SuperTrended Moving Averages Strategyself use
used in 1 second timeframe
please let me publish it aaa
Penunjuk dan strategi
Tunç ŞatıroğluTunç Şatıroğlu's Technical Analysis Suite
Description:
This comprehensive Pine Script indicator, inspired by the technical analysis teachings of Tunç Şatıroğlu, integrates six powerful TradingView indicators into a single, user-friendly suite for robust trend, momentum, and divergence analysis. Each component has been carefully selected and enhanced by beytun to improve functionality, performance, and visual clarity, aligning with Şatıroğlu's approach to technical analysis. The default configuration is meticulously set to match the exact settings of the individual indicators as used by Tunç Şatıroğlu in his training, ensuring authenticity and ease of use for followers of his methodology. Whether you're a beginner or an experienced trader, this suite provides a versatile toolkit for analyzing markets across multiple timeframes.
Included Indicators:
1. WaveTrend with Crosses (by LazyBear, modified): A momentum oscillator that identifies overbought/oversold conditions and trend reversals with clear buy/sell signals via crosses and bar color highlights.
2. Kaufman Adaptive Moving Average (KAMA) (by HPotter, modified): A dynamic moving average that adapts to market volatility, offering a smoother trend-following signal.
3. SuperTrend (by Alex Orekhov, modified): A trend-following indicator that plots dynamic support/resistance levels with buy/sell signals and optional wicks for enhanced accuracy.
4. Nadaraya-Watson Envelope (by LuxAlgo, modified): A non-linear envelope that highlights potential reversals with customizable repainting options for smoother outputs.
5. Divergence for Many Indicators v4 (by LonesomeTheBlue, modified): Detects regular and hidden divergences across multiple indicators (MACD, RSI, Stochastic, CCI, Momentum, OBV, VWMA, CMF, MFI, and more) for early reversal signals.
6. Ichimoku Cloud (TradingView built-in, modified): A multi-faceted indicator for trend direction, support/resistance, and momentum, with enhanced visuals for the Kumo Cloud.
Key Features:
- Authentic Default Settings : Pre-configured to mirror the exact parameters used by Tunç Şatıroğlu for each indicator, ensuring alignment with his proven technical analysis approach.
- Customizable Settings : Enable/disable individual indicators and fine-tune parameters to suit your trading style while retaining the option to revert to Şatıroğlu’s defaults.
- Enhanced User Experience : Modifications improve visual clarity, performance, and usability, with options like repainting smoothing for Nadaraya-Watson and adjustable Ichimoku projection periods.
- Multi-Timeframe Analysis : Combines trend-following, momentum, and divergence tools for a holistic view of market dynamics.
- Alert Conditions : Built-in alerts for SuperTrend direction changes, buy/sell signals, and divergence detections to keep you informed.
- Visual Clarity : Overlays (KAMA, SuperTrend, Nadaraya-Watson, Ichimoku) and pane-based indicators (WaveTrend, Divergences) are clearly distinguished, with customizable colors and styles.
Notes:
- The Nadaraya-Watson Envelope and Ichimoku Cloud may repaint in their default modes. Use the "Repainting Smoothing" option for Nadaraya-Watson or adjust Ichimoku settings to mitigate repainting if preferred.
- Published under the MIT License, with components licensed under GPL-3.0 (SuperTrend), CC BY-NC-SA 4.0 (Nadaraya-Watson), MPL 2.0 (Divergence), and TradingView's terms (Ichimoku Cloud).
Usage:
Add this indicator to your TradingView chart to leverage Tunç Şatıroğlu’s exact indicator configurations out of the box. Customize settings as needed to align with your strategy, and use the combined signals to identify trends, reversals, and divergences. Ideal for traders following Şatıroğlu’s methodologies or anyone seeking a powerful, all-in-one technical analysis tool.
Credits:
Original authors: LazyBear, HPotter, Alex Orekhov, LuxAlgo, LonesomeTheBlue, and TradingView.
Modifications and integration by beytun .
License:
Published under the MIT License, incorporating code under GPL-3.0, CC BY-NC-SA 4.0, MPL 2.0, and TradingView’s terms where applicable.
Aggregated Scores Oscillator [Alpha Extract]A sophisticated risk-adjusted performance measurement system that combines Omega Ratio and Sortino Ratio methodologies to create a comprehensive market assessment oscillator. Utilizing advanced statistical band calculations with expanding and rolling window analysis, this indicator delivers institutional-grade overbought/oversold detection based on risk-adjusted returns rather than traditional price movements. The system's dual-ratio aggregation approach provides superior signal accuracy by incorporating both upside potential and downside risk metrics with dynamic threshold adaptation for varying market conditions.
🔶 Advanced Statistical Framework
Implements dual statistical methodologies using expanding and rolling window calculations to create adaptive threshold bands that evolve with market conditions. The system calculates cumulative statistics alongside rolling averages to provide both historical context and current market regime sensitivity with configurable window parameters for optimal performance across timeframes.
🔶 Dual Ratio Integration System
Combines Omega Ratio analysis measuring excess returns versus deficit returns with Sortino Ratio calculations focusing on downside deviation for comprehensive risk-adjusted performance assessment. The system applies configurable smoothing to both ratios before aggregation, ensuring stable signal generation while maintaining sensitivity to regime changes.
// Omega Ratio Calculation
Excess_Return = sum((Daily_Return > Target_Return ? Daily_Return - Target_Return : 0), Period)
Deficit_Return = sum((Daily_Return < Target_Return ? Target_Return - Daily_Return : 0), Period)
Omega_Ratio = Deficit_Return ≠ 0 ? (Excess_Return / Deficit_Return) : na
// Sortino Ratio Framework
Downside_Deviation = sqrt(sum((Daily_Return < Target_Return ? (Daily_Return - Target_Return)² : 0), Period) / Period)
Sortino_Ratio = (Mean_Return / Downside_Deviation) * sqrt(Annualization_Factor)
// Aggregated Score
Aggregated_Score = SMA(Omega_Ratio, Omega_SMA) + SMA(Sortino_Ratio, Sortino_SMA)
🔶 Dynamic Band Calculation Engine
Features sophisticated threshold determination using both expanding historical statistics and rolling window analysis to create adaptive overbought/oversold levels. The system incorporates configurable multipliers and sensitivity adjustments to optimize signal timing across varying market volatility conditions with automatic band convergence logic.
🔶 Signal Generation Framework
Generates overbought conditions when aggregated score exceeds adjusted upper threshold and oversold conditions below lower threshold, with neutral zone identification for range-bound markets. The system provides clear binary signal states with background zone highlighting and dynamic oscillator coloring for intuitive market condition assessment.
🔶 Enhanced Visual Architecture
Provides modern dark theme visualization with neon color scheme, dynamic oscillator line coloring based on signal states, and gradient band fills for comprehensive market condition visualization. The system includes zero-line reference, statistical band plots, and background zone highlighting with configurable transparency levels.
snapshot
🔶 Risk-Adjusted Performance Analysis
Utilizes target return parameters for customizable risk assessment baselines, enabling traders to evaluate performance relative to specific return objectives. The system's focus on downside deviation through Sortino analysis provides superior risk-adjusted signals compared to traditional volatility-based oscillators that treat upside and downside movements equally.
🔶 Multi-Timeframe Adaptability
Features configurable calculation periods and rolling windows to optimize performance across various timeframes from intraday to long-term analysis. The system's statistical foundation ensures consistent signal quality regardless of timeframe selection while maintaining sensitivity to market regime changes through adaptive band calculations.
🔶 Performance Optimization Framework
Implements efficient statistical calculations with optimized variable management and configurable smoothing parameters to balance responsiveness with signal stability. The system includes automatic band adjustment mechanisms and rolling window management for consistent performance across extended analysis periods.
This indicator delivers sophisticated risk-adjusted market analysis by combining proven statistical ratios in a unified oscillator framework. Unlike traditional overbought/oversold indicators that rely solely on price movements, the ASO incorporates risk-adjusted performance metrics to identify genuine market extremes based on return quality rather than price volatility alone. The system's adaptive statistical bands and dual-ratio methodology provide institutional-grade signal accuracy suitable for systematic trading approaches across cryptocurrency, forex, and equity markets with comprehensive visual feedback and configurable risk parameters for optimal strategy integration.
MTF State of Delivery by @traderprimezOverview
This indicator provides a comprehensive, multi-timeframe view of institutional orderflow, a core concept from Inner Circle Trader (ICT) methodologies.
It is designed to objectively identify the market's "State of Delivery"—whether price is currently in a bullish or bearish orderflow—on both your current chart (Lower Timeframe) and a relevant Higher Timeframe.
By visualizing these key directional shifts, the indicator helps traders align with the dominant market bias, identify high-probability setups, and avoid trading against the underlying institutional intent.
Core Concept: The Orderflow Switch
The entire logic is built upon a specific two-candle price action pattern called a "Switch," which signals a potential turning point in the market.
Bullish Switch: A bullish candle followed immediately by a bearish candle. This duo creates a short-term resistance level. Orderflow is confirmed Bullish when a later bullish candle closes above this level.
Bearish Switch: A bearish candle followed immediately by a bullish candle. This duo creates a short-term support level. Orderflow is confirmed Bearish when a later bearish candle closes below this level.
Features & How to Read the Chart
This indicator plots several visual elements to provide a complete picture of the market's state:
Status Table: Located at the top of the chart, this table provides an at-a-glance summary of the current State of Delivery for both the Higher Timeframe (HTF) and Lower Timeframe (LTF). The status cells dynamically change color to reflect the current bias (Blue for Bullish, Red for Bearish).
Confirmed Orderflow Lines:
Thick Solid Lines: These represent the confirmed orderflow on the Higher Timeframe. A thick blue line indicates the HTF is in a bullish state, while a thick red line indicates a bearish state.
Thin Solid Lines: These represent the confirmed orderflow on your current chart (LTF). A thin blue line confirms a local bullish shift, and a thin red line confirms a local bearish shift.
Pending Switch Levels (Dotted Lines):
These forward-extending dotted lines mark the most recent switch levels that have not yet been broken. They represent the "lines in the sand"—the exact price levels that need to be breached to confirm the next shift in orderflow on both the LTF and HTF.
Multi-Timeframe Analysis
The indicator's power comes from its ability to sync LTF price action with the HTF narrative. It automatically determines the relevant HTF based on your current chart, using the following logical pairings:
1m or 3m chart 15 Minute
5m chart 1 Hour
15m chart 4 Hour
1h chart 1 Day
4h chart 1 Week
1d chart 1 Month
Note: The HTF feature will be inactive on unmapped timeframes.
How to Use in Your Trading
This tool is designed to be a confluence factor in your trading system, not a standalone signal generator.
High-Probability Setups: The strongest signals occur when the LTF confirms an orderflow shift that is in the same direction as the established HTF bias. For example, look for long entries after a thin blue LTF line appears while the dominant HTF line is also blue.
Confirmation: Use the break of a pending (dotted) line as a final confirmation for an entry you have already identified through your own analysis (e.g., at a Fair Value Gap or Order Block).
Risk Management: An opposing orderflow shift can serve as an early warning to manage a trade or take profits. For instance, if you are long and a bearish (red) LTF orderflow is confirmed, it may signal that the short-term momentum is shifting against you.
Settings
The indicator is fully customizable, allowing you to:
Toggle the visibility of the Status Table, HTF/LTF confirmed lines, and HTF/LTF pending lines.
Customize the colors and line widths for all elements to match your chart theme.
Disclaimer: This tool is for educational and analytical purposes only. It is not financial advice. All trading involves substantial risk, and past performance is not indicative of future results. Please perform your own due diligence and risk management.
Fisher Transform Trend Navigator [QuantAlgo]🟢 Overview
The Fisher Transform Trend Navigator applies a logarithmic transformation to normalize price data into a Gaussian distribution, then combines this with volatility-adaptive thresholds to create a trend detection system. This mathematical approach helps traders identify high-probability trend changes and reversal points while filtering market noise in the ever-changing volatility conditions.
🟢 How It Works
The indicator's foundation begins with price normalization, where recent price action is scaled to a bounded range between -1 and +1:
highestHigh = ta.highest(priceSource, fisherPeriod)
lowestLow = ta.lowest(priceSource, fisherPeriod)
value1 = highestHigh != lowestLow ? 2 * (priceSource - lowestLow) / (highestHigh - lowestLow) - 1 : 0
value1 := math.max(-0.999, math.min(0.999, value1))
This normalized value then passes through the Fisher Transform calculation, which applies a logarithmic function to convert the data into a Gaussian normal distribution that naturally amplifies price extremes and turning points:
fisherTransform = 0.5 * math.log((1 + value1) / (1 - value1))
smoothedFisher = ta.ema(fisherTransform, fisherSmoothing)
The smoothed Fisher signal is then integrated with an exponential moving average to create a hybrid trend line that balances statistical precision with price-following behavior:
baseTrend = ta.ema(close, basePeriod)
fisherAdjustment = smoothedFisher * fisherSensitivity * close
fisherTrend = baseTrend + fisherAdjustment
To filter out false signals and adapt to market conditions, the system calculates dynamic threshold bands using volatility measurements:
dynamicRange = ta.atr(volatilityPeriod)
threshold = dynamicRange * volatilityMultiplier
upperThreshold = fisherTrend + threshold
lowerThreshold = fisherTrend - threshold
When price momentum pushes through these thresholds, the trend line locks onto the new level and maintains direction until the opposite threshold is breached:
if upperThreshold < trendLine
trendLine := upperThreshold
if lowerThreshold > trendLine
trendLine := lowerThreshold
🟢 Signal Interpretation
Bullish Candles (Green): indicate normalized price distribution favoring bulls with sustained buying momentum = Long/Buy opportunities
Bearish Candles (Red): indicate normalized price distribution favoring bears with sustained selling pressure = Short/Sell opportunities
Upper Band Zone: Area above middle level indicating statistically elevated trend strength with potential overbought conditions approaching mean reversion zones
Lower Band Zone: Area below middle level indicating statistically depressed trend strength with potential oversold conditions approaching mean reversion zones
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant developments without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency and clarity
Configuration Presets: Three parameter sets available - Default (balanced settings), Scalping (faster response with higher sensitivity), and Swing Trading (slower response with enhanced smoothing)
Color Customization: Four color schemes including Classic, Aqua, Cosmic, and Custom options for personalized chart aesthetics
LW Outside Day Strategy[SpeculationLab]This strategy is based on the concept of the Outside Day Pattern described by Larry Williams in his book “Long-Term Secrets to Short-Term Trading”.
The Outside Day is a classic price action pattern often seen during market reversals or acceleration phases.
Strategy Logic
Outside Bar Detection
Current day’s high is higher than the previous high, and the low is lower than the previous low.
A body-size filter is applied: only bars with significantly larger bodies than the previous bar are considered valid.
Directional Confirmation
Close below the previous day’s low → Buy signal.
Close above the previous day’s high → Sell signal.
Stop Loss Options
Prev Low/High: Uses the previous swing low/high with buffer adjustment.
ATR: Stop loss based on volatility (ATR).
Fixed Pips: Uses a fixed pip distance defined by the user.
Take Profit Options
Prev High/Low (PHL): Targets the previous swing high/low.
Risk-Reward (RR): Targets based on user-defined risk-to-reward ratio.
Following Price Open (FPO): Exits at the next day’s open if price opens in profit.
Signal Markers
Buy/Sell signals are plotted on the chart (triangles).
Stop loss and target reference lines are drawn automatically.
Usage Notes
Timeframe: Best suited for Daily charts.
Markets: Works across stocks, forex, and crypto markets.
Disclaimer: This strategy is for educational and research purposes only. It does not guarantee profits and should not be considered financial advice. Please manage your own risk responsibly.
本策略基于美国著名交易大师 Larry Williams 在其著作《Long-Term Secrets to Short-Term Trading（短线交易的长线秘诀）》中提出的 Outside Day（外包线形态）。外包线是一种典型的价格行为形态，常出现在趋势反转或加速阶段。
策略逻辑
外包线识别
当日最高价高于前一日最高价，且当日最低价低于前一日最低价，即形成外包线。
同时过滤掉较小实体的 K 线，仅保留实体显著大于前一根的形态。
方向过滤
收盘价低于前一日最低价 → 视为买入信号。
收盘价高于前一日最高价 → 视为卖出信号。
止损设置（可选参数）
前低/高止损：以形态前低/前高为止损，带有缓冲倍数。
ATR 止损：根据平均波动率（ATR）动态调整。
固定点数止损：按照用户设定的点数作为止损范围。
止盈设置（可选参数）
前高/低止盈（PHL）：以前高/前低为目标。
固定盈亏比（RR）：根据用户设定的风险回报比自动计算。
隔夜开盘（FPO）：若次日开盘价高于进场价（多单）或低于进场价（空单），则平仓。
信号标记
在图表中标注买入/卖出信号（三角形标记）。
绘制止损与目标位参考线。
使用说明
适用周期：建议用于 日线图（Daily）。
适用市场：股票、外汇、加密货币等各类市场均可。
提示：此策略为历史研究与学习用途，不构成投资建议。实际交易请结合自身风险管理。
💎DrFX Diamond Algo 💎Diamond Algo - Multi-Feature Trading System
Advanced trading system combining Supertrend signals with multiple confirmation filters, risk management tools, and a comprehensive market analysis dashboard.
═══ CORE FEATURES ═══
• Smart Buy/Sell signals using modified Supertrend algorithm
• Multi-timeframe trend analysis (M1 to D1)
• Support & Resistance zone detection
• Risk management with automatic TP/SL levels (1:1, 2:1, 3:1)
• Real-time market dashboard with key metrics
• Multiple trend cloud overlays for visual confirmation
═══ SIGNAL GENERATION ═══
BUY Signal:
• Supertrend bullish crossover
• Price above SMA filter
• Optional smart signals (EMA 200 confirmation)
SELL Signal:
• Supertrend bearish crossunder
• Price below SMA filter
• Optional smart signals (EMA 200 confirmation)
═══ DASHBOARD COMPONENTS ═══
• Multi-timeframe trend status (8 timeframes)
• Current position indicator
• Market state analysis (Trending/Ranging/No trend)
• Volatility percentage
• Institutional activity monitor
• Trading session tracker (NY/London/Tokyo/Sydney)
• Trend pressure indicator
═══ VISUAL OVERLAYS ═══
• Trend Cloud: Long-term trend visualization
• Trend Follower: Adaptive trend line
• Comulus Cloud: Dual ALMA-based trend zones
• Cirrus Cloud: Short-term trend bands
• Smart Trail: Fibonacci-based trailing stop
• Dynamic trend lines with breakout alerts
═══ RISK MANAGEMENT ═══
• Automatic Stop-Loss placement (ATR-based)
• Three Take-Profit levels with Risk:Reward ratios
• Entry price labeling
• Optional distance and decimal customization
• Visual lines connecting entry to targets
═══ INPUT PARAMETERS ═══
Sensitivity (1-20): Controls signal frequency
Smart Signals Only: Filters for high-probability setups
Bar Coloring: Trend-based or gradient coloring
Dashboard Location/Size: Customizable placement
Multiple overlay toggles for clean charts
═══ BEST PRACTICES ═══
• Lower sensitivity (1-5) for swing trading
• Higher sensitivity (10-20) for scalping
• Enable Smart Signals for conservative approach
• Use dashboard to confirm multi-timeframe alignment
• Monitor volatility % before entering trades
═══ ALERT CONDITIONS ═══
• Buy Alert: Triggered on bullish signal
• Sell Alert: Triggered on bearish signal
• Trend line breakout alerts (automated)
═══ VERSION INFO ═══
Pine Script: v5
Max Labels: 500
Repainting: Minimal (uses confirmed bars for signals)
```
TTM Squeeze Screener [Pineify]TTM Squeeze Screener for Multiple Crypto Assets and Timeframes
This advanced TradingView Pine script, TTM Squeeze Screener, helps traders scan multiple crypto symbols and timeframes simultaneously, unlocking new dimensions in momentum and volatility analysis.
Key Features
Screen up to 8 crypto symbols across 4 different timeframes in one pane
TTM Squeeze indicator detects volatility contraction and expansion (“squeeze”) phases
Momentum filter reveals potential breakout direction and strength
Visual screener table for intuitive multi-asset monitoring
Fully customizable for symbols and timeframes
How It Works
The heart of this screener is the TTM Squeeze algorithm—a hybrid volatility and momentum indicator leveraging Bollinger Bands, Keltner Channels, and linear momentum analysis. The script checks whether Bollinger Bands are “squeezed” inside Keltner Channels, flagging periods of low volatility primed for expansion. Once a squeeze is released, the included momentum calculation suggests the likely breakout direction.
For each selected symbol and timeframe, the screener runs the TTM Squeeze logic, outputs “SQUEEZE” or “NO SQZ”, and tags momentum values. A table layout organizes the results, allowing rapid pattern recognition across symbols.
Trading Ideas and Insights
Spot multi-symbol volatility clusters—ideal for finding synchronized market moves
Assess breakout potential and direction before entering trades
Scalping and swing trading decisions are enhanced by cross-timeframe momentum filtering
Portfolio managers can quickly identify which assets are about to move
How Multiple Indicators Work Together
This screener unites three essential concepts:
Bollinger Bands : Measure volatility using standard deviation of price
Keltner Channels : Define expected price range based on average true range (ATR)
Momentum : Linear regression calculation to evaluate the direction and intensity after a squeeze
By combining these, the indicator not only signals when volatility compresses and releases, but also adds directional context—filtering false signals and helping traders time entries and exits more precisely.
Unique Aspects
Multi-symbol, multi-timeframe architecture—optimized for crypto traders and market scanners
Advanced table visualization—see all signals at a glance, minimizing cognitive overload
Modular calculation functions—easy to adapt and extend for other asset classes or strategies
Real-time, low-latency screening—built for actionable alerts on fast-moving markets
How to Use
Add the script to a TradingView chart (works on custom layouts)
Select up to 8 symbols and 4 timeframes using input fields (defaults to BTCUSD, ETHUSD, etc.)
Monitor the screener table; “SQUEEZE” highlights assets in potential breakout phase
Use momentum values to judge if the squeeze is likely bullish or bearish
Combine screener insights with manual chart analysis for optimal results
Customization
Symbols: Easily set any ticker for deep market scanning
Timeframes: Adjust to match your trading horizon (scalping, swing, long-term)
Indicator parameters: Refine Bollinger/Keltner/Momentum settings for sensitivity
Visuals: Personalize table layout, color codes, and formatting for clarity
Conclusion
In summary, the TTM Squeeze Screener is a robust, original TradingView indicator designed for crypto traders who demand a sophisticated multi-symbol, multi-timeframe edge. Its combination of volatility and momentum analytics makes it ideal for catching explosive breakouts, managing risk, and scanning the market efficiently. Whether you’re a scalper or swing trader, this screener provides the insights needed to stay ahead of the curve.
RSI: chart overlay
This indicator maps RSI thresholds directly onto price. Since the EMA of price aligns with RSI’s 50-line, it draws a volatility-based band around the EMA to reveal levels such as 70 and 30.
By converting RSI values into visible price bands, the overlay lets you see exactly where price would have to move to hit traditional RSI boundaries. These bands adapt in real time to both price movement and market volatility, keeping the classic RSI logic intact while presenting it in the context of price action. This approach helps traders interpret RSI signals without leaving the main chart window.
The calculation uses the same components as the RSI: alternative derivation script: Wilder’s EMA for smoothing, a volatility-based unit for scaling, and a normalization factor. The result is a dynamic band structure on the chart, representing RSI boundary levels in actual price terms.
Key components and calculation breakdown:
Wilder’s EMA
Used as the anchor point for measuring price position.
myEMA = ta.rma(close, Length)
Volatility Unit
Derived from the EMA of absolute close-to-close price changes.
CC_vol = ta.rma(math.abs(close - close ), Length)
Normalization Factor
Scales the volatility unit to align with the RSI formula’s structure.
normalization_factor = 1 / (Length - 1)
Upper and Lower Boundaries
Defines price bands corresponding to selected RSI threshold values.
up_b = myEMA + ((upper - 50) / 50) * (CC_vol / normalization_factor)
down_b = myEMA - ((50 - lower) / 50) * (CC_vol / normalization_factor)
Inputs
RSI length
Upper boundary – RSI level above 50
Lower boundary – RSI level below 50
ON/OFF toggle for 50-point line (EMA of close prices)
ON/OFF toggle for overbought/oversold coloring (use with line chart)
Interpretation:
Each band on the chart represents a chosen RSI level.
When price touches a band, RSI is at that threshold.
The distance between moving average and bands adjusts automatically with volatility and your selected RSI length.
All calculations remain fully consistent with standard RSI values.
Feedback and code suggestions are welcome, especially regarding implementation efficiency and customization.
Directional Indicator Crossovers [JopAlgo]Directional Indicator Crossovers — read trend intent at a glance, on any timeframe
Most traders ask two questions before they click: who’s in control right now and is control getting stronger or weaker?
The Directional Indicator (DI) answers the first one cleanly. +DI tracks upward directional movement; –DI tracks downward directional movement. When +DI crosses above –DI, buyers have the initiative; when –DI crosses above +DI, sellers do. DI Xover focuses on that simple, tradeable signal—the crossover—and keeps the pane uncluttered so you can layer it with your location/flow tools.
(If you add screenshots: image #1 can label +DI, –DI and a bullish crossover; image #2 can show a failed crossover in chop next to a successful one at a strong level.)
What you’re seeing (and how it’s built)
This indicator plots two lines in a separate pane:
+DI (green): smoothed positive directional movement.
–DI (red): smoothed negative directional movement.
Under the hood (length = 14 by default):
It measures how much today’s high exceeded yesterday’s high (up move) and how much today’s low fell below yesterday’s low (down move).
It keeps only the dominant side each bar (if up > down and up > 0 → up counts; vice-versa for down).
It normalizes by True Range (so moves are scaled by volatility) and smooths with RMA (so you don’t get jitter).
It raises alerts when +DI crosses above –DI (bullish) or –DI crosses above +DI (bearish).
How to read it, fast:
Cross up = buyers just took initiative.
Cross down = sellers just took initiative.
Wider distance between the lines = stronger control.
Lines braided/tight = balance/chop → expect more fake crosses.
DI is about directional control. It doesn’t tell you where to trade—that’s your location (e.g., Volume Profile, AVWAP). Use DI as a timing/confirmation layer, not as a standalone level generator.
Using DI Crossovers on any timeframe
The framework doesn’t change; only your expectations do as you zoom.
Scalping (1–5m)
Treat crossovers as triggers at levels. If price is tagging VAL/VAH/LVN (from Volume Profile v3.2) or Anchored VWAP, a fresh +DI cross up is your green light for a quick long; –DI cross up flips that logic for shorts.
Avoid taking every crossover mid-range—wait for location first.
In fast tape, require the lines to separate for 1–2 bars after the cross before you click.
Intraday (15m–1H)
In trend days, the first pullback into your level (POC/VA boundary/AVWAP) that prints a fresh +DI cross up is often the cleanest add/entry.
In balance days, fade DI crosses at edges back to POC—only if your flow tool isn’t screaming absorption against you.
Swing (2H–4H)
Look for confluence: at Weekly AVWAP or composite VAL/VAH, a DI crossover that stays separated for several bars is a solid momentum confirmation.
Failed crossover (lines recross quickly) near a level is a useful fail signal—expect a move back into value.
Position (1D–1W)
Use fewer, bigger signals: a weekly DI cross at Monthly/Quarterly AVWAP or at composite value edges marks a regime change.
Add on pullbacks when the controlling DI stays dominant (distance holds or widens).
Entries, exits, and risk (simple rules)
Entry (with level): wait for price to reach your level (e.g., VAL/VAH or AVWAP), then take the trade with the DI cross in that direction.
Filter: skip crosses when the two lines are braided (tiny separation) unless you’re trading a tight scalp with strict risk.
Exit / reduce: if your trade was based on a bullish cross, consider reducing when –DI recaptures +DI or the lines flatten at your target HVN/POC.
Stops: put them beyond the level (not just on a DI recross), but treat a fast recross as a warning to tighten.
Settings that actually matter (and how to tune them)
DI Length (default 14):
Shorter (7–10) = faster signals, more noise (good for scalps with filters).
Longer (20–30) = fewer but stronger signals (good for swing/position).
If you often see flip-flops, lengthen the setting or take crosses only at VP/AVWAP levels.
Pro tip: Define a minimum separation rule for yourself (e.g., after a cross, require the gap between +DI and –DI to increase on the next bar). You don’t need extra code for this—just enforce it visually.
What to look for (pattern cheatsheet)
Cross + hold at a level: The lines cross at your level and keep separating → high-quality entry in that direction.
Sneaky fail: Cross, then immediate recross back → treat it as a fade signal back into value (especially near VAH/VAL).
Strength confirmation: After a breakout, +DI stays above –DI on pullbacks → trend is healthy; buy dips at AVWAP/POC.
Pre-move tell: DI lines unbraid and begin diverging before price leaves a range; wait for location + trigger.
Combining DI Xover with other tools
Cumulative Volume Delta v1 (CVDv1):
Use DI for direction, and CVDv1 for quality. A bullish DI cross with ALIGN OK + Imbalance strong + no Absorption is a far better long than DI alone.
If DI crosses up but CVDv1 flags Absorption (red), don’t chase—look for the fail/reclaim instead.
Volume Profile v3.2 :
Let VP choose the battleground (POC/VAH/VAL/LVNs). Take the DI crossover at those references.
Classic: bearish DI cross at VAH → fade toward POC; bullish DI cross at VAL → rotate to POC—assuming CVDv1 isn’t vetoing with Absorption.
Anchored VWAP :
Treat reclaims/rejections of AVWAP as the location and DI cross as the trigger.
Example: price reclaims Weekly AVWAP, then on the next pullback, a +DI cross up confirms the add.
Common pitfalls this helps you avoid
Trading crosses in the middle of nowhere. DI is a trigger, not a level; wait for VP/AVWAP.
Chasing every wiggle. When the lines are braided, you’re likely in balance—expect fake crosses.
Ignoring flow. A DI cross against CVDv1 Absorption is often a trap; quality > quantity.
Practical defaults to start with
Length: 14
Timeframes: Works out of the box on 15m–4H. For 1–5m scalps try 10–12; for daily/weekly swings try 20–30.
Process: Only act on crosses at levels (VP v3.2 / Anchored VWAP), and prefer those where CVDv1 says ALIGN OK and no Absorption.
Alerts (what they tell you)
Bullish DI Crossover: +DI crossed above –DI → buyers just took initiative. Look to your chart for location and CVDv1 quality before entering.
Bearish DI Crossover: –DI crossed above +DI → sellers took initiative. Same rule: confirm at a level with flow.
Open source & disclaimer
This indicator is published open source so traders can learn, adapt, and build rules they trust. No tool guarantees outcomes; risk management remains essential.
Disclaimer — Not Financial Advice.
The “Directional Indicator Crossovers ” indicator and this description are provided for educational purposes only and do not constitute financial or investment advice. Trading involves risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
John Bollinger's Bollinger BandsJapanese below / 日本語説明は下記
This indicator replicates how John Bollinger, the inventor of Bollinger Bands, uses Bollinger Bands, displaying Bollinger Bands, %B and Bandwidth in one indicator with alerts and signals.
Bollinger Bands is created by John Bollinger in 1980s who is an American financial trader and analyst. He introduced %B and Bandwidth 30 years later.
🟦 What's different from other Bollinger Bands indicator?
Unlike the default Bollinger Bands or other custom Bollinger Bands indicators on TradingView, this indicator enables to display three Bollinger Bands tools into a single indicator with signals and alerts capability.
You can plot the classic Bollinger Bands together with either %B or Bandwidth or three tools altogether which requires the specific setting(see below settings).
This makes it easy to quantitatively monitor volatility changes and price position in relation to Bollinger Bands in one place.
🟦 Features:
Plots Bollinger Bands (Upper, Basis, Lower) with fill between bands.
Option to display %B or Bandwidth with Bollinger Bands.
Plots highest and lowest Bandwidth levels over a customizable lookback period.
Adds visual markers when Bandwidth reaches its highest (Bulge) or lowest (Squeeze) value.
Includes ready-to-use alert conditions for Bulge and Squeeze events.
📈Chart
Green triangles and red triangles in the bottom chart mark Bulges and Squeezes respectively.
🟦 Settings:
Length: Number of bars used for Bollinger Band middleline calculation.
Basis MA Type: Choose SMA, EMA, SMMA (RMA), WMA, or VWMA for the midline.
StdDev: Standard deviation multiplier (default = 2.0).
Option: Select "Bandwidth" or "%B" (add the indicator twice if you want to display both).
Period for Squeeze and Bulge: Lookback period for detecting the highest and lowest Bandwidth levels.(default = 125 as specified by John Bollinger )
Style Settings: Colors, line thickness, and transparency can be customized.
📈Chart
The chart below shows an example of three Bollinger Bands tools: Bollinger Band, %B and Bandwidth are in display.
To do this, you need to add this indicator TWICE where you select %B from Option in the first addition of this indicator and Bandwidth from Option in the second addition.
🟦 Usage:
🟠Monitor Volatility:
Watch Bandwidth values to spot volatility contractions (Squeeze) and expansions (Bulge) that often precede strong price moves.
John Bollinger defines Squeeze and Bulge as follows;
Squeeze:
The lowest bandwidth in the past 125 period, where trend is born.
Bulge:
The highest bandwidth in the past 125 period where trend is going to die.
According to John Bollinger, this 125 period can be used in any timeframe.
📈Chart1
Example of Squeeze
You can see uptrends start after squeeze(red triangles)
📈Chart2
Example of Bulge
You can see the trend reversal from downtrend to uptrends at the bulge(green triangles)
📈Chart3
Bulge DOES NOT NECESSARILY mean the beginning of a trend in opposite direction.
For example, you can see a bulge happening in the right side of the chart where green triangles are marked. Nevertheless, uptrend still continues after the bulge.
In this case, the bulge marks the beginning of a consolidation which lead to the continuation of the trend. It means that a phase of the trend highlighted in the light blue box came to an end.
Note: light blue box is not drawn by the indicator.
Like other technical analysis methods or tools, these setups do not guarantee birth of new trends and trend reversals. Traders should be carefully observing these setups along with other factors for making decisions.
🟠Track Price Position:
Use %B to see where price is located in relation to the Bollinger Bands.
If %B is close to 1, the price is near upper band while %B is close to 0, the price is near lower band.
🟠Set Alerts:
Receive alerts when Bandwidth hits highest and lowest values of bandwidth, helping you prepare for potential breakout, ending of trends and trend reversal opportunities.
🟠Combine with Other Tools:
This indicator would work best when combined with price action, trend analysis, or
market environmental analysis.
—————————————————————————————
このインジケーターはボリンジャーバンドの考案者であるジョン・ボリンジャー氏が提唱するボリンジャーバンドの使い方を再現するために、ボリンジャーバンド、%B、バンドウィズ(Bandwidth) の3つを1つのインジケーターで表示可能にしたものです。シグナルやアラートにも対応しています。
ボリンジャーバンドは1980年代にアメリカ人トレーダー兼アナリストのジョン・ボリンジャー氏によって開発されました。彼はその30年後に%Bとバンドウィズを導入しました。
🟦 他のボリンジャーバンドとの違い
TradingView標準のボリンジャーバンドや他のボリンジャーバンドとは異なり、このインジケーターでは3つのボリンジャーバンドツールを1つのインジケーターで表示し、シグナルやアラート機能も利用できるようになっています。
一般的に知られている通常のボリンジャーバンドに加え、%Bやバンドウィズを組み合わせて表示でき、設定次第では3つすべてを同時にモニターすることも可能です。これにより、価格とボリンジャーバンドの位置関係とボラティリティ変化をひと目で、かつ定量的に把握することができます。
🟦 機能:
ボリンジャーバンド（アッパーバンド・基準線・ロワーバンド）を描画し、バンド間を塗りつぶし表示。
オプションで%Bまたはバンドウィズを追加表示可能。
バンドウィズの最高値・最安値を、任意の期間で検出して表示。
バンドウィズが指定期間の最高値（バルジ※）または最安値（スクイーズ）に達した際にシグナルを表示。
※バルジは一般的にボリンジャーバンドで用いられるエクスパンションとほぼ同じ意味ですが、定義が異なります。(下記参照)
バルジおよびスクイーズ発生時のアラート設定が可能。
📈 チャート例
下記チャートの緑の三角と赤の三角は、それぞれバルジとスクイーズを示しています。
🟦 設定:
Length: ボリンジャーバンドの基準線計算に使う期間。
Basis MA Type: SMA, EMA, SMMA (RMA), WMA, VWMAから選択可能。
StdDev: 標準偏差の乗数（デフォルト2.0）。
Option: 「Bandwidth」または「%B」を選択（両方表示するにはこのインジケーターを2回追加）。
Period for Squeeze and Bulge: Bandwidthの最高値・最安値を検出する期間（デフォルトはジョン・ボリンジャー氏が推奨する125）。
Style Settings: 色、線の太さ、透明度などをカスタマイズ可能。
📈 チャート例
下のチャートは「ボリンジャーバンド」「%B」「バンドウィズ」の3つを同時に表示した例です。
この場合、インジケーターを2回追加し、最初に追加した方ではOptionを「%B」に、次に追加した方では「Bandwidth」を選択します。
🟦 使い方:
🟠 ボラティリティを監視する:
バンドウィズの値を見ることで、価格変動の収縮（スクイーズ）や拡大（バルジ）を確認できます。
これらはしばしば強い値動きの前兆となります。
ジョン・ボリンジャー氏はスクイーズとバルジを次のように定義しています：
スクイーズ: 過去125期間の中で最も低いバンドウィズ→ 新しいトレンドが生まれる場所。
バルジ: 過去125期間の中で最も高いバンドウィズ → トレンドが終わりを迎える場所。
この「125期間」はどのタイムフレームでも利用可能とされています。
📈 チャート1
スクイーズの例
赤い三角のスクイーズの後に上昇トレンドが始まっているのが確認できます。
📈 チャート2
バルジの例
緑の三角のバルジの箇所で下降トレンドから上昇トレンドへの反転が見られます。
📈 チャート3
バルジが必ずしも反転を意味しない例
下記のチャート右側の緑の三角で示されたバルジの後も、上昇トレンドが継続しています。
この場合、バルジは反転ではなく「トレンド一時的な調整（レンジ入り）」を示しており、結果的に上昇トレンドが継続しています。
この場合、バルジは水色のボックスで示されたトレンドのフェーズの終わりを示しています。
※水色のボックスはインジケーターが描画したものではありません。
また、他のテクニカル分析と同様に、これらのセットアップは必ず新しいトレンドの発生やトレンド転換を保証するものではありません。トレーダーは他の要素も考慮し、慎重に意思決定する必要があります。
🟠 価格とボリンジャーバンドの位置関係を確認する:
%Bを利用すれば、価格がバンドのどこに位置しているかを簡単に把握できます。
%Bが1に近ければ価格はアッパーバンド付近、0に近ければロワーバンド付近にあります。
🟠 アラートを設定する:
バンドウィズが一定期間の最高値または最安値に到達した際にアラートを設定することで、ブレイクアウトやトレンド終了、反転の可能性に備えることができます。
🟠 他のツールと組み合わせる:
このインジケーターは、プライスアクション、トレンド分析、環境認識などと組み合わせて活用すると最も効果的です。
VERITAS originale## **The Fundamental Characteristics of Moving Averages: Theoretical Principles and Strategic Applications**
### **The Non-Parallelism Principle: Mathematical Foundation**
The first fundamental principle governing moving averages establishes that **any moving average can never be parallel to its linear regression**. This is not coincidental or anomalous, but a direct consequence of the mathematical nature of moving averages.
**Theoretical explanation:** A moving average is a low-pass filter that removes high-frequency components from price data, while a linear regression represents the optimal linear trend over the considered period. Since the moving average maintains trace of oscillations around the trend (albeit attenuated), while the regression completely eliminates these oscillations to provide only the general direction, the two curves can never be identical or parallel.
**Crucial implication:** This characteristic certifies that **moving averages always have a curvilinear pattern** relative to their regression. The curvature is not an imperfection in the calculation, but the manifestation of the intrinsic dynamics of market data filtered through the moving average.
### **System Energy: Derivation from Curvature**
It is precisely this curvilinear characteristic that allows us to determine fundamental parameters such as **system energy**.
**Physical basis:** In physics, the potential energy of a curvilinear system is proportional to the deviation from the equilibrium trajectory (represented by the linear regression). In our context:
- **Potential energy** = Distance between moving average and its regression
- **Kinetic energy** = Speed of approach or separation between the two curves
- **Total system energy** = Sum of potential and kinetic energy
**Practical application:** When the moving average moves away from its regression, it accumulates potential energy that must be released. When it approaches rapidly, it manifests kinetic energy that can lead to overshooting the equilibrium point.
### **The Hierarchical Rolling Principle**
The second fundamental principle establishes that **curves roll around each other starting from longer periods toward shorter ones**. This phenomenon has deep roots in dynamical systems theory.
**Theoretical explanation:** Moving averages with longer periods have greater inertia and resistance to change (analogous to mass in physics). When a trend change occurs, it propagates first in long-period averages (which represent the dominant forces of the system), then progressively diffuses toward shorter-period averages.
**Propagation mechanism:**
1. **Macro level** (long averages): Change in direction of principal forces
2. **Medium level** (intermediate averages): Signal transmission
3. **Micro level** (short averages): Final manifestation of the change
### **Derived Strategic Formations**
This hierarchical rolling allows us to identify **important formations** for the strategy:
**Rolling Confluence:** When multiple averages of different periods simultaneously begin the rolling process, a high-probability reversal zone is created.
**Alignment Cascade:** The temporal sequence with which averages roll provides information about the strength and persistence of the imminent movement.
**Dynamic Resistance Zones:** Points where rolling encounters resistance indicate critical levels where opposing forces temporarily balance.
### **Strategic Implications**
These theoretical principles translate into concrete operational advantages:
1. **Energy predictability:** We can quantify the energy accumulated in the system and predict the strength of future movements
2. **Entry timing:** Hierarchical rolling provides a temporal sequence to optimize entry points
3. **Risk management:** Understanding system energy allows proper position sizing
The combination of these two principles - non-parallelism and hierarchical rolling - transforms moving averages from simple trend indicators into sophisticated tools for energetic and dynamic analysis of financial markets.
Mitigation Blocks — Lite (ICT) + Arrows + Stats📌 Mitigation Blocks — Lite (ICT-Based) + Arrows
This indicator detects mitigation blocks based on price structure shifts, inspired by ICT (Inner Circle Trader) concepts. It works by identifying strong impulses and highlighting the last opposite candle, forming a mitigation block zone for potential reversal or continuation trades.
🔍 Features:
✅ Automatic detection of bullish and bearish mitigation blocks
🟩 Box visualization with border color change on mitigation (first touch)
📉 ATR-based impulse filtering
📌 Entry arrows on first mitigation (touch)
📊 Autoscale anchors for better chart readability
📈 Real-time HUD info panel
📉 Backtest-friendly design (stable, deterministic logic)
🛠️ How it works:
Detects swing highs/lows using pivot points.
Confirms impulse candles breaking recent structure.
Locates the last opposite candle as the mitigation block.
Displays a block box until price revisits the zone.
On the first touch (mitigation), the block is marked and arrows are drawn.
💡 Ideal Use Case:
Apply this on higher timeframes (e.g., 4H) to identify potential limit order zones.
Use the blocks as entry zones and combine with confluence: FVGs, imbalance, S&D, or liquidity levels.
🧠 Extra Tip:
You can extend this script to include:
Win-rate tracking
Auto TP/SL levels based on ATR
Confluence detection (e.g., FVG, order blocks)
Market Bias (CEREBR)Market Bias (CEREBR) — quick read of who’s in control
What it does, in one line:
It builds a clean, smoothed Heikin-Ashi view (optionally from a higher timeframe) and an oscillator that says: bullish, bearish, or cooling off. You use it to decide directional bias and to avoid trading against that bias.
What you see on the chart
Smoothed HA candles (optional): green = bullish bias, red = bearish bias.
A soft fill band around the HA body:
Brighter = bias is strengthening.
Faded = bias is weakening.
(In Data Window) “Bias High / Low / Average” = the smoothed HA range and midline.
If you only look at one thing: green means look for longs, red means look for shorts. Faded color = be picky or trim.
How to use it (simple playbook)
Pick your higher timeframe (HTF) for the bias.
On a 4H chart, try HTF = 12H or 1D.
Rule: HTF must be equal to or higher than your chart TF.
Trade with the bias at real levels.
Longs only when the bias is green.
Shorts only when the bias is red.
Take entries at location: Volume Profile v3.2 levels (VAH/VAL/POC/LVNs) or Anchored VWAP.
Quality check (optional but strong):
Before clicking, glance at CVDv1.
Green bias + CVD Alignment OK and no Absorption = better odds.
If CVD shows Absorption against you, skip or wait for a retest.
When to pass:
Color flips every other bar (chop) → do less.
Color is fading (weakening) into your entry → size down or wait.
Timeframe guidance
Scalps (1–5m): HTF = 15m/30m. Use bias to filter direction; enter on pullbacks at AVWAP/VA edges.
Intraday (15m–1H): HTF = 4H. Buy dips in green / sell pops in red at VP levels.
Swing (2H–4H): HTF = 12H/1D. First pullback after a fresh flip is usually the best.
Position (1D–1W): HTF = 1W. Hold while color stays consistent; reduce on weakening near HVNs.
Entries, exits, and stops
Entry with trend:
Bias green, price pulls back to AVWAP / VAL / prior HA mid, then holds.
Click the long. Reverse for shorts in red.
Exit / reduce:
When “Trend Weakens” alert fires, or color fades while hitting your POC/HVN target.
Hard exit on opposite flip (green→red or red→green) if your idea was pure trend-follow.
Stops:
Behind structure/level (not just on color).
If the next bar flips bias against you and CVD also disagrees, cut it early.
Inputs that matter (keep these simple)
Timeframe (HA Market Bias): your HTF. Must be ≥ chart TF.
Period (default 100): smoothing for the base OHLC. Higher = steadier.
Smoothing (default 100): extra smoothing for the HA feed. Higher = fewer flips.
Oscillator Period (default 7): affects how fast strengthening/weakening shows in the fill color. Lower = quicker.
Tip: If you see too many flips, raise Period/Smoothing or pick a higher HTF. If it feels slow, lower them one notch.
Alerts (plain meaning)
Bullish Trend Switch: bias turned bearish → bullish.
Bullish Trend Strengthens / Weakens: same direction, momentum building / cooling.
Bearish Trend Switch: bullish → bearish.
Bearish Trend Strengthens / Weakens: same idea for shorts.
Use “Switch” to prepare for new setups; use “Strengthens/Weakens” to add/trim or tighten risk.
How it works (one paragraph, no math)
The script smooths price, builds Heikin-Ashi values on your chosen HTF, smooths those again, and doesn’t repaint on closed bars. From the HA open/close difference it creates a simple bias oscillator: above zero = bullish, below zero = bearish. The fill brightness tells you if that bias is getting stronger or weaker right now.
Good combos (optional, but recommended)
Volume Profile v3.2 : use VAH/VAL/POC/LVNs as your battleground.
Anchored VWAP : use reclaims/rejections for timing.
CVDv1 : sanity-check flow quality before entry.
FAQ (quick)
Does it repaint?
No on closed bars. HTF values are requested with a safe offset.
Best starting setup?
4H chart, HTF = 1D, Period/Smoothing 100/100, Oscillator 7.
Can I hide the HA candles?
Yes—toggle “Show HA Candles.” Keep only the bias fill if you want a cleaner price chart.
Short disclaimer
Educational tool, not advice. Markets carry risk. Test first, size small, and trade with your plan.
DTM 444 BANDS 🚀DTM 444 BANDS 🚀:
The DTM 444 BANDS 🚀 is a powerful, multi-purpose trading indicator combining Supertrend, Dynamic Band Levels, Breakout Signals, and Volume Confirmation to help traders identify high-probability trade setups across different timeframes.
🔧 Key Features
✅ Multi-Timeframe Support
Analyze price action across any timeframe using the Timeframe input.
All band calculations (High, Low, Midline, and Supertrend) are pulled from a higher timeframe for clearer context.
✅ Dynamic Bands Based on Supertrend
High Band: Rolling highest of Supertrend over hiLen period.
Low Band: Rolling lowest of Supertrend over loLen period.
Midline: Midpoint of the above.
Acts like dynamic support/resistance, ideal for trend-following and breakout strategies.
✅ Dual Signal System
Breakout Signals (Buy and Sell): Triggered when price breaks the bands with volume confirmation.
Supertrend Crossover Signals (Buy1 and Sell1): Classic momentum entries with a confirmation twist.
Exit Signals: Optional take-profit/neutral indicators when price reverses.
✅ Volume Confirmation Filter (Optional)
Only triggers signals if the volume exceeds its 20-period SMA.
Helps filter out false breakouts and weak trends in low-liquidity periods.
✅ Visual Enhancements
Color-coded candles based on band positioning (e.g., red = weak, green = strong, etc.)
On-chart labels for each signal for quick reference.
Real-time Signal Dashboard using Pine Script tables showing:
Current signal
Volume filter status
Live volume vs volume SMA
🧪 Practical Use Cases
Trend Traders: Use the Supertrend cross and band breakouts to ride trends early.
Breakout Traders: Catch high-probability moves outside established ranges.
Swing Traders: Time entries and exits using color-coded bars and exit labels.
Volume-Sensitive Traders: Focus on trades with strong volume backing.
📊 Backtest Snapshot
Based on the example chart for Reliance Industries (RELIANCE.NS) on the weekly timeframe:
Several profitable buy and breakout signals during uptrends.
Timely exits and breakdown alerts before reversals.
Volume filter keeps trades clean and avoids noise.
⚙️ Customizable Parameters
High Length and Low Length (default: 19)
Supertrend Multiplier and ATR Length
Volume Filter: Toggle ON/OFF
Volume SMA Length: Default 20
Custom Timeframe: Choose any higher timeframe for multi-timeframe analysis
📢 Alerts Ready
Fully integrated with TradingView alerts:
Breakout & Breakdown
Supertrend crossovers
All alerts respect the volume filter setting
🏁 Final Thoughts
DTM 444 BANDS 🚀 is a versatile and adaptive trading system that blends trend analysis, volatility bands, and volume validation. Whether you're a trend trader, breakout hunter, or swing trader — this tool gives you a structured edge with clear visual cues and real-time alerts.
AutoDay MA (Session-Normalized)📊 AutoDay MA (Session-Normalized Moving Average)
⚡ Daily power, intraday precision.
AutoDay MA automatically converts any N-day moving average into the exact equivalent on your current intraday timeframe.
💡 Concept inspired by Brian Shannon (Alphatrends) – mapping daily MAs onto intraday charts by normalizing session minutes.
🛠 How it works
Set Days (N) (e.g., 5, 10, 20).
Define Session Minutes per Day (⏱ 390 = US RTH, 🌍 1440 = 24h).
The indicator detects your chart’s timeframe and computes:
Length = (Days × SessionMinutes) / BarMinutes
Applies your chosen MA type (📐 SMA / EMA / RMA / WMA) with rounding (nearest, up, down).
Displays all details in a clear corner info panel.
✅ Why use it
Consistency 🔄: Same 5-day smoothing across all intraday charts.
Session-aware 🕒: Works for equities, futures, FX, crypto.
Transparency 🔍: Always shows the math & final MA length.
Alerts built-in 🔔: Cross up/down vs. price.
📈 Examples
5-Day on 1m → 1950-period MA
5-Day on 15m → 130-period MA
5-Day on 65m → 30-period MA
10-Day on 24h/15m (crypto) → 960-period MA
FRAMA Channel [JopAlgo]FRAMA Channel — let the market tell you how fast to move
Most moving averages make you pick a speed and hope it fits every regime. FRAMA (Fractal Adaptive Moving Average, popularized by John Ehlers) does the opposite: it adapts its smoothing to market structure. When price action is “trendy” (more directional, less jagged), FRAMA speeds up; when it’s choppy (more fractal noise), FRAMA slows down and filters the rubble.
FRAMA Channel wraps that adaptive core with a volatility channel and clean color logic so you can read trend, mean-reversion windows, and breakouts in one glance—on any timeframe.
What you’re seeing (plain-English tour)
FRAMA midline (Filt): the adaptive average. It’s computed from a fractal dimension of price over Length (N).
Trendy tape → lower fractal dimension → FRAMA tracks price tighter.
Choppy tape → higher fractal dimension → FRAMA smooths harder.
Channel bands (Filt ± distance × volatility): the “breathing room.” Volatility here is a long lookback average of (high − low).
Upper band = potential resistance in down/neutral or trend-walk path in uptrends.
Lower band = mirror logic for shorts.
Color logic (simple and strict):
Green when price breaks above the upper band → bullish regime (momentum present).
Red when price breaks below the lower band → bearish regime.
White when price crosses the FRAMA midline → neutral/reset.
Optional candle coloring: toggle Color Candles to tint the chart itself with the regime color—handy for quick reads.
(When you add screenshots: image #1 should label FRAMA, bands, and the three colors in a small trend + pullback. Image #2 can show a “squeeze → expansion” sequence: channel tightens, then price breaks and walks the band.)
How it’s built (without the jargon)
The script measures three ranges over your Length (N): two half-windows and the full window.
It converts those into a fractal dimension (Dimen). That number says “how zig-zaggy” price is right now.
It turns Dimen into an alpha (smoothing factor): alpha = exp(−4.6 × (Dimen − 1)), clamped so it never explodes or flatlines.
It updates FRAMA each bar using that alpha.
It builds bands using a long average of (high − low) multiplied by your Bands Distance setting.
It changes color only on confirmed bar events:
hlc3 crosses above the upper band → green
hlc3 crosses below the lower band → red
close crosses the midline → white
Result: a channel that tightens in balance, widens in trend, and doesn’t flicker on partial bars.
How to use FRAMA Channel on any timeframe
Same framework everywhere. Your job is to choose where to act (objective levels) and let FRAMA tell you trend/mean-reversion context and breakout quality.
Scalping (1–5m)
Pullback-to-midline (trend): When color is green, buy pullbacks that hold at/above the midline; when red, short pullbacks that fail at/below it.
Invalidation: a white flip (midline cross back) right after entry → tighten or bail.
Squeeze → break: A narrowing channel often precedes a move. Only chase the break if color flips to green/red and the first pullback holds the band/midline.
Intraday (15m–1H)
Trend rides: In green/red, expect price to walk the outer band. Entries on midline kisses are cleaner than chasing the band itself.
Balance fades: In white (neutral) with a tight channel, fade outer band → midline—but only at a real level (see “Pairing” below).
Swing (2H–4H)
Regime compass: Color changes that stick (several bars) often mark swing regime shifts. Combine with Weekly/Event AVWAP and composite VP levels.
Add/Trim: In an uptrend, add on midline holds; trim as the channel widens and price spikes beyond the upper band into HVNs.
Position (1D–1W)
Context first: A persistent green weekly channel is constructive; a persistent red is distributive.
Patience: Wait for midline retests at higher-TF levels rather than chasing outer-band prints.
Entries, exits, and risk (keep it simple)
Continuation entry (trend):
Color already green/red.
Price pulls back to FRAMA midline (or shallowly toward it) and holds.
Take the trend side.
Stop: beyond the opposite side of the midline or behind local structure.
Targets: your Volume Profile HVN/POC or prior swing, not the band alone.
Breakout entry:
Channel had tightened; price breaks a key level.
Color flips green/red and the first retest holds.
Enter with the break.
Avoid: breaks that flip color but immediately white-flip on the next bar.
Mean-reversion entry (balance):
Color white and channel tight.
At a VP edge (VAL/VAH), fade outer band → midline.
Stop: just outside the band; Exit: at midline/POC.
Settings that actually matter (and how to tune them)
Length (N) — default 26
Controls how FRAMA “reads” structure.
Shorter (14–20): faster, more responsive (good for scalps/intraday), more flips in chop.
Longer (30–40): steadier (good for swings/position), slower to acknowledge new trends.
Bands Distance — default 1.5
Scales the channel width.
If you’re constantly tagging bands, increase slightly (1.7–2.0).
If nothing ever reaches the band, decrease (1.2–1.4) to make context meaningful.
Color Candles — on/off
Great for quick regime reads. If your chart feels too busy, leave bands colored and turn candle coloring off.
Warm-up note: FRAMA references N bars. Right after switching timeframes or symbols, give it N–2N bars to settle before you judge the current state.
(You may see an input named “Signals Data” in this version; it’s reserved for future enhancements.)
What to look for (pattern cheat sheet)
Walk-the-band: After a green/red flip, price hugs the outer band while the midline slopes. Ride pullbacks to the midline, don’t fade the band.
Squeeze → Expansion: Channel pinches, then color flips and bands widen—that’s the move. The first midline retest is your best entry.
False break tell: Brief color flip to green/red that immediately reverts to white on the next bar—skip chasing; plan for a reclaim.
Midline reclaims: In chop, repeated white↔green/white↔red flips say “mean reversion”; stay tactical and target the midline/POC.
Pairing FRAMA Channel with other tools
Cumulative Volume Delta v1 (CVDv1):
FRAMA tells you trend/mean-reversion context; CVDv1 tells you flow quality.
Breakout quality: FRAMA flips green and CVDv1 ALIGN = OK, Imbalance strong, Absorption ≠ red → higher odds the break sticks.
If Absorption is red on a FRAMA green flip, do not chase—wait for retest or look for a fail/reclaim.
Volume Profile v3.2:
Use VAH/VAL/LVNs/POC for where.
Green + VAL retest → rotate toward POC/HVN.
Red + VAH rejection → rotate back to POC.
LVN + green flip → expect fast travel toward the next HVN; set targets there.
Anchored VWAP :
Treat AVWAP as fair-value rails.
AVWAP reclaim + FRAMA green → excellent trend-resume entry.
AVWAP rejection + FRAMA red → high-quality short; use midline as your risk guide.
Common pitfalls this helps you avoid
Chasing every poke: FRAMA’s white → green/red state change helps you wait for confirmation (or a retest) instead of reacting to the first wick.
Fading a real trend: A sloped midline with price walking the band is telling you not to fight it.
Stops too tight: In expansion, give the trade room to the midline or local structure, not just inside the channel.
Practical defaults to start with
Length: 26
Bands Distance: 1.5
Color Candles: on (turn off if your chart is busy)
Timeframes: works out of the box on 15m–4H; for 1–5m try Length=20; for daily swings try Length=34–40.
Open source & disclaimer
This indicator is published open source so traders can learn, tweak, and build rules they trust. No tool guarantees outcomes; risk management is essential.
Disclaimer — Not Financial Advice.
The “FRAMA Channel ” indicator and this description are provided for educational purposes only and do not constitute financial or investment advice. Trading involves risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
Use FRAMA Channel for context (trend vs balance, squeeze vs expansion), Volume Profile v3.2 and Anchored VWAP for locations, and CVDv1 for flow quality. That trio keeps your trades selective and your rules consistent on any timeframe.
Multi-TF FVG Kerze Break AlertHere's a breakdown of the key files:
App.tsx: This is the main component that orchestrates the entire user interface. It manages the application's state, including the input Pine Script, the selected target language, the resulting converted code, and the loading/error states.
services/geminiService.ts: This file handles all communication with the Google Gemini API. It takes the Pine Script and the target language, constructs a detailed prompt instructing the AI on how to perform the conversion, sends the request, and processes the response.
components/CodeEditor.tsx: A reusable UI component that provides a styled for both displaying the input Pine Script and the read-only output.
constants.ts: This file centralizes static data. It contains the list of target languages for the dropdown menu and the default Pine Script code that loads when the application first starts.
index.html & index.tsx: These are the standard entry points for the React application, responsible for setting up the web page and mounting the main App component.
In essence, the application provides a user-friendly interface for developers to convert financial trading algorithms written in TradingView's Pine Script into other popular programming languages, leveraging the power of the Gemini AI model to perform the translation.
Multi-Market Trend-Pullback Alerts (EMA20/50 + RSI) [v6]//@version=6 replaces 5
Some functions (like label.delete) need to be called as methods
Minor syntax tightening around string concatenation and label management
All alertcondition() and table logic still works, but must be explicitly version 6 compatible
Trendline Breakouts With Targets [ omerprıme ]Indicator Explanation (English)
This indicator is designed to detect trendline breakouts and provide early trading signals when the price breaks key support or resistance levels.
Trendline Detection
The indicator identifies recent swing highs and lows to construct dynamic trendlines.
These trendlines act as support in an uptrend and resistance in a downtrend.
Breakout Confirmation
When the price closes above a resistance trendline, the indicator generates a bullish breakout signal.
When the price closes below a support trendline, it generates a bearish breakout signal.
Filtering False Signals
To reduce false breakouts, additional conditions (such as candle confirmation, volume filters, or price momentum) can be applied.
Only significant and confirmed breakouts are highlighted.
Trading Logic
Buy signals are triggered when the price breaks upward through resistance with confirmation.
Sell signals are triggered when the price breaks downward through support with confirmation.
Smart Choppy Index v1 [JopAlgo]Smart Choppy Index v1 — decide trend vs. chop in seconds
What it does (one line):
Measures the percent range of price over a lookback and tells you if the market is choppy (do less, fade edges) or trending (go with breaks/pullbacks).
Range% = (Highest High − Lowest Low) / Close × 100 over length
Below Choppy Threshold → likely range (red tint / X marker)
Above Trending Threshold → likely trend (green tint / ● marker)
Between them = mixed/transition (no background)
Read the pane fast
Orange line: the live Range%.
Red dashed line: Choppy Threshold.
Green dashed line: Trending Threshold.
Background: soft red during chop, soft green during trend.
Markers: X at the top when chop is detected, ● at the bottom when trend is detected.
TL;DR: Red = play defense / mean-revert. Green = play offense / trend-follow.
Simple playbook (copy this into your process)
Identify regime
Choppy (Range% < red line): prefer mean-reversion at VP edges / AVWAP; smaller targets, quicker exits.
Trending (Range% > green line): prefer breakouts + pullbacks; hold to POC/HVNs or structure.
Only execute at real locations
Volume Profile v3.2 : VAH/VAL/POC/LVNs for entries/targets.
Anchored VWAP : reclaims/rejections for timing.
Quality check (optional, recommended)
CVDv1 : execute with flow (Alignment OK, strong Imbalance, no Absorption against your side).
Risk
Stops go beyond structure/level, not on indicator flips.
If regime flips right after entry (green → red or red → green), consider tightening or exiting early.
Timeframe guidance
1–5m (scalps): length 14–20. You’ll see more flips—use thresholds a touch wider and execute only at edges.
15m–1H (intraday): length 14–34. Sweet spot for day trading bias.
2H–4H (swing): length 20–50. Fewer, cleaner signals; great for planning.
1D+ (position): length 50–100. Use as backdrop; trigger on lower TFs.
Settings that actually matter (and how to tune)
Lookback Period (length)
Shorter = faster regime changes; longer = smoother, fewer flips.
Choppy Threshold (%) / Trending Threshold (%)
Calibrate by history: scroll back and mark typical Range% during range days vs trend days for your market/TF.
If you get too many trend flags, raise the green threshold.
If everything looks “choppy,” lower the red threshold slightly.
Background color
Turn off if your chart feels busy; markers remain.
How to trade it with other tools
In Chop (red):
Fade VAH/VAL/AVWAP touches toward POC with tight stops. Confirm with CVDv1 (avoid longs if Absorption is red, etc.).
In Trend (green):
Break + retest at VP levels/AVWAP. Add on pullbacks that hold while Range% stays above the green line.
Patterns to recognize
Squeeze → Expansion: Range% ramps from below red toward/through green → expect a trend phase.
Exhaustion → Balance: After a long green phase, Range% falls back toward the middle → take profits into HVNs, expect more two-way trade.
False break tell: Level poke while Range% sits near red → low odds of follow-through; prefer reclaims.
Practical defaults to start
length = 14
Choppy Threshold = 1.5%
Trending Threshold = 2.5%
Process: Regime → Location → Flow → Execute with structure-based risk
Serious Disclaimer & Licensing
This script and description are provided for educational purposes only and do not constitute financial, investment, or trading advice. Markets are risky; you can lose some or all of your capital. Past performance does not guarantee future results. You are solely responsible for your trading decisions, including evaluating the suitability of this tool in your process, testing it on historical and simulated data, and managing risk.
This indicator relies on exchange data that may vary across venues; differences in volume, liquidity, and price feeds can impact results. No warranty is made—express or implied—regarding accuracy, completeness, or fitness for a particular purpose. assumes no liability for any direct or consequential losses arising from the use of this script or description.
License: This Pine Script® code is released under the Mozilla Public License 2.0 (MPL 2.0), © JopAlgo. You may use, modify, and distribute the code in accordance with MPL 2.0 terms.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229.
Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327.
Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915.
Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44.
Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764.
Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue.
Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228.
Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108.
Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business.
Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007.
Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576.
Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61.
Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79.
Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49.
Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465.
Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78.
Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720.
Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin.
Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill.
Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384.
Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley.
Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*.
Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91.
Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292.
Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170.
Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467.
Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676.
Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419.
Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91.
Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433.
Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41.
Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press.
Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360.
Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449.
Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138.
Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462.
Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17.
Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
