🔥 Volatility Squeeze Breakout Strategy (TP/SL in Points)This strategy is designed to catch explosive breakout moves from low-volatility consolidations using a "volatility squeeze" + breakout + momentum" approach. It identifies high-probability buy opportunities when the market is in a tight range and preparing for expansion.
✅ Entry Condition:
- Previous candle is in a squeeze
- Current candle breaks above channel high
- Momentum is positive (ROC)
🎯 Exit Conditions:
- Take Profit in fixed points above entry price
- Stop Loss in fixed points below entry price
🧰 Inputs:
- ATR Length for volatility
- Channel Length for breakout levels
- ROC Length for momentum
- Squeeze threshold (ATR/close)
- TP/SL in absolute price points
📊 Plots:
- Buy signals shown as green triangles
- Channel high/low plotted
- TP/SL levels shown as live lines when in position
Suitable for intraday breakout scalping or directional trades
when price expands from compression zones.
Komut dosyalarını "scalping" için ara
Percent Change of Range Candles - FullTHIS IS THE FINAL VERSION
Description of the Percent Change of Range Candles - Full Indicator
This indicator displays the percentage change relative to the price range (high-low) over a specified period and combines multiple timeframes to provide a more accurate view of market trends and momentum. The idea is to not rely solely on classic closing prices but also to consider the opening price, highest, and lowest values within the selected period, offering deeper insight into price movements.
What makes this indicator better than the previous two?
Combination of multiple timeframes and parameters — the indicator calculates not only one level of percentage change but also uses additional calculations for shorter periods (e.g., 25% of the main period length). This allows it to better track subtle changes and provide signals less prone to noise.
Visual representation with colors and horizontal levels — it clearly displays key percentage change levels and whether the current trend is positive or negative, making interpretation easier without additional analysis.
Candle visualization within the indicator (plotcandle) — unlike previous indicators that might display only lines, this one shows candles within the indicator window, allowing you to follow visual patterns that often add extra value.
Better adaptation to trend changes — the indicator dynamically adjusts input parameters depending on price direction (positive or negative change), making it more flexible and suitable for different market conditions.
How the indicator works
It first calculates the percentage change of the closing price relative to the high-low range over a chosen period (default 100 candles).
Then, it calculates the average change value from the previous five periods to smooth out sudden oscillations.
Additionally, it calculates values for a shorter period (25% of the total length), which are dynamically used as "high" or "low" values depending on the current trend (positive or negative).
Horizontal lines mark important percentage change levels (100%, 70%, 0%, -70%, -100%), serving as visual guides for trend strength and direction.
Candles inside the indicator further visualize the relationship between average and current values.
Purpose and application
This indicator is designed to track trend strength and market movements through percentage analysis of price ranges. It can be used for:
Detecting the start and end of trends,
Recognizing strong reversals or confirming trend continuation,
Complementary analysis alongside other technical indicators,
Facilitating decision-making in scalping and swing trading strategies.
What to watch out for
Like any indicator based on historical data, it does not guarantee success and should be used alongside other tools to confirm signals.
The length period (length) should be adjusted according to the market type and trading timeframe — longer periods provide more stable but slower signals, while shorter ones are more sensitive to noise.
Pay attention to the colors and levels on the indicator, as crossing horizontal lines may indicate potential trend reversals or strengthening.
Candles within the indicator window can help identify formations signaling entries or exits.
Choppiness ZONE OverlayPurpose
This script overlays choppiness zones directly onto the price chart to help traders identify whether the market is trending or ranging. It is designed to filter out low-probability trades during high choppiness conditions.
How It Works
Calculates the Choppiness Index over a user-defined period using ATR and price range.
Divides choppiness into four zones:
30 to 40: Low choppiness, possible trend initiation, shown in yellow.
40 to 50: Moderate choppiness, transition zone, shown in orange.
50 to 60: High choppiness, weakening momentum, shown in red.
60 and above: Extreme choppiness, avoid trading, shown in purple.
Highlights each zone with customizable color fills between the high and low of the selected range.
Triggers a real-time alert when choppiness exceeds 60.
Features
Customizable choppiness zones and color settings.
Real-time alert when market becomes extremely choppy (choppiness ≥ 60).
Visual zone overlay on the price chart.
Compatible with all timeframes.
Lightweight and responsive for scalping, intraday, or swing trading.
Tip
Use this tool as a volatility or trend filter. Combine it with momentum or trend-following indicators to improve trade selection.
ATR Bands PRO + Sweep Label + Divergence [MASTER]🔰 ATR Bands PRO + Liquidity Sweep & Divergence (RSI/MACD)
## 🔰 ATR Bands PRO + Liquidity Sweep & Divergence
A powerful institutional-grade toolkit that combines advanced ATR band visualization, customizable stop bands, dynamic grid lines, real-time liquidity sweep detection, and built-in swing-point divergence signals (RSI/MACD) – all fully adjustable.
**Key Features:**
- **Multi-Timeframe ATR Bands:**
Visualize ATR-based bands from any higher timeframe, fully customizable in color, width, style, and extension.
- **Smart Stops & Grid:**
Add stop bands and dynamic ATR grid lines with user control over appearance and step.
- **Liquidity Sweep Detection:**
Instantly see “Bull Sweep” or “Bear Sweep” labels every time price touches high/low liquidity sweeps from your chosen timeframe.
- **Divergence Alerts (RSI/MACD):**
Detect bullish or bearish divergence at swing highs/lows (on the main timeframe) – complete with highly visible, color-customizable labels.
- **Professional, Non-Cluttered Visuals:**
All labels and lines are managed with smart array handling – zero repaint, zero overlay clutter.
**How to Use:**
1. Choose your ATR “base” timeframe and customize band/stop/grid appearance.
2. Pick the Liquidity Sweep timeframe (e.g., H1, H4, D1) for institutional swing levels.
3. Enable divergence detection (RSI or MACD) to reveal hidden reversal signals at market pivots.
4. Adjust label offsets and colors for maximum clarity on your chart.
**Perfect for:**
- Liquidity-driven scalping, swing, and positional strategies.
- Spotting liquidity grabs, institutional sweeps, and “trap” price action.
- Fast visual confirmation of potential reversal zones using built-in divergence signals.
- Traders who demand high-performance, flexible visuals without chart clutter.
---
**Credit:**
Original logic inspired by RunStrat, AlgoAlpha, and custom adaptations by MILO888.
---
*For educational and professional use. Test on your own symbol/timeframe before live trading. Enjoy an edge!*
내 스크립트//@version=5
indicator("Support/Resistance Scalping Strategy", overlay=true)
// === 사용자 설정 ===
support_level = input.float(101000, title="지지선", step=10)
resistance_level = input.float(104000, title="저항선", step=10)
rsi = ta.rsi(close, 14)
bb_upper = ta.bb(close, 20, 2).upper
bb_lower = ta.bb(close, 20, 2).lower
// === 조건 ===
// 롱 조건: 지지선 근처 도달 + RSI < 40 + 볼린저 하단 근접
long_condition = (low <= support_level * 1.002) and (rsi < 40) and (close <= bb_lower)
plotshape(long_condition, title="Long Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="LONG")
// 숏 조건: 저항선 근처 도달 + RSI > 60 + 볼린저 상단 근접
short_condition = (high >= resistance_level * 0.998) and (rsi > 60) and (close >= bb_upper)
plotshape(short_condition, title="Short Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SHORT")
// 시각적 지지/저항선 표시
hline(support_level, "지지선", color=color.green, linestyle=hline.style_dashed)
hline(resistance_level, "저항선", color=color.red, linestyle=hline.style_dashed)
내 스크립트//@version=5
indicator("Support/Resistance Scalping Strategy", overlay=true)
// === 사용자 설정 ===
support_level = input.float(101000, title="지지선", step=10)
resistance_level = input.float(104000, title="저항선", step=10)
rsi = ta.rsi(close, 14)
bb_upper = ta.bb(close, 20, 2).upper
bb_lower = ta.bb(close, 20, 2).lower
// === 조건 ===
// 롱 조건: 지지선 근처 도달 + RSI < 40 + 볼린저 하단 근접
long_condition = (low <= support_level * 1.002) and (rsi < 40) and (close <= bb_lower)
plotshape(long_condition, title="Long Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="LONG")
// 숏 조건: 저항선 근처 도달 + RSI > 60 + 볼린저 상단 근접
short_condition = (high >= resistance_level * 0.998) and (rsi > 60) and (close >= bb_upper)
plotshape(short_condition, title="Short Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SHORT")
// 시각적 지지/저항선 표시
hline(support_level, "지지선", color=color.green, linestyle=hline.style_dashed)
hline(resistance_level, "저항선", color=color.red, linestyle=hline.style_dashed)
1-Min Scalping Strategy with Trailing Stop (1 Contract)This is a 1 min scalp strategy specifically written for NQ futures with consistency in mind and stop losses with trailing stops. Happy trading. *** Not an investment advice***
Mean Absolute Deviation Trend | Lyro RSMean Absolute Deviation Trend
Introduction
Mean Absolute Deviation (MAD) Trend is a precision tool designed to capture directional bias using the Mean Absolute Deviation from a dynamic moving average. It identifies trend shifts by measuring average volatility around price, highlighting bullish and bearish phases through adaptive bands.
Signal Insight
The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 plots a dynamic bands around a user-defined moving average, using Mean Absolute Deviation (MAD) to reflect volatility-adjusted boundaries.
A bullish signal is generated when price breaks above the upper MAD band—indicating positive momentum and potential trend continuation to the upside.
A bearish signal occurs when price falls below the lower MAD band—signaling increased downside pressure and possible trend continuation to the downside.
This approach gives traders a volatility-sensitive trend filter that can enhance signal quality across different market environments.
Real-World Example
𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 delivers a clear and timely long signal, capturing a +22.90% move. Upon exit, it seamlessly flips to a short position, securing an additional +13.34% —demonstrating its strength in both trending directions.
Framework
The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 indicator identifies directional shifts by measuring price deviation from a dynamic moving average. At its core, it calculates the Mean Absolute Deviation (MAD) of price around a user-selected moving average.
The indicator builds adaptive upper and lower bands by multiplying the MAD value above and below the moving average. When price crosses above the upper band, it triggers a bullish signal. When price crosses below the lower band, it signals bearish momentum which gives a bearish signal.
This method provides an elegant balance between volatility sensitivity and trend clarity, adapting in real-time to changing market behavior. The moving average type and band sensitivity can be tuned to fit various strategies—from scalping to swing trading.
Recommended Settings
Long-Term Investing: 1D, EMA, 40, 2
Mid-Term Investing: 1D, Default Settings
Swing Trading: 4h, EMA, 20, 2.5
Day/Intraday Trading: 15mins, 25, 2.5
⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT.
(Mustang Algo) Stochastic RSI + Triple EMAStochastic RSI + Triple EMA (StochTEMA)
Overview
The Stochastic RSI + Triple EMA indicator combines the Stochastic RSI oscillator with a Triple Exponential Moving Average (TEMA) overlay to generate clear buy and sell signals on the price chart. By measuring RSI overbought/oversold conditions and confirming trend direction with TEMA, this tool helps traders identify high-probability entries and exits while filtering out noise in choppy markets.
Key Features
Stochastic RSI Calculation
Computes a standard RSI over a user-defined period (default 50).
Applies a Stochastic oscillator to the RSI values over a second user-defined period (default 50).
Smooths the %K line by taking an SMA over a third input (default 3), and %D is an SMA of %K over another input (default 3).
Defines oversold when both %K and %D are below 20, and overbought when both are above 80.
Triple EMA (TEMA)
Calculates three successive EMAs on the closing price with the same length (default 9).
Combines them using TEMA = 3×(EMA1 – EMA2) + EMA3, producing a fast-reacting trend line.
Bullish trend is identified when price > TEMA and TEMA is rising; bearish trend when price < TEMA and TEMA is falling; neutral/flat when TEMA change is minimal.
Signal Logic
Strong Buy: Previous bar’s Stoch RSI was oversold (both %K and %D < 20), %K crosses above %D, and TEMA is in a bullish trend.
Medium Buy: %K crosses above %D (without requiring oversold), TEMA is bullish, and previous %K < 50.
Weak Buy: Previous bar’s %K and %D were oversold, %K crosses above %D, TEMA is flat or bullish (not bearish).
Strong Sell: Previous bar’s Stoch RSI was overbought (both %K and %D > 80), %K crosses below %D, and TEMA is bearish.
Medium Sell: %K crosses below %D (without requiring overbought), TEMA is bearish, and previous %K > 50.
Weak Sell: Previous bar’s %K and %D were overbought, %K crosses below %D, TEMA is flat or bearish (not bullish).
Visual Elements on Chart
TEMA Line: Plotted in cyan (#00BCD4) with a medium-thick line for clear trend visualization.
Buy/Sell Markers:
BUY STRONG: Lime label below the candle
BUY MEDIUM: Green triangle below the candle
BUY WEAK: Semi-transparent green circle below the candle
SELL STRONG: Red label above the candle
SELL MEDIUM: Orange triangle above the candle
SELL WEAK: Semi-transparent orange circle above the candle
Candle & Background Coloring: When a strong buy or sell signal occurs, the candle body is tinted (semi-transparent lime/red) and the chart background briefly flashes light green (buy) or light red (sell).
Dynamic Support/Resistance:
On a strong buy signal, a green dot is plotted under that bar’s low as a temporary support marker.
On a strong sell signal, a red dot is plotted above that bar’s high as a temporary resistance marker.
Alerts
Strong Buy Alert: Triggered when Stoch RSI is oversold, %K crosses above %D, and TEMA is bullish.
Strong Sell Alert: Triggered when Stoch RSI is overbought, %K crosses below %D, and TEMA is bearish.
General Buy Alert: Triggered on any bullish crossover (%K > %D) when TEMA is not bearish.
General Sell Alert: Triggered on any bearish crossover (%K < %D) when TEMA is not bullish.
Inputs
Stochastic RSI Settings (group “Stochastic RSI”):
K (smoothK): Period length for smoothing the %K line (default 3, minimum 1)
D (smoothD): Period length for smoothing the %D line (default 3, minimum 1)
RSI Length (lengthRSI): Number of bars used for the RSI calculation (default 50, minimum 1)
Stochastic Length (lengthStoch): Number of bars for the Stochastic oscillator applied to RSI (default 50, minimum 1)
RSI Source (src): Price source for the RSI (default = close)
TEMA Settings (group “Triple EMA”):
TEMA Length (lengthTEMA): Number of bars used for each of the three EMAs (default 9, minimum 1)
How to Use
Add the Script
Copy and paste the indicator code into TradingView’s Pine Editor (version 6).
Save the script and add it to your chart as “Stochastic RSI + Triple EMA (StochTEMA).”
Adjust Inputs
Choose shorter lengths for lower timeframes (e.g., intraday scalping) and longer lengths for higher timeframes (e.g., swing trading).
Fine-tune the Stochastic RSI parameters (K, D, RSI Length, Stochastic Length) to suit the volatility of the instrument.
Modify TEMA Length if you prefer a faster or slower moving average response.
Interpret Signals
Primary Entries/Exits: Focus on “BUY STRONG” and “SELL STRONG” signals, as they require both oversold/overbought conditions and a confirming TEMA trend.
Confirmation Signals: Use “BUY MEDIUM”/“BUY WEAK” to confirm or add to an existing position when the market is trending. Similarly, “SELL MEDIUM”/“SELL WEAK” can be used to scale out or confirm bearish momentum.
Support/Resistance Dots: These help identify recent swing lows (green dots) and swing highs (red dots) that were tagged by strong signals—useful to place stop-loss or profit-target orders.
Set Alerts
Open the Alerts menu (bell icon) in TradingView, choose this script, and select the desired alert condition (e.g., “BUY Signal Strong”).
Configure notifications (popup, email, webhook) according to your trading workflow.
Notes & Best Practices
Filtering False Signals: By combining Stoch RSI crossovers with TEMA trend confirmation, most false breakouts during choppy price action are filtered out.
Timeframe Selection: This indicator works on all timeframes, but shorter timeframes may generate frequent signals—consider higher-timeframe confirmation when trading lower timeframes.
Risk Management: Always use proper position sizing and stop-loss placement. An “oversold” or “overbought” reading can remain extended for some time in strong trends.
Backtesting/Optimization: Before live trading, backtest different parameter combinations on historical data to find the optimal balance between sensitivity and reliability for your chosen instrument.
No Guarantee of Profits: As with any technical indicator, past performance does not guarantee future results. Use in conjunction with other forms of analysis (volume, price patterns, fundamentals).
Author: Your Name or Username
Version: 1.0 (Pine Script v6)
Published: June 2025
Feel free to customize input values and visual preferences. If you find bugs or have suggestions for improvements, open an issue or leave a comment below. Trade responsibly!
Quantum Edge Pro - Adaptive AICategorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics)
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
We don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
Categories
Primary: Trend Analysis
Secondary: Mathematical Indicators
Tertiary: Educational Tools
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
3x MTF EMA + VWAP + Daily CPR3x MTF EMA + VWAP + Daily CPR
A Complete Trend & Structure Toolkit for Informed Decisions
This all-in-one indicator blends the power of multi-timeframe analysis, volume-weighted price action, and daily structure zones to give you high-confidence entries and real-time market context.
📌 Key Features:
✅ 3x Multi-Timeframe EMAs
Plot up to three EMAs from any timeframe (e.g., 15m, 1H, Daily) on your current chart. Each EMA comes with:
Custom length
Custom source (close, hl2, etc.)
Independent timeframe
Color and visibility toggles
Use them for dynamic support/resistance, trend direction, and confluence zones.
✅ VWAP (Volume-Weighted Average Price)
Industry-standard intraday VWAP to track the true average traded price. Essential for:
Volume-weighted mean reversion
Institutional support/resistance
Intraday directional bias
Auto-hides on higher timeframes for precision.
✅ Daily CPR (Central Pivot Range)
Maps out key market structure levels for the day:
Central Pivot (P)
Top Central (TC)
Bottom Central (BC)
Widely used by pros for reversal zones, trend continuation, and opening range setups.
🎯 Why Use This Script?
Whether you're scalping intraday or swinging higher timeframes, this indicator gives you:
Instant clarity on market structure
High-probability trend confluence
Reliable institutional price zones
Perfect for SMC, ICT, VWAP traders, or anyone seeking an edge with precision levels.
⚙️ Fully Customizable
Toggle visibility for each layer (EMA, VWAP, CPR)
Adjust EMA sources, lengths, timeframes
Lightweight & optimized for performance [/
Previous Two Days HL + Asia H/L + 4H Vertical Lines📊 Indicator Overview
This custom TradingView indicator visually marks key market structure levels and session data on your chart using lines, labels, boxes, and vertical guides. It is designed for traders who analyze intraday and multi-session behavior — especially around the New York and Asia sessions — with a focus on 4-hour price ranges.
🔍 What the Indicator Tracks
1. Previous Two Days' Ranges (6PM–5PM NY Time)
PDH/PDL (Day 1 & Day 2): Draws horizontal lines marking the previous two trading days’ highs and lows.
Midlines: Calculates and displays the midpoint between each day’s high and low.
Color-Coded: Uses strong colors for Day 1 and more transparent versions for Day 2, to help differentiate them.
2. Asia Session High/Low (6 PM – 2 AM NY Time)
Automatically tracks the high and low during the Asia session.
Extends these levels until the following day’s NY close (4 PM).
Shows a midline of the Asia session (optional dotted line).
Highlights the Asia session background in gray.
Labels Asia High and Low on the chart for easy reference.
3. Last Closed 4-Hour Candle Range
At the start of every new 4H candle, it:
Draws a box from the last closed 4H candle.
Box spans horizontally across a set number of bars (adjustable).
Top and bottom lines indicate the high and low of that 4H candle.
Midline, 25% (Q1) and 75% (Q3) levels are also drawn inside the box using dotted lines.
Helps traders identify premium/discount zones within the previous 4H range.
4. Vertical 4H Time Markers
Draws vertical dashed lines to mark the start and end of the last 4H candle range.
Based on the standard 4H bar timing in NY (e.g. 5:00, 9:00, 13:00, 17:00).
⚙️ Inputs & Options
Line thickness, color customization for all levels.
Option to place labels on the right or left side of the chart.
Toggle for enabling/disabling the 4H box.
Adjustable box extension length (how far to extend the range visually).
✅ Ideal Use Cases
Identifying reaction zones from prior highs/lows.
Spotting reversals during Asia or NY session opens.
Trading intraday setups based on 4H structure.
Anchoring scalping or swing entries off major session levels.
Advanced VW SMI w/ Divergence, Confirmations & TableVolume-Weighted SMI with Dynamic Divergence and Confirmation
Description:
This advanced indicator combines the Stochastic Momentum Index (SMI) with volume weighting, dynamic overbought/oversold bands, and robust divergence detection to help you spot true momentum reversals confirmed by volume, trend, and momentum.
Features
Volume-Weighted SMI
The SMI is amplified or dampened based on normalized volume, filtering out low-interest price moves and highlighting those with real conviction.
Dynamic OB/OS Bands
Overbought and oversold levels adapt automatically to current volatility and trend using moving average and standard deviation bands, keeping signals relevant across all market regimes.
Divergence Detection with Visuals
Real-time bullish and bearish divergence signals are drawn right on the SMI line, including lines and labels, making reversal setups easy to spot.
Triple Confirmation
Divergence signals are filtered by:
Volume surge (user adjustable)
RSI extremes (oversold/overbought)
Higher timeframe trend (optional EMA filter)
Customizable Volume Weighting
Adjust how much influence volume has on SMI signals—tune sensitivity to your market and style.
Performance Table
Track bullish/bearish divergence counts in real time.
How to Use
Add to your chart.
(Move to a separate pane for best results.)
Adjust settings to fit your market (lengths, volume power, trend filter, etc.).
Watch for colored SMI moves outside dynamic bands for momentum extremes.
Look for divergences marked by arrows, lines, and labels on the SMI.
Use table count for an overview of signal frequency.
Tips
Works on all timeframes; try adjusting dynamic band length for higher timeframes.
For scalping, lower the SMI and pivot lengths.
For swing trading, enable trend and volume confirmations for higher confidence.
Use with other price action signals for best results.
Created with Pine Script v5.
If you find this helpful, please give it a like or comment!
Combined ATPC & MACD DivergenceTrend Optimizer + Divergence Finder in One Unified Tool
🔍 Overview:
This powerful dual-system indicator merges two proven analytical engines:
✅ The Algorganic Typical Price Channel (ATPC) — a custom trend oscillator that highlights mean-reversion and directional bias.
✅ A refined MACD system with divergence detection, enhanced with an adjusted Donchian midline for real-time trend strength filtering.
Together, they provide a high-confidence, multi-signal system ideal for swing trading, scalping, or confirming reversals with context.
⚙️ Core Components & Logic
🧠 1. ATPC Engine (Trend Commodity Index)
A momentum and volatility-normalized oscillator based on the typical price (H+L+C)/3:
TrendCI Line (Blue) – Main trend signal based on smoothed CCI logic.
TrendLine2 (Orange) – A slower smoothing of TrendCI for crossovers.
Key Zones (customizable):
🔴 Ultra Overbought: +73
🟣 Overbought: +58
🟣 Oversold: -58
🔴 Ultra Oversold: -73
Trade Logic:
✅ Buy Signal: TrendCI crosses above TrendLine2 while in oversold zone
❌ Sell Signal: TrendCI crosses below TrendLine2 while in overbought zone
Additional visual feedback:
Histogram Bars show strength and direction of momentum shift
Green/Red Circles highlight potential long/short setups
📉 2. MACD System + Divergence Finder
Classic MACD enhanced with a Donchian Midline overlay to filter trend bias.
🔷 MACD Line and 🟠 Signal Line show crossover momentum
🟩/🟥 Histogram shows distance from the signal line
🟪 Adjusted Donchian Midline dynamically adapts to range-bound vs trending environments
Background Color provides real-time trend state:
✅ Green = Bullish Trend
❌ Red = Bearish Trend
No color = Neutral / Choppy
MACD Boundaries (user-defined):
Overbought: +1.0
Oversold: -1.0
🔀 3. Divergence Detection
Spot hidden power shifts before price reacts:
🔼 Positive Divergence – Price makes lower lows, but MACD histogram rises
🔽 Negative Divergence – Price makes higher highs, but MACD histogram weakens
These are visually marked with:
Green “+Div” label (bullish reversal cue)
Red “–Div” label (bearish exhaustion signal)
🎯 How to Use It
For Trend Traders:
Stay in sync with macro trend using MACD histogram + background
Use ATPC crossovers for precision entries
Avoid signals during neutral background (chop filter)
For Reversal Traders:
Look for bullish +Div with ATPC buy signal in oversold zone
Look for bearish –Div with ATPC sell signal in overbought zone
Mid-Donchian line can act as confluence or breakout trigger
For Scalpers & Intraday Traders:
Combine with VWAP, liquidity zones, or order flow levels
ATPC crossovers + MACD histogram zero-line flip = potential scalp entry
Use histogram slope and divergence to avoid false momentum traps
🧩 Customizable Inputs
🎛️ ATPC: Channel & Smoothing lengths, overbought/oversold thresholds
🎛️ MACD: Fast/slow EMAs, signal smoothing, Donchian period, bounds
🎨 Fully theme-compatible with adjustable colors and line styles
🔔 Alerts (Add Your Own)
While this version doesn’t contain built-in alerts, you can easily add alerts based on:
buySignal or sellSignal from ATPC logic
Histogram cross zero or trend flip
MACD Divergence event
📜 “This indicator doesn't just show signals—it tells a story about who’s in control of the market, and when that control might be slipping.”
NY ORB + Fakeout Detector🗽 NY ORB + Fakeout Detector
This indicator automatically plots the New York Opening Range (ORB) based on the first 15 minutes of the NY session (15:30–15:45 CEST / 13:30–13:45 UTC) and detects potential fakeouts (false breakouts).
🔍 Key Features:
✅ Plots ORB high and low based on the 15-minute NY open range
✅ Automatically detects fake breakouts (price wicks beyond the box but closes back inside)
✅ Visual markers:
🔺 "Fake ↑" if a fake breakout occurs above the range
🔻 "Fake ↓" if a fake breakout occurs below the range
✅ Gray background highlights the ORB session window
✅ Designed for scalping and short-term breakout strategies
🧠 Best For:
Intraday traders looking for NY volatility setups
Scalpers using ORB-based entries
Traders seeking early-session fakeout traps to avoid false signals
Those combining with EMA 12/21, volume, or other confluence tools
CHN BUY SELL with EMA 200Overview
This indicator combines RSI 7 momentum signals with EMA 200 trend filtering to generate high-probability BUY and SELL entry points. It uses colored candles to highlight key market conditions and displays clear trading signals with built-in cooldown periods to prevent signal spam.
Key Features
Colored Candles: Visual momentum indicators based on RSI 7 levels
Trend Filtering: EMA 200 confirms overall market direction
Signal Cooldown: Prevents over-trading with adjustable waiting periods
Clean Interface: Simple BUY/SELL labels without clutter
How It Works
Candle Coloring System
Yellow Candles: Appear when RSI 7 ≥ 70 (overbought momentum)
Purple Candles: Appear when RSI 7 ≤ 30 (oversold momentum)
Normal Candles: All other market conditions
Trading Signals
BUY Signal: Triggered when closing price > EMA 200 AND yellow candle appears
SELL Signal: Triggered when closing price < EMA 200 AND purple candle appears
Signal Cooldown
After a BUY or SELL signal appears, the same signal type is suppressed for a specified number of candles (default: 5) to prevent excessive signals in ranging markets.
Settings
RSI 7 Length: Period for RSI calculation (default: 7)
RSI 7 Overbought: Threshold for yellow candles (default: 70)
RSI 7 Oversold: Threshold for purple candles (default: 30)
EMA Length: Period for trend filter (default: 200)
Signal Cooldown: Candles to wait between same signal type (default: 5)
How to Use
Apply the indicator to your chart
Look for yellow or purple colored candles
For LONG entries: Wait for yellow candle above EMA 200, then enter BUY when signal appears
For SHORT entries: Wait for purple candle below EMA 200, then enter SELL when signal appears
Use appropriate risk management and position sizing
Best Practices
Works best on timeframes M15 and higher
Suitable for Forex, Gold, Crypto, and Stock markets
Consider market volatility when setting stop-loss and take-profit levels
Use in conjunction with proper risk management strategies
Technical Details
Overlay: True (plots directly on price chart)
Calculation: Based on RSI momentum and EMA trend analysis
Signal Logic: Combines momentum exhaustion with trend direction
Visual Feedback: Colored candles provide immediate market condition awareness
DB1800 Gann Angle Levels Table (CMP Based)Gann Angles for Resistance and Support
2 = 360 degree for 1 month
1 = 180 degree for 1 week
0.5 = 90 degree for 1 to 2 days
0.25 = 45 degree for next day
0.125 = 22.5 degree for more granular than next day (scalping)
The only thing that multiplies when you share it is knowledge.
Inspired by Sudhir Sharma Sir
www.youtube.com
EMA flow trend buy AnhDuong🎯 1. Indicator Purpose
This indicator combines:
BUY/SELL signals based on crossovers between EMA and LWMA (with some noise filtering).
A multi-timeframe dashboard (M1 → H4) showing trend direction using EMA34 and EMA89.
📈 2. Crossover Signal Logic
BUY signal occurs when:
text
Sao chép
Chỉnh sửa
EMA89 < EMA34 (Bullish crossover)
LWMA45 > EMA34 (Price is rising above the fast EMA)
LWMA45 < EMA89 (Price hasn't broken the long EMA yet)
➡️ Meaning: A bullish move is forming (early stage) → potential entry point.
SELL signal occurs when:
text
Sao chép
Chỉnh sửa
EMA89 > EMA34 (Bearish crossover)
LWMA45 < EMA34 (Price is below the fast EMA)
LWMA45 > EMA89 (Price hasn't broken the long EMA downward)
➡️ Meaning: A bearish move is forming → possible short setup.
📊 3. Multi-Timeframe Dashboard
This dashboard shows trend direction using EMA34 vs EMA89:
UP: EMA34 > EMA89 → uptrend
DOWN: EMA34 < EMA89 → downtrend
Example:
makefile
Sao chép
Chỉnh sửa
DASHBOARD (EMA34/89)
M1: DOWN
M5: DOWN
M15: UP
H1: UP
H4: UP
🟢 Recommended strategy:
BUY when most timeframes show UP
SELL when most timeframes show DOWN
Avoid trades if the trend is mixed (e.g., M1 says UP but H1 says DOWN)
📌 4. Suggested Strategy
For Scalping (e.g., on M5):
Ensure that H1 and H4 are aligned with your direction.
Wait for a BUY/SELL signal on M5.
Check the Dashboard to confirm higher-timeframe trend agreement.
⚠️ 5. Important Notes
Don’t rely solely on BUY/SELL signals — always confirm with the Dashboard.
Consider adding RSI, Bollinger Bands, or SMC levels to improve filtering.
Avoid trading during choppy or sideways conditions — frequent EMA crossovers = many false signals.
Consolidation Range with Signals (Zeiierman)█ Overview
Consolidation Range with Signals (Zeiierman) is a precision tool for identifying and trading market consolidation zones, where price contracts into tight ranges before significant movement. It provides dynamic range detection using either ADX-based trend strength or volatility compression metrics, and offers built-in take profit and stop loss signals based on breakout dynamics.
Whether you trade breakouts, range reversals, or trend continuation setups, this indicator visualizes the balance between supply and demand with clearly defined mid-bands, breakout zones, and momentum-sensitive TP/SL placements.
█ How It Works
⚪ Multi-Method Range Detection
ADX Mode
Uses the Average Directional Index (ADX) to detect low-trend-strength environments. When ADX is below your selected threshold, price is considered to be in consolidation.
Volatility Mode
This mode detects consolidation by identifying periods of volatility compression. It evaluates whether the following metrics are simultaneously below their respective historical rolling averages:
Standard Deviation
Variance
Average True Range (ATR)
⚪ Dynamic Range Band System
Once a range is confirmed, the system builds a dynamic band structure using a volatility-based filter and price-jump logic:
Middle Line (Trend Filter): Reacts to price imbalance using adaptive jump logic.
Upper & Lower Bands: Calculated by expanding from the middle line using a configurable multiplier.
This creates a clean, visual box that reflects current consolidation conditions and adapts as price fluctuates within or escapes the zone.
⚪ SL/TP Signal Engine
On detection of a breakout from the range, the indicator generates up to 3 Take Profit levels and one Stop Loss, based on the breakout direction:
All TP/SL levels are calculated using the filtered base range and multipliers.
Cooldown logic ensures signals are not spammed bar-to-bar.
Entries are visualized with colored lines and labeled levels.
This feature is ideal for traders who want automated risk and reward reference points for range breakout plays.
█ How to Use
⚪ Breakout Traders
Use the SL/TP signals when the price breaks above or below the range bands, especially after extended sideways movement. You can customize how far TP1, TP2, and TP3 sit from the entry using your own risk/reward profile.
⚪ Mean Reversion Traders
Use the bands to locate high-probability reversion zones. These serve as reference zones for scalping or fade entries within stable consolidation phases.
█ Settings
Range Detection Method – Choose between ADX or Volatility compression to define range criteria.
Range Period – Determines how many bars are used to compute trend/volatility.
Range Multiplier – Scales the width of the consolidation zone.
SL/TP System – Optional levels that project TP1/TP2/TP3 and SL from the base price using multipliers.
Cooldown – Prevents repeated SL/TP signals from triggering too frequently.
ADX Threshold & Smoothing – Adjusts sensitivity of trend strength detection.
StdDev / Variance / ATR Multipliers – Fine-tune compression detection logic.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Brian Shannon 5-Day MA BackgroundBrian Shannon 5-Day Moving Average with Dynamic Background Fill
OVERVIEW
This indicator implements Brian Shannon's renowned 5-Day Moving Average methodology from his acclaimed work "Technical Analysis Using Multiple Timeframes." The indicator provides instant visual clarity on short-term trend direction and momentum, making it an essential tool for swing traders and active investors.
KEY FEATURES
• True 5-Day Moving Average: Dynamically calculates the correct period across all timeframes (1min, 5min, 15min, 1H, etc.)
• Visual Price-to-MA Relationship: Color-coded fill between price and the moving average
- Green Fill: Price is above the 5-day MA (bullish short-term momentum)
- Red Fill: Price is below the 5-day MA (bearish short-term momentum)
• Multi-Timeframe Compatible: Works seamlessly on any chart timeframe while maintaining the true 5-day calculation
BRIAN SHANNON'S STRATEGIC APPLICATION
Primary Uses:
1. Trend Identification: Quickly identify short-term momentum shifts
2. Dynamic Support/Resistance: The 5-day MA acts as a moving support level in uptrends and resistance in downtrends
3. Entry Signal Confirmation: Look for pullbacks to the 5-day MA as potential entry points in trending stocks
4. Multi-Timeframe Analysis: Essential component of Shannon's multiple timeframe approach
Perfect Combination with:
• AVWAP (Anchored Volume Weighted Average Price): Use together to identify high-probability setups where price is above both the 5-day MA and AVWAP
• Longer-term Moving Averages: Combine with 20-day and 50-day MAs for complete trend analysis
• Volume Analysis: Confirm 5-day MA signals with volume patterns
TRADING APPLICATIONS
For Swing Traders:
• Bullish Setup: Price above 5-day MA + above AVWAP + above longer-term MAs = Strong uptrend
• Bearish Setup: Price below 5-day MA + below AVWAP + below longer-term MAs = Strong downtrend
• Entry Timing: Use pullbacks to the 5-day MA as entry opportunities in the direction of the primary trend
For Day Traders:
• Quick visual confirmation of intraday momentum
• Dynamic support/resistance levels for scalping opportunities
• Clear trend bias for directional trades
WHY THIS INDICATOR WORKS
Brian Shannon's approach emphasizes that the 5-day moving average represents the short-term sentiment of market participants. When price is consistently above this level, it indicates buyers are in control of short-term price action. Conversely, when price falls below, it suggests selling pressure is dominating.
The visual fill makes it immediately obvious:
• How far price is from the 5-day MA
• The strength of the current short-term trend
• Potential areas where price might find support or resistance
BEST PRACTICES
1. Never use in isolation - Always combine with longer timeframe analysis
2. Volume confirmation - Look for volume expansion on moves away from the 5-day MA
3. Multiple timeframe approach - Check higher timeframes for overall trend direction
4. Combine with AVWAP - Most powerful when both indicators align
INSTALLATION NOTES
This indicator automatically adjusts for any timeframe, ensuring you always get a true 5-trading-day moving average regardless of whether you're viewing 1-minute or hourly charts.
Based on the technical analysis methodology of Brian Shannon, author of "Technical Analysis Using Multiple Timeframes"
Volume pressure by GSK-VIZAG-AP-INDIA🔍 Volume Pressure by GSK-VIZAG-AP-INDIA
🧠 Overview
“Volume Pressure” is a multi-timeframe, real-time table-based volume analysis tool designed to give traders a clear and immediate view of buying and selling pressure across custom-selected timeframes. By breaking down buy volume, sell volume, total volume, and their percentages, this indicator helps traders identify demand/supply imbalances and volume momentum in the market.
🎯 Purpose / Trading Use Case
This indicator is ideal for intraday and short-term traders who want to:
Spot aggressive buying or selling activity
Track volume dynamics across multiple timeframes *1 min time frame will give best results*
Use volume pressure as a confirming tool alongside price action or trend-based systems
It helps determine when large buying/selling activity is occurring and whether such behavior is consistent across timeframes—a strong signal of institutional interest or volume-driven trend shifts.
🧩 Key Features & Logic
Real-Time Table Display: A clean, dynamic table showing:
Buy Volume
Sell Volume
Total Volume
Buy % of total volume
Sell % of total volume
Multi-Time frame Analysis: Supports 8 user-selectable custom time frames from 1 to 240 minutes, giving flexibility to analyze volume pressure at various granularities.
Color-Coded Volume Bias:
Green for dominant Buy pressure
Red for dominant Sell pressure
Yellow for Neutral
Intensity-based blinking for extreme values (over 70%)
Dynamic Data Calculation:
Uses volume * (close > open) logic to estimate buy vs sell volumes bar-by-bar, then aggregates by timeframe.
⚙️ User Inputs & Settings
Timeframe Selectors (TF1 to TF8): Choose any 8 timeframes you want to monitor volume pressure across.
Text & Color Settings:
Customize text colors for Buy, Sell, Total volumes
Choose Buy/Sell bias colors
Enable/disable blinking for visual emphasis on extremes
Table Appearance:
Set header color, metric background, and text size
Table positioning: top-right, bottom-right, etc.
Blinking Highlight Toggle: Enable this to visually highlight when Buy/Sell % exceeds 70%—a sign of strong pressure.
📊 Visual Elements Explained
The table has 6 rows and 10 columns:
Row 0: Headers for Today and TF1 to TF8
Rows 1–3: Absolute values (Buy Vol, Sell Vol, Total Vol)
Rows 4–5: Relative percentages (Buy %, Sell %), with dynamic background color
First column shows the metric names (e.g., “Buy Vol”)
Cells blink using alternate background colors if volume pressure crosses thresholds
💡 How to Use It Effectively
Use Buy/Sell % rows to confirm potential breakout trades or identify volume exhaustion zones
Look for multi-timeframe confluence: If 5 or more TFs show >70% Buy pressure, buyers are in control
Combine with price action (e.g., breakouts, reversals) to increase conviction
Suitable for equities, indices, futures, crypto, especially on lower timeframes (1m to 15m)
🏆 What Makes It Unique
Table-based MTF Volume Pressure Display: Most indicators only show volume as bars or histograms; this script summarizes and color-codes volume bias across timeframes in a tabular format.
Customization-friendly: Full control over colors, themes, and timeframes
Blinking Alerts: Rare visual feature to capture user attention during extreme pressure
Designed with performance and readability in mind—even for fast-paced scalping environments.
🚨 Alerts / Extras
While this script doesn’t include TradingView alert functions directly, the visual blinking serves as a strong real-time alert mechanism.
Future versions may include built-in alert conditions for buy/sell bias thresholds.
🔬 Technical Concepts Used
Volume Dissection using close > open logic (to estimate buyer vs seller pressure)
Simple aggregation of volume over custom timeframes
Table plotting using Pine Script table.new, table.cell
Dynamic color logic for bias identification
Custom blinking logic using na(bar_index % 2 == 0 ? colorA : colorB)
⚠️ Disclaimer
This indicator is a tool for analysis, not financial advice. Always backtest and validate strategies before using any indicator for live trading. Past performance is not indicative of future results. Use at your own risk and apply proper risk management.
✍️ Author & Signature
Indicator Name: Volume Pressure
Author: GSK-VIZAG-AP-INDIA
TradingView Username: prowelltraders
Candle Range Trading (CRT) with Alerts
📌 Description:
The Candle Range Trading (CRT) indicator identifies potential reversal or continuation setups based on specific two-candle price action patterns.
It analyzes pairs of candles to detect Bullish or Bearish CRT patterns and provides visual signals (triangles) and alert notifications to support scalp or swing trading strategies.
🔍 How It Works:
🔻 Bearish CRT Pattern:
Candle 1 is bullish
Candle 2 is bearish
Candle 2's high > Candle 1's high
Candle 2 closes within Candle 1’s range
🔺 Red triangle above candle
🔺 Bullish CRT Pattern:
Candle 1 is bearish
Candle 2 is bullish
Candle 2's low < Candle 1's low
Candle 2 closes within Candle 1’s range
🔻 Green triangle below candle
📈 Visual Features:
🔺 Red triangle = Bearish CRT
🔻 Green triangle = Bullish CRT
📏 Optional box showing CRT High and CRT Low
🔔 Built-in Alerts:
Bullish CRT Alert: "Bullish CRT Pattern Detected"
Bearish CRT Alert: "Bearish CRT Pattern Detected"
Set alerts to get notified instantly when a pattern is detected.
⚠️ Note:
Use in conjunction with trend filters, support/resistance, or volume for best results.
Ideal for scalping or short-term trades.
Avoid trading in choppy or low-volume markets.
⚠️ Disclaimer:
This script was generated with the assistance of ChatGPT by OpenAI and is intended for educational and informational purposes only.
All strategies, alerts, and signals derived from this indicator should be thoroughly backtested and validated before using in live trading.
Trading involves substantial risk, and past performance is not indicative of future results. The author and ChatGPT bear no responsibility for any trading losses or financial decisions made using this script.
Users are solely responsible for the risks associated with their trading actions. Always apply proper risk management and perform your own due diligence before making any financial decisions.
