Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting.
The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions.
The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart.
Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets.
The script plots:
Fit Line — in-sample fitted data;
Forecast Line — out-of-sample projection;
Trend Segments — color-coded bullish/bearish sections;
Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias.
Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™.
🧩 Ridge AR Settings
Training Window — number of bars used for model training;
Forecast Horizon — forecast length (bars ahead);
AR Order — number of lags used as features;
Ridge Strength (λ) — regularization coefficient;
Damping Factor — exponential trend decay rate;
Trend Length — period for trend/volatility estimation;
Momentum Weight — strength of the recent move;
Mean Reversion — pullback intensity toward the mean.
🧮 Data Processing
Prefilter:
None — raw close price;
EMA — exponential smoothing;
SuperSmoother — Ehlers filter for noise reduction.
EMA Length, SuperSmoother Length — smoothing parameters.
🖥️ Display Settings
Update Mode:
Lock — static model;
Update Once Reached — rebuild after forecast horizon;
Continuous — update every bar.
Forecast Color — projection line color;
Bullish/Bearish Colors — colors for trend segments.
🐂🐻 Bull/Bear Signal System
The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation.
Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) .
Displayed below the bar, colored with Bullish Color.
Bear (🐻) — appears when downward momentum is dominant (momentum < 0) .
Displayed above the bar, colored with Bearish Color.
Signals are generated during model recalculations or when the directional bias changes in Continuous mode.
These visual markers are analytical aids , not trading triggers.
🧠 Core Algorithmic Components
Regularized AutoRegression (Ridge AR):
Solves: (X′X+λI)−1X′y
to derive stable regression coefficients.
Matrix and Pseudoinverse Operations — implemented natively in Pine Script™.
Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data.
Forecast Dynamics — integrates damping, momentum, and mean reversion.
Trend Visualization — color-coded bullish/bearish line segments.
Bull/Bear Signal Engine — visualizes real-time impulse direction.
📊 Applications
Academic and educational purposes;
Demonstration of Ridge Regression and AR models;
Analysis of bull/bear market phase transitions;
Visualization of time-series dependencies.
⚠️ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading or investment advice.
The author assumes no liability for financial losses resulting from its use.
Use responsibly and at your own risk.
Educational
Market Structure ICT Screener [TradingFinder] BoS ChoCh🔵 Introduction
Market Structure is the foundation of every Smart Money and ICT based trading model. It describes how price moves through a sequence of highs and lows, forming clear phases of expansion, retracement and reversal. Understanding this structure allows traders to read institutional order flow and align their positions with the true direction of liquidity.
Two of the most critical components in Market Structure are the Break of Structure (BOS) and Change of Character (CHOCH). A BOS represents trend continuation, confirming strength within the current direction. In contrast, CHOCH also known as a Market Structure Shift (MSS) signals the first sign of a trend reversal or liquidity shift where order flow begins to change from bullish to bearish or vice versa.
Because the market is fractal, structure can exist at multiple levels known as Major (External) and Minor (Internal). Major structure defines the overall trend on higher timeframes while minor or internal structure reveals short term swings and early reversals within that larger move.
🔵 How to Use
Understanding Market Structure starts with identifying how price interacts with previous swing highs and swing lows. Every trend in the market, whether bullish or bearish, is built from a sequence of impulsive and corrective moves. Impulsive legs show strong displacement in the direction of liquidity flow, while corrective legs represent temporary pullbacks as the market rebalances before the next expansion. Recognizing these sequences is essential for reading the story of price and anticipating what may happen next.
A Break of Structure (BOS) occurs when price decisively moves beyond a previous structural point by breaking above the last high in an uptrend or falling below the last low in a downtrend. This event confirms that the current trend remains intact and that liquidity has been successfully taken from one side of the market. A BOS acts as confirmation of continuation and reflects strength within the existing directional bias.
A Change of Character (CHOCH) appears when price violates structure in the opposite direction of the prevailing trend. This is the first signal that market sentiment and order flow may be shifting. For example, during a downtrend if price breaks above a previous high, it indicates that sellers are losing control and a potential bullish reversal may be developing. In an uptrend, when price drops below a recent low, it suggests a possible bearish transition.
Because the market is fractal, structure exists across multiple layers. Major structure reflects the dominant movement visible on higher timeframes and defines the broader directional bias. Minor or internal structure represents smaller swings within that move and helps identify early transitions before they appear on the higher timeframe. When internal and external structures align, they offer a high probability signal for trend continuation or reversal.
By observing BOS and CHOCH across both internal and external structures, traders can clearly visualize when the market is expanding, contracting or preparing to shift direction. This structured understanding of price movement forms the foundation for precise trend analysis and high quality decision making in any Smart Money or ICT based trading approach.
🔵 Settings
🟣 Display Settings
Table on Chart : Allows users to choose the position of the signal dashboard either directly on the chart or below it, depending on their layout preference.
Number of Symbols : Enables users to control how many symbols are displayed in the screener table, from 10 to 20, adjustable in increments of 2 symbols for flexible screening depth.
Table Mode : This setting offers two layout styles for the signal table :
Basic : Mode displays symbols in a single column, using more vertical space.
Extended : Mode arranges symbols in pairs side-by-side, optimizing screen space with a more compact view.
Table Size : Lets you adjust the table’s visual size with options such as: auto, tiny, small, normal, large, huge.
Table Position : Sets the screen location of the table. Choose from 9 possible positions, combining vertical (top, middle, bottom) and horizontal (left, center, right) alignments.
🟣 Symbol Settings
Each of the 20 symbol slots comes with a full set of customizable parameters :
Symbol : Define or select the asset (e.g., XAUUSD, BTCUSD, EURUSD, etc.).
Timeframe : Set your desired timeframe for each symbol (e.g., 15, 60, 240, 1D).
Pivot Period : Set the length used to detect swing highs and lows. Shorter values increase sensitivity, longer ones focus on major structures.
🔵 Conclusion
Mastering Market Structure and understanding the relationship between BOS and CHOCH allows traders to see the market with greater clarity and confidence. These two elements reveal how liquidity moves through different phases of expansion and retracement and how institutional order flow shifts between accumulation and distribution.
By analyzing both internal and external structures, traders can align short term and long term perspectives and anticipate where price is most likely to react. The ability to read these structural shifts helps identify continuation points, reversals and areas where liquidity is engineered or collected.
Incorporating Market Structure into a consistent trading process transforms the way a trader views the chart. Instead of reacting to random movements, each swing, break and shift becomes part of a logical framework that reflects the true behavior of the market. Understanding BOS and CHOCH is not just a concept but a complete language of price that guides every professional decision in Smart Money and ICT based trading.
Custom Text Display📋 Custom Text Display - User Guide
Overview
This indicator allows you to display custom text anywhere on your TradingView chart with full control over appearance and positioning.
Features
📝 Text Settings
Custom Text: Write any text you want to display on your chart. Can be multiple lines.
Text Alignment: Choose how your text is aligned within the box
Left
Center
Right
Text Color: Pick any color for your text
Background Color: Choose the background color of the text box (includes transparency control)
Text Size: Select from 5 different text sizes
Tiny
Small
Normal
Large
Huge
📍 Position Settings
Table Position: Choose where the text box appears on your chart
Top Left
Top Center
Top Right
Bottom Left
Bottom Center
Bottom Right
Vertical Offset (0-20): Fine-tune the vertical position
0 = Default position (no offset)
Higher values = Move the text box further down
Use this to avoid overlapping with other indicators or chart elements
How to Use
Add the indicator to your TradingView chart
Open the settings by clicking the gear icon next to the indicator name
Enter your text in the "Custom Text" field
Customize appearance:
Choose your preferred colors for text and background
Select text size and alignment
Position the box:
Select one of the 6 main positions
Use "Vertical Offset" to fine-tune the exact position (add empty space above the text)
Click OK to apply changes
Use Cases
📊 Display trading notes or reminders
📈 Show key levels or targets
💡 Add strategy descriptions
⚠️ Display risk warnings
📝 Create custom labels for specific chart setups
🎯 Mark important price zones with descriptions
Tips
Use transparent backgrounds (adjust the transparency slider) for a cleaner look
Vertical Offset is useful when you have multiple indicators at the top/bottom and need to avoid overlap
Large/Huge text works well for important notes you want to see at a glance
Combine different text alignments with different positions for better layout control
Note: The text box updates in real-time as you change settings, so you can preview your changes before applying them.
Adaptive Square Levels (Prev + Curr Month, Configurable)
The Adaptive Square Levels (Configurable Edition) indicator dynamically plots price levels based on perfect squares — a concept derived from harmonic market behavior and geometric scaling.
Each month, the script automatically detects the new monthly open and generates square levels both above and below the opening price.
This version introduces full configurability, allowing traders to adjust how many square levels they want to visualize on either side of the base level. The indicator also visually separates previous and current month levels for easy reference.
⚙️ Features
🔢 User-Configurable Range: Choose how many levels to plot above and below the base level.
🧮 Mathematically Derived Levels: Based on perfect squares up to a user-defined max price.
📅 Monthly Auto-Reset: Automatically refreshes at the start of each new month.
🎨 Color-Coded Levels:
Orange → Major levels (square roots divisible by 3)
Yellow → Regular levels
Star (★) → Base level (nearest to monthly open)
🕰️ Dual Month Display: Shows both current and previous month levels for trend comparison.
💡 How to Use
Add the indicator to any symbol and timeframe (preferably daily or higher).
Adjust:
Max Price Level → The upper bound of your price universe.
Number of Levels Each Side → Controls the density of levels.
Observe how price reacts around these mathematically significant zones.
Use in confluence with your own price action, volume, or support/resistance analysis.
📊 Ideal For
Swing traders analyzing monthly trend reversals
Price structure and geometry enthusiasts
Traders exploring market harmonics or square-of-nine–based frameworks
🧠 Note
The script doesn’t provide buy/sell signals — it offers a structural map of key levels derived from square relationships.
Use it as a visual guide to align entries and exits with natural market geometry.
15-Min RSI Scalper [SwissAlgo]15-Min RSI Scalper
Tracks RSI Momentum Loss and Gain to Generate Signals
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WHAT THIS INDICATOR CALCULATES
This indicator attempts to identify RSI directional changes (RSI momentum) using a step-by-step "ladder" method. It reads RSI(14) from the next higher timeframe relative to your chart. On a 15-minute chart, it uses 1-hour RSI. On a 5-minute chart, it uses 15-minute RSI, and so on.
How the ladder logic works:
The indicator doesn't track RSI all the time. It only starts tracking when RSI crosses into potentially extreme territory (these are called "events" in the code):
For sell signals : when RSI crosses above a dynamic upper threshold (typically between 60-80, calculated as the 90th percentile of recent RSI)
For buy signals : when RSI crosses below a dynamic lower threshold (typically between 20-40, calculated as the 10th percentile of recent RSI)
Once tracking begins, RSI movement is divided into 2-point steps (boxes). The indicator counts how many boxes RSI climbs or falls.
A signal generates only when:
RSI reverses direction by at least 2 boxes (4 RSI points) from its extreme
RSI holds that reversal for 3 consecutive confirmed bars
Example: Dynamic threshold is at 68. RSI crosses above 68 → tracking starts. RSI climbs to 76 (4 boxes up). Then it drops back to 72 and stays below that level for 3 bars → sell signal prints. The buy signal works the same way in reverse.
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SIGNAL GENERATION METHODOLOGY
Sell Signal (Red Triangle)
RSI crosses above a dynamic start level (calculated as the 90th percentile of the last 1000 bars, constrained between 60-80)
Indicator tracks upward progression in 2-point boxes
RSI reverses and drops below a boundary 2 boxes below the highest box reached
RSI remains below that boundary for 3 confirmed bars
Red triangle plots above price
Reset condition: RSI returns below 50
Buy Signal (Green Triangle)
RSI crosses below a dynamic start level (10th percentile of last 1000 bars, constrained between 20-40)
Indicator tracks downward progression in 2-point boxes
RSI reverses and rises above a boundary 2 boxes above the lowest box reached
RSI remains above that boundary for 3 confirmed bars
Green triangle plots below price
Reset condition: RSI returns above 50
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TECHNICAL PARAMETERS
All parameters are hardcoded:
RSI Period: 14
Box Size: 2 RSI points
Reversal Threshold: 2 boxes (4 RSI points)
Confirmation Period: 3 bars
Reset Level: RSI 50
Sell Start Range: 60-80 (dynamic)
Buy Start Range: 20-40 (dynamic)
Lookback for Percentile: 1000 bars
Note: Since the code is open source, users can modify these hardcoded values directly in the script to adjust sensitivity. For example, increasing the confirmation period from 3 to 5 bars will produce fewer but more conservative signals. Decreasing the box size from 2 to 1 will make the indicator more responsive to smaller RSI movements.
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KEY FEATURES
Automatic Higher Timeframe RSI
When applied to a 15-minute chart, the indicator automatically reads 1-hour RSI data. This is the next standard timeframe above 15 minutes in the indicator's logic.
Dynamic Adaptive Start Levels
Sell signals use the 90th percentile of RSI over the last 1000 bars, constrained between 60-80. Buy signals use the 10th percentile, constrained between 20-40. These thresholds recalculate on each bar based on recent data.
Ladder Box System
RSI movements are tracked in 2-point boxes. The indicator requires a 2-box reversal followed by 3 consecutive bars maintaining that reversal before generating a signal.
Dual Signal Output
Red down-triangles plot above price when the sell signal conditions are met. Green up-triangles plot below the price when buy signal conditions are met.
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REPAINTING
This indicator does not repaint. All calculations use "barstate.isconfirmed" to ensure signals appear only on closed bars. The request.security() call uses lookahead=barmerge.lookahead_off to prevent forward-looking bias.
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INTENDED CHART TIMEFRAME
This indicator is designed for use on 15-minute charts. The visual reminder table at the top of the chart indicates this requirement.
On a 15-minute chart:
RSI data comes from the 1-hour timeframe
Signals reflect 1-hour momentum shifts
3-bar confirmation equals 45 minutes of price action
Using it on other timeframes will change the higher timeframe RSI source and may produce different behavior.
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WHAT THIS INDICATOR DOES NOT DO
Does not predict future price movements
Does not provide entry or exit advice
Does not guarantee profitable trades
Does not replace comprehensive technical analysis
Does not account for fundamental factors, news events, or market structure
Does not adapt to all market conditions equally
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EDUCATIONAL USE
This indicator demonstrates one approach to momentum reversal detection using:
Multi-timeframe analysis
Adaptive thresholds via percentile calculation
Step-wise momentum tracking
Multi-bar confirmation logic
It is designed as a technical study, not a trading system. Signals represent calculated conditions based on RSI behavior, not trade recommendations. Always do your own analysis before taking market positions.
-------------------------------------------------------
RISK DISCLOSURE
Trading involves substantial risk of loss. This indicator:
Is for educational and informational purposes only
Does not constitute financial, investment, or trading advice
Should not be used as the sole basis for trading decisions
Has not been tested across all market conditions
May produce false signals, late signals, or no signals in certain conditions
Past performance of any indicator does not predict future results. Users must conduct their own analysis and risk assessment before making trading decisions. Always use proper risk management, including stop losses and position sizing appropriate to your account and risk tolerance.
MIT LICENSE
This code is open source and provided as-is without warranties of any kind. You may use, modify, and distribute it freely under the MIT License.
Algo Trading Signals - Buy/Sell System# 📊 Algo Trading Signals - Dynamic Buy/Sell System
## 🎯 Overview
**Algo Trading Signals** is a sophisticated intraday trading indicator designed for algorithmic traders and active day traders. This system generates precise buy and sell signals based on a dynamic box breakout strategy with intelligent position management, add-on entries, and automatic target adjustment.
The indicator creates a reference price box during a specified time window (default: 9:15 AM - 9:45 AM IST) and generates high-probability signals when price breaks out of this range with confirmation.
---
## ✨ Key Features
### 📍 **Smart Signal Generation**
- **Primary Entry Signals**: Clear buy/sell signals on confirmed breakouts above/below the reference box
- **Confirmation Bars**: Reduces false signals by requiring multiple bar confirmation before entry
- **Cooldown System**: Prevents overtrading with configurable cooldown periods between trades
- **Add-On Positions**: Automatically identifies optimal pullback entries for scaling into positions
### 📦 **Dynamic Reference Box**
- Creates a high/low range during your chosen time window
- Automatically updates after each successful trade
- Visual box display with color-coded boundaries (red=resistance, green=support)
- Mid-level reference line for market structure analysis
### 🎯 **Intelligent Position Management**
- **Automatic Target Calculation**: Sets profit targets based on average move distance
- **Add-On System**: Up to 3 additional entries on optimal pullbacks
- **Position Tracking**: Monitors active trades and remaining add-on capacity
- **Auto Box Shift**: Adjusts reference box after target hits for continued trading
### 📊 **Visual Clarity**
- **Color-Coded Labels**:
- 🟢 Green for BUY signals
- 🔴 Red for SELL signals
- 🔵 Blue for ADD-ON buys
- 🟠 Orange for ADD-ON sells
- ✓ Yellow for Target hits
- **TP Level Lines**: Dotted lines showing current profit targets
- **Hover Tooltips**: Detailed information on entry prices, targets, and add-on numbers
### 📈 **Real-Time Statistics**
Live performance dashboard showing:
- Total buy and sell signals generated
- Number of add-on positions taken
- Take profit hits achieved
- Current trade status (LONG/SHORT/None)
- Cooldown timer status
### 🔔 **Comprehensive Alerts**
Built-in alert conditions for:
- Primary buy entry signals
- Primary sell entry signals
- Add-on buy positions
- Add-on sell positions
- Buy take profit hits
- Sell take profit hits
---
## 🛠️ Configuration Options
### **Time Settings**
- **Box Start Hour/Minute**: Define when to begin tracking the reference range
- **Box End Hour/Minute**: Define when to lock the reference box
- **Default**: 9:15 AM - 9:45 AM (IST) - Perfect for Indian market opening range
### **Trade Settings**
- **Target Points (TP)**: Average move distance for profit targets (default: 40 points)
- **Breakout Confirmation Bars**: Number of bars to confirm breakout (default: 2)
- **Cooldown After Trade**: Bars to wait after closing position (default: 3)
- **Add-On Distance Points**: Minimum pullback for add-on entry (default: 40 points)
- **Max Add-On Positions**: Maximum additional positions allowed (default: 3)
### **Display Options**
- Toggle buy/sell signal labels
- Show/hide trading box visualization
- Show/hide TP level lines
- Show/hide statistics table
---
## 💡 How It Works
### **Phase 1: Box Formation (9:15 AM - 9:45 AM)**
The indicator tracks the high and low prices during your specified time window to create a reference box representing the opening range.
### **Phase 2: Breakout Detection**
After the box is locked, the system monitors for:
- **Bullish Breakout**: Price closes above box high for confirmation bars
- **Bearish Breakout**: Price closes below box low for confirmation bars
### **Phase 3: Signal Generation**
When confirmation requirements are met:
- Entry signal is generated with clear visual label
- Target price is calculated (Entry ± Target Points)
- Position tracking activates
- Cooldown timer starts
### **Phase 4: Position Management**
During active trade:
- **Add-On Logic**: If price pulls back by specified distance but stays within favorable range, additional entry signal fires
- **Target Monitoring**: Continuously checks if price reaches TP level
- **Box Adjustment**: After TP hit, box automatically shifts to new range for next opportunity
### **Phase 5: Trade Exit & Reset**
On target hit:
- Position closes with TP marker
- Statistics update
- Box repositions for next setup
- Cooldown activates
- System ready for next signal
---
## 📌 Best Use Cases
### **Ideal For:**
- ✅ Intraday breakout trading strategies
- ✅ Algorithmic trading systems (via alerts/webhooks)
- ✅ Opening range breakout (ORB) strategies
- ✅ Index futures (Nifty, Bank Nifty, Sensex)
- ✅ High-liquidity stocks with clear ranges
- ✅ Automated trading bots
- ✅ Scalping and day trading
### **Markets:**
- Indian Stock Market (NSE/BSE)
- Futures & Options
- Forex pairs
- Cryptocurrency (adjust timing for 24/7 markets)
- Global indices
---
## ⚙️ Integration with Algo Trading
This indicator is **algo-ready** and can be integrated with automated trading systems:
1. **TradingView Alerts**: Set up alert conditions for each signal type
2. **Webhook Integration**: Connect alerts to trading platforms via webhooks
3. **API Automation**: Use with brokers supporting TradingView integration (Zerodha, Upstox, Interactive Brokers, etc.)
4. **Signal Data Access**: All signals are plotted for external data retrieval
---
## 📖 Quick Start Guide
1. **Add Indicator**: Apply to your chart (works best on 1-5 minute timeframes)
2. **Configure Time Window**: Set your desired box formation period
3. **Adjust Parameters**: Tune confirmation bars, targets, and add-on settings to your trading style
4. **Set Alerts**: Create alert conditions for automated notifications
5. **Backtest**: Review historical signals to validate strategy performance
6. **Go Live**: Enable alerts and start receiving real-time trading signals
---
## ⚠️ Risk Disclaimer
This indicator is a **tool for analysis** and does not guarantee profits. Trading involves substantial risk of loss. Always:
- Use proper position sizing
- Implement stop losses (not included in this indicator)
- Test thoroughly before live trading
- Understand market conditions
- Never risk more than you can afford to lose
- Consider your risk tolerance and trading experience
**Past performance does not indicate future results.**
## 🔄 Version History
**v1.0** - Initial Release
- Dynamic box formation system
- Confirmed breakout signals
- Add-on position management
- Visual signal labels and statistics
- Comprehensive alert system
- Auto-adjusting target boxes
---
## 📞 Support & Feedback
If you find this indicator helpful:
- ⭐ Please leave a like/favorite
- 💬 Share your feedback in comments
- 📊 Share your results and improvements
- 🤝 Suggest features for future updates
---
## 🏷️ Tags
`breakout` `daytrading` `signals` `algo` `automated` `intraday` `ORB` `opening-range` `buy-sell` `scalping` `futures` `nifty` `banknifty` `algorithmic` `box-strategy`
*Remember: The best indicator is combined with proper risk management and trading discipline.* Use it at your own rist, not as financial advie
KCP Support & Resistance [Dr.K.C.PRAKASH]ChatGPT said:
This indicator “KCP Support & Resistance ” (Pine Script v5) is a multi-featured support & resistance tool that combines pivots, slope-based channels, Fibonacci options, and SMA200 trend reference.
🔎 Core Concept
The script identifies pivot highs and lows and uses them to draw support and resistance levels on the chart.
It allows you to visualize them in two ways:
Horizontal lines (flat support/resistance at pivot values).
Parallel slope-based lines (trend-adjusted, drawn with slope factor).
⚙️ Settings & Options
Theme
useDark: Switches to a dark-color palette with bright neon-style lines for better visibility on dark charts.
Basic Settings
length: Pivot length (bars used to detect swing high/low).
lookback: How many past pivot points to use for plotting lines.
Slope: Multiplier applied to slope calculations (for slanted trendline-style S/R).
Extend Horizontal Lines Left?: Option to extend horizontal lines to both sides.
Extend Parallel Lines Left?: Same for slope-based lines.
Show/Hide Controls
Show Parallel Lines?: Toggle diagonal support/resistance.
Show Horizontal Lines?: Toggle flat levels.
Show SMA 200 Line?: Toggle long-term SMA(200) reference.
Hide Fibonacci Lines? / Show Fib Trend Line? / Show All Fibonacci Lines?: (reserved for Fib functionality).
Line Colors
Customizable line colors for parallel & horizontal high/low lines.
If Dark Theme is enabled → Uses preset colors:
Electric Blue (Resistance - Parallel Highs)
Neon Green (Support - Parallel Lows)
Deep Red/Pink (Horizontal Highs)
Warm Yellow (Horizontal Lows)
📐 Logic & Calculations
Pivot Detection
Uses ta.pivothigh & ta.pivotlow with length to mark swing points.
Stores them in arrays for drawing multiple levels.
Slope Calculation
Uses covariance/variance of price vs. time (bar_index) to estimate slope.
Multiplied by Slope factor.
Makes trend-following parallel support/resistance lines possible.
Line Drawing
Parallel lines: Slanted, based on pivot highs/lows + slope.
Horizontal lines: Flat support & resistance levels extended across the chart.
SMA200 Plot
Plots SMA(200) for long-term trend direction.
Colored white if EMA(200) > SMA(200), else yellow (trend bias visual).
📊 What You See on Chart
Support & Resistance drawn dynamically from pivots.
Choice of horizontal (classic S/R) or sloped (trend-following) lines.
Dark theme colors → Electric blue, neon green, deep pink, warm yellow (if enabled).
SMA200 reference line → Helps identify bullish/bearish long-term bias.
Optional Fibonacci lines (future expansion).
Cnagda Pure Price ActionCnagda Pure Price Action (CPPA) indicator is a pure price action-based system designed to provide traders with real-time, dynamic analysis of the market. It automatically identifies key candles, support and resistance zones, and potential buy/sell signals by combining price, volume, and multiple popular trend indicators.
How Price Action & Volume Analysis Works
Silver Zone – Logic, Reason, and Trade Planning
Logic & Visualization:
The Silver Zone is created when the closing price is the lowest in the chosen window and volume is the highest in that window.
Visually, a large silver-colored box/rectangle appears on the chart.
Thick horizontal lines (top and bottom) are drawn at the high and low of that candle/bar, extending to the right.
Reasoning:
This combination typically occurs at strong “accumulation” or support areas:
Sellers push the price down to the lowest point, but aggressive buyers step in with high volume, absorbing supply.
Indicates potential exhaustion of selling and likely shift in market control to buyers.
How to Plan Trades Using Silver Zone:
Watch if price returns to the Silver Zone in the future: It often acts as powerful support.
Bullish entries (buys) can be planned when price tests or slightly pierces this zone, especially if new buy signals occur (like yellow/green candle labels).
Place your stop-loss below the bottom line of the Silver Zone.
Target: Look for the nearest resistance or opposing zone, or use indicator’s bullish label as confirmation.
Extra Tip:
Multiple touches of the Silver Zone reinforce its importance, but if price closes deeply below it with high volume, that’s a caution signal—support may be breaking.
Black Zone – Logic, Reason, and Trade Planning (as CPPA):
Logic & Visualization:
The Black Zone is created when the closing price is the highest in the chosen window and volume is the lowest in that window.
Visually, a large black-colored box/rectangle appears on the chart, along with thick horizontal lines at the top (high) and bottom (low) of the candle, extending to the right.
Reasoning:
This combination signals a strong “distribution” or resistance area:
Buyers push the price up to a local high, but low volume means there is not much follow-through or conviction in the move.
Often marks exhaustion where uptrend may pause or reverse, as sellers can soon step in.
How to Plan Trades Using Black Zone:
If price revisits the Black Zone in the future, it often acts as major resistance.
Bearish entries (sells) are considered when price is near, testing, or slightly above the Black Zone—especially if new sell signals appear (like blue/red candle labels).
Place your stop-loss just above the top line of the Black Zone.
Target: Nearest support zone (such as a Silver Zone) or next indicator’s bearish label.
Extra Tip:
Multiple touches of the Black Zone make it stronger, but if price closes far above with rising volume, be cautious—resistance might be breaking.
Support Line – Logic, Reason, and Trade Planning (as Cppa):
Logic & Visualization:
The Support Line is a dynamically drawn dashed line (usually blue) that marks key price levels where the market has previously shown significant buying interest.
The line is generated whenever a candle forms a high price with high volume (orange logic).
The script checks for historical pivot lows, past support zones, and even higher timeframe (HTF) supports, and then extends a blue dashed line from that price level to the right, labeling it (sometimes as “Prev Support Orange, HTF”).
Reasoning:
This line helps you visually identify where demand has been strong enough to hold price from falling further—essentially a floor in the market used by professional traders.
If price approaches or re-tests this line, there’s a good chance buyers will defend it again.
How to Plan Trades Using Support Line:
Watch for price to approach the Support Line during down moves. If you see a bullish candlestick pattern, buy labels (yellow/green), or other indicators aligning, this can be a high-probability entry zone.
Great for planning stop-loss for long trades: place stops just below this line.
Target: Next resistance zone, Black Zone, or the top of the last swing.
Extra Tip:
Multiple confirmations (support line + Silver Zone + bullish label) provide powerful entry signals.
If price closes strongly below the Support Line with volume, be cautious—support may be breaking, and a trend reversal or deeper correction could follow.
Resistance Line – Logic, Reason, and Trade Planning (from CPPA):
Logic & Visualization:
The Resistance Line is a dynamically drawn dashed line (usually purple or red) that identifies price levels where the market has previously faced significant selling pressure.
This line is created when a candle reaches a high price combined with high volume (orange logic), or from a historical pivot high/resistance,
The script also tracks higher timeframe (HTF) resistance lines, labeled as “Prev Resistance Orange, HTF,” and extends these dashed lines to the right across the chart.
Reasoning:
Resistance Lines are visual markers of “supply zones,” where buyers previously failed, and sellers took control.
If the price returns to this line later, sellers may get active again to defend this level, halting the uptrend.
How to Plan Trades Using Resistance Line:
Watch for price to approach the Resistance Line during up moves. If you see bearish candlestick patterns, sell labels (blue/red), or bearish indicator confirmation, this becomes a strong shorting opportunity.
Perfect for placing stop-loss in short trades—put your stop just above the Resistance Line.
Target: Next support zone (Silver Zone) or bottom of the last swing.
If the price breaks above with high volume, avoid shorting—resistance may be failing.
Extra Tip:
Multiple resistances (Resistance Line + Black Zone + bearish label) make short signals stronger.
Choppy movement around this line often signals indecision; wait for a clear rejection before entering trades.
Bullish / Bearish Label – Logic, Reason, and Trade Planning:
Logic & Visualization:
The indicator constantly calculates a "Bull Score" and a "Bear Score" based on several factors:
Trend direction from price slope
Confirmation by popular indicators (RSI, ADX, SAR, CMF, OBV, CCI, Bollinger Bands, TWAP)
Adaptive scoring (higher score for each bullish/bearish condition met)
If Bull Score > Bear Score, the chart displays a green "BULLISH" label (usually below the bar).
If Bear Score > Bull Score, the chart displays a red "BEARISH" label (usually above the bar).
If neither dominates, a "NEUTRAL" label appears.
Reasoning:
The labels summarize complex price action and indicator analysis into a simple, actionable sentiment cue:
Bullish: Majority of conditions indicate buying strength; trend is up.
Bearish: Majority signals show selling pressure; trend is down.
How to Use in Trade Planning:
Use the Bullish label as confirmation to enter or hold long (buy) positions, especially if near support/Silver Zone.
Use the Bearish label to enter/hold short (sell) positions, especially if near resistance/Black Zone.
For best results, combine with candle color, volume analysis, or other labels (yellow/green for buys, blue/red for sells).
Avoid trading against these labels unless you have strong confluence from zones/support levels.
Yellow Label (Buy Signal) – Logic, Reason & Trade Planning:
Logic & Visualization:
The yellow label appears below a candle (label.style_label_up, yloc.belowbar) and marks a potential buy signal.
Script conditions:
The candle must be a “yellow candle” (which means it’s at the local lowest close, not a high, with normal volume).
Volume is decreasing for 2 consecutive candles (current volume < previous volume, previous volume < second previous).
When these conditions are met, a yellow label is plotted below the candle.
Reasoning:
This scenario often marks the end of selling pressure and start of possible accumulation—buyers may be stepping in as sellers exhaust.
Decreasing volume during a local price low means selling is slowing, possibly hinting at a reversal.
How to Trade Using Yellow Label:
Entry: Consider buying at/just above the yellow-labeled candle’s close.
Stop-loss: A bit below the candle’s low (or Silver Zone line, if present).
Target: Next resistance level, Black Zone, or chart’s bullish label.
Extra Tip:
If the yellow label is found at/near a Silver Zone or Support Line, and trend is “Bullish,” the setup gets even stronger.
Avoid trading if overall indicator shows “Bearish.”
Green Label (Buy with Increasing Volume) – Logic, Reason & Trade Planning:
Logic & Visualization:
The green label is plotted below a candle (label.style_label_up, yloc.belowbar) and marks a strong buy signal.
Script conditions:
The candle must be a “yellow candle” (at the local lowest close, normal volume).
Volume is increasing for 2 consecutive candles (current volume > previous volume, previous volume > second previous).
When these conditions are met, a green label is plotted below the candle.
Reasoning:
This scenario signals that buyers are stepping in aggressively at a local price low—the end of a downtrend with strong, rising activity.
Increasing volume at a price low is a classic sign of accumulation, where institutions or large players may be buying.
How to Trade Using Green Label:
Entry: Consider buying at/just above the green-labeled candle’s close for a momentum-based reversal.
Stop-loss: Slightly below the candle’s low, or the Silver Zone/support line if present.
Target: Nearest resistance zone/Black Zone, indicator’s bullish label, or next swing high.
Extra Tip:
If the green label is near other supports (Silver Zone, Support Line), the setup is extra strong.
Use confirmation from Bullish labels or trend signals for best results.
Green label setups are suitable for quick, high momentum trades due to increasing volume
Blue Label (Sell Signal on Decreasing Volume) – Logic, Reason & Trade Planning:
Logic & Visualization:
The blue label is plotted above a candle (label.style_label_down, yloc.abovebar) as a potential sell signal.
Script conditions:
The candle is a “blue candle” (local highest close, but not also lowest, and volume is neither highest nor lowest).
Volume is decreasing over 2 consecutive candles (current volume < previous, previous < two ago).
When these match, a blue label appears above the candle.
Reasoning:
This typically signals buyer exhaustion at a local high: price has gone up, but volume is dropping, suggesting big players may not be buying any more at these levels.
The trend is losing strength, and a reversal or pullback is likely.
How to Trade Using Blue Label:
Entry: Look to sell at/just below the candle with the blue label.
Stop-loss: Just above the candle’s high (or above the Black Zone/resistance if present).
Target: Nearest support, Silver Zone, or a swing low.
Extra Tip:
Blue label signals are stronger if they appear near Black Zones or Resistance Lines, or when the general market label is "Bearish."
As with buy setups, always check for confirmation from trend or volume before trading aggressively.
Blue Label (Sell Signal on Decreasing Volume) – Logic, Reason & Trade Planning:
Logic & Visualization:
The blue label is plotted above a candle (label.style_label_down, yloc.abovebar) as a potential sell signal.
Script conditions:
The candle is a “blue candle” (local highest close, but not also lowest, and volume is neither highest nor lowest).
Volume is decreasing over 2 consecutive candles (current volume < previous, previous < two ago).
When these match, a blue label appears above the candle.
Reasoning:
This typically signals buyer exhaustion at a local high: price has gone up, but volume is dropping, suggesting big players may not be buying any more at these levels.
The trend is losing strength, and a reversal or pullback is likely.
How to Trade Using Blue Label:
Entry: Look to sell at/just below the candle with the blue label.
Stop-loss: Just above the candle’s high (or above the Black Zone/resistance if present).
Target: Nearest support, Silver Zone, or a swing low.
Extra Tip:
Blue label signals are stronger if they appear near Black Zones or Resistance Lines, or when the general market label is "Bearish."
As with buy setups, always check for confirmation from trend or volume before trading aggressively.
Here’s a summary of all key chart labels, zones, and trading logic of your Price Action script:
Silver Zone: Powerful support zone. Created at lowest close + highest volume. Best for buy entries near its lines.
Black Zone: Strong resistance zone. Created at highest close + lowest volume. Ideal for short trades near its levels.
Support Line: Blue dashed line at historical demand; buyers defend here. Look for bullish setups when price approaches.
Resistance Line: Purple/red dashed line at supply; sellers defend here. Great for bearish setups when price nears.
Bullish/Bearish Labels: Summarize trend direction using price action + multiple indicator confirmations. Plan buys, holds on bullish; sells, shorts on bearish.
Yellow Label: Buy signal on decreasing volume and local price low. Entry above candle, stop below, target next resistance.
Green Label: Strong buy on increasing volume at a price low. Entry for momentum trade, stop below, target next zone.
Blue Label: Sell signal on dropping volume and local price high. Entry below candle, stop above, target next support.
Best Practices:
Always combine zone/label signals for higher probability trades.
Use stop-loss near zones/lines for risk management.
Prefer trading in the trend direction (bullish/bearish label agrees with your entry).
if Any Question, Suggestion Feel free to ask
Disclaimer:
All information provided by this indicator is for educational and analysis purposes only, and should not be considered financial advice.
Market Structure (BOS on Break, HH/HL/LH/LL)Market Structure (BOS on Break, HH/HL/LH/LL) is a clean and reactive market structure tool designed for traders who want clear visual feedback of trend direction and structure changes.
It automatically identifies:
Higher Highs (HH)
Higher Lows (HL)
Lower Highs (LH)
Lower Lows (LL)
Breaks of Structure (BOS) when price breaches previous swing levels.
✳️ Features
Automatic Structure Detection: Detects swing highs/lows based on your left/right pivot settings.
Break of Structure Alerts: Instantly updates bias when price breaks a previous swing level (no delay).
Real-Time BOS Response: Structure flips as soon as price takes out the previous high or low.
Color-Coded Bars: Bars can auto-paint white for bullish and black for bearish conditions.
Optional Fading Dots: Visual fading dots track trend duration and strength for quick bias reading.
Customizable Pivots: Choose how many bars to use for left/right swing confirmation.
Alerts: Fully configured for HH, HL, LH, LL, BOS Up, and BOS Down events.
⚙️ Inputs
Left Pivot Bars / Right Pivot Bars: Control how many candles define a valid swing point.
Paint Bars by Trend: Toggle bar coloring to match bullish or bearish bias.
Show Fading Trend Dots: Add fading dots that shrink and fade as a trend matures.
🔔 Alerts
Break of Structure Up / Down
Higher High (HH) confirmed
Higher Low (HL) confirmed
Lower High (LH) confirmed
Lower Low (LL) confirmed
🎯 Use Case
Perfect for:
Price action traders
Smart money concept (SMC) practitioners
Trend structure analysts
Scalpers and swing traders looking for quick structure flips.
🧠 Notes
Works on any timeframe and any instrument.
BOS signals update immediately when price breaks structure (no lag).
For additional BOS markers, you can uncomment the last few lines in the script to show arrows when structure breaks.
TokMaz – Signal Plain🧭 Overview
A clean, lightweight, and non-repainting directional signal indicator built for serious traders.
This version is fully optimized and locked to ensure stable performance and consistent signal output.
⚙️ Features
1. Designed to follow price precisely (no delay or offset).
2. Non-repainting structure — all signals confirmed on bar close.
3. Automatic Buy/Sell/Rejection alerts ready for webhook or automation use.
4. Minimal visual design — clear lines, direct chart attachment, no background clutter.
5. Integrated trend reference line (SMA200) for long-term view.
💡 Signal Display:-
🔵 BUY Solid – Bullish directional setup
🔴 SELL Solid – Bearish directional setup
⚪ REJ→BUY / REJ→SELL – Potential rejection or reversal zones
All signals appear directly on candles for instant readability and execution timing.
📢 Alerts:-
✅ BUY Solid
✅ SELL Solid
⚡ REJ→BUY
⚡ REJ→SELL
Works seamlessly with TradingView notifications or external automation.
📈 Style:-
EMA50 (dynamic price reference)
SMA200 (trend backbone)
Clean, professional visual — perfect for live trading or educational setups.
PulseWave StrategyThe PulseWave Strategy is designed to identify trend-aligned momentum bursts in price action, using a combination of EMA trend filters, RSI momentum, and volume surge detection.
📌 Features
– Dual EMA crossover for trend direction
– RSI filter to avoid overbought/sold traps
– Volume surge detection to catch market "pulses"
– ATR-based trailing exit and trend-reversal stop
⚙️ Inputs You Can Customize
– EMA lengths
– RSI period and thresholds
– Volume surge sensitivity
– ATR length and trailing logic
📈 Backtest Sample (BTCUSD 1h)
– Return: +127%
– Win Rate: 54%
– Max Drawdown: 18%
– Period: Jan 2023 – Oct 2025
(Replace with your actual test results)
🔧 Built for educational use. Always validate on your own data and strategy framework before using live.
⚠️ Disclaimer
This tool is for research and educational purposes only. It is not financial advice. Trade responsibly and use proper risk management.
EMA with VolNew EMA 9 20 setup with Volume for educational purpose to identify the moves and everything.
Smooth Cloud + ZigZag VPOC CORE v6📌 Description
The Smooth Cloud + ZigZag VPOC indicator is designed to help traders visualize market structure and potential confluence zones.
Smooth Cloud: Built from smoothed moving averages (EMA, RMA, or HMA), this cloud highlights the underlying short-term trend by shading bullish and bearish phases.
Pivots (ZigZag style): Marks confirmed swing highs and lows, helping to identify support/resistance and breakout areas without repainting.
VPOC (Volume Point of Control): Plots the price level with the highest traded volume, either from a rolling lookback or anchored to a custom date. This often acts as a magnet or reaction level.
ATR Bands: Optional dynamic bands based on volatility to frame potential extension zones.
Signals & Alerts: Generates long/short labels when price breaks pivot levels in line with trend filters, with optional confluence from HTF trend, VPOC, and ATR.
This tool combines trend context, structure, and volume confluence in a single view to support decision-making.
✅ Notes
This script is intended for technical analysis and educational use only.
It does not provide financial advice or guaranteed outcomes.
Signals are purely analytical and should be combined with independent risk management.
Smooth Cloud + ZigZag VPOC📝 Indicator Description
The Smooth Cloud + ZigZag VPOC Indicator is a custom tool that combines three well-known concepts into one study:
Smooth Cloud Trend Filter – built from two smoothed EMAs, this visual “cloud” highlights the prevailing trend direction.
When the fast line is above the slow line, the background cloud shades teal (bullish bias).
When the fast line is below the slow line, the cloud shades red (bearish bias).
Confirmed ZigZag Pivots – plots non-repainting swing highs and swing lows using pivot confirmation. This helps traders see important structural turning points and potential breakout zones.
VPOC Approximation (Volume Point of Control) – within a lookback window, the indicator marks the price level with the highest traded volume. This level often acts as a magnet for price or an area of confluence.
Signals & Alerts
A long signal appears when price is trending up, breaks above the last confirmed pivot high, and (optionally) is above the VPOC line.
A short signal appears when price is trending down, breaks below the last confirmed pivot low, and (optionally) is below the VPOC line.
Alerts can be enabled to notify when these conditions occur.
Customization
Inputs allow adjusting the EMA lengths, smoothing factor, pivot sensitivity, and VPOC lookback.
Users can toggle on/off the cloud fill, pivot markers, bar coloring, and VPOC line to match their charting style.
✅ Notes (for compliance)
This script is for technical analysis and educational purposes only.
It does not provide financial advice or guaranteed results.
Signals are intended to highlight trend direction and breakout areas — traders should always confirm with their own risk management and strategy.
TTM Squeeze Range Lines (with Forward Extension) By Gautam KumarThis TTM Squeeze Range Lines script helps visualize breakout levels by marking the recent squeeze’s high and low, making it easier to identify potential trade setups. Each signal line is extended for visibility, showing possible entry levels after a squeeze.
Interpreting the LinesLight blue background marks periods when the TTM squeeze is active (tight volatility).
Green line is drawn at the highest price during the squeeze, extended forward—this is commonly used as the breakout level for long entries.
Red line shows the lowest price during the squeeze, indicating the bottom of the range—potential stop loss positioning or an invalidation level.
When the squeeze background disappears, the horizontal lines will have just appeared and extended forward for several bars after the squeeze ends.
If the price breaks above the green line (the squeeze high), it signals a possible momentum breakout, which traders often use as a long entry.
The red line can be used for placing stop losses or monitoring failed breakouts if price falls below this level.
Best Practices
Combine these levels with volume and momentum confirmation for strong entries.
Adjust the extension length (number of bars forward) from the settings menu to fit your preference.
For systematic trading, use these breakout signals alongside chart pattern or histogram confirmation.
This makes it easy to visualize strong entry zones based on the end of squeeze compression, supporting both discretionary and automated swing trading approaches
Square Root Price Calculator By ABPinescript to Calculate Square root of Price usefull for Gann Lover
IDX Utility Set [zidaniee]Purpose
This indicator is not a technical analysis tool. It’s a companion overlay designed to guide your analysis of the uniquely structured Indonesia Stock Exchange (IDX).
Core Features
Centered Ticker Display – Clean, readable ticker shown at the center of the chart.
Company Name – Displays the listed company’s full name.
Active Timeframe – Shows the currently selected timeframe.
Additional Features
ATH & ATL Markers – Labels the All-Time High (ATH) and All-Time Low (ATL) and shows the percentage distance from the latest price to each level, so you can quickly gauge upside/downside room.
IDX Fraction (Tick) Levels – Visualizes Indonesia’s price-fraction (tick) brackets. This matters because tick size changes by price range—very useful for scalpers and fast traders.
ARA/ARB Levels (Realtime) – Plots Auto-Reject Upper (ARA) and Auto-Reject Lower (ARB) levels in real time. Levels refresh in line with IDX trading hours 09:00–16:00 WIB (UTC+7), so your view stays consistent both during and outside market hours. This feature already complies with the latest rules and adjustments set by the Indonesia Stock Exchange (IDX).
Suspension Status – Shows SUSPENDED if the stock is halted/suspended, helping you avoid unnecessary analysis. The suspension check compares today’s date with the last available candle date and accounts for weekends.
Note: WIB = Western Indonesia Time (UTC+7).
Opening Range Gaps [TakingProphets]What is an Opening Range Gap (ORG)?
In ICT, the Opening Range Gap is defined as the price difference between the previous session’s close (e.g., 4:00 PM EST in U.S. indices) and the current day’s open (9:30 AM EST).
That gap is a liquidity void—an area where no trading occurred during regular hours.
Why ICT Traders Care About ORG
Liquidity Void (Gap Fill Logic)
-Because the gap is an untraded area, it naturally acts as a draw on liquidity.
-Price often seeks to rebalance by retracing into or fully filling this void.
Premium/Discount Sensitivity
-Once the ORG is defined, ICT treats it as a mini dealing range.
-Above EQ (Consequent Encroachment) = algorithmic premium (sell-sensitive).
-Below EQ = algorithmic discount (buy-sensitive).
-Price reaction at these levels gives a precise read on institutional intent intraday.
Support/Resistance from ORG
-If the session opens above prior close, the gap often acts as support until violated.
-If the session opens below prior close, the gap often acts as resistance until reclaimed.
Key ICT Concepts Anchored to ORG
Consequent Encroachment (CE): The midpoint of the gap. The algo is highly sensitive to CE as a decision point: reject → continuation; reclaim → reversal.
Draw on Liquidity (DoL): Price is algorithmically “pulled” toward gap fills, CE, or the opposite side of the ORG.
Order Flow Confirmation: If price ignores the gap and runs away from it, this signals strong institutional order flow in that direction.
Confluence with Other Tools: FVGs, OBs, and HTF PD arrays often overlap with ORG levels, strengthening setups.
Practical Application for Traders
Bias Formation:
Use ORG EQ as a line in the sand for intraday bias.
If price trades below ORG EQ after the open → look for short setups into the prior day’s low or external liquidity.
If price trades above ORG EQ → favor longs into highs/liquidity pools.
Execution Framework:
Wait for liquidity raids or market structure shifts at ORG edges (.00, .25, .50, .75).
Target: EQ, opposite quarter, or full gap fill.
Precision Reads:
ORG lines let traders anticipate where algorithms are likely to respond, providing mechanical invalidation and clear targets without clutter.
Impulse Range Compression & Expansion (IRCE)📌 Impulse Range Compression & Expansion (IRCE) – Visualizing Price Traps Before Breakouts
📖 Overview
The IRCE Indicator is a precision breakout detection tool designed to identify consolidation traps and price coil zones before expansion moves occur. Unlike traditional volatility indicators that rely solely on statistical thresholds (e.g., Bollinger Bands or ATR), IRCE focuses on behavioral price compression, detecting tight-range candle clusters and validating breakouts through body expansion and/or volume surges.
This makes it ideal for traders looking to:
• Catch breakouts from range traps
• Avoid choppy and premature signals
• Spot early-stage momentum moves based on clean price behavior
⸻
⚙️ How It Works
1. Impulse Range Compression Detection
• Measures the high-low range of each candle
• Compares it to a user-defined average range (default 7 bars)
• Flags candles where the range is significantly smaller (e.g., <60% of average)
• Groups these into tight clusters, indicating compression zones or potential “trap ranges”
2. Cluster Box Construction
• When a valid cluster (e.g., 3 or more tight candles) is detected, the indicator:
• Marks the high and low of the cluster
• Draws a shaded box over this “trap zone”
• This helps visually track where price has coiled before a breakout
3. Breakout Confirmation Logic
A breakout from the trap zone is only validated when:
• Price closes above the cluster high (bullish) or below the cluster low (bearish)
• One or both of the following confirm strength:
• Body Expansion: Current candle body is 120%+ of recent average
• Volume Expansion: Volume exceeds recent volume average
4. Optional Trend Filter
• An optional EMA filter (default: 50 EMA) ensures breakout signals align with trend direction
• Helps filter out countertrend noise in ranging markets
5. Signal Cooldown
• Prevents repeated signals by enforcing a cooldown period (e.g., 10 bars) between entries
⸻
🖥️ Visual Elements
• 📦 Yellow compression boxes represent tight price traps
• 🟢 Buy labels appear when price breaks above the trap with confirmation
• 🔴 Sell labels appear when price breaks below with confirmation
• All visuals are non-repainting and updated in real-time
🧠 How to Use
1. Wait for a yellow trap box to appear
2. Watch for a confirmed breakout from the trap zone
3. Take the trade in the direction of the breakout:
• Only if it satisfies body or volume confirmation
• And if trend alignment is enabled, it must match EMA direction
4. Place stops just outside the opposite end of the trap zone
5. Use risk/reward ratios or structure levels for exits
This logic works great on:
• Lower timeframes (scalping breakouts)
• Higher timeframes (detecting price coiling before major moves)
• Any market: Stocks, Crypto, FX, Commodities
⸻
🔒 Technical Notes
• ✅ No repainting
• ✅ No future-looking logic
• ✅ Suitable for both discretionary and systematic traders
• ✅ Built in Pine Script v6
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
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PSP [ANAY]PSP and TPD with ES NQ and YM. When NQ closoes up and ES closes down that marked uot a TPD
RSI: chart overlay
This indicator maps RSI thresholds directly onto price. Since the EMA of price aligns with RSI’s 50-line, it draws a volatility-based band around the EMA to reveal levels such as 70 and 30.
By converting RSI values into visible price bands, the overlay lets you see exactly where price would have to move to hit traditional RSI boundaries. These bands adapt in real time to both price movement and market volatility, keeping the classic RSI logic intact while presenting it in the context of price action. This approach helps traders interpret RSI signals without leaving the main chart window.
The calculation uses the same components as the RSI: alternative derivation script: Wilder’s EMA for smoothing, a volatility-based unit for scaling, and a normalization factor. The result is a dynamic band structure on the chart, representing RSI boundary levels in actual price terms.
Key components and calculation breakdown:
Wilder’s EMA
Used as the anchor point for measuring price position.
myEMA = ta.rma(close, Length)
Volatility Unit
Derived from the EMA of absolute close-to-close price changes.
CC_vol = ta.rma(math.abs(close - close ), Length)
Normalization Factor
Scales the volatility unit to align with the RSI formula’s structure.
normalization_factor = 1 / (Length - 1)
Upper and Lower Boundaries
Defines price bands corresponding to selected RSI threshold values.
up_b = myEMA + ((upper - 50) / 50) * (CC_vol / normalization_factor)
down_b = myEMA - ((50 - lower) / 50) * (CC_vol / normalization_factor)
Inputs
RSI length
Upper boundary – RSI level above 50
Lower boundary – RSI level below 50
ON/OFF toggle for 50-point line (EMA of close prices)
ON/OFF toggle for overbought/oversold coloring (use with line chart)
Interpretation:
Each band on the chart represents a chosen RSI level.
When price touches a band, RSI is at that threshold.
The distance between moving average and bands adjusts automatically with volatility and your selected RSI length.
All calculations remain fully consistent with standard RSI values.
Feedback and code suggestions are welcome, especially regarding implementation efficiency and customization.
