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RSI + Elder Bull-Bear pressure RSI + Bull/Bear (Elder-Ray enhanced RSI) What it is An extended RSI that overlays Elder-Ray Bull/Bear Power on the same, zero-centered scale. You get classic RSI regime cues plus a live read of buy/sell pressure, with optional smoothing, bands, and right-edge value labels. Key features RSI with bands – default bands 30 / 50 / 70 (editable). Bull/Bear Power (Elder) – ATR-normalized; optional EMA/SMA/RMA/HMA smoothing. One-pane overlay – RSI and Bull/Bear share a common midline (RSI-50 ↔ panel 0). Right-edge labels – always visible at the chart’s right margin with adjustable offsets. How to read it Cyan line = RSI (normalized) Above the mid band = bullish regime; below = bearish regime. Green = Bull Power, Red = Bear Power Columns/lines above 0 show buy pressure; below 0 show sell pressure. Smoothing reduces noise; zero-line remains your key reference. Trade logic (simple playbook) Entry BUY (primary): RSI crosses up through 50 (regime turns bullish), and Bull (green) crosses up through 0 (buy pressure confirms). SELL (primary): RSI crosses down through 50, and Bear (red) crosses down through 0 (sell pressure confirms). Alternative momentum entries Aggressive BUY: Bull (green) pushes above RSI-80 band (strong upside impulse). Aggressive SELL: Bear (red) pushes below RSI-30 band (strong downside impulse). Exits / trade management In a long: consider exiting or tightening stops if Bear (red) dips below the 0 line (rising sell pressure) or RSI loses 50. In a short: consider exiting or tightening if Bull (green) rises above 0 or RSI reclaims 50. Tip: “0” on the panel is your pressure zero-line (maps to RSI-50). Most whipsaws happen near this line; smoothing (e.g., EMA 21) helps. Defaults (on first load) RSI bands: 30 / 50 / 70 with subtle fills. Labels: tiny, pushed far right (large offsets). Bull/Bear smoothing: EMA(21), smoothed line plot mode. RSI plotted normalized so it overlaps the pressure lines cleanly. Tighten or loosen the Bull/Bear thresholds (e.g., Bull ≥ +0.5 ATR, Bear ≤ −0.5 ATR) to demand stronger confirmation. Settings that matter Smoothing length/type – balances responsiveness vs. noise. Power/RSI Gain – visual scaling only (doesn’t change logic). Band placement – keep raw 30/50/80 or switch to “distance from 50” if you prefer symmetric spacing. Label offsets – move values clear of the last bar/scale clutter. Good practices Combine with structure/ATR stops (e.g., 1–1.5× ATR, swing high/low). In trends, hold while RSI stays above/below 50 and the opposite pressure line doesn’t dominate. In ranges, favor signals occurring near the mid band and take profits at the opposite band. Disclaimer: This is a research/visual tool, not financial advice at any kind. Test your rules on multiple markets/timeframes and size positions responsibly.
Chỉ báo Pine Script®
của PDK1977
Dow Jones Trading System with PivotsThis TradingView indicator, tailored for the 30-minute Dow Jones (^DJI) chart, supports DIA options trading with a trend-following approach. It features a 30-period SMA (blue) and a 60-period SMA (red), with an optional 90-period SMA (orange) drawn from rauItrades' Dow SMA outfit. A bullish crossover (30 SMA > 60 SMA) displays a green "BUY" triangle below the bar for potential DIA longs, while a bearish crossunder (30 SMA < 60 SMA) shows a red "SELL" triangle above for shorts or exits. The background turns green (bullish) or red (bearish) to indicate trend bias. Pivot points highlight recent highs (orange circles) and lows (purple circles) for support/resistance, using a 5-bar lookback. Alerts notify for crossovers.
Chỉ báo Pine Script®
của DarkMoneyMusic
CHOCH + FVG Signals [30m Optimized]CHOCH + FVG Signals 🎯 What It Does: This script automatically scans your chart for high-probability Smart Money Concepts (SMC) setups based on two key institutional trading principles: Change of Character (CHOCH) – A shift in market structure signaling potential reversal Fair Value Gap (FVG) – An imbalance zone where price moved too fast, often acting as support/resistance When both conditions align, the script plots clear Buy (▲) and Sell (▼) signals directly on your chart — ideal for intraday trading on the 30-minute timeframe (but works on any timeframe). ✅ Key Features: 🔹 Visual Fair Value Gaps Green shaded zones = Bullish FVGs (potential support) Red shaded zones = Bearish FVGs (potential resistance) Toggle on/off in settings 🔹 Smart CHOCH Detection Detects breaks of recent swing highs/lows with proper context Avoids false signals by confirming prior price structure 🔹 Clear Trade Signals Green ▲ below bar = Buy signal (Bullish CHOCH + FVG confluence) Red ▼ above bar = Sell signal (Bearish CHOCH + FVG confluence) 🔹 Customizable Filters Option to require FVG for a signal (recommended for higher accuracy) Adjust sensitivity via swing detection settings (default optimized for 30m) 🔹 Alert-Ready Built-in alert conditions for instant notifications on TradingView mobile/desktop ⚙️ How to Use: Apply to a 30-minute chart (e.g., EURUSD, Gold, NAS100, BTC) Wait for at least 50–100 bars to load (so swing points appear) Look for: A green triangle (▲) → consider long entry near FVG support A red triangle (▼) → consider short entry near FVG resistance Confirm with price action: Wait for a strong candle close or rejection at the FVG zone Use stop-loss below/above the FVG and target recent liquidity pools 💡 Pro Tip: Best used during high-volume sessions (e.g., London Open 7–10 AM UTC, NY Open 12:30–3:30 PM UTC). 🛠️ Settings (Inputs): Show Fair Value Gaps ✅ Enabled Visualize FVG zones Max FVG History 100 bars Prevent chart clutter Require FVG for Signal? ✅ Enabled Higher-quality setups (disable to test CHOCH-only) ⚠️ Important Notes: This is a signal generator, not financial advice. Always manage risk. Works best in trending or breaking markets — avoid during low-volatility ranges. FVGs may get filled (tested) before price continues — patience improves results. Backtest on historical data before live trading. 📣 Ideal For: Retail traders learning Smart Money Concepts (SMC) Price action traders seeking institutional-level confluence Intraday scalpers & swing traders on 30m–1H timeframes
Chỉ báo Pine Script®
của Spoiltbrat
ICT HTF Volume Candles (Based on HTF Candles by Fadi)# ICT HTF Volume Candles - Multi-Timeframe Volume Analysis ## Overview This indicator provides multi-timeframe volume visualization designed to complement price action analysis. It displays volume data from up to 6 higher timeframes simultaneously in a separate panel, allowing traders to identify volume spikes, divergences, and institutional activity without switching between timeframes. **Original Concept Credits:** This indicator builds upon the HTF Candles framework by Fadi, adapting it specifically for volume analysis with enhanced features including gap-filling for extended hours, multiple scaling methods, and advanced synchronization. ## What Makes This Script Original ### Key Innovations: 1. **Three Volume Scaling Methods:** - **Per-HTF Auto Scale:** Each timeframe scales independently for detailed comparison - **Global Auto Scale:** All timeframes use unified scale for relative volume comparison - **Manual Scale:** User-defined maximum for consistent analysis across sessions 2. **Bullish/Bearish Volume Differentiation:** - Volume bars colored based on price movement (close vs open) - Separate styling for bullish (green) and bearish (red) volume periods - Helps identify whether volume supports price direction 3. **Advanced Time Synchronization:** - Custom daily candle open times (Midnight, 8:30 AM, 9:30 AM ET) - Timezone-aware calculations for New York trading hours - Real-time countdown timers for each timeframe - **Gap-filling technology** for continuous display during extended hours and weekends 4. **Flexible Display Options:** - Configurable spacing and positioning - Label placement (top, bottom, or both) - Day-of-week or time interval labels on candles - Works reliably in backtesting and live trading ## How It Works ### Volume Calculation The indicator uses `request.security()` with optimized parameters to fetch volume data from higher timeframes: - **Volume Open/High/Low/Close (OHLC):** Tracks volume changes within each HTF candle - **Color Logic:** Compares HTF close vs open prices to determine bullish/bearish classification - **Alignment:** All volume bars share a common baseline for easy visual comparison - **Gap Handling:** Uses `gaps=barmerge.gaps_off` to maintain continuity during non-trading hours ### Technical Implementation ``` 1. Monitors HTF timeframe changes using request.security() with lookahead 2. Creates new VolumeCandle object when HTF bar opens 3. Updates current candle's volume H/L/C on each chart bar 4. Applies selected scaling method to normalize display height 5. Repositions all candles and labels on each bar update 6. Fills gaps automatically during extended hours for consistent display ``` ### Scaling Methods Explained **Method 1 - Auto Scale per HTF:** Each timeframe displays volume relative to its own maximum. Best for identifying patterns within each individual timeframe. **Method 2 - Global Auto Scale:** All timeframes share the same scale based on the highest volume across all HTFs. Best for comparing relative volume strength between timeframes. **Method 3 - Manual Scale:** User sets maximum volume value. Best for maintaining consistent scale across different trading sessions or instruments. ## How to Use This Indicator ### Setup 1. Add indicator to your chart (it appears in a separate panel below price) 2. Configure up to 6 higher timeframes (default: 5m, 15m, 1H, 4H, 1D, 1W) 3. Set number of candles to display for each timeframe 4. Choose volume scaling method based on your analysis needs 5. Enable "Fix gaps in non-trading hours" for extended hours trading (enabled by default) ### Interpretation **Volume Spikes:** - Sudden increase in volume height indicates institutional activity or strong conviction - Compare volume between timeframes to identify where the real money is moving - Look for volume spikes that appear across multiple timeframes simultaneously **Bullish vs Bearish Volume:** - **Green volume bars:** Price closed higher (buying pressure) - **Red volume bars:** Price closed lower (selling pressure) - High green volume during uptrend = confirmation of strength - High red volume during downtrend = confirmation of weakness - High volume opposite to trend = potential reversal warning **Multi-Timeframe Context:** - **5m/15m:** Scalping and day trading activity - **1H/4H:** Swing trading and intraday institutional flows - **Daily/Weekly:** Major position building and long-term trends **Divergences:** - Price making new highs but volume declining = weakening trend - Volume increasing while price consolidates = potential breakout brewing - Price breaks level but volume doesn't confirm = likely false breakout ### Practical Examples **Example 1 - Institutional Confirmation:** Price breaks above resistance. Check volume across timeframes: - 5m shows spike = retail interest - 15m + 1H + 4H all show spikes = institutional confirmation - **Trade confidence: HIGH** **Example 2 - False Breakout Detection:** Price breaks resistance with: - High volume on 5m only - Normal/low volume on 1H and 4H - **Interpretation:** Likely retail trap, institutions not participating - **Action:** Wait for pullback or avoid **Example 3 - Accumulation Phase:** Price ranges sideways but: - Daily volume gradually increasing - Weekly volume above average - **Interpretation:** Smart money accumulating - **Action:** Prepare for breakout in direction of volume **Example 4 - Volume Divergence:** Price makes new high: - Current high has lower volume than previous high across all timeframes - **Interpretation:** Weakening momentum - **Action:** Consider profit-taking or reversal trade ## Configuration Parameters ### Timeframe Settings - **HTF 1-6:** Select timeframes (must be higher than chart timeframe) - **Max Display:** Number of candles to show per timeframe (1-50) - **Limit to Next HTFs:** Display only first N enabled timeframes (1-6) ### Styling - **Bull/Bear Colors:** Separate colors for body, border, and wick - **Padding from current candles:** Distance offset from live price action - **Space between candles:** Gap between individual volume bars - **Space between Higher Timeframes:** Gap between different timeframe groups - **Candle Width:** Thickness of volume bars (1-4, multiplied by 2) ### Volume Settings - **Volume Scale Method:** Choose 1, 2, or 3 - 1 = Auto Scale per HTF (each TF independent) - 2 = Global Auto Scale (all TF unified) - 3 = Manual Scale (user-defined max) - **Auto Scale Volume:** Enable/disable automatic scaling - **Manual Scale Max Volume:** Set maximum when using Method 3 ### Label Settings - **HTF Label:** Show/hide timeframe names with color and size options - **Label Positions:** Display at Top, Bottom, or Both - **Label Alignment:** Align centered or Follow Candles - **Remaining Time:** Show countdown timer until next HTF candle - **Interval Value:** Display day-of-week or time on each candle ### Custom Daily Candle - **Enable Custom Daily:** Override default daily candle timing - **Open Time Options:** - **Midnight:** Standard 00:00 ET daily open - **8:30 AM:** Align with economic data releases - **9:30 AM:** Align with NYSE market open - Useful for specific trading strategies or market alignment ### Advanced Settings - **Fix gaps in non-trading hours:** Maintains alignment during extended hours and weekends (recommended: ON) - Prevents visual gaps during forex weekend closures - Ensures consistent display during crypto 24/7 trading - Improves backtesting reliability ## Best Practices 1. **Pair with Price Action:** Use alongside HTF price candles indicator for complete picture 2. **Start Simple:** Enable 2-3 timeframes initially (e.g., 15m, 1H, 4H), add more as needed 3. **Match Settings:** Use same candle width/spacing as companion price indicator for visual alignment 4. **Scale Appropriately:** - Use **Global scale** (Method 2) when comparing timeframes - Use **Per-HTF scale** (Method 1) for pattern analysis within each timeframe - Use **Manual scale** (Method 3) for consistent day-to-day comparison 5. **Watch for Volume Clusters:** High volume appearing simultaneously across multiple HTFs signals significant market events 6. **Confirm Breakouts:** Always check if volume supports the price movement across higher timeframes 7. **Extended Hours:** Keep "Fix gaps" enabled for 24/7 markets (Forex, Crypto) and weekend analysis ## Technical Notes - **Timezone:** All calculations use America/New_York timezone for consistency - **Real-time Updates:** Volume and timers update on each tick during market hours - **Performance:** Optimized with max_bars_back=5000 for extensive historical analysis - **Compatibility:** Works on all instruments with volume data (Stocks, Forex, Crypto, Futures) - **Gap Handling:** Uses `barmerge.gaps_off` to fill data gaps during non-trading periods - **Backtesting:** Uses `lookahead=barmerge.lookahead_on` for stable historical data without repainting - **Data Continuity:** Automatically handles market closures, weekends, and extended hours ## Updates & Improvements **Version 2.0 (Current):** - ✅ Fixed alignment issues during extended hours and weekends - ✅ Eliminated repainting in backtesting - ✅ Added gap-filling technology for continuous display - ✅ Improved data synchronization across all timeframes - ✅ Enhanced NA value handling for data integrity - ✅ Added advanced settings group for user control ## Support For questions, suggestions, or feedback, please comment on the publication or message the author. --- **Disclaimer:** This indicator is for educational and informational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always perform your own analysis and implement proper risk management before making trading decisions.
Chỉ báo Pine Script®
của Pitt4
RSI Donchian Channel [DCAUT]█ RSI Donchian Channel 📊 ORIGINALITY & INNOVATION The RSI Donchian Channel represents an important synthesis of two complementary analytical frameworks: momentum oscillators and breakout detection systems. This indicator addresses a common limitation in traditional RSI analysis by replacing fixed overbought/oversold thresholds with adaptive zones derived from historical RSI extremes. Key Enhancement: Traditional RSI analysis relies on static threshold levels (typically 30/70), which may not adequately reflect changing market volatility regimes. This indicator adapts the reference zones dynamically based on the actual RSI behavior over the lookback period, helping traders identify meaningful momentum extremes relative to recent price action rather than arbitrary fixed levels. The implementation combines the proven momentum measurement capabilities of RSI with Donchian Channel's breakout detection methodology, creating a framework that identifies both momentum exhaustion points and potential continuation signals through the same analytical lens. 📐 MATHEMATICAL FOUNDATION Core Calculation Process: Step 1: RSI Calculation The Relative Strength Index measures momentum by comparing the magnitude of recent gains to recent losses: Calculate price changes between consecutive periods Separate positive changes (gains) from negative changes (losses) Apply selected smoothing method (RMA standard, also supports SMA, EMA, WMA) to both gain and loss series Compute Relative Strength (RS) as the ratio of smoothed gains to smoothed losses Transform RS into bounded 0-100 scale using the formula: RSI = 100 - (100 / (1 + RS)) Step 2: Donchian Channel Application The Donchian Channel identifies the highest and lowest RSI values within the specified lookback period: Upper Channel: Highest RSI value over the lookback period, represents the recent momentum peak Lower Channel: Lowest RSI value over the lookback period, represents the recent momentum trough Middle Channel (Basis): Average of upper and lower channels, serves as equilibrium reference Channel Width Dynamics: The distance between upper and lower channels reflects RSI volatility. Wide channels indicate high momentum variability, while narrow channels suggest momentum consolidation and potential breakout preparation. The indicator monitors channel width over a 100-period window to identify squeeze conditions that often precede significant momentum shifts. 📊 COMPREHENSIVE SIGNAL ANALYSIS Primary Signal Categories: Breakout Signals: Upper Breakout: RSI crosses above the upper channel, indicates momentum reaching new relative highs and potential trend continuation, particularly significant when accompanied by price confirmation Lower Breakout: RSI crosses below the lower channel, suggests momentum reaching new relative lows and potential trend exhaustion or reversal setup Breakout strength is enhanced when the channel is narrow prior to the breakout, indicating a transition from consolidation to directional movement Mean Reversion Signals: Upper Touch Without Breakout: RSI reaches the upper channel but fails to break through, may indicate momentum exhaustion and potential reversal opportunity Lower Touch Without Breakout: RSI reaches the lower channel without breakdown, suggests potential bounce as momentum reaches oversold extremes Return to Basis: RSI moving back toward the middle channel after touching extremes signals momentum normalization Trend Strength Assessment: Sustained Upper Channel Riding: RSI consistently remains near or above the upper channel during strong uptrends, indicates persistent bullish momentum Sustained Lower Channel Riding: RSI stays near or below the lower channel during strong downtrends, reflects persistent bearish pressure Basis Line Position: RSI position relative to the middle channel helps identify the prevailing momentum bias Channel Compression Patterns: Squeeze Detection: Channel width narrowing to 100-period lows indicates momentum consolidation, often precedes significant directional moves Expansion Phase: Channel widening after a squeeze confirms the initiation of a new momentum regime Persistent Narrow Channels: Extended periods of tight channels suggest market indecision and accumulation/distribution phases 🎯 STRATEGIC APPLICATIONS Trend Continuation Strategy: This approach focuses on identifying and trading momentum breakouts that confirm established trends: Identify the prevailing price trend using higher timeframe analysis or trend-following indicators Wait for RSI to break above the upper channel in uptrends (or below the lower channel in downtrends) Enter positions in the direction of the breakout when price action confirms the momentum shift Place protective stops below the recent swing low (long positions) or above swing high (short positions) Target profit levels based on prior swing extremes or use trailing stops to capture extended moves Exit when RSI crosses back through the basis line in the opposite direction Mean Reversion Strategy: This method capitalizes on momentum extremes and subsequent corrections toward equilibrium: Monitor for RSI reaching the upper or lower channel boundaries Look for rejection signals (price reversal patterns, volume divergence) when RSI touches the channels Enter counter-trend positions when RSI begins moving back toward the basis line Use the basis line as the initial profit target for mean reversion trades Implement tight stops beyond the channel extremes to limit risk on failed reversals Scale out of positions as RSI approaches the basis line and closes the position when RSI crosses the basis Breakout Preparation Strategy: This approach positions traders ahead of potential volatility expansion from consolidation phases: Identify squeeze conditions when channel width reaches 100-period lows Monitor price action for consolidation patterns (triangles, rectangles, flags) during the squeeze Prepare conditional orders for breakouts in both directions from the consolidation Enter positions when RSI breaks out of the narrow channel with expanding width Use the channel width expansion as a confirmation signal for the breakout's validity Manage risk with stops just inside the opposite channel boundary Multi-Timeframe Confluence Strategy: Combining RSI Donchian Channel analysis across multiple timeframes can improve signal reliability: Identify the primary trend direction using a higher timeframe RSI Donchian Channel (e.g., daily or weekly) Use a lower timeframe (e.g., 4-hour or hourly) to time precise entry points Enter long positions when both timeframes show RSI above their respective basis lines Enter short positions when both timeframes show RSI below their respective basis lines Avoid trades when timeframes provide conflicting signals (e.g., higher timeframe below basis, lower timeframe above) Exit when the higher timeframe RSI crosses its basis line in the opposite direction Risk Management Guidelines: Effective risk management is essential for all RSI Donchian Channel strategies: Position Sizing: Calculate position sizes based on the distance between entry point and stop loss, limiting risk to 1-2% of capital per trade Stop Loss Placement: For breakout trades, place stops just inside the opposite channel boundary; for mean reversion trades, use stops beyond the channel extremes Profit Targets: Use the basis line as a minimum target for mean reversion trades; for trend trades, target prior swing extremes or use trailing stops Channel Width Context: Increase position sizes during narrow channels (lower volatility) and reduce sizes during wide channels (higher volatility) Correlation Awareness: Monitor correlations between traded instruments to avoid over-concentration in similar setups 📋 DETAILED PARAMETER CONFIGURATION RSI Source: Defines the price data series used for RSI calculation: Close (Default): Standard choice providing end-of-period momentum assessment, suitable for most trading styles and timeframes High-Low Average (HL2): Reduces the impact of closing auction dynamics, useful for markets with significant end-of-day volatility High-Low-Close Average (HLC3): Provides a more balanced view incorporating the entire period's range Open-High-Low-Close Average (OHLC4): Offers the most comprehensive price representation, helpful for identifying overall period sentiment Strategy Consideration: Use Close for end-of-period signals, HL2 or HLC3 for intraday volatility reduction, OHLC4 for capturing full period dynamics RSI Length: Controls the number of periods used for RSI calculation: Short Periods (5-9): Highly responsive to recent price changes, produces more frequent signals with increased false signal risk, suitable for short-term trading and volatile markets Standard Period (14): Widely accepted default balancing responsiveness with stability, appropriate for swing trading and intermediate-term analysis Long Periods (21-28): Produces smoother RSI with fewer signals but more reliable trend identification, better for position trading and reducing noise in choppy markets Optimization Approach: Test different lengths against historical data for your specific market and timeframe, consider using longer periods in ranging markets and shorter periods in trending markets RSI MA Type: Determines the smoothing method applied to price changes in RSI calculation: RMA (Relative Moving Average - Default): Wilder's original smoothing method providing stable momentum measurement with gradual response to changes, maintains consistency with classical RSI interpretation SMA (Simple Moving Average): Treats all periods equally, responds more quickly to changes than RMA but may produce more whipsaws in volatile conditions EMA (Exponential Moving Average): Weights recent periods more heavily, increases responsiveness at the cost of potential noise, suitable for traders prioritizing early signal generation WMA (Weighted Moving Average): Applies linear weighting favoring recent data, offers a middle ground between SMA and EMA responsiveness Selection Guidance: Maintain RMA for consistency with traditional RSI analysis, use EMA or WMA for more responsive signals in fast-moving markets, apply SMA for maximum simplicity and transparency DC Length: Specifies the lookback period for Donchian Channel calculation on RSI values: Short Periods (10-14): Creates tight channels that adapt quickly to changing momentum conditions, generates more frequent trading signals but increases sensitivity to short-term RSI fluctuations Standard Period (20): Balances channel responsiveness with stability, aligns with traditional Bollinger Bands and moving average periods, suitable for most trading styles Long Periods (30-50): Produces wider, more stable channels that better represent sustained momentum extremes, reduces signal frequency while improving reliability, appropriate for position traders and higher timeframes Calibration Strategy: Match DC length to your trading timeframe (shorter for day trading, longer for swing trading), test channel width behavior during different market regimes, consider using adaptive periods that adjust to volatility conditions Market Adaptation: Use shorter DC lengths in trending markets to capture momentum shifts earlier, apply longer periods in ranging markets to filter noise and focus on significant extremes Parameter Combination Recommendations: Scalping/Day Trading: RSI Length 5-9, DC Length 10-14, EMA or WMA smoothing for maximum responsiveness Swing Trading: RSI Length 14, DC Length 20, RMA smoothing for balanced analysis (default configuration) Position Trading: RSI Length 21-28, DC Length 30-50, RMA or SMA smoothing for stable signals High Volatility Markets: Longer RSI periods (21+) with standard DC length (20) to reduce noise Low Volatility Markets: Standard RSI length (14) with shorter DC length (10-14) to capture subtle momentum shifts 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Adaptive Threshold Mechanism: Unlike traditional RSI analysis with fixed 30/70 thresholds, this indicator's Donchian Channel approach provides several improvements: Context-Aware Extremes: Overbought/oversold levels adjust automatically based on recent momentum behavior rather than arbitrary fixed values Volatility Adaptation: In low volatility periods, channels narrow to reflect tighter momentum ranges; in high volatility, channels widen appropriately Market Regime Recognition: The indicator implicitly adapts to different market conditions without manual threshold adjustments False Signal Reduction: Adaptive channels help reduce premature reversal signals that often occur with fixed thresholds during strong trends Signal Quality Characteristics: The indicator's dual-purpose design provides distinct advantages for different trading objectives: Breakout Trading: Channel boundaries offer clear, objective breakout levels that update dynamically, eliminating the ambiguity of when momentum becomes "too high" or "too low" Mean Reversion: The basis line provides a natural profit target for reversion trades, representing the midpoint of recent momentum extremes Trend Strength: Persistent channel boundary riding offers an objective measure of trend strength without additional indicators Consolidation Detection: Channel width analysis provides early warning of potential volatility expansion from compression phases Comparative Analysis: When compared to traditional RSI implementations and other momentum frameworks: vs. Fixed Threshold RSI: Provides market-adaptive reference levels rather than static values, helping to reduce false signals during trending markets where RSI can remain "overbought" or "oversold" for extended periods vs. RSI Bollinger Bands: Offers clearer breakout signals and more intuitive extreme identification through actual high/low boundaries rather than statistical standard deviations vs. Stochastic Oscillator: Maintains RSI's momentum measurement advantages (unbounded calculation avoiding scale compression) while adding the breakout detection capabilities of Donchian Channels vs. Standard Donchian Channels: Applies breakout methodology to momentum space rather than price, providing earlier signals of potential trend changes before price breakouts occur Performance Characteristics: The indicator exhibits specific behavioral patterns across different market conditions: Trending Markets: Excels at identifying momentum continuation through channel breakouts, RSI tends to ride one channel boundary during strong trends, providing trend confirmation Ranging Markets: Channel width narrows during consolidation, offering early preparation signals for potential breakout trading opportunities High Volatility: Channels widen to reflect increased momentum variability, automatically adjusting signal sensitivity to match market conditions Low Volatility: Channels contract, making the indicator more sensitive to subtle momentum shifts that may be significant in calm market environments Transition Periods: Channel squeezes often precede major trend changes, offering advance warning of potential regime shifts Limitations and Considerations: Users should be aware of certain operational characteristics: Lookback Dependency: Channel boundaries depend entirely on the lookback period, meaning the indicator has no predictive element beyond identifying current momentum relative to recent history Lag Characteristics: As with all moving average-based indicators, RSI calculation introduces lag, and channel boundaries update only as new extremes occur within the lookback window Range-Bound Sensitivity: In extremely tight ranges, channels may become very narrow, potentially generating excessive signals from minor momentum fluctuations Trending Persistence: During very strong trends, RSI may remain at channel extremes for extended periods, requiring patience for mean reversion setups or commitment to trend-following approaches No Absolute Levels: Unlike traditional RSI, this indicator provides no fixed reference points (like 50), making it less suitable for strategies that depend on absolute momentum readings USAGE NOTES This indicator is designed for technical analysis and educational purposes to help traders understand momentum dynamics and identify potential trading opportunities. The RSI Donchian Channel has limitations and should not be used as the sole basis for trading decisions. Important considerations: Performance varies significantly across different market conditions, timeframes, and instruments Historical signal patterns do not guarantee future results, as market behavior continuously evolves Effective use requires understanding of both RSI momentum principles and Donchian Channel breakout concepts Risk management practices (stop losses, position sizing, diversification) are essential for any trading application Consider combining with additional analytical tools such as volume analysis, price action patterns, or trend indicators for confirmation Backtest thoroughly on your specific instruments and timeframes before live trading implementation Be aware that optimization on historical data may lead to curve-fitting and poor forward performance The indicator performs best when used as part of a comprehensive trading methodology that incorporates multiple forms of market analysis, sound risk management, and realistic expectations about win rates and drawdowns.
Chỉ báo Pine Script®
của DCAUT
Extreme Pressure Zones Indicator (EPZ) [BullByte]Extreme Pressure Zones Indicator(EPZ) The Extreme Pressure Zones (EPZ) Indicator is a proprietary market analysis tool designed to highlight potential overbought and oversold "pressure zones" in any financial chart. It does this by combining several unique measurements of price action and volume into a single, bounded oscillator (0–100). Unlike simple momentum or volatility indicators, EPZ captures multiple facets of market pressure: price rejection, trend momentum, supply/demand imbalance, and institutional (smart money) flow. This is not a random mashup of generic indicators; each component was chosen and weighted to reveal extreme market conditions that often precede reversals or strong continuations. What it is? EPZ estimates buying/selling pressure and highlights potential extreme zones with a single, bounded 0–100 oscillator built from four normalized components. Context-aware weighting adapts to volatility, trendiness, and relative volume. Visual tools include adaptive thresholds, confirmed-on-close extremes, divergence, an MTF dashboard, and optional gradient candles. Purpose and originality (not a mashup) Purpose: Identify when pressure is building or reaching potential extremes while filtering noise across regimes and symbols. Originality: EPZ integrates price rejection, momentum cascade, pressure distribution, and smart money flow into one bounded scale with context-aware weighting. It is not a cosmetic mashup of public indicators. Why a trader might use EPZ EPZ provides a multi-dimensional gauge of market extremes that standalone indicators may miss. Traders might use it to: Spot Reversals: When EPZ enters an "Extreme High" zone (high red), it implies selling pressure might soon dominate. This can hint at a topside reversal or at least a pause in rallies. Conversely, "Extreme Low" (green) can highlight bottom-fish opportunities. The indicator's divergence module (optional) also finds hidden bullish/bearish divergences between price and EPZ, a clue that price momentum is weakening. Measure Momentum Shifts: Because EPZ blends momentum and volume, it reacts faster than many single metrics. A rising MPO indicates building bullish pressure, while a falling MPO shows increasing bearish pressure. Traders can use this like a refined RSI: above 50 means bullish bias, below 50 means bearish bias, but with context provided by the thresholds. Filter Trades: In trend-following systems, one could require EPZ to be in the bullish (green) zone before taking longs, or avoid new trades when EPZ is extreme. In mean-reversion systems, one might specifically look to fade extremes flagged by EPZ. Multi-Timeframe Confirmation: The dashboard can fetch a higher timeframe EPZ value. For example, you might trade a 15-minute chart only when the 60-minute EPZ agrees on pressure direction. Components and how they're combined Rejection (PRV) – Captures price rejection based on candle wicks and volume (see Price Rejection Volume). Momentum Cascade (MCD) – Blends multiple momentum periods (3,5,8,13) into a normalized momentum score. Pressure Distribution (PDI) – Measures net buy/sell pressure by comparing volume on up vs down candles. Smart Money Flow (SMF) – An adaptation of money flow index that emphasizes unusual volume spikes. Each of these components produces a 0–100 value (higher means more bullish pressure). They are then weighted and averaged into the final Market Pressure Oscillator (MPO), which is smoothed and scaled. By combining these four views, EPZ stands out as a comprehensive pressure gauge – the whole is greater than the sum of parts Context-aware weighting: Higher volatility → more PRV weight Trendiness up (RSI of ATR > 25) → more MCD weight Relative volume > 1.2x → more PDI weight SMF holds a stable weight The weighted average is smoothed and scaled into MPO ∈ with 50 as the neutral midline. What makes EPZ stand out Four orthogonal inputs (price action, momentum, pressure, flow) unified in a single bounded oscillator with consistent thresholds. Adaptive thresholds (optional) plus robust extreme detection that also triggers on crossovers, so static thresholds work reliably too. Confirm Extremes on Bar Close (default ON): dots/arrows/labels/alerts print on closed bars to avoid repaint confusion. Clean dashboard, divergence tools, pre-alerts, and optional on-price gradients. Visual 3D layering uses offsets for depth only,no lookahead. Recommended markets and timeframes Best: liquid symbols (index futures, large-cap equities, major FX, BTC/ETH). Timeframes: 5–15m (more signals; consider higher thresholds), 1H–4H (balanced), 1D (clear regimes). Use caution on illiquid or very low TFs where wick/volume geometry is erratic. Logic and thresholds MPO ∈ ; 50 = neutral. Above 50 = bullish pressure; below 50 = bearish. Static thresholds (defaults): thrHigh = 70, thrLow = 30; warning bands 5 pts inside extremes (65/35). Adaptive thresholds (optional): thrHigh = min(BaseHigh + 5, mean(MPO,100) + stdev(MPO,100) × ExtremeSensitivity) thrLow = max(BaseLow − 5, mean(MPO,100) − stdev(MPO,100) × ExtremeSensitivity) Extreme detection High: MPO ≥ thrHigh with peak/slope or crossover filter. Low: MPO ≤ thrLow with trough/slope or crossover filter. Cooldown: 5 bars (default). A new extreme will not print until the cooldown elapses, even if MPO re-enters the zone. Confirmation "Confirm Extremes on Bar Close" (default ON) gates extreme markers, pre-alerts, and alerts to closed bars (non-repainting). Divergences Pivot-based bullish/bearish divergence; tags appear only after left/right bars elapse (lookbackPivot). MTF HTF MPO retrieved with lookahead_off; values can update intrabar and finalize at HTF close. This is disclosed and expected. Inputs and defaults (key ones) Core: Sensitivity=1.0; Analysis Period=14; Smoothing=3; Adaptive Thresholds=OFF. Extremes: Base High=70, Base Low=30; Extreme Sensitivity=1.5; Confirm Extremes on Bar Close=ON; Cooldown=5; Dot size Small/Tiny. Visuals: Heatmap ON; 3D depth optional; Strength bars ON; Pre-alerts OFF; Divergences ON with tags ON; Gradient candles OFF; Glow ON. Dashboard: ON; Position=Top Right; Size=Normal; MTF ON; HTF=60m; compact overlay table on price chart. Advanced caps: Max Oscillator Labels=80; Max Extreme Guide Lines=80; Divergence objects=60. Dashboard: what each element means Header: EPZ ANALYSIS. Large readout: Current MPO; color reflects state (extreme, approaching, or neutral). Status badge: "Extreme High/Low", "Approaching High/Low", "Bullish/Neutral/Bearish". HTF cell (when MTF ON): Higher-timeframe MPO, color-coded vs extremes; updates intrabar, settles at HTF close. Predicted (when MTF OFF): Simple MPO extrapolation using momentum/acceleration—illustrative only. Thresholds: Current thrHigh/thrLow (static or adaptive). Components: ASCII bars + values for PRV, MCD, PDI, SMF. Market metrics: Volume Ratio (x) and ATR% of price. Strength: Bar indicator of |MPO − 50| × 2. Confidence: Heuristic gauge (100 in extremes, 70 in warnings, 50 with divergence, else |MPO − 50|). Convenience only, not probability. How to read the oscillator MPO Value (0–100): A reading of 50 is neutral. Values above ~55 are increasingly bullish (green), while below ~45 are increasingly bearish (red). Think of these as "market pressure". Extreme Zones: When MPO climbs into the bright orange/red area (above the base-high line, default 70), the chart will display a dot and downward arrow marking that extreme. Traders often treat this as a sign to tighten stops or look for shorts. Similarly, a bright green dot/up-arrow appears when MPO falls below the base-low (30), hinting at a bullish setup. Heatmap/Candles: If "Pressure Heatmap" is enabled, the background of the oscillator pane will fade green or red depending on MPO. Users can optionally color the price candles by MPO value (gradient candles) to see these extremes on the main chart. Prediction Zone(optional): A dashed projection line extends the MPO forward by a small number of bars (prediction_bars) using current MPO momentum and acceleration. This is a heuristic extrapolation best used for short horizons (1–5 bars) to anticipate whether MPO may touch a warning or extreme zone. It is provisional and becomes less reliable with longer projection lengths — always confirm predicted moves with bar-close MPO and HTF context before acting. Divergences: When price makes a higher high but EPZ makes a lower high (bearish divergence), the indicator can draw dotted lines and a "Bear Div" tag. The opposite (lower low price, higher EPZ) gives "Bull Div". These signals confirm waning momentum at extremes. Zones: Warning bands near extremes; Extreme zones beyond thresholds. Crossovers: MPO rising through 35 suggests easing downside pressure; falling through 65 suggests waning upside pressure. Dots/arrows: Extreme markers appear on closed bars when confirmation is ON and respect the 5-bar cooldown. Pre-alert dots (optional): Proximity cues in warning zones; also gated to bar close when confirmation is ON. Histogram: Distance from neutral (50); highlights strengthening or weakening pressure. Divergence tags: "Bear Div" = higher price high with lower MPO high; "Bull Div" = lower price low with higher MPO low. Pressure Heatmap : Layered gradient background that visually highlights pressure strength across the MPO scale; adjustable intensity and optional zone overlays (warning / extreme) for quick visual scanning. A typical reading: If the oscillator is rising from neutral towards the high zone (green→orange→red), the chart may see strong buying culminating in a stall. If it then turns down from the extreme, that peak EPZ dot signals sell pressure. Alerts EPZ: Extreme Context — fires on confirmed extremes (respects cooldown). EPZ: Approaching Threshold — fires in warning zones if no extreme. EPZ: Divergence — fires on confirmed pivot divergences. Tip: Set alerts to "Once per bar close" to align with confirmation and avoid intrabar repaint. Practical usage ideas Trend continuation: In positive regimes (MPO > 50 and rising), pullbacks holding above 50 often precede continuation; mirror for bearish regimes. Exhaustion caution: E High/E Low can mark exhaustion risk; many wait for MPO rollover or divergence to time fades or partial exits. Adaptive thresholds: Useful on assets with shifting volatility regimes to maintain meaningful "extreme" levels. MTF alignment: Prefer setups that agree with the HTF MPO to reduce countertrend noise. Examples Screenshots captured in TradingView Replay to freeze the bar at close so values don't fluctuate intrabar. These examples use default settings and are reproducible on the same bars; they are for illustration, not cherry-picking or performance claims. Example 1 — BTCUSDT, 1h — E Low MPO closed at 26.6 (below the 30 extreme), printing a confirmed E Low. HTF MPO is 26.6, so higher-timeframe pressure remains bearish. Components are subdued (Momentum/Pressure/Smart$ ≈ 29–37), with Vol Ratio ≈ 1.19x and ATR% ≈ 0.37%. A prior Bear Div flagged weakening impulse into the drop. With cooldown set to 5 bars, new extremes are rate-limited. Many traders wait for MPO to curl up and reclaim 35 or for a fresh Bull Div before considering countertrend ideas; if MPO cannot reclaim 35 and HTF stays weak, treat bounces cautiously. Educational illustration only. Example 2 — ETHUSD, 30m — E High A strong impulse pushed MPO into the extreme zone (≥ 70), printing a confirmed E High on close. Shortly after, MPO cooled to ~61.5 while a Bear Div appeared, showing momentum lag as price pushed a higher high. Volume and volatility were elevated (≈ 1.79x / 1.25%). With a 5-bar cooldown, additional extremes won't print immediately. Some treat E High as exhaustion risk—either waiting for MPO rollover under 65/50 to fade, or for a pullback that holds above 50 to re-join the trend if higher-timeframe pressure remains constructive. Educational illustration only. Known limitations and caveats The MPO line itself can change intrabar; extreme markers/alerts do not repaint when "Confirm Extremes on Bar Close" is ON. HTF values settle at the close of the HTF bar. Illiquid symbols or very low TFs can be noisy; consider higher thresholds or longer smoothing. Prediction line (when enabled) is a visual extrapolation only. For coders Pine v6. MTF via request.security with lookahead_off. Extremes include crossover triggers so static thresholds also yield E High/E Low. Extreme markers and pre-alerts are gated by barstate.isconfirmed when confirmation is ON. Arrays prune oldest objects to respect resource limits; defaults (80/80/60) are conservative for low TFs. 3D layering uses negative offsets purely for drawing depth (no lookahead). Screenshot methodology: To make labels legible and to demonstrate non-repainting behavior, the examples were captured in TradingView Replay with "Confirm Extremes on Bar Close" enabled. Replay is used only to freeze the bar at close so plots don't change intrabar. The examples use default settings, include both Extreme Low and Extreme High cases, and can be reproduced by scrolling to the same bars outside Replay. This is an educational illustration, not a performance claim. Disclaimer This script is for educational purposes only and does not constitute financial advice. Markets involve risk; past behavior does not guarantee future results. You are responsible for your own testing, risk management, and decisions.
Chỉ báo Pine Script®
của BullByte
16
Dr.Yazdani V063 Session OR + A-Lines **ACD Indicator: Mark Fisher's Opening Range Breakout Strategy** **Overview** The ACD system, developed by legendary trader Mark Fisher in his book *The Logical Trader*, is a powerful methodology for identifying high-probability trade setups based on the market's opening range (OR). This indicator automates Layers 1 and 2 of the ACD strategy, helping you spot breakout opportunities, trend direction, and key support/resistance levels. Perfect for day traders, scalpers, and swing traders in forex, stocks, futures, or crypto. **How It Works** 1. **Opening Range (OR)**: Calculated from the high/low of the first X minutes (default: 30-60 min) of major sessions (e.g., Tokyo, London, New York). 2. **A Levels**: Drawn at a percentage (default: 0.5% of OR range or ATR-based) above/below the OR. A breakout above A-Up signals a bullish setup; below A-Down signals bearish. 3. **C Levels**: Wider levels (default: 1-2% or ATR multiplier) for stronger confirmation. Breakouts here confirm trend strength and filter fakeouts. 4. **Pivot Ranges**: Includes daily and N-day pivots to gauge overall market bias (above pivots = bullish; below = bearish). **Key Features** - **Customizable Sessions**: Tokyo (00:00-01:00 GMT), London (08:00-09:00 GMT), New York (13:30-14:30 GMT) – adjustable. - **ATR Integration**: Uses Average True Range for dynamic A/C levels (period: 14 by default). - **Visual Alerts**: Color-coded lines (green for bullish, red for bearish) + optional labels for breakouts. - **Pivot Display**: Show/hide daily or multi-day pivots with customizable colors. - **Risk Management**: Built-in stop-loss suggestions based on OR width. **Trading Rules** - **Bullish Setup**: Price breaks and holds above A-Up → Enter long at C-Up confirmation. Target: Next pivot or 1:2 risk-reward. - **Bearish Setup**: Price breaks below A-Down → Enter short at C-Down. - **Avoid Fakeouts**: Wait for stabilization (e.g., close above/below level). - **Trend Filter**: Combine with PMA (Pivot Moving Average) for Layer 3 confirmation (search "ACD PMA" in TradingView). **Settings Guide** - **OR Timeframe**: Session start time and duration (e.g., 30 min). - **A Multiplier (%)**: Distance for A levels (default: 0.5). - **C Multiplier (%)**: Distance for C levels (default: 1.0). - **ATR Period**: For volatility-based levels (default: 14). - **Show Pivots**: Toggle daily/N-day ranges. This indicator balances supply/demand by analyzing volume and price action within the opening range. Backtest on your favorite pairs (e.g., EURUSD, BTCUSD) and adjust for your style. Not financial advice – always use proper risk management! **Inspired by**: Mark Fisher's ACD Methodology. Open-source for community review. Questions? Comment below! #ACD #OpeningRange #Breakout #DayTrading #FisherStrategy
Chỉ báo Pine Script®
của mortezagafti
Tape Speed Pulse (Pace + Direction) [v6 + Climax]Tape Speed Pulse (Pace + Direction) One-liner: A lightweight “tape pulse” that turns intraday bursts of buying/selling into an easy-to-read histogram, with surge, slowdown, and climax (exhaustion) markers for fast decision-making. Use on sec and min charts. What it measures Pace (RVOL): current bar volume vs the recent average (smoothed). Direction proxy: uptick/downtick by comparing close to close . Pulse (histogram): direction × pace, so you see who’s pushing and how fast. Colors - Lime = Buy surge (pace ≥ threshold & upticking) - Red = Sell surge (pace ≥ threshold & downticking) - Teal = Buy pressure, sub-threshold - Orange = Sell pressure, sub-threshold - Faded/gray = Near-neutral pace (below the Neutral Band) Lines (toggleable) -White = Pace (RVOL) - Yellow = Slowdown line = a drop of X% from the last 30-bar peak pace Background tint mirrors the current state so you can glance risk: greenish for buy pressure, reddish for sell pressure. Signals & alerts - BUY surge – fires when pace crosses above the surge threshold with uptick direction (optional acceleration & uptick streak filters; cooldown prevents spam). - SELL surge – mirror logic to downside. - Slowdown – fires when pace crosses below the yellow slowdown line while direction ≤ 0 (early fade warning). Climax (exhaustion) - Buy Climax: previous bar was a buy surge with a large upper wick; current bar slows (below slowdown line) and direction ≤ 0. - Sell Climax: mirror (large lower wick → slowdown → direction ≥ 0). - Great for trimming/tight stops or fade setups at obvious spikes. - Create alerts via Add alert → Condition: this indicator → choose the specific alert (BUY surge, SELL surge, Slowdown, Buy Climax, Sell Climax). How to use it (playbook) - Longs (e.g., VWAP reclaim / micro pullback) - Only take entries when the pulse is teal→lime (buy pressure to buy surge). - Into prior highs/VWAP bands, take partials on lime spikes. - If you get a Slowdown dot and bars turn orange/red, tighten or exit. Shorts (failed reclaim / lower-high) - Look for teal→orange→red with rising pace at a level. - Add confidence if a Buy Climax printed right before (exhaustion). - Risk above the spike; don’t fight true ignitions out of bases. Simple guardrails - Avoid new longs when the histogram is orange/red; avoid new shorts when teal/lime. - Use with VWAP + 9/20 EMA or your levels. The pulse is confirmation, not the whole thesis. Inputs (what they do & when to tweak) - Pace lookback (bars) – window for average volume. Lower = faster; higher = steadier. Too jumpy? raise it. Missing quick bursts? lower it. - Smoothing EMA (bars) – smooths pace. Higher = calmer. Use 4–6 during the open; 3–4 midday. - Surge threshold (× RVOL) – how fast counts as a surge. Too many surges? raise it. Too late? lower it slightly. - Slowdown drop from 30-bar max (%) – how far below the recent peak pace to call a slowdown. Higher % = later slowdown; lower % = earlier warning. - Neutral band (× RVOL) – paces below this fade to gray. Raise to clean up noise; lower to see subtle pressure. - Min seconds between signals – cooldown to prevent spam. Increase in chop; reduce if you want more pings. - BUY/SELL: min consecutive upticks/downticks – tiny streak filter. Raise to avoid wiggles; lower for earlier signals. Require pace accelerating into signal – ON = avoid stall breakouts; OFF = earlier pings. Climax options: wick % threshold & “require slowdown cross”. Raise wick% / require cross to be stricter; lower to catch more fades. Quick presets - Low-float runner, 5–10s chart - Lookback 20, Smoothing 3–4, Surge 2.2–2.8, Slowdown 35–45, Neutral 1.0–1.2, Cooldown 15–25s, Streaks 2–3, Accel ON. - Thick large-cap, 1-min - Lookback 20–30, Smoothing 5–7, Surge 1.5–1.9, Slowdown 25–35, Neutral 0.8–1.0, Cooldown 30–60s, Streaks 2, Accel ON. - Open vs Midday vs Power Hour - Open: higher Surge, more Smoothing, longer Cooldown. - Midday: lower Surge, less Smoothing to catch subtler pushes. - Power hour: moderate Surge; keep Slowdown on for exits. Reading common patterns - Ignition (likely continuation): lime spike out of a base that holds above a level while pace stays above yellow. - Exhaustion (likely fade): lime spike late in a run with upper wick → Slowdown → orange/red. The Buy Climax diamond is your tell. Limits / notes This is an OHLCV-based proxy (TradingView Pine can’t read raw tape/DOM). It won’t match Bookmap/Jigsaw tick-for-tick, but it’s fast and objective. Use with levels and a risk plan. Past performance ≠ future results. Educational only.
Chỉ báo Pine Script®
của THEBUNKER27
1
Weinstein Stage Analyzer — Table Only (more padding)What it does This indicator applies Stan Weinstein’s Stage Analysis (Stages 1–4) and presents the result in a clean, compact table only—no lines, labels, or overlays. It shows: • Previous Stage • Current Stage (with Early / Mature / Late tag) • Duration (how long price has been in the current stage, in HTF bars) • Sentiment (Bullish / Bearish / Balanced / Cautious, derived from stage & maturity) Timeframe-aware logic • Weekly charts: classic 30-period MA (Weinstein’s original 30-week concept). • Daily & Intraday: computed on Daily 150 as a practical daily translation of the 30-week idea. • Monthly: ~7-period MA (~30 weeks ≈ 7 months). The stage classification itself is evaluated on this HTF context and then displayed on your active chart. EMA/SMA toggle Choose EMA (default) or SMA for the trend line used in stage detection. How stages are decided (practical rules) • Stage 2 (Advance): MA rising with price above an upper band. • Stage 4 (Decline): MA falling with price below a lower band. • Flat MA zones become Stage 1 (Base) or Stage 3 (Top) depending on the prior trend. “Maturity” tags (Early/Mature/Late) come from run length and extension beyond the band. Inputs you can tweak • MA Type: EMA / SMA • Price Band (±%) and Slope Threshold to tighten/loosen stage flips • Maturity thresholds: min/max bars & late-extension % Notes • Duration is for the entire current stage (e.g., total time in Stage 4), not just the maturity slice. • A Top Padding Rows input is included to nudge the table lower if it overlaps your OHLC readout. Disclaimer For educational use only. Not financial advice. Always confirm with your own analysis, risk management, and market context.
Chỉ báo Pine Script®
của gautam_tradingipad
2
Premarket Hi/Lo + Prior Day O/C LevelsPremarket Hi/Lo + Prior Day O/C (today only) shows four clear reference levels for the current regular trading session: the Premarket High and Premarket Low (taken from a user-defined premarket window, 04:00–09:30 by default) and Yesterday’s 09:30 Open and 15:59 Close (sourced from the 1-minute feed for accuracy). The premarket levels “lock” at the opening bell so they don’t move for the rest of the day. All four lines are displayed only during today’s regular hours to keep the chart focused. Small right-edge labels and an optional top-right mini-table show the exact values at a glance. This indicator is designed to give immediate context without technical jargon. The premarket high/low summarize where price traveled before the bell; the prior-day open/close summarize where the last session began and ended. Checking whether price is above or below these markers helps you quickly judge strength or weakness and anticipate where price may pause, bounce, or break. Typical uses include watching for a clean break and hold above Premarket High (often bullish), a break and hold below Premarket Low (often bearish), drift back toward Prior Day Close after a gap (a common “magnet”), and flips around Prior Day Open that can lead to continuation. Setup: Turn on Extended Hours in TradingView so premarket bars are visible (Chart Settings → Symbol → Extended Hours). Apply the indicator to any intraday timeframe. In Inputs, you can change the premarket window to match your market, adjust colors and line widths, and toggle the floating labels and the mini-table. Times use the chart’s exchange time (for US stocks, Eastern Time). Notes and limits: Lines show only for today’s session (default 09:30–16:00). The script looks at the previous calendar day for “prior day,” so values may be empty after weekends or holidays when markets were closed. If your instrument uses different regular hours or you trade futures/crypto, adjust the premarket session in Inputs and—if needed—edit the regular-hours window in code to match. If your data source does not include premarket, the premarket lines will be blank. Best practice: The first 15–30 minutes after the open are where these levels have the most impact. Reactions are more meaningful when a line aligns with another tool you use (e.g., VWAP or your opening range). If price does not react clearly at a line, avoid forcing a trade.
Chỉ báo Pine Script®
của Blk0ut
RSI ADX Bollinger Analysis High-level purpose and design philosophy This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management. ________________________________________ What each indicator contributes (component-by-component) RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side. ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates. Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move. Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy. 4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results. 5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results. These exercises teach both statistical thinking and the specific failure modes of the mashup. ________________________________________ Limitations, failure modes and caveats (explicit & teachable) • ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions. • Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof. • RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns. • Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios. Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent. ________________________________________ Originality & mashup justification (text you can paste) This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength. ________________________________________ TradingView publication guidance & compliance checklist To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code): 1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter). 2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each. 3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.” 4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size). 5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest). 6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits. 7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes. 8. Trademark clause: If you used trademark symbols, remove or provide registration proof. 9. Risk disclaimer: Add the copy-ready disclaimer below. This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning. ________________________________________ Copy-ready short publication description (paste into TradingView) Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity. ________________________________________ Copy-ready risk & misuse disclaimer (paste into description or help file) This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls. ________________________________________ Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
Chỉ báo Pine Script®
của aiTrendview-Charting-System
Smart Money Precision Structure [BullByte]Smart Money Precision Structure Advanced Market Structure Analysis Using Institutional Order Flow Concepts --- OVERVIEW Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis. --- PURPOSE AND ORIGINALITY Why This Indicator Was Developed • Addresses the gap between retail and institutional analysis methods • Consolidates multiple analysis techniques that professionals use separately • Automates complex market structure evaluation into actionable insights • Eliminates the need for multiple indicators by providing comprehensive analysis What Makes SMPS Original • Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering • Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns • Adaptive Intelligence - Parameters automatically adjust based on detected market conditions • Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100% • Structure Break Detection - Advanced pivot analysis identifies trend reversals early --- HOW IT WORKS - TECHNICAL METHODOLOGY 1. Market Regime Analysis Engine The indicator evaluates five core market dimensions: • Volatility Score - Measures current volatility against 50-period historical baseline • Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs • Momentum Score - Combines RSI divergence with MACD signal alignment • Structure Score - Evaluates pivot point formation clarity • Efficiency Score - Calculates directional movement efficiency ratio These scores combine to classify markets into five regimes: • TRENDING - Strong directional movement with aligned indicators • RANGING - Sideways movement with mixed directional signals • VOLATILE - Elevated volatility with unpredictable price swings • QUIET - Low volatility consolidation periods • TRANSITIONAL - Market shifting between different regimes 2. Market Structure Analysis Advanced pivot point analysis identifies: • Higher Highs and Higher Lows for bullish structure • Lower Highs and Lower Lows for bearish structure • Structure breaks when established patterns fail • Dynamic support and resistance from recent pivot points • Key level proximity detection using ATR-based buffers 3. Volume Flow Decoding Institutional activity detection through: • Volume surge identification when volume exceeds 2x average • Buy versus sell pressure analysis using price-volume correlation • Flow strength measurement through directional volume consistency • Divergence detection between volume and price movements • Institutional threshold alerts when unusual volume patterns emerge 4. Multi-Period Momentum Synthesis Weighted momentum calculation across four timeframes: • 1-period momentum weighted at 40% • 3-period momentum weighted at 30% • 5-period momentum weighted at 20% • 8-period momentum weighted at 10% Result smoothed with 6-period EMA for noise reduction. 5. Price Action Quality Assessment Each bar evaluated for: • Range quality relative to 20-period average • Body-to-range ratio for directional conviction • Wick analysis for rejection pattern identification • Pattern recognition including engulfing and hammer formations • Sequential price movement analysis 6. Adaptive Parameter System Parameters automatically adjust based on detected regime: • Trending markets reduce sensitivity and confirmation requirements • Volatile markets increase filtering and require additional confirmations • Ranging markets maintain neutral settings • Transitional markets use moderate adjustments --- COMPLETE SETTINGS GUIDE Section 1: Core Analysis Settings Analysis Sensitivity (0.3-2.0) • Default: 1.0 • Lower values require stronger price movements • Higher values detect more subtle patterns • Scalpers use 0.8-1.2, swing traders use 1.5-2.0 Noise Reduction Level (2-7) • Default: 4 • Controls filtering of false patterns • Higher values reduce pattern frequency • Increase in volatile markets Minimum Move % (0.05-0.50) • Default: 0.15% • Sets minimum price movement threshold • Adjust based on instrument volatility • Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50% High Confirmation Mode • Default: True (Enabled) • Requires all technical conditions to align • Reduces frequency but increases reliability • Disable for more aggressive pattern detection Section 2: Market Regime Detection Enable Regime Analysis • Default: True (Enabled) • Activates market environment evaluation • Essential for adaptive features • Keep enabled for best results Regime Analysis Period (20-100) • Default: 50 bars • Determines regime calculation lookback • Shorter for responsive, longer for stable • Scalping: 20-30, Swing: 75-100 Minimum Market Clarity (0.2-0.8) • Default: 0.4 • Quality threshold for pattern generation • Higher values require clearer conditions • Lower for more patterns, higher for quality Adaptive Parameter Adjustment • Default: True (Enabled) • Enables automatic parameter optimization • Adjusts based on market regime • Highly recommended to keep enabled Section 3: Market Structure Analysis Enable Structure Validation • Default: True (Enabled) • Validates patterns against support/resistance • Confirms trend structure alignment • Essential for reliability Structure Analysis Period (15-50) • Default: 30 bars • Period for structure pattern analysis • Affects support/resistance calculation • Match to your trading timeframe Minimum Structure Alignment (0.3-0.8) • Default: 0.5 • Required structure score for valid patterns • Higher values need stronger structure • Balance with desired frequency Section 4: Analysis Configuration Minimum Strength Level (3-5) • Default: 4 • Minimum confirmations for pattern display • 5 = Maximum reliability, 3 = More patterns • Beginners should use 4-5 Required Technical Confirmations (4-6) • Default: 5 • Number of aligned technical factors • Higher = fewer but better patterns • Works with High Confirmation Mode Pattern Separation (3-20 bars) • Default: 8 bars • Minimum bars between patterns • Prevents clustering and overtrading • Increase for cleaner charts Section 5: Technical Filters Momentum Validation • Default: True (Enabled) • Requires momentum alignment • Filters counter-trend patterns • Essential for trend following Volume Confluence Analysis • Default: True (Enabled) • Requires volume confirmation • Identifies institutional participation • Critical for reliability Trend Direction Filter • Default: True (Enabled) • Only shows patterns with trend • Reduces counter-trend signals • Disable for reversal hunting Section 6: Volume Flow Analysis Institutional Activity Threshold (1.2-3.5) • Default: 2.0 • Multiplier for unusual volume detection • Lower finds more institutional activity • Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5 Volume Surge Multiplier (1.8-4.5) • Default: 2.5 • Defines significant volume increases • Adjust per instrument characteristics • Higher for stocks, lower for forex Volume Flow Period (12-35) • Default: 18 bars • Smoothing for volume analysis • Shorter = responsive, longer = smooth • Match to timeframe used Section 7: Analysis Frequency Control Maximum Analysis Points Per Hour (1-5) • Default: 3 • Limits pattern frequency • Prevents overtrading • Scalpers: 4-5, Swing traders: 1-2 Section 8: Target Level Configuration Target Calculation Method • Default: Market Adaptive • Three modes available: - Fixed: Uses set point distances - Dynamic: ATR-based calculations - Market Adaptive: Structure-based levels Minimum Target/Risk Ratio (1.0-3.0) • Default: 1.5 • Minimum acceptable reward vs risk • Higher filters lower probability setups • Professional standard: 1.5-2.0 Fixed Mode Settings: • Fixed Target Distance: 50 points default • Fixed Invalidation Distance: 30 points default • Use for consistent instruments Dynamic Mode Settings: • Dynamic Target Multiplier: 1.8x ATR default • Dynamic Invalidation Multiplier: 1.0x ATR default • Adapts to volatility automatically Market Adaptive Settings: • Use Structure Levels: True (default) • Structure Level Buffer: 0.1% default • Places levels at actual support/resistance Section 9: Visual Display Settings Color Theme Options • Professional (Teal/Red) - Bullish: Teal (#26a69a) - Bearish: Red (#ef5350) - Neutral: Gray (#78909c) - Best for: Traditional traders, clean appearance • Dark (Neon Green/Pink) - Bullish: Neon Green (#00ff88) - Bearish: Hot Pink (#ff0044) - Neutral: Dark Gray (#333333) - Best for: Dark theme users, high contrast • Light (Green/Red Classic) - Bullish: Green (#4caf50) - Bearish: Red (#f44336) - Neutral: Light Gray (#9e9e9e) - Best for: Light backgrounds, traditional colors • Vibrant (Cyan/Magenta) - Bullish: Cyan (#00ffff) - Bearish: Magenta (#ff00ff) - Neutral: Medium Gray (#888888) - Best for: High visibility, modern appearance Dashboard Position • Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right • Default: Top Right • Choose based on chart layout preference Dashboard Size • Full: Complete information display (desktop) • Mobile: Compact view for small screens • Default: Full Analysis Display Style • Arrows : Simple directional markers • Labels : Detailed text information • Zones : Colored areas showing pattern regions • Default: Labels (most informative) Display Options: • Display Analysis Strength: Shows star rating • Display Target Levels: Shows target/invalidation lines • Display Market Regime: Shows regime in pattern labels --- HOW TO USE SMPS - DETAILED GUIDE Understanding the Dashboard Top Row - Header • SMPS Dashboard title • VALUE column: Current readings • STATUS column: Condition assessments Market Regime Row • Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL • Color coding: Green = Favorable, Red = Caution • Status: FAVORABLE or CAUTION trading conditions Market Score Row • Percentage from 0-100% • Above 60% = Strong conditions • 40-60% = Moderate conditions • Below 40% = Weak conditions Structure Row • Direction: BULLISH, BEARISH, or NEUTRAL • Status: INTACT or BREAK • Orange BREAK indicates structure failure Volume Flow Row • Direction: BUYING or SELLING • Intensity: STRONG or WEAK • Color indicates dominant pressure Momentum Row • Numerical momentum value • Positive = Upward pressure • Negative = Downward pressure Volume Status Row • INST = Institutional activity detected • HIGH = Above average volume • NORM = Normal volume levels Adaptive Mode Row • ACTIVE = Parameters adjusting • STATIC = Fixed parameters • Shows required confirmations Analysis Level Row • Minimum strength level setting • Pattern separation in bars Market State Row • Current analysis: BULLISH, BEARISH, NEUTRAL • Shows analysis price level when active T:R Ratio Row • Current target to risk ratio • GOOD = Meets minimum requirement • LOW = Below minimum threshold Strength Row • BULL or BEAR dominance • Numerical strength value 0-100 Price Row • Current price • Percentage change Last Analysis Row • Previous pattern direction • Bars since last pattern Reading Pattern Signals Bullish Structure Pattern • Upward triangle or "Bullish Structure" label • Star rating shows strength (★★★★★ = strongest) • Green line = potential target level • Red dashed line = invalidation level • Appears below price bars Bearish Structure Pattern • Downward triangle or "Bearish Structure" label • Star rating indicates reliability • Green line = potential target level • Red dashed line = invalidation level • Appears above price bars Pattern Strength Interpretation • ★★★★★ = 6 confirmations (exceptional) • ★★★★☆ = 5 confirmations (strong) • ★★★☆☆ = 4 confirmations (moderate) • ★★☆☆☆ = 3 confirmations (minimum) • Below minimum = filtered out Visual Elements on Chart Lines and Levels: • Gray Line = 21 EMA trend reference • Green Stepline = Dynamic support level • Red Stepline = Dynamic resistance level • Green Solid Line = Active target level • Red Dashed Line = Active invalidation level Pattern Markers: • Triangles = Arrow display mode • Text Labels = Label display mode • Colored Boxes = Zone display mode Target Completion Labels: • "Target" = Price reached target level • "Invalid" = Pattern invalidated by price --- RECOMMENDED USAGE BY TIMEFRAME 1-Minute Charts (Scalping) • Sensitivity: 0.8-1.2 • Noise Reduction: 3-4 • Pattern Separation: 3-5 bars • High Confirmation: Optional • Best for: Quick intraday moves 5-Minute Charts (Precision Intraday) • Sensitivity: 1.0 (default) • Noise Reduction: 4 (default) • Pattern Separation: 8 bars • High Confirmation: Enabled • Best for: Day trading 15-Minute Charts (Short Swing) • Sensitivity: 1.0-1.5 • Noise Reduction: 4-5 • Pattern Separation: 10-12 bars • High Confirmation: Enabled • Best for: Intraday swings 30-Minute to 1-Hour (Position Trading) • Sensitivity: 1.5-2.0 • Noise Reduction: 5-7 • Pattern Separation: 15-20 bars • Regime Period: 75-100 • Best for: Multi-day positions Daily Charts (Swing Trading) • Sensitivity: 1.8-2.0 • Noise Reduction: 6-7 • Pattern Separation: 20 bars • All filters enabled • Best for: Long-term analysis --- MARKET-SPECIFIC SETTINGS Forex Pairs • Minimum Move: 0.05-0.10% • Institutional Threshold: 1.5-2.0 • Volume Surge: 1.8-2.2 • Target Mode: Dynamic or Market Adaptive Stock Indices (ES, NQ, YM) • Minimum Move: 0.10-0.15% • Institutional Threshold: 2.0-2.5 • Volume Surge: 2.5-3.0 • Target Mode: Market Adaptive Individual Stocks • Minimum Move: 0.15-0.25% • Institutional Threshold: 2.0-2.5 • Volume Surge: 2.5-3.5 • Target Mode: Dynamic Cryptocurrency • Minimum Move: 0.20-0.50% • Institutional Threshold: 2.5-3.5 • Volume Surge: 3.0-4.5 • Target Mode: Dynamic • Increase noise reduction --- PRACTICAL APPLICATION EXAMPLES Example 1: Strong Trending Market Dashboard Reading: • Market Regime: TRENDING • Market Score: 75% • Structure: BULLISH, INTACT • Volume Flow: BUYING, STRONG • Momentum: +0.45 Interpretation: • Strong uptrend environment • Institutional buying present • Look for bullish patterns as continuation • Higher probability of success • Consider using lower sensitivity Example 2: Range-Bound Conditions Dashboard Reading: • Market Regime: RANGING • Market Score: 35% • Structure: NEUTRAL • Volume Flow: SELLING, WEAK • Momentum: -0.05 Interpretation: • No clear direction • Low opportunity environment • Patterns are less reliable • Consider waiting for regime change • Or switch to a range-trading approach Example 3: Structure Break Alert Dashboard Reading: • Previous: BULLISH structure • Current: Structure BREAK • Volume: INST flag active • Momentum: Shifting negative Interpretation: • Trend reversal potentially beginning • Institutional participation detected • Watch for bearish pattern confirmation • Adjust bias accordingly • Increase caution on long positions Example 4: Volatile Market Dashboard Reading: • Market Regime: VOLATILE • Market Score: 45% • Adaptive Mode: ACTIVE • Confirmations: Increased to 6 Interpretation: • Choppy conditions • Parameters auto-adjusted • Fewer but higher quality patterns • Wider stops may be needed • Consider reducing position size Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action. • Example 1 – Bullish Structure Detection on SOLUSD 5m • Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m --- TROUBLESHOOTING GUIDE No Patterns Appearing Check these settings: • High Confirmation Mode may be too restrictive • Minimum Strength Level may be too high • Market Clarity threshold may be too high • Regime filter may be blocking patterns • Try increasing sensitivity Too Many Patterns Adjust these settings: • Enable High Confirmation Mode • Increase Minimum Strength Level to 5 • Increase Pattern Separation • Reduce Sensitivity below 1.0 • Enable all technical filters Dashboard Shows "CAUTION" This indicates: • Market conditions are unfavorable • Regime is RANGING or QUIET • Market score is low • Consider waiting for better conditions • Or adjust expectations accordingly Patterns Not Reaching Targets Consider: • Market may be choppy • Volatility may have changed • Try Dynamic target mode • Reduce target/risk ratio requirement • Check if regime is VOLATILE --- ALERTS CONFIGURATION Alert Message Format Alerts include: • Pattern type (Bullish/Bearish) • Strength rating • Market regime • Analysis price level • Target and invalidation levels • Strength percentage • Target/Risk ratio • Educational disclaimer Setting Up Alerts • Click Alert button on TradingView • Select SMPS indicator • Choose alert frequency • Customize message if desired • Alerts fire on pattern detection --- DATA WINDOW INFORMATION The Data Window displays: • Market Regime Score (0-100) • Market Structure Bias (-1 to +1) • Bullish Strength (0-100) • Bearish Strength (0-100) • Bull Target/Risk Ratio • Bear Target/Risk Ratio • Relative Volume • Momentum Value • Volume Flow Strength • Bull Confirmations Count • Bear Confirmations Count --- BEST PRACTICES AND TIPS For Beginners • Start with default settings • Use High Confirmation Mode • Focus on TRENDING regime only • Paper trade first • Learn one timeframe thoroughly For Intermediate Users • Experiment with sensitivity settings • Try different target modes • Use multiple timeframes • Combine with price action analysis • Track pattern success rate For Advanced Users • Customize per instrument • Create setting templates • Use regime information for bias • Combine with other indicators • Develop systematic rules --- IMPORTANT DISCLAIMERS • This indicator is for educational and informational purposes only • Not financial advice or a trading system • Past performance does not guarantee future results • Trading involves substantial risk of loss • Always use appropriate risk management • Verify patterns with additional analysis • The author is not a registered investment advisor • No liability accepted for trading losses --- VERSION NOTES Version 1.0.0 - Initial Release • Six-layer confluence system • Adaptive parameter technology • Institutional volume detection • Market regime classification • Structure break identification • Real-time dashboard • Multiple display modes • Comprehensive settings ## My Final Thoughts Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve. The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system. - BullByte
Chỉ báo Pine Script®
của BullByte
6
DYNAMIC TRADING DASHBOARDStudy Material for the "Dynamic Trading Dashboard" This Dynamic Trading Dashboard is designed as an educational tool within the TradingView environment. It compiles commonly used market indicators and analytical methods into one visual interface so that traders and learners can see relationships between indicators and price action. Understanding these indicators, step by step, can help traders develop discipline, improve technical analysis skills, and build strategies. Below is a detailed explanation of each module. ________________________________________ 1. Price and Daily Reference Points The dashboard displays the current price, along with percentage change compared to the day’s opening price. It also highlights whether the price is moving upward or downward using directional symbols. Alongside, it tracks daily high, low, open, and daily range. For traders, daily levels provide valuable reference points. The daily high and low are considered intraday support and resistance, while the median price of the day often acts as a pivot level for mean reversion traders. Monitoring these helps learners see how price oscillates within daily ranges. ________________________________________ 2. VWAP (Volume Weighted Average Price) VWAP is calculated as a cumulative average price weighted by volume. The dashboard compares the current price with VWAP, showing whether the market is trading above or below it. For traders, VWAP is often a guide for institutional order flow. Price trading above VWAP suggests bullish sentiment, while trading below VWAP indicates bearish sentiment. Learners can use VWAP as a training tool to recognize trend-following vs. mean reversion setups. ________________________________________ 3. Volume Analysis The system distinguishes between buy volume (when the closing price is higher than the open) and sell volume (when the closing price is lower than the open). A progress bar highlights the ratio of buying vs. selling activity in percentage. This is useful because volume confirms price action. For instance, if prices rise but sell volume dominates, it can signal weakness. New traders learning with this tool should focus on how volume often precedes price reversals and trends. ________________________________________ 4. RSI (Relative Strength Index) RSI is a momentum oscillator that measures price strength on a scale from 0 to 100. The dashboard classifies RSI readings into overbought (>70), oversold (<30), or neutral zones and adds visual progress bars. RSI helps learners understand momentum shifts. During training, one should notice how trending markets can keep RSI extended for longer periods (not immediate reversal signals), while range-bound markets react more sharply to RSI extremes. It is an excellent tool for practicing trend vs. range identification. ________________________________________ 5. MACD (Moving Average Convergence Divergence) The MACD indicator involves a fast EMA, slow EMA, and signal line, with focus on crossovers. The dashboard shows whether a “bullish cross” (MACD above signal line) or “bearish cross” (MACD below signal line) has occurred. MACD teaches traders to identify trend momentum shifts and divergence. During practice, traders can explore how MACD signals align with VWAP trends or RSI levels, which helps in building a structured multi-indicator analysis. ________________________________________ 6. Stochastic Oscillator This indicator compares the current close relative to a range of highs and lows over a period. Displayed values oscillate between 0 and 100, marking zones of overbought (>80) and oversold (<20). Stochastics are useful for students of trading to recognize short-term momentum changes. Unlike RSI, it reacts faster to price volatility, so false signals are common. Part of the training exercise can be to observe how stochastic “flips” can align with volume surges or daily range endpoints. ________________________________________ 7. Trend & Momentum Classification The dashboard adds simple labels for trend (uptrend, downtrend, neutral) based on RSI thresholds. Additionally, it provides quick momentum classification (“bullish hold”, “bearish hold”, or neutral). This is beneficial for beginners as it introduces structured thinking: differentiating long-term market bias (trend) from short-term directional momentum. By combining both, traders can practice filtering signals instead of trading randomly. ________________________________________ 8. Accumulation / Distribution Bias Based on RSI levels, the script generates simplified tags such as “Accumulate Long”, “Accumulate Short”, or “Wait”. This is purely an interpretive guide, helping learners think in terms of accumulation phases (when markets are low) and distribution phases (when markets are high). It reinforces the concept that trading is not only directional but also involves timing. ________________________________________ 9. Overall Market Status and Score Finally, the dashboard compiles multiple indicators (VWAP position, RSI, MACD, Stochastics, and price vs. median levels) into a Market Score expressed as a percentage. It also labels the market as Overbought, Oversold, or Normal. This scoring system isn’t a recommendation but a learning framework. Students can analyze how combining different indicators improves decision-making. The key training focus here is confluence: not depending on one indicator but observing when several conditions align. Extended Study Material with Formulas ________________________________________ 1. Daily Reference Levels (High, Low, Open, Median, Range) • Day High (H): Maximum price of the session. DayHigh=max⁡(Hightoday)DayHigh=max(Hightoday) • Day Low (L): Minimum price of the session. DayLow=min⁡(Lowtoday)DayLow=min(Lowtoday) • Day Open (O): Opening price of the session. DayOpen=OpentodayDayOpen=Opentoday • Day Range: Range=DayHigh−DayLowRange=DayHigh−DayLow • Median: Mid-point between high and low. Median=DayHigh+DayLow2Median=2DayHigh+DayLow These act as intraday guideposts for seeing how far the price has stretched from its key reference levels. ________________________________________ 2. VWAP (Volume Weighted Average Price) VWAP considers both price and volume for a weighted average: VWAPt=∑i=1t(Pricei×Volumei)∑i=1tVolumeiVWAPt=∑i=1tVolumei∑i=1t(Pricei×Volumei) Here, Price_i can be the average price (High + Low + Close) ÷ 3, also known as hlc3. • Interpretation: Price above VWAP = bullish bias; Price below = bearish bias. ________________________________________ 3. Volume Buy/Sell Analysis The dashboard splits total volume into buy volume and sell volume based on candle type. • Buy Volume: BuyVol=Volumeif Close > Open, else 0BuyVol=Volumeif Close > Open, else 0 • Sell Volume: SellVol=Volumeif Close < Open, else 0SellVol=Volumeif Close < Open, else 0 • Buy Ratio (%): VolumeRatio=BuyVolBuyVol+SellVol×100VolumeRatio=BuyVol+SellVolBuyVol×100 This helps traders gauge who is in control during a session—buyers or sellers. ________________________________________ 4. RSI (Relative Strength Index) RSI measures strength of momentum by comparing gains vs. losses. Step 1: Compute average gains (AG) and losses (AL). AG=Average of Upward Closes over N periodsAG=Average of Upward Closes over N periodsAL=Average of Downward Closes over N periodsAL=Average of Downward Closes over N periods Step 2: Calculate relative strength (RS). RS=AGALRS=ALAG Step 3: RSI formula. RSI=100−1001+RSRSI=100−1+RS100 • Used to detect overbought (>70), oversold (<30), or neutral momentum zones. ________________________________________ 5. MACD (Moving Average Convergence Divergence) • Fast EMA: EMAfast=EMA(Close,length=fast)EMAfast=EMA(Close,length=fast) • Slow EMA: EMAslow=EMA(Close,length=slow)EMAslow=EMA(Close,length=slow) • MACD Line: MACD=EMAfast−EMAslowMACD=EMAfast−EMAslow • Signal Line: Signal=EMA(MACD,length=signal)Signal=EMA(MACD,length=signal) • Histogram: Histogram=MACD−SignalHistogram=MACD−Signal Crossovers between MACD and Signal are used in studying bullish/bearish phases. ________________________________________ 6. Stochastic Oscillator Stochastic compares the current close against a range of highs and lows. %K=Close−LowestLowHighestHigh−LowestLow×100%K=HighestHigh−LowestLowClose−LowestLow×100 Where LowestLow and HighestHigh are the lowest and highest values over N periods. The %D line is a smooth version of %K (using a moving average). %D=SMA(%K,smooth)%D=SMA(%K,smooth) • Values above 80 = overbought; below 20 = oversold. ________________________________________ 7. Trend and Momentum Classification This dashboard generates simplified trend/momentum logic using RSI. • Trend: • RSI < 40 → Downtrend • RSI > 60 → Uptrend • In Between → Neutral • Momentum Bias: • RSI > 70 → Bullish Hold • RSI < 30 → Bearish Hold • Otherwise Neutral This is not predictive, only a classification framework for educational use. ________________________________________ 8. Accumulation/Distribution Bias Based on extreme RSI values: • RSI < 25 → Accumulate Long Bias • RSI > 80 → Accumulate Short Bias • Else → Wait/No Action This helps learners understand the idea of accumulation at lows (strength building) and distribution at highs (profit booking). ________________________________________ 9. Overall Market Status and Score The tool adds up 5 bullish conditions: 1. Price above VWAP 2. RSI > 50 3. MACD > Signal 4. Stochastic > 50 5. Price above Daily Median BullishScore=ConditionsMet5×100BullishScore=5ConditionsMet×100 Then it categorizes the market: • RSI > 70 or Stoch > 80 → Overbought • RSI < 30 or Stoch < 20 → Oversold • Else → Normal This encourages learners to think in terms of probabilistic conditions instead of single-indicator signals. ________________________________________ ⚠️ Warning: • Trading financial markets involves substantial risk. • You can lose more money than you invest. • Past performance of indicators does not guarantee future results. • This script must not be copied, resold, or republished without authorization from aiTrendview. By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice. ________________________________________ ⚠️ Disclaimer and Warning (From aiTrendview) This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment. The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
Chỉ báo Pine Script®
của aiTrendview-Charting-System
VWAP For Loop [BackQuant]VWAP For Loop What this tool does—in one sentence A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts. Plain-English overview Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context. Under the hood • Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:   Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year. • For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .   – If VWAP > VWAP , add +1.   – Else, add −1.   The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45. • Signal logic (stateful)   – Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).   – Short on crossunder of lower (e.g., dropping below −10).   – A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence. Why VWAP + a breadth score? • VWAP aggregates both price and volume—where participants actually traded. • The breadth-style count rewards consistency of the anchored trend, not one-off spikes. • Thresholds give you binary structure when you need it (alerts, automation), without complex math. What you’ll see on the chart • Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral). • Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors). • Threshold guides — Horizontal lines for the long/short bands (toggle). • Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors. Input map (quick reference) • VWAP Anchor Period — Day, Week, Month, Quarter, Year. • Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons. • Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below). • UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors. Interpreting the score • Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength. • Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness. • Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise. Why the asymmetric default thresholds? • Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.” • Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:   – Capture sustained uptrends only when they’re very strong.   – Flag downside turns as they occur, rather than waiting for an extreme negative breadth. Tuning guide Choose an anchor that matches your horizon   – Intraday scalps : Day anchor on intraday charts.   – Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles. Pick the for-loop window   – Larger N (bigger end) = stronger evidence requirement, smoother oscillator.   – Smaller N = faster, more reactive score. Set achievable thresholds   – Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.   – Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior. Match visuals to intent   – Enabling VWAP coloring lets you see regime directly on price.   – Background shading is useful for discretionary reading; turn it off for cleaner automation displays. Playbook examples • Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing. • Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off. • Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder. Behavior around resets (important) Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes. Alerts included • VWAP FL Long — Fires when the long condition is true (score > upper and not in short). • VWAP FL Short — Fires on crossunder of the lower threshold (event-driven). Messages include {{ticker}} and {{interval}} placeholders for routing. Strengths • Simple, transparent math — Easy to reason about and validate. • Volume-aware by construction — Decisions reference VWAP, not just price. • Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output). Limitations & cautions • Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N. • Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N. • Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature. • VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic. Suggested starting profiles • Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON. • Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF. • Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired. Implementation notes • The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss). • HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range. • For-loop cap is kept modest (≤50) for performance and clarity. How to use this responsibly Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns. Summary VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
Chỉ báo Pine Script®
của BackQuant
2
NAS100 Component Sentiment Scanner# NAS100 Component Sentiment Scanner ## 🎯 Overview The NAS100 Component Sentiment Scanner analyzes the top-weighted stocks in the NASDAQ-100 index to provide real-time bullish/bearish sentiment signals that can help predict NAS100 price movements. This indicator combines multiple technical analysis methods to give traders a comprehensive view of underlying market sentiment. ## 📊 How It Works The indicator calculates sentiment scores for major NASDAQ-100 components (AAPL, MSFT, NVDA, GOOGL, AMZN, META, TSLA, AVGO, COST, NFLX) using: - **RSI Analysis**: Identifies overbought/oversold conditions - **Moving Average Trends**: Compares fast vs slow MA positioning - **Volume Confirmation**: Validates moves with volume thresholds - **Price Momentum**: Analyzes recent price direction - **Market Cap Weighting**: Uses actual NASDAQ-100 weightings for accuracy ## 🚀 Key Features ### Real-Time Sentiment Analysis - Weighted composite score based on individual stock analysis - Color-coded sentiment line (Green = Bullish, Red = Bearish) - Dynamic background coloring for strong signals ### Interactive Data Table - Shows individual stock scores and signals - Bullish/Bearish stock count summary - Customizable position and size ### Smart Signal System - **Bullish Signals**: Green triangle up when sentiment crosses threshold - **Bearish Signals**: Red triangle down when sentiment falls below threshold - **Alert Conditions**: Automatic notifications for signal changes ## ⚙️ Customization Options ### Technical Analysis Settings - **RSI Period**: Adjust lookback period (default: 14) - **RSI Levels**: Set overbought/oversold thresholds - **Moving Averages**: Configure fast/slow MA periods - **Volume Threshold**: Set volume confirmation multiplier ### Signal Thresholds - **Bullish/Bearish Levels**: Customize trigger points - **Strong Signal Levels**: Set extreme sentiment thresholds - Fine-tune sensitivity to market conditions ### Display Options - **Toggle Table**: Show/hide sentiment data table - **Table Position**: 6 position options (Top/Bottom/Middle + Left/Right) - **Table Size**: Choose from Tiny, Small, Normal, or Large - **Background Colors**: Enable/disable signal backgrounds - **Signal Arrows**: Show/hide buy/sell indicators ### Stock Selection - **Individual Control**: Enable/disable any of the 10 major stocks - **Dynamic Weighting**: Automatically adjusts calculations based on selected stocks - **Flexible Analysis**: Focus on specific sectors or market leaders ## 📈 How to Use ### 1. Basic Setup 1. Add the indicator to your NAS100 chart 2. Default settings work well for most traders 3. Observe the sentiment line and signals ### 2. Signal Interpretation - **Score > 30**: Bullish bias for NAS100 - **Score > 50**: Strong bullish signal - **Score -30 to 30**: Neutral/consolidation - **Score < -30**: Bearish bias for NAS100 - **Score < -50**: Strong bearish signal ### 3. Trading Strategies **Trend Following:** - Buy NAS100 when bullish signals appear - Sell/short when bearish signals trigger - Use background colors for quick visual confirmation **Divergence Trading:** - Watch for sentiment/price divergences - Strong sentiment with weak NAS100 price = potential breakout - Weak sentiment with strong NAS100 price = potential reversal **Consensus Trading:** - Monitor bullish/bearish stock counts in table - 8+ stocks aligned = strong directional bias - Mixed signals = wait for clearer consensus ### 4. Advanced Usage - Combine with your existing NAS100 trading strategy - Use multiple timeframes for confirmation - Adjust thresholds based on market volatility - Focus on specific stocks by disabling others ## 🔔 Alert Setup The indicator includes built-in alert conditions: 1. Go to TradingView Alerts 2. Select "NAS100 Component Sentiment Scanner" 3. Choose from available alert types: - NAS100 Bullish Signal - NAS100 Bearish Signal - Strong Bullish Consensus - Strong Bearish Consensus ## 💡 Pro Tips ### Optimization - **High Volatility**: Increase signal thresholds (±40, ±60) - **Low Volatility**: Decrease thresholds (±20, ±40) - **Day Trading**: Use smaller table, focus on real-time signals - **Swing Trading**: Enable background colors, larger thresholds ### Best Practices - Don't use as a standalone system - combine with price action - Check individual stock table for context - Monitor during market open for most reliable signals - Consider earnings seasons for individual stock impacts ### Market Conditions - **Trending Markets**: Higher accuracy, use with trend following - **Ranging Markets**: Watch for false signals, increase thresholds - **News Events**: Individual stock news can skew sentiment temporarily ## 🎨 Visual Guide - **Green Line Above Zero**: Bullish sentiment building - **Red Line Below Zero**: Bearish sentiment building - **Background Color Changes**: Strong signal confirmation - **Triangle Arrows**: Entry/exit signal points - **Table Colors**: Quick sentiment overview ## ⚠️ Important Notes - This indicator analyzes component stocks, not NAS100 directly - Market cap weightings approximate real NASDAQ-100 weightings - Sentiment can change rapidly during volatile periods - Always use proper risk management - Combine with other technical analysis tools ## 🔧 Troubleshooting - **No signals**: Check if thresholds are too extreme - **Too many signals**: Increase threshold sensitivity - **Table not showing**: Ensure "Show Sentiment Table" is enabled - **Missing stocks**: Verify individual stock toggles in settings --- **Suitable for**: Day traders, swing traders, NAS100 specialists, index traders **Best Timeframes**: 5min, 15min, 1H, 4H **Market Sessions**: US market hours for highest accuracy
Chỉ báo Pine Script®
của TroutyTrades
Chart-Only Scanner — Pro Table v2.5.1Chart-Only Scanner — Pro Table v2.5 User Manual (Pine Script v6) What this tool does (in one line) A compact, on-chart table that scores the current chart symbol (or an optional override) using momentum, volume, trend, volatility, and pattern checks—so you can quickly decide UP, DOWN, or WAIT. Quick Start (90 seconds) Add the indicator to any chart and timeframe (1m…1M). Leave “Override chart symbol” = OFF to auto-use the chart’s symbol. Choose your layout: Row (wide horizontal strip), or Grid (title + labeled cells). Pick a size preset (Micro, Small, Medium, Large, Mobile). Optional: turn on “Use Higher TF (EMA 20/50)” and set HTF Multiplier (e.g., 4 ⇒ if chart is 15m, HTF is 60m). Watch the table: DIR (↑/↓/→), ROC%, MOM, VOL, EMA stack, HTF, REV, SCORE, ACT. Add an alert if you want: the script fires when |SCORE| ≥ Action threshold. What to expect A small table appears on the chart corner you choose, updating each bar (or only at bar close if you keep default smart-update). The ACT cell shows 🔥 (strong), 👀 (medium), or ⏳ (weak). Panels & Settings (every option explained) Core Momentum Period: Lookback for rate-of-change (ROC%). Shorter = more reactive; longer = smoother. ROC% Threshold: Minimum absolute ROC% to call direction UP (↑) or DOWN (↓); otherwise →. Require Volume Confirmation: If ON and VOL ≤ 1.0, the SCORE is forced to 0 (prevents low-volume false positives). Override chart symbol + Custom symbol: By default, the indicator uses the chart’s symbol. Turn this ON to lock to a specific ticker (e.g., a perpetual). Higher TF Use Higher TF (EMA 20/50): Compares EMA20 vs EMA50 on a higher timeframe. HTF Multiplier: Higher TF = (chart TF × multiplier). Example: on 3H chart with multiplier 2 ⇒ HTF = 6H. Volatility & Oscillators ATR Length: Used to show ATR% (ATR relative to price). RSI Length: Standard RSI; colors: green ≤30 (oversold), red ≥70 (overbought). Stoch %K Length: With %D = SMA(%K, 3). MACD Fast/Slow/Signal: Standard MACD values; we display Line, Signal, Histogram (L/S/H). ADX Length (Wilder): Wilder’s smoothing (internal derivation); also shows +DI / −DI if you enable the ADX column. EMAs / Trend EMA Fast/Mid/Slow: We compute EMA(20/50/200) by default (editable). EMA Stack: Bull if Fast > Mid > Slow; Bear if Fast < Mid < Slow; Flat otherwise. Benchmark (optional, OFF by default) Show Relative Strength vs Benchmark: Displays RS% = ROC(symbol) − ROC(benchmark) over the Momentum Period. Benchmark Symbol: Ticker used for comparison (e.g., BTCUSDT as a market proxy). Columns (show/hide) Toggle which fields appear in the table. Hiding unused fields keeps the layout clean (especially on mobile). Display Layout Mode: Row = a single two-row strip; each column is a metric. Grid = a title row plus labeled pairs (label/value) arranged in rows. Size Preset: Micro, Small, Medium, Large, Mobile change text size and the grid density. Table Corner: Where the panel sits (e.g., Top Right). Opaque Table Background: ON = dark card; OFF = transparent(ish). Update Every Bar: ON = update intra-bar; OFF = smart update (last bar / real-time / confirmed history). Action threshold (|score|): The cutoff for 🔥 and alert firing (default 70). How to read each field CHART: The active symbol name (or your custom override). DIR: ↑ (ROC% > threshold), ↓ (ROC% < −threshold), → otherwise. ROC%: Rate of change over Momentum Period. Formula: (Close − Close ) / Close × 100. MOM: A scaled momentum score: min(100, |ROC%| × 10). VOL: Volume ratio vs 20-bar SMA: Volume / SMA(Volume,20). 1.5 highlights as yellow (significant participation). ATR%: (ATR / Close) × 100 (volatility relative to price). RSI: Colored for extremes: ≤30 green, ≥70 red. Stoch K/D: %K and %D numbers. MACD L/S/H: Line, Signal, Histogram. Histogram color reflects sign (green > 0, red < 0). ADX, +DI, −DI: Trend strength and directional components (Wilder). ADX ≥ 25 is highlighted. EMA 20/50/200: Current EMA values (editable lengths). STACK: Bull/Bear/Flat as defined above. VWAP%: (Close − VWAP) / Close × 100 (premium/discount to VWAP). HTF: ▲ if HTF EMA20 > EMA50; ▼ if <; · if flat/off. RS%: Symbol’s ROC% − Benchmark ROC% (positive = outperforming). REV (reversal): 🟢 Eng/Pin = bullish engulfing or bullish pin detected, 🔴 Eng/Pin = bearish engulfing or bearish pin, · = none. SCORE (absolute shown as a number; sign shown via DIR and ACT): Components: base = MOM × 0.4 volBonus = VOL > 1.5 ? 20 : VOL × 13.33 htfBonus = use_mtf ? (HTF == DIR ? 30 : HTF == 0 ? 15 : 0) : 0 trendBonus = (STACK == DIR) ? 10 : 0 macdBonus = 0 (placeholder for future versions) scoreRaw = base + volBonus + htfBonus + trendBonus + macdBonus SCORE = DIR ≥ 0 ? scoreRaw : −scoreRaw If Require Volume Confirmation and VOL ≤ 1.0 ⇒ SCORE = 0. ACT: 🔥 if |SCORE| ≥ threshold 👀 if 50 < |SCORE| < threshold ⏳ otherwise Practical examples Strong long (trend + participation) DIR = ↑, ROC% = +3.2, MOM ≈ 32, VOL = 1.9, STACK = Bull, HTF = ▲, REV = 🟢 SCORE: base(12.8) + volBonus(20) + htfBonus(30) + trend(10) ≈ 73 → ACT = 🔥 Action idea: look for longs on pullbacks; confirm risk with ATR%. Weak long (no volume) DIR = ↑, ROC% = +1.0, but VOL = 0.8 and Require Volume Confirmation = ON SCORE forced to 0 → ACT = ⏳ Action: wait for volume > 1.0 or turn off confirmation knowingly. Bearish reversal warning DIR = →, REV = 🔴 (bearish engulfing), RSI = 68, HTF = ▼ SCORE may be mid-range; ACT = 👀 Action: watch for breakdown and rising VOL. Alerts (how to use) The script calls alert() whenever |SCORE| ≥ Action threshold. To receive pop-ups, sounds, or emails: click “⏰ Alerts” in TradingView, choose this indicator, and pick “Any alert() function call.” The alert message includes: symbol, |SCORE|, DIR. Layout, Size, and Corner tips Row is best when you want a compact status ribbon across the top. Grid is clearer on big screens or when you enable many columns. Size: Mobile = one pair per row (tall, readable) Micro/Small = dense; good for many fields Large = presentation/screenshots Corner: If the table overlaps price, change the corner or set Opaque Background = OFF. Repaint & timeframe behavior Default smart update prefers stability (last bar / live / confirmed history). For a stricter, “close-only” behavior (less repaint): turn Update Every Bar = OFF and avoid Heikin Ashi when you want raw market OHLC (HA modifies price inputs). HTF logic is derived from a clean, integer multiple of your chart timeframe (via multiplier). It works with 3H/4H and any TF. Performance notes The script analyzes one symbol (chart or override) with multiple metrics using efficient tuple requests. If you later want a multi-symbol grid, do it with pages (10–15 per page + rotate) to stay within platform limits (recommended future add-on). Troubleshooting No table visible Ensure the indicator is added and not hidden. Try toggling Opaque Background or switch Corner (it might be behind other drawings). Keep Columns count reasonable for the chosen Size. If you turned ON Override, verify the Custom symbol exists on your data provider. Numbers look different on HA candles Heikin Ashi modifies OHLC; switch to regular candles if you need raw price metrics. 3H/4H issues Use integer HTF Multiplier (e.g., 2, 4). The tool builds the correct string internally; no manual timeframe strings needed. Power user tips Volume gating: keeping Require Volume Confirmation = ON filters most fake moves; if you’re a scalper, reduce strictness or turn it off. Action threshold: 60–80 is typical. Higher = fewer but stronger signals. Benchmark RS%: great for spotting leaders/laggards; positive RS% = outperformance vs benchmark. Change policy & safety This version doesn’t alter your historical logic you tested (no radical changes). Any future “radical” change (score weights, HTF logic, UI hiding data) will ship with a toggle and an Impact Statement so you can keep old behavior if you prefer. Glossary (quick) ROC%: Percent change over N bars. MOM: Scaled momentum (0–100). VOL ratio: Volume vs 20-bar average. ATR%: ATR as % of price. ADX/DI: Trend strength / direction components (Wilder). EMA stack: Relationship between EMAs (bullish/bearish/flat). VWAP%: Premium/discount to VWAP. RS%: Relative strength vs benchmark.
Chỉ báo Pine Script®
của humoudq8
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster Plain-English overview This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism. What Monte Carlo is and why quants rely on it • Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve. • Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon. • Core strengths in quant finance . – Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?” – Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic. – Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess. • Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.” How this indicator builds its probability cone 1) Seasonal pattern discovery The script builds two day-of-year maps as new data arrives: • A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information. • A volatility map that tracks the typical absolute return for the same calendar day. It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day. 2) Choice of randomness engine You can pick how the future shocks are generated: • Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock. • Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution. Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands. 3) Volatility scaling to current conditions Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa. 4) Many futures, summarized by percentiles The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles: • 5th and 95th → approximate 95% band (outer cone). • 16th and 84th → approximate 68% band (inner cone). • 50th → the median or “expected path.” These are drawn as polylines so you can immediately see central tendency and dispersion. 5) A historical overlay (optional) Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward. Inputs you control and how to think about them Monte Carlo Simulation • Price Series for Calculation . The source series, typically close. • Enable Probability Forecasts . Master switch for simulation and drawing. • Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon. • Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty. • Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice). • Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history. • Volatility Scaling . On by default so the cone respects today’s range context. Plotting & UI • Probability Cone . Plots the outer and inner percentile envelopes. • Expected Path . Plots the median line through the cone. • Historical Overlay . Dotted seasonal-only projection for context. • Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts. What appears on your chart • A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band. • A median path (default blue) running through the center of the cone. • An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent. • Optional historical seasonal path drawn as dotted segments for the next 30 bars. How to use it in trading 1) Position sizing and stop logic The cone translates “volatility plus seasonality” into distances. • Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play. • Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable. • If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality. 2) Entry timing with seasonal bias When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system. 3) Target selection Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive. 4) Scenario planning & “what-ifs” Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through. 5) Options and vol tactics • When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate. • When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics. Reading the probability cone like a pro • Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite. • Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained. • Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone. • Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst. Methodological notes (what the code actually does) • Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price. • Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives. • Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable. • Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation. • Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better. • Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones. • Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive. • Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history. Strengths and limitations • Strengths : – Probabilistic thinking replaces single-point guessing. – Seasonality adds a small but meaningful directional bias that many markets exhibit. – Volatility scaling adapts to the current regime so the cone stays realistic. • Limitations : – Seasonality can break around structural changes, policy shifts, or one-off events. – The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision. – The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts. – Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories. Tuning guide • Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets. • Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes. • Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong. • Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence. Workflow examples • Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band. • Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated. • Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range. Good habits • Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality. • Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt. • Validate seasonality for your symbol and timeframe; not every market has strong calendar effects. Summary The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Chỉ báo Pine Script®
của BackQuant
2
VWAP CALENDARThe VWAP CALENDAR indicator plots up to 20 anchored Volume-Weighted Average Price (VWAP) lines on your chart, each starting from a user-defined date and time (e.g., April 20, 2024). Designed for simplicity, it helps traders visualize VWAPs for key events or dates, with customizable labels and colors. The indicator is optimized for crypto markets (e.g., BTC/USD) but works with any symbol providing volume data. Features: Multiple VWAPs: Configure up to 20 independent VWAPs, each with a custom anchor date and time. Dynamic Labels: Labels update in real-time, aligning precisely with each VWAP line’s price level, positioned to the right of the chart for clarity. Customizable Settings: Adjust label text (e.g., “Event A”), line colors, line widths (1–5 pixels), text colors, and text sizes (8–40 points, default 22). Bubble or No-Background Labels: Choose between bubble-style labels (with colored backgrounds) or plain text labels without backgrounds. Timeframe Support: Accurate on daily, 4-hour, 1-hour, and 30-minute charts for anchors within ~1.5 years (e.g., April 20, 2024, from August 2025). Limitations: VWAP accuracy for anchors like April 20, 2024 (~477 days back) is reliable on 1-hour and larger timeframes. Below 30-minute (e.g., 15-minute, 24-minute), VWAPs may start later or be unavailable due to TradingView’s 5,000-bar historical data limit. For distant anchors, use 4-hour or daily charts to ensure accuracy. Requires sufficient chart history (e.g., premium account or deep exchange data) for older anchors on 1-hour or 30-minute charts. Usage Notes: Set anchor dates via the indicator settings (e.g., “2024-04-20 00:00”). Enable/disable individual VWAPs as needed. Zoom out to load maximum chart history for best results, especially on 1-hour or 30-minute timeframes. Ideal for crypto symbols with continuous trading data, but verify data availability for other markets. Disclaimer: This is a free indicator provided as-is
Chỉ báo Pine Script®
của fragilety
GOLD_30MIN_ALLINONEA comprehensive 30 minute trading tool for XAUUSD trading. Use in combination of the indicator: 1 minute Easy Scalping Sys v3.0 (by BulltradingAM). Rules: 1. A solid break out (measure breakout strength from the other indicator mentioned above) from the London session high or low (Orange Boxes), during the first 3 30Min candles of NYC session (Blue Boxes). 2. open position in the direction of the break out, set SL on London session high/low and TP on 1:1 RR or Bollinger Band outer line (for trending trades) and Bollinger Band Base line (for pullbacks and trend reversal trades). 3. No long trades in Bollinger red section and no short trades in Bollinger green section. More Information: You need the indicator only for the breakout candle momentum strength with the following indicator settings: Timeframe 1: 1 Day Timeframe 2: 30 Minutes Timeframe 3: 30 Minutes Timeframe 4: 30 Minutes and set the week candles fill to blank for easy identification. You will not need ATRs or Hulls lines or anything else from the other indicator.
Chỉ báo Pine Script®
của syedsaifalicpa
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research. The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007). Theoretical Foundation The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin. The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing. The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006). Scientific Implementation This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research: Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly). Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies. Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration. ASSET SPECIFIC IMPLEMENTATION Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities. Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010). Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013). Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations. Risk Management Integration The calculator integrates sophisticated risk assessment through two primary modes: Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class. Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics. Fractional Kelly Approaches Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate. The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles. Empirical Validation and Performance Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period. Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies. Practical Implementation Guidelines Successful Kelly implementation requires systematic application with attention to several critical factors: Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures. Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations. Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly. Professional Features and Customization The calculator provides comprehensive customization options for professional applications: Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments. Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity. Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class. Limitations and Considerations Like any analytical tool, the Kelly Criterion has important limitations that users must understand: Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999). Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications. Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high. Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003). Advanced Applications and Extensions Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments. Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations. Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources. Comprehensive Usage Instructions and Practical Examples Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters. Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth. Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility. Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences. Detailed Practical Examples Example 1: SPY Swing Trading Strategy Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses. Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application. The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile. Example 2: EURUSD Day Trading with Stop Losses A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters. Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example. Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves. Example 3: ES1! Futures Systematic Strategy Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms. Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading. Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading. Example 4: MES1! Micro-Futures for Smaller Accounts Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity. Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments. Example 5: Bitcoin Swing Trading Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators. Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics. Advanced Usage Scenarios Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins. Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur. Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions. Troubleshooting and Optimization Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist. Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves. Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk. Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies. Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management. Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements. Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure. Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current. Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives. Professional Applications This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies. Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics. Conclusion The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications. Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods. The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results. References Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons. Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92. Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press. Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press. Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176. Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147. Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons. Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926. Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press. MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954. MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42. Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423. Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill. Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific. Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education. Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons. Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31. Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR. Further Reading For comprehensive understanding of Kelly Criterion applications and advanced implementations: MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons. Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House. Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons. Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Chỉ báo Pine Script®
của EdgeTools
VWAP CALENDARThe VWAP CALENDAR indicator plots up to 20 anchored Volume-Weighted Average Price (VWAP) lines on your chart, each starting from a user-defined date and time (e.g., April 20, 2024). Designed for simplicity, it helps traders visualize VWAPs for key events or dates, with customizable labels and colors. The indicator is optimized for crypto markets (e.g., BTC/USD) but works with any symbol providing volume data. Features: Multiple VWAPs: Configure up to 20 independent VWAPs, each with a custom anchor date and time. Dynamic Labels: Labels update in real-time, aligning precisely with each VWAP line’s price level, positioned to the right of the chart for clarity. Customizable Settings: Adjust label text (e.g., “Event A”), line colors, line widths (1–5 pixels), text colors, and text sizes (8–40 points, default 22). Bubble or No-Background Labels: Choose between bubble-style labels (with colored backgrounds) or plain text labels without backgrounds. Timeframe Support: Accurate on daily, 4-hour, 1-hour, and 30-minute charts for anchors within ~1.5 years (e.g., April 20, 2024, from August 2025). Limitations: VWAP accuracy for anchors like April 20, 2024 (~477 days back) is reliable on 1-hour and larger timeframes. Below 30-minute (e.g., 15-minute, 24-minute), VWAPs may start later or be unavailable due to TradingView’s 5,000-bar historical data limit. For distant anchors, use 4-hour or daily charts to ensure accuracy. Requires sufficient chart history (e.g., premium account or deep exchange data) for older anchors on 1-hour or 30-minute charts. Usage Notes: Set anchor dates via the indicator settings (e.g., “2024-04-20 00:00”). Enable/disable individual VWAPs as needed. Zoom out to load maximum chart history for best results, especially on 1-hour or 30-minute timeframes. Ideal for crypto symbols with continuous trading data, but verify data availability for other markets. Disclaimer: This is a free indicator provided as-is.
Chỉ báo Pine Script®
của fragilety
Daily High/Low Close Breakout - GOLD### **Daily High/Low Close Breakout Indicator** This indicator is a powerful tool for identifying potential breakout opportunities based on the previous day's price action. It's built on a unique time-based logic that defines key support and resistance levels for the trading day. --- ### **How the Indicator Works** The indicator operates in two main phases: 1. **Calculation Period (00:00 to 16:30 Tehran Time):** The indicator first observes the price action from the start of the day until 16:30. During this time, it records the highest and lowest **closing prices** of all candles. The chart background is shaded gray to visually mark this period. 2. **Trading Period (16:30 to 16:30 the next day):** At 16:30, the highest and lowest close levels are finalized and drawn as horizontal lines. These levels then become the primary breakout zones for the next 24 hours. The indicator will generate signals whenever the price crosses these lines. --- ### **Trading Signals** The indicator uses a simple and effective crossover logic for its signals: * **BUY Signal:** A signal is generated when a candle's closing price **crosses above** the high close line. * **SELL Signal:** A signal is generated when a candle's closing price **crosses below** the low close line. --- ### **Important Usage Guidelines** For optimal performance, please follow these specific recommendations: * **Timeframe:** This indicator is designed and optimized to be used exclusively on the **15-minute timeframe**. Using it on other timeframes may produce inconsistent or unreliable results. * **Primary Asset:** The logic for this indicator was developed and backtested primarily for **Gold (XAUUSD)**. Its performance and win rate have been observed to be the most consistent on this asset. * **Asset Restriction:** It is strongly recommended to **avoid using this indicator on other currency pairs or assets**, as it has not been optimized for their specific market behavior. --- ### **Disclaimer** *This indicator is provided for informational and educational purposes only. It is not financial advice. Past performance is not a guarantee of future results. All trading decisions should be based on your own research and risk analysis. Always use proper risk management.*
Chỉ báo Pine Script®
của Chartistant
BUY in HASH RibbonsHash Ribbons Indicator (BUY Signal) A TradingView Pine Script v6 implementation for identifying Bitcoin miner capitulation (“Springs”) and recovery phases based on hash rate data. It marks potential low-risk buying opportunities by tracking short- and long-term moving averages of the network hash rate. ⸻ Key Features • Hash Rate SMAs • Short-term SMA (default: 30 days) • Long-term SMA (default: 60 days) • Phase Markers • Gray circle: Short SMA crosses below long SMA (start of capitulation) • White circles: Ongoing capitulation, with brighter white when the short SMA turns upward • Yellow circle: Short SMA crosses back above long SMA (end of capitulation) • Orange circle: Buy signal once hash rate recovery aligns with bullish price momentum (10-day price SMA crosses above 20-day price SMA) • Display Modes • Ribbons: Plots the two SMAs as colored bands—red for capitulation, green for recovery • Oscillator: Shows the percentage difference between SMAs as a histogram (red for negative, blue for positive) • Optional Overlays • Bitcoin halving dates (2012, 2016, 2020, 2024) with dashed lines and labels • Raw hash rate data in EH/s • Alerts • Configurable alerts for capitulation start, recovery, and buy signals ⸻ How It Works 1. Data Source: Fetches daily hash rate values from a selected provider (e.g., IntoTheBlock, Quandl). 2. Capitulation Detection: When the 30-day SMA falls below the 60-day SMA, miners are likely capitulating. 3. Recovery Identification: A rising 30-day SMA during capitulation signals miner recovery. 4. Buy Signal: Confirmed when the hash rate recovery coincides with a bullish shift in price momentum (10-day price SMA > 20-day price SMA). ⸻ Inputs Hash Rate Short SMA: 30 days Hash Rate Long SMA: 60 days Plot Signals: On Plot Halvings: Off Plot Raw Hash Rate: Off ⸻ Considerations • Timeframe: Best applied on daily charts to capture meaningful miner behavior. • Data Reliability: Ensure the chosen hash rate source provides consistent, gap-free data. • Risk Management: Use alongside other technical indicators (e.g., RSI, MACD) and fundamental analysis. • Backtesting: Evaluate performance over different market cycles before live deployment.
Chỉ báo Pine Script®
của Tin2100