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Fibo Swing MFI by julzALGOOVERVIEW Fibo Swing MFI by julzALGO blends MFI → RSI → Least-Squares smoothing to flag overbought/oversold swings and continuously plot Fibonacci retracements from the rolling high/low of the last 200 bars. It’s built to spot momentum shifts while giving you a clean, always-current fib map of the recent market range. CORE PRINCIPLES Hybrid Momentum Signal - Uses MFI to integrate price and volume. - Applies RSI to MFI for momentum clarity. - Smooths the result with Least Squares regression to reduce noise. Swing Identification - Marks potential swing highs when momentum is overbought. - Marks potential swing lows when momentum is oversold. Fixed-Window Fibonacci Mapping - Always calculates fib levels from the highest high and lowest low of the last 200 bars. - This keeps fib zones consistent, independent of swing point detection. Visual Clarity & Non-Repainting Logic - Clean labels for OB/OS zones. - Lines and levels update only as new bars confirm changes. Adaptability - Works on any market and timeframe. - Adjustable momentum length, OB/OS thresholds, and smoothing. HOW IT WORKS - Computes Money Flow Index (MFI) from price & volume. - Applies RSI to the MFI for clearer OB/OS momentum. - Smooths the hybrid with a Least Squares (linear regression) filter. - Swing labels appear when OB/OS conditions are met (green = swing low, red = swing high). - Fibonacci retracements are always drawn from the highest high and lowest low of the last 200 bars (rolling window), independent of swing labels. HOW TO USE - Watch for OB/OS flips to mark potential swing highs/lows. - Use the 200-bar fib grid as your active map of pullback levels and reaction zones. - Combine fib reactions with your price action/volume cues for confirmation. - Works across markets and timeframes. SETTINGS - Length – Period for both MFI and RSI. - OB/OS Levels – Overbought/oversold thresholds (default 70/30). - Smooth – Least-Squares smoothing length. - Fibonacci Window – Fixed at 200 bars in this version (changeable in code via fibLen). NOTES - Logic is non-repainting aside from standard bar/label confirmation. - Increase Length on very low timeframes to reduce noise. - Swing labels help context; fibs are always based on the most recent 200-bar high/low range. SUMMARY Fibo Swing MFI by julzALGO is a momentum-plus-price action tool that merges MFI → RSI → smoothing to identify overbought/oversold swings and automatically plot Fibonacci retracements based on the rolling high/low of the last 200 bars. It’s designed to help traders quickly see potential reversal points and pullback zones, offering visual confluence between momentum shifts and fixed-window price structure. DISCLAIMER For educational purposes only. Not financial advice. Trade responsibly with proper risk management.
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julzALGO tarafından
2
Janmay's Fractal Price FilterJanmay’s Fractal Price Flow Filter A precision-crafted market bias tool that maps major and minor fractal levels while overlaying a proprietary Price Flow curve. Built for traders who want structure clarity and momentum insight without lag or noise. Quick-start strategy: Uptrend: When the minor fractal sits above the major fractal and price candles stay above the Price Flow curve, conditions favor buying. Exit/Sell: If price slips back under the Price Flow curve, momentum may be reversing. Downtrend: Simply flip the logic — minor fractal below major fractal and candles trading below the curve favors selling. Best use: Optimized for 10–15m charts and currently tested on BTCUSD. That’s just the tip of the iceberg. To unlock the full potential of this indicator and advanced setups, contact tradejanmay@gmail.com for further guidance.
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JEDSBN tarafından
Session Volatility MonitorOverview Session Volatility Monitor is a versatile volatility indicator tailored for intraday and session-based trading. It computes the average maximum price deviation (either up or down) from the session's opening price over a user-specified number of prior days, providing insights into expected "room to move" in the current session. This helps traders gauge potential exhaustion points, set realistic targets or stops, and identify when a directional move has reached historical norms (flagged as "REACHED" with the exact price level). Displayed via a customisable table and optional horizontal target lines, it's ideal for markets like forex, crypto, futures, or stocks where session volatility matters. The indicator supports custom sessions with timezone adjustments, making it adaptable to global trading hours (e.g., London, NY, or Asia kill zones). For assets with small tick sizes (e.g., forex pairs at 0.0001), a multiplier scales values for readability (e.g., showing pips as 67.0 instead of 0.00670). Key Features Session-Based Calculations: Defines sessions via presets (e.g., "NY Kill Zone: 07:00-10:00") or custom HHMM-HHMM inputs. (please note that preset sessions are mainly for futures e.g. "Full Day18:01-17:00", but also can be useful for forex and crypto) Adjustable UTC offset (e.g., -5 for ET) to align with your asset's timezone—ensures accurate detection regardless of TradingView's UTC internal clock. Tracks the max one-sided move (high - open or open - low) per session, averaging over 1–N previous days (default: 14). Table Display: Avg Max Move: Historical average deviation, labeled with days averaged and session time. Current Move: Real-time displacement from session open (positive for up, negative for down). Room to Go Up/Down: Remaining distance to reach the average, updating live; appends "REACHED (price)" if hit during the session. Customisable: Text color, font size (tiny to huge), position (e.g., bottom_left), and value scaling via multiplier/decimal places. Target Lines: Optional horizontal lines at "Up Target" (open + avg move) and "Down Target" (open - avg move). Lines start at the session open bar and extend only through the session duration (e.g., stops at 12:00 for a 07:00-12:00 session)—no further projection post-session. Fully customisable: Toggle on/off, color, style (solid/dotted/dashed), width, label text/background. Display Adjustments for Forex/Crypto: Multiplier: Scales raw values (e.g., set to 10000 for EURUSD to show pips like 45.0 instead of 0.0045). Decimals: Controls precision (0–5 places) for table values. How to Use Add to Chart: Search for "Session Volatility Monitor" in TradingView's indicators and apply to your symbol (e.g., EURUSD for forex, NQ1! for futures, BTCUSD for crypto). Configure Settings: Select a session preset or custom range; adjust UTC offset if needed (e.g., +0 for UTC symbols like crypto). Set "Number of Previous Days to Average" (e.g., 14 for a two-week look back). For small-tick assets, set Multiplier (e.g., 100 for crypto points, 10000 for forex pips) and Decimals (e.g., 0 for whole numbers). Customise table position/size/color and target lines for visibility. Interpret Outputs: Monitor the table for "room to go"—if Room Up is low/negative, upside might be limited; "REACHED" signals a potential reversal or exhaustion. Use target lines as visual S/R levels; they auto-start at session open and halt at close. Combine with price action, volume, or other indicators for entries (e.g., buy near down target if bullish bias). Example Scenario: Forex (GBPUSD, 1-min): Set session to "London Kill Zone: 02:00-05:00" (UTC+0), multiplier=10000. Table shows pips; lines mark expected highs/lows. Limitations and Tips Historical Data Limits: Averages are capped by TradingView's bar history (e.g., ~14 days on 1-min for free plans). Upgrade for deeper look backs or use higher timeframes. Session Accuracy: Ensure UTC offset matches your chart—test with the "In Session" plot (enable in Style tab, zoom y-axis if columns are tiny). No Alerts/Signals: Purely informational; add custom alerts via TradingView for "REACHED" conditions. Performance: On very low timeframes with long sessions, lines might consume line limits (max ~50)—toggle off if needed. Tips: For crypto/forex, experiment with multiplier to match your preferred units (e.g., points vs. decimals). Hide debug plot in Style tab for clean charts. If "REACHED" doesn't trigger, verify on historical data where moves exceed averages. This tool draws from concepts like Average Daily Range but focuses on directional, session-specific volatility for precise intraday decision-making. Feedback welcome! Disclaimer This indicator is for educational purposes only and does not constitute financial advice. Always consult a professional before trading.
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mr-bal tarafından
Becak I-series: Indicator Floating Panels v.80Becak I-series: Floating Panels v.80th (Indonesia Independence Days) What it does: This indicator creates three floating overlay panels that display MACD, RSI, and Stochastic oscillators directly on your price chart. Unlike traditional separate panes, these panels hover over your chart with customizable positioning and transparency, providing a clean, space-efficient way to monitor multiple technical indicators simultaneously. When to use: When you need to monitor momentum, trend strength, and overbought/oversold conditions without cluttering your workspace Perfect for traders who want quick visual access to multiple oscillators while maintaining focus on price action Ideal for any timeframe and asset class (stocks, crypto, forex, commodities) How it works: The script calculates standard MACD (12,26,9), RSI (14), and Stochastic (14,3,3) values, then renders them as floating panels with: MACD Panel: Shows MACD line (blue), Signal line (orange), and histogram (green/red bars) RSI Panel: Displays RSI line (purple) with overbought (70) and oversold (30) reference levels Stochastic Panel: Shows %K (blue) and %D (orange) lines with optional buy/sell signals and highlighted overbought/oversold zones Customization options: Position: Choose Top, Bottom, or Auto-Center placement Size: Adjust panel height (15-35% of chart) and spacing between panels Positioning: Fine-tune vertical center offset and horizontal positioning Appearance: Toggle panel backgrounds and adjust transparency (50-95%) Parameters: Modify all indicator lengths and overbought/oversold levels Signals: Enable/disable Stochastic crossover signals Display: Control lookback period (30-100 bars) and right margin spacing Universal compatibility: Works seamlessly across all asset types with automatic range detection and scaling. DIRGAHAYU HARI KEMERDEKAAN KE 80 - INDONESIA ... MERDEKA!!!!!
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kromut76 tarafından
5
Previous Day Fibonacci + Opening RangePrev Day Fibonacci & Opening Range Levels This indicator is designed for professional traders who want to combine yesterday’s market structure with today’s intraday levels. 🔹 Features: Automatic Fibonacci Retracements: Draws customizable Fibonacci retracement/extension levels based on the previous day’s High & Low. Full Customization: Users can adjust the Fibonacci ratios and colors directly in settings. Opening Range Levels: Plots today’s first candle High & Low (user-selectable timeframe for OR). Clear Visuals: Helps identify key reversal zones, breakout levels, and confluence areas between higher timeframe structure and intraday moves. 🔹 Usage Ideas: Spot potential reversal zones when price reacts to previous-day Fib levels. Combine Opening Range breakout strategies with daily Fib levels for high-probability setups. Use as confluence levels with your existing price action or indicator-based strategy. ⚡ Pro Tip: Look for overlaps between the Opening Range and Fibonacci retracements — these zones often act as strong support/resistance areas.
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drashtayoga11 tarafından
HZ Key LevelsThe HZ Key Levels script is a powerful tool designed to help traders identify sharp and precise entry and take profit levels on their charts. Utilizing a unique proprietary formula, this indicator provides a clear visual guide for strategic trading decisions. The levels are plotted as solid lines with corresponding price values, ensuring they remain relevant across different timeframes. Ideal for traders seeking reliable reference points to enhance their market analysis and execution precision.
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huzaifahzaba tarafından
Currency Strength v3.0Currency Strength v3.0 Summary The Currency Strength indicator is a powerful tool designed to gauge the relative strength of major and emerging market currencies. By plotting the True Strength Index (TSI) of various currency indices, it provides a clear visual representation of which currencies are gaining momentum and which are losing it. This indicator automatically detects the currency pair on your chart and highlights the corresponding strength lines, simplifying analysis and helping you quickly identify potential trading opportunities based on currency dynamics. Key Features Comprehensive Currency Analysis: Tracks the strength of 19 currencies, including major pairs and several emerging market currencies. Automatic Pair Detection: Intelligently identifies the base and quote currency of the active chart, automatically highlighting the relevant strength lines. Dynamic Coloring: The base currency is consistently colored blue, and the quote currency is colored gold, making it easy to distinguish between the two at a glance. Non-Repainting TSI Calculation: Uses the True Strength Index (TSI) for smooth and reliable momentum readings that do not repaint. Customizable Settings: Allows for adjustment of the fast and slow periods for the TSI calculation to fit your specific trading style. Clean Interface: Features a minimalist legend table that only displays the currencies relevant to your current chart, keeping your workspace uncluttered. How It Works The indicator pulls data from major currency indices (like DXY for the US Dollar and EXY for the Euro). For currencies that don't have a dedicated index, it uses their USD pair (e.g., USDCNY) and inverts the calculation to derive the currency's strength relative to the dollar. It then applies the True Strength Index (TSI) to this data. The TSI is a momentum oscillator that is less volatile than other oscillators, providing a more reliable measure of strength. The resulting values are plotted on the chart, allowing you to see how different currencies are performing against each other in real-time. How to Use Trend Confirmation: When the base currency's line is rising and above the zero line, and the quote currency's line is falling, it can confirm a bullish trend for the pair. The opposite would suggest a bearish trend. Identifying Divergences: Look for divergences between the currency strength lines and the price action of the pair. For example, if the price is making higher highs but the base currency's strength is making lower highs, it could signal a potential reversal. Crossovers: A crossover of the base and quote currency lines can signal a shift in momentum. A bullish signal occurs when the base currency line crosses above the quote currency line. A bearish signal occurs when it crosses below. Overbought/Oversold Levels: The horizontal dashed lines at 0.5 and -0.5 can be used as general guides for overbought and oversold conditions, respectively. Strength moving beyond these levels may indicate an unsustainable move that is due for a correction. Settings Fast Period: The short-term period for the TSI calculation. Default is 7. Slow Period: The long-term period for the TSI calculation. Default is 15. Index Source: The price source used for the calculations (e.g., Close, Open). Default is Close. Base Currency Color: The color for the base currency line. Default is Royal Blue. Quote Currency Color: The color for the quote currency line. Default is Goldenrod. Disclaimer This indicator is intended for educational and analytical purposes only. It is not financial advice. Trading involves substantial risk, and past performance is not indicative of future results. Always conduct your own research and risk management before making any trading decisions.
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DOSALGO tarafından
8
MF Logarithmic Log Trendline with Alerts by MigueFinanceWhat is Unique on This Indicator? It allows you to set alerts for logarithmic trendlines including cross and touches and it works! Not even tradingview has alerts for drawn logarithmic trendlines ! When on Logaritmic scale it looks like a normal trendline but when on the linear scale it looks like a real logarithmic curve! And is free to use! Overview Finally an Indicator that has alerts/alarms that works with Logarithmic trendlines! A- This indicator plots a customizable logarithmic (or linear) trendline based on user-defined start/end dates, extending forward to highlight support/resistance. B- It shows green upward triangles for bullish breakouts and red downward triangles for bearish breakdowns or dots for touches that are not effective crosses of the trendline C- It is also possible to set alert conditions with custom message for helping to trade the signals above/bellow the trendline How to Use 1. Apply indicator to chart. 2. Adjust inputs: log/linear, price source, start/end dates, custom prices , label/triangle visibility. 3. Setting -1 for the price source adjusts the trendline automatically to the height of the chart for the specified date based on high, low, close or open of the candle for that date. 4. Watch for triangle signals on bar close. 5. Set alerts for breakouts/breakdowns or touches via TradingView. Multiple Logarithmic Trendlines are possible! You can put many logarithmic trendlines in your chart with alerts!, just as many as many alerts your current tradingview plan allows you to. How to? - Just use this indicator, get the logaritmic trendline you desire, set the alert, make sure is well set, and after setting the alert draw yourself on tradingview the logarithmic trendline that you are setting the alarm to (a new manual drawing). - Then modify the settings of your indicator to create a new trendline, the previous one exists even if not visible as long as you have correctly set the alert, and if you did a drawing you can see your previous alert trendline. Custom Messages on Alerts - There are different options for the alerts, the one named "Any alert() function call" allows you to put custom messages that could be used on platforms like Wundertrade in order to set and execute automatic trades on buy/sell. - The other options for the alerts give you fix messages and allows you to decide if get you an alert on a cross over or under or on a touch of the trendline. Notes - Signals on confirmed closes (no repainting). - Designed for log-scaled charts but also works on linear charts. - Tested on stocks, crypto, and more. - Make sure that this indicator and your plotted chart are on the same scale, otherwise will look like floating, will move and would seem like it does not matches the chart. Important Note - The alerts and shapes only trigger after the "End Date" of the slope, not before or between them You are welcome to follow me on my socials! MigueFinance.
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miguefinance tarafından
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.
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21-50 Crossover - Sharad21/50 EMA Crossover Strategy with Alerts This indicator is designed around the classic 21 EMA and 50 EMA crossover method, a time-tested approach for identifying potential trend shifts. Bullish Signal: When the 21 EMA crosses above the 50 EMA, a possible long setup is indicated. Bearish Signal: When the 21 EMA crosses below the 50 EMA, a possible short setup is indicated. Custom Alerts: You can set alerts for crossover events to plan your entries and exits without constantly watching the charts. How to Use: Combine with price action, support/resistance zones, or other technical tools for better accuracy. Works on multiple timeframes and markets (crypto, forex, stocks, indices). ⚠ Disclaimer: Trading involves substantial risk and is not suitable for everyone. This tool is provided for educational purposes only and should be used with caution. Past performance does not guarantee future results. You are solely responsible for your trading decisions — trade wisely and manage your risk.
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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.
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macvv tarafından
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.
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