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.
趨勢分析
RBD/DBR Zone HelperUpdated ORB that builds on prvious rendetions and forward thinkers to beat the retail markets
EU Session Only StrategyThe name of the strategy is the EU session only, but you choose which time is important for you to follow, it can also be the beginning of the US session, a few hours after the news (2 hours after the US open level) or based on the daily open level.
📌 Indicator Description: "EU Session Only Strategy"
This TradingView indicator, written in Pine Script version 6, represents a simple yet effective intraday trading strategy focused exclusively on the European trading session.
🎯 Purpose and Use
The goal of this strategy is to:
Automatically identify the European session open price for the current trading day.
Trade only during a defined intraday time window (e.g., between 08:00 and 18:00 UTC).
Enter a trade only if the price moves a certain distance (in pips) away from the EU open level.
Limit the number of trades per day to avoid overtrading.
Automatically close all open positions at the end of the day to minimize overnight risk.
⚙️ How It Works
🔹 1. EU Open Level
When the European session opens (e.g., 09:00 UTC), the strategy records the opening price at that moment (eu_open_price).
This level is displayed as a red horizontal line on the chart.
🔹 2. Entry Conditions
The strategy checks if the current price:
Is above the EU open level by at least a defined number of pips → Buy signal.
Is below the EU open level by at least a defined number of pips → Sell signal.
Trading is allowed only within the specified time range (e.g., 08:00 to 18:00 UTC).
A maximum number of trades per day is enforced (e.g., 2 trades max).
🔹 3. Exit Conditions
If an opposite signal appears during the day, the strategy automatically closes the current position.
At the start of each new day, all open positions are closed, regardless of direction or profit.
✅ Advantages
A clear and efficient system based on price reaction around a key daily level.
Suitable for automated backtesting and optimization on TradingView.
Reduces risk with daily trade limits and end-of-day auto-closing.
Ideal for forex pairs that show volatility during the European session (e.g.,GOLD, EUR/USD, GBP/USD, etc.).
Zero Lag Trend Strategy (MTF) [AlgoAlpha]# Zero Lag Trend Strategy (MTF) - Complete Guide
## Overview
The Zero Lag Trend Strategy is a sophisticated trading system that combines zero-lag exponential moving averages with volatility bands and EMA-based entry/exit filtering. This strategy is designed to capture trending movements while minimizing false signals through multiple confirmation layers.
## Core Components
### 1. Zero Lag EMA (ZLEMA)
- **Purpose**: Primary trend identification with reduced lag
- **Calculation**: Uses a modified EMA that compensates for inherent lag by incorporating price momentum
- **Formula**: `EMA(price + (price - price ), length)` where lag = (length-1)/2
- **Default Length**: 70 periods (adjustable)
### 2. Volatility Bands
- **Purpose**: Define trend strength and entry/exit zones
- **Calculation**: Based on ATR (Average True Range) multiplied by a user-defined multiplier
- **Upper Band**: ZLEMA + (ATR * multiplier)
- **Lower Band**: ZLEMA - (ATR * multiplier)
- **Default Multiplier**: 1.2 (adjustable)
### 3. EMA Filter/Exit System
- **Purpose**: Entry filtering and exit signal generation
- **Default Length**: 9 periods (fully customizable)
- **Color**: Blue line on chart
- **Function**: Prevents counter-trend entries and provides clean exit signals
## Entry Logic
### Long Entry Conditions
1. **Primary Signal**: Price crosses above the upper volatility band (strong bullish momentum)
2. **Additional Entries**: Price crosses above ZLEMA while already in an uptrend (if enabled)
3. **EMA Filter**: Price must be above the EMA filter line
4. **Confirmation**: All conditions must align simultaneously
### Short Entry Conditions
1. **Primary Signal**: Price crosses below the lower volatility band (strong bearish momentum)
2. **Additional Entries**: Price crosses below ZLEMA while already in a downtrend (if enabled)
3. **EMA Filter**: Price must be below the EMA filter line
4. **Confirmation**: All conditions must align simultaneously
## Exit Logic
**Simple and Clean**: Positions are closed when price crosses the EMA filter line in the opposite direction:
- **Long Exit**: Price crosses below the EMA filter
- **Short Exit**: Price crosses above the EMA filter
## Multi-Timeframe Analysis
The strategy includes a real-time table showing trend direction across 5 different timeframes:
- Default timeframes: 5m, 15m, 1h, 4h, 1D (all customizable)
- Color-coded signals: Green for bullish, Red for bearish
- Helps confirm overall market direction before taking trades
## Key Parameters
### Main Calculations
- **Length (70)**: Zero-lag EMA calculation period
- **Band Multiplier (1.2)**: Controls volatility band width
### Strategy Settings
- **Enable Additional Trend Entries**: Allow multiple entries during strong trends
- **EMA Exit Length (9)**: Period for the entry filter and exit EMA
### Timeframes
- **5 customizable timeframes** for multi-timeframe trend analysis
### Appearance
- **Bullish Color**: Default green (#00ffbb)
- **Bearish Color**: Default red (#ff1100)
## Visual Elements
### Chart Display
- **ZLEMA Line**: Color-coded trend line (green/red based on trend direction)
- **Volatility Bands**: Dynamic upper/lower bands that appear based on trend
- **EMA Filter**: Blue line for entry filtering and exits
- **Entry Signals**:
- Large arrows (▲▼) for primary trend signals
- Small arrows for additional trend entries
- Tiny letters (L/S) for actual strategy entries
### Information Table
- **Position**: Top-right corner
- **Content**: Real-time trend status across all configured timeframes
- **Updates**: Continuously updated with current market conditions
## Strategy Advantages
### Trend Following Excellence
- Captures strong trending moves with reduced whipsaws
- Multiple confirmation layers prevent false entries
- Dynamic bands adapt to market volatility
### Risk Management
- Clear, objective exit rules
- EMA filter prevents counter-trend trades
- Multi-timeframe confirmation reduces bad trades
### Flexibility
- Fully customizable parameters
- Works across different timeframes and instruments
- Optional additional trend entries for maximum profit potential
### Visual Clarity
- Clean, professional chart display
- Easy-to-read signals and trends
- Comprehensive multi-timeframe overview
## Best Practices
### Parameter Optimization
- **Length**: Higher values (50-100) for longer-term trends, lower values (20-50) for shorter-term
- **Band Multiplier**: Higher values (1.5-2.0) reduce signals but increase quality
- **EMA Length**: Shorter periods (5-13) for quick exits, longer periods (20-50) for trend riding
### Market Conditions
- **Trending Markets**: Enable additional trend entries for maximum profit
- **Choppy Markets**: Use higher band multiplier and longer EMA for fewer, higher-quality signals
- **Different Timeframes**: Adjust all parameters proportionally when changing chart timeframes
### Multi-Timeframe Usage
- Align trades with higher timeframe trends
- Use lower timeframes for precise entry timing
- Avoid trades when timeframes show conflicting signals
## Risk Considerations
- Like all trend-following strategies, may struggle in ranging/choppy markets
- EMA exit system prioritizes trend continuation over quick profit-taking
- Multiple timeframe analysis requires careful interpretation
- Backtesting recommended before live trading with any parameter changes
## Conclusion
The Zero Lag Trend Strategy provides a comprehensive approach to trend trading with built-in risk management and multi-timeframe analysis. Its combination of advanced technical indicators, clear entry/exit rules, and customizable parameters makes it suitable for both novice and experienced traders seeking to capture trending market movements.
GStrategy XRP 4hRSI + Smart Money Trading Strategy
This strategy combines RSI (Relative Strength Index) with Smart Money detection to identify high-probability reversal trades in trending markets. It uses strict entry/exit rules with a 10% hard stop-loss to manage risk.
Strategy Logic
1. Entry Conditions
Long Entry (Buy):
RSI < 30 (Oversold condition)
Smart Money Confirmation:
Bullish candle (close > open)
Volume > 35-period SMA (unusual buying pressure)
Price hits a 5-bar low (potential reversal level)
Short Entry (Sell):
RSI > 70 (Overbought condition)
Smart Money Confirmation:
Bearish candle (close < open)
Volume > 20-period SMA (unusual selling pressure)
Price hits a 5-bar high (potential rejection level)
2. Exit Conditions
Long Exit: RSI ≥ 70 (Take profit at overbought)
Short Exit: RSI ≤ 40 (Take profit at mid-level)
Stop-Loss: Hard 10% stop on all trades
3. Position Management
No overlapping trades (only 1 position at a time).
Stop-loss visualized on the chart (red line).
Key Features
✅ RSI Filter: Avoids false reversals by requiring extreme RSI levels.
✅ Smart Money Detection: Confirms institutional activity via volume + price action.
✅ Asymmetric Exits:
Longs exit at RSI 70 (full overbought).
Shorts exit earlier at RSI 40 (conservative profit-taking).
✅ Strict Risk Control: 10% stop-loss prevents large drawdowns.
Indicators Used
RSI (14-period)
Volume SMA (20 for shorts, 35 for longs)
5-bar High/Low for price extremes.
P4H SFP StrategySignals Long or Short Entries based on Previous 4H low/high. Entry criteria are SFP/Rejection of P4h L/H and candle close in opposite direction. RSI must be 65/35 but can customize. Stop/TP 1% from entry. All of this is customizable. Stats are shown and you can change the time range of that as well.
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version)
**Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory.
It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk.
⚠️ _For educational and research purposes only. Past performance does not guarantee future results._
---
## 🎯 Strategy Objective
- Smooth price noise using Kaufman Filter smoothing
- Detect the strength and direction of trends with a normalized oscillator
- Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic
---
## ✨ Key Features
- **Kaufman Filter Trend Detection**
Extracts directional signal using a state space model.
- **Multi-Stage Profit-Taking**
Automatically takes partial profits based on color changes and zero-cross events.
- **ATR-Based Volatility Stops**
Stops adjust based on swing highs/lows and current market volatility.
---
## 📊 Entry & Exit Logic
**Long Entry**
- `trend_strength ≥ 60`
- Green trend signal
- Price above the Kaufman average
**Short Entry**
- `trend_strength ≤ -60`
- Red trend signal
- Price below the Kaufman average
**Exit (Long/Short)**
- Blue trend color → TP1 (50%)
- Oscillator crosses 0 → TP2 (25%)
- Trend weakens → Final exit (25%)
- ATR + swing-based stop loss
---
## 💰 Risk Management
- Initial capital: `$3,000`
- Order size: `$100` per trade (realistic, low-risk sizing)
- Commission: `0.002%`
- Slippage: `2 ticks`
- Pyramiding: `1` max position
- Estimated risk/trade: `~0.1–0.5%` of equity
> ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._
---
## ⚙️ Default Parameters
- **1st Take Profit**: 50%
- **2nd Take Profit**: 25%
- **Final Exit**: 25%
- **ATR Period**: 14
- **Swing Lookback**: 10
- **Entry Threshold**: ±60
- **Exit Threshold**: ±40
---
## 📅 Backtest Summary
- **Symbol**: USD/JPY
- **Timeframe**: 1H
- **Date Range**: Jan 3, 2022 – Jun 4, 2025
- **Trades**: 924
- **Win Rate**: 41.67%
- **Profit Factor**: 1.108
- **Net Profit**: +$1,659.29 (+54.56%)
- **Max Drawdown**: -$1,419.73 (-31.87%)
---
## ✅ Summary
This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness.
It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users.
---
## 📌 Disclaimer
This script is for educational and informational purposes only and should not be considered financial advice.
Use with proper risk controls and always test in a demo environment before live trading.
Frequent Swing Trading Supertrend Strategy (Daily)Made By Riddhiman Bandyopadhyay
How to Use-
Set Chart to Daily: Ensure your TradingView chart is set to a daily timeframe (D).
Add Strategy: Copy the Pine Script code into TradingView’s Pine Editor, compile, and add it to your NIFTY chart.
Logic Behind the Backtest : Use TradingView’s Strategy Tester to evaluate performance over the past few months (e.g., March to June 2025). Check if the buy/sell signals occur more frequently and capture shorter swings.
Fine-Tune: If signals are too frequent (leading to whipsaws), increase atr Period to 12 or factor to 3.5. If still not frequent enough, reduce maPeriod to 8 or lower the RSI thresholds to 65/35.
Why This Should Work Better
Increased Sensitivity: The Supertrend (ATR 10, factor 3.0) and 10-period SMA make the strategy more responsive to daily price movements, generating more signals.
Fewer Restrictions: Removing the 50-period SMA filter and loosening entry conditions allow trades in a wider range of market conditions.
Quicker Exits: The 3% profit target encourages faster exits, freeing up capital for new trades, thus increasing frequency.
Balanced Filtering: The RSI (70/30) still filters out extreme conditions, but it’s less restrictive, allowing more trades.
🔥Scalping Fusion Strategy v6🔥Scalping Fusion (v6)
✅ Overview:
This is a powerful intraday scalping strategy that combines two Super Trend systems:
Pivot Super Trend – uses dynamic pivot highs/lows and ATR-based bands.
Classic Super Trend – a traditional ATR-based trailing trend filter.
By combining both, the strategy ensures strong trend confirmation before taking trades.
⚙️ Core Logic:
Buy Entry:
Pivot Super Trend turns Bullish (Trend = 1)
Classic Super Trend also Bullish
Pivot Trend must have just changed from Bearish to Bullish
Sell Entry:
Pivot Super Trend turns Bearish (Trend = -1)
Classic Super Trend also Bearish
Pivot Trend must have just changed from Bullish to Bearish
🎯 Stop Loss / Take Profit:
Based on ATR (14):
Stop Loss = Entry ± 1.5 × ATR
Target = Entry ± 3.0 × ATR
This ensures dynamic SL/TP based on market volatility.
📈 Key Features:
Dual Super Trend Confirmation = Reduces false entries
ATR-based Stop Loss & Target = Adaptive to volatility
Pivot-based Trend Detection = Detects strong reversals
Buy/Sell labels + alerts for easy visual and automated trading
⏱️ Recommended Timeframe:
3-Minute or 5-Minute charts
Ideal for fast scalping and high-frequency trading sessions.
🧪 Backtest Suggestions:
Use during high volume hours (e.g., 9:15 AM – 2:30 PM)
Filter trades using volume or session-based logic
Consider adding maximum trades per day for better risk control
Trend Signals StrategyThis strategy is designed to follow the dominant market trend and only take trades in the direction of that trend. It uses two moving averages for trend detection and candlestick confirmation for entries. The strategy can be used on any timeframe but works best on 15m to 1H for intraday trading.
Volatility Break + Trend Bias Scalper [Enhanced Visuals]Volatility Break + Trend Bias Scalper \
Overview
This strategy is designed to help traders catch high-probability breakout moves by combining real-time volatility surges with higher timeframe trend confirmation. It is particularly useful in markets like AAPL, BTC, NASDAQ, and Forex pairs where volatility and momentum often occur in bursts.
📈 Strategy Logic
🎯 1. Volatility Spike Detection
The core entry trigger is based on identifying sudden ATR-based volatility bursts:
* ATR(7) is compared to its EMA(14) smoothing.
* A volatility spike is confirmed when ATR exceeds `1.5x` the smoothed ATR.
This helps avoid entering during sideways price action and focuses only on explosive breakouts.
🧭 2. Higher Timeframe Trend Filter
To improve signal quality, the strategy checks the EMA(200) slope from a higher timeframe (e.g., 15min while trading on 3/5/45min charts):
* Bullish trend: EMA rising
* Bearish trend: EMA falling
This ensures we only trade in the direction of larger momentum.
🧠 3. Structure Break Entry
A simple but effective price action confirmation:
* Long: Close > highest close of the last 2 candles
* Short: Close < lowest close of the last 2 candles
This avoids "fake" moves and choppy zones.
🎛️ 4. Risk/Reward and Exit Logic
* Take Profit (TP) = 1.5× ATR (configurable)
* Stop Loss (SL) = 1.0× ATR (configurable)
* You can adjust this for more aggressive or conservative setups.
✅ All exits are calculated dynamically using the current ATR at trade entry.
🖥️ Visual Enhancements
This version includes:
✅ Signal markers (🔴 for Short, 🟢 for Long)
✅ Trend-colored background zones
✅ TP/SL lines drawn on chart
✅ Toggle options to enable/disable labels and TP/SL lines
These visuals help traders quickly validate signals, backtest more effectively, and share setups with clarity.
🧪 Backtest Settings
* Position Size: 1% of equity
* Commission: 0
* Slippage: Assumed minimal
* Recommended Markets: AAPL (45m), BTCUSD (5m–15m), NAS100 (15m), EUR/USD (5m)
> You can tune the strategy further using `PineScriptsAI`.
⚠️ Disclaimer
This strategy is for **educational purposes only**. It does **not constitute financial advice** or guarantees of profitability. Backtest results may vary across assets, timeframes, and market conditions.
Always validate with forward testing and sound risk management.
🔗 Built With Help From PineScriptsAI
Want to build your own version or add:
* Time filters (e.g., NY or London session)?
* Multi-take-profits or trailing stop?
* Auto alert bots to Telegram/Discord?
Supply/Demand Zones + Engulfment-based ExecutionSupply/Demand Zones + Engulfment-Based Execution
Strategy Overview
This strategy combines institutional trading concepts—supply/demand zones and engulfing candle patterns—to generate high-probability long and short trade setups. The system uses aggregated price action to identify potential reversal zones and confirms entries with engulfing candle patterns, ensuring trades are only taken when market structure shows commitment in the direction of the trade.
Core Concepts
• Supply & Demand Zones: These are automatically detected by analyzing aggregated bullish and bearish candle structures over user-defined intervals. Supply zones are formed after bearish continuation patterns; demand zones appear after bullish continuation patterns.
• Engulfing Entries: Once price enters a zone, the strategy waits for a bullish engulfing pattern (in a demand zone) or a bearish engulfing pattern (in a supply zone) before executing a trade. This adds confirmation and reduces false signals.
• Risk Management: Stop-loss is placed at the low (for long trades) or high (for short trades) of the engulfed candle. Take-profit can be calculated using a fixed R-multiple (risk-to-reward ratio) or a user-defined target price.
Key Features
Fully customizable aggregation factor for zone detection
Visual zone boxes, entry/SL/TP boxes, and engulfing pattern labels
Optional removal of mitigated zones for cleaner charting
Configurable trade mode (Long only, Short only, or Both)
Support for trading sessions and date filtering
Alerts for price entering supply or demand zones
How to Use
Select Aggregation Factor: Choose how many candles to group together for identifying key zones (e.g., 4x timeframe).
Enable Zones: Turn on supply and/or demand zones as needed.
Set Execution Parameters:
– Choose R-multiple (e.g., 2:1 risk-reward)
– Or use a fixed take-profit price
Define Trade Time Window:
– Set the date and time ranges to restrict execution
– Use Start Hour and End Hour to limit trades to specific sessions (e.g., London/New York)
Run on Desired Timeframe: Typically used on 15m–4H charts, depending on your strategy and the asset’s volatility.
Ideal For
• Traders using Smart Money Concepts (SMC)
• Those who value high-confluence entries
• Intraday to swing traders looking for structure-based automation
⚠️ Important Notes
• The strategy requires engulfing confirmation within the zone to enter a position.
• This script does not repaint and executes trades on a bar close basis.
• Backtest results may vary based on session filters and aggregation factor.
© Attribution
This strategy was developed by The_Forex_Steward and is licensed under the Mozilla Public License 2.0.
You are free to use, modify, and distribute it under the terms of that license.
3 EMA Trend Strategy (Locks Trailing Stop Tightening)3 EMA Trend Strategy (with Trailing Stop Tightening)
This open-source strategy uses three Exponential Moving Averages (7, 21, 35) to detect bullish alignment and trigger long entries during strong upward trends.
* Entry Logic:
A long trade is triggered when EMA 7 > EMA 21 > EMA 35. This alignment signals a confirmed uptrend.
* Exit Logic:
The strategy uses a trailing stop mechanism.
An initial stop (e.g., 10%) follows the high since entry.
Once profit reaches a customizable threshold (e.g., 20%), the trailing stop tightens (e.g., to 5%) to help lock in gains.
* Backtest Settings (default):
Starting capital: $10,000
Commission: 0.1%
Slippage: 1 tick
Position sizing: 100% of equity per trade (can be reduced to lower risk)
* Customization:
All trailing logic and EMA settings are configurable.
Designed for swing trading and adaptable for multiple timeframes.
⚠️ This is for educational purposes only. Always test on different symbols and timeframes before using in live environments.
Golden Triangle Strategy (1H, Setup 1 & 2)🔺 Golden Triangle Strategy – Setup 1 & 2 with Dynamic Trailing Stop (Optimized for 1H Chart)
### 📘 Strategy Summary
This strategy blends **technical pattern recognition** with **volume confirmation** and **dynamic risk management** to capture high-probability breakouts. It features two independent entry setups . More details can be found at thepatternsite.com
I have added intelligent trailing stop that **tightens once a profit threshold is reached**. Please note that this is not mentioned in GoldenTriangle strategy. I just added to capture the profits.
### ✅ Entry Setups
#### **Setup 1 – Golden Triangle Breakout**
* Detects **triangle formations** using recent pivot highs and lows.
* A **bullish breakout** is confirmed when:
* Price **closes above the triangle top**, and
* Price is also **above the 50-period SMA**.
* Entry: At breakout candle close.
* Ideal for early momentum trades after consolidation.
#### **Setup 2 – Price + Volume Confirmation**
* Based on **mean reversion followed by volume surge**:
* Price drops **below the 50 SMA**, then closes **back above it**.
* Requires at least one **"up day"** (current close > previous close).
* Volume must be:
* Above its 50-SMA, **and**
* Higher than each of the **previous 4 days**.
* Entry: At the close of volume-confirmation day.
* Useful when triangle patterns are not clear, but accumulation is strong.
---
### 📈 Entry Logic Recap
| Condition | Setup 1 | Setup 2 |
| ------------------ | --------------------- | --------------------------------------- |
| Pattern | Triangle Breakout | SMA Reclaim + Volume Surge |
| SMA Filter | Close > 50 SMA | Price drops < 50 SMA, then closes above |
| Volume Requirement | Not Required | > Volume SMA + > last 4 bars |
| Entry Trigger | Breakout candle close | After volume confirmation |
---
### 🚪 Exit Strategy
#### 🔁 **Trailing Stop Loss (TSL)**
* **Initial stop:** 10% below the **highest price reached** after entry.
* **Tightening rule:**
* When profit reaches **10%**, the trailing stop is **tightened to 5%**.
* This keeps you in the trade while locking in more profit as the trade moves in your favor.
#### 🔻 **Manual Close**
* If the price drops below the trailing stop, the position is automatically closed using `strategy.close()`.
---
### 🌈 Visual Aids & Additions
* Green background shading while in a trade.
* Real-time dashboard showing:
* SMA values
* Entry signals
* Plots for:
* Dynamic trailing stop
* Weekly Fibonacci R3 and S3 levels as outer support/resistance zones.
---
### 🧠 Ideal Use Cases
* Works well on **1-hour charts** for intraday to short swing trades.
* Especially effective in **sideways-to-bullish markets**.
* Helps avoid false breakouts by using SMA and volume filters.
---
Tip: I also showed weekly R3 on the chart. When the price touches at this level lock your profits. You Dont have to wait until price hits trailing stop loss.
warning : This strategy is published educational purposes only.
EMA Trend Cross Signal
LOGIC :
This strategy opens position if shorter period Exponential Moving Average (EMA) crosses over or crosses under the longer period EMA and exits position if any of the 3 exit conditions mentioned below is fulfilled
ENTRY CONDITIONS :
LONG ENTRY -
shorter period EMA crosses over longer period EMA
SHORT ENTRY -
shorter period EMA crosses under longer period EMA
EXIT CONDITIONS :
BOTH EMA CROSSED -
LONG EXIT - If price closes below both the shorter period EMA & longer period EMA
SHORT EXIT - If price closes above both the shorter period EMA & longer period EMA
STOP-LOSS HIT -
LONG EXIT - If price closes below the LOW created at the time of ema crossover
SHORT EXIT - If price closes above the HIGH created at the time of ema crossover
EMA CROSS -
LONG EXIT - If shorter period EMA crosses under longer period EMA
SHORT EXIT - If shorter period EMA crosses above longer period EMA
EXAMPLES :
1. TESLA (1-DAY) -
2. APPLE (1-WEEK) -
PYRAMID CLOSING -
Positions will be closed pyramidically in 5 levels and price of each level will be calculated by multiplying current market price with the percentage of each pyramid level's value user has entered
SETTINGS OPTIONS -
MA TYPE -
There is option to choose the type of moving average among SMA, EMA, RMA, WMA, VWMA on chart
MA LENGTH -
There is option to change the length of short period MA & large period MA
FIELD TYPE -
There is also option to choose the price field among open, close, low, high etc. for the selected MA
HISTORICAL BACKTEST -
We can also backtest the strategy for a certain duration of time using this option by changing the start time and end time
SHOW BACKGROUND COLORS FOR EVERY POSITION -
There is option to show background color as green whenever a bullish position is opened and as red whenever a bearish position is opened
SHOW BACKGROUND COLORS FOR EVERY PROFIT & LOSS -
There is option to show green circle in background whenever profit is made and red circle whenever loss is made
SHOW TABLE -
If selected then it will show a table at the top-right corner with all the pyramid levels at which position will be closed for the current scrip
PAUSE TRADING -
If this option is selected then no position will opened on the chart
Grid TLong V1The “Grid TLong V1” strategy is based on the classic Grid strategy, but in the mode of buying and selling in favor of the trend and only on Long. This allows to take advantage of large uptrend movements to maximize profits in bull markets. For this reason, excessively sideways or bearish markets may not be very conducive to this strategy.
Like our Grid strategies in favor of the trend, you can enter and exit with the balance with controlled risk, as the distance between each grid functions as a natural and adaptable stop loss and take profit. What differentiates it from bidirectional strategies is that Short uses a minimum amount of follow-through, so that the percentage distance between the grids is maintained.
In this version of the script the entries and exits can be chosen at market or limit , and are based on the profit or loss of the current position, not on the percentage change in price.
The user may also notice that the strategy setup is risk-controlled, because it risks 5% on each trade, has a fairly standard commission and modest initial capital, all in order to protect the strategy user from unrealistic results.
As with all strategies, it is strongly recommended to optimize the parameters for the strategy to be effective for each asset and for each time frame.
Volatility Bias ModelVolatility Bias Model
Overview
Volatility Bias Model is a purely mathematical, non-indicator-based trading system that detects directional probability shifts during high volatility market phases. Rather than relying on classic tools like RSI or moving averages, this strategy uses raw price behavior and clustering logic to determine potential breakout direction based on recent market bias.
How It Works
Over a defined lookback window (default 10 bars), the strategy counts how many candles closed in the same direction (i.e., bullish or bearish).
Simultaneously, it calculates the price range during that window.
If volatility is above a minimum threshold and a clear directional bias is detected (e.g., >60% of closes are bullish), a trade is opened in the direction of that bias.
This approach assumes that when high volatility is coupled with directional closing consistency, the market is probabilistically more likely to continue in that direction.
ATR-based stop-loss and take-profit levels are applied, and trades auto-exit after 20 bars if targets are not hit.
Key Features
- 100% non-indicator-based logic
- Statistically-driven directional bias detection
- Works across all timeframes (1H, 4H, 1D)
- ATR-based risk management
- No pyramiding, slippage and commissions included
- Compatible with real-world backtesting conditions
Realism & Assumptions
To make this strategy more aligned with actual trading environments, it includes 0.05% commission per trade and a 1-point slippage on every entry and exit.
Additionally, position sizing is set at 10% of a $10,000 starting capital, and no pyramiding is allowed.
These assumptions help avoid unrealistic backtest results and make the performance metrics more representative of live conditions.
Parameter Explanation
Bias Window (10 bars): Number of past candles used to evaluate directional closings
Bias Threshold (0.60): Required ratio of same-direction candles to consider a bias valid
Minimum Range (1.5%): Ensures the market is volatile enough to avoid noise
ATR Length (14): Used to dynamically define stop-loss and target zones
Risk-Reward Ratio (2.0): Take-profit is set at twice the stop-loss distance
Max Holding Bars (20): Trades are closed automatically after 20 bars to prevent stagnation
Originality Note
Unlike common strategies based on oscillators or moving averages, this script is built on pure statistical inference. It models the market as a probabilistic process and identifies directional intent based on historical closing behavior, filtered by volatility. This makes it a non-linear, adaptive model grounded in real-world price structure — not traditional technical indicators.
Disclaimer
This strategy is for educational and experimental purposes only. It does not constitute financial advice. Always perform your own analysis and test thoroughly before applying with real capital.
Price Statistical Strategy-Z Score V 1.01
Price Statistical Strategy – Z Score V 1.01
Overview
A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”.
This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included.
Core Methodology
The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits.
Signal Conditions
Entry Condition:
A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met.
Exit Condition:
The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow.
Signal Gapping:
A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise.
Momentum Filter:
Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles.
Z-Score Function
The Z-Score is calculated as:
Z = (Close - SMA(Close, N)) / STDEV(Close, N)
Where N is the base period selected by the user.
Input Parameters
Enable Smoothed Z-Score Strategy
Enables or disables the Z-Score strategy logic. When disabled, no trades are executed.
Z-Score Base Period
Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes.
Short-Term Smoothing
Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility.
Long-Term Smoothing
Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic.
Bars gap between identical signals
Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals.
Trade Visualization Table
A table positioned at the bottom-right displays live PnL for open trades:
Entry Price
Unrealized PnL %
Text colors adapt based on whether unrealized profit is positive, negative, or neutral.
Technical Notes
This strategy uses only close prices — no trend indicators or volume components are applied.
All calculations are based on simple moving averages and standard deviation over user-defined windows.
Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Multi-Indicator Trend-Following Strategy v6Multi-Indicator Trend-Following Strategy v6
This strategy uses a combination of technical indicators to identify potential trend-following trade entries and exits. It is intended for educational and research purposes.
How it works:
Moving Averages (EMA): Entry signals are generated on crossovers between a fast and slow exponential moving average.
RSI Filter: Confirms momentum with a threshold above/below 50 for long/short entries.
Volume Confirmation: Requires volume to exceed a moving average multiplied by a user-defined factor.
ATR-Based Risk Management: Stop loss and take profit levels are calculated using the Average True Range (ATR), allowing for dynamic risk control based on market volatility.
Customizable Inputs:
Fast/Slow MA lengths
RSI length and levels
MACD settings (used in calculation, not directly in signal)
Volume MA and multiplier
ATR period and multipliers for stop loss and take profit
Notes:
This strategy does not guarantee future results.
It is provided for analysis and backtesting only.
Alerts are available for buy/sell conditions.
Feel free to adjust parameters to explore different market conditions and asset classes.
Long Explosive V1The “Long Explosive V1” strategy calculates the percentage change in price from the last closing price of the candlestick, so that if it increases by a certain percentage it goes long, but if it decreases by another percentage it sends an exit order, so that the percentage limits above and below the current price function as inherent stop loss and take profit, with the benefit of taking advantage of the volatility of the bull market.
Entries and exits are always at the market and based on percentage changes in the price. Of course, the default configuration of the strategy considers a position with a 5% risk control, modest initial capital and standard commissions, which helps to obtain realistic results and protect the user from unexpectedly controlled potential losses.
It is again emphasized that it is always advisable to adjust the parameters of the strategy well, so that the risk-reward is well controlled.
Dual MACD Strategy [Js.k]Strategy Overview
The Dual MACD Strategy leverages two MACD indicators with different parameters to generate buy and sell signals. By combining the trend-following properties of MACD with specific entry/exit criteria, this strategy aims to capture significant price movements while effectively managing risk.
Entry and Exit Conditions
Long Entry: A buy signal is triggered when:
The histogram of MACD1 crosses above zero.
The histogram of MACD2 is positive and rising.
Short Entry: A sell signal is triggered when:
The histogram of MACD1 crosses below zero.
The histogram of MACD2 is negative and declining.
Risk Management
Stop Loss and Take Profit:
Stop Loss is set at 1% below the entry price for long positions and 1% above the entry price for short positions.
Take Profit is set at 1.5% above the entry price for long positions and 1.5% below the entry price for short positions.
Position Sizing: Each trade risks a maximum of 10% of account equity, keeping potential losses manageable and in line with standard trading practices.
Backtesting Results
The strategy is tested on BTCUSDT with a time frame of 1 hour, resulting in 200+ trades.
The initial capital for backtesting is set to $10,000, with a realistic commission of 0.04% and a slippage of 2 ticks.
Conclusion
This strategy is inspired by Dreadblitz's Double MACD Buy and Sell, as well as some YouTube videos. My purpose in redeveloping them into this strategy is to validate the practicality of the Double MACD. After multiple modifications, this is the final version. I believe its profitability is limited and may lead to losses; please do not use this strategy for live trading.
NY Opening Range Breakout - MA StopCore Concept
This strategy trades breakouts from the New York opening range (9:30-9:45 AM NY time) on intraday timeframes, designed for scalping and day trading.
Setup Requirements
Timeframe: Works on any timeframe under 15 minutes (1m, 2m, 3m, 5m, 10m)
Session: New York market hours
Range Period: 9:30-9:45 AM NY time (15-minute opening range)
Entry Rules
Long Entries:
Wait for a candle to close above the opening range high
Enter long on the next candle (before 12:00 PM NY time)
Must be above moving average if using MA-based take profit
Short Entries:
Wait for a candle to close below the opening range low
Enter short on the next candle (before 12:00 PM NY time)
Must be below moving average if using MA-based take profit
Risk Management
Stop Loss:
Long trades: Opening range low
Short trades: Opening range high
Take Profit Options:
Fixed Risk Reward: 1.5x the range size (customizable ratio)
Moving Average: Exit when price crosses back through MA
Both: Whichever comes first
Key Features
Trade Direction Options:
Long Only
Short Only
Both directions
Moving Average Filter:
Prevents entries that would immediately hit stop loss
Uses EMA/SMA/WMA/VWMA with customizable length
Acts as dynamic support/resistance
Time Restrictions:
No entries after 12:00 PM NY time (customizable cutoff)
One trade per direction per day
Daily reset of all variables
Visual Elements
Red/green lines showing opening range
Purple line for moving average
Entry and breakout signals with shapes
Take profit and stop loss levels plotted
Information table with current status
Strategy Logic Flow
Morning: Capture 9:30-9:45 range high/low
Wait: Monitor for breakout (previous candle close outside range)
Filter: Check MA condition if using MA-based exits
Enter: Trade on next candle after breakout
Manage: Exit at fixed TP, MA cross, or stop loss
Reset: Start fresh next trading day
This is a momentum-based breakout strategy that capitalizes on early market volatility while using the opening range as natural support/resistance levels.
magic wand STSM"Magic Wand STSM" Strategy: Trend-Following with Dynamic Risk Management
Overview:
The "Magic Wand STSM" (Supertrend & SMA Momentum) is an automated trading strategy designed to identify and capitalize on sustained trends in the market. It combines a multi-timeframe Supertrend for trend direction and potential reversal signals, along with a 200-period Simple Moving Average (SMA) for overall market bias. A key feature of this strategy is its dynamic position sizing based on a user-defined risk percentage per trade, and a built-in daily and monthly profit/loss tracking system to manage overall exposure and prevent overtrading.
How it Works (Underlying Concepts):
Multi-Timeframe Trend Confirmation (Supertrend):
The strategy uses two Supertrend indicators: one on the current chart timeframe and another on a higher timeframe (e.g., if your chart is 5-minute, the higher timeframe Supertrend might be 15-minute).
Trend Identification: The Supertrend's direction output is crucial. A negative direction indicates a bearish trend (price below Supertrend), while a positive direction indicates a bullish trend (price above Supertrend).
Confirmation: A core principle is that trades are only considered when the Supertrend on both the current and the higher timeframe align in the same direction. This helps to filter out noise and focus on stronger, more confirmed trends. For example, for a long trade, both Supertrends must be indicating a bearish trend (price below Supertrend line, implying an uptrend context where price is expected to stay above/rebound from Supertrend). Similarly, for short trades, both must be indicating a bullish trend (price above Supertrend line, implying a downtrend context where price is expected to stay below/retest Supertrend).
Trend "Readiness": The strategy specifically looks for situations where the Supertrend has been stable for a few bars (checking barssince the last direction change).
Long-Term Market Bias (200 SMA):
A 200-period Simple Moving Average is plotted on the chart.
Filter: For long trades, the price must be above the 200 SMA, confirming an overall bullish bias. For short trades, the price must be below the 200 SMA, confirming an overall bearish bias. This acts as a macro filter, ensuring trades are taken in alignment with the broader market direction.
"Lowest/Highest Value" Pullback Entries:
The strategy employs custom functions (LowestValueAndBar, HighestValueAndBar) to identify specific price action within the recent trend:
For Long Entries: It looks for a "buy ready" condition where the price has found a recent lowest point within a specific number of bars since the Supertrend turned bearish (indicating an uptrend). This suggests a potential pullback or consolidation before continuation. The entry trigger is a close above the open of this identified lowest bar, and also above the current bar's open.
For Short Entries: It looks for a "sell ready" condition where the price has found a recent highest point within a specific number of bars since the Supertrend turned bullish (indicating a downtrend). This suggests a potential rally or consolidation before continuation downwards. The entry trigger is a close below the open of this identified highest bar, and also below the current bar's open.
Candle Confirmation: The strategy also incorporates a check on the candle type at the "lowest/highest value" bar (e.g., closevalue_b < openvalue_b for buy signals, meaning a bearish candle at the low, suggesting a potential reversal before a buy).
Risk Management and Position Sizing:
Dynamic Lot Sizing: The lotsvalue function calculates the appropriate position size based on your Your Equity input, the Risk to Reward ratio, and your risk percentage for your balance % input. This ensures that the capital risked per trade remains consistent as a percentage of your equity, regardless of the instrument's volatility or price. The stop loss distance is directly used in this calculation.
Fixed Risk Reward: All trades are entered with a predefined Risk to Reward ratio (default 2.0). This means for every unit of risk (stop loss distance), the target profit is rr times that distance.
Daily and Monthly Performance Monitoring:
The strategy tracks todaysWins, todaysLosses, and res (daily net result) in real-time.
A "daily profit target" is implemented (day_profit): If the daily net result is very favorable (e.g., res >= 4 with todaysLosses >= 2 or todaysWins + todaysLosses >= 8), the strategy may temporarily halt trading for the remainder of the session to "lock in" profits and prevent overtrading during volatile periods.
A "monthly stop-out" (monthly_trade) is implemented: If the lres (overall net result from all closed trades) falls below a certain threshold (e.g., -12), the strategy will stop trading for a set period (one week in this case) to protect capital during prolonged drawdowns.
Trade Execution:
Entry Triggers: Trades are entered when all buy/sell conditions (Supertrend alignment, SMA filter, "buy/sell situation" candle confirmation, and risk management checks) are met, and there are no open positions.
Stop Loss and Take Profit:
Stop Loss: The stop loss is dynamically placed at the upTrendValue for long trades and downTrendValue for short trades. These values are derived from the Supertrend indicator, which naturally adjusts to market volatility.
Take Profit: The take profit is calculated based on the entry price, the stop loss, and the Risk to Reward ratio (rr).
Position Locks: lock_long and lock_short variables prevent immediate re-entry into the same direction once a trade is initiated, or after a trend reversal based on Supertrend changes.
Visual Elements:
The 200 SMA is plotted in yellow.
Entry, Stop Loss, and Take Profit lines are plotted in white, red, and green respectively when a trade is active, with shaded areas between them to visually represent risk and reward.
Diamond shapes are plotted at the bottom of the chart (green for potential buy signals, red for potential sell signals) to visually indicate when the buy_sit or sell_sit conditions are met, along with other key filters.
A comprehensive trade statistics table is displayed on the chart, showing daily wins/losses, daily profit, total deals, and overall profit/loss.
A background color indicates the active trading session.
Ideal Usage:
This strategy is best applied to instruments with clear trends and sufficient liquidity. Users should carefully adjust the Your Equity, Risk to Reward, and risk percentage inputs to align with their individual risk tolerance and capital. Experimentation with different ATR Length and Factor values for the Supertrend might be beneficial depending on the asset and timeframe.
