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Timebender - Fractal CloseTimebender – Fractal Close displays which higher-timeframe candles (Daily, Weekly, Monthly) are scheduled to close within the next 24 hours — helping traders anticipate potential volatility and liquidity shifts around key session or higher-TF closes. It automatically scans: • Daily: 1D → 11D • Weekly: 1W → 3W • Monthly: 1M → 12M The detected timeframes are shown in a compact on-chart table that can be positioned anywhere (top, middle, bottom — left, center, or right). You can also customize text color, background, and font size for visual clarity. Use it to align intraday setups with higher-timeframe structure, or to prepare for major session transitions as multiple fractal closes converge.
אינדיקטור Pine Script®
מאת ‎bon_appetrade‎
Timebender - 90 Minute KillzonesTimebender – 90 Minute Killzones This indicator divides each trading day into sixteen 90-minute blocks based on New York Time. Each zone is color-coded by session: 🔴 Asian  🟢 London  🔵 New York AM  🟣 New York PM It helps visualize recurring intraday rhythms and session overlaps without adding signals or bias. Includes an optional Daily Close Line (18:00 NYT) to mark the end of the trading day, now zoom-safe and toggleable. Built for structure, clarity, and visual balance — nothing more, nothing less.
אינדיקטור Pine Script®
מאת ‎bon_appetrade‎
Real Relative Strength Breakout & BreakdownReal Relative Strength Breakout & Breakdown Indicator What It Does Identifies high-probability trading setups by combining: Technical Breakouts/Breakdowns - Price breaking support/resistance zones Real Relative Strength (RRS) - Volatility-adjusted performance vs benchmark (SPY) Key Insight: The strongest signals occur when price action contradicts market direction—breakouts during market weakness or breakdowns during market strength show exceptional buying/selling pressure. Real Relative Strength (RRS) Calculation RRS measures outperformance/underperformance on a volatility-adjusted basis: Power Index = (Benchmark Price Move) / (Benchmark ATR) RRS = (Stock Price Move - Power Index × Stock ATR) / Stock ATR RRS (smoothed) = 3-period SMA of RRS Interpretation: RRS > 0 = Relative Strength (outperforming) RRS < 0 = Relative Weakness (underperforming) Signal Types 🟢 Large Green Triangle (Premium Long) Condition: Breakout + RRS > 0 Meaning: Stock breaking resistance WHILE outperforming benchmark Best when: Market is weak but stock breaks out anyway = exceptional strength Use: High-conviction long entries 🔵 Small Blue Triangle (Standard Breakout) Condition: Breakout + RRS ≤ 0 Meaning: Breaking resistance but underperforming benchmark Typical: "Rising tide lifts all boats" scenario during market rally Use: Lower conviction—may just be following market 🟠 Large Orange Triangle (Premium Short) Condition: Breakdown + RRS < 0 Meaning: Stock breaking support WHILE underperforming benchmark Best when: Market is strong but stock breaks down anyway = severe weakness Use: High-conviction short entries 🔴 Small Red Triangle (Standard Breakdown) Condition: Breakdown + RRS ≥ 0 Meaning: Breaking support but outperforming benchmark Typical: Stock falling less than market during selloff Use: Lower conviction—may recover when market does Why Large Triangles Matter Large signals show divergence = genuine institutional flow: Stock breaking out while market falls → Aggressive buying despite headwinds Stock breaking down while market rallies → Aggressive selling despite tailwinds These setups reveal where real conviction lies, not just momentum-following behavior. Quick Settings RRS: 12-period lookback, 3-bar smoothing, vs SPY Breakouts: 5-period pivots, 200-bar lookback, 3% zone width, 2 minimum tests
אינדיקטור Pine Script®
מאת ‎william2003wakeford‎
Simplified Percentile ClusteringSimplified Percentile Clustering (SPC) is a clustering system for trend regime analysis. Instead of relying on heavy iterative algorithms such as k-means, SPC takes a deterministic approach: it uses percentiles and running averages to form cluster centers directly from the data, producing smooth, interpretable market state segmentation that updates live with every bar. Most clustering algorithms are designed for offline datasets, they require recomputation, multiple iterations, and fixed sample sizes. SPC borrows from both statistical normalization and distance-based clustering theory , but simplifies them. Percentiles ensure that cluster centers are resistant to outliers , while the running mean provides a stable mid-point reference. Unlike iterative methods, SPC’s centers evolve smoothly with time, ideal for charts that must update in real time without sudden reclassification noise. SPC provides a simple yet powerful clustering heuristic that: Runs continuously in a charting environment, Remains interpretable and reproducible, And allows traders to see how close the current market state is to transitioning between regimes. Clustering by Percentiles Traditional clustering methods find centers through iteration. SPC defines them deterministically using three simple statistics within a moving window: Lower percentile (p_low) → captures the lower basin of feature values. Upper percentile (p_high) → captures the upper basin. Mean (mid) → represents the central tendency. From these, SPC computes stable “centers”: // K = 2 → two regimes (e.g., bullish / bearish) = // K = 3 → adds a neutral zone = These centers move gradually with the market, forming live regime boundaries without ever needing convergence steps. Two clusters capture directional bias; three clusters add a neutral ‘range’ state. Multi-Feature Fusion While SPC can cluster a single feature such as RSI, CCI, Fisher Transform, DMI, Z-Score, or the price-to-MA ratio (MAR), its real strength lies in feature fusion. Each feature adds a unique lens to the clustering system. By toggling features on or off, traders can test how each dimension contributes to the regime structure. In “Clusters” mode, SPC measures how far the current bar is from each cluster center across all enabled features, averages these distances, and assigns the bar to the nearest combined center. This effectively creates a multi-dimensional regime map , where each feature contributes equally to defining the overall market state. The fusion distance is computed as: dist := (rsi_d * on_off(use_rsi) + cci_d * on_off(use_cci) + fis_d * on_off(use_fis) + dmi_d * on_off(use_dmi) + zsc_d * on_off(use_zsc) + mar_d * on_off(use_mar)) / (on_off(use_rsi) + on_off(use_cci) + on_off(use_fis) + on_off(use_dmi) + on_off(use_zsc) + on_off(use_mar)) Because each feature can be standardized (Z-Score), the distances remain comparable across different scales. Fusion mode combines multiple standardized features into a single smooth regime signal. Visualizing Proximity - The Transition Gradient Most indicators show binary or discrete conditions (e.g., bullish/bearish). SPC goes further, it quantifies how close the current value is to flipping into the next cluster. It measures the distances to the two nearest cluster centers and interpolates between them: rel_pos = min_dist / (min_dist + second_min_dist) real_clust = cluster_val + (second_val - cluster_val) * rel_pos This real_clust output forms a continuous line that moves smoothly between clusters: Near 0.0 → firmly within the current regime Around 0.5 → balanced between clusters (transition zone) Near 1.0 → about to flip into the next regime Smooth interpolation reveals when the market is close to a regime change. How to Tune the Parameters SPC includes intuitive parameters to adapt sensitivity and stability: K Clusters (2–3): Defines the number of regimes. K = 2 for trend/range distinction, K = 3 for trend/neutral transitions. Lookback: Determines the number of past bars used for percentile and mean calculations. Higher = smoother, more stable clusters. Lower = faster reaction to new trends. Lower / Upper Percentiles: Define what counts as “low” and “high” states. Adjust to widen or tighten cluster ranges. Shorter lookbacks react quickly to shifts; longer lookbacks smooth the clusters. Visual Interpretation In “Clusters” mode, SPC plots: A colored histogram for each cluster (red, orange, green depending on K) Horizontal guide lines separating cluster levels Smooth proximity transitions between states Each bar’s color also changes based on its assigned cluster, allowing quick recognition of when the market transitions between regimes. Cluster bands visualize regime structure and transitions at a glance. Practical Applications Identify market regimes (bullish, neutral, bearish) in real time Detect early transition phases before a trend flip occurs Fuse multiple indicators into a single consistent signal Engineer interpretable features for machine-learning research Build adaptive filters or hybrid signals based on cluster proximity Final Notes Simplified Percentile Clustering (SPC) provides a balance between mathematical rigor and visual intuition. It replaces complex iterative algorithms with a clear, deterministic logic that any trader can understand, and yet retains the multidimensional insight of a fusion-based clustering system. Use SPC to study how different indicators align, how regimes evolve, and how transitions emerge in real time. It’s not about predicting; it’s about seeing the structure of the market unfold. Disclaimer This indicator is intended for educational and analytical use. It does not generate buy or sell signals. Historical regime transitions are not indicative of future performance. Always validate insights with independent analysis before making trading decisions.
אינדיקטור Pine Script®
מאת ‎InvestorUnknown‎
2
ALISH WEEK LABELS THE ALISH WEEK LABELS Overview This indicator programmatically delineates each trading week and encapsulates its realized price range in a live-updating, filled rectangle. A week is defined in America/Toronto time from Monday 00:00 to Friday 16:00. Weekly market open to market close, For every week, the script draws: a vertical start line at the first bar of Monday 00:00, a vertical end line at the first bar at/after Friday 16:00, and a white, semi-transparent box whose top tracks the highest price and whose bottom tracks the lowest price observed between those two temporal boundaries. The drawing is timeframe-agnostic (M1 → 1D): the box expands in real time while the week is open and freezes at the close boundary. Time Reference and Session Boundaries All scheduling decisions are computed with time functions called using the fixed timezone string "America/Toronto", ensuring correct behavior across DST transitions without relying on chart timezone. The start condition is met at the first bar where (dayofweek == Monday && hour == 0 && minute == 0); on higher timeframes where an exact 00:00 bar may not exist, a fallback checks for the first Monday bar using ta.change(dayofweek). The close condition is met on the first bar at or after Friday 16:00 (Toronto), which guarantees deterministic closure on intraday and higher timeframes. State Model The indicator maintains minimal persistent state using var globals: week_open (bool): whether the current weekly session is active. wk_hi / wk_lo (float): rolling extrema for the active week. wk_box (box): the graphical rectangle spanning × . wk_start_line and a transient wk_end_line (line): vertical delimiters at the week’s start and end. Two dynamic arrays (boxes, vlines) store object handles to support bounded history and deterministic garbage collection. Update Cycle (Per Bar) On each bar the script executes the following pipeline: Start Check: If no week is open and the start condition is satisfied, instantiate wk_box anchored at the current bar_index, prime wk_hi/wk_lo with the bar’s high/low, create the start line, and push both handles to their arrays. Accrual (while week_open): Update wk_hi/wk_lo using math.max/min with current bar extremes. Propagate those values to the active wk_box via box.set_top/bottom and slide box.set_right to the current bar_index to keep the box flush with live price. Close Check: If at/after Friday 16:00, finalize the week by freezing the right edge (box.set_right), drawing the end line, pushing its handle, and flipping week_open false. Retention Pruning: Enforce a hard cap on historical elements by deleting the oldest objects when counts exceed configured limits. Drawing Semantics The range container is a filled white rectangle (bgcolor = color.new(color.white, 100 − opacity)), with a solid white border for clear contrast on dark or light themes. Start/end boundaries are full-height vertical white lines (y1=+1e10, y2=−1e10) to guarantee visibility across auto-scaled y-axes. This approach avoids reliance on price-dependent anchors for the lines and is robust to large volatility spikes. Multi-Timeframe Behavior Because session logic is driven by wall-clock time in the Toronto zone, the indicator remains consistent across chart resolutions. On coarse timeframes where an exact boundary bar might not exist, the script legally approximates by triggering on the first available bar within or immediately after the boundary (e.g., Friday 16:00 occurs between two 4-hour bars). The box therefore represents the true realized high/low of the bars present in that timeframe, which is the correct visual for that resolution. Inputs and Defaults Weeks to keep (show_weeks_back): integer, default 40. Controls retention of historical boxes/lines to avoid UI clutter and resource overhead. Fill opacity (fill_opacity): integer 0–100, default 88. Controls how solid the white fill appears; border color is fixed pure white for crisp edges. Time zone is intentionally fixed to "America/Toronto" to match the strategy definition and maintain consistent historical backtesting. Performance and Limits Objects are reused only within a week; upon closure, handles are stored and later purged when history limits are exceeded. The script sets generous but safe caps (max_boxes_count/max_lines_count) to accommodate 40 weeks while preserving Editor constraints. Per-bar work is O(1), and pruning loops are bounded by the configured history length, keeping runtime predictable on long histories. Edge Cases and Guarantees DST Transitions: Using a fixed IANA time zone ensures Friday 16:00 and Monday 00:00 boundaries shift correctly when DST changes in Toronto. Weekend Gaps/Holidays: If the market lacks bars exactly at boundaries, the nearest subsequent bar triggers the start/close logic; range statistics still reflect observed prices. Live vs Historical: During live sessions the box edge advances every bar; when replaying history or backtesting, the same rules apply deterministically. Scope (Intentional Simplicity) This tool is strictly a visual framing indicator. It does not compute labels, statistics, alerts, or extended S/R projections. Its single responsibility is to clearly present the week’s realized range in the Toronto session window so you can layer your own execution or analytics on top.
אינדיקטור Pine Script®
מאת ‎alishzcadillac101‎
⚡ Elite Momentum Pro🎯 Key Features 1. Smart Signal Engine 3 Signal Modes: Aggressive, Balanced, Conservative 7-Point Scoring System - Ensures high-quality signals Anti-Flip Protection - Prevents rapid signal changes Multiple confirmations: Supertrend, MACD, RSI, EMA alignment, momentum 2. Advanced Risk Management 3 Take Profit Levels (TP1, TP2, TP3) for scaling out ATR-Based Dynamic Stops - Adapts to volatility Customizable Risk:Reward (default 2.5:1) Visual stop and target levels 3. Clean Visual Design Color-coded price bars based on trend strength EMA Ribbon (9, 21, 50, 200) for trend clarity
אינדיקטור Pine Script®
מאת ‎jmslimus‎
Quantum Flux Universal Strategy Summary in one paragraph Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version. Scope and intent • Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto • Timeframes. One minute to daily • Default demo used in the publication. QQQ on one hour • Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns • Limits. This is a strategy. Orders are simulated on standard candles only Originality and usefulness • Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps. • What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise. • Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state. • Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols. Method overview in plain language The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction. Base measures • Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale. • Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop. • Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves. Components • Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner. • Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends. • Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response. • Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance. • Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot. Fusion rule • Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent. • The blend power input scales the driver mix. Zero means fixed spans. One means full driver control. • Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes. Signal rule • Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one. • Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one. • When polarity flips from plus to minus, the strategy closes any long and enters a short. • When flux crosses above the guide, the strategy closes any short. What you will see on the chart • White polarity plot around the zero line • A dotted reference line at zero named Zen • Green background tint for positive polarity and red background tint for negative polarity • Strategy long and short markers placed by the TradingView engine at entry and at close conditions • No table in this version to keep the visual clean and portable Inputs with guidance Setup • Price source. Default ohlc4. Stable for noisy symbols. • Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive. • Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price. Logic • Guide span. Typical range 4 to 12. A small guide smooths without eating turns. • Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing. • Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work. • Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise. • Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve. • Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak. • Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong. • Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline. • Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols. • Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs. Filters • Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends. • Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping. • Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version. Alerts • This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows. Strategy has been currently adapted for the QQQ asset with 30/60min timeframe. For other assets may require new optimization Properties visible in this publication • Initial capital 25000 • Base currency Default • Default order size method percent of equity with value 5 • Pyramiding 1 • Commission 0.05 percent • Slippage 10 ticks • Process orders on close ON • Bar magnifier ON • Recalculate after order is filled OFF • Calc on every tick OFF Honest limitations and failure modes • Past results do not guarantee future outcomes • Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency • Gap heavy symbols may benefit from the MAD Z normalization • Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context • Session time is the exchange time of the chart • If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference Open source reuse and credits • None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank • Method and fusion are original in construction and disclosure Legal Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs. Strategy add on block Strategy notice Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used. Entries and exits • Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero • Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short • Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed • Tie handling. Not applicable in this version because there are no fixed stops or targets Position sizing • Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice Properties used on the published chart • Initial capital 25000 • Base currency Default • Default order size percent of equity with value 5 • Pyramiding 1 • Commission 0.05 percent • Slippage 10 ticks • Process orders on close ON • Bar magnifier ON • Recalculate after order is filled OFF • Calc on every tick OFF Dataset and sample size • Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour • Trade count in sample 324 on the example chart Release notes template for future updates Version 1.1. • Add alertcondition lines for long, short, and exit short • Add optional table with component readouts • Add optional stop model with a distance unit expressed as ATR or a percent of price Notes. Backward compatibility Yes. Inputs migrated Yes.
אסטרטגיית Pine Script®
מאת ‎exlux‎
US30 Quarter Levels (125-point grid) by FxMogul🟦 US30 Quarter Levels — Trade the Index Like the Banks Discover the Dow’s hidden rhythm. This indicator reveals the institutional quarter levels that govern US30 — spaced every 125 points, e.g. 45125, 45250, 45375, 45500, 45625, 45750, 45875, 46000, and so on. These are the liquidity magnets and reaction zones where smart money executes — now visualized directly on your chart. 💼 Why You Need It See institutional precision: The Dow respects 125-point cycles — this tool exposes them. Catch reversals before retail sees them: Every impulse and retracement begins at one of these zones. Build confluence instantly: Perfectly aligns with your FVGs, OBs, and session highs/lows. Trade like a professional: Turn chaos into structure, and randomness into rhythm. ⚙️ Key Features Automatically plots US30 quarter levels (…125 / …250 / …375 / …500 / …625 / …750 / …875 / …000). Color-coded hierarchy: 🟨 xx000 / xx500 → major institutional levels ⚪ xx250 / xx750 → medium-impact levels ⚫ xx125 / xx375 / xx625 / xx875 → intraday liquidity pockets Customizable window size, label spacing, and line extensions. Works across all timeframes — from 1-minute scalps to 4-hour macro swings. Optimized for clean visualization with no clutter. 🎯 How to Use It Identify liquidity sweeps: Smart money hunts stops at these quarter zones. Align structure: Combine with session opens, order blocks, or FVGs. Set precision entries & exits: Trade reaction-to-reaction with tight risk. Plan daily bias: Watch how New York respects these 125-point increments. 🧭 Designed For Scalpers, day traders, and swing traders who understand that US30 doesn’t move randomly — it moves rhythmically. Perfect for traders using ICT, SMC, or liquidity-based frameworks. ⚡ Creator’s Note “Every 125 points, the Dow breathes. Every 1000, it shifts direction. Once you see the rhythm, you’ll never unsee it.” — FxMogul
אינדיקטור Pine Script®
מאת ‎leonelndam400‎
Time Line Indicator - by LMTime Line Indicator – by LM Description: The Time Line Indicator is a simple, clean, and customizable tool designed to visualize specific time periods within each hour directly in a dedicated indicator pane. It allows traders to mark important intraday minute ranges across multiple past hours, providing a clear visual reference for time-based analysis. This indicator is perfect for identifying recurring hourly windows, session patterns, or custom time-based events in your charts. Unlike traditional overlays, this indicator does not interfere with price candles and draws its lines in a separate pane at the bottom of your chart for clarity. Key Features: Custom Hourly Lines: Draw horizontal lines for a specific minute range within each hour, e.g., from the 45th minute to the 15th minute of the next hour. Multi-Hour Support: Choose how many past hours to display. The indicator will replicate the line for each selected hourly period, following the same minute logic. Automatic Start/End Logic: If your chosen start minute is in the previous hour, the line correctly begins at that time. The end minute can cross into the next hour when applicable. If the selected end minute does not yet exist in the current chart data, the line will extend to the latest available bar. Dedicated Indicator Pane: Lines appear in a fixed, non-intrusive y-axis within the indicator pane (overlay=false), keeping your price chart clean. Customizable Appearance: Line Color: Choose any color to match your chart theme. Line Thickness: Adjust the width of the lines for better visibility. Inputs: Input Name Type Default Description Line Color Color Orange The color of the horizontal lines. Line Thickness Integer 2 The thickness of each line (1–5). Start Minute Integer 5 The minute within the hour where the line begins (0–59). End Minute Integer 25 The minute within the hour where the line ends (0–59). Hours Back Integer 3 Number of past hours to display lines for. Use Cases: Intraday Analysis: Quickly visualize recurring minute ranges across multiple hours. Session Tracking: Mark critical time windows for trading sessions or market events. Pattern Recognition: Easily identify time-based patterns or setups without cluttering the price chart. How It Works: The indicator calculates the nearest bars corresponding to your start and end minutes. It draws horizontal lines at a fixed y-axis value within the indicator pane. Lines are drawn for each selected past hour, replicating the chosen minute span. All logic respects the actual chart data; lines never extend into the future beyond the most recent bar. Notes: Overlay is set to false, so lines appear in a dedicated pane below the price chart. The indicator is fully compatible with any timeframe. Lines adjust automatically to match the chart’s bar spacing. You can change the number of hours displayed at any time without affecting existing lines. If you want, I can also draft a shorter “TradingView Store / Public Library description” version under 500 characters for the “Short Description” field — concise and punchy for users scrolling through indicators.
אינדיקטור Pine Script®
מאת ‎LouisMont‎
Londen & New York Sessies (UTC+2)This script highlights the London and New York trading sessions on the chart, adjusted for UTC+2 timezone. It's designed to help traders easily visualize the most active and liquid periods of the Forex and global markets directly on their TradingView charts. The London session typically provides strong volatility, while the New York session brings increased momentum and overlaps with London for powerful trading opportunities. Ideal for intraday and session-based strategies.
אינדיקטור Pine Script®
מאת ‎Amelusions‎
Wyckoff Stage Approximator (MTF Alerts)Wyckoff Stage Approximator (MTF Context) This indicator is a powerful tool designed for traders who use a top-down, multi-timeframe approach based on Wyckoff principles. Its primary function is to identify the market's current stage—consolidation (Stage 1) or trend (Stage 2)—on a higher Context (C) timeframe and project that analysis onto your lower Validation (V) and Entry (E) charts. This ensures you are always trading in alignment with the "big picture" trend, preventing you from taking low-probability trades based on lower-timeframe noise. Core Concept: Top-Down Analysis The script solves a common problem for multi-timeframe traders: losing sight of the primary trend. By locking the background color to your chosen Context timeframe (e.g., 15-minute), you are constantly reminded of the market's true state. 🟡 Yellow Background (Stage 1): The Context timeframe is in consolidation. This is a time to be patient and wait for a clear directional bias to emerge. 🟢 Green Background (Stage 2 - Markup): The Context timeframe is in a confirmed uptrend. This is your green light to look for bullish pullback opportunities on your lower timeframes. 🔴 Red Background (Stage 2 - Markdown): The Context timeframe is in a confirmed downtrend. This is your signal to look for bearish rally opportunities. How It Works The indicator uses a combination of moving averages and trend strength to objectively define each stage: Trend Alignment: It checks if the 5 EMA, 10 EMA, and 20 EMA are properly stacked above or below the 50 SMA to determine the potential trend direction. Trend Strength: It uses the ADX to measure the strength of the trend. A trend is only confirmed as Stage 2 if the ADX is above a user-defined threshold (default is 23), filtering out weak or choppy moves. Stage Definition: Any period that is not a confirmed, strong Stage 2 Markup or Markdown is classified as a Stage 1 consolidation phase. Key Features Multi-Timeframe (MTF) Projection: Select your master "Context" timeframe, and its analysis will be displayed on any chart you view. Customizable Inputs: Easily adjust the moving average lengths and ADX threshold to fit your specific strategy and the asset you are trading. Clear Visual Cues: The intuitive background coloring makes it easy to assess the market environment at a glance. Stage Transition Alerts: Set up specific alerts to be notified the moment your Context timeframe shifts from a Stage 1 consolidation to a Stage 2 trend, ensuring you never miss a potential setup. How to Use This Indicator Add the indicator to your chart. In the settings, set the "Context Timeframe" to your highest timeframe (e.g., "15" for 15-minute). Create alerts for the "Stage 1 -> Stage 2" conditions. When you receive an alert, it signals that a potential trend is beginning on your Context chart. Switch to your lower Validation and Entry timeframes. The background color will confirm the higher-timeframe trend, giving you the confidence to look for your specific entry patterns. Disclaimer: This tool is designed for confluence and environmental analysis. It is not a standalone signal generator. It should be used in conjunction with your own price action, volume, or order flow analysis to validate trade entries.
אינדיקטור Pine Script®
מאת ‎JavMasta097‎
Wyckoff Accumulation / Distribution Detector (v3)🌱 Spring (Bullish Wyckoff Signature) 🧠 Definition A Spring happens when price dips below a well-defined support level, usually near the end of an accumulation phase, then quickly reverses back above support. This is not ordinary volatility — it's usually intentional by large operators (“Composite Man”) to: Trigger stop-losses of weak holders Create the illusion of a breakdown to scare late sellers in Absorb all remaining supply at low prices Launch the next markup leg once weak hands are flushed out 🧭 Typical Spring Characteristics Feature Behavior Location Near the bottom of a trading range after a decline Price Action Temporary breakdown below support, then sharp reversal above Volume Usually low to average on the break, indicating lack of real selling pressure. Sometimes a volume surge on the reversal as strong hands step in Candle Often shows a long lower wick, closes back inside the range Intent Shakeout of weak holders, allow institutions to accumulate more quietly 📈 Why It's Bullish Springs typically mark the final test of supply. If price can dip below support and immediately recover, it means: Selling pressure is exhausted (no follow-through) Strong hands are absorbing remaining shares A bullish breakout is often imminent 🪤 Upthrust (Bearish Wyckoff Signature) 🧠 Definition An Upthrust is the mirror image of a Spring. It happens when price pokes above a resistance level, usually near the end of a distribution phase, but then fails to hold above it and falls back inside the range. This is typically smart money distributing to eager buyers: Late breakout traders pile in Institutions sell into that strength Price collapses back into the range, trapping breakout buyers 🧭 Typical Upthrust Characteristics Feature Behavior Location Near the top of a trading range after a rally Price Action Temporary breakout above resistance, then quick reversal down Volume Frequently low on the breakout, suggesting a lack of real buying interest — or sometimes high but with no progress, showing hidden selling Candle Often shows a long upper wick, closes back inside the range Intent Trap breakout buyers, provide liquidity for institutional sellers to unload near highs 📉 Why It's Bearish Upthrusts show demand failure and supply swamping: Buyers cannot sustain the breakout. The sharp reversal signals large players are exiting. Typically precedes markdown phases or sharp declines. 📝 Trading Implications Spring → Often followed by a sign of strength rally → good long entry if confirmed with volume expansion and follow-through. Upthrust → Often followed by a sign of weakness → short setups, especially if the next rally fails at lower highs. The script looks for: 🌱 Spring: Price makes a low below recent pivot support, Closes back above, Does so on low volume → likely a shakeout. 🪤 Upthrust: Price makes a high above recent pivot resistance, Closes back below, On low volume → likely a bull trap.
אינדיקטור Pine Script®
מאת ‎padinroc‎
QUANTUM MOMENTUMOverview Quantum Momentum is a sophisticated technical analysis tool designed to help traders identify relative strength between assets through advanced momentum comparison. This cyberpunk-themed indicator visualizes momentum dynamics between your current trading symbol and any comparison asset of your choice, making it ideal for pairs trading, crypto correlation analysis, and multi-asset portfolio management. Key Features 📊 Multi-Asset Momentum Comparison Dual Symbol Analysis: Compare momentum between your chart symbol and any other tradable asset Real-Time Tracking: Monitor relative momentum strength as market conditions evolve Difference Visualization: Clear histogram display showing which asset has stronger momentum 🎯 Multiple Momentum Calculation Methods Choose from four different momentum calculation types: ROC (Rate of Change): Traditional percentage-based momentum measurement RSI (Relative Strength Index): Oscillator-based momentum from 0-100 range Percent Change: Simple percentage change over the lookback period Raw Change: Absolute price change in native currency units 📈 Advanced Trend Filtering System Enable optional trend filters to align momentum signals with prevailing market direction: SMA (Simple Moving Average): Classic trend identification EMA (Exponential Moving Average): Responsive trend detection Price Action: Identifies trends through higher highs/lows or lower highs/lows patterns ADX (Average Directional Index): Measures trend strength with customizable threshold 🎨 Futuristic Cyberpunk Design Neon Color Scheme: Eye-catching cyan, magenta, and matrix green color palette Glowing Visual Effects: Enhanced visibility with luminescent plot lines Dynamic Background Shading: Subtle trend state visualization Real-Time Data Table: Sleek information panel displaying current momentum values and trend status How It Works The indicator calculates momentum for both your current chart symbol and a comparison symbol (default: BTC/USDT) using your selected method and lookback period. The difference between these momentum values reveals which asset is exhibiting stronger momentum at any given time. Positive Difference (Green): Your chart symbol has stronger momentum than the comparison asset Negative Difference (Pink/Red): The comparison asset has stronger momentum than your chart symbol When the trend filter is enabled, the indicator will only display signals that align with the detected market trend, helping filter out counter-trend noise. Settings Guide Symbol Settings Compare Symbol: Choose any tradable asset to compare against (e.g., major indices, cryptocurrencies, forex pairs) Momentum Settings Momentum Length: Lookback period for momentum calculations (default: 14 bars) Momentum Type: Select your preferred momentum calculation method Display Options Toggle visibility of current symbol momentum line Toggle visibility of comparison symbol momentum line Toggle visibility of momentum difference histogram Optional zero line reference Trend Filter Settings Use Trend Filter: Enable/disable trend-based signal filtering Trend Method: Choose from SMA, EMA, Price Action, or ADX Trend Length: Period for trend calculations (default: 50) ADX Threshold: Minimum ADX value to confirm trend strength (default: 25) Best Use Cases ✅ Pairs Trading: Identify divergences in momentum between correlated assets ✅ Crypto Market Analysis: Compare altcoin momentum against Bitcoin or Ethereum ✅ Stock Market Rotation: Track sector or index relative strength ✅ Forex Strength Analysis: Monitor currency pair momentum relationships ✅ Multi-Timeframe Confirmation: Use alongside other indicators for confluence ✅ Mean Reversion Strategies: Spot extreme momentum divergences for potential reversals Visual Indicators ⚡ Cyan Line: Your chart symbol's momentum ⚡ Magenta Line: Comparison symbol's momentum 📊 Green/Pink Histogram: Momentum difference (positive = green, negative = pink) ▲ Green Triangle: Bullish trend detected (when filter enabled) ▼ Red Triangle: Bearish trend detected (when filter enabled) ◈ Yellow Diamond: Neutral/sideways trend (when filter enabled) Pro Tips 💡 Look for crossovers between the momentum lines as potential trade signals 💡 Combine with volume analysis for stronger confirmation 💡 Use momentum divergence (price making new highs/lows while momentum doesn't) for reversal signals 💡 Enable trend filter during ranging markets to reduce false signals 💡 Experiment with different momentum types to find what works best for your trading style Technical Requirements TradingView Pine Script Version: v6 Chart Type: Works on all chart types Indicator Placement: Separate pane (overlay=false) Data Requirements: Needs access to comparison symbol data
אינדיקטור Pine Script®
מאת ‎AlgoCollective‎
1
Forecast PriceTime Oracle [CHE] Forecast PriceTime Oracle — Prioritizes quality over quantity by using Power Pivots via RSI %B metric to forecast future pivot highs/lows in price and time Summary This indicator identifies potential pivot highs and lows based on out-of-bounds conditions in a modified RSI %B metric, then projects future occurrences by estimating time intervals and price changes from historical medians. It provides visual forecasts via diagonal and horizontal lines, tracks achievement with color changes and symbols, and displays a dashboard for statistical overview including hit rates. Signals are robust due to median-based aggregation, which reduces outlier influence, and optional tolerance settings for near-misses, making it suitable for anticipating reversals in ranging or trending markets. Motivation: Why this design? Standard pivot detection often lags or generates false signals in volatile conditions, missing the timing of true extrema. This design leverages out-of-bounds excursions in RSI %B to capture "Power Pivots" early—focusing on quality over quantity by prioritizing significant extrema rather than every minor swing—then uses historical deltas in time and price to forecast the next ones, addressing the need for proactive rather than reactive analysis. It assumes that pivot spacing follows statistical patterns, allowing users to prepare entries or exits ahead of confirmation. What’s different vs. standard approaches? - Reference baseline: Diverges from traditional ta.pivothigh/low, which require fixed left/right lengths and confirm only after bars close, often too late for dynamic markets. - Architecture differences: - Detects extrema during OOB runs rather than post-bar symmetry. - Aggregates deltas via medians (or alternatives) over a user-defined history, capping arrays to manage resources. - Applies tolerance thresholds for hit detection, with options for percentage, absolute, or volatility-adjusted (ATR) flexibility. - Freezes achieved forecasts with visual states to avoid clutter. - Practical effect: Charts show proactive dashed projections instead of retrospective dots; the dashboard reveals evolving hit rates, helping users gauge reliability over time without manual calculation. How it works (technical) The indicator first computes a smoothed RSI over a specified length, then applies Bollinger Bands to derive %B, flagging out-of-bounds below zero or above one hundred as potential run starts. During these runs, it tracks the extreme high or low price and bar index. Upon exit from the OOB state, it confirms the Power Pivot at that extreme and records the time delta (bars since prior) and price change percentage to rolling arrays. For forecasts, it calculates the median (or selected statistic) of recent deltas, subtracts the confirmation delay (bars from apex to exit), and projects ahead by that adjusted amount. Price targets use the median change applied to the origin pivot value. Lines are drawn from the apex to the target bar and price, with a short horizontal at the endpoint. Arrays store up to five active forecasts, pruning oldest on overflow. Tolerance adjusts hit checks: for highs, if the high reaches or exceeds the target (adjusted by tolerance); for lows, if the low drops to or below. Once hit, the forecast freezes, changing colors and symbols, and extends the horizontal to the hit bar. Persistent variables maintain last pivot states across bars; arrays initialize empty and grow until capped at history length. Parameter Guide Source: Specifies the data input for the RSI computation, influencing how price action is captured. Default is close. For conservative signals in noisy environments, switch to high; using low boosts responsiveness but may increase false positives. RSI Length: Sets the smoothing period for the RSI calculation, with longer values helping to filter out whipsaws. Default is 32. Opt for shorter lengths like 14 to 21 on faster timeframes for quicker reactions, or extend to 50 or more in strong trends to enhance stability at the cost of some lag. BB Length: Defines the period for the Bollinger Bands applied to %B, directly affecting how often out-of-bounds conditions are triggered. Default is 20. Align it with the RSI length: shorter periods detect more potential runs but risk added noise, while longer ones provide better filtering yet might overlook emerging extrema. BB StdDev: Controls the multiplier for the standard deviation in the bands, where wider settings reduce false out-of-bounds alerts. Default is 2.0. Narrow it to 1.5 for highly volatile assets to catch more signals, or broaden to 2.5 or higher to emphasize only major movements. Show Price Forecast: Enables or disables the display of diagonal and target lines along with their updates. Default is true. Turn it off for simpler chart views, or keep it on to aid in trade planning. History Length: Determines the number of recent pivot samples used for median-based statistics, where more history leads to smoother but potentially less current estimates. Default is 50. Start with a minimum of 5 to build data; limit to 100 to 200 to prevent outdated regimes from skewing results. Max Lookahead: Limits the number of bars projected forward to avoid overly extended lines. Default is 500. Reduce to 100 to 200 for intraday focus, or increase for longer swing horizons. Stat Method: Selects the aggregation technique for time and price deltas: Median for robustness against outliers, Trimmed Mean (20%) for a balanced trim of extremes, or 75th Percentile for a conservative upward tilt. Default is Median. Use Median for even distributions; switch to Percentile when emphasizing potential upside in trending conditions. Tolerance Type: Chooses the approach for flexible hit detection: None for exact matches, Percentage for relative adjustments, Absolute for fixed point offsets, or ATR for scaling with volatility. Default is None. Begin with Percentage at 0.5 percent for currency pairs, or ATR for adapting to cryptocurrency swings. Tolerance %: Provides the relative buffer when using Percentage mode, forgiving small deviations. Default is 0.5. Set between 0.2 and 1.0 percent; higher values accommodate gaps but can overstate hit counts. Tolerance Points: Establishes a fixed offset in price units for Absolute mode. Default is 0.0010. Tailor to the asset, such as 0.0001 for forex pairs, and validate against past wick behavior. ATR Length: Specifies the period for the Average True Range in dynamic tolerance calculations. Default is 14. This is the standard setting; shorten to 10 to reflect more recent volatility. ATR Multiplier: Adjusts the ATR scale for tolerance width in ATR mode. Default is 0.5. Range from 0.3 for tighter precision to 0.8 for greater leniency. Dashboard Location: Positions the summary table on the chart. Default is Bottom Right. Consider Top Left for better visibility on mobile devices. Dashboard Size: Controls the text scaling for dashboard readability. Default is Normal. Choose Tiny for dense overlays or Large for detailed review sessions. Text/Frame Color: Sets the color scheme for dashboard text and borders. Default is gray. Align with your chart theme, opting for lighter shades on dark backgrounds. Reading & Interpretation Forecast lines appear as dashed diagonals from confirmed pivots to projected targets, with solid horizontals at endpoints marking price levels. Open targets show a target symbol (🎯); achieved ones switch to a trophy symbol (🏆) in gray, with lines fading to gray. The dashboard summarizes median time/price deltas, sample counts, and hit rates—rising rates indicate improving forecast alignment. Colors differentiate highs (red) from lows (lime); frozen states signal validated projections. Practical Workflows & Combinations - Trend following: Enter long on low forecast hits during uptrends (higher highs/lower lows structure); filter with EMA crossovers to ignore counter-trend signals. - Reversal setups: Short above high projections in overextended rallies; use volume spikes as confirmation to reduce false breaks. - Exits/Stops: Trail stops to prior pivot lows; conservative on low hit rates (below 50%), aggressive above 70% with tight tolerance. - Multi-TF: Apply on 1H for entries, 4H for time projections; combine with Ichimoku clouds for confluence on targets. - Risk management: Position size inversely to delta uncertainty (wider history = smaller bets); avoid low-liquidity sessions. Behavior, Constraints & Performance Confirmation occurs on OOB exit, so live-bar pivots may adjust until close, but projections update only on events to minimize repaint. No security or HTF calls, so no external lookahead issues. Arrays cap at history length with shifts; forecasts limited to five active, pruning FIFO. Loops iterate over small fixed sizes (e.g., up to 50 for stats), efficient on most hardware. Max lines/labels at 500 prevent overflow. Known limits: Sensitive to OOB parameter tuning—too tight misses runs; assumes stationary pivot stats, which may shift in regime changes like low vol. Gaps or holidays distort time deltas. Sensible Defaults & Quick Tuning Defaults suit forex/crypto on 1H–4H: RSI 32/BB 20 for balanced detection, Median stats over 50 samples, None tolerance for exactness. - Too many false runs: Increase BB StdDev to 2.5 or RSI Length to 50 for filtering. - Lagging forecasts: Shorten History Length to 20; switch to 75th Percentile for forward bias. - Missed near-hits: Enable Percentage tolerance at 0.3% to capture wicks without overcounting. - Cluttered charts: Reduce Max Lookahead to 200; disable dashboard on lower TFs. What this indicator is—and isn’t This is a forecasting visualization layer for pivot-based analysis, highlighting statistical projections from historical patterns. It is not a standalone system—pair with price action, volume, and risk rules. Not predictive of all turns; focuses on OOB-derived extrema, ignoring volume or news impacts. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
אינדיקטור Pine Script®
מאת ‎chervolino‎
Herd Flow Oscillator — Volume Distribution Herd Flow Oscillator — Scientific Volume Distribution (herd-accurate rev) A composite order-flow oscillator designed to surface true herding behavior — not just random bursts of buying or selling. It’s built to detect when market participants start acting together, showing persistent, one-sided activity that statistically breaks away from normal market randomness. Unlike traditional volume or momentum indicators, this tool doesn’t just look for “who’s buying” or “who’s selling.” It tries to quantify crowd behavior by blending multiple statistical tests that describe how collective sentiment and coordination unfold in price and volume dynamics. What it shows The Herd Flow Oscillator works as a multi-layer detector of crowd-driven flow in the market. It examines how signed volume (buy vs. sell pressure) evolves, how persistent it is, and whether those actions are unusually coordinated compared to random expectations. HerdFlow Composite (z) — the main signal line, showing how statistically extreme the current herding pressure is. When this crosses above or below your set thresholds, it suggests a high probability of collective buying or selling. You can optionally reveal component panels for deeper insight into why herding is detected: DVI (Directional Volume Imbalance): Measures the ratio of bullish vs. bearish volume. If it’s strongly positive, more volume is hitting the ask (buying); if negative, more is hitting the bid (selling). LSV-style Herd Index : Inspired by academic finance measures of “herding.” It compares how often volume is buying vs. selling versus what would happen by random chance. If the result is significantly above chance, it means traders are collectively biased in one direction. O rder-Flow Persistence (ρ 1..K): Averages autocorrelation of signed volume over several lags. In simpler terms: checks if buying/selling pressure tends to continue in the same direction across bars. Positive persistence = ongoing coordination, not just isolated trades. Runs-Test Herding (−Z) : Statistical test that checks how often trade direction flips. When there are fewer direction changes than expected, it means trades are clustering — a hallmark of herd behavior. Skew (signed volume): Measures whether signed volume is heavily tilted to one side. A positive skew means more aggressive buying bursts; a negative skew means more intense selling bursts. CVD Slope (z): Looks at the slope of the Cumulative Volume Delta — essentially how quickly buy/sell pressure is accelerating. It’s a short-term flow acceleration measure. Shapes & background ▲ “BH” at the bottom = Bull Herding; ▼ “BH-” at the top = Bear Herding. These markers appear when all conditions align to confirm a herding regime. Persistence and clustering both confirm coordinated downside flow. Core Windows Primary Window (N) — the main sample length for herding calculations. It’s like the "memory span" for detecting coordinated behavior. A longer N means smoother, more reliable signals. Short Window (Nshort) — used for short-term measurements like imbalance and slope. Smaller values react faster but can be noisy; larger values are steadier but slower. Long Window (Nlong) — used for z-score normalization (statistical scaling). This helps the indicator understand what’s “normal” behavior over a longer horizon, so it can spot when things deviate too far. Autocorr lags (acLags) — how many steps to check when measuring persistence. Higher values (e.g., 3–5) look further back to see if trends are truly continuing. Calculation Options Price Proxy for Tick Rule — defines how to decide if a trade is “buy” or “sell.” hlc3 (average of high, low, and close) works as a neutral, smooth price proxy. Use ATR for scaling — keeps signals comparable across assets and timeframes by dividing by volatility (ATR). Prevents high-volatility periods from dominating the signal. Median Filter (bars) — smooths out erratic data spikes without heavily lagging the response. Odd values like 3 or 5 work best. Signal Thresholds Composite z-threshold — determines how extreme behavior must be before it counts as “herding.” Higher values = fewer, more confident signals. Imbalance threshold — the minimum directional volume imbalance to trigger interest. Plotting Show component panels — useful for analysts and developers who want to inspect the math behind signals. Fill strong herding zones — purely visual aid to highlight key periods of coordinated trading. How to use it (practical tips) Understand the purpose: This is not just a “buy/sell” tool. It’s a behavioral detector that identifies when traders or algorithms start acting in the same direction. Timeframe flexibility: 15m–1h: reveals short-term crowd shifts. 4h–1D: better for swing-trade context and institutional positioning. Combine with structure or trend: When HerdFlow confirms a bullish regime during a breakout or retest, it adds confidence. Conversely, a bearish cluster at resistance may hint at a crowd-driven rejection. Threshold tuning: To make it more selective, increase zThr and imbThr. To make it more sensitive, lower those thresholds but expand your primary window N for smoother results. Cross-market consistency: Keep “Use ATR for scaling” enabled to maintain consistency across different instruments or timeframes. Denoising: A small median filter (3–5 bars) removes flicker from volume spikes but still preserves the essential crowd patterns. Reading the components (why signals fire) Each sub-metric describes a unique “dimension” of crowd behavior: DVI: how imbalanced buying vs selling is. Herd Index: how biased that imbalance is compared to random expectation. Persistence (ρ): how continuous those flows are. Runs-Test: how clumped together trades are — clustering means the crowd’s acting in sync. Skew: how lopsided the volume distribution is — sudden surges of one-sided aggression. CVD Slope: how strongly accelerating the current directional flow is. When all of these line up, you’re seeing evidence that market participants are collectively moving in the same direction — i.e., true herding.
אינדיקטור Pine Script®
מאת ‎Cmo22‎
Seasonality Heatmap [QuantAlgo]🟢 Overview The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns. 🟢 How It Works 1. Monthly Heatmap How % Return is Calculated: The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month: (Current Month Price - Previous Month Price) / Previous Month Price × 100 Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns What Averages Mean: The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.) This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed What Months Up % Mean: Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month) Calculated as: (Number of Months with Positive Returns / Total Months) × 100 Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months Example: If October shows "64%", then 64% of all historical Octobers had positive returns What Consistency Score Means: A 1-10 rating that measures how predictable and stable a month's returns have been Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year Medium scores (5-7, gray): Moderate consistency with some variability Low scores (1-4, red): High variability with unpredictable behavior across different years Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation What Best Means: Shows the highest percentage return achieved for that specific month, along with the year it occurred Reveals the maximum observed upside and identifies outlier years with exceptional performance Useful for understanding the range of possible outcomes beyond the average Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain What Worst Means: Shows the most negative percentage return for that specific month, along with the year it occurred Reveals maximum observed downside and helps understand the range of historical outcomes Important for risk assessment even in months with positive averages Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss 2. Day-of-Week Heatmap How % Return is Calculated: Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection) Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.) Each cell shows the average performance for that specific day-month combination across all historical data Formula: (Current Day Price - Previous Day Close) / Previous Day Close × 100 What Averages Mean: The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday Identifies broad weekly patterns across the entire dataset Calculated by summing all daily returns for that weekday across all months and dividing by total observations Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset What Days Up % Mean: Shows the percentage of historical occurrences where that weekday had a positive return Calculated as: (Number of Positive Days / Total Days Observed) × 100 Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns What Consistency Score Means: A 1-10 rating measuring how stable that weekday's performance has been across different months Based on the coefficient of variation of daily returns for that weekday across all 12 months High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month Medium scores (5-7, gray): Moderate consistency with some month-to-month variation Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year What Best Means: Shows which calendar month had the strongest average performance for that specific weekday Identifies favorable day-month combinations based on historical data Format shows the month abbreviation and the average return achieved Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset What Worst Means: Shows which calendar month had the weakest average performance for that specific weekday Identifies historically challenging day-month combinations Useful for understanding which month-weekday pairings have shown weaker performance Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset 3. Optimal Timing Table/Summary Table → Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower Based on the observation that buying during historically weaker months may position for subsequent recovery Shows the month name, its average return, and color-coded performance Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period → Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher Based on historical strength patterns in that month Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains → 2nd Best Month to BUY: The second-lowest performing month based on average returns Provides an alternative timing option based on historical patterns Offers flexibility for staged entries or when the primary month doesn't align with strategy Example: Identifies the next-most favorable historical buying period → 2nd Best Month to SELL: The second-highest performing month based on average returns Provides an alternative exit timing based on historical data Useful for staged profit-taking or multiple exit opportunities Identifies the secondary historical strength period Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days). 🟢 Examples Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year. Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent. Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
אינדיקטור Pine Script®
מאת ‎QuantAlgo‎
2
Volume Rate of Change (VROC)# Volume Rate of Change (VROC) **What it is:** VROC measures the rate of change in trading volume over a specified period, typically expressed as a percentage. Formula: `((Current Volume - Volume n periods ago) / Volume n periods ago) × 100` ## **Obvious Uses** **1. Confirming Price Trends** - Rising VROC with rising prices = strong bullish trend - Rising VROC with falling prices = strong bearish trend - Validates that price movements have conviction behind them **2. Spotting Divergences** - Price makes new highs but VROC doesn't = weakening momentum - Price makes new lows but VROC doesn't = potential reversal **3. Identifying Breakouts** - Sudden VROC spikes often accompany legitimate breakouts from consolidation patterns - Helps distinguish real breakouts from false ones **4. Overbought/Oversold Conditions** - Extreme VROC readings (very high or very low) suggest exhaustion - Mean reversion opportunities when volume extremes occur --- ## **Non-Obvious Uses** **1. Smart Money vs. Dumb Money Detection** - Declining VROC during price rallies may indicate retail FOMO while institutions distribute - Rising VROC during selloffs with price stability suggests institutional accumulation **2. News Impact Measurement** - Compare VROC before/after earnings or announcements - Low VROC on "significant" news = market doesn't care (fade the move) - High VROC = genuine market reaction (respect the move) **3. Market Regime Changes** - Persistent shifts in average VROC levels can signal transitions between bull/bear markets - Declining baseline VROC over months = waning market participation/topping process **4. Intraday Liquidity Profiling** - VROC patterns across trading sessions identify best execution times - Avoid trading when VROC is abnormally low (wider spreads, poor fills) **5. Sector Rotation Analysis** - Compare VROC across sector ETFs to identify where capital is flowing - Rising VROC in defensive sectors + falling VROC in cyclicals = risk-off rotation **6. Options Expiration Effects** - VROC typically drops significantly post-options expiration - Helps avoid false signals from mechanically-driven volume changes **7. Algorithmic Activity Detection** - Unusual VROC patterns (regular spikes at specific times) may indicate algo programs - Can front-run or avoid periods of heavy algorithmic interference **8. Liquidity Crisis Early Warning** - Sharp, sustained VROC decline across multiple assets = liquidity withdrawal - Can precede market stress events before price volatility emerges **9. Cryptocurrency Wash Trading Detection** - Comparing VROC across exchanges for same asset - Discrepancies suggest artificial volume on certain platforms **10. Pair Trading Optimization** - Use relative VROC between correlated pairs - Enter when VROC divergence is extreme, exit when it normalizes The key to advanced VROC usage is context: combining it with price action, market structure, and other indicators rather than using it in isolation.
אינדיקטור Pine Script®
מאת ‎needfulCheetah31773‎