ATR Horizontal Lines from EMA and SMA with TableHow it works:
The script calculates ATR levels (of your choosing)
Instead of plotting curves, it creates horizontal lines
The lines are deleted and recreated on each bar to show current levels
Lines extend to the right or can be limited to a certain width
Customization options:
Line width (1-10 pixels)
Individual colors for each of the 4 lines
All the original parameters (EMA/SMA lengths, ATR length, multipliers)
The horizontal lines will now show the current ATR-based support/resistance levels and move dynamically as the EMAs, SMA, and ATR values change with new price data.
個人檔案管理
Performance-based Asset Weighting(MTF)**Performance-Based Asset Weighting (MTF/Symbol Free Setting)**
#### Overview
This indicator is a tool that visualizes the relative strength of performance (price change rate) as “weight (allocation ratio)” for **four user-defined stocks**.
By setting any specified past point in time as the baseline (where all symbols are equally weighted at 25%), it aims to provide an intuitive understanding of which symbols outperformed others and attracted capital, or underperformed and saw capital outflows.
**【Default Settings and Application Scenario: Pension Fund Rebalancing Analysis】**
The default settings reference the basic portfolio of Japan's Government Pension Investment Fund (GPIF), configuring four major asset classes: domestic equities, foreign equities, domestic bonds, and foreign bonds. It is known that when market fluctuations cause deviations from this equal-weighted ratio, rebalancing occurs to restore the original ratio (selling assets whose weight has increased and buying assets whose weight has decreased).
Analyzing using this default setting can serve as a reference point for considering **“whether rebalancing sales (or purchases) by pension funds and similar entities are likely to occur in the future.”**
**【Important: Usage Notes】**
The weights shown by this indicator are **theoretical reference values** calculated solely based on performance from the specified start date. Even if large investors conduct significant rebalancing (asset buying/selling) during the period, those transactions themselves are not reflected in this chart's calculations.
Therefore, please understand that the actual portfolio ratios may differ. **Use this solely as a rough guideline. **
#### Key Features
* **Freely configure the 4 assets for analysis:** You can freely set any 4 assets (stocks, indices, currencies, cryptocurrencies, etc.) you wish to compare via the settings screen.
* **Performance-based weight calculation:** Rather than simple price composition ratios, it calculates each asset's price change since the specified start date as a “performance index” and displays each asset's proportion of the total sum.
* **Freely set analysis start date:** You can set any desired starting point for analysis, such as “after the XX shock” or “after earnings announcements,” using the calendar.
* **Multi-Timeframe (MTF) Support:** Independently of the timeframe displayed on the chart, you can freely select the timeframe (e.g., 1-hour, 4-hour, daily) used by the indicator for calculations.
#### Calculation Principle
This indicator calculates weights in the following three steps:
1. **Obtaining the Base Price**
Obtain the closing price for each of the four stocks on the user-set “Start Date for Weight Calculation.” This becomes the **base price** for analysis.
2. **Calculating the Performance Index**
Divide the current price of each stock by the **base price** obtained in Step 1 to calculate the “Performance Index”.
`Performance Index = Current Price ÷ Base Date Price`
This quantifies how many times the current performance has increased compared to the base date performance, which is set to “1”.
3. **Calculating Weights**
Sum the “Performance Indexes” of the four stocks. Then, calculate the percentage contribution of each stock's Performance Index to this total sum and plot it on the chart.
`Weight (%) = (Individual Performance Index ÷ Total Performance Index of 4 Stocks) × 100`
Using this logic, on the analysis start date, all stocks' performance indices are set to “1”, so the weights start equally at 25%.
#### Usage
* **Application Example 1: Market Sentiment Analysis (Using Default Settings)**
Analyze using the default asset classes. By observing the relative strength between “Equities” and “Bonds”, you can assess whether the market is risk-on or risk-off.
* **Application Example 2: Sector/Theme Strength Analysis**
Configure settings for groups like “Top 4 semiconductor stocks” or “4 GAFAM stocks.” Setting the start date to the beginning of the year or earnings season allows you to instantly compare which stocks within the same sector are performing best.
* **Application Example 3: Cryptocurrency Power Map Analysis**
By setting major cryptocurrencies like “BTC, ETH, SOL, ADA,” you can analyze which currencies are attracting market capital.
**【About Legend Display】**
Due to Pine Script specification constraints, the legend on the chart will display fixed names: **“Stock 1” to “Stock 4”. **
Please note that the symbol you entered for “Symbol 1” in the settings corresponds to the “Symbol 1” line on the chart.
#### Settings
* **Symbol 1 to Symbol 4:** Set the four symbols you wish to analyze.
* **Timeframe for Calculation:** Select the timeframe the indicator references when calculating weights.
* **Start Date for Weight Calculation:** This serves as the base date for comparing performance.
#### Disclaimer
This script is solely a tool to assist with market analysis and does not recommend buying or selling any specific financial instruments. Please make all final investment decisions at your own discretion.
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**Performance-based Asset Weighting（MTF・シンボル自由設定）**
#### 概要
このインジケーターは、**ユーザーが自由に設定した4つの銘柄**について、パフォーマンス（騰落率）の相対的な強さを「ウェイト（構成比率）」として可視化するツールです。
指定した過去の任意の時点を基準（全銘柄が均等な25%）として、そこからどの銘柄のパフォーマンスが他の銘柄を上回り、資金が向かっているのか、あるいは下回っているのかを直感的に把握することを目的としています。
**【デフォルト設定と活用シナリオ：年金基金のリバランス考察】**
デフォルト設定では、日本の年金積立金管理運用独立行政法人（GPIF）の基本ポートフォリオを参考に、主要4資産クラス（国内株式, 外国株式, 国内債券, 外国債券）が設定されています。市場の変動によってこの均等な比率に乖離が生じると、元の比率に戻すためのリバランス（比率が増えた資産を売り、減った資産を買う）が行われることが知られています。
このデフォルト設定で分析することで、**「今後、年金基金などによるリバランスの売り（買い）が発生する可能性があるか」を考察するための、一つの目安として利用できます。**
**【重要：利用上の注意点】**
このインジケーターが示すウェイトは、あくまで指定した開始日からのパフォーマンスのみを基に算出した**理論上の参考値**です。実際に大口投資家などが途中で大規模なリバランス（資産の売買）を行ったとしても、その取引自体はこのチャートの計算には反映されません。
そのため、実際のポートフォリオ比率とは異なる可能性があることをご理解の上、**あくまで大まかな目安としてご活用ください。**
#### 主な特徴
* **分析対象の4銘柄を自由に設定可能:** 設定画面から、比較したい4つの銘柄（株式、指数、為替、仮想通貨など）を自由に設定できます。
* **パフォーマンス基準のウェイト計算:** 単純な価格の構成比ではなく、指定した開始日からの各銘柄の騰落を「パフォーマンス指数」として算出し、その合計に占める各銘柄の割合を表示します。
* **分析開始日の自由な設定:** 「〇〇ショック後」「決算発表後」など、分析したい任意の時点をカレンダーから設定できます。
* **マルチタイムフレーム（MTF）対応:** チャートに表示している時間足とは別に、インジケーターが計算に使う時間足（1時間足、4時間足、日足など）を自由に選択できます。
#### 計算の原理
このインジケーターは、以下の3ステップでウェイトを算出しています。
1. **基準価格の取得**
ユーザーが設定した「ウェイト計算の開始日」における、4つの各銘柄の終値を取得し、これを分析の**基準価格**とします。
2. **パフォーマンス指数の算出**
現在の各銘柄の価格を、ステップ1で取得した**基準価格**で割ることで、「パフォーマンス指数」を算出します。
`パフォーマンス指数 = 現在の価格 ÷ 基準日の価格`
これにより、基準日のパフォーマンスを「1」とした場合、現在のパフォーマンスが何倍になっているかが数値化されます。
3. **ウェイトの算出**
4つの銘柄の「パフォーマンス指数」の合計値を算出します。そして、合計値に占める各銘柄のパフォーマンス指数の割合（%）を計算し、チャートに描画します。
`ウェイト (%) = (個別のパフォーマンス指数 ÷ 4銘柄のパフォーマンス指数の合計) × 100`
このロジックにより、分析開始日には全銘柄のパフォーマンス指数が「1」となるため、ウェイトは均等に25%からスタートします。
#### 使用方法
* **応用例1：市場のセンチメント分析（デフォルト設定利用）**
デフォルト設定の資産クラスで分析し、「株式」と「債券」の力関係を見ることで、市場がリスクオンなのかリスクオフなのかを判断する材料になります。
* **応用例2：セクター・テーマ別の強弱分析**
設定画面で、例えば「半導体関連の主要4銘柄」や「GAFAMの4銘柄」などを設定します。開始日を年初や決算時期に設定することで、同セクター内でどの銘柄が最もパフォーマンスが良いかを一目で比較できます。
* **応用例3：仮想通貨の勢力図分析**
「BTC, ETH, SOL, ADA」など、主要な仮想通貨を設定することで、市場の資金がどの通貨に向かっているのかを分析できます。
**【凡例の表示について】**
Pine Scriptの仕様上の制約により、チャート上の凡例は**「銘柄1」〜「銘柄4」という固定名で表示されます。**
お手数ですが、設定画面でご自身が「銘柄1」に入力したシンボルが、チャート上の「銘柄1」のラインに対応する、という形でご覧ください。
#### 設定項目
* **銘柄1〜銘柄4:** 分析したい4つのシンボルをそれぞれ設定します。
* **計算に使う時間足:** インジケーターがウェイトを計算する際に参照する時間足を選択します。
* **ウェイト計算の開始日:** パフォーマンスを比較する上での基準日となります。
#### 免責事項
このスクリプトはあくまで市場分析を補助するためのツールであり、特定の金融商品の売買を推奨するものではありません。投資の最終的な判断は、ご自身の責任において行ってください。
BFM Yen Carry to Risk Ratio (Dynamic Rates)Shows risk of yen carry trade unwinding. Based on cost to borrow from Japan to buy us stocks compared to interest rate in USA.
Position Sizer SimplifiedThis is a Pine Script® indicator for TradingView called "Position Sizer Simplified". Its primary function is to help a trader quickly calculate the appropriate position size for a trade based on their chosen risk tolerance, account size, and the trade's entry/stop-loss levels. The results are displayed neatly in a customizable table on the chart.
This tool is essential for proper risk management in trading.
Core Functionality & Inputs
The script uses a few key inputs to perform its calculations:
Account & Risk Configuration
Account Size: You can define and switch between two account sizes (account_size_1 and account_size_2) using the account_option toggle ("P1" or "P2"). The chosen size determines the total capital.
Risk % per Trade (risk_percent): This is the percentage of your chosen account size that you are willing to lose on a single trade. Example: 0.5% risk on a $180,000 account means you risk $900 per trade.
Trade Parameters
Entry Price, Stop Loss Price, Target Price: These are the manual prices a trader enters for their planned trade.
Reset All Inputs (enable_reset): A toggle to quickly clear the three price inputs by setting them to 0.
🧮 Key Calculations
The script calculates several critical values to determine the position size:
Risk per Trade: The actual dollar amount you are risking:
Account Size×(100Risk %)
Stop Distance: The price difference between the entry and stop-loss:
Entry Price−Stop Loss Price
(This assumes a Long trade; for a Short trade, the calculation would be reversed, but the magnitude must be positive for the next step).
Position Size: The maximum number of shares/contracts you can buy/sell while keeping the dollar risk within your Risk per Trade amount. This is the main output:
Position Size=Floor(Stop DistanceRisk per Trade)
The math.floor() function ensures the position size is a whole number (no fractional shares).
Capital Required: The total cost to open the calculated position:
Position Size×Entry Price
Risk/Reward (R:R) Ratio: The potential reward compared to the risk taken:
Stop DistanceTarget Price−Entry Price
Table Display & Customization
The script's output is displayed in a customizable table on the chart.
Display Toggles
A large section of boolean (input.bool) variables (e.g., show_position_size, show_rr_ratio) allows the user to turn on/off individual rows in the results table, customizing what information is shown.
Visual Settings
Table Position: The user can select one of four corners for the table (Top Right, Bottom Right, Top Left, Bottom Left).
Colors and Size: Extensive inputs are provided to customize the table's background, border, font size, and text colors.
Conditional Coloring
The script uses colors to provide quick visual warnings and checks on key metrics:
Risk % per Trade:
Green/Lime for ≤1.0% (Low Risk)
Orange for >1.0% and ≤2.0% (Medium Risk)
Red for >2.0% (High Risk)
R:R Ratio:
Green/Lime for ≥2 (Good)
Red for <2 (Bad)
Capital Check:
Green if Capital Required ≤ Account Size (Within Limit)
Red if Capital Required > Account Size (Exceeds Account)
Displayed Outputs
The table provides a comprehensive set of calculated metrics, including:
Current Ticker: The symbol of the asset being traded.
Position Size: The calculated share/contract quantity.
Risk per Trade: The dollar amount risked.
Stop Distance (pts/%): How far the stop-loss is from the entry price, in both price points and a percentage of the entry price.
Target Reward ($/%): The potential profit in dollars and as a percentage.
R:R Ratio: The calculated Risk/Reward ratio.
Target 1 (50%): Half the distance to the full target (potential partial take profit).
Target 2 (100%): The full target_price.
Capital Check: A quick status on whether the trade exceeds the total account size.
Summary: A single line detailing the trade direction (Long/Short), prices, size, and R:R ratio.
This indicator is a powerful tool for traders who want to maintain strict, quantifiable risk control on every position they take.
Institutional elite indicator
🎯 Overview
An institutional-grade technical analysis indicator that combines 10+ professional indicators into a unified, easy-to-read signal system. Designed for precision trading across all timeframes (1min to 1month) with Heikin Ashi compatibility.
📈 Signal Types
Breakout Signals: Early detection before major price movements
Divergence Detection: RSI and MACD bullish/bearish divergences
Bottom Fishing: Identification of lowest price points before rebounds
Reversal Signals: Trend reversal detection in overbought/oversold zones
MA Crossovers: Enhanced 50/200 MA cross detection with volume confirmation
⚙️ Technical Specifications
Compatible: Heikin Ashi candles, all timeframes
Configurable: 25+ adjustable parameters
Performance: Optimized for institutional-grade accuracy
Interface: Clean, non-cluttered visual design with compact signals
🚀 Use Cases
Day Trading: Precise entry/exit points on lower timeframes
Swing Trading: Trend identification and reversal detection
Institutional Analysis: Multi-indicator confirmation system
Risk Management: Volume-confirmed signals with ADX trend strength
Perfect for traders seeking institutional-level precision with simplified execution.
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.
Apex Flow | OquantOverview
Apex Flow is a rotational allocation indicator designed for cryptocurrency traders seeking to dynamically shift capital between a selection of assets based on trend strength and relative performance. It aims to capture upside in trending markets while reducing exposure during weaker periods by incorporating a multi-factor trend detection system and a relative strength ranking mechanism. This tool is built to provide a structured approach to portfolio allocation in crypto environments, emphasizing risk management through trend confirmation and a built-in risk-on filter to avoid drawdowns in non-conducive conditions. Its originality lies in the ensemble of customized trend filters combined with pairwise relative strength comparisons, which together create a robust scoring system for asset selection—going beyond simple momentum or single-indicator rotations to offer a more nuanced, adaptive strategy.
Key Factors/Components
Multi-Trend Detection Ensemble: Utilizes a blend of moving average-based filters, deviation bands, and momentum oscillators to evaluate overall market trend direction.
Relative Strength Ranking: Compares assets pairwise to determine dominance, assigning scores that influence allocation priorities.
Allocation Splits: Supports configurable splits between the top-performing asset(s) and secondary ones, allowing for concentrated or diversified exposure.
risk-on Filter: Applies smoothing techniques to the simulated portfolio equity to confirm uptrend viability, acting as a macro-level risk overlay.
Performance Metrics and Visuals: Includes built-in tables for allocations, metrics (like Sharpe, Sortino, Omega ratios, max drawdown, and net profit), and an asset matrix for transparency in decision-making.
How It Works
The indicator first assesses each asset's trend state using an ensemble of seven trend-following components, each contributing to a composite score that signals whether the asset is in a bullish trend, or bearish trend. Thresholds determine when an asset qualifies for allocation (e.g., requiring a majority positive score for inclusion). Next, eligible assets are ranked via relative strength calculations derived from pairwise trend comparisons, producing a dominance score for each. The highest-scoring asset(s) receive primary allocation, with optional secondary allocation to the next tier based on user-defined splits (e.g., 80/20). Daily returns are then used to simulate a portfolio equity curve, which is filtered through multiple smoothing methods to ensure the overall strategy is in an "up" state before committing capital—otherwise, it defaults to cash. This process helps prioritize stronger assets while incorporating safeguards against prolonged downtrends, though it may lag in rapidly reversing markets due to its confirmatory nature.
For Who It Is Best/Recommended Use Cases
This indicator is ideal for intermediate to advanced cryptocurrency traders who appreciate an active, systematic strategy for rotating capital across a basket of assets (e.g., major cryptos like BTC, ETH, SOL, and SUI). It's suited for medium-term on the 1D timeframe, where the ensemble of trend filters and relative strength rankings can identify and capitalize on multi-day to weekly momentum shifts in trending markets. Recommended for those actively managing diversified crypto portfolios to potentially outperform buy-and-hold benchmarks with controlled volatility. It's not optimized for short-term scalping (e.g., intraday), highly illiquid assets, or prolonged range-bound conditions, where its confirmatory logic may lead to delayed/false signals. Always integrate it with your own risk management practices.
Settings and Default Settings
Strategy Start Date: Timestamp for "1 Jan 2023" – Defines the backtest start; adjust to test different periods.
Assets: Asset 1 ("INDEX:BTCUSD"), Asset 2 ("INDEX:ETHUSD"), Asset 3 ("CRYPTO:SOLUSD"), Asset 4 ("CRYPTO:SUIUSD") – Select up to four cryptos; defaults focus on major ones.
Allocation Split: "100/0" – Options include 80/20, 70/30, 60/40; default fully allocates to the top asset(s).
Plot Equity Curves: Strategy equity and btc equity enabled by default – Toggle to visualize strategy and individual assets.
Show Tables: All enabled – Display allocation, metrics, and asset matrix for real-time insights.
Internal parameters like trend lengths and multipliers are fixed to balance sensitivity and reliability, optimized for daily crypto charts.
Conclusion
Apex Flow offers a systematic way to navigate crypto rotations by blending trend confirmation with relative strength, potentially enhancing returns in bullish cycles while preserving capital in others. Its ensemble approach and equity filter provide a layer of robustness not found in simpler rotators, making it a valuable addition for trend-oriented portfolios.
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
Portfolio Simulator & BacktesterMulti-asset portfolio simulator with different metrics and ratios, DCA modeling, and rebalancing strategies.
Core Features
Portfolio Construction
Up to 5 assets with customizable weights (must total 100%)
Support for any tradable symbol: stocks, ETFs, crypto, indices, commodities
Real-time validation of allocations
Dollar Cost Averaging
Monthly or Quarterly contributions
Applies to both portfolio and benchmark for fair comparison
Model real-world investing behavior
Rebalancing
Four strategies: None, Monthly, Quarterly, Yearly
Automatic rebalancing to target weights
Transaction cost modeling (customizable fee %)
Key Metrics Table
CAGR: Annualized compound return (S&P 500 avg: ~10%)
Alpha: Excess return vs. benchmark (positive = outperformance)
Sharpe Ratio: Return per unit of risk (>1.0 is good, >2.0 excellent)
Sortino Ratio: Like Sharpe but only penalizes downside (better metric)
Calmar Ratio: CAGR / Max Drawdown (>1.0 good, >2.0 excellent)
Max Drawdown: Largest peak-to-trough decline
Win Rate: % of positive days (doesn't indicate profitability)
Visualization
Dual-chart comparison - Portfolio vs. Benchmark
Dollar or percentage view toggle
Customizable colors and line width
Two tables: Statistics + Asset Allocation
Adjustable table position and text size
🚀 Quick Start Guide
Enter 1-5 ticker symbols (e.g., SPY, QQQ, TLT, GLD, BTCUSD)
Make sure percentage weights total 100%
Choose date range (ensure chart shows full period - zoom out!)
Configure DCA and rebalancing (optional)
Select benchmark (default: SPX)
Analyze results in statistics table
💡 Pro Tips
Chart data matters: Load SPY or your longest-history asset as main chart
If you select an asset that was not available for the selected period, the chart will not show up! E.g. BTCUSD data: Only available from ~2017 onwards.
Transaction fees: 0.1% default (adjust to match your broker)
⚠️ Important Notes
Requires visible chart data (zoom out to show full date range)
Limited by each asset's historical data availability
Transaction fees and costs are modeled, but taxes/slippage are not
Past performance ≠ future results
Use for research and education only, not financial advice
Let me know if you have any suggestions to improve this simulator.
THOR SignalTHOR Signal Indicator
Trend Regime Detection via Volatility-Normalized Acceleration Scoring
The THOR Signal Indicator classifies market direction into “long-favorable” or “short/risk-off” regimes using a three-layer signal process that adapts to volatility, momentum strength, and directional consistency. This script is specifically designed for swing traders looking to reduce false positives during choppy or trendless periods.
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How It Works
THOR does not use standard technical indicators like RSI, MACD, or moving average crossovers. Instead, it creates a composite signal using three custom-calculated conditions:
1. Volatility-Normalized Price Deviation:
• Measures how far price has moved relative to recent volatility.
• Helps distinguish between meaningful trend movement and noise.
2. Directional Acceleration Score:
• Calculates the second derivative (rate of change of momentum) of a smoothed trend backbone.
• Signals regime shifts only when acceleration exceeds a dynamic threshold.
3. Persistence Filter:
• Applies a custom smoothing layer (similar to a Kalman filter) to confirm that directional strength is sustainable and not short-term whipsaw.
• Filters out one-bar momentum spikes.
A signal is plotted only when all three layers agree:
• Green dot = Long-side favorable regime
• Red dot = Short-side or risk-off regime
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Why This Is Different
Unlike standard momentum tools that rely on fixed thresholds (like RSI overbought/oversold levels), THOR dynamically adapts its regime criteria based on the asset’s own behavior. It avoids laggy confirmation signals by using real-time volatility conditioning and trend persistence scoring.
It is not a mashup of public indicators. No MA crossovers, Bollinger Bands, or known oscillator logic is used. The architecture is original and built entirely from low-level functions and mathematical modeling.
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How to Use It
• Best timeframes: Weekly, Daily, or Renko
• Use bar-close confirmation only (do not trade intrabar signals)
• Green dot: Consider long position or hold existing longs
• Red dot: Consider exiting longs or entering defensive stance
• Use with existing risk management and discretionary context
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Use Case
• Swing trade filter
• Trend regime switch detector
• Allocation toggling (risk-on vs risk-off)
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Disclosures
This is a closed-source script. Logic has been explained conceptually to comply with TradingView script publishing policies. No proprietary code is exposed. The tool is not intended as financial advice and does not guarantee accuracy or profitability.
DOT BOMB: Outlier Mark + Candle ColoringThis indicator helps us to get in where the probability of good trade is the highest which in my terms its called as the casino way of trading.
Works with every asset class/ every chart.
Useful for any trader to progress/recover their account which is mathematically made and proven.
DOT BOMB: Outlier Mark + Candle Coloringthis indicator helps us to get in where the probability of good trade trade is the highest which in my terms its called as the casino way of trading
works with asset class useful for any trader to progress/recover their account which is mathematically made and proven
Laguerre Filter Trend Navigator [QuantAlgo]🟢 Overview
The Laguerre Filter Trend Navigator employs advanced polynomial filtering mathematics to smooth price data while minimizing lag, creating a responsive yet stable trend-following system. Unlike simple moving averages that apply equal weight to historical data, the Laguerre filter uses recursive calculations with exponentially weighted polynomials to extract meaningful directional signals from noisy market conditions. Combined with dynamic volatility-adjusted boundaries, this creates an adaptive framework for identifying high-probability trend reversals and continuations across all tradable instruments and timeframes.
🟢 How It Works
The indicator leverages Laguerre polynomial filtering, a mathematical technique originally developed for digital signal processing applications. The core mechanism processes price data through four cascaded filter stages (L0, L1, L2, L3), each applying the gamma coefficient to recursively smooth incoming information while preserving phase relationships. This multi-stage architecture eliminates random fluctuations more effectively than traditional moving averages while responding quickly to genuine directional shifts.
The gamma coefficient serves as the primary smoothing control, determining how aggressively the filter dampens noise versus tracking price movements. Lower gamma values reduce smoothing and increase filter responsiveness, while higher values prioritize stability over reaction speed. Each filter stage compounds this effect, creating progressively smoother output that converges toward true underlying trend direction.
Surrounding the filtered price line, the algorithm constructs adaptive boundaries using dynamic volatility regime measurements. These calculations quantify current market turbulence independently of direction, expanding during active trading periods and contracting during quiet phases. By multiplying this volatility assessment by a user-defined scaling factor, the system creates self-adjusting bands that automatically conform to changing market conditions without manual intervention.
The trend-following engine monitors price position relative to these volatility-adjusted boundaries. When the upper band falls below the current trend line, the system shifts downward to track bearish momentum. Conversely, when the lower band rises above the trend line, it elevates to follow bullish movement. These crossover events trigger color transitions between bullish (green) and bearish (red) states, providing clear visual confirmation of directional changes validated by volatility-normalized thresholds.
🟢 How to Use
Green/Bullish Trend Line: Laguerre filter positioned in upward trajectory, indicating momentum-confirmed conditions favorable for establishing or maintaining long positions (buy)
Red/Bearish Trend Line: Laguerre filter trending downward, signaling regime-validated environment suitable for initiating or holding short positions (sell)
Rising Green Line: Accelerating bullish filter with expanding separation from price lows, demonstrating strengthening upward momentum and increasing confidence in trend persistence with optimal long entry timing
Declining Red Line: Steepening bearish filter creating growing distance from price highs, revealing intensifying downside pressure and enhanced probability of continued decline with favorable short positioning opportunities
Flattening Trends: Horizontal or oscillating filter movement regardless of color suggests directional uncertainty where price action contradicts filter positioning, potentially indicating consolidation phases or impending volatility expansion requiring cautious trade management
🟢 Pro Tips for Trading and Investing
→ Preset Selection Framework: Match presets to your trading style - Scalping preset employs aggressive gamma (0.4) with tight volatility bands (1.0x) for rapid signal generation on sub-15-minute charts, Day Trading preset balances responsiveness and stability for hourly timeframes, while Swing Trading preset maximizes smoothing (0.8 gamma) with wide bands (2.5x) to filter intraday noise on daily and weekly charts.
→ Gamma Coefficient Calibration: Adjust gamma based on market personality - reduce values (0.3-0.5) for highly liquid, fast-moving assets like major currency pairs and tech stocks where quick filter adaptation prevents lag-induced losses, increase values (0.7-0.9) for slower instruments or trending markets where excessive sensitivity generates false reversals and whipsaw trades.
→ Volatility Period Optimization: Tailor the volatility measurement window to information cycles. Deploy shorter lookback periods (7-10) for instruments with rapid regime changes like individual equities during earnings seasons, standard periods (14-20) for balanced assessment across general market conditions, and extended periods (21-30) for commodities and indices exhibiting persistent volatility characteristics.
→ Band Width Multiplier Adaptation: Scale boundary distance to current market phase. Contract multipliers (1.0-1.5) during range-bound consolidations to capture early breakout signals as soon as genuine momentum emerges, expand multipliers (2.0-3.0) during trending markets or high-volatility events to avoid premature exits caused by normal retracement activity rather than authentic reversals.
→ Multi-Timeframe Filter Alignment: Implement the indicator across multiple timeframes, using higher intervals (4H/Daily) to identify primary trend direction via filter slope and lower intervals (15min/1H) for precision entry timing when filter colors align, ensuring trades flow with dominant momentum while optimizing execution at favorable price levels.
→ Alert-Driven Systematic Execution: Configure trend change alerts to capture every filter-validated directional shift from bullish to bearish conditions or vice versa, enabling consistent signal response without continuous chart monitoring and eliminating emotional decision-making during critical transition moments.
Bayesian Trend Navigator [QuantAlgo]🟢 Overview
The Bayesian Trend Navigator uses Bayesian statistics to continuously update trend probabilities by combining long-term expectations (prior beliefs) and short-term observations (likelihood evidence), rather than relying solely on recent price data like many conventional indicators. This mathematical framework produces robust directional signals that naturally balance responsiveness with stability, making it suitable for traders and investors seeking statistically-grounded trend identification across diverse market environments and asset types.
🟢 How It Works
The indicator operates on Bayesian inference principles, a statistical method for updating beliefs when new evidence emerges. The system begins by establishing a prior belief - a long-term trend expectation calculated from historical price behavior. This represents the "baseline hypothesis" about market direction before considering recent developments.
Simultaneously, the algorithm collects recent market evidence through short-term trend analysis, representing the likelihood component. This captures what current price action suggests about directional momentum independent of historical context.
The core Bayesian engine then combines these elements using conjugate normal distributions and precision weighting. It calculates prior precision (inverse variance) and likelihood precision, combining them to determine a posterior precision. The resulting posterior mean represents the mathematically optimal trend estimate given both historical patterns and current reality. This posterior calculation includes intervals derived from the posterior variance, providing probabilistic confidence bounds around the trend estimate.
Finally, volatility-based standard deviation bands create adaptive boundaries around the Bayesian estimate. The trend line adjusts within these constraints, generating color transitions between bullish (green) and bearish (red) states when the posterior calculation crosses these probabilistic thresholds.
🟢 How to Use
Green/Bullish Trend Line: Posterior probability favoring upward momentum, indicating statistically favorable conditions for long positions (buy)
Red/Bearish Trend Line: Posterior probability favoring downward momentum, signaling mathematically supported timing for short positions (sell)
Rising Green Line: Strengthening bullish posterior as new evidence reinforces upward beliefs, showing increasing probabilistic confidence in trend continuation with favorable long entry conditions
Declining Red Line: Intensifying bearish posterior with accumulating downside evidence, indicating growing statistical certainty in downtrend persistence and optimal short positioning opportunities
Flattening Trends: Diminishing posterior confidence regardless of color suggests equilibrium between prior beliefs and contradictory evidence, potentially signaling consolidation or insufficient statistical clarity for high-conviction trades
🟢 Pro Tips for Trading and Investing
→ Preset Configuration Strategy: Deploy presets based on your trading horizon - Scalping preset maximizes evidence weight (0.8) for rapid Bayesian updates on 1-15 minute charts, Default preset balances prior and likelihood for general applications, while Swing Trading preset equalizes weights (0.5/0.5) for stable inference on hourly and daily timeframes.
→ Prior Weight Adjustment: Calibrate prior weight according to market regime - increase values (0.5-0.7) in stable trending markets where historical patterns remain predictive, decrease values (0.2-0.3) during regime changes or news-driven volatility when recent evidence should dominate the posterior calculation.
→ Evidence Period Tuning: Modify the evidence period based on information flow velocity. Use shorter periods (5-8 bars) for assets with continuous price discovery like cryptocurrencies, medium periods (10-15) for liquid stocks, and longer periods (15-20) for slower-moving markets to ensure adequate likelihood sample size.
→ Likelihood Weight Optimization: Adjust likelihood weight inversely to market noise levels. Higher values (0.7-0.8) work well in clean trending conditions where recent data is reliable, while lower values (0.4-0.6) help during choppy periods by maintaining stronger reliance on established prior beliefs.
→ Multi-Timeframe Bayesian Confluence: Apply the indicator across multiple timeframes, using higher timeframes (Daily/Weekly) to establish prior belief direction and lower timeframes (Hourly/15-minute) for likelihood-driven entry timing, ensuring posterior probabilities align across temporal scales for maximum statistical confidence.
→ Standard Deviation Multiplier Management: Adapt the multiplier to match current uncertainty levels. Use tighter multipliers (1.0-1.5) during low-volatility consolidations to capture early trend emergence, and wider multipliers (2.0-2.5) during high-volatility events to avoid premature signals caused by statistical noise rather than genuine posterior shifts.
→ Variance-Based Position Sizing: Monitor the implicit posterior variance through trend line stability - smooth consistent movements indicate low uncertainty warranting larger positions, while erratic fluctuations suggest high statistical uncertainty calling for reduced exposure until clearer probabilistic convergence emerges.
→ Alert-Based Probabilistic Execution: Utilize trend change alerts to capture every statistically significant posterior shift from bullish to bearish states or vice versa without constantly monitoring the charts.
ICT Multi-Timeframe Market Structure Tracker [SwissAlgo]ICT Multi-Timeframe Market Structure Tracker
Tracks the ICT market structure across three core timeframes (1-Week, 1-Day, 1-Hour) simultaneously.
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Why this Indicator?
You know market structure matters, whether you trade stocks, Forex, commodities, or crypto.
You've studied ICT concepts - " Change of Character ", " Break of Structure ", " Premium/discount zones ". You understand that multi-timeframe alignment is where the edge lives.
But here's what's probably happening while you apply the ICT concepts for your trading decisions:
You're manually drawing structural highs and lows across three timeframes
You're calculating Fibonacci retracements by hand for each timeframe
You're switching between weekly, daily, and hourly charts, trying to remember where each pivot was, trying to detect the critical events you're waiting for
By the time you've mapped it all out, the setup is gone. Or worse, you missed that the 1-hour just broke the structure while you were checking the weekly bias.
What about seeing all three timeframes at once instead? You need to know immediately when the price enters a premium or discount zone. You need alerts that fire when structure breaks or character changes - across all timeframes - without babysitting your screen.
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The Indicator, at a Glance
This indicator:
tracks ICT market structure across three core timeframes (1-Week, 1-Day, 1-Hour) simultaneously .
automatically plots Fibonacci retracement levels from your defined structural pivots
monitors price position (during retracements) in real-time
sends consolidated alerts when actionable events occur on any timeframe
The 1-Week View: Mid-Term Trend Bias for lower timeframes
The 1-Day View: Swings nested within the 1-Week Structure
The 1-Hour View: Swings nested within the 1-Day Structure
One glance tells you:
* Current trend direction per timeframe
* Exact Fib zone price is trading right now
* Whether the structure just broke or the character changed
* If you're in a potential long/short setup zone
The indicator helps you reduce chart-hopping, manual calculations, and minimize the missed structural shifts.
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Who is this for?
This tool is built for day traders who understand ICT concepts and need efficient multi-timeframe structure tracking. If you know what a Change of Character is, why 0.382-0.5 retracements matter in uptrends, and how to trade external structure, this indicator eliminates the manual structure tracking so you can focus on confirming and executing your trading tactics.
New to ICT? This indicator assumes foundational knowledge of the Inner Circle Trader methodology developed by Michael J. Huddleston. Before using this tool, familiarize yourself with concepts like market structure breaks, premium/discount arrays, and liquidity engineering. The ICT framework offers a unique perspective on institutional order flow and price action - but this indicator is designed for those already applying these concepts, not learning them for the first time.
Critical Skill Required : You must understand the difference between external structure (key swing highs/lows that define market direction) and internal structure (minor fluctuations within the range).
Selecting incorrect pivots - such as marking internal noise instead of true structural points - will generate false signals and undermine the entire analysis. This indicator tracks structure based on YOUR inputs. If those inputs are wrong, every Fibonacci level, alert, and bias signal will be wrong. Learn to identify clean structural breaks before using this tool.
Trading Experience Matters: This tool tracks structure and fires alerts, but interpreting those signals requires understanding context, confluences, and risk management. If you're early in your trading journey, consider this a professional-grade instrument that becomes powerful once you have the conceptual foundation to use it effectively.
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How It Works
Step 1: Define Your Structure
You, the ICT expert or student, define the structural high and low for each timeframe, with their exact dates. This empowers you to control the analysis.
Based on your entries, the indicator establishes trend direction by timeframe and calculates Fibonacci retracement levels automatically.
* Structural High/Low: Key swing points that define external structure per ICT methodology
* Auto-Validation: Built-in autoscan feature confirms your pivot entries match actual price extremes
* Deterministic Behavior: Date stamps ensure the indicator behaves consistently across all sessions
Step 2: Monitor The Tables
Two tables provide a structural context:
Multi-Timeframe Analysis Table (top-right):
Current close, high, low, and 0.5 Fib for all three timeframes
Trend direction (↑/↓)
Days since structure established (i.e., "age" or maturity)
Current Fibonacci zone
Real-time alerts: Trend changes, breakouts, and trade bias signals
Detailed Fibonacci Table (middle-right):
All nine Fib retracement levels (1.0 to 0.0) for the selected timeframe
Exact price at each level
Percentage distance from current price
Visual marker showing current position
Step 3: Monitor The Chart
Visual elements show structure at a glance:
Fibonacci Retracement Zones: Color-coded bands show premium (red), discount (green), and equilibrium (gray) areas based on trend direction
Structural Lines: Red (high) and green (low) horizontal lines mark your defined pivots with automatic fill showing the current range (based on higher timeframe pivots)
Pivot Dots: Optional small markers highlight potential structural turning points on your current timeframe (reference only - always validate pivots yourself)
Trend Indicator: Top-center banner displays the selected timeframe's current trend
Auto-pivot points
Step 4: Get Alerts and Decide the Way Forward
Set one alert on the 1-hour chart only (if you set the alert on other timeframes, you may get delayed feedback).
You'll receive notifications when ANY of these events occur on ANY timeframe:
* Change of Character (ChoC): Trend reversal confirmed by price breaking the opposite structural level
* Break of Structure (BoS): Continuation confirmed by price breaking the same-direction structural level
* Trade Bias Signals: Price entering key Fibonacci zones (0.382-0.5 for longs in uptrend, 0.5-0.618 for shorts in downtrend, with + and ++ variants for deeper retracements)
* Reversal Warnings: Price entering extreme zones (0.882-1.0 or 0.0-0.118), suggesting potential trend exhaustion and reversal towards the opposite direction
All alerts fire once per bar close with a consolidated message showing which timeframes triggered and what conditions were met.
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Understanding the 3 Timeframes Hierarchy
The three timeframes may be conceived as nested layers of structure:
* 1-Week (Macro Bias) : May help you determine your core directional bias (long/short) in a mid-term perspective. The 1-Week TF may operate as your highest-conviction filter and help you contextualize shorter-term market moves (which may align or misalign with the trend appearing on such a timeframe).
* 1-Day (Swing Structure) : Operates within the weekly range. The daily structure can contradict the weekly structure temporarily (due to retracements, consolidations). This is where you may identify intermediate swing opportunities.
* 1-Hour (Execution Structure) : Operates within the daily range. It may help you identify entry timing and short-term bias. Can show opposite trends during retracements, and some traders look for alignment with higher timeframes as part of their setup criteria.
Example: Weekly uptrend (bullish bias) → Daily pulls back into downtrend (retracement phase) → Hourly shows uptrend resumption (this may be interpreted as an entry signal). All three trends can differ simultaneously, but when all three align (in one direction or another), you may start evaluating your moves.
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Using the Tool effectively
When this indicator signals a potential setup (entering key Fibonacci zones, structure breaks, or bias shifts), treat it as a starting point for deeper analysis, not a direct entry signal.
Before executing, consider using additional tools to refine timing:
Fair Value Gaps (FVG) : Identify imbalances where the price moved too quickly, leaving potential fill zones
Order Blocks : Locate the last opposing candle before a strong move - often institutional entry points
Liquidity Zones : Map where stop losses likely cluster (equal highs/lows, round numbers)
Premium/Discount Confirmation: Verify you're buying at a discount or selling at a premium relative to the current range
Session Timing/Kill Zones : Align entries with high-liquidity sessions (London/New York opens)
This indicator shows you where the structure sits and when it shifts. Your job is to combine that context with precise entry models. The alerts narrow your focus to high-probability zones - then you apply your edge within those zones.
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How to Set Up Alerts
This indicator monitors all three timeframes simultaneously and fires consolidated alerts when any condition triggers. Follow these steps to configure alerts properly:
Step 1: Set Your Chart to the 1-Hour Timeframe
Alerts must be created on the 1-hour chart for optimal timing
Do not use higher timeframes (4H, 1D, 1W) or alerts may be delayed
Lower timeframes (15M, 5M) will work but may generate more frequent notifications
Step 2: Open the Alert Menu
Click the "Alert" button (clock icon) in the top toolbar
Or use keyboard shortcut: Alt+A (Windows) / Option+A (Mac)
Step 3: Configure Alert Settings
Condition: Select "ICT Multi-Timeframe Market Structure Tracker "
Alert Type: Choose "Any alert() function call"
Options: Select "Once Per Bar Close"
Expiration: Set to "Open-ended alert" (no expiration)
Alert Name: Choose a descriptive name (e.g., "BTC Market Structure Alerts")
Step 4: Configure Notifications
Notification Methods: Check your preferred channels (app notification, email, webhook, etc.)
Sound: Optional — choose alert sound if desired
Step 5: Create Alert
Click the "Create" button
Alert is now active and will monitor all three timeframes
Important Notes:
You only need ONE alert setup total — it monitors 1W, 1D, and 1H simultaneously
Alert messages show which timeframe(s) triggered and what conditions were met
Alerts fire once per bar close to avoid mid-bar noise
If you change your structural pivot inputs, the alert continues working with new parameters
Example Alert Message:
BTC Market Structure Alert:
🟢 1D Bullish BoS
📈 1H Long Setup (0.382-0.5)
This tells you the 1-Day broke structure bullishly AND the 1-Hour entered a long setup zone — both events happened on the same bar close.
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Key Features
* Tracks 1-Week, 1-Day, and 1-Hour structure simultaneously
* Automatic Fibonacci retracement calculation (9 levels + extensions up or down, depending on timeframe trend)
* Real-time Change of Character and Break of Structure detection
* Color-coded premium/discount zone visualization
* Multi-condition alerts across all timeframes (single alert setup required)
* Autoscan validation to confirm manual pivot entry accuracy
* Timezone-adjustable for global markets
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Important Notes
* Requires ICT Knowledge: This is not a plug-and-play system. Understanding market structure, liquidity concepts, and Fibonacci confluence is essential for effective use.
* Manual Structure Definition: You define the structural pivots. The indicator tracks and alerts - it doesn't make trading decisions.
* Chart Timeframe: Set alerts on the 1-hour chart for optimal timing across all three monitored timeframes.
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Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial, investment, or trading advice.
The indicator:
* Makes no guarantees about future market performance
* Cannot predict market movements with certainty
* May generate false indications
* Relies on historical patterns that may not repeat
* Should not be used as the sole basis for trading decisions
Users are responsible for:
* Conducting independent research and analysis
* Understanding the risks of trading
* Making their own investment/divestment decisions
* Managing position sizes and risk exposure appropriately
Trading involves substantial risk and may not be suitable for all investors. Past performance does not guarantee future results. Users should only invest what they can afford to lose and consult qualified professionals before making financial decisions. The indicator’s assumptions may be invalidated by changing market conditions.
By using this tool, users acknowledge these limitations and accept full responsibility for their trading decisions.
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView.
It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%.
All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC.
🚀 Goal
This script helps traders and investors:
• Understand the combined performance of their portfolio.
• Normalize diverse assets into a single synthetic chart .
• Make portfolio-level insights without relying on external spreadsheets.
🎯 Use Cases
• Backtest your portfolio allocations directly on the chart.
• Compare your portfolio vs. benchmarks like SPY, QQQ, BTC.
• Track thematic baskets (commodities, EV supply chain, regional ETFs).
• Visualize how each component contributes to overall performance.
📊 Features
• Weighted Portfolio Performance : Combines selected assets into a synthetic value series.
• Base Price Alignment : Each asset is normalized to its starting price at the chosen date.
• Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%.
• Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol.
⚙️ Configuration
Flexible Portfolio Setup
• Add up to 15 assets with custom weight inputs.
• You can enter any arbitrary numbers (e.g. 30, 15, 55).
• The script automatically normalizes all weights so the total allocation always equals 100%.
Start Date Selection
• Choose any custom start date to normalize all assets.
• The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ.
Chart Styles
• Candlestick chart
• Heikin Ashi chart
• Line chart
Custom Display
• Adjustable colors and line widths
• Optionally display asset list, normalized weights, and equivalent shares
⚙️ How It Works
• Fetch OHLC data for each asset.
• Normalizes weights internally so totals = 100%.
• Stores each asset’s base price at the selected start date.
• Calculates equivalent “shares” for each allocation.
• Builds a synthetic portfolio value series by summing weighted contributions.
• Renders as Candlestick, Heikin Ashi, or Line chart.
• Adds a portfolio info table for clarity.
⚠️ Notes
• This script is for visualization only . It does not place trades or auto-rebalance.
• Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
Position Size Calculator includes Acct % Risk (Improved)Uses account size to determine position size. Sets the stop at the low of the day for swing trading.
DCA vs One-ShotCompare a DCA strategy by choosing the payment frequency (daily, weekly, or monthly), and by choosing whether or not to pay on weekends for cryptocurrency. You can add fees and the reference price (opening, closing, etc.).
🔵Blue Mark📌 Blue Mark – TradingView Indicator
The Blue Mark indicator highlights extreme price points across multiple timeframes (15m, 5m, 1m), helping intraday traders identify liquidity zones and areas of institutional interest. It is designed for traders who want to spot high-probability entry and exit points based on market structure and volume concentration.
✔️ Marks extreme highs and lows on 15m, 5m, and 1m charts
✔️ Ideal for intraday trading and short-term strategies
✔️ Helps detect liquidity zones where institutional orders are likely concentrated
✔️ Supports tactical entries and exits aligned with market structure
How to use:
Apply the indicator to your chart.
Observe the marked extreme points on different timeframes.
Use these levels to plan entries, exits, or confirm areas of institutional interest.
Altcoins Exit Executor: 3Commas-Integrated [SwissAlgo]Title: Altcoins Exit Executor: 3Commas-Integrated
Plan and Execute your Altcoins Exits via 3Commas Integration
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1. Facing These Struggles?
You're holding a portfolio of altcoins, and the question keeps nagging you: when should you exit? how?
If you're like many crypto traders, you might recognize these familiar struggles:
The Planning Problem : You know you should have an exit strategy, but every time you sit down to plan it, you get overwhelmed. Should you sell at 2x? 5x? What about that resistance level you spotted last month? You end up postponing the decision again and again.
The Execution Headache : You use 3Commas (or an Exchange directly) for your trades, but setting up Smart Trades for multiple coins means endless manual data entry. Price levels, percentages, quantities - by the time you finish entering everything, the market may have already moved.
The Portfolio Scale Problem : Managing 5 altcoins is challenging enough, but what about 15? Or 30? The complexity grows exponentially with each additional position. What started as a manageable analysis for a few coins becomes an overwhelming juggling act that may lead to rushed decisions or complete paralysis.
The Consistency Challenge : You approach each coin differently. Maybe you're conservative with one position and aggressive with another, without any systematic reasoning. Your portfolio becomes a patchwork of random decisions rather than a coherent strategy. With dozens of positions, maintaining any consistent approach becomes nearly impossible.
The "What If" Anxiety : What happens if the market crashes while you're sleeping? You know you should have stop-losses, but setting them up properly across multiple positions feels overwhelming. The more coins you hold, the more potential failure points you need to monitor.
The Information Overload : You collect multiple data points, but how do you synthesize all this information into actionable exit points? Multiply this analysis across 20+ different altcoins, and the task becomes nearly impossible to execute consistently.
This indicator may help address these challenges by providing you with:
A systematic approach to analyzing potential resistance levels across multiple technical frameworks. All potential resistances (including Fibonacci levels) are calculated automatically
Tools to structure your exit plan with clear take-profit levels and position sizing
Automated generation of 3Commas 'Smart Trades' that match your exit strategy exactly, without manual entry
Optional emergency exit protection that could potentially guard against sudden market reversals (exit managed within the 3Commas 'Smart Trade' itself)
A consistent methodology you can apply across your entire altcoin portfolio, regardless of size
The goal is to transform exit planning from a source of stress and procrastination into a structured, repeatable process that may help you execute your trading plan in a consistent fashion, whether you're managing 3 coins or 30.
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2. Is this for You?
This indicator is designed for cryptocurrency traders who:
Hold a portfolio of multiple altcoins (typically 5+ positions)
Are actively seeking a systematic solution to plan and execute exit strategies
Have an active 3Commas account connected to their exchange
Understand 3Commas basics: Smart Trades, API connections, and account management
Have an account tier that supports their portfolio size (3Commas Free Plan: up to 3 trades/alts, Pro Plan: up to 50+ trades/alts)
Important: This tool provides analysis and automation assistance, not trading advice. All exit decisions require your individual judgment and proper risk management.
If you don't use 3Commas, you may still find value in the resistance analysis components, though the automated execution features require a 3Commas account and basic platform knowledge.
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3. How does it work?
This indicator streamlines your exit planning process into four steps:
Step 1: Analyze Your Coin & Define Exit Plan
The indicator automatically calculates multiple types of resistance levels that may act as potential exit points:
Fibonacci Extensions (projected resistance from recent price swings)
Fibonacci Retracements (resistance from previous cycle highs)
Major Pivot Highs (historical price rejection points)
Volume Imbalances (PVSRA analysis showing institutional activity zones)
Price Multipliers (2x, 3x, 4x, 5x psychological levels)
Market Trend Analysis (bull/bear market strength assessment)
You can view all resistance types together or focus on specific categories to identify potential exit zones.
Step 2: Enter Your Exit Plan.
Define your sequential take-profit strategy:
Set up to 5 take-profit levels with specific prices
Assign percentage of coins to sell at each level
Add your total coin quantity and average entry price
Optionally enable emergency exit (stop-loss) protection. The indicator validates your plan in real-time, ensuring percentages sum to 100% and prices follow logical sequences.
Step 3: Connect with 3Commas
Relay Secret
3Commas API keys (Public and Private)
Account ID (your exchange account on 3Commas)
Step 4: Generate Smart Trade on 3Commas
Create a TradingView alert that automatically:
Sends your complete exit plan to 3Commas
Creates a Smart Trade with all your take-profit levels
Includes stop-loss protection if enabled
Requires no manual data entry on the 3Commas platform
The entire process is designed to streamline the time required to move from analysis to execution, providing a standardized methodology across your altcoin positions.
User Experience Features:
Step-by-step guided workflow
Interactive submission helper with status tracking
Exit plan table with detailed projections
Comprehensive legend and educational tooltips
Dark/light theme compatibility
Organized visual presentation of all resistance levels
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4. Using the Indicator
Complete the 4-step guided workflow within the indicator to set up an Exit Plan and submit it to 3Commas.
At the end of the process, you will see a Smart Trade created on 3Commas reflecting your custom Exit Plan (inclusive of Stop Loss, if enabled).
Recommended Settings
Analyze your Exit Plan on the 1-Day timeframe
Use the Tradingview's Dark-Theme for high visual contrast
Set candles to 'Bar-Type' to view volumr-based candle colors (PVSRA analysis)
Use desktop for full content visibility
Analyzing Resistance Levels
Enable "Show all Resistance Levels" to view comprehensive analysis across your chart
Focus on resistance clusters where multiple resistance seem to converge - these may indicate stronger potential exit zones
Note the color-coded system: gray lines indicate closer levels, red lines suggest stronger resistance or potentially "out-of-reach" targets
Pay attention to the Golden Zone (Fibonacci 0.618-0.786 area) highlighted in green, it might act as a significant price magnet for average altcoins
Decide how many Take Profit Steps to use (min. 1 - max- 5)
Setting up your Plan
Enter the total number of coins you want to sell with the script
Enter your average entry price, if known (otherwise the script will use the current price as backup)
Enter the TP levels you decided to activate (price, qty to sell at each TP level)
Decide about the Emergency Exit (the price that, when broken, will trigger the sale of 100% of your coins with a close limit order)
Setting Up Your 3Commas Connection
Generate API keys in your 3Commas account with (User Profile→3Commas API→New API Access Token→System Generated→Permission: "Smart Trades Only" (leave all other permissions unchecked) + Whitelisted IP→Create→Save API public/private key securely)
Find your Account ID in the 3Commas exchange URL (My Portfolio→View Exchange→Look at the last number in the url of the webpage - should be a 8-digit number)
Enter all credentials in the indicator's connection section
Verify the green checkmarks appear on the Exit Table, confirming that plan and connection are validated
Deploying Your Plan
Check box "Step 1: Check and confirm Exit Plan" in section 4 of User Settings
Create a TradingView alert (Alert→Select Altcoins Exit Planner PRO→Any alert() function call→Interval Same as Chart→Open Ended→Message: coin name→Notifications: enable Webhook→save and exit
Your Smart Trade appears automatically in 3Commas within minutes
IMPORTANT: Delete the alert after successful deployment to prevent duplicated Smart Trades
To modify the Exit Plan: Delete the Smart Trade on 3Commas and repeat the process above
Monitor your Smart Trade execution through your 3Commas dashboard
Important Notes
Always verify your plan in the Exit Table before deployment
Test with smaller positions initially to familiarize yourself with the process
The indicator provides analysis - final trading decisions remain yours
Manage your API keys and Relay secret with caution: do not share with third parties, store them securely, use malware protection on your PC
Your API keys, trading data, and credentials are transmitted securely through direct API connections and are never stored, logged, or accessible to the indicator author - all communication occurs directly between your browser and the target platforms that support the service.
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5. Understanding the Resistance Analysis
Fibonacci Extensions: Calculated from three key points: 2022 bear market bottom → early 2024 bull market high → 2025 retracement low. These project where price might encounter resistance during future rallies based on mathematical ratios (0.618, 1.0, 1.618, 2.0, etc.).
Fibonacci Retracements: For established altcoins: calculated from 2021 cycle peak to 2022 bottom. For newer altcoins: from all-time high to subsequent major low. These show potential resistance zones where price may struggle to reclaim previous highs.
Major Pivot Highs: Historical price levels where significant reversals occurred. These act as potential resistance because traders may remember these levels and place sell orders near them.
Volume Imbalances (PVSRA) : Areas where price moved rapidly on abnormal volume, creating gaps that may attract future price action or orders. The indicator uses volume-to-price-range analysis (PVSRA candles or "Vector Candles") to identify these zones.
Price Multipliers: Reference lines showing 2x, 3x, 4x, 5x current price to help you assess the feasibility of your exit targets. These serve as a "reality check" - if you're setting a take-profit at 4x current price, you can quickly evaluate whether that level seems reasonable given current market conditions and your risk tolerance.
Market Trend Analysis: Uses EMA combined with ADX/DMI indicators to assess current market phase (bull/strong bull, bear/strong/bear, weakening trend)
This technical foundation helps explain why certain price levels appear as potential exit zones, though market conditions ultimately determine actual price behavior.
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6. FAQs
GENERAL FAQS
Can I use one indicator for multiple altcoins?
Answer: No, each altcoin needs its own chart layout with a separate indicator installation. Resistance levels are calculated from each coin's unique price history, and your exit plan will be different for each position. When you deploy an alert, it creates one Smart Trade on 3Commas for that specific coin only.
To manage multiple coins, create separate TradingView layouts for each altcoin, configure the indicator individually on each chart, then deploy one alert per coin when ready to execute. This ensures each position gets personalized analysis and allows different exit strategies across your portfolio.
EXIT PLAN ANALYSIS/RESISTANCE LEVELS
Are resistance lines calculated automatically by the script?
Answer: Yes, all resistance lines are calculated automatically based on your coin's price history and market data. You don't need to manually identify or draw any levels. The script analyzes historical pivots, calculates Fibonacci ratios from key price swings, identifies volume imbalance zones, and plots everything on your chart.
Simply enable "Show all Resistance Levels" in the settings and the indicator will display all potential resistance zones with color-coded lines and labels showing the exact price levels and their significance.
What's the difference between Fibonacci Extensions and Fibonacci Retracements?
Answer: Fibonacci Retracements look at completed moves from the past and show where price might struggle to reclaim previous highs. For established coins, they're calculated from 2021 peaks down to 2022 bottoms.
Fibonacci Extensions project forward from recent price swings to estimate where ongoing rallies might encounter resistance. They use three points: 2022 bottom, 2024 high, and 2025 retracement low.
Retracements ask "where might recovery stall based on old highs" while Extensions ask "where might this current rally run into trouble." Both use the same mathematical ratios but different reference points to give you complementary resistance perspectives.
Why are some resistance lines gray and others red?
Answer: The color coding helps you assess the potential difficulty of reaching different resistance levels. Gray lines represent closer resistance levels, while red lines indicate stronger resistance or potentially "out-of-reach" targets that may require exceptional market conditions to break through.
This visual system helps you prioritize your exit planning by distinguishing between near-term targets and more ambitious longer-term objectives when setting your take-profit levels.
What is the resistance from major pivot highs?
Answer: Major pivot highs are historical price levels where significant reversals occurred in the past. These levels often act as resistance because traders remember these previous "ceiling" points where price failed to break higher and may place sell orders near them again.
The indicator automatically identifies these pivot points from your coin's price history and draws horizontal lines at those levels. When price approaches these areas again, it may struggle to break through due to psychological resistance and clustered sell orders from traders who expect similar rejection patterns.
What is the resistance from abnormal volumes?
Answer: Volume imbalances occur when price moves rapidly on abnormally high volume, creating gaps or zones where institutions moved large amounts quickly. These areas often act as resistance when price returns to them because institutional traders may want to "fill" these gaps or add to their positions at those levels.
The indicator uses PVSRA analysis to identify candles with abnormal volume-to-price ratios and marks these zones on your chart. When price approaches these imbalance areas again, it may encounter resistance from institutional activity or algorithmic trading systems programmed to react at these levels.
What are price multipliers?
Answer: Price multipliers are reference lines showing 2x, 3x, 4x, and 5x the current price. They serve as a reality check when setting your take-profit targets. If you're considering a take-profit at $10 and current price is $2, you can quickly see that's a 5x target and evaluate whether that seems realistic given current market conditions.
These lines help you assess the feasibility of your exit goals and avoid setting unrealistic expectations. They're not resistance levels themselves, but visual aids to help you gauge whether your planned targets are conservative, aggressive, or somewhere in between
How is the EMA calculated and why does it represent bull/bear market intensity?
Answer: The indicator uses a 147-period EMA (1D tf) combined with ADX and DMI indicators to assess market phases. The EMA provides the basic trend direction - when price is above the EMA, it suggests bullish conditions, and when below, bearish conditions.
The intensity comes from the ADX/DMI analysis. Strong bull markets occur when price is above the EMA, ADX is above 25 (indicating strong trend), and the positive directional indicator dominates. Strong bear markets show the opposite pattern with negative directional movement dominating.
The system also uses weekly ADX slope to confirm trend strength is increasing rather than fading. This combination helps distinguish between weak sideways markets and genuine strong trending phases, giving you context at the time of exit planning.
EXIT PLAN
Why does my exit plan show errors?
Answer: The indicator validates your plan in real-time and shows specific error messages to help you fix issues. Common problems include take-profit percentages that don't sum to exactly 100%, price levels set in wrong order (TP2 must be higher than TP1), or gaps in your sequence (you can't use TP3 without filling TP1 and TP2 first).
Check the Exit Plan Validation section in the table - it will show exactly what needs fixing with messages like "TP percentages must sum to exactly 100%" or "Fill TPs consecutively starting from TP1." Fix the highlighted issue and the error will clear automatically, turning your validation checkmark green when everything is correct.
Why do I need to provide my coin quantity and average entry price?
Answer: The coin quantity is essential because the indicator calculates exact amounts to sell at each take-profit level based on your percentages. If you set TP1 to sell 25% of your position, the script needs to know your total quantity to calculate that 25% means exactly X coins in your 3Commas Smart Trade.
The average entry price helps calculate your projected gains and portfolio performance in the Exit Table. If you don't know your exact entry price, leave it at zero and the indicator will use current price as a fallback for calculations. Both pieces of information ensure your Smart Trade matches your actual position size and gives you accurate profit projections.
What is the emergency exit price?
Answer: The emergency exit price is an optional stop-loss feature that automatically sells 100% of your coin position if price falls to your specified level. This is critical to understand because once triggered, 3Commas will execute the sale immediately without further confirmation.
When price hits your emergency exit level, 3Commas places a limit sell order at 3% below that price to avoid poor market execution. However, execution is not guaranteed because limit orders may not fill during extreme volatility or if price gaps below your limit level. Use this feature cautiously and set the emergency price well below normal support levels to account for typical market fluctuations.
This sells your entire position regardless of your take-profit plan, so only enable it if you want automated crash protection and understand the risks of potential false breakdowns triggering unnecessary exits.
3COMMAS CONNECTION
How do I get my 3Commas API keys and Account ID?
Answer:
For API Keys: Log into 3Commas, go to User Profile → 3Commas API → New API Access Token → System Generated. Set permissions to "Smart Trades Only" (leave all other permissions unchecked) and add your IP to the whitelist for security. Save both the public and private keys securely after creation.
For Account ID: Go to My Portfolio → View Exchange in 3Commas. Look at the URL in your browser - the Account ID is the 8-digit number at the end of the webpage address (example: if the URL shows "/accounts/12345678" then your Account ID is 12345678).
Important: Never share these credentials with anyone. The indicator transmits them directly to 3Commas through secure API connections without storing or logging them. If you suspect your keys are compromised, revoke them immediately in your 3Commas account and generate new ones.
ALERTS
I have set up my exit plan, what's next?
Answer: Once your exit plan is configured and shows green checkmarks in the validation section, follow the 4-step workflow in the indicator. Check "Step 1: Check and confirm Exit Plan" to enable alert firing, then create a TradingView alert using the Altcoins Exit Planner PRO condition with "Any alert() function call" trigger.
The alert fires immediately and sends your plan to 3Commas. Within minutes, you should see a new Smart Trade appear in your 3Commas dashboard matching your exact exit strategy. After confirming the Smart Trade was created successfully, delete the TradingView alert to prevent duplicate submissions.
From that point, 3Commas manages your exit automatically according to your plan. Monitor execution through your 3Commas dashboard and let the platform handle the sequential take-profit levels as price moves.
How do I create the TradingView alert?
Answer: Click the "Alert" button in TradingView (bell icon in the top toolbar). In the alert setup window, set Condition to "Altcoins Exit Planner PRO" and Trigger to "Any alert() function call." Keep Interval as "Same as Chart" and Expiration as "Open Ended."
In the Message section, you can name your alert anything you want. In the Notifications section, enable the webhook option (leave the URL field as you'll handle that separately). You can also enable email or sound notifications if desired.
Click "Create" to activate the alert. If Step 1 is already checked in your indicator, the alert will fire immediately and send your exit plan to 3Commas. Remember to delete this alert after your Smart Trade appears to prevent duplicates.
I got the Smart Trade on 3Commas, what's next?
Answer: Congratulations! Your exit plan is now active and automated. Delete the TradingView alert immediately to prevent duplicate Smart Trades from being created. You can now monitor your Smart Trade's progress through your 3Commas dashboard.
3Commas will automatically execute your take-profit levels as price reaches each target, selling the specified percentages of your position according to your plan. If you enabled emergency exit protection, that stop-loss is also active and monitoring for downside protection.
Your job is essentially done - let 3Commas handle the execution while you monitor overall market conditions. You can view trade progress, modify the Smart Trade if needed, or manually close it early through your 3Commas interface. The platform will manage all the sequential selling according to your original exit strategy.
Can I cancel my exit plan and resubmit to 3Commas?
Answer: Yes, you can modify your exit strategy by first deleting the existing Smart Trade in your 3Commas dashboard, then resubmitting a new plan through the indicator.
To cancel and resubmit: Go to your 3Commas Smart Trades section and delete the current trade. Return to the TradingView indicator, modify your exit plan settings (prices, percentages, emergency exit, etc.), then repeat the deployment process by checking Step 1 and creating a new alert.
This creates a fresh Smart Trade with your updated parameters. Always ensure you delete the old Smart Trade first to avoid having multiple conflicting exit plans running simultaneously. The new deployment will overwrite nothing automatically - you must manually clean up the old trade before submitting the revised plan.
Why did I get a second Smart Trade after the first one?
Answer: This happens when you forget to delete the TradingView alert after your first Smart Trade was created successfully. The alert remains active and continues firing, creating duplicate Smart Trades each time it triggers.
Always delete your TradingView alert immediately after confirming your Smart Trade appears in 3Commas. Go to your TradingView alerts list, find the alert you created for this exit plan, and delete it completely. Also delete any duplicate Smart Trades in your 3Commas dashboard to avoid confusion.
To prevent this in future deployments, remember the workflow: create alert → Smart Trade appears → delete alert immediately. Each exit plan should only generate one Smart Trade, and keeping alerts active will cause unwanted duplicates.
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7. Limitations and Disclaimer
Limitations:
Doesn't provide trading signals or entry points
Doesn't guarantee resistance levels will hold
Requires manual monitoring of 3Commas execution
Works for exit planning only, not position building
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial, investment, or trading advice.
The indicator:
Makes no guarantees about future market performance
Cannot predict market movements with certainty
May generate false indications
Relies on historical patterns that may not repeat
Should not be used as the sole basis for trading decisions
Users are responsible for:
Conducting independent research and analysis
Understanding the risks of cryptocurrency trading
Making their own investment/divestment decisions
Managing position sizes and risk exposure appropriately
Managing API keys and secret codes diligently (do not share with third parties, store them securely, use malware protection on your PC)
Cryptocurrency trading involves substantial risk and may not be suitable for all investors. Past performance does not guarantee future results. Users should only invest what they can afford to lose and consult qualified professionals before making financial decisions.
The indicator’s assumptions may be invalidated by changing market conditions.
By using this tool, users acknowledge these limitations and accept full responsibility for their trading decisions.
Sortable Relative Performance | viResearchSortable Relative Performance | viResearch
Conceptual Foundation and Purpose
The Sortable Relative Performance indicator from viResearch is designed as a multi-asset ranking and comparison system that allows traders to evaluate the relative strength of up to 14 different assets over a user-defined lookback period. Unlike single-symbol indicators, this tool provides a comparative view of performance, making it ideal for traders seeking to understand how assets perform relative to each other within a watchlist, sector, or market segment. The indicator calculates the percentage return of each asset from a chosen starting point and presents the results both graphically and in a sorted, tabular format, helping traders identify outperformers and underperformers at a glance.
Technical Composition and Methodology
At its core, the script calculates the relative performance of each selected asset by comparing its current closing price with the closing price from the lookback period. This performance metric is expressed as a percentage and computed using Pine Script’s request.security() function, allowing for seamless cross-asset analysis within a single pane. Each asset is visually represented as a vertical column, color-coded according to a predefined identity map that reflects common asset branding. The best-performing asset is dynamically labeled on the chart, displaying its name and current return, while a real-time performance table updates and ranks all active assets in descending order based on their return values. The table and columns automatically adjust based on the user’s selection, creating an interactive and responsive comparative dashboard.
Features and Configuration
The indicator includes a customizable date filter, allowing traders to activate the display from a specific start date. This is particularly useful for performance reviews tied to events, such as earnings reports, Fed meetings, or macroeconomic releases. The lookback period is adjustable and determines how far back in time performance is measured, making the tool adaptable to both short-term and long-term strategies. Traders can toggle individual assets on or off, enabling focused analysis on specific coins, stocks, or indices. Up to 14 assets can be analyzed simultaneously, with each one clearly distinguished by unique, branded colors in both the plot and the ranking table. The script intelligently highlights the top performer with a floating label, drawing immediate attention to the strongest asset within the group.
Strategic Use and Application
This indicator is especially valuable for traders employing relative strength or momentum-based strategies. By visualizing asset performance in real time, it becomes easier to rotate capital into strong assets and away from laggards. Whether tracking cryptocurrencies, sectors, or forex pairs, the ability to assess comparative returns without switching charts provides an operational edge. The tool supports portfolio analysis, sector rotation, and cross-market studies, making it suitable for discretionary traders, systematic investors, and even macro analysts looking for a visual breakdown of market behavior.
Conclusion and Practical Value
The Sortable Relative Performance indicator by viResearch delivers a clean and effective way to measure and rank asset performance over time. By combining visual clarity with real-time calculation and dynamic sorting, it offers a powerful lens through which traders can evaluate market leadership and laggard behavior. Its flexibility and modular design ensure it can be integrated into a wide range of strategies and trading styles. Whether you're managing a crypto portfolio or monitoring traditional markets, this tool provides essential insights into where momentum resides and how capital is flowing across assets.
Note: Backtests are based on past results and are not indicative of future performance.
Alpha VolumeThis script is a comprehensive trading toolkit designed to integrate position sizing, risk management, and key data metrics directly onto your chart. It goes beyond a simple volume indicator by providing two interactive tables and a special volume signal to aid in trade planning and analysis.
What It Does
The "Alpha Volume" indicator is a multi-functional tool that helps traders make more informed decisions. Its core components are:
- A Position Size Calculator that dynamically determines how many shares to trade based on your account size, risk tolerance, and different stop-loss strategies.
- A Data Metrics Table that displays essential fundamental information like Market Cap, Industry, Sector, and Float shares.
- An Episodic Pivot (EP) signal that highlights bars with exceptionally high volume, pinpointing potentially significant market events.
Key Features
Dynamic Position Sizing: Automatically calculates the ideal trade size based on various stop-loss points:
- The low or high of the day.
- The midpoint of the current candle.
- Three customizable fixed percentage stop-losses (e.g., 0.75%, 1.00%, 1.25%).
Interactive Risk Management: After you enter a trade, you can input your actual entry price and quantity. The script will then calculate:
- The exact stop-loss price required to meet your predefined risk.
- The distance to your stop-loss in both percentage and currency.
- Up to 10 R-Multiple price targets to help with profit-taking.
On-Chart Fundamental Data: The Data Metrics table provides a quick snapshot of the company's financial health and classification, saving you from switching between screens.
- Episodic Pivot Signal: A simple triangle appears below a daily candle when its volume surpasses a user-defined threshold (e.g., 9 million shares), drawing your attention to stocks under significant accumulation or distribution.
How to Use
Pre-Trade Planning:
- In the indicator settings, enter your Capital and define your Risk per trade (either as a percentage like 0.5% or a fixed currency amount like $5000).
- The "Position Size Table" will instantly show you the quantity you can trade based on different potential stop-loss levels. For example, Q shows the quantity if your stop is the day's low, and SQ shows quantities for fixed percentage stops.
Trade Execution & Management:
- Once you're in a trade, enter your Position Opened (PO) price and Quantity Actual (QA) in the settings.
- The second table will update to show your calculated stop-loss (PC), the distance to it (DA), and your R-Multiple targets (RM), giving you a clear plan for managing the trade.
Market Analysis:
- Use the Episodic Pivot signal on the daily chart to identify stocks experiencing unusual volume, which often precedes significant price moves.
- Glance at the Data Metrics Table to quickly understand the company's size (Market Cap) and business (Industry/Sector).
Cumulative Outperformance | viResearchCumulative Outperformance | viResearch
Conceptual Foundation and Innovation
The "Cumulative Outperformance" indicator by viResearch is a relative strength analysis tool designed to measure an asset’s cumulative performance against a chosen benchmark over a user-defined period. Rooted in comparative return analysis, this indicator allows traders and analysts to assess whether an asset is outperforming or underperforming a broader market or sector, offering insights into trend strength and leadership.
Unlike traditional relative strength indicators that may rely on static ratio comparisons, this script uses cumulative return differentials to provide a more contextual understanding of long-term performance trends. A clean visual representation and dynamic text summary are provided to highlight not only the degree of outperformance but also the directional status — making it accessible to both novice and advanced users.
Technical Composition and Calculation
The indicator compares the cumulative returns of the selected asset and a benchmark symbol over a specified lookback period (length). Returns are calculated as the percent change from the current price to the price length bars ago.
This differential is plotted and color-coded, with a baseline zero line to make outperformance and underperformance visually distinct. A dynamic table in the bottom-right corner displays real-time values for the benchmark symbol, the current outperformance percentage, and a status label (e.g., "Outperforming", "Underperforming", or "Even").
Additionally, a floating label is plotted directly on the chart to make the latest outperformance value immediately visible.
Features and User Inputs
The script includes the following customizable inputs:
Start Date: Defines the point from which to begin tracking outperformance data.
Length: The period over which cumulative returns are measured.
Benchmark Symbol: Select any market index, stock, or crypto as the benchmark (e.g., INDEX:BTCUSD, SPX, etc.).
Practical Applications
This indicator is especially effective in:
Identifying Market Leaders: Compare sectors, stocks, or altcoins against a leading benchmark to identify outperformers.
Sector Rotation Strategies: Monitor when certain assets begin to outperform or lag behind the broader market.
Cross-Market Analysis: Compare crypto pairs, equities, or commodities to their sector benchmarks to find relative strength opportunities.
Visual Aids and Alerts
A purple outperformance line highlights the degree of cumulative difference.
A horizontal dotted white line marks the baseline (zero performance difference).
Real-time table overlay updates the benchmark name, performance delta, and relative status.
Alerts are built-in to notify users when assets begin to outperform or underperform, helping you stay ahead of major shifts.
Advantages and Strategic Value
Benchmark Flexibility: Analyze any asset class against any benchmark of your choice.
Visual Clarity: Dynamic labels and tables make performance tracking intuitive and immediate.
No Repainting: Calculations are based on closed bar data for consistent backtesting and real-time use.
Summary and Usage Tips
The "Cumulative Outperformance | viResearch" script offers a clean and effective way to visualize relative strength between any asset and its benchmark. By focusing on cumulative returns over time, it filters out short-term noise and gives a strategic view of long-term strength or weakness. Use this tool in combination with other momentum or trend-following indicators to refine your market entries and asset selection.
Note: Backtests are based on past results and are not indicative of future performance.
