Multi-Timeframe RSIRSI Divergence (Time-Based Engine)
This script is a powerful and highly customizable tool designed to automatically detect and display RSI divergences from up to three independent, user-defined timeframes directly on your chart. It eliminates the need to manually switch between timeframes to find these critical trading signals, allowing you to see long-term and short-term divergences all in one place.
The engine is built to be flexible, supporting both regular (reversal) divergences and hidden (trend-continuation) divergences. It's designed for traders who rely on divergence analysis as a core part of their strategy.
Key Features
Multi-Timeframe (MTF) Analysis: Configure and display divergences from up to three different timeframes simultaneously (e.g., show 4-Hour, Daily, and Weekly divergences on your 1-Hour chart). Each timeframe operates independently with its own settings.
Regular & Hidden Divergence: The script can detect both standard regular divergences that signal potential reversals and hidden divergences that suggest a trend may continue.
Configurable Pivot Strength: You have full control over the sensitivity of pivot detection. The 'Left Strength' and 'Right Strength' settings allow you to define what qualifies as a significant price pivot, filtering out market noise.
Bar Count Filter: Refine your signals by setting the minimum and maximum number of bars allowed between two pivots. This ensures you only see divergences that fit your specific strategic timeframe.
Dedicated Alerts: Each of the three timeframes has its own "Enable Alerts" toggle. When a new divergence line is drawn on the chart for a specific timeframe, a corresponding alert can be triggered, ensuring you never miss a potential setup.
Full Visual Customization: Tailor the look and feel of the indicator to your preference. Each timeframe has unique color settings for its bullish and bearish lines, allowing for easy visual identification. You can also toggle the visibility of various chart markers to keep your view clean.
How to Use
1. Add the indicator to your chart.
2. Open the Settings panel.
3. For each timeframe you wish to use (1, 2, or 3), check the "Enable Timeframe" box.
4. Select the desired Timeframe, RSI Length, and Pivot Strength for each active engine.
5. Adjust the Min/Max Bars filter to match your trading style.
6. If you want to receive notifications, check the "Enable Alerts" box for the desired timeframe(s). Then, create an alert using TradingView's alert manager, selecting the indicator and choosing the "Any alert() function call" option.
Wyszukaj w skryptach "weekly"
Whaley Thrust — ADT / UDT / SPT (2010) + EDT (EMA) + Info BoxDescription
Implements Wayne Whaley’s 2010 Dow Award breadth-thrust framework on daily data, with a practical extension:
• ADT (Advances Thrust) — 5-day ratio of advances to (adv+dec). Triggers: > 73.66% (thrust), < 19.05% (capitulation).
• UDT (Up-Volume Thrust) — 5-day ratio of up-volume to (up+down). Triggers: > 77.88%, < 16.41%. Defaults to USI:UVOL / USI:DVOL (edit if your feed differs).
• SPT (Price Thrust) — 5-day % change of a benchmark (default SPY, toggle to use chart symbol). Triggers: > +10.05%, < −13.85%.
• EDT (EMA extension) — Declines-share thrust derived from WBT logic (not in Whaley’s paper): EMA/SMA of Declines / (Adv+Decl). Triggers: > 0.8095 (declines thrust), < 0.2634 (declines abating).
• All-Clear — Prints when ADT+ and UDT+ occur within N days (default 10); marks the second event and shades brighter green.
Visuals & Controls
• Shape markers for each event; toggle text labels on/off.
• Optional background shading (green for thrusts, red for capitulations; brighter green for All-Clear).
• Compact info box showing live ADT / UDT / SPT (white by default; turns green/red at thresholds).
• Min-spacing filter to prevent duplicate prints.
Tips
• Use on Daily charts (paper uses 5 trading days). Weekly views can miss mid-week crosses.
• If UDT shows 100%, verify your Down Volume symbol; the script requires both UVOL and DVOL to be > 0.
• Best use: treat capitulations (−) as setup context; act on thrusts (+)—especially when ADT+ & UDT+ cluster (All-Clear).
Credit
Core method from Wayne Whaley (2010), Planes, Trains and Automobiles (Dow Award). EDT is an added, complementary interpretation using WBT-style smoothing.
X σ mirrorX σ Mirror — Volatility Projection & Price Action Guide
The X σ Mirror is a volatility-mapping tool that measures the prior period’s trading range, then mirrors and projects that range onto the current period. Anchored from the current period’s opening price, the indicator divides this projected range into quartiles, creating a structured price map that adapts to the asset’s recent volatility profile.
Core Methodology
Range Measurement – At the close of each user-selected higher timeframe (daily, 4-hour, weekly, etc.), the indicator captures the prior period’s high, low, and midpoint (equilibrium). This defines the “volatility envelope” for the next period.
Projection from the Open – The full prior range is projected above and below the current period’s open. This symmetrical mirroring anchors the volatility measurement to a logical starting point for intraperiod price movement.
Quartile Breakdown – The projected range is segmented into precise increments: 0.25×, 0.50×, 0.75×, 1.0×, 1.25×, 1.5×, and 2.0× of the prior range. These serve as price “checkpoints” that reflect proportional expansions or contractions relative to historical volatility.
How It Guides Price Action
Dynamic Support & Resistance – Quartile levels often act as temporary barriers or accelerators for price movement, highlighting areas where order flow may cluster.
Momentum Tracking – Price acceptance above successive quartiles suggests sustained directional strength, while repeated failures to breach a quartile indicate exhaustion.
Risk Management – The mirrored range and quartile levels help traders size positions, define stop placements, and set profit targets with volatility-adjusted precision.
Market Context – By anchoring the projection from the open, the indicator aligns volatility expectations with the session’s actual market structure, rather than static fixed levels.
Application
The X σ Mirror is adaptable across assets and timeframes, making it suitable for intraday traders tracking the unfolding session, as well as swing traders monitoring multi-day expansion potential. By combining historical range analysis with real-time market positioning, it provides a balanced framework for anticipating price behavior within a probabilistic structure.
Nifty50 Swing Trading Super Indicator# 🚀 Nifty50 Swing Trading Super Indicator - Complete Guide
**Created by:** Gaurav
**Date:** August 8, 2025
**Version:** 1.0 - Optimized for Indian Markets
---
## 📋 Table of Contents
1. (#quick-start-guide)
2. (#indicator-overview)
3. (#installation-instructions)
4. (#parameter-settings)
5. (#signal-interpretation)
6. (#trading-strategy)
7. (#risk-management)
8. (#optimization-tips)
9. (#troubleshooting)
---
## 🎯 Quick Start Guide
### What You Get
✅ **2 Complete Pine Script Indicators:**
- `swing_trading_super_indicator.pine` - Universal version for all markets
- `nifty_optimized_super_indicator.pine` - Specifically optimized for Nifty50 & Indian stocks
✅ **Key Features:**
- Multi-component signal confirmation system
- Optimized for daily and 3-hour timeframes
- Built-in risk management with dynamic stops and targets
- Real-time signal strength monitoring
- Gap analysis for Indian market characteristics
### Immediate Setup
1. Copy the Pine Script code from `nifty_optimized_super_indicator.pine`
2. Paste into TradingView Pine Editor
3. Add to chart on daily or 3-hour timeframe
4. Look for 🚀BUY and 🔻SELL signals
5. Use the information table for signal confirmation
---
## 🔍 Indicator Overview
### Core Components Integration
**🎯 Range Filter (35% Weight)**
- Primary trend identification using adaptive volatility filtering
- Optimized sampling period: 21 bars for Indian market volatility
- Enhanced range multiplier: 3.0 to handle market gaps
- Provides trend direction and strength measurement
**⚡ PMAX (30% Weight)**
- Volatility-adjusted trend confirmation using ATR-based calculations
- Dynamic multiplier adjustment based on market volatility
- 14-period ATR with 2.5 multiplier for swing trading sensitivity
- Offers trailing stop functionality
**🏗️ Support/Resistance (20% Weight)**
- Dynamic level identification using pivot point analysis
- Tighter channel width (3%) for precise Indian market levels
- Enhanced strength calculation with historical interaction weighting
- Provides entry/exit timing and breakout signals
**📊 EMA Alignment (15% Weight)**
- Multi-timeframe moving average confirmation
- Key EMAs: 9, 21, 50, 200 (popular in Indian markets)
- Hierarchical alignment scoring for trend strength
- Additional trend validation layer
### Advanced Features
**🌅 Gap Analysis**
- Automatic detection of significant price gaps (>2%)
- Gap strength measurement and impact on signals
- Specific optimization for Indian market overnight gaps
- Visual gap markers on chart
**⏰ Multi-Timeframe Integration**
- Higher timeframe bias from daily/weekly data
- Configurable daily bias weight (default 70%)
- 3-hour confirmation for precise entry timing
- Prevents counter-trend trades against major timeframe
**🛡️ Risk Management**
- Dynamic stop-loss calculation using multiple methods
- Automatic profit target identification
- Position sizing guidance based on signal strength
- Anti-whipsaw logic to prevent false signals
---
## 📥 Installation Instructions
### Step 1: Access TradingView
1. Open TradingView.com
2. Navigate to Pine Editor (bottom panel)
3. Create a new indicator
### Step 2: Copy the Code
**For Nifty50 & Indian Stocks (Recommended):**
```pinescript
// Copy entire content from nifty_optimized_super_indicator.pine
```
**For Universal Use:**
```pinescript
// Copy entire content from swing_trading_super_indicator.pine
```
### Step 3: Configure and Apply
1. Click "Add to Chart"
2. Select daily or 3-hour timeframe
3. Adjust parameters if needed (defaults are optimized)
4. Enable alerts for signal notifications
### Step 4: Verify Installation
- Check that all components are visible
- Confirm information table appears in top-right
- Test with known trending stocks for signal validation
---
## ⚙️ Parameter Settings
### 🎯 Range Filter Settings
```
Sampling Period: 21 (optimized for Indian market volatility)
Range Multiplier: 3.0 (handles overnight gaps effectively)
Source: Close (most reliable for swing trading)
```
### ⚡ PMAX Settings
```
ATR Length: 14 (standard for daily/3H timeframes)
ATR Multiplier: 2.5 (balanced for swing trading sensitivity)
Moving Average Type: EMA (responsive to price changes)
MA Length: 14 (matches ATR period for consistency)
```
### 🏗️ Support/Resistance Settings
```
Pivot Period: 8 (shorter for Indian market dynamics)
Channel Width: 3% (tighter for precise levels)
Minimum Strength: 3 (higher quality levels only)
Maximum Levels: 4 (focus on strongest levels)
Lookback Period: 150 (sufficient historical data)
```
### 🚀 Super Indicator Settings
```
Signal Sensitivity: 0.65 (balanced for swing trading)
Trend Strength Requirement: 0.75 (high quality signals)
Gap Threshold: 2.0% (significant gap detection)
Daily Bias Weight: 0.7 (strong higher timeframe influence)
```
### 🎨 Display Options
```
Show Range Filter: ✅ (trend visualization)
Show PMAX: ✅ (trailing stops)
Show S/R Levels: ✅ (key price levels)
Show Key EMAs: ✅ (trend confirmation)
Show Signals: ✅ (buy/sell alerts)
Show Trend Background: ✅ (visual trend state)
Show Gap Markers: ✅ (gap identification)
```
---
## 📊 Signal Interpretation
### 🚀 BUY Signals
**Requirements for BUY Signal:**
- Price above Range Filter with upward trend
- PMAX showing bullish direction (MA > PMAX line)
- Support/resistance breakout or favorable positioning
- EMA alignment supporting upward movement
- Higher timeframe bias confirmation
- Overall signal strength > 75%
**Signal Strength Indicators:**
- **90-100%:** Extremely strong - Maximum position size
- **80-89%:** Very strong - Large position size
- **75-79%:** Strong - Standard position size
- **65-74%:** Moderate - Reduced position size
- **<65%:** Weak - Wait for better opportunity
### 🔻 SELL Signals
**Requirements for SELL Signal:**
- Price below Range Filter with downward trend
- PMAX showing bearish direction (MA < PMAX line)
- Resistance breakdown or unfavorable positioning
- EMA alignment supporting downward movement
- Higher timeframe bias confirmation
- Overall signal strength > 75%
### ⚖️ NEUTRAL Signals
**Characteristics:**
- Conflicting signals between components
- Low overall signal strength (<65%)
- Range-bound market conditions
- Wait for clearer directional bias
### 📈 Information Table Guide
**Component Status:**
- **BULL/BEAR:** Current signal direction
- **Strength %:** Component contribution strength
- **Status:** Additional context (STRONG/WEAK/ACTIVE/etc.)
**Overall Signal:**
- **🚀 STRONG BUY:** All systems aligned bullish
- **🔻 STRONG SELL:** All systems aligned bearish
- **⚖️ NEUTRAL:** Mixed or weak signals
---
## 💼 Trading Strategy
### Daily Timeframe Strategy
**Setup:**
1. Apply indicator to daily chart of Nifty50 or Indian stocks
2. Wait for 🚀BUY or 🔻SELL signal with >75% strength
3. Confirm higher timeframe bias alignment
4. Check for significant support/resistance levels
**Entry:**
- Enter on signal bar close or next bar open
- Use 3-hour chart for precise entry timing
- Avoid entries during major news events
- Consider gap analysis for overnight positions
**Position Sizing:**
- **>90% Strength:** 3-4% of portfolio
- **80-89% Strength:** 2-3% of portfolio
- **75-79% Strength:** 1-2% of portfolio
- **<75% Strength:** Avoid or minimal size
### 3-Hour Timeframe Strategy
**Setup:**
1. Confirm daily timeframe bias first
2. Apply indicator to 3-hour chart
3. Look for signals aligned with daily trend
4. Use for entry/exit timing optimization
**Entry Refinement:**
- Wait for 3H signal confirmation
- Enter on pullbacks to key levels
- Use tighter stops for better risk/reward
- Monitor intraday support/resistance
### Risk Management Rules
**Stop Loss Placement:**
1. **Primary:** Use indicator's dynamic stop level
2. **Secondary:** Below/above nearest support/resistance
3. **Maximum:** 2-3% of portfolio per trade
4. **Trailing:** Move stops with PMAX line
**Profit Taking:**
1. **Target 1:** First resistance/support level (50% position)
2. **Target 2:** Second resistance/support level (30% position)
3. **Runner:** Trail remaining 20% with PMAX
**Position Management:**
- Review positions at daily close
- Adjust stops based on new signals
- Exit if trend changes to opposite direction
- Reduce size during high volatility periods
---
## 🎯 Optimization Tips
### For Nifty50 Trading
- Use daily timeframe for primary signals
- Monitor sector rotation impact
- Consider index futures for better liquidity
- Watch for RBI policy and global cues impact
### For Individual Stocks
- Verify stock follows Nifty correlation
- Check sector-specific news and events
- Ensure adequate liquidity for position size
- Monitor earnings calendar for volatility
### Market Condition Adaptations
**Trending Markets:**
- Increase position sizes for strong signals
- Use wider stops to avoid whipsaws
- Focus on trend continuation signals
- Reduce counter-trend trading
**Range-Bound Markets:**
- Reduce position sizes
- Use tighter stops and quicker profits
- Focus on support/resistance bounces
- Increase signal strength requirements
**High Volatility Periods:**
- Reduce overall exposure
- Use smaller position sizes
- Increase stop-loss distances
- Wait for clearer signals
### Performance Monitoring
- Track win rate and average profit/loss
- Monitor signal quality over time
- Adjust parameters based on market changes
- Keep trading journal for pattern recognition
---
## 🔧 Troubleshooting
### Common Issues
**Q: Signals appear too frequently**
A: Increase "Trend Strength Requirement" to 0.8-0.9
**Q: Missing obvious trends**
A: Decrease "Signal Sensitivity" to 0.5-0.6
**Q: Too many false signals**
A: Enable "3H Confirmation" and increase strength requirements
**Q: Indicator not loading**
A: Check Pine Script version compatibility (requires v5)
### Parameter Adjustments
**For More Sensitive Signals:**
- Decrease Signal Sensitivity to 0.5-0.6
- Decrease Trend Strength Requirement to 0.6-0.7
- Increase Range Filter multiplier to 3.5-4.0
**For More Conservative Signals:**
- Increase Signal Sensitivity to 0.7-0.8
- Increase Trend Strength Requirement to 0.8-0.9
- Enable all confirmation features
### Performance Issues
- Reduce lookback periods if chart loads slowly
- Disable some visual elements for better performance
- Use on liquid stocks/indices for best results
---
## 📞 Support & Updates
This super indicator combines the best of Range Filter, PMAX, and Support/Resistance analysis specifically optimized for Indian market swing trading. The multi-component approach significantly improves signal quality while the built-in risk management features help protect capital.
**Remember:** No indicator is 100% accurate. Always combine with proper risk management, market analysis, and your trading experience for best results.
**Happy Trading! 🚀**
EMA72 com Difusor - Cor Dinâmica e Espessuras Ajustadas17 EMA
72 EMA (with diffuser included, green signals buy, red signals sell)
72 EMA on the weekly chart
💎 ENJOYBLUE ⏰ Open Price AlertThis Pine Script (version 6) is designed for TradingView to monitor the closing of a user-selected Timeframe (TF) — for example, M30, H1, H4, or D1 — and trigger an alert immediately when that TF’s candle closes. Along with the alert, it displays the current open prices from four higher-level timeframes:
Open MN: Open price of the current monthly candle
Open W1: Open price of the current weekly candle
Open D1: Open price of the current daily candle
Open H4: Open price of the current 4-hour candle
The alert message is formatted into a single compact line to ensure it is fully visible on mobile devices!
~ENJOYBLUE 💎
Advanced Market TheoryADVANCED MARKET THEORY (AMT)
This is not an indicator. It is a lens through which to see the true nature of the market.
Welcome to the definitive application of Auction Market Theory. What you have before you is the culmination of decades of market theory, fused with state-of-the-art data analysis and visual engineering. It is an institutional-grade intelligence engine designed for the serious trader who seeks to move beyond simplistic indicators and understand the fundamental forces that drive price.
This guide is your complete reference. Read it. Study it. Internalize it. The market is a complex story, and this tool is the language with which to read it.
PART I: THE GRAND THEORY - A UNIVERSE IN AN AUCTION
To understand the market, you must first understand its purpose. The market is a mechanism of discovery, organized by a continuous, two-way auction.
This foundational concept was pioneered by the legendary trader J. Peter Steidlmayer at the Chicago Board of Trade in the 1980s. He observed that beneath the chaotic facade of ticking prices lies a beautifully organized structure. The market's primary function is not to go up or down, but to facilitate trade by seeking a price level that encourages the maximum amount of interaction between buyers and sellers. This price is "value."
The Organizing Principle: The Normal Distribution
Over any given period, the market's activity will naturally form a bell curve (a normal distribution) turned on its side. This is the blueprint of the auction.
The Point of Control (POC): This is the peak of the bell curve—the single price level where the most trade occurred. It represents the point of maximum consensus, the "fairest price" as determined by the market participants. It is the gravitational center of the session.
The Value Area (VA): This is the heart of the bell curve, typically containing 70% of the session's activity (one standard deviation). This is the zone of "accepted value." Prices within this area are considered fair and are where the market is most comfortable conducting business.
The Extremes: The thin areas at the top and bottom of the curve are the "unfair" prices. These are levels where one side of the auction (buyers at the top, sellers at the bottom) was shut off, and trade was quickly rejected. These are areas of emotional trading and excess.
The Narrative of the Day: Balance vs. Imbalance
Every trading session is a story of the market's search for value.
Balance: When the market rotates and builds a symmetrical, bell-shaped profile, it is in a state of balance . Buyers and sellers are in agreement, and the market is range-bound.
Imbalance: When the market moves decisively away from a balanced area, it is in a state of imbalance . This is a trend. The market is actively seeking new information and a new area of value because the old one was rejected.
Your Purpose as a Trader
Your job is to read this story in real-time. Are we in balance or imbalance? Is the auction succeeding or failing at these new prices? The Advanced Market Theory engine is your Rosetta Stone to translate this complex narrative into actionable intelligence.
PART II: THE AMT ENGINE - AN EVOLUTION IN MARKET VISION
A standard market profile tool shows you a picture. The AMT Engine gives you the architect's full schematics, the engineer's stress tests, and the psychologist's behavioral analysis, all at once.
This is what makes it the Advanced Market Theory. We have fused the timeless principles with layers of modern intelligence:
TRINITY ANALYSIS: You can view the market through three distinct lenses. A Volume Profile shows where the money traded. A TPO (Time) Profile shows where the market spent its time. The revolutionary Hybrid Profile fuses both, giving you a complete picture of market conviction—marrying volume with duration.
AUTOMATED STRUCTURAL DECODING: The engine acts as your automated analyst, identifying critical structural phenomena in real-time:
Poor Highs/Lows: Weak auction points that signal a high probability of reversal.
Single Prints & Ledges: Footprints of rapid, aggressive market moves and areas of strong institutional acceptance.
Day Type Classification: The engine analyzes the session's personality as it develops ("Trend Day," "Normal Day," etc.), allowing you to adapt your strategy to the market's current character.
MACRO & MICRO FUSION: Via the Composite Profile , the engine merges weeks of data to reveal the major institutional battlegrounds that govern long-term price action. You can see the daily skirmish and the multi-month war on a single chart.
ORDER FLOW INTELLIGENCE: The ultimate advancement is the integrated Cumulative Volume Delta (CVD) engine. This moves beyond structure to analyze the raw aggression of buyers versus sellers. It is your window into the market's soul, automatically detecting critical Divergences that often precede major trend shifts.
ADAPTIVE SIGNALING: The engine's signal generation is not static; it is a thinking system. It evaluates setups based on a multi-factor Confluence Score , understands the market Regime (e.g., High Volatility), and adjusts its own confidence ( Probability % ) based on the complete context.
This is not a tool that gives you signals. This is a tool that gives you understanding .
PART III: THE VISUAL KEY - A LEXICON OF MARKET STRUCTURE
Every element on your chart is a piece of information. This is your guide to reading it fluently.
--- THE CORE ARCHITECTURE ---
The Profile Histogram: The primary visual on the left of each session. Its shape is the story. A thin profile is a trend; a fat, symmetrical profile is balance.
Blue Box : The zone of accepted, "fair" value. The heart of the session's business.
Bright Orange Line & Label : The Point of Control. The gravitational center. The price of maximum consensus. The most significant intraday level.
Dashed Blue Lines & Labels : The boundaries of value. Critical inflection points where the market decides to either remain in balance or seek value elsewhere.
Dashed Cyan Lines & Labels : The major, long-term structural levels derived from weeks of data. These are institutional reference points and carry immense weight. Treat them as primary support and resistance.
Dashed Orange Lines & Labels : Marks a Poor or Unfinished Auction . These represent emotional, weak extremes and are high-probability targets for future price action.
Diamond Markers : Mark Single Prints , which are footprints of aggressive, one-sided moves that left a "liquidity vacuum." Price is often drawn back to these levels to "repair" the poor structure.
Arrow Markers : Mark Ledges , which are areas of strong horizontal acceptance. They often act as powerful support/resistance in the future.
Dotted Gray Lines & Labels : The projected daily range based on multiples of the Initial Balance . Use them to set realistic profit targets and gauge the day's potential.
--- THE SIGNAL SUITE ---
Colored Triangles : These are your high-probability entry signals. The color is a strategic playbook:
Gold Triangle : ELITE Signal. An A+ setup with overwhelming confluence. This is the highest quality signal the engine can produce.
Yellow Triangle : FADE Signal. A counter-trend setup against an exhausted move at a structural extreme.
Cyan Triangle : BREAKOUT Signal. A momentum setup attempting to capitalize on a breakout from the value area.
Purple Triangle : ROTATION Signal. A mean-reversion setup within the value area, typically from one edge towards the POC.
Magenta Triangle : LIQUIDITY Signal. A sophisticated setup that identifies a "stop run" or liquidity sweep.
Percentage Number: The engine's calculated probability of success . This is not a guarantee, but a data-driven confidence score.
Dotted Gray Line: The signal's Entry Price .
Dashed Green Lines: The calculated Take Profit Targets .
Dashed Red Line: The calculated Stop Loss level.
PART IV: THE DASHBOARD - YOUR STRATEGIC COMMAND CENTER
The dashboard is your real-time intelligence briefing. It synthesizes all the engine's analysis into a clear, concise, and constantly updating summary.
--- CURRENT SESSION ---
POC, VAH, VAL: The live values for the core structure.
Profile Shape: Is the current auction top-heavy ( b-shaped ), bottom-heavy ( P-shaped ), or balanced ( D-shaped )?
VA Width: Is the value area expanding (trending) or contracting (balancing)?
Day Type: The engine's judgment on the day's personality. Use this to select the right strategy.
IB Range & POC Trend: Key metrics for understanding the opening sentiment and its evolution.
--- CVD ANALYSIS ---
Session CVD: The raw order flow. Is there more net buying or selling pressure in this session?
CVD Trend & DIVERGENCE: This is your order flow intelligence. Is the order flow confirming the price action? If "DIVERGENCE" flashes, it is a critical, high-alert warning of a potential reversal.
--- MARKET METRICS ---
Volume, ATR, RSI: Your standard contextual metrics, providing a quick read on activity, volatility, and momentum.
Regime: The engine's assessment of the broad market environment: High Volatility (favor breakouts), Low Volatility (favor mean reversion), or Normal .
--- PROFILE STATS, COMPOSITE, & STRUCTURE ---
These sections give you a quick quantitative summary of the profile structure, the major long-term Composite levels, and any active Poor Structures.
--- SIGNAL TYPES & ACTIVE SIGNAL ---
A permanent key to the signal colors and their meanings, along with the full details of the most recent active signal: its Type , Probability , Entry , Stop , and Target .
PART V: THE INPUTS MENU - CALIBRATING YOUR LENS
This engine is designed to be calibrated to your specific needs as a trader. Every input is a lever. This is not a "one size fits all" tool. The extensive tooltips are your built-in user manual, but here are the key areas of focus:
--- MARKET PROFILE ENGINE ---
Profile Mode: This is the most fundamental choice. Volume is the standard for price-based support and resistance. TPO is for analyzing time-based acceptance. Hybrid is the professional's choice, fusing both for a complete picture.
Profile Resolution: This is your zoom lens. Lower values for scalping and intraday precision. Higher values for a cleaner, big-picture view suitable for swing trading.
Composite Sessions: Your timeframe for macro analysis. 5-10 sessions for a weekly view; 20-30 sessions for a monthly, structural view.
--- SESSION & VALUE AREA ---
These settings must be configured correctly for your specific asset. The Session times are critical. The Initial Balance should reflect the key opening period for your market (60 minutes is standard for equities).
--- SIGNAL ENGINE & RISK MANAGEMENT ---
Signal Mode: THIS IS YOUR PERSONAL RISK PROFILE. Set it to Conservative to see only the absolute best A+ setups. Use Elite or Balanced for a standard approach. Use Aggressive only if you are an experienced scalper comfortable with managing more frequent, lower-probability setups.
ATR Multipliers: This suite gives you full, dynamic control over your risk/reward parameters. You can precisely define your initial stop loss distance and profit targets based on the market's current volatility.
A FINAL WORD FROM THE ARCHITECT
The creation of this engine was a journey into the very heart of market dynamics. It was born from a frustrating truth: that the most profound market theories were often confined to books and expensive institutional platforms, inaccessible to the modern retail trader. The goal was to bridge that gap.
The challenge was monumental. Making each discrete system—the volume profile, the TPO counter, the composite engine, the CVD tracker, the signal generator, the dynamic dashboard—work was a task in itself. But the true struggle, the frustrating, painstaking process that consumed countless hours, was making them work in unison . It was about ensuring the CVD analysis could intelligently inform the signal engine, that the day type classification could adjust the probability scores, and that the composite levels could provide context to the intraday structure, all in a seamless, real-time dance of data.
This engine is the result of that relentless pursuit of integration. It is built on the belief that a trader's greatest asset is not a signal, but clarity . It was designed to clear the noise, to organize the chaos, and to present the elegant, underlying logic of the market auction so that you can make better, more informed, and more confident decisions.
It is now in your hands. Use it not as a crutch, but as a lens. See the market for what it truly is.
"The market can remain irrational longer than you can remain solvent."
- John Maynard Keynes
DISCLAIMER
This script is an advanced analytical tool provided for informational and educational purposes only. It is not financial advice. All trading involves substantial risk, and past performance is not indicative of future results. The signals, probabilities, and metrics generated by this indicator do not constitute a recommendation to buy or sell any financial instrument. You, the user, are solely responsible for all trading decisions, risk management, and outcomes. Use this tool to supplement your own analysis and trading strategy.
PUBLISHING CATEGORIES
Volume Profile
Market Profile
Order Flow
RSI and MACD Table with Cross [BY UKT]This script displays a compact, real-time dashboard of RSI and MACD values across multiple timeframes, along with the MACD cross direction (↑ / ↓) to help traders quickly assess momentum and trend strength.
▶️ Key Features:
RSI values for Weekly, Daily, 1H, 30M, 15M, 5M, and 3M
MACD values and cross status for each timeframe
Color-coded values for visual clarity (Green = Bullish, Red = Bearish)
Useful for both scalping and swing trading to get a multi-timeframe momentum overview
📌 Works on all asset classes: stocks, forex, crypto, and indices
👨💻 Developed in Pine Script v5
Multi-Timeframe SeparatorThis indicator draws vertical separator lines at the start of each candle from a higher timeframe, allowing you to visually align your current chart with key multi-timeframe structure such as 4H, Daily, Weekly, etc.
StratNinjaTableAuthor’s Instructions for StratNinjaTable
Purpose:
This indicator is designed to provide traders with a clear and dynamic table displaying The Strat candle patterns across multiple timeframes of your choice.
Usage:
Use the input panel to select which timeframes you want to monitor in the table.
Choose the table position on the chart (top left, center, right, or bottom).
The table will update each bar, showing the candle type, direction arrow, and remaining time until the candle closes for each selected timeframe.
Hover over or inspect the table to understand current market structure per timeframe using The Strat methodology.
Notes:
The Strat pattern is displayed as "1", "2U", "2D", or "3" based on the relationship of current and previous candle highs and lows.
The timer updates in real-time and adapts to daily, weekly, monthly, and extended timeframes.
This script requires Pine Script version 6. Please use it on supported platforms.
MFI or other indicators are not included in this base version but can be integrated separately if desired.
Credits:
Developed and inspired by shayy110 — thanks for your foundational work on The Strat in Pine Script.
Disclaimer:
This script is for educational and informational purposes only. Always verify signals and manage risk accordingly.
TSD Quantum [Moeinudin Montazerfaraj] 🔸 "TSD" stands for **Trend 1-2-3 and Supply & Demand**, which is the foundation of the trading style this indicator is built upon.
🔹 TSD Quantum is a specialized indicator designed exclusively for day traders who trade EURUSD, XAUUSD (Gold), and DAX40 on the 1H, 15M, and 5M timeframes using a Supply & Demand-based strategy.
This indicator is **not suitable for other symbols** and has been tailored specifically for these three assets to ensure high precision and effectiveness.
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### 🔍 Key Features:
✅ **Trading Checklist Panel**
A built-in checklist helps you track every rule in your trading plan. If even one condition is left unchecked, the system highlights it in red and marks the trade as "Not Allowed." This feature enhances trading discipline.
✅ **Spread & ATR Control Panel**
Supports both auto-calculated and fixed values for spread and ATR. This is especially helpful when placing stop-losses quickly and accurately.
✅ **Inside & Outside Candle Detection**
A dedicated panel highlights whether the last candle is inside or outside. Hovering your mouse over the chart elements automatically colorizes the candles:
🔵 Blue = Outside candle
🔴 Red = Inside candle
Also displays the high/low of the latest outside bar.
✅ **Weekly Trade Stats Panel**
Custom-built for the mentioned three assets. You can enter your trades using either fixed risk or floating risk models.
✅ **Performance Metrics**
Helps you build and adjust a floating risk model—so you don’t have to enter every trade with the same lot size. Improves risk management across multiple trades.
✅ **Base Candles Display**
Grey and white base candles are marked based on supply and demand zones.
✅ **EOT Candles**
Candles with a green dot underneath indicate valid EOT opportunities for potential move-outs.
✅ **RC (Rejection Candle) Detection**
RC candles are automatically detected to alert you of potential traps or weaknesses during Supply/Demand formations.
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### ⚠️ Disclaimer
This indicator does **not** issue buy/sell signals and **cannot guarantee profit or prevent loss**. It is a **tool for discretionary trading**, not an automated expert advisor.
All decisions must be made by the trader based on their own strategy and risk tolerance.
This is the **latest tested version** of TSD Quantum. All features have been validated and function as intended. Future updates will be provided if needed.
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🙏 Thank you for reviewing this script. We hope it becomes a valuable addition to your day trading toolkit!
MTF Dashboard 9 Timeframes + Signals# MTF Dashboard Pro - Multi-Timeframe Confluence Analysis System
## WHAT THIS SCRIPT DOES
This script creates a comprehensive dashboard that simultaneously analyzes market conditions across 9 different timeframes (1m, 5m, 15m, 30m, 1H, 4H, Daily, Weekly, Monthly) using a proprietary confluence scoring methodology. Unlike simple multi-timeframe displays that show individual indicators separately, this script combines trend analysis, momentum, volatility signals, and volume analysis into unified confluence scores for each timeframe.
## WHY THIS COMBINATION IS ORIGINAL AND USEFUL
**The Problem Solved:** Most traders manually check multiple timeframes and struggle to quickly assess overall market bias when different timeframes show conflicting signals. Existing MTF scripts typically display individual indicators without synthesizing them into actionable intelligence.
**The Solution:** This script implements a mathematical confluence algorithm that:
- Weights each indicator's signal strength (trend direction, RSI momentum, MACD volatility, volume analysis)
- Calculates normalized scores across all active timeframes
- Determines overall market bias with statistical confidence levels
- Provides instant visual feedback through color-coded symbols and star ratings
**Unique Features:**
1. **Confluence Scoring Algorithm**: Mathematically combines multiple indicator signals into a single confidence rating per timeframe
2. **Market Bias Engine**: Automatically calculates overall directional bias with percentage strength across all selected timeframes
3. **Dynamic Display System**: Real-time updates with customizable layouts, color schemes, and selective timeframe activation
4. **Statistical Analysis**: Provides bullish/bearish vote counts and overall confluence percentages
## HOW THE SCRIPT WORKS TECHNICALLY
### Core Calculation Methodology:
**1. Trend Analysis (EMA-based):**
- Fast EMA (default: 9) vs Slow EMA (default: 21) crossover analysis
- Returns values: +1 (bullish), -1 (bearish), 0 (neutral)
**2. Momentum Analysis (RSI-based):**
- RSI levels: >70 (strong bullish +2), >50 (bullish +1), <30 (strong bearish -2), <50 (bearish -1)
- Provides overbought/oversold context for trend confirmation
**3. Volatility Analysis (MACD-based):**
- MACD line vs Signal line positioning
- Histogram strength comparison with previous bar
- Combined score considering both direction and momentum strength
**4. Volume Analysis:**
- Current volume vs 20-period moving average
- Thresholds: >150% MA (strong +2), >100% MA (bullish +1), <50% MA (weak -2)
**5. Confluence Calculation:**
```
Confluence Score = (Trend + RSI + MACD + Volume) / 4.0
```
**6. Market Bias Determination:**
- Counts bullish vs bearish signals across all active timeframes
- Calculates bias strength percentage: |Bullish Count - Bearish Count| / Total Active TFs * 100
- Determines overall market direction: BULLISH, BEARISH, or NEUTRAL
### Multi-Timeframe Implementation:
Uses `request.security()` calls to fetch data from each timeframe, ensuring all calculations are performed on the respective timeframe's data rather than current chart timeframe, providing accurate multi-timeframe analysis.
## HOW TO USE THIS SCRIPT
### Initial Setup:
1. **Timeframe Selection**: Enable/disable specific timeframes in "Timeframe Selection" group based on your trading style
2. **Indicator Configuration**: Adjust EMA periods (Fast: 9, Slow: 21), RSI length (14), and MACD settings (12/26/9) to match your analysis preferences
3. **Display Options**: Choose table position, text size, and color scheme for optimal visibility
### Reading the Dashboard:
**Symbol Interpretation:**
- ⬆⬆ = Strong bullish signal (score ≥ 2)
- ⬆ = Bullish signal (score > 0)
- ➡ = Neutral signal (score = 0)
- ⬇ = Bearish signal (score < 0)
- ⬇⬇ = Strong bearish signal (score ≤ -2)
**Confluence Stars:**
- ★★★★★ = Very high confidence (score > 0.75)
- ★★★★☆ = High confidence (score > 0.5)
- ★★★☆☆ = Medium confidence (score > 0.25)
- ★★☆☆☆ = Low confidence (score > 0)
- ★☆☆☆☆ = Very low confidence (score > -0.25)
**Market Bias Section:**
- Shows overall market direction across all active timeframes
- Strength percentage indicates conviction level
- Overall confluence score represents average agreement across timeframes
### Trading Applications:
**Entry Signals:**
- Look for high confluence (4-5 stars) across multiple timeframes in same direction
- Higher timeframe alignment provides stronger signal validation
- Use confluence percentage >75% for high-probability setups
**Risk Management:**
- Lower timeframe conflicts may indicate choppy conditions
- Neutral bias suggests ranging market - adjust position sizing
- Strong bias with high confluence supports larger position sizes
**Timeframe Harmony:**
- Short-term trades: Focus on 1m-1H alignment
- Swing trades: Emphasize 1H-Daily alignment
- Position trades: Prioritize Daily-Monthly confluence
## SCRIPT SETTINGS EXPLANATION
### Dashboard Settings:
- **Table Position**: Choose optimal location (Top Right recommended for most layouts)
- **Text Size**: Adjust based on screen resolution and preferences
- **Color Scheme**: Professional (default), Classic, Vibrant, or Dark themes
- **Background Color/Transparency**: Customize table appearance
### Timeframe Selection:
All timeframes optional - activate based on trading timeframe preference:
- **Lower Timeframes (1m-30m)**: Scalping and day trading
- **Medium Timeframes (1H-4H)**: Swing trading
- **Higher Timeframes (D-M)**: Position trading and long-term bias
### Indicator Parameters:
- **Fast EMA (Default: 9)**: Shorter period for trend sensitivity
- **Slow EMA (Default: 21)**: Longer period for trend confirmation
- **RSI Length (Default: 14)**: Standard momentum calculation period
- **MACD Settings (12/26/9)**: Standard MACD configuration for volatility analysis
### Alert Configuration:
- **Strong Signals**: Alerts when confluence >75% with clear directional bias
- **High Confluence**: Alerts when multiple timeframes strongly agree
- All alerts use `alert.freq_once_per_bar` to prevent spam
## VISUAL FEATURES
### Chart Elements:
- **Background Coloring**: Subtle background tint reflects overall market bias
- **Signal Labels**: Strong buy/sell labels appear on chart during high-confluence signals
- **Clean Presentation**: Dashboard overlays chart without interfering with price action
### Color Coding:
- **Green/Bullish**: Various green shades for positive signals
- **Red/Bearish**: Various red shades for negative signals
- **Gray/Neutral**: Neutral color for conflicting or weak signals
- **Transparency**: Configurable transparency maintains chart readability
## IMPORTANT USAGE NOTES
**Realistic Expectations:**
- This tool provides analysis framework, not trading signals
- Always combine with proper risk management
- Past performance does not guarantee future results
- Market conditions can change rapidly - use appropriate position sizing
**Best Practices:**
- Verify signals with additional analysis methods
- Consider fundamental factors affecting the instrument
- Use appropriate timeframes for your trading style
- Regular parameter optimization may be beneficial for different market conditions
**Limitations:**
- Effectiveness may vary across different instruments and market conditions
- Confluence scoring is mathematical model - not predictive guarantee
- Requires understanding of underlying indicators for optimal use
This script serves as a comprehensive analysis tool for traders who need quick, organized access to multi-timeframe market information with statistical confidence levels.
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.
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Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Parabolic SAR Buy Zone📈 Parabolic SAR Buy Zone — Early Trend Reversal Indicator
This script highlights bullish reversals based on the Parabolic SAR (Stop and Reverse) indicator.
🧠 Key Features:
Uses SAR parameters: Start: 0.02, Increment: 0.005, Max: 0.2
Visually marks the Buy Zone when SAR falls below the price
Background is light blue to show accumulation or early reversal zones
Yellow SAR dots help identify trend direction and potential exits
Includes alerts when SAR flips from bearish to bullish, signaling potential entry points
✅ Best Used For:
Identifying early trend reversals
Swing trading setups on daily or weekly charts
Combining with volume, RSI, or support zones for confirmation
🛎️ Customize alert to stay notified when new buy zones appear on your favorite stocks or cryptos.
Cryptokazancev Strategy PackCryptokazancev Strategy Pack
Комплексный инструмент для анализа рыночной структуры / Comprehensive Market Structure Analysis Tool
🇷🇺 Описание на русском
Cryptokazancev Strategy Pack by ZeeZeeMon - это мощный набор инструментов для технического анализа, включающий:
• Ордерблоки (Order Blocks) с настройкой количества и цветов
• Пивоты (Pivot Points) различных таймфреймов
• Рыночную структуру с зонами Фибоначчи (0.618, 0.786)
• Разворотные конструкции (пинбары и поглощения)
• Зоны интереса на основе скопления свингов
📊 Основные функции:
1. Ордерблоки
- Автоматическое определение бычьих/медвежьих OB
- Настройка максимального количества блоков (до 30)
- Кастомизация цветов
2. Пивоты
- Поддержка таймфреймов: Дневные/Недельные/Месячные/Квартальные/Годовые
- Уровни Camarilla (P, R1-R4, S1-S4)
3. Рыночная структура
- Четкое определение тренда (UP/DOWN)
- Ключевые уровни Фибо (0.618 и 0.786)
- Настройка глубины анализа (10-1000 баров)
4. Разворотные конструкции
- Обнаружение пинбаров
- Обнаружение поглощений
- Настройка чувствительности
5. Зоны интереса
- Алгоритм кластеризации свингов
- Настройка через ATR-мультипликатор
- Лимит отображаемых зон
🇬🇧 English Description
ZeeZeeMon Pack is a comprehensive market analysis toolkit featuring:
• Order Blocks with customizable count and colors
• Pivot Points for multiple timeframes
• Market Structure with Fibonacci zones
• Reversal patterns (pinbars and engulfings)
• Interest Zones based on swing clustering
📊 Key Features:
1. Order Blocks
- Auto-detection of bullish/bearish OB
- Configurable max blocks (up to 30)
- Custom color schemes
2. Pivot Points
- Supports: Daily/Weekly/Monthly/Quarterly/Yearly
- Camarilla levels (P, R1-R4, S1-S4)
3. Market Structure
- Clear trend detection (UP/DOWN)
- Key Fibonacci levels (0.618 & 0.786)
- Adjustable analysis depth (10-1000 bars)
4. Reversal Patterns
- Smart pinbar detection
- ATR-based engulfing filter
- Sensitivity adjustment
5. Interest Zones
- Swing clustering algorithm
- ATR-multiplier configuration
- Display limit (up to 10 zones)
⚙️ Technical Highlights:
• Built with Pine Script v5
• Performance-optimized
• Well-commented code
• Flexible settings system
⚠️ Важно / Important:
Индикатор в бета-версии. Тестируйте перед использованием в реальной торговле.
This is BETA version. Please test before live trading.
💬 Поддержка / Support:
Комментарии к скрипту / Script comments section
Previous Levels by HAZEDPrevious Day/Week/Month High/Low Levels with 50% Equilibrium
🎯 Key Features:
- Previous Period Levels: Automatically plots previous Day, Week, and Month highs and lows
- 50% Equilibrium Zones: Shows the midpoint between each period's high and low
- Precise Line Placement: Lines start from the exact bar where the high/low occurred (not period beginning)
- Clean Visual Design: Solid lines for key levels, semi-transparent for equilibrium zones
- Customizable Display: Toggle each timeframe independently with custom colors and styles
📊 How It Works:
The indicator identifies the previous period's high and low points, then draws horizontal lines starting from the exact time those levels were created. The 50% equilibrium levels mark the midpoint between each period's range, providing additional support/resistance reference points.
⚙️ Settings:
- Timeframe Controls: Enable/disable Daily, Weekly, Monthly levels
- Line Styles: Choose between solid, dashed, or dotted lines
- Color Customization: Set individual colors for each timeframe
- Label Options: Show/hide price values, adjust label size
- 50% Levels: Toggle equilibrium zones with semi-transparent styling
💡 Trading Applications:
- Support & Resistance: Previous highs/lows act as key S/R levels
- Breakout Trading: Monitor price action around these critical levels
- Mean Reversion: 50% equilibrium zones often act as magnet levels
- Multi-Timeframe Analysis: See how different timeframe levels interact
🔧 Technical Notes:
- Lines extend to the right for future reference
- Only shows levels when chart timeframe is equal or lower than the level timeframe
- Uses precise historical data to ensure accurate line placement
- Optimized for performance with clean code structure
Perfect for swing traders, day traders, and anyone using support/resistance analysis!
Feel free to leave feedback and suggestions for future updates!
EMA 10/20 Crossover BackgroundThis script works best on a weekly chart and it taints the background green if the EMA-10 is larger than EMA-20 (EMA lengths can be configured) and red otherwise. I use this script to immediately determine if a market is trending upwards or downwards.
Lucas Scalia Maximums and minimums of the day, week, and month. Basically, it automatically marks and labels the highs and lows of the previous daily , weekly, and monthly candles. The labels can be added or removed at your discretion, leaving only the dotted lines.
ASK Screener by AshpreetThe ASK Indicator is a custom-built breakout and trend continuation system designed for swing traders seeking high-probability entries with strong risk-reward ratios. Built using a combination of moving averages, momentum filters, volume confirmation, and price structure, this indicator helps identify stocks poised for explosive moves.
It uses three key moving averages: the 44-period SMA (medium trend), 20-period DEMA (short-term strength, custom-coded), and 50-period WEMA (institutional trendline). Trades are only triggered when the price is above 50 WEMA, and the 20 DEMA is above the 44 SMA.
Momentum is confirmed using RSI(14) within a healthy zone of 40–60, ensuring the stock is not overbought or oversold. To focus on breakout candidates, the stock must be trading within 10% of its 52-week high, and the weekly candle range must be under 10%, signaling compression before expansion.
A valid ASK Signal occurs when these conditions are met along with a breakout above the previous day’s high and volume exceeding 1.5× the 20-day average. Once triggered, the indicator auto-plots the stop-loss (1× ATR) and two profit targets: 1:2 (TP1) and 1:4 (TP2).
Additionally, the system detects a narrow range setup, where the last 3 daily candles are inside the previous 3-day range — a powerful consolidation signal. Alerts for both ASK entries and narrow ranges are included.
This system is ideal for positional and short-term swing traders who want to combine structure, momentum, and volume in one powerful tool.
ASK Indicator by AshpreetThe ASK Indicator is a custom-built breakout and trend continuation system designed for swing traders seeking high-probability entries with strong risk-reward ratios. Built using a combination of moving averages, momentum filters, volume confirmation, and price structure, this indicator helps identify stocks poised for explosive moves.
It uses three key moving averages: the 44-period SMA (medium trend), 20-period DEMA (short-term strength, custom-coded), and 50-period WEMA (institutional trendline). Trades are only triggered when the price is above 50 WEMA, and the 20 DEMA is above the 44 SMA.
Momentum is confirmed using RSI(14) within a healthy zone of 40–60, ensuring the stock is not overbought or oversold. To focus on breakout candidates, the stock must be trading within 10% of its 52-week high, and the weekly candle range must be under 10%, signaling compression before expansion.
A valid ASK Signal occurs when these conditions are met along with a breakout above the previous day’s high and volume exceeding 1.5× the 20-day average. Once triggered, the indicator auto-plots the stop-loss (1× ATR) and two profit targets: 1:2 (TP1) and 1:4 (TP2).
Additionally, the system detects a narrow range setup, where the last 3 daily candles are inside the previous 3-day range — a powerful consolidation signal. Alerts for both ASK entries and narrow ranges are included.
This system is ideal for positional and short-term swing traders who want to combine structure, momentum, and volume in one powerful tool.
Recession Warning Model [BackQuant]Recession Warning Model
Overview
The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments.
Purpose
The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly.
Core Features
- Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress.
- Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries.
- Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix).
- Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads.
- Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability).
- Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals.
- Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals.
- Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R.
Model Complexity Levels
Users can select from four tiers to balance simplicity and depth:
1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring.
2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule.
3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth.
4. Research : Unlocks all indicators plus experimental settings for advanced users.
Key Indicators
Below is a summary of the 13 indicators, their data sources, and economic significance:
- Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress.
- Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs.
- Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions.
- Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator.
- Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending.
- PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction.
- VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress.
- S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption.
- M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction.
- NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions.
- Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress.
- JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns.
- Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating.
Data Processing
- Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period).
- Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback).
- Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise.
- Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations.
Probability Calculation
1. Each indicator’s binary signal is weighted according to user settings or dynamic performance.
2. Weights are normalized to sum to 100% across active indicators.
3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix.
4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes.
5. Optional smoothing ensures stable outputs.
Display and Visualization
- Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands.
- Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards.
- Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence.
- Ensemble Mode : Averages traditional and split probabilities for a balanced view.
- Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis).
- Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses.
Practical Applications
- Asset Allocation : Adjust equity or bond exposures based on sustained probability increases.
- Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis.
- Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%.
- Trading Filters : Disable short-term strategies during high-risk regimes.
- Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated.
Configuration Guidelines
- Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred.
- Use dynamic weighting with at least one economic cycle of data for optimal performance.
- Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals.
- Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization.
- Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading.
Limitations
- Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading.
- Historical data availability may constrain dynamic weighting on shorter timeframes.
- Model accuracy depends on the quality and timeliness of economic data feeds.
Final Note
The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
VWAP Multi-Period with SD & Value ZonesVWAP Multi-Period with SD & Value Zones
A dynamic VWAP indicator that works on Weekly, Monthly, Quarterly (3M) and Yearly (12M) timeframes.
VWAP line: true volume-weighted average price
±1, ±2, ±3 SD bands: volume-weighted volatility levels
Value Zone: filled area between ±1 SD
Prior Value Zone: last period’s ±1 SD area extended into the new period
Usage: Add to chart, select your period (W/M/3M/12M), and use the bands and zones as volume-weighted support/resistance and risk boundaries.
Demander à ChatGPT
