Smarter Money Concepts - Wyckoff Springs & Upthrusts [PhenLabs]📊Smarter Money Concepts - Wyckoff Springs & Upthrusts
Version: PineScript™v6
📌Description
Discover institutional manipulation in real-time with this advanced Wyckoff indicator that detects Springs (accumulation phases) and Upthrusts (distribution phases). It identifies when price tests support or resistance on high volume, followed by a strong recovery, signaling potential reversals where smart money accumulates or distributes positions. This tool solves the common problem of missing these subtle phase transitions, helping traders anticipate trend changes and avoid traps in volatile markets.
By combining volume spike detection, ATR-normalized recovery strength, and a sigmoid probability model, it filters out weak signals and highlights only high-confidence setups. Whether you’re swing trading or day trading, this indicator provides clear visual cues to align with institutional flows, improving entry timing and risk management.
🚀Points of Innovation
Sigmoid-based probability threshold for signal filtering, ensuring only statistically significant Wyckoff patterns trigger alerts
ATR-normalized recovery measurement that adapts to market volatility, unlike static recovery checks in traditional indicators
Customizable volume spike multiplier to distinguish institutional volume from retail noise
Integrated dashboard legend with position and size options for personalized chart visualization
Hidden probability plots for advanced users to analyze underlying math without chart clutter
🔧Core Components
Support/Resistance Calculator: Scans a user-defined lookback period to establish dynamic levels for Spring and Upthrust detection
Volume Spike Detector: Compares current volume to a 10-period SMA, multiplied by a configurable factor to identify significant surges
Recovery Strength Analyzer: Uses ATR to measure price recovery after breaks, normalizing for different market conditions
Probability Model: Applies sigmoid function to combine volume and recovery data, generating a confidence score for each potential signal
🔥Key Features
Spring Detection: Spots accumulation when price dips below support but recovers strongly, helping traders enter longs at potential bottoms
Upthrust Detection: Identifies distribution when price spikes above resistance but falls back, alerting to possible short opportunities at tops
Customizable Inputs: Adjust lookback, volume multiplier, ATR period, and probability threshold to match your trading style and market
Visual Signals: Clear + (green) and - (red) labels on charts for instant recognition of accumulation and distribution phases
Alert System: Triggers notifications for signals and probability thresholds, keeping you informed without constant monitoring
🎨Visualization
Spring Signal: Green upward label (+) below the bar, indicating strong recovery after support break for accumulation
Upthrust Signal: Red downward label (-) above the bar, showing failed breakout above resistance for distribution
Dashboard Legend: Customizable table explaining signals, positioned anywhere on the chart for quick reference
📖Usage Guidelines
Core Settings
Support/Resistance Lookback
Default: 20
Range: 5-50
Description: Sets bars back for S/R levels; lower for recent sensitivity, higher for stable long-term zones – ideal for spotting Wyckoff phases
Volume Spike Multiplier
Default: 1.5
Range: 1.0-3.0
Description: Multiplies 10-period volume SMA; higher values filter to significant spikes, confirming institutional involvement in patterns
ATR for Recovery Measurement
Default: 5
Range: 2-20
Description: ATR period for recovery strength; shorter for volatile markets, longer for smoother analysis of post-break recoveries
Phase Transition Probability Threshold
Default: 0.9
Range: 0.5-0.99
Description: Minimum sigmoid probability for signals; higher for strict filtering, ensuring only high-confidence Wyckoff setups
Display Settings
Dashboard Position
Default: Top Right
Range: Various positions
Description: Places legend table on chart; choose based on layout to avoid overlapping price action
Dashboard Text Size
Default: Normal
Range: Auto to Huge
Description: Adjusts legend text; larger for visibility, smaller for minimal space use
✅Best Use Cases
Swing Trading: Identify Springs for long entries in downtrends turning to accumulation
Day Trading: Catch Upthrusts for short scalps during intraday distribution at resistance
Trend Reversal Confirmation: Use in conjunction with other indicators to validate phase shifts in ranging markets
Volatility Plays: Spot signals in high-volume environments like news events for quick reversals
⚠️Limitations
May produce false signals in low-volume or sideways markets where volume spikes are unreliable
Depends on historical data, so performance varies in unprecedented market conditions or gaps
Probability model is statistical, not predictive, and cannot account for external factors like news
💡What Makes This Unique
Probability-Driven Filtering: Sigmoid model combines multiple factors for superior signal quality over basic Wyckoff detectors
Adaptive Recovery: ATR normalization ensures reliability across assets and timeframes, unlike fixed-threshold tools
User-Centric Design: Tooltips, customizable dashboard, and alerts make it accessible yet powerful for all trader levels
🔬How It Works
Calculate S/R Levels:
Uses the highest high and the lowest low over the lookback period to set dynamic zones
Establishes baseline for detecting breaks in Wyckoff patterns
Detect Breaks and Recovery:
Checks for price breaking support/resistance, then recovering on volume
Measures recovery strength via ATR for volatility adjustment
Apply Probability Model:
Combines volume spike and recovery into a sigmoid function for confidence score
Triggers signal only if above threshold, plotting visuals and alerts
💡Note:
For optimal results, combine with price action analysis and test settings on historical charts. Remember, Wyckoff patterns are most effective in trending markets – use lower probability thresholds for practice, then increase for live trading to focus on high-quality setups.
Tìm kiếm tập lệnh với "20年美元汇率"
Range Percent Histogram📌 Range Percent Histogram – Indicator Description
The Range Percent Histogram is a custom indicator that behaves like a traditional volume histogram, but instead of showing traded volume it displays the percentage range of each candle.
In other words, the height of each bar represents how much the price moved (in percentage terms) within that candle, from its low to its high.
🔧 What it shows
The indicator has two main components:
Component Description
Histogram Bars Columns plotted in red or green depending on the candle direction (green = bullish candle, red = bearish). The height of each bar = (high - low) / low * 100. That means a candle that moved, for example, 1 % from its lowest point to its highest point will show a bar with 1 % height.
Moving Average (optional) A 20-period Simple Moving Average applied directly to the bar values. It can be turned ON/OFF via a checkbox and helps you detect whether current range activity is above or below the average range of the past candles.
⚙️ How it works
Every time a new candle closes, the indicator calculates its range and converts it into a percentage.
This value is drawn as a column under the chart.
If the closing price is above the opening price → the bar is green (bullish range).
If the closing price is below the opening price → the bar is red (bearish range).
When the Show Moving Average option is enabled, a smooth line is plotted on top of the histogram representing the average percentage range of the last 20 candles.
📈 How to use it
This indicator is very helpful for detecting moments of range expansion or contraction.
One powerful way to use it is similar to a volume exhaustion / low-volume pattern:
Situation Interpretation
Consecutive bars with very low height Price is in a period of low volatility → possible accumulation or "pause" phase.
A sudden large bar after a series of small ones Indicates a strong pickup in volatility → often marks the start of a new impulse in the direction of the breakout.
RRG Relative Strength# RRG Relative Strength (RRG RS)
Compare any symbol to a benchmark using two RRG-style lines: **RS-Ratio** (trend of relative strength) and **RS-Momentum** (momentum of that trend). Both are centered at **100**:
- **RS-Ratio > 100** → outperforming the benchmark
- **RS-Ratio < 100** → underperforming
- **RS-Momentum** often **leads** RS-Ratio (crosses 100 earlier)
# How it works
1) Relative Strength (RS): RS = Close(symbol) / Close(benchmark)
2) Normalize around 100: smooth RS with EMA and divide RS by that EMA
3) RS-Ratio: EMA( RS / EMA(RS, Length), LenSmooth ) * 100
4) RS-Momentum: RS-Ratio / EMA(RS-Ratio, LenSmooth) * 100
# Inputs
- Length (default 14): normalization window for RS
- Length Smooth (default 20): smoothing window for RS-Ratio & RS-Momentum
# Benchmark (auto)
- US: SP:SPX (S&P 500)
- Vietnam: HOSE:VNINDEX
- Crypto: INDEX:BTCUSD
(Modify the mapping if needed, or replace with your own input.symbol().)
# How to read
- Improving: RS-Momentum crosses above 100 while RS-Ratio turns up
- Leading: RS-Ratio > 100 with RS-Momentum ≥ 100
- Weakening: RS-Momentum drops below 100; RS-Ratio often follows
# Timeframes & presets
- Works on Daily and Weekly charts
- Daily (fast): 14 / 20
- Approx. weekly behavior on Daily: 50 / 60
Note: Values usually hover near 100 (e.g., ~90–110) but are not strictly bounded. Ensure your symbol and benchmark trade in comparable sessions/currencies.
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.
XAUUSD Strength Dashboard with VolumeXAUUSD Strength Dashboard with Volume Analysis
📌 Description
This advanced Pine Script indicator provides a multi-timeframe dashboard for XAUUSD (Gold vs. USD), combining price action analysis with volume confirmation to generate high-probability trading signals. It detects:
✅ Break of Structure (BOS)
✅ Fair Value Gaps (FVG)
✅ Change of Character (CHOCH)
✅ Trendline Breaks (9/21 SMA Crossover)
✅ Volume Spikes (Confirmation of Strength)
The dashboard displays strength scores (0-100%) and action recommendations (Strong Buy/Buy/Neutral/Sell/Strong Sell) across multiple timeframes, helping traders identify confluences for better trade decisions.
🎯 How It Works
1. Multi-Timeframe Analysis
Fetches data from 1m, 5m, 15m, 30m, 1h, 4h, Daily, and Weekly timeframes.
Compares trend direction, BOS, FVG, CHOCH, and volume spikes across all timeframes.
2. Volume-Confirmed Strength Score
The Strength Score (0-100%) is calculated using:
Trend Direction (25 points) → 9 SMA vs. 21 SMA
Break of Structure (20 points) → New highs/lows with momentum
Fair Value Gaps (10 points) → Imbalance zones
Change of Character (10 points) → Shift in market structure
Trendline Break (20 points) → SMA crossover confirmation
Volume Spike (15 points) → High volume confirms moves
Score Interpretation:
≥75% → Strong Buy (High confidence bullish move)
60-74% → Buy (Bullish but weaker confirmation)
40-59% → Neutral (No strong bias)
25-39% → Sell (Bearish but weaker confirmation)
≤25% → Strong Sell (High confidence bearish move)
3. Dashboard & Chart Markers
Dashboard Table: Shows Trend, BOS, Volume, CHOCH, TL Break, Strength %, Key Level, and Action for each timeframe.
Chart Markers:
🟢 Green Triangles → Bullish BOS
🔴 Red Triangles → Bearish BOS
🟢 Green Circles → Bullish CHOCH
🔴 Red Circles → Bearish CHOCH
📈 Green Arrows → Bullish Trendline Break
📉 Red Arrows → Bearish Trendline Break
"Vol↑" (Lime) → Bullish Volume Spike
"Vol↓" (Maroon) → Bearish Volume Spike
🚀 How to Use
1. Dashboard Interpretation
Higher Timeframes (D/W) → Show the dominant trend.
Lower Timeframes (1m-4h) → Help with entry timing.
Strength Score ≥75% or ≤25% → Look for high-confidence trades.
Volume Spikes → Confirm breakouts/reversals.
2. Trading Strategy
📈 Long (Buy) Setup:
Higher TFs (D/W/4h) show bullish trend (↑).
Current TF has BOS & Volume Spike.
Strength Score ≥60%.
Key Level (Low) holds as support.
📉 Short (Sell) Setup:
Higher TFs (D/W/4h) show bearish trend (↓).
Current TF has BOS & Volume Spike.
Strength Score ≤40%.
Key Level (High) holds as resistance.
3. Customization
Adjust Volume Spike Multiplier (Default: 1.5x) → Controls sensitivity to volume spikes.
Toggle Timeframes → Enable/disable higher/lower timeframes.
🔑 Key Benefits
✔ Multi-Timeframe Confluence → Avoids false signals.
✔ Volume Confirmation → Filters low-quality breakouts.
✔ Clear Strength Scoring → Removes emotional bias.
✔ Visual Chart Markers → Easy to spot key signals.
This indicator is ideal for gold traders who follow institutional order flow, market structure, and volume analysis to improve their trading decisions.
🎯 Best Used With:
Support/Resistance Levels
Fibonacci Retracements
Price Action Confirmation
🚀 Happy Trading! 🚀
Key Indicators Dashboard (KID)Key Indicators Dashboard (KID) — Comprehensive Market & Trend Metrics
📌 Overview
The Key Indicators Dashboard (KID) is an advanced multi-metric market analysis tool designed to consolidate essential technical, volatility, and relative performance data into a single on-chart table. Instead of switching between multiple indicators, KID centralizes these key measures, making it easier to assess a stock’s technical health, volatility state, trend status, and relative strength at a glance.
🛠 Key Features
⦿ Average Daily Range (ADR %): Measures average daily price movement over a specified period. It is calculated by averaging the daily price range (high - low) over a set number of days (default 20 days).
⦿ Average True Range (ATR): Measures volatility by calculating the average of a true range over a specific period (default 14). It helps traders gauge the typical extent of price movement, regardless of the direction.
⦿ ATR%: Expresses the Average True Range as a percentage of the price, which allows traders to compare the volatility of stocks with different prices.
⦿ Relative Strength (RS): Compares a stock’s performance to a chosen benchmark index (default NIFTYMIDSML400) over a specific period (default 50 days).
⦿ RS Score (IBD-style): A normalized 1–100 rating inspired by Investor’s Business Daily methodology.
How it works: The RS Score is based on a weighted average of price changes over 3 months (40%), 6 months (20%), 9 months (20%), and 12 months (20%).
The raw value is converted into a percentage return, then normalized over the past 252 trading days so the lowest value maps to 1 and the highest to 100.
This produces a percentile-style score that highlights the strongest stocks in relative terms.
⦿ Relative Volume (RVol): Compares a stock's current volume to its average volume over a specific period (default 50). It is calculated by dividing the current volume by the average historical volume.
⦿ Average ₹ Volume (Turnover): Represents the total monetary value of shares traded for a stock. It's calculated by multiplying a day's closing price by its volume, with the final value converted to crores for clarity. This metric is a key indicator of a stock's liquidity and overall market interest.
⦿ Moving Average Extension: Measures how far a stock's current price has moved from from a selected moving average (EMA or SMA). This deviation is normalized by the stock's volatility (ATR%), with a default threshold of 6 ATR used to indicate that the stock is significantly extended and is marked with a selected shape (default Red Flag).
⦿ 52-Weeks High & Low: Measures a stock's current price in relation to its highest and lowest prices over the past year. It calculates the percentage a stock is below its 52-week high and above its 52-week low.
⦿ Market Capitalization: Market Cap represents the total value of all outstanding.
⦿ Free Float: It is the value of shares readily available for public trading, with the Free Float Percentage showing the proportion of shares available to the public.
⦿ Trend: Uses Supertrend indicator to identify the current trend of a stock's price. A factor (default 3) and an ATR period (default 10) is used to signal whether the trend is up or down.
⦿ Minervini Trend Template (MTT): It is a set of technical criteria designed to identify stocks in strong uptrends.
Price > 50-DMA > 150-DMA > 200-DMA
200-DMA is trending up for at least 1 month
Price is at least 30% above its 52-week low.
Price is within at least 25 percent of its 52-week high
Table highlights when a stock meets all above criteria.
⦿ Sector & Industry: Display stock's sector and industry, provides categorical classification to assist sector-based analysis. The sector is a broad economic classification, while the industry is a more specific group within that sector.
⦿ Moving Averages (MAs): Plot up to four customizable Moving Averages on a chart. You can independently set the type (Simple or Exponential), the source price, and the length for each MA to help visualize a stock's underlying trend.
MA1: Default 10-EMA
MA2: Default 20-EMA
MA3: Default 50-EMA
MA4: Default 200-EMA
⦿ Moving Average (MA) Crossover: It is a trend signal that occurs when a shorter-term moving average crosses a longer-term one. This script identifies these crossover events and plots a marker on the chart to visually signal a potential change in trend direction.
User-configurable MAs (short and long).
A bullish crossover occurs when the short MA crosses above the long MA.
A bearish crossover occurs when the short MA crosses below the long MA.
⦿ Inside Bar (IB): An Inside Bar is a candlestick whose entire price range is contained within the range of the previous bar. This script identifies this pattern, which often signals consolidation, and visually marks bullish and bearish inside bars on the chart with distinct colors and labels.
⦿ Tightness: Identifies periods of low volatility and price consolidation. It compares the price range over a short lookback period (default 3) to the average daily range (ADR). When the lookback range is smaller than the ADR, the indicator plots a marker on the chart to signal consolidation.
⦿ PowerBar (Purple Dot): Identifies candles with a strong price move on high volume. By default, it plots a purple dot when a stock moves up or down by at least 5% and has a minimum volume of 500,000. More dots indicate higher volatility and liquidity.
⦿ Squeezing Range (SQ): Identifies periods of low volatility, which can often precede a significant price move. It checks if the Bollinger Bands have narrowed to a range that is smaller than the Average True Range (ATR) for a set number of consecutive bars (default 3).
(UpperBB - LowerBB) < (ATR × 2)
⦿ Mark 52-Weeks High and Low: Marks and labels a stock's 52-Week High and Low prices directly on the chart. It draws two horizontal lines extending from the candles where the highest and lowest prices occurred over the past year, providing a clear visual reference for long-term price extremes.
⏳PineScreener Filters
The indicator’s alert conditions act as filters for PineScreener.
Price Filter: Minimum and maximum price cutoffs (default ₹25 - ₹10000).
Daily Price Change Filter: Minimum and maximum daily percent change (default -5% and 5%).
🔔 Built-in Alerts
Supports alert creation for:
ADR%, ATR/ATR %, RS, RS Rating, Turnover
Moving Average Crossover (Bullish/Bearish)
Minervini Trend Template
52-Week High/Low
Inside Bars (Bullish/Bearish)
Tightness
Squeezing Range (SQ)
⚙️ Customizable Visualization
Switchable between vertical or horizontal layout.
Works in dark/light mode
User-configurable to toggle any indicator ON or OFF.
User-configurable Moving (EMA/SMA), Period/Lengths and thresholds.
⦿ (Optional) : For horizontal table orientation increase Top Margin to 16% in Chart (Canvas) settings to avoid chart overlapping with table.
⚡ Add this script to your chart and start making smarter trade decisions today! 🚀
Momentum_EMABand📢 Reposting Notice
I am reposting this script because my earlier submission was hidden due to description requirements under TradingView’s House Rules. This updated version fully explains the originality, the reason for combining these indicators, and how they work together. Follow me for future updates and refinements.
🆕 Momentum EMA Band, Rule-Based System
Momentum EMA Band is not just a mashup — it is a purpose-built trading tool for intraday traders and scalpers that integrates three complementary technical concepts into a single rules-based breakout & retest framework.
Originality comes from the specific sequence and interaction of these three filters:
Supertrend → Sets directional bias.
EMA Band breakout with retest logic → Times precise entries.
ADX filter → Confirms momentum strength and avoids noise.
This system is designed to filter out weak setups and false breakouts that standalone indicators often fail to avoid.
🔧 How the Indicator Works — Combined Logic
1️⃣ EMA Price Band — Dynamic Zone Visualization
Plots upper & lower EMA bands (default: 9-period EMA).
Green Band → Price above upper EMA = bullish momentum
Red Band → Price below lower EMA = bearish pressure
Yellow Band → Price within band = neutral zone
Acts as a consolidation zone and breakout trigger level.
2️⃣ Supertrend Overlay — Reliable Trend Confirmation
ATR-based Supertrend adapts to volatility:
Green Line = Uptrend bias
Red Line = Downtrend bias
Ensures trades align with the prevailing trend.
3️⃣ ADX-Based No-Trade Zone — Choppy Market Filter
Manual ADX calculation (default: length 14).
If ADX < threshold (default: 20) and price is inside EMA Band → gray background marks low-momentum zones.
🧩 Why This Mashup Works
Supertrend confirms trend direction.
EMA Band breakout & retest validates the breakout’s strength.
ADX ensures the market has enough trend momentum.
When all align, entries are higher probability and whipsaws are reduced.
📈 Example Trade Walkthrough
Scenario: 5-minute chart, ADX threshold = 20.
Supertrend turns green → trend bias is bullish.
Price consolidates inside the yellow EMA Band.
ADX rises above 20 → trend momentum confirmed.
Price closes above the green EMA Band after retesting the band as support.
Entry triggered on candle close, stop below band, target based on risk-reward.
Exit when Supertrend flips red or ADX momentum drops.
This sequence prevents premature entries, keeps trades aligned with trend, and avoids ranging markets.
🎯 Key Features
✅ Multi-layered confirmation for precision trading
✅ Built-in no-trade zone filter
✅ Fully customizable parameters
✅ Clean visuals for quick decision-making
⚠ Disclaimer: This is Version 1. Educational purposes only. Always use with risk management.
Fibs Has Lied 🌟 Fibs Has Lied - Indicator Overview 🌟
Designed for indices like US30, NQ, and SPX, this indicator highlights setups where price interacts with key EMA levels during specific trading sessions (default: 6:30–11:30 AM EST).
🌟 Key Features & Levels 🌟
🔹EMA Crossover Setups
The indicator uses the 100-period and 200-period EMAs to identify bullish and bearish setups:
- Bullish Setup: Triggers when the 100 EMA crosses above the 200 EMA, followed by two consecutive candles opening above the 100 EMA, with the low within a specified point distance (e.g., 20 points for US30).
- Bearish Setup: Triggers when the 100 EMA crosses below the 200 EMA, followed by two consecutive candles opening below the 100 EMA, with the high within the point distance.
- Signals are marked with green (buy) or red (sell) triangles and text, ensuring you don’t miss a setup. 📈
🔹 Reset Conditions for Re-Entries
After an initial setup, the indicator watches for “reset” opportunities:
- Buy Reset: If price moves below the 200 EMA after a bullish crossover, then returns with two consecutive candles where lows are above the 100 EMA (within point distance), a new buy signal is plotted.
- Sell Reset: If price moves above the 200 EMA after a bearish crossover, then returns with two consecutive candles where highs are below the 100 EMA (within point distance), a new sell signal is plotted.
This feature captures additional entries after liquidity grabs or fakeouts, aligning with ICT’s manipulation concepts. 🔄
🔹 Session-Based Filtering
Focus your trades during high-liquidity windows! The default session (6:30–11:30 AM EST, New York timezone) targets the London/NY overlap, where price often seeks liquidity or sets up for reversals. Toggle the time filter off for 24/7 signals if desired. 🕒
🔹Symbol-Specific Point Distance
Customizable entry zones based on your chosen index:
- US30: 20 points from the 100 EMA.
- NQ: 3 points from the 100 EMA.
- SPX: 2.5 points from the 100 EMA.
This ensures setups are tailored to the volatility of your market, maximizing relevance. 🎯
🔹 Market Structure Markers (Optional)
Visualize swing points with pivot-based labels:
- HH (Higher High): Signals uptrend continuation.
- HL (Higher Low): Indicates potential bullish support.
- LH (Lower High): Suggests weakening uptrend or reversal.
- LL (Lower Low): Points to downtrend continuation.
- Toggle these on/off to keep your chart clean while analyzing trend direction. 📊
🔹 EMA Visualization
Optionally plot the 100 EMA (blue) and 200 EMA (red) to see key levels where price reacts. These act as dynamic support/resistance, perfect for spotting liquidity pools or ICT’s Power of 3 setups. ⚖️
🌟 Customization Options 🌟
- Symbol Selection: Choose US30, NQ, or SPX to adjust point distance for entries.
- Time Filter: Enable/disable the 6:30–11:30 AM EST session to focus on high-liquidity periods.
- EMA Display: Toggle 100/200 EMAs on/off to reduce chart clutter.
- Market Structure: Show/hide HH/HL/LH/LL labels for cleaner analysis.
- Signal Markers: Green (buy) and red (sell) triangles with text are auto-plotted for easy identification.
🌟 Usage Tips 🌟
- Best Timeframes: Use on 3m for intraday scalping and 30m for swing trades.
- Combine with ICT Tools: Pair with order blocks, fair value gaps, or kill zones for stronger setups.
- Focus on Session: The default 6:30–11:30 AM EST session captures London/NY volatility—perfect for liquidity-driven moves.
- Avoid Overcrowding: Disable market structure or EMAs if you only want setup signals.
DXY Opening Zones - FixedFull Description:
Overview:
This indicator automates the identification of DXY (Dollar Index) opening zones, a cornerstone of the Funded Trader Academy's "Dixie Open" strategy. It marks the critical gap between market close and open, which acts as a magnetic attraction level for price action throughout the trading day.
Key Features:
✅ Automatic Gap Detection: Identifies opening gaps between market close (6:00 PM EST) and open (7:45 PM EST Sunday, 7:45 PM Mon-Thu)
✅ Smart Zone Expansion: Automatically expands zones when gaps are smaller than 20 pips to include prior candle highs/lows for better trading ranges
✅ Session Highlighting: Visual overlays for London (3 AM - 12 PM EST) and New York (8 AM - 5 PM EST) sessions
✅ Phantom Candle Filter: Ignores glitch/phantom candles smaller than 2 pips to prevent false zones
✅ Time-Based Zone Extension: Zones automatically extend to 5 PM EST (US market close) for full-day relevance
✅ 15-Minute Chart Optimization: Specifically designed for the 15-minute timeframe where the strategy performs best
✅ DXY-Only Protection: Built-in safeguards ensure the indicator only works on Dollar Index symbols
Trading Strategy Context:
The DXY Opening Level strategy capitalizes on the market's tendency to return to opening gaps, offering approximately 70-75% win rate when traded correctly. Best entries occur during London session (after 2:30 AM EST) when volume increases.
Ideal For:
Forex traders using DXY correlation strategies
Mean reversion and gap trading enthusiasts
Traders seeking high-probability setups with defined risk
Those following the Funded Trader Academy methodology
Settings Explained:
Zone Color: Customize the visual appearance of zones
Expand Zone Threshold: Adjust when zones should expand (default 20 pips)
Phantom Filter: Set minimum candle size to consider valid (default 2 pips)
Session Display: Toggle London/NY session backgrounds
Debug Mode: View detailed gap measurements and timing information
Important Notes:
Must be used on 15-minute DXY/Dollar Index charts
Zones mark attraction levels, not direct entry points
Always wait for valid entry signals (engulfing, pin bar, 3-bar reversal)
Trade correlated forex pairs, not DXY directly
Best results during London session (2:30 AM - 12 PM EST)
Risk Disclaimer:
This indicator identifies potential trading zones based on historical patterns. Always use proper risk management and never risk more than you can afford to lose. Past performance does not guarantee future results.
Supertrend - Support & ResistanceSupertrend – Multi-Timeframe Support & Resistance
This script overlays multiple Supertrend bands from higher timeframes on a single chart and treats them as dynamic support and resistance. The goal is simple: see the bigger picture without leaving your current timeframe.
What it does
• Calculates Supertrend using the same ATR Length and Factor across 5m, 15m, 30m, 1h, 4h, 8h, 12h, and 1D.
• Pulls each timeframe via request.security(..., lookahead_off) so values only update on candle close. No look-ahead, no “teleporting” lines.
• Plots each timeframe’s Supertrend as an on-chart band with increasing transparency the higher you go, so you can visually separate short-term vs higher-timeframe structure.
• Colors indicate direction:
• Green = bearish band above price (acting as resistance)
• Red = bullish band below price (acting as support)
• Drops compact labels (5m, 15m, 30m, etc.) every 20 bars right on the corresponding Supertrend level, so you can quickly identify which line belongs to which timeframe.
Why this helps
Supertrend is great for trend definition and trailing stops. But one timeframe alone can whipsaw you. By stacking multiple timeframes:
• Confluence stands out. When several higher-TF bands cluster, price often reacts.
• You see where intraday pullbacks are likely to pause (lower TF bands) and where trend reversals are more meaningful (higher TF bands).
• It’s easier to align entries with the dominant trend while still timing them on your working timeframe.
How it works (quick refresher)
Supertrend uses ATR to offset a median price with a multiplier (Factor). When price crosses the band, direction flips and the trailing line switches sides. This script exposes:
• ATR Length (default 10): sensitivity of the ATR. Smaller = tighter band, more flips. Larger = smoother, fewer flips.
• Factor (default 3.0): multiplier applied to ATR. Larger = wider band, more conservative.
The same settings are used for all timeframes for clean, apples-to-apples comparisons.
How to use it
• Trend alignment: Prefer longs when most higher-TF lines are below price (red support). Prefer shorts when most are above price (green resistance).
• Pullback entries: In an uptrend, look for pullbacks into a lower-TF red band that lines up near a higher-TF red band. That overlap is your “zone.”
• Breakout confirmation: A strong break and close beyond a higher-TF band carries more weight than a lower-TF poke.
• Stops and targets: Use the nearest opposing band as a logic point. For example, in a long, if price loses the lower-TF red band and the next higher-TF band is close overhead, trim or tighten.
Signals you can read at a glance
• Stacking: Multiple red bands beneath price = strong bullish structure. Multiple green bands above price = strong bearish structure.
• Compression: Bands from different TFs squeezing together often precede expansion.
• Flip zones: When a higher-TF band flips side, treat that level as newly minted support/resistance.
Design choices in the code
• lookahead_off on all request.security calls avoids repainting from future data.
• Increasing transparency as the timeframe rises makes lower-TF context visible without drowning the chart.
• Labels every 20 bars keep the chart readable while still giving you frequent anchors.
Good to know (limits and tips)
• This is an overlay of closed-bar Supertrend values from higher TFs. Intrabar moves can still exceed a band before close; final signal prints at candle close of that timeframe.
• Using the same ATR/factor across TFs makes confluence easier to judge. If you need independent tuning per TF, you can clone the security calls and add separate inputs.
• On very low timeframes with many symbols, multiple request.security calls can be heavy. If performance drops, hide one or two higher TFs or increase the label spacing.
Risk note
This is a context tool, not an auto-trader. Combine it with structure (HH/HL vs LH/LL), volume, and your execution rules. Always test on your market and timeframe before committing real capital.
Multi - Timeframe 3 EMA Bull/Bear Table此指标是一个图标指标，适用于短线交易以及中线交易，它明确的显示出来了用EMA来表示方向指示，1分钟不可使用，此图表更新了多次以及修改了多次，在实际回测中有明显的提醒作用，不过多用于参考，不可作为主要指标使用，代码稍复杂如有加以改进的地方请提出，其中核心使用了EMA的20，50，200周期来作为参考，目的是能识别多周期和时间的方向指示，注意：此指标建议仅用于方向参考，不用于主要指标交易。
This indicator is a graphical indicator suitable for short-term and medium-term trading. It clearly shows the direction indicated by the EMA. It cannot be used for 1-minute intervals. This chart has been updated and modified multiple times, and it has a significant alerting effect in actual backtesting; however, it is mainly for reference and should not be used as the primary indicator. The code is somewhat complex, so please suggest improvements if there are any. The core uses the 20, 50, and 200 EMA periods as references, with the aim of identifying the direction indicators across multiple periods and timeframes. Note: This indicator is recommended only for directional reference and not for main indicator trading.
Breakout Volume Momentum [5m]Breakout Volume Momentum Indicator (Pine Script v5)
This TradingView Pine Script v5 indicator plots a green dot below a 5-minute price bar whenever all the breakout and volume conditions are met. It is optimized for live intraday trading (not backtesting) and includes customizable inputs for thresholds and trading session times. Key features and conditions of this indicator:
Gap Up Threshold: Current price is up at least X% (default 20%) from the previous day’s close (uses higher-timeframe daily data) before any signal can trigger.
Relative Volume (RVOL): Current bar’s volume is at least Y× (default 2×) the average volume of the last 20 bars. This ensures unusually high volume is present, indicating strong interest.
Trend Alignment: Price is trading above the VWAP (Volume-Weighted Average Price) and above a fast EMA. In addition, the fast EMA (default 9) is above the slower EMA (default 20) to confirm bullish momentum
tradingview.com
tradingview.com
. These filters ensure the stock is in an intraday uptrend (above the average price and rising EMAs).
Intraday Breakout (optional): Optionally require the price to break above the recent intraday high (default last 30 bars). If enabled, a signal only occurs when the stock exceeds its prior range high, confirming a breakout. This can be toggled on/off in the settings.
Avoid Parabolic Spikes: The script skips any bar with an excessively large range (default >12% from low to high), to avoid triggering on spiky or unsustainable parabolic candles.
Time Window Filter: Signals are restricted to a specific session window (by default 09:30 – 11:00 exchange time, typically the morning session) and will not trigger outside these hours. The session window is adjustable via inputs
stackoverflow.com
.
Alerts: An alert condition is provided so you can set a Trading View alert to send a push notification when a green dot signal fires. The alert message includes the ticker and price at the time of signal.
IU Indicators DashboardDESCRIPTION
The IU Indicators Dashboard is a comprehensive multi-stock monitoring tool that provides real-time technical analysis for up to 10 different stocks simultaneously. This powerful indicator creates a customizable table overlay that displays the trend status of multiple technical indicators across your selected stocks, giving you an instant overview of market conditions without switching between charts.
Perfect for portfolio monitoring, sector analysis, and quick market screening, this dashboard consolidates critical technical data into one easy-to-read interface with color-coded trend signals.
USER INPUTS
Stock Selection (10 Configurable Stocks):
- Stock 1-10: Customize any symbols (Default: NSE:CDSL, NSE:RELIANCE, NSE:VEDL, NSE:TCS, NSE:BEL, NSE:BHEL, NSE:TATAPOWER, NSE:TATASTEEL, NSE:ITC, NSE:LT)
Technical Indicator Parameters:
- EMA 1 Length: First Exponential Moving Average period (Default: 20)
- EMA 2 Length: Second Exponential Moving Average period (Default: 50)
- EMA 3 Length: Third Exponential Moving Average period (Default: 200)
- RSI Length: Relative Strength Index calculation period (Default: 14)
- SuperTrend Length: SuperTrend indicator period (Default: 10)
- SuperTrend Factor: SuperTrend multiplier factor (Default: 3.0)
Visual Customization:
- Table Size: Choose from Normal, Tiny, Small, or Large
- Table Background Color: Customize dashboard background
- Table Frame Color: Set frame border color
- Table Border Color: Configure border styling
- Text Color: Set text display color
- Bullish Color: Color for positive/bullish signals (Default: Green)
- Bearish Color: Color for negative/bearish signals (Default: Red)
LOGIC OF THE INDICATOR
The dashboard employs a multi-timeframe analysis approach using five key technical indicators:
1. Triple EMA Analysis
- Compares current price against three different EMA periods (20, 50, 200)
- Bullish Signal: Price above EMA level
- Bearish Signal: Price below EMA level
- Provides short-term, medium-term, and long-term trend perspective
2. RSI Momentum Analysis
- Uses 14-period RSI with 50-level threshold
- Bullish Signal: RSI > 50 (upward momentum)
- Bearish Signal: RSI < 50 (downward momentum)
- Identifies momentum strength and potential reversals
3. SuperTrend Direction
- Utilizes SuperTrend with configurable length and factor
- Bullish Signal: SuperTrend direction = -1 (uptrend)
- Bearish Signal: SuperTrend direction = 1 (downtrend)
- Provides clear trend direction with volatility-adjusted signals
4. MACD Histogram Analysis
- Uses standard MACD (12, 26, 9) histogram values
- Bullish Signal: Histogram > 0 (bullish momentum)
- Bearish Signal: Histogram < 0 (bearish momentum)
- Identifies momentum shifts and trend confirmations
5. Real-time Data Processing
- Implements request.security() for multi-symbol data retrieval
- Uses barstate.isrealtime logic for accurate live data
- Processes data only on the last bar for optimal performance
WHY IT IS UNIQUE
Multi-Stock Monitoring
- Monitor up to 10 different stocks simultaneously on a single chart
- No need to switch between multiple charts or timeframes
Highly Customizable Interface
- Full color customization for personalized visual experience
- Adjustable table size and positioning
- Clean, professional dashboard design
Real-time Analysis
- Live data processing with proper real-time handling
- Instant visual feedback through color-coded signals
- Optimized performance with smart data retrieval
Comprehensive Technical Coverage
- Combines trend-following, momentum, and volatility indicators
- Multiple timeframe perspective through different EMA periods
- Balanced approach using both lagging and leading indicators
Flexible Configuration
- Easy symbol switching for different markets (NSE, BSE, NYSE, NASDAQ)
- Adjustable indicator parameters for different trading styles
- Suitable for both swing trading and position trading
HOW USERS CAN BENEFIT FROM IT
Portfolio Management
- Quick Portfolio Health Check: Instantly assess the technical status of your entire stock portfolio
- Diversification Analysis: Monitor stocks across different sectors to ensure balanced exposure
- Risk Management: Identify which positions are showing bearish signals for potential exit strategies
- Rebalancing Decisions: Spot strongest performers for potential position increases
Market Screening and Analysis
- Sector Rotation: Compare different sector stocks to identify rotation opportunities
- Relative Strength Analysis: Quickly identify which stocks are outperforming or underperforming
- Market Breadth Assessment: Gauge overall market sentiment by monitoring diverse stock selections
- Trend Confirmation: Validate market trends by observing multiple stock behaviors
Time-Efficient Trading
- Single-Glance Analysis: Get complete technical overview without chart-hopping
- Pre-Market Preparation: Quickly assess overnight changes across multiple positions
- Intraday Monitoring: Track multiple opportunities simultaneously during trading hours
- End-of-Day Review: Efficiently review all watched stocks for next-day planning
Strategic Decision Making
- Entry Point Identification: Spot stocks showing bullish alignment across multiple indicators
- Exit Signal Recognition: Identify positions showing deteriorating technical conditions
- Swing Trading Opportunities: Find stocks with favorable technical setups for swing trades
- Long-term Investment Guidance: Use 200 EMA signals for long-term position decisions
Educational Benefits
- Pattern Recognition: Learn how different indicators behave across various market conditions
- Correlation Analysis: Understand how stocks move relative to each other
- Technical Analysis Learning: Observe multiple indicator interactions in real-time
- Market Sentiment Understanding: Develop better market timing skills through multi-stock observation
Workflow Optimization
- Reduced Chart Clutter: Keep your main chart clean while monitoring multiple stocks
- Faster Analysis: Complete technical analysis of 10 stocks in seconds instead of minutes
- Consistent Methodology: Apply the same technical criteria across all monitored stocks
- Alert Integration: Easy visual identification of stocks requiring immediate attention
This indicator is designed for traders and investors who want to maximize their market awareness while minimizing analysis time. Whether you're managing a portfolio, screening for opportunities, or learning technical analysis, the IU Indicators Dashboard provides the comprehensive overview you need for better trading decisions.
DISCLAIMER :
This indicator is not financial advice, it's for educational purposes only highlighting the power of coding( pine script) in TradingView, I am not a SEBI-registered advisor. Trading and investing involve risk, and you should consult with a qualified financial advisor before making any trading decisions. I do not guarantee profits or take responsibility for any losses you may incur.
Multi-EMAMulti-EMA Indicator
This script plots five commonly used Exponential Moving Averages (EMAs) on your chart for trend identification and trade timing.
Included EMAs & Colors:
EMA 8 — Red
EMA 20 — Orange
EMA 50 — Yellow
EMA 100 — Cyan
EMA 200 — Blue
How to use:
Shorter EMAs (8 & 20) help identify short-term momentum and potential entry/exit points.
Mid-range EMA (50) gives a broader view of intermediate trends.
Longer EMAs (100 & 200) are used to confirm long-term trend direction and key support/resistance zones.
Crossovers between EMAs can signal potential trend changes.
Price trading above most EMAs often signals bullish conditions, while trading below suggests bearish momentum.
Designed to work on any timeframe or market.
Defense Mode Dashboard ProWhat it is
A one‑look market regime dashboard for ES, NQ, YM, RTY, and SPY that tells you when to play defense, when you might have an offense cue, and when to chill. It blends VIX, VIX term structure, ATR 5 over 60, and session gap signals with clean alerts and a compact table you can park anywhere.
Why traders like it
Because it filters out the noise. Regime first, tactics second. You avoid trading size into landmines and lean in when volatility cooperates.
What it measures
Volatility stress with VIX level and VIX vs 20‑SMA
Term structure using VX1 vs VX2 with two modes
Diff mode: VX1 minus VX2
Ratio mode: VX1 divided by VX2
Realized volatility using ATR5 over ATR60 with optional smoothing
Session risk from RTH opening gaps and overnight range, normalized by ATR
How to use in 30 seconds
Pick a preset in the inputs. ES, NQ, YM, RTY, SPY are ready.
Leave thresholds at defaults to start.
Add one TradingView alert using “Any alert() function call”.
Trade smaller or stand aside when the header reads DEFENSE ON. Consider leaning in only when you see OFFENSE CUE and your playbook agrees.
Defaults we recommend
VIX triggers: 22 and 1.25× the 20‑SMA
Term mode: Diff with tolerance 0.00. Use Ratio at 1.00+ for choppier markets
ATR 5/60 defense: 1.25. Offense cue: 0.85 or lower
ATR smoothing: 1. Try 2 to 3 if you want fewer flips
Gap mode: RTH. Turn Both on if you want ON range to count too
RTH wild gap: 0.60× ATR5. ON wild range: 0.80× ATR5
Alert cadence: Once per RTH session
Snooze: Quick snooze first 30 minutes on. Fire on snooze exit off, unless you really want the catch‑up ping
New since the last description
Multi‑asset presets set symbols and RTH windows for ES, NQ, YM, RTY, SPY
Term ratio mode with near‑flat warning when ratio is between 1.00 and your trigger
ATR smoothing for the 5 over 60 ratio
RTH keying for cadence, so “Once per RTH session” behaves like a trader expects
Snooze upgrades with quick snooze tied to the first N minutes of RTH and an optional fire‑on‑snooze‑exit
Compact title merge and user color controls for labels, values, borders, and background
Exposed series for integrations: DefenseOn(1=yes) and OffenseCue(1=yes)
Debug toggle to visualize gap points, ON range, and term readings
Stronger NA handling with a clear “No core data” row when feeds are missing
Notes
Dynamic alerts require “Any alert() function call”.
Works on any chart timeframe. Daily reads and 1‑minute anchors handle the regime logic.
Return Volatility (σ) — auto-annualized [v6]Overview
This indicator calculates and visualizes the return-based volatility (standard deviation) of any asset, automatically adjusting for your chart's timeframe to provide both absolute and annualized volatility values.
It’s designed for traders who want to filter trades, adjust position sizing, and detect volatility events based on statistically significant changes in market activity.
Key Features
Absolute Volatility (abs σ%) – Standard deviation of returns for the current timeframe (e.g., 1H, 4H, 1D).
Annualized Volatility (ann σ%) – Converts abs σ% into an annualized figure for easier cross-timeframe and cross-asset comparison.
Relative Volatility (rel σ) – Ratio of current volatility to the long-term average (default: 120 periods).
Z-Score – Number of standard deviations the current volatility is above or below its historical average.
Auto-Timeframe Adjustment – Detects your chart’s bar size (seconds per bar) and calculates bars/year automatically for crypto’s 24/7 market.
Highlight Mode – Optional yellow background when volatility exceeds set thresholds (rel σ ≥ threshold OR z-score ≥ threshold).
Alert Conditions – Alerts trigger when relative volatility or z-score exceed defined limits.
How It Works
Return Calculation
Log returns: ln(Pt / Pt-1) (default)
or Simple returns: (Pt / Pt-1) – 1
Volatility Measurement
Standard deviation of returns over the lookback period N (default: 20 bars).
Absolute volatility = σ × 100 (% per bar).
Annualization
Uses: σₐₙₙ = σ × √(bars/year) × 100 (%)
Bars/year auto-calculated based on timeframe:
1H = 8,760 bars/year
4H ≈ 2,190 bars/year
1D = 365 bars/year
Relative and Statistical Context
Relative σ = Current σ / Historical average σ (baseLen, default: 120)
Z-score = (Current σ – Historical average σ) / Std. dev. of σ over baseLen
Trading Applications
Volatility Filter – Only allow trade entries when volatility exceeds historical norms (trend traders often benefit from this).
Risk Management – Reduce position size during high volatility spikes to manage risk; increase size in low-volatility trending environments.
Market Scanning – Identify assets with the highest relative volatility for momentum or breakout strategies.
Event Detection – Highlight significant volatility surges that may precede large moves.
Suggested Settings
Lookback (N): 20 bars for short/medium-term trading.
Base Length (M): 120 bars to establish long-term volatility baseline.
Relative Threshold: 1.5× baseline σ.
Z-score Threshold: ≥ 2.0 for statistically significant volatility shifts.
Use Log Returns: Recommended for more consistent scaling across prices.
Notes & Limitations
Volatility measures movement magnitude, not direction. Combine with trend or momentum filters for directional bias.
Very low volatility may still produce false breakouts; combine with volume and market structure analysis.
Crypto markets trade 24/7 — annualization assumes no market closures; adjust for other asset classes if needed.
💡 Best Practice: Use this indicator as a pre-trade filter for breakout or trend-following strategies, or as a risk control overlay in mean-reversion systems.
Cycle Phase & ETA Tracker [Robust v4]
Cycle Phase & ETA Tracker
Description
The Cycle Phase & ETA Tracker is a powerful tool for analyzing market cycles and predicting the completion of the current cycle (Estimated Time of Arrival, or ETA). It visualizes the cycle phase (0–100%) using a smoothed signal and displays the forecasted completion date with an optional confidence band based on cycle length variability. Ideal for traders looking to time their trades based on cyclical patterns, this indicator offers flexible settings for robust cycle analysis.
Key Features
Cycle Phase Visualization: Tracks the current cycle phase (0–100%) with color-coded zones: green (0–33%), blue (33–66%), orange (66–100%).
ETA Forecast: Shows a vertical line and label indicating the estimated date of cycle completion.
Confidence Band (±σ): Displays a band around the ETA to reflect uncertainty, calculated using the standard deviation of cycle lengths.
Multiple Averaging Methods: Choose from three methods to calculate average cycle length:
Median (Robust): Uses the median for resilience against outliers.
Weighted Mean: Prioritizes recent cycles with linear or quadratic weights.
Simple Mean: Applies equal weights to all cycles.
Adaptive Cycle Length: Automatically adjusts cycle length based on the timeframe or allows a fixed length.
Debug Histogram: Optionally displays the smoothed signal for diagnostic purposes.
Setup and Usage
Add the Indicator:
Search for "Cycle Phase & ETA Tracker " in TradingView’s indicator library and apply it to your chart.
Configure Parameters:
Core Settings:
Track Last N Cycles: Sets the number of recent cycles used to calculate the average cycle length (default: 20). Higher values provide stability but may lag market shifts.
Source: Selects the data source for analysis (e.g., close, open, high; default: close price).
Use Adaptive Cycle Length?: Enables automatic cycle length adjustment based on timeframe (e.g., shorter for intraday, longer for daily) or uses a fixed length if disabled.
Fixed Cycle Length: Defines the cycle length in bars when adaptive mode is off (default: 14). Smaller values increase sensitivity to short-term cycles.
Show Debug Histogram: Enables a histogram of the smoothed signal for debugging signal behavior.
Cycle Length Estimation:
Average Mode: Selects the method for calculating average cycle length: "Median (Robust)", "Weighted Mean", or "Simple Mean".
Weights (for Weighted Mean): For "Weighted Mean", chooses "linear" (moderate emphasis on recent cycles) or "quadratic" (strong emphasis on recent cycles).
ETA Visualization:
Show ETA Line & Label: Toggles the display of the ETA line and date label.
Show ETA Confidence Band (±σ): Toggles the confidence band around the ETA, showing the uncertainty range.
Band Transparency: Adjusts the transparency of the confidence band (0 = fully transparent, 100 = fully opaque; default: 85).
ETA Color: Sets the color for the ETA line, label, and confidence band (default: orange).
Interpretation:
The cycle phase (0–100%) indicates progress: green for the start, blue for the middle, and orange for the end of the cycle.
The ETA line and label show the predicted cycle completion date.
The confidence band reflects the uncertainty range (±1 standard deviation) of the ETA.
If a warning "Insufficient cycles for ETA" appears, wait for the indicator to collect at least 3 cycles.
Limitations
Requires at least 3 cycles for reliable ETA and confidence band calculations.
On low timeframes or low-volatility markets, zero-crossings may be infrequent, delaying ETA updates.
Accuracy depends on proper cycle length settings (adaptive or fixed).
Notes
Test the indicator across different assets and timeframes to optimize settings.
Use the debug histogram to troubleshoot if the ETA appears inaccurate.
For feedback or suggestions, contact the author via TradingView.
Cycle Phase & ETA Tracker
Описание
Индикатор Cycle Phase & ETA Tracker предназначен для анализа рыночных циклов и прогнозирования времени завершения текущего цикла (ETA — Estimated Time of Arrival). Он отслеживает фазы цикла (0–100%) на основе сглаженного сигнала и отображает предполагаемую дату завершения цикла с опциональной доверительной полосой, основанной на стандартном отклонении длин циклов. Индикатор идеально подходит для трейдеров, которые хотят выявлять циклические закономерности и планировать свои действия на основе прогнозируемого времени.
Ключевые особенности
Фазы цикла: Визуализирует текущую фазу цикла (0–100%) с цветовой кодировкой: зеленый (0–33%), синий (33–66%), оранжевый (66–100%).
Прогноз ETA: Показывает вертикальную линию и метку с предполагаемой датой завершения цикла.
Доверительная полоса (±σ): Отображает зону неопределенности вокруг ETA, основанную на стандартном отклонении длин циклов.
Гибкие методы усреднения: Поддерживает три метода расчета средней длины цикла:
Median (Robust): Медиана, устойчивая к выбросам.
Weighted Mean: Взвешенное среднее, где недавние циклы имеют больший вес (линейный или квадратичный).
Simple Mean: Простое среднее с равными весами.
Адаптивная длина цикла: Автоматически подстраивает длину цикла под таймфрейм или позволяет задать фиксированную длину.
Отладочная гистограмма: Опционально отображает сглаженный сигнал для анализа.
Настройка и использование
Добавьте индикатор:
Найдите "Cycle Phase & ETA Tracker " в библиотеке индикаторов TradingView и добавьте его на график.
Настройте параметры:
Core Settings:
Track Last N Cycles: Количество последних циклов для расчета средней длины (по умолчанию 20). Большие значения дают более стабильные результаты, но могут запаздывать.
Source: Источник данных (по умолчанию цена закрытия).
Use Adaptive Cycle Length?: Включите для автоматической настройки длины цикла по таймфрейму или отключите для использования фиксированной длины.
Fixed Cycle Length: Длина цикла в барах, если адаптивная длина отключена (по умолчанию 14).
Show Debug Histogram: Включите для отображения сглаженного сигнала (полезно для отладки).
Cycle Length Estimation:
Average Mode: Выберите метод усреднения: "Median (Robust)", "Weighted Mean" или "Simple Mean".
Weights (for Weighted Mean): Для режима "Weighted Mean" выберите "linear" (умеренный вес для новых циклов) или "quadratic" (сильный вес для новых циклов).
ETA Visualization:
Show ETA Line & Label: Включите для отображения линии и метки ETA.
Show ETA Confidence Band (±σ): Включите для отображения доверительной полосы.
Band Transparency: Прозрачность полосы (0 — полностью прозрачная, 100 — полностью непрозрачная, по умолчанию 85).
ETA Color: Цвет для линии, метки и полосы (по умолчанию оранжевый).
Интерпретация:
Фаза цикла (0–100%) показывает прогресс текущего цикла: зеленый — начало, синий — середина, оранжевый — конец.
Линия и метка ETA указывают предполагаемую дату завершения цикла.
Доверительная полоса показывает диапазон неопределенности (±1 стандартное отклонение).
Если отображается предупреждение "Insufficient cycles for ETA", дождитесь, пока индикатор соберет минимум 3 цикла.
Ограничения
Требуется минимум 3 цикла для надежного расчета ETA и доверительной полосы.
На низких таймфреймах или рынках с низкой волатильностью пересечения нуля могут быть редкими, что замедляет обновление ETA.
Эффективность зависит от правильной настройки длины цикла (fixedL или адаптивной).
Примечания
Протестируйте индикатор на разных таймфреймах и активах, чтобы подобрать оптимальные параметры.
Используйте отладочную гистограмму для анализа сигнала, если ETA кажется неточным.
Для вопросов или предложений по улучшению свяжитесь через TradingView.
Keltner Channel Based Grid Strategy # KC Grid Strategy - Keltner Channel Based Grid Trading System
## Strategy Overview
KC Grid Strategy is an innovative grid trading system that combines the power of Keltner Channels with dynamic position sizing to create a mean-reversion trading approach. This strategy automatically adjusts position sizes based on price deviation from the Keltner Channel center line, implementing a systematic grid-based approach that capitalizes on market volatility and price oscillations.
## Core Principles
### Keltner Channel Foundation
The strategy builds upon the Keltner Channel indicator, which consists of:
- **Center Line**: Moving average (EMA or SMA) of the price
- **Upper Band**: Center line + (ATR/TR/Range × Multiplier)
- **Lower Band**: Center line - (ATR/TR/Range × Multiplier)
### Grid Trading Logic
The strategy implements a sophisticated grid system where:
1. **Position Direction**: Inversely correlated to price position within the channel
- When price is above center line → Short positions
- When price is below center line → Long positions
2. **Position Size**: Proportional to distance from center line
- Greater deviation = Larger position size
3. **Grid Activation**: Positions are adjusted only when the difference exceeds a predefined grid threshold
### Mathematical Foundation
The core calculation uses the KC Rate formula:
```
kcRate = (close - ma) / bandWidth
targetPosition = kcRate × maxAmount × (-1)
```
This creates a mean-reversion system where positions increase as price moves further from the mean, expecting eventual return to equilibrium.
## Parameter Guide
### Time Range Settings
- **Start Date**: Beginning of strategy execution period
- **End Date**: End of strategy execution period
### Core Parameters
1. **Number of Grids (NumGrid)**: Default 12
- Controls grid sensitivity and position adjustment frequency
- Higher values = More frequent but smaller adjustments
- Lower values = Less frequent but larger adjustments
2. **Length**: Default 10
- Period for moving average and volatility calculations
- Shorter periods = More responsive to recent price action
- Longer periods = Smoother, less noisy signals
3. **Grid Coefficient (kcRateMult)**: Default 1.33
- Multiplier for channel width calculation
- Higher values = Wider channels, less frequent trades
- Lower values = Narrower channels, more frequent trades
4. **Source**: Default Close
- Price source for calculations (Close, Open, High, Low, etc.)
- Close price typically provides most reliable signals
5. **Use Exponential MA**: Default True
- True = Uses EMA (more responsive to recent prices)
- False = Uses SMA (equal weight to all periods)
6. **Bands Style**: Default "Average True Range"
- **Average True Range**: Smoothed volatility measure (recommended)
- **True Range**: Current bar's volatility only
- **Range**: Simple high-low difference
## How to Use
### Setup Instructions
1. **Apply to Chart**: Add the strategy to your desired timeframe and instrument
2. **Configure Parameters**: Adjust settings based on market characteristics:
- Volatile markets: Increase Grid Coefficient, reduce Number of Grids
- Stable markets: Decrease Grid Coefficient, increase Number of Grids
3. **Set Time Range**: Define your backtesting or live trading period
4. **Monitor Performance**: Watch strategy performance metrics and adjust as needed
### Optimal Market Conditions
- **Range-bound markets**: Strategy performs best in sideways trending markets
- **High volatility**: Benefits from frequent price oscillations around the mean
- **Liquid instruments**: Ensures efficient order execution and minimal slippage
### Position Management
The strategy automatically:
- Calculates optimal position sizes based on account equity
- Adjusts positions incrementally as price moves through grid levels
- Maintains risk control through maximum position limits
- Executes trades only during specified time periods
## Risk Warnings
### ⚠️ Important Risk Considerations
1. **Trending Market Risk**:
- Strategy may underperform or generate losses in strong trending markets
- Mean-reversion assumption may fail during sustained directional moves
- Consider market regime analysis before deployment
2. **Leverage and Position Size Risk**:
- Strategy uses pyramiding (up to 20 positions)
- Large positions may accumulate during extended moves
- Monitor account equity and margin requirements closely
3. **Volatility Risk**:
- Sudden volatility spikes may trigger multiple rapid position adjustments
- Consider volatility filters during high-impact news events
- Backtest across different volatility regimes
4. **Execution Risk**:
- Strategy calculates on every tick (calc_on_every_tick = true)
- May generate frequent orders in volatile conditions
- Ensure adequate execution infrastructure and consider transaction costs
5. **Parameter Sensitivity**:
- Performance highly dependent on parameter optimization
- Over-optimization may lead to curve-fitting
- Regular parameter review and adjustment may be necessary
## Suitable Scenarios
### Ideal Market Conditions
- **Sideways/Range-bound markets**: Primary use case
- **Mean-reverting instruments**: Forex pairs, some commodities
- **Stable volatility environments**: Consistent ATR patterns
- **Liquid markets**: Major currency pairs, popular stocks/indices
## Important Notes
### Strategy Limitations
1. **No Stop Loss**: Strategy relies on mean reversion without traditional stop losses
2. **Capital Requirements**: Requires sufficient capital for grid-based position sizing
3. **Market Regime Dependency**: Performance varies significantly across different market conditions
## Disclaimer
This strategy is provided for educational and research purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Users should thoroughly test the strategy and understand its mechanics before risking real capital. The author assumes no responsibility for trading losses incurred through the use of this strategy.
---
# KC网格策略 - 基于肯特纳通道的网格交易系统
## 策略概述
KC网格策略是一个创新的网格交易系统，它将肯特纳通道的力量与动态仓位调整相结合，创建了一个均值回归交易方法。该策略根据价格偏离肯特纳通道中心线的程度自动调整仓位大小，实施系统化的网格方法，利用市场波动和价格振荡获利。
## 核心原理
### 肯特纳通道基础
该策略建立在肯特纳通道指标之上，包含：
- **中心线**: 价格的移动平均线（EMA或SMA）
- **上轨**: 中心线 + (ATR/TR/Range × 乘数)
- **下轨**: 中心线 - (ATR/TR/Range × 乘数)
### 网格交易逻辑
该策略实施复杂的网格系统：
1. **仓位方向**: 与价格在通道中的位置呈反向关系
- 当价格高于中心线时 → 空头仓位
- 当价格低于中心线时 → 多头仓位
2. **仓位大小**: 与距离中心线的距离成正比
- 偏离越大 = 仓位越大
3. **网格激活**: 只有当差异超过预定义的网格阈值时才调整仓位
### 数学基础
核心计算使用KC比率公式：
```
kcRate = (close - ma) / bandWidth
targetPosition = kcRate × maxAmount × (-1)
```
这创建了一个均值回归系统，当价格偏离均值越远时仓位越大，期望最终回归均衡。
## 参数说明
### 时间范围设置
- **开始日期**: 策略执行期间的开始时间
- **结束日期**: 策略执行期间的结束时间
### 核心参数
1. **网格数量 (NumGrid)**: 默认12
- 控制网格敏感度和仓位调整频率
- 较高值 = 更频繁但较小的调整
- 较低值 = 较少频繁但较大的调整
2. **长度**: 默认10
- 移动平均线和波动率计算的周期
- 较短周期 = 对近期价格行为更敏感
- 较长周期 = 更平滑，噪音更少的信号
3. **网格系数 (kcRateMult)**: 默认1.33
- 通道宽度计算的乘数
- 较高值 = 更宽的通道，较少频繁的交易
- 较低值 = 更窄的通道，更频繁的交易
4. **数据源**: 默认收盘价
- 计算的价格来源（收盘价、开盘价、最高价、最低价等）
- 收盘价通常提供最可靠的信号
5. **使用指数移动平均**: 默认True
- True = 使用EMA（对近期价格更敏感）
- False = 使用SMA（对所有周期等权重）
6. **通道样式**: 默认"平均真实范围"
- **平均真实范围**: 平滑的波动率测量（推荐）
- **真实范围**: 仅当前K线的波动率
- **范围**: 简单的高低价差
## 使用方法
### 设置说明
1. **应用到图表**: 将策略添加到您所需的时间框架和交易品种
2. **配置参数**: 根据市场特征调整设置：
- 波动市场：增加网格系数，减少网格数量
- 稳定市场：减少网格系数，增加网格数量
3. **设置时间范围**: 定义您的回测或实盘交易期间
4. **监控表现**: 观察策略表现指标并根据需要调整
### 最佳市场条件
- **区间震荡市场**: 策略在横盘趋势市场中表现最佳
- **高波动性**: 受益于围绕均值的频繁价格振荡
- **流动性强的品种**: 确保高效的订单执行和最小滑点
### 仓位管理
策略自动：
- 根据账户权益计算最优仓位大小
- 随着价格在网格水平移动逐步调整仓位
- 通过最大仓位限制维持风险控制
- 仅在指定时间段内执行交易
## 风险警示
### ⚠️ 重要风险考虑
1. **趋势市场风险**:
- 策略在强趋势市场中可能表现不佳或产生损失
- 在持续方向性移动期间均值回归假设可能失效
- 部署前考虑市场制度分析
2. **杠杆和仓位大小风险**:
- 策略使用金字塔加仓（最多20个仓位）
- 在延长移动期间可能积累大仓位
- 密切监控账户权益和保证金要求
3. **波动性风险**:
- 突然的波动性激增可能触发多次快速仓位调整
- 在高影响新闻事件期间考虑波动性过滤器
- 在不同波动性制度下进行回测
4. **执行风险**:
- 策略在每个tick上计算（calc_on_every_tick = true）
- 在波动条件下可能产生频繁订单
- 确保充足的执行基础设施并考虑交易成本
5. **参数敏感性**:
- 表现高度依赖于参数优化
- 过度优化可能导致曲线拟合
- 可能需要定期参数审查和调整
## 适用场景
### 理想市场条件
- **横盘/区间震荡市场**: 主要用例
- **均值回归品种**: 外汇对，某些商品
- **稳定波动性环境**: 一致的ATR模式
- **流动性市场**: 主要货币对，热门股票/指数
## 注意事项
### 策略限制
1. **无止损**: 策略依赖均值回归而无传统止损
2. **资金要求**: 需要充足资金进行基于网格的仓位调整
3. **市场制度依赖性**: 在不同市场条件下表现差异显著
## 免责声明
该策略仅供教育和研究目的。过往表现不保证未来结果。交易涉及重大损失风险，并非适合所有投资者。用户应在投入真实资金前彻底测试策略并理解其机制。作者对使用此策略产生的交易损失不承担任何责任。
---
**Strategy Version**: Pine Script v6
**Author**: Signal2Trade
**Last Updated**: 2025-8-9
**License**: Open Source (Mozilla Public License 2.0)
ATR%指標概要 / Overview
ATR Percentage (MTF)：把 ATR 轉為百分比（ATR%）或保留為絕對值，並在該「波動序列」上套用布林帶。支援多週期（MTF）計算：例如在 5 分圖顯示 4H / D1 的 ATR%。內建白色點狀水平線作為固定門檻（預設 1%）。
ATR Percentage (MTF): Converts ATR to a percentage of price (ATR%) or keeps it as absolute ATR, then applies Bollinger Bands on this volatility series. Supports multi-timeframe (MTF) calculation (e.g., show 4H/D1 ATR% on a 5-min chart). Includes a configurable white dotted horizontal threshold line (default 1%).
⸻
設計目的 / Purpose
• 以 ATR% 衡量相對波動，利於跨品種比較。
Use ATR% for relative volatility to compare across markets.
• 以 布林帶 標示「高/低波動區」，觀察擴張與壓縮。
Use Bollinger Bands on volatility to highlight expansion/squeeze.
• 提供 固定閾值（1%） 作為策略濾網或告警門檻。
Provide a fixed threshold (1%) for filters/alerts.
• 以 MTF 方式，讓低週期策略用高週期波動做濾網。
MTF lets lower-TF strategies filter by higher-TF volatility.
⸻
參數說明 / Inputs
• Use ATR as % of Close：切換 ATR（絕對值）/ ATR%（建議）。
Toggle between absolute ATR and ATR% (recommended).
• ATR Periods：ATR 計算長度（預設 22）。
ATR lookback (default 22).
• Show Bollinger Bands / BB Periods / StdDev：布林帶開關、長度與倍數（預設 20 / 2）。
Bollinger Bands on/off, length, and deviation (default 20 / 2).
• Source Timeframe：計算用週期（如 60、240、D、W；留空/Chart ＝ 跟隨圖表）。
Timeframe used for calculations (e.g., 60, 240, D, W; empty/“Chart” = current).
• Threshold Line (%)：白色點線門檻，預設 1.0（即 1%）。
White dotted threshold line, default 1.0 (1%).
提醒：當 非 ATR% 模式時，Threshold 值代表「價格單位」而非百分比。
Note: In non-ATR% mode, the threshold is in price units, not percent.
⸻
訊號解讀 / How to Read
• ATR% > 上軌：波動顯著擴張（趨勢啟動或加速常見）。
ATR% above upper band: significant expansion; often trend ignition/acceleration.
• ATR% < 下軌：波動明顯壓縮（常見於突破前）。
ATR% below lower band: volatility squeeze; often precedes breakouts.
• ATR% 穿越 Threshold（1%）：達到固定波動標準，可作策略開關或風控分水嶺。
ATR% crossing the 1% threshold: fixed volatility bar for filters/risk gates.
⸻
內建告警 / Built-in Alerts
• Volatility Breakout (MTF)：ATR/ATR% 向上穿越上軌。
Triggers when ATR/ATR% crosses above the upper band.
• Volatility Squeeze (MTF)：ATR/ATR% 向下穿越下軌。
Triggers when ATR/ATR% crosses below the lower band.
⸻
使用建議 / Suggested Uses
• 當沖濾網：於 1–5 分圖選擇 4H / D1 作為 Source Timeframe；僅在 ATR% > 1% 且位於中線以上時允許趨勢進場。
Intraday filter: on 1–5m charts, set 4H/D1 as source TF; allow trend entries only when ATR% > 1% and above the midline.
• 突破前偵測：ATR% 長時間貼近下軌 → 留意可能的波動擴張。
Pre-breakout scan: prolonged ATR% near lower band can foreshadow expansion.
• 跨品種比較：用 ATR% 統一指數、外匯、商品的波動刻度。
Cross-asset comparison: ATR% normalizes volatility across indices/FX/commodities.
⸻
已知限制 / Notes
• MTF 對齊：使用 request.security() 對映高週期資料到當前圖表；在歷史回補與即時邊界棒可能略有差異。
MTF alignment: request.security() maps higher-TF data; boundary bars may differ slightly between historical and realtime.
• 百分比分母：ATR% 的分母為同一週期的 close；若需更平滑可改 ATR / SMA(close, N) × 100。
Denominator: ATR% uses same-TF close; for smoother values consider ATR / SMA(close, N) × 100.
• 風險聲明：僅供研究/教育用途，非投資建議，請自行控管風險。
Disclaimer: For research/education only. Not investment advice.
⸻
版本與更新 / Version & Updates
• v1.0：ATR/ATR% + BB（MTF）、1% 白色點線、兩組告警。
v1.0: ATR/ATR% + BB (MTF), 1% white dotted line, two alert conditions.
Mutanabby_AI __ OSC+ST+SQZMOMMutanabby_AI OSC+ST+SQZMOM: Multi-Component Trading Analysis Tool
Overview
The Mutanabby_AI OSC+ST+SQZMOM indicator combines three proven technical analysis components into a unified trading system, providing comprehensive market analysis through integrated oscillator signals, trend identification, and volatility assessment.
Core Components
Wave Trend Oscillator (OSC): Identifies overbought and oversold market conditions using exponential moving average calculations. Key threshold levels include overbought zones at 60 and 53, with oversold areas marked at -60 and -53. Crossover signals between the two oscillator lines generate entry opportunities, displayed as colored circles on the chart for easy identification.
Supertrend Indicator (ST): Determines overall market direction using Average True Range calculations with a 2.5 factor and 10-period ATR configuration. Green lines indicate confirmed uptrends while red lines signal downtrend conditions. The indicator automatically adapts to market volatility changes, providing reliable trend identification across different market environments.
Squeeze Momentum (SQZMOM): Compares Bollinger Bands with Keltner Channels to identify consolidation periods and potential breakout scenarios. Black squares indicate squeeze conditions representing low volatility periods, green triangles signal confirmed upward breakouts, and red triangles mark downward breakout confirmations.
Signal Generation Logic
Long Entry Conditions:
Green triangles from Squeeze Momentum component
Supertrend line transitioning to green
Bullish crossovers in Wave Trend Oscillator from oversold territory
Short Entry Conditions:
Red triangles from Squeeze Momentum component
Supertrend line transitioning to red
Bearish crossovers in Wave Trend Oscillator from overbought territory
Automated Risk Management
The indicator incorporates comprehensive risk management through ATR-based calculations. Stop losses are automatically positioned at 3x ATR distance from entry points, while three progressive take profit targets are established at 1x, 2x, and 3x ATR multiples respectively. All risk management levels are clearly displayed on the chart using colored lines and informative labels.
When trend direction changes, the system automatically clears previous risk levels and generates new calculations, ensuring all risk parameters remain current and relevant to existing market conditions.
Alert and Notification System
Comprehensive alert framework includes trend change notifications with complete trade setup details, squeeze release alerts for breakout opportunity identification, and trend weakness warnings for active position management. Alert messages contain specific trading pair information, timeframe specifications, and all relevant entry and exit level data.
Implementation Guidelines
Timeframe Selection: Higher timeframes including 4-hour and daily charts provide the most reliable signals for position trading strategies. One-hour charts demonstrate good performance for day trading applications, while 15-30 minute timeframes enable scalping approaches with enhanced risk management requirements.
Risk Management Integration: Limit individual trade risk to 1-2% of total capital using the automatically calculated stop loss levels for precise position sizing. Implement systematic profit-taking at each target level while adjusting stop loss positions to protect accumulated gains.
Market Volatility Adaptation: The indicator's ATR-based calculations automatically adjust to changing market volatility conditions. During high volatility periods, risk management levels appropriately widen, while low volatility conditions result in tighter risk parameters.
Optimization Techniques
Combine indicator signals with fundamental support and resistance level analysis for enhanced signal validation. Monitor volume patterns to confirm breakout strength, particularly when Squeeze Momentum signals develop. Maintain awareness of scheduled economic events that may influence market behavior independent of technical indicator signals.
The multi-component design provides internal signal confirmation through multiple alignment requirements, significantly reducing false signal occurrence while maintaining reasonable trade frequency for active trading strategies.
Technical Specifications
The Wave Trend Oscillator utilizes customizable channel length (default 10) and average length (default 21) parameters for optimal market sensitivity. Supertrend calculations employ ATR period of 10 with factor multiplier of 2.5 for balanced signal quality. Squeeze Momentum analysis uses Bollinger Band length of 20 periods with 2.0 multiplication factor, combined with Keltner Channel length of 20 periods and 1.5 multiplication factor.
Conclusion
The Mutanabby_AI OSC+ST+SQZMOM indicator provides a systematic approach to technical market analysis through the integration of proven oscillator, trend, and momentum components. Success requires thorough understanding of each element's functionality and disciplined implementation of proper risk management principles.
Practice with demo trading accounts before live implementation to develop familiarity with signal interpretation and trade management procedures. The indicator's systematic approach effectively reduces emotional decision-making while providing clear, objective guidelines for trade entry, management, and exit strategies across various market conditions.
Standard Deviation BandsStandard Deviation Bands
คำอธิบายอินดิเคเตอร์:
อินดิเคเตอร์ SD Bands (Standard Deviation Bands) เป็นเครื่องมือวิเคราะห์ทางเทคนิคที่ออกแบบมาเพื่อวัดความผันผวนของราคาและระบุโอกาสในการเทรดที่อาจเกิดขึ้น อินดิเคเตอร์นี้จะแสดงผลเป็นเส้นขอบ 2 เส้นบนกราฟราคาโดยตรง โดยอ้างอิงจากค่าเฉลี่ยเคลื่อนที่ (Moving Average) และค่าส่วนเบี่ยงเบนมาตรฐาน (Standard Deviation)
* เส้นบน (Upper Band): แสดงระดับที่ราคาเคลื่อนไหวสูงกว่าค่าเฉลี่ย
* เส้นล่าง (Lower Band): แสดงระดับที่ราคาเคลื่อนไหวต่ำกว่าค่าเฉลี่ย
ความกว้างของช่องระหว่างเส้นทั้งสองบ่งบอกถึงระดับความผันผวนของตลาดในปัจจุบัน
วิธีการใช้งานอย่างละเอียด:
คุณสามารถนำอินดิเคเตอร์ SD Bands ไปประยุกต์ใช้ได้หลายวิธีเพื่อประกอบการตัดสินใจ ดังนี้:
1. การใช้เป็นแนวรับ-แนวต้านแบบไดนามิก (Dynamic Support & Resistance)
* แนวรับ: เมื่อราคาวิ่งลงมาแตะหรือเข้าใกล้เส้นล่าง (เส้นสีน้ำเงิน) เส้นนี้อาจทำหน้าที่เป็นแนวรับชั่วคราวและมีโอกาสที่ราคาจะเด้งกลับขึ้นไปหาเส้นกลาง
* แนวต้าน: เมื่อราคาวิ่งขึ้นไปแตะหรือเข้าใกล้เส้นบน (เส้นสีแดง) เส้นนี้อาจทำหน้าที่เป็นแนวต้านชั่วคราวและมีโอกาสที่ราคาจะย่อตัวลงมา
2. การวัดความผันผวนและสัญญาณ Breakout
* ช่วงตลาดสงบ (Low Volatility): เมื่อเส้น SD ทั้งสองเส้นบีบตัวเข้าหากันเป็นช่องที่แคบมาก (คล้ายกับ Bollinger Squeeze) แสดงว่าตลาดมีความผันผวนต่ำมาก ซึ่งมักจะเป็นสัญญาณว่ากำลังจะเกิดการเคลื่อนไหวครั้งใหญ่ (Breakout)
* ช่วงตลาดเป็นเทรนด์ (High Volatility): เมื่อเส้น SD ขยายตัวกว้างออกอย่างรวดเร็ว พร้อมกับที่ราคาวิ่งอยู่นอกขอบ แสดงว่าตลาดเข้าสู่ช่วงเทรนด์ที่แข็งแกร่งและมีโมเมนตัมสูง
3. สัญญาณการกลับตัว (Reversal Signals)
* เมื่อราคาปิดแท่งเทียน นอกเส้น SD Bands อย่างชัดเจน (โดยเฉพาะหลังจากที่เทรนด์นั้นดำเนินมานาน) อาจเป็นสัญญาณว่าแรงซื้อ/แรงขายเริ่มอ่อนกำลังลง และมีโอกาสที่จะเกิดการกลับตัวของราคาในไม่ช้า
การตั้งค่าอินพุต (Input Parameters):
* ระยะเวลา (Length): กำหนดจำนวนแท่งเทียนที่ใช้ในการคำนวณค่าเฉลี่ยและ SD
* 20: สำหรับการวิเคราะห์ระยะสั้นถึงกลาง
* 50 หรือ 100: สำหรับการวิเคราะห์ระยะยาว
* ตัวคูณ (Multiplier): กำหนดระยะห่างของเส้น SD จากค่าเฉลี่ย
* 1.0 - 2.0: เส้นจะอยู่ใกล้ราคามากขึ้น ทำให้เกิดสัญญาณบ่อยขึ้น
* 2.0 - 3.0: เส้นจะอยู่ห่างจากราคามากขึ้น ทำให้เกิดสัญญาณที่น่าเชื่อถือมากขึ้น แต่จะเกิดไม่บ่อย
ข้อควรระวังและคำเตือน:
* อินดิเคเตอร์นี้เป็นเพียง เครื่องมือวิเคราะห์ เพื่อช่วยในการตัดสินใจ ไม่ใช่สัญญาณการซื้อขายที่ถูกต้อง 100%
* ควรใช้ร่วมกับเครื่องมืออื่นๆ เช่น RSI, MACD, หรือ Volume เพื่อยืนยันสัญญาณ
* การเทรดมีความเสี่ยงสูง ควรบริหารจัดการความเสี่ยงและตั้งจุด Stop Loss ทุกครั้ง
คุณสามารถใช้โครงสร้างนี้ในการเขียนโพสต์บน TradingView ได้เลยนะครับ ขอให้ประสบความสำเร็จกับการโพสต์อินดิเคเตอร์ของคุณครับ!
English
Standard Deviation Bands
Indicator Description:
The SD Bands (Standard Deviation Bands) indicator is a powerful technical analysis tool designed to measure price volatility and identify potential trading opportunities. The indicator displays two dynamic bands directly on the price chart, based on a moving average and a customizable standard deviation multiplier.
* Upper Band: Indicates price levels above the moving average.
* Lower Band: Indicates price levels below the moving average.
The width of the channel between these two bands provides a clear picture of current market volatility.
Detailed User Guide:
You can use SD Bands in several ways to enhance your trading decisions:
1. Dynamic Support and Resistance:
These bands can act as dynamic support and resistance levels.
* Support: When the price moves down and touches or approaches the lower band, it can act as support, offering the possibility of a rebound to the average.
* Resistance: When the price moves up and touches or approaches the upper band, it can act as resistance, offering the possibility of a rebound.
2. Volatility Measurement and Breakout Signals:
* Low Volatility (Squeeze): When the two bands converge and form a narrow channel. Indicates very low market volatility. This condition often occurs before significant price movements or breakouts.
* High Volatility (Expansion): When the bands expand and widen rapidly, it indicates that the market is entering a period of strong trending momentum with high momentum.
3. Reversal Signals:
* When the price closes significantly outside the SD Bands (especially after a long-term trend), it may signal that the current momentum has expired and a reversal may be imminent.
Input Parameters:
The indicator's parameters are fully customizable to suit your trading style:
* Length: Defines the number of bars used to calculate the moving average and standard deviation.
* 20: Suitable for short- to medium-term analysis.
* 50 or 100: Suitable for long-term trend analysis.
* Multiplier: Adjusts the sensitivity of the signal bars.
* 1.0 - 2.0: Creates narrower signal bars, leading to more frequent signals.
* 2.0 - 3.0: Creates wider signal bars, providing fewer but potentially more significant signals.
Important Warning:
* This indicator is an analytical tool only. It does not provide guaranteed buy or sell signals.
* Always use it in conjunction with other indicators (such as RSI, MACD, and Volume) for confirmation.
* Trading involves high risk. Proper risk management, including the use of stop-loss orders, is recommended.
You can use this structure for your posts on TradingView. Good luck with your indicators!
ai quant oculusAI QUANT OCULUS
Version 1.0 | Pine Script v6
Purpose & Innovation
AI QUANT OCULUS integrates four distinct technical concepts—exponential trend filtering, adaptive smoothing, momentum oscillation, and Gaussian smoothing—into a single, cohesive system that delivers clear, objective buy and sell signals along with automatically plotted stop-loss and three profit-target levels. This mash-up goes beyond a simple EMA crossover or standalone TRIX oscillator by requiring confluence across trend, adaptive moving averages, momentum direction, and smoothed price action, reducing false triggers and focusing on high‐probability turning points.
How It Works & Why Its Components Matter
Trend Filter: EMA vs. Adaptive MA
EMA (20) measures the prevailing trend with fixed sensitivity.
Adaptive MA (also EMA-based, length 10) approximates a faster-responding moving average, standing in for a KAMA-style filter.
Bullish bias requires AMA > EMA; bearish bias requires AMA < EMA. This ensures signals align with both the underlying trend and a more nimble view of recent price action.
Momentum Confirmation: TRIX
Calculates a triple-smoothed EMA of price over TRIX Length (15), then converts it to a percentage rate-of-change oscillator.
Positive TRIX reinforces bullish entries; negative TRIX reinforces bearish entries. Using TRIX helps filter whipsaws by focusing on sustained momentum shifts.
Gaussian Price Smoother
Applies two back-to-back 5-period EMAs to the price (“gaussian” smoothing) to remove short-term noise.
Price above the smoothed line confirms strength for longs; below confirms weakness for shorts. This layer avoids entries on erratic spikes.
Confluence Signals
Buy Signal (isBull) fires only when:
AMA > EMA (trend alignment)
TRIX > 0 (momentum support)
Close > Gaussian (price strength)
Sell Signal (isBear) fires under the inverse conditions.
Requiring all three conditions simultaneously sharply reduces false triggers common to single-indicator systems.
Automatic Risk & Reward Plotting
On each new buy or sell signal (edge detection via not isBull or not isBear ), the script:
Stores entryPrice at the signal bar’s close.
Draws a stop-loss line at entry minus ATR(14) × Stop Multiplier (1.5) by default.
Plots three profit-target lines at entry plus ATR × Target Multiplier (1×, 1.5×, and 2×).
All previous labels and lines are deleted on each new signal, keeping the chart uncluttered and focusing only on the current trade.
Inputs & Customization
Input Description Default
EMA Length Period for the main trend EMA 20
Adaptive MA Length Period for the faster adaptive EM A substitute 10
TRIX Length Period for the triple-smoothed momentum oscillator 15
Dominant Cycle Length (Reserved) 40
Stop Multiplier ATR multiple for stop-loss distance 1.5
Target Multiplier ATR multiple for first profit target 1.5
Show Buy/Sell Signals Toggle on-chart labels for entry signals On
How to Use
Apply to Chart: Best on 15 m–1 h timeframes for swing entries or 5 m for agile scalps.
Wait for Full Confluence:
Look for the AMA to cross above/below the EMA and verify TRIX and Gaussian conditions on the same bar.
A bright “LONG” or “SHORT” label marks your entry.
Manage the Trade:
Place your stop where the red or green SL line appears.
Scale or exit at the three yellow TP1/TP2/TP3 lines, automatically drawn by volatility.
Repeat Cleanly: Each new signal clears prior annotations, ensuring you only track the active setup.
Why This Script Stands Out
Multi-Layer Confluence: Trend, momentum, and noise-reduction must all align, addressing the weaknesses of single-indicator strategies.
Automated Trade Management: No manual plotting—stop and target lines appear seamlessly with each signal.
Transparent & Customizable: All logic is open, adjustable, and clearly documented, allowing traders to tweak lengths and multipliers to suit different instruments.
Disclaimer
No indicator guarantees profit. Always backtest AI QUANT OCULUS extensively, combine its signals with your own analysis and risk controls, and practice sound money management before trading live.
Correlation HeatMap [TradingFinder] Sessions Data Science Stats🔵 Introduction
n financial markets, correlation describes the statistical relationship between the price movements of two assets and how they interact over time. It plays a key role in both trading and investing by helping analyze asset behavior, manage portfolio risk, and understand intermarket dynamics. The Correlation Heatmap is a visual tool that shows how the correlation between multiple assets and a central reference asset (the Main Symbol) changes over time.
It supports four market types forex, stocks, crypto, and a custom mode making it adaptable to different trading environments. The heatmap uses a color-coded grid where warmer tones represent stronger negative correlations and cooler tones indicate stronger positive ones. This intuitive color system allows traders to quickly identify when assets move together or diverge, offering real-time insights that go beyond traditional correlation tables.
🟣 How to Interpret the Heatmap Visually ?
Each cell represents the correlation between the main symbol and one compared asset at a specific time.
Warm colors (e.g. red, orange) suggest strong negative correlation as one asset rises, the other tends to fall.
Cool colors (e.g. blue, green) suggest strong positive correlation both assets tend to move in the same direction.
Lighter shades indicate weaker correlations, while darker shades indicate stronger correlations.
The heatmap updates over time, allowing users to detect changes in correlation during market events or trading sessions.
One of the standout features of this indicator is its ability to overlay global market sessions such as Tokyo, London, New York, or major equity opens directly onto the heatmap timeline. This alignment lets traders observe how correlation structures respond to real-world session changes. For example, they can spot when assets shift from being inversely correlated to moving together as a new session opens, potentially signaling new momentum or macro flow. The customizable symbol setup (including up to 20 compared assets) makes it ideal not only for forex and crypto traders but also for multi-asset and sector-based stock analysis.
🟣 Use Cases and Advantages
Analyze sector rotation in equities by tracking correlation to major indices like SPX or DJI.
Monitor altcoin behavior relative to Bitcoin to find early entry opportunities in crypto markets.
Detect changes in currency alignment with DXY across trading sessions in forex.
Identify correlation breakdowns during market volatility, signaling possible new trends.
Use correlation shifts as confirmation for trade setups or to hedge multi-asset exposure
🔵 How to Use
Correlation is one of the core concepts in financial analysis and allows traders to understand how assets behave in relation to one another. The Correlation Heatmap extends this idea by going beyond a simple number or static matrix. Instead, it presents a dynamic visual map of how correlations shift over time.
In this indicator, a Main Symbol is selected as the reference point for analysis. In standard modes such as forex, stocks, or crypto, the symbol currently shown on the main chart is automatically used as the main symbol. This allows users to begin correlation analysis right away without adjusting any settings.
The horizontal axis of the heatmap shows time, while the vertical axis lists the selected assets. Each cell on the heatmap shows the correlation between that asset and the main symbol at a given moment.
This approach is especially useful for intermarket analysis. In forex, for example, tracking how currency pairs like OANDA:EURUSD EURUSD, FX:GBPUSD GBPUSD, and PEPPERSTONE:AUDUSD AUDUSD correlate with TVC:DXY DXY can give insight into broader capital flow.
If these pairs start showing increasing positive correlation with DXY say, shifting from blue to light green it could signal the start of a new phase or reversal. Conversely, if negative correlation fades gradually, it may suggest weakening relationships and more independent or volatile movement.
In the crypto market, watching how altcoins correlate with Bitcoin can help identify ideal entry points in secondary assets. In the stock market, analyzing how companies within the same sector move in relation to a major index like SP:SPX SPX or DJ:DJI DJI is also a highly effective technique for both technical and fundamental analysts.
This indicator not only visualizes correlation but also displays major market sessions. When enabled, this feature helps traders observe how correlation behavior changes at the start of each session, whether it's Tokyo, London, New York, or the opening of stock exchanges. Many key shifts, breakouts, or reversals tend to happen around these times, and the heatmap makes them easy to spot.
Another important feature is the market selection mode. Users can switch between forex, crypto, stocks, or custom markets and see correlation behavior specific to each one. In custom mode, users can manually select any combination of symbols for more advanced or personalized analysis. This makes the heatmap valuable not only for forex traders but also for stock traders, crypto analysts, and multi-asset strategists.
Finally, the heatmap's color-coded design helps users make sense of the data quickly. Warm colors such as red and orange reflect stronger negative correlations, while cool colors like blue and green represent stronger positive relationships. This simplicity and clarity make the tool accessible to both beginners and experienced traders.
🔵 Settings
Correlation Period: Allows you to set how many historical bars are used for calculating correlation. A higher number means a smoother, slower-moving heatmap, while a lower number makes it more responsive to recent changes.
Select Market: Lets you choose between Forex, Stock, Crypto, or Custom. In the first three options, the chart’s active symbol is automatically used as the Main Symbol. In Custom mode, you can manually define the Main Symbol and up to 20 Compared Symbols.
Show Open Session: Enables the display of major trading sessions such as Tokyo, London, New York, or equity market opening hours directly on the timeline. This helps you connect correlation shifts with real-world market activity.
Market Mode: Lets you select whether the displayed sessions relate to the forex or stock market.
🔵 Conclusion
The Correlation Heatmap is a robust and flexible tool for analyzing the relationship between assets across different markets. By tracking how correlations change in real time, traders can better identify alignment or divergence between symbols and gain valuable insights into market structure.
Support for multiple asset classes, session overlays, and intuitive visual cues make this one of the most effective tools for intermarket analysis.
Whether you’re looking to manage portfolio risk, validate entry points, or simply understand capital flow across markets, this heatmap provides a clear and actionable perspective that you can rely on.
