Black FridayJusqu'à 80% en MOINS

Volume

Orderbook Table1. Indicator Name Orderbook Table This is an order book style trading volume map that upgraded the price from my first script to label 2. One-line Introduction A visual heatmap-style orderbook simulator that displays volume and delta clustering across price levels. 3. Overall Description Orderbook Table is a powerful visual tool designed to replicate an on-chart approximation of a traditional order book. It scans historical candles within a specified lookback window and accumulates traded volume into price "bins" or levels. Each level is color-coded based on total volume and directional bias (delta), offering a layered view of where market interest was concentrated. The indicator approximates order flow by analyzing each candle's directional volume, separating bullish and bearish volume. With adjustable parameters such as level depth, price bin density, delta sensitivity, and opacity, it provides a highly customizable visualization. Displayed directly on the chart, each level shows the volume at that price zone, along with a price label, offset to the right of the current bar. Traders can use this tool to detect high liquidity zones, support/resistance clusters, and volume imbalances that may precede future price movements. 4. Key Benefits (Title + Description) ✅ On-Chart Volume Heatmap Shows volume distribution across price levels in real-time directly on the price chart, creating a live “orderbook” view. ✅ Delta-Based Bias Coloring Color changes based on net buying/selling pressure (delta), making aggressive demand/supply zones easy to spot. ✅ High Customizability Users can adjust lookback bars, price bins, opacity levels, and delta usage to fit any market condition or asset class. ✅ Lightweight Simulation Approximates orderbook depth using candle data without needing L2 feed access—works on all assets and timeframes. ✅ Clear Visual Anchoring Volume quantities and price levels are offset to the right for easy viewing without cluttering the active chart area. ✅ Fast Market Context Recognition Quickly identify price levels where volume concentrated historically, improving decision-making for entries/exits. 5. Indicator User Guide 📌 Basic Concept Orderbook Table analyzes a configurable number of past bars and distributes traded volume into price "bins." Each bin shows how much volume occurred around that price level, optionally adjusted for bullish/bearish candle direction. ⚙️ Settings Overview Lookback Bars: Number of candles to scan for volume history Levels (Total): Number of price levels to display around the current price Price Bins: Granularity of price segmentation for volume distribution Shift Right: How far to offset labels to the right of the current bar Max/Min Opacity: Controls visual strength of volume coloring Use Candle Delta Approx.: If enabled, colors the volume based on candle direction (green for up, red for down) 📈 Example Timing Look for green clusters (bullish bias) below current price → possible strong demand zones Price enters a high-volume level with previously aggressive buyers (green), suggesting support 📉 Example Timing Red clusters (bearish bias) above current price can act as resistance or supply zones Price stalling at a red-heavy volume band may indicate exhaustion or reversal opportunity 🧪 Recommended Use Use as a support/resistance mapping tool in ranging and trending markets Pair with candlestick analysis or momentum indicators for refined entry/exit points Combine with VWAP or volume profile for multi-dimensional volume insight 🔒 Cautions This is an approximation, not a true L2 orderbook—volume is based on historical candles, not actual limit order data In low-volume markets or higher timeframes, bin granularity may be too coarse—adjust "Price Bins" accordingly Delta calculation is based on open-close direction and does not reflect true buy/sell volume splits Avoid overinterpreting low-opacity (light color) zones—they may indicate low interest rather than true resistance/support +++
Indicateur Pine Script®
par c9indicator
Pure Wyckoff V50R [Region Based]Pure Wyckoff V50R — Regional Wyckoff Volume-Price Structure Scanner This script implements a semi-automatic Wyckoff volume–price analysis based purely on regional behaviour, not on single candles. Instead of trying to label every bar, it analyses the last N candles (default ≥ 50) and their volume distribution to estimate whether the market is in an accumulation, distribution or trend phase. Main features: 🔍 Region-based structure detection Scans the last regLen bars to find the trading range, then attempts to locate key Wyckoff points such as SC (Selling Climax), AR, ST, Spring, UT, LPSY, and draws the SC–AR band when a structure is active. ⚖️ Supply–demand balance Uses regional bullish vs bearish volume to show whether Demand > Supply, Supply > Demand, or Balanced for the current range. 🧠 Phase & decision panel For the current bar the panel summarises: overall structure (bullish / bearish / ranging), approximate Wyckoff phase (e.g. “A phase: SC→AR rally”, “B phase: top distribution zone”, “Bottom testing zone”), VSA-style bar reading (no supply, effort vs result, SOW, etc.), current key signal (Spring / UT / LPSY / ST / Trend), one-line short-term and long-term trading bias. 📊 Scoreboard Simple scores for structure, volume and trend to give a quick “bullish / bearish / neutral” overview. Recommended use: Designed mainly for higher timeframes (Daily / 4H) where Wyckoff structures are clearer. Parameters (window length, volume averages, multipliers) should be tuned to the instrument and timeframe. This is a structure helper, not an automatic signal provider – always combine it with your own discretion and risk management. Disclaimer: This script is for educational and analytical purposes only and does not constitute financial advice. Use at your own risk and feel free to share feedback or improvements.
Indicateur Pine Script®
par hello_zenshara
Delta Force Index - DFI [TCMaster]This indicator provides a proxy measurement of hidden buying and selling pressure inside each candle by combining tick volume with candle direction. It calculates a simulated delta volume (buy vs. sell imbalance), applies customizable scaling factors, and displays three components: Delta Columns (green/red): Show estimated hidden buy or sell pressure per candle. Delta Moving Average (orange line): Smooths delta values to highlight underlying momentum. Cumulative Delta (blue line): Tracks the long-term accumulation of hidden order flow. How to use: Rising green columns with a positive Delta MA and upward Cumulative Delta suggest strong hidden buying pressure. Falling red columns with a negative Delta MA and downward Cumulative Delta suggest strong hidden selling pressure. Scaling parameters allow you to adjust the visual balance between columns and lines for different timeframes. Note: This tool uses tick volume and candle direction as a proxy for order flow. It does not display actual bid/ask data or Level II market depth. For professional order flow analysis, footprint charts or DOM data are required.
Indicateur Pine Script®
par TCMaster
ProCrypto OI Candles — by ruben_procryptoThis indicator visualizes aggregated Open Interest (OI) from multiple futures exchanges (Binance, Bybit, OKX). It plots OI as colored candles (blue for increasing OI, orange for decreasing OI), combined with a smoothed OI line for clearer trend reading. Key Features: Multiple exchange support (Binance / Bybit / OKX) Aggregated OI calculation OI candlesticks with custom opacity Smoothed OI trend line Optional OI Delta bars Adjustable smoothing length, range offset, and lookback settings Works on all timeframes What it helps with: Spotting liquidity traps Identifying fake pumps / fake dumps Detecting aggressive long/short positioning Reading funding cycles and OI expansions Tracking market strength/weakness behind price movements OI is one of the most powerful tools for understanding leverage behavior and true market intent. This script gives a clear, clean, real-time view of OI so traders can see where momentum is actually coming from. Built for traders who use liquidity, leverage, OI shifts, and momentum to understand price movement more accurately. Created by @ruben_procrypto.
Indicateur Pine Script®
par Ruben_ProCrypto
Filter Volume1. Indicator Name Filter Volume 2. One-line Introduction A regression-based trend filter that quantifies and visualizes market direction and strength using price behavior. 3. Overall Description Filter Volume+ is a trend-detection indicator that uses linear regression to evaluate the dominant direction of price movement over a given period. It compares historical regression values to determine whether the market is in a bullish, bearish, or neutral state. The indicator applies a percentage threshold to filter out weak or indecisive trends, highlighting only significant movements. Each trend state is visualized through distinct colors: bullish (greenish), bearish (reddish), and neutral (gray), with intensity reflecting trend strength. To reduce noise and create smooth visual signals, a three-step smoothing process is applied to the raw trend intensity. Users can customize the regression source, lookback period, and sensitivity, allowing the indicator to adapt to various assets and timeframes. This tool is especially useful in filtering entry signals based on clear directional bias, making it suitable for trend-following or confirmation strategies. 4. Key Benefits (Title + Description) ✅ Quantified Trend Strength Only displays trend signals when a statistically significant direction is detected using linear regression comparisons. ✅ Visual Clarity with Color Coding Each market state (bullish, bearish, neutral) is represented with distinct colors and transparency, enabling fast interpretation. ✅ Custom Regression Source Users can define the data input (e.g., close, open, indicator output) for regression calculation, increasing strategic flexibility. ✅ Multi-Level Smoothing Applies three layers of smoothing (via moving averages) to eliminate noise and produce a stable, flowing trend curve. ✅ Area Fill Visualization Plots a colored band between the trend value and zero-line, helping users quickly gauge the market's dominant force. ✅ Adjustable Sensitivity Settings Includes tolerance and lookback controls, allowing traders to fine-tune how reactive or conservative the trend detection should be. 5. Indicator User Guide 📌 Basic Concept Filter Volume+ assesses the direction of price by comparing regression values over a selected period. If the percentage of upward comparisons exceeds a threshold, a bullish state is shown; if downward comparisons dominate, it shows a bearish state. ⚙️ Settings Overview Lookback Period (n): The number of bars to compare for trend analysis Range Tolerance (%): Minimum threshold for declaring a strong trend Regression Source: The data used for regression (e.g., close, open) Linear Regression Length: Number of bars used to compute each regression value Bull/Bear Color: Custom colors for bullish and bearish trends 📈 Example Timing When the trend line stays above zero and the green color intensity increases → trend gaining strength After a neutral phase (gray), the color shifts quickly to greenish → early trend reversal 📉 Example Timing When the trend line stays below zero with deepening red color → strong bearish continuation Sudden change from bullish to bearish color with rising intensity 🧪 Recommended Use Use as a trend confirmation filter alongside entry/exit strategies Ideal for swing or position trades in trending markets Combine with oscillators like RSI or MACD for improved signal validation 🔒 Cautions In ranging (sideways) markets, the color may change frequently – avoid relying solely on this indicator in those zones. Low-intensity colors (faded) suggest weak trends – better to stay on the sidelines. A short lookback period may cause over-sensitivity and false signals. When using non-price regression sources, expect the indicator to behave differently – test before deploying. +++
Indicateur Pine Script®
par c9indicator
CVD [able0.1]# CVD Overlay iOS Style - Complete User Guide ## 📖 Table of Contents 1. (#what-is-cvd) 2. (#installation-guide) 3. (#understanding-the-display) 4. (#reading-the-info-table) 5. (#settings--customization) 6. (#trading-strategies) 7. (#common-mistakes-to-avoid) --- ## 🎯 What is CVD? **CVD (Cumulative Volume Delta)** tracks the **difference between buying and selling pressure** over time. ### Simple Explanation: - **Positive CVD** (Orange) = More buying than selling = Bulls winning - **Negative CVD** (Gray) = More selling than buying = Bears winning - **Rising CVD** = Increasing buying pressure = Potential uptrend - **Falling CVD** = Increasing selling pressure = Potential downtrend ### Why It Matters: CVD helps you see **who's really in control** of the market - not just price movement, but actual buying/selling volume. --- ## 🚀 Installation Guide ### Step 1: Open Pine Editor 1. Go to TradingView 2. Click the **"Pine Editor"** tab at the bottom of the screen 3. Click **"New"** or open an existing script ### Step 2: Copy & Paste the Code 1. Select all existing code (Ctrl+A / Cmd+A) 2. Delete it 3. Copy the entire CVD iOS Style code 4. Paste it into Pine Editor ### Step 3: Add to Chart 1. Click **"Save"** button (or Ctrl+S / Cmd+S) 2. Click **"Add to Chart"** button 3. The indicator will appear on your chart! ### Step 4: Initial Setup - The indicator appears as an **overlay** on your price chart - You'll see an **orange/gray line** following price - An **info table** appears in the top-right corner --- ## 📊 Understanding the Display ### Main Chart Elements: #### 1. **CVD Line** (Orange/Gray) - **Orange Line** = Positive CVD (buying pressure) - **Gray Line** = Negative CVD (selling pressure) - This line moves with your price chart but shows volume delta #### 2. **CVD Zone** (Shaded Area) - Light shaded box around the CVD line - Shows the "range" of CVD movement - Helps visualize CVD boundaries #### 3. **Center Line** (Dotted) - Gray dotted line in the middle of the zone - Represents the "neutral" point - CVD crossing this = shift in market control #### 4. **Reference Asset Line** (Light Gray) - Shows Bitcoin (BTC) price movement for comparison - Helps you see if your asset moves with or against BTC - Can be changed to any asset you want #### 5. **CVD Label** - Shows current CVD value - Positioned above/below zone to avoid overlap - Updates in real-time #### 6. **Reset Background** (Very Light Gray) - Appears when CVD resets - Indicates a new calculation period --- ## 📋 Reading the Info Table The info table (top-right) shows **8 key metrics**: ### Row 1: **Header** ``` ╔═ CVD able ═╗ | 15m | ████████ | able ``` - **CVD able** = Indicator name + creator - **15m** = Current timeframe - **████████** = Visual decoration - **able** = Creator signature ### Row 2: **CVD Value** ``` CVD▲ | 7.39K | ████████ | █ █ █ ``` - **CVD▲** = CVD with trend arrow - ▲ = CVD increasing - ▼ = CVD decreasing - ► = CVD unchanged - **7.39K** = Actual CVD number - **Progress Bar** = Visual strength (darker = stronger) - **Vertical Bars** = Height shows intensity ### Row 3: **Delta** ``` ◆DELTA | -1.274K | ████░░░░ | ░ ░ ``` - **Delta** = Volume change THIS BAR ONLY - **Negative** = More selling this bar - **Positive** = More buying this bar - Shows **immediate** pressure (not cumulative) ### Row 4: **UP Volume** ``` UP↑ | -1.263K | ████████ | █ █ █ ``` - Total **buying volume** this bar - Higher = Stronger buying pressure - Green/Orange vertical bars = Bullish strength ### Row 5: **DOWN Volume** ``` DN↓ | 2.643K | ████████ | ░ ░ ░ ``` - Total **selling volume** this bar - Higher = Stronger selling pressure - Gray vertical bars = Bearish strength ### Row 6-7: **Reference Asset** (if enabled) ``` ══ REF ══ | ══════ | ████████ | █ █ PRICE▲ | 4130.300 | ████████ | █ █ ``` - **REF** = Reference asset header - **PRICE▲** = Reference price with trend - Shows if BTC (or chosen asset) is rising/falling - Compare with your chart to see correlation ### Row 8: **Market Status** ``` ◄STATUS► | NEUT | ████░░░░ | ▒ ▒ ``` - **BULL** = CVD positive + Delta positive = Strong buying - **BEAR** = CVD negative + Delta negative = Strong selling - **NEUT** = Mixed signals = Wait for clarity **Status Colors:** - **Orange background** = Bullish (good for long) - **Gray background** = Bearish (good for short) - **White background** = Neutral (no clear signal) --- ## ⚙️ Settings & Customization ### Main Settings (⚙️) #### **CVD Reset** - **None** = CVD never resets (from beginning of data) - **On Higher Timeframe** = Resets when HTF candle closes - 15m chart → Resets hourly - 1h chart → Resets daily - Recommended for most traders - **On Session Start** = Resets at market open - **On Visible Chart** = Resets from leftmost visible bar #### **Precision** - **Low (Fast)** = Uses 1m data, faster but less accurate - **Medium** = Uses 5m data, balanced (recommended) - **High** = Uses 15m data, most accurate but slower #### **Cumulative** - ✅ On = CVD accumulates over time (recommended) - ❌ Off = Shows only current bar delta #### **Show Labels** - ✅ On = Shows CVD value label on chart - ❌ Off = Cleaner chart, no label #### **Show Info Table** - ✅ On = Shows info table (recommended for beginners) - ❌ Off = Hide table for minimalist view --- ### 🎨 iOS Style Colors You can customize **every color** to match your chart theme: #### **Primary Colors** - **Primary (Orange)** = Main bullish color (#FF9500) - **Secondary (Gray)** = Main bearish color (#8E8E93) - **Background** = Table background (#FFFFFF) - **Text** = Text color (#1C1C1E) #### **Bullish/Bearish** - **Bullish (Orange)** = Positive CVD color - **Bearish (Gray)** = Negative CVD color - **Opacity** = Zone transparency (0-100%) - **Show Zone** = Enable/disable shaded area #### **Table Colors** (📋) - **Header Background** = Top row background - **Header Text** = Top row text color - **Cell Background** = Data cells background - **Cell Text** = Data cells text color - **Border** = Table border color - **Accent Background** = Special rows background - **Alert Background** = Warning/status background --- ### 📊 Reference Asset Settings #### **Enable** - ✅ On = Shows reference asset line - ❌ Off = Hide reference asset #### **Symbol** - Default: `BINANCE:BTCUSDT` - Can change to any asset: - `BINANCE:ETHUSDT` (Ethereum) - `SPX` (S&P 500) - `DXY` (US Dollar Index) - Any ticker symbol #### **Color & Width** - Customize line appearance - Width: 1-4 (thickness) --- ## 💡 Trading Strategies ### Strategy 1: CVD Divergence (Beginner-Friendly) **What to Look For:** - Price making **higher highs** but CVD making **lower highs** = Bearish divergence - Price making **lower lows** but CVD making **higher lows** = Bullish divergence **How to Trade:** 1. Wait for divergence to form 2. Look for confirmation (price reversal, candlestick pattern) 3. Enter trade in divergence direction 4. Stop loss beyond recent high/low **Example:** ``` Price: /\ /\ /\ (higher highs) CVD: /\ / \/ (lower highs) = Bearish signal ``` ### Strategy 2: CVD Trend Following (Intermediate) **What to Look For:** - **Strongly rising CVD** + **rising price** = Strong uptrend - **Strongly falling CVD** + **falling price** = Strong downtrend **How to Trade:** 1. Wait for CVD and price moving in same direction 2. Enter on pullbacks to support/resistance 3. Stay in trade while CVD trend continues 4. Exit when CVD trend breaks **Signals:** - CVD ▲▲▲ + Price ↑ = Go LONG - CVD ▼▼▼ + Price ↓ = Go SHORT ### Strategy 3: CVD + Reference Asset (Advanced) **What to Look For:** - Your asset **rising** but BTC (reference) **falling** = Relative strength - Your asset **falling** but BTC (reference) **rising** = Relative weakness **How to Trade:** 1. Compare CVD movement with BTC 2. If your CVD rises faster than BTC = Buy signal 3. If your CVD falls faster than BTC = Sell signal 4. Use for **pair trading** or **asset selection** ### Strategy 4: Volume Delta Confirmation **What to Look For:** - **Large positive Delta** = Strong buying this bar - **Large negative Delta** = Strong selling this bar **How to Trade:** 1. Price breaks resistance + Large positive Delta = Confirmed breakout 2. Price breaks support + Large negative Delta = Confirmed breakdown 3. Use Delta to **confirm** price moves, not predict them **Rules:** - Delta > 2x average = Very strong pressure - Delta near zero at key level = Weak move, likely false breakout --- ## 🎓 Reading Real Scenarios ### Scenario 1: Strong Buying Pressure ``` Table Shows: CVD▲ | 12.5K | ████████ | ████ (CVD rising) ◆DELTA | +2.8K | ████████ | ▲ (Positive delta) UP↑ | 3.1K | ████████ | ████ (High buy volume) DN↓ | 0.3K | ██░░░░░░ | ░ (Low sell volume) ◄STATUS► | BULL | ████████ | ████ (Orange background) ``` **Interpretation:** Strong buying, good for LONG trades ### Scenario 2: Distribution (Hidden Selling) ``` Table Shows: CVD► | 8.2K | ████░░░░ | ▒▒ (CVD flat) ◆DELTA | -1.5K | ████████ | ▼ (Negative delta) UP↑ | 0.8K | ███░░░░░ | ░ (Low buy volume) DN↓ | 2.3K | ████████ | ████ (High sell volume) ◄STATUS► | BEAR | ████████ | ░░░░ (Gray background) ``` **Interpretation:** Price may look stable, but selling increasing = Prepare for drop ### Scenario 3: Neutral/Choppy Market ``` Table Shows: CVD► | 5.1K | ████░░░░ | ▒ (CVD sideways) ◆DELTA | +0.2K | ██░░░░░░ | ─ (Small delta) UP↑ | 1.2K | ████░░░░ | ▒ (Medium buy) DN↓ | 1.0K | ████░░░░ | ▒ (Medium sell) ◄STATUS► | NEUT | ████░░░░ | ▒▒ (White background) ``` **Interpretation:** No clear direction = Stay out or reduce position size --- ## ⚠️ Common Mistakes to Avoid ### Mistake 1: Trading on CVD Alone - ❌ **Wrong:** "CVD is rising, I'll buy immediately" - ✅ **Right:** "CVD is rising, let me check price structure, support/resistance, and wait for confirmation" ### Mistake 2: Ignoring Delta - ❌ **Wrong:** Looking only at cumulative CVD - ✅ **Right:** Watch both CVD (trend) and Delta (momentum) - Delta shows **immediate** pressure changes ### Mistake 3: Wrong Timeframe - ❌ **Wrong:** Using 1m chart with High Precision (too slow) - ✅ **Right:** Match precision to timeframe: - 1m-5m → Low Precision - 15m-1h → Medium Precision - 4h+ → High Precision ### Mistake 4: Not Using Reset - ❌ **Wrong:** Using "None" reset for intraday trading - ✅ **Right:** Use "On Higher Timeframe" to see fresh CVD each session ### Mistake 5: Overtrading Neutral Status - ❌ **Wrong:** Forcing trades when STATUS = NEUT - ✅ **Right:** Only trade clear BULL or BEAR status ### Mistake 6: Ignoring Reference Asset - ❌ **Wrong:** Trading altcoin without checking BTC - ✅ **Right:** Always check if BTC CVD agrees with your asset --- ## 🔥 Pro Tips ### Tip 1: Multi-Timeframe Analysis - Check CVD on **3 timeframes**: - Lower TF (15m) = Entry timing - Current TF (1h) = Trade direction - Higher TF (4h) = Overall trend ### Tip 2: Volume Confirmation - Big price move + Small Delta = **Weak move** (likely reversal) - Small price move + Big Delta = **Strong accumulation** (continuation) ### Tip 3: CVD Reset Zones - Pay attention to **reset backgrounds** (light gray) - Often marks **session starts** = High volatility periods ### Tip 4: Divergence + Status - Bearish divergence + STATUS = BEAR = **Strongest short signal** - Bullish divergence + STATUS = BULL = **Strongest long signal** ### Tip 5: Color Psychology - **Orange** (Bullish) is **warm** = Buying energy - **Gray** (Bearish) is **cool** = Selling pressure - Train your eye to read colors instantly ### Tip 6: Table as Quick Scan - Glance at table without reading numbers: - **All orange** = Bullish - **All gray** = Bearish - **Mixed** = Wait --- ## 📱 Quick Reference Card | Signal | CVD | Delta | Status | Action | |--------|-----|-------|--------|--------| | **Strong Buy** | ▲▲ High | ++ Positive | BULL | Long Entry | | **Strong Sell** | ▼▼ Low | -- Negative | BEAR | Short Entry | | **Divergence Buy** | ▲ Rising | Price ▼ | → BULL | Long Setup | | **Divergence Sell** | ▼ Falling | Price ▲ | → BEAR | Short Setup | | **Neutral** | → Flat | ~0 Near Zero | NEUT | Stay Out | | **Accumulation** | → Flat | ++ Positive | NEUT→BULL | Watch for Breakout | | **Distribution** | → Flat | -- Negative | NEUT→BEAR | Watch for Breakdown | --- ## 🆘 Troubleshooting ### Issue: "Indicator not showing" - **Solution:** Make sure overlay=true in code, re-add to chart ### Issue: "Table overlaps with price" - **Solution:** Change table position in code or use TradingView's "Move" feature ### Issue: "CVD line too far from price" - **Solution:** This is normal! CVD is volume-based, not price-based. Focus on CVD direction, not position ### Issue: "Too many lines on chart" - **Solution:** Disable "Show Zone" and "Show Labels" in settings for cleaner view ### Issue: "Calculations too slow" - **Solution:** Change Precision to "Low (Fast)" or use higher timeframe ### Issue: "Reference asset not showing" - **Solution:** Check if "Enable" is ON and symbol is valid (e.g., BINANCE:BTCUSDT) --- ## 🎬 Getting Started Checklist - Install indicator on TradingView - Set precision to "Medium" - Set reset to "On Higher Timeframe" - Enable info table - Add reference asset (BTC) - Practice reading the table on demo account - Test on different timeframes (15m, 1h, 4h) - Compare CVD with your current strategy - Paper trade for 1 week before going live - Keep a trading journal of CVD signals --- ## 📚 Summary **CVD shows WHO is winning: Buyers or Sellers** **Key Points:** 1. **Orange/Rising CVD** = Buying pressure = Bullish 2. **Gray/Falling CVD** = Selling pressure = Bearish 3. **Delta** = Immediate momentum THIS BAR 4. **Status** = Overall market condition 5. **Always confirm** with price action & other indicators **Remember:** - CVD is a **tool**, not a crystal ball - Use with proper risk management - Practice makes perfect - Stay disciplined! --- **Created by: able** **Version:** iOS Style v1.0 **Contact:** For questions, refer to TradingView community Happy Trading! 🚀📈
Indicateur Pine Script®
par krittaphat6
QQE MOD + Integrated RSI 14 (2in1)This is a custom modification of the popular QQE MOD by Mihkel00. I have re-engineered the script to maximize utility for free TradingView users who are limited to 3 indicators per chart. Key Changes: Secondary Trend Line: The secondary line has been reprogrammed to function exactly like a Standard RSI (Length 14). 2-in-1 Efficiency: You no longer need to load a separate RSI indicator. This script now displays market momentum (QQE) and standard price strength (RSI 14) in a single pane. This modification frees up a valuable indicator slot, allowing you to add a third indicator of your choice without upgrading your plan.
Indicateur Pine Script®
par XAUUSD2026CHK
Float Rotation TrackerFloat Rotation Tracker - Quick Reference Guide What is Float Rotation? Float Rotation = Cumulative Daily Volume ÷ Float Example: Float = 5,000,000 shares Day Volume = 7,500,000 shares Rotation = 7.5M ÷ 5M = 1.5x (150%) When rotation hits 1x (100%), every available share has theoretically changed hands at least once during the trading day. Why It Matters RotationMeaningImplication0.5x50% of float tradedInterest building1.0x 🔥Full rotationExtreme interest confirmed2.0x 🔥🔥Double rotationVery high volatility3.0x 🔥🔥🔥Triple rotationRare - maximum volatility Key insight: High rotation on a low-float stock = explosive potential Float Classification Float SizeClassificationRotation Impact≤ 2M🔥 MICROExtremely volatile, fast rotation≤ 5M🔥 VERY LOWExcellent momentum potential≤ 10MLOWGood for rotation plays> 10MNORMALNeeds massive volume to rotate Rule of thumb: Focus on stocks with float under 10M for meaningful rotation signals. Reading the Indicator Rotation Line (Yellow) Shows current rotation level Rises throughout the day as volume accumulates Crosses horizontal level lines at milestones Level Lines LineColorMeaning0.5Gray dotted50% rotation1.0Orange solidFull rotation2.0Red solidDouble rotation3.0Fuchsia solidTriple rotation Volume Bars (Bottom) ColorMeaningGrayBelow average volumeBlueNormal volume (1-2x avg)GreenHigh volume (2-5x avg)LimeExtreme volume (5x+ avg) Milestone Markers Circles appear when rotation crosses key levels Labels show "50%", "1x", "2x", "3x🔥" Background Color Changes as rotation increases Darker = higher rotation level Info Table Explained FieldDescriptionFloatShare count + classification (MICRO/LOW/NORMAL)SourceAuto ✓ = TradingView data / Manual = user enteredRotationCurrent rotation with emoji indicatorRotation %Same as rotation × 100Day VolumeCumulative volume todayTo XxVolume needed to reach next milestoneBar RVolCurrent bar's relative volumeMilestonesWhich levels have been hit todayPer RotationShares equal to one full rotationEst. TimeBars until next milestone (at current pace) Trading with Float Rotation Entry Signals Early Entry (Higher Risk, Higher Reward) Rotation approaching 0.5x Strong price action (bull flag, breakout) Rising relative volume bars Confirmation Entry (Lower Risk) Rotation at or above 1x Price holding above VWAP Continuous green/lime volume bars Late Entry (Highest Risk) Rotation above 2x Only enter on clear pullback pattern Tight stop required Exit Signals Warning Signs: Rotation very high (2x+) with declining volume bars Reversal candle after milestone Price breaking below key support Volume bars turning gray/blue after being green/lime Take Profits: Partial profit at each rotation milestone Trail stop as rotation increases Full exit on reversal pattern after 2x+ rotation Best Setups Ideal Float Rotation Play ✓ Float under 10M (preferably under 5M) ✓ Stock up 5%+ on the day ✓ News catalyst driving interest ✓ Rotation approaching or exceeding 1x ✓ Price above VWAP ✓ Volume bars green or lime ✓ Clear chart pattern (bull flag, flat top) Red Flags to Avoid ✗ Float over 50M (hard to rotate meaningfully) ✗ Rotation high but price declining ✗ Volume bars turning gray after spike ✗ No clear catalyst ✗ Price below VWAP with high rotation ✗ Late in day (3pm+) after 2x rotation Float Data Sources If auto-detect doesn't work, get float from: SourceHow to FindFinvizfinviz.com → ticker → "Shs Float"Yahoo FinanceFinance.yahoo.com → Statistics → "Float"MarketWatchMarketwatch.com → ticker → ProfileYour BrokerUsually in stock details/fundamentals Note: Float can change due to offerings, buybacks, lockup expirations. Check recent data. Settings Guide Conservative Settings Alert Level 1: 0.75 (75%) Alert Level 2: 1.0 (100%) Alert Level 3: 2.0 (200%) Alert Level 4: 3.0 (300%) High Vol Multiplier: 2.0 Extreme Vol Multiplier: 5.0 Aggressive Settings Alert Level 1: 0.3 (30%) Alert Level 2: 0.5 (50%) Alert Level 3: 1.0 (100%) Alert Level 4: 2.0 (200%) High Vol Multiplier: 1.5 Extreme Vol Multiplier: 3.0 Alert Setup Recommended Alerts 100% Rotation (1x) - Primary signal Most important milestone Confirms extreme interest High Rotation + Extreme Volume Combined condition Very high probability signal How to Set Right-click chart → Add Alert Condition: Float Rotation Tracker Select desired milestone Set notification (popup/email/phone) Set expiration Common Questions Q: Why is my float showing "Manual (no data)"? A: TradingView doesn't have float data for this stock. Enter the float manually in settings after looking it up on Finviz or Yahoo Finance. Q: The rotation seems too high/low - is the float wrong? A: Possibly. Cross-check float on Finviz. Recent offerings or share structure changes may not be reflected in TradingView's data. Q: What if float rotates early in the day? A: Early 1x rotation (within first hour) is very bullish - indicates massive interest. Watch for continuation patterns. Q: High rotation but price is dropping? A: This is distribution - large holders are selling into demand. High rotation doesn't guarantee price direction, just volatility. Q: Can I use this for swing trading? A: The indicator resets daily, so it's designed for intraday use. You could note multi-day rotation patterns manually. Quick Decision Matrix RotationPrice ActionVolumeDecision<0.5xStrong upHighWatch, early stage0.5-1xConsolidatingSteadyPrepare entry1x+Breaking outIncreasingEntry on pattern1x+DroppingHighAvoid - distribution2x+Strong upExtremePartial profit, trail stop2x+Reversal candleDecliningExit or avoid Workflow Integration MORNING ROUTINE: 1. Scan for gappers (5%+, high volume) 2. Check float on each candidate 3. Apply Float Rotation Tracker 4. Prioritize lowest float with building rotation DURING SESSION: 5. Watch rotation levels on active trades 6. Enter on patterns when rotation confirms (0.5-1x) 7. Scale out as rotation increases 8. Exit or trail after 2x rotation END OF DAY: 9. Note which stocks hit 2x+ rotation 10. Review rotation vs price action 11. Learn patterns for future trades Combining with Other Indicators IndicatorHow to Use Together5 PillarsScreen for low-float stocks firstGap & GoCheck rotation on gappersBull FlagEnter bull flags with 1x+ rotationVWAPOnly trade rotation plays above VWAPRSIWatch for divergence at high rotation Key Takeaways Float size matters - Lower float = faster rotation = more volatility 1x is the key level - Full rotation confirms extreme interest Volume quality matters - Green/lime bars better than gray Combine with price action - Rotation confirms, patterns trigger Know when you're late - 2x+ rotation is late stage Check your float data - Wrong float = wrong rotation calculation Happy Trading! 🔥
Indicateur Pine Script®
par DayTradingMD1992
MTF EMA Hariss 369The strategy has been prepared in a simplistic manner and easy to understand the concept by any novice trader. Indicators used: Current Time frame 20 EMA- Gives clear look about current time frame dynamic support and resistance and trend as well. Higher Time Frame 20 EMA: Gives macro level trend, support and resistance Kama: Capture volatility and trend direction. RVOL: Main factor of price movement. Buy when price closes above current time frame 20 ema and current time frame 20 ema is above higher time frame 20 ema. Stop loss just below the low of last candle. One can use current time frame 20 ema, higher time frame 20 ema or kama as stop loss depending upon type of asset class and risk appetite. The ideal way is to keep 20 ema as trailing sl if one wants to trail with trend. Sell when price closes below current time frame 20 ema and current time frame 20 ema is lower than higher time frame 20 ema. Stop loss just above high of last candle. Ideal target is 1.5 or 2 times of stop loss. Entry and exit time depends on trading style. Eg. if you want to enter and exit in 5 min time frame, then choose 15 min or 1h as higher time frame as trend filter. Buy and sell signals are also plotted based on this strategy. One should always go with the higher time frame trend. Opting higher time frame trend filter always filters out market noises.
Stratégie Pine Script®
par Hariss369
Ichimoku+RVOL Strategy Hariss 369Trading with Ichimoku Cloud is very simple. It is the one of the self sufficient indicator takes every parameter into account. Buy when price closes above the cloud. Stop loss just below the cloud and target 1.5 or 2 times of stop loss. Sell when price closes below the cloud. Stop loss just above the cloud and target 1.5 or 2 times of stop loss. No trade when price is inside the cloud as price consolidates here. RVOL has been taken into account to initiate the trade with volume.
Stratégie Pine Script®
par Hariss369
Dynamic Equity Allocation Model//@version=6 indicator('Dynamic Equity Allocation Model', shorttitle = 'DEAM', overlay = false, precision = 1, scale = scale.right, max_bars_back = 500) // DYNAMIC EQUITY ALLOCATION MODEL // Quantitative framework for dynamic portfolio allocation between stocks and cash. // Analyzes five dimensions: market regime, risk metrics, valuation, sentiment, // and macro conditions to generate allocation recommendations (0-100% equity). // // Uses real-time data from TradingView including fundamentals (P/E, ROE, ERP), // volatility indicators (VIX), credit spreads, yield curves, and market structure. // INPUT PARAMETERS group1 = 'Model Configuration' model_type = input.string('Adaptive', 'Allocation Model Type', options = , group = group1, tooltip = 'Conservative: Slower to increase equity, Aggressive: Faster allocation changes, Adaptive: Dynamic based on regime') use_crisis_detection = input.bool(true, 'Enable Crisis Detection System', group = group1, tooltip = 'Automatic detection and response to crisis conditions') use_regime_model = input.bool(true, 'Use Market Regime Detection', group = group1, tooltip = 'Identify Bull/Bear/Crisis regimes for dynamic allocation') group2 = 'Portfolio Risk Management' target_portfolio_volatility = input.float(12.0, 'Target Portfolio Volatility (%)', minval = 3, maxval = 20, step = 0.5, group = group2, tooltip = 'Target portfolio volatility (Cash reduces volatility: 50% Equity = ~10% vol, 100% Equity = ~20% vol)') max_portfolio_drawdown = input.float(15.0, 'Maximum Portfolio Drawdown (%)', minval = 5, maxval = 35, step = 2.5, group = group2, tooltip = 'Maximum acceptable PORTFOLIO drawdown (not market drawdown - portfolio with cash has lower drawdown)') enable_portfolio_risk_scaling = input.bool(true, 'Enable Portfolio Risk Scaling', group = group2, tooltip = 'Scale allocation based on actual portfolio risk characteristics (recommended)') risk_lookback = input.int(252, 'Risk Calculation Period (Days)', minval = 60, maxval = 504, group = group2, tooltip = 'Period for calculating volatility and risk metrics') group3 = 'Component Weights (Total = 100%)' w_regime = input.float(35.0, 'Market Regime Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_risk = input.float(25.0, 'Risk Metrics Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_valuation = input.float(20.0, 'Valuation Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_sentiment = input.float(15.0, 'Sentiment Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_macro = input.float(5.0, 'Macro Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) group4 = 'Crisis Detection Thresholds' crisis_vix_threshold = input.float(40, 'Crisis VIX Level', minval = 30, maxval = 80, group = group4, tooltip = 'VIX level indicating crisis conditions (COVID peaked at 82)') crisis_drawdown_threshold = input.float(15, 'Crisis Drawdown Threshold (%)', minval = 10, maxval = 30, group = group4, tooltip = 'Market drawdown indicating crisis conditions') crisis_credit_spread = input.float(500, 'Crisis Credit Spread (bps)', minval = 300, maxval = 1000, group = group4, tooltip = 'High yield spread indicating crisis conditions') group5 = 'Display Settings' show_components = input.bool(false, 'Show Component Breakdown', group = group5, tooltip = 'Display individual component analysis lines') show_regime_background = input.bool(true, 'Show Dynamic Background', group = group5, tooltip = 'Color background based on allocation signals') show_reference_lines = input.bool(false, 'Show Reference Lines', group = group5, tooltip = 'Display allocation percentage reference lines') show_dashboard = input.bool(true, 'Show Analytics Dashboard', group = group5, tooltip = 'Display comprehensive analytics table') show_confidence_bands = input.bool(false, 'Show Confidence Bands', group = group5, tooltip = 'Display uncertainty quantification bands') smoothing_period = input.int(3, 'Smoothing Period', minval = 1, maxval = 10, group = group5, tooltip = 'Smoothing to reduce allocation noise') background_intensity = input.int(95, 'Background Intensity (%)', minval = 90, maxval = 99, group = group5, tooltip = 'Higher values = more transparent background') // Styling Options color_scheme = input.string('EdgeTools', 'Color Theme', options = , group = 'Appearance', tooltip = 'Professional color themes') use_dark_mode = input.bool(true, 'Optimize for Dark Theme', group = 'Appearance') main_line_width = input.int(3, 'Main Line Width', minval = 1, maxval = 5, group = 'Appearance') // DATA RETRIEVAL // Market Data sp500 = request.security('SPY', timeframe.period, close) sp500_high = request.security('SPY', timeframe.period, high) sp500_low = request.security('SPY', timeframe.period, low) sp500_volume = request.security('SPY', timeframe.period, volume) // Volatility Indicators vix = request.security('VIX', timeframe.period, close) vix9d = request.security('VIX9D', timeframe.period, close) vxn = request.security('VXN', timeframe.period, close) // Fixed Income and Credit us2y = request.security('US02Y', timeframe.period, close) us10y = request.security('US10Y', timeframe.period, close) us3m = request.security('US03MY', timeframe.period, close) hyg = request.security('HYG', timeframe.period, close) lqd = request.security('LQD', timeframe.period, close) tlt = request.security('TLT', timeframe.period, close) // Safe Haven Assets gold = request.security('GLD', timeframe.period, close) usd = request.security('DXY', timeframe.period, close) yen = request.security('JPYUSD', timeframe.period, close) // Financial data with fallback values get_financial_data(symbol, fin_id, period, fallback) => data = request.financial(symbol, fin_id, period, ignore_invalid_symbol = true) na(data) ? fallback : data // SPY fundamental metrics spy_earnings_per_share = get_financial_data('AMEX:SPY', 'EARNINGS_PER_SHARE_BASIC', 'TTM', 20.0) spy_operating_earnings_yield = get_financial_data('AMEX:SPY', 'OPERATING_EARNINGS_YIELD', 'FY', 4.5) spy_dividend_yield = get_financial_data('AMEX:SPY', 'DIVIDENDS_YIELD', 'FY', 1.8) spy_buyback_yield = get_financial_data('AMEX:SPY', 'BUYBACK_YIELD', 'FY', 2.0) spy_net_margin = get_financial_data('AMEX:SPY', 'NET_MARGIN', 'TTM', 12.0) spy_debt_to_equity = get_financial_data('AMEX:SPY', 'DEBT_TO_EQUITY', 'FY', 0.5) spy_return_on_equity = get_financial_data('AMEX:SPY', 'RETURN_ON_EQUITY', 'FY', 15.0) spy_free_cash_flow = get_financial_data('AMEX:SPY', 'FREE_CASH_FLOW', 'TTM', 100000000) spy_ebitda = get_financial_data('AMEX:SPY', 'EBITDA', 'TTM', 200000000) spy_pe_forward = get_financial_data('AMEX:SPY', 'PRICE_EARNINGS_FORWARD', 'FY', 18.0) spy_total_debt = get_financial_data('AMEX:SPY', 'TOTAL_DEBT', 'FY', 500000000) spy_total_equity = get_financial_data('AMEX:SPY', 'TOTAL_EQUITY', 'FY', 1000000000) spy_enterprise_value = get_financial_data('AMEX:SPY', 'ENTERPRISE_VALUE', 'FY', 30000000000) spy_revenue_growth = get_financial_data('AMEX:SPY', 'REVENUE_ONE_YEAR_GROWTH', 'TTM', 5.0) // Market Breadth Indicators nya = request.security('NYA', timeframe.period, close) rut = request.security('IWM', timeframe.period, close) // Sector Performance xlk = request.security('XLK', timeframe.period, close) xlu = request.security('XLU', timeframe.period, close) xlf = request.security('XLF', timeframe.period, close) // MARKET REGIME DETECTION // Calculate Market Trend sma_20 = ta.sma(sp500, 20) sma_50 = ta.sma(sp500, 50) sma_200 = ta.sma(sp500, 200) ema_10 = ta.ema(sp500, 10) // Market Structure Score trend_strength = 0.0 trend_strength := trend_strength + (sp500 > sma_20 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_50 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_200 ? 2 : -2) trend_strength := trend_strength + (sma_50 > sma_200 ? 2 : -2) // Volatility Regime returns = math.log(sp500 / sp500 ) realized_vol_20d = ta.stdev(returns, 20) * math.sqrt(252) * 100 realized_vol_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 ewma_vol = ta.ema(math.pow(returns, 2), 20) realized_vol = math.sqrt(ewma_vol * 252) * 100 vol_premium = vix - realized_vol // Drawdown Calculation running_max = ta.highest(sp500, risk_lookback) current_drawdown = (running_max - sp500) / running_max * 100 // Regime Score regime_score = 0.0 // Trend Component (40%) if trend_strength >= 4 regime_score := regime_score + 40 regime_score else if trend_strength >= 2 regime_score := regime_score + 30 regime_score else if trend_strength >= 0 regime_score := regime_score + 20 regime_score else if trend_strength >= -2 regime_score := regime_score + 10 regime_score else regime_score := regime_score + 0 regime_score // Volatility Component (30%) if vix < 15 regime_score := regime_score + 30 regime_score else if vix < 20 regime_score := regime_score + 25 regime_score else if vix < 25 regime_score := regime_score + 15 regime_score else if vix < 35 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Drawdown Component (30%) if current_drawdown < 3 regime_score := regime_score + 30 regime_score else if current_drawdown < 7 regime_score := regime_score + 20 regime_score else if current_drawdown < 12 regime_score := regime_score + 10 regime_score else if current_drawdown < 20 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Classify Regime market_regime = regime_score >= 80 ? 'Strong Bull' : regime_score >= 60 ? 'Bull Market' : regime_score >= 40 ? 'Neutral' : regime_score >= 20 ? 'Correction' : regime_score >= 10 ? 'Bear Market' : 'Crisis' // RISK-BASED ALLOCATION // Calculate Market Risk parkinson_hl = math.log(sp500_high / sp500_low) parkinson_vol = parkinson_hl / (2 * math.sqrt(math.log(2))) * math.sqrt(252) * 100 garman_klass_vol = math.sqrt((0.5 * math.pow(math.log(sp500_high / sp500_low), 2) - (2 * math.log(2) - 1) * math.pow(math.log(sp500 / sp500 ), 2)) * 252) * 100 market_volatility_20d = math.max(ta.stdev(returns, 20) * math.sqrt(252) * 100, parkinson_vol) market_volatility_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 market_drawdown = current_drawdown // Initialize risk allocation risk_allocation = 50.0 if enable_portfolio_risk_scaling // Volatility-based allocation vol_based_allocation = target_portfolio_volatility / math.max(market_volatility_20d, 5.0) * 100 vol_based_allocation := math.max(0, math.min(100, vol_based_allocation)) // Drawdown-based allocation dd_based_allocation = 100.0 if market_drawdown > 1.0 dd_based_allocation := max_portfolio_drawdown / market_drawdown * 100 dd_based_allocation := math.max(0, math.min(100, dd_based_allocation)) dd_based_allocation // Combine (conservative) risk_allocation := math.min(vol_based_allocation, dd_based_allocation) // Dynamic adjustment current_equity_estimate = 50.0 estimated_portfolio_vol = current_equity_estimate / 100 * market_volatility_20d estimated_portfolio_dd = current_equity_estimate / 100 * market_drawdown vol_utilization = estimated_portfolio_vol / target_portfolio_volatility dd_utilization = estimated_portfolio_dd / max_portfolio_drawdown risk_utilization = math.max(vol_utilization, dd_utilization) risk_adjustment_factor = 1.0 if risk_utilization > 1.0 risk_adjustment_factor := math.exp(-0.5 * (risk_utilization - 1.0)) risk_adjustment_factor := math.max(0.5, risk_adjustment_factor) risk_adjustment_factor else if risk_utilization < 0.9 risk_adjustment_factor := 1.0 + 0.2 * math.log(1.0 / risk_utilization) risk_adjustment_factor := math.min(1.3, risk_adjustment_factor) risk_adjustment_factor risk_allocation := risk_allocation * risk_adjustment_factor risk_allocation else vol_scalar = target_portfolio_volatility / math.max(market_volatility_20d, 10) vol_scalar := math.min(1.5, math.max(0.2, vol_scalar)) drawdown_penalty = 0.0 if current_drawdown > max_portfolio_drawdown drawdown_penalty := (current_drawdown - max_portfolio_drawdown) / max_portfolio_drawdown drawdown_penalty := math.min(1.0, drawdown_penalty) drawdown_penalty risk_allocation := 100 * vol_scalar * (1 - drawdown_penalty) risk_allocation risk_allocation := math.max(0, math.min(100, risk_allocation)) // VALUATION ANALYSIS // Valuation Metrics actual_pe_ratio = spy_earnings_per_share > 0 ? sp500 / spy_earnings_per_share : spy_pe_forward actual_earnings_yield = nz(spy_operating_earnings_yield, 0) > 0 ? spy_operating_earnings_yield : 100 / actual_pe_ratio total_shareholder_yield = spy_dividend_yield + spy_buyback_yield // Equity Risk Premium (multi-method calculation) method1_erp = actual_earnings_yield - us10y method2_erp = actual_earnings_yield + spy_buyback_yield - us10y payout_ratio = spy_dividend_yield > 0 and actual_earnings_yield > 0 ? spy_dividend_yield / actual_earnings_yield : 0.4 sustainable_growth = spy_return_on_equity * (1 - payout_ratio) / 100 method3_erp = spy_dividend_yield + sustainable_growth * 100 - us10y implied_growth = spy_revenue_growth * 0.7 method4_erp = total_shareholder_yield + implied_growth - us10y equity_risk_premium = method1_erp * 0.35 + method2_erp * 0.30 + method3_erp * 0.20 + method4_erp * 0.15 ev_ebitda_ratio = spy_enterprise_value > 0 and spy_ebitda > 0 ? spy_enterprise_value / spy_ebitda : 15.0 debt_equity_health = spy_debt_to_equity < 1.0 ? 1.2 : spy_debt_to_equity < 2.0 ? 1.0 : 0.8 // Valuation Score base_valuation_score = 50.0 if equity_risk_premium > 4 base_valuation_score := 95 base_valuation_score else if equity_risk_premium > 3 base_valuation_score := 85 base_valuation_score else if equity_risk_premium > 2 base_valuation_score := 70 base_valuation_score else if equity_risk_premium > 1 base_valuation_score := 55 base_valuation_score else if equity_risk_premium > 0 base_valuation_score := 40 base_valuation_score else if equity_risk_premium > -1 base_valuation_score := 25 base_valuation_score else base_valuation_score := 10 base_valuation_score growth_adjustment = spy_revenue_growth > 10 ? 10 : spy_revenue_growth > 5 ? 5 : 0 margin_adjustment = spy_net_margin > 15 ? 5 : spy_net_margin < 8 ? -5 : 0 roe_adjustment = spy_return_on_equity > 20 ? 5 : spy_return_on_equity < 10 ? -5 : 0 valuation_score = base_valuation_score + growth_adjustment + margin_adjustment + roe_adjustment valuation_score := math.max(0, math.min(100, valuation_score * debt_equity_health)) // SENTIMENT ANALYSIS // VIX Term Structure vix_term_structure = vix9d > 0 ? vix / vix9d : 1 backwardation = vix_term_structure > 1.05 steep_backwardation = vix_term_structure > 1.15 // Safe Haven Flows gold_momentum = ta.roc(gold, 20) dollar_momentum = ta.roc(usd, 20) yen_momentum = ta.roc(yen, 20) treasury_momentum = ta.roc(tlt, 20) safe_haven_flow = gold_momentum * 0.3 + treasury_momentum * 0.3 + dollar_momentum * 0.25 + yen_momentum * 0.15 // Advanced Sentiment Analysis vix_percentile = ta.percentrank(vix, 252) vix_zscore = (vix - ta.sma(vix, 252)) / ta.stdev(vix, 252) vix_momentum = ta.roc(vix, 5) vvix_proxy = ta.stdev(vix_momentum, 20) * math.sqrt(252) risk_reversal_proxy = (vix - realized_vol) / realized_vol // Sentiment Score base_sentiment = 50.0 vix_adjustment = 0.0 if vix_zscore < -1.5 vix_adjustment := 40 vix_adjustment else if vix_zscore < -0.5 vix_adjustment := 20 vix_adjustment else if vix_zscore < 0.5 vix_adjustment := 0 vix_adjustment else if vix_zscore < 1.5 vix_adjustment := -20 vix_adjustment else vix_adjustment := -40 vix_adjustment term_structure_adjustment = backwardation ? -15 : steep_backwardation ? -30 : 5 vvix_adjustment = vvix_proxy > 2.0 ? -10 : vvix_proxy < 1.0 ? 10 : 0 sentiment_score = base_sentiment + vix_adjustment + term_structure_adjustment + vvix_adjustment sentiment_score := math.max(0, math.min(100, sentiment_score)) // MACRO ANALYSIS // Yield Curve yield_spread_2_10 = us10y - us2y yield_spread_3m_10 = us10y - us3m // Credit Conditions hyg_return = ta.roc(hyg, 20) lqd_return = ta.roc(lqd, 20) tlt_return = ta.roc(tlt, 20) hyg_duration = 4.0 lqd_duration = 8.0 tlt_duration = 17.0 hyg_log_returns = math.log(hyg / hyg ) lqd_log_returns = math.log(lqd / lqd ) hyg_volatility = ta.stdev(hyg_log_returns, 20) * math.sqrt(252) lqd_volatility = ta.stdev(lqd_log_returns, 20) * math.sqrt(252) hyg_yield_proxy = -math.log(hyg / hyg ) * 100 lqd_yield_proxy = -math.log(lqd / lqd ) * 100 tlt_yield = us10y hyg_spread = (hyg_yield_proxy - tlt_yield) * 100 lqd_spread = (lqd_yield_proxy - tlt_yield) * 100 hyg_distance = (hyg - ta.lowest(hyg, 252)) / (ta.highest(hyg, 252) - ta.lowest(hyg, 252)) lqd_distance = (lqd - ta.lowest(lqd, 252)) / (ta.highest(lqd, 252) - ta.lowest(lqd, 252)) default_risk_proxy = 2.0 - (hyg_distance + lqd_distance) credit_spread = hyg_spread * 0.5 + (hyg_volatility - lqd_volatility) * 1000 * 0.3 + default_risk_proxy * 200 * 0.2 credit_spread := math.max(50, credit_spread) credit_market_health = hyg_return > lqd_return ? 1 : -1 flight_to_quality = tlt_return > (hyg_return + lqd_return) / 2 // Macro Score macro_score = 50.0 yield_curve_score = 0 if yield_spread_2_10 > 1.5 and yield_spread_3m_10 > 2 yield_curve_score := 40 yield_curve_score else if yield_spread_2_10 > 0.5 and yield_spread_3m_10 > 1 yield_curve_score := 30 yield_curve_score else if yield_spread_2_10 > 0 and yield_spread_3m_10 > 0 yield_curve_score := 20 yield_curve_score else if yield_spread_2_10 < 0 or yield_spread_3m_10 < 0 yield_curve_score := 10 yield_curve_score else yield_curve_score := 5 yield_curve_score credit_conditions_score = 0 if credit_spread < 200 and not flight_to_quality credit_conditions_score := 30 credit_conditions_score else if credit_spread < 400 and credit_market_health > 0 credit_conditions_score := 20 credit_conditions_score else if credit_spread < 600 credit_conditions_score := 15 credit_conditions_score else if credit_spread < 1000 credit_conditions_score := 10 credit_conditions_score else credit_conditions_score := 0 credit_conditions_score financial_stability_score = 0 if spy_debt_to_equity < 0.5 and spy_return_on_equity > 15 financial_stability_score := 20 financial_stability_score else if spy_debt_to_equity < 1.0 and spy_return_on_equity > 10 financial_stability_score := 15 financial_stability_score else if spy_debt_to_equity < 1.5 financial_stability_score := 10 financial_stability_score else financial_stability_score := 5 financial_stability_score macro_score := yield_curve_score + credit_conditions_score + financial_stability_score macro_score := math.max(0, math.min(100, macro_score)) // CRISIS DETECTION crisis_indicators = 0 if vix > crisis_vix_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if vix > 60 crisis_indicators := crisis_indicators + 2 crisis_indicators if current_drawdown > crisis_drawdown_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if current_drawdown > 25 crisis_indicators := crisis_indicators + 1 crisis_indicators if credit_spread > crisis_credit_spread crisis_indicators := crisis_indicators + 1 crisis_indicators sp500_roc_5 = ta.roc(sp500, 5) tlt_roc_5 = ta.roc(tlt, 5) if sp500_roc_5 < -10 and tlt_roc_5 < -5 crisis_indicators := crisis_indicators + 2 crisis_indicators volume_spike = sp500_volume > ta.sma(sp500_volume, 20) * 2 sp500_roc_1 = ta.roc(sp500, 1) if volume_spike and sp500_roc_1 < -3 crisis_indicators := crisis_indicators + 1 crisis_indicators is_crisis = crisis_indicators >= 3 is_severe_crisis = crisis_indicators >= 5 // FINAL ALLOCATION CALCULATION // Convert regime to base allocation regime_allocation = market_regime == 'Strong Bull' ? 100 : market_regime == 'Bull Market' ? 80 : market_regime == 'Neutral' ? 60 : market_regime == 'Correction' ? 40 : market_regime == 'Bear Market' ? 20 : 0 // Normalize weights total_weight = w_regime + w_risk + w_valuation + w_sentiment + w_macro w_regime_norm = w_regime / total_weight w_risk_norm = w_risk / total_weight w_valuation_norm = w_valuation / total_weight w_sentiment_norm = w_sentiment / total_weight w_macro_norm = w_macro / total_weight // Calculate Weighted Allocation weighted_allocation = regime_allocation * w_regime_norm + risk_allocation * w_risk_norm + valuation_score * w_valuation_norm + sentiment_score * w_sentiment_norm + macro_score * w_macro_norm // Apply Crisis Override if use_crisis_detection if is_severe_crisis weighted_allocation := math.min(weighted_allocation, 10) weighted_allocation else if is_crisis weighted_allocation := math.min(weighted_allocation, 25) weighted_allocation // Model Type Adjustment model_adjustment = 0.0 if model_type == 'Conservative' model_adjustment := -10 model_adjustment else if model_type == 'Aggressive' model_adjustment := 10 model_adjustment else if model_type == 'Adaptive' recent_return = (sp500 - sp500 ) / sp500 * 100 if recent_return > 5 model_adjustment := 5 model_adjustment else if recent_return < -5 model_adjustment := -5 model_adjustment // Apply adjustment and bounds final_allocation = weighted_allocation + model_adjustment final_allocation := math.max(0, math.min(100, final_allocation)) // Smooth allocation smoothed_allocation = ta.sma(final_allocation, smoothing_period) // Calculate portfolio risk metrics (only for internal alerts) actual_portfolio_volatility = smoothed_allocation / 100 * market_volatility_20d actual_portfolio_drawdown = smoothed_allocation / 100 * current_drawdown // VISUALIZATION // Color definitions var color primary_color = #2196F3 var color bullish_color = #4CAF50 var color bearish_color = #FF5252 var color neutral_color = #808080 var color text_color = color.white var color bg_color = #000000 var color table_bg_color = #1E1E1E var color header_bg_color = #2D2D2D switch color_scheme // Apply color scheme 'Gold' => primary_color := use_dark_mode ? #FFD700 : #DAA520 bullish_color := use_dark_mode ? #FFA500 : #FF8C00 bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #C0C0C0 : #808080 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A00 : #FFFEF0 header_bg_color := use_dark_mode ? #2D2600 : #F5F5DC header_bg_color 'EdgeTools' => primary_color := use_dark_mode ? #4682B4 : #1E90FF bullish_color := use_dark_mode ? #4CAF50 : #388E3C bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #708090 : #696969 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0F1419 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A3A : #E6F3FF header_bg_color 'Behavioral' => primary_color := #808080 bullish_color := #00FF00 bearish_color := #8B0000 neutral_color := #FFBF00 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A1A : #F8F8F8 header_bg_color := use_dark_mode ? #2D2D2D : #E8E8E8 header_bg_color 'Quant' => primary_color := #808080 bullish_color := #FFA500 bearish_color := #8B0000 neutral_color := #4682B4 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0D0D0D : #FAFAFA header_bg_color := use_dark_mode ? #1A1A1A : #F0F0F0 header_bg_color 'Ocean' => primary_color := use_dark_mode ? #20B2AA : #008B8B bullish_color := use_dark_mode ? #00CED1 : #4682B4 bearish_color := use_dark_mode ? #FF4500 : #B22222 neutral_color := use_dark_mode ? #87CEEB : #2F4F4F text_color := use_dark_mode ? #F0F8FF : #191970 bg_color := use_dark_mode ? #001F3F : #F0F8FF table_bg_color := use_dark_mode ? #001A2E : #E6F7FF header_bg_color := use_dark_mode ? #002A47 : #CCF2FF header_bg_color 'Fire' => primary_color := use_dark_mode ? #FF6347 : #DC143C bullish_color := use_dark_mode ? #FFD700 : #FF8C00 bearish_color := use_dark_mode ? #8B0000 : #800000 neutral_color := use_dark_mode ? #FFA500 : #CD853F text_color := use_dark_mode ? #FFFAF0 : #2F1B14 bg_color := use_dark_mode ? #2F1B14 : #FFFAF0 table_bg_color := use_dark_mode ? #261611 : #FFF8F0 header_bg_color := use_dark_mode ? #3D241A : #FFE4CC header_bg_color 'Matrix' => primary_color := use_dark_mode ? #00FF41 : #006400 bullish_color := use_dark_mode ? #39FF14 : #228B22 bearish_color := use_dark_mode ? #FF073A : #8B0000 neutral_color := use_dark_mode ? #00FFFF : #008B8B text_color := use_dark_mode ? #C0FF8C : #003300 bg_color := use_dark_mode ? #0D1B0D : #F0FFF0 table_bg_color := use_dark_mode ? #0A1A0A : #E8FFF0 header_bg_color := use_dark_mode ? #112B11 : #CCFFCC header_bg_color 'Arctic' => primary_color := use_dark_mode ? #87CEFA : #4169E1 bullish_color := use_dark_mode ? #00BFFF : #0000CD bearish_color := use_dark_mode ? #FF1493 : #8B008B neutral_color := use_dark_mode ? #B0E0E6 : #483D8B text_color := use_dark_mode ? #F8F8FF : #191970 bg_color := use_dark_mode ? #191970 : #F8F8FF table_bg_color := use_dark_mode ? #141B47 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A5C : #E0F0FF header_bg_color // Transparency settings bg_transparency = use_dark_mode ? 85 : 92 zone_transparency = use_dark_mode ? 90 : 95 band_transparency = use_dark_mode ? 70 : 85 table_transparency = use_dark_mode ? 80 : 15 // Allocation color alloc_color = smoothed_allocation >= 80 ? bullish_color : smoothed_allocation >= 60 ? color.new(bullish_color, 30) : smoothed_allocation >= 40 ? primary_color : smoothed_allocation >= 20 ? color.new(bearish_color, 30) : bearish_color // Dynamic background var color dynamic_bg_color = na if show_regime_background if smoothed_allocation >= 70 dynamic_bg_color := color.new(bullish_color, background_intensity) dynamic_bg_color else if smoothed_allocation <= 30 dynamic_bg_color := color.new(bearish_color, background_intensity) dynamic_bg_color else if smoothed_allocation > 60 or smoothed_allocation < 40 dynamic_bg_color := color.new(primary_color, math.min(99, background_intensity + 2)) dynamic_bg_color bgcolor(dynamic_bg_color, title = 'Allocation Signal Background') // Plot main allocation line plot(smoothed_allocation, 'Equity Allocation %', color = alloc_color, linewidth = math.max(1, main_line_width)) // Reference lines (static colors for hline) hline_bullish_color = color_scheme == 'Gold' ? use_dark_mode ? #FFA500 : #FF8C00 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4CAF50 : #388E3C : color_scheme == 'Behavioral' ? #00FF00 : color_scheme == 'Quant' ? #FFA500 : color_scheme == 'Ocean' ? use_dark_mode ? #00CED1 : #4682B4 : color_scheme == 'Fire' ? use_dark_mode ? #FFD700 : #FF8C00 : color_scheme == 'Matrix' ? use_dark_mode ? #39FF14 : #228B22 : color_scheme == 'Arctic' ? use_dark_mode ? #00BFFF : #0000CD : #4CAF50 hline_bearish_color = color_scheme == 'Gold' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'EdgeTools' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'Behavioral' ? #8B0000 : color_scheme == 'Quant' ? #8B0000 : color_scheme == 'Ocean' ? use_dark_mode ? #FF4500 : #B22222 : color_scheme == 'Fire' ? use_dark_mode ? #8B0000 : #800000 : color_scheme == 'Matrix' ? use_dark_mode ? #FF073A : #8B0000 : color_scheme == 'Arctic' ? use_dark_mode ? #FF1493 : #8B008B : #FF5252 hline_primary_color = color_scheme == 'Gold' ? use_dark_mode ? #FFD700 : #DAA520 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4682B4 : #1E90FF : color_scheme == 'Behavioral' ? #808080 : color_scheme == 'Quant' ? #808080 : color_scheme == 'Ocean' ? use_dark_mode ? #20B2AA : #008B8B : color_scheme == 'Fire' ? use_dark_mode ? #FF6347 : #DC143C : color_scheme == 'Matrix' ? use_dark_mode ? #00FF41 : #006400 : color_scheme == 'Arctic' ? use_dark_mode ? #87CEFA : #4169E1 : #2196F3 hline(show_reference_lines ? 100 : na, '100% Equity', color = color.new(hline_bullish_color, 70), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 80 : na, '80% Equity', color = color.new(hline_bullish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 60 : na, '60% Equity', color = color.new(hline_bullish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(50, '50% Balanced', color = color.new(hline_primary_color, 50), linestyle = hline.style_solid, linewidth = 2) hline(show_reference_lines ? 40 : na, '40% Equity', color = color.new(hline_bearish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 20 : na, '20% Equity', color = color.new(hline_bearish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 0 : na, '0% Equity', color = color.new(hline_bearish_color, 70), linestyle = hline.style_dotted, linewidth = 1) // Component plots plot(show_components ? regime_allocation : na, 'Regime', color = color.new(#4ECDC4, 70), linewidth = 1) plot(show_components ? risk_allocation : na, 'Risk', color = color.new(#FF6B6B, 70), linewidth = 1) plot(show_components ? valuation_score : na, 'Valuation', color = color.new(#45B7D1, 70), linewidth = 1) plot(show_components ? sentiment_score : na, 'Sentiment', color = color.new(#FFD93D, 70), linewidth = 1) plot(show_components ? macro_score : na, 'Macro', color = color.new(#6BCF7F, 70), linewidth = 1) // Confidence bands upper_band = plot(show_confidence_bands ? math.min(100, smoothed_allocation + ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Upper Band') lower_band = plot(show_confidence_bands ? math.max(0, smoothed_allocation - ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Lower Band') fill(upper_band, lower_band, color = show_confidence_bands ? color.new(neutral_color, zone_transparency) : na, title = 'Uncertainty') // DASHBOARD if show_dashboard and barstate.islast var table dashboard = table.new(position.top_right, 2, 20, border_width = 1, bgcolor = color.new(table_bg_color, table_transparency)) table.clear(dashboard, 0, 0, 1, 19) // Header header_color = color.new(header_bg_color, 20) dashboard_text_color = text_color table.cell(dashboard, 0, 0, 'DEAM', text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) table.cell(dashboard, 1, 0, model_type, text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) // Core metrics table.cell(dashboard, 0, 1, 'Equity Allocation', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 1, str.tostring(smoothed_allocation, '##.#') + '%', text_color = alloc_color, text_size = size.small) table.cell(dashboard, 0, 2, 'Cash Allocation', text_color = dashboard_text_color, text_size = size.small) cash_color = 100 - smoothed_allocation > 70 ? bearish_color : primary_color table.cell(dashboard, 1, 2, str.tostring(100 - smoothed_allocation, '##.#') + '%', text_color = cash_color, text_size = size.small) // Signal signal_text = 'NEUTRAL' signal_color = primary_color if smoothed_allocation >= 70 signal_text := 'BULLISH' signal_color := bullish_color signal_color else if smoothed_allocation <= 30 signal_text := 'BEARISH' signal_color := bearish_color signal_color table.cell(dashboard, 0, 3, 'Signal', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 3, signal_text, text_color = signal_color, text_size = size.small) // Market Regime table.cell(dashboard, 0, 4, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_color_display = market_regime == 'Strong Bull' or market_regime == 'Bull Market' ? bullish_color : market_regime == 'Neutral' ? primary_color : market_regime == 'Crisis' ? bearish_color : bearish_color table.cell(dashboard, 1, 4, market_regime, text_color = regime_color_display, text_size = size.small) // VIX table.cell(dashboard, 0, 5, 'VIX Level', text_color = dashboard_text_color, text_size = size.small) vix_color_display = vix < 20 ? bullish_color : vix < 30 ? primary_color : bearish_color table.cell(dashboard, 1, 5, str.tostring(vix, '##.##'), text_color = vix_color_display, text_size = size.small) // Market Drawdown table.cell(dashboard, 0, 6, 'Market DD', text_color = dashboard_text_color, text_size = size.small) market_dd_color = current_drawdown < 5 ? bullish_color : current_drawdown < 10 ? primary_color : bearish_color table.cell(dashboard, 1, 6, '-' + str.tostring(current_drawdown, '##.#') + '%', text_color = market_dd_color, text_size = size.small) // Crisis Detection table.cell(dashboard, 0, 7, 'Crisis Detection', text_color = dashboard_text_color, text_size = size.small) crisis_text = is_severe_crisis ? 'SEVERE' : is_crisis ? 'CRISIS' : 'Normal' crisis_display_color = is_severe_crisis or is_crisis ? bearish_color : bullish_color table.cell(dashboard, 1, 7, crisis_text, text_color = crisis_display_color, text_size = size.small) // Real Data Section financial_bg = color.new(primary_color, 85) table.cell(dashboard, 0, 8, 'REAL DATA', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) table.cell(dashboard, 1, 8, 'Live Metrics', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) // P/E Ratio table.cell(dashboard, 0, 9, 'P/E Ratio', text_color = dashboard_text_color, text_size = size.small) pe_color = actual_pe_ratio < 18 ? bullish_color : actual_pe_ratio < 25 ? primary_color : bearish_color table.cell(dashboard, 1, 9, str.tostring(actual_pe_ratio, '##.#'), text_color = pe_color, text_size = size.small) // ERP table.cell(dashboard, 0, 10, 'ERP', text_color = dashboard_text_color, text_size = size.small) erp_color = equity_risk_premium > 2 ? bullish_color : equity_risk_premium > 0 ? primary_color : bearish_color table.cell(dashboard, 1, 10, str.tostring(equity_risk_premium, '##.##') + '%', text_color = erp_color, text_size = size.small) // ROE table.cell(dashboard, 0, 11, 'ROE', text_color = dashboard_text_color, text_size = size.small) roe_color = spy_return_on_equity > 20 ? bullish_color : spy_return_on_equity > 10 ? primary_color : bearish_color table.cell(dashboard, 1, 11, str.tostring(spy_return_on_equity, '##.#') + '%', text_color = roe_color, text_size = size.small) // D/E Ratio table.cell(dashboard, 0, 12, 'D/E Ratio', text_color = dashboard_text_color, text_size = size.small) de_color = spy_debt_to_equity < 0.5 ? bullish_color : spy_debt_to_equity < 1.0 ? primary_color : bearish_color table.cell(dashboard, 1, 12, str.tostring(spy_debt_to_equity, '##.##'), text_color = de_color, text_size = size.small) // Shareholder Yield table.cell(dashboard, 0, 13, 'Dividend+Buyback', text_color = dashboard_text_color, text_size = size.small) yield_color = total_shareholder_yield > 4 ? bullish_color : total_shareholder_yield > 2 ? primary_color : bearish_color table.cell(dashboard, 1, 13, str.tostring(total_shareholder_yield, '##.#') + '%', text_color = yield_color, text_size = size.small) // Component Scores component_bg = color.new(neutral_color, 80) table.cell(dashboard, 0, 14, 'Components', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 1, 14, 'Scores', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 0, 15, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_score_color = regime_allocation > 60 ? bullish_color : regime_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 15, str.tostring(regime_allocation, '##'), text_color = regime_score_color, text_size = size.small) table.cell(dashboard, 0, 16, 'Risk', text_color = dashboard_text_color, text_size = size.small) risk_score_color = risk_allocation > 60 ? bullish_color : risk_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 16, str.tostring(risk_allocation, '##'), text_color = risk_score_color, text_size = size.small) table.cell(dashboard, 0, 17, 'Valuation', text_color = dashboard_text_color, text_size = size.small) val_score_color = valuation_score > 60 ? bullish_color : valuation_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 17, str.tostring(valuation_score, '##'), text_color = val_score_color, text_size = size.small) table.cell(dashboard, 0, 18, 'Sentiment', text_color = dashboard_text_color, text_size = size.small) sent_score_color = sentiment_score > 60 ? bullish_color : sentiment_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 18, str.tostring(sentiment_score, '##'), text_color = sent_score_color, text_size = size.small) table.cell(dashboard, 0, 19, 'Macro', text_color = dashboard_text_color, text_size = size.small) macro_score_color = macro_score > 60 ? bullish_color : macro_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 19, str.tostring(macro_score, '##'), text_color = macro_score_color, text_size = size.small) // ALERTS // Major allocation changes alertcondition(smoothed_allocation >= 80 and smoothed_allocation < 80, 'High Equity Allocation', 'Equity allocation reached 80% - Bull market conditions') alertcondition(smoothed_allocation <= 20 and smoothed_allocation > 20, 'Low Equity Allocation', 'Equity allocation dropped to 20% - Defensive positioning') // Crisis alerts alertcondition(is_crisis and not is_crisis , 'CRISIS DETECTED', 'Crisis conditions detected - Reducing equity allocation') alertcondition(is_severe_crisis and not is_severe_crisis , 'SEVERE CRISIS', 'Severe crisis detected - Maximum defensive positioning') // Regime changes regime_changed = market_regime != market_regime alertcondition(regime_changed, 'Regime Change', 'Market regime has changed') // Risk management alerts risk_breach = enable_portfolio_risk_scaling and (actual_portfolio_volatility > target_portfolio_volatility * 1.2 or actual_portfolio_drawdown > max_portfolio_drawdown * 1.2) alertcondition(risk_breach, 'Risk Breach', 'Portfolio risk exceeds target parameters') // USAGE // The indicator displays a recommended equity allocation percentage (0-100%). // Example: 75% allocation = 75% stocks, 25% cash/bonds. // // The model combines market regime analysis (trend, volatility, drawdowns), // risk management (portfolio-level targeting), valuation metrics (P/E, ERP), // sentiment indicators (VIX term structure), and macro factors (yield curve, // credit spreads) into a single allocation signal. // // Crisis detection automatically reduces exposure when multiple warning signals // converge. Alerts available for major allocation shifts and regime changes. // // Designed for SPY/S&P 500 portfolio allocation. Adjust component weights and // risk parameters in settings to match your risk tolerance. View in Pine
Indicateur Pine Script®
par Alfred773
Moving VWAP-KAMA CloudMoving VWAP-KAMA Cloud Overview The Moving VWAP-KAMA Cloud is a high-conviction trend filter designed to solve a major problem with standard indicators: Noise. By combining a smoothed Volume Weighted Average Price (MVWAP) with Kaufman’s Adaptive Moving Average (KAMA), this indicator creates a "Value Zone" that identifies the true structural trend while ignoring choppy price action. Unlike brittle lines that break constantly, this cloud is "slow" by design—making it exceptionally powerful for spotting genuine trend reversals and filtering out fakeouts. How It Works This script uses a unique "Double Smoothing" architecture: The Anchor (MVWAP): We take the standard VWAP and smooth it with a 30-period EMA. This represents the "Fair Value" baseline where volume has supported price over time. The Filter (KAMA): We apply Kaufman's Adaptive Moving Average to the already smoothed MVWAP. KAMA is unique because it flattens out during low-volatility (choppy) periods and speeds up during high-momentum trends. The Cloud: Green/Teal Cloud: Bullish Structure (MVWAP > KAMA) Purple Cloud: Bearish Structure (MVWAP < KAMA) 🔥 The "Reversal Slingshot" Strategy Backtests reveal a powerful behavior during major trend changes, particularly after long bear markets: The Resistance Phase: During a long-term downtrend, price will repeatedly rally into the Purple Cloud and get rejected. The flattened KAMA line acts as a "concrete ceiling," keeping the bearish trend intact. The Breakout & Flip: When price finally breaks above the cloud with conviction, and the cloud flips Green, it signals a structural regime change. The "Slingshot" Retest: Often, immediately after this flip, price will drop back into the top of the cloud. This is the "Slingshot" moment. The old resistance becomes new, hardened support. The Rally: From this support bounce, stocks often launch into a sustained, multi-month bull run. This setup has been observed repeatedly at the bottom of major corrections. How to Use This Indicator 1. Dynamic Support & Resistance The KAMA Wall: When price retraces into the cloud, the KAMA line often flattens out, acting as a hard "floor" or "wall." A break of this wall usually signals a genuine trend change, not just a stop hunt. 2. Trend Confirmation (Regime Filter) Bullish Regime: If price is holding above the cloud, only look for Long setups. Bearish Regime: If price is holding below the cloud, only look for Short setups. No-Trade Zone: If price is stuck inside the cloud, the market is traversing fair value. Stand aside until a clear winner emerges. 3. Multi-Timeframe Versatility While designed for trend confirmation on higher timeframes (4H, Daily), this indicator adapts beautifully to lower timeframes (5m, 15m) for intraday scalping. On Lower Timeframes: The cloud reacts much faster, acting as a dynamic "VWAP Band" that helps intraday traders stay on the right side of momentum during the session. Settings Moving VWAP Period (30): The lookback period for the base VWAP smoothing. KAMA Settings (10, 10, 30): Controls the sensitivity of the adaptive filter. Cloud Transparency: Adjust to keep your chart clean. Alerts Included Price Cross Over/Under MVWAP Price Cross Over/Under KAMA Cloud Flip (Bullish/Bearish Trend Change) Tip for Traders This is not a signal entry indicator. It is a Trend Conviction tool. Use it to filter your entries from faster indicators (like RSI or MACD). If your fast indicator signals "Buy" but the cloud is Purple, the probability is low. Wait for the Cloud Flip
Indicateur Pine Script®
par SovereignCharts