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อินดิเคเตอร์และกลยุทธ์

Kalman Exponential SuperTrendThe Kalman Exponential SuperTrend is a new, smoother & superior version of the famous "SuperTrend". Using Kalman smoothing, a concept from the EMA (Exponential Moving Average), this script leverages the best out of each and combines it into a single indicator. How does it work? First, we need to calculate the Kalman smoothed source. This is a kind of complex calculation, so you need to study it if you want to know how it works precisely. It smooths the source of the SuperTrend, which helps us smooth the SuperTrend. Then, we calculate "a" where: n = user defined ATR length a = 2/(n+1) Now we calculate the ATR over "n" period. Classical calculation, nothing changed here. Now we calculate the SuperTrend using the Kalman smoothed source & ATR where: kalman = kalman smoothed source ATR = Average True Range m = Factor chosen by user. Upper Band = kalman + ATR * m Lower Band = kalman - ATR * m Now we just smooth it a bit further using the "a" and a concept from the EMA. u1 = Upper Band a bar ago l1 = Lower Band a bar ago u = Upper Band l = Lower Band Upper = u1 * (1-a) + u * a Lower = l1 * (1-a) + u * a When the classical (not Kalman) source crosses above the Upper, it indicates an uptrend. When it crosses below the Lower, it indicates a downtrend. Methodology & Concepts When I took a look at the classical SuperTrend => It was just far too slow, and if I made it faster it was noisy as hell. So I decided I would try to make up for it. I tried the gaussian, bilateral filter, but then I tried kalman and that worked the best, so I added it. Now it was still too noisy and unconsistent, so I revisited my knowledge of concepts and picked the one from the EMA, and it kinda solved it. In the core of the indicator, all it does is combine them in a really simple way, but if you go more deeply you see how it fits the puzzlé really well. It is not about trying out random things´=> but about seeking what it is missing and trying to lessen its bad side. That is the entire point of this indicator => Offer a unique approach to the SuperTrend type, that lessen the bad sides of it. I also added different plotting types, this is so everyone can find their favorite Enjoy Gs!
อินดิเคเตอร์ Pine Script®
โดย MisinkoMaster
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
อินดิเคเตอร์ Pine Script®
โดย ire0n
RBLR - GSK Vizag AP IndiaThis indicator identifies the Opening Range High (ORH) and Low (ORL) based on the first 15 minutes of the Indian equity market session (9:15 AM to 9:30 AM IST). It draws horizontal lines extending these levels until market close (3:30 PM IST) and generates visual signals for price breakouts above ORH or below ORL, as well as reversals back into the range. Key features: - **Range Calculation**: Captures the high and low during the opening period using real-time bar data. - **Line Extension**: Lines are dynamically extended bar-by-bar within the session for clear visualization. - **Signals**: - Green triangle up: Crossover above ORH (potential bullish breakout). - Red triangle down: Crossunder below ORL (potential bearish breakout). - Yellow labels: Reversals from breakout levels back into the range. - **Labels**: "RAM BAAN" marks the ORH (inspired by a precise arrow from the Ramayana), and "LAKSHMAN REKHA" marks the ORL (inspired by a protective boundary line from the same epic). - **Customization**: Toggle signals on/off and select line styles (Dotted, Dashed, Solid, or Smoothed, with transparency for Smoothed). The state-tracking logic prevents redundant signals by monitoring if price remains outside the range after a breakout. This helps users observe range-bound behavior or directional moves without built-in alerts. This indicator is particularly useful for day trading on longer intraday timeframes (e.g., 15-minute charts) to identify session-wide trends and avoid noise in shorter frames. For best results, apply on intraday timeframes on NSE/BSE symbols. Note that lines and labels are limited to the script's max counts to avoid performance issues on long histories. **Disclaimer**: This indicator is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Trading in financial markets involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Users should conduct their own research, consider their financial situation, and consult with qualified professionals before making any investment decisions. The author and TradingView assume no liability for any losses incurred from its use.
อินดิเคเตอร์ Pine Script®
โดย prowelltraders
Grok's xAI Signal (GXS) Indicator for BTC V6Grok's xAI Signal (GXS) Indicator: A Simple Guide Imagine trying to decide if Bitcoin is a "buy," "sell," or "wait" without staring at 10 different charts. The GXS Indicator does that for you—it's like a smart dashboard for BTC traders, overlaying signals right on your price chart. It boils down complex market clues into one easy score (from -1 "super bearish" to +1 "super bullish") and flashes green/red arrows or shaded zones when action's needed. No fancy math overload; just clear visuals like tiny triangles for trades, colored clouds for trends, and a bottom "mood bar" (green=up vibe, red=down, gray=meh). At its core, GXS mixes three big-picture checks: Price Momentum (50% weight): Quick scans of RSI (overbought/oversold vibes), MACD (speed of ups/downs), EMAs (is price riding the trend wave?), and Bollinger Bands (is the market squeezing for a breakout?). This catches short-term "hot or not" energy. Network Health (30% weight): A simple "NVT" hack using trading volume vs. price to spot if BTC feels undervalued (buy hint) or overhyped (sell warning). It's like checking if the crowd's too excited or chill. Trend Strength (20% weight): ADX filter ensures signals only fire in "trending" markets (not choppy sideways noise), plus a MACD boost for extra momentum nudge. Why this approach? BTC's wild—pure price charts give false alarms in flat times, while ignoring volume/network ignores the "why" behind moves. GXS blends old-school TA (reliable for patterns) with on-chain smarts (crypto-specific "under the hood" data) and a trend gate (skips 70% of bad trades). It's conservative: Signals need the score to cross ±0.08 and a strong trend, reducing noise for swing/position traders. Result? Fewer emotional guesses, more "wait for confirmation" patience—perfect for volatile assets like BTC where hype kills. Quick Tips to Tweak for Better Results Start with defaults, then experiment on historical charts (backtest via TradingView's strategy tester if pairing with one): Fewer False Signals: Bump thresholds to ±0.15 (buy/sell)—trades only on stronger conviction, cutting whipsaws by 20-30% in choppy markets. Or raise ADX thresh to 28 for "only big trends." Faster/Slower Response: Shorten EMAs (e.g., 5/21) or RSI (10) for quicker scalps; lengthen (12/50) for swing holds. Test on 4H/daily BTC. Volume Sensitivity: If NVT flips too often, extend its length to 20—smooths on-chain noise in bull runs. Visual Polish: Crank cloud opacity to 80% for subtler fills; toggle off EMAs if they clutter. Enable table for score breakdowns during live trades. Risk Tip: Always pair with stops (e.g., 2-3% below signals). On BTC, tweak in bull markets (looser thresh) vs. bears (tighter). In short, GXS is your BTC "sixth sense"—balanced, not black-box. Tweak small, track win rate, and let trends lead. Happy trading!
อินดิเคเตอร์ Pine Script®
โดย ralis24
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.
อินดิเคเตอร์ Pine Script®
โดย EdgeTools
1
PG ATM Strike Line with Call & Put PremiumsPine Script: ATM Strike Line with Call & Put Premiums (Simplified)This Pine Script for TradingView displays the At-The-Money (ATM) strike price, futures price, call/put premiums (time value), and two ratios—Premium Ratio (PR) and Volume Ratio (VR)—for a user-selected underlying asset (e.g., NIFTY, BANKNIFTY, or stocks). It helps traders gauge near-term market direction using options data.How the Script WorksInputs:Expiry: Select year (e.g., '25), month (01–12), day (01–31) for option expiry (e.g., '251028'). Timeframe: Choose data timeframe (e.g., Daily, 15-min). Symbol: Auto-detects chart symbol or select from Indian indices/stocks. Strike: Auto-ATM (based on futures) or manual strike input. Interval: Auto (e.g., 100 for NIFTY) or custom strike interval. Colors: Customizable for ATM line, labels (Futures Price, CPR, PPR, VR, PR). Calculations:Futures Price (FP): Fetches front-month futures price (e.g., NSE:NIFTY1!). ATM Strike: Rounds futures price to nearest strike interval. Option Data: Retrieves Last Traded Price (LTP) and volume for ATM call/put options (e.g., NSE:NIFTY251028C24200). Call Premium (CPR): Call LTP minus intrinsic value (max(0, FP - Strike)). Put Premium (PPR): Put LTP minus intrinsic value (max(0, Strike - FP)). Premium Ratio (PR): PPR / CPR. Volume Ratio (VR): Put Volume / Call Volume. Visuals:Draws ATM strike line on chart. Displays labels: FP (futures price), CPR (call premium), PPR (put premium), VR, PR. VR/PR labels: Red (≥ 1.25, bearish), Green (≤ 0.75, bullish), Gray (0.75–1.25, neutral). Updates on last confirmed bar to avoid redraws. Using PR and VR for Market DirectionPremium Ratio (PR):PR ≥ 1.25 (Red): High put premiums suggest bearish sentiment (expect price drop). PR ≤ 0.75 (Green): High call premiums suggest bullish sentiment (expect price rise). 0.75 < PR < 1.25 (Gray): Neutral, no clear direction. Use: High PR favors bearish trades (e.g., buy puts); low PR favors bullish trades (e.g., buy calls). Volume Ratio (VR):VR ≥ 1.25 (Red): High put volume indicates bearish activity. VR ≤ 0.75 (Green): High call volume indicates bullish activity. 0.75 < VR < 1.25 (Gray): Neutral trading activity. Use: High VR suggests bearish moves; low VR suggests bullish moves. Combined Signals:High PR & VR: Strong bearish signal; consider put buying or call selling. Low PR & VR: Strong bullish signal; consider call buying or put selling. Mixed/Neutral: Use price action or support/resistance for confirmation. Tips:Combine with technical analysis (e.g., trends, levels). Match timeframe to trading horizon (e.g., 15-min for intraday). Monitor FP for context; check volatility or news for accuracy. ExampleNIFTY: FP = 24,237.50, ATM = 24,200, CPR = 120.25, PPR = 180.50, PR = 1.50 (Red), VR = 1.30 (Red). Insight: High PR/VR suggests bearish bias; consider bearish trades if price nears resistance. Action: Buy puts or exit longs, confirm with price action. Conclusion: This script provides a concise tool for options traders, showing ATM strike, premiums, and PR/VR ratios. High PR/VR (≥ 1.25) signals bearish sentiment, low PR/VR (≤ 0.75) signals bullish sentiment, and neutral (0.75–1.25) suggests indecision. Combine with technical analysis for robust trading decisions in the Indian options market.
อินดิเคเตอร์ Pine Script®
โดย ipuneet
SC_Reversal Confirmation 30 minutes by Claude (Version 1)📉 When to Use Use this setup when the stock is in a downtrend and a bullish reversal is anticipated. 🔍 Recommended Usage This model is designed for pullback phases, where the asset is declining and a reversal is expected. It helps filter out weak signals and waits for technical confirmation before triggering an entry. ✅ Entry Signal Green triangles appear only when all reversal conditions are fully met. Entry may occur slightly after the bottom, but with a reduced likelihood of false signals. 📊 Suggested Settings Apply on a 30-minute chart using a 100-period Exponential Moving Average (EMA) based on close. Recommended for Cobalt Chart 0. --------------------------------------------------------------------------------------
อินดิเคเตอร์ Pine Script®
โดย torotrap
Yield Curve RegimesCurrently we are seeing equities and all other risk assets rallying to new all time high. But when will this stop? There are multiple risks/signals i am monitoring to stay at the right side of the macro trade. Macro is everything: “When you get the Big-Picture wrong you wont live long.” So lets go through a major risk that could be the catalyst for the next deeper correction Capital needs to begin to move BACK across the risk curve as the yield curve steepens. We don't know if the source of the the crash will be from bear steepening or bull steepening because its unclear if long end rates blowing out will be the source of the crash. If the Fed continues to make the policy error of being overly accommodative at this high level of nominal GDP + Inflation risk, the long end of the curve will price this. Simple: If the Fed is to lose the long end can move up to price the inflation risk, which could ultimately pull risk assets down. We have not seen this yet because the last inflation prints came in flat, but I expect these to come in higher over the next 6 months. This means watching long end rates and their potential drag on equities will be critical. We are not seeing this yet as the Russell is sitting at all time highs and capital continues to move into low quality factors. Look where the long end is moving + the attribution analysis for the move. → Down growth risk → Up Inflation risk + look what the 2s10s & the 10s30 are pricing and how these changes in the curve connect to the current yield curve regimes. You can get the Trading view Skript 100% free here
อินดิเคเตอร์ Pine Script®
โดย The-white-goat
SMC by ASHY-JAYASHY-JAY "Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money". Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades. The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized. The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies. 🟣Key Terms of Smart Money Concept (SMC) • Market Structure (Trend) • Change of Character (ChoCh) • Break of Structure (BoS) • Order Blocks (Supply and Demand) • Imbalance (IMB) • Inefficiency (IFC) • Fair Value Gap (FVG) • Liquidity • Premium and Discount
อินดิเคเตอร์ Pine Script®
โดย Jay-Jover
Svopex Session Highlighter# Session Highlighter ## Description **Session Highlighter** is a powerful Pine Script indicator designed to visually identify and mark specific trading hours on your chart. This tool helps traders focus on their preferred trading sessions by highlighting the background during active hours and marking the session start with customizable visual markers. ## Key Features - **📊 Session Background Highlighting**: Automatically shades the chart background during your defined trading hours (default: 7:00 - 23:00) - **🎯 Smart Session Start Marker**: Places a marker on the last candle before session start, intelligently adapting to your timeframe: - 1 Hour chart: Marker at 6:00 - 15 Minute chart: Marker at 6:45 - 5 Minute chart: Marker at 6:55 - 1 Minute chart: Marker at 6:59 - **🌍 Timezone Support**: Choose from multiple timezones (Europe/Prague, Europe/London, America/New_York, UTC) - **🎨 5 Marker Styles**: Customize your session start indicator: - Triangle - Circle - Diamond - Label with time text - Vertical line - **⚙️ Fully Customizable**: Adjust start/end hours, timezone, and marker style through simple settings ## Settings - **Start Hour**: Set your session start time (0-23) - **End Hour**: Set your session end time (0-23) - **Timezone**: Select your trading timezone - **Marker Style**: Choose your preferred visual marker ## Use Cases - Identify London/New York trading sessions - Mark Asian session hours - Highlight your personal trading windows - Avoid trading during off-hours - Perfect for day traders and scalpers ## Installation 1. Copy the Pine Script code 2. Open TradingView Pine Editor 3. Paste the code and click "Add to Chart" 4. Configure settings to match your trading schedule
อินดิเคเตอร์ Pine Script®
โดย svopex