SPY200SMA (+4%/-3%) TQQQ/QQQ STRATEGYSummary of the Improved Strategy: When the price of AMEX:SPY is +4% above the 200SMA BUY NASDAQ:TQQQ and when the price of SPY drops to -3% under the SPY 200SMA SELL everything and slowly DCA into NASDAQ:QQQ over the next 6-12 months or until price returns to +4% above the SPY 200SMA at which point you will go back into 100% TQQQ.
Note: (if the price of QQQ goes 30% above the 200SMA of QQQ deleverage to QQQ or Sell to protect yourself from dot com level event)
More info and stats -https://www.reddit.com/r/LETFs/comments/1nhye66/spy_200sma_43_tqqqqqq_long_term_investment/
Portföy Yönetimi
MNQ TopStep 50K | Ultra Quality v3.0MNQ TopStep 50K | Ultra Quality v3.0 - Publish Summary
📊 Overview
A professional-grade trading indicator designed specifically for MNQ futures traders using TopStep funded accounts. Combines 7 technical confirmations with 5 advanced safety filters to deliver high-quality trade signals while managing drawdown risk.
🎯 Key Features
Core Signal System
7-Point Confirmation: VWAP, EMA crossovers, 15-min HTF trend, MACD, RSI, ADX, and Volume
Signal Grading: Each signal is rated A+ through D based on 7 quality factors
Quality Threshold: Adjustable minimum grade requirement (A+, A, B, C, D)
Advanced Safety Filters (Customizable)
Mean Reversion Filter - Prevents chasing extended moves beyond VWAP bands
ATR Spike Filter - Avoids trading during extreme volatility events
EMA Spacing Filter - Ensures proper trend separation (optional)
Momentum Filter - Requires consecutive directional bars (optional)
Multi-Timeframe Confirmation - Aligns with 15-min trend (optional)
TopStep Risk Management
Real-time drawdown tracking
Position sizing calculator based on remaining cushion
Daily loss limit monitoring
Consecutive loss protection
Max trades per day limiter
Visual Components
VWAP with 1σ, 2σ, 3σ bands
EMA 9/21 with cloud fill
15-min EMA 50 for HTF trend
Comprehensive metrics dashboard
Risk management panel
Filter status panel
Detailed trade labels with entry, stops, and targets
⚙️ Default Settings (Balanced for Regular Signals)
Technical Indicators
Fast EMA: 9 | Slow EMA: 21 | HTF EMA: 50 (15-min)
MACD: 10/22/9
RSI: 14 period | Thresholds: 52 (buy) / 48 (sell)
ADX: 14 period | Minimum: 20
ATR: 14 period | Stop: 2x | TP1: 2x | TP2: 3x
Volume: 1.2x average required
Session Settings
Default: 9:30 AM - 11:30 AM ET (adjustable)
Avoids first 15 minutes after market open
Customizable trading hours
Safety Filters (Default Configuration)
✅ Mean Reversion: Enabled (2.5σ max from VWAP)
✅ ATR Spike: Enabled (2.0x threshold)
❌ EMA Spacing: Disabled (can enable for quality)
❌ Momentum: Disabled (can enable for quality)
❌ MTF Confirmation: Disabled (can enable for quality)
Risk Controls
Minimum Signal Quality: C (adjustable to A+ for fewer/better signals)
Min Bars Between Signals: 10
Max Trades Per Day: 5
Stop After Consecutive Losses: 2
📈 Expected Performance
With Default Settings:
Signals per week: 10-15 trades
Estimated win rate: 55-60%
Risk-Reward: 1:2 (TP1) and 1:3 (TP2)
With Aggressive Settings (Min Quality = D, All Filters Off):
Signals per week: 20-25 trades
Estimated win rate: 50-55%
With Conservative Settings (Min Quality = A, All Filters On):
Signals per week: 3-5 trades
Estimated win rate: 65-70%
🚀 How to Use
Basic Setup:
Add indicator to MNQ 5-minute chart
Adjust TopStep account settings in inputs
Set your risk per trade percentage (default: 0.5%)
Configure trading session hours
Set minimum signal quality (Start with C for balanced results)
Signal Interpretation:
Green Triangle (BUY): Long signal - all confirmations aligned
Red Triangle (SELL): Short signal - all confirmations aligned
Label Details: Shows entry, stop loss, take profit levels, position size, and signal grade
Signal Grade: A+ = Elite (6-7 points) | A = Strong (5) | B = Good (4) | C = Fair (3)
Dashboard Monitoring:
Top Right: Technical metrics and market conditions
Top Left: Filter status (which filters are passing/blocking)
Bottom Right: TopStep risk metrics and position sizing
⚡ Customization Tips
For More Signals:
Lower "Minimum Signal Quality" to D
Decrease ADX threshold to 18-20
Lower RSI thresholds to 50/50
Reduce Volume multiplier to 1.1x
Disable additional filters
For Higher Quality (Fewer Signals):
Raise "Minimum Signal Quality" to A or A+
Increase ADX threshold to 25-30
Enable all 5 advanced filters
Tighten VWAP distance to 2.0σ
Increase momentum requirement to 3-4 bars
For TopStep Compliance:
Adjust "Max Total Drawdown" and "Daily Loss Limit" to match your account
Update "Already Used Drawdown" daily
Monitor the Risk Panel for cushion remaining
Use recommended contract sizing
🛡️ Risk Disclaimer
IMPORTANT: This indicator is for educational and informational purposes only.
Past performance does not guarantee future results
All trading involves substantial risk of loss
Use proper risk management and position sizing
Test thoroughly in paper trading before live use
The indicator does not guarantee profitable trades
Adjust settings based on your risk tolerance and trading style
Always comply with your broker's and TopStep's rules
Risk-Reward Position SizerRisk-Reward Position Sizer – Features Checklist
Purpose:
A visual calculator and position sizing tool for day traders, providing realistic risk, stop-loss, take-profit, and reward-to-risk information based on account size and position constraints.
Features:
Flexible Risk Settings
Set risk as a percentage of your account or a fixed dollar amount per trade.
Automatically calculates position size based on desired risk and stop distance.
Stop Loss Options
Stop distance can be defined as a percent of entry price or a fixed price.
Automatically adjusts stop distance when position is cash-limited to achieve your target risk.
Take Profit Options
TP can be defined as a fixed R multiple (e.g., 2R) or fixed absolute price.
Cash-Limited Position Handling
Optional “Cap Position to Account Size” prevents buying more shares than your cash allows.
Shows actual achievable risk if your cash limits position size.
Realistic Risk / Reward Calculations
Calculates Actual Risk $ based on position size and stop distance.
Calculates Projected Win $ based on take profit and position size.
Calculates Actual Reward-to-Risk (R:R) ratio using actual stop and TP.
Position Metrics
Estimated quantity of shares/contracts to buy.
Estimated position value.
Estimated leverage used relative to account size.
Top-Right Table Display
Clear, compact table showing:
Account size
Target risk $
Actual risk $
Stop distance
Quantity
Position value
Take profit and stop-loss prices
Projected win $ and %
Projected loss %
Actual R:R
Leverage
Trading Decision Aid
Gives traders a realistic snapshot of achievable risk and reward before entering a trade.
Helps avoid the common trap of setting tight stops that don’t actually match desired account risk.
Why It’s Useful:
This indicator turns abstract risk/reward concepts into concrete, actionable numbers, helping day traders size positions safely, plan stops and targets realistically, and maintain consistent risk management across trades.
21day Structure + 1xATR Extension LineThis is a 21-day structure script that is used by Alex Desjardins (Prime Trading) along with a 1xATR line to make sure entries aren't bought extended from this structure.
Market Regime IndexThe Market Regime Index is a top-down macro regime nowcasting tool that offers a consolidated view of the market’s risk appetite. It tracks 32 of the world’s most influential markets across asset classes to determine investor sentiment by applying trend-following signals to each independent asset. It features adjustable parameters and a built-in alert system that notifies investors when conditions transition between Risk-On and Risk-Off regimes. The selected markets are grouped into equities (7), fixed income (9), currencies (7), commodities (5), and derivatives (4):
Equities = S&P 500 E-mini Index Futures, Nasdaq-100 E-mini Index Futures, Russell 2000 E-mini Index Futures, STOXX Europe 600 Index Futures, Nikkei 225 Index Futures, MSCI Emerging Markets Index Futures, and S&P 500 High Beta (SPHB)/Low Beta (SPLV) Ratio.
Fixed Income = US 10Y Treasury Yield, US 2Y Treasury Yield, US 10Y-02Y Yield Spread, German 10Y Bund Yield, UK 10Y Gilt Yield, US 10Y Breakeven Inflation Rate, US 10Y TIPS Yield, US High Yield Option-Adjusted Spread, and US Corporate Option-Adjusted Spread.
Currencies = US Dollar Index (DXY), Australian Dollar/US Dollar, Euro/US Dollar, Chinese Yuan/US Dollar, Pound Sterling/US Dollar, Japanese Yen/US Dollar, and Bitcoin/US Dollar.
Commodities = ICE Brent Crude Oil Futures, COMEX Gold Futures, COMEX Silver Futures, COMEX Copper Futures, and S&P Goldman Sachs Commodity Index (GSCI) Futures.
Derivatives = CBOE S&P 500 Volatility Index (VIX), ICE US Bond Market Volatility Index (MOVE), CBOE 3M Implied Correlation Index, and CBOE VIX Volatility Index (VVIX)/VIX.
All assets are directionally aligned with their historical correlation to the S&P 500. Each asset contributes equally based on its individual bullish or bearish signal. The overall market regime is calculated as the difference between the number of Risk-On and Risk-Off signals divided by the total number of assets, displayed as the percentage of markets confirming each regime. Green indicates Risk-On and occurs when the number of Risk-On signals exceeds Risk-Off signals, while red indicates Risk-Off and occurs when the number of Risk-Off signals exceeds Risk-On signals.
Bullish Signal = (Fast MA – Slow MA) > (ATR × ATR Margin)
Bearish Signal = (Fast MA – Slow MA) < –(ATR × ATR Margin)
Market Regime = (Risk-On signals – Risk-Off signals) ÷ Total assets
This indicator is designed with flexibility in mind, allowing users to include or exclude individual assets that contribute to the market regime and adjust the input parameters used for trend signal detection. These parameters apply to each independent asset, and the overall regime signal is smoothed by the signal length to reduce noise and enhance reliability. Investors can position according to the prevailing market regime by selecting factors that have historically outperformed under each regime environment to minimise downside risk and maximise upside potential:
Risk-On Equity Factors = High Beta > Cyclicals > Low Volatility > Defensives.
Risk-Off Equity Factors = Defensives > Low Volatility > Cyclicals > High Beta.
Risk-On Fixed Income Factors = High Yield > Investment Grade > Treasuries.
Risk-Off Fixed Income Factors = Treasuries > Investment Grade > High Yield.
Risk-On Commodity Factors = Industrial Metals > Energy > Agriculture > Gold.
Risk-Off Commodity Factors = Gold > Agriculture > Energy > Industrial Metals.
Risk-On Currency Factors = Cryptocurrencies > Foreign Currencies > US Dollar.
Risk-Off Currency Factors = US Dollar > Foreign Currencies > Cryptocurrencies.
In summary, the Market Regime Index is a comprehensive macro risk-management tool that identifies the current market regime and helps investors align portfolio risk with the market’s underlying risk appetite. Its intuitive, color-coded design makes it an indispensable resource for investors seeking to navigate shifting market conditions and enhance risk-adjusted performance by selecting factors that have historically outperformed. While it has proven historically valuable, asset-specific characteristics and correlations evolve over time as market dynamics change.
Risk Recommender — (Heatmap)📊 Risk Recommender — Per-Trade & Annualized (Heatmap Columns)
Estimate the optimal risk percentage for any market regime.
This tool dynamically recommends how much of your account equity to risk — either per trade or at a portfolio (annualized) level — using volatility as the guide.
⚙️ How it works
Two distinct modes give you flexibility:
1️⃣ Per-Trade (ATR-based)
• Calculates the current Average True Range (ATR) compared to its long-term baseline.
• When volatility is high (ATR ↑), risk per trade decreases to maintain constant dollar risk.
• When volatility is low (ATR ↓), risk per trade increases within your defined floor and ceiling.
• The display is normalized by stop distance (× ATR) and smoothed to avoid noise.
2️⃣ Annualized (Volatility Targeting)
• Computes realized volatility (standard deviation of log returns) and an EWMA forecast of future volatility.
• Blends current and forecast volatilities to estimate “effective” volatility.
• Scales your base risk so that portfolio volatility converges toward your chosen annual target (e.g., 20%).
• Useful for portfolio-level or systematic strategies that maintain constant volatility exposure.
🎨 Heatmap Visualization
The vertical column graph acts like a thermometer:
• 🟥 Red → “Reduce risk” (volatility high).
• 🟩 Green → “Increase risk” (volatility low).
• Smoothed and bounded between your Floor and Ceiling risk levels.
• Optional dotted guides mark those bounds.
• Label shows the current mode, recommended risk %, and key metrics (ATR ratio or effective volatility).
🔧 Key Inputs
• Base max risk per trade (%) — your normal per-trade risk budget.
• ATR length / Baseline ATR length — control sensitivity to short- vs. long-term volatility.
• Target annualized volatility (%) — portfolio volatility target for quant mode.
• λ (lambda) — smoothing factor for the EWMA volatility forecast (0.90–0.99 typical).
• Floor & Ceiling — clamps the output to avoid extreme sizing.
• Smoothing & Hysteresis — prevent rapid changes in risk recommendations.
🧮 Interpreting the Output
• “Recommended Risk (%)” = suggested portion of equity to risk on the next trade (or current exposure).
• In Per-Trade mode: reflects current ATR ÷ baseline ATR .
• In Annualized mode: reflects target volatility ÷ effective volatility .
• Use the color and height of the column as a quick visual cue for aggressiveness.
💡 Typical Use Cases
• Position-sizing overlay for discretionary traders.
• Volatility-targeting component for algorithmic or multi-asset systems.
• Educational tool to understand how volatility governs prudent risk management.
📘 Notes
• This indicator provides risk suggestions only ; it does not place trades.
• Works on any symbol or timeframe.
• Combine with your own strategy or alerts for full automation.
• All calculations use built-in Pine functions; no proprietary logic.
Tags:
#RiskManagement #ATR #Volatility #Quant #PositionSizing #SystematicTrading #AlgorithmicTrading #Portfolio #TradingStrategy #Heatmap #EWMA #Risk
15-Min RSI Scalper [SwissAlgo]15-Min RSI Scalper
Tracks RSI Momentum Loss and Gain to Generate Signals
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WHAT THIS INDICATOR CALCULATES
This indicator attempts to identify RSI directional changes (RSI momentum) using a step-by-step "ladder" method. It reads RSI(14) from the next higher timeframe relative to your chart. On a 15-minute chart, it uses 1-hour RSI. On a 5-minute chart, it uses 15-minute RSI, and so on.
How the ladder logic works:
The indicator doesn't track RSI all the time. It only starts tracking when RSI crosses into potentially extreme territory (these are called "events" in the code):
For sell signals : when RSI crosses above a dynamic upper threshold (typically between 60-80, calculated as the 90th percentile of recent RSI)
For buy signals : when RSI crosses below a dynamic lower threshold (typically between 20-40, calculated as the 10th percentile of recent RSI)
Once tracking begins, RSI movement is divided into 2-point steps (boxes). The indicator counts how many boxes RSI climbs or falls.
A signal generates only when:
RSI reverses direction by at least 2 boxes (4 RSI points) from its extreme
RSI holds that reversal for 3 consecutive confirmed bars
Example: Dynamic threshold is at 68. RSI crosses above 68 → tracking starts. RSI climbs to 76 (4 boxes up). Then it drops back to 72 and stays below that level for 3 bars → sell signal prints. The buy signal works the same way in reverse.
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SIGNAL GENERATION METHODOLOGY
Sell Signal (Red Triangle)
RSI crosses above a dynamic start level (calculated as the 90th percentile of the last 1000 bars, constrained between 60-80)
Indicator tracks upward progression in 2-point boxes
RSI reverses and drops below a boundary 2 boxes below the highest box reached
RSI remains below that boundary for 3 confirmed bars
Red triangle plots above price
Reset condition: RSI returns below 50
Buy Signal (Green Triangle)
RSI crosses below a dynamic start level (10th percentile of last 1000 bars, constrained between 20-40)
Indicator tracks downward progression in 2-point boxes
RSI reverses and rises above a boundary 2 boxes above the lowest box reached
RSI remains above that boundary for 3 confirmed bars
Green triangle plots below price
Reset condition: RSI returns above 50
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TECHNICAL PARAMETERS
All parameters are hardcoded:
RSI Period: 14
Box Size: 2 RSI points
Reversal Threshold: 2 boxes (4 RSI points)
Confirmation Period: 3 bars
Reset Level: RSI 50
Sell Start Range: 60-80 (dynamic)
Buy Start Range: 20-40 (dynamic)
Lookback for Percentile: 1000 bars
Note: Since the code is open source, users can modify these hardcoded values directly in the script to adjust sensitivity. For example, increasing the confirmation period from 3 to 5 bars will produce fewer but more conservative signals. Decreasing the box size from 2 to 1 will make the indicator more responsive to smaller RSI movements.
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KEY FEATURES
Automatic Higher Timeframe RSI
When applied to a 15-minute chart, the indicator automatically reads 1-hour RSI data. This is the next standard timeframe above 15 minutes in the indicator's logic.
Dynamic Adaptive Start Levels
Sell signals use the 90th percentile of RSI over the last 1000 bars, constrained between 60-80. Buy signals use the 10th percentile, constrained between 20-40. These thresholds recalculate on each bar based on recent data.
Ladder Box System
RSI movements are tracked in 2-point boxes. The indicator requires a 2-box reversal followed by 3 consecutive bars maintaining that reversal before generating a signal.
Dual Signal Output
Red down-triangles plot above price when the sell signal conditions are met. Green up-triangles plot below the price when buy signal conditions are met.
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REPAINTING
This indicator does not repaint. All calculations use "barstate.isconfirmed" to ensure signals appear only on closed bars. The request.security() call uses lookahead=barmerge.lookahead_off to prevent forward-looking bias.
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INTENDED CHART TIMEFRAME
This indicator is designed for use on 15-minute charts. The visual reminder table at the top of the chart indicates this requirement.
On a 15-minute chart:
RSI data comes from the 1-hour timeframe
Signals reflect 1-hour momentum shifts
3-bar confirmation equals 45 minutes of price action
Using it on other timeframes will change the higher timeframe RSI source and may produce different behavior.
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WHAT THIS INDICATOR DOES NOT DO
Does not predict future price movements
Does not provide entry or exit advice
Does not guarantee profitable trades
Does not replace comprehensive technical analysis
Does not account for fundamental factors, news events, or market structure
Does not adapt to all market conditions equally
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EDUCATIONAL USE
This indicator demonstrates one approach to momentum reversal detection using:
Multi-timeframe analysis
Adaptive thresholds via percentile calculation
Step-wise momentum tracking
Multi-bar confirmation logic
It is designed as a technical study, not a trading system. Signals represent calculated conditions based on RSI behavior, not trade recommendations. Always do your own analysis before taking market positions.
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RISK DISCLOSURE
Trading involves substantial risk of loss. This indicator:
Is for educational and informational purposes only
Does not constitute financial, investment, or trading advice
Should not be used as the sole basis for trading decisions
Has not been tested across all market conditions
May produce false signals, late signals, or no signals in certain conditions
Past performance of any indicator does not predict future results. Users must conduct their own analysis and risk assessment before making trading decisions. Always use proper risk management, including stop losses and position sizing appropriate to your account and risk tolerance.
MIT LICENSE
This code is open source and provided as-is without warranties of any kind. You may use, modify, and distribute it freely under the MIT License.
Ajay R5.41🔻 Ajay Gold 3H Power Indicator 🔻
Precision-Based Smart Sell System for Gold (XAU/USD)
💡 Overview
This indicator is specifically designed for Gold (XAU/USD) and delivers best results on the 3-Hour Timeframe (3H TF).
It is a Smart Money Logic-based Sell Confirmation System, combining institutional structure and candle behavior to generate highly accurate bearish signals.
⚙️ Technical Foundation
The indicator uses multiple advanced confirmations:
📉 EMA Trend Filter → Confirms downtrend
💪 RSI Overbought Rejection → Momentum reversal signal
📊 MACD Bearish Cross → Confirms trend strength
🕯️ Bearish Candle Structure → Price action validation
When all conditions align, a clear 🔻 Sell Signal is plotted on the chart.
💎 Hidden Feature
This indicator includes a hidden feature that activates only when the correct market structure forms.
It helps reduce false signals and increases accuracy without being visible on the chart — fully automated internal logic.
📆 Recommended Settings
Symbol: XAU/USD (Gold)
Timeframe: 3-Hour (3H)
Market: Forex / Commodity
Mode: Sell-Only Confirmation Indicator
Performance: Best precision and consistency on 3H TF
📈 How to Use
Select XAU/USD on chart and set 3H timeframe.
Add the indicator to the chart.
Wait for the 🔻 Sell Signal and confirm the market structure after candle close.
Take entry according to your risk management.
⚠️ Disclaimer
This indicator is for educational and analytical purposes only.
No system is 100% accurate — always backtest and demo trade before using in real trading.
💬 Credits
Developed by Ajay Sahu (India)
Based on Institutional & Smart Money Logic
Best results on 3H TF
Hidden Algorithm for XAU/USD traders
PnL PortfolioThis indicator provides a comprehensive, real-time overview of your open trading portfolio directly on the chart. It allows you to track up to 20 different trading pairs simultaneously.
For each asset, simply input the Pair Symbol, Average Entry Price, and Position Quantity. The script securely fetches the current market price and dynamically calculates and displays a customizable table showing:
Real-Time Profit/Loss ($)
Percentage PnL (%)
Entry Price and Position Quantity
The table uses color coding to clearly highlight profitable (green) or losing (red) positions, and its location on the chart (top/bottom, left/right) is fully adjustable.
PnL TrackerThis script allows you to manually input the details for up to 64 unique positions in the settings, each requiring a Symbol, Average Cost, and Quantity (Qty).
Key Features:
Average Cost Line: Plots a horizontal line on the chart corresponding to your recorded Average Cost for the security currently being viewed.
Real-Time PnL Label: A dynamic label attached to the Average Cost line provides an instant summary of your PnL in both percentage and currency for the last visible bar.
Detailed PnL Box: Displays a consolidated, easy-to-read table in the bottom-right corner of the chart, clearly showing:
The Symbol and Quantity of your position.
Your Average Cost.
The current PnL in percentage (%) and base currency (e.g., USD, EUR).
Visibility Controls: Toggles in the settings allow you to show or hide the Average Cost line and the PnL summary box independently.
This tool is perfect for actively managing and visualizing your multi-asset portfolio positions without leaving your main trading chart. Simply enter your positions in the indicator's settings, and the script will automatically track the PnL for the symbol matching the current chart.
Stop Loss and TargetsEnter your purchase price, SL% and up to 3x TP%s. Automatically plots them on your chart to enable quicker set up of alerts.
PnL PortfolioThis script allows you to input the details for up to 20 active positions across various trading pairs or markets. Stop manually calculating your trades—get instant, real-time feedback on your performance.
Key Features:
Multi-Pair Tracking: Monitor up to 20 unique symbols simultaneously.
Required Inputs: Easily define the Symbol, Entry Price, and Position Quantity (size) for each trade in the indicator settings.
Real-Time PnL: Instantly calculates and displays two critical metrics based on the current market price:
% PnL (Percentage Profit/Loss)
Absolute Profit/Loss (in currency)
Color-Coded Feedback: The PnL columns are color-coded (green/teal for profit, red/maroon for loss) for immediate visual confirmation of your trade health.
Customizable Layout: Choose where the dashboard table appears on your chart (top-left, top-right, bottom-left, or bottom-right) to keep your trading view clean.
This is an essential overlay for any trader managing multiple active positions and needing a consolidated, easy-to-read overview.
Performance-based Asset Weighting(MTF)**Performance-Based Asset Weighting (MTF/Symbol Free Setting)**
#### Overview
This indicator is a tool that visualizes the relative strength of performance (price change rate) as “weight (allocation ratio)” for **four user-defined stocks**.
By setting any specified past point in time as the baseline (where all symbols are equally weighted at 25%), it aims to provide an intuitive understanding of which symbols outperformed others and attracted capital, or underperformed and saw capital outflows.
**【Default Settings and Application Scenario: Pension Fund Rebalancing Analysis】**
The default settings reference the basic portfolio of Japan's Government Pension Investment Fund (GPIF), configuring four major asset classes: domestic equities, foreign equities, domestic bonds, and foreign bonds. It is known that when market fluctuations cause deviations from this equal-weighted ratio, rebalancing occurs to restore the original ratio (selling assets whose weight has increased and buying assets whose weight has decreased).
Analyzing using this default setting can serve as a reference point for considering **“whether rebalancing sales (or purchases) by pension funds and similar entities are likely to occur in the future.”**
**【Important: Usage Notes】**
The weights shown by this indicator are **theoretical reference values** calculated solely based on performance from the specified start date. Even if large investors conduct significant rebalancing (asset buying/selling) during the period, those transactions themselves are not reflected in this chart's calculations.
Therefore, please understand that the actual portfolio ratios may differ. **Use this solely as a rough guideline. **
#### Key Features
* **Freely configure the 4 assets for analysis:** You can freely set any 4 assets (stocks, indices, currencies, cryptocurrencies, etc.) you wish to compare via the settings screen.
* **Performance-based weight calculation:** Rather than simple price composition ratios, it calculates each asset's price change since the specified start date as a “performance index” and displays each asset's proportion of the total sum.
* **Freely set analysis start date:** You can set any desired starting point for analysis, such as “after the XX shock” or “after earnings announcements,” using the calendar.
* **Multi-Timeframe (MTF) Support:** Independently of the timeframe displayed on the chart, you can freely select the timeframe (e.g., 1-hour, 4-hour, daily) used by the indicator for calculations.
#### Calculation Principle
This indicator calculates weights in the following three steps:
1. **Obtaining the Base Price**
Obtain the closing price for each of the four stocks on the user-set “Start Date for Weight Calculation.” This becomes the **base price** for analysis.
2. **Calculating the Performance Index**
Divide the current price of each stock by the **base price** obtained in Step 1 to calculate the “Performance Index”.
`Performance Index = Current Price ÷ Base Date Price`
This quantifies how many times the current performance has increased compared to the base date performance, which is set to “1”.
3. **Calculating Weights**
Sum the “Performance Indexes” of the four stocks. Then, calculate the percentage contribution of each stock's Performance Index to this total sum and plot it on the chart.
`Weight (%) = (Individual Performance Index ÷ Total Performance Index of 4 Stocks) × 100`
Using this logic, on the analysis start date, all stocks' performance indices are set to “1”, so the weights start equally at 25%.
#### Usage
* **Application Example 1: Market Sentiment Analysis (Using Default Settings)**
Analyze using the default asset classes. By observing the relative strength between “Equities” and “Bonds”, you can assess whether the market is risk-on or risk-off.
* **Application Example 2: Sector/Theme Strength Analysis**
Configure settings for groups like “Top 4 semiconductor stocks” or “4 GAFAM stocks.” Setting the start date to the beginning of the year or earnings season allows you to instantly compare which stocks within the same sector are performing best.
* **Application Example 3: Cryptocurrency Power Map Analysis**
By setting major cryptocurrencies like “BTC, ETH, SOL, ADA,” you can analyze which currencies are attracting market capital.
**【About Legend Display】**
Due to Pine Script specification constraints, the legend on the chart will display fixed names: **“Stock 1” to “Stock 4”. **
Please note that the symbol you entered for “Symbol 1” in the settings corresponds to the “Symbol 1” line on the chart.
#### Settings
* **Symbol 1 to Symbol 4:** Set the four symbols you wish to analyze.
* **Timeframe for Calculation:** Select the timeframe the indicator references when calculating weights.
* **Start Date for Weight Calculation:** This serves as the base date for comparing performance.
#### Disclaimer
This script is solely a tool to assist with market analysis and does not recommend buying or selling any specific financial instruments. Please make all final investment decisions at your own discretion.
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**Performance-based Asset Weighting（MTF・シンボル自由設定）**
#### 概要
このインジケーターは、**ユーザーが自由に設定した4つの銘柄**について、パフォーマンス（騰落率）の相対的な強さを「ウェイト（構成比率）」として可視化するツールです。
指定した過去の任意の時点を基準（全銘柄が均等な25%）として、そこからどの銘柄のパフォーマンスが他の銘柄を上回り、資金が向かっているのか、あるいは下回っているのかを直感的に把握することを目的としています。
**【デフォルト設定と活用シナリオ：年金基金のリバランス考察】**
デフォルト設定では、日本の年金積立金管理運用独立行政法人（GPIF）の基本ポートフォリオを参考に、主要4資産クラス（国内株式, 外国株式, 国内債券, 外国債券）が設定されています。市場の変動によってこの均等な比率に乖離が生じると、元の比率に戻すためのリバランス（比率が増えた資産を売り、減った資産を買う）が行われることが知られています。
このデフォルト設定で分析することで、**「今後、年金基金などによるリバランスの売り（買い）が発生する可能性があるか」を考察するための、一つの目安として利用できます。**
**【重要：利用上の注意点】**
このインジケーターが示すウェイトは、あくまで指定した開始日からのパフォーマンスのみを基に算出した**理論上の参考値**です。実際に大口投資家などが途中で大規模なリバランス（資産の売買）を行ったとしても、その取引自体はこのチャートの計算には反映されません。
そのため、実際のポートフォリオ比率とは異なる可能性があることをご理解の上、**あくまで大まかな目安としてご活用ください。**
#### 主な特徴
* **分析対象の4銘柄を自由に設定可能:** 設定画面から、比較したい4つの銘柄（株式、指数、為替、仮想通貨など）を自由に設定できます。
* **パフォーマンス基準のウェイト計算:** 単純な価格の構成比ではなく、指定した開始日からの各銘柄の騰落を「パフォーマンス指数」として算出し、その合計に占める各銘柄の割合を表示します。
* **分析開始日の自由な設定:** 「〇〇ショック後」「決算発表後」など、分析したい任意の時点をカレンダーから設定できます。
* **マルチタイムフレーム（MTF）対応:** チャートに表示している時間足とは別に、インジケーターが計算に使う時間足（1時間足、4時間足、日足など）を自由に選択できます。
#### 計算の原理
このインジケーターは、以下の3ステップでウェイトを算出しています。
1. **基準価格の取得**
ユーザーが設定した「ウェイト計算の開始日」における、4つの各銘柄の終値を取得し、これを分析の**基準価格**とします。
2. **パフォーマンス指数の算出**
現在の各銘柄の価格を、ステップ1で取得した**基準価格**で割ることで、「パフォーマンス指数」を算出します。
`パフォーマンス指数 = 現在の価格 ÷ 基準日の価格`
これにより、基準日のパフォーマンスを「1」とした場合、現在のパフォーマンスが何倍になっているかが数値化されます。
3. **ウェイトの算出**
4つの銘柄の「パフォーマンス指数」の合計値を算出します。そして、合計値に占める各銘柄のパフォーマンス指数の割合（%）を計算し、チャートに描画します。
`ウェイト (%) = (個別のパフォーマンス指数 ÷ 4銘柄のパフォーマンス指数の合計) × 100`
このロジックにより、分析開始日には全銘柄のパフォーマンス指数が「1」となるため、ウェイトは均等に25%からスタートします。
#### 使用方法
* **応用例1：市場のセンチメント分析（デフォルト設定利用）**
デフォルト設定の資産クラスで分析し、「株式」と「債券」の力関係を見ることで、市場がリスクオンなのかリスクオフなのかを判断する材料になります。
* **応用例2：セクター・テーマ別の強弱分析**
設定画面で、例えば「半導体関連の主要4銘柄」や「GAFAMの4銘柄」などを設定します。開始日を年初や決算時期に設定することで、同セクター内でどの銘柄が最もパフォーマンスが良いかを一目で比較できます。
* **応用例3：仮想通貨の勢力図分析**
「BTC, ETH, SOL, ADA」など、主要な仮想通貨を設定することで、市場の資金がどの通貨に向かっているのかを分析できます。
**【凡例の表示について】**
Pine Scriptの仕様上の制約により、チャート上の凡例は**「銘柄1」〜「銘柄4」という固定名で表示されます。**
お手数ですが、設定画面でご自身が「銘柄1」に入力したシンボルが、チャート上の「銘柄1」のラインに対応する、という形でご覧ください。
#### 設定項目
* **銘柄1〜銘柄4:** 分析したい4つのシンボルをそれぞれ設定します。
* **計算に使う時間足:** インジケーターがウェイトを計算する際に参照する時間足を選択します。
* **ウェイト計算の開始日:** パフォーマンスを比較する上での基準日となります。
#### 免責事項
このスクリプトはあくまで市場分析を補助するためのツールであり、特定の金融商品の売買を推奨するものではありません。投資の最終的な判断は、ご自身の責任において行ってください。
BFM Yen Carry to Risk Ratio (Dynamic Rates)Shows risk of yen carry trade unwinding. Based on cost to borrow from Japan to buy us stocks compared to interest rate in USA.
ATR Horizontal Lines from EMA and SMA with TableHow it works:
The script calculates ATR levels (of your choosing)
Instead of plotting curves, it creates horizontal lines
The lines are deleted and recreated on each bar to show current levels
Lines extend to the right or can be limited to a certain width
Customization options:
Line width (1-10 pixels)
Individual colors for each of the 4 lines
All the original parameters (EMA/SMA lengths, ATR length, multipliers)
The horizontal lines will now show the current ATR-based support/resistance levels and move dynamically as the EMAs, SMA, and ATR values change with new price data.
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
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Portfolio Simulator & BacktesterMulti-asset portfolio simulator with different metrics and ratios, DCA modeling, and rebalancing strategies.
Core Features
Portfolio Construction
Up to 5 assets with customizable weights (must total 100%)
Support for any tradable symbol: stocks, ETFs, crypto, indices, commodities
Real-time validation of allocations
Dollar Cost Averaging
Monthly or Quarterly contributions
Applies to both portfolio and benchmark for fair comparison
Model real-world investing behavior
Rebalancing
Four strategies: None, Monthly, Quarterly, Yearly
Automatic rebalancing to target weights
Transaction cost modeling (customizable fee %)
Key Metrics Table
CAGR: Annualized compound return (S&P 500 avg: ~10%)
Alpha: Excess return vs. benchmark (positive = outperformance)
Sharpe Ratio: Return per unit of risk (>1.0 is good, >2.0 excellent)
Sortino Ratio: Like Sharpe but only penalizes downside (better metric)
Calmar Ratio: CAGR / Max Drawdown (>1.0 good, >2.0 excellent)
Max Drawdown: Largest peak-to-trough decline
Win Rate: % of positive days (doesn't indicate profitability)
Visualization
Dual-chart comparison - Portfolio vs. Benchmark
Dollar or percentage view toggle
Customizable colors and line width
Two tables: Statistics + Asset Allocation
Adjustable table position and text size
🚀 Quick Start Guide
Enter 1-5 ticker symbols (e.g., SPY, QQQ, TLT, GLD, BTCUSD)
Make sure percentage weights total 100%
Choose date range (ensure chart shows full period - zoom out!)
Configure DCA and rebalancing (optional)
Select benchmark (default: SPX)
Analyze results in statistics table
💡 Pro Tips
Chart data matters: Load SPY or your longest-history asset as main chart
If you select an asset that was not available for the selected period, the chart will not show up! E.g. BTCUSD data: Only available from ~2017 onwards.
Transaction fees: 0.1% default (adjust to match your broker)
⚠️ Important Notes
Requires visible chart data (zoom out to show full date range)
Limited by each asset's historical data availability
Transaction fees and costs are modeled, but taxes/slippage are not
Past performance ≠ future results
Use for research and education only, not financial advice
Let me know if you have any suggestions to improve this simulator.
Macro Momentum – 4-Theme, Vol Target, RebalanceMacro Momentum — 4-Theme, Vol Target, Rebalance
Purpose. A macro-aware strategy that blends four economic “themes”—Business Cycle, Trade/USD, Monetary Policy, and Risk Sentiment—into a single, smoothed Composite signal. It then:
gates entries/exits with hysteresis bands,
enforces optional regime filters (200-day bias), and
sizes the position via volatility targeting with caps for long/short exposure.
It’s designed to run on any chart (index, ETF, futures, single stocks) while reading external macro proxies on a chosen Signal Timeframe.
How it works (high level)
Build four theme signals from robust macro proxies:
Business Cycle: XLI/XLU and Copper/Gold momentum, confirmed by the chart’s price vs a long SMA (default 200D).
Trade / USD: DXY momentum (sign-flipped so a rising USD is bearish for risk assets).
Monetary Policy: 10Y–2Y curve slope momentum and 10Y yield trend (steepening & falling 10Y = risk-on; rising 10Y = risk-off).
Risk Sentiment: VIX momentum (bearish if higher) and HYG/IEF momentum (bullish if credit outperforms duration).
Normalize & de-noise.
Optional Winsorization (MAD or stdev) clamps outliers over a lookback window.
Optional Z-score → tanh mapping compresses to ~ for stable weighting.
Theme lines are SMA-smoothed; the final Composite is LSMA-smoothed (linreg).
Decide direction with hysteresis.
Enter/hold long when Composite ≥ Entry Band; enter/hold short when Composite ≤ −Entry Band.
Exit bands are tighter than entry bands to avoid whipsaws.
Apply regime & direction constraints.
Optional Long-only above 200MA (chart symbol) and/or Short-only below 200MA.
Global Direction control (Long / Short / Both) and Invert switch.
Size via volatility targeting.
Realized close-to-close vol is annualized (choose 9-5 or 24/7 market profile).
Target exposure = TargetVol / RealizedVol, capped by Max Long/Max Short multipliers.
Quantity is computed from equity; futures are rounded to whole contracts.
Rebalance cadence & execution.
Trades are placed on Weekly / Monthly / Quarterly rebalance bars or when the sign of exposure flips.
Optional ATR stop/TP for single-stock style risk management.
Inputs you’ll actually tweak
General
Signal Timeframe: Where macro is sampled (e.g., D/W).
Rebalance Frequency: Weekly / Monthly / Quarterly.
ROC & SMA lengths: Defaults for theme momentum and the 200D regime filter.
Normalization: Z-score (tanh) on/off.
Winsorization
Toggle, lookback, multiplier, MAD vs Stdev.
Risk / Sizing
Target Annualized Vol & Realized Vol Lookback.
Direction (Long/Short/Both) and Invert.
Max long/short exposure caps.
Advanced Thresholds
Theme/Composite smoothing lengths.
Entry/Exit bands (hysteresis).
Regime / Execution
Long-only above 200MA, Short-only below 200MA.
Stops/TP (optional)
ATR length and SL/TP multiples.
Theme Weights
Per-theme scalars so you can push/pull emphasis (e.g., overweight Policy during rate cycles).
Macro Proxies
Symbols for each theme (XLI, XLU, HG1!, GC1!, DXY, US10Y, US02Y, VIX, HYG, IEF). Swap to alternatives as needed (e.g., UUP for DXY).
Signals & logic (under the hood)
Business Cycle = ½ ROC(XLI/XLU) + ½ ROC(Copper/Gold), then confirmed by (price > 200SMA ? +1 : −1).
Trade / USD = −ROC(DXY).
Monetary Policy = 0.6·ROC(10Y–2Y) − 0.4·ROC(10Y).
Risk Sentiment = −0.6·ROC(VIX) + 0.4·ROC(HYG/IEF).
Each theme → (optional Winsor) → (robust z or scaled ROC) → tanh → SMA smoothing.
Composite = weighted average → LSMA smoothing → compare to bands → dir ∈ {−1,0,+1}.
Rebalance & flips. Orders fire on your chosen cadence or when the sign of exposure changes.
Position size. exposure = clamp(TargetVol / realizedVol, maxLong/Short) × dir.
Note: The script also exposes Gross Exposure (% equity) and Signed Exposure (× equity) as diagnostics. These can help you audit how vol-targeting and caps translate into sizing over time.
Visuals & alerts
Composite line + columns (color/intensity reflect direction & strength).
Entry/Exit bands with green/red fills for quick polarity reads.
Hidden plots for each Theme if you want to show them.
Optional rebalance labels (direction, gross & signed exposure, σ).
Background heatmap keyed to Composite.
Alerts
Enter/Inc LONG when Composite crosses up (and on rebalance bars).
Enter/Inc SHORT when Composite crosses down (and on rebalance bars).
Exit to FLAT when Composite returns toward neutral (and on rebalance bars).
Practical tips
Start higher timeframes. Daily signals with Monthly rebalance are a good baseline; weekly signals with quarterly rebalances are even cleaner.
Tune Entry/Exit bands before anything else. Wider bands = fewer trades and less noise.
Weights reflect regime. If policy dominates markets, raise Monetary Policy weight; if credit stress drives moves, raise Risk Sentiment.
Proxies are swappable. Use UUP for USD, or futures-continuous symbols that match your data plan.
Futures vs ETFs. Quantity auto-rounds for futures; ETFs accept fractional shares. Check contract multipliers when interpreting exposure.
Caveats
Macro proxies can repaint at the selected signal timeframe as higher-TF bars form; that’s intentional for macro sampling, but test live.
Vol targeting assumes reasonably stationary realized vol over the lookback; if markets regime-shift, revisit volLook and targetVol.
If you disable normalization/winsorization, themes can become spikier; expect more hysteresis band crossings.
What to change first (quick start)
Set Signal Timeframe = D, Rebalance = Monthly, Z-score on, Winsor on (MAD).
Entry/Exit bands: 0.25 / 0.12 (defaults), then nudge until trade count and turnover feel right.
TargetVol: try 10% for diversified indices; lower for single stocks, higher for vol-sell strategies.
Leave weights = 1.0 until you’ve inspected the four theme lines; then tilt deliberately.
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView.
It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%.
All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC.
🚀 Goal
This script helps traders and investors:
• Understand the combined performance of their portfolio.
• Normalize diverse assets into a single synthetic chart .
• Make portfolio-level insights without relying on external spreadsheets.
🎯 Use Cases
• Backtest your portfolio allocations directly on the chart.
• Compare your portfolio vs. benchmarks like SPY, QQQ, BTC.
• Track thematic baskets (commodities, EV supply chain, regional ETFs).
• Visualize how each component contributes to overall performance.
📊 Features
• Weighted Portfolio Performance : Combines selected assets into a synthetic value series.
• Base Price Alignment : Each asset is normalized to its starting price at the chosen date.
• Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%.
• Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol.
⚙️ Configuration
Flexible Portfolio Setup
• Add up to 15 assets with custom weight inputs.
• You can enter any arbitrary numbers (e.g. 30, 15, 55).
• The script automatically normalizes all weights so the total allocation always equals 100%.
Start Date Selection
• Choose any custom start date to normalize all assets.
• The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ.
Chart Styles
• Candlestick chart
• Heikin Ashi chart
• Line chart
Custom Display
• Adjustable colors and line widths
• Optionally display asset list, normalized weights, and equivalent shares
⚙️ How It Works
• Fetch OHLC data for each asset.
• Normalizes weights internally so totals = 100%.
• Stores each asset’s base price at the selected start date.
• Calculates equivalent “shares” for each allocation.
• Builds a synthetic portfolio value series by summing weighted contributions.
• Renders as Candlestick, Heikin Ashi, or Line chart.
• Adds a portfolio info table for clarity.
⚠️ Notes
• This script is for visualization only . It does not place trades or auto-rebalance.
• Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
DCA vs One-ShotCompare a DCA strategy by choosing the payment frequency (daily, weekly, or monthly), and by choosing whether or not to pay on weekends for cryptocurrency. You can add fees and the reference price (opening, closing, etc.).
RSI Momentum ScalperOverview
The "RSI Momentum Scalper" is a Pine Script v5 strategy crafted for trading highly volatile markets, with a special focus on newly listed cryptocurrencies. This strategy harnesses the Relative Strength Index (RSI) alongside volume analysis and momentum thresholds to pinpoint short-term trading opportunities. It supports both long and short trades, managed with customizable take profit, stop loss, and trailing stop levels, which are visually plotted on the chart for easy tracking.
Why I Created This Strategy
I developed the "RSI Momentum Scalper" because I was seeking a reliable trading strategy tailored to newly listed, highly volatile cryptocurrencies. These assets often experience rapid price fluctuations, rendering traditional strategies less effective. I aimed to create a tool that could exploit momentum and volume spikes while managing risk through adaptable exit parameters. This strategy is designed to address that need, offering a flexible approach for traders in dynamic crypto markets.
How It Works
The strategy utilizes RSI to identify momentum shifts, combined with volume confirmation, to trigger long or short entries. Trades are controlled with take profit, stop loss, and trailing stop levels, which adjust dynamically as the price moves in your favor. The trailing stop helps lock in profits, while the plotted exit levels provide clear visual cues for trade management.
Customizable Settings
The script is highly customizable, allowing you to adjust it to various market conditions and trading styles. Here’s a brief overview of the key settings:
Trade Mode: Select "Both," "Long Only," or "Short Only" to determine the trade direction.
(Default: Both)
RSI Length: Sets the lookback period for the RSI calculation (2 to 30).
(Default: 8)
A shorter length increases RSI sensitivity, suitable for volatile assets.
RSI Overbought: Defines the upper RSI threshold (60 to 99) for short entries.
(Default: 90)
Higher values signal stronger overbought conditions.
RSI Oversold: Defines the lower RSI threshold (1 to 40) for long entries.
(Default: 10)
Lower values indicate stronger oversold conditions.
RSI Momentum Threshold: Sets the minimum RSI momentum change (1 to 15) to trigger entries.
(Default: 14)
Adjusts the sensitivity to price momentum.
Volume Multiplier: Multiplies the volume moving average to filter high-volume bars (1.0 to 3.0).
(Default: 1)
Higher values require stronger volume confirmation.
Volume MA Length: Sets the lookback period for the volume moving average (5 to 50).
(Default: 13)
Influences the volume trend sensitivity.
Take Profit %: Sets the profit target as a percentage of the entry price (0.1 to 10.0).
(Default: 4.15)
Determines when to close a winning trade.
Stop Loss %: Sets the loss limit as a percentage of the entry price (0.1 to 6.0).
(Default: 1.85)
Protects against significant losses.
Trailing Stop %: Sets the trailing stop distance as a percentage (0.1 to 4.0).
(Default: 2.55)
Locks in profits as the price moves favorably.
Visual Features
Exit Levels: Take profit (green), fixed stop loss (red), and trailing stop (orange) levels are plotted when in a position.
Performance Table: Displays win rate, total trades, and net profit in the top-right corner.
How to Use
Add the strategy to your chart in TradingView.
Adjust the input settings based on the cryptocurrency and timeframe you’re trading.
Monitor the plotted exit levels for trade management.
Use the performance table to assess the strategy’s performance over time.
Notes
Test the strategy on a demo account or with historical data before live trading.
The strategy is optimized for short-term scalping; adjust settings for longer timeframes if needed.
