crosses above EMA 21 + RSI > 55 + ADX > 20 (if enabled)
SHORT: EMA 9 crosses below EMA 21 + RSI < 45 + ADX > 20 (if enabled)
Exit Signals:
Opposite EMA crossover (auto-closes current position)
Stop loss hit at 2% from entry price (tracks candle wicks)
Technical Indicators:
Fast EMA: 9-period (short-term trend)
Slow EMA: 21-period (primary trend)
RSI: 14-period with 55/45 thresholds (momentum confirmation)
ADX: 14-period with 20 threshold (trend strength filter - optional)
Market-Specific Settings
Traditional Markets (NIFTY, Bank NIFTY, S&P Futures, Options)
Recommended Settings:
ADX Filter: Turn OFF (less choppy, cleaner trends)
Timeframe: 15-minute chart
Win Rate: 50%+ on NIFTY Futures
Why No ADX: Traditional markets have more institutional participation and smoother price action, making ADX unnecessary
Cryptocurrency Markets (BTC, ETH, Altcoins)
Recommended Settings:
ADX Filter: Turn ON (ADX > 20)
Timeframe: 15-minute to 1-hour
Extensive backtesting required before live trading
Why ADX: Crypto markets are highly volatile and prone to false breakouts; ADX filters low-quality chop
Best Practices
✅ Backtest thoroughly on your specific instrument and timeframe
✅ Use larger timeframes (1H, 4H) for higher quality signals and better risk/reward
✅ Adjust RSI thresholds based on market volatility (try 52/48 for more signals, 60/40 for fewer but stronger)
✅ Monitor ADX effectiveness - disable for traditional markets, enable for crypto
✅ Proper position sizing - adjust default_qty_value based on your capital and instrument price
✅ Paper trade first - test for 2-4 weeks before risking real capital
Risk Management
Fixed 2% stop loss per trade (adjustable)
Stop loss tracks candle wicks for accurate execution
Positions auto-reverse on opposite signals (no manual intervention needed)
0.075% commission built into backtest (adjust for your broker)
Customization Options
All parameters are adjustable via inputs:
EMA periods (default: 9/21)
RSI length and thresholds (default: 14-period, 55/45 levels)
ADX length and threshold (default: 14-period, 20 threshold)
Stop loss percentage (default: 2%)
Webhook Automation
This strategy includes six distinct alert messages for automated trading:
"Entry Buy" - Long position opened
"Entry Sell" - Short position opened
"Close Long" - Long position closed on opposite crossover
"Close Short" - Short position closed on opposite crossover
"SL Hit Long" - Long stop loss triggered
"SL Hit Short" - Short stop loss triggered
Compatible with Delta Exchange, Binance Futures, 3Commas, Alertatron, and other webhook platforms.
Important Notes
⚠️ Crypto markets require extensive backtesting - volatility patterns differ significantly from traditional markets
⚠️ Higher timeframes = better results - 15min works but 1H/4H provide cleaner signals
⚠️ ADX toggle is critical - OFF for traditional markets, ON for crypto
⚠️ Not financial advice - always conduct your own research and use proper risk management
⚠️ Past performance ≠ future results - backtest results may not reflect live trading conditions
Disclaimer
This strategy is for educational and informational purposes only. Trading futures and options involves substantial risk of loss. Always backtest thoroughly, start with paper trading, and never risk more than you can afford to lose. The author assumes no responsibility for any trading losses incurred using this strategy.
Put Option Profits inspired by Travis Wilkerson; SPX BacktesterPut Option Profits — Travis Wilkerson inspired. This tester evaluates a simple monthly SPX at-the-money credit-spread timing idea: enter on a fixed calendar rule (e.g., 1st Friday or 8th day with business-day shifting) at Open or Close, then exit exactly N calendar days later (first tradable day >= target, at Close). A trade is marked WIN if price at exit is above the entry price (1:1 risk proxy).
The book suggests forward testing 60-day and 180-day expirations to prove the concept. This tool lets you backtest both (and more) to see what actually works best. In the book, profits are taken when the spread reaches ~80% of max credit; losers are left to expire and cash-settle. This backtester does not model early profit-taking—every trade is held to the configured hold period and evaluated on price vs entry at the exit close. Think of it as a pure “set it and forget it” stress test. In live trading, you can still follow Travis’s 80% take-profit rule; TradingView just doesn’t simulate that here. Happy trading!
Features:
Schedule: Day-of-Month (with Prev/Next business-day shift, optional “stay in month”) or Nth Weekday (e.g., 1st Friday).
Entry timing: Open or Close.
Exit: N calendar days later at Close (holiday/weekend aware).
Filters: Optional EMA-200 “risk-on” filter.
Scope: Date range limiter.
Visuals: Entry/exit bubbles (paired colors) or simple win/loss dots.
Table: Overall Win% and N (within range).
Alerts: Entry alert (static condition + dynamic alert() message).
How to use:
[* ]Choose Start Mode (NthWeekday or DayOfMonth) and parameters (e.g., 1st Friday or DOM=8, PrevBizDay).
Pick Entry Timing (Open or Close).
Set Days In Trade (e.g., 150).
(Optional) Enable EMA filter and set Date Range.
Turn Bubbles on/off and/or Dots on/off.
Create alert:
Simple ping: Condition = this indicator -> Monthly Entry Signal -> “Once per bar” (Open) or “Once per bar close” (Close).
Rich message: Condition = this indicator -> Any alert() function call.
Notes:
Keep DOM shift in same month: when a DOM falls on a weekend/holiday, PrevBizDay/NextBizDay shift will stay inside the month if enabled; otherwise it can spill into the prior/next month. (Ignored for NthWeekday.)
Credits: Concept sparked by “Put Option Profits – How to turn ten minutes of free time into consistent cash flow each month” by Travis Wilkerson; this script is a neutral research tool (not financial advice).
Force DashboardScalping Dashboard - Complete User Guide
Overview
This scalping system consists of two complementary TradingView indicators designed for intraday trading with no overnight holds:
Force Dashboard - Single-row table showing real-time market bias and entry signals
Large Order Detection - Visual diamonds showing institutional order flow
Together, they provide a complete at-a-glance view of market conditions optimized for quick entries and exits.
Recommended Timeframes
Primary Scalping Timeframes
1-minute chart: Ultra-fast scalps (30 seconds - 3 minutes hold time)
2-minute chart: Quick scalps (2-5 minutes hold time)
5-minute chart: Standard scalps (5-15 minutes hold time)
Best Practices
Use 1-2 minute for highly liquid instruments (ES, NQ, major forex pairs)
Use 5-minute for less liquid markets or if you prefer fewer signals
Never hold past the last hour of trading to avoid overnight risk
Set hard stop times (e.g., exit all positions by 3:45 PM EST)
Dashboard Components Explained
Core Indicators (Circles ●)
MACD (5/13/5)
Green ● = Bullish momentum (MACD histogram positive)
Red ● = Bearish momentum (MACD histogram negative)
Gray ● = No clear momentum
Use: Confirms trend direction and momentum shifts
EMA (9/20/50)
Green ● = Price > EMA9 > EMA20 (uptrend)
Red ● = Price < EMA9 < EMA20 (downtrend)
Gray ● = Choppy/sideways
Use: Identifies the immediate micro-trend
Stoch (5-period Stochastic)
Green ● = Oversold (<20) - potential reversal up
Red ● = Overbought (>80) - potential reversal down
Gray ● = Neutral zone (20-80)
Use: Spots reversal opportunities at extremes
RSI (7-period)
Green ● = Oversold (<30)
Red ● = Overbought (>70)
Gray ● = Neutral
Use: Confirms overbought/oversold conditions
CVD (Cumulative Volume Delta)
Green ● = CVD above its moving average (buying pressure)
Red ● = CVD below its moving average (selling pressure)
Gray ● = Neutral
Use: Shows overall buying vs selling pressure
ΔCVD (Delta CVD - Rate of Change)
Green ● = CVD accelerating upward (buying acceleration)
Red ● = CVD accelerating downward (selling acceleration)
Gray ● = No acceleration
Use: Detects momentum shifts in order flow
Imbal (Order Flow Imbalance)
Green ● = Buy pressure >2x sell pressure
Red ● = Sell pressure >2x buy pressure
Gray ● = Balanced
Use: Identifies extreme one-sided order flow
Vol (Volume Strength)
Green ● = Volume >1.5x average (strong interest)
Red ● = Volume <0.7x average (low interest)
Gray ● = Normal volume
Yellow background = Volume surge (>2x average) - BIG MOVE ALERT
Use: Confirms conviction behind price moves
Tape (Tape Speed)
Green ● = Fast order flow (>1.3x normal)
Red ● = Slow order flow (<0.7x normal)
Gray ● = Normal speed
Yellow background = Very fast tape (>1.5x) - RAPID EXECUTION ALERT
Use: Measures urgency and speed of orders
Key Levels
Support (Supp)
Shows the nearest high-volume support level below current price
Bright Green background = Price is AT support (within 0.3%) - BOUNCE ZONE
Green background = Price above support (healthy)
Red background = Price below support (broken support, now resistance)
Resistance (Res)
Shows the nearest high-volume resistance level above current price
Bright Orange background = Price is AT resistance (within 0.3%) - REJECTION ZONE
Red background = Price below resistance (facing overhead supply)
Green background = Price above resistance (breakout)
These levels update automatically every 3 bars based on volume profile
Entry Signal Components
Score
Displays format: "6L" (6 long indicators) or "4S" (4 short indicators)
Bright Green = 6-7 indicators aligned for long
Light Green = 5 indicators aligned for long
Yellow = 4 indicators aligned (weaker setup)
Gray = No alignment
Red/Orange colors = Same scale for short setups
Score of 5+ indicates high-probability setup
SCALP (Main Entry Signal)
BRIGHT GREEN "LONG" = High-quality long scalp (Score 5+)
Green "LONG" = Decent long scalp (Score 4)
BRIGHT ORANGE "SHORT" = High-quality short scalp (Score 5+)
Red "SHORT" = Decent short scalp (Score 4)
Gray "WAIT" = No clear setup - STAY OUT
Entry Strategies
Strategy 1: High-Probability Scalps (Conservative)
When to Enter:
SCALP column shows BRIGHT GREEN "LONG" or BRIGHT ORANGE "SHORT"
Score is 5 or higher
Vol or Tape has yellow background (volume surge)
Example Long Setup:
SCALP = BRIGHT GREEN "LONG"
Score = 6L
Vol = Yellow background
Price AT Support (bright green Supp cell)
EMA, MACD, CVD, ΔCVD, Imbal all green
Entry: Enter immediately on next candle
Target: 0.5-1% move or resistance level
Stop: Below support or -0.3%
Hold Time: 2-10 minutes
Strategy 2: Momentum Scalps (Aggressive)
When to Enter:
Tape has yellow background (fast tape)
Vol has yellow background (volume surge)
ΔCVD is green (for longs) or red (for shorts)
Imbal shows strong imbalance in your direction
Score is 4+
Example Short Setup:
Tape & Vol = Yellow backgrounds
ΔCVD = Red, Imbal = Red
Price AT Resistance (bright orange)
Score = 5S
Entry: Enter immediately
Target: Quick 0.3-0.7% move
Stop: Tight -0.2%
Hold Time: 1-5 minutes
Strategy 3: Reversal Scalps (Mean Reversion)
When to Enter:
Stoch shows oversold (green) or overbought (red)
RSI confirms the extreme
Price is AT Support (for longs) or AT Resistance (for shorts)
ΔCVD and Imbal start reversing direction
Score is 4+
Example Long Setup:
Stoch = Green (oversold)
RSI = Green (oversold)
Supp = Bright green (at support)
ΔCVD turns green
Imbal turns green
Score = 4L or 5L
Entry: Wait for confirmation candle
Target: Move back to EMA9 or mid-range
Stop: Below the low
Hold Time: 3-8 minutes
Large Order Detection Usage
Diamond Signals
Green diamonds below bar = Large buy orders (institutional buying)
Red diamonds above bar = Large sell orders (institutional selling)
Size matters: Larger diamonds = larger order flow
How to Use with Dashboard
Confirmation Entries
Dashboard shows "LONG" signal
Green diamond appears
Enter immediately - institutions are buying
Divergence Alerts (CAUTION)
Dashboard shows "LONG" signal
RED diamond appears (institutions selling)
DO NOT ENTER - conflicting order flow
Cluster Patterns
Multiple green diamonds in row = Strong accumulation, stay long
Multiple red diamonds in row = Strong distribution, stay short
Alternating colors = Chop, avoid trading
Risk Management Rules
Position Sizing
Risk 0.5-1% of account per scalp
Maximum 3 concurrent positions
Reduce size after 2 consecutive losses
Stop Loss Guidelines
Tight stops: 0.2-0.3% for 1-2 min charts
Standard stops: 0.3-0.5% for 5 min charts
Always use stop loss - no exceptions
Place stops below support (longs) or above resistance (shorts)
Take Profit Targets
Target 1: 0.3-0.5% (take 50% off)
Target 2: 0.7-1% (take remaining 50%)
Move stop to breakeven after Target 1 hit
Trail stop if Score remains high
Time-Based Exits
Exit immediately if:
SCALP changes from LONG/SHORT to WAIT
Score drops below 3
Large diamond appears in opposite direction
Maximum hold time: 15 minutes (even if profitable)
Hard exit time: 30 minutes before market close
Trading Sessions
Best Times to Scalp
High-Liquidity Sessions
9:30-11:00 AM EST (Market open, highest volume)
2:00-3:30 PM EST (Afternoon session, good moves)
Avoid
11:30 AM-1:30 PM EST (Lunch, low volume)
Last 30 minutes (unpredictable, don't initiate new trades)
News releases (wait 5 minutes for volatility to settle)
Common Patterns & Setups
The Perfect Storm (Highest Probability)
Score = 6L or 7L
SCALP = BRIGHT GREEN
Vol + Tape = Yellow backgrounds
Green diamond appears
Price AT Support
Win rate: ~70-80%
The Fade Setup (Counter-Trend)
Price hits resistance (bright orange)
Stoch + RSI overbought (red)
Red diamond appears
CVD starts turning red
SCALP shows "SHORT"
Win rate: ~60-70%
The Breakout Continuation
Price breaks resistance (Res turns green)
EMA, MACD green
Vol surge (yellow)
Multiple green diamonds
SCALP = "LONG"
Win rate: ~65-75%
Warning Signs - DO NOT TRADE
Red Flags
❌ SCALP shows "WAIT"
❌ Score below 3
❌ Vol and Tape both gray (no volume)
❌ Conflicting signals (dashboard says LONG but red diamonds appearing)
❌ Alternating green/red circles (choppy market)
❌ Support and Resistance very close together (tight range)
Market Conditions to Avoid
Low volume periods
Major news releases (first 5 minutes after)
First 2 minutes after market open
Wide spreads
Consecutive losing trades (take a break after 2 losses)
Quick Reference Checklist
Before Taking ANY Trade:
☑ SCALP shows LONG or SHORT (not WAIT)
☑ Score is 4 or higher
☑ Vol or Tape shows activity
☑ No conflicting diamond signals
☑ Stop loss level identified
☑ Target profit level identified
☑ Not in restricted time periods
After Entering:
☑ Set stop loss immediately
☑ Set profit targets
☑ Watch SCALP column - exit if changes to WAIT
☑ Watch for opposite-colored diamonds
☑ Move stop to breakeven after first target
☑ Exit all by market close
Advanced Tips
Scalping Psychology
Be patient: Wait for Score 5+ setups
Be decisive: When signal appears, act immediately
Be disciplined: Follow your stop loss always
Be flexible: Exit quickly if dashboard reverses
Optimization
Backtest on your specific instrument
Adjust RSI/Stoch levels for your market
Fine-tune volume thresholds
Keep a trade journal to track which setups work best
Multi-Timeframe Confirmation
Use 5-min dashboard as "trend filter"
Take 1-min trades only in direction of 5-min SCALP signal
Increases win rate by ~10-15%
Troubleshooting
Q: Dashboard shows WAIT most of the time
Normal - scalping is about patience. Quality > Quantity
3-8 good setups per day is excellent
Q: Too many false signals
Increase minimum Score requirement to 5 or 6
Only trade with volume surge (yellow backgrounds)
Add large order detection confirmation
Q: Signals too slow
You may be on too high a timeframe
Try 1-minute chart for faster signals
Ensure real-time data feed is active
Q: Support/Resistance not updating
Normal - updates every 3 bars
If completely stuck, remove and re-add indicator
Summary
This scalping system works best when:
✅ Multiple indicators align (Score 5+)
✅ Volume and tape speed confirm the move
✅ Order flow (diamonds) confirms direction
✅ Price is at key levels (support/resistance)
✅ You manage risk strictly
✅ You exit before market close
The golden rule: When SCALP says WAIT, you WAIT. Discipline beats frequency.
ICMR — Chrono Maker Range (v12.7.1)✅ ICMR — Chrono Maker Range (v12.7) — Description (Balanced Technical + Friendly)
ICMR — Chrono Maker Range is a hybrid market-structure tool designed to help traders clearly identify directional bias and high-quality breakouts using either Higher-Timeframe (HTF) ranges or Initial Balance (IB) ranges. The indicator automatically builds the range, colors candles by market state, and highlights breakout signals using smart filters to reduce noise.
The concept is simple:
Price is either above the range (Bullish), inside the range (Neutral), or below the range (Bearish)—and ICMR keeps this state stable and easy to follow.
🔷 How It Works
ICMR constructs a tradable range using one of two modes:
1) HTF Range Mode
Pulls the High / Low from a higher timeframe (e.g., Daily, 4H).
You can choose:
Previous HTF candle → stable, non-moving range
Current HTF candle → dynamic, expands until HTF close
Perfect for tracking market bias across smaller timeframes.
2) Initial Balance (IB) Mode
Builds the range from the first N minutes of the session (e.g., first 60 minutes).
After the IB period ends, the range locks and becomes the day’s framework.
🔷 Market State Logic
The indicator evaluates where price is relative to the range and classifies the market into:
✅ Bullish → price breaks above the range
⚪ Neutral → price stays inside
❌ Bearish → price breaks below
You can optionally enable an EMA Trend Filter (fast vs slow EMA) to ensure breakouts align with trend direction.
🔷 Smart Signal System
ICMR includes compact signal shapes (triangles/circles), but only when conditions are strong:
✔️ Minimum breakout distance beyond the range
✔️ Candle body must exceed a % of ATR
✔️ Optional volume expansion filter
✔️ Cooldown between signals to avoid over-trading
✔️ Option to trigger signals only on state flips
These filters help keep signals actionable and reduce noise.
🔷 Visual Tools
HTF/IB Range High, Range Low, Midline
Optional shaded box
Segmented extend-right lines that reset when HTF/IB changes
Bar coloring (Bull/Neutral/Bear)
Soft background tint (optional)
Built-in info panel with range & filter stats
Alerts on state flips
Everything is designed to keep the chart clean and readable.
🔷 Presets
The indicator includes two ready-to-use profiles:
Conservative
Stable HTF ranges, confirmed breaks, trend-filtered signals, and fewer alerts.
Aggressive
Dynamic HTF ranges, more flexible break rules, and more frequent signals.
Each preset can be fully customized.
🔷 How Traders Use It
Intraday traders use HTF ranges (D, 4H) for bias on 1m–15m charts.
Day traders use IB to track the opening range and breakout opportunities.
Swing traders use conservative settings to filter false moves.
Scalpers enable aggressive mode with ATR/volume filters.
Session SFPThis script is a powerful, multi-timeframe tool designed to identify high-probability Swing Failure Patterns (SFPs) at key historical levels.
Instead of looking for traditional "pivots" (like a 3-bar swing), this indicator finds the actual high and low of a previous higher-timeframe (HTF) bar (e.g., the previous weekly high/low) and waits for a lower-timeframe (LTF) candle to sweep that level and fail.
This allows you to spot liquidity sweeps and potential reversals at significant, structural price points.
How It Works
The indicator's logic is based on a simple, two-timeframe process:
Level Detection: First, it finds the high and low of the previous bar on your chosen "Level Timeframe" (e.g., W for Weekly, D for Daily). It plots these as small 'x' markers on your chart.
SFP Identification: Second, it watches price action on a lower "SFP Timeframe" (e.g., 240 for 4H). A potential SFP is identified when a candle's wick sweeps above a key high or below a key low.
Confirmation: The SFP is only confirmed after the SFP candle closes back below the high (for a bearish SFP) or above the low (for a bullish SFP). It then waits for a set number of "Confirmation Bars" to pass. If price does not close back over the level during this window, the signal is locked in, and a label is printed.
How to Use (Key Settings)
Level Timeframe (Most Important): This is the timeframe for the levels you want to trade. Set this to W to find SFPs of the previous weekly high/low. Set it to D to find SFPs of the previous daily high/low.
SFP Timeframe: This is the timeframe you want to use to find the SFP candle itself. This should be lower than your Level Timeframe (e.g., 240 or 60).
Level Lookback: This controls how many old levels the script will track. A value of 10 on a W Level Timeframe will track the highs and lows of the last 10 weeks.
Confirmation Bars: This is your "patience" filter. It's the number of SFP Timeframe bars that must close without reclaiming the level after the SFP. A value of 0 will confirm the SFP immediately on the candle's close.
Enable Wick % Filter: A quality filter. If checked, this ensures the SFP candle's rejection wick is a significant percentage of the candle's total range.
Chart Visuals
'x' Markers: These are the historical highs and lows from your "Level Timeframe". You can turn these on or off in the settings.
SFP Label: When an SFP is fully confirmed, a label (Bearish SFP or Bullish SFP) will appear, detailing the level that was swept and the timeframes used.
SFP Line: A solid horizontal line is drawn from the 'x' marker to the SFP candle to highlight the sweep.
Colored Boxes (Optional): If you are viewing a chart timeframe lower than your "SFP Timeframe", you can enable background boxes to highlight the exact SFP candle and its confirmation bars.
Realtime Squeeze Box [CHE] Realtime Squeeze Box — Detects lowvolatility consolidation periods and draws trimmed price range boxes in realtime to highlight potential breakout setups without clutter from outliers.
Summary
This indicator identifies "squeeze" phases where recent price volatility falls below a dynamic baseline threshold, signaling potential energy buildup for directional moves. By requiring a minimum number of consecutive bars in squeeze, it reduces noise from fleeting dips, making signals more reliable than simple threshold crosses. The core innovation is realtime box visualization: during active squeezes, it builds and updates a box capturing the price range while ignoring extreme values via quantile trimming, providing a cleaner view of consolidation bounds. This differs from static volatility bands by focusing on trimmed ranges and suppressing overlapping boxes, which helps traders spot genuine setups amid choppy markets. Overall, it aids in anticipating breakouts by combining volatility filtering with visual containment of price action.
Motivation: Why this design?
Traders often face whipsaws during brief volatility lulls that mimic true consolidations, leading to premature entries, or miss setups because standard volatility measures lag in adapting to changing market regimes. This design addresses that by using a hold requirement on consecutive lowvolatility bars to denoise signals, ensuring only sustained squeezes trigger visuals. The core idea—comparing rolling standard deviation to a smoothed baseline—creates a responsive yet stable filter for lowenergy periods, while the trimmed box approach isolates the core price cluster, making it easier to gauge breakout potential without distortion from spikes.
What’s different vs. standard approaches?
Reference baseline: Traditional squeeze indicators like the Bollinger Band Squeeze or TTM Squeeze rely on fixed multiples of bands or momentum oscillators crossing zero, which can fire on isolated bars or ignore range compression nuances.
Architecture differences:
Realtime box construction that updates barbybar during squeezes, using arrays to track and trim price values.
Quantilebased outlier rejection to define box bounds, focusing on the bulk of prices rather than full range.
Overlap suppression logic that skips redundant boxes if the new range intersects heavily with the prior one.
Hold counter for consecutive bar validation, adding persistence before signaling.
Practical effect: Charts show fewer, more defined orange boxes encapsulating tight price action, with a horizontal line extension marking the midpoint postsqueeze—visibly reducing clutter in sideways markets and highlighting "coiled" ranges that standard plots might blur with full highs/lows. This matters for quicker visual scanning of multitimeframe setups, as boxes selflimit to recent history and avoid piling up.
How it works (technical)
The indicator starts by computing a rolling average and standard deviation over a userdefined length on the chosen source price series. This deviation measure is then smoothed into a baseline using either a simple or exponential average over a longer window, serving as a reference for normal volatility. A squeeze triggers when the current deviation dips below this baseline scaled by a multiplier less than one, but only after a minimum number of consecutive bars confirm it, which resets the counter on breaks.
Upon squeeze start, it clears a buffer and begins collecting source prices barbybar, limited to the first few bars to keep computation light. For visualization, if enabled, it sorts the buffer and finds a quantile threshold, then identifies the minimum value at or below that threshold to set upper and lower box bounds—effectively clamping the range to exclude tails above the quantile. The box draws from the start bar to the current one, updating its right edge and levels dynamically; if the new bounds overlap significantly with the last completed box, it suppresses drawing to avoid redundancy.
Once the hold limit or squeeze ends, the box freezes: its final bounds become the last reference, a midpoint line extends rightward from the end, and a tiny circle label marks the point. Buffers and states reset on new squeezes, with historical boxes and lines capped to prevent overload. All logic runs on every bar but uses confirmed historical data for calculations, with realtime updates only affecting the active box's position—no future peeking occurs. Initialization seeds with null values, building states progressively from the first bars.
Parameter Guide
Source: Selects the price series (e.g., close, hl2) for deviation and box building; influences sensitivity to wicks or bodies. Default: close. Tradeoffs/Tips: Use hl2 for balanced range view in volatile assets; stick to close for pure directional focus—test on your timeframe to avoid oversmoothing trends.
Length (Mean/SD): Sets window for average and deviation calculation; shorter values make detection quicker but noisier. Default: 20. Tradeoffs/Tips: Increase to 30+ for stability in higher timeframes, reducing false starts; below 10 risks overreacting to singlebar noise.
Baseline Length: Defines smoothing window for the deviation baseline; longer periods create a steadier reference, filtering regime shifts. Default: 50. Tradeoffs/Tips: Pair with Length at 1:2 ratio for calm markets; shorten to 30 if baselines lag during fast volatility drops, but watch for added whips.
Squeeze Multiplier (<1.0): Scales the baseline downward to set the squeeze threshold; lower values tighten criteria for rarer, stronger signals. Default: 0.8. Tradeoffs/Tips: Tighten to 0.6 for highvol assets like crypto to cut noise; loosen to 0.9 in forex for more frequent but shallower setups—balances hit rate vs. depth.
Baseline via EMA (instead of SMA): Switches baseline smoothing to exponential for faster adaptation to recent changes vs. equalweighted simple average. Default: false. Tradeoffs/Tips: Enable in trending markets for quicker baseline drops; disable for uniform history weighting in rangebound conditions to avoid overreacting.
SD: Sample (len1) instead of Population (len): Adjusts deviation formula to divide by length minus one for smallsample bias correction, slightly inflating values. Default: false. Tradeoffs/Tips: Use sample in short windows (<20) for more conservative thresholds; population suits long looks where bias is negligible, keeping signals tighter.
Min. Hold Bars in Squeeze: Requires this many consecutive squeeze bars before confirming; higher denoise but may clip early setups. Default: 1. Tradeoffs/Tips: Bump to 35 for intraday to filter ticks; keep at 1 for swings where quick consolidations matter—trades off timeliness for reliability.
Debug: Plot SD & Threshold: Toggles lines showing raw deviation and threshold for visual backtesting of squeeze logic. Default: false. Tradeoffs/Tips: Enable during tuning to eyeball crossovers; disable live to declutter—great for verifying multiplier impact without alerts.
Tint Bars when Squeeze Active: Overlays semitransparent color on bars during open box phases for quick squeeze spotting. Default: false. Tradeoffs/Tips: Pair with low opacity for subtlety; turn off if using boxes alone, as tint can obscure candlesticks in dense charts.
Tint Opacity (0..100): Controls background tint strength during active squeezes; higher values darken for emphasis. Default: 85. Tradeoffs/Tips: Dial to 60 for light touch; max at 100 risks hiding price action—adjust per chart theme for visibility.
Stored Price (during Squeeze): Price series captured in the buffer for box bounds; defaults to source but allows customization. Default: close. Tradeoffs/Tips: Switch to high/low for wider boxes in gappy markets; keep close for midline focus—impacts trim effectiveness on outliers.
Quantile q (0..1): Fraction of sorted prices below which tails are cut; higher q keeps more data but risks including spikes. Default: 0.718. Tradeoffs/Tips: Lower to 0.5 for aggressive trim in noisy assets; raise to 0.8 for fuller ranges—tune via debug to match your consolidation depth.
Box Fill Color: Sets interior shade of squeeze boxes; semitransparent for layering. Default: orange (80% trans.). Tradeoffs/Tips: Soften with more transparency in multiindicator setups; bold for standalone use—ensures boxes pop without overwhelming.
Box Border Color: Defines outline hue and solidity for box edges. Default: orange (0% trans.). Tradeoffs/Tips: Match fill for cohesion or contrast for edges; thin width keeps it clean—helps delineate bounds in zoomed views.
Keep Last N Boxes: Limits historical boxes/lines/labels to this count, deleting oldest for performance. Default: 10. Tradeoffs/Tips: Increase to 50 for weekly reviews; set to 0 for unlimited (risks lag)—balances history vs. speed on long charts.
Draw Box in Realtime (build/update): Enables live extension of boxes during squeezes vs. waiting for end. Default: true. Tradeoffs/Tips: Disable for confirmedonly views to mimic backtests; enable for proactive trading—adds minor repaint on live bars.
Box: Max First N Bars: Caps buffer collection to initial squeeze bars, freezing after for efficiency. Default: 15. Tradeoffs/Tips: Shorten to 510 for fast intraday; extend to 20 in dailies—prevents bloated arrays but may truncate long squeezes.
Reading & Interpretation
Squeeze phases appear as orange boxes encapsulating the trimmed price cluster during lowvolatility holds—narrow boxes signal tight consolidations, while wider ones indicate looser ranges within the threshold. The box's top and bottom represent the quantilecapped high and low of collected prices, with the interior fill shading the containment zone; ignore extremes outside for "true" bounds. Postsqueeze, a solid horizontal line extends right from the box's midpoint, acting as a reference level for potential breakout tests—drifting prices toward or away from it can hint at building momentum. Tiny orange circles at the line's start mark completion points for easy scanning. Debug lines (if on) show deviation hugging or crossing the threshold, confirming hold logic; a persistent hug below suggests prolonged calm, while spikes above reset counters.
Practical Workflows & Combinations
Trend following: Enter long on squeezeend close above the box top (or midpoint line) confirmed by higher high in structure; filter with rising 50period average to avoid countertrend traps. Use boxes as support/resistance proxies—short below bottom in downtrends.
Exits/Stops: Trail stops to the box midpoint during postsqueeze runs for conservative holds; go aggressive by exiting on retest of opposite box side. If debug shows repeated threshold grazes, tighten stops to curb drawdowns in ranging followups.
Multiasset/MultiTF: Defaults work across stocks, forex, and crypto on 15min+ frames; scale Length proportionally (e.g., x2 on hourly). Layer with highertimeframe boxes for confluence—e.g., daily squeeze + 1H box for entry timing. (Unknown/Optional: Specific multiTF scaling recipes beyond proportional adjustment.)
Behavior, Constraints & Performance
Repaint/confirmation: Core calculations use historical closes, confirming on bar close; active boxes repaint their right edge and levels live during squeezes if enabled, but freeze irrevocably on hold limit or end—mitigates via barbybar buffer adds without future leaks. No lookahead indexes.
security()/HTF: None used, so no external timeframe repaints; all native to chart resolution.
Resources: Caps at 300 boxes/lines/labels total; small arrays (up to 20 elements) and short loops in sorting/minfinding keep it light—suitable for 10k+ bar charts without throttling. Persistent variables track state across bars efficiently.
Known limits: May lag on ultrasharp volatility spikes due to baseline smoothing; gaps or thin markets can skew trims if buffer hits cap early; overlaps suppress visuals but might hide chained squeezes—(Unknown/Optional: Edge cases in nonstandard sessions).
Sensible Defaults & Quick Tuning
Start with defaults for most liquid assets on 1Hdaily: Length 20, Multiplier 0.8, Hold 1, Quantile 0.718—yields balanced detection without excess noise. For too many false starts (choppy charts), increase Hold to 3 and Baseline Length to 70 for stricter confirmation, reducing signals by 3050%. If squeezes feel sluggish or miss quick coils, shorten Length to 14 and enable EMA baseline for snappier adaptation, but monitor for added flips. In highvol environments like options, tighten Multiplier to 0.6 and Quantile to 0.6 to focus on core ranges; reverse for calm pairs by loosening to 0.95. Always backtest tweaks on your asset's history.
What this indicator is—and isn’t
This is a volatilityfiltered visualization tool for spotting and bounding consolidation phases, best as a signal layer atop price action and trend filters—not a standalone predictor of direction or strength. It highlights setups but ignores volume, momentum, or news context, so pair with discreteness rules like higher highs/lows. Never use it alone for entries; always layer risk management, such as 12% stops beyond box extremes, and position sizing based on account drawdown tolerance.
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 HeikinAshi, Renko, Kagi, PointandFigure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
MPO4 Lines – Modal Engine█ OVERVIEW
MPO4 Lines – Modal Engine is an advanced multi-line modal oscillator for TradingView, designed to detect momentum shifts, trend strength, and reversal points through candle-based pressure analysis with multiple fast lines and a reference slow line. It features divergence detection on Fast Line A, overbought/oversold return signals, dynamic coloring modes, and layered gradient visualizations for enhanced clarity and decision-making.
█ CONCEPT
The indicator is built upon the Market Pressure Oscillator (MPO) and serves as its expanded evolution, aimed at enabling broader market analysis through multiple lines with varying parameters. It calculates modal pressure using candle body size and direction, weighted against average body size over a lookback period, then normalized and smoothed via EMA. It generates four distinct oscillator lines: a heavily smoothed Slow Line (trend reference), two Fast Lines (A & B) for momentum and support/resistance, and an optional Line 4 for additional confirmation. Divergence is calculated solely on Fast Line A, with visual gradients between lines and bands for intuitive interpretation.
█ WHY USE IT?
- Multi-Layer Momentum: Combines slow trend reference with dual fast lines for precise entry/exit timing.
- Divergence Precision: Bullish/bearish divergences on Fast Line A with labeled confirmation.
- OB/OS Return Signals: Clear buy/sell markers when Fast Line A exits oversold/overbought zones.
- Dynamic Visuals: Gradient fills, line-to-line shading, and band gradients for instant market state recognition.
- Flexible Coloring: Slow Line color by direction or zero-position; fast lines by sign.
- Full Customization: Independent lengths, smoothing, visibility, and transparency — by adjusting the lengths of different lines, you can tailor results for various strategies; for example, enabling Line 4 and tuning its length allows trading based on crossovers between different lines.
█ HOW IT WORKS?
- Candle Pressure Calculation: Body = math.abs(close - open); avgBody = ta.sma(body, len). Direction = +1 (bull), –1 (bear), 0 (neutral). Weight = body / avgBody. Contribution = direction × weight.
- Rolling Sum & Normalization: Sums contributions over lookback, normalizes to ±100 scale (÷ (len × 2) × 100).
Smoothing: Applies primary EMA (smoothLen), with extra EMA on Slow Line for stability.
Line Structure:
- Slow Line = calcCPO(len1=20, smoothLen1=5) → extra EMA (5)
- Fast Line A = calcCPO(len2=6, smoothLen2=7)
- Fast Line B = calcCPO(len3=6, smoothLen3=10)
- Line 4 = calcCPO(len4=14, smoothLen4=1)
Divergence Detection: Uses ta.pivothigh/low on price and Fast Line A (pivotLength left/right). Bullish: lower price low + higher osc low. Bearish: higher price high + lower osc high. Valid within 5–60 bar window.
Signals:
- Buy: Fast Line A crosses above oversold (–30)
- Sell: Fast Line A crosses below overbought (+30)
- Slow Line color flip (direction or zero-cross)
- Divergence labels ("Bull" / "Bear")
- Band Coloring as Momentum Signal:
When Fast Line A ≤ Fast Line B → Overbought band turns red (bearish pressure building)
When Fast Line A > Fast Line B → Oversold band turns green (bullish pressure building) This dynamic coloring serves as visual confirmation of momentum shift following fast line crossovers
Visualization:
- Gradients: Fast B → Zero (multi-layer fade), Fast A ↔ B fill, OB/OS bands
- Dynamic colors: Green/red based on sign or trend
- Zero line + dashed OB/OS thresholds
Alerts: Trigger on OB/OS returns, Slow Line changes, and divergences.
█ SETTINGS AND CUSTOMIZATION
- Line Visibility: Toggle Slow, Fast A, Fast B, Line 4 independently.
Line Lengths:
- Slow Line: Base (20), Primary EMA (5), Extra EMA (5)
- Fast A: Lookback (6), EMA (7)
- Fast B: Lookback (6), EMA (10)
- Line 4: Lookback (14), EMA (1)
- Slow Line Coloring Mode: “Direction” (trend-based) or “Position vs Zero”.
- Bands & Thresholds: Overbought (+30), Oversold (–30), step 0.1.
- Signals: Enable Fast A OB/OS return markers (default: on).
- Divergence: Enable/disable, Pivot Length (default: 2, min 1).
- Colors & Appearance: Full control over bullish/bearish hues for all lines, zero, bands, divergence, and text.
Gradients & Transparency:
- Fast B → Zero: 75 (default)
- Fast A ↔ B fill: 50
- Band gradients: 40
- Toggle each gradient independently
█ USAGE EXAMPLES
The indicator allows users to configure various strategies manually, though no built-in alerts exist for them. Entry signals can include color of fast lines, crossovers between different lines, alignment of colors across lines, or consistency in direction.
- Trend Confirmation: Slow Line above zero + green = bullish bias; below + red = bearish.
- Entry Timing: Buy on Fast A crossing above –30 (circle marker), especially if Slow Line is rising or near zero.
- Reversal Setup: Bullish divergence (“Bull” label) + Fast A in oversold + green gradient band = high-probability long.
- Scalping: Fast A vs Fast B crossover in direction of Slow Line trend.
- Noise Reduction: Increase extraSmoothLen on Slow Line
█ USER NOTES
- Best combined with volume, support/resistance, or trend channels.
- Adjust lookback and smoothing to asset volatility.
- Divergence delay = pivotLength; plan entries accordingly.
Analog Flow [KedArc Quant]Overview
AnalogFlow is an advanced analogue based market projection engine that reconstructs future price tendencies by matching current price behavior to historical analogues in the same instrument. Instead of using traditional indicators such as moving averages, RSI, or regression, AnalogFlow applies pattern vector similarity analysis - a data driven technique that identifies historically similar sequences and aggregates their subsequent movements into a smooth, forward looking curve.
Think of it as a market memory system:
If the current pattern looks like one we have seen before, how did price move afterward?
Why AnalogFlow Is Unique
1. Pattern centric - it does not rely on any standard indicator formula; it directly analyzes price movement vectors.
2. Adaptive - it learns from the same instrument's past behavior, making it self calibrating to volatility and regime shifts.
3. Non repainting - the projection is generated on the latest completed bar and remains fixed until new data is available.
4. Noise resistant - the EMA Blend engine smooths the projected trajectory, reducing random variance between analogues.
Inputs and Configuration
Pattern Bars
Number of bars in the reference pattern window: 40
Projection Bars
Number of bars forward to project: 30
Search Depth
Number of bars back to look for matching analogues: 600
Distance Metric
Comparison method: Euclidean, Manhattan, or Cosine (default Euclidean)
Matches
Number of top analogues to blend (1-5): Top 3
Build Mode
Projection type: Cumulative, MeanStep, or EMA Blend (default EMA Blend)
EMA Blend Length
Smoothness of the projected path: 15
Normalize Pattern
Enable Z score normalization for shape matching: true
Dissimilarity Mode
If true, finds inverse analogues for mean reversion analysis: false
Line Color and Width
Style settings for projection curve: Blue, width 2
How It Works with Past Data
1. The system builds a memory bank of patterns from the last N bars based on the scanDepth value.
2. It compares the latest Pattern Bars segment to each historical segment.
3. It selects the Top K most similar or dissimilar analogues.
4. For each analogue, it retrieves what happened after that pattern historically.
5. It averages or smooths those forward moves into a single composite forecast curve.
6. The forecast (blue line) is drawn ahead of the current candle using line.new with no repainting.
Output Explained
Blue Path
The weighted mean future trajectory based on historical analogues.
Smoother when EMA Blend mode is enabled.
Flat Section
Indicates low directional consensus or equilibrium across analogues.
Upward or Downward Slope
Represents historical tendency toward continuation or reversal following similar conditions.
Recommended Timeframes
Scalping / Short Term
1m - 5m : Short winLen (20-30), small ahead (10-15)
Swing Trading
15m - 1h : Balanced settings (winLen 40-60, ahead 20-30)
Positional / Multi Day
4h - 1D : Large windows (winLen 80-120, ahead 30-50)
Instrument Compatibility
Works seamlessly on:
Stocks and ETFs
Indices
Cryptocurrency
Commodities (Gold, Crude, etc.)
Futures and F&O (both intraday and positional)
Forex
No symbol specific calibration needed. It self adapts to volatility.
How Traders Can Use It
Forecast Context
Identify likely short term price path or drift direction.
Reversal Detection
Flip seekOpp to true for mean reversion pattern analysis.
Scenario Comparison
Observe whether the current regime tends to continue or stall.
Momentum Confirmation
Combine with trend tools such as EMA or MACD for directional bias.
Backtesting Support
Compare projected path versus realized price to evaluate reliability.
FAQ
Q1. Does AnalogFlow repaint?
No. It calculates only once per completed bar and projects forward. The future path remains static until a new bar closes.
Q2. Is it a neural network or AI model?
Not in the machine learning sense. It is a deterministic analogue matching engine using statistical distance metrics.
Q3. Why does the projection sometimes flatten?
That means similar historical setups had no clear consensus in direction (neutral expectation).
Q4. Can I use it for live trading signals?
AnalogFlow is not a signal generator. It provides probabilistic context for upcoming movement.
Q5. Does higher scanDepth improve accuracy?
Up to a point. More depth gives more analogues, but too much can dilute recency. Try 400 to 800.
Glossary
Analogue
A past pattern similar to the current price behavior.
Distance Metric
Mathematical formula for pattern similarity.
Step Vector
Difference between consecutive closing prices.
EMA Blend
Exponential smoothing of the projected path.
Cumulative Mode
Adds sequential historical deltas directly.
Z Score Normalization
Rescaling to mean 0 and variance 1 for shape comparison.
Summary
AnalogFlow converts the market's historical echoes into a structured, statistically weighted forward projection. It gives traders a contextual roadmap, not a signal, showing how similar past setups evolved and allowing better informed entries, exits, and scenario planning across all asset classes.
Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and proper risk management when applying this strategy.
MCL RSI Conflux v2.5 — Multi-Timeframe Momentum & Z-Score Full Description
Overview
The MCL RSI Conflux v2.5 is a multi-timeframe momentum model that integrates daily, weekly, and monthly RSI values into a unified composite. It extends the classical RSI framework with adaptive overbought/oversold thresholds and statistical normalization (Z-score confluence).
This combination allows traders to visualize cross-timeframe alignment, identify synchronized momentum shifts, and detect exhaustion zones with higher statistical confidence.
Methodology
The script extracts RSI data from three major time horizons:
Daily RSI (short-term momentum)
Weekly RSI (intermediate trend)
Monthly RSI (macro bias)
Each RSI is optionally smoothed, weighted, and aggregated into a Composite RSI.
A Z-score transformation then measures how far each RSI deviates from its historical mean, revealing when momentum strength is statistically extreme or aligned across timeframes.
Key Features
Multi-Timeframe RSI Engine – Computes RSI across D/W/M intervals with individual weighting controls.
Adaptive Overbought/Oversold Bands – Automatically adjusts OB/OS thresholds based on rolling volatility (standard deviation of daily RSI).
Composite RSI Score – Weighted consensus RSI that represents total market momentum.
Z-Score Confluence Analysis – Identifies when all three timeframes are statistically synchronized.
Z-Composite Histogram – Displays aggregated Z-score strength around the midline (50).
Divergence Detection – Flags confirmed pivot-based bull and bear divergences on the daily RSI.
Dynamic Gradient Background – Shifts from red to green based on composite momentum regime.
Customizable Control Panel – Displays RSI values, Z-scores, state, and adaptive bands for each timeframe.
Integrated Alerts – For crossovers, risk-on/off thresholds, alignment, and Z-confluence events.
Interpretation
All RSI values above 50: multi-timeframe bullish alignment.
All RSI values below 50: multi-timeframe bearish alignment.
Composite RSI > 60: risk-on environment; momentum expansion.
Composite RSI < 45: risk-off environment; momentum contraction.
Adaptive OB/OS hits: potential exhaustion or mean reversion setup.
Green Z-ribbon: all Z-scores positive and aligned (statistical confirmation).
Red Z-ribbon: all Z-scores negative and aligned (broad market weakness).
Divergences: short-term warning signals against the prevailing momentum bias.
Practical Application
Use the Composite RSI as a global momentum gauge for position bias.
Trade only in the direction of higher-timeframe alignment (avoid countertrend RSI).
Combine Z-ribbon confirmation with Composite RSI crosses to filter noise.
Use divergence labels and adaptive thresholds for risk reduction or exit timing.
Ideal for swing traders and macro momentum models seeking trend synchronization filters.
Recommended Settings
Market Mode k-Band Lookback Use Case
Stocks / ETFs Adaptive 0.85 200 Medium-term rotation filter
Crypto Adaptive 1.00 150 Volatility-responsive swing filter
Commodities Fixed 70/30 100 Mean reversion model
Alerts Included
Daily RSI crossed above/below Weekly RSI
Composite RSI > Risk-On threshold
Composite RSI < Risk-Off threshold
All RSI aligned above/below 50
Z-Score Conformity (All positive or all negative)
Overbought/Oversold triggers
Author’s Note
This indicator was designed for research and systematic confluence analysis within Mongoose Capital Labs.
It is not financial advice and should be used in combination with independent risk assessment, volume confirmation, and higher-timeframe context.
