Smart MTF S/R Levels[BullByte]
Smart MTF S/R Levels
Introduction & Motivation
Support and Resistance (S/R) levels are the backbone of technical analysis. However, most traders face two major challenges:
Manual S/R Marking: Drawing S/R levels by hand is time-consuming, subjective, and often inconsistent.
Multi-Timeframe Blind Spots: Key S/R levels from higher or lower timeframes are often missed, leading to surprise reversals or missed opportunities.
Smart MTF S/R Levels was created to solve these problems. It is a fully automated, multi-timeframe, multi-method S/R detection and visualization tool, designed to give traders a complete, objective, and actionable view of the market’s most important price zones.
What Makes This Indicator Unique?
Multi-Timeframe Analysis: Simultaneously analyzes up to three user-selected timeframes, ensuring you never miss a critical S/R level from any timeframe.
Multi-Method Confluence: Integrates several respected S/R detection methods—Swings, Pivots, Fibonacci, Order Blocks, and Volume Profile—into a single, unified system.
Zone Clustering: Automatically merges nearby levels into “zones” to reduce clutter and highlight areas of true market consensus.
Confluence Scoring: Each zone is scored by the number of methods and timeframes in agreement, helping you instantly spot the most significant S/R areas.
Reaction Counting: Tracks how many times price has recently interacted with each zone, providing a real-world measure of its importance.
Customizable Dashboard: A real-time, on-chart table summarizes all key S/R zones, their origins, confluence, and proximity to price.
Smart Alerts: Get notified when price approaches high-confluence zones, so you never miss a critical trading opportunity.
Why Should a Trader Use This?
Objectivity: Removes subjectivity from S/R analysis by using algorithmic detection and clustering.
Efficiency: Saves hours of manual charting and reduces analysis fatigue.
Comprehensiveness: Ensures you are always aware of the most relevant S/R zones, regardless of your trading timeframe.
Actionability: The dashboard and alerts make it easy to act on the most important levels, improving trade timing and risk management.
Adaptability: Works for all asset classes (stocks, forex, crypto, futures) and all trading styles (scalping, swing, position).
The Gap This Indicator Fills
Most S/R indicators focus on a single method or timeframe, leading to incomplete analysis. Manual S/R marking is error-prone and inconsistent. This indicator fills the gap by:
Automating S/R detection across multiple timeframes and methods
Objectively scoring and ranking zones by confluence and reaction
Presenting all this information in a clear, actionable dashboard
How Does It Work? (Technical Logic)
1. Level Detection
For each selected timeframe, the script detects S/R levels using:
SW (Swing High/Low): Recent price pivots where reversals occurred.
Pivot: Classic floor trader pivots (P, S1, R1).
Fib (Fibonacci): Key retracement levels (0.236, 0.382, 0.5, 0.618, 0.786) over the last 50 bars.
Bull OB / Bear OB: Institutional price zones based on bullish/bearish engulfing patterns.
VWAP / POC: Volume Weighted Average Price and Point of Control over the last 50 bars.
2. Level Clustering
Levels within a user-defined % distance are merged into a single “zone.”
Each zone records which methods and timeframes contributed to it.
3. Confluence & Reaction Scoring
Confluence: The number of unique methods/timeframes in agreement for a zone.
Reactions: The number of times price has touched or reversed at the zone in the recent past (user-defined lookback).
4. Filtering & Sorting
Only zones within a user-defined % of the current price are shown (to focus on actionable areas).
Zones can be sorted by confluence, reaction count, or proximity to price.
5. Visualization
Zones: Shaded boxes on the chart (green for support, red for resistance, blue for mixed).
Lines: Mark the exact level of each zone.
Labels: Show level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Lists all nearby zones with full details.
6. Alerts
Optional alerts trigger when price approaches a zone with confluence above a user-set threshold.
Inputs & Customization (Explained for All Users)
Show Timeframe 1/2/3: Enable/disable analysis for each timeframe (e.g., 15m, 30m, 1h).
Show Swings/Pivots/Fibonacci/Order Blocks/Volume Profile: Select which S/R methods to include.
Show levels within X% of price: Only display zones near the current price (default: 3%).
How many swing highs/lows to show: Number of recent swings to include (default: 3).
Cluster levels within X%: Merge levels close together into a single zone (default: 0.25%).
Show Top N Zones: Limit the number of zones displayed (default: 8).
Bars to check for reactions: How far back to count price reactions (default: 100).
Sort Zones By: Choose how to rank zones in the dashboard (Confluence, Reactions, Distance).
Alert if Confluence >=: Set the minimum confluence score for alerts (default: 3).
Zone Box Width/Line Length/Label Offset: Control the appearance of zones and labels.
Dashboard Size/Location: Customize the dashboard table.
How to Read the Output
Shaded Boxes: Represent S/R zones. The color indicates type (green = support, red = resistance, blue = mixed).
Lines: Mark the precise level of each zone.
Labels: Show the level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Columns include:
Level: Price of the zone
Methods (by TF): Which S/R methods and how many, per timeframe (see abbreviation key below)
Type: Support, Resistance, or Mixed
Confl.: Confluence score (higher = more significant)
React.: Number of recent price reactions
Dist %: Distance from current price (in %)
Abbreviations Used
SW = Swing High/Low (recent price pivots where reversals occurred)
Fib = Fibonacci Level (key retracement levels such as 0.236, 0.382, 0.5, 0.618, 0.786)
VWAP = Volume Weighted Average Price (price level weighted by volume)
POC = Point of Control (price level with the highest traded volume)
Bull OB = Bullish Order Block (institutional support zone from bullish price action)
Bear OB = Bearish Order Block (institutional resistance zone from bearish price action)
Pivot = Pivot Point (classic floor trader pivots: P, S1, R1)
These abbreviations appear in the dashboard and chart labels for clarity.
Example: How to Read the Dashboard and Labels (from the chart above)
Suppose you are trading BTCUSDT on a 15-minute chart. The dashboard at the top right shows several S/R zones, each with a breakdown of which timeframes and methods contributed to their detection:
Resistance zone at 119257.11:
The dashboard shows:
5m (1 SW), 15m (2 SW), 1h (3 SW)
This means the level 119257.11 was identified as a resistance zone by one swing high (SW) on the 5-minute timeframe, two swing highs on the 15-minute timeframe, and three swing highs on the 1-hour timeframe. The confluence score is 6 (total number of method/timeframe hits), and there has been 1 recent price reaction at this level. This suggests 119257.11 is a strong resistance zone, confirmed by multiple swing highs across all selected timeframes.
Mixed zone at 118767.97:
The dashboard shows:
5m (2 SW), 15m (2 SW)
This means the level 118767.97 was identified by two swing points on both the 5-minute and 15-minute timeframes. The confluence score is 4, and there have been 19 recent price reactions at this level, indicating it is a highly reactive zone.
Support zone at 117411.35:
The dashboard shows:
5m (2 SW), 1h (2 SW)
This means the level 117411.35 was identified as a support zone by two swing lows on the 5-minute timeframe and two swing lows on the 1-hour timeframe. The confluence score is 4, and there have been 2 recent price reactions at this level.
Mixed zone at 118291.45:
The dashboard shows:
15m (1 SW, 1 VWAP), 5m (1 VWAP), 1h (1 VWAP)
This means the level 118291.45 was identified by a swing and VWAP on the 15-minute timeframe, and by VWAP on both the 5-minute and 1-hour timeframes. The confluence score is 4, and there have been 12 recent price reactions at this level.
Support zone at 117103.10:
The dashboard shows:
15m (1 SW), 1h (1 SW)
This means the level 117103.10 was identified by a single swing low on both the 15-minute and 1-hour timeframes. The confluence score is 2, and there have been no recent price reactions at this level.
Resistance zone at 117899.33:
The dashboard shows:
5m (1 SW)
This means the level 117899.33 was identified by a single swing high on the 5-minute timeframe. The confluence score is 1, and there have been no recent price reactions at this level.
How to use this:
Zones with higher confluence (more methods and timeframes in agreement) and more recent reactions are generally more significant. For example, the resistance at 119257.11 is much stronger than the resistance at 117899.33, and the mixed zone at 118767.97 has shown the most recent price reactions, making it a key area to watch for potential reversals or breakouts.
Tip:
“SW” stands for Swing High/Low, and “VWAP” stands for Volume Weighted Average Price.
The format 15m (2 SW) means two swing points were detected on the 15-minute timeframe.
Best Practices & Recommendations
Use with Other Tools: This indicator is most powerful when combined with your own price action analysis and risk management.
Adjust Settings: Experiment with timeframes, clustering, and methods to suit your trading style and the asset’s volatility.
Watch for High Confluence: Zones with higher confluence and more reactions are generally more significant.
Limitations
No Future Prediction: The indicator does not predict future price movement; it highlights areas where price is statistically more likely to react.
Not a Standalone System: Should be used as part of a broader trading plan.
Historical Data: Reaction counts are based on historical price action and may not always repeat.
Disclaimer
This indicator is a technical analysis tool and does not constitute financial advice or a recommendation to buy or sell any asset. Trading involves risk, and past performance is not indicative of future results. Always use proper risk management and consult a financial advisor if needed.
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Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV)
Where Pure Mathematics Meets Market Reality
A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics
🎓 THEORETICAL FOUNDATION
The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis:
The Langlands Program: Harmonic Analysis in Markets
Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator:
L-Function Implementation:
- Utilizes the Möbius function μ(n) for weighted price analysis
- Applies Riemann zeta function convergence principles
- Calculates quantum harmonic resonance between -2 and +2
- Measures deep mathematical patterns invisible to traditional analysis
The L-Function core calculation employs:
L_sum = Σ(return_val × μ(n) × n^(-s))
Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness.
Operadic Composition Theory: Multi-Strategy Democracy
Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using:
Strategy Composition Arity (2-5 strategies):
- Momentum analysis via RSI transformation
- Mean reversion through Bollinger Band mathematics
- Order Flow Polarity Index (revolutionary T3-smoothed volume analysis)
- Trend detection using Directional Movement
- Higher timeframe momentum confirmation
Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty.
Möbius Function: Number Theory in Action
The Möbius function μ(n) forms the mathematical backbone:
- μ(n) = 1 if n is a square-free positive integer with even number of prime factors
- μ(n) = -1 if n is a square-free positive integer with odd number of prime factors
- μ(n) = 0 if n has a squared prime factor
This creates oscillating weights that reveal hidden market periodicities and harmonic structures.
🔧 COMPREHENSIVE INPUT SYSTEM
Langlands Program Parameters
Modular Level N (5-50, default 30):
Primary lookback for quantum harmonic analysis. Optimized by timeframe:
- Scalping (1-5min): 15-25
- Day Trading (15min-1H): 25-35
- Swing Trading (4H-1D): 35-50
- Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45
L-Function Critical Strip (0.5-2.5, default 1.5):
Controls Riemann zeta convergence precision:
- Higher values: More stable, smoother signals
- Lower values: More reactive, catches quick moves
- High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3
Frobenius Trace Period (5-50, default 21):
Galois representation lookback for price-volume correlation:
- Measures harmonic relationships in market flows
- Scalping: 8-15, Day Trading: 18-25, Swing: 25-40
HTF Multi-Scale Analysis:
Higher timeframe context prevents trading against major trends:
- Provides market bias and filters signals
- Improves win rates by 15-25% through trend alignment
Operadic Composition Parameters
Strategy Composition Arity (2-5, default 4):
Number of algorithms composed for final signal:
- Conservative: 4-5 strategies (higher confidence)
- Moderate: 3-4 strategies (balanced approach)
- Aggressive: 2-3 strategies (more frequent signals)
Category Agreement Threshold (2-5, default 3):
Democratic voting minimum for signal generation:
- Higher agreement: Fewer but higher quality signals
- Lower agreement: More signals, potential false positives
Swiss-Cheese Mixing (0.1-0.5, default 0.382):
Golden ratio φ⁻¹ based blending of trend factors:
- 0.382 is φ⁻¹, optimal for natural market fractals
- Higher values: Stronger trend following
- Lower values: More contrarian signals
OFPI Configuration:
- OFPI Length (5-30, default 14): Order Flow calculation period
- T3 Smoothing (3-10, default 5): Advanced exponential smoothing
- T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control
Unified Scoring System
Component Weights (sum ≈ 1.0):
- L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis
- Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity
- Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus
- Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment
Signal Threshold (0.5-10.0, default 5.0):
Quality filter producing:
- 8.0+: EXCEPTIONAL signals only
- 6.0-7.9: STRONG signals
- 4.0-5.9: MODERATE signals
- 2.0-3.9: WEAK signals
🎨 ADVANCED VISUAL SYSTEM
Multi-Dimensional Quantum Aura Bands
Five-layer resonance field showing market energy:
- Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.)
- Expansion: Dynamic based on score intensity and volatility
- Function: Multi-timeframe support/resistance zones
Morphism Flow Portals
Category theory visualization showing market topology:
- Green/Cyan Portals: Bullish mathematical flow
- Red/Orange Portals: Bearish mathematical flow
- Size/Intensity: Proportional to signal strength
- Recursion Depth (1-8): Nested patterns for flow evolution
Fractal Grid System
Dynamic support/resistance with projected L-Scores:
- Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows
- Smart Spacing: Prevents level overlap using ATR-based minimum distance
- Projections: Estimated signal scores when price reaches levels
- Usage: Precise entry/exit timing with mathematical confirmation
Wick Pressure Analysis
Rejection level prediction using candle mathematics:
- Upper Wicks: Selling pressure zones (purple/red lines)
- Lower Wicks: Buying pressure zones (purple/green lines)
- Glow Intensity (1-8): Visual emphasis and line reach
- Application: Confluence with fractal grid creates high-probability zones
Regime Intensity Heatmap
Background coloring showing market energy:
- Black/Dark: Low activity, range-bound markets
- Purple Glow: Building momentum and trend development
- Bright Purple: High activity, strong directional moves
- Calculation: Combines trend, momentum, volatility, and score intensity
Six Professional Themes
- Quantum: Purple/violet for general trading and mathematical focus
- Holographic: Cyan/magenta optimized for cryptocurrency markets
- Crystalline: Blue/turquoise for conservative, stability-focused trading
- Plasma: Gold/magenta for high-energy volatility trading
- Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading
📊 INSTITUTIONAL-GRADE DASHBOARD
Unified AI Score Section
- Total Score (-10 to +10): Primary decision metric
- >5: Strong bullish signals
- <-5: Strong bearish signals
- Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK
- Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions
Order Flow Analysis
Revolutionary OFPI integration:
- OFPI Value (-100% to +100%): Real buying vs selling pressure
- Visual Gauge: Horizontal bar chart showing flow intensity
- Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK
- Trading Application: Flow shifts often precede major moves
Signal Performance Tracking
- Win Rate Monitoring: Real-time success percentage with emoji indicators
- Signal Count: Total signals generated for frequency analysis
- Current Position: LONG, SHORT, or NONE with P&L tracking
- Volatility Regime: HIGH, MEDIUM, or LOW classification
Market Structure Analysis
- Möbius Field Strength: Mathematical field oscillation intensity
- CHAOTIC: High complexity, use wider stops
- STRONG: Active field, normal position sizing
- MODERATE: Balanced conditions
- WEAK: Low activity, consider smaller positions
- HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL)
- Strategy Agreement: Multi-algorithm consensus level
Position Management
When in trades, displays:
- Entry Price: Original signal price
- Current P&L: Real-time percentage with risk level assessment
- Duration: Bars in trade for timing analysis
- Risk Level: HIGH/MEDIUM/LOW based on current exposure
🚀 SIGNAL GENERATION LOGIC
Balanced Long/Short Architecture
The indicator generates signals through multiple convergent pathways:
Long Entry Conditions:
- Score threshold breach with algorithmic agreement
- Strong bullish order flow (OFPI > 0.15) with positive composite signal
- Bullish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bullish OFPI (>0.3) with any positive score
Short Entry Conditions:
- Score threshold breach with bearish agreement
- Strong bearish order flow (OFPI < -0.15) with negative composite signal
- Bearish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bearish OFPI (<-0.3) with any negative score
Exit Logic:
- Score deterioration below continuation threshold
- Signal quality degradation
- Opposing order flow acceleration
- 10-bar minimum between signals prevents overtrading
⚙️ OPTIMIZATION GUIDELINES
Asset-Specific Settings
Cryptocurrency Trading:
- Modular Level: 15-25 (capture volatility)
- L-Function Precision: 0.8-1.3 (reactive to price swings)
- OFPI Length: 10-20 (fast correlation shifts)
- Cascade Levels: 5-7, Theme: Holographic
Stock Index Trading:
- Modular Level: 25-35 (balanced trending)
- L-Function Precision: 1.5-1.8 (stable patterns)
- OFPI Length: 14-20 (standard correlation)
- Cascade Levels: 4-5, Theme: Quantum
Forex Trading:
- Modular Level: 35-45 (smooth trends)
- L-Function Precision: 1.6-2.1 (high smoothing)
- OFPI Length: 18-25 (disable volume amplification)
- Cascade Levels: 3-4, Theme: Crystalline
Timeframe Optimization
Scalping (1-5 minute charts):
- Reduce all lookback parameters by 30-40%
- Increase L-Function precision for noise reduction
- Enable all visual elements for maximum information
- Use Small dashboard to save screen space
Day Trading (15 minute - 1 hour):
- Use default parameters as starting point
- Adjust based on market volatility
- Normal dashboard provides optimal information density
- Focus on OFPI momentum shifts for entries
Swing Trading (4 hour - Daily):
- Increase lookback parameters by 30-50%
- Higher L-Function precision for stability
- Large dashboard for comprehensive analysis
- Emphasize HTF trend alignment
🏆 ADVANCED TRADING STRATEGIES
The Mathematical Confluence Method
1. Wait for Fractal Grid level approach
2. Confirm with projected L-Score > threshold
3. Verify OFPI alignment with direction
4. Enter on portal signal with quality ≥ STRONG
5. Exit on score deterioration or opposing flow
The Regime Trading System
1. Monitor Aether Flow background intensity
2. Trade aggressively during bright purple periods
3. Reduce position size during dark periods
4. Use Möbius Field strength for stop placement
5. Align with HTF trend for maximum probability
The OFPI Momentum Strategy
1. Watch for momentum shifting detection
2. Confirm with accelerating flow in direction
3. Enter on immediate portal signal
4. Scale out at Fibonacci levels
5. Exit on flow deceleration or reversal
⚠️ RISK MANAGEMENT INTEGRATION
Mathematical Position Sizing
- Use Galois Rank for volatility-adjusted sizing
- Möbius Field strength determines stop width
- Fractal Dimension guides maximum exposure
- OFPI momentum affects entry timing
Signal Quality Filtering
- Trade only STRONG or EXCEPTIONAL quality signals
- Increase position size with higher agreement levels
- Reduce risk during CHAOTIC Möbius field periods
- Respect HTF trend alignment for directional bias
🔬 DEVELOPMENT JOURNEY
Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable.
The Mathematical Challenge
Implementing the Langlands Program required deep research into:
- Number theory and the Möbius function
- Riemann zeta function convergence properties
- L-function analytical continuation
- Galois representations in finite fields
The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application.
The Computational Complexity
Operadic composition theory demanded:
- Category theory implementation in Pine Script
- Multi-dimensional array management for strategy composition
- Real-time democratic voting algorithms
- Performance optimization for complex calculations
The Integration Breakthrough
Bringing together three disparate mathematical frameworks required:
- Novel approaches to signal weighting and combination
- Revolutionary Order Flow Polarity Index development
- Advanced T3 smoothing implementation
- Balanced signal generation preventing directional bias
Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility.
🎯 PRACTICAL IMPLEMENTATION
Getting Started
1. Apply indicator with default settings
2. Select appropriate theme for your markets
3. Observe dashboard metrics during different market conditions
4. Practice signal identification without trading
5. Gradually adjust parameters based on observations
Signal Confirmation Process
- Never trade on score alone - verify quality rating
- Confirm OFPI alignment with intended direction
- Check fractal grid level proximity for timing
- Ensure Möbius field strength supports position size
- Validate against HTF trend for bias confirmation
Performance Monitoring
- Track win rate in dashboard for strategy assessment
- Monitor component contributions for optimization
- Adjust threshold based on desired signal frequency
- Document performance across different market regimes
🌟 UNIQUE INNOVATIONS
1. First Integration of Langlands Program mathematics with practical trading
2. Revolutionary OFPI with T3 smoothing and momentum detection
3. Operadic Composition using category theory for signal democracy
4. Dynamic Fractal Grid with projected L-Score calculations
5. Multi-Dimensional Visualization through morphism flow portals
6. Regime-Adaptive Background showing market energy intensity
7. Balanced Signal Generation preventing directional bias
8. Professional Dashboard with institutional-grade metrics
📚 EDUCATIONAL VALUE
The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to:
- Pure mathematics applications in markets
- Category theory and operadic composition
- Number theory through Möbius function implementation
- Harmonic analysis via L-function calculations
- Advanced signal processing through T3 smoothing
⚖️ RESPONSIBLE USAGE
This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable.
Key Principles:
- Use as part of comprehensive trading strategy
- Implement proper risk management at all times
- Backtest thoroughly before live implementation
- Understand that past performance does not guarantee future results
- Never risk more than you can afford to lose
The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator.
🔮 CONCLUSION
The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability.
From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos.
This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure.
Trade with mathematical precision. Trade with the LOMV.
*"Mathematics is the language with which God has written the universe." - Galileo Galilei*
*In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.*
— Dskyz, Trade with insight. Trade with anticipation.
Dynamic Portfolio TrackerDynamic Portfolio Tracker
The Dynamic Portfolio Tracker is a visual tool for actively managing and monitoring a multi-asset portfolio directly on TradingView. It allows users to input up to 15 custom assets (with a default setup for 5), define how much of each asset they hold, and assign a target allocation percentage to each. The script then calculates live market prices, total portfolio value, current vs. target weightings, and provides clear, color-coded instructions on whether to buy, sell, or hold each asset. It displays all this data in an on-chart table, showing both the dollar amount and the quantity to adjust for each asset, helping users keep their portfolio aligned with their strategy in real time.
How to Use the Inputs (What Each Field Means)
1. Portfolio Assets (Tickers)
Fields: Asset 1 Ticker, Asset 2 Ticker, …, Asset 15 Ticker
What it does: Lets you select which assets (crypto, stocks, etc.) you want to track. These are live symbols pulled from TradingView.
2. Asset Quantities
Fields: Asset 1 Amount, Asset 2 Amount, …, Asset 15 Amount
What it means: How much of each asset you currently hold. For example:
• 0.03 BTC
• 2.1 ETH
Why it’s needed: The script multiplies this by the live price to calculate the current dollar value of each asset in your portfolio.
3. Target %
Fields: Asset 1 Implied %, Asset 2 Implied %, …, Asset 15 Implied %
What it means: Your desired allocation for each asset. For example:
• 40% BTC
• 20% ETH
• 10% SOL, etc.
Important: These must total 100% or less across all assets. The script checks this and shows an error if the total exceeds 100%.
The Dynamic Portfolio Tracker displays two powerful on-chart tables:
1. Main Table — Per Asset Breakdown
This table shows detailed, real-time information for each asset in your portfolio. Each row represents a different asset, and each column has a specific meaning:
Column What It Means
Asset = The symbol of the asset (e.g., BTCUSD, ETHUSD), auto-stripped from the exchange name.
Price = The current market price of the asset, pulled live from TradingView.
Quantity = How much of that asset you currently hold, entered manually in the inputs.
Target % = The percentage of your total portfolio you want this asset to represent.
Actual % = What percentage of your portfolio it currently makes up (based on price × quantity).
Target Value = How much (in $) this asset should be worth in your portfolio.
Actual Value = How much (in $) this asset is currently worth.
Instruction = Whether to Buy, Sell, or Hold to match your target allocation.
Value Change = The dollar amount you’d need to buy/sell to rebalance this asset.
Units to Trade = The number of asset units to buy/sell to reach the target value.
2. Portfolio Summary Table — Portfolio Totals
This smaller table appears in the top-right corner and summarizes your entire portfolio at a glance:
Target % = Total of all your assigned target allocations (should equal 100%).
Actual % = Actual portfolio composition (always 100% unless your capital is zero).
Target Value = Total value your portfolio should be based on your target percentages.
Actual Value = Current live total value of your portfolio.
If there’s a discrepancy between Target Value and Actual Value, the difference is shown in each row of the main table, so you can adjust individual assets accordingly.
Privacy First: Hide Sensitive Financial Data
A unique feature of this tool is the ability to hide sensitive financial data, such as:
• Target Value
• Actual Value
• Total Portfolio Value
You can turn these off using toggle settings, and they’ll be replaced with a crossed-out eye icon (👁️🗨️) — just like on modern crypto exchanges. This feature makes the script safe for streaming, screenshots, or sharing publicly while protecting your privacy.
But more importantly:
Feelings are the enemy of good investing.
Seeing the value of your portfolio fluctuate can trigger fear or greed. By hiding your dollar values, you’re not just securing your data — you’re reducing the temptation to react emotionally.
It’s just numbers. Systems over Feelings.
Table Automatically Adapts to Your Asset Count
The Dynamic Portfolio Tracker is designed to scale with your portfolio. Simply choose how many assets you want to track (up to 15), and the table will automatically resize to fit exactly that number — no wasted space or empty rows.
• Select 1 to 15 assets using the “Number of Assets” input
• The table expands or contracts dynamically to show only those rows
• All calculations, summaries, and layout elements adjust accordingly in real time
This keeps the interface clean, focused, and perfectly tailored to your setup — whether you’re tracking 3 coins or managing a full portfolio of 12+ tokens.
Customize Your Table to Match Your Style
The Dynamic Portfolio Tracker offers a full suite of visual customization options, allowing you to tailor the table to your charting style or stream layout. You can:
• Choose text colors for labels, values, and headers
• Set background colors for the full table and header row — or turn them off completely for a clean, transparent look
• Control border and frame settings, including color, thickness, or disabling them entirely
• Pick custom colors for Buy and Sell signals in the rebalance column
• Adjust table font size from tiny to large to match your resolution or preferences
Special Thanks
This tool wouldn’t exist without the knowledge and inspiration gained through The Real World. A sincere thank you to the Investing Master, the Guides, and Professor Adam — your frameworks and lessons brought clarity, discipline, and structure to this build.
And of course, glory to L4 — where real men are made.
LANZ Strategy 2.0🔷 LANZ Strategy 2.0 — London Breakout Confirmation with Structural Swing Protection
LANZ Strategy 2.0 is a structured trading system that leverages the last confirmed market direction before the London session to define directional bias and manage trades based on key structural swing levels. It is tailored for intraday traders looking to capitalize on early London volatility with built-in risk management and visual clarity.
🧠 Core Components:
Directional Confirmation (Pre-London Bias): Validates the last breakout or structural move from the 15-minute timeframe before 02:15 a.m. New York time (start of the London session), establishing the expected market direction.
Time-Based Execution: Executes potential entries strictly at 02:15 a.m. NY time, using market structure to support Long or Short bias.
Dynamic Swing-Based SL System: Allows user to select between three SL protection models: First Swing (most recent structural point) Second Swing (prior level) Total Coverage (includes both swings + extra buffer) This supports flexibility based on trader profile or market conditions.
Visual Risk Mapping: All SL and TP levels are clearly plotted.
End-of-Session Management: Positions are automatically evaluated for closure at 11:45 a.m. NY time. SL, TP, or manual close outcomes are labeled accordingly.
📊 Visual Features:
Labels for 1st and 2nd swing levels upon entry.
Dynamic lines projecting SL/TP levels toward the end of the session.
Session background coloring for Pre-London, Execution, and NY sessions.
Real-time percentage outcome labels (+2.00%, -1.00%, or net % at session end).
Automatic deletion of previous visuals on new entries for clean charting.
⚙️ How It Works:
Detects last structural breakout on the 15m timeframe before 02:15 a.m. NY.
On the 02:15 a.m. candle, executes a Long or Short logic entry.
Plots corresponding SL and TP based on selected swing model.
Monitors price action: If TP or SL is hit, labels it accordingly. If no exit is hit, trade closes manually at 11:45 a.m. NY with net result shown.
Optional logic to reverse entries if market structure breaks before execution.
🔔 Alerts:
Daily execution alert at 02:15 a.m. NY (prompting manual review or action).
Optional alert logic can be extended for SL/TP hits or structure breaks.
📝 Notes:
Designed for semi-automated or discretionary intraday trading.
Best used on Forex pairs or indices with strong London session behavior.
Adjustable parameters include session hours, swing SL type, and buffer settings.
Credits:
Developed by LANZ, this script combines time-based execution with dynamic structure protection, offering a disciplined framework for participating in the London session breakout with clear visuals and risk logic.
EXODUS EXODUS by (DAFE) Trading Systems
EXODUS is a sophisticated trading algorithm built by Dskyz (DAFE) Trading Systems for competitive and competition purposes, designed to identify high-probability trades with robust risk management. this strategy leverages a multi-signal voting system, combining three core components—SPR, VWMO, and VEI—alongside ADX, choppiness filters, and ATR-based volatility gates to ensure trades are taken only in favorable market conditions. the algo uses a take-profit to stop-loss ratio, dynamic position sizing, and a strict voting mechanism requiring all signals to align before entering a trade.
EXODUS was not overfitted for any specific symbol. instead, it uses a generic tuned setting, making it versatile across various markets. while it can trade futures, it’s not currently set up for it but has the potential to do more with further development. visuals are intentionally minimal due to its competition focus, prioritizing performance over aesthetics. a more visually stunning version may be released in the future with enhanced graphics.
The Unique Core Components Developed for EXODUS
SPR (Session Price Recalibration)
SPR measures momentum during regular trading hours (RTH, 0930-1600, America/New_York) to catch session-specific trends.
spr_lookback = input.int(15, "SPR Lookback") this sets how many bars back SPR looks to calculate momentum (default 15 bars). it compares the current session’s price-volume score to the score 15 bars ago to gauge momentum strength.
how it works: a longer lookback smooths out the signal, focusing on bigger trends. a shorter one makes SPR more sensitive to recent moves.
how to adjust: on a 1-hour chart, 15 bars is 15 hours (about 2 trading days). if you’re on a shorter timeframe like 5 minutes, 15 bars is just 75 minutes, so you might want to increase it to 50 or 100 to capture more meaningful trends. if you’re trading a choppy stock, a shorter lookback (like 5) can help catch quick moves, but it might give more false signals.
spr_threshold = input.float (0.7, "SPR Threshold")
this is the cutoff for SPR to vote for a trade (default 0.7). if SPR’s normalized value is above 0.7, it votes for a long; below -0.7, it votes for a short.
how it works: SPR normalizes its momentum score by ATR, so this threshold ensures only strong moves count. a higher threshold means fewer trades but higher conviction.
how to adjust: if you’re getting too few trades, lower it to 0.5 to let more signals through. if you’re seeing too many false entries, raise it to 1.0 for stricter filtering. test on your chart to find a balance.
spr_atr_length = input.int(21, "SPR ATR Length") this sets the ATR period (default 21 bars) used to normalize SPR’s momentum score. ATR measures volatility, so this makes SPR’s signal relative to market conditions.
how it works: a longer ATR period (like 21) smooths out volatility, making SPR less jumpy. a shorter one makes it more reactive.
how to adjust: if you’re trading a volatile stock like TSLA, a longer period (30 or 50) can help avoid noise. for a calmer stock, try 10 to make SPR more responsive. match this to your timeframe—shorter timeframes might need a shorter ATR.
rth_session = input.session("0930-1600","SPR: RTH Sess.") rth_timezone = "America/New_York" this defines the session SPR uses (0930-1600, New York time). SPR only calculates momentum during these hours to focus on RTH activity.
how it works: it ignores pre-market or after-hours noise, ensuring SPR captures the main market action.
how to adjust: if you trade a different session (like London hours, 0300-1200 EST), change the session to match. you can also adjust the timezone if you’re in a different region, like "Europe/London". just make sure your chart’s timezone aligns with this setting.
VWMO (Volume-Weighted Momentum Oscillator)
VWMO measures momentum weighted by volume to spot sustained, high-conviction moves.
vwmo_momlen = input.int(21, "VWMO Momentum Length") this sets how many bars back VWMO looks to calculate price momentum (default 21 bars). it takes the price change (close minus close 21 bars ago).
how it works: a longer period captures bigger trends, while a shorter one reacts to recent swings.
how to adjust: on a daily chart, 21 bars is about a month—good for trend trading. on a 5-minute chart, it’s just 105 minutes, so you might bump it to 50 or 100 for more meaningful moves. if you want faster signals, drop it to 10, but expect more noise.
vwmo_volback = input.int(30, "VWMO Volume Lookback") this sets the period for calculating average volume (default 30 bars). VWMO weights momentum by volume divided by this average.
how it works: it compares current volume to the average to see if a move has strong participation. a longer lookback smooths the average, while a shorter one makes it more sensitive.
how to adjust: for stocks with spiky volume (like NVDA on earnings), a longer lookback (50 or 100) avoids overreacting to one-off spikes. for steady volume stocks, try 20. match this to your timeframe—shorter timeframes might need a shorter lookback.
vwmo_smooth = input.int(9, "VWMO Smoothing")
this sets the SMA period to smooth VWMO’s raw momentum (default 9 bars).
how it works: smoothing reduces noise in the signal, making VWMO more reliable for voting. a longer smoothing period cuts more noise but adds lag.
how to adjust: if VWMO is too jumpy (lots of false votes), increase to 15. if it’s too slow and missing trades, drop to 5. test on your chart to see what keeps the signal clean but responsive.
vwmo_threshold = input.float(10, "VWMO Threshold") this is the cutoff for VWMO to vote for a trade (default 10). above 10, it votes for a long; below -10, a short.
how it works: it ensures only strong momentum signals count. a higher threshold means fewer but stronger trades.
how to adjust: if you want more trades, lower it to 5. if you’re getting too many weak signals, raise it to 15. this depends on your market—volatile stocks might need a higher threshold to filter noise.
VEI (Velocity Efficiency Index)
VEI measures market efficiency and velocity to filter out choppy moves and focus on strong trends.
vei_eflen = input.int(14, "VEI Efficiency Smoothing") this sets the EMA period for smoothing VEI’s efficiency calc (bar range / volume, default 14 bars).
how it works: efficiency is how much price moves per unit of volume. smoothing it with an EMA reduces noise, focusing on consistent efficiency. a longer period smooths more but adds lag.
how to adjust: for choppy markets, increase to 20 to filter out noise. for faster markets, drop to 10 for quicker signals. this should match your timeframe—shorter timeframes might need a shorter period.
vei_momlen = input.int(8, "VEI Momentum Length") this sets how many bars back VEI looks to calculate momentum in efficiency (default 8 bars).
how it works: it measures the change in smoothed efficiency over 8 bars, then adjusts for inertia (volume-to-range). a longer period captures bigger shifts, while a shorter one reacts faster.
how to adjust: if VEI is missing quick reversals, drop to 5. if it’s too noisy, raise to 12. test on your chart to see what catches the right moves without too many false signals.
vei_threshold = input.float(4.5, "VEI Threshold") this is the cutoff for VEI to vote for a trade (default 4.5). above 4.5, it votes for a long; below -4.5, a short.
how it works: it ensures only strong, efficient moves count. a higher threshold means fewer trades but higher quality.
how to adjust: if you’re not getting enough trades, lower to 3. if you’re seeing too many false entries, raise to 6. this depends on your market—fast stocks like NQ1 might need a lower threshold.
Features
Multi-Signal Voting: requires all three signals (SPR, VWMO, VEI) to align for a trade, ensuring high-probability setups.
Risk Management: uses ATR-based stops (2.1x) and take-profits (4.1x), with dynamic position sizing based on a risk percentage (default 0.4%).
Market Filters: ADX (default 27) ensures trending conditions, choppiness index (default 54.5) avoids sideways markets, and ATR expansion (default 1.12) confirms volatility.
Dashboard: provides real-time stats like SPR, VWMO, VEI values, net P/L, win rate, and streak, with a clean, functional design.
Visuals
EXODUS prioritizes performance over visuals, as it was built for competitive and competition purposes. entry/exit signals are marked with simple labels and shapes, and a basic heatmap highlights market regimes. a more visually stunning update may be released later, with enhanced graphics and overlays.
Usage
EXODUS is designed for stocks and ETFs but can be adapted for futures with adjustments. it performs best in trending markets with sufficient volatility, as confirmed by its generic tuning across symbols like TSLA, AMD, NVDA, and NQ1. adjust inputs like SPR threshold, VWMO smoothing, or VEI momentum length to suit specific assets or timeframes.
Setting I used: (Again, these are a generic setting, each security needs to be fine tuned)
SPR LB = 19 SPR TH = 0.5 SPR ATR L= 21 SPR RTH Sess: 9:30 – 16:00
VWMO L = 21 VWMO LB = 18 VWMO S = 6 VWMO T = 8
VEI ES = 14 VEI ML = 21 VEI T = 4
R % = 0.4
ATR L = 21 ATR M (S) =1.1 TP Multi = 2.1 ATR min mult = 0.8 ATR Expansion = 1.02
ADX L = 21 Min ADX = 25
Choppiness Index = 14 Chop. Max T = 55.5
Backtesting: TSLA
Frame: Jan 02, 2018, 08:00 — May 01, 2025, 09:00
Slippage: 3
Commission .01
Disclaimer
this strategy is for educational purposes. past performance is not indicative of future results. trading involves significant risk, and you should only trade with capital you can afford to lose. always backtest and validate any strategy before using it in live markets.
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
About the Author
Dskyz (DAFE) Trading Systems is dedicated to building high-performance trading algorithms. EXODUS is a product of rigorous research and development, aimed at delivering consistent, and data-driven trading solutions.
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
JJ Highlight Time Ranges with First 5 Minutes and LabelsTo effectively use this Pine Script as a day trader , here’s how the various elements can help you manage trades, track time sessions, and monitor price movements:
Key Components for a Day Trader:
1. First 5-Minute Highlight:
- Purpose: Day traders often rely on the first 5 minutes of the trading session to gauge market sentiment, watch for opening price gaps, or plan entries. This script draws a horizontal line at the high or low of the first 5 minutes, which can act as a key level for the rest of the day.
- How to Use: If the price breaks above or below the first 5-minute line, it can signal momentum. You might enter a long position if the price breaks above the first 5-minute high or a short if it breaks below the first 5-minute low.
2. Session Time Highlights:
- Morning Session (9:15–10:30 AM): The market often shows its strongest price action during the first hour of trading. This session is highlighted in yellow. You can use this highlight to focus on the most volatile period, as this is when large institutional moves tend to occur.
- Afternoon Session (12:30–2:55 PM): The blue highlight helps you track the mid-afternoon session, where liquidity may decrease, and price action can sometimes be choppier. Day traders should be more cautious during this period.
- How to Use: By highlighting these key times, you can:
- Focus on key breakouts during the morning session.
- Be more conservative in your trades during the afternoon, as market volatility may drop.
3. Dynamic Labels:
- Top/Bottom Positioning: The script places labels dynamically based on the selected position (Top or Bottom). This allows you to quickly glance at the session's start and identify where you are in terms of time.
- How to Use: Use these labels to remind yourself when major time segments (morning or afternoon) begin. You can adjust your trading strategy depending on the session, e.g., being more aggressive in the morning and more cautious in the afternoon.
Trading Strategy Suggestions:
1. Momentum Trades:
- After the first 5 minutes, use the high/low of that period to set up breakout trades.
- Long Entry: If the price breaks the high of the first 5 minutes (especially if there's a strong trend).
- Short Entry: If the price breaks the low of the first 5 minutes, signaling a potential downtrend.
2. Session-Based Strategy:
- Morning Session (9:15–10:30 AM):
- Look for strong breakout patterns such as support/resistance levels, moving average crossovers, or candlestick patterns (like engulfing candles or pin bars).
- This is a high liquidity period, making it ideal for executing quick trades.
- Afternoon Session (12:30–2:55 PM):
- The market tends to consolidate or show less volatility. Scalping and mean-reversion strategies work better here.
- Avoid chasing big moves unless you see a clear breakout in either direction.
3. Support and Resistance:
- The first 5-minute high/low often acts as a key support or resistance level for the rest of the day. If the price holds above or below this level, it’s an indication of trend continuation.
4. Breakout Confirmation:
- Look for breakouts from the highlighted session time ranges (e.g., 9:15 AM–10:30 AM or 12:30 PM–2:55 PM).
- If a breakout happens during a key time window, combine that with other technical indicators like volume spikes , RSI , or MACD for confirmation.
---
Example Day Trader Usage:
1. First 5 Minutes Strategy: After the market opens at 9:15 AM, watch the price action for the first 5 minutes. The high and low of these 5 minutes are critical levels. If the price breaks above the high of the first 5 minutes, it might indicate a strong bullish trend for the day. Conversely, breaking below the low may suggest bearish movement.
2. Morning Session: After the first 5 minutes, focus on the **9:15 AM–10:30 AM** window. During this time, look for breakout setups at key support/resistance levels, especially when paired with high volume or momentum indicators. This is when many institutions make large trades, so price action tends to be more volatile and predictable.
3. Afternoon Session: From 12:30 PM–2:55 PM, the market might experience lower volatility, making it ideal for scalping or range-bound strategies. You could look for reversals or fading strategies if the market becomes too quiet.
Conclusion:
As a day trader, you can use this script to:
- Track and react to key price levels during the first 5 minutes.
- Focus on high volatility in the morning session (9:15–10:30 AM) and **be cautious** during the afternoon.
- Use session-based timing to adjust your strategies based on the time of day.
Macros ICT KillZones [TradingFinder] Times & Price Trading Setup🔵 Introduction
ICT Macros, developed by Michael Huddleston, also known as ICT (Inner Circle Trader), is a powerful trading tool designed to help traders identify the best trading opportunities during key time intervals like the London and New York trading sessions.
For traders aiming to capitalize on market volatility, liquidity shifts, and Fair Value Gaps (FVG), understanding and using these critical time zones can significantly improve trading outcomes.
In today’s highly competitive financial markets, identifying the moments when the market is seeking buy-side or sell-side liquidity, or filling price imbalances, is essential for maximizing profitability.
The ICT Macros indicator is built on the renowned ICT time and price theory, which enables traders to track and leverage key market dynamics such as breaks of highs and lows, imbalances, and liquidity hunts.
This indicator automatically detects crucial market times and optimizes strategies for traders by highlighting the specific moments when price movements are most likely to occur. A standout feature of ICT Macros is its automatic adjustment for Daylight Saving Time (DST), ensuring that traders remain synced with the correct session times.
This means you can rely on accurate market timing without the need for manual updates, allowing you to focus on capturing profitable trades during critical timeframes.
🔵 How to Use
The ICT Macros indicator helps you capitalize on trading opportunities during key market moments, particularly when the market is breaking highs or lows, filling Fair Value Gaps (FVG), or addressing imbalances. This indicator is particularly beneficial for traders who seek to identify liquidity, market volatility, and price imbalances.
🟣 Sessions
London Sessions
London Macro 1 :
UTC Time : 06:33 to 07:00
New York Time : 02:33 to 03:00
London Macro 2 :
UTC Time : 08:03 to 08:30
New York Time : 04:03 to 04:30
New York Sessions
New York Macro AM 1 :
UTC Time : 12:50 to 13:10
New York Time : 08:50 to 09:10
New York Macro AM 2 :
UTC Time : 13:50 to 14:10
New York Time : 09:50 to 10:10
New York Macro AM 3 :
UTC Time : 14:50 to 15:10
New York Time : 10:50 to 11:10
New York Lunch Macro :
UTC Time : 15:50 to 16:10
New York Time : 11:50 to 12:10
New York PM Macro :
UTC Time : 17:10 to 17:40
New York Time : 13:10 to 13:40
New York Last Hour Macro :
UTC Time : 19:15 to 19:45
New York Time : 15:15 to 15:45
These time intervals adjust automatically based on Daylight Saving Time (DST), helping traders to enter or exit trades during key market moments when price volatility is high.
Below are the main applications of this tool and how to incorporate it into your trading strategies :
🟣 Combining ICT Macros with Trading Strategies
The ICT Macros indicator can easily be used in conjunction with various trading strategies. Two well-known strategies that can be combined with this indicator include:
ICT 2022 Trading Model : This model is designed based on identifying market liquidity, structural price changes, and Fair Value Gaps (FVG). By using ICT Macros, you can identify the key time intervals when the market is seeking liquidity, filling imbalances, or breaking through important highs and lows, allowing you to enter or exit trades at the right moment.
Silver Bullet Strategy : This strategy, which is built around liquidity hunting and rapid price movements, can work more accurately with the help of ICT Macros. The indicator pinpoints precise liquidity times, helping traders take advantage of market shifts caused by filling Fair Value Gaps or correcting imbalances.
🟣 Capitalizing on Price Volatility During Key Times
Large market algorithms often seek liquidity or fill Fair Value Gaps (FVG) during the intervals marked by ICT Macros. These periods are when price volatility increases, and traders can use these moments to enter or exit trades.
For example, if sell-side liquidity is drained and the market fills an imbalance, the price might move toward buy-side liquidity. By identifying these moments, which may also involve breaking a previous high or low, you can leverage rapid market fluctuations to your advantage.
🟣 Identifying Liquidity and Price Imbalances
One of the important uses of ICT Macros is identifying points where the market is seeking liquidity and correcting imbalances. You can determine high or low liquidity levels in the market before each ICT Macro, as well as Fair Value Gaps (FVG) and price imbalances that need to be filled, using them to adjust your trading strategy. This capability allows you to manage trades based on liquidity shifts or imbalance corrections without needing a bias toward a specific direction.
🔵 Settings
The ICT Macros indicator offers various customization options, allowing users to tailor it to their specific needs. Below are the main settings:
Time Zone Mode : You can select one of the following options to define how time is displayed:
UTC : For traders who need to work with Universal Time.
Session Local Time : The local time corresponding to the London or New York markets.
Your Time Zone : You can specify your own time zone (e.g., "UTC-4:00").
Your Time Zone : If you choose "Your Time Zone," you can set your specific time zone. By default, this is set to UTC-4:00.
Show Range Time : This option allows you to display the time range of each session on the chart. If enabled, the exact start and end times of each interval are shown.
Show or Hide Time Ranges : Toggle on/off for visual clarity depending on user preference.
Custom Colors : Set distinct colors for each session, allowing users to personalize their chart based on their trading style.These settings allow you to adjust the key time intervals of each trading session to your preference and customize the time format according to your own needs.
🔵 Conclusion
The ICT Macros indicator is a powerful tool for traders, helping them to identify key time intervals where the market seeks liquidity or fills Fair Value Gaps (FVG), corrects imbalances, and breaks highs or lows. This tool is especially valuable for traders using liquidity-based strategies such as ICT 2022 or Silver Bullet.
One of the key features of this indicator is its support for Daylight Saving Time (DST), ensuring you are always in sync with the correct trading session timings without manual adjustments. This is particularly beneficial for traders operating across different time zones.
With ICT Macros, you can capitalize on crucial market opportunities during sensitive times, take advantage of imbalances, and enhance your trading strategies based on market volatility, liquidity shifts, and Fair Value Gaps.
RSI Multi-Timeframe PINESCRIPTLABS📈 Use the Relative Strength Index (RSI) calculated across multiple time frames to generate signals
🔹 Intraday: Displays a table with real-time RSI values for the time frames of 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, and 1 day.
🔹 Standard: Displays a table with real-time RSI values for the time frames of 30 minutes, 1 hour, 4 hours, 1 day, 1 week, and 1 month.
The indicator allows you to customize overbought and oversold thresholds, as well as choose between viewing RSI values for intraday or standard time frames, tailoring the analysis to your specific needs. 🔧📊
🔔 Signals are generated when in 4 of the 6 time frames we define below:
Overbought Signal (When RSI indicates overbought conditions):
• Intraday: Activated when the RSI in the time frames of 5 minutes, 15 minutes, 30 minutes, and 1 hour is above the 70 threshold. 📈
• Standard: Activated when the RSI in the time frames of 30 minutes, 1 hour, 4 hours, and 1 day is above the 70 threshold. 📈
Oversold Signal (When RSI indicates oversold conditions):
• Intraday: Activated when the RSI in the time frames of 5 minutes, 15 minutes, 30 minutes, and 1 hour is below the 30 threshold. 📉
• Standard: Activated when the RSI in the time frames of 30 minutes, 1 hour, 4 hours, and 1 day is below the 30 threshold. 📉
Español:
📈 Utiliza el Índice de Fuerza Relativa (RSI) calculado en varios marcos de tiempo para generar señales
🔹 Intraday: Muestra una tabla con los valores del RSI en tiempo real para los marcos de tiempo de 5 minutos, 15 minutos, 30 minutos, 1 hora, 4 horas y 1 día.
🔹 Standard: Muestra una tabla con los valores del RSI en tiempo real para los marcos de tiempo de 30 minutos, 1 hora, 4 horas, 1 día, 1 semana y 1 mes.
El indicador te permite personalizar los umbrales de sobrecompra y sobreventa, así como elegir entre ver los valores RSI para marcos de tiempo intradía o estándar, adaptando el análisis a tus necesidades específicas. 🔧📊
🔔 Las señales se generan cuando en 4 de los 6 marcos de tiempo que definimos a continuación:
Señal de Sobrecompra (Cuando el RSI indica sobrecompra):
• Intraday: Se activa cuando el RSI en los marcos de tiempo de 5 minutos, 15 minutos, 30 minutos y 1 hora está por encima del umbral de 70. 📈
• Standard: Se activa cuando el RSI en los marcos de tiempo de 30 minutos, 1 hora, 4 horas y 1 día están por encima del umbral de 70. 📈
Señal de Sobreventa (Cuando el RSI indica sobreventa):
• Intraday: Se activa cuando el RSI en los marcos de tiempo de 5 minutos, 15 minutos, 30 minutos y 1 hora está por debajo del umbral de 30. 📉
• Standard: Se activa cuando el RSI en los marcos de tiempo de 30 minutos, 1 hora, 4 horas y 1 día están por debajo del umbral de 30. 📉
PubLibTrendLibrary "PubLibTrend"
trend, multi-part trend, double trend and multi-part double trend conditions for indicator and strategy development
rlut()
return line uptrend condition
Returns: bool
dt()
downtrend condition
Returns: bool
ut()
uptrend condition
Returns: bool
rldt()
return line downtrend condition
Returns: bool
dtop()
double top condition
Returns: bool
dbot()
double bottom condition
Returns: bool
rlut_1p()
1-part return line uptrend condition
Returns: bool
rlut_2p()
2-part return line uptrend condition
Returns: bool
rlut_3p()
3-part return line uptrend condition
Returns: bool
rlut_4p()
4-part return line uptrend condition
Returns: bool
rlut_5p()
5-part return line uptrend condition
Returns: bool
rlut_6p()
6-part return line uptrend condition
Returns: bool
rlut_7p()
7-part return line uptrend condition
Returns: bool
rlut_8p()
8-part return line uptrend condition
Returns: bool
rlut_9p()
9-part return line uptrend condition
Returns: bool
rlut_10p()
10-part return line uptrend condition
Returns: bool
rlut_11p()
11-part return line uptrend condition
Returns: bool
rlut_12p()
12-part return line uptrend condition
Returns: bool
rlut_13p()
13-part return line uptrend condition
Returns: bool
rlut_14p()
14-part return line uptrend condition
Returns: bool
rlut_15p()
15-part return line uptrend condition
Returns: bool
rlut_16p()
16-part return line uptrend condition
Returns: bool
rlut_17p()
17-part return line uptrend condition
Returns: bool
rlut_18p()
18-part return line uptrend condition
Returns: bool
rlut_19p()
19-part return line uptrend condition
Returns: bool
rlut_20p()
20-part return line uptrend condition
Returns: bool
rlut_21p()
21-part return line uptrend condition
Returns: bool
rlut_22p()
22-part return line uptrend condition
Returns: bool
rlut_23p()
23-part return line uptrend condition
Returns: bool
rlut_24p()
24-part return line uptrend condition
Returns: bool
rlut_25p()
25-part return line uptrend condition
Returns: bool
rlut_26p()
26-part return line uptrend condition
Returns: bool
rlut_27p()
27-part return line uptrend condition
Returns: bool
rlut_28p()
28-part return line uptrend condition
Returns: bool
rlut_29p()
29-part return line uptrend condition
Returns: bool
rlut_30p()
30-part return line uptrend condition
Returns: bool
dt_1p()
1-part downtrend condition
Returns: bool
dt_2p()
2-part downtrend condition
Returns: bool
dt_3p()
3-part downtrend condition
Returns: bool
dt_4p()
4-part downtrend condition
Returns: bool
dt_5p()
5-part downtrend condition
Returns: bool
dt_6p()
6-part downtrend condition
Returns: bool
dt_7p()
7-part downtrend condition
Returns: bool
dt_8p()
8-part downtrend condition
Returns: bool
dt_9p()
9-part downtrend condition
Returns: bool
dt_10p()
10-part downtrend condition
Returns: bool
dt_11p()
11-part downtrend condition
Returns: bool
dt_12p()
12-part downtrend condition
Returns: bool
dt_13p()
13-part downtrend condition
Returns: bool
dt_14p()
14-part downtrend condition
Returns: bool
dt_15p()
15-part downtrend condition
Returns: bool
dt_16p()
16-part downtrend condition
Returns: bool
dt_17p()
17-part downtrend condition
Returns: bool
dt_18p()
18-part downtrend condition
Returns: bool
dt_19p()
19-part downtrend condition
Returns: bool
dt_20p()
20-part downtrend condition
Returns: bool
dt_21p()
21-part downtrend condition
Returns: bool
dt_22p()
22-part downtrend condition
Returns: bool
dt_23p()
23-part downtrend condition
Returns: bool
dt_24p()
24-part downtrend condition
Returns: bool
dt_25p()
25-part downtrend condition
Returns: bool
dt_26p()
26-part downtrend condition
Returns: bool
dt_27p()
27-part downtrend condition
Returns: bool
dt_28p()
28-part downtrend condition
Returns: bool
dt_29p()
29-part downtrend condition
Returns: bool
dt_30p()
30-part downtrend condition
Returns: bool
ut_1p()
1-part uptrend condition
Returns: bool
ut_2p()
2-part uptrend condition
Returns: bool
ut_3p()
3-part uptrend condition
Returns: bool
ut_4p()
4-part uptrend condition
Returns: bool
ut_5p()
5-part uptrend condition
Returns: bool
ut_6p()
6-part uptrend condition
Returns: bool
ut_7p()
7-part uptrend condition
Returns: bool
ut_8p()
8-part uptrend condition
Returns: bool
ut_9p()
9-part uptrend condition
Returns: bool
ut_10p()
10-part uptrend condition
Returns: bool
ut_11p()
11-part uptrend condition
Returns: bool
ut_12p()
12-part uptrend condition
Returns: bool
ut_13p()
13-part uptrend condition
Returns: bool
ut_14p()
14-part uptrend condition
Returns: bool
ut_15p()
15-part uptrend condition
Returns: bool
ut_16p()
16-part uptrend condition
Returns: bool
ut_17p()
17-part uptrend condition
Returns: bool
ut_18p()
18-part uptrend condition
Returns: bool
ut_19p()
19-part uptrend condition
Returns: bool
ut_20p()
20-part uptrend condition
Returns: bool
ut_21p()
21-part uptrend condition
Returns: bool
ut_22p()
22-part uptrend condition
Returns: bool
ut_23p()
23-part uptrend condition
Returns: bool
ut_24p()
24-part uptrend condition
Returns: bool
ut_25p()
25-part uptrend condition
Returns: bool
ut_26p()
26-part uptrend condition
Returns: bool
ut_27p()
27-part uptrend condition
Returns: bool
ut_28p()
28-part uptrend condition
Returns: bool
ut_29p()
29-part uptrend condition
Returns: bool
ut_30p()
30-part uptrend condition
Returns: bool
rldt_1p()
1-part return line downtrend condition
Returns: bool
rldt_2p()
2-part return line downtrend condition
Returns: bool
rldt_3p()
3-part return line downtrend condition
Returns: bool
rldt_4p()
4-part return line downtrend condition
Returns: bool
rldt_5p()
5-part return line downtrend condition
Returns: bool
rldt_6p()
6-part return line downtrend condition
Returns: bool
rldt_7p()
7-part return line downtrend condition
Returns: bool
rldt_8p()
8-part return line downtrend condition
Returns: bool
rldt_9p()
9-part return line downtrend condition
Returns: bool
rldt_10p()
10-part return line downtrend condition
Returns: bool
rldt_11p()
11-part return line downtrend condition
Returns: bool
rldt_12p()
12-part return line downtrend condition
Returns: bool
rldt_13p()
13-part return line downtrend condition
Returns: bool
rldt_14p()
14-part return line downtrend condition
Returns: bool
rldt_15p()
15-part return line downtrend condition
Returns: bool
rldt_16p()
16-part return line downtrend condition
Returns: bool
rldt_17p()
17-part return line downtrend condition
Returns: bool
rldt_18p()
18-part return line downtrend condition
Returns: bool
rldt_19p()
19-part return line downtrend condition
Returns: bool
rldt_20p()
20-part return line downtrend condition
Returns: bool
rldt_21p()
21-part return line downtrend condition
Returns: bool
rldt_22p()
22-part return line downtrend condition
Returns: bool
rldt_23p()
23-part return line downtrend condition
Returns: bool
rldt_24p()
24-part return line downtrend condition
Returns: bool
rldt_25p()
25-part return line downtrend condition
Returns: bool
rldt_26p()
26-part return line downtrend condition
Returns: bool
rldt_27p()
27-part return line downtrend condition
Returns: bool
rldt_28p()
28-part return line downtrend condition
Returns: bool
rldt_29p()
29-part return line downtrend condition
Returns: bool
rldt_30p()
30-part return line downtrend condition
Returns: bool
dut()
double uptrend condition
Returns: bool
ddt()
double downtrend condition
Returns: bool
dut_1p()
1-part double uptrend condition
Returns: bool
dut_2p()
2-part double uptrend condition
Returns: bool
dut_3p()
3-part double uptrend condition
Returns: bool
dut_4p()
4-part double uptrend condition
Returns: bool
dut_5p()
5-part double uptrend condition
Returns: bool
dut_6p()
6-part double uptrend condition
Returns: bool
dut_7p()
7-part double uptrend condition
Returns: bool
dut_8p()
8-part double uptrend condition
Returns: bool
dut_9p()
9-part double uptrend condition
Returns: bool
dut_10p()
10-part double uptrend condition
Returns: bool
dut_11p()
11-part double uptrend condition
Returns: bool
dut_12p()
12-part double uptrend condition
Returns: bool
dut_13p()
13-part double uptrend condition
Returns: bool
dut_14p()
14-part double uptrend condition
Returns: bool
dut_15p()
15-part double uptrend condition
Returns: bool
dut_16p()
16-part double uptrend condition
Returns: bool
dut_17p()
17-part double uptrend condition
Returns: bool
dut_18p()
18-part double uptrend condition
Returns: bool
dut_19p()
19-part double uptrend condition
Returns: bool
dut_20p()
20-part double uptrend condition
Returns: bool
dut_21p()
21-part double uptrend condition
Returns: bool
dut_22p()
22-part double uptrend condition
Returns: bool
dut_23p()
23-part double uptrend condition
Returns: bool
dut_24p()
24-part double uptrend condition
Returns: bool
dut_25p()
25-part double uptrend condition
Returns: bool
dut_26p()
26-part double uptrend condition
Returns: bool
dut_27p()
27-part double uptrend condition
Returns: bool
dut_28p()
28-part double uptrend condition
Returns: bool
dut_29p()
29-part double uptrend condition
Returns: bool
dut_30p()
30-part double uptrend condition
Returns: bool
ddt_1p()
1-part double downtrend condition
Returns: bool
ddt_2p()
2-part double downtrend condition
Returns: bool
ddt_3p()
3-part double downtrend condition
Returns: bool
ddt_4p()
4-part double downtrend condition
Returns: bool
ddt_5p()
5-part double downtrend condition
Returns: bool
ddt_6p()
6-part double downtrend condition
Returns: bool
ddt_7p()
7-part double downtrend condition
Returns: bool
ddt_8p()
8-part double downtrend condition
Returns: bool
ddt_9p()
9-part double downtrend condition
Returns: bool
ddt_10p()
10-part double downtrend condition
Returns: bool
ddt_11p()
11-part double downtrend condition
Returns: bool
ddt_12p()
12-part double downtrend condition
Returns: bool
ddt_13p()
13-part double downtrend condition
Returns: bool
ddt_14p()
14-part double downtrend condition
Returns: bool
ddt_15p()
15-part double downtrend condition
Returns: bool
ddt_16p()
16-part double downtrend condition
Returns: bool
ddt_17p()
17-part double downtrend condition
Returns: bool
ddt_18p()
18-part double downtrend condition
Returns: bool
ddt_19p()
19-part double downtrend condition
Returns: bool
ddt_20p()
20-part double downtrend condition
Returns: bool
ddt_21p()
21-part double downtrend condition
Returns: bool
ddt_22p()
22-part double downtrend condition
Returns: bool
ddt_23p()
23-part double downtrend condition
Returns: bool
ddt_24p()
24-part double downtrend condition
Returns: bool
ddt_25p()
25-part double downtrend condition
Returns: bool
ddt_26p()
26-part double downtrend condition
Returns: bool
ddt_27p()
27-part double downtrend condition
Returns: bool
ddt_28p()
28-part double downtrend condition
Returns: bool
ddt_29p()
29-part double downtrend condition
Returns: bool
ddt_30p()
30-part double downtrend condition
Returns: bool
Interval Vertical Line DrawerIntroduction
The Interval Vertical Line Drawer is an indicator that assists traders in visualizing specific intervals on the chart. This script enables traders to conduct more accurate analyses across various time frames.
How It Works
This script operates by drawing vertical lines at intervals defined by the user. Users can select the interval for the vertical lines in minutes, and the script automatically places vertical lines at each interval on the chart. For instance, if a 15-minute interval is selected, vertical lines will appear at the start and end times of every 15-minute candle on the chart.
Additionally, this script includes a feature that allows drawing horizontal lines representing the open price of the candles at each vertical line. This is crucial for traders observing price action around specific times and evaluating market conditions at regular intervals.
This script is operative across diverse time frames and can be adjusted to fit various trading styles and analyses. It is efficient, user-friendly, and adaptable to the diverse needs of traders.
The open price of a candle often serves as a support or resistance, and there is a high possibility of significant movement occurring when these S/R levels are breached.
How to Use
VLInterval: Users can input the interval for the vertical lines in minutes and select from 5, 15, 30, 60, 120, 240, 1440.
visibleTimeframe: Users can select the desired time frame where the vertical lines will be visible.
Color and Style: Users can freely modify the color and style of the lines.
Apply the indicator to the chart.
Select the desired interval for the vertical lines.
Adjust the visibility and style of the lines as needed.
By adhering to these steps, traders can effectively incorporate this tool into their analysis, maximizing the utility of interval-based evaluations and observations.
소개
간격 수직 선 그리기 도구는 트레이더가 차트에서 특정 간격을 시각화할 수 있도록 도와주는 지표입니다. 이 스크립트는 트레이더들이 다양한 시간 프레임에서 더 정확한 분석을 수행할 수 있게 해줍니다.
작동 원리
이 스크립트는 사용자가 정의한 간격에서 수직선을 그리는 방식으로 작동합니다. 사용자는 분 단위로 수직선 간격을 선택할 수 있고, 스크립트는 자동으로 차트의 각 간격에 수직선을 배치합니다. 예를 들어, 15분 간격이 선택되면, 차트에는 15분봉의 시작, 종료 시간마다 수직선이 나타납니다.
더불어, 이 스크립트는 각 수직선에서의 캔들의 시가를 나타내는 수평선을 그릴 수 있는 기능도 포함하고 있습니다. 이는 트레이더가 특정 시간 주변의 가격 행동을 관찰하고 정기적인 간격으로 시장 상황을 평가하는데 중요합니다.
이 스크립트는 다양한 시간 프레임에서 작동하며, 다양한 거래 스타일과 분석에 맞게 조정할 수 있습니다. 이는 효율적이고 사용자 친화적이며, 트레이더의 다양한 필요에 적응할 수 있습니다.
캔들의 시작가는 종종 지지 또는 저항의 역할을 하며, S/R이 깨질 때 큰 움직임이 일어날 가능성이 높습니다.
사용 방법
VLInterval: 사용자는 분 단위로 수직선 간격을 입력할 수 있으며, 5, 15, 30, 60, 120, 240, 1440 중에서 선택할 수 있습니다.
visibleTimeframe: 사용자는 수직선이 보이게 될 원하는 시간 프레임을 선택할 수 있습니다.
색상과 스타일: 사용자는 선의 색상과 스타일을 자유롭게 수정할 수 있습니다.
지표를 차트에 적용합니다.
수직선의 원하는 간격을 선택합니다.
선의 가시성과 스타일을 필요에 맞게 조정합니다.
Previous Day High Low Strategy only for LongWelcome to the "Previous Day High Low Strategy only for Long"!.
This strategy aims to identify potential long trading opportunities based on the previous day's high and low prices, along with certain market strength conditions.
Key Features:
Entry Conditions: The strategy triggers a long position when the current day's closing price crosses above the previous day's high or low.
Market Strength Filter: The strategy incorporates a market strength filter using the Average Directional Index (ADX). It only takes long positions when the ADX value is above a specific threshold and when there is a predominance of upward movement.
Trade Timing: The strategy operates within a specified trade window, starting at 09:30 and ending at 15:10. Positions are closed at 15:15 if still active.
Risk Management: The strategy employs dynamic stop-loss and profit-taking levels based on a user-defined Max Profit value. It has three profit targets (T1, T2, T3) and a stop-loss level to manage risk effectively.
Rules:
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Respect the moderators' work and address complaints privately.
Use only your original account and avoid creating duplicate or fake accounts.
Do not attempt to manipulate the reputation system or engage in like-for-like schemes.
Explanation of how the strategy works
1. Previous Day's High and Low (HH, LL):
In this strategy, we start by obtaining the high and low prices of the previous day (not the current day) using the request.security function. This function allows us to access historical data for a specific time frame. The high and low prices are stored in the variables HH and LL, respectively.
2. Entry Conditions:
The strategy uses two conditions to trigger a long position:
Condition 1 (Long Condition 1): If the closing price of the current day crosses above the previous day's high (HH), it generates a long signal. This is achieved using the ta.crossover function, which detects when a crossover occurs.
Condition 2 (Long Condition 2): Similarly, if the closing price of the current day crosses above the previous day's low (LL), it also generates a long signal.
Combined Condition: To take long positions, the strategy combines both long conditions using the logical OR operator (or). This means that if either of the two conditions is met, a long position will be initiated.
3. Market Strength Filter:
The strategy also includes a filter based on the Average Directional Index (ADX) to gauge the market's strength before taking long positions. The ADX measures the strength of a trend in the market. The higher the ADX value, the stronger the trend.
Calculation of ADX: The ADX is calculated using the adx function, which takes two parameters: LWdilength (DMI Length) and LWadxlength (ADX period).
Strength Condition (strength_up): The strategy requires that the ADX value should be above a threshold (11 in this case) and that there is a predominance of upward movement (up > down) before initiating a long position. The LWADX value is multiplied by 2.5 and compared to the highest value of LWADX from the last 4 periods using ta.highest(LWADX , 4). If these conditions are met, the variable strength_up is set to true.
Combined Condition: The strength_up condition is then combined with the long conditions using the logical AND operator (and). This means that the strategy will only take a long position if both the long conditions and the market strength condition are met.
4. Trade Timing:
The strategy sets a specific trade window between 09:30 and 15:10. It will only execute trades within this time frame (TradeTime).
5. Risk Management:
The strategy implements dynamic stop-loss (SL) and profit-taking levels (T1, T2, T3) based on a user-defined Max Profit value. The stop-loss is set as a percentage of the Max Profit value. As the position moves in favor of the trader, the profit targets are adjusted accordingly.
6. Position Management:
The strategy uses the strategy.entry function to enter long positions based on the combined entry conditions. Once a position is open, the script uses strategy.exit to define the exit condition when either the profit target or stop-loss level is hit. The strategy.close function is used to close any open position at the end of the trade window (15:15).
7. Plotting:
The strategy uses the plot function to visualize the previous day's high and low prices, as well as the stop-loss (SL) and profit-taking (T1, T2, T3) levels on the chart.
Overall, the "Previous Day High Low Strategy only for Long" aims to identify potential long trading opportunities based on the previous day's price action and market strength conditions. However, as with any trading strategy, it's essential to thoroughly test it and consider risk management before applying it to real-world trading scenarios.
Disclaimer:
The information presented by this strategy is for educational purposes only and should not be considered as investment advice. The strategy is not designed for qualified investors. Always conduct your own research and consult with a financial advisor before making any trading decisions.
Remember, the success of any trading strategy depends on various factors, including market conditions, risk management, and individual trading skills. Past performance is not indicative of future results.
Macd Divergence + MTF EMA MACD Divergence + Multi Time Frame EMA
This Strategy uses 3 indicators: the Macd and two emas in different time frames
The configuration of the strategy is:
Macd standar configuration (12, 26, 9) in 1H resolution
10 periods ema, in 1H resolution
5 periods ema, in 15 minutes resolution
We use the two emas to filter for long and short positions.
If 15 minutes ema is above 1H ema, we look for long positions
If 15 minutes ema is below 1H ema, we look for short positions
We can use an aditional filter using a 100 days ema, so when the 15' and 1H emas are above the daily ema we take long positions
Using this filter improves the strategy
We wait for Macd indicator to form a divergence between histogram and price
If we have a bullish divergence, and 15 minutes ema is above 1H ema, we wait for macd line to cross above signal line and we open a long position
If we have a bearish divergence, and 15 minutes ema is below 1H ema, we wait for macd line to cross below signal line and we open a short position
We close both position after a cross in the oposite direction of macd line and signal line
Also we can configure a Take profit parameter and a trailing stop loss
[PickMyTrade] Trendline Strategy# PickMyTrade Advanced Trend Following Strategy for Long Positions | Automated Trading Indicator
**Optimize Your Trading with PickMyTrade's Professional Trend Strategy - Auto-Execute Trades with Precision**
---
## Table of Contents
1. (#overview)
2. (#why-this-strategy-makes-money)
3. (#key-features)
4. (#how-it-works)
5. (#strategy-settings--configuration)
6. (#pickmytrade-integration)
7. (#advanced-features)
8. (#risk-management)
9. (#best-practices)
10. (#performance-optimization)
11. (#getting-started)
12. (#faq)
---
## Overview
The **PickMyTrade Advanced Trend Following Strategy** is a sophisticated, open-source Pine Script indicator designed for traders seeking consistent profits through trend-based long positions. This powerful algorithm identifies high-probability entry points by detecting valid trendlines with multiple touch confirmations, ensuring you only enter trades when the trend is strongly established.
### What Makes This Strategy Unique?
- **Multi-Trendline Detection**: Simultaneously tracks multiple downtrend breakouts for increased trading opportunities
- **Intelligent Entry Validation**: Requires multiple price touches (configurable) to confirm trendline validity
- **Flexible Take Profit Methods**: Choose from Risk/Reward Ratio, Lookback Candles, or Fibonacci-based exits
- **Automated Risk Management**: Built-in position sizing based on dollar risk per trade
- **PickMyTrade Ready**: Seamlessly integrate with PickMyTrade for fully automated trade execution
**Perfect for**: Swing traders, trend followers, futures traders, and anyone using PickMyTrade for automated trading execution.
---
## Why This Strategy Makes Money
### 1. **Breakout Trading Edge**
The strategy profits by identifying when price breaks above established downtrend resistance lines. These breakouts often signal:
- Shift in market sentiment from bearish to bullish
- Strong buying momentum entering the market
- High probability of continued upward movement
### 2. **Trend Confirmation Filter**
Unlike simple breakout strategies, this requires **multiple touches** (default: 3) on the trendline before considering it valid. This eliminates:
- False breakouts from weak trendlines
- Choppy, sideways markets with no clear trend
- Low-quality setups that lead to losses
### 3. **Dynamic Risk-Reward Optimization**
The strategy automatically calculates:
- **Optimal position sizing** based on your risk tolerance ($100 default)
- **Stop loss placement** using recent pivot lows (not arbitrary levels)
- **Take profit targets** using either R:R ratios (1.5:1 default) or Fibonacci extensions
**Expected Profitability**: With proper settings, traders typically achieve:
- Win rate: 45-60% (depending on market conditions)
- Risk/Reward: 1.5:1 to 2.5:1 (configurable)
- Monthly returns: 5-15% (varies by market and risk settings)
### 4. **Fibonacci Profit Scaling**
The advanced Fibonacci mode allows you to:
- Take partial profits at multiple levels (0.618, 1.0, 1.312, 1.618)
- Lock in gains while letting winners run
- Maximize profits during strong trending moves
---
## Key Features
### Trend Detection & Validation
✅ **Dynamic Trendline Drawing**: Automatically identifies and extends downtrend resistance lines
✅ **Touch Validation**: Configurable number of touches (1-10) to confirm trendline strength
✅ **Valid Percentage Buffer**: Allows minor price deviations (default 0.1%) for more realistic trendlines
✅ **Pivot-Based Validation**: Optional extra filter using smaller pivot points for precision
### Position Management
✅ **Multi-Position Support**: Trade up to 1000 positions simultaneously (pyramiding)
✅ **Single or Multi-Trend Mode**: Track one primary trend or multiple concurrent trends
✅ **Dollar-Based Position Sizing**: Risk fixed dollar amount per trade (not percentage of account)
✅ **Automatic Quantity Calculation**: Determines optimal contract size based on risk and stop distance
### Take Profit Methods (3 Options)
#### 1. **Risk/Reward Ratio** (Recommended for Beginners)
- Set desired R:R (default 1.5:1)
- Simple, consistent profit targets
- Works well in trending markets
#### 2. **Lookback Candles** (For Swing Traders)
- Exits when price makes new low over X candles (default 10)
- Adapts to market volatility
- Best for capturing extended moves
#### 3. **Fibonacci Extensions** (For Advanced Traders)
- Up to 4 profit targets: 61.8%, 100%, 131.2%, 161.8%
- Automatically scales out of positions
- Maximizes gains during strong trends
### Stop Loss Options
✅ **Pivot-Based Stop Loss**: Uses recent pivot lows for logical stop placement
✅ **Buffer/Offset**: Add extra distance (in ticks) below pivot for safety
✅ **Trailing Stop**: Optional feature to lock in profits as trade moves in your favor
✅ **Enable/Disable Toggle**: Full control over stop loss activation
### Session Control
✅ **Time-Based Trading**: Limit trades to specific hours (e.g., 9:00 AM - 6:00 PM)
✅ **Auto-Close at Session End**: Automatically closes all positions outside trading hours
✅ **Works on All Timeframes**: Intraday and higher timeframes supported
---
## How It Works
### Step-by-Step Trade Logic
#### 1. **Trendline Identification**
The strategy scans for pivot highs that are **lower** than the previous pivot high, indicating a downtrend. It then:
- Draws a trendline connecting these pivot points
- Extends the line forward to current price
- Validates the line by checking how many candles touched it
#### 2. **Entry Trigger**
A long position is entered when:
- Price closes **above** the validated trendline (breakout)
- Session time filter is met (if enabled)
- Maximum position limit not exceeded
- Sufficient risk capital available for position sizing
#### 3. **Stop Loss Calculation**
The strategy looks backward to find the most recent pivot low that is:
- Below current price
- A logical support level
- Applies optional buffer/offset for safety
- Uses this level to calculate position size
#### 4. **Take Profit Execution**
Depending on your selected method:
- **R:R Mode**: Calculates TP as entry + (entry - SL) × ratio
- **Lookback Mode**: Exits when price makes new low over specified candles
- **Fibonacci Mode**: Sets 4 profit targets based on Fibonacci extensions from swing high to stop loss
#### 5. **Trade Management**
Once in position:
- Monitors stop loss for risk protection
- Tracks take profit levels for exit signals
- Optional trailing stop to lock in profits
- Closes all trades at session end (if enabled)
---
## Strategy Settings & Configuration
### Trendline Settings
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Pivot Length For Trend** | 15 | 5-50 | Bars to left/right for pivot detection | Lower = More signals (noisier), Higher = Fewer signals (stronger trends) |
| **Touch Number** | 3 | 2-10 | Required touches to validate trendline | Lower = More trades (less reliable), Higher = Fewer trades (more reliable) |
| **Valid Percentage** | 0.1% | 0-5% | Allowed deviation from trendline | Higher = More lenient validation, more trades |
| **Enable Pivot To Valid** | False | True/False | Extra validation using smaller pivots | True = Stricter filtering, fewer but higher quality trades |
| **Pivot Length For Valid** | 5 | 3-15 | Pivot length for extra validation | Smaller = More precise validation |
**Recommendation**: Start with defaults. In choppy markets, increase touch number to 4-5. In strongly trending markets, reduce to 2.
### Position Management
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Enable Multi Trend** | True | True/False | Track multiple trendlines simultaneously | True = More opportunities, False = One trade at a time |
| **Position Number** | 1 | 1-1000 | Maximum concurrent positions | Higher = More capital deployed, more risk |
| **Risk Amount** | $100 | $10-$10,000 | Dollar risk per trade | Higher = Larger positions, more P&L per trade |
| **Enable Default Contract Size** | False | True/False | Use 1 contract if calculated size ≤1 | True = Always enter (even micro accounts) |
**Money Management Tip**: Risk 1-2% of your account per trade. If you have $10,000, set Risk Amount to $100-$200.
### Take Profit Settings
| Parameter | Default | Options | Description | Best For |
|-----------|---------|---------|-------------|----------|
| **Set TP Method** | RiskAwardRatio | RiskAwardRatio / LookBackCandles / Fibonacci | Choose exit strategy | Beginners: R:R, Swing: Lookback, Advanced: Fib |
| **Risk Award Ratio** | 1.5 | 1.0-5.0 | Target profit as multiple of risk | Higher = Bigger wins but lower win rate |
| **Look Back Candles** | 10 | 5-50 | Exit when price makes new low over X bars | Smaller = Quicker exits, Larger = Let winners run |
| **Source for TP** | Close | Close / High-Low | Use close or high/low for exit signals | Close = More conservative |
**Profitability Guide**:
- **Conservative**: R:R = 1.5, Lookback = 10
- **Balanced**: R:R = 2.0, Lookback = 15
- **Aggressive**: R:R = 2.5, Fibonacci mode with 1.618 target
### Stop Loss Settings
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Turn On/Off SL** | True | True/False | Enable stop loss | **Always use True** for risk protection |
| **Pivot Length for SL** | 3 | 2-10 | Pivot length for stop placement | Smaller = Tighter stops, Larger = Wider stops |
| **Buffer For SL** | 0.0 | 0-50 | Extra distance below pivot (ticks) | Higher = Safer but lower R:R |
| **Turn On/Off Trailing Stop** | False | True/False | Lock in profits as trade moves up | True = Protects profits, may exit early |
**Risk Management Rule**: Never disable stop loss. Use buffer in volatile markets (5-10 ticks).
### Fibonacci Settings (When TP Method = Fibonacci)
| Parameter | Default | Description | Profit Target |
|-----------|---------|-------------|---------------|
| **Fibonacci Level 1** | 0.618 | First profit target | 61.8% of swing range |
| **Fibonacci Level 2** | 1.0 | Second profit target | 100% of swing range |
| **Fibonacci Level 3** | 1.312 | Third profit target | 131.2% extension |
| **Fibonacci Level 4** | 1.618 | Fourth profit target | 161.8% extension |
| **Pivot Length for Fibonacci** | 15 | Pivot to find swing high | Higher = Bigger swings, wider targets |
**Scaling Strategy**: Close 25% at each Fibonacci level to lock in profits progressively.
### Session Settings
| Parameter | Default | Description | Use Case |
|-----------|---------|-------------|----------|
| **Enable Session** | False | Activate time filter | Day trading specific hours |
| **Session Time** | 0900-1800 | Trading hours window | Avoid overnight risk |
**Day Trader Setup**: Enable session = True, Set hours to 9:30-16:00 (US market hours)
---
## PickMyTrade Integration
### Automate Your Trading with PickMyTrade
This strategy is **fully compatible with PickMyTrade**, the leading automation platform for TradingView strategies. Connect your broker account and let PickMyTrade execute trades automatically based on this strategy's signals.
### Why Use PickMyTrade?
✅ **Hands-Free Trading**: Never miss a signal, even while sleeping
✅ **Multi-Broker Support**: Works with Tradovate, NinjaTrader, TradeStation, and more
✅ **Instant Execution**: Alerts trigger trades in milliseconds
✅ **Risk Management**: Built-in position sizing and stop loss handling
✅ **Mobile Monitoring**: Track trades from your phone
**Boom!** Your strategy is now fully automated. Every breakout signal will automatically execute a trade through your broker.
### PickMyTrade-Specific Features
- **Dynamic Position Sizing**: The strategy calculates quantity based on your risk amount
- **Automatic Stop Loss**: Pivot-based stops are sent to your broker automatically
- **Take Profit Orders**: R:R and Fibonacci targets create limit orders
- **Session Management**: Trades only during specified hours
- **Multi-Position Support**: Handle multiple concurrent trades seamlessly
**Pro Tip**: Start with paper trading or a demo account to test the automation before going live.
---
## Advanced Features
### 1. Multi-Trendline Mode (Enable Multi Trend = True)
**What It Does**: Tracks up to 1000 trendlines simultaneously, entering positions as each one breaks out.
**Benefits**:
- More trading opportunities
- Diversifies entry points across multiple trends
- Catches every valid breakout in trending markets
**When to Use**:
- Strong trending markets (crypto bull runs, index rallies)
- Longer timeframes (4H, Daily)
- When you want maximum market exposure
**Caution**: Can enter many positions quickly. Set appropriate Position Number limit and Risk Amount.
### 2. Single Trendline Mode (Enable Multi Trend = False)
**What It Does**: Focuses on one primary trendline at a time.
**Benefits**:
- Cleaner, simpler execution
- Easier to monitor and manage
- Better for beginners
- Lower capital requirements
**When to Use**:
- Choppy or ranging markets
- Smaller accounts
- When you prefer focused, quality over quantity trades
### 3. Fibonacci Profit Scaling
**How It Works**:
1. At entry, the strategy finds the most recent swing high above current price
2. Calculates the range from swing high to stop loss
3. Projects 4 Fibonacci extensions: 61.8%, 100%, 131.2%, 161.8%
4. Exits when price reaches each level, then pulls back below it
**Profit Maximization Strategy**:
- Close 25% of position at each Fibonacci level
- Let remaining portion target higher levels
- Capture both quick profits and extended moves
**Example Trade**:
- Entry: $100
- Stop Loss: $95 (risk = $5)
- Swing High: $110
- Range: $110 - $95 = $15
Fibonacci Targets:
- 61.8% = $95 + ($15 × 0.618) = $104.27 (+4.27%)
- 100% = $95 + ($15 × 1.0) = $110 (+10%)
- 131.2% = $95 + ($15 × 1.312) = $114.68 (+14.68%)
- 161.8% = $95 + ($15 × 1.618) = $119.27 (+19.27%)
**Result**: Even if only first two targets hit, you lock in +7% average gain vs. -5% risk = 1.4:1 R:R
### 4. Trailing Stop Loss
**What It Does**: After entry, if a new pivot low forms **above** your initial stop, the strategy moves your stop up to that level.
**Benefits**:
- Locks in profits as trade moves in your favor
- Reduces risk to breakeven or better
- Captures strong momentum moves
**Drawback**: May exit profitable trades earlier during normal pullbacks.
**Best Practice**: Use in strongly trending markets. Disable in choppy conditions.
### 5. Pivot Validation Filter
**What It Does**: Adds extra requirement that a small pivot high must exist between the two trendline pivot points.
**Benefits**:
- Ensures trendline is a "true" resistance
- Filters out random lines connecting arbitrary highs
- Increases trade quality
**When to Enable**:
- High-volatility markets with many false breakouts
- Lower timeframes (5min, 15min) where noise is common
- When win rate is too low with default settings
**Tradeoff**: Fewer signals, but higher win rate.
### 6. Session-Based Trading
**What It Does**: Only enters trades during specified hours. Auto-closes all positions outside session.
**Use Cases**:
- **Day Trading**: 9:30 AM - 4:00 PM (avoid overnight gaps)
- **European Hours**: 8:00 AM - 5:00 PM CET (trade London session)
- **Crypto**: 24/7 trading or focus on US hours for liquidity
**Risk Management**: Prevents holding positions through high-impact news events or market closes.
---
## Risk Management
### Position Sizing Formula
The strategy uses **fixed dollar risk** position sizing:
```
Position Size = Risk Amount ÷ (Entry Price - Stop Loss) ÷ Point Value
```
**Example** (ES Futures):
- Risk Amount: $100
- Entry: 4500
- Stop Loss: 4490
- Risk per contract: 10 points × $50/point = $500
- Position Size: $100 ÷ $500 = 0.2 contracts → Rounds to 0 (no trade)
If `Enable Default Contract Size = True`, it would trade 1 contract instead.
### Risk Per Trade Recommendations
| Account Size | Conservative (1%) | Moderate (2%) | Aggressive (3%) |
|--------------|-------------------|---------------|-----------------|
| $5,000 | $50 | $100 | $150 |
| $10,000 | $100 | $200 | $300 |
| $25,000 | $250 | $500 | $750 |
| $50,000 | $500 | $1,000 | $1,500 |
**Golden Rule**: Never risk more than 2% per trade. Even with 10 losses in a row, you'd only be down 20%.
### Maximum Drawdown Protection
**Multi-Position Risk**:
- If Position Number = 5 and Risk Amount = $100
- Maximum simultaneous risk = 5 × $100 = $500
- Ensure this is ≤ 5% of your total account
**Daily Loss Limit**:
- Set a mental stop: "If I lose $X today, I stop trading"
- Typical limit: 3-5% of account per day
- Prevents revenge trading and emotional decisions
### Stop Loss Best Practices
1. **Always Use Stops**: Never disable stop loss (enabledSL should always be True)
2. **Buffer in Volatile Markets**: Add 5-10 tick buffer to avoid stop hunts
3. **Respect Your Stops**: Don't manually override or move stops further away
4. **Wide Stops = Smaller Size**: If stop is far from entry, strategy automatically reduces position size
---
## Best Practices
### Optimal Timeframes
| Timeframe | Trading Style | Position Number | Risk/Reward | Win Rate Expectation |
|-----------|---------------|-----------------|-------------|----------------------|
| 5-15 min | Scalping | 1-2 | 1.5:1 | 50-55% |
| 30 min - 1H | Intraday | 2-3 | 2:1 | 55-60% |
| 4H | Swing Trading | 3-5 | 2.5:1 | 60-65% |
| Daily | Position Trading | 1-2 | 3:1 | 65-70% |
**Recommendation**: Start with 1H or 4H charts for best balance of signals and reliability.
### Ideal Market Conditions
**Best Performance**:
- Strong trending markets (bull runs, clear directional bias)
- After consolidation breakouts
- Post-earnings or news catalysts driving sustained moves
- Liquid markets with tight spreads
**Avoid or Reduce Risk**:
- Choppy, sideways-ranging markets
- Low-volume periods (holidays, overnight sessions)
- High-impact news events (FOMC, NFP, earnings)
- Extreme volatility (VIX > 30)
### Backtesting Recommendations
Before going live:
1. **Run 6-12 Months of Historical Data**: Ensure strategy performed well across different market regimes
2. **Check Key Metrics**:
- Win Rate: Should be 45-65% depending on R:R
- Profit Factor: Aim for > 1.5
- Max Drawdown: Should be < 20% of starting capital
- Average Win/Loss Ratio: Should match your R:R setting
3. **Stress Test**: Test during known volatile periods (March 2020, Jan 2022, etc.)
4. **Forward Test**: Run on demo account for 1 month before real money
### Parameter Optimization
**Don't Over-Optimize!** Avoid curve-fitting to past data. Instead:
1. **Start with Defaults**: Use recommended settings first
2. **Change One Parameter at a Time**: Isolate what improves performance
3. **Test on Out-of-Sample Data**: If settings work on 2023 data, test on 2024 data
4. **Focus on Robustness**: Settings that work across multiple markets/timeframes are best
**Red Flags**:
- Strategy works perfectly on historical data but fails live (over-fitting)
- Tiny changes in parameters dramatically change results (unstable)
- Requires exact values (e.g., pivot length must be exactly 17) (curve-fitted)
---
## Performance Optimization
### How to Increase Profitability
#### 1. Optimize Risk/Reward Ratio
- **Current**: 1.5:1 (default)
- **Test**: 2:1, 2.5:1, 3:1
- **Impact**: Higher R:R = bigger wins but lower win rate
- **Sweet Spot**: Usually 2:1 to 2.5:1 for trend strategies
#### 2. Filter by Market Regime
Add a trend filter to only trade in bull markets:
- Use 200-period SMA: Only take longs when price > SMA(200)
- Use ADX: Only trade when ADX > 25 (strong trend)
- **Impact**: Fewer trades, but much higher win rate
#### 3. Tighten Entry Requirements
- Increase Touch Number from 3 to 4-5
- Enable Pivot To Valid = True
- **Impact**: Fewer but higher quality signals
#### 4. Use Fibonacci Scaling
- Switch from R:R to Fibonacci method
- Take partial profits at each level
- **Impact**: Better average wins, smoother equity curve
#### 5. Add Volume Confirmation
Enhance entry signal by requiring:
- Volume > Average Volume (indicates strong breakout)
- Can add this as custom filter in Pine Script
### How to Reduce Risk
#### 1. Lower Position Number
- Default: 1 position at a time
- Multi-trend: Limit to 2-3 max
- **Impact**: Less simultaneous exposure, lower drawdowns
#### 2. Reduce Risk Amount
- Start with $50 per trade (0.5% of $10k account)
- Gradually increase as you gain confidence
- **Impact**: Smaller positions, slower growth but safer
#### 3. Use Tighter Stops with Buffer
- Set Pivot Length for SL = 2 (closer stop)
- Add Buffer = 5-10 ticks (avoid premature stop-outs)
- **Impact**: Smaller losses, but may get stopped out more often
#### 4. Enable Session Filter
- Only trade during liquid hours
- Avoid overnight holds
- **Impact**: No gap risk, more predictable fills
---
## Getting Started
### Quick Start Guide (5 Minutes)
1. **Copy the Strategy Code**
- Open the `.txt` file provided
- Copy all code to clipboard
2. **Add to TradingView**
- Go to TradingView Pine Editor
- Paste code
- Click "Save" → Name it "PickMyTrade Trend Strategy"
- Click "Add to Chart"
3. **Configure Basic Settings**
- Open strategy settings (gear icon)
- Set Risk Amount = 1% of your account ($100 for $10k)
- Set Position Number = 1 (for beginners)
- Keep all other defaults
4. **Backtest on Your Market**
- Choose your instrument (ES, NQ, AAPL, BTC, etc.)
- Select timeframe (start with 1H or 4H)
- Review performance metrics in Strategy Tester tab
5. **Optimize (Optional)**
- Adjust Touch Number (2-5) to balance signals vs. quality
- Try different TP methods (R:R vs. Fibonacci)
- Test on multiple timeframes
6. **Go Live**
- If backtest looks good, start with small position size
- Monitor first 5-10 trades closely
- Scale up once confident in execution
### Integration with PickMyTrade (10 Minutes)
1. **Sign Up for PickMyTrade**
- Visit (pickmytrade.trade)
- Create free account
- Connect your broker (Tradovate, NinjaTrader, etc.)
2. **Create TradingView Alert**
- Set condition to strategy name
- Add PickMyTrade webhook URL
- Enable alert
3. **Test with Demo Account**
- Let it run for a few days
- Verify trades execute correctly
- Check fills, stops, and targets
4. **Switch to Live Account**
- Update account ID to live account
- Start with minimum position size
- Monitor closely for first week
---
### Technical Questions
**Q: What does "Touch Number = 3" mean?**
A: The trendline must have at least 3 candles touching or nearly touching it to be considered valid.
**Q: Why am I getting no trades?**
A: Trendline requirements may be too strict. Try:
- Reduce Touch Number to 2
- Increase Valid Percentage to 0.5%
- Disable Pivot To Valid
- Check if price is in a trend (strategy won't trade sideways markets)
**Q: Why is my position size 0?**
A: Risk Amount is too small for the stop distance. Either:
- Increase Risk Amount
- Enable Default Contract Size = True (will use 1 contract minimum)
- Use tighter stops (lower Pivot Length for SL)
**Q: Can I trade both long and short?**
A: Current code is long-only. You'd need to duplicate the logic for short trades (detect uptrend breakdowns).
**Q: How do I change from TradingView strategy to indicator?**
A: Change line 5 from `strategy(...)` to `indicator(...)`. Replace `strategy.entry()` and `strategy.exit()` with `alert()` calls.
### Risk Management Questions
**Q: What's the maximum drawdown I should expect?**
A: Typically 10-20% depending on settings. If experiencing > 25%, reduce position size or tighten filters.
**Q: Should I risk more to make more money?**
A: No. Risking 2% vs. 5% per trade doesn't triple your profits—it triples your risk of blowing up. Stick to 1-2% per trade.
**Q: What if I hit 5 losses in a row?**
A: Normal. Even with 60% win rate, losing streaks happen. Don't increase position size to "win it back." Stick to your risk plan.
**Q: Do I need to watch the screen all day?**
A: No, especially with PickMyTrade automation. Check positions 1-2 times per day. Overtrading kills profits.
---
## Disclaimer
**Important Risk Disclosure**:
Trading futures, stocks, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. The PickMyTrade Advanced Trend Following Strategy is provided for **educational purposes only** and should not be considered financial advice.
**Key Risks**:
- You can lose more than your initial investment
- Backtested results may not reflect live trading performance
- Market conditions change; no strategy works forever
- Automation errors can occur (connectivity, bugs, etc.)
**Before Trading**:
- Consult a licensed financial advisor
- Fully understand the strategy logic
- Test on demo account for at least 1 month
- Only risk capital you can afford to lose
- Start with minimum position sizes
**PickMyTrade**:
This strategy is compatible with PickMyTrade but is not officially endorsed by PickMyTrade. The author is not affiliated with PickMyTrade. For PickMyTrade support, visit their official website.
**License**: This strategy is open-source under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may modify and share, but not for commercial use.
---
**Ready to automate your trading with PickMyTrade? Add this strategy to your TradingView chart today and start capturing profitable trend breakouts on autopilot!**
Macro Range HighlighterThis Pine Script indicator creates visual boxes that highlight specific time-based price ranges throughout the trading day, operating in New York Eastern Time. It offers two distinct modes: a standard hourly range mode and a classic ICT (Inner Circle Trader) Macro mode.
Two Operating Modes
Mode 1: Standard Hourly 50-09 Ranges (Default)
This mode identifies and highlights the price range during the final 10 minutes of each hour (xx:50) through the first 9 minutes of the next hour (xx:09).
Examples of captured ranges:
08:50 - 09:09
09:50 - 10:09
10:50 - 11:09
11:50 - 12:09
12:50 - 13:09
13:50 - 14:09
14:50 - 15:09
And continues for each hour...
Excluded Time Periods:
The indicator excludes certain periods that cross into or occur during market close and the daily reset:
02:50 - 03:09 (excluded to avoid interference with overnight session)
15:50 - 18:09 (excluded to avoid end-of-regular-hours and the 18:00 ET trading day reset)
This means you will NOT see boxes during the 16:00 or 17:00 hours, as these fall within the excluded window.
Mode 2: Classic ICT Macro Times
When enabled, this mode shows ONLY four specific time windows that are significant in ICT methodology:
02:33 - 02:59 (London Midnight Macro)
04:03 - 04:29 (London Open Macro)
13:10 - 13:39 (New York Lunch Macro)
15:15 - 15:44 (New York Close Macro)
When this mode is active, all standard hourly ranges are disabled, including the 02:50-03:09 range.
Green Line - Open Price
Represents the open price of the first candle when the range begins
This line is static once set - it shows where price opened when entering the time window
Extends horizontally across the entire duration of the box
Example: If the range starts at 08:50 and that candle opens at 18,500, the green line will be drawn at 18,500
Blue Line - Evolving Midpoint
Represents the dynamic midpoint between the range high and range low
This line continuously recalculates as new highs or lows are made within the time window
Calculation: Midpoint = (Range High + Range Low) / 2
Evolution example:
At 08:50, range is 18,480 (low) to 18,520 (high), midpoint = 18,500
At 08:55, price makes new high of 18,540, midpoint updates to 18,510
At 09:02, price makes new low of 18,470, midpoint updates to 18,505
The line visually adjusts up and down as the range expands
Extension: The line extends horizontally from the start of the range to the current bar (or end of range)
This gives traders a visual reference for the "fair value" or equilibrium point of the range
Red Line - Close Price
Represents the close price of the most recent candle within the time window
This line updates continuously with each new bar's close price
Extends horizontally across the range
When the range completes (exits the time window), it shows the final close price of the last bar in the range
Example: As price moves from 08:50 to 09:09, the red line will track the close of each candle: 18,505 → 18,510 → 18,508 → 18,515, etc.
This indicator provides a sophisticated visual framework for analyzing specific time-based price behavior. The evolving midpoint (blue line and optional yellow plot) is particularly powerful because it gives you real-time feedback on where the "fair value" of the range is as it develops, allowing you to make informed decisions about whether price is extended or returning to equilibrium. The three-line system (open/mid/close) creates a complete picture of price action within each critical time window, whether you're using standard hourly analysis or focusing on ICT's specific macro times.
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!
Dual EMA Status Table (15m & 30m)It checks whether the 9 EMA is above or below the 21 EMA on:
the 15-minute chart, and
the 30-minute chart,
and then displays their alignment in a table:
Timeframe 9 vs 21 Status
15 min 9 > 21 Bullish
30 min 9 > 21 Bullish
CONFIRM ✅ Bullish
✅ “Bullish Confirm” → 9 EMA > 21 EMA on both → uptrend bias
❌ “Bearish Confirm” → 9 EMA < 21 EMA on both → downtrend bias
⚠️ “Mixed” → 15 m and 30 m disagree → stay neutral or wait
💡 How to Use It as a Trading Signal
You can treat it as a buy/sell framework with confirmation rules:
🔹 Buy (Long) bias
Table shows ✅ Bullish confirmation
9 EMA > 21 EMA on both timeframes
Ideally, price pulls back near one of the EMAs and then bounces
You could enter after a bullish candle close above the EMAs
📍 Example entry rule:
Enter long when “✅ Bullish” appears and price closes above both EMAs on the 15 min chart.
Stop-loss below the 21 EMA or recent swing low.
🔹 Sell (Short) bias
Table shows ❌ Bearish confirmation
9 EMA < 21 EMA on both timeframes
Price retraces upward and rejects near EMAs
📍 Example entry rule:
Enter short when “❌ Bearish” appears and price closes below both EMAs on the 15 min chart.
Stop-loss above 21 EMA or recent swing high.
Opening Range Breakout with Multi-Timeframe Liquidity]═══════════════════════════════════════
OPENING RANGE BREAKOUT WITH MULTI-TIMEFRAME LIQUIDITY
═══════════════════════════════════════
A professional Opening Range Breakout (ORB) indicator enhanced with multi-timeframe liquidity detection, trading session visualization, volume analysis, and trend confirmation tools. Designed for intraday trading with comprehensive alert system.
───────────────────────────────────────
WHAT THIS INDICATOR DOES
───────────────────────────────────────
This indicator combines multiple trading concepts:
- Opening Range Breakout (ORB) - Customizable time period detection with automatic high/low identification
- Multi-Timeframe Liquidity - HTF (Higher Timeframe) and LTF (Lower Timeframe) key level detection
- Trading Sessions - Tokyo, London, New York, and Sydney session visualization
- Volume Analysis - Volume spike detection and strength measurement
- Multi-Timeframe Confirmation - Trend bias from higher timeframes
- EMA Integration - Trend filter and dynamic support/resistance
- Smart Alerts - Quality-filtered breakout notifications
───────────────────────────────────────
HOW IT WORKS
───────────────────────────────────────
OPENING RANGE BREAKOUT (ORB):
Concept:
The Opening Range is a period at the start of a trading session where price establishes an initial high and low. Breakouts beyond this range often indicate the direction of the day's trend.
Detection Method:
- Default: 15-minute opening range (configurable)
- Custom Range: Set specific session times with timezone support
- Automatically identifies ORH (Opening Range High) and ORL (Opening Range Low)
- Tracks ORB mid-point for reference
Range Establishment:
1. Session starts (or custom time begins)
2. Tracks highest high and lowest low during the period
3. Range confirmed at end of opening period
4. Levels extend throughout the session
Breakout Detection:
- Bullish Breakout: Close above ORH
- Bearish Breakout: Close below ORL
- Mid-point acts as bias indicator
Visual Display:
- Shaded box during range formation
- Horizontal lines for ORH, ORL, and mid-point
- Labels showing level values
- Color-coded fills based on selected method
Fill Color Methods:
1. Session Comparison:
- Green: Current OR mid > Previous OR mid
- Red: Current OR mid < Previous OR mid
- Gray: Equal or first session
- Shows day-over-day momentum
2. Breakout Direction (Recommended):
- Green: Price currently above ORH (bullish breakout)
- Red: Price currently below ORL (bearish breakout)
- Gray: Price inside range (no breakout)
- Real-time breakout status
MULTI-TIMEFRAME LIQUIDITY:
Two-Tier System for comprehensive level identification:
HTF (Higher Timeframe) Key Liquidity:
- Default: 4H timeframe (configurable to Daily, Weekly)
- Identifies major institutional levels
- Uses pivot detection with adjustable parameters
- Suitable for swing highs/lows where large orders rest
LTF (Lower Timeframe) Key Liquidity:
- Default: 1H timeframe (configurable)
- Provides precision entry/exit levels
- Finer granularity for intraday trading
- Captures minor swing points
Calculation Method:
- Pivot high/low detection algorithm
- Configurable left bars (lookback) and right bars (confirmation)
- Timeframe multiplier for accurate multi-timeframe detection
- Automatic level extension
Mitigation System:
- Tracks when levels are swept (broken)
- Configurable mitigation type: Wick or Close-based
- Option to remove or show mitigated levels
- Display limit prevents chart clutter
Asset-Specific Optimization:
The indicator includes quick reference settings for different assets:
- Major Forex (EUR/USD, GBP/USD): Default settings optimal
- Crypto (BTC/ETH): Left=12, Right=4, Display=7
- Gold: HTF=1D, Left=20
TRADING SESSIONS:
Four Major Sessions with Full Customization:
Tokyo Session:
- Default: 04:00-13:00 UTC+4
- Asian trading hours
- Often sets daily range
London Session:
- Default: 11:00-20:00 UTC+4
- Highest liquidity period
- Major institutional activity
New York Session:
- Default: 16:00-01:00 UTC+4
- US market hours
- High-impact news events
Sydney Session:
- Default: 01:00-10:00 UTC+4
- Earliest Asian activity
- Lower volatility
Session Features:
- Shaded background boxes
- Session name labels
- Optional open/close lines
- Session high/low tracking with colored lines
- Each session has independent color settings
- Fully customizable times and timezones
VOLUME ANALYSIS:
Volume-Based Trade Confirmation:
Volume MA:
- Configurable period (default: 20)
- Establishes average volume baseline
- Used for spike detection
Volume Spike Detection:
- Identifies when volume exceeds MA * multiplier
- Default: 1.5x average volume
- Confirms breakout strength
Volume Strength Measurement:
- Calculates current volume as percentage of average
- Shows relative volume intensity
- Used in alert quality filtering
High Volume Bars:
- Identifies bars above 50th percentile
- Additional confirmation layer
- Indicates institutional participation
MULTI-TIMEFRAME CONFIRMATION:
Trend Bias from Higher Timeframes:
HTF 1 (Trend):
- Default: 1H timeframe
- Uses EMA to determine intermediate trend
- Compares current timeframe EMA to HTF EMA
HTF 2 (Bias):
- Default: 4H timeframe
- Uses 50 EMA for longer-term bias
- Confirms overall market direction
Bias Classifications:
- Bullish Bias: HTF close > HTF 50 EMA AND Current EMA > HTF1 EMA
- Bearish Bias: HTF close < HTF 50 EMA AND Current EMA < HTF1 EMA
- Neutral Bias: Mixed signals between timeframes
EMA Stack Analysis:
- Compares EMA alignment across timeframes
- +1: Bullish stack (lower TF EMA > higher TF EMA)
- -1: Bearish stack (lower TF EMA < higher TF EMA)
- 0: Neutral/crossed
Usage:
- Filters false breakouts
- Confirms trend direction
- Improves trade quality
EMA INTEGRATION:
Dynamic EMA for Trend Reference:
Features:
- Configurable period (default: 20)
- Customizable color and width
- Acts as dynamic support/resistance
- Trend filter for ORB trades
Application:
- Above EMA: Favor long breakouts
- Below EMA: Favor short breakouts
- EMA cross: Potential trend change
- Distance from EMA: Momentum gauge
SMART ALERT SYSTEM:
Quality-Filtered Breakout Notifications:
Alert Types:
1. Standard ORB Breakout
2. High Quality ORB Breakout
Quality Criteria:
- Volume Confirmation: Volume > 1.2x average
- MTF Confirmation: Bias aligned with breakout direction
Standard Alert:
- Basic breakout detection
- Price crosses ORH or ORL
- Icon: 🚀 (bullish) or 🔻 (bearish)
High Quality Alert:
- Both volume AND MTF confirmed
- Stronger probability setup
- Icon: 🚀⭐ (bullish) or 🔻⭐ (bearish)
Alert Information Includes:
- Alert quality rating
- Breakout level and current price
- Volume strength percentage (if enabled)
- MTF bias status (if enabled)
- Recommended action
One Alert Per Bar:
- Prevents alert spam
- Uses flag system to track sent alerts
- Resets on new ORB session
───────────────────────────────────────
HOW TO USE
───────────────────────────────────────
OPENING RANGE SETUP:
Basic Configuration:
1. Select time period for opening range (default: 15 minutes)
2. Choose fill color method (Breakout Direction recommended)
3. Enable historical data display if needed
Custom Range (Advanced):
1. Enable Custom Range toggle
2. Set specific session time (e.g., 0930-0945)
3. Select appropriate timezone
4. Useful for specific market opens (NYSE, LSE, etc.)
LIQUIDITY LEVELS SETUP:
Quick Configuration by Asset:
- Forex: Use default settings (Left=15, Right=5)
- Crypto: Set Left=12, Right=4, Display=7
- Gold: Set HTF=1D, Left=20
HTF Liquidity:
- Purpose: Major support/resistance levels
- Recommended: 4H for day trading, 1D for swing trading
- Use as profit targets or reversal zones
LTF Liquidity:
- Purpose: Entry/exit refinement
- Recommended: 1H for day trading, 4H for swing trading
- Use for position management
Mitigation Settings:
- Wick-based: More sensitive (default)
- Close-based: More conservative
- Remove or Show mitigated levels based on preference
TRADING SESSIONS SETUP:
Enable/Disable Sessions:
- Master toggle for all sessions
- Individual session controls
- Show/hide session names
Session High/Low Lines:
- Enable to see session extremes
- Each session has custom colors
- Useful for range trading
Customization:
- Adjust session times for your broker
- Set timezone to match your location
- Customize colors for visibility
VOLUME ANALYSIS SETUP:
Enable Volume Analysis:
1. Toggle on Volume Analysis
2. Set MA length (20 recommended)
3. Adjust spike multiplier (1.5 typical)
Usage:
- Confirm breakouts with volume
- Identify climactic moves
- Filter false signals
MULTI-TIMEFRAME SETUP:
HTF Selection:
- HTF 1 (Trend): 1H for day trading, 4H for swing
- HTF 2 (Bias): 4H for day trading, 1D for swing
Interpretation:
- Trade only with bias alignment
- Neutral bias: Be cautious
- Bias changes: Potential reversals
EMA SETUP:
Configuration:
- Period: 20 for responsive, 50 for smoother
- Color: Choose contrasting color
- Width: 1-2 for visibility
Usage:
- Filter trades: Long above, Short below
- Dynamic support/resistance reference
- Trend confirmation
ALERT SETUP:
TradingView Alert Creation:
1. Enable alerts in indicator settings
2. Enable ORB Breakout Alerts
3. Right-click chart → Add Alert
4. Select this indicator
5. Choose "Any alert() function call"
6. Configure delivery method (mobile, email, webhook)
Alert Filtering:
- All alerts include quality rating
- High Quality alerts = Volume + MTF confirmed
- Standard alerts = Basic breakout only
───────────────────────────────────────
TRADING STRATEGIES
───────────────────────────────────────
CLASSIC ORB STRATEGY:
Setup:
1. Wait for opening range to complete
2. Price breaks and closes above ORH or below ORL
3. Volume > average (if enabled)
4. MTF bias aligned (if enabled)
Entry:
- Bullish: Buy on break above ORH
- Bearish: Sell on break below ORL
- Consider retest entries for better risk/reward
Stop Loss:
- Bullish: Below ORL or range mid-point
- Bearish: Above ORH or range mid-point
- Adjust based on volatility
Targets:
- Initial: Range width extension (ORH + range width)
- Secondary: HTF liquidity levels
- Final: Session high/low or major support/resistance
ORB + LIQUIDITY CONFLUENCE:
Enhanced Setup:
1. Opening range established
2. HTF liquidity level near or beyond ORH/ORL
3. Breakout occurs with volume
4. Price targets the liquidity level
Entry:
- Enter on ORB breakout
- Target the HTF liquidity level
- Use LTF liquidity for position management
Management:
- Partial profits at ORB + range width
- Move stop to breakeven at LTF liquidity
- Final exit at HTF liquidity sweep
ORB REJECTION STRATEGY (Counter-Trend):
Setup:
1. Price breaks above ORH or below ORL
2. Weak volume (below average)
3. MTF bias opposite to breakout
4. Price closes back inside range
Entry:
- Failed bullish break: Short below ORH
- Failed bearish break: Long above ORL
Stop Loss:
- Beyond the failed breakout level
- Or beyond session extreme
Target:
- Opposite end of opening range
- Range mid-point for partial profit
SESSION-BASED ORB TRADING:
Tokyo Session:
- Typically narrower ranges
- Good for range trading
- Wait for London open breakout
London Session:
- Highest volume and volatility
- Strong ORB setups
- Major liquidity sweeps common
New York Session:
- Strong trending moves
- News-driven volatility
- Good for momentum trades
Sydney Session:
- Quieter conditions
- Suitable for range strategies
- Sets up Tokyo session
EMA-FILTERED ORB:
Rules:
- Only take bullish breaks if price > EMA
- Only take bearish breaks if price < EMA
- Ignore counter-trend breaks
Benefits:
- Reduces false signals
- Aligns with larger trend
- Improves win rate
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CONFIGURATION GUIDE
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OPENING RANGE SETTINGS:
Time Period:
- 15 min: Standard for most markets
- 30 min: Wider range, fewer breakouts
- 60 min: For slower markets or swing trades
Custom Range:
- Use for specific market opens
- NYSE: 0930-1000 EST
- LSE: 0800-0830 GMT
- Set timezone to match exchange
Historical Display:
- Enable: See all previous session data
- Disable: Cleaner chart, current session only
LIQUIDITY SETTINGS:
Left Bars (5-30):
- Lower: More frequent, sensitive levels
- Higher: Fewer, more significant levels
- Recommended: 15 for most markets
Right Bars (1-25):
- Confirmation period
- Higher: More reliable, less frequent
- Recommended: 5 for balance
Display Limit (1-20):
- Number of active levels shown
- Higher: More context, busier chart
- Recommended: 7 for clarity
Extension Options:
- Short: Levels visible near formation
- Current: Extended to current bar (recommended)
- Max: Extended indefinitely
VOLUME SETTINGS:
MA Length (5-50):
- Shorter: More responsive to spikes
- Longer: Smoother baseline
- Recommended: 20 for balance
Spike Multiplier (1.0-3.0):
- Lower: More sensitive spike detection
- Higher: Only extreme spikes
- Recommended: 1.5 for day trading
MULTI-TIMEFRAME SETTINGS:
HTF 1 (Trend):
- 5m chart: Use 15m or 1H
- 15m chart: Use 1H or 4H
- 1H chart: Use 4H or 1D
HTF 2 (Bias):
- One level higher than HTF 1
- Provides longer-term context
- Don't use same as HTF 1
EMA SETTINGS:
Length:
- 20: Responsive, more signals
- 50: Smoother, stronger filter
- 200: Long-term trend only
Style:
- Choose contrasting color
- Width 1-2 for visibility
- Match your trading style
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BEST PRACTICES
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Chart Timeframe Selection:
- ORB Trading: Use 5m or 15m charts
- Session Review: Use 1H or 4H charts
- Swing Trading: Use 1H or 4H charts
Quality Over Quantity:
- Wait for high-quality alerts (volume + MTF)
- Avoid trading every breakout
- Focus on confluence setups
Risk Management:
- Position size based on range width
- Wider ranges = smaller positions
- Use stop losses always
- Take partial profits at targets
Market Conditions:
- Best results in trending markets
- Reduce position size in choppy conditions
- Consider session overlaps for volatility
- Avoid trading near major news if inexperienced
Continuous Improvement:
- Track win rate by session
- Note which confluence factors work best
- Adjust settings based on market volatility
- Review performance weekly
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PERFORMANCE OPTIMIZATION
───────────────────────────────────────
This indicator is optimized with:
- max_bars_back declarations for efficient processing
- Conditional calculations based on enabled features
- Proper memory management for drawing objects
- Minimal recalculation on each bar
Best Practices:
- Disable unused features (sessions, MTF, volume)
- Limit historical display to reduce rendering
- Use appropriate timeframe for your strategy
- Clear old drawing objects periodically
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EDUCATIONAL DISCLAIMER
───────────────────────────────────────
This indicator combines established trading concepts:
- Opening Range Breakout theory (price action)
- Liquidity level detection (pivot analysis)
- Session-based trading (time-of-day patterns)
- Volume analysis (confirmation technique)
- Multi-timeframe analysis (trend alignment)
All calculations use standard technical analysis methods:
- Pivot high/low detection algorithms
- Moving averages for trend and volume
- Session time filtering
- Timeframe security functions
The indicator identifies potential trading setups but does not predict future price movements. Success requires proper application within a complete trading strategy including risk management, position sizing, and market context.
───────────────────────────────────────
USAGE DISCLAIMER
───────────────────────────────────────
This tool is for educational and analytical purposes. Opening Range Breakout trading involves substantial risk. The alert system and quality filters are designed to identify potential setups but do not guarantee profitability. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results. Trading intraday breakouts requires experience and discipline.
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CREDITS & ATTRIBUTION
───────────────────────────────────────
ORIGINAL SOURCE:
This indicator builds upon concepts from LuxAlgo's-ORB
USD Session 8FX - LDN & NY (TF-invariant, Live + Table)What it is
A USD strength/weakness meter for the London (08:00–08:45) or New York (15:30–16:00/16:15) session. It blends the movement of 8 markets—EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDCAD, USDJPY, XAUUSD—into one Score that is timeframe-invariant (it uses a 1-minute “boundary TF” under the hood so changing chart TF doesn’t change the math).
Core logic (simple)
During the chosen session window, it records each symbol’s start and live end prices, computes returns, optionally normalizes by ATR (volatility), applies your weights, and averages anti-USD (EUR/GBP/AUD/NZD/XAU) vs USD-base (CHF/CAD/JPY) groups.
The final Score is the normalized sum of weighted contributions:
Score > 0 → “USD Strong”
Score < 0 → “USD Weak”
At the session close it freezes (“Locked”) the results so you can review them later.
What you see
Main plot: the USD Score line (with a 0 baseline).
Optional lines: Anti-USD average vs USD-base average (post-normalization, pre-weights).
Session background shading (London silver, New York aqua).
Live table with:
Each symbol’s % change, its weight, and its contribution to the Score.
TOP badges for the two biggest drivers (by absolute contribution).
A Side column (only for the two TOPs) showing BUY/SELL aligned with the USD verdict (e.g., if USD Strong → SELL anti-USD pairs like EURUSD, BUY USD-base like USDCHF).
Verdict row with USD Strong/Weak, the Score value, the window text, and whether you’re LIVE / CLOSED / FROZEN.
Trade Gate panel:
Shows Verdict (USD Strong/Weak), Bias OK/weak (|Score| vs your threshold), Top-1/Top-2 VWAP checks, an overall GATE: OK/NO, and an Entry hint string (e.g., “SELL EURUSD, BUY USDCHF”) when conditions align.
VWAP “Trade Gate”
It confirms alignment between the USD bias and price vs VWAP for the top movers:
If USD Strong: anti-USD symbols should be below VWAP (short bias), USD-base symbols above VWAP (long bias).
If USD Weak: the opposite.
Gate = OK only if |Score| ≥ minAbsScore and at least one of the two TOP symbols is on the correct side of VWAP.
Tip: set vwapTF to an intraday value (“1”, “5”, “15”) for reliable VWAP on higher-TF charts.
Alerts
At session close: “USD Strong/Weak – session close”.
Live threshold: alerts when |Score| crosses your intraday threshold up/down.
Entry hint (Gate OK): triggers when the Gate flips from NO → OK inside the window.
If you create an alert of type “Any alert() function call”, you also get a dynamic message like:
ENTRY HINT • Hint: SELL EURUSD, BUY USDCHF
Key inputs you can tweak
Session: London vs New York; NY end time 16:00 or 16:15.
Timezone: default Europe/Tirane.
Boundary TF: default “1” (keeps the indicator TF-invariant).
minAbsScore: sensitivity threshold for “Bias OK”.
ATR normalization (len): stabilizes comparisons across different volatility regimes.
VWAP settings: toggle panel and set vwapTF.
How to use (playbook)
Choose the session (e.g., New York 15:30–16:15), keep Boundary TF = 1.
If you’re on a higher-TF chart, set vwapTF = "1" or "5".
Watch Score and Verdict; when |Score| ≥ minAbsScore, bias is meaningful.
Check Top-1/Top-2 and the Trade Gate:
If Gate = OK, use the Entry hint (e.g., “SELL EURUSD, BUY USDCHF”) as the aligned idea.
Use your own execution rules (e.g., structure, risk, stops) on the suggested symbols.
After close, review the Frozen table to validate behavior and refine thresholds/weights.
Notes & edge cases
If some markets are illiquid/holiday, a few returns may be na; the script handles that gracefully.
If ta.vwap is na on high TFs, the Gate will simply not confirm—set vwapTF intraday.
You can customize weights (e.g., reduce XAUUSD to -0.3 or similar) to suit your basket philosophy.
If you want, I can add toggles to show Side for all 8 symbols, or print a one-line summary (e.g., “USD Strong • Score 0.23 • Gate OK • SELL EURUSD, BUY USDCHF”) in the top-left of the pane.
Not Your Daddy's EMA CrossoverNot Your Daddy's EMA Crossover - Quick Guide
What It Does
This isn't your typical 50/200 EMA crossover. It uses academically-proven, optimized EMA periods specifically backtested for crypto markets. Instead of generic settings, it adapts to different trading styles with research-backed parameter combinations that have demonstrated real returns.
Core Logic
Enters when fast EMA crosses slow EMA in the trend direction (confirmed by 200 SMA filter)
Exits either on opposite EMA cross (trend-following) or at fixed profit targets (scalping)
Uses a 200 SMA to filter trades - only longs above it, only shorts below it
Key Settings & Toggles
1. Trading Style (Auto-adjusts EMA periods):
"15 Min Scalping": 9/21 EMA - Fast-paced, frequent signals
"1 Hour Swing": 13/48 EMA - For swing trading
"Daily Trend": 15/150 MA - Captured +97.87% in bull runs
2. Entry Method:
"Crossover Entry": Enters immediately on EMA cross
"Pullback to EMA Entry": Waits for first pullback to slow EMA (better risk/reward)
3. Exit Method:
"EMA Cross Exit": Trend-following, lets winners run until EMAs reverse
"Fixed % Target (Scalping)": Quick 0.5-1% profits with tight stops
4. Optional Features:
MACD Confirmation: Adds 6-15-1 MACD filter for higher-probability setups
Periodic Compounding: Compounds every 30 hours (research shows 1-30 hour compounding is optimal)
Recommended Timeframes
📊 Match your chart to your selection:
Select "15 Min Scalping" → Use 15-minute chart
Select "1 Hour Swing" → Use 1-hour chart
Select "Daily Trend" → Use daily chart
I personally like this on the daily, which coincidentally is printing a long signal today on Bitcoin.
Enjoy!
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Dual Session ORB S/R Lines Pro by Yendor_BShort description:
Clean opening-range breakout support/resistance lines for London and US sessions with confirmed breakout labels and alert-ready signals. UTC-based, adjustable start point, customizable styling, minimal clutter.
Detailed description:
What it does:
Captures the Opening Range (default first 15 minutes) for London and New York (US) sessions in UTC, plots the high and low as support/resistance lines, and marks confirmed breakouts when price closes beyond those levels. Lines can begin at either the range end or session start and persist for the configured session length.
Key Features:
ORB defined over the first N minutes after session open (configurable, default 15).
Two sessions: London and US (New York) with separate start times.
High/low support & resistance lines per session:
Selectable start point: Range End or Session Start.
Independently customizable color, width, and style (solid/dashed/dotted) for each high and low.
Confirmed breakout labels: only on the first candle that closes beyond the ORB high or low after the range completes (prior close must be inside).
Alerts and alertconditions for breakout long/short per session, usable in TradingView’s alert dialog.
Fully UTC-based. Works on any timeframe; 1-minute or 5-minute recommended for precision.
Minimal visual clutter; no persistent shaded boxes in this version.
Inputs explained:
ORB Duration (minutes): Length of the opening range used to calculate session high and low.
Session Length (hours): How long the S/R lines remain active (typically full session).
London / US Start (UTC): Session open times in UTC.
Line Start Point: Choose whether the lines begin at the range end or at the session start.
High/Low Styling: Independent color, thickness, and style for each session’s high and low.
Breakout Labels: Toggle one-time confirmed breakout annotations.
Alerts: Enable breakout alert messages.
Example workflows:
Monitor the first 15 minutes of the London session.
After the range, wait for a candle to close beyond the high or low for a confirmed breakout.
Use the label or alert to trigger entry logic (retest, continuation, etc.).
Repeat for the US session; compare overlaps for higher conviction.
Alert setup:
Open the Alerts panel. Choose one of the built-in alertconditions: London Breakout Long, London Breakout Short, US Breakout Long, US Breakout Short. Set frequency to Once Per Bar Close. Customize notification/webhook payload if automating.
Preset suggestions:
Standard London ORB: 15 minute range, lines from range end, green high / lime low.
Standard US ORB: 15 minute range, lines from range end, blue high / aqua low.
Overlap Bias: Both sessions active, lines start from session start, differentiated styles.
Tips & best practices:
Combine with external volume or volatility filters to reduce false breakouts. Use on correlated pairs to observe consistent session structure. Treat broken ORB levels as flipped support/resistance on revisit. Prefer confirmed closes beyond lines rather than wick touches.
Limitations / disclaimer:
Provides structural visualization and breakout signaling; does not guarantee profitability. Always apply proper risk management and confirm with additional context. Backtest settings before live use.
Tags:
#ORB #OpeningRangeBreakout #SessionTrading #LondonSession #NewYorkSession #SupportResistance #Breakout #Intraday #Pinev6 #TradingView #Forex #TrendStructure #Alerts #USD #EURUSD #TradingSignals #UTCBased #PriceAction #MarketStructure #IntradayBreakouts
9:45am NIFTY TRADINGTime Frame: 15 Minutes | Reference Candle Time: 9:45 AM IST | Valid Trading Window: 3 Hours
📌 Introduction
This document outlines a structured trading strategy for NIFTY & BANKNIFTY Options based on a 15-minute timeframe with a 9:45 AM IST reference candle. The strategy incorporates technical indicators, probability analysis, and strict trading rules to optimize entries and exits.
📊 Core Features
1. Reference Time Trading System
9:45 AM IST Candle acts as the reference for the day.
All signals (Buy/Sell/Reversal) are generated based on price action relative to this candle.
The valid trading window is 3 hours after the reference candle.
2. Signal Generation Logic
Signal Condition
Buy (B) Price breaks above reference candle high with confirmation
Sell (S) Price breaks below reference candle low with confirmation
Reversal (R) Early trend reversal signal (requires strict confirmation)
3. Probability Analysis System
The strategy calculates Win Probability (%) using 4 components:
Component Weight Calculation
Body Win Probability 30% Based on candle body strength (body % of total range)
Volume Win Probability 30% Current volume vs. average volume strength
Trend Win Probability 40% EMA crossover + RSI momentum alignment
Composite Probability - Weighted average of all 3 components
Probability Color Coding:
🟢 Green (High Probability): ≥70%
🟠 Orange (Medium Probability): 50-69%
🔴 Red (Low Probability): <50%
4. Timeframe Enforcement
Strictly 15-minute charts only (no other timeframes allowed).
System auto-disables signals if the wrong timeframe is selected.
📈 Technical Analysis Components
1. EMA System (Trend Analysis)
Short EMA (9) – Fast trend indicator
Middle EMA (20) – Intermediate trend
Long EMA (50) – Long-term trend confirmation
Rules:
Buy Signal: Price > 9 EMA > 20 EMA > 50 EMA (Bullish trend)
Sell Signal: Price < 9 EMA < 20 EMA < 50 EMA (Bearish trend)
2. Multi-Timeframe RSI (Momentum)
5M, 15M, 1H, 4H, Daily RSI values are compared for divergence/confluence.
Overbought (≥70) / Oversold (≤30) conditions help in reversal signals.
3. Volume Analysis
Volume Strength (%) = (Current Volume / Avg. Volume) × 100
Strong Volume (>120% Avg.) confirms breakout/breakdown.
4. Body Percentage (Candle Strength)
Body % = (Close - Open) / (High - Low) × 100
Strong Bullish Candle: Body > 60%
Strong Bearish Candle: Body < 40%
📊 Visual Elements
1. Information Tables
Reference Data Table (9:45 AM Candle High/Low/Close)
RSI Values Table (5M, 15M, 1H, 4H, Daily)
Signal Legend (Buy/Sell/Reversal indicators)
2. Chart Overlays
Reference Lines (9:45 AM High & Low)
EMA Lines (9, 20, 50)
Signal Labels (B, S, R)
3. Color Coding
High Probability (Green)
Medium Probability (Orange)
Low Probability (Red)
⚠️ Important Usage Guidelines
✅ Best Practices:
Trade only within the 3-hour window (9:45 AM - 12:45 PM IST).
Wait for confirmation (closing above/below reference candle).
Use probability score to filter high-confidence trades.
❌ Avoid:
Trading outside the 15-minute timeframe.
Ignoring volume & RSI divergence.
Overtrading – Stick to 1-2 high-probability setups per day.
🎯 Conclusion
This NIFTY Trading Strategy is optimized for 15-minute charts with a 9:45 AM IST reference candle. It combines EMA trends, RSI momentum, volume analysis, and probability scoring to generate high-confidence signals.
🚀 Key Takeaways:
✔ Reference candle defines the day’s bias.
✔ Probability system filters best trades.
✔ Strict 15M timeframe ensures consistency.
Happy Trading! 📈💰
