Chart-prepFxxDanny Chart-Prep
A practical multi-tool script for clean and structured chart preparation.
✨ Features
Weekly Close Levels
Automatically plots the previous week’s close and the week before that, with clear styling to distinguish current and past levels.
Trading Sessions
Colored session boxes for the three key market sessions:
Asia (20:00–23:00 UTC-4)
Europe (02:00–05:00 UTC-4)
New York (08:00–11:00 UTC-4)
Each session box automatically adapts to the session’s high/low range and only keeps the last 5 visible to avoid clutter.
Previous Day’s High & Low
Plots the prior day’s high and low with lines that extend into the current session. Up to 10 days are kept on the chart.
Daily & Weekly Separators
Vertical lines to visually separate days (dotted) and weeks (solid, colored).
Anchored to a rolling price window so the Y-axis scaling stays clean and unaffected.
✅ Benefits
Stay focused with key price levels and session ranges marked automatically.
No need for manual drawing or constant adjustments.
Optimized performance – old objects are automatically removed.
No axis distortion from “infinite” lines or boxes.
基本面分析
Triple RSI [JopAlgo]Triple RSI — a cleaner RSI with a built-in trigger
Core idea
This is RSI + a moving average of RSI (you choose SMA / EMA / VWAP) plus the classic 70 / 50 / 30 rails. That gives you:
Regime (above/below 50),
Trigger (RSI crosses its RSI-MA),
Stretch (near 70/30).
Pick EMA for speed, SMA for smooth bias, or VWAP to weight RSI by volume (helpful when participation spikes matter).
What you’ll see
RSI (blue) and Selected MA of RSI (orange).
Static levels: 70 / 50 / 30.
Built-in alerts for RSI↗ MA (Buy) and RSI↘ MA (Sell).
Pane shapes are available but hidden by default—turn them on if you want markers.
Read it fast: Which side of 50? Is RSI above or below its MA? Are we near 70/30?
How to use it (simple playbook)
Direction filter (regime):
Focus longs while RSI ≥ 50.
Focus shorts while RSI ≤ 50.
Trigger:
Enter on RSI crossing its MA in the direction of the regime (RSI↗MA above 50 for longs; RSI↘MA below 50 for shorts).
If RSI is already stretched (near 70/30), wait for the retest/hold at a level instead of chasing.
Location first (always):
Act at real references: Volume Profile v3.2 (VAH/VAL/POC/LVNs) and Anchored VWAP (session/weekly/event).
No level, no trade.
Quality check (optional, strong):
If you use CVDv1 , prefer signals with Alignment OK and no Absorption against your side.
Entries, exits, risk
Continuation long: Regime ≥ 50, price pulls to VAL / AVWAP / MA cluster, RSI crosses up its MA → enter.
Stop: below structure/last swing. Targets: POC/HVNs or next swing high.
Reclaim short: Regime ≤ 50, failed retest at VAH/AVWAP, RSI crosses down its MA → enter.
Invalidation: quick reclaim of the level and RSI re-cross up.
Trim/avoid: RSI marching into 70 with weak follow-through at an HVN → take profits / don’t chase. Mirror at 30 for shorts.
Settings that matter (and how to tune)
RSI Length (default 10):
Lower = faster/more signals; higher = smoother/fewer.
MA Length (default 7):
Controls how quickly your trigger reacts. Shorter = earlier crossovers (more noise).
MA Type (SMA / EMA / VWAP):
EMA: fastest trigger (popular for intraday).
SMA: calmer trigger (good for swing).
VWAP (on RSI): volume-weighted RSI baseline—use when participation matters (crypto, news hours).
Starter presets
Scalp (1–5m): RSI 7–9, MA 5–7, EMA
Intraday (15m–1H): RSI 10–14, MA 7–9, EMA
Swing (2H–4H): RSI 14–20, MA 9–12, SMA
Daily backdrop: RSI 14, MA 9–10, SMA (execute on lower TF)
Pattern cheat sheet
Regime + Trigger: RSI >50 and crosses up its MA at a level → continuation.
Mean-revert stretch: RSI tags 70 (or 30) into VAH/VAL and crosses back through its MA → rotation to value.
Failure tell: Price pokes a level but RSI can’t hold above 50 (or below 50 for shorts) → likely fake; wait for the reclaim.
Best combos (kept simple)
Volume Profile v3.2: Entries at VAH/VAL/LVNs, targets at POC/HVNs.
Anchored VWAP: Reclaims/rejections with RSI regime + trigger in the same direction = cleaner timing.
CVDv1 (optional): Take RSI-aligned trades with flow (ALIGN OK, no Absorption).
Common mistakes this helps you avoid
Trading against the 50-line regime.
Chasing crosses far from value (wait for the retest).
Taking every cross in chop—use levels and the 50-line filter.
Disclaimer
This indicator and write-up are for education only, not financial advice. Trading is risky; results vary by market, venue, and settings. Test first, trade at defined levels, and manage risk. No guarantees or warranties are provided.
CoT Bias Tracker [DOSALGO]Unlock a powerful new dimension in your market analysis with the CoT Bias Tracker . This advanced tool goes beyond price charts to reveal the positioning of the market's largest players, allowing you to track the "smart money" and make more informed trading decisions.
By harnessing the weekly Commitment of Traders (CoT) report, this indicator automatically fetches, processes, and displays the net positioning of Commercials (Hedgers), Non-Commercials (Large Speculators), and Retail traders. Its standout feature is the unique dual-asset analysis for Forex pairs, which automatically breaks down a pair like EURUSD into its Base (EUR) and Quote (USD) components, giving you a crystal-clear view of the capital flows driving the market.
Stop guessing the trend and start tracking the institutional bias that truly matters.
Key Features
📈 Complete CoT Data Analysis: Automatically fetches and displays the latest weekly net positions for three key market participants: Commercials, Non-Commercials, and Retail Traders.
🌍 Unique Forex Pair Analysis: The only tool you'll need for Forex. It intelligently separates pairs (e.g., AUDJPY) into their Base (AUD) and Quote (JPY) currencies and displays a full CoT analysis for each, revealing which currency is truly in demand.
📊 Advanced Bias Dashboard: A comprehensive and fully customizable dashboard provides an at-a-glance summary of the market's sentiment, including current positions, weekly changes, and both short-term and long-term bias readings.
🧠 Conviction Analysis: This indicator goes deeper than just net positions. By analyzing the relationship between positioning changes and Open Interest, it gauges the conviction behind a move, distinguishing between a "Strong Long" (new money entering) and a "Weak Long" (short covering).
🚀 POIV Metric: Includes the Position x Open Interest Volume (POIV) metric, an advanced tool for measuring the cumulative force behind positioning changes over time.
📉 Historical Data Plotting: Visualize the net positioning data and its moving average directly on your chart's indicator pane. This is perfect for identifying historical extremes, divergences, and long-term trends in positioning.
⚙️ Automatic Symbol Recognition: The indicator intelligently detects the asset on your chart—from Forex pairs to indices like the S&P 500 and commodities like Gold—and automatically fetches the correct CoT data.
🎨 Full Customization: Tailor the entire tool to your workspace. Control the dashboard's position, size, and colors. Toggle the visibility of any data row or plot to focus only on what matters to you.
The Dashboard Explained
The dashboard gives you a complete, multi-faceted view of the market's positioning.
Participant Groups:
Commercials: Often considered the "smart money." They use futures to hedge their business operations and typically fade trends, buying into lows and selling into highs.
Non-Commercials: Large speculators like hedge funds and institutions. They are typically trend-followers, and their positioning is a powerful indicator of the current dominant trend.
Retail Traders: Small, non-reportable speculators. They are often seen as a contrarian indicator.
Net Positions & Change: See the raw net long or short positions from the current and previous week's report, along with the net change to understand the weekly capital flow.
S-Term Bias (Short-Term): Based on the weekly net change, this tells you who was buying and who was selling since the last report.
L-Term Bias (Long-Term): Compares the current net position to its moving average to define the dominant positioning trend. (Note: This reading is most effective on the Weekly chart timeframe.)
Conviction (via Open Interest): Found in the "Open Interest" row under the L-Term Bias column, this powerful metric tells you how positions are changing:
Strong Long: New buyers are entering the market with conviction.
Weak Long: Existing shorts are covering their positions.
Strong Short: New sellers are entering the market with conviction.
Weak Short: Existing longs are closing their positions.
Use Cases & Strategy
Trend Confirmation: Use the positioning of Non-Commercials to confirm the strength and direction of a trend you've identified with technical analysis.
Reversal Signals: Look for extreme net positioning levels or divergences between Commercial and Non-Commercial sentiment, which can often precede major market reversals.
Forex Strength Analysis: When trading a pair like GBPJPY, use the dashboard to see if Non-Commercials are strongly bullish on GBP while being bearish on JPY. This "double confirmation" can highlight high-probability trade setups.
Important Notes
Understanding CoT Data: The Commitment of Traders report is released by the CFTC every Friday afternoon (~3:30 PM ET). Crucially, it reflects the positions that were held on the preceding Tuesday. It is a tool for gauging medium- to long-term sentiment, not for intraday signals.
Disclaimer: This tool is for analytical and educational purposes only and should not be considered financial advice. All forms of trading involve risk. Always conduct your own research and apply robust risk management.
Volume Profile v3.2 [JopAlgo]Volume Profile v3.2 — where the market actually traded, and why that matters
Price shows you where bids and offers met. Volume Profile shows you how much business was done at each price. Put simply: it maps the market’s preference. In any auction, price tends to accept and revisit heavy-traded areas and reject low-participation areas. VP v3.2 turns that logic into a clear picture you can use on any timeframe—from scalps to multi-week swings.
This version focuses on three things that matter in real trading:
a clean, configurable price–volume distribution (your profile),
key levels derived from that distribution (POC, VAH, VAL), and
a POC Shift detector that highlights structural change when the market’s center of mass moves.
If you attach screenshots to your script page, one image should label POC/VAH/VAL and the histogram shape; a second should show a POC Shift during a trend transition.
What you’re seeing (and how to read it)
The histogram you see beside price is the profile: each horizontal bar measures traded volume at that price within a chosen range. It reveals:
POC (Point of Control): the single most-traded price in your range (the market’s center of gravity).
Value Area (VA): the price band where a chosen percentage of all volume traded (default 70%). Its boundaries are the VAH (top) and VAL (bottom).
HVNs / LVNs: High-Volume Nodes (bulges) tend to attract price; Low-Volume Nodes (gaps/voids) tend to repel price and act like funnels—price often moves quickly through them, then pauses at the next HVN.
In practice: trade from value edges back to POC in balance, or trade breakouts through LVNs toward the next HVN when the auction is trending.
Choosing the range: Anchored vs Visible
Your range defines the story. VP v3.2 lets you pick it two ways:
Visible Range (default): the profile is built from the bars currently on screen. This adapts as you pan/zoom and is ideal for quick reads and intraday work.
Anchored Range: toggle “Anchored Range” and pick an Anchor Time. Now the profile starts at that timestamp and extends to the most recent bar. Use this for event-based composites (e.g., listing day, policy announcement, ETF approval), weekly/monthly composites, or to study a trend leg in isolation.
Tip: for scalping, a visible range that covers the current session is enough. For swings, anchor to the start of the move, start of the week/month, or the event that changed regime.
Important settings (why they exist, and simple defaults)
Rows: how many price buckets form the histogram. More rows = finer detail, more CPU. Start around 120; go higher when zoomed in.
Value Area %: how much volume should sit inside the VA band. 70% is standard; lower it (e.g., 60–65%) in strong trends to keep VA tight, raise it in dull balance to widen the area you consider “fair.”
Max Width, Horizontal Offset, Row Height %: purely visual—how wide the histogram can draw, how far it sits from the last bar, and how thick each row appears.
Histogram Emphasis: a gentle power curve that makes big nodes pop and tiny nodes fade. Leave at 1.0 until you know why you want more/less contrast.
FAST vs ACCURATE:
FAST puts each bar’s volume into its mid-price bucket. It’s very fast and stable for live trading.
ACCURATE spreads each bar’s volume across its full high-low range. This gives a smoother profile (especially on wide candles) at the cost of more computation.
If your machine lags, use FAST for intraday and ACCURATE when you’re doing end-of-day review.
Plot POC/VA series / Extend Left: draws POC/VAH/VAL as dotted lines and (optionally) extends them left across your chart. Extending is useful when you want those levels to act as “attractors/repellers” beyond the immediate profile.
Theme & Colors: there’s a dark/light toggle so the profile remains readable on any chart theme.
POC Shift — when “fair value” moves
Markets rotate around “fair value.” When that value shifts by a meaningful amount and sticks, the auction has changed. VP v3.2 detects this with three parameters:
Shift Min Ticks: the minimum distance POC must move to count as a new candidate.
Shift Confirm Bars: how many consecutive bars must hold that new area for the shift to be confirmed.
Shift Cooldown: how long to ignore re-triggers after a confirmed shift (avoids spamming in chop).
A POC Shift Up says buyers migrated the center of business higher (typical of an acceptance above prior value). POC Shift Down says the opposite. These are structure events: combine them with your directional tools (e.g., CVDv1’s Absorption/Efficiency read) to avoid chasing when the shift is fragile.
Using Volume Profile on any timeframe
The logic is the same everywhere: trade at value boundaries, target the POC or the next HVN, and respect LVNs as fast-pass corridors.
Scalping (1–5m charts)
Range: Visible; cover the current session or the last 2–4 hours.
Use: Fade VAH→POC and VAL→POC only when the tape supports it (e.g., CVDv1 not showing absorption against you). If price pushes into an LVN, expect fast movement to the other side—don’t fight mid-void.
Intraday (15m–1H)
Range: Visible covering the day, or Anchored to the day’s open.
Use: First seek balance trades (VAL/VAH to POC). When a real POC Shift confirms after a break, switch to trend-following: use pullbacks to prior VA boundaries as entries.
Swing (2H–4H)
Range: Anchored to start of week, start of trend leg, or major event.
Use: Enter on retests of VAL/VAH that hold, target the composite POC/HVNs. If you see sequential POC Shifts in trend direction, it’s a sign to trail rather than constantly fade back to POC.
Position (1D–1W)
Range: Anchored to YTD, quarter, or cycle low/high.
Use: Treat LVNs as structural gaps; acceptance through an LVN often leads to the next HVN. Weekly VA boundaries are strong reference levels; a weekly POC Shift is notable regime information.
How to act at the levels (a simple, durable playbook)
In balance: fade VAL/VAH back to POC—but only if your flow read doesn’t scream “absorption against you.”
In trend: ignore the first touch fade. Wait for acceptance (close outside), then use pullbacks to the broken VA boundary to join.
At LVNs: don’t expect chop. Plan for quick travel to the next HVN, place stops accordingly, and avoid mid-void entries.
Alerts (what they mean, what you do)
Cross POC / VAH / VAL: price just interacted with a key reference. Use your secondary signal (e.g., CVDv1 alignment/absorption) to decide fade vs follow.
POC Shift Up/Down: a structure change just confirmed. In balance, you may flip your bias. In trend, you can add on pullbacks toward the shifted area.
Compatible tools (optional, but powerful)
Volume Profile v3.2 is designed to work cleanly with other tools:
Cumulative Volume Delta v1 (CVDv1): lets you judge flow quality at VP levels. For example, a poke above VAH with CVD Absorption is a veto to chase—look for a failed breakout or reclaim. A retest of VAL with Imbalance strong and Alignment OK is a higher-quality bounce back to POC.
Weekly AVWAP v3 : the market’s mean/anchor. Confluence of Weekly AVWAP with VA boundaries or HVNs creates A-tier levels. Reclaims of Weekly AVWAP near VAL are excellent swing entries; rejections at Weekly AVWAP into VAH are high-quality fades in balance.
(If you post images, one good example is a VAL retest at Weekly AVWAP with CVDv1 showing “Efficient”—that story clicks instantly.)
Practical defaults
Rows: 120
Value Area: 70%
FAST mode for live work; ACCURATE for deep review or wide-range composites
Anchored Range: off (Visible) for intraday; on for weekly/monthly/“since event” studies
POC Shift: start with 10 ticks, 2 confirm bars, 10 cooldown; tighten for very small-tick futures, loosen in highly volatile regimes
Common pitfalls this solves
“Why did it stall here?” Check the profile: you hit an HVN. That’s where business likes to be done. Expect chop or mean-reversion to POC.
“Breakout straight back in.” You broke into an LVN without acceptance; or CVDv1 flagged Absorption. Wait for acceptance, then take the retest.
“Levels feel arbitrary.” POC/VAH/VAL come from where traders actually transacted, not where a simple moving average happens to sit.
Open source & disclaimer
This indicator is published open source so you can learn from it, tune it, and build rules you trust. Trading is risky; no tool eliminates that risk.
Disclaimer — Not Financial Advice.
The “Volume Profile v3.2 ” indicator and this description are provided for educational purposes only and do not constitute financial or investment advice. Markets involve risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
Use VP v3.2 to decide where the market is likely to accept or reject. Then use your flow read (e.g., CVDv1) to decide when to act. That combination—location + flow—is what keeps you on the right side of the auction across any timeframe.
MARA / mNAV=1 (x)What it does
This script overlays two signals on the MARA chart:
mNAV=1 fair-value line — the MARA price implied by Bitcoin NAV:
mNAV1 = (BTC price × BTC holdings) / MARA shares
Premium/Discount ratio — how far MARA trades vs. its NAV fair value:
Ratio = Close / mNAV1 (1.00 = fair; >1 = premium; <1 = discount)
Inputs
Shares outstanding (default: 370,460,000)
BTC holdings (official or estimated; you can roll forward +25 BTC/day if you want)
BTC symbol used for pricing (e.g., BTCUSD, BTCUSDT, BTCUSDTPERP)
How to use
When Price < mNAV=1 and Ratio < 1.00 → MARA trades at a discount to BTC NAV (potential mean-reversion if BTC is stable).
When Price > mNAV=1 and Ratio > 1.00 → premium (premium often compresses during BTC chop/weakness).
Rule of thumb (with ~53k BTC and 370.46M shares): +$1,000 BTC ≈ +$0.14 on the mNAV=1 line.
Visuals
Blue line = mNAV=1 (fair value) plotted directly on the MARA chart.
Purple line = Ratio (×) on a separate right-hand scale centered around 1.00.
Optional shading: green when Ratio > 1.05 (+5% premium), red when Ratio < 0.95 (−5% discount).
Alerts (suggested)
Premium > +5%: Ratio > 1.05
Discount < −5%: Ratio < 0.95
Notes
This is a proxy for NAV parity; it assumes your BTC holdings input is correct (official last report or your estimate).
Choice of BTC symbol matters; use the feed that best matches your workflow (spot, perp, or index).
The ratio is most informative when BTC is range-bound; during fast BTC moves MARA can overshoot temporarily.
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).
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COT - Weekly Summary# COT Weekly Summary Dashboard - User Manual
## 📊 Overview
**COT Weekly Summary** is an advanced Pine Script dashboard that provides comprehensive real-time analysis of CFTC COT (Commitment of Traders) data. This professional tool allows you to monitor net positions of non-commercial traders across currencies, precious metals, and commodities, all within a single elegant and customizable interface.
### 🎯 Key Features
- **Multi-Asset Analysis**: Major currencies, precious metals, and commodities
- **Real-Time Data**: Automatic weekly updates from CFTC reports
- **BestPair Detector**: Automatically identifies the best trading opportunities
- **Customizable Interface**: Colors, sizes, and positioning fully configurable
- **Dynamic Watermark**: Motivational phrases that change with each new bar
---
## ⚙️ Configuration and Customization
### 📍 Table Position
**Setting**: `Table Position`
- **Top Left**: Top left corner
- **Top Center**: Top center
- **Top Right**: Top right corner
- **Middle Left**: Middle left
- **Middle Center**: Screen center
- **Middle Right**: Middle right
- **Bottom Left**: Bottom left corner
- **Bottom Center**: Bottom center
- **Bottom Right**: Bottom right corner
### 📊 Data Type
**Setting**: `Data Type`
- **Futures Only**: Futures contracts only
- **Futures and Options**: Futures + options (more complete data)
### 🎨 Color Customization
#### Base Table Colors
- **Header Background**: Header background color
- **Header Text**: Header text color
- **Frame Color**: Table frame color
- **Border Color**: Cell border color
#### Dynamic Row Colors
- **Positive Background**: Background for positive changes
- **Positive Text**: Text for positive changes
- **Negative Background**: Background for negative changes
- **Negative Text**: Text for negative changes
- **Neutral Background**: Background for neutral changes
#### BestPair Colors (Dynamic Gradient)
- **Red Background**: Pairs with strength 0-10
- **Orange Background**: Pairs with strength 10-20
- **Yellow Background**: Pairs with strength 20-30
- **Light Green Background**: Pairs with strength 30-70
- **Dark Green Background**: Pairs with strength 70+
### 📏 Text Size
**Available options**: Tiny, Small, Normal, Large, Huge
---
## 📋 Asset Categories
### 💱 Major Currencies
**Activation**: `Show Major Currencies`
Monitors the 8 main Forex currencies:
- **EUR** - Euro
- **GBP** - British Pound
- **JPY** - Japanese Yen
- **CHF** - Swiss Franc
- **CAD** - Canadian Dollar
- **AUD** - Australian Dollar
- **NZD** - New Zealand Dollar
- **USD** - US Dollar
### 🥇 Metals
**Activation**: `Show Metals`
Includes the main precious metals:
- **GOLD** - Gold
- **SILVER** - Silver
- **PLATINUM** - Platinum
- **PALLADIUM** - Palladium
- **COPPER** - Copper
### 🌾 Commodities
**Activation**: `Show Commodities`
Covers the most important commodities:
- **WTI** - West Texas Intermediate Oil
- **NATURAL GAS** - Natural Gas
- **COCOA** - Cocoa
- **CORN** - Corn
- **SUGAR** - Sugar
- **COFFEE** - Coffee
- **SOYBEANS** - Soybeans
### 🎯 BestPair Detection
**Activation**: `Show BestPair`
The BestPair system automatically analyzes all currencies and identifies the best trading opportunities by combining:
- Currencies with positive changes (bullish trend)
- Currencies with negative changes (bearish trend)
- Calculation of "Strength Score" for each pair
**How it works**:
1. Identifies currencies with opposite trends
2. Calculates relative strength of each pair
3. Applies dynamic colors based on strength
4. Shows only the most promising pairs
---
## 📊 Data Interpretation
### Table Columns
1. **ASSET**: Asset name (currency, metal, commodity)
2. **NET POSITIONS**: Current net positions of non-commercial traders
3. **# CHANGE**: Numerical change from previous week
4. **% CHANGE**: Percentage change (determines row color)
### 🎨 Color System
- **Green**: Positive changes (bullish sentiment)
- **Red**: Negative changes (bearish sentiment)
- **Neutral**: Minimal or no changes
### 📈 BestPair Strength Score
The color system for BestPair indicates pair strength:
- **🔴 Red (0-10)**: Low strength
- **🟠 Orange (10-20)**: Moderate strength
- **🟡 Yellow (20-30)**: Good strength
- **🟢 Light Green (30-70)**: High strength
- **🟢 Dark Green (70+)**: Excellent strength
---
## 💡 Usage Tips
### ⏰ **IMPORTANT: Weekly Timeframe Only**
**This indicator works ONLY on Weekly (1W) timeframe. Make sure your chart is set to Weekly before using the dashboard.**
### 📊 Weekly Analysis
- COT data is updated every Tuesday (previous Friday's data)
- Focus on percentage changes rather than absolute values
- Changes above 10% are generally significant
### 🎯 Trading with BestPair
- Dark green pairs (70+) offer the best opportunities
- Combine COT analysis with technical analysis
- Consider opposite sentiment as potential reversal
### ⚡ Performance Optimization
- Disable unnecessary categories to reduce loading times
- Use "Futures Only" if you don't need options data
- Position the table where it doesn't interfere with your charts
---
## ❓ Troubleshooting
### Common Issues
1. **Table not appearing**: Check that you're on Weekly timeframe
2. **Data not updating**: COT data is released with a 3-day delay
3. **Performance issues**: Disable unused asset categories
4. **Colors not showing**: Verify color settings in Style tab
### Technical Requirements
- **Timeframe**: Weekly (1W) only
- **Market**: Works on any symbol (data is independent)
- **Account**: Works with any TradingView account type
---
## 🤝 Support the Project
If this indicator has been helpful for your trading analysis, consider supporting its development:
**☕ Buy Me a Coffee**: buymeacoffee.com
Your support helps maintain and improve this tool for the entire trading community!
---
**Happy Trading! 📈**
---
AutoDay MA (Session-Normalized)📊 AutoDay MA (Session-Normalized Moving Average)
⚡ Daily power, intraday precision.
AutoDay MA automatically converts any N-day moving average into the exact equivalent on your current intraday timeframe.
💡 Concept inspired by Brian Shannon (Alphatrends) – mapping daily MAs onto intraday charts by normalizing session minutes.
🛠 How it works
Set Days (N) (e.g., 5, 10, 20).
Define Session Minutes per Day (⏱ 390 = US RTH, 🌍 1440 = 24h).
The indicator detects your chart’s timeframe and computes:
Length = (Days × SessionMinutes) / BarMinutes
Applies your chosen MA type (📐 SMA / EMA / RMA / WMA) with rounding (nearest, up, down).
Displays all details in a clear corner info panel.
✅ Why use it
Consistency 🔄: Same 5-day smoothing across all intraday charts.
Session-aware 🕒: Works for equities, futures, FX, crypto.
Transparency 🔍: Always shows the math & final MA length.
Alerts built-in 🔔: Cross up/down vs. price.
📈 Examples
5-Day on 1m → 1950-period MA
5-Day on 15m → 130-period MA
5-Day on 65m → 30-period MA
10-Day on 24h/15m (crypto) → 960-period MA
RWE (MASTER CƯỜNG BOSS)Tôi là một nhà giao dịch master, tôi muốn chia sẻ đến các bạn những chỉ báo tuyệt vời nhất
Session LevelsSession Levels
Overview
Session Levels is a Pine Script v6 indicator for TradingView that plots key price levels from previous and current sessions. It overlays Previous Day High/Low, Pre-Market High/Low, Previous Close, and Today’s Open/High/Low as horizontal lines—levels that traders commonly reference as potential support/resistance. The script updates dynamically and offers customizable timing and visuals for intraday and multi-day analysis.
How It Works
Previous Day High, Low, and Close are retrieved via request.security on the daily timeframe. Pre-Market High/Low are tracked inside a user-defined window (default: 4:00–8:30 America/New_York) using timestamp and rolling math.max/math.min. Today’s Open is captured at 9:30 America/New_York, and Today’s High/Low update throughout the session. Lines are plotted with user-selectable style (solid/dotted/dashed), width, and color, and labeled (e.g., PDH, PDL, PMH, PML). Lines extend to the right for ongoing context.
Key Features
• Previous Day, Pre-Market, and Today’s levels in one view
• Custom pre-market window (America/New_York, DST-aware)
• Flexible line styles, widths, and colors
• Per-level visibility toggles
• Ongoing updates to Today’s High/Low
• Clear labels for quick identification
What It Displays
A consolidated set of session-based reference levels to help study potential support, resistance, and breakout zones across intraday and multi-day contexts.
Originality
The Pine v6 indicator implementation uses TradingView built-ins (request.security, timestamp, math.max, math.min). No external open-source code is incorporated.
Common Uses
• Day traders reviewing intraday levels and potential breakouts
• Swing traders monitoring multi-day reference zones
• Technical analysts annotating key price areas
Configuration Notes
Set the pre-market window (default: 4:00–8:30 America/New_York) and choose which levels to display (e.g., Previous Day, Pre-Market, Today’s Open). Adjust line styles, widths, and colors to fit your chart.
Legal Disclaimer
For informational and educational purposes only—not investment, financial, or trading advice. Past performance does not guarantee future results; trading involves significant risk. Provided “as is,” without warranties. Consult a qualified professional before making decisions. By using, you accept all risks and agree to this disclaimer.
ORB Range Only (Customizable Time Zone)ORB Range Only (Customizable Time Zone)
Overview
The ORB Range Only (Customizable Time Zone) is a Pine Script v6 indicator for TradingView that identifies and visualizes Opening Range Breakout (ORB) levels for intraday trading. It plots the high and low during a user-defined window (default: 15 minutes) as key reference levels and extends them through the session. A tick-snapping option aligns the start to the first bar in the window. A 1-minute fallback and optional breakout markers provide a clear, display-based workflow for studying breakout conditions.
How It Works
The indicator tracks the session window’s highest and lowest prices using TradingView’s ta.highest and ta.lowest. A snap-to-first-bar mechanism anchors the window’s start to the first bar at/after the intended timestamp to help on thin charts. For sparse data, a 1-minute fallback retrieves display-based highs/lows via request.security (no lookahead). While the window is open, a dynamic box updates in real time; once it closes, a final box is drawn and extended to 16:00 in the selected time zone, with dotted lines at the ORB high/low. Breakout triangles appear when price crosses these locked levels.
Key Features
• Customizable ORB window (duration and 12-hour start with AM/PM)
• Tick-aligned start (snap to first available bar)
• 1-minute fallback for sparse data
• Live “building” box and post-window final box with dotted levels
• Optional breakout markers (bullish/bearish triangles)
• Time zone presets (Chart, major regions, UTC, or Custom Olson ID) and color controls
What It Displays
A real-time ORB range box during the window and a locked range for the rest of the session, with optional breakout markers to study intraday momentum and reference levels across stocks, forex, futures, or crypto.
Originality
This Pine v6 indicator implementation uses TradingView’s built-in request.security, ta.highest, and ta.lowest functions.
Common Ways People Use It
• Day traders reviewing opening-range breakouts
• Scalpers focusing on early session volatility
• Technical analysts marking intraday reference levels
Configuration Notes
Set the ORB window (default: 15 minutes at 8:00 AM in the selected time zone, default preset: Chart), enable/disable tick-snapping and the 1-minute fallback, choose a time zone preset or a custom Olson ID, and adjust colors. Use breakout triangles to visualize when price crosses the ORB high/low.
Legal Disclaimer
This indicator is for informational and educational purposes only—not investment, financial, or trading advice. Past performance is not indicative of future results; trading involves risk of loss. Provided “as is” with no warranties. Consult a qualified professional before making decisions. By using it, you assume all risk and agree to this disclaimer.
Volume Intensity MeterVolume Intensity Meter
Overview
The Volume Intensity Meter is a Pine Script v6 indicator for TradingView that measures market momentum and direction using a volume-weighted intensity metric. It renders a normalized percentage score in a compact table with a gradient meter and directional arrows indicating bullish, bearish, or neutral conditions. A hybrid formula balances direction and volatility, and users can tune sensitivity and display settings.
How It Works
The indicator builds a hybrid intensity score from:
• Volatility component: (high − low) × volume (30% weight)
• Directional component: (close − open) × volume (70% weight)
The raw score is smoothed with an EMA (default 5) and normalized by a baseline SMA (default 20) to produce a percentage metric, clamped between −150% and +150%. A gradient row (red → yellow → green) provides context, and a pointer highlights the current intensity. Directional arrows (▲ bullish, ▼ bearish, ⎯ neutral) switch based on a user sensitivity threshold of 1% by default (0.01 in normalized units).
Key Features
• Hybrid Intensity Calculation: Blends directional and volatility signals with volume weighting.
• Gradient Meter Display: Color-coded table with a live pointer and center readout.
• Customizable Parameters: Lookback, smoothing, number of segments, and flip sensitivity.
• Momentum Pointer : Marks current intensity from bearish to bullish.
• Normalized Scale: Clamped output (±150%) for consistent reading across symbols.
• Toggleable Display: Show/hide the meter for a clean chart.
What It Displays
A normalized momentum readout with directional context, shown as a gradient meter plus arrows. This helps quickly assess shifts in pressure for scalping, day trading, or swing trading across stocks, forex, futures, or crypto.
Originality
The Pine v6 implementation uses TradingView’s built-in ta.ema and ta.sma for smoothing and baseline calculations.
Common Uses
• Monitoring short-term momentum changes.
• Visualizing intensity around key sessions.
• Adding a quick-glance pressure gauge to existing setups.
Configuration Notes
Set the lookback period (default 20), smoothing (default 5), and segments (default 21). The flip sensitivity is specified in normalized units (default 0.01 = 1%). Use the gradient meter and arrows to gauge momentum strength and direction.
Legal Disclaimer
This indicator is for informational and educational purposes only—not investment, financial, or trading advice. Past performance does not guarantee future results; trading involves risk. Provided “as is” with no warranties. Consult a qualified professional before making decisions.
Dynamic S/R Zones (Verified Pivots)Dynamic S/R Zones (Verified Pivots)
Overview
Dynamic S/R Zones is a Pine Script v6 indicator for TradingView that draws support/resistance (S/R) zones and Fibonacci retracement levels from verified pivot points. It plots minor S/R on the current timeframe and major S/R from a user-selected higher timeframe. Optional verification logic colors Fibonacci levels when price action or a trend filter aligns. The tool is intended for chart study and annotation.
How It Works
Pivots → S/R: Minor levels come from ta.pivothigh/ta.pivotlow on the chart’s timeframe. Major levels use request.security(lookahead=barmerge.lookahead_off) to read higher-timeframe data without lookahead. A distance buffer deduplicates nearby levels.
Labels & Coloring: Each level is shown as support or resistance based on the bar’s close relative to the level. When enabled, coloring reflects which side of the level price is on.
Fibonacci (33%, 50%, 66%): Fibs are drawn between the most recent verified swing high/low pair. No real-time/unverified pivots are used.
Verification Modes (optional):
Bounce/Reject: A level is marked “verified” when a bar closes at/above (bounce) or at/below (rejection) that level.
Trend-Based: Levels are marked when price closes through the level in the direction of either the most recent swing (pivot-based) or an HTF EMA filter (default: 21/50 EMAs on a user-set HTF).
When a level becomes verified, its color updates and a small arrow can be plotted at the bar where verification occurred.
Key Features
• Minor (current TF) and major (HTF) S/R from verified pivots.
• Optional Fibonacci 33% / 50% / 66% retracements from the latest verified swing.
• Two optional verification styles: bounce/rejection or trend-based (pivot or HTF EMA).
• Side-of-level coloring and compact labels for quick context.
• Adjustable pivot strength, lookback range, HTF selection, colors, and visibility.
What It Displays
The indicator visualizes pivot-derived S/R levels, optional Fibonacci retracements, and—when enabled—verification marks that indicate where price action or trend criteria aligned with a level. It’s designed to help document areas of interest on charts across assets and timeframes.
No-Repaint Notes
• Uses verified pivots only (no real-time/unconfirmed pivots).
• HTF series are requested with lookahead off.
• Verification occurs on bar close conditions.
Originality
Original Pine v6 implementation using TradingView built-ins: ta.pivothigh, ta.pivotlow, ta.ema, and request.security.
Configuration Notes
Set Pivot Strength (e.g., 5), Lookback Range (e.g., 300), and choose an HTF (e.g., Daily) for major S/R. Enable Fibonacci and choose a Verification Mode (bounce/reject or HTF trend-based). Toggle arrows/labels and adjust colors to fit your chart style.
Legal Disclaimer
This indicator is for informational and educational purposes only and does not constitute investment, financial, or trading advice. Markets involve risk, and past results do not guarantee future outcomes. Provided “as is” without warranties. Consider consulting a qualified professional before making decisions.
Adaptive Trend FinderAdaptive Trend Finder
Overview
Adaptive Trend Finder is a Pine Script v6 indicator for TradingView that visualizes adaptive regression channels across short, mid, and long horizons. For each horizon it selects the period with the highest Pearson’s R to the log of price, then draws a midline and deviation bands. Optional table rows summarize the selected period, a plain-language trend-fit label (from R), and an Annualized Price Change metric for daily/weekly charts (descriptive of historical price movement only).
How It Works
The indicator computes logarithmic linear regressions for short (~20–200 bars), mid (~200–1100 bars), and long (~300–1200 bars) horizons using built-ins like math.log and math.exp . For each horizon, it picks the candidate period with the highest Pearson’s R, then uses the slope, intercept, and standard deviation to plot:
• Midline (regression)
• Upper/lower bands (default multipliers: 2.0 / 2.5 / 3.0)
Channel color can follow price-action (close vs. previous close), direction (sign of slope), or a fixed color.
Key Features
• Adaptive channels: period auto-selection per horizon using Pearson’s R.
• Flexible visuals: configurable band multipliers, styles, and transparency.
• Readable stats: optional table for selected period, R-based trend-fit label, and Annualized Price Change (historical/descriptive; optional).
• Clean display: channels can extend left/right or not at all; table position and text size are configurable.
What It Displays
Three regression channels (short/mid/long) with configurable coloring, plus optional table rows describing the selected period and how closely the regression fits recent data (via R). This is a chart-visualization tool intended to help review historical trend behavior across timeframes.
Use Notes
Examples of chart review tasks include examining channel slope/width, noting where price interacts with bands, and comparing short vs. longer-term channels for context. This indicator does not generate signals or trade recommendations.
Configuration Notes
Adjust deviation multipliers (defaults: 2.0 / 2.5 / 3.0), line styles/colors, and toggle short/mid/long channels. Choose table position and contents. The Annualized Price Change row (if enabled) is shown on daily/weekly charts as a descriptive price metric—not a performance claim.
Originality
Original Pine v6 implementation using TradingView built-ins ( math.log , math.exp , math.sqrt , math.pow ).
Legal Disclaimer
For informational and educational purposes only—not investment, financial, or trading advice. Past results do not guarantee future outcomes; trading involves risk. Provided “as is,” without warranties. Use at your own risk.
HH/HL/LH/LL Swing MapHH/HL/LH/LL Swing Map
Overview
The HH/HL/LH/LL Swing Map is a Pine Script v6 indicator for TradingView that classifies market structure swings in real time. It identifies and labels Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) using verified pivot logic, ensuring accuracy without repainting. Custom non-repainting comparisons support reliable structure mapping. Designed for price action traders, it provides a clear, systematic view of evolving swing structure directly on the chart.
How It Works
The indicator uses ta.pivothigh and ta.pivotlow functions with configurable left/right bar settings to verify swing points—delaying labels until full verification for no repaints. Each verified pivot is compared to the previous swing of the same type: If a new high is above the prior high, it is marked as HH; otherwise, LH. If a new low is above the prior low, it is marked as HL; otherwise, LL. Labels and optional triangle markers are plotted on the exact bar where the swing confirms, with customizable offsets in ticks for consistent placement. Users can also enable connection lines between consecutive highs or lows, providing a simple zigzag visualization of structure shifts.
Key Features
• Non-Repainting Logic: Uses verified pivots (bar_index - rightBars) for accurate HH/HL/LH/LL classification.
• Customizable Labels: Adjustable colors, offsets, and visibility for clean chart integration.
• Optional Connection Lines: Connects successive highs and lows to highlight market structure flow.
• Trend Clarity: Quickly distinguishes bullish (HH/HL) and bearish (LH/LL) conditions.
• Alerts Built-In: Alert conditions trigger when new HH, HL, LH, or LL points are verified.
• Lightweight Design: Refined for fast rendering without cluttering the chart.
What It Displays
The indicator plots visual labels and markers for each verified structural pivot (HH, HL, LH, LL). It can also draw connecting lines between pivots to form a simplified swing map of price action. These elements help traders spot shifts in trend direction, continuation, and reversal zones.
Originality
This is an original Pine v6 implementation. It applies verified pivot logic, label/line management, and alert conditions.
Common Ways People Use It
• Price action traders mapping bullish or bearish structure (HH/HL vs. LH/LL).
• Swing traders validating entry/exit signals with structural verification.
• Technical analysts combining market structure with support/resistance or supply/demand zones.
• Day traders monitoring micro-structure shifts for intraday scalping strategies.
Configuration Notes
Users can adjust left/right bar counts for swing verification, label and triangle visibility, line drawing options, and color schemes. Offsets allow labels to remain readable across symbols and timeframes. Alerts may be set for specific structure changes (e.g., “New Higher High Verified”).
Legal Disclaimer
These indicators are for informational and educational purposes only—not investment, financial, or trading advice. Past performance is not indicative of future results; trading involves high risk of loss. Provided "as is" with no warranties. Consult a qualified professional before decisions. By using, you assume all risk and agree to this disclaimer.
Hypothetical (Swing Explorer)Hypothetical (Swing Explorer)
Overview
The Hypothetical (Swing Explorer) is a Pine Script v6 indicator for TradingView, designed to analyze market structure and illustrate price movements. It combines ZigZag-based pivot detection, liquidity sweeps, order blocks, trendlines, trail lines, and hypothetical illustrative swing marks, with optional custom-colored candles. We use integrated hypotheticals support forward estimations. A status table summarizes trend direction, trail status, and hypothetical illustrative marks, making it a versatile tool for traders reviewing commonly monitored setups.
How It Works
The indicator identifies market structure using a ZigZag algorithm (ta.highest, ta.lowest, default length: 9 bars) to plot swing highs/lows, verified by pivot detection (ta.pivothigh, ta.pivotlow)—ensuring non-repainting verification. Trend direction is determined by comparing fast (default: 8-period) and slow (default: 21-period) EMAs. Liquidity sweeps are detected at pivot highs/lows, and order blocks are drawn using ATR-based sizing. Trendlines connect pivot points, and trail lines track price or EMA trends with optional gradient coloring. Hypotheticals (next swing, illustrative swing, reversal, echo swing) are calculated using historical swing ranges, momentum, and ATR, with estimated arrival times based on velocity patterns. Custom candles reflect ZigZag leg direction, with illustrative or verified coloring options.
Key Features
• ZigZag Market Structure: Visualizes swing highs/lows with customizable lines.
• Liquidity Sweeps: Marks liquidity grabs at pivot points with dynamic lines.
• Order Blocks: Displays bullish/bearish order blocks with configurable limits.
• Trendlines: Draws bullish/bearish trendlines based on pivot connections.
• Hypothetical Swing Marks: Illustrates hypothetical (next swing, reversal, and echo swing levels) with time estimates.
• Trail and Zero-Lag Lines: Tracks price with customizable, gradient-colored lines synced to price or EMA trends.
• Custom Plot Candles: Overlays candles colored by ZigZag leg direction (illustrative or verified).
• Status Table: Summarizes trend, trail, and illustrative hypothetical mark states.
• Customizable Settings: Adjust lookback, colors, and visibility for all components.
What It Displays
This indicator integrates multiple price action tools—ZigZag structure, liquidity sweeps, order blocks, and hypothetical illustrative marks—into a cohesive system, offering a comprehensive view of market dynamics via velocity-based estimations derived from historical swing ranges and momentum. Its customizable visuals and illustrative features make it adaptable for day trading, scalping, or swing trading in stocks, forex, futures, or crypto, enhancing trade chart review.
Originality
This indicator is a Pine v6 implementation using TradingView’s built-in ta.ema, ta.atr, ta.pivothigh, ta.pivotlow, ta.highest, ta.lowest, and ta.barssince functions.
Common Ways People Use It
• Day traders reviewing intraday breakouts and reversals.
• Scalpers studying liquidity sweeps and order block zones.
• Technical analysts illustrating market structure and swings.
Configuration Notes
Configure the ZigZag length (default: 9), pivot lookback (default: 10), and EMA periods (default: 8/21). Adjust visibility and colors for market structure, liquidity sweeps, order blocks, trendlines, and trail lines. Enable custom candles for enhanced visualization and use the status table to monitor trend and illustrative markers/labels for trade planning.
Legal Disclaimer
These indicators are for informational and educational purposes only—not investment, financial, or trading advice. Past performance is not indicative of future results; trading involves high risk of loss. Provided "as is" with no warranties. Consult a qualified professional before decisions. By using, you assume all risk and agree to this disclaimer.
FMK Mum Koşulları (Tüm Filtreler)Yönü pozitife dönmüş ve alım fırsatı veren mumları bulmaya yarayan bir indikatör.
Correlation Table 5хThe script allows you to determine the correlation in real time, which makes it possible to effectively select trading pairs.
Enhanced Std Dev Oscillator (Z-Score)Enhanced Std Dev Oscillator (Z-Score)
Overview
The Enhanced Std Dev Oscillator (ESDO) is a refined Z-Score indicator that normalizes price deviations from a moving mean using standard deviation, smoothed for clarity and equipped with divergence detection. This oscillator shines in identifying extreme overbought/oversold conditions and potential reversals, making it ideal for mean-reversion strategies in stocks, forex, or crypto. By highlighting when prices stray too far from the norm, it helps traders avoid chasing trends and focus on high-probability pullbacks.
Key Features
Customisable Mean & Deviation: Choose SMA or EMA for the mean (default: SMA, length 14); opt for Population or Sample standard deviation for precise statistical accuracy.
Smoothing for Clarity: Apply a simple moving average (default: 3) to the raw Z-Score, reducing noise without lagging signals excessively.
Zone Highlighting: Background colours flag extreme zones—red tint above +2 (overbought), green below -2 (oversold)—for quick visual scans.
Divergence Alerts: Automatically detects bullish (price lows lower, Z-Score higher) and bearish (price highs higher, Z-Score lower) divergences using pivot points (default length: 5), with labeled shapes for easy spotting.
Built-in Alerts: Notifications for Z-Score crossovers into OB/OS zones and divergence events to keep you informed without constant monitoring.
How It Works
Core Calculation: Computes the mean (SMA/EMA) over the specified length, then standard deviation (Population or adjusted Sample formula for N>1). Z-Score = (Source - Mean) / Std Dev, handling edge cases like zero deviation.
Smoothing: Averages the Z-Score with an SMA to create a cleaner plot oscillating around zero.
Levels & Zones: Plots horizontal lines at ±1 (orange dotted) and ±2 (red dashed) for reference; backgrounds activate in extreme zones.
Divergence Logic: Scans for pivot highs/lows in price and Z-Score; flags divergences when price extremes diverge from oscillator extremes (looking back 2 pivots for confirmation).
Visualisation: Blue line for the smoothed Z-Score; green/red labels for bull/bear divergences.
Usage Tips
Buy Signal: Z-Score crosses below -2 (oversold) or bullish divergence forms—pair with volume spike for confirmation.
Sell Signal: Z-Score crosses above +2 (overbought) or bearish divergence—watch for resistance alignment.
Customisation: Use EMA mean for trendier assets; enable Sample std dev for smaller datasets. Increase pivot length (7-10) in volatile markets to filter false signals.
Timeframes: Excels on daily/4H for swing trades; test smoothing on lower frames to avoid over-smoothing. Always combine with trend filters like a 200-period MA.
This open-source script is licensed under Mozilla Public License 2.0. Backtest thoroughly—past performance isn't indicative of future results. Trade with discipline! 📈
© HighlanderOne
Quarterly Earnings - v1This script shows company fundamentals in a TradingView table: Earnings Per Share (EPS), Price-to-Earnings Ratio (P/E, TTM), Sales (in Crores), Operating Margin (OPM %), Return on Assets (ROA %), and Return on Equity (ROE %).
Quarterly Earnings - v1This script shows company fundamentals in a TradingView table: Earnings Per Share (EPS), Price-to-Earnings Ratio (P/E, TTM), Sales (in Crores), Operating Margin (OPM %), Return on Assets (ROA %), and Return on Equity (ROE %).
Liquidity Sweep ReversalThe Liquidity Sweep Reversal indicator is a sophisticated price-action-based tool designed for TradingView that identifies high-probability reversal setups by combining institutional liquidity concepts with session-based market structure. It detects potential reversals after price "sweeps" key support/resistance levels—such as prior day/week highs and lows or session extremes (Asian, London, New York)—followed by a rejection pattern.
The core logic revolves around two main signal types:
CISD (Close Inside, Sweep, Divergence) patterns that confirm liquidity grabs on higher timeframes.
Engulfing candlestick reversals occurring shortly after a touch of a key level within a defined lookback window.
To enhance relevance and reduce noise, the indicator optionally restricts signals to high-volatility “Killzone” sessions—including Asian, London, and New York AM/PM overlap periods—where institutional activity is typically concentrated.
Users can fully customize:
Timezone and higher timeframe (HTF) settings
Which key levels to monitor (PDH, PDL, PWH, PWL, session highs/lows)
Visual styling (line types, colors, labels)
Signal sensitivity (max bars after touch, signal size)
Display options (background highlights, level visibility, historical signal filtering)
Additionally, the script draws vertical lines for today’s and tomorrow’s London (08:00 CET) and New York (09:30 EST) market opens to provide contextual reference.
This tool is ideal for traders using auction market theory, order flow, or institutional footprint strategies who seek confluence between liquidity pools, session structure, and price rejection.
