RSI Divergence Buy/Sell Alertsbuy or sell when bullish or bearish divergence occurs in and over sold or over bought condition
Educational
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.
U Table • LITEA compact, educational version of my workflow that combines trend, momentum, trend strength, and a clean trigger:
Trend: EMA Fast vs EMA Slow (auto-lengths by chart TF)
Momentum: RSI > 50 for longs / < 50 for shorts
Strength: ADX above a user-set threshold (fallback implementation; can be replaced by ta.adx() when available)
Trigger: price crosses the Bollinger basis (center line)
Signals
LONG: crossover(close, BB basis) while EMA Fast > EMA Slow, RSI > 50, ADX > threshold
SHORT: crossunder(close, BB basis) while EMA Fast < EMA Slow, RSI < 50, ADX > threshold
Visuals
EMA Fast / EMA Slow / BB basis
Markers “L” / “S” on triggers
Latest confirmed pivot high/low (broken line style)
Small diagnostics table (ADX, EMA relation, RSI, last pivots) on the last bar
Inputs
Pivot length: pivot confirmation window (default 5)
ADX threshold: minimum trend strength to allow signals (default 20)
Notes
Signals are intended to be evaluated on bar close. Intrabar values may change until the bar closes.
Pivot lines appear after confirmation; they do not repaint once confirmed.
No external data or security() calls are used.
This LITE build focuses on clarity and speed (few calculations, overlay-friendly). It can be used as a stand-alone study or as a scaffold for your own research and risk management.
EMA Cross by TejasFor all Free Sub users. Feel free to use it everywhere. Mostly ASTA students. Very Eaasy to use with signals.
Index Options Expirations and Calendar EffectsFeatures
- Highlights monthly equity options expiration (opex) dates.
- Marks VIX options expiration dates based on standard 30-day offset.
- Shows configurable vanna/charm pre-expiration window (green shading).
- Shows configurable post-opex weakness window (red shading).
- Adjustable colors, start/end offsets, and on/off toggles for each element.
What this does
This overlay highlights option-driven calendar windows around monthly equity options expiration (opex) and VIX options expiration. It draws:
- Solid blue lines on the third Friday of each month (typical monthly opex).
- Dashed orange lines on the Wednesday ~30 days before next month’s opex (typical VIX expiration schedule).
- Green shading during a pre-expiration window when vanna/charm effects are often strongest.
- Red shading during the post-expiration "window of non-strength" often observed into the Tuesday after opex.
How it works
1. Monthly opex is detected when Friday falls between the 15th–21st of the month.
2. VIX expiration is calculated by finding next month’s opex date, then subtracting 30 calendar days and marking that Wednesday.
3. Vanna/charm window (green) : starts on the Monday of the week before opex and ends on Tuesday of opex week.
4. Post-opex weakness window (red) : starts Wednesday of opex week and ends Tuesday after opex.
How to use
- Add to any chart/timeframe.
- Adjust inputs to toggle VIX/opex lines, choose colors, and fine-tune the start/end offsets for shaded windows.
- This is an educational visualization of typical timing and not a trading signal.
Limitations
- Exchange holidays and contract-specific exceptions can shift expirations; this script uses standard calendar rules.
- No forward-looking data is used; all dates are derived from historical and current bar time.
- Past patterns do not guarantee future behavior.
Originality
Provides a single, adjustable visualization combining opex, VIX expiration, and configurable vanna/charm/weakness windows into one tool. Fully explained so non-coders can use it without reading the source code.
Smart Wick AnalyzerSmart Wick Analyzer (SWA)
Purpose: Highlight potential liquidity‑grab candles (long wicks) and turn them into actionable, rule‑based buy/sell signals with trend, volume, and cooldown filters.
Type: Indicator (not a strategy). Educational tool to contextualize wick events.
🧠 What This Script Does
SWA looks for candles where the wick is large relative to its body—a common signature of liquidity sweeps / rejection. It then adds three confirmations before marking a trade signal:
1. Wick Event
• Upper‑wick event (possible rejection from above)
• Lower‑wick event (possible rejection from below)
• Condition: wick length > body × Wick‑to‑Body Ratio
2. Context Filters
• Trend filter : closing price vs. SMA of lookbackBars
• Volume filter : current volume vs. average volume × volumeThreshold
3. Signal Hygiene
• Cooldown : prevents clustering; a minimum number of bars must pass before a new signal is allowed.
If a candle passes these checks:
• Buy Signal (triangle up): long lower wick + price above SMA + relative‑high volume + cooldown passed
• Sell Signal (triangle down): long upper wick + price below SMA + relative‑high volume + cooldown passed
The signal candle is also bar‑colored black for quick visual focus.
⸻
✳️ What the Dotted Lines Mean (including the green one)
On every signal bar the script draws two dotted horizontal levels, extended to the right:
• Open line of the signal candle
• Close line of the signal candle
• They use the signal color: green for Buy, red for Sell.
How to interpret (example: green = Buy signal):
• The green dotted close line represents the momentum validation level. If subsequent candles close above this line, it indicates follow‑through after the wick rejection (buyers defended into the close).
• The green dotted open line is a risk context / invalidation reference. If price falls back below it soon after the signal, the wick event may have failed or devolved into chop.
In your annotated chart: the candle initially looked constructive (“closing above could be positive momentum”), but later price failed and rotated down—hence a sell signal interpreted when an upper‑wick event occurred under down‑trend conditions.
⸻
⚙️ Inputs & What They Control
• Wick‑to‑Body Ratio (wickThreshold): how “extreme” a wick must be to count as a liquidity‑grab.
• Lookback Period (lookbackBars):
• SMA period for trend context
• Volume MA for relative‑volume check
• Volume Multiplier (volumeThreshold): strengthens/loosens volume confirmation.
• Cooldown Bars (cooldownBars): minimum spacing between consecutive signals.
• Enable Alerts (showAlerts): turns on alert conditions.
⸻
🔔 Alerts (exact titles)
• “SWA Buy Alert” — potential reversal / Buy signal detected
• “SWA Sell Alert” — potential reversal / Sell signal detected
⸻
📌 How to Use (practical guide)
1. Scan for the black‑colored signal candle and its dotted lines.
2. For Buy signals (green): Prefer continuation if price closes above the green close line within the next few bars. Manage risk using the open line or your own level.
3. For Sell signals (red): Prefer continuation if price closes below the red close line.
4. Avoid chasing during low‑volume / counter‑trend signals; the filters help, but structure (HTF trend, S/R, session context) still matters.
5. Use the cooldown to reduce noise on fast time frames.
⸻
✅ Why This Isn’t Just “Another Wick Indicator”
• The script does not flag every long‑wick; it requires trend alignment and relative volume to suggest participation.
• The two reference lines (open/close) provide post‑signal state tracking—a simple, visual framework to judge follow‑through vs. failure without additional tools.
• Cooldown logic discourages clustered, low‑quality repeats around the same zone.
⸻
⚠️ Notes & Limitations
• Works across markets/time frames, but wick behavior varies by instrument and session. Parameters may need adjustment.
• Signals are contextual, not guarantees. Consolidation and news spikes can invalidate wick reads.
• This indicator is not a strategy; it does not backtest performance on its own.
⸻
📄 Disclaimer
This tool is for educational purposes only and should be combined with personal analysis and risk management. Markets are uncertain; past behavior does not guarantee future results.
Average hourly move by @zeusbottradingThis Pine Script called "Average hourly move by @zeusbottrading" calculates and displays the average percentage price movement for each hour of the day using the full available historical data.
How the script works:
It tracks the high and low price within each full hour (e.g., 10:00–10:59).
It calculates the percentage move as the range between high and low relative to the average price during that hour.
For each hour of the day, it stores the total of all recorded moves and the count of occurrences across the full history.
At the end, the script computes the average move for each hour (0 to 23) and determines the minimum and maximum averages.
Using these values, it creates a color gradient, where the hours with the lowest average volatility are red and the highest are green.
It then displays a table in the top-right corner of the chart showing each hour and its average percentage move, color‑coded according to volatility.
What it can be used for:
Identifying when the market is historically most volatile or calm during the day.
Helping plan trade entries and exits based on expected volatility.
Comparing hourly volatility patterns across different markets or instruments.
Adjusting position size and risk management according to the anticipated volatility in a particular hour.
Using long-term historical data to understand recurring daily volatility patterns.
In short, this script is a useful tool for traders who want to fine‑tune their trading strategies and risk management by analyzing time‑based volatility profiles.
YM Confluence Panel - Dual SMA (fast/slow)This script displays a YM Confluence Panel for the mini Dow Jones (YM), using six correlated/inversely correlated assets (ES, NQ, RTY, ZN, GC, VIX) and two simple moving averages (fast: 9 / slow: 20).
The logic determines bullish or bearish conditions for each asset based on SMA relationships and price, generating arrows and an aggregated BUY / SELL / WAIT signal.
🔹 How it works:
• Correlated assets (ES, NQ, RTY): bullish when SMA(9) > SMA(20) and price above SMA(20).
• Inverse assets (ZN, GC, VIX): bullish when SMA(9) < SMA(20) and price below SMA(20).
• All bullish → BUY
• All bearish → SELL
• Otherwise → WAIT
✅ Customizable:
• Adjust assets and timeframes.
• Change SMA periods.
• Set panel position.
⚠️ Disclaimer: For educational purposes only. Not financial advice.
Lot Size + Margin InfoThis indicator is designed to give Futures & Options traders instant access to lot size and estimated margin requirements for the instrument they are viewing — directly on their TradingView chart. It combines real-time symbol detection with a built-in, regularly updated margin lookup table (sourced from Kotak Securities’ published margin requirements), while also handling fallback logic for unknown or unsupported symbols.
---
### What It Does
* Automatically Detects the Instrument Type
Identifies whether the current chart’s symbol is a futures contract, option, or a cash/spot instrument.
* Shows Accurate Lot Size
For supported F\&O symbols, it fetches the correct lot size directly from exchange data.
For options, it retrieves the lot size from the option’s point value.
For cash/spot symbols with linked futures, it uses the futures lot size.
* Calculates Estimated Margin
* For futures: `Lot Size × Current Price × Margin%` (Margin% sourced from the internal lookup table).
* For options: `Lot Size × Current Price` (simple multiplication, as options margin ≈ premium cost).
* For unsupported or non-FnO symbols: Displays "No FnO".
* Fallback Margin Logic
If a symbol is missing from the margin lookup table, the script applies a user-defined default margin percentage and highlights the data in orange to indicate it’s using fallback values.
* Debug Mode for Transparency
A toggle to display the exact symbol string used for fetching lot size and margin, so traders can verify the data source.
---
### How It Works
1. Symbol Normalization
The script standardizes symbol names to match the margin table format (e.g., converting `"NIFTY1!"` to `"NIFTY"`).
2. Type-Based Handling
* Futures – Uses point value for lot size, applies specific margin % from the table.
* Options – Uses option point value for lot size, margin is simply premium × lot size.
* Cash Symbols with Linked Futures – Attempts to find and use the associated futures contract for lot/margin data.
* Unsupported Symbols – Displays `"No FnO"`.
3. Margin Table Integration
The margin % table is manually updated from a reliable broker’s margin sheet (Kotak Securities) — ensuring alignment with real trading conditions.
4. Customizable Display
* Position (Top Right / Bottom Left / Bottom Right)
* Table background color, text color, font size, border width
* Editable label text for lot size and margin display
* Toggleable lot size and margin sections
---
### How to Use
1. Add the Indicator to Your Chart – Works on any NSE Futures, Options, or Cash symbol with linked F\&O.
2. Configure Display Settings – Choose whether to show lot size, margin, or both, and place the info table where you prefer.
3. Adjust Fallback Margin % – If you trade less common contracts, set your default margin % to reflect your broker’s requirement.
4. Enable Debug Mode (Optional) – To see the exact symbol source the script is using.
---
### Best For
* Intraday & Positional F\&O Traders who need instant clarity on lot size and margin before entering trades.
* Options Sellers & Buyers who want quick cost estimates.
* Traders Switching Symbols Quickly — saves time by removing the need to check the broker’s margin sheet manually.
---
💡 Pro Tip: Since margin requirements can change, keep the script updated whenever your broker revises margin data. This version’s margin table is updated as of 13-08-2025.
Crypto Macro CockpitCrypto Macro Cockpit — Institutional Liquidity Regime Detection
🔍 Overview
This script introduces a modern macro framework for crypto market regime detection, leveraging newly added stablecoin market data on TradingView. It’s designed to guide traders through the evolving institutional era of crypto — where liquidity, not just price, is king.
🌐 Why This Matters
Historically, traditional proxies like M2 money supply or bond yields were referenced to infer macro liquidity shifts. But with the regulatory green light and institutional embrace of stablecoins, on-chain fiat liquidity is now directly observable.
Stablecoins = The new M2 for crypto.
This script utilizes real-time data from:
📊 CRYPTOCAP:STABLE.C (Total Stablecoin Market Cap)
📊 CRYPTOCAP:STABLE.C.D (Stablecoin Dominance)
to assess dry powder, risk appetite, and macro regime transitions.
📋 How to Read the Crypto Macro Cockpit
This dashboard updates every few bars and is organized into four actionable segments:
1️⃣ Macro Spreads
Metric --> Interpretation
Risk Flow --> Measures capital flow between stablecoins and total crypto market cap. → Green = risk deploying.
ETH vs BTC --> Shift in dominance between ETH and BTC → rotation gauge.
ETHBTC --> Price ratio movement → confirms leadership tilt.
ALTs (TOTAL3ES) --> Momentum in altcoin market, excluding BTC/ETH/stables → key for alt season timing.
2️⃣ Liquidity & Risk Appetite
Metric --> Interpretation
Liquidity --> Directional change in stablecoin cap → more stables = more dry powder.
Risk Appetite --> Inverse of stablecoin dominance → falling dominance = capital rotating into risk.
3️⃣ Stablecoin Context
Metric --> Interpretation
StableCap ROC --> Growth rate of stablecoin market cap → proxy for fiat inflows.
StableDom ROC --> Change in stablecoin dominance → reflects market caution or aggression.
4️⃣ Composite Labels
Label --> Interpretation
Rotation --> Sector tilt (BTC-led vs ETH/Alts)
Regime --> Synthesized macro environment → "Risk-ON", "Caution", "Waiting", or "Risk-OFF"
Background Color --> Optional tint reflecting regime for quick glance validation
All metrics are evaluated with directional arrows (▲/▼/•) and acceleration overlays, using user-defined thresholds scaled by timeframe for precision.
🔔 Built-in Alerts
Predefined, non-repainting alerts include:
Regime transitions
Sector rotations
Confirmed ETH/ALT rotations
Stablecoin market cap spikes
Risk Flow acceleration
You can use these alerts for discretionary trading or automated system triggers.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice. Trading cryptocurrencies involves risk, and past performance does not guarantee future results. Always do your own research and manage risk responsibly.
✅ Ready to Use
No configuration needed — just load the script
Works on all timeframes (optimized for 1D)
Thresholds and smoothing are customizable
Table positioning and sizing is user-controlled
If you find this helpful, feel free to ⭐️ favorite or leave feedback. Questions welcome in the comments.
Let’s trade with macro awareness in this new era.
Prime NumbersPrime Numbers highlights prime numbers (no surprise there 😅), tokens and the recent "active" feature in "input".
🔸 CONCEPTS
🔹 What are Prime Numbers?
A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers.
Wikipedia: Prime number
🔹 Prime Factorization
The fundamental theorem of arithmetic states that every integer larger than 1 can be written as a product of one or more primes. More strongly, this product is unique in the sense that any two prime factorizations of the same number will have the same number of copies of the same primes, although their ordering may differ. So, although there are many different ways of finding a factorization using an integer factorization algorithm, they all must produce the same result. Primes can thus be considered the "basic building blocks" of the natural numbers.
Wikipedia: Fundamental theorem of arithmetic
Math Is Fun: Prime Factorization
We divide a given number by Prime Numbers until only Primes remain.
Example:
24 / 2 = 12 | 24 / 3 = 8
12 / 3 = 4 | 8 / 2 = 4
4 / 2 = 2 | 4 / 2 = 2
|
24 = 2 x 3 x 2 | 24 = 3 x 2 x 2
or | or
24 = 2² x 3 | 24 = 2² x 3
In other words, every natural/integer number above 1 has a unique representation as a product of prime numbers, no matter how the number is divided. Only the order can change, but the factors (the basic elements) are always the same.
🔸 USAGE
The Prime Numbers publication contains two use cases:
Prime Factorization: performed on "close" prices, or a manual chosen number.
List Prime Numbers: shows a list of Prime Numbers.
The other two options are discussed in the DETAILS chapter:
Prime Factorization Without Arrays
Find Prime Numbers
🔹 Prime Factorization
Users can choose to perform Prime Factorization on close prices or a manually given number.
❗️ Note that this option only applies to close prices above 1, which are also rounded since Prime Factorization can only be performed on natural (integer) numbers above 1.
In the image below, the left example shows Prime Factorization performed on each close price for the latest 50 bars (which is set with "Run script only on 'Last x Bars'" -> 50).
The right example shows Prime Factorization performed on a manually given number, in this case "1,340,011". This is done only on the last bar.
When the "Source" option "close price" is chosen, one can toggle "Also current price", where both the historical and the latest current price are factored. If disabled, only historical prices are factored.
Note that, depending on the chosen options, only applicable settings are available, due to a recent feature, namely the parameter "active" in settings.
Setting the "Source" option to "Manual - Limited" will factorize any given number between 1 and 1,340,011, the latter being the highest value in the available arrays with primes.
Setting to "Manual - Not Limited" enables the user to enter a higher number. If all factors of the manual entered number are in the 1 - 1,340,011 range, these factors will be shown; however, if a factor is higher than 1,340,011, the calculation will stop, after which a warning is shown:
The calculated factors are displayed as a label where identical factors are simplified with an exponent notation in superscript.
For example 2 x 2 x 2 x 5 x 7 x 7 will be noted as 2³ x 5 x 7²
🔹 List Prime Numbers
The "List Prime Numbers" option enables users to enter a number, where the first found Prime Number is shown, together with the next x Prime Numbers ("Amount", max. 200)
The highest shown Prime Number is 1,340,011.
One can set the number of shown columns to customize the displayed numbers ("Max. columns", max. 20).
🔸 DETAILS
The Prime Numbers publication consists out of 4 parts:
Prime Factorization Without Arrays
Prime Factorization
List Prime Numbers
Find Prime Numbers
The usage of "Prime Factorization" and "List Prime Numbers" is explained above.
🔹 Prime Factorization Without Arrays
This option is only there to highlight a hurdle while performing Prime Factorization.
The basic method of Prime Factorization is to divide the base number by 2, 3, ... until the result is an integer number. Continue until the remaining number and its factors are all primes.
The division should be done by primes, but then you need to know which one is a prime.
In practice, one performs a loop from 2 to the base number.
Example:
Base_number = input.int(24)
arr = array.new()
n = Base_number
go = true
while go
for i = 2 to n
if n % i == 0
if n / i == 1
go := false
arr.push(i)
label.new(bar_index, high, str.tostring(arr))
else
arr.push(i)
n /= i
break
Small numbers won't cause issues, but when performing the calculations on, for example, 124,001 and a timeframe of, for example, 1 hour, the script will struggle and finally give a runtime error.
How to solve this?
If we use an array with only primes, we need fewer calculations since if we divide by a non-prime number, we have to divide further until all factors are primes.
I've filled arrays with prime numbers and made libraries of them. (see chapter "Find Prime Numbers" to know how these primes were found).
🔹 Tokens
A hurdle was to fill the libraries with as many prime numbers as possible.
Initially, the maximum token limit of a library was 80K.
Very recently, that limit was lifted to 100K. Kudos to the TradingView developers!
What are tokens?
Tokens are the smallest elements of a program that are meaningful to the compiler. They are also known as the fundamental building blocks of the program.
I have included a code block below the publication code (// - - - Educational (2) - - - ) which, if copied and made to a library, will contain exactly 100K tokens.
Adding more exported functions will throw a "too many tokens" error when saving the library. Subtracting 100K from the shown amount of tokens gives you the amount of used tokens for that particular function.
In that way, one can experiment with the impact of each code addition in terms of tokens.
For example adding the following code in the library:
export a() => a = array.from(1) will result in a 100,041 tokens error, in other words (100,041 - 100,000) that functions contains 41 tokens.
Some more examples, some are straightforward, others are not )
// adding these lines in one of the arrays results in x tokens
, 1 // 2 tokens
, 111, 111, 111 // 12 tokens
, 1111 // 5 tokens
, 111111111 // 10 tokens
, 1111111111111111111 // 20 tokens
, 1234567890123456789 // 20 tokens
, 1111111111111111111 + 1 // 20 tokens
, 1111111111111111111 + 8 // 20 tokens
, 1111111111111111111 + 9 // 20 tokens
, 1111111111111111111 * 1 // 20 tokens
, 1111111111111111111 * 9 // 21 tokens
, 9999999999999999999 // 21 tokens
, 1111111111111111111 * 10 // 21 tokens
, 11111111111111111110 // 21 tokens
//adding these functions to the library results in x tokens
export f() => 1 // 4 tokens
export f() => v = 1 // 4 tokens
export f() => var v = 1 // 4 tokens
export f() => var v = 1, v // 4 tokens
//adding these functions to the library results in x tokens
export a() => const arraya = array.from(1) // 42 tokens
export a() => arraya = array.from(1) // 42 tokens
export a() => a = array.from(1) // 41 tokens
export a() => array.from(1) // 32 tokens
export a() => a = array.new() // 44 tokens
export a() => a = array.new(), a.push(1) // 56 tokens
What if we could lower the amount of tokens, so we can export more Prime Numbers?
Look at this example:
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
Eight numbers contain the same number 8291.
If we make a function that removes recurrent values, we get fewer tokens!
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
//is transformed to:
829111, 21, 23, 51, 59, 77, 87, 93
The code block below the publication code (// - - - Educational (1) - - - ) shows how these values were reduced. With each step of 100, only the first Prime Number is shown fully.
This function could be enhanced even more to reduce recurrent thousands, tens of thousands, etc.
Using this technique enables us to export more Prime Numbers. The number of necessary libraries was reduced to half or less.
The reduced Prime Numbers are restored using the restoreValues() function, found in the library fikira/Primes_4.
🔹 Find Prime Numbers
This function is merely added to show how I filled arrays with Prime Numbers, which were, in turn, added to libraries (after reduction of recurrent values).
To know whether a number is a Prime Number, we divide the given number by values of the Primes array (Primes 2 -> max. 1,340,011). Once the division results in an integer, where the divisor is smaller than the dividend, the calculation stops since the given number is not a Prime.
When we perform these calculations in a loop, we can check whether a series of numbers is a Prime or not. Each time a number is proven not to be a Prime, the loop starts again with a higher number. Once all Primes of the array are used without the result being an integer, we have found a new Prime Number, which is added to the array.
Doing such calculations on one bar will result in a runtime error.
To solve this, the findPrimeNumbers() function remembers the index of the array. Once a limit has been reached on 1 bar (for example, the number of iterations), calculations will stop on that bar and restart on the next bar.
This spreads the workload over several bars, making it possible to continue these calculations without a runtime error.
The result is placed in log.info() , which can be copied and pasted into a hardcoded array of Prime Number values.
These settings adjust the amount of workload per bar:
Max Size: maximum size of Primes array.
Max Bars Runtime: maximum amount of bars where the function is called.
Max Numbers To Process Per Bar: maximum numbers to check on each bar, whether they are Prime Numbers.
Max Iterations Per Bar: maximum loop calculations per bar.
🔹 The End
❗️ The code and description is written without the help of an LLM, I've only used Grammarly to improve my description (without AI :) )
ATR: Body % + Ranges and AnomaliesATR: Body % + Ranges and Anomalies
This indicator provides a dual analysis of price bars to help you better understand market dynamics and volatility. It combines two powerful concepts into one tool: a candle body percentage and a range analysis with an anomaly-excluding average.
Key Features:
1. Candle Body Percentage
This feature plots the size of the candle's body as a percentage of its total high-low range.
A high percentage (e.g., above the 50% gray line) indicates strong, directional movement. The more solid the body is relative to its wicks, the more conviction is behind that move.
The 100% red line marks "Marubozu" candles—bars with no wicks, showing absolute control by buyers or sellers.
2. Range Analysis with Anomalies
This is a unique part of the indicator that helps you identify and understand normal vs. abnormal volatility.
Custom SMA: It calculates an average range of the last N bars, but it smartly excludes "anomalous" bars (spikes or unusually small ranges) from the calculation. This gives you a more reliable baseline for normal volatility.
Anomaly Detection: Bars are colored differently based on their range:
Blue: Small anomalies (range less than 0.5 * ATR). These often occur during periods of low liquidity or indecision.
Red: Large anomalies (range greater than 1.8 * ATR). These can signal a sudden burst of volatility, breakout events, or capitulation.
ATR Range % Label: The label on the chart shows the current bar's range as a percentage of the custom SMA. This tells you how much larger or smaller the current bar's range is compared to a clean average.
How to Use:
Spotting Trends: Use the Body % to confirm the strength of a trend. A series of bars with high body percentages can indicate a strong, healthy trend.
Identifying Volatility: Use the Range Analysis to find areas of interest. A large red anomaly bar could signal a significant event, while a series of blue anomalies might suggest the market is in a tight consolidation before a breakout.
Contextual Analysis: The combination of these tools can provide powerful context. For example, a bar with a high Body % and a red anomaly color suggests a strong, volatile move that could be a turning point or the start of a major trend.
Experiment with the input settings to fine-tune the ATR and SMA periods for different timeframes and assets.
Volume Delta Pressure Tracker by GSK-VIZAG-AP-INDIA📢 Title:
Volume Delta Pressure Tracker by GSK-VIZAG-AP-INDIA
📝 Short Description (for script title box):
Real-time volume pressure tracker with estimated Buy/Sell volumes and Delta visualization in an Indian-friendly format (K, L, Cr).
📃 Full Description
🔍 Overview:
This indicator estimates buy and sell volumes using candle structure (OHLC) and displays a real-time delta table for the last N candles. It provides traders with a quick view of volume imbalance (pressure) — often indicating strength behind price moves.
📊 Features:
📈 Buy/Sell Volume Estimation using the candle’s OHLC and Volume.
⚖️ Delta Calculation (Buy Vol - Sell Vol) to detect pressure zones.
📅 Time-stamped Table displaying:
Time (HH:MM)
Buy Volume (Green)
Sell Volume (Red)
Delta (Color-coded)
🔢 Indian Number Format (K = Thousands, L = Lakhs, Cr = Crores).
🧠 Fully auto-calculated — no need for tick-by-tick bid/ask feed.
📍 Neatly placed bottom-right table, customizable number of rows.
🛠️ Inputs:
Show Table: Toggle the table on/off
Number of Bars to Show: Choose how many recent candles to include (5–50)
🎯 Use Cases:
Identify hidden buyer/seller strength
Detect volume absorption or exhaustion
✅ Compatibility:
Works on any timeframe
Ideal for intraday instruments like NIFTY, BANKNIFTY, etc.
Ideal for volume-based strategy confirmation.
🖋️ Developed by:
GSK-VIZAG-AP-INDIA
Intraday Volume Pulse GSK-VIZAG-AP-INDIAIntraday Volume Pulse Indicator
Overview
This indicator is designed to track and visualize intraday volume dynamics during a user-defined trading session. It calculates and displays key volume metrics such as buy volume, sell volume, cumulative delta (difference between buy and sell volumes), and total volume. The data is presented in a customizable table overlay on the chart, making it easy to monitor volume pulses throughout the session. This can help traders identify buying or selling pressure in real-time, particularly useful for intraday strategies.
The indicator resets its calculations at the start of each new day and only accumulates volume data from the specified session start time onward. It uses simple logic to classify volume as buy or sell based on candle direction:
Buy Volume: Assigned to green (up) candles or half of neutral (doji) candles.
Sell Volume: Assigned to red (down) candles or half of neutral (doji) candles.
All calculations are approximate and based on available volume data from the chart. This script does not incorporate external data sources, order flow, or tick-level information—it's purely derived from standard OHLCV (Open, High, Low, Close, Volume) bars.
Key Features
Session Customization: Define the start time of your trading session (e.g., market open) and select from common timezones like Asia/Kolkata, America/New_York, etc.
Volume Metrics:
Buy Volume: Total volume attributed to bullish activity.
Sell Volume: Total volume attributed to bearish activity.
Cumulative Delta: Net difference (Buy - Sell), highlighting overall market bias.
Total Volume: Sum of all volume during the session.
Formatted Display: Volumes are formatted for readability (e.g., in thousands "K", lakhs "L", or crores "Cr" for large numbers).
Color-Coded Table: Uses a patriotic color scheme inspired by general themes (Saffron, White, Green) with dynamic backgrounds based on positive/negative values for quick visual interpretation.
Table Options: Toggle visibility and position (top-right, top-left, etc.) for a clean chart layout.
How to Use
Add to Chart: Apply this indicator to any symbol's chart (works best on intraday timeframes like 1-min, 5-min, or 15-min).
Configure Inputs:
Session Start Hour/Minute: Set to your market's open time (default: 9:15 for Indian markets).
Timezone: Choose the appropriate timezone to align with your trading hours.
Show Table: Enable/disable the metrics table.
Table Position: Place the table where it doesn't obstruct your view.
Interpret the Table:
Monitor for spikes in buy/sell volume or shifts in cumulative delta.
Positive delta (green) suggests buying pressure; negative (red) suggests selling.
Use alongside price action or other indicators for confirmation—e.g., high total volume with positive delta could indicate bullish momentum.
Limitations:
Volume classification is heuristic and not based on actual order flow (e.g., it splits doji volume evenly).
Data accumulation starts from the session time and resets daily; historical backtesting may be limited by the max_bars_back=500 setting.
This is for educational and visualization purposes only—do not use as sole basis for trading decisions.
Calculation Details
Session Filter: Uses timestamp() to define the session start and filters bars with time >= sessionStart.
New Day Detection: Resets volumes on daily changes via ta.change(time("D")).
Volume Assignment:
Buy: Full volume if close > open; half if close == open.
Sell: Full volume if close < open; half if close == open.
Cumulative Metrics: Accumulated only during the session.
Formatting: Custom function f_format() scales large numbers for brevity.
Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice or signals to buy/sell any security. Always perform your own analysis and consult a qualified financial professional before making trading decisions.
© 2025 GSK-VIZAG-AP-INDIA
TRI - Smart Zones============================================================================
# TRI - SMART ZONES v2.0
## Professional Smart Money Concepts Indicator for Pine Script v6
============================================================================
## 📊 OVERVIEW
**TRI - Smart Zones** is a comprehensive Smart Money Concepts indicator that
combines multiple institutional trading concepts into a single, powerful tool.
Built with Pine Script v6 for optimal performance and reliability.
## 🎯 CORE FEATURES
### **Fair Value Gaps (FVG)**
- **Detection**: Automatic identification of price imbalances
- **Types**: Bullish and Bearish Fair Value Gaps
- **Threshold**: Customizable gap size requirements (0.1% default)
- **Extension**: Configurable zone projection length
- **Mitigation**: Real-time tracking of gap fills
### **Order Blocks (OB)**
- **Detection**: Volume-based institutional footprint identification
- **Types**: Bullish and Bearish Order Blocks
- **Method**: Pivot-based volume analysis with configurable lookback
- **Validation**: Market structure confirmation required
- **Extension**: Adjustable zone projection
### **BSL/SSL Liquidity Levels**
- **Multi-Timeframe**: Automatic higher timeframe reference
- **Dynamic**: Real-time level updates and extensions
- **Visual**: Clear line markings with timeframe labels
- **Smart**: Adaptive timeframe selection based on current chart
### **Fibonacci Extensions**
- **ZigZag Integration**: Advanced pivot point detection
- **Levels**: Customizable Fibonacci ratios (38.2%, 61.8%, 100%, 161.8%)
- **Projection**: Dynamic extension from swing points
- **Visual**: Subtle dashed lines with level/price labels
### **Smart Dashboard**
- **Zone Statistics**: Real-time FVG and OB counts
- **Success Rates**: Mitigation percentages for each zone type
- **Market Bias**: Intelligent bullish/bearish/neutral assessment
- **Positioning**: Customizable location and size
### **Zone Analysis Engine**
- **Technical Confluence**: RSI, ADX, ATR, Volume analysis
- **VWAP Integration**: Institutional price reference
- **Confidence Scoring**: High/Mid/Low signal classification
- **Signal Arrows**: Visual trade direction indicators
## 🔔 ALERT SYSTEM
### **Market Structure Alerts**
- `Market Bias Changed` - Shift in overall market sentiment
- `BSL Touched` - Buy Side Liquidity level reached
- `SSL Touched` - Sell Side Liquidity level reached
### **Zone Touch Alerts**
- `OB Touched` - Any Order Block interaction
- `Bullish OB Touched` - Bullish Order Block touch
- `Bearish OB Touched` - Bearish Order Block touch
- `FVG Touched` - Any Fair Value Gap interaction
- `Bullish FVG Touched` - Bullish FVG touch
- `Bearish FVG Touched` - Bearish FVG touch
- `Zone Touched` - Any Smart Zone interaction
- `Bullish Zone Touched` - Any bullish zone touch
- `Bearish Zone Touched` - Any bearish zone touch
## ⚙️ CONFIGURATION
### **Zone Detection**
- Enable/disable FVG and OB detection independently
- Maximum zones per type (3-15, default: 8)
- Zone-specific threshold and extension settings
### **Visual Customization**
- Individual color schemes for each zone type
- Adjustable transparency levels
- Configurable line styles and widths
- Dashboard positioning and sizing options
### **Technical Analysis**
- RSI, ADX, ATR period customization
- Volume threshold multipliers
- Confidence level color coding
- Signal display toggle
## 🚀 PINE SCRIPT v6 OPTIMIZATIONS
- **User-Defined Types**: Structured data for zones and statistics
- **Methods**: Type-specific operations for better code organization
- **Enhanced Arrays**: Optimized memory management
- **Switch Statements**: Improved performance for zone classification
- **Error Handling**: Robust input validation and edge case management
- **Performance**: Efficient algorithms for real-time analysis
## 📈 TRADING APPLICATIONS
### **Entry Strategies**
- Zone confluence for high-probability setups
- Multi-timeframe confirmation via BSL/SSL
- Fibonacci extension targets
- Signal arrows for directional bias
### **Risk Management**
- Zone mitigation for stop-loss placement
- Market bias for position sizing
- Dashboard statistics for strategy validation
### **Market Analysis**
- Institutional footprint identification
- Liquidity level mapping
- Market structure assessment
- Trend continuation vs reversal analysis
## 🔧 TECHNICAL SPECIFICATIONS
- **Version**: Pine Script v6
- **Overlay**: True (draws on price chart)
- **Max Objects**: 100 boxes, 100 lines, 50 labels
- **Performance**: Optimized for real-time analysis
- **Compatibility**: All TradingView chart types and timeframes
ICT NY Opening Price Lines (12AM/8:30AM/9:30AM) ICT NY Opens (12AM / 8:30AM / 9:30AM)
This indicator plots three key New York session reference levels used by ICT traders and intraday scalpers: the Midnight Open (12:00 AM EST), the 8:30 AM EST level , and the 9:30 AM EST RTH open. Each line is drawn at that day’s opening price for the specified time and extends horizontally to 4:15 PM true daily close so you always have clean, fixed anchors for the entire trading day.
Bar TimeBar Time is a simple utility for traders who rely on backtesting, Bar Replay, and detailed price action analysis. It solves a common but frustrating problem: knowing the exact time of the bar you are looking at.
While most time indicators show your computer's live clock time, this tool displays the bar's own timestamp, perfectly synchronized with your chart's data and timezone.
Why Is This Important?
When you are deep in a Bar Replay session or analyzing a historical setup, the live clock is irrelevant. You need to know when that critical breakout or reversal candle actually happened. Was it during the pre-market? At the London open? In the last five minutes of the US session? This indicator provides that vital context instantly, without you needing to squint at the small print on the x-axis.
Key Use Cases
1. Mastering Bar Replay
As you click through bars in Replay mode, the displayed time updates with each new bar. This allows you to simulate a live trading session with full awareness of the time of day, helping you train your decision-making under more realistic conditions.
2. Analyzing Screener Signals
This is one of the most powerful uses. Imagine your screener finds a "BUY" signal on a stock from two bars ago. You switch to that stock's chart to investigate. Instead of hunting for the exact bar, this tool instantly shows you the date and time of the bar you are currently hovering over. It dramatically speeds up the workflow of moving from a screener alert to actionable analysis.
3. Detailed Price Action Study
Quickly identify key session timings, see how price reacts to news events at a specific time, or analyze intraday volume patterns with complete temporal clarity.
Features & Customization
The tool is designed to be lightweight, efficient, and fully customizable to match your charting environment.
Timezone-Aware Accuracy: Automatically detects your chart's timezone for a perfect match between the label and the x-axis.
Fully Customizable Position: Place the time display in any of nine screen positions (e.g., Top Left, Bottom Center) using a simple dropdown menu.
Custom Colors: Easily set the background and text colors to blend seamlessly with your chart's theme.
915 Opening Range RaysDraws the high and low of the 09:15–09:20 first 5-min candle each day as horizontal rays with options for extension and alerts.
