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3:55 S&D + CE 📊 High Probability S&D Trading System (90%+ Win Rate)Transform your trading with institutional-grade Supply & Demand zones built from the critical 3:55 PM EST candle - the last key reference point of the regular trading session where smart money positions for the next day.✨ KEY FEATURES🔥 Consequent Encroachment (CE) Integration Yellow CE lines mark the 50% optimal entry point within each zone Signals only fire after CE is touched (institutional rebalancing level) Dramatically increases win rate by catching precise reversals 📦 Clean Supply & Demand Zones Supply Zones (Red) - Resistance areas above 3:55 PM high Demand Zones (Green) - Support areas below 3:55 PM low Automatically extends zones to the right for easy visualization Adjustable zone height (default 15% of candle range) 🎯 Intelligent Scoring System (0-12 Points) Every setup is scored based on: ✅ CE Touch (+3 points) 🥇 Zone Freshness (+1-2 points) 📊 Volume Confirmation (+2-3 points) 🕯️ Rejection Wick Quality (+2 points) 💪 Order Flow Direction (+1 point) Signals only fire when score ≥ 9 (Strict Mode) or ≥ 6 (Balanced Mode)🛡️ Two Trading ModesStrict Mode (90%+ Win Rate): Requires ALL confirmations CE touch mandatory 60%+ rejection wicks 1.5x volume minimum 1-3 perfect setups per week Balanced Mode (80%+ Win Rate): More flexible requirements 6+ point minimum 2-5 setups per week 📅 Extended Historical View View up to 50 days of supply & demand zones Perfect for swing traders and position traders Automatically removes exhausted zones (3+ touches)
Pine Script® indicator
by RichForEternity_
MACD Trend & Momentum Dashboard (Weighted, 3 TFs)This indicator provides a multi-timeframe MACD trend and momentum dashboard that works independently of your current chart timeframe. It displays MACD zero-line bias and MACD-vs-Signal trend state across three user-selectable timeframes, using clear color-coded cells for instant visual interpretation. A weighted scoring system combines all six signals into a single market bias classification (Strong Bullish → Strong Bearish). This helps traders quickly understand higher- and lower-timeframe alignment, market regime, and overall trend quality. Ideal for trend- and momentum-followers who want a clean, actionable market overview at a glance.
Pine Script® indicator
by hungryHead54637
Engulfing Candlestick Pattern - BB FilterBeen working on doing a better version of this. This is like version 2.0. Usese this definition of an engulfing candle: tradeciety.com/how-to-trade-the-engulfing-candlestick-pattern As you change the parameters of the Bollinger band the signals will change. You can also set the distance away from the band using ATR muliplier to catch moves near the BB. Per Claude, This setup should give you much higher quality signals since you're filtering for engulfing patterns that occur at the extremes of the Bollinger Bands - exactly like the Tradeciety article recommends. Those are the setups with the best context and highest probability. A few tips for using it: You can adjust the BB Touch Distance slider if you want to be stricter or more lenient about what counts as "touching" the bands Try enabling Strict Mode if you want only the strongest engulfing patterns (where the full range including wicks is engulfed) Works great on higher timeframes like Daily and Weekly for the most reliable signals on NQ and ES I personally use this on the 1000 tick NQ chart. It's not perfect but 2x better than my first attempt. Enjoy. Open to suggestions as well. For entertainment purposes only.
Pine Script® indicator
by theAdamWarner
1
Engulfing Candlestick Pattern - BB FilterBeen working on doing a better version of this. This is like version 2.0. Usese this definition of an engulfing candle: tradeciety.com As you change the parameters of the Bollinger band the signals will change. You can also set the distance away from the band using ATR muliplier to catch moves near the BB. Per Claude, This setup should give you much higher quality signals since you're filtering for engulfing patterns that occur at the extremes of the Bollinger Bands - exactly like the Tradeciety article recommends. Those are the setups with the best context and highest probability. A few tips for using it: You can adjust the BB Touch Distance slider if you want to be stricter or more lenient about what counts as "touching" the bands Try enabling Strict Mode if you want only the strongest engulfing patterns (where the full range including wicks is engulfed) Works great on higher timeframes like Daily and Weekly for the most reliable signals on NQ and ES I personally use this on the 1000 tick NQ chart. It's not perfect but 2x better than my first attempt. Enjoy. Open to suggestions as well. For entertainment purposes only.
Pine Script® indicator
by theAdamWarner
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention . Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve. Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume. The Problem This System Solves Traditional Technical Analysis Limitations Most trading systems suffer from three fundamental challenges : Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes. Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions. Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution. How RPD Machine Learning Addresses These Challenges Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation. Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly. Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success. Machine Learning Architecture: What Makes This "Real" ML Component 1: Contextual Multi-Armed Bandits (LinUCB) What Is a Multi-Armed Bandit Problem? Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies. Academic Foundation: RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems. How It Works: Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains: Reward History: Tracks wins/losses for each policy Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume) Uncertainty Estimates: Confidence in each policy's performance UCB Formula: predicted_reward + α × uncertainty The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies. Why This Matters: Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics. Component 2: Random Fourier Features (RFF) The Non-Linearity Challenge: Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions. Academic Foundation: RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading. How It Works: The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations: Input: 8 base features Projection: Through random Gaussian weights Transformation: cos(W×features + b) Output: 16 RFF dimensions This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space. Why This Matters: Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection. Component 3: Reinforcement Learning Stack Beyond bandits, RPD implements a complete RL framework : Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades. TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades. Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction. Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases. Why This Matters: Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm. Component 4: Policy Momentum Tracking (v2 Feature) The Recency Challenge: Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance. RPD's Solution: Each policy maintains a ring buffer of the last 10 outcomes. The system calculates: Momentum: recent_win_rate - global_win_rate (range: -1 to +1) Confidence: consistency of recent results (1 - variance) Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty. Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits. Signal Generation: The Core Algorithm Multi-Timeframe Fractal Detection RPD identifies reversal points using three complementary methods : 1. Quantum State Analysis: Divides price range into discrete states (default: 6 levels) Peak signals require price in top states (≥ state 5) Valley signals require price in bottom states (≤ state 1) Prevents mid-range signals that may struggle in strong trends 2. Fractal Geometry: Identifies swing highs/lows using configurable fractal strength Confirms local extremum with neighboring bars Validates reversal only if price crosses prior extreme 3. Multi-Timeframe Confirmation: Analyzes higher timeframe (4× default) for alignment MTF confirmation adds probability bonus Designed to reduce false signals while preserving valid setups Probability Scoring System Each signal receives a dynamic probability score (40-99%) based on: Base Components: Trend Strength: EMA(velocity) / ATR × 30 points Entropy Quality: (1 - entropy) × 10 points Starting baseline: 40 points Enhancement Bonuses: Divergence Detection: +20 points (price/momentum divergence) RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys) Volume Confirmation: +5 points (volume >1.2× average) Adaptive Momentum: +10 points (strong directional velocity) MTF Alignment: +12 points (higher timeframe confirms) Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment) Regime Bonus: +8 points (trending ADX >25 with directional agreement) Penalties: High Entropy: -5 points (entropy >0.85, chaotic price action) Consolidation Regime: -10 points (ADX <20, no directional conviction) Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%) Entropy-Based Quality Filter What Is Entropy? Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions. Calculation: Count up/down price changes over adaptive period Calculate probability: p = ups / total_changes Shannon entropy: -p×log(p) - (1-p)×log(1-p) Normalized to 0-1 range Application: Entropy <0.5: Highly ordered (ELITE signals possible) Entropy 0.5-0.75: Mixed (GOOD signals) Entropy >0.85: Chaotic (signals blocked or heavily penalized) Why This Matters: Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable. Regime Detection & Market Microstructure - ADX-Based Regime Classification RPD uses Wilder's Average Directional Index to classify markets: Bull Trend: ADX >25, +DI > -DI (directional conviction bullish) Bear Trend: ADX >25, +DI < -DI (directional conviction bearish) Consolidation: ADX <20 (no directional conviction) Transitional: ADX 20-25 (forming direction, ambiguous) Filter Logic: Blocks all signals during Transitional regime (avoids trading during uncertain conditions) Blocks Consolidation signals unless ADX ≥ Min Trend Strength Adds probability bonus during strong trends (ADX >30) Effect: Designed to reduce signal frequency while focusing on higher-quality setups. Divergence Detection Bearish Divergence: Price makes higher high Velocity (price momentum) makes lower high Indicates weakening upward pressure → SHORT signal quality boost Bullish Divergence: Price makes lower low Velocity makes higher low Indicates weakening downward pressure → LONG signal quality boost Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing. Hierarchical Policy System - The Six Trading Policies 1. AggressiveTrend (Policy 0): Probability Threshold: 60% (trades more frequently) Entropy Threshold: 0.70 (tolerates moderate chaos) Stop Multiplier: 2.5× ATR (wider stops for trends) Target Multiplier: 5.0R (larger targets) Entry Mode: Pyramid (scales into winners) Best For: Strong trending markets, breakouts, momentum continuation 2. ConservativeRange (Policy 1): Probability Threshold: 75% (more selective) Entropy Threshold: 0.60 (requires order) Stop Multiplier: 1.8× ATR (tighter stops) Target Multiplier: 3.0R (modest targets) Entry Mode: Single (one-shot entries) Best For: Range-bound markets, low volatility, mean reversion 3. VolatilityBreakout (Policy 2): Probability Threshold: 65% (moderate) Entropy Threshold: 0.80 (accepts high entropy) Stop Multiplier: 3.0× ATR (wider stops) Target Multiplier: 6.0R (larger targets) Entry Mode: Tiered (splits entry) Best For: Compression breakouts, post-consolidation moves, gap opens 4. EntropyScalp (Policy 3): Probability Threshold: 80% (very selective) Entropy Threshold: 0.40 (requires extreme order) Stop Multiplier: 1.5× ATR (tightest stops) Target Multiplier: 2.5R (quick targets) Entry Mode: Single Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns 5. DivergenceHunter (Policy 4): Probability Threshold: 70% (quality-focused) Entropy Threshold: 0.65 (balanced) Stop Multiplier: 2.2× ATR (moderate stops) Target Multiplier: 4.5R (balanced targets) Entry Mode: Tiered Best For: Divergence-confirmed reversals, exhaustion moves, trend climax 6. AdaptiveBlend (Policy 5): Probability Threshold: 68% (balanced) Entropy Threshold: 0.75 (balanced) Stop Multiplier: 2.0× ATR (standard) Target Multiplier: 4.0R (standard) Entry Mode: Single Best For: Mixed conditions, general trading, fallback when no clear regime Policy Clustering (Advanced/Extreme Modes) Policies are grouped into three clusters based on regime affinity: Cluster 1 (Trending): AggressiveTrend, DivergenceHunter High regime affinity (0.8): Performs well when ADX >25 Moderate vol affinity (0.6): Works in various volatility Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend Low regime affinity (0.3): Better suited for ADX <20 Low vol affinity (0.4): Optimized for calm markets Cluster 3 (Breakout): VolatilityBreakout Moderate regime affinity (0.6): Works in multiple regimes High vol affinity (0.9): Requires high volatility for optimal characteristics Hierarchical Selection Process: Calculate cluster scores based on current regime and volatility Select best-matching cluster Run UCB selection within chosen cluster Apply momentum boost/penalty This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster. Risk Management & Position Sizing Dynamic Kelly Criterion Sizing (Optional) Traditional Fixed Sizing Challenge: Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions. Kelly Formula: f = (p × b - q) / b Where: p = win probability (from signal score) q = loss probability (1 - p) b = win/loss ratio (average_win / average_loss) f = fraction of capital to risk RPD Implementation: Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits. Enhancements: Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier Divergence Bonus: Additional sizing on divergence signals Regime Bonus: Additional sizing during strong trends (ADX >30) Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction Safety Rails: Minimum: 1 contract (floor) Maximum: User-defined cap (default 10 contracts) Portfolio Heat: Max total risk across all positions (default 4% equity) Multi-Mode Stop Loss System ATR Mode (Default): Stop = entry ± (ATR × base_mult × policy_mult) Consistent risk sizing Ignores market structure Best for: Futures, forex, algorithmic trading Structural Mode: Finds swing low (long) or high (short) over last 20 bars Identifies fractal pivots within lookback Places stop below/above structure + buffer (0.1× ATR) Best for: Stocks, instruments that respect structure Hybrid Mode (Intelligent): Attempts structural stop first Falls back to ATR if: Structural level is invalid (beyond entry) Structural stop >2× ATR away (too wide) Best for: Mixed instruments, adaptability Dynamic Adjustments: Breakeven: Move stop to entry + 1 tick after 1.0R profit Trailing: Trail stop 0.8R behind price after 1.5R profit Timeout: Force close after 30 bars (optional) Tiered Entry System Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality. Solution: Tier 1 (40% of size): Enters immediately on all signals Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%) Example: Calculated optimal size: 10 contracts Signal probability: 72% Tier 2 trigger: 75% Result: Enter 4 contracts only (Tier 1) Same signal at 80% probability Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2) Effect: Automatically scales size to signal quality, optimizing capital allocation. Performance Optimization & Learning Curve Warmup Phase (First 50 Trades) Purpose: Ensure all policies get tested before system focuses on preferred strategies. Modifications During Warmup: Probability thresholds reduced 20% (65% becomes 52%) Entropy thresholds increased 20% (more permissive) Exploration rate stays high (30%) Confidence width (α) doubled (more exploration) Why This Matters: Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies. Curriculum Learning Phase 1 (Trades 1-50): Exploration Warmup active All policies tested High exploration (30%) Learning fundamental patterns Phase 2 (Trades 50-100): Refinement Warmup ended, thresholds normalize Exploration decaying (30% → 15%) Policy preferences emerging Meta-learning optimizing Phase 3 (Trades 100-200): Specialization Exploration low (15% → 8%) Clear policy preferences established Momentum tracking fully active System focusing on learned patterns Phase 4 (Trades 200+): Maturity Exploration minimal (8% → 5%) Regime-policy relationships learned Auto-adaptation to market shifts Stable performance expected Convergence Indicators System is learning well when: Policy switch rate decreasing over time (initially ~50%, should drop to <20%) Exploration rate decaying smoothly (30% → 5%) One or two policies emerge with >50% selection frequency Performance metrics stabilizing over time Consistent behavior in similar market conditions System may need adjustment when: Policy switch rate >40% after 100 trades (excessive exploration) Exploration rate not decaying (parameter issue) All policies showing similar selection (not differentiating) Performance declining despite relaxed thresholds (underlying signal issue) Highly erratic behavior after learning phase Advanced Features Attention Mechanism (Extreme Mode) Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally. Solution: Each RFF dimension has an importance weight . After each trade: Calculate correlation: sign(feature - 0.5) × sign(reward) Update importance: importance += correlation × 0.01 Clamp to range Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes. Temporal Context (Extreme Mode) Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information. Solution: Includes 3-period historical context with exponential decay (0.85): Current features (weight 1.0) 1 bar ago (weight 0.85) 2 bars ago (weight 0.72) Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves. Transfer Learning via Episodic Memory Short-Term Memory (STM): Last 20 trades Fast adaptation to immediate regime High learning rate Long-Term Memory (LTM): Condensed historical patterns Preserved knowledge from past regimes Low learning rate Transfer Mechanism: When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch. Practical Implementation Guide - Recommended Settings by Instrument Futures (ES, NQ, CL): Adaptive Period: 20-25 ML Mode: Advanced RFF Dimensions: 16 Policies: 6 Base Risk: 1.5% Stop Mode: ATR or Hybrid Timeframe: 5-15 min Forex Majors (EURUSD, GBPUSD): Adaptive Period: 25-30 ML Mode: Advanced RFF Dimensions: 16 Policies: 6 Base Risk: 1.0-1.5% Stop Mode: ATR Timeframe: 5-30 min Cryptocurrency (BTC, ETH): Adaptive Period: 20-25 ML Mode: Extreme (handles non-stationarity) RFF Dimensions: 32 (captures complexity) Policies: 6 Base Risk: 1.0% (volatility consideration) Stop Mode: Hybrid Timeframe: 15 min - 4 hr Stocks (Large Cap): Adaptive Period: 25-30 ML Mode: Advanced RFF Dimensions: 16 Policies: 5-6 Base Risk: 1.5-2.0% Stop Mode: Structural or Hybrid Timeframe: 15 min - Daily Scaling Strategy Phase 1 (Testing - First 50 Trades): Max Contracts: 1-2 Goal: Validate system on your instrument Monitor: Performance stabilization, learning progress Phase 2 (Validation - Trades 50-100): Max Contracts: 2-3 Goal: Confirm learning convergence Monitor: Policy stability, exploration decay Phase 3 (Scaling - Trades 100-200): Max Contracts: 3-5 Enable: Kelly sizing (1/4 Kelly) Goal: Optimize capital efficiency Monitor: Risk-adjusted returns Phase 4 (Full Deployment - Trades 200+): Max Contracts: 5-10 Enable: Full momentum tracking Goal: Sustained consistent performance Monitor: Ongoing adaptation quality Limitations & Disclaimers Statistical Limitations Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior. Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment). Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing. Technical Limitations Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded. Pine Script Constraints: No true matrix inversion (uses diagonal approximation for LinUCB) No cryptographic RNG (uses market data as entropy) No proper random number generation for RFF (uses deterministic pseudo-random) These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications. Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality. Forward-Looking Bias Disclaimer Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108. Why This is Disclosed: Entry signals do NOT look ahead - decisions use only data up to entry bar Forward data used for learning only, not signal generation In live trading, system learns identically as bars unfold in real-time Simulates natural learning process (outcomes are only known after trades complete) Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if: - Positions held longer than 8 bars - Slippage/commissions differ from backtest settings - Market microstructure changes (wider spreads, different execution quality) Risk Warnings No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events. Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations. Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses. Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance. System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards. Appropriate Use This System Is: ✅ A machine learning framework for adaptive strategy selection ✅ A signal generation system with probabilistic scoring ✅ A risk management system with dynamic sizing ✅ A learning system designed to adapt over time This System Is NOT: ❌ A price prediction system (does not forecast exact prices) ❌ A guarantee of profits (can and will experience losses) ❌ A replacement for due diligence (requires monitoring and understanding) ❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals) Recommended Use: Paper trade for 100 signals before risking capital Start with minimal position sizing (1-2 contracts) regardless of calculated size Monitor learning progress via dashboard Scale gradually over several months only after consistent results Combine with fundamental analysis and broader market context Set account-level risk limits (e.g., maximum drawdown threshold) Never risk more than you can afford to lose What Makes This System Different RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization: ✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.) ✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht) ✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook ✅ Meta-Learning - Learning rate adaptation based on feature correlation ✅ Online Learning - Real-time updates from streaming data ✅ Hierarchical Policies - Two-stage selection with clustering ✅ Momentum Tracking - Recent performance analysis for faster adaptation ✅ Attention Mechanism - Feature importance weighting ✅ Transfer Learning - Episodic memory consolidation Key Differentiators: Actually learns from trade outcomes (not just parameter optimization) Updates model parameters in real-time (true online learning) Adapts to changing market regimes (not static rules) Improves over time through reinforcement learning Implements published ML algorithms with proper citations Conclusion RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market. The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static. Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® strategy
by DskyzInvestments
1
ORB + INMERELO ADR + ATRThis indicator provides **two completely different but complementary lines of information** for intraday traders: # **1. The ORB Line (ADR-Based Context Line)** The ORB portion of the script focuses on **range expansion** relative to typical daily behavior. ### **What it measures** * **20-day ADR (Average Daily Range)** * **Today’s range as a % of ADR** * **How much of the average range has been “used”** by the time you’re considering an Opening Range Breakout ### **Why it matters for ORB trading** Successful ORBs thrive when: * **ADR used% is low** (green) → plenty of fuel left for expansion * **ADR used% is moderate** (orange) → breakout still possible but less explosive * **ADR used% is high** (red) → breakout attempts often fail or reverse ### **What the indicator gives you** A clean, color-coded readout of: * ADR * Today’s range * Used% * A simple green/orange/red evaluation of ORB quality This allows a trader to quickly judge whether **conditions favor ORB continuation or mean-reversion reversal**—without manually calculating ranges or switching charts. --- # **2. The INMERELO Line (ATR Stretch + MA Interaction)** The INMERELO portion of the script is built around **mean-reversion mechanics**: the market tends to revert back toward the **first daily MA it crosses under**. ### **How it determines the active MA** At the start of each session, the script waits for price to cross under: * **EMA10** * **EMA21** * **SMA50** Whichever MA is crossed first becomes the **active MA** for the day. If no cross has occurred yet, the indicator shows the **nearest MA**, so traders know exactly what the likely “INMERELO magnet” will be. ### **What it measures** * **Stretch from the active MA (in ATR units)** * **20-day ATR regime direction (expanding or contracting)** * **Daily MA context: E10, E21, or S50** ### **Why it matters for INMERELOs** This provides: * The **target MA** * The **distance to that MA in ATRs** * A color-coded stretch score: * **0.6–1.2 ATR** → prime INMERELO zone (Green) * Moderately stretched → Orange * Overstretched or dead zone → Red An up/down arrow shows whether **volatility is expanding or compressing**, which affects expected retrace behavior. ### **What the indicator gives you** All INMERELO data is displayed in a second compact line: * Stretch to MA * Active MA label (E10/E21/S50) * ATR regime arrow This allows fast identification of high-probability **mean-reversion trades back to the MA**. --- # **Summary** This indicator shows: ### **Line 1 → ORB Context (ADR)** * Is the stock setup for a powerful breakout? * How much ADR is left? * Are you early (good) or late (risky)? ### **Line 2 → INMERELO Context (ATR + MA Stretch)** * Which MA is in control today (EMA10, EMA21, or SMA50)? * How many ATRs away from that MA are we? * Is volatility expanding or contracting? * Is this a clean INMERELO setup or not? Together, these two lines give traders the **two most important intraday lenses**: **range expansion (ORB)** and **mean reversion (INMERELO)**—updated every bar, without clutter.
Pine Script® indicator
by machine917
Gann Master System - CompleteGann Master Trading System - Multi-Factor Confluence Indicator Advanced implementation of W.D. Gann methodology combining Square of 9 calculations, Octave Theory projections, Time Cycle analysis, and Planetary Aspect windows into a systematic confluence-based trading system. Key Features: Square of 9 geometric price levels (180°, 270°, 360° rotations) Octave Theory targets with harmonic divisions (0.5x, 1x, 2x, 4x) Time cycle tracking with sub-cycle analysis 10 configurable planetary aspect windows (manual input from ephemeris) Automatic swing pivot detection Multi-factor confluence scoring (0-20+ points) Visual signals: Blue (score 3-6), Red (7-10), Purple (11+) Real-time info panel with factor status Built-in alerts for high-probability setups How It Works: System calculates multiple Gann factors simultaneously and awards points when price aligns with key levels. Higher confluence scores indicate stronger probability of reversal. Combines objective mathematics with astronomical timing for systematic edge. Best For: Daily/4H charts on Gold, Forex majors, Indices Signal Frequency: 2-4 high-quality setups per month (score 11+) Recommended Min Score: 7 for trading, 11+ for highest probability Setup Required: Configure Square of 9 pivot, Octave base range, Time cycle start date, and planetary aspect dates. See full documentation for detailed guide.
Pine Script® indicator
by rudolfm777
EMA 5-8 Crossover Indicator//@version=5 indicator("EMA 5-8 Crossover Indicator", shorttitle="EMA Cross", overlay=false) // EMA calculations ema5 = ta.ema(close, 5) ema8 = ta.ema(close, 8) // Crossover signals bullishCross = ta.crossover(ema5, ema8) // EMA5 crosses above EMA8 (BUY) bearishCross = ta.crossunder(ema5, ema8) // EMA5 crosses below EMA8 (SELL) // Trend state (for continuous background) var int trend = 0 if bullishCross trend := 1 else if bearishCross trend := -1 // EMA lines (in lower pane) plot(ema5, title="EMA 5", color=color.new(color.blue, 0), linewidth=2) plot(ema8, title="EMA 8", color=color.new(color.orange, 0), linewidth=2) // Continuous background colors bgcolor(trend == 1 ? color.new(color.green, 85) : trend == -1 ? color.new(color.red, 85) : na, title="Trend Background") // Alert conditions alertcondition(bullishCross, title="BUY Signal", message="EMA 5 crossed above EMA 8 - BUY!") alertcondition(bearishCross, title="SELL Signal", message="EMA 5 crossed below EMA 8 - SELL!")
Pine Script® indicator
by drfatihgul
INMERELO EMA Reclaim HighlighterOverview The INMERELO EMA Reclaim indicator highlights intraday candles reclaiming a configurable EMA on any timeframe. It identifies candles based on customizable candle geometry filters and confirms momentum using a custom MACD setup. Features Configurable Intraday EMA Any EMA length and timeframe. Default: 6-period EMA on chart timeframe. Highlights when price reclaims the EMA after a configurable number of prior closes below it. Candle Geometry Filters (ORB-Style) Open Position: Maximum position of open relative to candle range (0–1). Default: 0.40 Close Position: Minimum position of close relative to candle range (0–1). Default: 0.70 Body Fraction: Minimum body size relative to candle range. Default: 0.50 Custom MACD Filter Fast line above slow line. Configurable: Fast (default 6), Slow (default 20), Signal (default 9). Prior Closes Below EMA Filter Configurable minimum number of prior closes below EMA. Default: 2 Visual Options Paint candle with configurable color. Optional arrow display above reclaim candle (toggleable). Flexible Works on any intraday timeframe, including 5-minute, 2-minute, 15-minute, etc. Settings Overview Setting Default Notes EMA Length 6 EMA used for reclaim detection EMA Timeframe Chart TF Can be set to any intraday timeframe Open ≤ 0.40 ORB-style filter Close ≥ 0.70 ORB-style filter Body Fraction 0.50 ORB-style filter Min Prior Closes Below EMA 2 Minimum closes below EMA before reclaim MACD Fast 6 Custom MACD fast line MACD Slow 20 Custom MACD slow line MACD Signal 9 Custom MACD signal line Paint Candle True Highlights valid candles Candle Color Lime Configurable Show Arrow False Optional visual Summary: The INMERELO EMA Reclaim indicator identifies intraday candles reclaiming a configurable EMA, filtered by customizable candle geometry and MACD momentum. Visual options include painted candles and optional arrows, and all settings are fully configurable.
Pine Script® indicator
by JackCorsellis
3
EMA Crossover with Supertrend + Ribbon + Multi TFThis is a multi indicator all in one, incorporates several indicators in one. Stay on the right side of the trend with this indicator, has customizable everything, a fast and slow ema ribbon, a second ema ribbon for longer ema lengths, a customizable multi time frame trend table, a customizable supertrend, the vwap, 2 background trend color changes , one for the ema's and one for the supertrend, daily support and resistance lines, follow up bearish or bullish signals on every candle. I am sure you will be able to find this multi indicator very useful!
Pine Script® indicator
by angel68roa
Volume Pressure and PercentVPP Volume Pressure and Percentage Indicator with a Volume Trendline that indicates which side is driving the flow. Features: 1. Buy/Sell Pressure Bars (Core Volume Split) The indicator separates each candle’s volume into buy volume (green) above the zero line and sell volume (red) below it. This gives you a real-time visualization of which side is more aggressive within the current bar. Instead of waiting for prices to move or candles to close, you can instantly see whether buyers or sellers are stepping in. 2. Dynamic Total Volume (Invisible Histogram + Status Line Color) The total volume of each bar is tracked behind the scenes and displayed in the pinned status line using a dynamic color—green when buyers dominate, red when sellers dominate. The histogram for total volume is invisible to keep the chart clean, but the total volume figure stays visible and changes color based on who is in control. This gives you instant confirmation of whether institutional-sized volume supports the direction shown by the buy/sell pressure, which is especially valuable when evaluating the risk or conviction behind a potential entry. 3. Percentage Mode (% of Bar Volume) When toggled on, the indicator converts each bar into percent buy vs percent sell, normalizing all flow to a 0–100% scale. This mode is incredibly useful when comparing pressure across different times of day, gaps, or varying volume conditions—such as early morning spikes versus lunchtime chop. By removing absolute volume from the equation, you gain a clean look at the actual imbalance between buyers and sellers. 4. 70% Pressure Band (Imbalance Threshold Zone) In percentage mode, the indicator displays a subtle 70% band (a light gray zone) above and below the zero line, showing where buy or sell pressure reaches extreme dominance (≥70%). When a bar’s buy or sell percentage enters this zone, it highlights moments of exhaustion, acceleration, or potential reversal. The band acts like a real-time overbought/oversold gauge specifically for volume imbalance, not price. 5. Trend Line (Net Pressure Trend / Reversal Detector) The trend line smooths out the net volume pressure (buy volume minus sell volume or its percentage equivalent) and shows the overall direction of order flow. When the line slopes upward, buyers are gaining control; when it slopes downward, sellers are taking over. This trend line acts as a real-time momentum indicator based directly on flow rather than price. Because it reacts quickly to intrabar shifts in buy/sell pressure, it often turns before price does—giving you a measurable timing edge. 6. Auto-Selecting Trend Source (Volume Net, Percent Net, or CVD) The indicator lets you choose how the trend line is calculated: Volume Net (buy minus sell volume), Percent Net (normalized imbalance), or CVD (Cumulative Volume Delta) for long-term flow bias. The default “Auto” mode automatically switches between Volume Net and Percent Net depending on which view you’re using. This flexibility allows the trend line to remain meaningful whether you’re analyzing raw volume or normalized percentage data. 7. Pinned (Status Line) Totals in K/M/B Format Regardless of whether you’re in volume or percentage mode, the indicator always displays Total Volume, Buy Volume, and Sell Volume in the status line using abbreviated K, M, B formatting. These values update in real time and are color-coded: green for bullish dominance, red for bearish. This gives you a concise snapshot of order flow strength on every bar. --------------------- How To Use: Support Level Zones • Watch for Buy bars increasing + Trend line flipping up right at or slightly below support. • This often signals absorption — market makers filling large buy orders before reversal. • Confirmation: Price reclaims VWAP ... enter calls / longs. Resistance Level Zones • Watch for Sell bars increasing + Trend line flattening/turning down near resistance. • This signals distribution or stop runs. • Confirmation: Price rejects VWAP ... enter puts / shorts. Breakout Traps • Sometimes you’ll see price break a level, but the flow doesn’t confirm (buy volume doesn’t expand). • That’s a false breakout — fade it with options opposite the move.
Pine Script® indicator
by bignac
Volume Climax Reversal (VCR) — Catch Exhaustion Tops & BottomsNew! VCR spots exhaustion spikes at highs/lows using volume extremes + price action + VWAP context. If you trade parabolic runners, indices, or mean-reversion edges, VCR helps you time the backside (shorts) and fade capitulation (longs) with clean, rule-based signals. What it does Detects volume climax: current volume > SMA(len) × multiplier and a new volume high in the lookback. Confirms price context: makes a higher high (for tops) or lower low (for bottoms). Filters with VWAP (optional): bearish signals only below VWAP, bullish signals only above VWAP. Optional wick filter: requires an exhaustion wick > body to reduce chop. Why traders like it Clear entries: “VCR↓” (bearish) at exhaustion tops, “VCR↑” (bullish) at washout lows. Fewer false signals: VWAP gating + wick filter focus on true climaxes. Built-in alerts: set once, get notified on your phone/desktop when a setup appears. How I trade it (simple playbook) Bearish reversal (short / puts) Wait for VCR↓ (exhaustion at/near HH). Look for a lower high that fails to reclaim the signal candle high. Enter on the break of that lower-high candle low. Stop above the signal wick high. Covers/targets: VWAP first; then 20–30% fade from the local top / prior demand. Bullish reversal (long / calls) Wait for VCR↑ (capitulation at/near LL). Look for a higher low that holds above the signal candle low. Enter on the break of the HL candle high. Stop below the signal wick low. Targets: VWAP first; then prior supply/MA bands. Tip for small-cap/“Dux” style: VCR pairs perfectly with a gap + high USD-rotation scan. Let them blow off, then use VCR for the timing. Inputs (tune to your market) Volume SMA Length (default 20) Volume Spike Multiplier (default 2.0) Lookback High / Low (default 10 / 10) Require VWAP confirmation? (on) Use wick filter? (on) Works on stocks, indices, futures, crypto. Timeframes: 1–15m for day trading; 1h–4h–D for swing. Alerts Set one (or both) alerts and forget it: Bearish Volume Climax — VCR↓ Bullish Volume Climax — VCR↑ You’ll get instant notifications when a qualified top/bottom prints. Best practices Don’t countertrend the first front-side ramp—wait for the VCR and a lower-high/higher-low. Respect VWAP: it’s your first profit-taking and a bias filter. Size small into volatility; widen stops in fast markets. Combine with your watchlist filters (gap %, float/O/S, USD rotation, session timing). What’s included Clean visual signals (triangles + subtle background shading) Session-anchored VWAP Alert conditions that appear in TradingView’s alert menu Sensible defaults + clear docs (this post) FAQ Q: Does it repaint? No. VCR uses completed-bar data; signals print end-of-bar. Q: Which markets? Anything with volume: US equities, futures, crypto, indices. Q: Can I use it for scalps? Yes—1–5m with wick filter on and VWAP required works well. Get more / upgrades I’m iterating fast (MTF filter, heatmap panel, combined “one-alert” mode). Want the pro template with dashboard & combined alerts? Message me on TV or DM / email you@domain.com . Risk Notice This is educational research, not financial advice. Markets carry risk—always manage position size and use stops. If this helped you, smash the 👍 and ⭐ — it really helps! #volume #vwap #reversal #exhaustion #trendreversal #smallcaps #scalping #daytrading #swingtrading #stocks #futures #crypto #indicator
Pine Script® indicator
by Rendon1
Qullamagi EMA Breakout Autotrade (Crypto Futures L+S)Title: Qullamagi EMA Breakout – Crypto Autotrade Overview A crypto-focused, Qullamagi-style EMA breakout strategy built for autotrading on futures and perpetual swaps. It combines a 5-MA trend stack (EMA 10/20, SMA 50/100/200), volatility contraction boxes, volume spikes and an optional higher-timeframe 200-MA filter. The script supports both long and short trades, partial take profit, trailing MA exits and percent-of-equity position sizing for automated crypto futures trading. Key Features (Crypto) Qullamagi MA Breakout Engine – trades only when price is aligned with a strong EMA/SMA trend and breaks out of a tight consolidation range. Longs use: Close > EMA10 > EMA20 > SMA50 > SMA100 > SMA200. Shorts are the mirror condition with all MAs sloping in the trend direction. Strict vs Loose Modes – Strict (Daily) is designed for cleaner swing trades on 1H–4H (full MA stack, box+ATR and volume filters, optional HTF filter). Loose (Intraday) focuses on 10/20/50 alignment with relaxed filters for more frequent 15m–30m signals. Volatility & Volume Filters for Crypto – ATR-based box height limit to detect volatility contraction, wide-candle filter to avoid chasing exhausted breakouts, and a volume spike condition requiring current volume to exceed an SMA of volume. Higher-Timeframe Trend Filter (Optional) – uses a 200-period SMA on a higher timeframe (default: 1D). Longs only when HTF close is above the HTF 200-SMA, shorts only when it is below, helping avoid trading against dominant crypto trends. Autotrade-Oriented Trade Management – position size as % of equity, initial stop anchored to a chosen MA (EMA10 / EMA20 / SMA50) with optional buffer, partial take profit at a configurable R-multiple, trailing MA exit for the remainder, and an optional cooldown after a full exit. Markets & Timeframes Best suited for BTC, ETH and major altcoin futures/perpetuals (Binance, Bybit, OKX, etc.). Strict preset: 1H–4H charts for classic Qullamagi-style trend structure and fewer fake breakouts. Loose preset: 15m–30m charts for higher trade frequency and more active intraday trading. Always retune ATR length, box length, volume multiplier and position size for each symbol and exchange. Strategy Logic (Quick Summary) Long (Strict): MA stack in bullish alignment with all MAs sloping up → tight volatility box (ATR-based) → volume spike above SMA(volume) × multiplier → breakout above box high (close or intrabar) → optional HTF close above 200-SMA. Short: Mirror logic: bearish MA stack, tight box, volume spike and breakdown below box low with optional HTF downtrend. Best Practices for Crypto Backtest on each symbol and timeframe you plan to autotrade, including commissions and slippage. Start on higher timeframes (1H/4H) to learn the behavior, then move to 15m–30m if you want more signals. Use the higher-timeframe filter when markets are strongly trending to reduce counter-trend trades. Keep position-size percentage conservative until you fully understand the drawdowns. Forward-test / paper trade before connecting to live futures accounts. Webhook / Autotrade Integration Designed to work with TradingView webhooks and external crypto trading bots. Alert messages include structured fields such as: EVENT=ENTRY / SCALE_OUT / EXIT, SIDE=LONG / SHORT, STRATEGY=Qullamagi_MA. Map each EVENT + SIDE combination to your bot logic (open long/short, partial close, full close, etc.) on your preferred exchange. Important Notes & Disclaimer Crypto markets are highly volatile and can change regime quickly. Backtest and forward-test thoroughly before using real capital. Higher timeframes generally produce cleaner MA structures and fewer fake breakouts. This strategy is for educational and informational purposes only and does not constitute financial advice. Trading leveraged crypto products involves substantial risk of loss. Always do your own research, manage risk carefully, and never trade with money you cannot afford to lose.
Pine Script® strategy
by asefzxizexvsadf
VWAP + Volume Spikes See Where Smart Money ExhaustsVolume tells the truth. VWAP tells the bias. This script shows both — live. If you trade intraday momentum, reversals, or liquidity sweeps, this indicator is built for you. It shows where volume spikes hit extreme levels, anchored around VWAP and its dynamic bands, so you can instantly spot capitulation or hidden absorption. 🎯 What This Indicator Does ✅ Plots VWAP — session-anchored, updates automatically ✅ Adds dynamic VWAP bands — standard deviation envelopes showing volatility context ✅ Highlights volume spikes — colored candles + background for abnormal prints ✅ Includes alerts — “Volume Spike”, “VWAP Cross”, or a combined alert with direction ✅ Clean visual design — instantly readable in fast markets It’s your visual orderflow radar — whether you’re trading gold, indices, or small caps. 🔍 Why It Works Institutions build and unwind positions around VWAP. Retail often chases volume… this script shows you when that volume becomes too extreme. A spike above VWAP near resistance? → Likely distribution. A spike below VWAP near support? → Likely capitulation. Combine volume exhaustion + VWAP context, and you’ll see market turning points form before most indicators react. ⚙️ Inputs You Can Tune Bands lookback: adjusts how reactive the VWAP bands are Band width (σ): set how tight or wide your deviation envelope is Volume baseline length: controls how “abnormal” a spike must be Spike threshold: multiplier vs. average volume Toggle color-coding, bands, and labels Default settings work well across 1m–15m intraday charts and 1h–4h swing frames. 💡 How Traders Use It 1️⃣ Fade Parabolics: When a green spike candle pierces upper VWAP band on high volume → smart money unloading. Look for rejection and short into VWAP. 2️⃣ Catch Capitulations: When a red spike candle dumps below lower VWAP band → panic selling. Watch for stabilization and long back to VWAP. 3️⃣ VWAP Rotation Plays: Alerts for price crossing VWAP help you spot shift in intraday control. Above VWAP = buyers in charge. Below VWAP = sellers in charge. 🧠 Best Practices Pair it with Volume Profile or Delta/Flow tools to confirm exhaustion. Don’t chase — wait for spike confirmation + reversal candle. Use it on liquid tickers (NASDAQ, SPY, GOLD, BTC, etc.). Great for Dux-style small-cap shorts or index pullbacks. 🔔 Alerts Ready Choose from: Volume Spike (single-bar explosion) VWAP Cross Up/Down (trend shift confirmation) One Combined Alert (any signal, includes ticker, price, and volume) Set once — get real-time push notifications, Telegram, or webhook signals. 📊 My Favorite Setups US100 / NASDAQ: fade rallies above VWAP + spike Gold / Silver: trade reversals from VWAP bands Small caps: short back-side after volume climax ES, DAX, Oil: scalp VWAP rotation with confluence ❤️ Support This Work I release free and premium scripts weekly — combining smart money concepts, VWAP tools, and volume analytics. 👉 Follow me on TradingView for more indicators and setups. 👉 Comment “🔥” if you want me to post the multi-timeframe VWAP + Volume Pressure version next. 👉 Share this with your team — it helps the community grow.
Pine Script® indicator
by Rendon1
SPY Levels on ES# SPY Levels on ES - Professional Support & Resistance Indicator ## 🎯 Overview Transform your S&P 500 futures trading with precision-engineered SPY support and resistance levels. This professional-grade indicator displays critical SPY price levels directly on your ES (E-mini S&P 500) and MES (Micro E-mini S&P 500) charts, providing institutional-quality analysis for retail traders. ## ⚡ Key Features ### 📊 Dual-Level System - Whole Number Levels : 10 closest round SPY levels around current price - Half Levels : 0.5 increment levels for granular analysis - Smart Scaling : Automatically converts SPY levels to ES/MES prices ### 🎨 Professional Visualization - Clean Design : Minimalist lines that don't clutter your chart - Customizable Colors : Choose your own colors for whole and half levels - Historical Extension : Lines extend across your entire chart for context - Dynamic Labels : Real-time SPY price display with clear level identification ### ⚙️ Intelligent Features - Auto-Detection : Works seamlessly on SPY, ES, and MES charts - Real-Time Updates : Levels adjust automatically as SPY price moves - Performance Optimized : Efficient code that won't slow down your charts - Flexible Settings : Toggle levels on/off based on your trading style ## 🎯 Perfect For - ES Futures Traders seeking SPY correlation levels - MES Micro Futures traders needing precise entry/exit points - SPY Options Traders analyzing support/resistance zones - Day Traders requiring quick visual reference points - Swing Traders identifying key technical levels ## 📈 How It Works The indicator fetches real-time SPY prices and calculates the 10 closest whole number levels (e.g., 580, 581, 582) plus half levels (580.5, 581.5, 582.5) around the current price. When applied to ES or MES charts, it automatically scales these levels to match futures pricing, giving you precise SPY-correlated support and resistance zones. ## 🔧 Customization Options - SPY Price Label : Toggle on/off - Whole Number Levels : Show/hide with custom colors - Half Levels : Show/hide with custom colors and transparency - Visual Styling : Personalize colors to match your chart theme ## 💡 Trading Applications - Support/Resistance : Identify key psychological levels - Entry/Exit Points : Use levels for precise trade execution - Risk Management : Set stops and targets at significant levels - Market Structure : Understand institutional price zones - Confluence Analysis : Combine with other technical indicators
Pine Script® indicator
by WizardCharts_com