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Buy And Hold Performance Screener - [JTCAPITAL]Buy And Hold Performance Screener – is a script designed to track and display multi-asset “buy and hold” performance curves and performance statistics over defined timeframes for selected symbols. It doesn’t attempt to time entries or exits; rather, it shows what would happen if one simply bought the asset at the defined start date and held it. The indicator works by calculating in the following steps: Start Date Definition The script begins by reading an input for the start date. This defines the bar from which the equity curves begin. Symbol Definitions & Close Price Retrieval The script allows the user to specify up to ten tickers. For each ticker it uses request.security() on the “1D” timeframe to retrieve the daily close price of that symbol. Plot Enable Inputs For each ticker there is an input boolean controlling whether the equity curve for that ticker should be plotted. Asset Name Cleaning The helper function clean_name(string asset) => … takes the asset string (e.g., “CRYPTO:SOLUSD”) and manipulates it (via string splitting and replacements) to derive a cleaned short name (e.g., “SOL”). This name is used for visuals (labels, table headers). Equity Curve Calculation (“HODL”) The helper function f_HODL(closez) defines a variable equity that assumes a starting equity of 1 unit at the start date and then multiplies by the ratio of each bar’s close to the prior bar’s close: i.e. daily compounding of returns. Performance Metrics Calculation The helper function f_performance(closez) calculates, for each symbol’s close series, the percentage change of the current close relative to its close 30 days ago, 90 days ago, 180 days ago, 1 year ago (365 days), 2 years ago (730 days) and 3 years ago (1095 days). Equity Curve Plots For each ticker, if the corresponding plot input is true, the script assigns a plotted variable equal to the equity curve value. Its then drawing each selected equity curve on the chart, each in a distinct color. Table Construction If the plottable input is true, the script constructs a table and populates it with rows and column corresponding to the assigned tickers and the set 6 timeframes used for display. Buy and Sell Conditions: Since this is strictly a “buy-and-hold” performance screener, there are no explicit buy or sell signals generated or plotted. The script assumes: buy at the defined start_date, hold continuously to present. There are no filters, no exit logic, no take-profit or stop-loss. The benefit of this approach is to provide a clean benchmark of how selected assets would have performed if one simply adopted a passive “buy & hold” approach from a given start date. Features and Parameters: start_date (input.time) : Defines the date from which performance and equity curves begin. ticker1 … ticker10 (input.symbol) : User-selectable asset symbols to include in the screener. plot1 … plot10 (input.bool) : Boolean flags to enable/disable plotting of each asset’s equity curve. plottable (input.bool) : Flag to enable/disable drawing the performance table. Colored plotting + Labels for identifying each asset curve on the chart. Specifications: Here is a detailed breakdown of every calculation/variable/function used in the script and what each part means: start_date This is defined via input.time(timestamp("1 Jan 2025"), title = "Start Date"). It allows the user to pick a specific calendar date from which the equity curves and performance calculations will start. ticker1 … ticker10 These inputs allow the user to select up to ten different assets (symbols) to monitor. The script uses each of these to fetch daily close prices. plot1 … plot10 Boolean inputs controlling which of the ten asset equity curves are plotted. If plotX is true, the equity curve for ticker X will be visible; otherwise it will be not plotted. This gives the user flexibility to include or exclude specific assets on the chart. Returns the cleaned asset short name. This provides friendly text labels like “BTC”, “ETH”, “SOL”, etc., instead of full symbol codes. The choice of distinct colours for each asset helps differentiate curves visually when multiple assets are overlaid. Colour definitions Variables color1…color10 are explicitly defined via color.rgb(r,g,b) to give each asset a unique colour (e.g., red, orange, yellow, green, cyan, blue, purple, pink, etc.). What are the benefits of combining these calculations? By computing equity curves for multiple assets from the same start date and overlaying them, you can visualise comparative performance of different assets under a uniform “buy & hold” assumption. The performance table adds multi-horizon returns (30 D, 90 D, 180 D, 1 Y, 2 Y, 3 Y) which helps the user see both short-term and longer-term performance without having to manually compute returns. The use of daily close data via request.security(..., "1D") removes dependency on the chart’s timeframe, thereby standardising the comparison across assets. The equity curve and table together provide both visual (curve) and numerical (table) summaries of performance, making it easier to spot trends, divergences, and cross-asset comparisons at a glance. Because it uses compounding (equity := equity * (closez / closez )), the curves reflect the real growth of a 1-unit investment held over time, rather than only simple returns. The labelling of curves and the color-coding make the multi-asset overlay easier to interpret. Using a clean start date ensures that all curves begin at the same point (1 unit at start_date), making relative performance intuitive. Because of this, the script is useful as a benchmarking tool: rather than trying to pick entries or exit points, you can simply compare “what if I had held these assets since Jan 1 2025” (or your chosen date), and see which assets out-/under-performed in that period. It helps an investor or trader evaluate the long-term benefits of passive vs. active management, or of allocation decisions. Please note: The script assumes continuous daily data and does not account for dividends, fees, slippage, or tax implications. It does not attempt to optimise timing or provide trading signals. Returns prior to the start date are ignored (equity only begins once time >= start_date). For newly listed assets with fewer than 365 or 730 or 1095 days of history, the longer-horizon returns may return na or misleading values. Because it uses request.security() without specifying lookahead, and on “1D” timeframe, it complies with standard usage but you should verify there is no look-ahead bias in your particular setup. ENJOY!
Pine Script®指标
由JTCapitalNL提供
Liquidity Levels - PMH/PWH/PDH/HODWhat is it? An indicator that tracks the main liquidity levels on TradingView, displaying the highs and lows of reference for month, week, previous day and current day. What's it for? It identifies price zones where there are many pending orders (liquidity). Traders use it to: Find support and resistance points Identify areas where price could bounce or break through Receive alerts when price touches or breaks these levels Which levels does it show? LevelDescriptionColorLinePMH/PMLPrevious month's high and lowPurpleSolidPWH/PWLPrevious week's high and lowBlueSolidPDH/PDLPrevious day's high and lowOrangeSolidHOD/LODCurrent day's high and lowGrayDotted How to use it? Apply the indicator to your chart Customize colors and enable/disable the levels you prefer Set alerts to receive notifications when price touches or breaks levels Use the levels to make trading decisions (entry, exit, stop loss) Perfect for: Scalping, Day Trading, Swing Trading on any asset (forex, crypto, stocks)
Pine Script®指标
由fabio23traders提供
Simple VWAP + BandsSimple VWAP + Bands A clean and customizable VWAP (Volume Weighted Average Price) indicator with standard deviation bands and RTH (Regular Trading Hours) session support. Features: - VWAP Line: Volume-weighted average price calculation - Three Standard Deviation Bands: Configurable bands at 1σ, 2σ, and 3σ levels (above and below VWAP) - RTH Session Support: Option to calculate VWAP only during regular trading hours - Customizable Session Times: Configure your own trading session hours and timezone - Clean Visualization: Line breaks between sessions prevent messy connections across non-trading periods - Toggle Bands: Show/hide individual standard deviation bands as needed Use Cases: - Identify overbought/oversold conditions relative to volume-weighted price - Track price deviation from VWAP during trading sessions - Support and resistance levels based on standard deviations - Mean reversion trading strategies
Pine Script®指标
由ire0n提供
J.P. Morgan Efficiente 5 IndexJ.P. MORGAN EFFICIENTE 5 INDEX REPLICATION Walk into any retail trading forum and you'll find the same scene playing out thousands of times a day: traders huddled over their screens, drawing trendlines on candlestick charts, hunting for the perfect entry signal, convinced that the next RSI crossover will unlock the path to financial freedom. Meanwhile, in the towers of lower Manhattan and the City of London, portfolio managers are doing something entirely different. They're not drawing lines. They're not hunting patterns. They're building fortresses of diversification, wielding mathematical frameworks that have survived decades of market chaos, and most importantly, they're thinking in portfolios while retail thinks in positions. This divide is not just philosophical. It's structural, mathematical, and ultimately, profitable. The uncomfortable truth that retail traders must confront is this: while you're obsessing over whether the 50-day moving average will cross the 200-day, institutional investors are solving quadratic optimization problems across thirteen asset classes, rebalancing monthly according to Markowitz's Nobel Prize-winning framework, and targeting precise volatility levels that allow them to sleep at night regardless of what the VIX does tomorrow. The game you're playing and the game they're playing share the same field, but the rules are entirely different. The question, then, is not whether retail traders can access institutional strategies. The question is whether they're willing to fundamentally change how they think about markets. Are you ready to stop painting lines and start building portfolios? THE INSTITUTIONAL FRAMEWORK: HOW THE PROFESSIONALS ACTUALLY THINK When Harry Markowitz published "Portfolio Selection" in The Journal of Finance in 1952, he fundamentally altered how sophisticated investors approach markets. His insight was deceptively simple: returns alone mean nothing. Risk-adjusted returns mean everything. For this revelation, he would eventually receive the Nobel Prize in Economics in 1990, and his framework would become the foundation upon which trillions of dollars are managed today (Markowitz, 1952). Modern Portfolio Theory, as it came to be known, introduced a revolutionary concept: through diversification across imperfectly correlated assets, an investor could reduce portfolio risk without sacrificing expected returns. This wasn't about finding the single best asset. It was about constructing the optimal combination of assets. The mathematics are elegant in their logic: if two assets don't move in perfect lockstep, combining them creates a portfolio whose volatility is lower than the weighted average of the individual volatilities. This "free lunch" of diversification became the bedrock of institutional investment management (Elton et al., 2014). But here's where retail traders miss the point entirely: this isn't about having ten different stocks instead of one. It's about systematic, mathematically rigorous allocation across asset classes with fundamentally different risk drivers. When equity markets crash, high-quality government bonds often rally. When inflation surges, commodities may provide protection even as stocks and bonds both suffer. When emerging markets are in vogue, developed markets may lag. The professional investor doesn't predict which scenario will unfold. Instead, they position for all of them simultaneously, with weights determined not by gut feeling but by quantitative optimization. This is what J.P. Morgan Asset Management embedded into their Efficiente Index series. These are not actively managed funds where a portfolio manager makes discretionary calls. They are rules-based, systematic strategies that execute the Markowitz framework in real-time, rebalancing monthly to maintain optimal risk-adjusted positioning across global equities, fixed income, commodities, and defensive assets (J.P. Morgan Asset Management, 2016). THE EFFICIENTE 5 STRATEGY: DECONSTRUCTING INSTITUTIONAL METHODOLOGY The Efficiente 5 Index, specifically, targets a 5% annualized volatility. Let that sink in for a moment. While retail traders routinely accept 20%, 30%, or even 50% annual volatility in pursuit of returns, institutional allocators have determined that 5% volatility provides an optimal balance between growth potential and capital preservation. This isn't timidity. It's mathematics. At higher volatility levels, the compounding drag from large drawdowns becomes mathematically punishing. A 50% loss requires a 100% gain just to break even. The institutional solution: constrain volatility at the portfolio level, allowing the power of compounding to work unimpeded (Damodaran, 2008). The strategy operates across thirteen exchange-traded funds spanning five distinct asset classes: developed equity markets (SPY, IWM, EFA), fixed income across the risk spectrum (TLT, LQD, HYG), emerging markets (EEM, EMB), alternatives (IYR, GSG, GLD), and defensive positioning (TIP, BIL). These aren't arbitrary choices. Each ETF represents a distinct factor exposure, and together they provide access to the primary drivers of global asset returns (Fama and French, 1993). The methodology, as detailed in replication research by Jungle Rock (2025), follows a precise monthly cadence. At the end of each month, the strategy recalculates expected returns and volatilities for all thirteen assets using a 126-day rolling window. This six-month lookback balances responsiveness to changing market conditions against the noise of short-term fluctuations. The optimization engine then solves for the portfolio weights that maximize expected return subject to the 5% volatility target, with additional constraints to prevent excessive concentration. These constraints are critical and reveal institutional wisdom that retail traders typically ignore. No single ETF can exceed 20% of the portfolio, except for TIP and BIL which can reach 50% given their defensive nature. At the asset class level, developed equities are capped at 50%, bonds at 50%, emerging markets at 25%, and alternatives at 25%. These aren't arbitrary limits. They're guardrails preventing the optimization from becoming too aggressive during periods when recent performance might suggest concentrating heavily in a single area that's been hot (Jorion, 1992). After optimization, there's one final step that appears almost trivial but carries profound implications: weights are rounded to the nearest 5%. In a world of fractional shares and algorithmic execution, why round to 5%? The answer reveals institutional practicality over mathematical purity. A portfolio weight of 13.7% and 15.0% are functionally similar in their risk contribution, but the latter is vastly easier to communicate, to monitor, and to execute at scale. When you're managing billions, parsimony matters. WHY THIS MATTERS FOR RETAIL: THE GAP BETWEEN APPROACH AND EXECUTION Here's the uncomfortable reality: most retail traders are playing a different game entirely, and they don't even realize it. When a retail trader says "I'm bullish on tech," they buy QQQ and that's their entire technology exposure. When they say "I need some diversification," they buy ten different stocks, often in correlated sectors. This isn't diversification in the Markowitzian sense. It's concentration with extra steps. The institutional approach represented by the Efficiente 5 is fundamentally different in several ways. First, it's systematic. Emotions don't drive the allocation. The mathematics do. When equities have rallied hard and now represent 55% of the portfolio despite a 50% cap, the system sells equities and buys bonds or alternatives, regardless of how bullish the headlines feel. This forced contrarianism is what retail traders know they should do but rarely execute (Kahneman and Tversky, 1979). Second, it's forward-looking in its inputs but backward-looking in its process. The strategy doesn't try to predict the next crisis or the next boom. It simply measures what volatility and returns have been recently, assumes the immediate future resembles the immediate past more than it resembles some forecast, and positions accordingly. This humility regarding prediction is perhaps the most institutional characteristic of all. Third, and most critically, it treats the portfolio as a single organism. Retail traders typically view their holdings as separate positions, each requiring individual management. The institutional approach recognizes that what matters is not whether Position A made money, but whether the portfolio as a whole achieved its risk-adjusted return target. A position can lose money and still be a valuable contributor if it reduced portfolio volatility or provided diversification during stress periods. THE MATHEMATICAL FOUNDATION: MEAN-VARIANCE OPTIMIZATION IN PRACTICE At its core, the Efficiente 5 strategy solves a constrained optimization problem each month. In technical terms, this is a quadratic programming problem: maximize expected portfolio return subject to a volatility constraint and position limits. The objective function is straightforward: maximize the weighted sum of expected returns. The constraint is that the weighted sum of variances and covariances must not exceed the volatility target squared (Markowitz, 1959). The challenge, and this is crucial for understanding the Pine Script implementation, is that solving this problem properly requires calculating a covariance matrix. This 13x13 matrix captures not just the volatility of each asset but the correlation between every pair of assets. Two assets might each have 15% volatility, but if they're negatively correlated, combining them reduces portfolio risk. If they're positively correlated, it doesn't. The covariance matrix encodes these relationships. True mean-variance optimization requires matrix algebra and quadratic programming solvers. Pine Script, by design, lacks these capabilities. The language doesn't support matrix operations, and certainly doesn't include a QP solver. This creates a fundamental challenge: how do you implement an institutional strategy in a language not designed for institutional mathematics? The solution implemented here uses a pragmatic approximation. Instead of solving the full covariance problem, the indicator calculates a Sharpe-like ratio for each asset (return divided by volatility) and uses these ratios to determine initial weights. It then applies the individual and asset-class constraints, renormalizes, and produces the final portfolio. This isn't mathematically equivalent to true mean-variance optimization, but it captures the essential spirit: weight assets according to their risk-adjusted return potential, subject to diversification constraints. For retail implementation, this approximation is likely sufficient. The difference between a theoretically optimal portfolio and a very good approximation is typically modest, and the discipline of systematic rebalancing across asset classes matters far more than the precise weights. Perfect is the enemy of good, and a good approximation executed consistently will outperform a perfect solution that never gets implemented (Arnott et al., 2013). RETURNS, RISKS, AND THE POWER OF COMPOUNDING The Efficiente 5 Index has, historically, delivered on its promise of 5% volatility with respectable returns. While past performance never guarantees future results, the framework reveals why low-volatility strategies can be surprisingly powerful. Consider two portfolios: Portfolio A averages 12% returns with 20% volatility, while Portfolio B averages 8% returns with 5% volatility. Which performs better over time? The arithmetic return favors Portfolio A, but compound returns tell a different story. Portfolio A will experience occasional 20-30% drawdowns. Portfolio B rarely draws down more than 10%. Over a twenty-year horizon, the geometric return (what you actually experience) for Portfolio B may match or exceed Portfolio A, simply because it never gives back massive gains. This is the power of volatility management that retail traders chronically underestimate (Bernstein, 1996). Moreover, low volatility enables behavioral advantages. When your portfolio draws down 35%, as it might with a high-volatility approach, the psychological pressure to sell at the worst possible time becomes overwhelming. When your maximum drawdown is 12%, as might occur with the Efficiente 5 approach, staying the course is far easier. Behavioral finance research has consistently shown that investor returns lag fund returns primarily due to poor timing decisions driven by emotional responses to volatility (Dalbar, 2020). The indicator displays not just target and actual portfolio weights, but also tracks total return, portfolio value, and realized volatility. This isn't just data. It's feedback. Retail traders can see, in real-time, whether their actual portfolio volatility matches their target, whether their risk-adjusted returns are improving, and whether their allocation discipline is holding. This transparency transforms abstract concepts into concrete metrics. WHAT RETAIL TRADERS MUST LEARN: THE MINDSET SHIFT The path from retail to institutional thinking requires three fundamental shifts. First, stop thinking in positions and start thinking in portfolios. Your question should never be "Should I buy this stock?" but rather "How does this position change my portfolio's expected return and volatility?" If you can't answer that question quantitatively, you're not ready to make the trade. Second, embrace systematic rebalancing even when it feels wrong. Perhaps especially when it feels wrong. The Efficiente 5 strategy rebalances monthly regardless of market conditions. If equities have surged and now exceed their target weight, the strategy sells equities and buys bonds or alternatives. Every retail trader knows this is what you "should" do, but almost none actually do it. The institutional edge isn't in having better information. It's in having better discipline (Swensen, 2009). Third, accept that volatility is not your friend. The retail mythology that "higher risk equals higher returns" is true on average across assets, but it's not true for implementation. A 15% return with 30% volatility will compound more slowly than a 12% return with 10% volatility due to the mathematics of return distributions. Institutions figured this out decades ago. Retail is still learning. The Efficiente 5 replication indicator provides a bridge. It won't solve the problem of prediction no indicator can. But it solves the problem of allocation, which is arguably more important. By implementing institutional methodology in an accessible format, it allows retail traders to see what professional portfolio construction actually looks like, not in theory but in executable code. The the colorful lines that retail traders love to draw, don't disappear. They simply become less central to the process. The portfolio becomes central instead. IMPLEMENTATION CONSIDERATIONS AND PRACTICAL REALITY Running this indicator on TradingView provides a dynamic view of how institutional allocation would evolve over time. The labels on each asset class line show current weights, updated continuously as prices change and rebalancing occurs. The dashboard displays the full allocation across all thirteen ETFs, showing both target weights (what the optimization suggests) and actual weights (what the portfolio currently holds after price movements). Several key insights emerge from watching this process unfold. First, the strategy is not static. Weights change monthly as the optimization recalibrates to recent volatility and returns. What worked last month may not be optimal this month. Second, the strategy is not market-timing. It doesn't try to predict whether stocks will rise or fall. It simply measures recent behavior and positions accordingly. If volatility has risen, the strategy shifts toward defensive assets. If correlations have changed, the diversification benefits adjust. Third, and perhaps most importantly for retail traders, the strategy demonstrates that sophistication and complexity are not synonyms. The Efficiente 5 methodology is sophisticated in its framework but simple in its execution. There are no exotic derivatives, no complex market-timing rules, no predictions of future scenarios. Just systematic optimization, monthly rebalancing, and discipline. This simplicity is a feature, not a bug. The indicator also highlights limitations that retail traders must understand. The Pine Script implementation uses an approximation of true mean-variance optimization, as discussed earlier. Transaction costs are not modeled. Slippage is ignored. Tax implications are not considered. These simplifications mean the indicator is educational and analytical, not a fully operational trading system. For actual implementation, traders would need to account for these real-world factors. Moreover, the strategy requires access to all thirteen ETFs and sufficient capital to hold meaningful positions in each. With 5% as the rounding increment, practical implementation probably requires at least $10,000 to avoid having positions that are too small to matter. The strategy is also explicitly designed for a 5% volatility target, which may be too conservative for younger investors with long time horizons or too aggressive for retirees living off their portfolio. The framework is adaptable, but adaptation requires understanding the trade-offs. CAN RETAIL TRULY COMPETE WITH INSTITUTIONS? The honest answer is nuanced. Retail traders will never have the same resources as institutions. They won't have Bloomberg terminals, proprietary research, or armies of analysts. But in portfolio construction, the resource gap matters less than the mindset gap. The mathematics of Markowitz are available to everyone. ETFs provide liquid, low-cost access to institutional-quality building blocks. Computing power is essentially free. The barriers are not technological or financial. They're conceptual. If a retail trader understands why portfolios matter more than positions, why systematic discipline beats discretionary emotion, and why volatility management enables compounding, they can build portfolios that rival institutional allocation in their elegance and effectiveness. Not in their scale, not in their execution costs, but in their conceptual soundness. The Efficiente 5 framework proves this is possible. What retail traders must recognize is that competing with institutions doesn't mean day-trading better than their algorithms. It means portfolio-building better than their average client. And that's achievable because most institutional clients, despite having access to the best managers, still make emotional decisions, chase performance, and abandon strategies at the worst possible times. The retail edge isn't in outsmarting professionals. It's in out-disciplining amateurs who happen to have more money. The J.P. Morgan Efficiente 5 Index Replication indicator serves as both a tool and a teacher. As a tool, it provides a systematic framework for multi-asset allocation based on proven institutional methodology. As a teacher, it demonstrates daily what portfolio thinking actually looks like in practice. The colorful lines remain on the chart, but they're no longer the focus. The portfolio is the focus. The risk-adjusted return is the focus. The systematic discipline is the focus. Stop painting lines. Start building portfolios. The institutions have been doing it for seventy years. It's time retail caught up. REFERENCES Arnott, R. D., Hsu, J., & Moore, P. (2013). Fundamental Indexation. Financial Analysts Journal, 61(2), 83-99. Bernstein, W. J. (1996). The Intelligent Asset Allocator. New York: McGraw-Hill. Dalbar, Inc. (2020). Quantitative Analysis of Investor Behavior. Boston: Dalbar. Damodaran, A. (2008). Strategic Risk Taking: A Framework for Risk Management. Upper Saddle River: Pearson Education. Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Hoboken: John Wiley & Sons. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Jorion, P. (1992). Portfolio optimization in practice. Financial Analysts Journal, 48(1), 68-74. J.P. Morgan Asset Management. (2016). Guide to the Markets. New York: J.P. Morgan. Jungle Rock. (2025). Institutional Asset Allocation meets the Efficient Frontier: Replicating the JPMorgan Efficiente 5 Strategy. Working Paper. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons. Swensen, D. F. (2009). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press.
Pine Script®指标
由EdgeTools提供
Directional EMA - For Loop | Lyro RSDirectional EMA - For Loop | Lyro RS Introduction This indicator combines multi-type moving averages, loop-based momentum scoring, and divergence detection for adaptive trend and reversal analysis. Key Features: Multiple Moving Average Selection System: Choose from 16 different MA types - HMA, ALMA and JMA etc. To match your style best. For Loop Based Scoring: Uses a From / To system to calculate cumulative buying/selling pressure across recent price action. Signal Threshold: Long / Short threshold levels to control the sensitivity for different market conditions. Divergence Detection: Regular bullish / bearish with clear labels for potential reversal points. Clean Visuals: Multiple color themes with table and color based indicator line for easy reading. How It Works: Core Calculation: The indicator first creates a directional signal by comparing price to your selected moving average, normalized for current volatility. Loop Analysis: This signal feeds into a for-loop that scores recent price history, generating a cumulative momentum value. Signal Generation: Bullish signals trigger when the score crosses above the Upper Threshold Bearish signals trigger when the score crosses below the Lower Threshold Divergence Alerts: Automatically detects when price makes new highs/lows that aren't confirmed by the oscillator. Practical Use: Trend Identification: The color-coded oscillator and signal table help confirm trend direction. Reversal Warning: Divergence labels highlight potential trend exhaustion points for careful watch. Customization: Adjust MA type and length for sensitivity tuning Modify loop parameters (From/To) to change analysis depth Fine-tune threshold levels for signal frequency Enable/disable divergence detection as needed ⚠️ Disclaimer This tool is for technical analysis education only. It does not guarantee results or constitute financial advice. Always use proper risk management and combine with other analysis methods. Past performance doesn't predict future results.
Pine Script®指标
由LyroRS提供
iFVG Strategie by Futures.RobbyiFVG Strategy Checklist by Futures.Robby Updated: October 27, 2025 Description This script is a manual checklist designed to help traders evaluate their setups based on the iFVG (Fair Value Gap) strategy. It serves solely as a visual aid and does not perform automatic analysis, signal generation, or trade execution. How It Works The script creates an interactive checklist directly on the chart. Traders manually select which criteria are met, and the script calculates a percentage score, displaying it with color coding: Green (≥ 60%): Good fulfillment of criteria Orange (40–59%): Partial fulfillment Red (< 40%): Poor fulfillment Checklist Criteria The checklist is divided into two main sections: 1. Trade Criteria (8 Points) Eight manually selectable criteria to assess setup quality: Trade im Bias → Trade in Bias: Trade follows the higher timeframe trend (H1/H4/Daily). BE Level → BE Level: Swing point between entry and target. Sweep → Sweep: Price hits a key swing before reversing. Displacement → Displacement: iFVG broken by strong candles. Leg FVG geschlossen → Leg FVG Closed: No open m1 to m5 FVGs to target. FVG Reaktion → FVG Reaction: Reaction at FVG during sweep (HTF). FVG Größe → FVG Size: 6 to 10 points. Anzahl Kerzen → Number of Candles: Maximum of 6 candles. 2. Goals (1 Point) Six optional goal conditions, counted together as 1 point: Equal H / L → Equal High/Low Session H / L → Session High/Low News H / L → News High/Low HTF Swing Point → HTF Swing Point HTF OB → HTF Order Block HTF FVG → HTF FVG Settings and Customization The script’s settings are translated as follows: Group: Trade Criteria Trade im Bias → Trade in Bias Tooltip: Trendrichtung folgt HTF (H1/H4/Täglich) – Trend follows HTF direction BE Level → BE Level Tooltip: Swingpunkt zwischen Einstieg und Ziel – Swing point between entry and target Sweep → Sweep Tooltip: Kurs erreicht markanten Swing – Price hits key swing before inverse Displacement → Displacement Tooltip: iFVG durch starke Kerzen gebrochen – iFVG broken by strong candles Leg FVG geschlossen → Leg FVG Closed Tooltip: Keine offenen m1 bis m5 FVGs bis Ziel – No open m1 to m5 FVGs to target FVG Reaktion → FVG Reaction Tooltip: Reaktion an FVG beim Sweep (HTF) – Reaction at FVG during sweep (HTF) FVG Größe → FVG Size Tooltip: 6 bis 10 Punkte – 6 to 10 points Anzahl Kerzen → Number of Candles Tooltip: Maximal 6 Kerzen – Maximum of 6 candles Group: Goals Equal H / L → Equal High/Low Session H / L → Session High/Low News H / L → News High/Low HTF Swing Point → HTF Swing Point HTF OB → HTF Order Block HTF FVG → HTF FVG ℹ️ Ziele zählen gemeinsam als 1 Punkt → ℹ️ Goals count together as 1 point Window Position & Size Fensterposition → Window Position oben rechts → top right oben links → top left unten rechts → bottom right unten links → bottom left Tabellengröße → Table Size normal → normal small → small tiny → tiny Translation of Chart Table Contents The table headers and entries on the chart are translated as follows: Table Headers: Trade Checkliste → Trade Checklist Ziele → Goals Status Symbols: ✅ → ✅ (Fulfilled) ❌ → ❌ (Not fulfilled) Individual Criteria (Trade Criteria): Trade im Bias → Trade in Bias BE Level → BE Level Sweep → Sweep Displacement → Displacement Leg FVG geschlossen → Leg FVG Closed FVG Reaktion → FVG Reaction FVG Größe → FVG Size Anzahl Kerzen → Number of Candles Individual Criteria (Goals): Equal H / L → Equal High/Low Session H / L → Session High/Low News H / L → News High/Low HTF Swing Point → HTF Swing Point HTF OB → HTF Order Block HTF FVG → HTF FVG Note Line: Ziele zählen gemeinsam als 1 Punkt → Goals count together as 1 point Important Note This tool is not an automated indicator, but a visual decision aid for traders who want to apply their strategy in a structured and conscious way.
Pine Script®指标
由kp_eu_de提供
FVG SizeFVG Size Indicator – Description Overview This Pine Script v5 indicator detects and visualizes Fair Value Gaps (FVGs) on the chart. It draws colored boxes for FVGs, center lines (CE), and displays the size of each FVG as a label. The indicator is designed for manual analysis, helping traders identify potential price imbalances. Key Features FVG Detection: Identifies bullish and bearish FVGs based on price structure. Draws colored boxes for FVGs and dotted center lines (CE). FVG Size Display: Shows the size of each FVG as a label inside the box. Customizable minimum size threshold to filter out smaller FVGs. Dynamic Adjustments: Extends FVG boxes to the right as new bars form. Removes FVGs that are filled (mitigated) by price action. Customizable Settings: Adjustable colors, text size, and display options. Settings and Translations Here are the German settings with their English translations: FVG Settings Long FVG Farbe → Long FVG Color (Color for bullish FVG boxes) Short FVG Farbe → Short FVG Color (Color for bearish FVG boxes) CE Farbe → CE Color (Color for the center line) Tage Rückblick → Lookback Days (Number of days to look back for FVGs) Lösche gefüllte Boxen & Linien → Delete Filled Boxes & Lines (Removes FVGs that have been filled by price) FVG Display FVG Größe anzeigen → Show FVG Size (Displays the size of each FVG as a label) Text → Text Size (Size of the FVG size label text) Mindestgröße → Minimum Size (Minimum FVG size to display, filtering out smaller FVGs) How It Works FVG Detection Logic: A bullish FVG is detected if the high of the 3rd bar is lower than the low of the 1st bar. A bearish FVG is detected if the low of the 3rd bar is higher than the high of the 1st bar. Drawing FVGs: The indicator draws a box between the high/low of the 1st and 3rd bars. A center line (CE) is drawn at the midpoint of the FVG. The size of the FVG is displayed as a label inside the box. Dynamic Adjustments: FVG boxes are extended to the right as new bars form. If the price fills the FVG, the box and line are removed (depending on settings). Mitigation Logic: If the price closes beyond the FVG boundaries, the FVG is considered "filled" and removed.
Pine Script®指标
由kp_eu_de提供
GARCH Range PredictorThis was inspired by deltatrendtrading's video on GARCH models to predict daily trading ranges and identify favorable trading conditions. Based on advanced volatility forecasting techniques, it predicts whether a trading day's true range will exceed a threshold, helping traders decide when to trade or skip a session. Key Features GARCH(1,1) Volatility Modeling: Uses log-transformed true ranges with exponential moving average centering Forward-Looking Predictions: Makes predictions at session start before the day unfolds Dynamic or Static Thresholds: Choose between fixed dollar thresholds or adaptive 20-day averages Accuracy Tracking: Monitors prediction accuracy with overall and recent (20-day) hit rates Visual Session Boxes: Colors trading sessions green (trade) or red (skip) based on predictions Real-Time Statistics: Displays current predictions, thresholds, and performance metrics How It Works Data Transformation: Log-transforms daily true ranges and centers them using an EMA Variance Modeling: Updates GARCH variance using: σ²ₜ = ω + α(residual²) + β(σ²ₜ₋₁) Prediction Generation: Back-transforms log predictions to dollar values Signal Generation: Compares predictions to threshold to generate trade/skip signals Performance Tracking: Validates predictions against actual outcomes Parameters GARCH Parameters (ω, α, β): Control volatility persistence and mean reversion EMA Period: Smoothing period for log range centering Threshold Settings: Static dollar amount or dynamic multiplier of recent averages Session Time: Define regular trading hours for analysis Best Use Cases Breakout and momentum strategies that perform better on high-range days Risk management by avoiding low-volatility sessions Futures day trading (optimized for MNQ/NQ detection) Any strategy where daily range impacts profitability Important Notes Requires 5+ sessions for initialization and warm-up Accuracy depends heavily on proper parameter tuning for your specific instrument Default parameters may need adjustment for different markets Monitor the hit rate to validate effectiveness on your timeframe
Pine Script®指标
由yundore提供
Order Blocks (SMC/ICT)Order Blocks (SMC/ICT) — TradingATH Precision. Intent. Structure. This refined tool automatically detects and displays Order Blocks (OBs) with clean, institutional clarity—mapping the exact zones where displacement originated and where price is most likely to rebalance or react. Built for SMC/ICT Traders , it presents bullish and bearish OBs with disciplined visuals, mitigation logic, and efficient performance on any timeframe. What You’ll See Bullish Order Blocks in balanced green shading with a mid-line, borders, and a compact label. Bearish Order Blocks in refined red tones, equally annotated and visually consistent. Controlled Extension of zones for a chosen number of bars—or dynamic extension until mitigation. Configurable Labels with selectable position (Top/Bottom, Left/Right) and custom label font color per side. Mid-Line (Average) through each OB for precise reference and execution alignment. Features Volume Pivot Logic : OBs are derived from a volume pivot length, focusing on zones that matter where displacement had participation. Mitigation Methods : Validate via Wick or Close, adapting to different liquidity conditions and your SMC playbook. ATR Size Filter : Enforce minimum/maximum size (×ATR) to remove insignificant zones and retain only the highest-quality blocks. Extend-Until-Mitigated : Keep active zones projected into future bars until price mitigates—ensuring they remain actionable. Clean Plotting : Size-safe arrays, time-anchored boxes/lines, and efficient updates for smooth operation in intraday and swing contexts. Alert Suite : OB Formed: Bullish/Bearish OB creation. OB Mitigated: Zone filled/invalidated. Price Entered OB: Live notification when price re-enters the most recent OB. Perfect For ICT/SMC Traders mapping institutional zones with precise mitigation behavior. Intraday Traders who need real-time OB formation and entry alerts without chart clutter. Swing Traders filtering only valid, well-sized OBs using ATR-based constraints. Analysts & Educators requiring a reliable, visually consistent tool for teaching structure and mitigation. Recommended Settings Volume Pivot Length : 5–14 (balance sensitivity vs. selectivity). Mitigation : Wick for responsiveness; Close for stricter confirmation. OB Length (bars) : 10–50 for controlled reach; enable Extend until mitigation for live tracking. ATR Filter : Min 0.25×, Max 3× to exclude noise while preserving meaningful blocks. Labels : Position Bottom-Right (default), adjust label font color for readability on your theme. In Short Institutional logic. Clean execution. Professional clarity. Order Blocks (SMC/ICT) — TradingATH presents structurally sound zones with precise mitigation handling and alerting—crafted for traders who demand both analytical accuracy and visual refinement. Created by: TradingATH
Pine Script®指标
由PueblaATH提供
27 ALERT INFY EQ | Manual JSON Only//@version=6 indicator("27 ALERT INFY EQ | Manual JSON Only", overlay=true) //──────────────────────────────────────────── // 🔧 USER INPUTS //──────────────────────────────────────────── symbol_name = input.string("INFY", "Equity Symbol (for labels only)") tradeMode = input.string("Intraday", "Trade Mode (for dashboard)", options= ) // Paste your exact Dhan payloads below (must be single-line strings) json_buy = input.string('{"secret":"hWMIU","alertType":"multi_leg_order","order_legs": }', "BUY JSON Script") json_sell = input.string('{"secret":"hWMIU","alertType":"multi_leg_order","order_legs": }', "SELL JSON Script") //──────────────────────────────────────────── // 📊 SIGNAL LOGIC (Heikin Ashi + MACD) //──────────────────────────────────────────── ha_close = (open + high + low + close) / 4.0 var float ha_open = na ha_open := na(ha_open ) ? (open + close)/2.0 : (ha_open + ha_close )/2.0 isBull = ha_close > ha_open isBear = ha_close < ha_open = ta.macd(close, 12, 26, 9) macdBull = macdLine > signalLine macdBear = macdLine < signalLine //──────────────────────────────────────────── // 🚀 ENTRY / EXIT (One-Candle Confirmation) //──────────────────────────────────────────── var bool inTrade = false var string lastEvent = "Waiting..." buyCondition = isBull and macdBull and not inTrade sellCondition = isBear and macdBear and inTrade if buyCondition inTrade := true lastEvent := "BUY" label.new(bar_index, low, "🟢 BUY " + symbol_name, style=label.style_label_up, color=color.new(color.green, 0)) alert(json_buy, alert.freq_once_per_bar_close) if sellCondition inTrade := false lastEvent := "SELL" label.new(bar_index, high, "🔴 SELL " + symbol_name, style=label.style_label_down, color=color.new(color.red, 0)) alert(json_sell, alert.freq_once_per_bar_close) //──────────────────────────────────────────── // 🧭 DASHBOARD //──────────────────────────────────────────── var table dash = table.new(position.top_right, 2, 6, border_width=1) if barstate.isfirst table.cell(dash, 0, 0, "📊 " + symbol_name + " EQ", bgcolor=color.new(color.black, 60), text_color=color.white) table.cell(dash, 0, 1, "Symbol") table.cell(dash, 0, 2, "Position") table.cell(dash, 0, 3, "Trend") table.cell(dash, 0, 4, "Mode") table.cell(dash, 0, 5, "Alert Type") trendTxt = isBull ? "BULLISH" : isBear ? "BEARISH" : "NEUTRAL" if barstate.islast table.cell(dash, 1, 1, symbol_name) table.cell(dash, 1, 2, inTrade ? "LONG" : "FLAT") table.cell(dash, 1, 3, trendTxt) table.cell(dash, 1, 4, tradeMode) table.cell(dash, 1, 5, "MANUAL JSON")
Pine Script®指标
由Dhananjay_0210提供
Enhanced Price Direction Predictor📊 Core Mechanism: Rule-Based Scoring: The indicator relies on a simplified scoring model where it checks for nine specific conditions on the bullish side and nine corresponding conditions on the bearish side. Bullish/Bearish Score Calculation:The script initializes bullish_score and bearish_score to $0.0$.It then checks a predefined list of features (e.g., $5$-period Rate of Change, $5/20$ EMA crossover, RSI level, Order Flow direction) and adds a fixed point value (weight) to the appropriate score if the condition is met. Overbought/Oversold Penalty:It includes a built-in risk-management element by applying a $-0.10$ penalty if the RSI is in extreme territory:RSI(14) $>$ 70 (Overbought) $\rightarrow$ Penalty to bullish_score.RSI(14) $<$ 30 (Oversold) $\rightarrow$ Penalty to bearish_score. Probability Conversion:The probability_up is calculated by taking the ratio of the bullish_score to the total_score (sum of bullish and bearish scores):$$\text{Probability\_Up} = \frac{\text{Bullish\_Score}}{\text{Bullish\_Score} + \text{Bearish\_Score}}$ MIL:IF the total_score is zero (i.e., no strong conditions are met), the probability defaults to $0.5$ (neutral).
Pine Script®指标
由virtualapeclub提供
Liquidity Grab + RSI Divergence═══════════════════════════════════════════════════════════════ LIQUIDITY GRAB + RSI DIVERGENCE INDICATOR ═══════════════════════════════════════════════════════════════ 📌 OVERVIEW This indicator identifies high-probability reversals by combining: • Liquidity sweeps (stop hunts) • RSI divergence confirmation • Filters false breakouts automatically ═══════════════════════════════════════════════════════════════ 🟢 BUY SIGNAL (Green Triangle Up) REQUIRES BOTH CONDITIONS: 1. Liquidity Grab Below Previous Low • Price breaks BELOW recent low • Candle CLOSES ABOVE that low • Traps sellers who shorted the breakdown 2. Bullish RSI Divergence • Price: Lower Low (LL) • RSI: Higher Low (HL) • Shows weakening downward momentum ➜ Result: Potential bullish reversal ═══════════════════════════════════════════════════════════════ 🔴 SELL SIGNAL (Red Triangle Down) REQUIRES BOTH CONDITIONS: 1. Liquidity Grab Above Previous High • Price breaks ABOVE recent high • Candle CLOSES BELOW that high • Traps buyers who bought the breakout 2. Bearish RSI Divergence • Price: Higher High (HH) • RSI: Lower High (LH) • Shows weakening upward momentum ➜ Result: Potential bearish reversal ═══════════════════════════════════════════════════════════════ 📊 VISUAL INDICATORS Main Signals: 🔺 Large Green Triangle = BUY (Liq Grab + Bullish Div) 🔻 Large Red Triangle = SELL (Liq Grab + Bearish Div) Reference Levels: ━ Red Line = Previous High Level ━ Green Line = Previous Low Level Additional Markers (Optional): ○ Small Green Circle = Liquidity grab low only ○ Small Red Circle = Liquidity grab high only ✕ Small Blue Cross = Bullish divergence only ✕ Small Orange Cross = Bearish divergence only ═══════════════════════════════════════════════════════════════ ⚙️ SETTINGS 1. Lookback Period (Default: 20) • Range: 5-100 • Sets how far back to identify previous highs/lows • Higher = fewer but stronger levels • Lower = more frequent but weaker levels 2. RSI Length (Default: 14) • Range: 5-50 • Standard RSI calculation period • 14 is industry standard 3. RSI Divergence Lookback (Default: 5) • Range: 3-20 • Controls pivot point sensitivity • Higher = fewer divergence signals • Lower = more divergence signals 4. Show Labels (Default: ON) • Toggle BUY/SELL text labels • Disable for cleaner chart view ═══════════════════════════════════════════════════════════════ 💡 HOW TO USE Step 1: WAIT FOR CONFIRMATION • Only trade LARGE TRIANGLE signals • Ignore small circles/crosses alone Step 2: CHECK TIMEFRAME • Best on: 15min, 1H, 4H, Daily • Avoid: 1min, 5min (too noisy) Step 3: CONFIRM CONTEXT • Check overall market trend • Identify key support/resistance • Look for confluence with price action Step 4: ENTRY & RISK MANAGEMENT • Enter on signal candle close or pullback • Stop loss below/above the liquidity grab wick • Target: Previous swing high/low or key levels • Risk/Reward: Minimum 1:2 ratio Step 5: SET ALERTS • Create alert for "BUY Signal" • Create alert for "SELL Signal" • Never miss opportunities ═══════════════════════════════════════════════════════════════ ✅ BEST PRACTICES DO: ✓ Use on multiple timeframes for confluence ✓ Combine with support/resistance zones ✓ Wait for both conditions (liq grab + divergence) ✓ Practice on demo account first ✓ Use proper position sizing DON'T: ✗ Trade every small circle/cross ✗ Use on very low timeframes (<15min) ✗ Ignore overall market context ✗ Trade without stop loss ✗ Risk more than 1-2% per trade ═══════════════════════════════════════════════════════════════ ⚠️ IMPORTANT NOTES • This is a CONFIRMATION tool, not a holy grail • No indicator is 100% accurate • Combine with your trading strategy • Backtest on your preferred instruments • Adjust parameters for your trading style • Higher timeframes = more reliable signals • Always use risk management ═══════════════════════════════════════════════════════════════ 🔔 ALERTS INCLUDED Two alert conditions are built-in: 1. "BUY Signal" - Liquidity Grab + Bullish RSI Divergence 2. "SELL Signal" - Liquidity Grab + Bearish RSI Divergence ═══════════════════════════════════════════════════════════════ 📈 RECOMMENDED SETTINGS BY TIMEFRAME 5-15 Min Charts: • Lookback: 10-15 • RSI Length: 14 • RSI Div Lookback: 3-5 1H-4H Charts: • Lookback: 20-30 • RSI Length: 14 • RSI Div Lookback: 5-7 Daily Charts: • Lookback: 30-50 • RSI Length: 14 • RSI Div Lookback: 7-10 ═══════════════════════════════════════════════════════════════ Good luck and trade safe! 🚀
Pine Script®指标
由hassanejaz1提供
Fair Value Gaps (SMC/ICT)Fair Value Gaps (SMC/ICT) — TradingATH Precision. Imbalance. Opportunity. This advanced indicator automatically detects and visualizes Fair Value Gaps (FVGs) revealing the inefficiencies left in price delivery where institutional order flow has displaced liquidity. Each gap is cleanly mapped, color-coded, and dynamically managed, helping traders identify where price may seek to rebalance in the future. Engineered for Smart Money Concepts (SMC/ICT) Traders , it delivers a disciplined, minimal, and highly responsive visualization of market inefficiencies — enabling precise alignment with institutional order flow and mitigation behavior. What You’ll See Bullish FVGs represented as soft green zones indicating bullish imbalances where price is likely to revisit for rebalancing. Bearish FVGs shown in refined red tones, marking bearish inefficiencies and potential short-term premium zones. Dynamic or Static Mode — choose between continuously updating live zones or static historical boxes extending for a controlled bar length. Smart Labels displayed within each zone identifying “Bullish FVG (Fair Value Gap)” or “Bearish FVG (Fair Value Gap)” with fully customizable text colors. Mitigation Lines optionally plotted once price closes through a gap, confirming rebalancing and structural cleanup. Optional Dashboard providing a clear count of active, mitigated, and total FVGs in both bullish and bearish contexts. Features Auto Detection Logic : Precisely identifies FVGs using refined three-candle displacement logic with adjustable threshold sensitivity. Dynamic Zones : Live FVGs compress and evolve in real-time as price re-enters inefficiencies, maintaining structural accuracy. Smart Mitigation : Automatically removes or softens zones once filled or mitigated, keeping your chart focused and clean. Visual Precision : Professional color balance, typography, and spacing ensure a refined institutional-grade presentation. Alert System : Configurable alerts for new FVG formations and mitigation events to stay aligned with live liquidity flow. Performance Optimized : Built with efficient arrays and conditional logic for flawless responsiveness on all timeframes. ICT/SMC Integration : Fully compatible with complementary SMC tools like Order Blocks, Liquidity Sweeps, and Market Structure Shifts. Perfect For SMC/ICT Traders analyzing price delivery imbalances and mitigation behavior. Intraday Traders tracking high-probability rebalancing setups within institutional killzones. Swing Traders identifying unmitigated imbalances as key areas of interest for future reversals. Analysts & Educators who demand clarity, precision, and reliability in their institutional models. Recommended Settings Timeframe : 1H–4H (for structural precision). Mode : Dynamic = off for backtesting; on for live reactive monitoring. Extend : 20 bars for balanced visibility. Threshold %: 0.10–0.25% for adaptive sensitivity. Mitigation Lines : Enabled for visual confirmation of rebalancing. In Short Institutional logic. Visual clarity. Trading precision. Fair Value Gaps (SMC/ICT) — TradingATH provides a clean, data-driven visualization of inefficiencies in price delivery, helping traders anticipate rebalancing and liquidity re-tests with exceptional clarity and professional design. Created by: TradingATH
Pine Script®指标
由PueblaATH提供
Devils Mark Plus Volume Imbalance Multi TimeframeFollowing the success of the devil marks multi timeframe indicator I decided to add volume imbalance. Devils mark code remains unchanged here. Functionality of the Devils mark remains the same as in when a candle prints without a wick at either end it indicates an area of price imbalance and it is assumed that the market will want to re-balance this level at some point in the future. The same can be said for volume imbalances where 2 adjacent candles bodies don't meet. Again it it assumed the market will come back at some point to readdress this imbalance. Once mitigated the volume imbalance will be removed by the indicator. These areas are best used to add confluence to trade ideas and shouldn't be used to formulate trade ideas on their own. A table is included for easy reference. Please note that data for timeframes lower than the current timeframe will not be shown. It is also worth noting that data on much higher timeframes than the current chart timeframe may not be shown due to data restrictions. If in doubt go up a timeframe ! I hope you find this indicator useful.
Pine Script®指标
由gj7rwt提供