Commencez

Oscillateurs

RSI VWAP EMA ON CHART1. Understand the components VWAP (Volume Weighted Average Price) Green: price is above VWAP → bullish trend Red: price is below VWAP → bearish trend Blue: price exactly at VWAP → neutral Acts as a dynamic trend line and support/resistance. 4 Moving Averages (MA1–MA4) Customizable lengths and type (SMA or EMA). Useful for trend confirmation and dynamic support/resistance. Typically: MA1 = fastest (short-term) MA4 = slowest (long-term) When price is above multiple MAs → strong bullish trend; below → bearish trend. RSI Overlay with VWAP Alignment RSI line normalized to price scale. Background shading indicates momentum aligned with VWAP trend: Green shading: RSI > 50 and price above VWAP → bullish momentum Red shading: RSI < 50 and price below VWAP → bearish momentum Gray areas: neutral or momentum does not align with VWAP. 2. Basic usage workflow Trend Confirmation Look at VWAP color: price above → bullish, below → bearish. Check RSI + VWAP shading: green confirms bullish momentum, red confirms bearish momentum. Check MA alignment: shorter MAs above longer MAs = stronger bullish trend; vice versa for bearish. Entry Signals (Scalping) Long (Buy) Setup Price above VWAP (green) RSI green shading (RSI > 50) Shorter MAs above longer MAs (trend support) Short (Sell) Setup Price below VWAP (red) RSI red shading (RSI < 50) Shorter MAs below longer MAs Exits / Stops Exit if price closes against VWAP trend (e.g., price drops below VWAP during a bullish trade). Use nearest MA support/resistance as stop-loss or take-profit zones. 3. Optional adjustments for scalping RSI length / thresholds Shorter RSI (7–10) → faster response for scalping. Standard RSI (14) → smoother, fewer false signals. MA lengths Short-term: 20–50 Medium-term: 50–100 Long-term: 100–200 Can tweak for the timeframe you trade (1m, 5m, 15m). Timeframe VWAP works best on intraday charts (1m, 5m, 15m). Use higher timeframe (e.g., 15m or 1h) for trend direction and lower timeframe (1m–5m) for entries. 4. Example Scalping Setup Bullish setup (buy): Price above VWAP → VWAP green RSI > 50 and green shading Shorter MAs above longer MAs Enter on small pullback or breakout Stop: below nearest MA or VWAP Bearish setup (sell): Price below VWAP → VWAP red RSI < 50 and red shading Shorter MAs below longer MAs Enter on minor bounce or breakdown Stop: above nearest MA or VWAP 5. Visual cues summary Element Interpretation VWAP Green Price above VWAP → bullish trend VWAP Red Price below VWAP → bearish trend RSI Green Shading Bullish momentum aligns with VWAP RSI Red Shading Bearish momentum aligns with VWAP MA Alignment Trend strength (short above long = bullish, short below long = bearish)
Indicateur Pine Script®
par Simon20cent
VWAP + 4 MAs with RSI Overlay & VWAP Alignment1. Understand the components VWAP (Volume Weighted Average Price) Green: price is above VWAP → bullish trend Red: price is below VWAP → bearish trend Blue: price exactly at VWAP → neutral Acts as a dynamic trend line and support/resistance. 4 Moving Averages (MA1–MA4) Customizable lengths and type (SMA or EMA). Useful for trend confirmation and dynamic support/resistance. Typically: MA1 = fastest (short-term) MA4 = slowest (long-term) When price is above multiple MAs → strong bullish trend; below → bearish trend. RSI Overlay with VWAP Alignment RSI line normalized to price scale. Background shading indicates momentum aligned with VWAP trend: Green shading: RSI > 50 and price above VWAP → bullish momentum Red shading: RSI < 50 and price below VWAP → bearish momentum Gray areas: neutral or momentum does not align with VWAP. 2. Basic usage workflow Trend Confirmation Look at VWAP color: price above → bullish, below → bearish. Check RSI + VWAP shading: green confirms bullish momentum, red confirms bearish momentum. Check MA alignment: shorter MAs above longer MAs = stronger bullish trend; vice versa for bearish. Entry Signals (Scalping) Long (Buy) Setup Price above VWAP (green) RSI green shading (RSI > 50) Shorter MAs above longer MAs (trend support) Short (Sell) Setup Price below VWAP (red) RSI red shading (RSI < 50) Shorter MAs below longer MAs Exits / Stops Exit if price closes against VWAP trend (e.g., price drops below VWAP during a bullish trade). Use nearest MA support/resistance as stop-loss or take-profit zones. 3. Optional adjustments for scalping RSI length / thresholds Shorter RSI (7–10) → faster response for scalping. Standard RSI (14) → smoother, fewer false signals. MA lengths Short-term: 20–50 Medium-term: 50–100 Long-term: 100–200 Can tweak for the timeframe you trade (1m, 5m, 15m). Timeframe VWAP works best on intraday charts (1m, 5m, 15m). Use higher timeframe (e.g., 15m or 1h) for trend direction and lower timeframe (1m–5m) for entries. 4. Example Scalping Setup Bullish setup (buy): Price above VWAP → VWAP green RSI > 50 and green shading Shorter MAs above longer MAs Enter on small pullback or breakout Stop: below nearest MA or VWAP Bearish setup (sell): Price below VWAP → VWAP red RSI < 50 and red shading Shorter MAs below longer MAs Enter on minor bounce or breakdown Stop: above nearest MA or VWAP 5. Visual cues summary Element Interpretation VWAP Green Price above VWAP → bullish trend VWAP Red Price below VWAP → bearish trend RSI Green Shading Bullish momentum aligns with VWAP RSI Red Shading Bearish momentum aligns with VWAP MA Alignment Trend strength (short above long = bullish, short below long = bearish)
Indicateur Pine Script®
par Simon20cent
1
RSI Divergence (Regular + Hidden, @darshakssc)This indicator detects regular and hidden divergence between price and RSI, using confirmed swing highs and swing lows (pivots) on both series. It is designed as a visual analysis tool, not as a signal generator or trading system. The goal is to highlight moments where price action and RSI momentum move in different directions, which some traders study as potential early warnings of trend exhaustion or trend continuation. All divergence signals are only drawn after a pivot is fully confirmed, helping to avoid repainting. The script supports four divergence types: Regular Bullish Divergence Regular Bearish Divergence Hidden Bullish Divergence Hidden Bearish Divergence Each type is drawn with a different color and labeled clearly on the chart. Core Concepts Used 1. RSI (Relative Strength Index) The script uses standard RSI, calculated on a configurable input source (default: close) and length (default: 14). RSI is treated purely as a momentum oscillator – the script does not enforce oversold/overbought interpretations. 2. Pivots / Swings The indicator defines swing highs and swing lows using ta.pivothigh() and ta.pivotlow(): A swing high forms when a bar’s high is higher than a specified number of bars to the left and to the right. A swing low forms when a bar’s low is lower than a specified number of bars to the left and to the right. The same pivot logic is applied to both price and RSI. Because pivots require “right side” bars to form, the indicator: Waits for the full pivot to be confirmed (no forward-looking referencing beyond the rightBars parameter). Only then considers that pivot for divergence detection. This helps prevent repainting of divergence signals. How Divergence Is Detected The script always uses the two most recent confirmed pivots for both price and RSI. It tracks: Last two swing lows in price and RSI Last two swing highs in price and RSI Their pivot bar indexes and values A basic minimum distance filter between the pivots (in bars) is also applied to reduce noise. 1. Regular Bullish Divergence Condition: Price makes a lower low (LL) between the last two lows RSI makes a higher low (HL) over the same two pivot lows The RSI difference between the two lows is greater than or equal to the user-defined minimum (Min RSI Difference) The two low pivots are separated by at least Min Bars Between Swings Interpretation: Some traders view this as bearish momentum weakening while price prints a new low. The script only marks this structure; it does not assume any outcome. On the chart: Drawn between the previous and current price swing lows Labeled: “Regular Bullish” Color: Green (by default in the script) 2. Regular Bearish Divergence Condition: Price makes a higher high (HH) between the last two highs RSI makes a lower high (LH) over the same two pivot highs RSI difference exceeds Min RSI Difference Pivots are separated by at least Min Bars Between Swings Interpretation: Some traders see this as bullish momentum weakening while price prints a new high. Again, the indicator simply highlights this divergence. On the chart: Drawn between the previous and current price swing highs Labeled: “Regular Bearish” Color: Red 3. Hidden Bullish Divergence Condition: Price makes a higher low (HL) between the last two lows RSI makes a lower low (LL) over the same two lows RSI difference exceeds Min RSI Difference Pivots meet the minimum distance requirement Interpretation: Some traders interpret hidden bullish divergence as a potential trend continuation signal within an existing uptrend. The indicator does not classify trends; it just tags the pattern when price and RSI pivots meet the conditions. On the chart: Drawn between the previous and current price swing lows Labeled: “Hidden Bullish” Color: Teal 4. Hidden Bearish Divergence Condition: Price makes a lower high (LH) between the last two highs RSI makes a higher high (HH) over those highs RSI difference exceeds Min RSI Difference Pivots meet the minimum distance filter Interpretation: Some traders associate hidden bearish divergence with potential downtrend continuation, but again, this script only visualizes the structure. On the chart: Drawn between the previous and current price swing highs Labeled: “Hidden Bearish” Color: Orange Inputs and Settings 1. RSI Settings RSI Source – Price source for RSI (default: close). RSI Length – Period for RSI calculation (default: 14). These control the responsiveness of the RSI. Shorter lengths may show more frequent divergence; longer lengths smooth the signal. 2. Swing / Pivot Settings Left Swing Bars (leftBars) Right Swing Bars (rightBars) These define how strict the pivot detection is: Higher values → fewer, more significant swings Lower values → more swings, more signals Because the script uses ta.pivothigh / ta.pivotlow, a pivot is only confirmed once rightBars candles have closed after the candidate bar. This is an intentional design to reduce repainting and make pivots stable. 3. Divergence Filters Min Bars Between Swings (Min Bars Between Swings) Requires a minimum bar distance between the two pivots used to form divergence. Helps avoid clutter from pivots that are too close to each other. Min RSI Difference (Min RSI Difference) Requires a minimum absolute difference between RSI values at the two pivots. Filters out very minor changes in RSI that may not be meaningful. 4. Visibility Toggles Show Regular Divergence Show Hidden Divergence You can choose to display: Both regular and hidden divergence, or Only regular divergence, or Only hidden divergence This is useful if you prefer to focus on one type of structure. 5. Alerts Enable Alerts When enabled, the script exposes four alert conditions: Regular Bullish Divergence Confirmed Regular Bearish Divergence Confirmed Hidden Bullish Divergence Confirmed Hidden Bearish Divergence Confirmed Each alert fires after the corresponding divergence has been fully confirmed based on the pivot and bar confirmation logic. The script does not issue rapid or intrabar signals; it uses confirmed historical conditions. You can set these in the TradingView Alerts dialog by choosing this indicator and selecting the desired condition. Visual Elements On the main price chart, the indicator: Draws a line between the two price pivots involved in the divergence. Adds a small label at the latest pivot, describing the divergence type. Colors are used to differentiate divergence categories (Green/Red/Teal/Orange). This makes it easy to visually scan the chart for zones where price and RSI have diverged. What to Look For (Analytical Use) This indicator is intended as a visual helper, especially when: You want to quickly see where price made new highs or lows while RSI did not confirm them in the same way. You are studying momentum exhaustion, shifts, or continuation using RSI divergence as one of many tools. You want to compare divergence occurrences across different timeframes or instruments. Important: The indicator does not tell you when to enter or exit trades. It does not rank or validate the “quality” of a divergence. Divergence can persist or fail; it is not a guarantee of reversal or continuation. Many traders combine divergence analysis with: Higher timeframe context Trend filters (moving averages, structure) Support/resistance zones or liquidity areas Volume, structure breaks, or other confirmations Disclaimer This script is provided for educational and analytical purposes only. It does not constitute financial advice, trading advice, or investment recommendations. No part of this indicator is intended to suggest, encourage, or guarantee any specific trading outcome. Users are solely responsible for their own decisions and risk management.
Indicateur Pine Script®
par darshakssc
12
Curvature Tensor Pivots - HIVECurvature Tensor Pivots - HIVE I. CORE CONCEPT & ORIGINALITY Curvature Tensor Pivots - HIVE is an advanced, multi-dimensional pivot detection system that combines differential geometry, reinforcement learning, and statistical physics to identify high-probability reversal zones before they fully form. Unlike traditional pivot indicators that rely on simple price comparisons or lagging moving averages, this system models price action as a smooth curve in geometric space and calculates its mathematical curvature (how sharply the price trajectory is "bending") to detect pivots with scientific precision. What Makes This Original: Differential Geometry Engine: The script calculates first and second derivatives of price using Kalman-filtered trajectory analysis, then computes true mathematical curvature (κ) using the classical formula: κ = |y''| / (1 + y'²)^(3/2). This approach treats price as a physical phenomenon rather than discrete data points. Ghost Vertex Prediction: A proprietary algorithm that detects pivots 1-3 bars BEFORE they complete by identifying when velocity approaches zero while acceleration is high—this is the mathematical definition of a turning point. Multi-Armed Bandit AI: Four distinct pivot detection strategies (Fast, Balanced, Strict, Tensor) run simultaneously in shadow portfolios. A Thompson Sampling reinforcement learning algorithm continuously evaluates which strategy performs best in current market conditions and automatically selects it. Hive Consensus System: When 3 or 4 of the parallel strategies agree on the same price zone, the system generates "confluence zones"—areas of institutional-grade probability. Dynamic Volatility Scaling (DVS): All parameters auto-adjust based on current ATR relative to historical average, making the indicator adaptive across all timeframes and instruments without manual re-optimization. II. HOW THE COMPONENTS WORK TOGETHER This is NOT a simple mashup —each subsystem feeds data into the others in a closed-loop learning architecture: The Processing Pipeline: Step 1: Geometric Foundation Raw price is normalized against a 50-period SMA to create a trajectory baseline A Zero-Lag EMA smooths the trajectory while preserving edge response Kalman filter removes noise while maintaining signal integrity Step 2: Calculus Layer First derivative (y') measures velocity of price movement Second derivative (y'') measures acceleration (rate of velocity change) Curvature (κ) is calculated from these derivatives, representing how sharply price is turning Step 3: Statistical Validation Z-Score measures how many standard deviations current price deviates from the Kalman-filtered "true price" Only pivots with Z-Score > threshold (default 1.2) are considered statistically significant This filters out noise and micro-fluctuations Step 4: Tensor Construction Curvature is combined with volatility (ATR-based) and momentum (ROC-based) to create a multidimensional "tensor score" This tensor represents the geometric stress in the price field High tensor magnitude = high probability of structural failure (reversal) Step 5: AI Decision Layer All 4 bandit strategies evaluate current conditions using different sensitivity thresholds Each strategy maintains a virtual portfolio that trades its signals in real-time Thompson Sampling algorithm updates Bayesian priors (alpha/beta distributions) based on each strategy's Sharpe ratio, win rate, and drawdown The highest-performing strategy's signals are displayed to the user Step 6: Confluence Aggregation When multiple strategies agree on the same price zone, that zone is highlighted as a confluence area. These represent "hive mind" consensus—the strongest setups Why This Integration Matters: Traditional indicators either detect pivots too late (lagging) or generate too many false signals (noisy). By requiring geometric confirmation (curvature), statistical significance (Z-Score), multi-strategy agreement (hive voting), and performance validation (RL feedback) , this system achieves institutional-grade precision. The reinforcement learning layer ensures the system adapts as market regimes change, rather than degrading over time like static algorithms. III. DETAILED METHODOLOGY A. Curvature Calculation (Differential Geometry) The system models price as a parametric curve where: x-axis = time (bar index) y-axis = normalized price The curvature at any point represents how quickly the direction of the tangent line is changing. High curvature = sharp turn = potential pivot. Implementation: Lookback window (default 8 bars) defines the local curve segment Smoothing (default 5 bars) applies adaptive EMA to reduce tick noise Curvature is normalized to 0-1 scale using local statistical bounds (mean ± 2 standard deviations) B. Ghost Vertex (Predictive Pivot Detection) Classical pivot detection waits for price to form a swing high/low and confirm. Ghost Vertex uses calculus to predict the turning point: Conditions for Ghost Pivot: Velocity (y') ≈ 0 (price rate of change approaching zero) Acceleration (y'') ≠ 0 (change is decelerating/accelerating) Z-Score > threshold (statistically abnormal position) This allows detection 1-3 bars before the actual high/low prints, providing an early entry edge. C. Multi-Armed Bandit Reinforcement Learning The system runs 4 parallel "bandits" (agents), each with different detection sensitivity: Bandit Strategies: Fast: Low curvature threshold (0.1), low Z-Score requirement (1.0) → High frequency, more signals Balanced: Standard thresholds (0.2 curvature, 1.5 Z-Score) → Moderate frequency Strict: High thresholds (0.4 curvature, 2.0 Z-Score) → Low frequency, high conviction Tensor: Requires tensor magnitude > 0.5 → Geometric-weighted detection Learning Algorithm (Thompson Sampling): Each bandit maintains a Beta distribution with parameters (α, β) After each trade outcome, α is incremented for wins, β for losses Selection probability is proportional to sampled success rate from the distribution This naturally balances exploration (trying underperformed strategies) vs exploitation (using best strategy) Performance Metrics Tracked: Equity curve for each shadow portfolio Win rate percentage Sharpe ratio (risk-adjusted returns) Maximum drawdown Total trades executed The system displays all metrics in real-time on the dashboard so users can see which strategy is currently "winning." D. Dynamic Volatility Scaling (DVS) Markets cycle between high volatility (trending, news-driven) and low volatility (ranging, quiet). Static parameters fail when regime changes. DVS Solution: Measures current ATR(30) / close as normalized volatility Compares to 100-bar SMA of normalized volatility Ratio > 1 = high volatility → lengthen lookbacks, raise thresholds (prevent noise) Ratio < 1 = low volatility → shorten lookbacks, lower thresholds (maintain sensitivity) This single feature is why the indicator works on 1-minute crypto charts AND daily stock charts without parameter changes. E. Confluence Zone Detection The script divides the recent price range (200 bars) into 200 discrete zones. On each bar: Each of the 4 bandits votes on potential pivot zones Votes accumulate in a histogram array Zones with ≥ 3 votes (75% agreement) are drawn as colored boxes Red boxes = resistance confluence, Green boxes = support confluence These zones act as magnet levels where price often returns multiple times. IV. HOW TO USE THIS INDICATOR For Scalpers (1m - 5m timeframes): Settings: Use "Aggressive" or "Adaptive" pivot mode, Curvature Window 5-8, Min Pivot Strength 50-60 Entry Signal: Triangle marker appears (🔺 for longs, 🔻 for shorts) Confirmation: Check that Hive Sentiment on dashboard agrees (3+ votes) Stop Loss: Use the dotted volatility-adjusted target line in reverse (if pivot is at 100 with target at 110, stop is ~95) Take Profit: Use the projected target line (default 3× ATR) Advanced: Wait for confluence zone formation, then enter on retest of the zone For Day Traders (15m - 1H timeframes): Settings: Use "Adaptive" mode (default settings work well) Entry Signal: Pivot marker + Hive Consensus alert Confirmation: Check dashboard—ensure selected bandit has Sharpe > 1.5 and Win% > 55% Filter: Only take pivots with Pivot Strength > 70 (shown in dashboard) Risk Management: Monitor the Live Position Tracker—if your selected bandit is holding a position, consider that as market structure context Exit: Either use target lines OR exit when opposite pivot appears For Swing Traders (4H - Daily timeframes): Settings: Use "Conservative" mode, Curvature Window 12-20, Min Bars Between Pivots 15-30 Focus on Confluence: Only trade when 4/4 bandits agree (unanimous hive consensus) Entry: Set limit orders at confluence zones rather than market orders at pivot signals Confirmation: Look for breakout diamonds (◆) after pivot—these signal momentum continuation Risk Management: Use wider stops (base stop loss % = 3-5%) Dashboard Interpretation: Top Section (Real-Time Metrics): κ (Curv): Current curvature. >0.6 = active pivot forming Tensor: Geometric stress. Positive = bullish bias, Negative = bearish bias Z-Score: Statistical deviation. >2.0 or <-2.0 = extreme outlier (strong signal) Bandit Performance Table: α/β: Bayesian parameters. Higher α = more wins in history Win%: Self-explanatory. >60% is excellent Sharpe: Risk-adjusted returns. >2.0 is institutional-grade Status: Shows which strategy is currently selected Live Position Tracker: Shows if the selected bandit's shadow portfolio is currently holding a position Displays entry price and real-time P&L Use this as "what the AI would do" confirmation Hive Sentiment: Shows vote distribution across all 4 bandits "BULLISH" with 3+ green votes = high-conviction long setup "BEARISH" with 3+ red votes = high-conviction short setup Alert Setup: The script includes 6 alert conditions: "AI High Pivot" = Selected bandit signals short "AI Low Pivot" = Selected bandit signals long "Hive Consensus BUY" = 3+ bandits agree on long "Hive Consensus SELL" = 3+ bandits agree on short "Breakout Up" = Resistance breakout (continuation long) "Breakdown Down" = Support breakdown (continuation short) Recommended Alert Strategy: Set "Hive Consensus" alerts for high-conviction setups Use "AI Pivot" alerts for active monitoring during your trading session Use breakout alerts for momentum/trend-following entries V. PARAMETER OPTIMIZATION GUIDE Core Geometry Parameters: Curvature Window (default 8): Lower (3-5): Detects micro-structure, best for scalping volatile pairs (crypto, forex majors) Higher (12-20): Detects macro-structure, best for swing trading stocks/indices Rule of thumb: Set to ~0.5% of your typical trade duration in bars Curvature Smoothing (default 5): Increase if you see too many false pivots (noisy instrument) Decrease if pivots lag (missing entries by 2-3 bars) Inflection Threshold (default 0.20): This is advanced. Lower = more inflection zones highlighted Useful for identifying order blocks and liquidity voids Most users can leave default Pivot Detection Parameters: Pivot Sensitivity Mode: Aggressive: Use in low-volatility range-bound markets Normal: General purpose Adaptive: Recommended—auto-adjusts via DVS Conservative: Use in choppy, whipsaw conditions or for swing trading Min Bars Between Pivots (default 8): THIS IS CRITICAL for visual clarity If chart looks cluttered, increase to 12-15 If missing pivots, decrease to 5-6 Match to your timeframe: 1m charts use 3-5, Daily charts use 20+ Min Z-Score (default 1.2): Statistical filter. Higher = fewer but stronger signals During news events (NFP, FOMC), increase to 2.0+ In calm markets, 1.0 works well Min Pivot Strength (default 60): Composite quality score (0-100) 80+ = institutional-grade pivots only 50-70 = balanced Below 50 = will show weak setups (not recommended) RL & DVS Parameters: Enable DVS (default ON): Leave enabled unless you want to manually tune for a specific market condition This is the "secret sauce" for cross-timeframe performance DVS Sensitivity (default 1.0): Increase to 1.5-2.0 for extremely volatile instruments (meme stocks, altcoins) Decrease to 0.5-0.7 for stable instruments (utilities, bonds) RL Algorithm (default Thompson Sampling): Thompson Sampling: Best for non-stationary markets (recommended) UCB1: Best for stable, mean-reverting markets Epsilon-Greedy: For testing only Contextual: Advanced—uses market regime as context Risk Parameters: Base Stop Loss % (default 2.0): Set to 1.5-2× your instrument's average ATR as a percentage Example: If SPY ATR = $3 and price = $450, ATR% = 0.67%, so use 1.5-2.0% Base Take Profit % (default 4.0): Aim for 2:1 reward/risk ratio minimum For mean-reversion strategies, use 1.5-2.0% For trend-following, use 3-5% VI. UNDERSTANDING THE UNDERLYING CONCEPTS Why Differential Geometry? Traditional technical analysis treats price as discrete data points. Differential geometry models price as a continuous manifold —a smooth surface that can be analyzed using calculus. This allows us to ask: "At what rate is the trend changing?" rather than just "Is price going up or down?" The curvature metric captures something fundamental: inflection points in market psychology . When buyers exhaust and sellers take over (or vice versa), the price trajectory must curve. By measuring this curvature mathematically, we detect these psychological shifts with precision. Why Reinforcement Learning? Markets are non-stationary —statistical properties change over time. A strategy that works in Q1 may fail in Q3. Traditional indicators have fixed parameters and degrade over time. The multi-armed bandit framework solves this by: Running multiple strategies in parallel (diversification) Continuously measuring performance (feedback loop) Automatically shifting capital to what's working (adaptation) This is how professional hedge funds operate—they don't use one strategy, they use ensembles with dynamic allocation. Why Kalman Filtering? Raw price contains two components: signal (true movement) and noise (random fluctuations). Kalman filters are the gold standard in aerospace and robotics for extracting signal from noisy sensors. By applying this to price data, we get a "clean" trajectory to measure curvature against. This prevents false pivots from bid-ask bounce or single-print anomalies. Why Z-Score Validation? Not all high-curvature points are tradeable. A sharp turn in a ranging market might just be noise. Z-Score ensures that pivots occur at statistically abnormal price levels —places where price has deviated significantly from its Kalman-filtered "fair value." This filters out 70-80% of false signals while preserving true reversal points. VII. COMMON USE CASES & STRATEGIES Strategy 1: Confluence Zone Reversal Trading Wait for confluence zone to form (red or green box) Wait for price to approach zone Enter when pivot marker appears WITHIN the confluence zone Stop: Beyond the zone Target: Opposite confluence zone or 3× ATR Strategy 2: Hive Consensus Scalping Set alert for "Hive Consensus BUY/SELL" When alert fires, check dashboard—ensure 3-4 votes Enter immediately (market order or 1-tick limit) Stop: Tight, 1-1.5× ATR Target: 2× ATR or opposite pivot signal Strategy 3: Bandit-Following Swing Trading On Daily timeframe, monitor which bandit has best Sharpe ratio over 30+ days Take ONLY that bandit's signals (ignore others) Enter on pivot, hold until opposite pivot or target line Position size based on bandit's current win rate (higher win% = larger position) Strategy 4: Breakout Confirmation Identify key support/resistance level manually Wait for pivot to form AT that level If price breaks level and diamond breakout marker appears, enter in breakout direction This combines support/resistance with geometric confirmation Strategy 5: Inflection Zone Limit Orders Enable "Show Inflection Zones" Place limit buy orders at bottom of purple zones Place limit sell orders at top of purple zones These zones represent structural change points where price often pauses VIII. WHAT THIS INDICATOR DOES NOT DO To set proper expectations: This is NOT: A "holy grail" with 100% win rate A strategy that works without risk management A replacement for understanding market fundamentals A signal copier (you must interpret context) This DOES NOT: Predict black swan events Account for fundamental news (you must avoid trading during major news if not experienced) Work well in extremely low liquidity conditions (penny stocks, microcap crypto) Generate signals during consolidation (by design—prevents whipsaw) Best Performance: Liquid instruments (SPY, ES, NQ, EUR/USD, BTC/USD, etc.) Clear trend or range conditions (struggles in choppy transition periods) Timeframes 5m and above (1m can work but requires experience) IX. PERFORMANCE EXPECTATIONS Based on shadow portfolio backtesting across multiple instruments: Conservative Mode: Signal frequency: 2-5 per week (Daily charts) Expected win rate: 60-70% Average RRR: 2.5:1 Adaptive Mode: Signal frequency: 5-15 per day (15m charts) Expected win rate: 55-65% Average RRR: 2:1 Aggressive Mode: Signal frequency: 20-40 per day (5m charts) Expected win rate: 50-60% Average RRR: 1.5:1 Note: These are statistical expectations. Individual results depend on execution, risk management, and market conditions. X. PRIVACY & INVITE-ONLY NATURE This script is invite-only to: Maintain signal quality (prevent market impact from mass adoption) Provide dedicated support to users Continuously improve the algorithm based on user feedback Ensure users understand the complexity before deploying real capital The script is closed-source to protect proprietary research in: Ghost Vertex prediction mathematics Tensor construction methodology Bandit reward function design DVS scaling algorithms XI. FINAL RECOMMENDATIONS Before Trading Live: Paper trade for minimum 2 weeks to understand signal timing Start with ONE timeframe and master it before adding others Monitor the dashboard —if selected bandit Sharpe drops below 1.0, reduce size Use confluence and hive consensus for highest-quality setups Respect the Min Bars Between Pivots setting —this prevents overtrading Risk Management Rules: Never risk more than 1-2% of account per trade If 3 consecutive losses occur, stop trading and review (possible regime change) Use the shadow portfolio as a guide—if ALL bandits are losing, market is in transition Combine with other analysis (order flow, volume profile) for best results Continuous Learning: The RL system improves over time, but only if you: Keep the indicator running (it learns from bar data) Don't constantly change parameters (confuses the learning) Let it accumulate at least 50 samples before judging performance Review the dashboard weekly to see which bandits are adapting CONCLUSION Curvature Tensor Pivots - HIVE represents a fusion of advanced mathematics, machine learning, and practical trading experience. It is designed for serious traders who want institutional-grade tools and understand that edge comes from superior methodology, not magic formulas. The system's strength lies in its adaptive intelligence —it doesn't just detect pivots, it learns which detection method works best right now, in this market, under these conditions. The hive consensus mechanism provides confidence, the geometric foundation provides precision, and the reinforcement learning provides evolution. Use it wisely, manage risk properly, and let the mathematics work for you. Disclaimer: This indicator is a tool for analysis and does not constitute financial advice. Past performance of shadow portfolios does not guarantee future results. Trading involves substantial risk of loss. Always perform your own due diligence and never trade with capital you cannot afford to lose. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Indicateur Pine Script®
par DskyzInvestments
SWRSI Trends (Source Out)Overview SWRSI Trends is a specialized momentum indicator based on the Relative Strength Index (RSI). While it functions as a visual trading aid with bar coloring and signal shapes, its primary purpose is to serve as a modular signal provider for other strategies and backtesting bots on TradingView. It detects trend reversals by monitoring RSI crossovers at specific custom levels (Default: 60 and 40), rather than the standard 70/30 extreme zones. Key Features 1. External Source Outputs (Connect to Bots) This script includes hidden plot outputs specifically designed to interface with other scripts. RSI LONG SIGNAL (Source): Outputs a value of 1 when a Long condition is met, 0 otherwise. RSI SHORT SIGNAL (Source): Outputs a value of 1 when a Short condition is met, 0 otherwise. Usage: You can select these outputs as the "Entry Source" in compatible Strategy scripts or Backtest Bots without needing to copy-paste code. 2. Signal Logic Long Signal: Triggered when the RSI line crosses OVER the Lower Threshold (Default: 40). This indicates momentum is recovering from the lower zone. Short Signal: Triggered when the RSI line crosses UNDER the Upper Threshold (Default: 60). This indicates momentum is cooling off from the upper zone. 3. Visual Aids Bar Coloring: Candles change color based on RSI position (Green above 60, Red below 40). Dynamic Line: The RSI line changes color to reflect the current zone. Settings RSI Length: The lookback period for calculation (Default: 14). Short Threshold: Level for bearish crossover (Default: 60). Long Threshold: Level for bullish crossover (Default: 40). Color Bars: Toggle candle painting on/off. How to Connect to Another Indicator Add SWRSI Trends to your chart. Open the settings of your Target Strategy/Bot (e.g., SwietcherBot). In the "Source" or "External Signal" input field, select "SWRSI Trends: RSI LONG SIGNAL" or "RSI SHORT SIGNAL" from the dropdown menu.
Indicateur Pine Script®
par SwSpace
Improved ADX – Responsive & Visual (manual ADX)just an ADX but more visual.... This indicator is a custom implementation of the Average Directional Index (ADX), designed to provide a responsive and visual representation of trend strength along with the DI+ and DI– lines. It uses manual calculations for the ADX and directional movement components for greater control over smoothing and responsiveness. Key Features: ADX Calculation: Computes directional movement (upMove / downMove) manually. Calculates True Range and smoothed directional movements using Wilder’s moving average (RMA). Computes the ADX using the smoothed DX and applies additional smoothing (adx_fast) for responsiveness. Directional Indicators (DI+ / DI–): DI+ and DI– lines are calculated independently. Lines are plotted on the same scale as the indicator. Colors: DI+ is green when above DI–, otherwise semi-transparent green. DI– is red when above DI+, otherwise semi-transparent red. ADX Coloring and Trend Fill: ADX line color changes dynamically: Green when above the trend strength threshold. Yellow when near the threshold (0.75 × threshold). Red when below the threshold. Optional fill highlights strong trends between ADX and the threshold line: Green fill for ADX above the threshold. Red fill for ADX below the threshold. Cross Markers: Triangle markers are plotted on the indicator scale when ADX crosses the threshold: Upward green triangle for ADX crossing above the threshold (strengthening trend). Downward red triangle for ADX crossing below the threshold (weakening trend). Live Values Label: Displays the current ADX, DI+, and DI– values at the top of the indicator pane. Automatically updates on the last bar. Inputs: len: ADX length (default 14) smooth: Smoothing factor for adx_fast (default 5) show_fill: Highlight strong trend area (true/false) highlight_level: Trend strength threshold (default 25) show_di: Show DI+ / DI– (true/false) show_adx: Show ADX line (true/false) Usage: Use the ADX to gauge trend strength. DI+ above DI– indicates bullish pressure; DI– above DI+ indicates bearish pressure. ADX color and fill provide visual cues for trend strength and potential entry signals. Cross markers alert when trend strength is increasing or decreasing relative to the threshold.
Indicateur Pine Script®
par ueuito
The Hunting GroundsLiquid Hunter - The Hunting Grounds Professional-grade reversal detection system designed for identifying high-probability entry zones. Overview The Hunting Grounds utilizes a proprietary framework, we call SBI (Snap Back Index) to identify extreme market conditions where price reversals are statistically more likely to occur. The indicator visualizes these zones through dynamic cloud formations and precision signal markers. Key Features 📊 Dual-Strength Signal System EXTREME Signals (🔥): Highest-probability reversal zones - rare but powerful MEDIUM Signals (💎/⚠️): Secondary reversal opportunities with solid statistical edge ☁️ Dynamic Cloud Visualization Clouds automatically form around price during extreme conditions Color-coded by signal strength and direction Adjustable size and transparency for personal preference Adapts to market volatility 🎨 Signal Types 🔥 EXTREME LONG (Green): Major oversold reversal zone 💎 MEDIUM LONG (Cyan): Secondary oversold opportunity 🔥 EXTREME SHORT (Red): Major overbought reversal zone ⚠️ MEDIUM SHORT (Yellow): Secondary overbought opportunity How It Works The indicator employs a sophisticated multi-layer system that processes price action, volume momentum, and volatility to identify market extremes. When conditions align, visual clouds appear to highlight the reversal zone, accompanied by precise entry markers. The underlying calculation methodology is proprietary and optimized through extensive back-testing across multiple timeframes and asset classes. Recommended Usage Best Timeframes: Works on small timeframes for scalping ; 15m-4H recommended for swing trading Asset Classes: Crypto, Forex, Stocks, Indices Strategy: Mean reversion, counter-trend entries, liquidation hunting Risk Management: Always use stop losses; EXTREME signals offer tighter stops Customization Options Signal direction filter (LONG only, SHORT only, or Both) Signal strength filter (EXTREME only, or include MEDIUM) Cloud display toggle and size adjustment Transparency control for visual preference Built-in alert system for all signal types What Makes This Different Unlike standard indicators, SBI is specifically calibrated to identify institutional moves and extreme market exhaustion. The cloud visualization provides clear, actionable entry zones rather than abstract numerical values. Note: This indicator does not repaint. All signals are confirmed in real-time and suitable for live trading. ⚠️ Disclaimer This indicator is a tool for technical analysis and should be used as part of a complete trading strategy. Past performance does not guarantee future results. Always practice proper risk management.
Indicateur Pine Script®
par Liquid-Hunter
VuManChu Filtered OverlayVuManChu Filtered Overlay is a price-overlay signal tool inspired by VuManChu Cipher B. Instead of plotting the full oscillator in a separate pane, this script focuses on generating clean long/short signals directly on the chart, combining WaveTrend, Money Flow–style momentum, and an adjustable overbought/oversold threshold. Under the hood, the script builds a smoothed “Inertia Wave” using a normalized (close–open)/(high–low) money-flow proxy and a long SMA. This is used together with a classic WaveTrend (wt1 / wt2) calculation. Signals are only triggered when: WaveTrend lines cross (wt1 vs wt2), The cross direction matches the expected bias Bull: cross up from below, WaveTrend below zero Bear: cross down from above, WaveTrend above zero The custom money-flow curve (rsiMFI) confirms direction Bull: rsiMFI > 0 Bear: rsiMFI < 0 The WaveTrend line is beyond a user-defined OS/OB magnitude (Wavetrendtrigger), so only meaningful extremes are considered. The “VuManChu WaveTrend OS/OB threshold (+/-)” input lets you control how aggressive the signals are: Lower values (e.g. 5–10) → more frequent, more sensitive signals Higher values (e.g. 40–60) → fewer signa ls, focused on strong exhaustion moves Bullish and bearish opportunities are plotted as green and red dots on the candles, and corresponding alerts are fired: 🟢 Optimized VuManChu LONG signal detected on timeframe: X 🔴 Optimized VuManChu SHORT signal detected on timeframe: X This script is meant as a filter / confirmation layer, not a standalone system. For best results, combine it with your own trend, volume, or higher-timeframe context. This is not financial advice and should be used for educational and experimental purposes only.
Indicateur Pine Script®
par BTraderMS
Ata Low rsi macd aomacd stochastic and divergensesBrief Description of the Script The script is a multi‑indicator trading tool for the TradingView platform (Pine Script v5) that combines several technical analysis elements to help traders identify market trends, potential reversals, and entry/exit points. эту версию скрипта не обновляю. для получения обновлений в лс. Key features: Multiple Oscillators The user can select one of four oscillators to display: RSI (Relative Strength Index) — identifies overbought/oversold conditions; Stoch (Stochastic Oscillator) — detects potential reversals via %K and %D line interactions; MACD (Moving Average Convergence/Divergence) — shows trend direction and momentum shifts; AO+MACD — combines Awesome Oscillator (AO) for momentum with MACD for trend confirmation. Divergence Detection Identifies four types of price‑oscillator divergences: Bullish regular (price lows vs. higher oscillator lows); Bullish hidden (higher price lows vs. lower oscillator lows); Bearish regular (price highs vs. lower oscillator highs); Bearish hidden (lower price highs vs. higher oscillator highs). Divergences are marked on the chart with labels and lines. Customizable Parameters Users can adjust: Oscillator periods (e.g., RSI length, Stoch K/D smoothing, MACD fast/slow/signal lengths); Source prices (close, high, low, etc.); Visual settings (colors, line widths, label styles); Divergence sensitivity (minimum bars between swing points). Trend and Volatility Analysis EMA crossover (fast/slow) to determine trend direction; ATR‑based volatility score (1–5 scale); RSI‑derived trend strength (1–50 scale); ADX filter to confirm trend strength (>20). Additional Signals Awesome Oscillator “Tea Saucer” patterns for potential long/short entries; Fibonacci‑Bollinger bands to spot price deviations and reversal zones; Volume filter to confirm reversals; Session timing table (optional) showing active/upcoming market sessions (Asia, London, NYSE, etc.). Visual Outputs Plots for selected oscillator (RSI, Stoch, MACD, or AO); Shaded zones (e.g., RSI overbought/oversold areas); Divergence lines and labels (color‑coded by type); Reversal “circles” (blue for bullish, red for bearish); Summary label with trend direction, volatility, and strength; Optional session timing table. Purpose: To provide a comprehensive view of market momentum, trend, and potential reversal setups by combining oscillator crossovers, divergences, volatility, volume, and session context — helping traders time entries and exits across multiple timeframes.
Indicateur Pine Script®
par vslvnvch
Chronos Reversal Labs - SPChronos Reversal Labs - Shadow Portfolio Chronos Reversal Labs - Shadow Portfolio: combines reinforcement learning optimization with adaptive confluence detection through a shadow portfolio system. Unlike traditional indicator mashups that force traders to manually interpret conflicting signals, this system deploys 4 multi-armed bandit algorithms to automatically discover which of 5 specialized confluence strategies performs best in current market conditions, then validates those discoveries through parallel shadow portfolios that track virtual P&L for each strategy independently. Core Innovation: Rather than relying on static indicator combinations, this system implements Thompson Sampling (Bayesian multi-armed bandits), contextual bandits (regime-specific learning), advanced chop zone detection (geometric pattern analysis), and historical pre-training to build a self-improving confluence detection engine. The shadow portfolio system runs 5 parallel virtual trading accounts—one per strategy—allowing the system to learn which confluence approach works best through actual position tracking with realistic exits. Target Users: Intermediate to advanced traders seeking systematic reversal signals with mathematical rigor. Suitable for swing trading and day trading across stocks, forex, crypto, and futures on liquid instruments. Requires understanding of basic technical analysis and willingness to allow 50-100 bars for initial learning. Why These Components Are Combined The Fundamental Problem No single confluence method works consistently across all market regimes. Kernel-based methods (entropy, DFA) excel during predictable phases but fail in chaos. Structure-based methods (harmonics, BOS) work during clear swings but fail in ranging conditions. Technical methods (RSI, MACD, divergence) provide reliable signals in trends but generate false signals during consolidation. Traditional solutions force traders to either manually switch between methods (slow, error-prone) or interpret all signals simultaneously (cognitive overload). Both fail because they assume the trader knows which regime the market is in and which method works best. The Solution: Meta-Learning Through Reinforcement Learning This system solves the problem through automated strategy selection : Deploy 5 specialized confluence strategies designed for different market conditions, track their real-world performance through shadow portfolios, then use multi-armed bandit algorithms to automatically select the optimal strategy for the next trade. Why Shadow Portfolios? Traditional bandit implementations use abstract "rewards." Shadow portfolios provide realistic performance measurement : Each strategy gets a virtual trading account with actual position tracking, stop-loss management, take-profit targets, and maximum holding periods. This creates risk-adjusted learning where strategies are evaluated on P&L, win rate, and drawdown—not arbitrary scores. The Five Confluence Strategies The system deploys 5 orthogonal strategies with different weighting schemes optimized for specific market conditions: Strategy 1: Kernel-Dominant (Entropy/DFA focused, optimal in predictable markets) Shannon Entropy weight × 2.5, DFA weight × 2.5 Detects low-entropy predictable patterns and DFA persistence/mean-reversion signals Failure mode: High-entropy chaos (hedged by Technical-Dominant) Strategy 2: Structure-Dominant (Harmonic/BOS focused, optimal in clear swing structures) Harmonics weight × 2.5, Liquidity (S/R) weight × 2.0 Uses swing detection, break-of-structure, and support/resistance clustering Failure mode: Range-bound markets (hedged by Balanced) Strategy 3: Technical-Dominant (RSI/MACD/Divergence focused, optimal in established trends) RSI weight × 2.0, MACD weight × 2.0, Trend weight × 2.0 Zero-lag RSI suite with 4 calculation methods, MACD analysis, divergence detection Failure mode: Choppy/ranging markets (hedged by chop filter) Strategy 4: Balanced (Equal weighting, optimal in unknown/transitional regimes) All components weighted 1.2× Baseline performance during regime uncertainty Strategy 5: Regime-Adaptive (Dynamic weighting by detected market state) Chop zones: Kernel × 2.0, Technical × 0.3 Bull/Bear trends: Trend × 1.5, DFA × 2.0 Ranging: Mean reversion × 1.5 Adapts explicitly to detected regime Multi-Armed Bandit System: 4 Core Algorithms What Is a Multi-Armed Bandit Problem? Formal Definition: K arms (strategies), each with unknown reward distribution. Goal: Maximize cumulative reward while learning which arms are best. Challenge: Balance exploration (trying uncertain strategies) vs. exploitation (using known-best strategy). Trading Application: Each confluence strategy is an "arm." After each trade, receive reward (P&L percentage). Bandits decide which strategy to trust for next signal. The 4 Implemented Algorithms 1. Thompson Sampling (DEFAULT) Category: Bayesian approach with probability distributions How It Works: Model each strategy as Beta(α, β) where α = wins, β = losses. Sample from distributions, select highest sample. Properties: Optimal regret O(K log T), automatic exploration-exploitation balance When To Use: Best all-around choice, adaptive markets, long-term optimization 2. UCB1 (Upper Confidence Bound) Category: Frequentist approach with confidence intervals Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i) Properties: Deterministic, interpretable, same optimal regret as Thompson When To Use: Prefer deterministic behavior, stable markets 3. Epsilon-Greedy Category: Simple baseline with random exploration How It Works: With probability ε (0.15): random strategy. Else: best average reward. Properties: Simple, fast initial learning When To Use: Baseline comparison, short-term testing 4. Contextual Bandit Category: Context-aware Thompson Sampling Enhancement: Maintains separate alpha/beta for Bull/Bear/Ranging regimes Learning: "Strategy 2: 60% win rate in Bull, 40% in Bear" When To Use: After 100+ bars, clear regime shifts Shadow Portfolio System Why Shadow Portfolios? Traditional bandits use abstract scores. Shadow portfolios provide realistic performance measurement through actual position simulation. How It Works Position Opening: When strategy generates validated signal: Opens virtual position for selected strategy Records: entry price, direction, entry bar, RSI method Optional: Open positions for ALL strategies simultaneously (faster learning) Position Management (Every Bar): Current P&L: pnl_pct = (close - entry) / entry × direction × 100 Exit if: pnl_pct <= -2.0% (stop-loss) OR pnl_pct >= +4.0% (take-profit) OR held ≥ 100 bars (time) Position Closing: Calculate final P&L percentage Update strategy equity, track win rate, gross profit/loss, max drawdown Calculate risk-adjusted reward: text base_reward = pnl_pct / 10.0 win_rate_bonus = (win_rate - 0.5) × 0.3 drawdown_penalty = -max_drawdown × 0.05 total_reward = sigmoid(base + bonus + penalty) Update bandit algorithms with reward Update RSI method bandit Statistics Tracked Per Strategy: Equity curve (starts at $10,000) Win rate percentage Max drawdown Gross profit/loss Current open position This creates closed-loop learning : Strategies compete → Best performers selected → Bandits learn quality → System adapts automatically. Historical Pre-Training System The Problem with Live-Only Learning Standard bandits start with zero knowledge and need 50-100 signals to stabilize. For weekly timeframe traders, this could take years. The Solution: Historical Training During Chart Load: System processes last 300-1000 bars (configurable) in "training mode": Detect signals using Balanced strategy (consistent baseline) For each signal, open virtual training positions for all 5 strategies Track positions through historical bars using same exit logic (SL/TP/time) Update bandit algorithms with historical outcomes CRITICAL TRANSPARENCY: Signal detection does NOT look ahead—signals use only data available at entry bar. Exit tracking DOES look ahead (uses future bars for SL/TP), which is acceptable because: ✅ Entry decisions remain valid (no forward bias) ✅ Learning phase only (not affecting shown signals) ✅ Real-time mirrors training (identical exit logic) Training Completion: Once chart reaches current bar, system transitions to live mode. Dashboard displays training vs. live statistics for comparison. Benefit: System begins live trading with 100-500 historical trades worth of learning, enabling immediate intelligent strategy selection. Advanced Chop Zone Detection Engine The Innovation: Multi-Layer Geometric Chop Analysis Traditional chop filters use simple volatility metrics (ATR thresholds) that can't distinguish between trending volatility (good for signals) and choppy volatility (bad for signals). This system implements three-layer geometric pattern analysis to precisely identify consolidation zones where reversal signals fail. Layer 1: Micro-Structure Chop Detection Method: Analyzes micro pivot points (5-bar left, 2-bar right) to detect geometric compression patterns. Slope Analysis: Calculates slope of pivot high trendline and pivot low trendline Compression ratio: compression = slope_high - slope_low Pattern Classification: Converging slopes (compression < -0.05) → "Rising Wedge" or "Falling Wedge" Flat slopes (|slope| < 0.05) → "Rectangle" Parallel slopes (|compression| < 0.1) → "Channel" Expanding slopes → "Expanding Range" Chop Scoring: Rectangle pattern: +15 points (highest chop) Low average slope (<0.05): +15 points Wedge patterns: +12 points Flat structures: +10 points Why This Works: Geometric patterns reveal market indecision. Rectangles and wedges create false breakouts that trap technical traders. By quantifying geometric compression, system detects these zones before signals fire. Layer 2: Macro-Structure Chop Detection Method: Tracks major swing highs/lows using ATR-based deviation threshold (default 2.0× ATR), projects channel boundaries forward. Channel Position Calculation: proj_high = last_swing_high + (swing_high_slope × bars_since) proj_low = last_swing_low + (swing_low_slope × bars_since) channel_width = proj_high - proj_low position = (close - proj_low) / channel_width Dead Zone Detection: Middle 50% of channel (position 0.25-0.75) = low-conviction zone Score increases as price approaches center (0.5) Chop Scoring: Price in dead zone: +15 points (scaled by centrality) Narrow channel width (<3× ATR): +15 points Channel width 3-5× ATR: +10 points Why This Works: Price in middle of range has equal probability of moving either direction. Institutional traders avoid mid-range entries. By detecting "dead zones," system avoids low-probability setups. Layer 3: Volume Chop Scoring Method: Low volume indicates weak conviction—precursor to ranging behavior. Scoring: Volume < 0.5× average: +20 points Volume 0.5-0.8× average: +15 points Volume 0.8-1.0× average: +10 points Overall Chop Intensity & Signal Filtering Total Chop Calculation: chop_intensity = micro_score + macro_score + (volume_score × volume_weight) is_chop = chop_intensity >= 40 Signal Filtering (Three-Tier Approach): 1. Signal Blocking (Intensity > 70): Extreme chop detected (e.g., tight rectangle + dead zone + low volume) ALL signals blocked regardless of confluence Chart displays red/orange background shading 2. Threshold Adjustment (Intensity 40-70): Moderate chop detected Confluence threshold increased: threshold += (chop_intensity / 50) Only highest-quality signals pass 3. Strategy Weight Adjustment: During Chop: Kernel-Dominant weight × 2.0 (entropy detects breakout precursors), Technical-Dominant weight × 0.3 (reduces false signals) After Chop Exit: Weights revert to normal Why This Three-Tier Approach Is Original: Most chop filters simply block all signals (loses breakout entries). This system adapts strategy selection during chop—allowing Kernel-Dominant (which excels at detecting low-entropy breakout precursors) to operate while suppressing Technical-Dominant (which generates false signals in consolidation). Result: System remains functional across full market regime spectrum. Zero-Lag Filter Suite with Dynamic Volatility Scaling Zero-Lag ADX (Trend Regime Detection) Implementation: Applies ZLEMA to ADX components: lag = (length - 1) / 2 zl_source = source + (source - source ) × strength Dynamic Volatility Scaling (DVS): Calculates volatility ratio: current_ATR / ATR_100period_avg Adjusts ADX length dynamically: High vol → shorter length (faster), Low vol → longer length (smoother) Regime Classification: ADX > 25 with +DI > -DI = Bull Trend ADX > 25 with -DI > +DI = Bear Trend ADX < 25 = Ranging Zero-Lag RSI Suite (4 Methods with Bandit Selection) Method 1: Standard RSI - Traditional Wilder's RSI Method 2: Ehlers Zero-Lag RSI ema1 = ema(close, length) ema2 = ema(ema1, length) zl_close = close + (ema1 - ema2) Method 3: ZLEMA RSI lag = (length - 1) / 2 zl_close = close + (close - close ) Method 4: Kalman-Filtered RSI - Adaptive smoothing with process/measurement noise RSI Method Bandit: Separate 4-arm bandit learns which calculation method produces best results. Updates independently after each trade. Kalman Adaptive Filters Fast Kalman: Low process noise → Responsive to genuine moves Slow Kalman: Higher measurement noise → Filters noise Application: Crossover logic for trend detection, acceleration analysis for momentum inflection What Makes This Original Innovation 1: Shadow Portfolio Validation First TradingView script to implement parallel virtual portfolios for multi-armed bandit reward calculation. Instead of abstract scoring metrics, each strategy's performance is measured through realistic position tracking with stop-loss, take-profit, time-based exits, and risk-adjusted reward functions (P&L + win rate + drawdown). This provides orders-of-magnitude better reward signal quality for bandit learning than traditional score-based approaches. Innovation 2: Three-Layer Geometric Chop Detection Novel multi-scale geometric pattern analysis combining: (1) Micro-structure slope analysis with pattern classification (wedges, rectangles, channels), (2) Macro-structure channel projection with dead zone detection, (3) Volume confirmation. Unlike simple volatility filters, this system adapts strategy weights during chop —boosting Kernel-Dominant (breakout detection) while suppressing Technical-Dominant (false signal reduction)—allowing operation across full market regime spectrum without blind signal blocking. Innovation 3: Historical Pre-Training System Implements two-phase learning : Training phase (processes 300-1000 historical bars on chart load with proper state isolation) followed by live phase (real-time learning). Training positions tracked separately from live positions. System begins live trading with 100-500 trades worth of learned experience. Dashboard displays training vs. live performance for transparency. Innovation 4: Contextual Multi-Armed Bandits with Regime-Specific Learning Beyond standard bandits (global strategy quality), implements regime-specific alpha/beta parameters for Bull/Bear/Ranging contexts. System learns: "Strategy 2: 60% win rate in ranging markets, 45% in bull trends." Uses current regime's learned parameters for strategy selection, enabling regime-aware optimization. Innovation 5: RSI Method Meta-Learning Deploys 4 different RSI calculation methods (Standard, Ehlers ZL, ZLEMA, Kalman) with separate 4-arm bandit that learns which calculation works best. Updates RSI method bandit independently based on trade outcomes, allowing automatic adaptation to instrument characteristics. Innovation 6: Dynamic Volatility Scaling (DVS) Adjusts ALL lookback periods based on current ATR ratio vs. 100-period average. High volatility → shorter lengths (faster response). Low volatility → longer lengths (smoother signals). Applied system-wide to entropy, DFA, RSI, ADX, and Kalman filters for adaptive responsiveness. How to Use: Practical Guide Initial Setup (5 Minutes) Theory Mode: Start with "BALANCED" (APEX for aggressive, CONSERVATIVE for defensive) Enable RL: Toggle "Enable RL Auto-Optimization" to TRUE, select "Thompson Sampling" Enable Confluence Modules: Divergence, Volume Analysis, Liquidity Mapping, RSI OB/OS, Trend Analysis, MACD (all recommended) Enable Chop Filter: Toggle "Enable Chop Filter" to TRUE, sensitivity 1.0 (default) Historical Training: Enable "Enable Historical Pre-Training", set 300-500 bars Dashboard: Enable "Show Dashboard", position Top Right, size Large Learning Phase (First 50-100 Bars) Monitor Thompson Sampling Section: Alpha/beta values should diverge from initial 1.0 after 20-30 trades Expected win% should stabilize around 55-60% (excellent), >50% (acceptable) "Pulls" column should show balanced exploration (not 100% one strategy) Monitor Shadow Portfolios: Equity curves should diverge (different strategies performing differently) Win rate > 55% is strong Max drawdown < 15% is healthy Monitor Training vs Live (if enabled): Delta difference < 10% indicates good generalization Large negative delta suggests overfitting Large positive delta suggests system adapting well Optimization: Too few signals: Lower "Base Confluence Threshold" to 2.5-3.0 Too many signals: Raise threshold to 4.0-4.5 One strategy dominates (>80%): Increase "Exploration Rate" to 0.20-0.25 Excessive chop blocking: Lower "Chop Sensitivity" to 0.7-0.8 Signal Interpretation Dashboard Indicators: "WAITING FOR SIGNAL": No confluence "LONG ACTIVE ": Validated long entry "SHORT ACTIVE ": Validated short entry Chart Visuals: Triangle markers: Entry signal (green = long, red = short) Orange/red background: Chop zone Lines: Support/resistance if enabled Position Management Entry: Enter on triangle marker, confirm direction matches dashboard, check confidence >60% Stop-Loss: Entry ± 1.5× ATR or at structural swing point Take-Profit: TP1: Entry + 1.5R (take 50%, move SL to breakeven) TP2: Entry + 3.0R (runner) or trail Position Sizing: Risk per trade = 1-2% of capital Position size = (Account × Risk%) / (Entry - SL) Recommended Settings by Instrument Stocks (Large Cap): Balanced mode, Threshold 3.5, Thompson Sampling, Chop 1.0, 15min-1H, Training 300-500 bars Forex Majors: Conservative-Balanced mode, Threshold 3.5-4.0, Thompson Sampling, Chop 0.8-1.0, 5min-30min, Training 400-600 bars Cryptocurrency: Balanced-APEX mode, Threshold 3.0-3.5, Thompson Sampling, Chop 1.2-1.5, 15min-4H, Training 300-500 bars Futures: Balanced mode, Threshold 3.5, UCB1 or Thompson, Chop 1.0, 5min-30min, Training 400-600 bars Technical Approximations & Limitations 1. Thompson Sampling: Pseudo-Random Beta Distribution Standard: Cryptographic RNG with true beta sampling This Implementation: Box-Muller transform using market data as entropy source Impact: Not cryptographically random but maintains exploration-exploitation balance. Sufficient for strategy selection. 2. Shadow Portfolio: Simplified Execution Model Standard: Order book simulation with slippage, partial fills This Implementation: Perfect fills at close price, no fees modeled Impact: Real-world performance ~0.1-0.3% worse per trade due to execution costs. 3. Historical Training: Forward-Looking for Exits Only Entry signals: Use only past data (causal, no bias) Exit tracking: Uses future bars to determine SL/TP (forward-looking) Impact: Acceptable because: (1) Entry logic remains valid, (2) Live trading mirrors training, (3) Improves learning quality. Training win rates reflect 8-bar evaluation window—live performance may differ if positions held longer. 4. Shannon Entropy & DFA: Simplified Calculations Impact: 10-15% precision loss vs. academic implementations. Still captures predictability and persistence signals effectively. General Limitations No Predictive Guarantee: Past performance ≠ future results Learning Period Required: Minimum 50-100 bars for stable statistics Overfitting Risk: May not generalize to unprecedented conditions Single-Instrument: No multi-asset correlation or sector context Execution Assumptions: Degrades in illiquid markets (<100k volume), major news events, flash crashes Risk Warnings & Disclaimers No Guarantee of Profit: All trading involves substantial risk of loss. This indicator is a tool, not a guaranteed profit system. System Failures: Software bugs possible despite testing. Use appropriate position sizing. Market Regime Changes: Performance may degrade during extreme volatility (VIX >40), low liquidity periods, or fundamental regime shifts. Broker-Specific Issues: Real-world execution includes slippage (0.1-0.5%), commissions, overnight financing costs, partial fills. Forward-Looking Bias in Training: Historical training uses 8-bar forward window for exit evaluation. Dashboard "Training Win%" reflects this method. Real-time performance may differ. Appropriate Use This Indicator IS: ✅ Entry trigger system with confluence validation ✅ Risk management framework (automated SL/TP) ✅ Adaptive strategy selection engine ✅ Learning system that improves over time This Indicator IS NOT: ❌ Complete trading strategy (requires position sizing, portfolio management) ❌ Replacement for due diligence ❌ Guaranteed profit generator ❌ Suitable for complete beginners Recommended Complementary Analysis: Market context, volume profile, fundamental catalysts, higher timeframe alignment, support/resistance from other sources. Conclusion Chronos Reversal Labs V2.0 - Elite Edition synthesizes research from multi-armed bandit theory (Thompson Sampling, UCB, contextual bandits), market microstructure (geometric chop detection, zero-lag filters), and machine learning (shadow portfolio validation, historical pre-training, RSI method meta-learning). Unlike typical indicator mashups, this system implements mathematically rigorous bandit algorithms with realistic performance validation, three-layer chop detection with adaptive strategy weighting, regime-specific learning, and full transparency on approximations and limitations. The system is designed for intermediate to advanced traders who understand that no indicator is perfect, but through proper machine learning and realistic validation, we can build systems that improve over time and adapt to changing markets without manual intervention. Use responsibly. Understand the limitations. Risk disclosure applies. Past performance does not guarantee future results. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Indicateur Pine Script®
par DskyzInvestments
1
Trade4Freedom## 🔷 Trade4Freedom – Market Logic Framework **Not a group of indicators. One continuous system of reading market behaviour.** The script is designed to follow the same decision flow I use in trading. Every tool here supports the others — there are no standalone modules. The market is analysed layer by layer, but always as one sequence: --- ### 🔄 **How the logic works (continuous process)** 1. **Structure first** – BOS/ChoCH levels show where the market changed behaviour. The projected dotted line is not a signal — it is a place where I wait and observe. I do not enter until price interacts with structure. 2. **Liquidity next** – if the structure level aligns with a liquidity bag (retest), the zone becomes important. Active liquidity lines are potential targets or reasons to avoid trading against the area. 3. **Context filter** – I use CCI only when structure + liquidity are already active. Example of long bias: −200 level is broken → candle closes above the MA → CCI rises from the channel. From this point I begin to trail stops and start building position if structure supports it. 4. **Confirmation & positioning** Stochastic heatmap is not for entries – it confirms pressure. Divergences on CCI or price are additional evidence when forming or adjusting a position. 5. **Execution zones** – only after structure → liquidity → context, I use deviation levels (1–5) to define where to place orders. On higher timeframes they work for accumulation models, on intraday levels they work for tactical entry zones. Dev1/Dev2 boxes exist only to make limit-order planning faster. --- ### 📌 **Purpose of the script** This tool does not predict price or generate signals. It creates the same structured environment on any chart: **Structure → Liquidity → Context → Deviation → Decision** This helps avoid random trading and replaces guessing with logic and observation.
Indicateur Pine Script®
par TRADE4FREEDOMM
EQT Stochastic RibbonEQT Stochastic Ribbon is a modified Stochastic Oscillator with ribbon fill visualization. Features: - Dynamic color ribbon that changes based on trend direction (Blue for bullish, White for bearish) - Crossover signals with triangle markers when %K crosses %D - Customizable colors and signal offset - Dashed lines at 80/20 levels for overbought/oversold zones How to use: - Blue ribbon = Bullish momentum (%K above %D) - White ribbon = Bearish momentum (%K below %D) - Triangle up = Buy signal (K crosses above D) - Triangle down = Sell signal (K crosses below D) Settings: - K, D, Smooth - Standard Stochastic parameters - Signal Offset - Distance of signal arrows from the line - Bullish/Bearish Colors - Customize ribbon and signal colors
Indicateur Pine Script®
par Openfort
Momentum Divergence Oscillator by JJMomentum Divergence Oscillator by JJ A powerful, all-in-one momentum tool designed to streamline trade confluence, combining multi-timeframe trend analysis with automatic divergence spotting and classic MACD signals. How to Use This Indicator This oscillator is designed to be used in the lower pane of your chart, beneath your primary price chart. It provides three main types of signals: 1. Multi-Timeframe (MTF) Trend Confirmation The background shading is your primary trend filter. It looks at the MACD trend on two higher timeframes (30m and 60m by default) to confirm the market's overarching direction. Green Shading: Indicates that both higher timeframes are in a bullish trend (MACD above signal line). Focus on looking for BUY signals during this time. Red Shading: Indicates that both higher timeframes are in a bearish trend. Focus on looking for SELL signals during this time. Grey/No Shading: The higher timeframes are not in agreement or are consolidating. Exercise caution or stick to standard price action rules. 2. Automatic Divergence Signals Divergence is a powerful early warning system where the indicator moves in the opposite direction of the price. The indicator automatically flags these occurrences: "Bull RSI Div" (Green Label-Up): Bullish divergence identified using the RSI oscillator. This suggests a potential reversal to the upside after a downtrend. "Bear RSI Div" (Red Label-Down): Bearish divergence identified using the RSI oscillator. This suggests a potential reversal to the downside after an uptrend. Tip: These signals are often most reliable when they occur within the corresponding MTF background colour (e.g., a "Bull RSI Div" during a Green MTF background). 3. Momentum Shifts and Crossovers The standard plots provide immediate insight into market momentum: Blue/Orange Lines: The traditional MACD line (Blue) and Signal line (Orange). Histogram (Green/Red Bars): Represents the momentum difference between the MACD and Signal lines. Zero-Line Crosses (Triangles): Tiny triangles appear when the MACD line crosses the zero line, indicating a shift in long-term momentum. Peaks & Troughs (X-Crosses): The 'X' markers identify local peaks and troughs in the histogram, sometimes indicating short-term exhaustion of the current move. Disclaimer: Trading involves significant risk and is not suitable for every investor. This indicator is for educational purposes only and should not be considered financial advice. Always use appropriate risk management.
Indicateur Pine Script®
par junaid8883
RayAlgo Flux Velocity & Volume OscillatorThe RayAlgo Oscilator uses a three-step calculation process: Volume-Weighted Momentum: It starts by calculating price momentum but weights the result by volume. If price moves strongly on low volume, the signal is dampened. If the move is supported by high volume, the signal is amplified. This filters out "fake" moves. The Fisher Transform: This is the secret sauce. The Fisher Transform converts the volume-weighted data into a Gaussian Normal Distribution. This process forces the data to create sharp, well-defined peaks and valleys, clearly defining statistical extremes (tops and bottoms) that standard oscillators simply blur. Hull Moving Average (HMA) Smoothing: The final signal is smoothed using the HMA. This provides the fast, liquid, wave-like motion you see, virtually eliminating lag without introducing choppiness.
Indicateur Pine Script®
par Ray_Algo
Cjack COT IndexHere's the updated description with the formula and additional context: --- **Cjack COT Index - Commitment of Traders Positioning Indicator** This indicator transforms raw Commitment of Traders (COT) data into normalized 0-100 index values, making it easy to identify extreme positioning across different trader categories. **How It Works:** The indicator calculates a min-max normalized index for three trader groups over your chosen lookback period (default 26 weeks): - **Large Speculators** (Non-commercial positions) - typically trend followers - **Small Speculators** (Non-reportable positions) - retail traders - **Commercial Hedgers** - producers and consumers hedging business risk The normalization formula is: **Index = (Current Position - Minimum Position) / (Maximum Position - Minimum Position) × 100** This calculation shows where current net positioning sits between the minimum and maximum levels observed in the lookback window. A reading of 100 means current positioning equals the maximum net long over that period, 0 equals the minimum (most net short), and 50 is the midpoint of the range. **Important:** The lookback period critically affects index readings - shorter lookbacks (13-26 weeks) make the index more sensitive to recent extremes, while longer lookbacks (52-78 weeks) provide broader historical context and identify truly exceptional positioning. Min-max normalization is essential because it makes positioning comparable across different contracts and time periods, regardless of the absolute size of positions. **What It's Good For:** The indicator excels at identifying **crowded trades** and potential reversals by tracking contrarian setups where commercials (smart money) position opposite to speculators. Background highlighting automatically flags: - **Long setups** (green): Commercials heavily long while speculators are heavily short - **Short setups** (red): Commercials heavily short while speculators are heavily long The "Shift Index" option (enabled by default) displays last week's tradeable COT data aligned with current price action, ensuring you're working with actionable information since COT reports publish with a delay. Works on weekly timeframes and below for commodities and futures with available COT data.
Indicateur Pine Script®
par Cjackjamie