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Previous Day & Week High/Low LevelsPrevious Day & Week High/Low Levels is a precision tool designed to help traders easily identify the most relevant price levels that often act as strong support or resistance areas in the market. It automatically plots the previous day’s and week’s highs and lows, as well as the current day’s developing internal high and low. These levels are crucial reference points for intraday, swing, and even position traders who rely on price action and liquidity behavior.
Key Features
Previous Day High/Low:
The indicator automatically draws horizontal lines marking the highest and lowest prices from the previous trading day.
These levels are widely recognized as potential zones where the market may react again — either rejecting or breaking through them.
Previous Week High/Low:
The script also tracks and displays the high and low from the last completed trading week.
Weekly levels tend to represent stronger liquidity pools and broader institutional zones, which makes them especially important when aligning higher timeframe context with lower timeframe entries.
Internal Daily High/Low (Real-Time Tracking):
While the day progresses, the indicator dynamically updates the current day’s internal high and low.
This allows traders to visualize developing market structure, identify intraday ranges, and anticipate potential breakouts or liquidity sweeps.
Multi-Timeframe Consistency:
All levels — daily and weekly — remain visible across any chart timeframe, from 1 minute to 1 day or higher.
This ensures traders can maintain perspective and avoid losing track of key zones when switching views.
Customizable Visuals:
The colors, line thickness, and label visibility can be easily adjusted to match personal charting preferences.
This makes the indicator adaptable to any trading style or layout, whether minimalistic or detailed.
How to Use
Identify Key Reaction Zones:
Observe how price interacts with the previous day and week levels. Rejections, consolidations, or clean breakouts around these lines often signal strong liquidity areas or potential directional moves.
Combine with Market Structure or Liquidity Concepts:
The indicator works perfectly with supply and demand analysis, liquidity sweeps, order block strategies, or simply classic support/resistance techniques.
Scalping and Intraday Trading:
On lower timeframes (1m–15m), the daily levels help identify intraday turning points.
On higher timeframes (1h–4h or daily), the weekly levels provide broader context and directional bias.
Risk Management and Planning:
Using these levels as reference points allows for more precise stop placement, target setting, and overall trade management.
Why This Indicator Helps
Markets often react strongly around previous highs and lows because these zones contain trapped liquidity, pending orders, or institutional decision points.
By having these areas automatically mapped out, traders gain a clear and objective view of where price is likely to respond — without needing to manually draw lines every day or week.
Whether you’re a beginner still learning about price structure, or an advanced trader refining entries within liquidity zones, this tool simplifies the process and keeps your charts clean, consistent, and data-driven.
ETH Smart Money Order Blocks Detector derek
This script helps identify potential Order Blocks (institutional price zones) on the chart based on Breaks of Structure (BOS).
It automatically highlights demand zones (green blocks) and supply zones (red blocks), making it easier to read institutional market flow.
Ideal for traders using Smart Money Concepts (SMC) or market structure analysis.
Compatible with any asset and timeframe.
🔹 Bullish OB = potential buy entry zone
🔹 Bearish OB = potential sell or resistance zone
Author: Derek 🧠💹
X 4H ORThis indicator plots the 30-second opening range (high/low) for six New-York–time anchors—2am, 6am, 10am, 2pm, 6pm, and 10pm—and extends each box to a fixed end time (e.g., 2am→9am, 6am→1pm, etc.). It samples true 30-second data regardless of the chart timeframe, so the captured highs/lows are precise.
What it does
Builds the first 30s OR for each selected anchor and draws a time-anchored box for that session.
Archives every day’s boxes (up to a cap) so you can study how price interacts with past ranges.
Adds per-anchor show toggles to display the latest box for that anchor.
Adds a global History toggle to show/hide all archived boxes without deleting them (clean view vs. context view).
Uses borderless, color-coded fills per anchor to avoid edge distortion while keeping levels easy to read.
Why it’s useful
Quickly spot session inflection zones where liquidity, breakouts, or reversals cluster.
Compare how current price trades relative to recent session ranges for bias and risk framing.
Perform lightweight post-session review/backtesting on OR breaks, retests, and range rotations.
Keep charts decluttered on demand (latest only), or flip on history for deeper context.
Gold and Bitcoin: The Evolution of Value!The Eternal Luster of Gold
In the dawn of time, when the earth was young and rivers whispered secrets to the stones, a wanderer named Elara found a gleam in the silt of a sun-kissed stream. It was pure gold, radiant like a captured star fallen from the heavens. She held it in her palm, feeling its warmth pulse like a heartbeat, and in that moment, humanity’s soul awakened to the allure of eternity.
As seasons turned to centuries, gold wove itself into the story of empires. In ancient Egypt, pharaohs crowned themselves with its glow, believing it to be the flesh of gods. It built pyramids that reached for the sky and tombs that guarded kings forever. Across the sands in Mesopotamia, merchants traded it for spices and silks, its weight a promise of power and trust.
Translation moment: Gold became the first universal symbol of value. People trusted it more than words or promises because it did not rust, fade, or vanish.
The Greeks saw in gold not only wealth but wisdom, the symbol of the sun’s eternal fire. Alexander the Great carried it across the continent, forging an empire of golden threads. Rome rose on its back, minting coins whose clink echoed through history.
Through the ages, gold endured the rush of California’s dreamers, the halls of Versailles, and the quiet vaults of modern fortunes. It has been both a curse and a blessing, the fuel of wars and the gift of love, whispering of beauty’s fragility and the human desire for something that lasts beyond the grave. In its shine, we see ourselves fragile yet forever chasing light.
The Digital Dawn of Bitcoin
Centuries later, under the glow of computer screens, a visionary named Satoshi dreamed of a new gold born not from the earth but from the ether of ideas. Bitcoin appeared in 2009 amid a world weary of banks and broken trust.
Like gold’s ancient gleam, Bitcoin was mined not with picks but with puzzles solved by machines. It promised freedom, a currency without kings, flowing from person to person, unbound by borders or empires.
Translation moment: Bitcoin works like digital gold. Instead of digging the ground, miners use computers to solve problems and unlock new coins. No one controls it, and that is what makes it powerful.
Through doubt and frenzy, it rose as a beacon for those seeking sovereignty in a digital world. Its volatility became its soul, a reminder that true value is built on belief. Bitcoin speaks to ingenuity and rebellion, a star of code guiding us toward a future where wealth is weightless yet profoundly honest.
Gold’s Cycles: Echoes of War and Crisis
In the early 20th century, gold was held under fixed prices until the Great Depression of 1929 shattered these illusions. The 1934 dollar devaluation lifted it from 20.67 to 35, restoring faith amid despair. When World War II erupted in 1939, gold’s role as a refuge was muted by controls, yet it quietly held its place as the world’s silent guardian.
The 1970s awakened its wild spirit. The Nixon Shock of 1971 freed gold from 35, sparking a bull run during the 1973 Oil Crisis. The 1979 Iranian Revolution led to a 1980 peak of 850, a leap of more than 2,000 percent, as investors sought safety from the chaos.
Translation moment: When fear rises, people rush to gold. Every major war or economic crisis has sent gold upward because it feels safe when paper money loses trust.
The 1987 stock crash caused brief dips, but the 1990 Gulf War reignited its glow. Around 2000, after the Dot-com Bust, gold found new life, climbing from $ 270 to over $1,900 during the 2008 Financial Crisis. It dipped to 1050 in 2015, then surged again past 2000 during the 2020 pandemic.
The 2022 Ukraine War added another chapter with prices climbing above 2700 by 2025. Across a century of crises, gold has risen whenever fear tested humanity’s resolve, teaching patience and fortitude through its quiet endurance.
Bitcoin’s Cycles: Echoes of Innovation and Crisis
Born from the ashes of the 2008 Financial Crisis, Bitcoin began its story at mere cents. It traded below $1 until 2011, when it reached $30 before crashing by 90 percent following the MTGOX collapse.
In 2013, it soared to 1242 only to fall again to 200 in 2015 as regulations tightened. The 2017 bull run lifted it to nearly 20000 before another long winter brought it to 3200 in 2018. Each fall taught resilience, each rise renewed belief.
During the 2020 pandemic, it fell below 5000 before rallying to 69000 in 2021. The Ukraine War and the FTX collapse of 2022 brought it down to 16000, but also proved its role in humanitarian aid. By 2024, the halving and ETF approvals helped it break 100000, marking Bitcoin’s rise as digital gold.
Translation moment: Bitcoin’s rhythm follows four-year halving cycles when mining rewards are cut in half. This keeps supply limited, which often triggers new bull runs as demand returns.
Every four years, it's halving cycles 2012, 2016, 2020, 2024, fueling new waves of adoption and correction. Bitcoin grows strongest in times of uncertainty, echoing humanity’s drive to evolve beyond limits.
The Harmony of Gold and Bitcoin Modern Parallels
In today’s markets, gold’s ancient glow meets Bitcoin’s electric pulse. As of October 17, 2025, their correlation stands near 0.85, close to its historic high of 0.9. Both rise as guardians against inflation and the erosion of trust in the dollar.
Gold trades near 4310 per ounce a record high while Bitcoin hovers around 104700 showing brief fractures in their unity. Gold offers the comfort of touch while Bitcoin provides the thrill of code. Together, they reflect fear and hope, the twin emotions that drive every market.
Translation moment: A correlation of 0.85 means they often move in the same direction. When fear or inflation rises, both gold and Bitcoin tend to rise in tandem.
Analysts warn of bubbles in stocks, gold, and crypto, yet optimism remains for Bitcoin’s growth through 2026, while gold holds its defensive strength.
Gold carries risks of storage cost and theft, but steadiness in chaos. Bitcoin carries volatility and regulatory challenges, but it also offers unmatched innovation and reach. One is the anchor, the other the dream, and both reward those who hold conviction through uncertainty.
Epilogue: The Timeless Balance
Gold and Bitcoin form a bridge between the ancient and the future. Gold, the earth’s eternal treasure, stands as a symbol of stability and truth. Bitcoin, the digital heir, shines with the spark of innovation and freedom.
Experts view gold as the ultimate inflation hedge, forged in fire and tested over centuries. They see Bitcoin as its digital counterpart, scarce by code and limitless in reach.
Gold’s weight grounds us in reality while Bitcoin’s light expands our imagination. In 2025, as gold surpasses $4,346 and Bitcoin hovers near $105,000, the wise investor sees not rivals but reflections.
Translation moment: Gold reminds us to protect what we have. Bitcoin reminds us to dream of what could be. Together, they balance caution and courage, the two forces every generation must master.
One whispers of legacy, the other of evolution, yet together they tell humanity’s oldest story, our unending quest to preserve value against time and to chase the light that never fades.
🙏 I ask (Allah) for guidance and success. 🤲
Wyckoff Accumulation / Distribution Detector (v3)🌱 Spring (Bullish Wyckoff Signature)
🧠 Definition
A Spring happens when price dips below a well-defined support level, usually near the end of an accumulation phase, then quickly reverses back above support.
This is not ordinary volatility — it's usually intentional by large operators (“Composite Man”) to:
Trigger stop-losses of weak holders
Create the illusion of a breakdown to scare late sellers in
Absorb all remaining supply at low prices
Launch the next markup leg once weak hands are flushed out
🧭 Typical Spring Characteristics
Feature Behavior
Location Near the bottom of a trading range after a decline
Price Action Temporary breakdown below support, then sharp reversal above
Volume Usually low to average on the break, indicating lack of real selling pressure. Sometimes a volume surge on the reversal as strong hands step in
Candle Often shows a long lower wick, closes back inside the range
Intent Shakeout of weak holders, allow institutions to accumulate more quietly
📈 Why It's Bullish
Springs typically mark the final test of supply. If price can dip below support and immediately recover, it means:
Selling pressure is exhausted (no follow-through)
Strong hands are absorbing remaining shares
A bullish breakout is often imminent
🪤 Upthrust (Bearish Wyckoff Signature)
🧠 Definition
An Upthrust is the mirror image of a Spring. It happens when price pokes above a resistance level, usually near the end of a distribution phase, but then fails to hold above it and falls back inside the range.
This is typically smart money distributing to eager buyers:
Late breakout traders pile in
Institutions sell into that strength
Price collapses back into the range, trapping breakout buyers
🧭 Typical Upthrust Characteristics
Feature Behavior
Location Near the top of a trading range after a rally
Price Action Temporary breakout above resistance, then quick reversal down
Volume Frequently low on the breakout, suggesting a lack of real buying interest — or sometimes high but with no progress, showing hidden selling
Candle Often shows a long upper wick, closes back inside the range
Intent Trap breakout buyers, provide liquidity for institutional sellers to unload near highs
📉 Why It's Bearish
Upthrusts show demand failure and supply swamping:
Buyers cannot sustain the breakout.
The sharp reversal signals large players are exiting.
Typically precedes markdown phases or sharp declines.
📝 Trading Implications
Spring → Often followed by a sign of strength rally → good long entry if confirmed with volume expansion and follow-through.
Upthrust → Often followed by a sign of weakness → short setups, especially if the next rally fails at lower highs.
The script looks for:
🌱 Spring:
Price makes a low below recent pivot support,
Closes back above,
Does so on low volume → likely a shakeout.
🪤 Upthrust:
Price makes a high above recent pivot resistance,
Closes back below,
On low volume → likely a bull trap.
TrendShield Pro | DinkanWorldSmart Trailing Trend System Powered by EMA + ATR
TrendShield Pro is a powerful trend detection and trailing stop indicator designed for traders who rely on pure price movement and volatility tracking.
It dynamically adapts to market conditions using a combination of EMA (Exponential Moving Average) and ATR (Average True Range) to identify the active trend and place a visual trailing stop line.
🔍 How It Works
TrendShield Pro combines trend direction and volatility to create a self-adjusting trailing system:
EMA (Exponential Moving Average):
Smooths price fluctuations and identifies the overall market bias.
ATR (Average True Range):
Measures volatility to determine how far the trailing stop should follow the trend.
Dynamic Bands:
Two invisible thresholds are formed — up and down — around the EMA using the ATR and your chosen Factor value.
Trailing Logic:
When the EMA is rising, the Trailing Stop (TUp) locks in higher lows.
When the EMA is falling, the Trailing Stop (TDown) locks in lower highs.
The indicator switches trend automatically based on price crossing these trailing levels.
🧭 Visuals & Features
Green Trailing Line (Demand Trend): Indicates an active bullish trend.
Red Trailing Line (Supply Trend): Indicates an active bearish trend.
Arrow Signals:
🟢 Up Arrow → Bullish Trend Reversal
🔴 Down Arrow → Bearish Trend Reversal
Diamond Markers: Show points where the trailing line shifts, marking dynamic volatility changes.
⚙️ Inputs
Input Description
EMA Period Length of the Exponential Moving Average
ATR Period Period used for Average True Range calculation
Factor Multiplier for ATR-based volatility expansion
Squeeze Weekday Frequency [CHE] Squeeze Weekday Frequency — Tracks historical frequency of low-volatility squeezes by weekday to inform timing of low-risk setups.
Summary
This indicator monitors periods of unusually low volatility, defined as when the average true range falls below a percentile threshold, and tallies their occurrences across each weekday. By aggregating these counts over the chart's history, it reveals patterns in squeeze frequency, helping traders avoid or target specific days for reduced noise. The approach uses persistent counters to ensure accurate daily tallies without duplicates, providing a robust view of weekday biases in volatility regimes.
Motivation: Why this design?
Traders often face inconsistent signal quality due to varying volatility patterns tied to the trading calendar, such as quieter mid-week sessions or busier Mondays. This indicator addresses that by binning low-volatility events into weekday buckets, allowing users to spot recurring low-activity days where trends may develop with less whipsaw. It focuses on historical aggregation rather than real-time alerts, emphasizing pattern recognition over prediction.
What’s different vs. standard approaches?
- Reference baseline: Traditional volatility trackers like simple moving averages of range or standalone Bollinger Band squeezes, which ignore temporal distribution.
- Architecture differences:
- Employs array-based persistent counters for each weekday to accumulate events without recounting.
- Includes duplicate prevention via day-key tracking to handle sparse data.
- Features on-demand sorting and conditional display modes for focused insights.
- Practical effect: Charts show a persistent table of ranked weekdays instead of transient plots, making it easier to glance at biases like higher squeezes on Fridays, which reduces the need for manual logging and highlights calendar-driven edges.
How it works (technical)
The indicator first computes the average true range over a specified lookback period to gauge recent volatility. It then ranks this value against its own history within a sliding window to identify squeezes when the rank drops below the threshold. Each bar's timestamp is resolved to a weekday using the selected timezone, and a unique day identifier is generated from the date components.
On detecting a squeeze and valid price data, it checks against a stored last-marked day for that weekday to avoid multiple counts per day. If it's a new occurrence, the corresponding weekday counter in an array increments. Total days and data-valid days are tracked separately for context.
At the chart's last bar, it sums all counters to compute shares, sorts weekdays by their squeeze proportions, and populates a table with the selected subset. The table alternates row colors and highlights the peak weekday. An info label above the final bar summarizes totals and the top day. Background shading applies a faint red to squeeze bars for visual confirmation. State persists via variable arrays initialized once, ensuring counts build incrementally without resets.
Parameter Guide
ATR Length — Sets the lookback for measuring average true range, influencing squeeze sensitivity to short-term swings. Default: 14. Trade-offs/Tips: Shorter values increase responsiveness but raise false positives in chop; longer smooths for stability, potentially missing early squeezes.
Percentile Window (bars) — Defines the history length for ranking the current ATR, balancing recent relevance with sample size. Default: 252. Trade-offs/Tips: Narrower windows adapt faster to regime shifts but amplify noise; wider ones stabilize ranks yet lag in fast markets—aim for 100-500 bars on daily charts.
Squeeze threshold (PR < x) — Determines the cutoff for low-volatility classification; lower values flag rarer, tighter squeezes. Default: 10.0. Trade-offs/Tips: Tighter thresholds (under 5) yield fewer but higher-quality signals, reducing clutter; looser (over 20) captures more events at the cost of relevance.
Timezone — Selects the reference for weekday assignment; exchange default aligns with asset's session. Default: Exchange. Trade-offs/Tips: Use custom for cross-market analysis, but verify alignment to avoid offset errors in global pairs.
Show — Toggles the results table visibility for quick on/off of the display. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to save screen space; re-enable for periodic reviews.
Pos — Positions the table on the chart pane for optimal viewing. Default: Top Right. Trade-offs/Tips: Bottom options suit long-term charts; test placements to avoid overlapping price action.
Font — Adjusts text size in the table for readability at different zooms. Default: normal. Trade-offs/Tips: Smaller fonts fit more data but strain eyes on small screens; larger for presentations.
Dark — Applies a dark color scheme to the table for contrast against chart backgrounds. Default: true. Trade-offs/Tips: Toggle false for light themes; ensures legibility without manual recoloring.
Display — Filters table rows to show all, top three, or bottom three weekdays by squeeze share. Default: All. Trade-offs/Tips: Use "Top 3" for focus on high-frequency days in active trading; "All" for full audits.
Reading & Interpretation
Red-tinted backgrounds mark individual squeeze bars, indicating current low-volatility conditions. The table's summary row shows the highest squeeze count, its percentage of total events, and the associated weekday in teal. Detail rows list selected weekdays with their absolute counts, proportional shares, and a left arrow for the peak day—higher percentages signal days where squeezes cluster, suggesting potential for calmer trend development. The info label reports overall days observed, valid data days, and reiterates the top weekday with its count. Drifting counts toward zero on a weekday imply rarity, while elevated ones point to habitual low-activity sessions.
Practical Workflows & Combinations
- Trend following: Scan for squeezes on high-frequency weekdays as entry filters, confirming with higher highs or lower lows in the structure; pair with momentum oscillators to time breaks.
- Exits/Stops: On low-squeeze days, widen stops for breathing room, tightening them during peak squeeze periods to guard against false breaks—use the table's percentages as a regime proxy.
- Multi-asset/Multi-TF: Defaults work across forex and indices on hourly or daily frames; for stocks, adjust percentile window to 100 for shorter histories. Scale thresholds up by 5-10 points for high-vol assets like crypto to maintain signal sparsity.
Behavior, Constraints & Performance
- Repaint/confirmation: Counts update only on confirmed bars via day-key changes, with no future references—live bars may shade red tentatively but tallies finalize at session close.
- security()/HTF: Not used, so no higher-timeframe repaint risks; all computations stay in the chart's resolution.
- Resources: Relies on a fixed-size array of seven elements and small loops for sorting and table fills, capped at 5000 bars back—efficient for most charts but may slow on very long intraday histories.
- Known limits: Ignores weekends and holidays implicitly via data presence; early chart bars lack full percentile context, leading to initial undercounting; assumes continuous sessions, so gaps in data (e.g., news halts) skew totals.
Sensible Defaults & Quick Tuning
Start with the built-in values for broad-market daily charts: ATR at 14, window at 252, threshold at 10. For noisier environments, lower the threshold to 5 and shorten the window to 100 to prioritize rare squeezes. If too few events appear, raise the threshold to 15 and extend ATR to 20 for broader capture. To combat overcounting in sparse data, widen the window to 500 while keeping others stock—monitor the info label's data-days count before trusting patterns.
What this indicator is—and isn’t
This serves as a statistical overlay for spotting calendar-based volatility biases, aiding in session selection and filter design. It is not a standalone signal generator, predictive model, or risk manager—integrate it with price action, volume, and broader strategy rules for decisions.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Orderblocks & BreakersThis indicator identifies potential orderblocks and breakers based on recent swing highs and lows. It is built to offer a structured, customizable, and noise-controlled view of how price interacts with supply and demand levels.
The script applies pivot-based swing detection to identify swing highs and lows.
Bullish Orderblocks: The script Identifies and stores the last down candle before a swing high is breached and confirms and plots the orderblock with a market structure break (close above the swing high).
Bearish Orderblocks: The script Identifies and stores the last up candle before a swing low is breached and confirms and plots the orderblock with a market structure break (close below the swing low).
When price later closes through an existing orderblock, it is reclassified as a Breaker and recolored accordingly. (all colors can be changed in the settings)
What Makes It Different
Unlike most orderblock tools that simply mark every swing-based block, this version introduces:
1. Chop Control – automatically hides breakers that price repeatedly closes through (2 closes after the orderblock becomes a breaker), keeping only relevant zones visible.
2. Recent Block Filtering – limits how many of the recent orderblocks or breakers are displayed, preventing chart clutter.
3. Dynamic Updating – orderblocks automatically convert to breakers when price closes beyond them, with clear color changes.
These features make it easier to study cleaner price structure without manually managing old or invalid zones. The optional Chop Control filter can reduce overlapping or repeatedly invalidated zones to keep the chart clearer.
Customizable Parameters
- Swing detection length (shorter means more aggressive pivot detection, longer means less aggressive so less highs/lows detected)
- Number of recent blocks to display
- Visibility toggles for orderblocks or breakers
- Color and transparency controls for each type
Alerts
Alerts can be set to trigger when price tests any defined zone.
Purpose
This indicator is designed as a price structure visualization and study tool.
It may assist in understanding how price interacts with previously active regions, but it does not produce signals or trade recommendations.
Illuminati Zone🟣 Illuminati Zone — Hidden Power of the 11 PM NZ Candle
The Illuminati Zone reveals the hidden footprints of liquidity and market imbalance formed by the 11 PM New Zealand 15-minute candle — a time when global liquidity transitions between major sessions.
This candle often defines key intraday supply and demand boundaries, serving as a magnet for price and a pivot point for high-probability reversals or breakouts.
🧠 How it works
Automatically detects and marks the 11 PM NZ 15-minute candle each day.
Draws a translucent zone box between its high and low.
Extends two reference lines at +1 × range and –1 × range above and below the zone — ideal for spotting overextensions or liquidity sweeps.
Supports custom lookback, colors, and visual options.
💡 How to use it
Watch how price interacts with the zone — rejection often signals smart-money activity.
Use +1 and –1 levels as overextended zones for potential reversals or breakout retests.
Combine with your own confluence tools or volume analysis for precision entries.
⚙️ Customization Options
Target hour (NZ time)
Days back to display
Zone and line colors
Transparency and visual preferences
🔮 Pro Tip: Pair it with a volume or imbalance indicator for surgical-level precision in identifying where smart money positions are built or released.
3-1-2 Strat Combo by NaturalBelleThe 3-1-2 Strat Combo by NaturalBelle automatically detects and highlights one of The Strat’s most powerful reversal patterns — the 3-1-2 setup.
When a 3 (outside bar) is followed by a 1 (inside bar) and then a 2 that breaks direction, this script plots yellow triangles and draws yellow box zones across the sequence, giving traders a clean visual cue for potential reversals or continuations.
Features:
Highlights both bullish (3-1-2-Up) and bearish (3-1-2-Down) sequences
Draws yellow boxes covering the 3-1-2 structure for easy zone recognition
Optional text labels for clarity
Adjustable box extension and transparency
Built-in alert conditions for both up and down setups
This clean, no-clutter version focuses purely on price action — no indicators, no noise. Just the pattern.
🟡 Best used on: Any timeframe
🟡 Strategy: Combine with market structure, EMAs, or supply & demand zones for confirmation
Created by NaturalBelle — keeping Strat analysis simple, visual, and precise.
Wyckoff Effort vs. Result📌 Wyckoff Effort vs. Result (E/R) – Visualizing Supply & Demand Imbalance with Volume Confirmation
📖 Overview
The Wyckoff Effort vs. Result (E/R) indicator is designed to help traders interpret market behavior through the lens of volume vs. price movement — a foundational concept in Richard Wyckoff’s methodology.
This tool aims to highlight moments where the “effort” (volume) is not in proportion to the “result” (price movement) — giving insight into potential accumulation or distribution events.
By detecting high-volume candles and classifying them based on their price direction, the indicator visualizes zones where smart money might be active .
⚙️ How It Works
1. Effort Accumulation (High Volume Down Bar):
• When a candle closes lower than it opens (down bar) and has above-average volume , it’s marked as potential absorption of selling pressure (effort to push down met by buying).
• These candles are colored red and the open level is plotted, acting as a potential support or re-test zone.
2. Effort Distribution (High Volume Up Bar):
• When a candle closes higher than it opens (up bar) and has above-average volume , it’s marked as potential distribution (effort to push up absorbed by sellers).
• These candles are colored green and the open level is plotted , acting as a potential resistance or rejection zone.
3. Average Volume Calculation:
• The script calculates a simple moving average (SMA) of volume over a user-defined lookback period.
• If current volume exceeds the average multiplied by a set threshold, it’s treated as a high-effort bar .
🧪 Inputs
Input Description
Average Volume Lookback - Number of bars used to calculate the volume average
High Volume Multiplier. - Multiplier to define what qualifies as “high volume”
🖥️ Visual Output
• 🔴 Red candles = High volume on a down bar → possible accumulation
• 🟢 Green candles = High volume on an up bar → possible distribution
• 📉 Horizontal lines at bar open price mark the potential zones where effort occurred
These zones can serve as:
• Areas of support/resistance
• Trap zones where smart money absorbs liquidity
• Entry/exit filters when combined with price action
🧠 How to Use
• Use in combination with price structure, support/resistance, and volume profile tools
• Watch how price reacts when it revisits the plotted lines
• Look for effort bars that fail to lead to continuation, signaling potential reversal
• Can be used in scalping, swing trading, or Wyckoff-style phase analysis
🔒 Technical Notes
• ✅ Does not repaint
• ✅ Built with Pine Script v6
• ✅ Lightweight and customizable
• ❌ Does not generate buy/sell signals — it provides context, not predictions
Supply/Demand Zones & EMA CrossSupport and Resistance Zone based on past ten days for daily, weekly, with this ema 8,20,50,200 and vwap also inclued
Nifty vs Nifty Fut Premium indicator This indicator compares Nifty Spot and Nifty Futures prices in real-time, displaying the premium (or discount) between them at the top of the pane.
Trading applications:
Arbitrage opportunities: When the premium becomes unusually high or low compared to fair value (based on cost of carry), traders can exploit the mispricing through cash-futures arbitrage
Market sentiment: A rising premium often indicates bullish sentiment as traders are willing to pay more for futures, while a declining or negative premium suggests bearish sentiment
Rollover strategy: Near expiry, monitoring the premium helps traders decide optimal timing for rolling positions from current month to next month contracts
Risk assessment: Sudden spikes in premium can signal increased demand for leveraged long positions, potentially indicating overbought conditions or strong momentum
Crypto Exchange PremiumDescription: Crypto Exchange Premium
The Crypto Exchange Premium indicator is designed to quantify and visualize price disparities between different types of crypto markets — specifically between spot and perpetual futures markets, or between any two customizable sources of price data. By consolidating live data from multiple major exchanges, it creates a unified, cross-market measure of premium (or discount), helping traders identify institutional activity (i. e. by comparing exchanges with high institutional activity against others), arbitrage opportunities, and shifts in market sentiment before they become visible in price action alone.
Concept and Purpose
In cryptocurrency markets, price divergence between spot and perpetual pairs reflects the real-time interaction of demand and liquidity across market segments.
When perpetual prices trade above spot, it implies aggressive long positioning or bullish leverage (positive funding expectations).
Conversely, when spot trades above perps, it may reflect net selling pressure in futures or strong spot accumulation.
Unlike most tools that rely on funding rates or open interest alone, this indicator measures the actual traded price spread dynamically across exchanges. This allows traders to visualize the “premium curve” of the crypto market in a clear, data-driven format.
How It Works
The indicator aggregates real-time prices from a wide selection of exchanges, normalizes them into groups, and computes the difference (“premium”) between two chosen reference markets.
1. Exchange Aggregation:
Users can toggle individual exchanges for both spot and perpetual aggregation groups.
The script automatically calculates group averages by dividing the sum of all enabled exchange prices by the number of valid feeds.
Non-USD exchanges (e.g., KRW pairs on Upbit or Bithumb) are automatically converted into USD using live FX data (USDKRW) for accurate normalization.
2. Flexible Comparison Logic:
Each leg of the comparison (First vs. Second Source) can be chosen as one of:
Local chart symbol
Custom symbol
Aggregated Spot group
Aggregated Perpetual group
This allows users to compare, for example:
Binance Spot vs. Global Perp Average
Coinbase Spot vs. Binance Perp
BTCUSD vs. BTCUSDT.P (or any cross-exchange combination)
3. Premium Calculation:
The final value is computed as:
Premium = First Source Price − Second Source Price
and is plotted as a histogram (positive = green, negative = red). This visual instantly shows whether the first source trades at a premium or discount relative to the second.
How to Use
Select Data Sources:
Configure the “First Symbol” and “Second Symbol” in the settings. For most use cases:
First Symbol → Perps (Aggregated)
Second Symbol → Spot (Aggregated)
Adjust Exchange Selection:
Enable or disable individual exchanges to fine-tune your data set. For instance, disabling Korean exchanges filters out regional FX distortions.
Originality and Value
While many exchange difference or “premium indicators” track one or two exchanges, this script introduces multi-exchange aggregation, cross-market normalization, and user-configurable pairing, resulting in a more holistic and accurate reflection of market structure.
It bridges a gap between macro market breadth and microstructural price dynamics, empowering traders to:
Detect arbitrage inefficiencies between spot and perps.
Track regional price dislocations (USD vs. KRW).
Gauge the intensity of speculative leverage over time.
Anticipate funding rate shifts and liquidation clusters before they happen.
US Opening 5-Minute Candle HighlighterUS Opening 5-Minute Candle Highlighter — True RVOL (Two-Tier + Label)
What it does (in plain English)
This indicator finds the first 5-minute bar of the US cash session (09:30–09:35 ET) and highlights it when the candle has the specific “strong open” look you want:
Opens near the low of its own range, and
Closes near the high of its own range, and
Has a decisive real body (not a wick-y doji), and
(Optionally) is a green candle, and
Meets a TRUE opening-bar RVOL filter (compares today’s 09:30–09:35 volume only to prior sessions’ 09:30–09:35 volumes).
You get two visual intensities based on opening RVOL:
Tier-1 (≥ threshold 1, default 1.0×) → light green highlight + lime arrow
Tier-2 (≥ threshold 2, default 1.5×) → darker green highlight + green arrow
An RVOL label (e.g., RVOL 1.84x) can be shown above or below the opening bar.
Designed for 5-minute charts. On other timeframes the “opening bar” will be the bar that starts at 09:30 on that timeframe (e.g., 15-minute 09:30–09:45). For best results keep the chart on 5m.
How the pattern is defined
For the opening 5-minute bar, we compute:
Range = high − low
Body = |close − open|
Then we measure where the open and close sit within the bar’s own range on a 0→1 scale:
0 means exactly at the low
1 means exactly at the high
Using two quantiles:
Open ≤ position in range (0–1) (default 0.20)
Example: 0.20 means “open must be in the lowest 20% of the bar’s range.”
Close ≥ position in range (0–1) (default 0.80)
Example: 0.80 means “close must be in the top 20% of the bar’s range.”
This keeps the logic range-normalized so it adapts across different tickers and vol regimes (you’re not using fixed cents or % of price).
Body ≥ fraction of range (0–1) (default 0.55)
Requires the real body to be at least that fraction of the total range.
0.55 = body fills ≥ 55% of the candle.
Purpose: filter out indecisive, wick-heavy bars.
Raise to 0.7–0.8 for only the fattest thrusts; lower to 0.3–0.4 to admit more bars.
Require green candle? (default ON)
If ON, close > open must be true. Turn OFF if you also want to catch strong red opens for shorts.
Minimum range (ticks)
Ignore tiny, illiquid opens: e.g., set to 2–5 ticks to suppress micro bars.
TRUE Opening-Bar RVOL (why it’s “true”)
Most “RVOL” compares against any recent bars, which isn’t fair at the open.
This indicator calculates only against prior opening bars:
At 09:30–09:35 ET, take today’s opening 5-minute volume.
Compare it to the average of the last N sessions’ opening 5-minute volumes.
RVOL = today_open_volume / average_prior_open_volumes.
So:
1.0× = equal to average prior opens.
1.5× = 150% of average prior opens.
2.0× = double the typical opening participation.
A minimum prior samples guard (default 10) ensures you don’t judge with too little history. Until enough samples exist, the RVOL gate won’t pass (you can disable RVOL temporarily if needed).
Visuals & tiers
Light green highlight + lime arrow → price filters pass and RVOL ≥ Tier-1 (default 1.0×)
Dark green highlight + green arrow → price filters pass and RVOL ≥ Tier-2 (default 1.5×)
Optional bar paint in matching green tones for extra visibility.
Optional RVOL label (e.g., RVOL 1.84x) above or below the opening bar.
You can show the label only when the candle qualifies, or on every open.
Inputs (step-by-step)
Price-action filters
Open ≤ position in range (0–1): default 0.20. Smaller = stricter (must open nearer the low).
Close ≥ position in range (0–1): default 0.80. Larger = stricter (must close nearer the high).
Body ≥ fraction of range (0–1): default 0.55. Raise to demand a “fatter” body.
Require green candle?: default ON. Turn OFF to also mark bearish thrusts.
Minimum range (ticks): default 0. Set to 2–5 for liquid mid/large caps.
Time settings
Timezone: default America/New_York. Leave as is for US equities.
Start hour / minute: defaults 09:30. The bar that starts at this time is evaluated.
TRUE Opening-Bar RVOL (two-tier)
Require TRUE opening-bar RVOL?: ON = must pass Tier-1 to highlight; OFF = price filters alone can highlight (still shows Tier-2 when hit).
RVOL lookback (prior opens count): default 20. How many prior openings to average.
Min prior opens required: default 10. Warm-up guard.
Tier-1 RVOL threshold (× avg): default 1.00× (light green).
Tier-2 RVOL threshold (× avg): default 1.50× (dark green).
Display
Also paint candle body?: OFF by default. Turn ON for instant visibility on a chart wall.
Arrow size: tiny/small/normal/large.
Light/Dark opacity: tune highlight strength.
Show RVOL label?: ON/OFF.
Show label only when candle qualifies?: ON by default; OFF to see RVOL every open.
Label position: Above candle or Below candle.
Label size: tiny/small/normal/large.
How to use (quick start)
Apply to a 5-minute chart.
Keep defaults: Open ≤ 0.20, Close ≥ 0.80, Body ≥ 0.55, Require green ON.
Turn RVOL required ON, with Tier-1 = 1.0×, Tier-2 = 1.5×, Lookback = 20, Min prior = 10.
Optional: enable Paint bar and set Arrow size = large for monitor-wall visibility.
Optional: show RVOL label below the bar to keep wicks clean.
Interpretation:
Dark green = A+ opening thrust with strong participation (≥ Tier-2).
Light green = Valid opening thrust with at least average participation (≥ Tier-1).
No highlight = one or more filters failed (quantiles, body, green, range, or RVOL if required).
Alerts
Two alert conditions are included:
Opening 5m Match — Tier-2 RVOL → fires when the opening candle passes price filters and RVOL ≥ Tier-2.
Opening 5m Match — Tier-1 RVOL → fires when the opening candle passes price filters and RVOL ≥ Tier-1 (but < Tier-2).
Recommended alert settings
Condition: choose the script + desired tier.
Options: Once Per Bar Close (you want the confirmed 09:30–09:35 bar).
Set your watchlist to symbols of interest (themes/sectors) and let the alerts pull you to the right charts.
Recommended starting values
Quantiles: Open ≤ 0.20, Close ≥ 0.80
Body fraction: 0.55
Require green: ON
RVOL: Required ON, Tier-1 = 1.0×, Tier-2 = 1.5×, Lookback 20, Min prior 10
Display: Paint bar ON, Arrow large, Label ON, Below candle
Tune tighter for A-plus selectivity:
Open ≤ 0.15, Close ≥ 0.85, Body ≥ 0.65, Tier-2 2.0×.
Notes, tips & limitations
5-minute timeframe is the intended use. On higher TFs, the 09:30 bar spans more than 5 minutes; geometry may not reflect the first 5 minutes alone.
RTH only: The opening detection looks at the clock (09:30 ET). Pre-market bars are ignored for the signal and for RVOL history.
Warm-up period: Until you have Min prior opens required samples, the RVOL gate won’t pass. You can temporarily toggle RVOL off.
DST & timezone: Leave timezone on America/New_York for US equities. If you trade non-US exchanges, set the appropriate TZ and opening time.
Illiquid tickers: Use Minimum range (ticks) and require RVOL to reduce noise.
No strategy orders: This is a visual/alert tool. Combine with your execution and risk plan.
Why this is useful on multi-monitor setups
Instant pattern recognition: the two-shade green makes A vs A+ opens pop at a glance.
Adaptive thresholds: quantiles & body are within-bar, so it works across $5 and $500 names.
Fair volume test: TRUE opening RVOL avoids comparing to pre-market or midday bars.
Optional labels: glanceable RVOL x-value helps triage the strongest themes quickly.
Dominant DATR [CHE] Dominant DATR — Directional ATR stream with dominant-side EMA, bands, labels, and alerts
Summary
Dominant DATR builds two directional volatility streams from the true range, assigns each bar’s range to the up or down side based on the sign of the close-to-close move, and then tracks the dominant side through an exponential average. A rolling band around the dominant stream defines recent extremes, while optional gradient coloring reflects relative magnitude. Swing-based labels mark new higher highs or lower lows on the dominant stream, and alerts can be enabled for swings, zero-line crossings, and band breakouts. The result is a compact pane that highlights regime bias and intensity without relying on price overlays.
Motivation: Why this design?
Conventional ATR treats all range as symmetric, which can mask directional pressure, cause late regime shifts, and produce frequent false flips during noisy phases. This design separates the range into up and down contributions, then emphasizes whichever side is stronger. A single smoothed dominant stream clarifies bias, while the band and swing markers help distinguish continuation from exhaustion. Optional normalization by close makes the metric comparable across instruments with different price scales.
What’s different vs. standard approaches?
Reference baseline: Classic ATR or a basic EMA of price.
Architecture differences:
Directional weighting of range using positive and negative close-to-close moves.
Separate moving averages for up and down contributions combined into one dominant stream.
Rolling highest and lowest of the dominant stream to form a band.
Optional normalization by close, window-based scaling for color intensity, and gamma adjustment for visual contrast.
Event logic for swing highs and lows on the dominant stream, with label buffering and pruning.
Configurable alerts for swings, zero-line crossings, and band breakouts.
Practical effect: You see when volatility is concentrated on one side, how strong that bias currently is, and when the dominant stream pushes through or fails at its recent envelope.
How it works (technical)
Each bar’s move is split into an up component and a down component based on whether the close increased or decreased relative to the prior close. The bar’s true range is proportionally assigned to up or down using those components as weights.
Each side is smoothed with a Wilder-style moving average. The dominant stream is the side with the larger value, recorded as positive for up dominance and negative for down dominance.
The dominant stream is then smoothed with an exponential moving average to reduce noise and provide a responsive yet stable signal line.
A rolling window tracks the highest and lowest values of the dominant EMA to form an envelope. Crossings of these bounds indicate unusual strength or weakness relative to recent history.
For visualization, the absolute value of the dominant EMA is scaled over a lookback window and passed through a gamma curve to modulate gradient intensity. Colors are chosen separately for up and down regimes.
Swing events are detected by comparing the dominant EMA to its recent extremes over a short lookback. Labels are placed when a prior bar set an extreme and the current bar confirms it. A managed array prunes older labels when the user-defined maximum is exceeded.
Alerts mirror these events and also include zero-line crossings and band breakouts. The script does not force closed-bar confirmation; users should configure alert execution timing to suit their workflow.
There are no higher-timeframe requests and no security calls. State is limited to simple arrays for labels and persistent color parameters.
Parameter Guide
Parameter — Effect — Default — Trade-offs/Tips
ATR Length — Smoothing of directional true range streams — fourteen — Longer reduces noise and may delay regime shifts; shorter increases responsiveness.
EMA Length — Smoothing of the dominant stream — twenty-five — Lower values react faster; higher values reduce whipsaw.
Band Length — Window for recent highs and lows of the dominant stream — ten — Short windows flag frequent breakouts; long windows emphasize only exceptional moves.
Normalize by Close — Divide by close price to produce a percent-like scale — false — Useful across assets with very different price levels.
Enable gradient color — Turn on magnitude-based coloring — true — Visual aid only; can be disabled for simplicity.
Gradient window — Lookback used to scale color intensity — one hundred — Larger windows stabilize the color scale.
Gamma (lines) — Adjust gradient intensity curve — zero point eight — Lower values compress variation; higher values expand it.
Gradient transparency — Transparency for gradient plots — zero, between zero and ninety — Higher values mute colors.
Up dark / Up neon — Base and peak colors for up dominance — green tones — Styling only.
Down dark / Down neon — Base and peak colors for down dominance — red tones — Styling only.
Show zero line / Background tint — Visual references for regime — true and false — Background tint can help quick scanning.
Swing length — Bars used to detect swing highs or lows — two — Larger values demand more structure.
Show labels / Max labels / Label offset — Label visibility, cap, and vertical offset — true, two hundred, zero — Increase cap with care to avoid clutter.
Alerts: HH/LL, Zero Cross, Band Break — Toggle alert rules — true, false, false — Enable only what you need.
Reading & Interpretation
The dominant EMA above zero indicates up-side dominance; below zero indicates down-side dominance.
Band lines show recent extremes of the dominant EMA; pushes through the band suggest unusual momentum on the dominant side.
Gradient intensity reflects local magnitude of dominance relative to the chosen window.
HH/LL labels appear when the dominant stream prints a new local extreme in the current regime and that extreme is confirmed on the next bar.
Zero-line crosses suggest regime flips; combine with structure or filters to reduce noise.
Practical Workflows & Combinations
Trend following: Consider entries when the dominant EMA is on the regime side and expands away from zero. Band breakouts add confirmation; structure such as higher highs or lower lows in price can filter signals.
Exits and stops: Tighten exits when the dominant stream stalls near the band or fades toward zero. Opposite swing labels can serve as early caution.
Multi-asset and multi-timeframe: Works across liquid assets and common timeframes. For lower noise instruments, reduce smoothing slightly; for high noise, increase lengths and swing length.
Behavior, Constraints & Performance
Repaint and confirmation: No security calls and no future-looking references. Swing labels confirm one bar later by design. Real-time crosses can change intra-bar; use bar-close alerts if needed.
Resources: `max_bars_back` is two thousand. The script uses an array for labels with pruning, gradient color computations, and a simple while loop that runs only when the label cap is exceeded.
Known limits: The EMA can lag at sharp turns. Normalization by close changes scale and may affect thresholds. Extremely gappy data can produce abrupt shifts in the dominant side.
Sensible Defaults & Quick Tuning
Starting point: ATR Length fourteen, EMA Length twenty-five, Band Length ten, Swing Length two, gradient enabled.
Too many flips: Increase EMA Length and swing length, or enable only swing alerts.
Too sluggish: Decrease EMA Length and Band Length.
Inconsistent scales across symbols: Enable Normalize by Close.
Visual clutter: Disable gradient or reduce label cap.
What this indicator is—and isn’t
This is a volatility-bias visualization and signal layer that highlights directional pressure and intensity. It is not a complete trading system and does not produce position sizing or risk management. Use it with market structure, context, and independent risk controls.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Aggression Bulbs v3.1 (Sessions + Bias, fixed)EYLONAggression Bulbs v3.2 (Sessions + Bias + Volume Surge)
This indicator highlights aggressive buy and sell activity during the London and New York sessions, using volume spikes and candle body dominance to detect institutional momentum.
⚙️ Main Logic
Compares each candle’s volume vs average volume (Volume Surge).
Checks body size vs full candle range to detect strong directional moves.
Uses an EMA bias filter to align signals with the current trend.
Displays green bubbles for aggressive buyers and red bubbles for aggressive sellers.
🕐 Sessions
London: 08:00–12:59 UTC+1
New York: 14:00–18:59 UTC+1
(Backgrounds: Yellow = London, Orange = New York)
📊 How to Read
🟢 Green bubble below bar → Aggressive BUY candle (strong demand).
🔴 Red bubble above bar → Aggressive SELL candle (strong supply).
Bubble size = relative strength (volume × candle dominance).
Use in confluence with key POI zones, volume profile, or delta clusters.
⚠️ Tips
Use on 1m–15m charts for scalping or intraday analysis.
Combine with your session bias or FVG zones for higher accuracy.
Set alerts when score ≥ threshold to catch early momentum.
Katana_Fox RSI Pro - Advanced Momentum Indicator with Clear BUOverview:
Connors RSI Pro is a sophisticated enhancement of the classic Connors RSI indicator, designed for traders who demand professional-grade tools. This premium version combines multiple momentum components with intelligent signaling and beautiful visualization to give you an edge in the markets.
Key Features:
🎯 Clear BUY/SELL Signal System
BUY signals in green when CRSI crosses above oversold level
SELL signals in red when CRSI crosses below overbought level
Clean, professional labels that are easy to read
Customizable overbought/oversold levels (70/30 default)
🎨 Professional Visualization
Modern color scheme that adapts to market conditions
Customizable background fills for better readability
Smooth, easy-to-read line plotting
⚡ Enhanced Calculations
Triple-component momentum analysis (RSI, UpDown RSI, Percent Rank)
EMA smoothing for reduced noise and false signals
Configurable lengths for each component
🔔 Advanced Alert System
4 distinct alert conditions for various market scenarios
Compatible with TradingView's native alert system
Perfect for automated trading strategies
Input Parameters:
RSI Length (3): Period for standard RSI calculation
UpDown Length (2): Period for UpDown RSI component
ROC Length (100): Period for Rate of Change percentile ranking
Signal Alerts: Toggle BUY/SELL signals on/off
Custom Colors: Choose between classic and modern color schemes
Trading Signals:
BUY (Green Label): Bullish signal when CRSI crosses above oversold level
SELL (Red Label): Bearish signal when CRSI crosses below overbought level
Background Colors: Visual zones indicating momentum strength
Ideal For:
Swing traders seeking momentum reversals
Day traders looking for overbought/oversold conditions
Algorithmic traders needing reliable signals
Technical analysts wanting multi-timeframe confirmation
How to Use:
Oversold Bounce: Enter long when CRSI shows BUY signal above 30
Overbought Rejection: Enter short when CRSI shows SELL signal below 70
Trend Confirmation: Use the 50-level crossover for trend direction
Divergence Trading: Look for price/indicator divergences at extremes
Upgrade your trading arsenal with Connors RSI Pro - where professional analytics meet clear trading signals!
No Supply (Low-Volume Down Bars) — IdoThis indicator flags classic Wyckoff/VSA “No Supply (NS)” events—down bars that print on unusually low volume, suggesting a lack of sellers rather than strong selling pressure. NS often appears near support, LPS, or within re-accumulation ranges as a test before continuation higher.
Signal definition (configurable):
Down bar: choose Close < PrevClose or Close < Open.
Low volume: Volume < SMA(Volume, len) × threshold (e.g., 0.7).
Optional volume lower than the prior two bars (reduces noise).
Optional narrow spread: range (H–L) below its average.
Optional close position: close in the upper half of the bar.
Optional trend filter: only mark NS above or below an EMA (or any).
Optional wide-bar exclusion: skip unusually wide bars.
Visuals & outputs
Blue dot below each NS bar (optional bar tint).
Separate pane showing Relative Volume (vol / volSMA) to gauge effort.
Built-in alertcondition to trigger notifications when NS prints.
Inputs (high level)
lenVol: Volume SMA length.
ratioVol: Volume threshold vs. average (e.g., 0.7 = 70%).
usePrev2: Require volume below each of the prior two bars.
useNarrow + lenRange + ratioRange: Narrow-bar filter.
useClosePos + minClosePos: Close in upper portion of the bar.
downBarMode: Define “down bar” logic.
trendFiltOn, trendLen, trendSide: EMA trend filter.
useWideFilter, lenRangeWide, wideThreshold: Skip wide bars.
How to use (Wyckoff/VSA context)
Treat NS as a test of supply: price dips, but volume is light and close holds up.
Stronger when it prints near support/LPS within a re-accumulation structure.
Confirmation (recommended): within 1–3 bars, see demand—e.g., break above the NS high with expanding volume (above average or above the prior two bars). Many traders place a buy-stop just above the NS high; common stops are below the NS low or the most recent swing low.
Scanning tip
TradingView’s stock screener can’t consume Pine directly.
Use a Watchlist Custom Column that reports “bars since NS” to sort symbols (0 = NS on the latest bar). A companion column script is provided separately.
Notes & limitations
Works on any timeframe (intraday/daily/weekly), but context matters.
Expect false positives around news, gaps, or illiquid symbols—combine with structure (trend, S/R, phases) and risk management.
© moshel — Educational use only; not financial advice.
TTM Squeeze Screener [Pineify]TTM Squeeze Screener for Multiple Crypto Assets and Timeframes
This advanced TradingView Pine script, TTM Squeeze Screener, helps traders scan multiple crypto symbols and timeframes simultaneously, unlocking new dimensions in momentum and volatility analysis.
Key Features
Screen up to 8 crypto symbols across 4 different timeframes in one pane
TTM Squeeze indicator detects volatility contraction and expansion (“squeeze”) phases
Momentum filter reveals potential breakout direction and strength
Visual screener table for intuitive multi-asset monitoring
Fully customizable for symbols and timeframes
How It Works
The heart of this screener is the TTM Squeeze algorithm—a hybrid volatility and momentum indicator leveraging Bollinger Bands, Keltner Channels, and linear momentum analysis. The script checks whether Bollinger Bands are “squeezed” inside Keltner Channels, flagging periods of low volatility primed for expansion. Once a squeeze is released, the included momentum calculation suggests the likely breakout direction.
For each selected symbol and timeframe, the screener runs the TTM Squeeze logic, outputs “SQUEEZE” or “NO SQZ”, and tags momentum values. A table layout organizes the results, allowing rapid pattern recognition across symbols.
Trading Ideas and Insights
Spot multi-symbol volatility clusters—ideal for finding synchronized market moves
Assess breakout potential and direction before entering trades
Scalping and swing trading decisions are enhanced by cross-timeframe momentum filtering
Portfolio managers can quickly identify which assets are about to move
How Multiple Indicators Work Together
This screener unites three essential concepts:
Bollinger Bands : Measure volatility using standard deviation of price
Keltner Channels : Define expected price range based on average true range (ATR)
Momentum : Linear regression calculation to evaluate the direction and intensity after a squeeze
By combining these, the indicator not only signals when volatility compresses and releases, but also adds directional context—filtering false signals and helping traders time entries and exits more precisely.
Unique Aspects
Multi-symbol, multi-timeframe architecture—optimized for crypto traders and market scanners
Advanced table visualization—see all signals at a glance, minimizing cognitive overload
Modular calculation functions—easy to adapt and extend for other asset classes or strategies
Real-time, low-latency screening—built for actionable alerts on fast-moving markets
How to Use
Add the script to a TradingView chart (works on custom layouts)
Select up to 8 symbols and 4 timeframes using input fields (defaults to BTCUSD, ETHUSD, etc.)
Monitor the screener table; “SQUEEZE” highlights assets in potential breakout phase
Use momentum values to judge if the squeeze is likely bullish or bearish
Combine screener insights with manual chart analysis for optimal results
Customization
Symbols: Easily set any ticker for deep market scanning
Timeframes: Adjust to match your trading horizon (scalping, swing, long-term)
Indicator parameters: Refine Bollinger/Keltner/Momentum settings for sensitivity
Visuals: Personalize table layout, color codes, and formatting for clarity
Conclusion
In summary, the TTM Squeeze Screener is a robust, original TradingView indicator designed for crypto traders who demand a sophisticated multi-symbol, multi-timeframe edge. Its combination of volatility and momentum analytics makes it ideal for catching explosive breakouts, managing risk, and scanning the market efficiently. Whether you’re a scalper or swing trader, this screener provides the insights needed to stay ahead of the curve.
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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