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Gann Square of Nine: Planetary Degrees█ Gann Square of Nine: Planetary Degrees maps planetary positions onto Gann's Square of Nine grid, tracking where pivot highs and lows accumulate by planetary degree. Use this indicator to identify recurring degree patterns on the So9, determine whether pivots cluster around cardinal, diagonal, or other significant angles, and project when the planet will return to those degrees. Powered by the open-source BlueprintResearch Planetary Ephemeris library , which implements truncated VSOP87 (planets) and ELP2000 (Moon) series for high-accuracy celestial calculations entirely within Pine Script. █ FEATURES • Anchor Point System — Select any significant price pivot (high or low) as your reference point; all subsequent pivot tracking begins from this timestamp • All 10 celestial bodies — Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto • Geocentric or Heliocentric views — Toggle between Earth-centered (traditional) and Sun-centered perspectives • Interactive Square of Nine table — Visual grid displaying the Gann spiral pattern with highlighted pivot degrees • Automatic pivot detection — Configurable bar sensitivity to identify price pivots (symmetric left/right) • Pivot degree labeling — Each detected pivot displays the planet's ecliptic longitude (0-360°) at that moment • Target degree alerts — Define specific So9 degrees to watch; triggers alerts when the planet crosses them • Preset So9 angles — Quick selection of degrees along major So9 lines (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°) • Custom degree input — Enter any degrees as comma-separated or newline-separated values • Future degree projections — Scans up to 500 bars ahead and shows when the planet will reach each target degree • Retrograde indicator — Shows ℞ symbol with red text when planets are in apparent retrograde motion • So9 overlay tools — Plot 90° and 45° angle relationships from any entered degree █ HOW IT WORKS The Square of Nine Concept: Gann's Square of Nine is a spiral grid where numbers flow outward from the center (1) in a square spiral pattern. Key angle relationships (0°, 45°, 90°, etc.) align along specific diagonals and cardinal lines. When planetary degrees land on the same So9 position as significant price pivots, it suggests potential support/resistance levels. This Indicator: 1. User selects an "anchor" timestamp at a significant price pivot 2. The indicator calculates the selected planet's ecliptic longitude (0-360°) at each bar 3. Price pivots detected after the anchor are labeled with their planetary degrees 4. These degrees accumulate on the So9 grid, revealing patterns 5. Target degrees can be set to receive alerts when crossed 6. Future projections show when the planet will reach those target degrees █ HOW TO USE 1. Click on the anchor timestamp input and select a significant high or low pivot on your chart 2. Choose "High" or "Low" pivot type based on your anchor point 3. Select your planet from the dropdown 4. Choose Geocentric (traditional) or Heliocentric view 5. The So9 table appears showing accumulated pivot degrees highlighted 6. Set target degrees using presets or custom input to receive crossing alerts 7. Future projections appear as vertical lines with date/time labels 8. Use the So9 overlay tools to visualize angle relationships from specific degrees █ VISUAL GUIDE So9 Table Colors: • Anchor degree: White (⚓ symbol) • Current planet position: Planet's assigned color with symbol • Pivot Highs: Green background • Pivot Lows: Red background • Equal (both high and low): Orange background • Diagonal crosses: Blue background • Cardinal crosses: Red background • Target degrees: Yellow highlight Chart Labels: • Pivot High labels appear above the price with the degree • Pivot Low labels appear below the price with the degree • Future projection lines: Yellow (upcoming) or Gray (already crossed since anchor) █ SETTINGS OVERVIEW 1. Anchor Point — Set the starting pivot timestamp and type (High/Low) 2. Planet Selection — Choose celestial body and coordinate system 3. Target Degree Alerts — Configure which degrees to watch and receive alerts 4. Pivot Detection — Set bar sensitivity for pivot high/low detection and degree rounding precision 5. Visual Style — Customize colors and label sizes 6. So9 Grid Overlay — Enter a degree to visualize its angular relationships 7. So9 Table — Position, sizing, and color options for the grid 8. So9 Diagonals — Toggle and color the diagonal/cardinal cross highlights █ LIMITATIONS & ACCURACY This indicator uses optimized VSOP87 and ELP2000 series tailored for Pine Script performance. It delivers excellent accuracy for trading and analytical purposes. Expected Accuracy: • Sun, Moon, Mercury, Venus, Mars: Within 1-10 arcseconds • Jupiter, Saturn: Within 10-30 arcseconds • Uranus, Neptune: Within 1-2 arcminutes • Pluto: Simplified Meeus method (valid 1900-2100) Degree Resolution: The So9 grid uses integer degrees (1-361). Planetary positions are rounded to the nearest whole degree for grid placement. Precise decimal degrees are retained for crossing calculations and alerts. Crossing Detection: Future projection lines and background highlights both point to the confirmation bar—the first bar where the crossing can be verified. Alerts also trigger on this bar. This ensures all visual elements align consistently: when the chart reaches a future projection line, that bar closes with the crossing confirmed and highlighted. █ CREDITS • Square of Nine grid visualization adapted from ThiagoSchmitz's "Gann Square of 9" (Feb 2023) • Ephemeris calculations via BlueprintResearch/lib_ephemeris open-source library
อินดิเคเตอร์ Pine Script®
โดย BlueprintResearch
3
All-in-One CVD: Failed Auction + Trap + Flow Classifications All-in-One CVD : Failed Auction/Trap + Flow Classifications (Colored Bars) Description: This script provides an advanced order flow and delta-based trading visualization designed to highlight key market microstructure events in real time. It combines Cumulative Volume Delta (CVD), failed auction detection, absorption tracking, continuation signals, and trap identification into a single, coherent tool with colored bars and visual markers. Unlike standard volume or trend-following indicators, this script focuses on aggressive order flow and price acceptance/rejection events, making it particularly suitable for scalping, intraday momentum trading, and identifying high-probability short-term setups. Originality and Purpose: Many scripts either show CVD or detect failed auctions separately, but this script integrates multiple advanced flow concepts into one indicator. By combining CVD, normalized delta, strong delta thresholds, failed auctions, absorption, traps, and continuation patterns, traders can identify where aggressive buying or selling is being absorbed, where price is likely to continue, and where traps are forming. The mashup is intentional: each component validates the other. For example, a failed auction signal without absorption is less significant, while a failed auction coinciding with absorption signals a true high-probability trap or reversal. Failed auctions typically align with "Failed 2" patterns from The Strat by Rob Smith, providing additional confirmation using a well-established price action methodology. How It Works: Volume and Delta Calculation: Computes buying and selling pressure from volume and bar structure (high/low/close). Supports UltraData mode for enhanced volume calculations using security data. Options for Cumulative Mode: Total, Periodic, or EMA-based CVD. Normalized Delta and Strong Delta Detection: Calculates normalized delta (z-score) to standardize flow across different volatility regimes. Flags strong buying or selling when delta exceeds user-defined thresholds. Failed Auction Detection: Highlights bars where price attempted to break previous highs/lows but failed to sustain, signaling trapped aggressive participants. True failed auctions can coincide with absorption for higher-probability setups. Absorption: Detects situations where strong aggressive flow is absorbed at key levels, showing institutional participation or liquidity consumption. Bullish absorption occurs when aggressive buying is absorbed at previous lows; bearish absorption occurs when aggressive selling is absorbed at previous highs. Flow Classification: Continuation: Aggressive flow accepted by the market — often the next candle continues in the direction of the delta. Important: A single continuation signal does not guarantee follow-through. Traders should view it as an indicator that aggressive participants are in control for the current candle, and consider market context, trend, and support/resistance before assuming continuation. Multiple consecutive continuation signals or confirmation with absorption/strong delta increases reliability. Trap: Aggressive flow trapped — the market reverses after failed auction. Absorption: Aggressive orders absorbed — market shows hesitation at tested levels. Colored CVD Bars and Visual Markers: Bars colored green/red/gray based on delta direction. Visual markers indicate flow state: circles for continuation, X-cross for traps, triangles for absorption. Works in real time — live candles are updated with flow state markers. Alerts: Custom alert conditions for each flow type: continuation, trap, and absorption. Alerts provide actionable signals for automated monitoring or manual trading. Trading Applications: Trap Trading: Identify aggressive buyers/sellers who fail to push price and get trapped. Use trap signals to fade reversals. Continuation Trading: Detect market acceptance of aggressive flow for trend-following or breakout strategies. Use caution: a single continuation signal indicates probability, not certainty, and should be confirmed with structural context. Absorption Analysis: Spot where institutional participants absorb liquidity before a potential directional move. Intraday Scalping: Combines delta, volume, failed auction logic, and Strat alignment for high-frequency setups. Key Notes: True failed auctions with significant market impact require absorption — otherwise, a simple failed attempt may be a weak signal. The script works across multiple markets (Forex, Crypto, Stock) and supports live bar updates. Users can adjust strong delta thresholds, period lengths, and cumulative modes to fit their preferred trading style or volatility regime. Conclusion: This all-in-one script provides traders with a comprehensive, visually intuitive, and real-time method to detect aggressive flow, failed auctions, absorption, and continuation patterns. By linking failed auctions to The Strat’s failed 2 patterns, and clarifying the probabilistic nature of continuation signals, it merges advanced delta analytics with proven price action methodology, making it highly original, actionable, and educational for understanding market order flow dynamics.
อินดิเคเตอร์ Pine Script®
โดย samb817
Fractal Market Geometry [JOAT] Fractal Market Geometry Overview Fractal Market Geometry is an open-source overlay indicator that combines fractal analysis with harmonic pattern detection, Fibonacci retracements and extensions, Elliott Wave concepts, and Wyckoff phase identification. It provides traders with a geometric framework for understanding market structure and identifying potential reversal patterns with multi-factor signal confirmation. What This Indicator Does The indicator calculates and displays: Fractal Detection - Identifies fractal highs and lows using Williams-style pivot analysis with configurable period Fractal Dimension - Calculates market complexity using range-based dimension estimation Harmonic Patterns - Detects Gartley, Butterfly, Bat, Crab, Shark, Cypher, and ABCD patterns using Fibonacci ratios Fibonacci Retracements - Key levels at 38.2%, 50%, and 61.8% Fibonacci Extensions - Projection level at 161.8% Elliott Wave Count - Simplified wave counting based on pivot detection (1-5) Wyckoff Phase - Volume-based phase identification (Accumulation, Markup, Distribution, Neutral) Golden Spiral Levels - ATR-based support and resistance levels using phi (1.618) ratio Trend Detection - EMA crossover trend identification (20/50 EMA) How It Works Fractal detection uses a configurable period to identify swing points: detectFractalHigh(simple int period) => bool result = true float centerVal = high for i = 0 to period - 1 if high >= centerVal or high >= centerVal result := false break Harmonic pattern detection uses Fibonacci ratio analysis between swing points. Each pattern has specific ratio requirements: Gartley: AB 0.382-0.618, BC 0.382-0.886, CD 1.27-1.618 Butterfly: AB 0.382-0.5, BC 0.382-0.886, CD 1.618-2.24 Bat: AB 0.5-0.618, BC 1.13-1.618, CD 1.618-2.24 Crab: AB 0.382-0.618, BC 0.382-0.886, CD 2.24-3.618 Shark: AB 0.382-0.618, BC 1.13-1.618, CD 1.618-2.24 Cypher: AB 0.382-0.618, BC 1.13-1.414, CD 0.786-0.886 Wyckoff phase detection analyzes volume relative to price movement: wyckoffPhase(simple int period) => float avgVol = ta.sma(volume, period) float priceChg = ta.change(close, period) string phase = "NEUTRAL" if volume > avgVol * 1.5 and math.abs(priceChg) < close * 0.02 phase := "ACCUMULATION" else if volume > avgVol * 1.5 and math.abs(priceChg) > close * 0.05 phase := "MARKUP" else if volume < avgVol * 0.7 phase := "DISTRIBUTION" phase Signal Generation Signals use multi-factor confirmation for accuracy: BUY Signal: Fractal low + Uptrend (EMA20 > EMA50) + RSI 30-55 + Bullish candle + Volume confirmation SELL Signal: Fractal high + Downtrend (EMA20 < EMA50) + RSI 45-70 + Bearish candle + Volume confirmation Pattern Detection: Label appears when harmonic pattern completes at current bar Dashboard Panel (Top-Right) Dimension - Fractal dimension value (market complexity measure) Last High - Most recent fractal high price Last Low - Most recent fractal low price Pattern - Current harmonic pattern name or NONE Elliott Wave - Current wave count (Wave 1-5) or OFF Wyckoff - Current market phase or OFF Trend - BULLISH, BEARISH, or NEUTRAL based on EMA crossover Signal - BUY, SELL, or WAIT status Visual Elements Fractal Markers - Small triangles at fractal highs (down arrow) and lows (up arrow) Geometry Lines - Dashed lines connecting the most recent fractal high and low Fibonacci Levels - Clean horizontal lines at 38.2%, 50%, and 61.8% retracement levels Fibonacci Extension - Horizontal line at 161.8% extension level Golden Spiral Levels - Support and resistance lines based on ATR x 1.618 3D Fractal Field - Optional depth layers around swing levels (OFF by default) Harmonic Pattern Markers - Small diamond shapes when Crab, Shark, or Cypher patterns detected Pattern Labels - Text label showing pattern name when detected Signal Labels - BUY/SELL labels on confirmed multi-factor signals Input Parameters Fractal Period (default: 5) - Bars on each side for fractal detection Geometry Depth (default: 3) - Complexity of geometric calculations Pattern Sensitivity (default: 0.8) - Tolerance for pattern ratio matching Show Fibonacci Levels (default: true) - Display retracement levels Show Fibonacci Extensions (default: true) - Display extension level Elliott Wave Detection (default: true) - Enable wave counting Wyckoff Analysis (default: true) - Enable phase detection Golden Spiral Levels (default: true) - Display spiral support/resistance Show Fractal Points (default: true) - Display fractal markers Show Geometry Lines (default: true) - Display connecting lines Show Pattern Labels (default: true) - Display pattern name labels Show 3D Fractal Field (default: false) - Display depth layers Show Harmonic Patterns (default: true) - Display pattern markers Show Buy/Sell Signals (default: true) - Display signal labels Suggested Use Cases Identify potential reversal zones using harmonic pattern completion Use Fibonacci levels for entry, stop-loss, and target planning Monitor Wyckoff phases for accumulation/distribution awareness Track Elliott Wave counts for trend structure analysis Use fractal dimension to gauge market complexity Wait for multi-factor signal confirmation before entering trades Timeframe Recommendations Best on 1H to Daily charts. Lower timeframes produce more fractals but with less significance. Higher timeframes provide stronger levels and more reliable signals. Limitations Harmonic pattern detection uses simplified ratio ranges and may not match all textbook definitions Elliott Wave counting is basic and does not include all wave rules Wyckoff phase detection is volume-based approximation Fractal dimension calculation is simplified Signals require fractal confirmation which has inherent lag equal to the fractal period Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
อินดิเคเตอร์ Pine Script®
โดย officialjackofalltrades
CT Market Fragility & Systemic Risk Monitor v1.0CT ⊕ Market Fragility & Systemic Risk Monitor v1.0 Systemic Stress & Market Regime Monitor OVERVIEW Wall Street-grade structural monitoring now open-source. CT ⊕ Market Fragility & Systemic Risk Monitor v1.0 is a real-time systemic risk tool designed to detect fragility before it hits price. Built by former institutional traders, it delivers structural insight typically reserved for desks inside hedge funds and global macro desks. This isn’t about finding entries or exits, it’s about understanding the environment you're trading in, and recognizing when it's shifting. WHAT IT DOES • Monitors six key market domains: Equities, Rates/Credit, FX (USD stress), Commodities, Crypto, and Macro • Detects volatility stress, cross-domain coupling, and regime synchronization • Classifies market structure into Normal → Fragile → Critical • Shows a live dashboard with scores, coupling levels, and structural state • Plots event markers (T1, T2, T3) for structural transitions • Implements hysteresis logic to model post-stress 'memory • Supports both single-domain ("Local Mode") and system-wide monitoring HOW IT WORKS This engine does not rely on traditional TA. No moving averages. No MACD. No patterns. No guesswork. Instead, it measures how markets are behaving beneath price detecting when stress is: • Building internally • Spreading across domains • Synchronizing into systemic fragility T1 (🟠) — Early instability: acceleration in market coupling T2 (🔵) — Fragile regime: multiple domains simultaneously stressed T3 (🔴) — Critical regime: synchronized, system-wide stress These are not buy/sell signals. They are structural regime alerts, the same kind used by institutions to cut risk before stress cascades. WHY IT MATTERS Most retail tools are reactive. They interpret surface-level patterns after the move. This tool is different. It’s proactive – measuring pressure before it breaks structure. Institutions have used structural fragility models like this for years. This script helps close that gap, giving everyday traders the same early warnings that pros use to reduce exposure and sidestep systemic blowups. It’s not about finding the edge. It’s about not getting crushed when the system breaks. Whether you trade crypto, stocks, FX, or macro, this engine helps answer: • Is the system stable right now? • Are stress levels rising across markets? • Is it time to tighten risk? Institutions don’t wait for breakouts. They monitor structure. Now, you can too. KEY FEATURES • Works on any asset class and any timeframe • Fully customizable domain selection • Three-tier structural alert system (T1–T3) • Real-time dashboard: stress scores, states, and coupling levels • Hysteresis modeling: post-stress “memory” detection • Supports single-domain (local) or multi-domain (systemic) monitoring • PineScript alerts built-in RECOMMENDED USE Active traders - all asset classes Use the dashboard and T1–T3 alerts to stay aware of structural risk in real time. Track multi-timeframe alignment to detect where risk originates and how it spreads across markets. Crypto trader s Monitor upstream domains (Equities, FX, Rates, Macro) to detect pressure before it reaches crypto. Identify reflexive stress before Bitcoin reacts — and stay ahead of contagion events. Macro & systematic traders Use T1–T3 transitions as volatility filters, exposure governors, or dynamic risk overlays. Build regime-aware models that adapt to shifting systemic conditions. Examples & Visuals Question: Would it have helped to know that at 9:30 on October 9th and again at 10:00 on October 10th that critical states were detected in the structural behavior of Bitcoin? Take a look: 30 min chart BTC shows two distinct T3 (critical) regime detections October 9th and 10:30 October 10th 5m BTC chart reveals high frequency instability for the same period, identifying instability, fragility, criticality The 30minute BTC chart at 16:30 Friday October 10th,, a few hours after first detecting critical systemic risk RISK DISCLAIMER This is a structural analysis tool, not a predictive signal. It does not provide financial advice, trade entries, or forecasts. Use at your own risk. Full disclaimer embedded in the script. Complexity Trading - From Wall St to Main St No patterns. No repainting. No mysticism. Just logic, math, science and market structure - now made accessible to everyone. Developer of LPPL Critical Pulse (LPPLCP), the Temporal Phase Model (TPM) and other other advanced structural and attractor based systems inspired by Sornette’s LPPL framework and other differentiated thinkers. Note on Methodology This tool is not predictive, and not designed for academic publication. It is a real-time structural monitoring system inspired by academically established concepts, including LPPL attractor dynamics, cross-asset coupling, reflexivity, and phase regime transitions, implemented within the real-time constraints of PineScript, and intended for visual, exploratory, and diagnostic use.
อินดิเคเตอร์ Pine Script®
โดย ComplexityTrading
14
Kinetic EMA & Volume with State EngineKinetic EMA & Volume with State Engine (EMVOL) 1. Introduction & Concept The EMVOL indicator converts a dense family of EMA signals and volume flows into a compact “state engine”. Instead of looking at individual EMA lines or simple crossovers, the script treats each EMA as part of a kinetic vector field and classifies the market into interpretable states: - Trend direction and strength (from a grid of prime‑period EMAs). - Volume regime (expansion, contraction, climax, dry‑up). - Order‑flow bias via delta (buy versus sell volume). - A combined scenario label that summarises how these three layers interact. The goal is educational: to help traders see that moving averages and volume become more meaningful when observed as a structure, not as isolated lines. EMVOL is therefore designed as a real‑time teaching tool, not as an automatic signal generator. 2. Volume Settings Group: “Volume Settings” A. Calculation Method - Geometry (Source File) – Default mode. Buy and sell volume are estimated from each candle’s geometry: the close is compared to the high/low range and the bar’s total volume is split proportionally between buyers and sellers. This approximation works on any TradingView plan and does not require lower‑timeframe data. - Intrabar (Precise) – Reconstructs buy/sell volume using a lower timeframe via requestUpAndDownVolume(). The script asks TradingView for historical intrabar data (e.g., 15‑second bars) and builds buy/sell volume and delta from that stream. This mode can produce a more accurate view of order flow, but coverage is limited by your account’s history limits and the symbol’s available lower‑timeframe data. B. Intrabar Resolution (If Precise) - Intrabar Resolution (If Precise) – Selected only when the calculation method is “Intrabar (Precise)”. It defines which lower timeframe (for example 15S, 30S, 1m) is used to compute up/down volume. Smaller intrabar timeframes may give smoother and more granular deltas, but require more historical depth from the platform. When “Intrabar (Precise)” is active, the dashboard’s extended section shows the resolution and the number of bars for which precise volume has been successfully retrieved, in the format: - Mode: Intrabar (15S) – where N is the count of bars with valid high‑resolution volume data. In Geometry mode this counter simply reflects the processed bars in the current session. 3. Kinetic Vector Settings Group: “Kinetic Vector” A. Vector Window - Vector Window – Controls the temporal smoothing applied to the aggregated vectors (trend, volume, delta, etc.). Internally, each bar’s vector value is averaged with a simple moving window of this length. - Shorter windows make the state engine more reactive and sensitive to local swings. - Longer windows make the states more stable and better suited to higher‑timeframe structure. B. Max Prime Period - Max Prime Period – Sets the largest prime number used in the EMA grid. The engine builds a family of EMAs on prime lengths (2, 3, 5, 7, …) up to this limit and converts their slopes into angles. - A higher limit increases the number of long‑horizon EMAs in the grid and makes the vectors sensitive to broader structure. - A lower limit focuses the analysis on short- and medium‑term behaviour. C. Price Source - Price Source – The price series from which the kinetic EMA grid is built (e.g., Close, HLC3, OHLC4). Changing the source modifies the context that the state engine is reading but does not change the core logic. 4. State Engine Settings Group: “State Engine Settings” These inputs define how the continuous vectors are translated into discrete states. A. Trend Thresholds - Strong Trend Threshold – Value above which the trend vector is treated as “extreme bullish” and below which it is “extreme bearish”. - Weak Trend Threshold – Inner boundary between neutral and directional conditions. Roughly: - |trend| < weak → Neutral trend state. - weak < |trend| ≤ strong → Bullish/Bearish. - |trend| > strong → Extreme Bullish/Extreme Bearish. B. Volume Thresholds - Volume Climax Threshold – Upper bound at which volume is considered “climax” (unusually expanded participation). - Volume Expansion Threshold – Boundary for normal expansion versus contraction. Conceptually: - Volume above “expansion” indicates increasing activity. - Volume near or above “climax” marks extreme participation. - Negative values below the symmetric thresholds map to contraction and extreme dry‑up (liquidity vacuum) states. C. Delta Thresholds - Strong Delta Threshold – Cut‑off for extreme buying or selling dominance in delta. - Weak Delta Threshold – Threshold for mild buy/sell bias versus neutral order flow. Combined with the sign of the delta vector, these thresholds classify order flow as: - Extreme Buy, Buy‑Dominant, Neutral, Sell‑Dominant, Extreme Sell. D. State Hysteresis Bars - State Hysteresis Bars – Minimum number of bars for which a new state must persist before the engine commits to the change. This prevents the dashboard from flickering during fast spikes and emphasises persistent market behaviour. - Smaller values switch states quickly; larger values demand more confirmation. 5. Visual Interface Group: “Visual Interface” A. Ribbon Base Color - Ribbon Base Color – Base hue for the multi‑layer EMA ribbon drawn around price. The script plots a dense grid of hidden EMAs and fills the gaps between them to form a semi‑transparent band. Narrow, overlapping bands hint at compression; wider separation hints at dispersion across EMA horizons. B. Show Dashboard - Show Dashboard – Toggles the on‑chart table which summarises the current state engine output. Disable this if you only want to keep the EMA ribbon and volume‑based structure on the price chart. C. Color Theme - Color Theme – Switch between a dark and light style for the dashboard background and text colours so that the table matches your chart theme. D. Table Position - Table Position – Places the dashboard at any corner or edge of the chart (Top / Middle / Bottom × Left / Centre / Right). E. Table Size - Table Size – Changes the dashboard’s text size (Tiny, Small, Normal, Large). Use a larger size on high‑resolution screens or when streaming. F. Show Extended Info - Show Extended Info – Adds diagnostic rows under the main state summary: - Mode / Primes / Vector – Shows the current calculation mode (Geometry / Intrabar), the selected intrabar resolution and coverage in bars ( ), how many prime periods are active, and the vector window. - Values – Displays the current aggregated vectors: - P: price vector - V: volume vector - B: buy‑volume vector - S: sell‑volume vector - D: delta vector Values are bounded between ‑1 and +1. - Volume Stats – Prints the last bar’s raw buy volume, sell volume and delta as formatted numbers. - Footer – A final row with the symbol and current time: #SYMBOL | HH:MM. These extended rows are meant for inspecting how the engine is behaving under the hood while you scroll the chart and compare different assets or timeframes. 6. Language Settings Group: “Language Settings” - Select Language – Switches the entire dashboard between English and Turkish. The underlying calculations and scenario logic are identical; only the labels, titles and comments in the table are translated. 7. Dashboard Structure & Reading Guide The table summarises the current situation in a few rows: 1. System Header – Shows the script name and the active calculation method (“Geometry” or “Intrabar”). 2. Scenario Title – High‑level description of the current combined scenario (e.g., “Trending Buy Confirmed”, “Sideways Balanced”, “Bull Trap”, “Blow‑Off Top”). The background colour is derived from the scenario family (trending, compression, exhaustion, anomaly, etc.). 3. Bias / Trend Line – States the dominant trend bias derived from the trend vector (Extreme Bullish, Bullish, Neutral, Bearish, Extreme Bearish). 4. Signal / Consideration Line – A short sentence giving qualitative guidance about the current state (for example: continuation risk, exhaustion risk, trap‑like behaviour, or compression). This is deliberately phrased as a consideration, not as a direct trading signal. 5. Trend / Volume / Delta Rows – Three separate rows explain, in plain language, how the trend, volume regime and delta are classified at this bar. 6. Extended Info (optional) – Mode / primes / vector settings, current vector values, and last‑bar volume statistics, as described above. Together, these rows are meant to be read as a narrative of what price, volume and order‑flow are doing, not as mechanical instructions. 8. State Taxonomy The state engine organizes market behaviour in three stages. 8.1 Trend States (from the Price Vector) - Extreme Bullish Trend – The prime‑grid price vector is strongly upward; most EMAs are aligned to the upside. - Bullish Trend – Upward bias is present, but less extreme. - Neutral Trend – EMAs are mixed or flat; price is effectively sideways relative to the grid. - Bearish Trend – Downward bias, with the EMA grid sloping down. - Extreme Bearish Trend – Strong downside alignment across the grid. 8.2 Volume Regime States (from the Volume Vector) - Volume Climax (Buy‑Side) – Strong positive volume vector; participation is unusually high in the current direction. - Volume Expansion – Activity above normal but below the climax threshold. - Neutral Volume – No major expansion or contraction versus recent history. - Volume Contraction – Activity is drying up compared with the past. - Extreme Dry‑Up / Liquidity Vacuum – Very low participation; the market is thin and prone to slippage. 8.3 Delta Behaviour States (from the Delta Vector) - Extreme Buy Delta – Buying pressure dominates strongly. - Buy‑Dominant Delta – Buy volume exceeds sell volume, but not at an extreme. - Neutral Delta – Buy and sell flows are roughly balanced. - Sell‑Dominant Delta – Selling pressure dominates. - Extreme Sell Delta – Aggressive, one‑sided selling. 8.4 Combined Scenario State s EMVOL uses the three base states above to generate a single scenario label. These scenarios are designed to be read as context, not as entry or exit signals. Trending Scenarios 1. Trending Buy Confirmed - Bullish or extreme bullish trend, supported by expanding or climax volume and buy‑side delta. - Educational idea: a healthy uptrend where both participation and order flow agree with the direction. 2. Trending Buy – Weak Volume - Bullish trend, but volume is neutral, contracting or in dry‑up while delta is still buy‑side. - Educational idea: price is advancing, yet participation is thinning; trend continuation becomes more fragile. 3. Trending Sell Confirmed - Bearish or extreme bearish trend, with expanding or climax volume and sell‑side delta. - Educational idea: strong downtrend with both volume and order‑flow confirmation. 4. Trending Sell – Weak Volume - Bearish trend, but volume is neutral, contracting or very low while delta remains sell‑side. - Educational idea: downside continues but with limited participation; vulnerable to short‑covering. Sideways / Range Scenarios 5. Sideways Balanced - Neutral trend, neutral delta, neutral volume. - Classic range environment; low directional edge, suitable for observation and context rather than trend trading. 6. Sideways with Buy Pressure - Neutral trend, but buy‑side delta is dominant or extreme. - Range with latent accumulation: price may still appear sideways, but buyers are quietly more active. 7. Sideways with Sell Pressure - Neutral trend with dominant or extreme sell‑side delta. - Distribution‑like environment where price chops while sellers are gradually more aggressive. Exhaustion & Volume Extremes 8. Exhaustion – Buy Risk - Extreme bullish trend, volume climax and strong buy‑side delta. - Educational idea: very strong up‑move where both participation and delta are already stretched; risk of exhaustion or blow‑off. 9. Exhaustion – Sell Risk - Extreme bearish trend, volume dry‑up and strong sell‑side delta. - Suggests one‑sided selling into increasingly thin liquidity. 10. Volume Climax (Buy) - Neutral trend, neutral delta, but volume at climax levels. - Often associated with a “big event” bar where participation spikes without a clear directional commitment. 11. Volume Climax (Sell / Dry‑Up) - Neutral trend and neutral delta, while the volume vector indicates an extreme dry‑up. - Highlights a stand‑still episode: very limited interest from both sides, increasing the sensitivity to future impulses. Divergences 12. Divergence – Bullish Context - Bullish or extreme bullish trend, but delta has faded back to neutral. - Price trend continues while order‑flow conviction softens; can precede pauses or complex corrections. 13. Divergence – Bearish Context - Bearish or extreme bearish trend with a neutral delta. - Downtrend persists, but selling pressure no longer dominates as clearly. Consolidation & Compression 14. Consolidation - Default state when no specific pattern dominates and the market is broadly balanced. - Educational use: treat this as a “no strong edge” label; focus on structure rather than direction. 15. Breakout Imminent - Neutral trend with contracting volume. - Compression phase where energy is building up; often precedes transitions into trending or shock scenarios. Traps & Hidden Divergences 16. Bull Trap - Bullish trend, with neutral or contracting volume and sell‑side delta. - Price appears strong, but order‑flow shifts against it; often seen near fake breakouts or failing rallies. 17. Bear Trap - Bearish trend, neutral or contracting volume, but buy‑side delta. - Downtrend “looks” intact, while buyers become more aggressive underneath the surface. 18. Hidden Bullish Divergence - Bullish trend, contracting volume, but strong buy‑side delta. - Educational idea: price dips or slows while aggressive buyers step in, often inside an ongoing uptrend. 19. Hidden Bearish Divergence - Bearish trend, volume expansion and strong sell‑side delta. - Reinforced downside pressure even if price is temporarily retracing. Reversal & Transition Patterns 20. Reversal to Bearish - Neutral trend, volume climax and strong sell‑side delta. - Suggests that heavy selling appears at the top of a move, turning a previously neutral or rising context into potential downside. 21. Reversal to Bullish - Neutral trend, extreme volume dry‑up and strong buy‑side delta. - Often associated with selling exhaustion where buyers start to take control. 22. Indecision Spike - Neutral trend with extreme volume (climax or dry‑up) but neutral delta. - Crowd participation changes sharply while order‑flow remains undecided; treat as an informational spike rather than a direction. Extended Compression & Acceleration 23. Coiling Phase - Neutral trend, contracting volume, and delta that is neutral or only mildly one‑sided. - Extended compression where price, volume and delta all contract into a tightly coiled range, often preceding a strong move. 24. Bullish Acceleration - Bullish trend with volume expansion and strong buy‑side delta. - Uptrend not only continues but gains kinetic strength; educationally, this illustrates how trend, volume and delta align in the strongest phases of a move. 25. Bearish Acceleration - Bearish trend with volume expansion and strong sell‑side delta. - Mirror image of Bullish Acceleration on the downside. Trend Exhaustion & Climax Reversal 26. Bull Exhaustion - Bullish or extreme bullish trend, with contraction or dry‑up in volume and buy‑side or neutral delta. - The move has already travelled far; participation fades while price is still elevated. 27. Bear Exhaustion - Bearish or extreme bearish trend, with volume climax or contraction and sell‑side or neutral delta. - Down‑move may be approaching a point where additional selling pressure has diminishing impact. 28. Blow‑Off Top - Extreme bullish trend, volume climax and extreme buy delta all at once. - Classic blow‑off behaviour: price, volume and order‑flow are simultaneously stretched in the same direction. 29. Selling Climax Reversal - Extreme bearish trend with extreme volume dry‑up and extreme sell‑side delta. - Marks a very aggressive capitulation phase that can precede major rebounds. Advanced VSA / Anomaly Scenarios 30. Absorption - Typically neutral trend with expanding or climax volume and extreme delta (either buy or sell). - Educational focus: large participants are aggressively absorbing liquidity from the opposite side, while price remains relatively contained. 31. Distribution - Scenario where volume remains elevated while directional conviction weakens and the trend slows. - Represents potential “selling into strength” or “buying into weakness”, depending on the active side. 32. Liquidity Vacuum - Combination of thin liquidity (extreme dry‑up) with a directional trend or strong delta. - Highlights environments where even small orders can move price disproportionately. 33. Anomaly / Shock Event - Triggered when the vector z‑scores detect rare combinations of price, volume and delta behaviour that deviate from their own historical distribution. - Intended as a warning label for unusual events rather than a specific tradeable pattern. 9. Educational Usage Notes - EMVOL does not produce mechanical “buy” or “sell” commands. Instead, it classes each bar into an interpretable state so that traders can study how trends, volume and order‑flow interact over time. - A common exercise is to overlay your usual EMA crossovers, support/resistance or price patterns and observe which EMVOL scenarios appear around entries, exits, traps and climaxes. - Because the vectors are normalized (bounded between ‑1 and +1) and then discretized, the same conceptual states can be compared across different symbols and timeframes. 10. Disclaimer & Educational Purpose This indicator is provided strictly as an educational and analytical tool. Its purpose is to help visualise how price, volume and order‑flow interact; it is not designed to function as a stand‑alone trading system. Please note: 1. No Automated Strategy – The script does not implement a complete trading strategy. Scenario labels and dashboard messages are descriptive and should not be followed as unconditional entry or exit signals. 2. No Financial Advice – All information produced by this indicator is general market analysis. It must not be interpreted as investment, financial or trading advice, or as a recommendation to buy or sell any instrument. 3. Risk Warning – Trading and investing involve substantial risk, including the risk of loss. Always perform your own analysis, use appropriate position sizing and risk management, and consult a qualified professional if needed. You are solely responsible for any decisions made using this tool. 4. Data Precision & Platform Limits – The “Intrabar (Precise)” mode depends on the availability of high‑resolution historical data at the chosen intrabar timeframe. If your TradingView plan or the symbol’s history does not provide sufficient depth, this mode may only partially cover the visible chart. In such cases, consider switching to “Geometry (Source File)” for a fully populated view.
อินดิเคเตอร์ Pine Script®
โดย ata_sabanci
2
The Quantum Leap: Renko + ML(Note: This indicator uses the BackQuant & SuperTrend which takes a 4-5 seconds to load) This strategy uses the following indicators (please see source code) Synthetic Renko: Ignores time and focuses purely on price movement to detect clear trend reversals (Red-to-Green). ATR (Average True Range): Measures volatility to calculate the Renko brick sizes and SuperTrend sensitivity. Adaptive SuperTrend: A trend filter that uses volatility clustering to confirm if the market is currently in a "Bearish" state. RSI (Relative Strength Index): A momentum gauge ensuring the asset is "Oversold" (exhausted) before we consider a setup. Monthly Pivots: Horizontal support lines based on last month's data acting as price "floors" (S1, S2, S3). SMA (Simple Moving Average): A 100-bar average ensuring we are strictly buying below the long-term mean (deep value). BackQuant (KNN): A Machine Learning engine that compares current data to historical patterns to predict immediate momentum. This is a sophisticated, multi-stage strategy script. It combines "Old School" price action (Renko) with "New School" Machine Learning (KNN and Clustering). Here is the high-level summary of how we will break this down: Topic 1: The "Bottom Hunter" Setup. How the script uses Renko bricks and aggressive filtering (SuperTrend, SMA, RSI, Pivots) to find a potential market bottom. Topic 2: The ML Engine (BackQuant & SuperTrend). How the script uses K-Nearest Neighbors (KNN) to predict momentum and Volatility Clustering to adjust the SuperTrend. Topic 3: The "Leap" Execution. How the script synchronizes the Setup (Topic 1) with the ML Trigger (Topic 2) using a time window. Topic 1: The "Bottom Hunter" Setup This script is designed as a Mean Reversion strategy (often called "catching a falling knife" or "bottom fishing"). It is trying to find the exact moment a downtrend stops and reverses. Most strategies buy when price is above the 200 SMA or above the SuperTrend. This script does the exact opposite. The Logic: Renko Bricks: It simulates Renko bricks internally (without changing your chart view). It waits for a specific pattern: A Red Brick followed immediately by a Green Brick (a reversal). The "Bearish" Filters: To generate a "WATCH" signal, the following must be true: Price < SuperTrend: The market must officially be in a downtrend. Price < SMA: Long-term trend is down. Price < Monthly Pivot: Price is deeply discounted. RSI < Threshold: The asset is oversold (exhausted). Recommended Settings for daily signals for Stocks : Confirmation : 10. (How many bars after Renko Buy signal the AI has to identify a bullish move). Percentage : 2 (This is the Renko bar size. This represents 2% move.) SMA: 100 (Signal must be found below 100 SMA) Price must be below: PIVOT (This is the monthly Pivot levels)
อินดิเคเตอร์ Pine Script®
โดย dr_lobo
TTP IFVG Signals With EMA /ICT Gold scalpingThis script uses original logic and alerting rules. in Japan finding ICT IFVG and EMA conditions. #IFVG, Forex, ICT, EMA, Scalping, Indicator This indicator automatically finds IFVG (Imbalance / Fair Value Gap) zones and gives you a buy or sell signal when price comes back and breaks out through that gap. It also draws a colored box over the gap so you can see the zone visually, and it raises alerts when a new signal appears. High-level logic: On every bar, the script looks back up to “IFVG_GapBars” bars. For each offset i it checks a 3-candle pattern: – If the low of the newer candle is above the high of the older candle: bullish FVG (price jumped up, leaving a gap). – If the high of the newer candle is below the low of the older candle: bearish FVG (price jumped down, leaving a gap). When a valid FVG is found: – For a bullish FVG it looks for a later close that breaks down through that gap (sell signal). – For a bearish FVG it looks for a later close that breaks up through that gap (buy signal). – A moving-average trend filter must agree (downtrend for sells, uptrend for buys). – It checks that price has not already “filled” the gap before the breakout. If all conditions are satisfied, it: – Sets signal_dir = 1 for a buy, or -1 for a sell. – Draws a box from the original FVG bar to the bar just before the breakout (extended a bit to the right), between the gap high and gap low. – Plots an ▲ label for buys or ▼ label for sells. – Triggers the corresponding alert conditions. Now the parameters: PipSizeMultilier (PipSizeManual) Multiplies the symbol’s minimum tick size (syminfo.mintick). It is used when converting “MinFVG_Pips” into an actual price distance. If you feel the indicator is too sensitive (too many small gaps), you can increase this multiplier to effectively require a larger price difference. TickSize Internal value = syminfo.mintick * PipSizeMultiplier. This is the actual price step the script uses as a “pip” when checking minimum gap size. FVG Search Lookback (IFVG_GapBars) How many bars back from the current bar the script will scan for a 3-candle FVG pattern. Larger value = it can find older FVGs, but loop cost is higher. Min FVG Size (Pips/Points) (MinFVG_Pips) Minimum allowed size of the gap, measured in “pips/points” using TickSize. If the vertical distance between the gap high and gap low is smaller than this, the gap is ignored. 0.0 means “no size filter” (every FVG is allowed). FVG Epsilon (Price Units) (FVG_EpsPoints) Tolerance for the FVG detection. It is subtracted/added in the condition that checks “low > old high” or “high < old low”. 0.0 means strict gap (no overlap at all). A small positive epsilon allows tiny overlaps to still count as a gap. Show IFVG Zones (ShowZones) If true, the script draws a box over the IFVG zone when a signal is confirmed. If false, no boxes are drawn; you only see the ▲ / ▼ markers and alerts. Buy Zone Color (ZoneColorBuy) Fill color and border color for boxes created from bearish FVGs that later produce a buy signal. Sell Zone Color (ZoneColorSell) Fill color and border color for boxes created from bullish FVGs that later produce a sell signal. Box Extension (Bars) (BoxExtension) How many extra bars to extend the right side of the box beyond the breakout bar. The internal right coordinate is “bar_index - 1 + BoxExtension”. Increase this if you want the zone to visually extend further into the future. MA Period (MA_Period) Lookback length of the moving average used as a trend filter. MA Type (MA_Kind) Type of moving average: “SMA” or “EMA”. If SMA is chosen, the script uses ta.sma; if EMA, it uses ta.ema. Moving-average filter behavior: For sell signals (from bullish FVG): MA must be sloping down (MA < MA ) and price must be below MA. For buy signals (from bearish FVG): MA must be sloping up (MA > MA ) and price must be above MA. If these conditions are not satisfied, the FVG is ignored even if the gap and breakout conditions are met. Signals and alerts: signal_dir = 1 → buy signal, ▲ label below the bar, “IFVG Buy Alert” / “IFVG Buy/Sell Alert” can fire. signal_dir = -1 → sell signal, ▼ label above the bar, “IFVG Sell Alert” / “IFVG Buy/Sell Alert” can fire. signal_dir = 0 → no new signal on this bar. In short: This indicator finds 3-candle IFVG gaps, filters them by size and trend, waits for a clean breakout through the gap, draws a box on the original gap zone, and gives you a clear buy or sell signal plus alerts.
อินดิเคเตอร์ Pine Script®
โดย stray34sk2
LJ Parsons Harmonic Time StampsPurpose of the Script This script is designed to divide a specific time period on a market chart (from startDate to endDate) into fractional segments based on mathematically significant ratios. It then plots vertical lines at the first candle that occurs at or after each of these fractional timestamps. Each line is labeled according to an interval scheme, as outlined by LJ Parsons "Structured Multiplicative, Recursive Systems in Financial Markets" papers.ssrn.com Providing a symbolic mapping of time fractions zenodo.org Start (00) and End (00): Marks the beginning and end of the period. Intermediate labels (m2, M2, m3, M3, …): Represent divisions of the time period that correspond to specific fractions of the whole. This creates a visual “resonance map” along the price chart, where the timing of price movements can be compared to mathematically significant points. Parsons Market Resonance Theory proposes that markets move in patterns that are not random but resonate with underlying mathematical structures, analogous to logarithmic relationships. The key ideas reflected in this script are: Temporal Fractional Resonance By marking fractional points of a defined time period, the script highlights potential moments when market activity might “resonate” due to cyclical patterns. These points are analogous to overtones in music—certain times may have stronger market reactions. Mapping Market Movements to "Just Intonation" Intervals Assigning Interval labels to fractional timestamps provides a symbolic framework for understanding market behaviour. For example, the midpoint (P5) may correspond to strong market turning points, while minor or major intervals (m3, M6) might correspond to subtler movements. Identifying Potentially Significant Points in Time The plotted lines do not predict price direction but rather identify temporal markers where price movements may be more likely to display structured behaviour. Traders or researchers can then study price reactions around these lines for correlations with market resonance patterns. In essence, the script turns a period of time into a harmonic structure, with each line and label acting like a “note” in the market’s temporal symphony. It’s a tool to visualize and test whether price behaviour aligns with the resonant fractions hypothesized in MRT.
อินดิเคเตอร์ Pine Script®
โดย wallabimba
Fibonacci Degree System This Pine Script creates a sophisticated technical analysis tool that combines Fibonacci retracements with a degree-based cycle system. Here's a comprehensive breakdown: Core Concept The indicator maps price movements onto a 360-degree circular framework, treating market cycles like geometric angles. It creates a visual "mesh" where Fibonacci ratios intersect in both price (horizontal) and time (vertical) dimensions. How It Works 1. Finding Reference Points The script looks back over a specified period (default 100 bars) to identify: Highest High: The peak price point Lowest Low: The trough price point Time Locations: Exactly which bars these extremes occurred on These two points form the boundaries of your analysis window. 2. Creating the Fibonacci Grid Horizontal Lines (Price Levels): The script divides the price range between high and low into seven key Fibonacci ratios: 0% (Low) - Bottom boundary in red 23.6% - Minor retracement in orange 38.2% - Shallow retracement in yellow 50% - Midpoint in lime green 61.8% - Golden ratio in aqua (most significant) 78.6% - Deep retracement in blue 100% (High) - Top boundary in purple Each line represents a potential support/resistance level where price might react. Vertical Lines (Time Cycles): The same Fibonacci ratios are applied to the time dimension between the high and low bars. If your high and low are 50 bars apart, vertical lines appear at: Bar 0 (0%) Bar 12 (23.6%) Bar 19 (38.2%) Bar 25 (50%) Bar 31 (61.8%) Bar 39 (78.6%) Bar 50 (100%) These suggest when price might make significant moves. 3. The Degree Mapping System The innovative feature maps the time progression to degrees: 0° = Start point (0% time) 85° = 23.6% through the cycle 138° = 38.2% through the cycle 180° = Midpoint (50%) 222° = 61.8% through the cycle (golden angle) 283° = 78.6% through the cycle 360° = Complete cycle (100%) This treats market movements as circular patterns, similar to how planets orbit or pendulums swing. Visual Output When you apply this indicator, you'll see: A rectangular mesh extending beyond your high-low range (by 150% default) Color-coded horizontal lines showing price Fibonacci levels Matching vertical lines showing time Fibonacci intervals Price labels on the right showing percentage levels Degree labels at the bottom showing the angular position in the cycle Intersection points creating a grid of potentially significant price-time coordinates Trading Application Traders use this to identify: Support/Resistance Zones: Where horizontal and vertical lines intersect Time Targets: When price might reverse (at vertical Fibonacci times) Cycle Completion: When approaching 360°, a new cycle may begin Harmonic Patterns: Geometric relationships between price and time Customization Features The script offers extensive control: Lookback period: Adjust cycle length (10-500 bars) Mesh extension: How far to project the grid forward Visual toggles: Show/hide horizontal lines, vertical lines, labels Styling: Line thickness, style (solid/dashed/dotted), colors Label positioning: Fine-tune text placement for readability The intersection at 61.8% time and 61.8% price at 222° becomes a key target zone. This tool essentially converts the abstract concept of market cycles into a concrete, visual geometric framework that traders can analyze and act upon. DISCLAIMER: This information is provided for educational purposes only and should not be considered financial, investment, or trading advice. No guarantee of profits: Past performance and theoretical models do not guarantee future results. Trading and investing involve substantial risk of loss. Not a recommendation: This script illustration does not constitute a recommendation to buy, sell, or hold any financial instrument. Do your own research: Always conduct thorough independent research and consider consulting with a qualified financial advisor before making any trading decisions.
อินดิเคเตอร์ Pine Script®
โดย kingshukghosh71
Bull Flag & Flat Top Breakout DetectorBull Flag & Flat Top Detector - Quick Reference Guide Pattern Overview 🚩 Bull Flag ╱╲ ╱ ╲ ← Pullback (2-5 red candles) ╱ ╲ ╱ ╲____ ╱ ╲ │ │ │ THE POLE │ ← Strong upward move (3+ green candles) │ │ └──────────────┘ What to look for: Strong initial move (the "pole") - 3+ green candles, 3%+ move Brief pullback - 2-5 candles, less than 50% retracement Pullback should "drift" lower, not crash Entry on first candle to make new high after pullback 📊 Flat Top Breakout ════════════════ ← Resistance (multiple touches) ↑ ↑ ↑ ╱╲ ╱╲ ╱╲ ╱ ╲╱ ╲╱ ╲ ← Consolidation ╱ ╲ ╱ ╲ What to look for: Multiple touches of same resistance level (2+) Tight consolidation range Each failed breakout builds pressure Entry on convincing break above resistance with volume Signal Types SignalShapeColorMeaningBull Flag Breakout▲ TriangleLimeEntry signal - go longFlat Top Breakout◆ DiamondAquaEntry signal - go longBear Flag Breakout▼ TriangleRedShort entry (if enabled)Pattern Forming🚩 FlagFaded GreenBull flag developingPattern Forming■ SquareFaded BlueFlat top developing Level Lines Explained LineColorStyleMeaningEntryLimeSolidBreakout trigger priceStop LossRedDashedExit if price falls hereTarget 1AquaDottedFirst profit target (2R)Target 2YellowDottedSecond profit target (3R) Info Table Reference FieldWhat It ShowsBull FlagScanning / Forming 🚩 / Breakout ✓Flat TopScanning / Forming 📊 / Breakout ✓PullbackCandle count + retracement %Rel VolumeCurrent bar vs averageEMA 20Above ✓ or Below ✗VWAPAbove ✓ or Below ✗Green StreakConsecutive green candles (pole)ResistanceTouch count for flat top Trading Checklist Before Entry ✅ Pattern status shows "FORMING" or "BREAKOUT" Price above EMA (table shows ✓) Price above VWAP (table shows ✓) Relative volume 1.5x+ (ideally 2x+) Stock is in play (up 5%+ on day, has catalyst) Market direction supportive (not fighting trend) Entry Execution Wait for breakout candle to form Confirm volume spike on breakout Enter as close to entry line as possible Set stop loss at red dashed line Know your target levels Trade Management If no immediate follow-through → consider exit ("breakout or bailout") Take 50% off at Target 1 Move stop to breakeven Let remainder run toward Target 2 Exit fully if price returns below entry Bull Flag Quality Checklist Pole Quality: FactorIdealAcceptableAvoidGreen candles5+3-4Less than 3Move size10%+3-10%Less than 3%VolumeIncreasingSteadyDecliningCandle bodiesLargeMediumSmall/doji Pullback Quality: FactorIdealAcceptableAvoidCandle count2-34-56+RetracementUnder 38%38-50%Over 50%VolumeDecliningSteadyIncreasingCharacterOrderly driftChoppySharp drop Flat Top Quality Checklist FactorGood SetupWeak SetupTouches3+ at same levelOnly 2, widely spacedToleranceVery tight (0.2%)Loose (1%+)Duration5-15 barsToo short or too longVolumeDrying upErraticPrior trendUpSideways/down Common Mistakes to Avoid ❌ Entering too early Wait for actual breakout, not anticipation "Forming" ≠ "Breakout" ❌ Ignoring volume No volume = likely false breakout Require 1.5x+ relative volume minimum ❌ Fighting the trend Check EMA and VWAP status Both should be ✓ for high probability ❌ Wide stops Stop should be below pullback low If stop is too wide, skip the trade ❌ Holding losers "Breakout or bailout" - if it doesn't work, exit Failed breakouts often reverse hard ❌ Chasing extended moves If you missed entry, wait for next pattern Don't chase 5+ candles after breakout Risk Management Rules Position Sizing Risk Amount = Account × Risk % (typically 1-2%) Position Size = Risk Amount ÷ (Entry - Stop) Example: Account: $25,000 Risk: 1% = $250 Entry: $5.00 Stop: $4.70 Risk per share: $0.30 Position Size: $250 ÷ $0.30 = 833 shares Risk-Reward Targets TargetR MultipleExample (risk $0.30)Target 12:1+$0.60 ($5.60)Target 23:1+$0.90 ($5.90) Timeframe Guide TimeframeProsConsBest For1-minMore patterns, precise entryNoisy, false signalsScalping5-minGood balance, cleaner patternsFewer signalsDay trading15-minHigh quality patternsMiss fast movesSwing entries Settings Quick Reference Default Settings (Balanced) Pole: 3 candles, 3% move Pullback: 2-5 candles, 50% max retrace Volume: 1.5x required Filters: EMA + VWAP ON Aggressive Settings Pole: 2 candles, 2% move Pullback: 2-6 candles, 60% max retrace Volume: 1.2x required Filters: VWAP OFF Conservative Settings Pole: 4 candles, 5% move Pullback: 2-4 candles, 40% max retrace Volume: 2.0x required Filters: Both ON Alert Setup Recommended Alerts "Bull Flag Forming" Get early warning as pattern develops Prepare your position size and levels "Bull Flag Breakout" Primary entry alert React quickly when triggered "Any Bullish Breakout" Catch both bull flags and flat tops Good for watchlist scanning Alert Setup Steps Right-click chart → Add Alert Condition: Select "Bull Flag & Flat Top Breakout Detector" Choose alert type from dropdown Set expiration and notification method Troubleshooting Q: Patterns not detecting? Lower the Min Pole Move % setting Reduce Min Pole Candles requirement Check that price is in acceptable range Q: Too many false signals? Increase volume multiplier to 2.0x Enable both EMA and VWAP filters Increase Min Pole Move % Q: Levels not showing? Enable "Show Entry Line", "Show Stop Loss", "Show Targets" Check "Max Patterns to Display" setting Q: Info table not visible? Enable "Show Info Table" in settings Try different table position Pattern Combinations Best Setups (A+ Quality) Bull flag on a gap day (Gap & Go → Bull Flag) Flat top at pre-market high resistance Pattern forming above VWAP with 5x+ volume Avoid These Bull flag below VWAP Flat top in downtrending stock Low volume patterns Patterns late in the day (after 2pm) Daily Routine Pre-Market (7-9am) Build watchlist of gappers (5%+, high volume) Apply indicator to top 3-5 candidates Note pre-market levels Market Open (9:30-10:30am) Watch for "FORMING" status on watchlist Prepare entries as patterns develop Execute on breakout signals Manage trades according to plan Midday (10:30am-2pm) Look for second-wave patterns Be more selective (less momentum) Consider tighter stops Close (2-4pm) Generally avoid new patterns Manage existing positions Review day's trades
อินดิเคเตอร์ Pine Script®
โดย DayTradingMD1992
Market Energy & Direction DashboardMarket Energy & Direction Dashboard - Daytrading Overview A comprehensive real-time market internals dashboard that combines NYSE TICK, NYSE Advance-Decline (ADD) momentum, VIX direction, and relative volume into a single visual traffic light system with intelligent signal synthesis. Designed for active daytraders who need instant confirmation of market direction and energy based on momentum alignment across all major internals. What It Does This indicator synthesizes multiple market internals using directional momentum analysis rather than static thresholds to provide clear, actionable signals: • Traffic Light System: Single glance confirmation of market state o Bright Green: Maximum bullish - all internals aligned (TICK + ADD rising + VIX falling + volume) o Bright Red: Maximum bearish - all internals aligned (TICK + ADD falling + VIX rising + volume) o Yellow: Exhaustion warning - TICK at extremes, potential reversal imminent o Moderate Colors: Partial alignment - some confirmation but not complete o Gray: Choppy, neutral, or conflicting signals • Real-Time Dashboard displays: o Current TICK value with exhaustion warnings o Current ADD with directional momentum indicator (↑ rising = breadth improving, ↓ falling = breadth deteriorating, ± compression) o VIX level with directional indicator (↓ declining = bullish, ↑ rising = bearish, ± compression = neutral) o Relative volume (current vs 20-period average) o Composite status message synthesizing all data into clear directional summary Key Features ✓ Momentum-based analysis - all indicators show direction/change, not just levels ✓ Intelligent signal hierarchy from "Maximum" to "Moderate" based on internal alignment ✓ ADD directional momentum - catches breadth shifts early, works in all market conditions ✓ VIX directional analysis - shows if fear is increasing, decreasing, or stagnant ✓ Color-coded traffic light for instant decision making ✓ Detects TICK/ADD divergences (conflicting signals = caution) ✓ Exhaustion warnings at extreme TICK levels (±1000+) ✓ Composite status messages - "Maximum Bull", "Strong Bull", "Moderate Bull", etc. ✓ Customizable thresholds for all parameters ✓ Moveable dashboard (9 position options) ✓ Built-in alerts for all signal strengths, exhaustion, and divergences How To Use Setup: 1. Add indicator to your main trading chart (SPY, ES, NQ, etc.) 2. Default settings work well for most traders, but you can customize: o TICK Extreme Level (default 1000) o ADD Compression Threshold (default 100 - detects when breadth is stagnant) o VIX Elevated Level (default 20) o VIX Compression Threshold (default 2% - detects low volatility) o Volume Threshold (default 1.5x average) 3. Position dashboard wherever convenient on your chart Reading The Signals: Signal Hierarchy (Strongest to Weakest): MAXIMUM SIGNALS ⭐ (Brightest colors - All 4 internals aligned) • "✓ MAXIMUM BULL": TICK bullish + ADD rising (↑) + VIX falling (↓) + Volume elevated o This is the holy grail setup - all momentum aligned, highest conviction longs • "✓ MAXIMUM BEAR": TICK bearish + ADD falling (↓) + VIX rising (↑) + Volume elevated o Perfect storm bearish - all momentum aligned, highest conviction shorts STRONG SIGNALS (Bright colors - Core internals aligned) • "✓ STRONG BULL": TICK bullish + ADD rising (↑) o Strong confirmation even without VIX/volume - breadth supporting the move • "✓ STRONG BEAR": TICK bearish + ADD falling (↓) o Strong confirmation - both momentum and breadth deteriorating MODERATE SIGNALS (Faded colors - Partial confirmation) • "MODERATE BULL": TICK bullish but ADD not confirming direction o Proceed with caution - momentum present but breadth questionable • "MODERATE BEAR": TICK bearish but ADD not confirming direction o Proceed with caution - selling but breadth not fully participating WARNING SIGNALS • "⚠ EXHAUSTION" (Yellow): TICK at ±1000+ extremes o Potential reversal zone - prepare to fade or take profits o Often marks blow-off tops or capitulation bottoms NEUTRAL/AVOID • "CHOPPY/NEUTRAL" (Gray): Conflicting signals or low conviction o Stay out or reduce size significantly Individual Indicator Interpretation: TICK: • Green: Bullish momentum (>+300) • Red: Bearish momentum (<-300) • Yellow: Exhaustion (±1000+) • Gray: Neutral ADD (Advance-Decline): • Green (↑): Breadth improving - more stocks participating in the move • Red (↓): Breadth deteriorating - fewer stocks participating • Gray (±): Breadth stagnant - no clear participation trend VIX: • Green (↓): Fear declining - healthy environment for rallies • Red (↑): Fear rising - risk-off mode, supports downward moves • Gray (±): Volatility compression - often precedes explosive moves Volume: • Green: High conviction (>1.5x average) • Gray: Low conviction Trading Strategy: 1. Wait for "MAXIMUM" or "STRONG" signals for highest probability entries o Maximum signals = go full size with confidence o Strong signals = good conviction, normal position sizing 2. Confirm directional alignment: o For longs: Want ADD ↑ (rising) and VIX ↓ (falling) o For shorts: Want ADD ↓ (falling) and VIX ↑ (rising) 3. Use exhaustion warnings (yellow) to: o Take profits on existing positions o Prepare counter-trend entries o Tighten stops 4. Avoid "MODERATE" signals unless you have strong conviction from other analysis o These work best as confirmation for existing setups o Not strong enough to initiate new positions alone 5. Never trade "CHOPPY/NEUTRAL" signals o Gray means stay out - preserve capital o Wait for clear alignment 6. Watch for divergences: o Price making new highs but ADD ↓ (falling) = distribution warning o Price making new lows but ADD ↑ (rising) = potential bottom o Divergence alert will notify you Best Practices: • Use on 1-5 minute charts for daytrading • Combine with your price action or technical setup (support/resistance, trendlines, patterns) • The dashboard confirms when to take your setup, not what setup to take • Most effective during regular market hours (9:30 AM - 4:00 PM ET) when volume is present • The strongest edge comes from "MAXIMUM" signals - wait for these for best risk/reward • Pay special attention to ADD direction - it's the most predictive breadth indicator • VIX compression (gray ±) often signals upcoming volatility expansion - prepare for bigger moves Customization Option All thresholds are adjustable in settings: • TICK Extreme: Higher = fewer exhaustion warnings (try 1200-1500 for less sensitivity) • ADD Compression Threshold: Change detection sensitivity o Default 100 = balanced o Lower (50) = more sensitive to small breadth changes o Higher (200-300) = only shows major breadth shifts • VIX Elevated: Adjust for current volatility regime (15-25 typical range) • VIX Compression Threshold: o Default 2% = balanced o Lower (0.5-1%) = catches subtle VIX changes o Higher (3-5%) = only shows significant VIX moves • Volume Threshold: Lower for quieter stocks/times, higher for more confirmation Alerts Available • Maximum Bullish: All 4 internals aligned bullish (TICK + ADD↑ + VIX↓ + Volume) • Maximum Bearish: All 4 internals aligned bearish (TICK + ADD↓ + VIX↑ + Volume) • Strong Bullish: TICK bullish + ADD rising • Strong Bearish: TICK bearish + ADD falling • Exhaustion Warning: TICK at extreme levels • Divergence Warning: TICK and ADD directions conflicting Understanding the Signal Synthesis The indicator uses intelligent logic to combine all internals: "MAXIMUM" Signals require: • TICK direction (bullish/bearish) • ADD momentum (rising/falling) in same direction • VIX direction (falling for bulls, rising for bears) • Volume elevated (>1.5x average) "STRONG" Signals require: • TICK direction (bullish/bearish) • ADD momentum (rising/falling) in same direction • (VIX and volume are bonuses but not required) "MODERATE" Signals: • TICK showing direction • But ADD not confirming or contradicting • Weakest actionable signal This hierarchy ensures you know exactly how much conviction the market has behind any move. Technical Details • Pulls real-time data from NYSE TICK (USI:TICK), NYSE ADD (USI:ADD), and CBOE VIX • ADD direction calculated using bar-to-bar change with compression detection • VIX direction calculated using bar-to-bar percentage change • Volume calculation uses 20-period simple moving average • Dashboard updates every bar • No repainting - all calculations based on closed bar data Who This Is For • Active daytraders of stocks, futures (ES/NQ), and options • Scalpers needing quick directional confirmation with multiple internal alignment • Swing traders looking to time intraday entries with maximum confluence • Volatility traders who monitor VIX behavior • Market makers and professionals who trade based on breadth and internals • Anyone who monitors market internals but wants intelligent synthesis vs raw data Tips For Success Trading Philosophy: • Quality over quantity - wait for "MAXIMUM" signals for best results • One "MAXIMUM" signal trade is worth five "MODERATE" signal trades • Gray/neutral is not a sign of missing opportunity - it's protecting your capital Signal Confidence Levels: 1. MAXIMUM (95%+ confidence) - Trade these aggressively with full size 2. STRONG (80-85% confidence) - Trade these with normal position sizing 3. MODERATE (60-70% confidence) - Only if confirmed by strong technical setup 4. CHOPPY/NEUTRAL - Do not trade, wait for clarity Advanced Techniques: • Breadth divergences: Watch for price making new highs while ADD shows ↓ (falling) = major warning • VIX/Price divergences: Rallies with rising VIX (↑) are usually false moves • Volume confirmation: "MAXIMUM" signals with 2x+ volume are the absolute best • Compression zones: When both ADD and VIX show compression (±), expect explosive breakout soon • Sequential signals: Back-to-back "MAXIMUM" signals in same direction = strong trending day Common Patterns: • Opening surge with "MAXIMUM BULL" that shifts to "EXHAUSTION" (yellow) = fade the high • Selloff with "MAXIMUM BEAR" followed by ADD ↑ (rising) divergence = potential reversal • Choppy morning followed by "MAXIMUM" signal afternoon = best trending opportunity Example Scenarios Perfect Bull Entry: • Bright green signal box • TICK: +650 • ADD: +1200 (↑) • VIX: 18.30 (↓) • Volume: 2.3x • Status: "✓ MAXIMUM BULL" → ALL SYSTEMS GO - Take aggressive long positions Strong Bull (Good Confidence): • Green signal box (slightly less bright) • TICK: +500 • ADD: +800 (↑) • VIX: 19.50 (±) • Volume: 1.2x • Status: "✓ STRONG BULL" → Good long setup - breadth confirming even without VIX/volume Caution Bull (Moderate): • Faded green signal box • TICK: +400 • ADD: +900 (↓) • VIX: 20.10 (↑) • Volume: 0.9x • Status: "MODERATE BULL" → CAUTION - TICK bullish but breadth deteriorating and VIX rising = weak rally Exhaustion Warning: • Yellow signal box • TICK: +1350 ⚠ • ADD: +2100 (↑) • VIX: 17.20 (↓) • Volume: 1.8x • Status: "⚠ EXHAUSTION" → Take profits or prepare to fade - TICK overextended despite good internals Divergence Setup (Potential Reversal): • Faded green signal • TICK: +300 • ADD: +1800 (↓) • VIX: 21.50 (↑) • Volume: 1.6x • Status: "MODERATE BULL" → WARNING - Price rallying but breadth collapsing and fear rising = distribution Perfect Bear Entry: • Bright red signal box • TICK: -780 • ADD: -1600 (↓) • VIX: 24.80 (↑) • Volume: 2.5x • Status: "✓ MAXIMUM BEAR" → Perfect short setup - all momentum bearish with conviction Compression (Wait Mode): • Gray signal box • TICK: +50 • ADD: -200 (±) • VIX: 16.40 (±) • Volume: 0.7x • Status: "CHOPPY/NEUTRAL" → STAY OUT - Volatility compression, no conviction, await breakout Performance Optimization Best Market Conditions: • Works excellent in trending markets (up or down) • Particularly powerful during high-volume sessions (first/last hours) • "MAXIMUM" signals most reliable during 9:45-11:00 AM and 2:00-3:30 PM ET Less Effective During: • Lunch period (11:30 AM - 1:30 PM) - lower volume reduces signal quality • Low-volatility environments - compression signals dominate • Major news events in first 5 minutes - wait for internals to stabilize Recommended Use Cases: • Scalping: Trade only "MAXIMUM" signals for quick 5-15 minute moves • Daytrading: Use "MAXIMUM" and "STRONG" signals for position entries • Swing entries: Use "MAXIMUM" signals for optimal intraday entry timing • Exit timing: Use "EXHAUSTION" (yellow) warnings to take profits ________________________________________ Pro Tip: Create a dedicated workspace with this indicator on SPY/ES/NQ charts. Set alerts for "MAXIMUM BULL", "MAXIMUM BEAR", and "EXHAUSTION" signals. Most professional traders only trade the "MAXIMUM" setups and ignore everything else - this alone can dramatically improve win rates.
อินดิเคเตอร์ Pine Script®
โดย BlouToro
Elliott Wave + SMC Fusion # Elliott Wave + SMC Fusion ## TITLE: Elliott Wave + Smart Money Concepts Fusion --- ## SHORT DESCRIPTION: Automated Elliott Wave pattern detection with Smart Money Concepts confirmation, EWO oscillator integration, and confluence scoring system. --- ## FULL DESCRIPTION: ### 📊 OVERVIEW This indicator combines three powerful trading methodologies into a unified system: - **Elliott Wave Theory** - Automated detection of Wave 1-2 impulse patterns - **Smart Money Concepts (SMC)** - Order Blocks and Fair Value Gaps for institutional confirmation - **Elliott Wave Oscillator (EWO)** - Momentum-based signal validation The core concept is to identify high-probability Wave 3 entries by detecting completed Wave 1-2 structures and validating them with SMC and momentum indicators. --- ### 🔧 HOW IT WORKS **1. Pattern Detection (ZigZag Method)** - Uses pivot high/low detection to identify swing points - Validates Wave 2 retracement using Fibonacci ratios (default: 38.2% - 88.6%) - Requires minimum wave size to filter noise - Applies confirmation bars to avoid premature signals **2. Wave Projections** - Wave 3 target: Fibonacci extension of Wave 1 (default: 1.618) - Wave 4 retracement: Percentage of Wave 3 (default: 38.2%) - Wave 5 projection: Extension of Wave 1 from Wave 4 **3. Smart Money Validation** - **Order Blocks**: Identifies last opposing candle before breakout (institutional footprint) - **Fair Value Gaps**: Detects price imbalances for potential support/resistance **4. EWO Confirmation** - Calculates momentum: (EMA5 / EMA34 - 1) × 100 - Signal line crossovers confirm trend direction - Strong signals occur at extremes (< -13 or > 13 threshold) **5. Confluence Scoring (0-100%)** Points awarded for: - Fibonacci quality of Wave 2 retracement (10-30 pts) - Order Block presence (15 pts) - Fair Value Gap presence (10 pts) - Volume confirmation (10-15 pts) - Trend alignment with EMA50 (10 pts) - EWO confirmation (10-20 pts) --- ### 🎯 UNIQUE FEATURES **Pattern Locking System** - Once a valid pattern is detected, it locks until: - Pattern invalidates (price breaks Wave 0) - Pattern completes (Wave 5 reached) - Auto-timeout (configurable bars) - Prevents rapid signal flipping and false alerts **Signal Stability Controls** - Adjustable cooldown between signals (default: 20 bars) - Minimum bar distance between wave points - Direction change requirement option - Confirmation bars after Wave 2 formation **Visual Wave Tracking** - Solid lines for impulse waves (0→1, 2→3, 4→5) - Dashed lines for corrective waves (1→2, 3→4) - Numbered labels on each wave point - Real-time projection lines to targets **Comprehensive Dashboard** - Current wave status and lock state - Pattern grade (A+ to D based on confluence) - Projected vs actual wave levels (✓ when completed) - SMC confirmation status - Risk/Reward ratio calculation - EWO trend direction --- ### 📈 TRADING APPLICATION **Entry Strategy** - Wait for Wave 1-2 pattern detection (diamond signal) - Check confluence score (>65% = higher probability) - Verify EWO alignment with pattern direction - Enter after 30% retracement of Wave 2 (customizable) **Risk Management** - Stop Loss: Below Wave 0 (with buffer) - Take Profit 1: Wave 3 projection - Take Profit 2: Wave 5 projection - R:R displayed in dashboard **Invalidation Rules** - Price breaks below Wave 0 (bullish) or above (bearish) - Wave 2 level violated before Wave 3 forms - Pattern timeout exceeded --- ### ⚙️ KEY SETTINGS **Elliott Wave** - ZigZag Length: Pivot detection sensitivity - Fib Tolerance: Acceptable retracement range - Min Wave Size: Filter small movements **Signal Stability** - Signal Cooldown: Minimum bars between signals - Lock Pattern Until Invalid: Prevent signal changes - Confirmation Bars: Wait after Wave 2 **Wave Projection** - Wave 3/4/5 Fibonacci extensions - Projection display distance **EWO Settings** - Fast/Slow EMA lengths - Signal smoothing - Strength threshold **SMC Settings** - Order Block lookback period - FVG minimum size percentage --- ### 🔔 ALERTS - New bullish/bearish pattern detected - High confluence setup (>75%) - Pattern invalidation - Wave completion --- ### ⚠️ IMPORTANT NOTES - This indicator identifies **potential** Elliott Wave patterns based on mathematical rules - Elliott Wave analysis is subjective - patterns may be interpreted differently - Always combine with other analysis methods and proper risk management - Past pattern performance does not guarantee future results - Pattern locking prevents repainting but delays new pattern detection - Best used on higher timeframes (1H+) for cleaner wave structures --- ### 📚 METHODOLOGY REFERENCES **Elliott Wave Theory** - Wave 2 typically retraces 38.2% - 88.6% of Wave 1 - Wave 3 is often the strongest, extending 161.8% of Wave 1 - Wave 4 usually retraces 38.2% of Wave 3 - Wave 5 completes the impulse structure **Smart Money Concepts** - Order Blocks represent institutional supply/demand zones - FVGs indicate price inefficiencies that may act as magnets **Elliott Wave Oscillator** - Developed to identify wave momentum - Crossovers signal potential wave transitions - Extreme readings often coincide with wave completions --- ### 🎨 VISUAL ELEMENTS - **Green**: Bullish patterns and projections - **Red**: Bearish patterns and projections - **Orange**: Wave projection levels - **Purple**: Order Block zones - **Yellow**: Fair Value Gaps - **Blue**: Entry levels - **Diamond shapes**: New pattern signals - **Triangle shapes**: EWO crossover signals --- ### 💡 TIPS FOR BEST RESULTS 1. Use on liquid markets with clear trend behavior 2. Higher timeframes produce more reliable patterns 3. Look for confluence scores above 65% 4. Verify EWO alignment before entry 5. Consider market context (overall trend, key levels) 6. Adjust ZigZag length based on your trading style 7. Increase cooldown period for longer-term signals --- **Indicator Type**: Overlay **Markets**: All (Crypto, Forex, Stocks, Commodities) **Timeframes**: All (1H+ recommended) **Style**: Pattern Recognition + Momentum + Price Action
อินดิเคเตอร์ Pine Script®
โดย Smart-Day-Trader
USDJPY Fair Value Gap + Session Strategy🎯 Overview This strategy combines Fair Value Gaps (FVGs) with session-based order flow analysis, specifically optimized for USDJPY. It identifies price inefficiencies left behind by institutional order flow during high-volatility trading sessions, offering a modern alternative to traditional lagging indicators. 🔬 What Are Fair Value Gaps? Fair Value Gaps represent areas where aggressive institutional buying or selling created "gaps" in the market structure: Bullish FVG: Price moves up so aggressively that it leaves unfilled buy orders behind Bearish FVG: Price moves down so quickly that it leaves unfilled sell orders behind Research shows approximately 80% of FVGs get "filled" (price returns to the gap) within 20-60 bars, making them highly predictable trading zones. (see the generated image above) (see the generated image above) FVG Detection Logic: text // Bullish FVG: Gap between high and current low bullishFVG = low > high and high > high // Bearish FVG: Gap between low and current high bearishFVG = high < low and low < low 🌏 Session-Based Trading Why Sessions Matter for USDJPY (see the generated image above) Tokyo Session (00:00-09:00 UTC) Highest volatility during first hour (00:00-01:00 UTC) Average movement: 51-60 pips Best for breakout strategies London/NY Overlap (13:00-16:00 UTC) Maximum liquidity and institutional participation Tightest spreads and most reliable FVG formations Optimal for continuation trades Monday Premium Effect USDJPY moves 120+ pips on Mondays due to weekend positioning Enhanced FVG formation during session opens 📊 Strategy Components (see the generated image above) 1. Fair Value Gap Detection Identifies bullish and bearish FVGs automatically Age limit: FVGs expire after 20 bars to avoid stale setups Size filter: Minimum gap size to filter out noise 2. Session Filtering Tokyo Open focus: Trades during first hour of Asian session London/NY Overlap: Captures high-liquidity institutional flows Weekend gap strategy: Enhanced signals on Monday opens 3. Volume Confirmation Requires 1.5x average volume spike Confirms institutional participation Reduces false signals 4. Trend Alignment 50 EMA filter ensures trades align with higher timeframe trend Long trades above EMA, short trades below Prevents costly counter-trend trades 5. Risk Management 2:1 Risk/Reward minimum ensures profitability with 40%+ win rate Percentage-based stops adapt to USDJPY volatility (0.3% default) Configurable position sizing 🎯 Entry Conditions (see the generated image above) Long Entry (BUY) ✅ Bullish FVG detected in previous bars ✅ Price returns to FVG zone during active trading session ✅ Volume spike above 1.5x average ✅ Price above 50 EMA (trend confirmation) ✅ Bullish candle closes within FVG zone ✅ Trading during Tokyo open OR London/NY overlap Short Entry (SELL) ✅ Bearish FVG detected in previous bars ✅ Price returns to FVG zone during active trading session ✅ Volume spike above 1.5x average ✅ Price below 50 EMA (trend confirmation) ✅ Bearish candle closes within FVG zone ✅ Trading during Tokyo open OR London/NY overlap 📈 Expected Performance Backtesting Results (Based on Similar Strategies): Win Rate: 44-59% (profitable due to high R:R ratio) Average Winner: 60-90 pips during London/NY sessions Average Loser: 30-40 pips (tight stops at FVG boundaries) Risk/Reward: 2:1 minimum, often 3:1 during strong trends Best Performance: Monday Tokyo opens and Wednesday London/NY overlaps Why This Works for USDJPY: 90% correlation with US-Japan bond yield spreads High volatility provides sufficient pip movement Heavy institutional/central bank participation creates clear FVGs Consistent volatility patterns across trading sessions ⚙️ Configurable Parameters Session Settings: Trade Tokyo Session (Enable/Disable) Trade London/NY Overlap (Enable/Disable) FVG Settings: FVG Minimum Size (Filter small gaps) Maximum FVG Age (20 bars default) Show FVG Markers (Visual display) Volume Settings: Use Volume Filter (Enable/Disable) Volume Multiplier (1.5x default) Volume Average Period (20 bars) Trend Settings: Use Trend Filter (Enable/Disable) Trend EMA Period (50 default) Risk Management: Risk/Reward Ratio (2.0 default) Stop Loss Percentage (0.3% default) 🎨 Visual Indicators 🟡 Yellow Line: 50 EMA trend filter 🟢 Green Triangles: Long entry signals 🔴 Red Triangles: Short entry signals 🟢 Green Dots: Bullish FVG zones 🔴 Red Dots: Bearish FVG zones 🟦 Blue Background: Tokyo open session 🟧 Orange Background: London/NY overlap 📊 Recommended Settings Optimal Timeframes: Primary: 5-minute charts (scalping) Secondary: 15-minute charts (swing trading) Parameter Optimization: Conservative: Stop Loss 0.2%, R:R 2:1, Volume 2.0x Balanced: Stop Loss 0.3%, R:R 2:1, Volume 1.5x (default) Aggressive: Stop Loss 0.4%, R:R 1.5:1, Volume 1.2x Risk Management: Maximum 1-2% of account per trade Daily loss limit: Stop after 3-5 consecutive losses Use fixed percentage position sizing ⚠️ Important Considerations Avoid Trading During: Major news events (BOJ interventions, NFP, FOMC) Holiday periods with reduced liquidity Low volatility Asian afternoon sessions When US-Japan yield differential narrows sharply Best Practices: Limit to 2-3 trades per session maximum Always respect the 50 EMA trend filter Never risk more than planned per trade Paper trade for 2-4 weeks before live implementation Track performance by session and day of week 🚀 How to Use Add the script to your USDJPY chart Set timeframe to 5-minute or 15-minute Adjust parameters based on your risk tolerance Enable strategy alerts for automated notifications Wait for visual signals (triangles) to appear Enter trades according to your risk management rules 📚 Strategy Foundation This strategy is based on: Smart Money Concepts (SMC): Institutional order flow tracking Market Microstructure: Understanding how FVGs form in electronic trading Quantified Risk Management: Statistical edge through proper R:R ratios Session Liquidity Patterns: Exploiting predictable volatility cycles
กลยุทธ์ Pine Script®
โดย Stockhock
Bitcoin Cycle History Visualization [SwissAlgo]BTC 4-Year Cycle Tops & Bottoms Historical visualization of Bitcoin's market cycles from 2010 to present, with projections based on weighted averages of past performance. ----------------------------------------------------------------- CALCULATION METHODOLOGY Why Bottom-to-Bottom Cycle Measurement? This indicator defines cycles as bottom-to-bottom periods. This is one of several valid approaches to Bitcoin cycle analysis: - Focuses on market behavior (price bottoms) rather than supply schedule events (halving-to-halving) - Bottoms may offer good reference points for some analytical purposes - Tops tend to be extended periods that are harder to define precisely - Aligns with how some traditional asset cycles are measured and the timing observed in the broader "risk-on" assets category - Halving events are shown separately (yellow backgrounds) for reference - Neither halving-based nor bottom-based measurement is inherently superior Different analysts prefer different cycle definitions based on their analytical goals. This approach prioritizes observable market turning points. Cycle Date Definitions - Approximate monthly ranges used for each event (e.g., Nov 2022 bottom = Nov 1-30, 2022) - Cycle 1: Jul 2010 bottom → Jun 2011 top → Nov 2011 bottom - Cycle 2: Nov 2011 bottom → Dec 2013 top → Jan 2015 bottom - Cycle 3: Jan 2015 bottom → Dec 2017 top → Dec 2018 bottom - Cycle 4: Dec 2018 bottom → Nov 2021 top → Nov 2022 bottom - Future cycles will be added as new top/bottom dates become firm Duration Calculations - Days = timestamp difference converted to days (milliseconds ÷ 86,400,000) - Bottom → Top: days from cycle bottom to peak - Top → Bottom: days from peak to next cycle bottom - Bottom → Bottom: full cycle duration (sum of above) Price Change Calculations - % Change = ((New Price - Old Price) / Old Price) × 100 - Example: $200 → $19,700 = ((19,700 - 200) / 200) × 100 = 9,750% gain - Approximate historical prices used (rounded to significant figures) Weighted Average Formula Recent cycles weighted more heavily to reflect the evolved market structure: - Cycle 1 (2010-2011): EXCLUDED (too early-stage, tiny market cap) - Cycle 2 (2011-2015): Weight = 1x - Cycle 3 (2015-2018): Weight = 3x - Cycle 4 (2018-2022): Weight = 5x Formula: Weighted Avg = (C2×1 + C3×3 + C4×5) / (1+3+5) Example for Bottom→Top days: (761×1 + 1065×3 + 1066×5) / 9 = 1,032 days Projection Method - Projected Top Date = Nov 2022 bottom + weighted avg Bottom→Top days - Projected Bottom Date = Nov 2022 bottom + weighted avg Bottom→Bottom days - Current days elapsed compared to weighted averages - Warning symbol (⚠) shown when the current cycle exceeds the historical average Technical Implementation - Historical cycle dates are hardcoded (not algorithmically detected) - Dates represent approximate monthly ranges for each event - The indicator will be updated as the Cycle 5 top and bottom dates become confirmed - Updates require manual code maintenance - not automatic - Users should verify they're using the latest version for current cycle data ----------------------------------------------------------------- FEATURES - Background highlights for historical tops (red), bottoms (green), and halving events (yellow) - Data table showing cycle durations and price changes - Visual cycle boundary boxes with subtle coloring - Projected timeframes displayed as dashed vertical lines - Toggle on/off for each visual element - Customizable background colors ----------------------------------------------------------------- DISPLAY SETTINGS - Show/hide cycle tops, bottoms, halvings, data table, and cycle boxes - Customizable background colors for each event type - Clean, institutional-grade visual design suitable for analysis UPDATES & MAINTENANCE This indicator is maintained as new cycle events occur. When Cycle 5's top and bottom are confirmed with sufficient time elapsed, the code and projections will be updated accordingly. Check for the latest version periodically. OPEN SOURCE Code available for review, modification, and improvement. Educational transparency is prioritized. ----------------------------------------------------------------- IMPORTANT LIMITATIONS ⚠ EXTREMELY SMALL SAMPLE SIZE Based on only 4 complete cycles (2011-2022). In statistical analysis, this is insufficient for reliable predictions. ⚠ CHANGED MARKET STRUCTURE Bitcoin's market has fundamentally evolved since early cycles: - 2010-2015: Tiny market cap, retail-only, unregulated - 2024-2025: Institutional adoption, spot ETFs, regulatory frameworks, macro correlation The environment that created past patterns no longer exists in the same form. ⚠ NO PREDICTIVE GUARANTEE Historical patterns can and do break. Market cycles are not laws of physics. Past performance does not guarantee future results. The next cycle may not follow historical averages. ⚠ LENGTHENING CYCLE THEORY Some analysts believe cycles are extending over time (diminishing returns, maturing market). If true, simple averaging underestimates future cycle lengths. ⚠ SELF-FULFILLING PROPHECY RISK The halving narrative may be partially circular - it works because people believe it works. Sufficient changes in market structure or participant behavior can invalidate the pattern. ⚠ APPROXIMATE DATA Historical prices rounded to significant figures. Exact bottom/top dates vary by exchange. Month-long ranges are used for simplicity. EDUCATIONAL USE ONLY This indicator is designed for historical analysis and understanding Bitcoin's past behavior. It is NOT: - Trading advice or financial recommendations - A guarantee or prediction of future price movements - Suitable as a sole basis for investment decisions - A replacement for fundamental or technical analysis The projections show "what if the pattern continues exactly" - not "what will happen." Always conduct independent research, understand the risks, and consult qualified financial advisors before making investment decisions. Only invest what you can afford to lose.
อินดิเคเตอร์ Pine Script®
โดย SwissAlgo
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork. Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret? The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously. This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation. Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio. The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally. Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional. The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply. The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach. 1. Introduction and Theoretical Background 1.1 The Limitations of Static Portfolio Allocation Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies. The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework. 1.2 Multi-Factor Approaches in Asset Allocation Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding. 2. Data Foundation and Data Quality 2.1 Data Sources Used The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics. First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures. Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model. Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator. Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data. 2.2 Handling Missing Data A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals. 3. Component 1: Market Regime Detection 3.1 The Concept of Market Regimes The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly. DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis. 3.2 Trend Analysis Through Moving Averages Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs. The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent. 3.3 Volatility Regimes Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986). DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices. The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989). 3.4 Drawdown Analysis A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric. The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%. 3.5 Regime Classification Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations. 4. Component 2: Risk-Based Allocation 4.1 Volatility Targeting as Risk Management Approach The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced. Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility. 4.2 Market Volatility Calculation Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure. Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice. The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation. 4.3 Drawdown-Based Position Size Adjustment In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979). The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25). 4.4 Dynamic Risk Adjustment An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure. Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability. 5. Component 3: Valuation Analysis 5.1 Theoretical Foundations of Fundamental Valuation DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets. Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets. 5.2 Equity Risk Premium as Central Valuation Metric The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation. DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs. The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003). The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone. The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth. The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this. The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10. 5.3 Quality Adjustments to Valuation Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure. Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points. Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies. Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010). 6. Component 4: Sentiment Analysis 6.1 The Role of Sentiment in Financial Markets Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions. Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops. 6.2 VIX-Based Sentiment Assessment DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods. A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities. 6.3 VIX Term Structure as Sentiment Signal The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D). Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines. 6.4 Safe-Haven Flows During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion. The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases. Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection. 7. Component 5: Macroeconomic Analysis 7.1 The Yield Curve as Economic Indicator The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity. DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points. The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006). 7.2 Credit Conditions and Spreads Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns. The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points. Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score. 7.3 Financial Stability at Corporate Level While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health. A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions. Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases. 8. Component 6: Crisis Detection 8.1 The Need for Systematic Crisis Detection Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components. Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators. 8.2 Multi-Signal Crisis Identification The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence. The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility. The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns. The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis. The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points. The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point. A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms. 8.3 Crisis-Based Allocation Override When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007). This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability. 9. Integration and Final Allocation Calculation 9.1 Component Weighting The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results. The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations. The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017). The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988). Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable. 9.2 Model Type Adjustments DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments. Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons. Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets. Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged. 9.3 Smoothing and Stability The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change. The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals. 10. Visualization and Interpretation 10.1 Main Output: Equity Allocation DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash. The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases. 10.2 Dashboard Metrics A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status. Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments. Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down. 10.3 Component Breakdown (Optional) Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning." This feature is disabled by default to keep the chart clean but can be activated for deeper analysis. 10.4 Confidence Bands The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations. This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources. 11. Alert System 11.1 Allocation Alerts DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning. These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts. 11.2 Crisis Alerts Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors). 11.3 Regime Change Alerts An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics. 11.4 Risk Breach Alerts A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions. 12. Practical Application and Limitations 12.1 Portfolio Implementation DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation. A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs. 12.2 Adaptation to Individual Preferences The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight. Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation. Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect. 12.3 Limitations and Disclaimers DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007). The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market. The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination. Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation. The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems. 12.4 Relationship to Other Strategies DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing. The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory. The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio. 13. Scientific Foundation and Evaluation 13.1 Theoretical Consistency DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998). This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990). 13.2 Empirical Validation While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements: In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation. Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance. Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters. 13.3 Comparison with Existing Literature DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources. Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals. Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion). References Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press. Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403. Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24. Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985. Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680. Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152. Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229. Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327. Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915. Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44. Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764. Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue. Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228. Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108. Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business. Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007. Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576. Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61. Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79. Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49. Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465. Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78. Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720. Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin. Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill. Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384. Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley. Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*. Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91. Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292. Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170. Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467. Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676. Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419. Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91. Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433. Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644. Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250. Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65. Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41. Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press. Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360. Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449. Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153. Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138. Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58. Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462. Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17. Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105. Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research. Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
อินดิเคเตอร์ Pine Script®
โดย EdgeTools
16
Outside the Bollinger Bands Alerting Indicator Overview The Outside the Bollinger Bands Alerting Indicator is a comprehensive technical analysis tool that combines multiple proven indicators into a single, powerful system designed to identify high-probability reversal patterns at Bollinger Band extremes. This indicator goes beyond simple band touches to detect sophisticated pattern formations that often signal strong directional moves. Key Features & Capabilities 🎯 Advanced Pattern Recognition Bollinger Band Breakout Patterns - Detects "pierce-and-reject" formations where price breaks through a Bollinger Band but immediately reverses back inside - Identifies failed breakouts that often lead to strong moves in the opposite direction - Combines multiple confirmation signals: engulfing candle patterns, MACD momentum, and ATR volatility filters - Visual alerts with symbols positioned below (bullish) or above (bearish) candles Tweezer Top & Bottom Patterns - Identifies consecutive candles with nearly identical highs (tweezer tops) or lows (tweezer bottoms) - Requires at least one candle to breach the respective Bollinger Band - Confirms reversal with directional close requirements - Customizable tolerance settings for pattern sensitivity - Visual alerts with ❙❙ symbols for easy identification 📊 Multi-Indicator Integration Bollinger Bands Indicator - Dual-band configuration with outer (2.0 std dev) and inner (1.5 std dev) bands that can be adjusted to suit your own parameters - Configurable MA types: SMA, EMA, SMMA (RMA), WMA, VWMA - Customizable length, source, and offset parameters - Color-coded band fills for visual clarity Moving Average Suite - EMA 9, 21, 50, and 200 (individually toggleable) - Special "SMA 3 High" for help visualizing and detecting Bollinger Band break-outs - Dynamic color coding based on price relationship Optional Ichimoku Cloud overlay - Complete Ichimoku implementation with customizable periods - Dynamic cloud coloring based on trend direction - Toggleable overlay that doesn't interfere with other indicators 🚨 Comprehensive Alert System Real-Time JSON Alerts - Sends structured data on every confirmed bar close - Includes all indicator values: BB levels, EMAs, MACD, RSI - Contains signal states and crossover conditions - Perfect for automated trading systems and webhooks {"timestamp":1753118700000,"symbol":"ETHUSD","timeframe":"5","price":3773.3,"bollinger_bands":{"upper":3826.95,"basis":3788.32,"lower":3749.68},"emas":{"ema_9":3780.45,"ema_21":3788.92,"ema_50":3800.79,"ema_200":3787.74,"sma_3_high":3789.45},"macd":{"macd":-10.1932,"signal":-11.3266,"histogram":1.1334},"rsi":{"rsi":40.5,"rsi_ma":39.32,"level":"neutral"}} Specific Alert Conditions - MACD histogram state changes (rising to falling, falling to rising) - RSI overbought/oversold crossovers - All pattern detections (BB Bounce, Tweezer patterns) - Bollinger Band breakout alerts 🎨 Visual Elements Pattern Identification - ♻ symbols for Bollinger Band breakout patterns (green for bullish, red for bearish) - ❙❙ symbols for tweezer patterns (green below for bottoms, red above for tops) - Color-coded band fills for trend visualization Chart Overlay Options - All moving averages with distinct colors - Bollinger Bands with inner and outer boundaries - Optional Ichimoku cloud with trend-based coloring Trading Applications Reversal Trading - Identify high-probability reversal points at extreme price levels - Use failed breakout patterns for entry signals - Combine multiple timeframes for enhanced accuracy Trend Analysis - Monitor moving average relationships for trend direction - Use Ichimoku cloud for trend strength assessment - Track momentum with MACD and RSI integration Risk Management - ATR-based volatility filtering reduces false signals - Multiple confirmation requirements improve signal quality - Real-time alerts enable prompt decision making Suggested Use - Use on multiple timeframes for confluence - Combine with support/resistance levels for enhanced accuracy - Set up alerts for hands-free monitoring - Customize settings based on market volatility and trading style - Consider volume confirmation for stronger signals
อินดิเคเตอร์ Pine Script®
โดย Chart0bserver
CVD Absorption + Confirmation [Orderflow & Volume]This indicator detects bullish and bearish absorption setups by combining Cumulative Volume Delta (CVD) with price action, candlestick, and volume confirmations. 🔹 What is Absorption? Absorption happens when aggressive buyers/sellers push CVD to new highs or lows, but price fails to follow through. Bearish absorption: CVD makes a higher high, but price does not. Bullish absorption: CVD makes a lower low, but price does not. This often signals that limit orders are absorbing aggressive market orders, creating potential reversal points. 🔹 Confirmation Patterns Absorption signals are only shown if they are validated by one of the following patterns: Engulfing candle with low volume → reversal faces little resistance. Engulfing candle with high volume → strong aggressive participation. Pin bar with high volume → absorption visible in the wick. CVD flattening / slope reversal → shift in aggressive order flow. 🔹 Signals ✅ Bullish absorption confirmed → Green label below the bar. ❌ Bearish absorption confirmed → Red label above the bar. Each label represents a potential reversal setup after orderflow absorption is validated. 🔹 Alerts Built-in alerts are included for both bullish and bearish confirmations, so you can track setups in real-time without watching the chart 24/7. 📌 How to Use: Best applied at key levels (supply/demand, VWAP, OR, liquidity zones). Look for confluence with your trading strategy before taking entries. Works on all markets and timeframes where volume is reliable.
อินดิเคเตอร์ Pine Script®
โดย ratipetya
1
Aethix Cipher DivergencesAethix Cipher Divergences v6 Core Hook: Custom indicator inspired by VuManChu B, Grok-enhanced for crypto intel—blends WaveTrend (WT) oscillator with multi-divergences for buy/sell circles (green/teal buys #00FFFF, red sells) and dots (divs, gold overbought alerts). Key Features: WaveTrend Waves: Dual waves (teal WT1, darker teal WT2) with VWAP (purple for neon vibe), overbought/oversold lines, crosses for signals. Divergences: Regular/hidden for WT, RSI, Stoch—red bearish, green bullish dots; extra range for deeper insights. RSI + MFI Area: Colored area (green positive, red negative) for sentiment/volume flow. Stochastic RSI: K/D lines with fill for overbought/oversold trends. Schaff Trend Cycle: Purple line for cycle smoothing. Sommi Patterns: Flags (pink bearish, blue bullish) and diamonds for HTF patterns, purple higher VWAP. MACD Colors on WT: Dynamic WT shading based on MACD for enhanced reads.
อินดิเคเตอร์ Pine Script®
โดย axi13
Consolidation Box1. Overview & Purpose The "Faithful Box" is a powerful TradingView indicator designed to automatically identify and visualize high-quality price consolidation zones, often known as 'trading ranges' or 'boxes'. This tool is a direct Pine Script translation of a specific, robust Python-based analysis strategy. Its primary goal is not just to find any sideways movement, but to qualify it based on a strict set of rules, ensuring that only meaningful and tradable consolidation patterns are highlighted on the chart. 2. Core Logic: How It Works The indicator's intelligence is based on several key rules translated from the original Python script: Box Definition (Based on Closing Prices): The most crucial rule is how the box is defined. Unlike many tools that use candle wicks (highs and lows), this indicator establishes the Ceiling (Resistance) and the Floor (Support) of the box using the highest Close price and the lowest Close price over the specified analysis period. This provides a more stable and representative view of the true consolidation zone, ignoring outlier price spikes. Quality Filters: A consolidation is only considered valid and drawn on the chart if it meets two strict criteria: Minimum Touches: The price must "test" the ceiling and floor a minimum number of times. A "touch" is counted with a tolerance — the High of a candle only needs to get close to the ceiling, and the Low close to the floor. This simulates how price interacts with support and resistance zones in the real world. Maximum Height: The consolidation range cannot be excessively wide or volatile. The box will only be drawn if its total height, as a percentage of its price, is below a user-defined limit. 3. Visual Features on the Chart When a valid consolidation pattern is detected, the indicator draws a semi-transparent box over the analysis period. The most powerful visual feature is its dynamic coloring, which functions as a built-in alert system: 🟥 Red Box: The box turns red when the price enters the upper "Action Zone" (e.g., the top 30% of the range). This visually signals that the price is near a key resistance level, alerting the trader to a potential reversal or breakout opportunity. 🟩 Green Box: The box turns green when the price enters the lower "Action Zone" (e.g., the bottom 30% of the range). This highlights that the price is testing a key support level. ⬜ Gray Box: The box remains a neutral gray when the price is trading in the middle of the range, which can often be considered a "no-trade" or "wait-and-see" zone. 4. Configuration (Indicator Inputs) You have full control over the indicator's sensitivity through its inputs: Analysis Period (days): Defines the lookback window (in trading days) to search for a consolidation pattern. Common values are 42 (approx. 2 months) or 63 (approx. 3 months). Maximum Box Height (%): Filters out consolidations that are too volatile or wide. A lower percentage will find tighter, more compressed consolidations. Minimum Ceiling/Floor Touches: Defines the "strength" of the support and resistance levels. Requiring more touches will result in fewer detected patterns, but likely of higher quality. Touch Tolerance (%): Defines how close the High/Low needs to get to the edge of the box to be counted as a touch. A value of 2% means a touch is registered if the price comes within 2% of the box's total height from the edge. Action Zone (%): Customizes the size of the red (resistance) and green (support) zones. A value of 30% means the top 30% of the box will be the red zone, and the bottom 30% will be the green zone.
อินดิเคเตอร์ Pine Script®
โดย tulyodetarso
Ultimate Market Structure [Alpha Extract]Ultimate Market Structure A comprehensive market structure analysis tool that combines advanced swing point detection, imbalance zone identification, and intelligent break analysis to identify high-probability trading opportunities.Utilizing a sophisticated trend scoring system, this indicator classifies market conditions and provides clear signals for structure breaks, directional changes, and fair value gap detection with institutional-grade precision. 🔶 Advanced Swing Point Detection Identifies pivot highs and lows using configurable lookback periods with optional close-based analysis for cleaner signals. The system automatically labels swing points as Higher Highs (HH), Lower Highs (LH), Higher Lows (HL), and Lower Lows (LL) while providing advanced classifications including "rising_high", "falling_high", "rising_low", "falling_low", "peak_high", and "valley_low" for nuanced market analysis. swingHighPrice = useClosesForStructure ? ta.pivothigh(close, swingLength, swingLength) : ta.pivothigh(high, swingLength, swingLength) swingLowPrice = useClosesForStructure ? ta.pivotlow(close, swingLength, swingLength) : ta.pivotlow(low, swingLength, swingLength) classification = classifyStructurePoint(structureHighPrice, upperStructure, true) significance = calculateSignificance(structureHighPrice, upperStructure, true) 🔶 Significance Scoring System Each structure point receives a significance level on a 1-5 scale based on its distance from previous points, helping prioritize the most important levels. This intelligent scoring system ensures traders focus on the most meaningful structure breaks while filtering out minor noise. 🔶 Comprehensive Trend Analysis Calculates momentum, strength, direction, and confidence levels using volatility-normalized price changes and multi-timeframe correlation. The system provides real-time trend state tracking with bullish (+1), bearish (-1), or neutral (0) direction assessment and 0-100 confidence scoring. // Calculate trend momentum using rate of change and volatility calculateTrendMomentum(lookback) => priceChange = (close - close ) / close * 100 avgVolatility = ta.atr(lookback) / close * 100 momentum = priceChange / (avgVolatility + 0.0001) momentum // Calculate trend strength using multiple timeframe correlation calculateTrendStrength(shortPeriod, longPeriod) => shortMA = ta.sma(close, shortPeriod) longMA = ta.sma(close, longPeriod) separation = math.abs(shortMA - longMA) / longMA * 100 strength = separation * slopeAlignment ❓How It Works 🔶 Imbalance Zone Detection Identifies Fair Value Gaps (FVGs) between consecutive candles where price gaps create unfilled areas. These zones are displayed as semi-transparent boxes with optional center line mitigation tracking, highlighting potential support and resistance levels where institutional players often react. // Detect Fair Value Gaps detectPriceImbalance() => currentHigh = high currentLow = low refHigh = high refLow = low if currentOpen > currentClose if currentHigh - refLow < 0 upperBound = currentClose - (currentClose - refLow) lowerBound = currentClose - (currentClose - currentHigh) centerPoint = (upperBound + lowerBound) / 2 newZone = ImbalanceZone.new( zoneBox = box.new(bar_index, upperBound, rightEdge, lowerBound, bgcolor=bullishImbalanceColor, border_color=hiddenColor) ) 🔶 Structure Break Analysis Determines Break of Structure (BOS) for trend continuation and Directional Change (DC) for trend reversals with advanced classification as "continuation", "reversal", or "neutral". The system compares pre-trend and post-trend states for each break, providing comprehensive trend change momentum analysis. 🔶 Intelligent Zone Management Features partial mitigation tracking when price enters but doesn't fully fill zones, with automatic zone boundary adjustment during partial fills. Smart array management keeps only recent structure points for optimal performance while preventing duplicate signals from the same level. 🔶 Liquidity Zone Detection Automatically identifies potential liquidity zones at key structure points for institutional trading analysis. The system tracks broken structure points and provides adaptive zone extension with configurable time-based limits for imbalance areas. 🔶 Visual Structure Mapping Provides clear visual indicators including swing labels with color-coded significance levels, dashed lines connecting break points with BOS/DC labels, and break signals for continuation and reversal patterns. The adaptive zones feature smart management with automatic mitigation tracking. 🔶 Market Structure Interpretation HH/HL patterns indicate bullish market structure with trend continuation likelihood, while LH/LL patterns signal bearish structure with downtrend continuation expected. BOS signals represent structure breaks in trend direction for continuation opportunities, while DC signals warn of potential reversals. 🔶 Performance Optimization Automatic cleanup of old structure points (keeps last 8 points), recent break tracking (keeps last 5 break events), and efficient array management ensure smooth performance across all timeframes and market conditions. Why Choose Ultimate Market Structure ? This indicator provides traders with institutional-grade market structure analysis, combining multiple analytical approaches into one comprehensive tool. By identifying key structure levels, imbalance zones, and break patterns with advanced significance scoring, it helps traders understand market dynamics and position themselves for high-probability trade setups in alignment with smart money concepts. The sophisticated trend scoring system and intelligent zone management make it an essential tool for any serious trader looking to decode market structure with precision and confidence.
อินดิเคเตอร์ Pine Script®
โดย AlphaExtract
3
Adaptive Cycle Oscillator with EMADescription of the Adaptive Cycle Oscillator with EMA Pine Script This Pine Script, titled "Adaptive Cycle Oscillator with EMA", is a custom technical indicator designed for TradingView to help traders analyze market cycles and identify potential buy or sell opportunities. It combines an Adaptive Cycle Oscillator (ACO) with multiple Exponential Moving Averages (EMAs), displayed as colorful, wavy lines, and includes features like buy/sell signals and divergence detection. Below is a beginner-friendly explanation of how the script works, adhering to TradingView's Script Publishing Rules. What This Indicator Does The Adaptive Cycle Oscillator with EMA helps you: Visualize market cycles using an oscillator that adapts to price movements. Track trends with seven EMAs of different lengths, plotted as a rainbow of wavy lines. Identify potential buy or sell signals when the oscillator crosses predefined thresholds. Spot divergences between the oscillator and price to anticipate reversals. Use customizable settings to adjust the indicator to your trading style. Note: This is a technical analysis tool and does not guarantee profits. Always combine it with other analysis methods and practice risk management. Step-by-Step Explanation for New Users 1. Understanding the Indicator Adaptive Cycle Oscillator (ACO): The ACO analyzes price data (based on high, low, and close prices, or HLC3) to detect market cycles. It smooths price movements to create an oscillator that swings between overbought and oversold levels. EMAs: Seven EMAs of different lengths are applied to the ACO and scaled based on the market's dominant cycle. These EMAs are plotted as colorful, wavy lines to show trend direction. Buy/Sell Signals: The script generates signals when the ACO crosses above or below user-defined thresholds, indicating potential entry or exit points. Divergence Detection: The script identifies bullish or bearish divergences between the ACO and the fastest EMA, which may signal potential reversals. Visual Style: The indicator uses a rainbow of seven colors (red, orange, yellow, green, blue, indigo, violet) for the EMAs, with wavy lines for a unique visual effect. Static levels (zero, overbought, oversold) are also wavy for consistency. 2. How to Add the Indicator to Your Chart Open TradingView and load the chart of any asset (e.g., stock, forex, crypto). Click on the Indicators button at the top of the chart. Search for "Adaptive Cycle Oscillator with EMA" (or paste the script into TradingView’s Pine Editor if you have access to it). Click to add the indicator to your chart. It will appear in a separate panel below the price chart. 3. Customizing the Indicator The script offers several input options to tailor it to your needs: Base Cycle Length (Default: 20): Sets the initial period for calculating the dominant cycle. Higher values make the indicator slower; lower values make it more sensitive. Alpha Smoothing (Default: 0.07): Controls how much the ACO smooths price data. Smaller values produce smoother results. Show Buy/Sell Signals (Default: True): Toggle to display green triangles (buy) and red triangles (sell) on the chart. Threshold (Default: 0.0): Defines overbought (above threshold) and oversold (below threshold) levels. Adjust to widen or narrow signal zones. EMA Base Length (Default: 10): Sets the starting length for the fastest EMA. Other EMAs are incrementally longer (12, 14, 16, etc.). Divergence Lookback (Default: 14): Determines how far back the script looks to detect divergences. To adjust these: Right-click the indicator on your chart and select Settings. Modify the inputs in the pop-up window. Click OK to apply changes. 4. Reading the Indicator Oscillator and EMAs: The ACO and seven EMAs are plotted in a separate panel. The EMAs (colored lines) move in a wavy pattern: Red (fastest) to Violet (slowest) represent different response speeds. When the faster EMAs (e.g., red, orange) are above slower ones (e.g., blue, violet), it suggests bullish momentum, and vice versa. Zero Line: A gray wavy line at zero acts as a neutral level. The ACO above zero indicates bullish conditions; below zero indicates bearish conditions. Overbought/Oversold Lines: Red (overbought) and green (oversold) wavy lines mark threshold levels. Extreme ACO values near these lines may suggest reversals. Buy/Sell Signals: Green Triangle (Bottom): Appears when the ACO crosses above the oversold threshold, suggesting a potential buy. Red Triangle (Top): Appears when the ACO crosses below the overbought threshold, suggesting a potential sell. Divergences: Green Triangle (Bottom): Indicates a bullish divergence (price makes a lower low, but the EMA makes a higher low), hinting at a potential upward reversal. Red Triangle (Top): Indicates a bearish divergence (price makes a higher high, but the EMA makes a lower high), hinting at a potential downward reversal. 5. Using Alerts You can set alerts for key events: Right-click the indicator and select Add Alert. Choose a condition (e.g., "ACO Buy Signal", "Bullish Divergence"). Configure the alert settings (e.g., notify via email, app, or pop-up). Click Create to activate the alert. Available alert conditions: ACO Buy Signal: When the ACO crosses above the oversold threshold. ACO Sell Signal: When the ACO crosses below the overbought threshold. Bullish Divergence: When a potential upward reversal is detected. Bearish Divergence: When a potential downward reversal is detected. 6. Tips for Using the Indicator Combine with Other Tools: Use the indicator alongside support/resistance levels, candlestick patterns, or other indicators (e.g., RSI, MACD) for confirmation. Test on Different Timeframes: The indicator works on any timeframe (e.g., 1-minute, daily). Shorter timeframes may produce more signals but with more noise. Practice Risk Management: Never rely solely on this indicator. Set stop-losses and position sizes to manage risk. Backtest First: Use TradingView’s Strategy Tester (if you convert the script to a strategy) to evaluate performance on historical data. Compliance with TradingView’s Script Publishing Rules This description adheres to TradingView’s Script Publishing Rules (as outlined in the provided link): No Performance Claims: The description avoids promising profits or specific results, emphasizing that the indicator is a tool for analysis. Clear Instructions: It provides step-by-step guidance for adding, customizing, and using the indicator. Risk Disclaimer: It notes that trading involves risks and the indicator should be used with other analysis methods. No Misleading Terms: Terms like “buy” and “sell” are used to describe signals, not guaranteed actions. Transparency: The description explains the indicator’s components (ACO, EMAs, signals, divergences) without exaggerating its capabilities. No External Links: The description avoids linking to external resources or soliciting users. Educational Tone: It focuses on educating users about the indicator’s functionality. Limitations Not a Standalone System: The indicator is not a complete trading strategy. It provides insights but requires additional analysis. Lagging Nature: As with most oscillators and EMAs, signals may lag behind price movements, especially in fast markets. False Signals: Signals and divergences may not always lead to successful trades, particularly in choppy markets. Market Dependency: Performance varies across assets and market conditions (e.g., trending vs. ranging markets).
อินดิเคเตอร์ Pine Script®
โดย Trading_Indicator_Pro
1
Real-Time Spring DetectorThis is a Pine Script for Trading View that creates a "Real-Time Spring Detector" indicator. This Pine Script is essentially a sophisticated pattern recognition tool that helps identify "spring" setups - a popular trading pattern where price briefly breaks below support but then bounces back strongly, often indicating that sellers are exhausted and buyers are ready to step in.What is a "Spring" in Trading? A spring is a technical analysis pattern that occurs when: Price breaks below a support level (like breaking below a floor) But then quickly bounces back up (like a spring rebounds) This often signals that sellers are weak and buyers are stepping in Think of it like testing the strength of a trampoline - you push down, but it springs back up stronger. What This Script Does This Pine Script automatically detects spring patterns on your chart and alerts you when they happen. Here's how it works: Main Components 1. Input Parameters (Settings You Can Adjust) Lookback Period (10): How many bars back to look for patterns Min Support Touches (2): How many times price must touch the support level Min Penetration % (0.1%): How far below support price must break Min Rejection % (30%): How much price must bounce back up Alert Settings: Choose when to get notifications 2. Support Level Detection The script finds "support levels" - price levels where buyers have stepped in before: It looks at recent low points Identifies areas where price has bounced multiple times Uses a small tolerance (0.5%) to account for minor price differences 3. Spring Detection Logic The script identifies three types of springs: Real-Time Spring (happening right now): Price breaks below support by the minimum amount Price bounces back strongly (rejection %) Current candle closes higher than it opened (bullish) Volume is reasonable Confirmed Spring (already completed): Same as real-time, but the candle has finished forming Potential Spring (early warning): Price is near support but hasn't fully formed the pattern yet 4. Visual Elements Markers on Chart: 🟢 Green Triangle: Confirmed spring (reliable signal) 🟡 Yellow Triangle: Spring forming right now (live signal) 🟠 Orange Circle: Potential spring (early warning) Labels: Show "SPRING" with the rejection percentage "FORMING" for developing patterns "?" for potential springs Support Line: Red dotted line showing the support level Background Colors: Light red when price penetrates support Light yellow for potential springs 5. Information Box A table in the top-left corner shows: Current support level price Whether penetration is happening Rejection percentage Current pattern status Live price 6. Alert System Two types of alerts: Real-time alerts: Notify when spring is forming (current bar) Confirmed alerts: Notify when spring is complete (bar closed) Alert cooldown: Prevents spam by waiting 5 bars between alerts How to Use This Script 1. Installation Copy the script code Open TradingView Go to Pine Editor Paste the code Click "Add to Chart" 2. Settings Adjust the input parameters based on your trading style: Lower lookback = more sensitive, faster signals Higher support touches = more reliable but fewer signals Lower penetration % = catches smaller springs Higher rejection % = only strong bounces 3. Interpretation Green triangles: High-confidence buy signals Yellow triangles: Watch closely, pattern developing Orange circles: Early warning, not tradeable yet 4. Best Practices Use on higher timeframes (15min+) for more reliable signals Combine with other indicators for confirmation Pay attention to volume - higher volume springs are more reliable Wait for confirmed signals if you're a conservative trader Key Features for Small Timeframes The script includes special detection for shorter timeframes: Quick bounce detection: Identifies rapid reversals Hammer pattern recognition: Spots candlestick patterns Relaxed volume requirements: Works when volume data is limited Advanced Features Volume Analysis Compares current volume to 10-bar average Requires at least 80% of average volume (flexible for small timeframes) Pattern Enhancement Looks for hammer-like candles (long lower wick, small upper wick) Identifies quick bounces where the upper wick is small Multiple Confirmation Combines multiple criteria to reduce false signals Stronger springs get priority for alerts Common Use Cases Entry Signals: Buy when confirmed springs appear Support Level Identification: Visual support lines help identify key levels Risk Management: Failed springs (continued breakdown) can be stop-loss triggers Market Structure: Understanding where buyers are defending price levels Limitations Works best in trending or ranging markets May produce false signals in very choppy conditions small timeframe signals can be noisy should be combined with other analysis methods.The key advantage is that it can catch these patterns as they happen, rather than you having to constantly watch charts. This is especially valuable for active traders who want to capitalize on quick reversals at support levels.
อินดิเคเตอร์ Pine Script®
โดย kingshukghosh71
1