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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.
Pine Script®策略
由DskyzInvestments提供
11
AndyB London Breakout Box StrategyThe AndyB London Breakout Box Strategy is a time-based breakout strategy designed for traders who focus on the London trading session. It automatically draws a range box from a configurable time window (default: 02:00–07:00 London time) and places simulated long or short entries when price breaks above or below this range. This script allows flexible configuration of: Box timing (start, end, and cutoff hours in London time) Take Profit (TP): either based on the box size or a multiple of the stop-loss Stop-Loss (SL): either half the box size or based on the distance from the breakout candle's close to the opposite box edge Two logic modes can be combined: Use the entry candle’s close for SL and calculate TP from that distance Or use the box size as the base for SL and TP Test on Gold M15 (or M5) for trading ideas.
Pine Script®策略
由abarrett69提供
2
Darren - Engulfing + MACD CrossDarren – Engulfing + MACD Cross Overall Behavior Identify an engulfing candle (bullish or bearish). Wait up to windowBars bars for the corresponding MACD crossover (bullish engulfing → MACD cross up; bearish engulfing → MACD cross down). If the crossover occurs within that window, trigger an entry (long or short) and close any opposite open trade. Inputs • macdFast (default 12): length of MACD fast EMA • macdSlow (default 26): length of MACD slow EMA • macdSignal (default 9): length of MACD signal line • windowBars (default 3): maximum bars allowed between an engulfing candle and a MACD crossover Indicators • macdLine and signalLine are calculated using ta.macd(close, macdFast, macdSlow, macdSignal) • macdHist = macdLine – signalLine, plotted as columns (green when ≥ 0, red when < 0) Engulfing Pattern Detection • Bullish engulfing (bullEngulfing) is true when the previous candle is bearish (close < open ), the current candle is bullish (close > open), and the current body fully engulfs the previous body (open < close and close > open ). • Bearish engulfing (bearEngulfing) is the inverse: previous candle bullish, current candle bearish, and current body fully engulfs the prior body. MACD Crossover Detection • macdCrossUp is true when macdLine crosses above signalLine. • macdCrossDown is true when macdLine crosses below signalLine. Timing Logic • barsSinceBull = ta.barssince(bullEngulfing) returns number of bars since the last bullish engulfing. • barsSinceBear = ta.barssince(bearEngulfing) returns number of bars since the last bearish engulfing. • longCondition occurs if a MACD cross up happens within windowBars bars of a bullish engulfing (barsSinceBull ≤ windowBars and macdCrossUp). • shortCondition occurs if a MACD cross down happens within windowBars bars of a bearish engulfing (barsSinceBear ≤ windowBars and macdCrossDown). Chart Markers • “Bull” label below bar whenever bullEngulfing is true. • “Bear” label above bar whenever bearEngulfing is true. • Small “Up” ▲ below bar when macdCrossUp is true. • Small “Down” ▼ above bar when macdCrossDown is true. • Triangle ▲ below bar for Long Entry (longCondition). • Triangle ▼ above bar for Short Entry (shortCondition). Entry & Exit Rules • On longCondition: enter “Long”, and close any existing “Short” position. • On shortCondition: enter “Short”, and close any existing “Long” position.
Pine Script®策略
由OEM_redartgnidoc提供
Qian-Kun Strategy SharingBuy when the K_ signal disappears, and close the long position when the Q_ signal disappears. Yin-Yang Mutual Exclusion Trading Strategy: The Wisdom of Tai Chi, The Way of the Market. In the chaos of market fluctuations, capturing the moments of Qian and Kun transitions, yin-yang mutual exclusion reveals the essence of order: Yang rises and Yin falls, mutually exclusive yet coexisting. When Yang energy prevails (price breaks above the cycle high), Yin retreats, signaling an upward move; when Yin energy dominates (price falls below the cycle low), Yang recedes, indicating a downward move.
Pine Script®策略
由gaotienqian提供
SY_Quant_AI_YJ📢 SY_Quant_AI_Alert Strategy Overview Name: SY_Quant_AI_Alert Purpose: Designed for real-time alerting when Long, Short, or Take-Profit signals are triggered. Alerts are structured in JSON format, ideal for integration with DingTalk, Telegram, or other bots. ✅ Key Features: Trend Detection using Supertrend, EMA/MA filters, and candle color analysis. Momentum Filtering with MACD crossover signals. Entry Alerts when a strong signal appears (Long/Short), showing ideal entry zone. Take-Profit Alerts when floating profit is valid and MACD reverses in favor. Full JSON Alerts for easy automation and third-party integrations.
Pine Script®策略
由mlngbeuho095提供
MACD 直方图背离 + 连续N柱动能确认 + 量能/RSI 方向过滤（含止盈止损）This strategy combines MACD histogram divergence detection with short-term momentum confirmation to identify potential trend reversal zones. It optionally includes volume surge and RSI direction filters to further refine signals, aiming to filter out weak setups.TotalNetProfit+2,894,973.64USDT(+289.50%) MaxDrawdown536,362.52USDT(34.76%) TotalTrades321 WinningRate30.84% ProfitFactor1.72 Trades are protected by configurable take profit and stop loss levels. Ideal for traders who prefer visual divergence-based setups with flexible confirmation logic. ⚙️ All key elements are adjustable: divergence sensitivity, histogram momentum length, volume filter, RSI behavior, and risk management. 📌 Try it on crypto, indices, or forex charts — especially effective on 2H or 4H timeframes. 👉 If you find it helpful, leave a like or follow for more advanced divergence-based strategies.
Pine Script®策略
由mengmei2016提供
RSI Reversal Pro [跨 10 周期 OR] (Vol + 阳线多/阴线空)Easily capture overbought/oversold reversals using RSI signals from 10 timeframes with OR logic, enhanced with a volume spike filter. Built-in stop loss and take profit protection. Entries rely on just three elements: RSI extreme → volume breakout → candle direction. Why Try It? • Works on any asset: Crypto, Forex, Indices, and more • Multi-timeframe ready: From 15min up to 3-day charts • No pyramiding: Fixed SL at 1.5% and TP at 2% for clean risk management Total Net Profit: +204,734.18 USDT (+20.47%) Max Equity Drawdown: 21,709.20 USDT (1.88%) Total Trades: 20 Winning Trades: 75.00% (15 wins / 5 losses) Profit Factor: 11.635 Note This is a lite showcase version with simplified logic for educational and testing purposes.
Pine Script®策略
由mengmei2016提供
1
BUY/SELL PEPEUSDT 80%WINThis Pine Script strategy is designed for trading XAUUSD (Gold) with a focus on trend-following entries and ATR-based risk management. Here's how it works and how to use it effectively: !! USE ONLY ON 3M TIMEFRAME !! Strategy Logic MA Crossover System: Uses customizable moving averages (12 types including TEMA, HullMA, ALMA) Tracks Close vs. Open price MAs for clearer trend signals Alternate timeframe analysis for higher timeframe confirmation Smart Risk Management: ATR-based stops (1.5x ATR) 3-tier take profit (1x, 2x, 3x ATR) with partial closing 2% equity risk per trade (adjustable) Flexible Trading Modes: Long-only, Short-only or Both directions Works on any timeframe (optimized for 15M-1H) How To Trade It Successfully ✅ Best Market Conditions: Trending markets (avoid choppy/ranging periods) London/NY overlap hours (high liquidity) Gold volatility > 1.5% daily ⚙️ Optimal Settings: MA Type: TEMA or HullMA (8-12 period) Alternate TF: 3x current chart TF (e.g. 45M when trading 15M) TP/SL Ratio: 1:2 or 1:3 (adjust ATR multipliers) 📊 Trade Execution Rules: Long Entry: MA crossover UP + Price > MA Confirm with RSI(14) > 50 (optional) Short Entry: MA crossover DOWN + Price < MA Confirm with RSI(14) < 50 (optional) Exit: Let partial TP1 (1x ATR) auto-close 50% Trail balance to TP2/TP3 ⚠️ Risk Warning: Max 2% account risk per trade Avoid trading during major news (NFP, FOMC) Disable during sideways markets (use ADX filter >25) Pro Tip: Combine with 200EMA on higher timeframe for trend confirmation! 👉 Backtest shows 64% win rate with proper risk management. Always forward test before live trading!
Pine Script®策略
由pr3data提供
Baseline Entry + RSI Divergence Exit + Triangle+DiamondThis strategy focuses on low-frequency trades with high win factor and winning probability. It uses momentum to enter trades, which is combined with moving averages and volume. It exits based on partial profit booking and when the momentum turns to the opposite direction. It can also exit on RSI divergences. First, check the backtest result for it. I like to use it on Flockusdt.p with the parameters Stop loss—1.2, Take profit—10,10, Ema—8 42, 9, 44, while keeping the normal CVD off. It has fantastic risk management, a Win rate of 56.52%, a win factor of 41 on a 4-hour time frame at 1000 capital, and a 0.05% commission on flockusdt on Blofin.
Pine Script®策略
由jy819989提供
CRODL | Risk Pilot v1CRODL | Risk Pilot v1 is a precision-engineered strategy built for traders who prioritize capital preservation and systematic risk control. This script is designed to automate trade execution based on predefined risk parameters, using dynamic stop-loss logic and optional breakeven mechanics. 🔍 Key Features: 🧮 Risk Management: Set your risk per trade in USD and let the strategy auto-calculate position size for you. 🎯 Smart TP/SL: Take profit and stop-loss are dynamically generated based on ATR or a percentage-based system. 🛡️ Breakeven Logic: Optionally move stop-loss to breakeven once 1R is achieved. ⚙️ VWMA & SMA Filters: Higher-timeframe VWMA and SMA filters help validate trade direction and reduce noise. 📉 Supertrend or Percent SL: Choose between Supertrend-based stop loss or a fixed percentage. 🚨 Custom Alerts: Built-in alert messages for entries and exits, customizable for webhook or manual execution setups. 📊 Visual Risk Lines: See your SL and TP levels drawn clearly on the chart with real-time updates. This strategy is ideal for those who want to control their risk down to the dollar, automate execution, and visually track trades on the chart. It’s flexible enough to adapt to your unique trading preferences while ensuring each trade respects your risk limits. Note: This script is for educational purposes only and does not constitute financial advice. Always test any strategy using a demo account or paper trading environment before applying it to live markets. 🔍 Entry Logic Explained How Longs and Shorts Are Determined: This strategy uses a combination of custom ATR trailing logic, higher-timeframe SMA filtering, and Supertrend direction to generate trade entries. Here's how it works: 🟢 Long Conditions: A long position is triggered when: Price breaks above the custom ATR trailing stop (bullish trend detection), The close is above the higher-timeframe SMA (default: 1-hour SMA 20), Either no position is open, or existing short positions are closed first (based on user setting), Supertrend confirms the direction (if enabled for SL). 🔴 Short Conditions: A short position is triggered when: Price breaks below the custom ATR trailing stop (bearish trend detection), The close is below the higher-timeframe SMA, Either no position is open, or existing long positions are closed first, Supertrend aligns in bearish direction (if used for SL). By combining momentum detection (ATR), trend filtering (SMA), and precision risk controls, the strategy aims to reduce false entries and only act during strong directional bias.
Pine Script®策略
由crodl提供