Zones Monitor V2Zones Monitor is a lightweight, reliable tool for tracking custom price zones on a single active symbol, with optional auto-mapping of zones to the current chart symbol. It focuses on stability and ease-of-use: minimal settings, clear visuals, and simple event logic (Touch & Breakout) that works smoothly without hitting TradingView’s object limits.
Educational
☸HH/LL & Support/Resistance Strategy [NHP]🔶This script finds pivot highs and pivot lows then calculates higher highs & lower lows. And also it calculates support/resistance by using HH-HL-LL-LH points.
🔶Generally HH and HL shows up-trend, LL and LH shows down-trend.
🔶If price breaks resistance levels it means the trend is up or if price breaks support level it means the trend is down, so the script changes bar color blue or black. if there is up-trend then bar color is blue, or if down-trend then bar color is black. also as you can see support and resistance levels change dynamically.
🔶If you use smaller numbers for left/right bars then it will be more sensitive.
🔶All content provided is for informational & educational purposes only. Past performance does not guarantee future results.
Position Sizer SimplifiedThis is a Pine Script® indicator for TradingView called "Position Sizer Simplified". Its primary function is to help a trader quickly calculate the appropriate position size for a trade based on their chosen risk tolerance, account size, and the trade's entry/stop-loss levels. The results are displayed neatly in a customizable table on the chart.
This tool is essential for proper risk management in trading.
Core Functionality & Inputs
The script uses a few key inputs to perform its calculations:
Account & Risk Configuration
Account Size: You can define and switch between two account sizes (account_size_1 and account_size_2) using the account_option toggle ("P1" or "P2"). The chosen size determines the total capital.
Risk % per Trade (risk_percent): This is the percentage of your chosen account size that you are willing to lose on a single trade. Example: 0.5% risk on a $180,000 account means you risk $900 per trade.
Trade Parameters
Entry Price, Stop Loss Price, Target Price: These are the manual prices a trader enters for their planned trade.
Reset All Inputs (enable_reset): A toggle to quickly clear the three price inputs by setting them to 0.
🧮 Key Calculations
The script calculates several critical values to determine the position size:
Risk per Trade: The actual dollar amount you are risking:
Account Size×(100Risk %)
Stop Distance: The price difference between the entry and stop-loss:
Entry Price−Stop Loss Price
(This assumes a Long trade; for a Short trade, the calculation would be reversed, but the magnitude must be positive for the next step).
Position Size: The maximum number of shares/contracts you can buy/sell while keeping the dollar risk within your Risk per Trade amount. This is the main output:
Position Size=Floor(Stop DistanceRisk per Trade)
The math.floor() function ensures the position size is a whole number (no fractional shares).
Capital Required: The total cost to open the calculated position:
Position Size×Entry Price
Risk/Reward (R:R) Ratio: The potential reward compared to the risk taken:
Stop DistanceTarget Price−Entry Price
Table Display & Customization
The script's output is displayed in a customizable table on the chart.
Display Toggles
A large section of boolean (input.bool) variables (e.g., show_position_size, show_rr_ratio) allows the user to turn on/off individual rows in the results table, customizing what information is shown.
Visual Settings
Table Position: The user can select one of four corners for the table (Top Right, Bottom Right, Top Left, Bottom Left).
Colors and Size: Extensive inputs are provided to customize the table's background, border, font size, and text colors.
Conditional Coloring
The script uses colors to provide quick visual warnings and checks on key metrics:
Risk % per Trade:
Green/Lime for ≤1.0% (Low Risk)
Orange for >1.0% and ≤2.0% (Medium Risk)
Red for >2.0% (High Risk)
R:R Ratio:
Green/Lime for ≥2 (Good)
Red for <2 (Bad)
Capital Check:
Green if Capital Required ≤ Account Size (Within Limit)
Red if Capital Required > Account Size (Exceeds Account)
Displayed Outputs
The table provides a comprehensive set of calculated metrics, including:
Current Ticker: The symbol of the asset being traded.
Position Size: The calculated share/contract quantity.
Risk per Trade: The dollar amount risked.
Stop Distance (pts/%): How far the stop-loss is from the entry price, in both price points and a percentage of the entry price.
Target Reward ($/%): The potential profit in dollars and as a percentage.
R:R Ratio: The calculated Risk/Reward ratio.
Target 1 (50%): Half the distance to the full target (potential partial take profit).
Target 2 (100%): The full target_price.
Capital Check: A quick status on whether the trade exceeds the total account size.
Summary: A single line detailing the trade direction (Long/Short), prices, size, and R:R ratio.
This indicator is a powerful tool for traders who want to maintain strict, quantifiable risk control on every position they take.
LP ChecklistLP Checklist — Rejection (ATR + calculator), D1-Fixed, Risk Position Sizing
This script is an on-chart checklist for rejection (bounce) trades based on Gerchik-style level logic, with a fixed daily ATR core and a simple risk calculator. ATR values are latched on D1, so SL/TP remain stable when you switch timeframes. The UI (inputs and panel) is in Russian.
What it does:
• Checklist: three groups (Prerequisites, Negatives, Waiting). You tick items; the panel shows a clean grouped list with counters.
• Rejection calculations: Entry, Stop-Loss, Take-Profit from your level price, k × ATR stop, and a preset RR ratio. A built-in entry offset ≈ 12.5% of the stop distance is applied automatically.
• Entry placement for rejection: after a false break and return back into the range, Long enters above the level (stop below), Short enters below the level (stop above). The built-in entry offset is applied relative to the level and stop side.
• ATR engine (D1): Classic ATR (selectable period; HL or True Range) — always computed and displayed as reference and daily progress. Classic ATR also drives a visible D1-progress metric (current D1 range as a percentage of ATR).
• Active ATR = either 5-day median ATR with optional paranormal bar filter (filters both oversized and undersized D1 ranges by percentage thresholds vs a blended reference) or Manual ATR if enabled.
• Risk calculator: given Deposit and Risk %, the script outputs the entry notional (USD) so an SL hit is approximately equal to your dollar risk; the panel also shows potential PnL to take-profit.
• Stable visuals: level, entry, SL, and TP lines with automatic cleanup on a new day; the panel can be placed in any corner (RU labels).
How to use:
Set Direction (Long/Short) and the Level Price.
Tick checklist items as the setup forms.
In ATR, leave Classic as reference (period + HL/TR), and choose the Active ATR mode: 5-day median (with optional paranormal filter) or Manual (manual value overrides).
Pick k for Stop = k × ATR; TP is placed by the RR preset relative to stop size.
In Calculator, set Deposit and Risk % — the panel returns the entry notional aligned with your risk.
Show or hide panel sections (calculations, ATR, calculator) as needed.
Quick notes:
• Classic ATR is always calculated and shown for context and D1 progress.
• Active ATR drives stop sizing: 5-day median (with filter) or Manual.
• Manual ATR, if enabled, fully replaces the active one in calculations.
• All ATR computations use closed D1 bars; values are cached per day.
• The built-in entry offset equals 12.5% of stop distance and applies automatically to both directions.
Описание (RU)
Чек-лист уровней (Отбой / ЛП) — ATR (классический показывается, активный = медианный 5-дневный или ручной), фиксация D1, расчёт позиции от риска
Скрипт для работы с отбойными (ложно-пробойными) сетапами по логике уровней Герчика: чек-лист на графике, фиксированный дневной ATR, расчёт ТВХ/SL/TP и простой калькулятор позиции от риска. Значения ATR фиксируются на D1, поэтому SL/TP не плавают при смене таймфреймов. Интерфейс (входы и панель) на русском.
Что делает:
• Чек-лист: три группы (Предпосылки, Минусы, Ожидаю). Отмечаете галочки — панель выводит список и счётчики.
• Расчёты отбоя: ТВХ, Стоп-лосс, Тейк-профит от цены уровня; стоп как k × ATR; тейк задаётся пресетом RR; встроен люфт входа ≈ 12.5% от дистанции стопа.
• Правило входа для отбоя: после ложного прокола и возврата внутрь диапазона вход по Long — выше уровня (стоп ниже), по Short — ниже уровня (стоп выше). Люфт входа прикладывается относительно уровня и стороны стопа.
• ATR (D1): Классический ATR (период, HL или True Range) всегда считается и показывается как справочное значение и для прогресса дня. Также отображается процент текущего D1-хода к ATR.
• Активный ATR = медианный за 5 дней с опциональным фильтром паранормальных баров (отсеивает одновременно слишком большие и слишком маленькие дневные диапазоны относительно «смешанного» референса, который равен среднему между mean и median последних D1-диапазонов) или Ручной ATR, если включён.
• Калькулятор риска: по Депозиту и Риску % отдаёт сумму входа (USD) так, чтобы убыток по стопу был близок к заданному риску; дополнительно показывает потенциальный PnL по тейку.
• Стабильная отрисовка: линии Уровень, ТВХ, SL и TP, автоочистка на новый день; позиция панели выбирается (русские подписи углов).
Как работать:
Задайте Направление (Long/Short) и Цену уровня.
Отметьте пункты чек-листа по текущей ситуации.
В ATR используйте Классический как справочный (период + HL/TR), а Активный выберите как медианный 5-дневный (с фильтром при необходимости) или Ручной (ручное значение замещает активный).
Укажите k для Стоп = k × ATR; тейк задаётся пресетом RR относительно размера стопа.
В Калькуляторе задайте Депозит и Риск % — получите сумму входа, согласованную с риском.
В панели можно скрывать или показывать секции (расчёты, ATR, калькулятор).
Важные примечания:
• Классический ATR всегда считается и отображается для контекста и прогресса по дню.
• Активный ATR используется для расчёта стопа: медианный 5D (с фильтром) или ручной.
• Если включён Ручной ATR, он полностью замещает активный ATR в расчётах стопа и RR.
• Все расчёты ATR делаются по закрытым барам D1; значения фиксируются на день.
• Встроенный люфт входа составляет 12.5% от дистанции стопа и применяется автоматически в обе стороны.
ICT Concepts(Liquidity, FVG & Liquidity Sweeps)📄 Description:
A Smart Money Concept (SMC)-based utility that blends ICT-style Liquidity Sweeps, Fair Value Gap (FVG) mapping, and Swing Structure proxies – designed for traders seeking clean precision in price imbalance analysis.
⸻
🔍 1. What This Script Does
T his indicator brings together three core Institutional Concepts:
• Liquidity Sweep Detection : Identifies buy/sell-side liquidity grabs (fakeouts) confirmed by volume spikes – a common precursor to institutional order flow shifts.
• Fair Value Gaps (FVGs) : Highlights inefficiencies between price legs using strict ICT-style 3-candle or gap-based rules. These are areas institutions often revisit.
• Swing Structure Proxy (OB Mapping) : Tracks dynamic swing highs/lows to act as proxy zones for potential order blocks and structural boundaries.
It also includes a cooldown-based signal filtering engine to prevent overfitting and noise, helping traders avoid false positives in choppy markets.
⚙️ 2. How It Works (Core Logic)
✅ A. Liquidity Sweep Engine
• Looks back N bars to find Equal Highs or Equal Lows.
• Triggers a signal only if price sweeps the level and closes on the other side with a volume spike.
• Customizable volume threshold (e.g., 1.5x average volume).
• Includes a signal cooldown period to reduce clutter and boost quality.
Bullish Sweep = Price dips below equal lows but closes higher
Bearish Sweep = Price spikes above equal highs but closes lower
Visuals: Signal arrows with alerts (BUY LQ / SELL LQ)
⸻
✅ B. Fair Value Gap (FVG) Zones
• Detects FVGs using:
• Sequential logic: Low > High (bullish), High < Low (bearish)
• Gap logic: Open gaps at bar open
• Dynamic box drawing:
• Automatically extends FVG zones until price fully closes through them.
• Different color coding for bullish (teal) and bearish (orange) gaps.
• Customizable:
• Opacity control
• Option to include/exclude gap-based FVGs
• Hide filled zones
• Limit total zones rendered (for performance)
⸻
✅ C. Swing High/Low Structure
• Uses a lookback period to find latest swing high/low levels.
• Acts as a proxy for Order Block zones or structural shift reference points.
• Plotted as red (high) and green (low) lines.
⸻
🚀 3. How to Use It
• Scalpers and Intraday Traders can use Liquidity Sweep + FVG Confluence to time reversals or catch early entries into trend continuation moves.
• Swing Traders can observe swing OB proxies and recent FVG zones to frame directional bias and target zones.
• Volume-Aware Traders benefit from the volume filter that confirms sweeps are meaningful – not just random stop hunts.
🔔 Set alerts on:
• Bullish Liquidity Sweeps
• Bearish Liquidity Sweeps
You can use this in combination with your own trend filters, or even confluence it with Order Blocks, VWAP, or EMA trend tools.
⸻
💡 What Makes It Original?
• The script doesn’t merely combine standard tools — it builds a cohesive ICT-style detection system using:
• A custom volume-confirmed liquidity sweep filter
• Dynamic FVG rendering with filled logic + performance optimization
• Visual hierarchy to avoid clutter: clean line plots, contextual boxes, and conditional signals
• Highly customizable yet lightweight, making it suitable for fast-paced decision making.
⸻
✅ Notes
• Invite-only script for serious traders interested in Smart Money and ICT concepts.
• Does not repaint signals.
• All visuals are dynamically managed for clarity and performance.
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.
12/21 x 50-100-200 MA - [RZ]👁️ - 12/21 x 50-100-200 MA
A comprehensive moving average overlay indicator designed to identify trend direction and key support/resistance levels using a dual fast/slow MA crossover system combined with three major moving averages.
⛓️ - FEATURES
Dual MA Crossover System: Configurable short (default 12) and long (default 21) period moving averages that change color based on trend direction
Triple Major MAs: 50, 100, and 200 period moving averages displayed in blue, yellow, and red respectively for identifying key market structure levels
Multiple MA Types: Choose from SMA, EMA, DEMA, TEMA, LSMA, WMA, or HMA for all calculations
Customizable Source: Apply the indicator to any price source (close, open, high, low)
Optional Bar Coloring: Visualize trend direction directly on price bars
Built-in Alerts: Automated alerts for trend reversals (Trend Up/Trend Down)
🎮 - HOW TO USE
Bullish Signal: When the short MA crosses above the long MA, both MAs turn green
Bearish Signal: When the short MA crosses below the long MA, both MAs turn red
The 50/100/200 MAs serve as dynamic support/resistance levels and help confirm overall market trend
Use bar coloring for quick visual identification of current trend state
🧰 - OPTIONS
Adjustable lengths for all moving averages
Color customization for bullish/bearish trends
Toggle bar coloring on/off
Select preferred MA calculation method
⚠️ - DISCLAIMER
This indicator is provided for educational and informational purposes only and should not be considered financial advice.
Trading and investing in financial markets involves substantial risk of loss and is not suitable for every investor.
Past performance is not indicative of future results.
The signals and information generated by this indicator do not guarantee profits and may result in losses.
Users should conduct their own research and due diligence, and consult with a qualified financial advisor before making any investment decisions.
The creator of this indicator assumes no responsibility for any financial losses incurred through the use of this tool.
By using this indicator, you acknowledge that you are solely responsible for your trading decisions and their outcomes.
👑 - CREDITS
@profmichaelg for Michael's EMA indicator
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.
Signal Tester EN [Abusuhil]Signal Tester - Complete Description
Overview
Signal Tester is a comprehensive trading tool designed to backtest and analyze external trading signals with advanced risk management capabilities. The indicator provides seven different calculation methods for stop-loss and take-profit levels, along with detailed performance statistics and real-time tracking of active trades.
Important Disclaimer: This indicator is a tool for analysis and education purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Always conduct your own research and consider seeking advice from a qualified financial advisor before making trading decisions.
Key Features
7 Calculation Methods for customizable risk management
External Signal Integration via any oscillator or indicator
Real-time Trade Tracking with visual entry/exit points
Comprehensive Statistics Table showing win rate, profit/loss, and active trades
Date Filtering for focused backtesting periods
Custom Alerts for new buy signals
Multi-Target System with up to 5 take-profit levels
How to Use
Step 1: Connect External Signal
The indicator requires an external signal source to generate buy signals.
Add your preferred indicator to the chart (RSI, MACD, Stochastic, custom indicator, etc.)
In Signal Tester settings, locate "External Indicator" input
Click the input and select your indicator's plot line
Buy signals are generated when the external source crosses above zero
Example: If using RSI, connect the RSI line. A buy signal triggers when RSI crosses above the zero reference (if plotted as oscillator).
Step 2: Choose Your Calculation Method
Select one of seven methods under "Calculation Method":
1. Percentage %
The simplest method using fixed percentage values.
Settings:
Stop Loss %: Distance from entry to stop-loss (default: 2%)
Target 1-5 %: Distance from entry to each take-profit level
Example: Entry at $100
Stop Loss (2%): $98
Target 1 (2%): $102
Target 2 (4%): $104
Best For: Beginners, markets with consistent volatility
2. ATR Multiplier
Uses Average True Range for dynamic levels based on market volatility.
Settings:
ATR Period: Calculation period (default: 14)
Stop Multiplier: ATR multiplier for stop-loss (default: 1.5)
Target Multipliers: ATR multipliers for each take-profit
Example: Entry at $100, ATR = $2
Stop Loss (1.5x ATR): $100 - $3 = $97
Target 1 (2x ATR): $100 + $4 = $104
Best For: Volatile markets, adapting to changing conditions
3. Risk:Reward Ratio
Calculates targets based on risk-to-reward ratios.
Settings:
Stop Loss %: Initial risk percentage
Target Ratios: R:R ratio for each target (1:1.5, 1:2, 1:3, etc.)
Example: Entry at $100, Stop at $98 (2% risk = $2)
Target 1 (1:1.5): $100 + ($2 × 1.5) = $103
Target 2 (1:2): $100 + ($2 × 2) = $104
Target 3 (1:3): $100 + ($2 × 3) = $106
Best For: Traders focused on risk management and position sizing
4. Swing High/Low
Places stop-loss at recent swing low with targets as multiples of the risk.
Settings:
Swing Lookback Candles: Number of bars to find swing low (default: 5)
Stop Safety Distance %: Buffer below swing low
Target Multipliers: Risk multiples for each target
Example: Entry at $105, Swing Low at $100
Stop Loss: $100 - 0.1% = $99.90 (risk = $5.10)
Target 1 (1.5x): $105 + ($5.10 × 1.5) = $112.65
Best For: Swing traders, respecting market structure
5. Partial Take Profit
Sells portions of the position at each target level, moving stop to entry after first target.
Settings:
Stop Loss %: Initial stop distance
Target 1-5 %: Price levels for partial exits
Sell % at TP1-4: Percentage of position to close at each level
Example: 100% position, 50% sell at each target
TP1 hit: Sell 50%, remaining 50%, stop moves to entry
TP2 hit: Sell 25% (50% of remaining), remaining 25%
TP3 hit: Sell 12.5%, remaining 12.5%
Best For: Conservative traders, locking in profits gradually
6. Trailing Stop
Similar to Partial Take Profit but trails the stop-loss to each achieved target.
Settings:
Stop Loss %: Initial stop distance
Target 1-5 %: Price levels for trailing stops
Sell % at TP1-4: Percentage to close at each level
Example:
TP1 ($102) hit: Sell 50%, stop trails to $102
TP2 ($104) hit: Sell 25%, stop trails to $104
Price retraces to $104: Exit with locked profits
Best For: Trend followers, maximizing profit in strong moves
7. Smart Exit
Advanced method that moves stop to entry after first target, then exits based on technical conditions.
Settings:
Stop Loss %: Initial stop distance
First Target %: When hit, stop moves to breakeven
Exit Method: Choose from 8 exit strategies
Exit Methods:
Close < EMA 21: Exits when price closes below 21-period EMA
Close < MA 20: Exits when price closes below 20-period Moving Average
Supertrend Flip: Exits when Supertrend indicator flips bearish
ATR Trailing Stop: Dynamic trailing stop based on ATR
MACD Crossover: Exits on MACD bearish crossover
RSI < 50: Exits when RSI drops below specified level
Parabolic SAR Flip: Exits when SAR flips above price
Bollinger Bands: Exits when price closes below middle or lower band
Best For: Advanced traders, letting winners run with protection
Date Filtering
Control which trades are included in backtesting.
Filter Types:
Specific Date: Only trades after selected date
Number of Weeks: Last X weeks (default: 12)
Number of Months: Last X months (default: 3)
How to Enable:
Check "Enable Date Filter"
Select filter type
Set the date or number of weeks/months
Use Case: Test strategy performance in recent market conditions or specific periods
Understanding the Statistics Table
The table displays the last 10 trades plus comprehensive statistics:
Trade Columns:
#: Trade number
Entry: Entry price
Stop: Current stop-loss level
TP1-TP5: Checkmarks (✅) when targets are hit
Profit %: Realized profit for the trade
Max %: Maximum unrealized profit reached (⬆️ indicates active trade)
Status:
🔄 Active trade
✅ Closed winner
❌ SL - Stopped out
Summary Row:
Total: Number of trades executed
Period: Duration of trading period (Years, Months, Days)
Statistics Row:
W: Number of winning trades
L: Number of losing trades
A: Number of active (open) trades
Win Rate %: (Wins / Total Trades) × 100
Performance Row:
Profit: Total profit from all winning trades
Loss: Total loss from all losing trades
Net: Net profit/loss (Profit - Loss)
Visual Elements
When a buy signal triggers, the indicator draws:
Blue Line: Entry price
Red Line: Stop-loss level
Green Lines: Take-profit levels (up to 5)
Green Label: Trade number below the entry bar
Green Triangle: Buy signal marker
Alerts
The indicator includes customizable alerts for new buy signals.
Setting Up Alerts:
Click the "⏰" icon in TradingView
Select "Signal Tester "
Choose condition: "Buy"
Configure notification preferences (popup, email, webhook)
Click "Create"
Alert Message Format:
🚀 New Buy Signal!
Price:
Trade #:
Best Practices
Backtest First: Test each calculation method on historical data before live trading
Match Timeframe: Use the indicator on the timeframe you plan to trade
Combine with Analysis: Use alongside support/resistance, trend analysis, and other tools
Risk Management: Never risk more than 1-2% of capital per trade
Review Statistics: Regularly check win rate and profit/loss metrics
Adjust Settings: Optimize parameters based on the asset's volatility and your risk tolerance
Limitations
Requires external signal source (does not generate signals independently)
Backtesting assumes perfect entry/exit execution (real trading includes slippage)
Past performance does not guarantee future results
Should be used as one component of a complete trading strategy
Version Information
Version: 1.0
Pine Script Version: v5
Type: Overlay Indicator
Author: Abusuhil
Support and Updates
This indicator is provided as-is for educational and analytical purposes. Users are responsible for their own trading decisions and should thoroughly test any strategy before implementing it with real capital.
Risk Warning: Trading financial instruments carries a high level of risk and may not be suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. Only trade with money you can afford to lose.
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
GOLDSNIPERThe Gold Sniper Indicator is a precision trading tool designed specifically for scalping and intraday trading Gold (XAUUSD) on TradingView.
It automatically plots institutional key levels, detects breakout & retest opportunities, and provides trade management levels (Stop Loss & Take Profit) for structured, disciplined trading.
Aug 6
Release Notes
The Gold Sniper Indicator is a precision trading tool designed specifically for scalping and intraday trading Gold (XAUUSD) on TradingView.
It automatically plots institutional key levels, detects breakout & retest opportunities, and provides trade management levels (Stop Loss & Take Profit) for structured, disciplined trading
Aug 13
Release Notes
The Gold Sniper Indicator is a precision trading tool designed specifically for scalping and intraday trading Gold (XAUUSD) on TradingView.
It automatically plots institutional key levels, detects breakout & retest opportunities, and provides trade management levels (Stop Loss & Take Profit) for structured, disciplined trading.
3 days ago
Release Notes
The Gold Sniper Indicator is a precision TradingView tool for scalping and intraday trading Gold (XAUUSD).
It is built around a break-and-retest strategy with clear trade management: 10 pip Stop Loss, 20 pip TP1, and 35 pip TP2.
The indicator automatically:
• Plots institutional key levels and supply & demand zones
• Detects breakout and retest opportunities in real time
• Provides stop loss and take profit levels for structured, disciplined trading
Whether you’re a scalper or day trader, Gold Sniper helps you catch high-probability setups on XAUUSD with precise risk-to-reward ratios (1:1 and 1:3).
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).
PRITESH@23Pritesh@23 (Protected)
Overview:
A flexible SMC-style indicator combining EMA trend, ADX/DMI confirmation, RSI filtering, SMC swing pivots (order-block detection), pre-entry markers, a 0–7 signalScore, and optional horizontal lines anchored to weak candles from a selected timeframe.
Key inputs:
• EMA Fast / EMA Slow
• ADX length & smoothing
• RSI length
• Swing lookback (order block detection)
• Show/hide SMC zones (order-block boxes & lines)
• Show signalScore (0–7)
• Horizontal lines TF & style controls (color, width, pattern)
• Max lines per type (to limit drawing objects)
Usage:
1. Add to chart and select preferred timeframe.
2. Adjust EMA/ADX/RSI to match instrument volatility (e.g., lower EMA for lower timeframes).
3. Use signalScore (0–7) to prioritize setups; pre-entry markers flag potential entries inside order-blocks.
4. Horizontal weak-candle lines help mark structural weakness/resilience across TFs.
Support & License:
• Protected source — code not visible to users.
• For questions/support: contact the author (provide non-sensitive contact).
• License: For personal use only. Redistribution or resale is prohibited without the author's express permission.
Version: 1.0
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.
Eng. Jaycob 💰It will show you where are the prices that may get reverse from them.
Numbers will be updated daily on the chart.
GOLDSNIPER
The Gold Sniper Indicator is a precision trading tool designed specifically for scalping and intraday trading Gold (XAUUSD) on TradingView.
It automatically plots institutional key levels, detects breakout & retest opportunities, and provides trade management levels (Stop Loss & Take Profit) for structured, disciplined trading.
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
Syed Shams - PSX Dashboard v2.0A compact dashboard that summarizes trend/strength context for Pakistan stocks and indices. It normalizes signals from widely-used tools into a single table so you can triage symbols quickly—no alerts, no buy/sell calls.
What’s inside (columns):
------------------------------
- Scrip / Price / Δ%: Symbol, last price, and percent change vs the previous bar close on the active timeframe (e.g., on 1D it’s vs prior daily close).
- LMH / LML / LWH / LWL: Last Month/Week High & Low. Optional setting to use closed prior M/W bars.
- EMAs 5/9/21/44/100/200: Six mini-squares. Green = price ≥ that EMA, Red = below.
- RS5 / RS21 (vs KSE100): Arrows show out/under-performance over two user-set return windows.
- RSI: Text = RSI value with slope arrow; Blue fill when RSI > its EMA (bullish bias), Red fill when below.
- OBV: Blue/Red fill for OBV vs its EMA; slope arrow uses the global Slope Lookback.
- MACD (M A C D): 4 tiny histogram bars colored by quadrant/acceleration for quick trend read.
- ADX / DMI: ADX value (color-coded: >50 red, 25–50 green, 20–25 orange, <20 red) + slope arrow. +DI / −DI arrows with neutral/green/red fill when +DI dominates/equals/−DI dominates.
- ST 5,1 / ST 8,2: Green/Red dots for SuperTrend state.
- Ichimoku: Cell fill for price vs cloud (above/inside/below). “Laser” dash appears on fresh HH/LL checks.
- BB Zone: Uses BB(20,1/2/3).
• price ≥ U2 → “BB3” (Dark Blue text, Light Blue fill)
• U1 < price < U2 → “BB2” (Dark Blue / Light Blue)
• L1 ≤ price ≤ U1 → “BB1” (Dark Green / Light Green)
• L2 < price < L1 → “BB2” (Dark Red / Light Red)
• price ≤ L2 → “BB3” (Dark Red / Light Red)
Also shows BB3 upper-band slope using the global lookback: “+” widening, “−” contracting, “=” flat.
- Grade (A/B/C/D): Optional composite score; rows sort by score when enabled.
Grade scoring:
------------------
Price ≥ each EMA +1 (max +6) · RS5>idx +2, RS21>idx +1 · OBV>EMA +2, OBV-EMA↑ +1 · RSI>50 +2, RSI>EMA +1, RSI slope↑ +2 / ↓ −2 · MACD hist: ≥0&rising +2, ≥0&falling +1, <0&falling −2, <0&rising −1 · +DI>−DI +1, +DI slope↑ +1 · ADX: >50 −2, 25–50 +2, 20–25 +1, ADX slope↑ +1 · ST(5,1) +1, ST(8,2) +1 · Ichimoku: above cloud +1, below −1, HH “laser” +2 / LL −2 · BB zone: inside BB1 +1; above BB1 +2; BB3 widening +2; shrinking −2; flat 0.
Controls & workflow:
-------------------------
- Universe selector (incl. sector lists and Custom Watchlist).
- Show KSE index rows (off by default).
- Slope Lookback (arrows): one control for RSI/ADX/DMI/OBV/BB3 slope checks.
- Closed bars for LM/LW H/L (off by default).
- Dark Mode (off by default): optimized table contrast for black charts.
- Show Grades toggle.
How to use:
---------------
1) Pick your universe and timeframe.
2) Adjust Slope Lookback (default 1) if you want a stricter/looser slope test.
3) Sort by Grade (on) to find leaders/laggards, then open charts for entries/exits using your own process.
Notes:
--------
- Timeframe-aware: all calculations—including Δ% and RS windows—use the active chart TF.
- Educational research tool. Not investment advice. No alerts.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229.
Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327.
Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915.
Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44.
Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764.
Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue.
Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228.
Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108.
Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business.
Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007.
Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576.
Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61.
Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79.
Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49.
Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465.
Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78.
Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720.
Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin.
Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill.
Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384.
Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley.
Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*.
Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91.
Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292.
Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170.
Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467.
Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676.
Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419.
Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91.
Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433.
Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41.
Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press.
Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360.
Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449.
Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138.
Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462.
Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17.
Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
12/21 EMA STRAT - [RZ]12/21 EMA Strategy with Performance Analytics
👁️ - OVERVIEW
This indicator implements a simple yet effective exponential moving average (EMA) crossover strategy that compares a 12-period EMA against a 21-period EMA. The system generates long signals when the 12 EMA is positioned above the 21 EMA, and moves to cash when the 12 EMA falls below the 21 EMA.
🧠 - STRATEGY LOGIC
Signal Generation:
Long Position: Activated when 12 EMA > 21 EMA
Cash Position: Activated when 12 EMA < 21 EMA
Technical Implementation:
Uses perpetual condition checks instead of crossover/crossunder functions to prevent signal misgeneration and ensure reliability
Implements barstate.isconfirmed validation to eliminate repainting issues and ensure all signals are confirmed on closed bars
Provides clean, reliable signals suitable for both backtesting and live trading
⚙️ - FEATURES
The indicator includes a comprehensive table displaying real-time performance metrics comparing the strategy against a buy-and-hold approach:
Sharpe Ratio: Risk-adjusted return measurement
Sortino Ratio: Downside risk-adjusted return measurement
Omega Ratio: Probability-weighted ratio of gains versus losses
Maximum Drawdown %: Largest peak-to-trough decline
Visual Components
Equity Curves: Plots both strategy equity and buy-and-hold equity for visual comparison
Status Table: Real-time display of current position (Long/Cash) and performance metrics
Clean Chart Interface: Easy-to-read visualization of strategy performance
Alert System
Long signal triggers
Cash signal triggers
📝 - How to Use
Add the indicator to your chart
Review the performance metrics table to compare strategy vs. buy-and-hold
Monitor the equity curves to visualize strategy performance
Set up alerts for long and cash signals if desired
Use the current position indicator to track strategy status
📊 - Multi-Timeframe Compatibility
This indicator works across multiple timeframes, however, performance characteristics vary significantly depending on the timeframe selected:
Different timeframes will produce different results
Strategy performance may be optimal on certain timeframes and underperform on others
DYOR (Do Your Own Research): Users are strongly encouraged to backtest the strategy on their preferred timeframes and market conditions before use
Test extensively with historical data to understand the strategy's behavior in your specific use case
ETH
SOL
⚠️ - DISCLAIMER
This indicator is provided for educational and informational purposes only. It is NOT financial advice, investment advice, or a recommendation to buy or sell any security or financial instrument.
Past performance does not guarantee future results
Trading involves substantial risk of loss and is not suitable for all investors
You should carefully consider your financial situation and risk tolerance before making any trading decisions
Always conduct your own research and consult with a qualified financial advisor before making investment decisions
The creator of this indicator assumes no responsibility for any financial losses incurred through the use of this tool
Use this indicator at your own risk
Aazam Jani Golden This indicator is designed to provide high-probability BUY/SELL signals only during strong trending market conditions.
It combines multiple confirmations from various indicators and Trend Strength filters to ignore sideways (choppy) markets.
