Multi-Timeframe EMA (5 Configurable)Here's a comprehensive description you can use for your indicator:
Multi-Timeframe EMA Indicator (5 Configurable Slots)
Description
This indicator displays up to 5 Exponential Moving Averages (EMAs) from different timeframes simultaneously on a single chart. Perfect for multi-timeframe analysis, it allows traders to visualize key EMAs from intraday to higher timeframes without switching charts.
Key Features
5 Independent EMA Slots: Each slot can be configured with its own timeframe, EMA length, and color
Flexible Configuration: Mix any timeframes and EMA lengths (e.g., 1m EMA 50, 15m EMA 200, 4h EMA 100)
Smart Label Formatting: Automatically displays timeframes in readable format (minutes, hours, or days)
Optional Data Table: Toggle a compact table showing EMA values and price distance percentages
Individual Toggle Controls: Enable/disable each EMA independently without losing settings
Customizable Styling: Adjust colors and line width to match your chart theme
Default Configuration
EMA 1: 1-minute timeframe, EMA 200 (Red)
EMA 2: 5-minute timeframe, EMA 200 (Purple)
EMA 3: 15-minute timeframe, EMA 200 (Yellow)
EMA 4: 1-hour timeframe, EMA 200 (Blue)
EMA 5: 4-hour timeframe, EMA 200 (Orange)
How to Use
Add the indicator to any chart
Configure each EMA slot in the settings:
Timeframe: Choose from 1m, 5m, 15m, 1h, 4h, D, W, M, or custom
Length: Set the EMA period (default 200)
Color: Select a color for easy identification
Enable "Show Line Labels" to see EMA identifiers on the right side
Enable "Show Values Table" for a detailed view of current values and distances
Use Cases
Trend Analysis: Identify alignment across multiple timeframes
Support/Resistance: Use higher timeframe EMAs as dynamic S/R levels
Entry/Exit Timing: Enter on lower timeframe signals near higher timeframe EMAs
Multi-Timeframe Confirmation: Validate setups when price is above/below key EMAs
Scalping: Monitor 1m/5m EMAs while respecting 1h/4h trend direction
Tips
All EMAs update in real-time and move with the chart
Use contrasting colors for easier visual distinction
Disable unused slots to declutter your chart
The table shows percentage distance from current price to each EMA
Works on any symbol and any chart timeframe
스크립트에서 " TABLE"에 대해 찾기
Material Color Palette Library█ OVERVIEW
Unlock a world of color in your Pine Script® projects with the Material Color Palette Library . This library provides a comprehensive and structured color system based on Google's Material Design palette, making it incredibly easy to create visually appealing and professional-looking indicators and strategies.
Forget about guessing hex codes. With this library, you have access to 19 distinct color families, each offering a wide range of shades. Every color can be fine-tuned with saturation, darkness, and opacity levels, giving you precise control over your script's appearance.
To make development even easier, the library includes a visual cheatsheet. Simply add the script to your chart to display a full table of all available colors and their corresponding parameters.
█ KEY FEATURES
Vast Spectrum: 19 distinct color families, from vibrant reds and blues to subtle greys and browns.
Fine-Tuned Control: Each color function accepts parameters for `saturationLevel` (1-13 or 1-9) and `darkLevel` (1-3) to select the perfect shade.
Opacity Parameter: Easily add transparency to any color for fills, backgrounds, or lines.
Quick Access Tones: A simple `tone()` function to grab base colors by name.
Visual Cheatsheet: An on-chart table displays the entire color palette, serving as a handy reference guide during development.
█ HOW TO USE
As a library, this script is meant to be imported into your own indicators or strategies.
1. Import the Library
Add the following line to the top of your script. Remember to replace `YourUsername` with your TradingView username.
import mastertop/ColorPalette/1 as colors
2. Call a Color Function
You can now use any of the exported functions to set colors for your plots, backgrounds, tables, and more.
The primary functions take three arguments: `functionName(saturationLevel, darkLevel, opacity)`
`saturationLevel`: An integer that controls the intensity of the color. Ranges from 1 (lightest) to 13 (most vibrant) for most colors, and 1-9 for `brown`, `grey`, and `blueGrey`.
`darkLevel`: An integer from 1 to 3 (1: light, 2: medium, 3: dark).
`opacity`: An integer from 0 (opaque) to 100 (invisible).
Example Usage:
Let's plot a moving average with a specific shade of teal.
// Import the library
import mastertop/ColorPalette/1 as colors
indicator("My Script with Custom Colors", overlay = true)
// Calculate a moving average
ma = ta.sma(close, 20)
// Plot the MA using a color from the library
// We'll use teal with saturation level 7, dark level 2, and 0% opacity
plot(ma, "MA", color = colors.teal(7, 2, 0), linewidth = 2)
3. Using the `tone()` Function
For quick access to a base color, you can use the `tone()` function.
// Set a red background with 85% transparency
bgcolor(colors.tone('red', 85))
█ VISUAL REFERENCE
To see all available colors at a glance, you can add this library script directly to your chart. It will display a comprehensive table showing every color variant. This makes it easy to pick the exact shade you need without guesswork.
This library is designed for fellow Pine Script® developers to streamline their workflow and enhance the visual quality of their scripts. Enjoy!
TradeVision Pro - Multi-Factor Analysis System═══════════════════════════════════════════════════════════════════
TRADEVISION PRO - MULTI-FACTOR ANALYSIS SYSTEM
Created by Zakaria Safri
═══════════════════════════════════════════════════════════════════
A comprehensive technical analysis tool combining multiple factors for
signal generation, trend analysis, and dynamic risk management visualization.
Designed for educational purposes to study multi-factor convergence trading
strategies across all markets and timeframes.
⚠️ IMPORTANT DISCLAIMER:
This indicator is provided for EDUCATIONAL and INFORMATIONAL purposes only.
It does NOT constitute financial advice, investment advice, or trading advice.
Past performance does not guarantee future results. Trading involves
substantial risk of loss. Always do your own research and consult a
financial advisor before making trading decisions.
🎯 KEY FEATURES
═══════════════════════════════════════════════════════════════════
✅ MULTI-FACTOR SIGNAL GENERATION
• Price Volume Trend (PVT) analysis
• Rate of Change (ROC) momentum confirmation
• Volume-Weighted Moving Average (VWMA) trend filter
• Simple Moving Average (SMA) price smoothing
• Signals only when all factors align
✅ DYNAMIC RISK VISUALIZATION (Educational Only)
• ATR-based stop loss calculation
• Risk-reward based take profit levels (1-5 targets)
• Visual lines and labels showing entry, SL, and TPs
• Automatically adapts to market volatility
• ⚠️ VISUAL REFERENCE ONLY - Does not execute trades
✅ SUPPORT & RESISTANCE DETECTION
• Automatic pivot-based level identification
• Red dashed lines for resistance zones
• Green dashed lines for support areas
• Helps identify key price levels
✅ VWMA TREND BANDS
• Volume-weighted moving average with standard deviation
• Color-changing bands (Green = Uptrend, Red = Downtrend)
• Filled band area for easy visualization
• Volume-confirmed trend strength
✅ TREND DETECTION SYSTEM
• Counting-based trend confirmation
• Three states: Up Trend, Down Trend, Ranging
• Requires threshold of consecutive bars
• Independent trend validation
✅ PRICE RANGE VISUALIZATION
• High/Low range lines showing market structure
• Filled area highlighting price volatility
• Helps identify breakout zones
✅ COMPREHENSIVE INFO TABLE
• Real-time trend status
• Last signal type (BUY/SELL)
• Entry price display
• Stop loss level
• All active take profit levels
• Clean, professional layout
✅ OPTIONAL FEATURES
• Bar coloring by trend direction
• Customizable alert notifications
• Toggle visibility for all components
• Fully configurable parameters
📊 HOW IT WORKS
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SIGNAL METHODOLOGY:
BUY SIGNAL generates when ALL conditions are met:
• Smoothed price > Moving Average (upward price trend)
• PVT > PVT Average (volume supporting uptrend)
• ROC > 0 (positive momentum)
• Close > VWMA (above volume-weighted average)
SELL SIGNAL generates when ALL conditions are met:
• Smoothed price < Moving Average (downward price trend)
• PVT < PVT Average (volume supporting downtrend)
• ROC < 0 (negative momentum)
• Close < VWMA (below volume-weighted average)
This multi-factor approach filters out weak signals and waits for
strong convergence before generating alerts.
RISK CALCULATION:
Stop Loss = Entry ± (ATR × SL Multiplier)
• Uses Average True Range for volatility measurement
• Automatically adjusts to market conditions
Take Profit Levels = Entry ± (Risk Distance × TP Multiplier × Level)
• Risk Distance = |Entry - Stop Loss|
• Creates risk-reward based targets
• Example: TP Multiplier 1.0 = 1:1, 2:2, 3:3 risk-reward
⚠️ NOTE: All risk levels are VISUAL REFERENCES for educational study.
They do not execute trades automatically.
⚙️ SETTINGS GUIDE
═══════════════════════════════════════════════════════════════════
SIGNAL SETTINGS:
• Signal Length (14): Main calculation period for averages
• Smooth Length (8): Price data smoothing period
• PVT Length (14): Price Volume Trend calculation period
• ROC Length (9): Rate of Change momentum period
RISK MANAGEMENT (Visual Only):
• ATR Length (14): Volatility measurement lookback
• SL Multiplier (2.2): Stop loss distance (× ATR)
• TP Multiplier (1.0): Risk-reward ratio per TP level
• TP Levels (1-5): Number of take profit targets to display
• Show TP/SL Lines: Toggle visual reference lines
SUPPORT & RESISTANCE:
• Pivot Lookback (10): Sensitivity for S/R detection
• Show SR: Toggle support/resistance lines
VWMA BANDS:
• VWMA Length (20): Volume-weighted average period
• Show Bands: Toggle band visibility
TREND DETECTION:
• Trend Threshold (5): Consecutive bars required for trend
PRICE LINES:
• Period (20): High/low calculation lookback
• Show: Toggle price range visualization
DISPLAY OPTIONS:
• Signals: Show/hide BUY/SELL labels
• Table: Show/hide information panel
• Color Bars: Enable trend-based bar coloring
ALERTS:
• Enable: Activate alert notifications for signals
💡 USAGE INSTRUCTIONS
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RECOMMENDED APPROACH:
• Works on all timeframes (1m to Monthly)
• Suitable for all markets (Stocks, Forex, Crypto, etc.)
• Best used with additional analysis and confirmation
• Always practice proper risk management
ENTRY STRATEGY:
1. Wait for BUY or SELL signal to appear
2. Check trend table for trend confirmation
3. Verify VWMA band color matches signal direction
4. Look for nearby support/resistance confluence
5. Consider entering on next candle open
6. Use visual SL level for risk management
EXIT STRATEGY:
1. Use TP levels as potential exit zones
2. Consider scaling out at multiple TP levels
3. Exit on opposite signal
4. Adjust stops as trade progresses
5. Account for spread and slippage
TREND TRADING:
• "Up Trend" → Focus on BUY signals
• "Down Trend" → Focus on SELL signals
• "Ranging" → Wait for clear trend or use range strategies
🎨 VISUAL ELEMENTS
═══════════════════════════════════════════════════════════════════
• GREEN VWMA BANDS → Bullish trend indication
• RED VWMA BANDS → Bearish trend indication
• ORANGE DASHED LINE → Entry price reference
• RED SOLID LINE → Stop loss level
• GREEN DOTTED LINES → Take profit targets
• RED DASHED LINES → Resistance levels
• GREEN DASHED LINES → Support levels
• GREY FILLED AREA → Price high/low range
• GREEN BUY LABEL → Long signal
• RED SELL LABEL → Short signal
• BLUE INFO TABLE → Current trade details
• GREEN/RED BARS → Trend direction (optional)
⚠️ IMPORTANT NOTES
═══════════════════════════════════════════════════════════════════
RISK WARNING:
• Trading involves substantial risk of loss
• You can lose more than your initial investment
• Past performance does not guarantee future results
• No indicator is 100% accurate
• Always use proper position sizing
• Never risk more than you can afford to lose
EDUCATIONAL PURPOSE:
• This tool is for learning and research
• Not a complete trading system
• Should be combined with other analysis
• Requires interpretation and context
• Test thoroughly before live use
• Consider consulting a financial advisor
TECHNICAL LIMITATIONS:
• Signals lag price action (all indicators lag)
• False signals occur in choppy markets
• Works better in trending conditions
• Support/resistance levels are approximate
• TP/SL levels are suggestions, not guarantees
📚 METHODOLOGY
═══════════════════════════════════════════════════════════════════
This indicator combines established technical analysis concepts:
• Price Volume Trend (PVT): Volume-weighted price momentum
• Rate of Change (ROC): Momentum measurement
• Volume-Weighted Moving Average (VWMA): Trend identification
• Average True Range (ATR): Volatility measurement (J. Welles Wilder)
• Pivot Points: Support/resistance detection
All methods are based on publicly available technical analysis
principles. No proprietary or "secret" algorithms are used.
⚖️ FULL DISCLAIMER
═══════════════════════════════════════════════════════════════════
LIABILITY:
The creator (Zakaria Safri) assumes NO liability for:
• Trading losses or damages of any kind
• Loss of capital or profits
• Incorrect signal interpretation
• Technical issues, bugs, or errors
• Any consequences of using this tool
USER RESPONSIBILITY:
By using this indicator, you acknowledge that:
• You are solely responsible for your trading decisions
• You understand the substantial risks involved
• You will not hold the creator liable for losses
• You will conduct your own research and analysis
• You may consult a licensed financial professional
• You are using this tool entirely at your own risk
AS-IS PROVISION:
This indicator is provided "AS IS" without warranty of any kind,
express or implied, including but not limited to warranties of
merchantability, fitness for a particular purpose, or non-infringement.
The creator is not a registered investment advisor, financial planner,
or broker-dealer. This tool is not approved or endorsed by any
financial authority.
📞 ABOUT THE CREATOR
═══════════════════════════════════════════════════════════════════
Created by: Zakaria Safri
Specialization: Technical analysis indicator development
Focus: Multi-factor analysis, risk visualization, trend detection
This is an educational tool designed to demonstrate technical
analysis concepts and multi-factor signal generation methods.
📋 VERSION INFO
═══════════════════════════════════════════════════════════════════
Version: 1.0
Platform: TradingView Pine Script v5
License: Mozilla Public License 2.0
Creator: Zakaria Safri
Year: 2024
═══════════════════════════════════════════════════════════════════
Study Carefully, Trade Wisely, Manage Risk Properly
TradeVision Pro - Educational Trading Tool
Created by Zakaria Safri
═══════════════════════════════════════════════════════════════════
Candle Body Break (M/W/D/4H/1H)v5# Candle Body Break (M/W/D/4H/1H) Multi-Timeframe Indicator
This indicator identifies and plots **Candle Body Breaks** across five key timeframes: Monthly (M), Weekly (W), Daily (D), 4-Hour (4H), and 1-Hour (1H).
## Core Logic: Candle Body Break
The core concept is a break in the swing high/low defined by the body of the previous counter-trend candle(s). It focuses purely on **closing price breaks** of remembered highs/lows established by full candle bodies (close > open or close < open).
1. **Remembering the Swing:**
* After a bullish break (upward trend), the indicator waits for the first **bearish (close < open) candle** to appear. This bearish candle's high (`rememberedHigh`) and low (`rememberedLow`) are saved as the **breakout level**.
* Subsequent bearish candles that make a new low update this saved level, continuously adjusting the level to the most significant recent resistance/support established by the body's range.
2. **Executing the Break:**
* **Bull Break (Long signal):** Occurs when a **bullish candle's closing price** exceeds the last remembered bearish high (`rememberedHigh`).
* **Bear Break (Short signal):** Occurs when a **bearish candle's closing price** falls below the last remembered bullish low (`rememberedLow_Bull`).
Once a break occurs, the memory is cleared, and the indicator waits for the next counter-trend candle to establish a new level.
## Features
* **Multi-Timeframe Analysis:** Displays break lines and labels for M, W, D, 4H, and 1H timeframes on any chart.
* **Timeframe Filtering:** Break lines are only shown for timeframes **equal to or higher** than the current chart timeframe (e.g., on a 4H chart, only 4H, D, W, and M breaks are displayed).
* **Candidate Lines (Dotted Green):** Plots the current potential breakout level (the remembered high/low) that must be broken to trigger the next signal.
* **Direction Table:** A table in the top right corner summarizes the latest break direction (⇧ Up / ⇩ Down) for all five timeframes. This can be optionally limited to the 4H chart only.
* **1H Alert:** Triggers an alert when a 1-Hour break is detected.
## Input Settings Translation (for Mod Compliance)
| English Input Text | Original Japanese Text |
| :--- | :--- |
| **Show Monthly Break Lines** | 月足ブレイクを描画する |
| **Show Weekly Break Lines** | 週足ブレイクを描画する |
| **Show Daily Break Lines** | 日足ブレイクを描画する |
| **Show 4-Hour Break Lines** | 4時間足ブレイクを描画する |
| **Show 1-Hour Break Lines** | 1時間足ブレイクを描画する |
| **Show Monthly Candidate Lines** | 月足ブレイク候補ラインを描画する |
| **Show Weekly Candidate Lines** | 週足ブレイク候補ラインを描画する |
| **Show Daily Candidate Lines** | 日足ブレイク候補ラインを描画する |
| **Show 4-Hour Candidate Lines** | 4時間足ブレイク候補ラインを描画する |
| **Show 1-Hour Candidate Lines** | 1時間足ブレイク候補ラインを描画する |
| **Show Only Current TF Candidate Lines** | チャート時間足の候補ラインのみ表示 |
| **Show Table Only on 4H Chart** | テーブルを4Hチャートのみ表示 |
*Please note: The default alert message "1-Hour Break Detected" is also in English.*
※日本語訳
ろうそく足実体ブレイク（M/W/D/4H/1H）マルチタイムフレーム・インジケーター（日本語訳）
このインジケーターは、月足（M）、週足（W）、日足（D）、4時間足（4H）、1時間足（1H）の5つの主要な時間足におけるろうそく足実体ブレイクを検出し、プロットします。
コアロジック：ろうそく足実体ブレイク
このロジックの中核は、直近の**逆行ろうそく足（カウンター・トレンド・キャンドル）**の実体によって定義されたスイングの高値/安値のブレイクです。終値が実体のレンジ外で確定することを純粋に追跡します。
スイングの記憶（Remembering the Swing）:
強気のブレイク（上昇トレンド）の後、インジケーターは最初に現れる弱気（終値＜始値）のろうそく足を待ちます。この弱気ろうそく足の高値（rememberedHigh）と安値（rememberedLow）が、ブレイクアウトレベルとして保存されます。
その後、安値を更新する弱気ろうそく足が続いた場合、この保存されたレベルが更新され、実体のレンジによって確立された最新の重要なレジスタンス/サポートにレベルが継続的に調整されます。
ブレイクの実行（Executing the Break）:
ブルブレイク（買いシグナル）: 最後に記憶された弱気ろうそく足の高値（rememberedHigh）を、強気ろうそく足の終値が上回ったときに発生します。
ベアブレイク（売りシグナル）: 最後に記憶された強気ろうそく足の安値（rememberedLow_Bull）を、弱気ろうそく足の終値が下回ったときに発生します。
一度ブレイクが発生すると、記憶されたレベルはクリアされ、インジケーターは次の逆行ろうそく足が出現し、新しいレベルを確立するのを待ちます。
機能
マルチタイムフレーム分析: 現在のチャートの時間足に関わらず、M、W、D、4H、1Hのブレイクラインとラベルを表示します。
時間足フィルタリング: ブレイクラインは、現在のチャート時間足と同じか、それよりも上位の時間足のもののみが表示されます（例：4時間足チャートでは、4H、D、W、Mのブレイクのみが表示されます）。
候補ライン（緑の点線）: 次のシグナルをトリガーするためにブレイクされる必要がある、現在の潜在的なブレイクアウトレベル（記憶された高値/安値）をプロットします。
方向テーブル: 右上隅のテーブルに、5つの全時間足の最新のブレイク方向（⇧ 上昇 / ⇩ 下降）をまとめて表示します。これは、オプションで4時間足チャートのみに表示するように制限できます。
1時間足アラート: 1時間足のブレイクが検出されたときにアラートをトリガーします。
入力設定の翻訳
コード内の入力設定（UIテキスト）の日本語訳は以下の通りです。
英語の入力テキスト 日本語訳
Show Monthly Break Lines 月足ブレイクを描画する
Show Weekly Break Lines 週足ブレイクを描画する
Show Daily Break Lines 日足ブレイクを描画する
Show 4-Hour Break Lines 4時間足ブレイクを描画する
Show 1-Hour Break Lines 1時間足ブレイクを描画する
Show Monthly Candidate Lines 月足ブレイク候補ラインを描画する
Show Weekly Candidate Lines 週足ブレイク候補ラインを描画する
Show Daily Candidate Lines 日足ブレイク候補ラインを描画する
Show 4-Hour Candidate Lines 4時間足ブレイク候補ラインを描画する
Show 1-Hour Candidate Lines 1時間足ブレイク候補ラインを描画する
Show Only Current TF Candidate Lines チャート時間足の候補ラインのみ表示
Show Table Only on 4H Chart テーブルを4Hチャートのみ表示
Alert Message: 1-Hour Break Detected アラートメッセージ: 1時間足ブレイク発生
Golden Cross Screener [Pineify]Golden Cross Screener Pineify – Multi-Symbol Trend Detection Screener for TradingView
Discover the Golden Cross Screener Pineify for TradingView: a multi-symbol, multi-timeframe indicator for crypto and other assets. Customizable Golden Cross detection, robust algorithm, and intuitive screener design for smarter portfolio trend analysis.
Key Features
Multi-symbol screening across major cryptocurrencies or assets – BTCUSD, ETHUSD, XRPUSD, USDT, BNB, SOLUSD, DOGEUSD, TRXUSD (fully customizable).
Multi-timeframe analysis (e.g., 1m, 5m, 10m, 30m), enabling robust trend detection from scalp to swing.
Customizable Moving Average settings for both Fast and Slow MA (source and length).
Efficient screener table, highlighting Golden Cross events and current asset trends in one panel.
Visual cues for bullish, bearish, and cross states using intuitive color-coding and labels.
Flexible symbol and timeframe inputs to tailor the screener to any portfolio or watchlist.
How It Works
The Golden Cross Screener Pineify leverages the classic Golden Cross methodology—a bullish trend signal triggered when a shorter-term moving average crosses above a longer-term moving average. To improve robustness, you are empowered to configure both Fast MA and Slow MA periods and sources, making the detection logic applicable to any symbol, timeframe, or asset class.
Internally, the script runs dedicated calculations on each chosen symbol and timeframe, generating independent signals using exponential moving averages (EMA). Using the TradingView `request.security` function, it fetches and processes price data for up to eight portfolio assets on four timeframes, displaying the detected Golden Cross, Bullish, or Bearish states in a central screener table.
Trading Ideas and Insights
Spot emerging bullish or bearish trends across your favorite crypto pairs or trading assets in real time.
Capture prime opportunities when multiple assets align with Golden Cross signals—ideal for portfolio rebalancing or rotational strategies.
Analyze trend consistency by monitoring cross events at multiple timeframes for a given asset.
Swiftly identify when short-term and long-term momentum diverge—flagging potential reversals or trend initiations.
The Golden Cross Screener Pineify is not just a trend signal; it’s a holistic multi-asset scanner built for traders who know the power of combining technical breadth with agile timing.
How Multiple Indicators Work Together
This screener stands out with its modular approach: each asset/timeframe pair is monitored in isolation, yet displayed collectively for multidimensional market insight. Each symbol’s price action is processed through independently configured EMAs—Fast and Slow—whose crossovers are analyzed for directional bias. The implementation’s real innovation is in its screener table engine: it aggregates signals, synchronizes timeframes, and color-codes market states, allowing users to see confluences, divergences, and sector trends at a glance.
Combining Golden Cross detection with customizable moving averages and flexible multi-timeframe, multi-symbol scanning means users can fine-tune sensitivity, focus on specific signals, and adapt screener logic for scalping, swing trading, or investing.
Unique Aspects
True multi-symbol screener within the TradingView indicator framework.
Full customization of screener assets, timeframes, and moving averages.
Advanced, efficient use of TradingView table for clear, actionable visualization.
No dependency on standard, static MA settings—adjust everything to match your strategy.
Big-picture and granular trend detection in one tool, designed for both active traders and portfolio managers.
How to Use
Add the Golden Cross Screener Pineify to your TradingView chart.
Choose up to eight symbols—crypto, stock, forex, or custom assets.
Set four timeframes for screening, from lower to higher intervals.
Adjust moving average sources (price, close, etc.) and period lengths for both Fast and Slow MAs to suit your trading style.
Interpret table cells: clear labels and color indicate Golden Cross (trend shift), Bullish (uptrend), Bearish (downtrend) states for each symbol/timeframe.
React to signal alignments—deploy or rebalance positions, increase alert sensitivity, or backtest sequence confluences.
Customization
The indicator’s inputs panel gives full control:
Select which symbols to screen, making it perfect for any asset watchlist.
Pick the desired timeframes—mix daily, hourly, or minute-based intervals.
Adjust Fast and Slow MA settings: switch source type, change period length, and fine-tune detection logic as needed.
Style your screener table via TradingView settings (colors, font sizes, alignment).
Every element is customizable—adapt the Golden Cross Screener Pineify for your specific portfolio, trading timeframe, and strategy focus.
Conclusion
The Golden Cross Screener Pineify elevates multi-symbol trend detection to a new level on TradingView. By combining configurable Golden Cross logic with a powerful screener engine, it serves both precision and broad market insight—crucial for agile traders and strategic portfolio managers. Whether you’re tracking crypto pairs, stocks, forex, or a mix, this tool transforms static trend analysis into an active, multi-dimensional trading edge.
GEX Options Flow Pro 100% free
INTRODUCTION
This script is designed to visualize advanced options-derived metrics and levels on TradingView charts, including Gamma Exposure (GEX) walls, gamma flip points, vanna levels, delta-neutral prices (DEX), max pain, implied moves, and more. It overlays dynamic lines, labels, boxes, and an info table to highlight potential support, resistance, volatility regimes, and flow dynamics based on options data.
These visualizations aim to help users understand how options market structure might influence price action, such as areas of potential stability (positive GEX) or volatility (negative GEX). All data is user-provided via pasted strings, as Pine Script cannot fetch external options data directly due to platform limitations (detailed below).
The script is open-source under TradingView's terms, allowing study, modification, and improvement. It draws inspiration from standard options Greeks and exposure metrics (e.g., gamma, vanna, charm) discussed in financial literature like Black-Scholes models and dealer positioning analyses. No external code is copied; all logic is original or based on mathematical formulas.
Disclaimer: This is an educational tool only. It does not provide investment advice, trading signals, or guarantees of performance. Past data is not indicative of future results. Use at your own risk, and combine with your own analysis. Not intended for qualified investors only.
How the Options Levels Are Calculated
Levels are not computed in Pine Script—they rely on pre-calculated values from external tools (e.g., Python scripts using libraries like yfinance for options chains). Here's how they're typically derived externally before pasting into the script:
Fetching Options Data: Retrieve options chain for a ticker: strikes, open interest (OI), volume, implied volatility (IV), expirations (e.g., shortest: 0-7 DTE, short: 7-14 DTE, medium: ~30 DTE, long: ~90 DTE). Get current price and 5-day history for context.
Gamma Walls (Put/Call Walls): Compute gamma for each option using Black-Scholes: gamma = N'(d1) / (S * σ * √T) where S = spot price, K = strike, T = time to expiration (years), σ = IV, N'(d1) = normal PDF. Aggregate GEX at strikes: GEX = sign * gamma * OI * 100 * S^2 * 0.01 (per 1% move, with sign based on dealer positioning: typically short calls/puts = negative GEX). Put Wall: Highest absolute GEX put strike below S (support via dealer buying on dips). Call Wall: Highest absolute GEX call strike above S (resistance via dealer selling on rallies). Secondary/Tertiary: Next highest levels. Historical walls track tier-1 levels over 5 days.
Gamma Flip: Net GEX profile across prices: Sum GEX for all options at hypothetical spots. Flip point: Interpolated price where net GEX changes sign (stable above, volatile below).
Vanna Levels: Vanna = -N'(d1) * d2 / σ. Weighted by OI; highest positive/negative strikes.
DEX (Delta-Neutral Price): Net dealer delta: Sum (delta * OI * 100 * sign), with delta from Black-Scholes. DEX: Price where net delta = 0 (interpolated).
Max Pain: Strike minimizing total intrinsic value for all options holders.
Skew: 25-delta skew: IV difference between 25-delta put and call (interpolated).
Net GEX/Delta: Total signed GEX/delta at current S.
Implied Move: ATM IV * √(DTE/365) for 1σ range.
C/P Ratio: (Call OI + volume) / (Put OI + volume).
Smart Stop Loss: Below lowest support (e.g., Put Wall, gamma flip), buffered by IV * √(DTE/30).
Other Metrics: IV: ATM average. 5-day metrics: Avg volume, high/low.
External tools handle dealer assumptions (e.g., short calls/puts) and scaling (per % move).
Effect as Support and Resistance in Technical Trading
Options levels reflect dealer hedging dynamics:
Put Wall (Gamma Support): High put GEX creates buying pressure on dips (dealers hedge short puts by buying stock). Use for long entries, bounces, or stops below.
Call Wall (Gamma Resistance): High call GEX leads to selling on rallies. Good for trims, shorts, or reversals.
Gamma Flip: Pivot for volatility—above: dampened moves (positive GEX, mean reversion); below: amplified trends (negative GEX, momentum).
Vanna Levels: Sensitivity to IV changes; crosses may signal vol shifts.
DEX: Dealer delta neutral—bullish if price below with positive delta.
Max Pain: Price magnet minimizing option payouts.
Implied Move/Confidence Bands: Expected ranges (1σ/2σ/3σ); breakouts suggest extremes.
Liquidity Zones: Wall ranges as price magnets.
Smart Stop Loss: Protective level below supports, IV-adjusted.
C/P Ratio & Skew: Sentiment (high C/P = bullish; high skew = put demand).
Net GEX: Positive = low vol strategies (e.g., condors); negative = momentum trades.
Combine with TA (e.g., volume, trends). High activity strengthens effects; alerts on crosses/proximities for awareness.
Limitations of the TradingView Platform for Data Pulling
Pine Script is sandboxed:
No API calls or internet access (can't fetch options data directly).
Limited to chart/symbol data; no real-time chains.
Inputs static per load; manual updates needed.
Caching not persistent across sessions.
This ensures lightweight scripts but requires external data sourcing.
Creative Solution for On-Demand Data Pulling
Users can use external tools (e.g., Python scripts with yfinance) to fetch/compute data on demand. Generate a formatted string (ticker,timestamp|term1_data|term2_data|...), paste into inputs. Tools can process multiple tickers, cache for ~15-30 min, and output strings for quick portfolio scanning. Run locally or via custom setups for near-real-time updates without platform violations.
For convenience, a free bot is available on my website that accepts commands like !gex to generate both current data strings (for all expiration terms) and historical walls data on demand. This allows users to easily obtain fresh or cached data (refreshed every ~30 min) for pasting into the indicator—ideal for scanning portfolios without manual coding.
Script Functionality Breakdown
Inputs: Data strings (current/historical); term selector (Shortest/Short/Medium/Long); toggles (historical walls, GEX profile, secondaries, vanna, table, max pain, DEX, stop loss, implied move, liquidity, bands); colors/styles.
Parsing: Extracts term-specific data; validates ticker match; gets timestamp for freshness.
Drawing: Dynamic lines/labels (width/color by GEX strength); boxes (moves, zones, bands); clears on updates.
Info Table: Dashboard with status (freshness emoji), Greeks (GEX/delta with emojis), vol (IV/skew), levels (distances), flow (C/P, vol vs 5D).
Historical Walls: Displays past tier-1 walls on daily+ timeframes.
Alerts: 20+ conditions (e.g., near/cross walls, GEX sign change, high IV).
Performance: Efficient for real-time; smart label positioning.
Release Notes
Initial release: Full features including multi-term support, enhanced table with emojis/sentiment, dynamic visuals, smart stop loss.
Data String Format: TICKER,TIMESTAMP|TERM1_DATA|TERM2_DATA|TERM3_DATA|TERM4_DATA Where each TERM_DATA = val0,val1,...,val30 (31 floats: current_price, prev_close, call_wall_1, call_wall_1_gex, ..., low_5d). Historical: TICKER|TERM1_HIST|... where TERM_HIST = date:cw,pw;date:cw,pw;...
Feedback welcome in comments. Educational only—not advice.
RVol+ Enhanced Relative Volume Indicator📊 RVol+ Enhanced Relative Volume Indicator
Overview
RVol+ (Relative Volume Plus) is an advanced time-based relative volume indicator designed specifically for swing traders and breakout detection. Unlike simple volume comparisons, RVol+ analyzes volume at the same time of day across multiple sessions, providing statistically significant insights into institutional activity and breakout potential.
🎯 Key Features
Core Volume Analysis
Time-Based RVol Calculation - Compares current cumulative volume to the average volume at this exact time over the past N days
Statistical Z-Score - Measures volume in standard deviations from the mean for true anomaly detection
Volume Percentile - Shows where current volume ranks historically (0-100%)
Sustained Volume Filter - 3-bar moving average prevents false signals from single-bar spikes
Breakout Detection
🚀 Confirmed Breakouts - Identifies price breakouts validated by high volume (RVol > 1.5x)
⚠️ False Breakout Warnings - Alerts when price breaks key levels on low volume (high failure risk)
Multi-Timeframe Context - Weekly volume overlay prevents chasing daily noise
Advanced Metrics
OBV Divergence Detection - Spots bullish/bearish accumulation/distribution patterns
Volume Profile Integration - Identifies institutional positioning
Money Flow Analysis - Tracks smart money vs retail activity
Extreme Volume Alerts - 🔥 Labels mark unusual spikes beyond the display cap
Visual Intelligence
Smart Color Coding:
🟢 Bright Teal = High activity (RVol ≥ 1.5x)
🟡 Medium Teal = Caution zone (RVol ≥ 1.2x)
⚪ Light Teal = Normal activity
🟠 Orange = Breakout confirmed
🔴 Red = False breakout risk
Comprehensive Stats Table:
Current Volume (formatted as M/K/B)
RVol ratio
Z-Score with significance
Volume percentile
Historical average and standard deviation
Sustained volume confirmation
📈 How to Use
For Swing Trading (1D - 3W Holds)
Perfect Setup:
✓ RVol > 1.5x (bright teal)
✓ Z-Score > 2.0 (⚡ alert)
✓ Percentile > 90%
✓ Sustained = ✓
✓ 🚀 Breakout label appears
Avoid:
✗ Red "Low Vol" warning during breakouts
✗ RVol < 1.0 at key levels
✗ Sustained volume not confirmed
Signal Interpretation
⚡ Z>2 Labels - Statistically significant volume (95th+ percentile) - highest probability moves
↗️ OBV+ Labels - Bullish accumulation (OBV rising while price consolidates)
↘️ OBV- Labels - Bearish distribution (OBV falling while price rises)
🔵 Blue Background - Weekly volume elevated (confirms daily strength)
⚙️ Customization
Basic Settings
N Day Average - Number of historical days for comparison (default: 5)
RVol Thresholds - Customize highlight levels (default: 1.2x, 1.5x)
Visual Display Cap - Prevent extreme spikes from compressing view (default: 4.0x)
Advanced Metrics (Toggle On/Off)
Z-Score analysis
Weekly RVol context
OBV divergence detection
Volume percentile ranking
Breakout signal generation
Table Customization
Position - 9 placement options to avoid chart overlap
Size - Tiny to Huge
Colors - Full customization of positive/negative/neutral values
Transparency - Adjustable background
Debug Mode
Enable Pine Logs for calculation transparency
Adjustable log frequency
Real-time calculation breakdown
🔬 Technical Details
Algorithm:
Binary search for historical lookups (O(log n) performance)
Time-zone aware session detection
DST-safe timestamp calculations
Exponentially weighted standard deviation
Anti-repainting architecture
Performance:
Optimized for max_bars_back = 5000
Efficient array management
Built-in function optimization
Memory-conscious data structures
📊 What Makes RVol+ Different?
vs. Standard Volume:
Context-aware (time-of-day matters)
Statistical significance testing
False breakout filtering
vs. Basic RVol:
Z-Score normalization (2-3 sigma detection)
Multi-timeframe confirmation
OBV divergence integration
Sustained volume filtering
Smart visual scaling
vs. Professional Tools:
Free and open-source
Fully customizable
No black-box algorithms
Educational debug logs
💡 Best Practices
Wait for Confirmation - Don't enter on first bar; wait for sustained volume ✓
Combine with Price Action - RVol validates, price structure determines entry
Weekly Context Matters - Blue background = institutional interest
Z-Score is King - Focus on ⚡ alerts for highest probability
Avoid Low Volume Breakouts - Red ⚠️ labels = high failure risk
🎓 Trading Psychology
Volume precedes price. When RVol+ shows:
High RVol + Rising OBV = Accumulation before breakout
High RVol at Resistance = Test of conviction
Low RVol on Breakout = Retail-driven (fade candidate)
Z-Score > 3 = Potential "whale" positioning
📝 Credits
Based on the time-based RVol concept from /u/HurlTeaInTheSea, enhanced with:
Statistical analysis (z-scores, percentiles)
Multi-timeframe integration
OBV divergence detection
Professional-grade visualization
Swing trading optimization
🔧 Version History
v2.0 - Enhanced Edition
Added Z-Score analysis
Multi-timeframe volume context
OBV divergence detection
Breakout confirmation system
Smart color coding
Customizable stats table
Debug logging mode
Performance optimizations
📚 Learn More
For optimal use with swing trading:
Combine with support/resistance levels
Watch for volume clusters in consolidation
Use weekly timeframe for trend confirmation
Monitor OBV divergence for early warnings
⚠️ Disclaimer
This indicator is for educational purposes. Volume analysis is one component of trading decisions. Always use proper risk management, consider multiple timeframes, and validate signals with price structure. Past performance does not guarantee future results.
🚀 Getting Started
Add indicator to chart
Adjust "N Day Average" to your preference (5-10 days typical)
Position stats table to avoid overlap
Enable features you want to monitor
Watch for 🚀 breakout confirmations!
Happy Trading! 📈
Intraday Rising & Reversal ScannerPine Script Description: Intraday Rising & Reversal ScannerThis Pine Script is a TradingView indicator designed to identify stocks with intraday (1-hour timeframe) potential for bullish (rising) or bearish (reversal) movements. It scans for stocks based on user-defined technical criteria, including price change, relative volume, RSI, EMA, ATR, and VWAP. The script plots signals on the chart, displays a summary table, and triggers alerts when conditions are met.FeaturesBullish Signal (Rising Stocks):1H Price Change: > 1% (configurable, e.g., >2% for volatile markets).
Relative Volume: > 2.0 (volume is at least twice the 20-period average).
RSI (14): Between 50 and 70 (strong but not overbought momentum).
Price vs EMA 13: Price above the 13-period EMA (confirms short-term uptrend).
ATR (14): Current ATR above its 20-period average (indicates volatility).
VWAP: Price above VWAP (optional, shown on chart for manual confirmation).
Bearish Signal (Reversal Stocks):1H Price Change: < -1% (configurable, e.g., <-2% for stronger reversals).
Relative Volume: > 2.0 (high volume confirms selling pressure).
RSI (14): > 70 (overbought, increasing reversal likelihood).
Price vs EMA 13: Price below the 13-period EMA (confirms short-term downtrend).
ATR (14): Current ATR above its 20-period average (indicates volatility).
VWAP: Price below VWAP (optional, shown on chart for manual confirmation).
Visualization:Bullish Signal: Green triangle below the bar.
Bearish Signal: Red triangle above the bar.
VWAP: Plotted as a blue line for manual verification.
Table: Displays real-time metrics (Change %, Relative Volume, RSI, Price vs EMA, ATR, VWAP) in the top-right corner, color-coded (green for bullish, red for bearish).
Alerts:Separate alerts for bullish ("Intraday Bullish Signal") and bearish ("Intraday Bearish Signal") conditions.
Customizable alert messages include parameter values for easy tracking.
How It WorksThe script runs on the 1-hour (1H) timeframe, ensuring all calculations are based on hourly data.
Indicators are computed:Change %: Percentage price change over the last hour.
Relative Volume: Current volume divided by the 20-period SMA of volume.
RSI: 14-period Relative Strength Index.
EMA 13: 13-period Exponential Moving Average.
ATR: 14-period Average True Range, compared to its 20-period SMA.
VWAP: Volume Weighted Average Price, plotted for visual confirmation.
Signals are generated when all conditions for either bullish or bearish criteria are met.
A table summarizes key metrics, and alerts can be set up for real-time notifications.
Usage InstructionsApply the Script:Open TradingView’s Pine Editor.
Copy and paste the script.
Click "Add to Chart" and set the chart to the 1-hour (1H) timeframe.
Set Up Alerts:Right-click on the chart > "Add Alert".
Select "Intraday Bullish Signal" or "Intraday Bearish Signal" as the condition.
Configure notifications (e.g., SMS, email, or TradingView alerts).
Manual VWAP Check:VWAP is plotted as a blue line. Verify that the price is above VWAP for bullish signals or below for bearish signals using the table or chart.
To make VWAP a mandatory filter, uncomment the VWAP conditions in the bull_signal and bear_signal definitions.
BOCS Channel Scalper Indicator - Mean Reversion Alert System# BOCS Channel Scalper Indicator - Mean Reversion Alert System
## WHAT THIS INDICATOR DOES:
This is a mean reversion trading indicator that identifies consolidation channels through volatility analysis and generates alert signals when price enters entry zones near channel boundaries. **This indicator version is designed for manual trading with comprehensive alert functionality.** Unlike automated strategies, this tool sends notifications (via popup, email, SMS, or webhook) when trading opportunities occur, allowing you to manually review and execute trades. The system assumes price will revert to the channel mean, identifying scalp opportunities as price reaches extremes and preparing to bounce back toward center.
## INDICATOR VS STRATEGY - KEY DISTINCTION:
**This is an INDICATOR with alerts, not an automated strategy.** It does not execute trades automatically. Instead, it:
- Displays visual signals on your chart when entry conditions are met
- Sends customizable alerts to your device/email when opportunities arise
- Shows TP/SL levels for reference but does not place orders
- Requires you to manually enter and exit positions based on signals
- Works with all TradingView subscription levels (alerts included on all plans)
**For automated trading with backtesting**, use the strategy version. For manual control with notifications, use this indicator version.
## ALERT CAPABILITIES:
This indicator includes four distinct alert conditions that can be configured independently:
**1. New Channel Formation Alert**
- Triggers when a fresh BOCS channel is identified
- Message: "New BOCS channel formed - potential scalp setup ready"
- Use this to prepare for upcoming trading opportunities
**2. Long Scalp Entry Alert**
- Fires when price touches the long entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "LONG scalp signal at 24731.75 | TP: 24743.2 | SL: 24716.5"
**3. Short Scalp Entry Alert**
- Fires when price touches the short entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "SHORT scalp signal at 24747.50 | TP: 24735.0 | SL: 24762.75"
**4. Any Entry Signal Alert**
- Combined alert for both long and short entries
- Use this if you want a single alert stream for all opportunities
- Message: "BOCS Scalp Entry: at "
**Setting Up Alerts:**
1. Add indicator to chart and configure settings
2. Click the Alert (⏰) button in TradingView toolbar
3. Select "BOCS Channel Scalper" from condition dropdown
4. Choose desired alert type (Long, Short, Any, or Channel Formation)
5. Set "Once Per Bar Close" to avoid false signals during bar formation
6. Configure delivery method (popup, email, webhook for automation platforms)
7. Save alert - it will fire automatically when conditions are met
**Alert Message Placeholders:**
Alerts use TradingView's dynamic placeholder system:
- {{ticker}} = Symbol name (e.g., NQ1!)
- {{close}} = Current price at signal
- {{plot_1}} = Calculated take profit level
- {{plot_2}} = Calculated stop loss level
These placeholders populate automatically, creating detailed notification messages without manual configuration.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This indicator is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Indicator**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the indicator ideal for active day traders who want continuous alert opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased signal frequency also means higher potential commission costs and requires disciplined trade selection when acting on alerts.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The indicator normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The indicator uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The indicator tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The indicator uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. Visual markers (arrows and labels) appear on chart, and configured alerts fire immediately.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents alert spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long alert will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The indicator includes a multi-timeframe ATR filter to avoid alerts during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while viewing 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Alerts enabled
- If ATR < threshold: No alerts fire
This prevents notifications during dead zones where mean reversion is unreliable due to insufficient price movement. The ATR status is displayed in the info table with visual confirmation (✓ or ✗).
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. These levels are displayed as visual lines with labels and included in alert messages for reference when manually placing orders.
### Stop Loss Placement:
Stop losses are calculated just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. SL levels are displayed on chart and included in alert notifications as suggested stop placement.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
## INPUT PARAMETERS:
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long alert generation on/off
- **Enable Short Scalps**: Toggle short alert generation on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between alerts (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for alert enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time indicator status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Long Color**: Customize long signal color (default: darker green for readability)
- **Short Color**: Customize short signal color (default: red)
- **TP/SL Colors**: Customize take profit and stop loss line colors
- **Line Length**: Visual length of TP/SL reference lines (5-200 bars)
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short alerts
- **TP/SL reference lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing channel status, last signal, entry/TP/SL prices, risk/reward ratio, and ATR filter status
- **Visual confirmation** when alerts fire via on-chart markers synchronized with notifications
## HOW TO USE:
### For 1-3 Minute Scalping with Alerts (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars to reduce alert spam
- **Alert Setup**: Configure "Any Entry Signal" for combined long/short notifications
- **Execution**: When alert fires, verify chart visuals, then manually place limit order at entry zone with provided TP/SL levels
### For 5-15 Minute Day Trading with Alerts:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- **Alert Setup**: Configure separate "Long Scalp Entry" and "Short Scalp Entry" alerts if you trade directionally based on bias
- **Execution**: Review channel structure on alert, confirm ATR filter shows ✓, then enter manually
### For 30-60 Minute Swing Scalping with Alerts:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- **Alert Setup**: Use "New Channel Formation" to prepare for setups, then "Any Entry Signal" for execution alerts
- **Execution**: Larger timeframes allow more analysis time between alert and entry
### Webhook Integration for Semi-Automation:
- Configure alert webhook URL to connect with platforms like TradersPost, TradingView Paper Trading, or custom automation
- Alert message includes all necessary order parameters (direction, entry, TP, SL)
- Webhook receives structured data when signal fires
- External platform can auto-execute based on alert payload
- Still maintains manual oversight vs full strategy automation
## USAGE CONSIDERATIONS:
- **Manual Discipline Required**: Alerts provide opportunities but execution requires judgment. Not all alerts should be taken - consider market context, trend, and channel quality
- **Alert Timing**: Alerts fire on bar close by default. Ensure "Once Per Bar Close" is selected to avoid false signals during bar formation
- **Notification Delivery**: Mobile/email alerts may have 1-3 second delay. For immediate execution, use desktop popups or webhook automation
- **Cooldown Necessity**: Without cooldown, rapidly touching price action can generate excessive alerts. Start with 3-bar cooldown and adjust based on alert volume
- **ATR Filter Impact**: Enabling ATR filter dramatically reduces alert count but improves quality. Track filter status in info table to understand when you're receiving fewer alerts
- **Commission Awareness**: High alert frequency means high potential trade count. Calculate if your commission structure supports frequent scalping before acting on all alerts
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features are not included in this indicator version. Multi-timeframe ATR requires higher-tier TradingView subscription for request.security() functionality on timeframes below chart timeframe.
## KNOWN LIMITATIONS:
- **Indicator does not execute trades** - alerts are informational only; you must manually place all orders
- **Alert delivery depends on TradingView infrastructure** - delays or failures possible during platform issues
- **No position tracking** - indicator doesn't know if you're in a trade; you must manage open positions independently
- **TP/SL levels are reference only** - you must manually set these on your broker platform; they are not live orders
- **Immediate touch entry can generate many alerts** in choppy zones without adequate cooldown
- **Channel deletion at 10-tick breaks** may be too aggressive or lenient depending on instrument tick size
- **ATR filter from lower timeframes** requires TradingView Premium/Pro+ for request.security()
- **Mean reversion logic fails** in strong breakout scenarios - alerts will fire but trades may hit stops
- **No partial closing capability** - full position management is manual; you determine scaling out
- **Alerts do not account for gaps** or overnight price changes; morning alerts may be stale
## RISK DISCLOSURE:
Trading involves substantial risk of loss. This indicator provides signals for educational and informational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Mean reversion strategies can experience extended drawdowns during trending markets. Alerts are not guaranteed to be profitable and should be combined with your own analysis. Stop losses may not fill at intended levels during extreme volatility or gaps. Never trade with capital you cannot afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Always verify alerts against current market conditions before executing trades manually.
## ACKNOWLEDGMENT & CREDITS:
This indicator is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based alert generation, comprehensive alert condition system with customizable notifications, multi-timeframe ATR volatility filtering, cooldown period for alert management, dual TP methods (fixed points vs channel percentage), visual TP/SL reference lines, and real-time status monitoring table. This indicator version is specifically designed for manual traders who prefer alert-based decision making over automated execution.
Macro & Earnings Dashboard — NY Fed CalendarMacro & Earnings Dashboard — NY Fed Calendar
This is an overlay indicator designed to provide a quick, real-time overview of the most critical upcoming US economic data releases and corporate earnings reports directly on your TradingView chart. It functions as a dynamic dashboard, removing the need to constantly check external calendars.
Key Features
1. Real-Time Economic Calendar (Bottom-Right Table)
The dashboard tracks the time remaining until the next release of five major, high-impact economic indicators. The data for these dates is pre-loaded directly from the New York Fed Economic Indicators Calendar (currently loaded for October through December 2025).
The tracked events include:
CPI (Consumer Price Index)
PPI (Producer Price Index)
Employment Situation (Non-Farm Payrolls / Unemployment Rate)
Interest Rate Decision (FOMC Meetings)
Consumer Sentiment (University of Michigan Survey)
2. Corporate Earnings Tracker (Top-Right Table)
This table uses TradingView's built-in data to calculate the estimated days remaining until the next Earnings Per Share (EPS) report for a curated list of high-profile NASDAQ tickers:
AAPL, NVDA, GOOG, TSLA, MSFT, AMZN, META
3. Color-Coded Urgency
The "Days" column for both macro and earnings tables uses a traffic light system to instantly communicate how soon the event is:
Red: The event is scheduled for Today or Tomorrow (0–1 day away).
Orange: The event is scheduled for the current week (within 6 days).
Teal: The event is more than a week away.
Gray: The date is currently unavailable or outside the loaded calendar range.
cd_indiCATor_CxGeneral:
This indicator is the redesigned, simplified, and feature-enhanced version of the previously shared indicators:
cd_cisd_market_Cx, cd_HTF_Bias_Cx, cd_sweep&cisd_Cx, cd_SMT_Sweep_CISD_Cx, and cd_RSI_divergence_Cx.
Within the holistic setup, the indicator tracks:
• HTF bias
• Market structure (trend) in the current timeframe
• Divergence between selected pairs (SMT)
• Divergence between price and RSI values
• Whether the price is in an important area (FVG, iFVG, and Volume Imbalance)
• Whether the price is at a key level
• Whether the price is within a user-defined special timeframe
The main condition and trigger of the setup is an HTF sweep with CISD confirmation on the aligned timeframe.
When the main condition occurs, the indicator provides the user with a real-time market status summary, enriched with other data.
________________________________________
What’s new?
-In the SMT module:
• Triad SMT analysis (e.g.: NQ1!, ES1!, and YM1!)
• Dyad SMT analysis (e.g.: EURUSD, GBPUSD)
• Alternative pair definition and divergence analysis for non-correlated assets
o For crypto assets (xxxUSDT <--> xxxUSDT.P) (e.g.: SOLUSDT.P, SOLUSDT)
o For stocks, divergence analysis by comparing the asset with its value in another currency
(BIST:xxx <--> BIST:xxx / EURTRY), (BAT:xxx <--> BAT:xxx / EURUSD)
-Special timeframe definition
-Configurable multi-option alarm center
-Alternative summary presentation (check list / status table / stickers)
________________________________________
Details and usage:
The user needs to configure four main sections:
• Pair and correlated pairs
• Timeframes (Auto / Manual)
• Alarm center
• Visual arrangement and selections
Pair Selections:
The user should adjust trading pairs according to their trade preferences.
Examples:
• Triad: NQ1!-ES1!-YM1!, BTC-ETH-Total3
• Dyad: NAS100-US500, XAUUSD-XAGUSD, XRPUSDT-XLMUSDT
Single pairs:
-Crypto Assets:
If crypto assets are not in the triad or dyad list, they are automatically matched as:
Perpetual <--> Spot (e.g.: DOGEUSDT.P <--> DOGEUSDT)
If the asset is already defined in a dyad list (e.g., DOGE – SHIB), the dyad definition takes priority.
________________________________________
-Stocks:
If stocks are defined in the dyad list (e.g.: BIST:THYAO <--> BIST:PGSUS), the dyad definition takes priority.
If not defined, the stock is compared with its value in the selected currency.
For example, in the Turkish Stock Exchange:
BIST:FENER stock, if EUR is chosen from the menu, is compared as BIST:FENER / OANDA:EURTRY.
Here, “OANDA” and the stock market currency (TRY) are automatically applied for the exchange rate.
For NYSE:XOM, its pair will be NYSE:XOM / EURUSD.
________________________________________
Timeframes:
By default, the menu is set to “Auto.” In this mode, aligned timeframes are automatically selected.
Aligned timeframes (LTF-HTF):
1m-15m, 3m-30m, 5m-1h, 15m-4h, 1h-D, 4h-W, D-M
Example: if monitoring the chart on 5m:
• 1h sweep + 5m CISD confirmation
• D sweep + 1h CISD confirmation (bias)
• 5m market structure
• 1h SMT and 1h RSI divergence analysis
For manual selections, the user must define the timeframes for Sweep and HTF bias.
FVG, iFVG, and Volume Imbalance timeframes must be manually set in both modes.
________________________________________
Alarm Center:
The user can choose according to preferred criteria.
Each row has options.
“Yes” → included in alarm condition.
“No” → not included in alarm condition.
If special timeframe criteria are added to the alarm, the hour range must also be entered in the same row, and the “Special Zone” tab (default: -4) should be checked.
Key level timeframes and plot options must be set manually.
Example alarm setup:
Alongside the main Sweep + CISD condition, if we also want HTF bias + Trend alignment + key level (W, D) and special timeframe (09:00–11:00), we should set up the menu as follows:
________________________________________
Visual Arrangement and Selections:
Users can control visibility with checkboxes according to their preferences.
In the Table & Sticker tab, table options and labels can be controlled.
• Summary Table has two options: Check list and Status Table
• From the HTF bias section, real-time bias and HTF sweep zone (optional) are displayed
• The RSI divergence section only shows divergence analysis results
• The SMT 2 sub-section only functions when triad is selected
Labels are shown on the bar where the sweep + CISD condition occurs, displaying the current situation.
With the Check box option, all criteria’s real-time status is shown (True/False).
Status Table provides a real-time summary table.
Although the menu may look crowded, most settings only need to be adjusted once during initial use.
________________________________________
What’s next?
• Suggestions from users
• Standard deviation projection
• Mitigation/order blocks (cd special mtg)
• PSP /TPD
________________________________________
Final note:
Every additional criterion in the alarm settings will affect alarm frequency.
Multiple conditions occurring at the same time is not, by itself, sufficient to enter a trade—you should always apply your own judgment.
Looking forward to your feedback and suggestions.
Happy trading! 🎉
EMA-RSI-ADX Trend Bands
📌 EMA-RSI-ADX Trend Bands (ERA Trend Bands)
🔥 Overview
The ERA Trend Bands indicator combines Exponential Moving Average (EMA), Relative Strength Index (RSI), and Average Directional Index (ADX) into a powerful multi-factor trend system.
It helps traders:
Identify trend direction (Bullish / Bearish)
Measure trend strength using EMA deviation bands
Confirm momentum with RSI & ADX filters
Visualize conditions with dynamic colors, labels, tables, and signals
⚡ Key Features
📍 EMA Trend Bands
EMA100 with gradient glow effect showing trend bias
Strength bands around EMA (Very Weak → Hyper levels)
Bands color-coded for bullish/bearish extremes
📊 RSI + ADX Confluence
Bullish Signal: RSI ≥ threshold & ADX ≥ threshold → 🟢
Bearish Signal: RSI ≤ threshold & ADX ≤ threshold → 🔴
Candles recolored when conditions are met
Auto-generated labels show live RSI/ADX values
🧩 Strength Levels
Classifies deviation from EMA into 8 levels:
Neutral → Very Weak → Weak → Moderate → Strong → Very Strong → Extreme → Hyper
Dashboard table shows deviation % ranges & strength colors
Dynamic labels display Trend, Strength, Deviation %, RSI & ADX
🎨 Visual Enhancements
Gradient EMA line with glow effect
Bullish (greens) & bearish (reds) vibrant palettes
Background coloring (optional) based on strength
Symbols & labels for entry confirmation
🎯 How to Use
Trend Direction – EMA color + deviation bands show whether market is bullish or bearish.
Strength Confirmation – Use strength labels & dashboard table to gauge overextension.
Entry Signals – Watch for RSI/ADX confluence (green/red labels on chart).
Exits – Monitor when strength fades back toward Neutral/Weak levels.
⚙️ Settings & Inputs
EMA Settings → Length, Line Width, Gradient Intensity
RSI Settings → Length & Thresholds (Bullish / Bearish)
ADX Settings → Length & Thresholds (Bullish / Bearish)
Bands → Enable/disable EMA deviation bands
Labels/Table → Toggle strength info display
Colors → Fully customizable vibrant palettes
🚨 Alerts & Signals
Bullish Condition → RSI & ADX above thresholds
Bearish Condition → RSI & ADX below thresholds
Visual confirmation with labels, candles, and background
⚠️ Disclaimer
This script is for educational purposes only.
It does not constitute financial advice.
Always backtest and use proper risk management before trading live.
✨ Add EMA-RSI-ADX Trend Bands (ERA Trend Bands) to your chart to trade with clarity, strength, and precision.
Strat Failed 2-Up/2-Down Scanner v2**Strat Failed 2-Up/2-Down Scanner**
The Strat Failed 2-Up/2-Down Scanner is designed for traders using The Strat methodology, developed by Rob Smith, to identify key reversal patterns in any market and timeframe. This indicator detects two specific candlestick patterns: Failed 2-Up (bearish) and Failed 2-Down (bullish), which signal potential reversals when a directional move fails to follow through.
**What It Does**
- **Failed 2-Up**: Identifies a bearish candle where the low and high are higher than the previous candle’s low and high, but the close is below the open, indicating a failed attempt to continue an uptrend. These are marked with a red candlestick, a red downward triangle above the bar, and a table entry.
- **Failed 2-Down**: Identifies a bullish candle where the high and low are lower than the previous candle’s high and low, but the close is above the open, signaling a failed downtrend. These are marked with a green candlestick, a green upward triangle below the bar, and a table entry.
- A table in the top-right corner displays the signal type ("Failed 2-Up" or "Failed 2-Down") and the ticker symbol for quick reference.
- Alerts are provided for both patterns, making the indicator compatible with TradingView’s screener for automated scanning.
**How It Works**
The indicator analyzes each candlestick’s high, low, and close relative to the previous candle:
- Failed 2-Up: `low > low `, `high > high `, `close < open`.
- Failed 2-Down: `high < high `, `low < low `, `close > open`.
When these conditions are met, the indicator applies visual markers (colored bars and triangles) and updates the signal table. Alert conditions trigger notifications for integration with TradingView’s alert system.
**How to Use**
1. Apply the indicator to any chart (stocks, forex, crypto, etc.) on any timeframe (e.g., 1-minute, hourly, daily).
2. Monitor the chart for red (Failed 2-Up) or green (Failed 2-Down) candlesticks with corresponding triangles.
3. Check the top-right table for the latest signal and ticker.
4. Set alerts by selecting “Failed 2-Up Detected” or “Failed 2-Down Detected” in TradingView’s alert menu to receive notifications (e.g., via email or app).
5. Use the signals to identify potential reversal setups in conjunction with other Strat-based analysis, such as swing levels or time-based strategies.
**Originality**
Unlike other Strat indicators that may focus on swing levels or complex candlestick combinations, this scanner specifically targets Failed 2-Up and Failed 2-Down patterns with clear, minimalist visualizations (bars, triangles, table) and robust alert functionality. Its simplicity makes it accessible for both novice and experienced traders using The Strat methodology.
**Ideal For**
Day traders, swing traders, and scalpers looking to capitalize on reversal signals in trending or ranging markets. The indicator is versatile for any asset class and timeframe, enhancing trade decision-making with The Strat’s pattern-based approach.
Trade Calculator {Phanchai}Trade Calculator 🧮 {Phanchai} — Documentation
A lightweight sizing helper for TradingView that turns your risk per trade into an estimated maximum nominal position size — using the most recent chart low as your stop reference. Built for speed and clarity right on the chart.
Key Features
Clean on-chart info table with configurable font size and position.
Row toggles: show/hide each line (Price, Last Low, Risk per Trade, Entry − Low, SL to Low %, Max. Nominal Value in USDT).
Configurable low reference: Last N bars or Running since load .
Low label placed exactly at the wick of the lowest bar (no horizontal line).
Custom padding: add extra rows above/below and blank columns left/right (with custom whitespace/text fillers) to fine-tune layout.
Integer display for Risk per Trade (USDT) and Max. Nominal Value (USDT); decimals configurable elsewhere.
Open source script — easy to read and extend.
How to Use
Add the indicator: open TradingView → Indicators → paste the source code → Add to chart.
Pick your low reference in settings:
Last N bars — uses the lowest low within your chosen lookback.
Running since load — tracks the lowest low since the script loaded.
Set your capital and risk:
Total Capital — your account size in USDT.
Max. invest Capital per Trade (%) — your risk per trade as a percent of Total Capital.
Tidy the table:
Use Table Position and Table Size to place it.
Add Extra rows/columns and set left/right fillers (spaces allowed) for padding.
Toggle individual rows (on/off) to show only what you need.
Read the numbers:
Act. Price in USDT — current close.
Last Low in USDT — stop reference price.
Risk per Trade — whole-USDT value of your risk budget for this trade.
Entry − Low — absolute risk per unit.
SL to Low (%) — percentage distance from price to low.
Max. Nominal Value in USDT — estimated max nominal position size given your risk budget and stop at the low.
Scope
This calculator is designed for long trades only (stop below price at the chart low).
Notes & Assumptions
Does not factor fees, funding, slippage, tick size, or broker/venue position limits.
“Running since load” updates as new lows appear; “Last N bars” uses only the selected lookback window.
If price equals the low (zero distance), sizing will be undefined (division by zero guarded as “—”).
Risk Warning
Trading involves substantial risk. Always double-check every value the calculator shows, confirm your stop distance, and verify position sizing with your broker/platform before entering any order. Never risk money you cannot afford to lose.
Open Source & Feedback
The source code is open. If you spot a bug or have an idea to improve the tool, feel free to share suggestions — I’m happy to iterate and make it better.
BPS Multi-MA 5 — 22/30, SMA/WMA/EMA# Multi-MA 5 — 22/30 base, SMA/WMA/EMA
**What it is**
A lightweight 5-line moving-average ribbon for fast visual bias and trend/mean-reversion reads. You can switch the MA type (SMA/WMA/EMA) and choose between two ways of setting lengths: by monthly “session-based” base (22 or 30) with multipliers, or by entering exact lengths manually. An optional info table shows the effective settings in real time.
---
## How it works
* Calculates five moving averages from the selected price source.
* Lengths are either:
* **Multipliers mode:** `Base × Multiplier` (e.g., base 22 → 22/44/66/88/110), or
* **Manual mode:** any five exact lengths (e.g., 10/22/50/100/200).
* Plots five lines with fixed legend titles (MA1…MA5); the **info table** displays the actual type and lengths.
---
## Inputs
**Length Mode**
* **Multipliers** — choose a **Base** of **22** (≈ trading sessions per month) or **30** (calendar-style, smoother) and set **×1…×5** multipliers.
* **Manual** — enter **Len1…Len5** directly.
**MA Settings**
* **MA Type:** SMA / WMA / EMA
* **Source:** any series (e.g., `close`, `hlc3`, etc.)
* **Use true close (ignore Heikin Ashi):** when enabled, the MA is computed from the underlying instrument’s real `close`, not HA candles.
* **Show info table:** toggles the on-chart table with the current mode, type, base, and lengths.
---
## Quick start
1. Add the indicator to your chart.
2. Pick **MA Type** (e.g., **WMA** for faster response, **SMA** for smoother).
3. Choose **Length Mode**:
* **Multipliers:** set **Base = 22** for session-based monthly lengths (stocks/FX), or **30** for heavier smoothing.
* **Manual:** enter your exact lengths (e.g., 10/22/50/100/200).
4. (Optional) On **Heikin Ashi** charts, enable **Use true close** if you want the lines based on the instrument’s real close.
---
## Tips & notes
* **1 month ≈ 21–22 sessions.** Using 30 as “monthly” yields a smoother, more delayed curve.
* **WMA** reacts faster than **SMA** at the same length; expect earlier signals but more whipsaws in chop.
* **Len = 1** makes the MA track the chosen source (e.g., `close`) almost exactly.
* If changing lengths doesn’t move the lines, ensure you’re editing fields for the **active Length Mode** (Multipliers vs Manual).
* For clean comparisons, use the **same timeframe**. If you later wrap this in MTF logic, keep `lookahead_off` and handle gaps appropriately.
---
## Use cases
* Trend ribbon and dynamic bias zones
* Pullback entries to the mid/slow lines
* Crossovers (fast vs slow) for confirmation
* Volatility filtering by spreading lengths (e.g., 22/44/88/132/176)
---
**Credits:** Built for clarity and speed; designed around session-based “monthly” lengths (22) or smoother calendar-style (30).
[c3s] CWS - M2 Global Liquidity Index & BTC Correlation CWS - M2 Global Liquidity Index with Offset BTC Correlation
This custom indicator visualizes and analyzes the relationship between the global M2 money supply and Bitcoin (BTC) price movements. It calculates the correlation between these two variables to provide insights into how changes in global liquidity may impact Bitcoin’s price over time.
Key Features:
Global M2 Liquidity Index Calculation:
Fetches M2 money supply data from multiple economies (China, US, EU, Japan, UK) and normalizes using currency exchange rates (e.g., CNY/USD, EUR/USD).
Combines all M2 data points and normalizes by dividing by 1 trillion (1e12) for easier visualization.
Offset for M2 Data:
The offset parameter allows users to shift the M2 data by a specified number of days, helping track the influence of past global liquidity on Bitcoin.
BTC Price Correlation:
Computes the correlation between shifted global M2 liquidity and Bitcoin (BTC) price, using a 52-day lookback period by default.
Correlation Quality Display:
Categorizes correlation quality as:
Excellent : Correlation >= 0.8
Good : Correlation >= 0.6 and < 0.8
Weak : Correlation >= 0.4 and < 0.6
Very Weak : Correlation < 0.4
Displays correlation quality as a label on the chart for easy assessment.
Visual Enhancements:
Labels : Displays dynamic labels on the chart with metrics like M2 value and correlation.
Plot Shapes : Uses shapes to indicate data availability for global M2 and correlation.
Data Table : Optionally shows a data table in the top-right corner summarizing:
Global M2 value (in trillions)
The correlation between global M2 and BTC
The correlation quality
Optional Debugging:
Debug plots help identify when data is missing for M2 or correlation, ensuring transparency and accurate functionality.
Inputs:
Offset: Shift the M2 data (in days) to see past liquidity effects on Bitcoin.
Lookback Period: Number of periods (default 52) used to calculate the correlation.
Show Labels: Toggle to show or hide labels for M2 and correlation values.
Show Table: Toggle to show or hide the data table in the top-right corner.
Usage:
Ideal for traders and analysts seeking to understand the relationship between global liquidity and Bitcoin price. The offset and lookback period can be adjusted to explore different timeframes and correlation strengths, aiding more informed trading decisions.
Correlation Heatmap Matrix [TradingFinder] 20 Assets Variable🔵 Introduction
Correlation is one of the most important statistical and analytical metrics in financial markets, data mining, and data science. It measures the strength and direction of the relationship between two variables.
The correlation coefficient always ranges between +1 and -1 : a perfect positive correlation (+1) means that two assets or currency pairs move together in the same direction and at a constant ratio, a correlation of zero (0) indicates no clear linear relationship, and a perfect negative correlation (-1) means they move in exactly opposite directions.
While the Pearson Correlation Coefficient is the most common method for calculation, other statistical methods like Spearman and Kendall are also used depending on the context.
In financial market analysis, correlation is a key tool for Forex, the Stock Market, and the Cryptocurrency Market because it allows traders to assess the price relationship between currency pairs, stocks, or coins. For example, in Forex, EUR/USD and GBP/USD often have a high positive correlation; in stocks, companies from the same sector such as Apple and Microsoft tend to move similarly; and in crypto, most altcoins show a strong positive correlation with Bitcoin.
Using a Correlation Heatmap in these markets visually displays the strength and direction of these relationships, helping traders make more accurate decisions for risk management and strategy optimization.
🟣 Correlation in Financial Markets
In finance, correlation refers to measuring how closely two assets move together over time. These assets can be stocks, currency pairs, commodities, indices, or cryptocurrencies. The main goal of correlation analysis in trading is to understand these movement patterns and use them for risk management, trend forecasting, and developing trading strategies.
🟣 Correlation Heatmap
A correlation heatmap is a visual tool that presents the correlation between multiple assets in a color-coded table. Each cell shows the correlation coefficient between two assets, with colors indicating its strength and direction. Warm colors (such as red or orange) represent strong negative correlation, cool colors (such as blue or cyan) represent strong positive correlation, and mid-range tones (such as yellow or green) indicate correlations that are close to neutral.
🟣 Practical Applications in Markets
Forex : Identify currency pairs that move together or in opposite directions, avoid overexposure to similar trades, and spot unusual divergences.
Crypto : Examine the dependency of altcoins on Bitcoin and find independent movers for portfolio diversification.
Stocks : Detect relationships between stocks in the same industry or find outliers that move differently from their sector.
🟣 Key Uses of Correlation in Trading
Risk management and diversification: Select assets with low or negative correlation to reduce portfolio volatility.
Avoiding overexposure: Prevent opening multiple positions on highly correlated assets.
Pairs trading: Exploit temporary deviations between historically correlated assets for arbitrage opportunities.
Intermarket analysis: Study the relationships between different markets like stocks, currencies, commodities, and bonds.
Divergence detection: Spot when two typically correlated assets move apart as a possible trend change signal.
Market forecasting: Use correlated asset movements to anticipate others’ behavior.
Event reaction analysis: Evaluate how groups of assets respond to economic or political events.
❗ Important Note
It’s important to note that correlation does not imply causation — it only reflects co-movement between assets. Correlation is also dynamic and can change over time, which is why analyzing it across multiple timeframes provides a more accurate picture. Combining correlation heatmaps with other analytical tools can significantly improve the precision of trading decisions.
🔵 How to Use
The Correlation Heatmap Matrix indicator is designed to analyze and manage the relationships between multiple assets at once. After adding the tool to your chart, start by selecting the assets you want to compare (up to 20).
Then, choose the Correlation Period that fits your trading strategy. Shorter periods (e.g., 20 bars) are more sensitive to recent price movements, making them suitable for short-term trading, while longer periods (e.g., 100 or 200 bars) provide a broader view of correlation trends over time.
The indicator outputs a color-coded matrix where each cell represents the correlation between two assets. Warm colors like red and orange signal strong negative correlation, while cool colors like blue and cyan indicate strong positive correlation. Mid-range tones such as yellow or green suggest correlations that are close to neutral. This visual representation makes it easy to spot market patterns at a glance.
One of the most valuable uses of this tool is in portfolio risk management. Portfolios with highly correlated assets are more vulnerable to market swings. By using the heatmap, traders can find assets with low or negative correlation to reduce overall risk.
Another key benefit is preventing overexposure. For example, if EUR/USD and GBP/USD have a high positive correlation, opening trades on both is almost like doubling the position size on one asset, increasing risk unnecessarily. The heatmap makes such relationships clear, helping you avoid them.
The indicator is also useful for pairs trading, where a trader identifies assets that are usually correlated but have temporarily diverged — a potential arbitrage or mean-reversion opportunity.
Additionally, the tool supports intermarket analysis, allowing traders to see how movements in one market (e.g., crude oil) may impact others (e.g., the Canadian dollar). Divergence detection is another advantage: if two typically aligned assets suddenly move in opposite directions, it could signal a major trend shift or a news-driven move.
Overall, the Correlation Heatmap Matrix is not just an analytical indicator but also a fast, visual alert system for monitoring multiple markets at once. This is particularly valuable for traders in fast-moving environments like Forex and crypto.
🔵 Settings
🟣 Logic
Correlation Period : Number of bars used to calculate correlation between assets.
🟣 Display
Table on Chart : Enable/disable displaying the heatmap directly on the chart.
Table Size : Choose the table size (from very small to very large).
Table Position : Set the table location on the chart (top, middle, or bottom in various alignments).
🟣 Symbol Custom
Select Market : Choose the market type (Forex, Stocks, Crypto, or Custom).
Symbol 1 to Symbol 20: In custom mode, you can define up to 20 assets for correlation calculation.
🔵 Conclusion
The Correlation Heatmap Matrix is a powerful tool for analyzing correlations across multiple assets in Forex, crypto, and stock markets. By displaying a color-coded table, it visually conveys both the strength and direction of correlations — warm colors for strong negative correlation, cool colors for strong positive correlation, and mid-range tones such as yellow or green for near-zero or neutral correlation.
This helps traders select assets with low or negative correlation for diversification, avoid overexposure to similar trades, identify arbitrage and pairs trading opportunities, and detect unusual divergences between typically aligned assets. With support for custom mode and up to 20 symbols, it offers high flexibility for different trading strategies, making it a valuable complement to technical analysis and risk management.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Volume Based Analysis V 1.00
Volume Based Analysis V1.00 – Multi-Scenario Buyer/Seller Power & Volume Pressure Indicator
Description:
1. Overview
The Volume Based Analysis V1.00 indicator is a comprehensive tool for analyzing market dynamics using Buyer Power, Seller Power, and Volume Pressure scenarios. It detects 12 configurable scenarios combining volume-based calculations with price action to highlight potential bullish or bearish conditions.
When used in conjunction with other technical tools such as Ichimoku, Bollinger Bands, and trendline analysis, traders can gain a deeper and more reliable understanding of the market context surrounding each signal.
2. Key Features
12 Configurable Scenarios covering Buyer/Seller Power convergence, divergence, and dominance
Advanced Volume Pressure Analysis detecting when both buy/sell volumes exceed averages
Global Lookback System ensuring consistency across all calculations
Dominance Peak Module for identifying strongest buyer/seller dominance at structural pivots
Real-time Signal Statistics Table showing bullish/bearish counts and volume metrics
Fully customizable inputs (SMA lengths, multipliers, timeframes)
Visual chart markers (S01 to S12) for clear on-chart identification
3. Usage Guide
Enable/Disable Scenarios: Choose which signals to display based on your trading strategy
Fine-tune Parameters: Adjust SMA lengths, multipliers, and lookback periods to fit your market and timeframe
Timeframe Control: Use custom lower timeframes for refined up/down volume calculations
Combine with Other Indicators:
Ichimoku: Confirm volume-based bullish signals with cloud breakouts or trend confirmation
Bollinger Bands: Validate divergence/convergence signals with overbought/oversold zones
Trendlines: Spot high-probability signals at breakout or retest points
Signal Tables & Peaks: Read buy/sell volume dominance at a glance, and activate the Dominance Peak Module to highlight key turning points.
4. Example Scenarios & Suggested Images
Image #1 – S01 Bullish Convergence Above Zero
S01 activated, Buyer Power > 0, both buyer power slope & price slope positive, above-average buy volume. Show S01 ↑ marker below bar.
Image #2 – Combined with Ichimoku
Display a bullish scenario where price breaks above Ichimoku cloud while S01 or S09 bullish signal is active. Highlight both the volume-based marker and Ichimoku cloud breakout.
Image #3 – Combined with Bollinger Bands & Trendlines
Show a bearish S10 signal at the upper Bollinger Band near a descending trendline resistance. Highlight the confluence of the volume pressure signal with the band touch and trendline rejection.
Image #4 – Dominance Peak Module
Pivot low with green ▲ Bull Peak and pivot high with red ▼ Bear Peak, showing strong dominance counts.
Image #5 – Statistics Table in Action
Bottom-left table showing buy/sell volume, averages, and bullish/bearish counts during an active market phase.
5. Feedback & Collaboration
Your feedback and suggestions are welcome — they help improve and refine this system. If you discover interesting use cases or have ideas for new features, please share them in the script’s comments section on TradingView.
6. Disclaimer
This script is for educational purposes only. It is not financial advice. Past performance does not guarantee future results. Always do your own analysis before making trading decisions.
Tip: Use this tool alongside trend confirmation indicators for the most robust signal interpretation.
The Visualized Trader (Fractal Timeframe)The **The Visualized Trader (Fractal Timeframe)** indicator for TradingView is a tool designed to help traders identify strong bullish or bearish trends by analyzing multiple technical indicators across two timeframes: the current chart timeframe and a user-selected higher timeframe. It visually displays trend alignment through arrows on the chart and a condition table in the top-right corner, making it easy to see when conditions align for potential trade opportunities.
### Key Features
1. **Multi-Indicator Analysis**: Combines five technical conditions to confirm trend direction:
- **Trend**: Based on the slope of the 50-period Simple Moving Average (SMA). Upward slope indicates bullish, downward indicates bearish.
- **Stochastic (Stoch)**: Uses Stochastic Oscillator (5, 3, 2) to measure momentum. Rising values suggest bullish momentum, falling values suggest bearish.
- **Momentum (Mom)**: Derived from the MACD fast line (5, 20, 30). Rising MACD line indicates bullish momentum, falling indicates bearish.
- **Dad**: Uses the MACD signal line. Rising signal line is bullish, falling is bearish.
- **Price Change (PC)**: Compares the current close to the previous close. Higher close is bullish, lower is bearish.
2. **Dual Timeframe Comparison**:
- Calculates the same five conditions on both the current timeframe and a user-selected higher timeframe (e.g., daily).
- Helps traders see if the trend on the higher timeframe aligns with the current chart, providing context for stronger trade decisions.
3. **Visual Signals**:
- **Arrows on Chart**:
- **Current Timeframe**: Blue upward arrows below bars for bullish alignment, red downward arrows above bars for bearish alignment.
- **Higher Timeframe**: Green upward triangles below bars for bullish alignment, orange downward triangles above bars for bearish alignment.
- Arrows appear only when all five conditions align (all bullish or all bearish), indicating strong trend potential.
4. **Condition Table**:
- Displays a table in the top-right corner with two rows:
- **Top Row**: Current timeframe conditions (Trend, Stoch, Mom, Dad, PC).
- **Bottom Row**: Higher timeframe conditions (labeled with "HTF").
- Each cell is color-coded: green for bullish, red for bearish.
- The table can be toggled on/off via input settings.
5. **User Input**:
- **Show Condition Boxes**: Toggle the table display (default: on).
- **Comparison Timeframe**: Choose the higher timeframe (e.g., "D" for daily, default setting).
### How It Works
- The indicator evaluates the five conditions on both timeframes.
- When all conditions are bullish (or bearish) on a given timeframe, it plots an arrow/triangle to signal a strong trend.
- The condition table provides a quick visual summary, allowing traders to compare the current and higher timeframe trends at a glance.
### Use Case
- **Purpose**: Helps traders confirm strong trend entries by ensuring multiple indicators align across two timeframes.
- **Example**: If you're trading on a 1-hour chart and see blue arrows with all green cells in the current timeframe row, plus green cells in the higher timeframe (e.g., daily) row, it suggests a strong bullish trend supported by both timeframes.
- **Benefit**: Reduces noise by focusing on aligned signals, helping traders avoid weak or conflicting setups.
### Settings
- Access the indicator settings in TradingView to:
- Enable/disable the condition table.
- Select a higher timeframe (e.g., 4H, D, W) for comparison.
### Notes
- Best used in trending markets; may produce fewer signals in choppy conditions.
- Combine with other analysis (e.g., support/resistance) for better decision-making.
- The higher timeframe signals (triangles) provide context, so prioritize trades where both timeframes align.
This indicator simplifies complex trend analysis into clear visual cues, making it ideal for traders seeking confirmation of strong momentum moves.
OBR 15min Session Opening Range Breakout + Volume Trend DeltaQuick Overview
This Pine Script plots the opening range for London and New York sessions, highlights breakout levels, draws previous session pivots, and offers a live volume delta table for trend confirmation.
Session Opening Range
- Captures the high/low of the first 15 minutes (configurable) for both London & NY sessions.
- Fills the range area with adjustable semi‑transparent colors.
- Optional alerts fire on breakout above the high or below the low.
Previous Session Levels
- Automatically draws previous day’s High, Low, Open and previous 4‑hour High/Low.
- Helps identify key S/R zones as price approaches ORB breakouts.
Volume Trend Delta
- Uses a CMO‑weighted moving average and ATR bands to detect trend state.
- Accumulates bullish vs. bearish volume during each trend.
- Displays Bull Vol, Bear Vol, and Delta % in a movable table for quick strength checks.
How to Use
1. Let the opening range complete (first 15 min).
2. Look for price closing above/below the ORB—enter long on an upside break, short on a downside break.
3. Check the Volume Delta table: positive delta confirms buying strength; negative delta confirms selling pressure.
4. Use previous day/4h levels as additional support/resistance filters.
Settings & Customization
- ORB Duration & Session Times (London/NY), fill colors, and toggles.
- Enable/disable Previous Day & 4H levels.
- Trend Period, Momentum Window, and Delta table position/size.
- Pre‑built alert conditions for all ORB breakouts.
Developer Notes
- Fully commented for easy adjustments.
- Modular sections: ORB, previous levels, trend delta, and alerts.
- No external libraries—pure Pine Script v6.
Tip
Combine ORB breakouts with Volume Delta and prior session pivots to filter false signals and trade stronger, more reliable moves.
ATR Buy, Target, Stop + OverlayATR Buy, Target, Stop + Overlay
This tool is to assist traders with precise trade planning using the Average True Range (ATR) as a volatility-based reference.
This script plots buy, target, and stop-loss levels on the chart based on a user-defined buy price and ATR-based multipliers, allowing for objective and adaptive trade management.
*NOTE* In order for the indicator to initiate plotted lines and table values a non-zero number must be entered into the settings.
What It Does:
Buy Price Input: Users enter a manual buy price (e.g., an executed or planned trade entry).
ATR-Based Target and Stop: The script calculates:
Target Price = Buy + (ATR × Target Multiplier)
Stop Price = Buy − (ATR × Stop Multiplier)
Customizable Timeframe: Optionally override the ATR timeframe (e.g., use daily ATR on a 1-hour chart).
Visual Overlay: Lines are drawn directly on the price chart for the Buy, Target, and Stop levels.
Interactive Table: A table is displayed with relevant levels and ATR info.
Customization Options:
Line Settings:
Adjust color, style (solid/dashed/dotted), and width for Buy, Target, and Stop lines.
Choose whether to extend lines rightward only or in both directions.
Table Settings:
Choose position (top/bottom, left/right).
Toggle individual rows for Buy, Target, Stop, ATR Timeframe, and ATR Value.
Customize text color and background transparency.
How to Use It for Trading:
Plan Your Trade: Enter your intended buy price when planning a trade.
Assess Risk/Reward: The script immediately visualizes the potential stop-loss and target level, helping assess R:R ratios.
Adapt to Volatility: Use ATR-based levels to scale stop and target dynamically depending on current market volatility.
Higher Timeframe ATR: Select a different timeframe for the ATR calculation to smooth noise on lower timeframe charts.
On-the-Chart Reference: Visually track trade zones directly on the price chart—ideal for live trading or strategy backtesting.
Ideal For:
Swing traders and intraday traders
Risk management and trade planning
Traders using ATR-based exits or scaling
Visualizing asymmetric risk/reward setups
How I Use This:
After entering a trade, adding an entry price will plot desired ATR target and stop level for visualization.
Adjusting ATR multiplier values assists in evaluating and planning trades.
Visualization assists in comparing ATR multiples to recent support and resistance levels.
Rolling VWAP LevelsRolling VWAP Levels Indicator
Overview
Dynamic horizontal lines showing rolling Volume Weighted Average Price (VWAP) levels for multiple timeframes (7D, 30D, 90D, 365D) that update in real-time as new bars form.
Who This Is For
Day traders using VWAP as support/resistance
Swing traders analyzing multi-timeframe price structure
Scalpers looking for mean reversion entries
Options traders needing volatility bands for strike selection
Institutional traders tracking volume-weighted fair value
Risk managers requiring dynamic stop levels
How To Trade With It
Mean Reversion Strategies:
Buy when price is below VWAP and showing bullish divergence
Sell when price is above VWAP and showing bearish signals
Use multiple timeframes - enter on shorter, confirm on longer
Target opposite VWAP level for profit taking
Breakout Trading:
Watch for price breaking above/below key VWAP levels with volume
Use 7D VWAP for intraday breakouts
Use 30D/90D VWAP for swing trade breakouts
Confirm breakout with move beyond first standard deviation band
Support/Resistance Trading:
VWAP levels act as dynamic support in uptrends
VWAP levels act as dynamic resistance in downtrends
Multiple timeframe VWAP confluence creates stronger levels
Use standard deviation bands as additional S/R zones
Risk Management:
Place stops beyond next VWAP level
Use standard deviation bands for position sizing
Exit partial positions at VWAP levels
Monitor distance table for overextended moves
Key Features
Real-time Updates: Lines move and extend as new bars form
Individual Styling: Custom colors, widths, styles for each timeframe
Standard Deviation Bands: Optional volatility bands with custom multipliers
Smart Labels: Positioned above, below, or diagonally relative to lines
Distance Table: Shows percentage distance from each VWAP level
Alert System: Get notified when price crosses VWAP levels
Memory Efficient: Automatically cleans up old drawing objects
Settings Explained
Display Group: Show/hide labels, font size, line transparency, positioning
Individual VWAP Groups: Color, line width (1-5), line style for each timeframe
Standard Deviation Bands: Enable bands with custom multipliers (0.5, 1.0, 1.5, 2.0, etc.)
Labels Group: Position (8 options including diagonal), custom text, price display
Additional Info: Distance table, alert conditions
Technical Implementation
Uses rolling arrays to maintain sliding windows of price*volume data. The core calculation function processes both VWAP and standard deviation efficiently. Lines are created dynamically and updated every bar. Memory management prevents object accumulation through automatic cleanup.
Best Practices
Start with 7D and 30D VWAP for most strategies
Add 90D/365D for longer-term context
Use standard deviation bands when volatility matters
Position labels to avoid chart clutter
Enable distance table during high volatility periods
Set alerts for key VWAP level breaks
Market Applications
Forex: Major pairs during London/NY sessions
Stocks: Large cap names with good volume
Crypto: Bitcoin, Ethereum, major altcoins
Futures: ES, NQ, CL, GC with continuous volume
Options: Use SD bands for strike selection and volatility assessment
