RiskCraft - Advanced Risk Management SystemRiskCraft – Risk Intelligence Dashboard
Trade like you actually respect risk
"I know the setup looks good… but how much am I actually risking right now?"
RiskCraft is an open-source Pine Script v6 indicator that keeps risk transparent directly on the chart. It is not a signal generator; it is a risk desk that calculates size, frames volatility, and reminds you when your behaviour drifts away from the plan.
Core utilities
Calculates professional-style position sizing in real time.
Reads volatility and market regime before position size is confirmed.
Adjusts risk based on the trader’s emotional state and confidence inputs.
Maps session risk across Asian, London, and New York hours.
Draws exactly one stop line and one target line in the preferred direction.
Provides rotating education tips plus contextual warnings when risk escalates.
It is intentionally conservative and keeps you in the game long enough for any separate entry logic to matter.
---
Chart layout checklist
Use a clean chart on a liquid symbol (e.g., AMEX:SPY or major FX pairs).
Main RiskCraft dashboard placed on the right edge.
Session Risk box on the left with UTC time visible.
Floating risk badge above price.
Stop/target guide lines enabled.
Education panel visible in the bottom-right corner.
---
1. On-chart components
Right-side dashboard : account risk %, position size/value, stop, target, risk/reward, regime, trend strength, emotional state, behavioural score, correlation, and preferred trade direction.
Session Risk box : highlights active session (Asian, London, NY), current UTC time, and risk label (High/Med/Low) per session.
Floating risk badge : keeps actual account risk percent visible with colour-coded wording from Ultra Cautious to Very Aggressive.
Stop/target lines : exactly one dashed stop and one dashed target aligned with the preferred bias.
Education panel : rotates core principles and AI-style warnings tied to volatility, risk %, and behaviour flags.
---
2. Volatility engine – ATR with context 📈
atr = ta.atr(atrLength)
atrPercent = (atr / close) * 100
atrSMA = ta.sma(atr, atrLength)
volatilityRatio = atr / atrSMA
isHighVol = volatilityRatio > volThreshold
ATR vs ATR SMA shows how wild price is relative to recent history.
Volatility ratio above the threshold flips isHighVol , which immediately trims risk.
An ATR percentile rank over the last 100 bars indicates calm versus chaotic regimes.
Daily ATR sampling via request.security() gives higher time-frame context for intraday sessions.
When volatility spikes the script dials position size down automatically instead of cheering for maximum exposure.
---
3. Market regime radar – Danger or Drift 🌊
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
trendScore = (close > ema20 ? 1 : -1) +
(ema20 > ema50 ? 1 : -1) +
(ema50 > ema200 ? 1 : -1)
= ta.dmi(14, 14)
Regimes covered:
Danger : high volatility with weak trend.
Volatile : volatility elevated but structure still directional.
Choppy : low ADX and noisy action.
Trending : directional flows without extreme volatility.
Mixed : anything between.
Each regime maps to a 1–10 risk score and a multiplier that feeds the final position size. Danger and Choppy clamp size; Trending restores normal risk.
---
4. Behaviour engine – trader inputs matter 🧠
You provide:
Emotional state : Confident, Neutral, FOMO, Revenge, Fearful.
Confidence : slider from 1 to 10.
Toggle for behavioural adjustment on/off.
Behind the scenes:
Each state triggers an emotional multiplier .
Confidence produces a confidence multiplier .
Combined they form behavioralFactor and a 0–100 Behavioural Score .
High-risk emotions or low conviction clamp the final risk. Calm inputs allow normal size. The dashboard prints both fields to keep accountability on-screen.
---
5. Correlation guardrail – avoid stacking identical risk 📊
Optional correlation mode compares the active symbol to a reference (default AMEX:SPY ):
corrClose = request.security(correlationSymbol, timeframe.period, close)
priceReturn = ta.change(close) / close
corrReturn = ta.change(corrClose) / corrClose
correlation = calcCorrelation()
Absolute correlation above the threshold applies a correlation multiplier (< 1) to reduce size.
Dashboard row shows the live correlation and reference ticker.
When disabled, the row simply echoes the current symbol, keeping the table readable.
---
6. Position sizing engine – heart of the script 💰
baseRiskAmount = accountSize * (baseRiskPercent / 100)
adjustedRisk = baseRiskAmount * behavioralFactor *
regimeAdjustment * volAdjustment *
correlationAdjustment
finalRiskAmount = math.min(adjustedRisk,
accountSize * (maxRiskCap / 100))
stopDistance = atr * atrStopMultiplier
takeProfit = atr * atrTargetMultiplier
positionSize = stopDistance > 0 ? finalRiskAmount / stopDistance : 0
positionValue = positionSize * close
Outputs shown on the dashboard:
Position size in units and value in currency.
Actual risk % back on account after adjustments.
Risk/Reward derived from ATR-based stop and target.
---
7. Intelligent trade direction – bias without signals 🎯
Direction score ingredients:
EMA stack alignment.
Price versus EMA20.
RSI momentum relative to 50.
MACD line vs signal.
Directional Movement (DI+/DI–).
The resulting Trade Direction row prints LONG, SHORT, or NEUTRAL. No orders are generated—this is guidance so you only risk capital when the structure supports it.
---
8. Stop/target guide lines – two lines only ✂️
if showStopLines
if preferLong
// long stop below, target above
else if preferShort
// short stop above, target below
Lines refresh each bar to keep clutter low.
When the direction score is neutral, no lines appear.
Use them as visual anchors, not auto-orders.
---
9. Session Risk map – global volatility clock 🌍
Tracks Asian, London, and New York windows via UTC.
Computes average ATR per session versus global ATR SMA.
Labels each session High/Med/Low and colours the cells accordingly.
Top row shows the active session plus current UTC time so you always know the regime you are trading.
One glance tells you whether you are trading quiet drift or the part of the day that hunts stops.
---
10. Floating risk badge – honesty above price 🪪
Text ranges from Ultra Cautious through Very Aggressive.
Colour matches the risk palette inputs (High/Med/Low).
Updates on the last bar only, keeping historical clutter off the chart.
Account risk becomes impossible to ignore while you stare at price.
---
11. Education engine & warnings 📚
Rotates evergreen principles (risk 1–2%, journal trades, respect plan).
Triggers contextual warnings when volatility and risk % conflict.
Flags when emotional state = FOMO or Revenge.
Highlights sub-standard risk/reward setups.
When multiple danger flags stack, an AI-style warning overrides the tip text so you can course-correct before capital is exposed.
---
12. Alerts – hard guard rails 🚨
Excessive Risk Alert : actual risk % crosses custom threshold.
High Volatility Alert : ATR behaviour signals danger regime.
Emotional State Warning : FOMO or Revenge selected.
Poor Risk/Reward Alert : risk/reward drops below your standard.
All alerts reinforce discipline; none suggest entries or exits.
---
13. Multi-market behaviour 🕒
Intraday (1m–1h): session box and badge react quickly; ideal for scalpers needing constant risk context.
Higher time frames (1D–1W): dashboard shifts slowly, supporting swing planning.
Asset classes confirmed in validation: crypto majors, large-cap equities, indices, major FX pairs, and liquid commodities.
Risk logic is price-based, so it adapts across markets without bespoke tuning.
15. Key inputs & recommended defaults
Account Size : 10,000 (modify to match actual account; min 100).
Base Risk % : 1.0 with a Maximum Risk Cap of 2.5%.
ATR Period : 14, Stop Multiplier 2.0, Target Multiplier 3.0.
High Vol Threshold : 1.5 for ATR ratio.
Behavioural Adjustment : enabled by default; disable for fixed risk.
Correlation Check : optional; default symbol AMEX:SPY , threshold 0.7.
Display toggles : main dashboard, risk badge, session map, education panel, and stop lines can be individually disabled to reduce clutter.
16. Usage notes & limits
Indicator mode only; no automated entries or exits.
Trade history panel intentionally disabled (requires strategy context).
Correlation analysis depends on additional data requests and may lag slightly on illiquid symbols.
Session timing uses UTC; adjust expectations if you trade localized instruments.
HTF ATR sampling uses daily data, so bar replay on lower charts may show brief data gaps while HTF loads.
What does everyone think RISK really means?
Buscar en scripts para "spy"
KING4R_swingGeneral
This script is called "KING4R_swing", designed to identify high-probability swing trading entries based on technical setups. It overlays the chart and uses conditions based on volume, EMAs, SPY index trend, and price structure.
Main Features
User Options:
Enable/disable SPY EMA conditions.
Show/hide checklist and final message.
Adjust label positions (vertical/horizontal).
Local EMAs:
Calculates 13-EMA and 48-EMA on the current symbol.
Flags whether EMA13 is above EMA48 and whether price is above EMA48.
Volume Spike Detection:
Searches for unusual volume spikes in the last 30 candles.
Then checks 60 candles after the spike for either:
Lateral movement (no new lows).
Higher lows (suggesting a change in structure).
Visual Tags:
When a volume spike is detected, it adds:
“📦 Post-volume Lateral” label if price goes sideways.
“📈 Structure Change” if higher lows are confirmed.
SPY Conditions:
Pulls EMAs from SPY on the daily timeframe.
Two optional conditions: EMA13 > EMA48 and EMA8 > EMA21.
Stopping Volume:
Checks if there's stopping volume in the last 30 candles (volume 1.5x above average).
Checklist + Scoring:
Assigns up to 6 points based on:
EMA13 > EMA48
Lateral structure after high volume
Price above EMA48
Stopping volume
SPY EMA13 > EMA48
SPY EMA8 > EMA21
Each condition adds 1 point.
Dynamic Labels:
Shows a red checklist, a final message with score, and a warning (“NO SETUP, NO TRADE”).
If score is 6/6, shows a 🚀 rocket icon above the bar.
Alert:
Triggers an alert when score = 6/6, indicating a possible high-probability entry.
Triad Macro Gauge__________________________________________________________________________________
Introduction
__________________________________________________________________________________
The Triad Macro Gauge (TMG) is designed to provide traders with a comprehensive view of the macroeconomic environment impacting financial markets. By synthesizing three critical market signals— VIX (volatility) , Credit Spreads (credit risk) , and the Stocks/Bonds Ratio (SPY/TLT) —this indicator offers a probabilistic assessment of market sentiment, helping traders identify bullish or bearish macro conditions.
Holistic Macro Analysis: Combines three distinct macroeconomic indicators for multi-dimensional insights.
Customization & Flexibility: Adjust weights, thresholds, lookback periods, and visualization styles.
Visual Clarity: Dynamic table, color-coded plots, and anomaly markers for quick interpretation.
Fully Consistent Scores: Identical values across all timeframes (4H, daily, weekly).
Actionable Signals: Clear bull/bear thresholds and volatility spike detection.
Optimized for timeframes ranging from 4 hour to 1 week , the TMG equips swing traders and long-term investors with a robust tool to navigate macroeconomic trends.
__________________________________________________________________________________
Key Indicators
__________________________________________________________________________________
VIX (CBOE:VIX): Measures market volatility (negatively weighted for bearish signals).
Credit Spreads (FRED:BAMLH0A0HYM2EY): Tracks high-yield bond spreads (negatively weighted).
Stocks/Bonds Ratio (SPY/TLT): Evaluates equity sentiment relative to treasuries (positively weighted).
__________________________________________________________________________________
Originality and Purpose
__________________________________________________________________________________
The TMG stands out by combining VIX, Credit Spreads, and SPY/TLT into a single, cohesive indicator. Its unique strength lies in its fully consistent scores across all timeframes, a critical feature for multi-timeframe analysis.
Purpose: To empower traders with a clear, actionable tool to:
Assess macro conditions
Spot market extremes
Anticipate reversals
__________________________________________________________________________________
How It Works
__________________________________________________________________________________
VIX Z-Score: Measures volatility deviations (inverted for bearish signals).
Credit Z-Score: Tracks credit spread deviations (inverted for bearish signals).
Ratio Z-Score: Assesses SPY/TLT strength (positively weighted for bullish signals).
TMG Score: Weighted composite of z-scores (bullish > +0.30, bearish < -0.30).
Anomaly Detection: Identifies extreme volatility spikes (z-score > 3.0).
All calculations are performed using daily data, ensuring that scores remain consistent across all chart timeframes.
__________________________________________________________________________________
Visualization & Interpretation
__________________________________________________________________________________
The script visualizes data through:
A dynamic table displaying TMG Score , VIX Z, Credit Z, Ratio Z, and Anomaly status, with color gradients (green for positive, red for negative, gray for neutral/N/A).
A plotted TMG Score in Area, Histogram, or Line mode , with adaptive opacity for clarity.
Bull/Bear thresholds as horizontal lines (+0.30/-0.30) to signal market conditions.
Anomaly markers (orange circles) for volatility spikes.
Crossover signals (triangles) for bull/bear threshold crossings.
The table provides an immediate snapshot of macro conditions, while the plot offers a visual trend analysis. All values are consistent across timeframes, simplifying multi-timeframe analysis.
__________________________________________________________________________________
Script Parameters
__________________________________________________________________________________
Extensive customization options:
Symbol Selection: Customize VIX, Credit Spreads, SPY, TLT symbols
Core Parameters: Adjust lookback periods, weights, smoothing
Anomaly Detection: Enable/disable with custom thresholds
Visual Style: Choose display modes and colors
__________________________________________________________________________________
Conclusion
__________________________________________________________________________________
The Triad Macro Gauge by Ox_kali is a cutting-edge tool for analyzing macroeconomic trends. By integrating VIX, Credit Spreads, and SPY/TLT, TMG provides traders with a clear, consistent, and actionable gauge of market sentiment.
Recommended for: Swing traders and long-term investors seeking to navigate macro-driven markets.
__________________________________________________________________________________
Credit & Inspiration
__________________________________________________________________________________
Special thanks to Caleb Franzen for his pioneering work on macroeconomic indicator blends – his research directly inspired the core framework of this tool.
__________________________________________________________________________________
Notes & Disclaimer
__________________________________________________________________________________
This is the initial public release (v2.5.9). Future updates may include additional features based on user feedback.
Please note that the Triad Macro Gauge is not a guarantee of future market performance and should be used with proper risk management. Past performance is not indicative of future results.
IBD Style Relative Strength RatingWelcome to the IBD Style Relative Strength Rating Indicator!
A powerful tool inspired by Investor's Business Daily (IBD), this indicator helps traders evaluate stock performance relative to a benchmark. It’s perfect for identifying strong or weak stocks compared to the broader market, specifically the S&P 500 (SPY). Whether you're a beginner or an experienced investor, this guide will walk you through its features and key concepts, including the RS Line and RS Rating, and how legendary trader Mark Minervini uses similar tools.
Understanding the RS Line & RS Rating
RS Line (Relative Strength Line)
A visual representation of how a stock’s price performs relative to SPY.
Calculated by dividing the stock’s closing price by SPY’s closing price and multiplying by 100.
Rising RS Line → Stock is outperforming SPY.
Falling RS Line → Stock is underperforming SPY.
Helps identify strength or weakness compared to the market.
RS Rating
A numerical score (1-99) measuring stock performance over 252 trading days (1 year) relative to SPY.
Above 80 → Top 20% of performers.
Above 90 → Top 10% (ideal for growth investors).
Weighted average of stock’s price changes over 63, 126, 189, and 252 days.
Key Features Explained
RS Line Color Mode:
Static (default white) or Dynamic (green when rising, red when falling) for quick trend identification.
Comparative Symbol:
Default: SPY. Can be changed to NASDAQ:NDX, AAPL, or other indices/stocks.
Ensure selected symbols have sufficient historical data.
Plot RS New Highs: Marks new 250-day highs with subtle blue circles
Indicates a stock significantly outperforming SPY (potential buy signal).
Plot RS New Lows: Marks new 250-day lows with red circles
Signals underperformance (possible sell or avoid indicator).
Lookback for Display: Adjustable up to 2000 bars for historical trend analysis.
RS Rating Color Scheme
Green: Upward trend (improving RS Rating).
Orange: Neutral/mixed trend.
Red: Downward trend (declining RS Rating).
Dynamic Color Settings
Rising Line Color: Green (default), customizable.
Falling Line Color: Red (default), adjustable.
Advanced Options
Enable Replay Mode: Uses fixed percentile values for consistent RS Rating calculations in backtesting.
RS Rating Table
Displays current RS Rating and values from previous day, week, and month in the top-right corner (daily charts).
Background color reflects trend: Green (up), Orange (neutral), Red (down).
Past values appear in neutral gray for a quick performance snapshot.
How Mark Minervini Uses This Indicator
Mark Minervini, a legendary trader, emphasizes Relative Strength as a core strategy:
Looks for stocks with:
Rising RS Line.
RS Rating above 80-90 (top performers).
RS New Highs to spot breakout candidates.
Avoids stocks with:
Declining RS Line.
RS Rating below 70.
Important Information for Beginners
RS vs. SPY
The indicator compares stock performance against SPY (S&P 500).
Rising RS Line → Stock is beating SPY.
Falling RS Line → Stock is lagging.
Why Use This Indicator?
Helps find strong relative strength stocks, crucial for bullish trends.
New highs/lows on the RS Line signal significant shifts.
The RS Rating quantifies percentile-based performance.
Customization Options
Adjust colors, lookback periods, and marker sizes to match your trading style.
Default SPY comparison is ideal for U.S. traders but can be customized.
Timeframe Considerations
Optimized for daily charts.
Weekly/monthly charts may have limited data availability.
Tips for Crypto Traders (Measuring Altcoins vs. Bitcoin or Total Market Cap)
If trading cryptocurrencies, this indicator can measure altcoins vs. Bitcoin (BTC) or the total crypto market cap (TOTAL):
Comparative Symbol Setup:
Set Comparative Symbol to BTCUSD to compare an altcoin (e.g., ETHUSD) against Bitcoin.
Rising RS Line → The altcoin is outperforming Bitcoin (bullish signal).
Use TOTAL (crypto market cap index) to assess an altcoin’s strength against the total market.
High RS Rating suggests the altcoin is a market leader.
Adjust Look-back Periods:
Crypto markets are volatile, so reduce Look-back for New Highs/Lows to 50-100 bars (about 2-4 months) for shorter-term trends.
Fine-tune based on your trading strategy.
New Highs and Lows:
Watch for new RS Line highs (blue dots) to identify altcoins breaking out against BTC or TOTAL (momentum trading).
New lows (red dots) may signal weakening altcoins to avoid.
RS Rating Interpretation:
Above 80 against BTC or TOTAL → The altcoin is a strong performer.
This aligns with Minervini’s growth strategy for stocks.
Color Dynamics:
Use Dynamic RS Line Color (green for rising, red for falling) to quickly spot altcoin trends against BTC or TOTAL.
Crypto data may have gaps—test indicator settings on different timeframes (e.g., 1-hour or 4-hour charts).
Tips for Getting Started
Apply the Indicator to a stock chart and set Comparative Symbol to SPY.
Watch the RS Line:
If trending upward with new highs and RS Rating > 80, it's a strong candidate.
Use the RS Rating Table to check for trend consistency.
Adjust Opacity Settings for markers to balance visibility and clarity.
This indicator is now ready for public use as of March 18, 2025. Enjoy trading with enhanced insights, and feel free to share feedback or suggestions for future updates!
TILT - Timed Index of Liquidity TrendsThe Timed Index of Liquidity Trends (TILT) is a tracking tool for high-market cap, high-volatility assets like Bitcoin (BTCUSD), the S&P 500 (SPY), the Nasdaq 100 (QQQ), and Gold. Liquidity drives markets; understanding when liquidity is expanding or contracting can help traders anticipate major market swings with greater confidence.
TILT’s M2 Calculation
TILT is based on a global M2 money supply proxy, which aggregates liquidity conditions from major economies. Since TradingView does not provide direct M2 data for all regions, the indicator uses market-based proxies instead:
🇺🇸 United States – S&P 500 Index (SPX)
🇨🇦 Canada – TSX Composite Index (TSX)
🇪🇺 Eurozone – EUR/USD Exchange Rate (EURUSD)
🇬🇧 United Kingdom – GBP/USD Exchange Rate (GBPUSD)
🇷🇺 Russia – Moscow Exchange Index (MOEX)
🇨🇳 China – China 50 Index (CN50USD)
🇯🇵 Japan – Nikkei 225 Index (JPN225)
🇦🇺 Australia – Gold (XAUUSD) as a liquidity proxy
🇮🇳 India – Nifty 50 Index (NIFTY)
🇰🇷 South Korea – KOSPI Index (KOSPI)
🇧🇷 Brazil – Bovespa Index (IBOV)
🇿🇦 South Africa – USD/ZAR Exchange Rate (USDZAR)
By summing these liquidity proxies, TILT provides a comprehensive view of global M2 conditions, allowing traders to see when money supply is expanding (bullish liquidity conditions) or contracting (bearish liquidity conditions).
How to Use TILT for Trading High-Volatility Assets
TILT is not a traditional price indicator. It is a macro tool designed to show whether liquidity is flowing into or out of the financial system. Assets like Bitcoin, QQQ, and Gold tend to perform well when liquidity is expanding and decline when liquidity is contracting.
₿ Bitcoin (BTCUSD) – The Ultimate Liquidity Sponge
Bitcoin thrives on excess liquidity because it is still a speculative asset with no central authority.
· Liquidity Expanding → BTC tends to rise, as speculative capital flows in.
· Liquidity Contracting → BTC struggles or enters a bear market as leverage dries up.
Example Use Case: If TILT turns green (expanding liquidity) and BTC is near a technical support zone, it may indicate a buying opportunity before the next rally.
📊 S&P 500 (SPY) & Nasdaq 100 (QQQ) – Growth & Risk Appetite
These indices are heavily influenced by liquidity conditions because they represent growth stocks and corporate credit access.
· SPY (🇺🇸) → Moves based on global liquidity, particularly Fed policy & M2 expansion.
· QQQ (🇺🇸) → Even more sensitive than SPY due to high exposure to tech stocks.
Example Use Case: If TILT shows liquidity expansion, QQQ often leads SPY higher, providing early signals for market-wide risk-on behavior.
🥇 Gold – Liquidity & Inflation Hedge
Gold is a monetary asset, meaning it benefits from liquidity expansion and inflation fears.
· Liquidity Expanding → Gold can rally as real yields decline.
· Liquidity Contracting → Gold struggles, especially if real yields rise.
Example Use Case: If TILT turns red (liquidity contracting) and bond yields are rising, gold could enter a bearish phase.
⏱️ Timing Market Swings with the Offset Function
The offset function in TILT allows traders to shift liquidity data forward or backward in time to find the best correlation with price action. However, the offset is not fixed and should be re-evaluated periodically to ensure it remains optimized as a leading indicator. Liquidity cycles and market conditions change over time, meaning an offset that worked well in one period may need adjustment in another.
🤔 Why Use an Offset?
Liquidity moves markets with a lag – The effect of M2 expansion/contraction takes time to show up in risk assets.
Finding the right lag helps confirm liquidity-driven price moves – This is crucial for Bitcoin, QQQ, and Gold, which react differently to liquidity shifts.
Since liquidity conditions evolve, the offset should be adjusted from time to time to maintain predictive accuracy.
👋 How to Fit the Offset Using Vertical Reference Lines
The best way to optimize the offset is by testing historical liquidity cycles and using vertical reference lines (and/or the Date Range tool) to align liquidity trends with major price swings.
Step 1: Plot TILT and the asset you’re analyzing (e.g., BTCUSD) on the same chart.
Step 2: Add vertical lines on significant price reversals (major tops & bottoms).
Step 3: Adjust TILT’s offset forward or backward to see if liquidity trends lead or lag those reversals.
Step 4: Periodically revisit the offset setting to ensure it still aligns well with current market conditions.
Example: If BTC topped 10 bars after TILT turned red, you might set the offset to +10 to better align liquidity changes with price action. If, over time, BTC begins reacting faster or slower to liquidity shifts, the offset should be updated accordingly.
💡 Advanced Tips for TILT Users
· Combine TILT With Sentiment Indicators Like the Fear & Greed Index
· Low Fear & Expanding Liquidity → Strong buy signal for BTC & risk assets
· High Greed & Contracting Liquidity → Caution: Market topping signal
· Use With Volume & On-Chain Metrics for BTC
· Rising TILT + Increasing BTC Volume → Confirms strong accumulation
· TILT Falling + Weak BTC Volume → Potential distribution & market risk
· Watch for Divergences
If BTC makes a new high but TILT is falling, it could indicate a liquidity-driven market top.
If BTC makes a new low but TILT is rising, it could indicate a bottom forming.
Conclusion: TILT = The Macro Liquidity Key for Volatile Assets
TILT is an effective tool for timing market swings in Bitcoin, QQQ, SPY, and Gold, as these assets are highly sensitive to liquidity cycles.
· Tracks global M2 trends using liquidity proxies from major economies
· Helps confirm major tops & bottoms in risk assets
· Offset function allows precise timing of liquidity-driven market moves
· Offset should be reviewed periodically to maintain optimal accuracy
· Pairs well with sentiment tools like the Fear & Greed Index for crypto
By using TILT correctly, traders can anticipate major market turns and position ahead of liquidity-driven moves.
Sector/Industry Relative StrengthOverview
The Sector/Industry Relative Strength (RS) Indicator is a powerful tool designed to help traders and investors analyze the performance of sectors and industries relative to the broader market (SPY). It provides real-time insights into sector and industry strength, helping you identify leading and lagging areas of the market.
Key Features
Sector and Industry Analysis:
Automatically detects the sector and industry of the current symbol.
Displays the corresponding sector and industry ETF.
Relative Strength (STS) Calculation:
Calculates the Sector/Industry Trend Strength (STS) by comparing the sector or industry ETF to SPY over the past 20 days.
STS is expressed as a percentile (0-100), indicating how strong the sector/industry ETF has been relative to SPY over the past 20 days.
Example: An STS of 70 means that during the past 20 days, the ETF’s relative strength against SPY was stronger than 70% of those days.
Sector Rank:
Ranks the current sector ETF against a predefined list of major sector ETFs.
Highlights whether the sector is outperforming or underperforming SPY (green if outperforming, red if underperforming).
Customizable Display:
Choose which elements to display (e.g., sector, industry, ETFs, STS, sector rank).
Customize table position, size, text alignment, and colors.
Real-Time Performance:
Tracks daily price changes for sector and industry ETFs.
Displays percentage change from open to close.
How to Use
Add the Indicator:
Apply the indicator to any stock or ETF chart.
The script will automatically detect the sector and industry of the selected symbol.
Interpret the Data:
Sector/Industry: Displays the current sector and industry.
ETF: Shows the corresponding sector and industry ETF.
STS (Sector/Industry Trend Strength): A percentile score (0-100) indicating the relative strength of the sector/industry ETF compared to SPY over the past 20 days.
Sector Rank: Ranks the sector ETF against other major sectors (e.g., "3/12" means the sector is ranked 3rd out of 12).
Customize the Display:
Use the input settings to:
Show/hide specific elements (e.g., sector, industry, ETFs, STS, sector rank).
Adjust the table position, size, and text alignment.
Change colors for positive/negative changes.
Make Informed Decisions:
Use the STS score and sector rank to identify potential trading opportunities.
Focus on sectors and industries with high STS scores and strong rankings (green).
Input Parameters
Table Settings:
Table Position: Choose where to display the table (Top Left, Top Right, Bottom Left, Bottom Right).
Table Size: Adjust the size of the table (Tiny, Small, Normal, Large).
Text Color: Customize the text color.
Background Color: Set the table background color.
Display Options:
Show ETFs: Toggle the display of sector and industry ETFs.
Show STS: Toggle the display of the Sector/Industry Trend Strength (STS) score.
Show Sector/Industry: Toggle the display of sector and industry information.
Show Sector Rank: Toggle the display of the sector rank.
Parameters:
Sector Rank Time Length: Set the number of days used for calculating the sector rank (default: 20).
Example Use Cases
Sector Rotation:
Identify sectors with high STS scores and strong rankings (green) to allocate capital.
Avoid sectors with low STS scores and weak rankings (red).
Industry Analysis:
Compare the STS scores of different industries within the same sector.
Use the STS score to gauge relative strength and identify potential opportunities.
Market Timing:
Use the STS score and sector rank to time entries and exits in sector-specific ETFs.
Combine with other technical indicators for confirmation.
Universal RPPI Indices & Futures [SS Premium]Hello everyone,
For the much-anticipated indicator release, the universal RPPI for Futures and Indices!
If you follow me, chances are you know this indicator by now, since its the basis of all of my analyses and target prices, but if not, let me introduce you!
What is it?
The RPPI for Indices & Futures is essentially a compendium indicator. It contains hundreds of, just over 100 different math models of various futures and indices.
These models are designed to forecast the current targets on multiple timeframes including:
1. The daily
2. The weekly
3. The monthly
4. The Three Month (for SPY and QQQ ONLY)
5. The 6 Month (for DJI, SPX and USOIL/CLI1! ONLY)
6. The annual (for DJI, SPX and USOIL/CLI1! ONLY)
7. The 3 hour
So I will go over the details of the models within the indicators compendium and how they are produced. If you are not interested, just skip to the next section!
What is a model and how is it produced?
Models are math equations and frameworks that attempt to predict future behavior. They are developed in many ways and through many methods. In this particular indicator, each index and future is unique and has been created in various ways, such as using principles of data smoothing, data interpolation, data substitution and data omission.
All this means is, I have manually adjusted model parameters to correct for rare, outlier events. The outcome is having a more accurate model that is better prepared to predict what you want it to predict.
Now let's get into the indicator use.
The first thing we need to talk about is selecting a model type. Different model types are available on a handful of stocks in the indicator, such as SPY, QQQ, DJI and DIA, and so it is important to explain the difference.
Corrected vs Uncorrected Models (i.e. Low Precision vs High Precision Models)
In the settings menu, you will see the second option that reads "Precision". This is where you have the ability to select the model type.
"High Precision" is a corrected model. It is a model that I have used data manipulation for (like the examples above) to enhance its accuracy.
"Low Precision" is a UNCORRECTED model. These models have undergone no data manipulation and are just raw projections.
Which do you use?
There are only a handful of tickers that have both models, like SPY, GLD1! and DJI (among others). Some tickers perform better with low precision models, others perform better with high precision models.
To know what model works best with which stock, the indicator will tell you. At the bottom of the settings table, simply select "Show Model Data":
Selecting this, you will get a table that looks like this:
It will tell you the available model types and which one works best. For IWM, the high-precision corrected model is best. This is true for QQQ and NQ1! as well. However, for SPY and ES1!, the uncorrected model is actually better:
Sometimes, different models perform better at various levels of precision, for example, high on the monthly but low on the daily.
This is why I have omitted this option for the majority of stocks. I don't want this to be confusing to use. For 90% of the included tickers, I have selected the model of best fit. However, for a few of the very popular and volatile tickers (ES, NQ specifically), I have included the ability to use both.
Rule of Thumb:
The rule of thumb with selecting high vs low, is essentially this:
a) If the market is hugely volatility with major swings intraday that exceed its normal behaviour, switch to the low precesion model. This will not be skewed by the massive swings.
b) If the market is stable, trendy or range bound, but not trending beyond its normal, general behaviour, keep it at high precision.
With that, you will be good to go!
Using the indicator:
The indicator is intended as a standalone indicator. Of course, you can combine other indicators that you like to help you out, but there is a strategy version of this that will be released within the coming days/weeks, as this is intended to be a full strategy in and of itself.
As with the universal forecaster, you are given threshold levels that are labelled "Bullish Condition" and "Bearish Condition", a break and hold of the "Bullish Condition" and it is a long to the high targets. Inverse for the bearish condition.
In addition to these conditionals, the indicator also provides you with a high probability retracement level. These are available on the weekly, monthly and higher timeframes. A special moving retracement level is available for SPY only, however it moves based on the PA to give you a sort of POC.
Testing Model Performance:
It is possible to see model performance. At the bottom of the settings menu, select the option to "Show Demographic Data". You need to be sure you are on the chart of the selected timeframe.
This is ES1! on the daily timeframe. It shows you the demographics, i.e. the extent targets are hit, the extent that the high prob retracement targets are missed, the extent that ES closes in and out of its daily range.
This is very valuable information. This table is essentially saying there is only a 10% chance that ES will close above its range and a 9% chance ES closes below its range. This means, that the most ideal setups are a move outside of its range!!
You can view it on all timeframes. If your chart isn't aligned with the lookback, you will get a warning sign:
Misc Functions:
Show price accumulation:
There is an option to toggle on price accumulation. It will show you the amount of accumulation in each of the ranges:
This will show where the accumulation of price rests in relation to the targets.
Autoregression Assessment:
You can have the indicator plot an autoregressive trendline of the expected stock trajectory. You can select the forecast length and it will plot the direction it suspects the stock will go:
Show Standard Deviation:
In the menu, you can toggle on the show standard deviation function. This will plot the standard deviation that each price rests at. The default timeframe for standard deviation is the daily. If you are looking at the weekly, please select the weekly timeframe.
This is helpful because you can see which targets are likely based on where the standard deviation rests. In the above example, a move to the low range would be a move to -2 standard deviations and beyond. This is not something that a ticker would normally do in general circumstances.
FAQ Table:
There is also an option to display an FAQ table. This will show you model revisions and pending revision dates. This will allow you to see when each model was last updated and when new updates will be pushed:
Which models does this contain?
The indicator contains models for the following stocks:
SPY
QQQ
DIA
DJI
ES1!
SPX
NQ1!
NDX
SOXX
IWM
RTY
GCL1! (Gold)
CL1! / USOIL (Oil)
XLE
XLF
YM1!
And some more are in the works (like JETS).
NOTE: Feel free to leave a comment of future ones you would like to see!
The indicator will automatically select the model for whichever ticker you are on.
Some models are cross-compatible, such as CL1! and USOIL, but the indicator is programmed to recognize those that are cross-compatible and auto-select those models.
From there, you just need to select the timeframe you wish to view!
And that is the indicator! I know very wordy explanation but wanted to cover all basis on the indicator so you can be well prepared!
As always, leave your questions, and comments below, and safe trades!
Bollinger Bands (Nadaraya Smoothed) | Flux ChartsTicker: AMEX:SPY , Timeframe: 1m, Indicator settings: default
General Purpose
This script is an upgrade to the classic Bollinger Bands. The idea behind Bollinger bands is the detection of price movements outside of a stock's typical fluctuations. Bollinger Bands use a moving average over period n plus/minus the standard deviation over period n times a multiplier. When price closes above or below either band this can be considered an abnormal movement. This script allows for the classic Bollinger Band interpretation while de-noising or "smoothing" the bands.
Efficacy
Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: Standard Dev: 2; Level 1 : off; Level 2: off; labels: off
Upper Band Key:
Blue: Bollinger No smoothing
Orange: Bollinger SMA smoothing period of 10
Purple: Bollinger EMA smoothing period of 10
Red: Nadaraya Smoothed Bollinger bandwidth of 6
Here we chose periods so that each would have a similar offset from the original Bollinger's. Notice that the Red Band has a much smoother result while on average having a similar fit to the other smoothing techniques. Increasing the EMA's or SMA's period would result in them being smoother however the offset would increase making them less accurate to the original data.
Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: Standard Dev: 2; Level 1: off; Level 2: off; labels: off
Upper Band Key:
Blue: Bollinger No smoothing
Orange: Bollinger SMA smoothing period of 20
Purple: Bollinger EMA smoothing period of 20
Red: Nadaraya Smoothed Bollinger bandwidth of 6
This makes the Nadaraya estimator a particularly efficacious technique in this use case as it achieves a superior smoothness to fit ratio.
How to Use
This indicator is not intended to be used on its own. Its use case is to identify outlier movements and periods of consolidation. The Smoothing Factor when lowered results in a more reactive but noisy graph. This setting is also known as the "bandwidth" ; it essentially raises the amplitude of the kernel function causing a greater weighting to recent data similar to lowering the period of a SMA or EMA. The repaint smoothing simply draws on the Bollinger's each chart update. Typically repaint would be used for processing and displaying discrete data however currently it's simply another way to display the Bollinger Bands.
What makes this script unique.
Since Bollinger bands use standard deviation they have excess noise. By noise we mean minute fluctuations which most traders will not find useful in their strategies. The Nadaraya-Watson estimator, as used, is essentially a weighted average akin to an ema. A gaussian kernel is placed at the candlestick of interest. That candlestick's value will have the highest weight. From that point the other candlesticks' values effect on the average will decrease with the slope of the kernel function. This creates a localized mean of the Bollinger Bands allowing for reduced noise with minimal distortion of the original Bollinger data.
Performance ComparatorThis indicator allows to compare the performance (% change) of a given symbol with the larger market ( AMEX:SPY ) and/or with a custom symbol, which defaults to AMEX:XLK (an ETF tracking technology companies from the S&P 500).
The performance for the current symbol is displayed as a blue histogram, while performance for the AMEX:SPY and the custom symbol are respectively displayed as orange and white lines, making it easy to spot when the symbol outperformed the market.
Features:
Configurable time resolution (default: same as chart)
Comparison using change percentage or its EMA/WMA/SMA (default: EMA)
Configurable moving average length
Optionally hide AMEX:SPY or the custom symbol from the chart
Vol ROC Indicator [ASM]Volatility crush indy
TL;DR: Vol Crush = market up.
Fundamental theory:
1) Vol must rise to be crushed. Why does it rise? Big guys expect “risk event” and buy insurance (put options). They are smart ok, they may see risk that we don’t. Then supply / demand law (or just put-seller rises prices) –> puts get expensive -> VIX go up = SPY usually down because of that risk event looming.
2) If risk event doesn’t happen (off the table)… Insurance no longer needed right? AMEX:SPY bravely goes up = puts cheap again = VIX go down.
Technicals:
Option market makers’ (MM’s) mechanical flows. Gamm, GEX,VEX, etc.
When investor buys puts = VIX goes up -> MM shorts $SPY.
Then if Vol crushes, puts decrease in value -> MM buys back AMEX:SPY that he shorted earlier.
Specifics:
In this example instead of VIX, we use Nations taildex index. It shows price crash protection put options.
Green triangle. If TDEX > 20 puts expensive and market expects risk event. When TDEX falls below 20 then risk event cancelled or not so scary anymore. Green trianlge signals.
Green background. Again if TDEX > 20, then falls -15% within 2 days, you get a background signal.
Other variants.
You can change TDEX to VIX, VOLI. Use another overbought levels or use another vol change percentages.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Volume Profile (Simple)Simple Volume Profile (Simple)
Master the Market's Structure with a Clear View of Volume
by mercaderoaurum
The Simple Volume Profile (Simple) indicator removes the guesswork by showing you exactly where the most significant trading activity has occurred. By visualizing the Point of Control (POC) and Value Area (VA) for today and yesterday, you can instantly identify the price levels that matter most, giving you a critical edge in your intraday trading.
This tool is specifically optimized for day trading SPY on a 1-minute chart, but it's fully customizable for any symbol or timeframe.
Key Features
Multi-Day Analysis: Automatically plots the volume profiles for the current and previous trading sessions, allowing you to see how today's market is reacting to yesterday's key levels.
Automatic Key Level Plotting: Instantly see the most important levels from each session:
Point of Control (POC): The single price level with the highest traded volume, acting as a powerful magnet for price.
Value Area High (VAH): The upper boundary of the area where 50% of the volume was traded. It often acts as resistance.
Value Area Low (VAL): The lower boundary of the 50% value area, often acting as support.
Extended Levels: The POC, VAH, and VAL from previous sessions are automatically extended into the current day, providing a clear map of potential support and resistance zones.
Customizable Sessions: While optimized for the US stock market, you can define any session time and time zone, making it a versatile tool for forex, crypto, and futures traders.
Core Trading Strategies
The Simple Volume Profile helps you understand market context. Instead of trading blind, you can now make decisions based on where the market has shown the most interest.
1. Identifying Support and Resistance
This is the most direct way to use the indicator. The extended lines from the previous day are your roadmap for the current session.
Previous Day's POC (pPOC): This is the most significant level. Watch for price to react strongly here. It can act as powerful support if approached from above or strong resistance if approached from below.
Previous Day's VAH (pVAH): Expect this level to act as initial resistance. A clean break above pVAH can signal a strong bullish trend.
Previous Day's VAL (pVAL): Expect this level to act as initial support. A firm break below pVAL can indicate a strong bearish trend.
Example Strategy: If SPY opens and rallies up to the previous day's VAH and stalls, this is a high-probability area to look for a short entry, with a stop loss just above the level.
2. The "Open-Drive" Rejection
How the market opens in relation to the previous day's value area is a powerful tell.
Open Above Yesterday's Value Area: If the market opens above the pVAH, it signals strength. The first pullback to test the pVAH is often a key long entry point. The level is expected to flip from resistance to support.
Open Below Yesterday's Value Area: If the market opens below the pVAL, it signals weakness. The first rally to test the pVAL is a potential short entry, as the level is likely to act as new resistance.
3. Fading the Extremes
When price pushes far outside the previous day's value area, it can become overextended.
Reversal at Highs: If price rallies significantly above the pVAH and then starts to lose momentum (e.g., forming bearish divergence on RSI or a topping pattern), it could be an opportunity to short the market, targeting a move back toward the pVAH or pPOC.
Reversal at Lows: Conversely, if price drops far below the pVAL and shows signs of bottoming, it can be a good opportunity to look for a long entry, targeting a reversion back to the value area.
Recommended Settings (SPY Intraday)
These settings are the default and are optimized for scalping or day trading SPY on a 1-minute chart.
Value Area (%): 50%. This creates a tighter, more sensitive value area, perfect for identifying the most critical intraday zones.
Number of Rows: 1000. This high resolution is essential for a low-volatility instrument like SPY, ensuring that the profile is detailed and the levels are precise.
Session Time: 0400-1800 in America/New_York. This captures the full pre-market and core session, which is crucial for understanding the day's complete volume story.
Ready to trade with an edge? Add the Simple Volume Profile (Multi-Day) to your chart now and see the market in a new light!
Intermarket Correlation Oscillator (ICO)The Intermarket Correlation Oscillator (ICO) is a TradingView indicator that helps traders analyze the relationship between two assets, such as stocks, indices, or cryptocurrencies, by measuring their price correlation. It displays this correlation as an oscillator ranging from -1 to +1, making it easy to spot whether the assets move together, oppositely, or independently. A value near +1 indicates strong positive correlation (assets move in the same direction), near -1 shows strong negative correlation (opposite movements), and near 0 suggests no correlation. This tool is ideal for confirming trends, spotting divergences, or identifying hedging opportunities across markets.
How It Works?
The ICO calculates the Pearson correlation coefficient between the chart’s primary asset (e.g., Apple stock) and a secondary asset you choose (e.g., SPY for the S&P 500) over a specified number of bars (default: 20). The oscillator is plotted in a separate pane below the chart, with key levels at +0.8 (overbought, strong positive correlation) and -0.8 (oversold, strong negative correlation). A midline at 0 helps gauge neutral correlation. When the oscillator crosses these levels or the midline, labels ("OB" for overbought, "OS" for oversold) and alerts notify you of significant shifts. Shaded zones highlight extreme correlations (red for overbought, green for oversold) if enabled.
Why Use the ICO?
Trend Confirmation: High positive correlation (e.g., SPY and QQQ both rising) confirms market trends.
Divergence Detection: Negative correlation (e.g., DXY rising while stocks fall) signals potential reversals.
Hedging: Identify negatively correlated assets to balance your portfolio.
Market Insights: Understand how assets like stocks, bonds, or crypto interact.
Easy Steps to Use the ICO in TradingView
Add the Indicator:
Open TradingView and load your chart (e.g., AAPL on a daily timeframe).
Go to the Pine Editor at the bottom of the TradingView window.
Copy and paste the ICO script provided earlier.
Click "Add to Chart" to display the oscillator below your price chart.
Configure Settings:
Click the gear icon next to the indicator’s name in the chart pane to open settings.
Secondary Symbol: Choose an asset to compare with your chart’s symbol (e.g., "SPY" for S&P 500, "DXY" for USD Index, or "BTCUSD" for Bitcoin). Default is SPY.
Correlation Lookback Period: Set the number of bars for calculation (default: 20). Use 10-14 for short-term trading or 50 for longer-term analysis.
Overbought/Oversold Levels: Adjust thresholds (default: +0.8 for overbought, -0.8 for oversold) to suit your strategy. Lower values (e.g., ±0.7) give more signals.
Show Midline/Zones: Check boxes to display the zero line and shaded overbought/oversold zones for visual clarity.
Interpret the Oscillator:
Above +0.8: Strong positive correlation (red zone). Assets move together.
Below -0.8: Strong negative correlation (green zone). Assets move oppositely.
Near 0: No clear relationship (midline reference).
Labels: "OB" or "OS" appears when crossing overbought/oversold levels, signaling potential correlation shifts.
Set Up Alerts:
Right-click the indicator, select "Add Alert."
Choose conditions like "Overbought Alert" (crossing above +0.8), "Oversold Alert" (crossing below -0.8), or zero-line crossings for bullish/bearish correlation shifts.
Configure notifications (e.g., email, SMS) to stay informed.
Apply to Trading:
Use positive correlation to confirm trades (e.g., buy AAPL if SPY is rising and correlation is high).
Spot divergences for reversals (e.g., stocks dropping while DXY rises with negative correlation).
Combine with other indicators like RSI or moving averages for stronger signals.
Tips for New Users
Start with related assets (e.g., SPY and QQQ for tech stocks) to see clear correlations.
Test on a demo account to understand signals before trading live.
Be aware that correlation is a lagging indicator; confirm signals with price action.
If the secondary symbol doesn’t load, ensure it’s valid on TradingView (e.g., use correct ticker format).
The ICO is a powerful, beginner-friendly tool to explore intermarket relationships, enhancing your trading decisions with clear visual cues and alerts.
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool
An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations.
---
💡 Why I Created This Indicator
As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term .
What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later.
Traditional DCA tracking tools exist, but they share similar limitations:
Require manual data entry and complex spreadsheets
Use fixed assumptions that don't reflect real market behavior
Can't show future projections overlaid on actual price charts
Lose the visual context of what's happening in the market
Make compound growth feel abstract rather than tangible
I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time.
This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore.
---
🎯 What This Indicator Does
This educational indicator provides DCA analysis tools. Users can input investment scenarios to study:
Theoretical Performance: Educational calculations based on historical return data
Comparative Analysis: Study differences between actual and theoretical scenarios
Historical Projections: Theoretical projections for educational analysis (not predictions)
Performance Metrics: CAGR, ROI, and other analytical metrics for study
Historical Analysis: Calculates historical return data for reference purposes
---
🚀 Key Features
Volatility-Adjusted Historical Return Calculation
Analyzes 3-20 years of actual price data for any symbol
Automatically detects high-volatility stocks (meme stocks, growth stocks)
Uses median returns for volatile stocks, standard CAGR for stable stocks
Provides conservative estimates when extreme outlier years are detected
Smart fallback to manual percentages when data insufficient
Customizable Performance Dashboard
Educational DCA performance analysis with compound growth calculations
Customizable table sizing (Tiny to Huge text options)
9 positioning options (Top/Middle/Bottom + Left/Center/Right)
Theme-adaptive colors (automatically adjusts to dark/light mode)
Multiple display layout options
Future Projection System
Visual future growth projections
Timeframe-aware calculations (Daily/Weekly/Monthly charts)
1-30 year projection options
Shows projected portfolio value and total investment amounts
Investment Insights
Performance vs benchmark comparison
ROI from initial investment tracking
Monthly average return analysis
Investment milestone alerts (25%, 50%, 100% gains)
Contribution tracking and next milestone indicators
---
📊 Step-by-Step Setup Guide
1. Investment Settings 💰
Initial Investment: Enter your starting lump sum (e.g., $60,000)
Monthly Contribution: Set your regular DCA amount (e.g., $500/month)
Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed %
Historical Period: Select 3-20 years for auto calculations (default: 10 years)
Start Year: When you began investing (e.g., 2020)
Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000)
2. Display Settings 📊
Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge
Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right
Visibility Toggles: Show/hide Main Table and Stats Table independently
3. Future Projection 🔮
Enable Projections: Toggle on to see future growth visualization
Projection Years: Set 1-30 years ahead for analysis
Live Example - NASDAQ:META Analysis:
Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value
---
🔬 Pine Script Code Examples
Core DCA Calculations:
// Calculate total invested over time
months_elapsed = (year - start_year) * 12 + month - 1
total_invested = initial_investment + (monthly_contribution * months_elapsed)
// Compound growth formula for initial investment
theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed)
// Future Value of Annuity for monthly contributions
monthly_rate = annual_return / 12
fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate)
// Total expected value
theoretical_total = theoretical_initial_growth + fv_contributions
Volatility Detection Logic:
// Detect extreme years for volatility adjustment
extreme_years = 0
for i = 1 to historical_years
yearly_return = ((price_current / price_i_years_ago) - 1) * 100
if yearly_return > 100 or yearly_return < -50
extreme_years += 1
// Use median approach for high volatility stocks
high_volatility = (extreme_years / historical_years) > 0.2
calculated_return = high_volatility ? median_of_returns : standard_cagr
Performance Metrics:
// Calculate key performance indicators
absolute_gain = actual_value - total_invested
total_return_pct = (absolute_gain / total_invested) * 100
roi_initial = ((actual_value - initial_investment) / initial_investment) * 100
cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100
---
📊 Real-World Examples
See the indicator in action across different investment types:
Stable Index Investments:
AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations
Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking.
MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches
Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections.
MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation
Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies.
NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record
Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used.
High-Volatility Growth Stocks:
NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings
High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods.
NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks
Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections.
NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations
Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments.
Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods.
---
📈 Performance Metrics Explained
Current Portfolio Value: Your actual investment worth today
Expected Value: What you should have based on historical returns (Auto) or your target return (Manual)
Total Invested: Your actual money invested (initial + all monthly contributions)
Total Gains/Loss: Absolute dollar difference between current value and total invested
Total Return %: Percentage gain/loss on your total invested amount
ROI from Initial Investment: How your starting lump sum has performed
CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy)
vs Benchmark: How you're performing compared to the expected returns
---
⚠️ Important Notes & Limitations
Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended)
CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy
Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary
Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported
Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking
---
📚 Educational Use & Disclaimer
This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns.
Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes.
EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions.
---
© 2025 TradeVizion. All rights reserved.
Options Oscillator [PRO] IVRank, IVx, Call/Put Volatility Skew𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗥𝗘𝗔𝗟 𝗜𝗩𝗥𝗮𝗻𝗸, 𝗜𝗩𝘅, 𝗮𝗻𝗱 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝘀𝗸𝗲𝘄 𝗱𝗮𝘁𝗮 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗥𝗘𝗔𝗟 𝗼𝗽𝘁𝗶𝗼𝗻 𝗰𝗵𝗮𝗶𝗻 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟲𝟱+ 𝗺𝗼𝘀𝘁 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨.𝗦. 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀
🔃 Auto-Updating Option Metrics without refresh!
🍒 Developed and maintained by option traders for option traders.
📈 Specifically designed for TradingView users who trade options.
🔶 Ticker Information:
This indicator is currently only available for over 165+ most liquid U.S. market symbols (eg. SP:SPX AMEX:SPY NASDAQ:QQQ NASDAQ:TLT NASDAQ:NVDA , etc.. ), and we are continuously expanding the compatible watchlist here: www.tradingview.com
🔶 How does the indicator work and why is it unique?
This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 The following data is displayed in the oscillator 🟨
We use Tastytrade formulas, so our numbers mostly align with theirs!
🔶 𝗜𝗩𝗥𝗮𝗻𝗸
The Implied Volatility Rank (IVR) helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options.
IV Rank formula = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVRank is default blue and you can adjust their settings:
🔶 𝗜𝗩𝘅 𝗮𝘃𝗴
The implied volatility (IVx) shown in the option chain is calculated like the VIX. The Cboe uses standard and weekly SPX options to measure expected S&P 500 volatility. A similar method is used for calculating IVx for each expiration cycle.
We aggregate the IVx values for the 35-70 day monthly expiration cycle, and use that value in the oscillator and info panel.
We always display which expiration the IVx values are averaged for when you hover over the IVx cell.
IVx main color is purple, but you can change the settings:
🔹 IVx 5 days change %
We are also displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
Important Note:
The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔶 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝗸𝗲𝘄 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at tastytrade binary expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
We calculate the interpolated strike price based on the expected move, taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive.
🔹 What Causes Pricing Skew? The Theory Behind It
The asymmetric pricing of PUT and CALL options is driven by the natural dynamics of the market. The theory is that when CALL options are more expensive than PUT options at the same distance from the current spot price, market participants are buying CALLs and selling PUTs, expecting a faster upward movement compared to a downward one .
In the case of PUT skew, it's the opposite: participants are buying PUTs and selling CALLs , as they expect a potential downward move to happen more quickly than an upward one.
An options trader can take advantage of this phenomenon by leveraging PUT pricing skew. For example, if they have a bullish outlook and both IVR and IVx are high and IV started decreasing, they can capitalize on this PUT skew with strategies like a jade lizard, broken wing butterfly, or short put.
🔴 PUT Skew 🔴
Put options are more expensive than call options, indicating the market expects a faster downward move (▽). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so faster in velocity compared to a potential upward movement.
🔹 SPY PUT SKEW example:
If AMEX:SPY PUT option prices are 46% higher than CALLs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so 46% faster in velocity compared to a potential upward movement
🟢 CALL Skew 🟢
Call options are more expensive than put options, indicating the market expects a faster upward move (△). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so faster in velocity compared to a potential downward movement.
🔹 INTC CALL SKEW example:
If NASDAQ:INTC CALL option prices are 49% higher than PUTs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so 49% faster in velocity compared to a potential downward movement .
🔶 USAGE example:
The script is compatible with our other options indicators.
For example: Since the main metrics are already available in this Options Oscillator, you can hide the main IVR panel of our Options Overlay indicator, freeing up more space on the chart. The following image shows this:
🔶 ADDITIONAL IMPORTANT COMMENTS
🔹 Historical Data:
Yes, we only using historical internal metrics dating back to 2024-07-01, when the TanukiTrade options brand launched. For now, we're using these, but we may expand the historical data in the future.
🔹 What distance does the indicator use to measure the call/put pricing skew?:
It is important to highlight that this oscillator displays the call/put pricing skew changes for the next optimal monthly expiration on a histogram.
The Binary Expected Move distance is calculated using the TastyTrade method for the next optimal monthly expiration: Formula = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
We interpolate the exact difference based on the neighboring strikes at the binary expected move distance using the TastyTrade method, and compare the interpolated call and put prices at this specific point.
🔹 - Why is there a slight difference between the displayed data and my live brokerage data?
There are two reasons for this, and one is beyond our control.
◎ Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator, and you can see the time elapsed since the last update at the bottom of the corner on daily TF.
◎ Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
🔹 - EOD data:
The indicator always displays end-of-day (EOD) data for IVR, IV, and CALL/PUT pricing skew. During trading hours, it shows the current values for the ongoing day with each update, and at market close, these values become final. From that point on, the data is considered EOD, provided the day confirms as a closed daily candle.
🔹 - U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Round Numbers Breakouts Smart Formula Signals and AlertsThis indicator uses Round Numbers breakouts and then uses smart formula with the near Round Numbers to determine best TP (take profit)/SL (stop loss) areas. Furthermore, it calculates win percentage, shows in-profit/in-loss peaks and the price amount result over a customizable date range, which when combined well with the smart formula provides decent profitable outcome. I have decided to write my own backtesting engine as the integrated TradingView strategy one has limitations and has shown inconsistencies when compared to manual backtesting…
There are many settings you can manually change to trade any instrument, any style, any approach and there are presets included for Bitcoin(BTCUSD), FOREX(EURUSD), SPY(S&P500), so you can start trading immediately! Alerts correspond to indicator settings and are turned on with a few clicks. There are 3 tables (each can be shown/hidden) showing everything you need to see/know to calibrate the indicator as you wish.
Labels, lines, tables explanations (everything can be hidden/shown):
- LONG Labels: medium-green: position open, dark-green: SL, bright-green: TP, blue: TP2
- SHORT Labels: medium-red: position open, dark-red: SL, bright-red: TP, purple: TP2
- Gray circles: position entry area | Yellow crosses: SL area
- Green line: Long TP1, Blue line: Long TP2 | Red Line: Short TP1, Purple line: Short TP2
- Grey lines: Round Numbers (customized via “Round Number up/down measure unit” input)
- Yellow labels at end of each week: end of week OVERALL total results
- Red colored background: power segment
- 3 tables: 1) INFO | STATS, 2) SPY Options Calculator, 3) Indicator Settings
If you decide to fully customize the indicator yourself, on the very top - under “PRESETS” select “MANUAL”! NOTE: If you select any of the pre-set presets, only GLOBAL settings can be changed, the rest of the settings will be “frozen” until you switch it to “MANUAL”!
- Global Settings are self-explanatory and mainly observational, show/hide, etc.
- Manual TP2 (Multi-Take-Profit) Settings:
>>>>> Include TP2 System? Turn on/off multi-profit system, with this unchecked, every trade will either end with SL or with TP1.
>>>>> TP2 System: NEAREST/FORMULA, NEAREST – after TP1 is taken > next TP2 will be a round number price target nearest to where TP1 was taken (sometimes it can be very near, sometimes further away…), FORMULA – 2nd round number price target will be optimally selected based on the distance behind and ahead of TP1 area. For TP2 – FORMULA would be the most logical choice as with multi-take-profit setting turned on – you’d want to ride it out as far as possible.
>>>>> TP1/TP2 division type: 1) Each price target (TP1, TP2) will be ½ of the position 2) TP1 will be 2/3 of the position and TP2 will be the remaining 1/3.
>>>>> TP2 hit type: “close” > candle has to close on top/crossing the price target line, “touch” > once candle touches the price target – you will be immediately alerted to take the partial profit (if you will use such setting – you will need to take the partial profits as soon as you receive the alert.
>>>>> TP1 > Back to Entry hit type: similar to TP2, “close” > candle close, “touch” > candle touch. Please note: this is a very tricky setting as if you use “close” option – your profitable trade may become a loss if a huge candle will close against your position eliminating your TP1 profit, however often the price will touch and cross the entry area to only bounce and continue with your position direction for even bigger profits… so experiment with the date range results to see what works best for your instrument/setting/strategy.
>>>>> TP2 count towards trades count: this can be a bit confusing, but it is simply how should TP2 be treated towards trades count. The indicator will show you Win Percentage and Win % is obtained from winning trades count divided by total trades count. While TP2 is not “a new trade”, it expands the profit of the trade. This is an experimental setting to count TP2 as the whole winning trade, ½ of a trade, or not count it at all.
- Manual Signals/TP1 Settings:
>>>>> TP1/TP2 offset: this one is really cool, with this feature you can hunt these conditions when the price comes very near the profit target area, but never touches it. With this setting turned on and with a good offset amount – you will be able to catch these for TP1 and TP2!
>>>>> TP1/TP2 offset amount: just what the title says, please be careful with this as this number varies significantly depending on the instrument you will be trading. Examples: 1) For SPY 0.1 would be $0.10 offset - if TP1 is $400 and price hits $399.90 > TP1 considered taken/signal shown/alert) | 2) For EURUSD, it is very different and if wrong will show TP1 immediately at position open, typical good offset for EURUSD is: 0.0005 | 3) For BTCUSD, 10 - $10 offset, if TP is $15,000 > $14,990, etc.
>>>>> Round Number up/down measure unit (in dollars $): this one is very important if you will be using “MANUAL” selection to build your own setup as it is very different for every instrument. For SPY, round numbers are single dollars or even half-dollar 50 cent numbers: 1 or 0.5 (350, 351, 352, etc. or 350.50, 351, 351.50, 352, etc.), while for Bitcoin (BTCUSD) a single unit ($1) is too small to be a round number as Bitoin moves much faster and wider every second and it would have to be at least 50 ($50) to make sense. Similar for FOREX (EUR/USD) a single 1 unit ($1) will be too big as EURUSD will never move a whole $1 in 15 minutes or even a day.. and would have to be something like 1.05500. You can easily determine if this number makes sense for your instrument by observing the grey Round Number lines which will correspond based on this setting. You can also visually observer if the price of the instrument appreciates these round numbers.
>>>>> Close Position Before Market Closes: just what the title says. Indicator will close the position 15 minutes before market closes (US session), update backtesting stats, alert you.
>>>>> Close Position Before Power Hour: 3PM – 4PM ET is the last hour of US trading session, where sudden move in any direction can happen with huge volatility, while sometimes nothing will happen at all… Many try to avoid it, so if you wish to avoid it as well - turn this on and it will alert you to close your positions 15 minutes before Power Hour starts, backtesting/stats will be adjusted accordingly.
>>>>> Skip OVERSIZED candles in signals: turn on this setting to skip signals, which happen to fall on big candles. This is basically a protection from huge volatility moves, which usually happen during financial news/events and if you are not a fan of these – you can set this option for indicator to not open anything based on the candle size.
>>>>> Color OVERSIZED candles: this will help you calibrate the size of the OVERSIZED candles if you decide to use this setting and overall visually see them.
>>>>> OVERSIZED candle size: OVERSIZED candle size must be input as it varies significantly. Please note: for each instrument – the size number is completely different, as for SPY: 2 would mean any candle bigger than $2 distance will be considered OVERSIZED, for Bitcoin it would have to be several hundred dollars, like 400-500. For FOREX, this would have to be a decimal, for EURUSD something like 0.0005. It’s best to experiment visually with this setting depending on the instrument you will be trading while setting up the size. To see a typical huge unusual candle – look up financial calendar for something like FOMC meeting, then measure the candle input it into this setting.
>>>>> OVERSIZED candle size calculation type: this is just more flexibility for your preference. If you wish to calculate the size of the candle based on the open/close – select “BODY”, if you wish to use high/low – select “STICKS (from tip to tip)”. Hard to say which one is better, so it is up to you to decide.
>>>>> Include EMA in signal formula: LONG signals will only be shown only if above EMA, SHORT if below EMA. EMA length is of course customizable in below.
>>>>> Skip opposite candle types in signals: signals where the candle color confirms the direction of the trade, but the candle type is opposite (like a green colored bearish hammer for example) will be avoided (such candles can be very uncertain/deceptive).
>>>>> Skip doji: signals where the signal candle is doji (uncertain) will be avoided.
>>>>> TP1 hit type/system: same thing as TP2 hit type/system.
>>>>> SL hit type/system: same as TP1 and TP2 types/systems.
>>>>> Intraday Session Signals Active Time in ET: time range during the day when indicator will show signals (open trades, alert you, etc.). This is specifically for intraday trading. You can turn it off completely by selecting a BLANK option.
>>>>> Intraday TP/SL Active Time in ET: same as above, but for taking profits/stop losses.
*** To add the alerts
-Right-click anywhere on the TradingView chart
-Click on Add alert
-Condition: Select this indicator by it’s name
-Alert name: Whatever you want
-Hit “Create”
-Note: If you change ANY Settings within the indicator – you must DELETE the current alert and create a new one per steps above, otherwise it will continue triggering alerts per old Settings!
If you wish to try this out for a week or so – please write me directly and I will give you access.
Market First Signals - Relative Strength/WeaknessThis market-first trading strategy gives BUY, SHORT, and CLOSE signals based on volume, trend, and relative strength or weakness to the market (SPY by default, can be customized). This indicator is useful for signaling day-trade entries and exits for tickers that are strong (or weak) against the market.
Stocks that are showing relative strength (or weakness) to the market, are trending, and have decent movement generate a buy (or short) signal. When the trend runs out, a CLOSE signal is fired.
Potential profit (based on ATR) and actual profit is calculated, predicting the type of move expected
Unique 'stay in trade' logic helps prevent unnecessary CLOSE signals if a trend is likely to continue
A colored plot indicates the strength of the current trend and turns orange/red when the strength is weakened.
Crypto traders can uncheck 'Trade during market hours' for 24-hour trading, and should change the comparison ticker from SPY to BTCUSD or something similar for their market.
Enjoy!
KEY CONCEPTS
The three- and five-minute timeframes are used to establish and verify trend (ADX/DI with custom logic)
Entries and exits are based on Parabolic SAR and confirmed on multiple timeframes, trend, and relative volume
Relative strength /weakness to the market compares ticker to SPY
Chop is avoided at all costs. I've experimented with choppiness indicator below 38, but found that the ADX DI+/- readings work even better.
Trend is established using ADX DI+/- readings over 20, confirmed by EMA 5/13 crossover and EMA5 slope
Signals will fire only if the average volume for the current 5-min bar is above normal
Only tickers with a five-bar / 13 period ATR of 1% the ticker's price generate signal.
Only longs above daily-anchored VWAP, shorts below daily-anchored VWAP
Signals fire on bar close to prevent repainting / look-ahead bias
Indicator labels and alerts generated
SIGNALS
BUY: up-trending tickers showing relative strength are bought on the three-minute PSAR
SELL: when the close price falls below the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
SHORT: down-trending tickers with relative weakness are shorted on the three-minute PSAR
COVER: when the close price moves above the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
ALERTS
Alerts are generated on BUY, SELL, SHORT, and COVER signals, as well as optional LOST RELATIVE STRENGTH and LOST RELATIVE WEAKNESS
INPUTS
Use relative strength/weakness comparison with the market : trigger trades based on the ticker's strength or weakness to the selected comparison ticker (usually SPY for equities or BTCUSD for crypto)
Comparison Ticker for relative strength/weakness : Ticker to compare against for relative strength/weakness
Trade during market hours only : Take buy/sells during specified hours. Disable this for crypto trading.
Market hours (market time) : Customize market hours - defaults to 9:30 to 16:00 EST
"Only trade very strong trends" : take trades only if an established trend is very strong (ADX over 40) (DEFAULT = OFF)
"Limit trade direction to VWAP" : Long trades only above VWAP, shorts below (DEFAULT = ON)
"Limit trade direction to Market direction" : Long trades only if SPY (or selected comparison ticker) is up, shorts if the market is down. (DEFAULT= OFF)
"Limit trades based on a ticker's green/red status for the day" : Long trades if the ticker is green for the day, shorts if red. (DEFAULT = OFF)
RSI and market strength with alerts By combining the true strength of the Nasdaq (NDX) and S&P500 ( SPY ) we can then compare that against the volatility /fear index ( VIX ). The true strength of the Volatility Index ( VIX ) is shown by the red area and the Nasdaq (NDX) and S&P500 ( SPY ) by the silver /teal areas.
The yellow EMA area shows the average difference between the VIX and the NDX/ SPY indexes. When the yellow area crosses above the zero line and is climbing it means that the NDX/ SPY are strengthening. When the yellow area is decreasing or crosses under the zero line it means the fear/volatility index ( VIX ) is becoming stronger and NDX/ SPY are weakening. The RSI is overlaid as a white line to show the correlation of the instrument strength compared to the market.
When looking at charts of companies/components listed on the Nasdaq and/or S&P 500 you will notice a frequent correlation between the movement of the yellow area depicting index health and the RSI of the chart you are viewing. It’s a very quick and effective method of seeing the strength and fear within the market indexes and how they are effecting your chart.
The background color changes when the market strength combined with the active symbol RSI and higher timeframe MACD signals are aligned. Green is a buy zone, Red is a sell zone.
There are also red and green indicator x’s at the top/bottom of the indicator. They show a drastic change in Relative strength within a short period. This often indicates a buy or sell opportunity.
It is tested and works well on all timeframes with Stocks listed on NASDAQ & S&P500 .
Buy, Sell, Exit Buy and Exit Sell alerts are included.
There are also red and green indicator x’s at the top/bottom of the indicator. They show a drastic change in Relative strength within a short period. This often indicates a buy or sell opportunity.
Market Index Strength by Keiron RichieBy combining the true strength of the Nasdaq (NDX) and S&P500 (SPY) we can then compare that against the volatility /fear index (VIX). The true strength of the Volatility Index (VIX) is shown by the red area and the Nasdaq (NDX) and S&P500 (SPY) by the silver/teal areas.
The yellow EMA line shows the average difference between the VIX and the NDX/SPY indexes. When the yellow line crosses above the zero line and is climbing it means that the NDX/SPY are strengthening. When the yellow line is decreasing or crosses under the zero line it means the fear (VIX) is becoming stronger and NDX/SPY are weakening.
When looking at charts of companies/components listed on the Nasdaq and/or S&P 500 you will notice a frequent correlation between the movement of the yellow line depicting index health and the strength of the chart you are viewing. It’s a very quick and effective method of seeing the strength and fear within the market indexes and how they are effecting your chart.
The background color changes based on the market strength combined with the active symbol true strength. Green is a buy zone, Red is a sell zone.
It is tested and works well on all timeframes with Stocks listed on NASDAQ & S&P500. It does not include buy/sell alerts.
Dynamic 15-Ticker Multi-Symbol Table 2025 EditionTitle:
Dynamic 15-Ticker Multi-Symbol Table 2025 Edition
Description:
This script provides a multi-ticker table for TradingView charts. It is fully open-source and free to use. The table displays up to 15 tickers, including SPY as the baseline symbol. The script updates in real-time on any timeframe.
Features:
SPY baseline: The first row always shows SPY for reference.
Custom tickers: Add up to 14 additional tickers via the input settings. Rows without tickers remain hidden.
Price and direction: Each ticker row displays the current price and an indicator of direction based on recent price movement.
RSI (14) indicator: Shows the current relative strength index value with a simple directional marker.
Volume formatting: Displays volume values in thousands, millions, or billions automatically. Volume change is indicated with directional markers.
Stable layout: The table uses alternating row colors for readability and maintains consistent row count without collapsing or disappearing rows.
Real-time updates: All displayed values refresh automatically on any chart timeframe.
How to use:
Add the script to your chart.
Enter your chosen tickers in the input settings. SPY will remain as the first ticker automatically.
Tickers not entered will remain hidden. When a ticker is removed, the row will be removed-dynamically.
Observe live prices, RSI values, and volume changes directly on your chart without switching symbols.
Additional notes:
The script is fully open-source; users are encouraged to modify or improve it.
No external links or references are required to understand its function.
This script does not repaint and does not require additional requests to update values.
Enhanced Oversold | 超跌信号 + 历史统计 + 模拟入出场 (v2.4)Enhanced Oversold | Oversold Signal + Historical Stats + Simulated Entries/Exits (v2.4) – Release Notes (EN)
1. Overview
This script is an advanced “buy-the-dip” toolkit for US stocks and ETFs. It detects rare, deep intraday selloffs on fundamentally strong names, then simulates a three-tier entry strategy around the event and tracks different exit paths.
The goal is to answer three questions:
* When did similar crashes happen in the past?
* How would a disciplined laddered entry have performed?
* How long did it take for price to recover under different exit rules?
2. Core idea
* Define an 8-hour “crash” relative to a robust reference price yBase = min(previous-day VWAP, previous close).
* Combine this with short-term RSI and 15m Z-score filters to avoid “random noise” dips.
* Filter out regime-level risk (index / sector crash, volatility spikes, liquidity stress, bad long-term trend).
* When a valid oversold event appears, simulate staged entries (E1/E2/E3) and exits, then record everything into a historical table and JSON for external analysis.
3. Signal logic (summary)
* Timeframe: designed for 15m / 5m charts, using US RTH session 09:30–16:00.
* Crash trigger (must all be true):
* 8h drawdown from yBase ≤ fixed threshold (default −6%) and the 8h low is recent within N×15m bars.
* RSI(1h) below an oversold level (default < 30).
* 15m return Z-score ≤ threshold (default ≤ −1.5) over a configurable window.
* Optional filters:
* Circuit breaker: SPY + sector ETF + VIX/VIX3M + VVIX conditions to avoid market-wide panic regimes.
* Liquidity stress: SPY 1h “stress index” (ATR/price, intraday range vs volume, and VIX Z-score) normalised to 0–100, with a user threshold.
* Shape filter: only accept “A-type” healthy long-term trend set-ups (6m / 12m performance vs VWAP/EMA and daily 200SMA slope).
4. Simulated entries (E1 / E2 / E3)
* E1: first ladder price anchored to the first RTH after the event, with optional “same-day RTH” entry if the event happens during RTH.
* E2: only becomes valid from the next RTH day onward, and only if the new RTH low breaks the E1-execution-day low. The target depth is based on E1 discount × (1+α).
* E3: only after E2, on a different day (not the E1 “anchor” day). Depth is based on the max discount of E1/E2 × (1+β).
* Stair and cap rules:
* A minimum tick step between ladders, adjustable in ticks.
* Optional cap so that every entry price must be below a multiple of the event price.
* Optional “chase on first RTH bar”: if nothing fills on the first RTH bar, prices can be lifted once toward the intraday low, while keeping ladder spacing and cap constraints.
* All actual fills are simulated against bar lows. The script records:
* Whether E1/E2/E3 filled.
* Actual execution prices.
* Average entry price and the entry sequence string (e.g. “13”, “123”).
5. Exit logic and timing metrics
Two exit rules are tracked in parallel:
* Exit Ref: exit when close returns to yBase.
* Exit Open+Y%: exit when close reaches min(event close, first post-event open) × (1+Y%).
For each event the script records:
* t_ref_d: days from event to first touch of yBase.
* tY_d: days until Open+Y% level is reached.
* tUp_d: days until price turns “bullish again” (RTH VWAP ≥ previous daily VWAP and close > previous close).
* tLow_d: days until the minimum price between event and t_ref (or end of window) is reached.
* lowToRef: that minimum price.
* ddMinPct: maximum drawdown (in %) from average entry to lowToRef.
Additional intraday stats for the first RTH after the event:
* dayFirstLow: low of the first RTH bar from the chosen statistics start.
* rthLow: overall RTH low of that day.
* eqFirst: whether the overall low equals the first-bar low.
* postDipAvg: average close after the daily low is formed (equal-weighted).
6. Historical table on chart
* The on-chart table shows up to maxRows events, most recent first.
* Columns include:
* Date, 8h drawdown, yBase, stress, circuit conditions, shape (A/B/C).
* Entry sequence and actual execution prices.
* Average entry price.
* Exit prices and PnL (in % and absolute) for both exit modes.
* Timing metrics (t_ref, tY, tUp, tLow).
* Min price to t_ref, max drawdown vs average entry.
* First RTH low, day RTH low, equality flag, post-dip average, and market flag (US/HK).
The table is only redrawn on bar close to reduce CPU load.
7. Liquidity stress pane
* Optional lower pane that plots the SPY-based liquidity stress index (0–100).
* Components (all on 60m SPY/VIX data):
* rvZ: Z-score of ATR/price.
* rpvZ: Z-score of intraday range divided by volume.
* vixZ: Z-score of VIX.
* Stress index = 50 + 10 × (rvZ + rpvZ + vixZ), clipped to .
* A horizontal line marks the current filter threshold.
8. Webhook JSON outputs
The indicator can send three types of alerts via alert():
* Signal
* Emitted only when a new oversold event fires.
* Contains ticker, market flag, event time, drop8h, RSI1h, Z15, yBase, shape, circuit reason, and stress.
* History
* Emitted when requested, containing a full snapshot of the latest event:
* All key metrics used in the table, including absolute PnL for both exit modes, timing metrics, drawdown stats, and post-dip averages.
* HistoryAll
* Compressed bulk export of all events as a compact JSON object:
* Short keys (d, dr8, yb, st, c, sh, e, px, avg, xr, pr, absR, xy, py, absY, tr, ty, tu, tl, l2r, dd, fl, rl, eq, pavg).
* Numbers rounded to 3 decimals to reduce payload size.
* Because TradingView enforces a payload size limit, HistoryAll is automatically split into multiple chunks (up to ~3200 characters each).
* When HistoryAll is selected and a manual “dump all” flag is turned on, the script will emit multiple alerts on the same bar until all chunks are sent.
9. What is new in v2.3
Compared with previous versions, v2.3 adds:
* Deeper risk metrics:
* Tracking of the minimum price until recovery (lowToRef) and its timing (tLow_d).
* Max drawdown vs average entry (ddMinPct) for each event.
* E1-execution-day RTH low tracking, used to decide whether later days truly “make a new low” before adding E2.
* Absolute PnL fields:
* absRef and absOY for both exit modes, calculated using user-defined share/contract sizes for E1/E2/E3.
* More compact and robust HistoryAll:
* Short-key JSON objects, 3-decimal numeric formatting, chunked output suitable for 3rd-party storage and analysis.
* Performance optimisations:
* Array length normalisation is done once per bar instead of inside the per-event loop.
* Table rendering only happens on bar close, and no longer clears the whole grid every bar.
* Same-day RTH pricing for event-day entries is restricted to the latest event only, reducing redundant work on historical events.
10. Usage notes and disclaimer
* Recommended canvas: 15m or 5m chart, US stocks / ETFs, with RTH session set to 09:30–16:00.
* For stable operation on TradingView’s servers, avoid extremely large lookback windows and oversized history tables if your symbol has very long history.
* This script is for educational and research purposes only.
* It is not financial advice and does not guarantee profitability. Always combine it with your own risk management, fundamental research, and market context.
KING4R_swing
### KING4R Swing: The High-Performance Trading Checklist
**KING4R\_swing** is a powerful indicator designed for **swing trading** that incorporates a **six-point checklist** to validate high-probability entry setups. It focuses on identifying bullish configurations by aligning local market strength with the overall strength of the S&P 500 ($\text{SPY}$) index.
🚀 **Key Features:**
* **Local EMA Alignment:** Checks if the price is above the 48-period EMA and if the 13-period EMA has bullishly crossed the 48-period EMA.
* **Post-Volume Context:** Detects periods of **sideways consolidation** or a **structure shift** (higher lows) following a candle with unusual volume (Stopping Volume), signaling potential accumulation.
* **Macro Filter (SPY Daily):** Uses the `request.security` function to integrate general market context, validating if $\text{SPY}$'s EMAs (8/21 and 13/48) are trending bullishly on the daily timeframe. These $\text{SPY}$ conditions are optional.
* **Scoring and Feedback:** Generates a **total score** out of $6$ and displays a dynamic **checklist** on the chart with $\text{✅}$ or $\text{❌}$.
* **Setup Alert:** A **"🚀"** label and a **configurable alert** are triggered when all $6$ conditions are met, indicating a fully aligned entry *setup*.
**Set your rules, wait for alignment, and only trade if you have the setup!**
High Probability TQQQ Call SignalMarket trend filter (both must be true)
SPY uptrend: SPY close > SPY 20-EMA and 50-EMA
QQQ uptrend: QQQ close > QQQ 20-EMA and 50-EMA
Leadership trigger (need at least one)
NVDA breakout: today’s NVDA close > yesterday’s high and today’s volume > 1.5 × 10-day avg vol
MSFT breakout: same rule for MSFT
Signal
If (1) AND (NVDA breakout OR MSFT breakout) → “TQQQ Call” signal prints on the chart and tints the background.
Trade idea implied by the setup
You’re using mega-cap leadership strength to confirm a broad tech uptrend, then buying TQQQ (or calls on TQQQ/QQQ) when leaders expand on high volume.
How to use it (practical rules)
Timeframe: Keep it on a Daily chart (or any chart, but the logic is all Daily).
Entry (simplest):
Enter TQQQ at next day’s open after a signal (or same-day close, but next-open is cleaner for testing).
Stops / invalidation (pick one):
QQQ close < 20-EMA (trend wobble → exit)
Or TQQQ close < 20-EMA
Or ATR stop: 2× ATR(14) below entry (fixed)
(Use whichever matches your style; for position trades, I like “QQQ close < 20-EMA”)
Profit taking (examples):
Partial at +8–12% on TQQQ, trail the rest with QQQ 20-EMA
Or time-based: 10 trading days then reassess, provided trend filters still pass
When to avoid:
Major macro days (CPI/PPI/NFP/FOMC) if you don’t want gap risk
If only one of SPY/QQQ is above EMAs (filter fails)
If breakouts happen on weak volume (below 1.5×)
Disclaimer:
This Pine Script is provided solely for informational and educational purposes. It is not investment advice or a recommendation to buy or sell any security, derivatives, or financial instrument. The signals and logic contained herein are based on historical data and technical analysis concepts, which may not reflect future market conditions.
Users are solely responsible for evaluating the risks associated with trading and investing. The author makes no guarantees regarding accuracy, reliability, or future performance. Past performance is not indicative of future results.
The author is not a registered financial advisor, broker, or dealer, and assumes no responsibility or liability for any financial losses incurred from the use or interpretation of this script.
Trading highly leveraged instruments such as TQQQ, futures, or options carries significant risk, including the possibility of losing more than your initial investment. Always conduct your own research and, if necessary, consult with a licensed financial professional.
By using this script, you agree that you are doing so at your own risk.
