התחל

חפש סקריפטים עבור "Futures"

Waindrops [Makit0]█  OVERALL Plot waindrops (custom volume profiles) on user defined periods, for each period you get high and low, it slices each period in half to get independent vwap, volume profile and the volume traded per price at each half. It works on intraday charts only, up to 720m (12H). It can plot balanced or unbalanced waindrops, and volume profiles up to 24H sessions. As example you can setup unbalanced periods to get independent volume profiles for the overnight and cash sessions on the futures market, or 24H periods to get the full session volume profile of EURUSD The purpose of this indicator is twofold: 1 — from a Chartist point of view, to have an indicator which displays the volume in a more readable way 2 — from a Pine Coder point of view, to have an example of use for two very powerful tools on Pine Script:    • the recently updated drawing limit to 500 (from 50)    • the recently ability to use drawings arrays (lines and labels) If you are new to Pine Script and you are learning how to code, I hope you read all the code and comments on this indicator, all is designed for you, the variables and functions names, the sometimes too big explanations, the overall structure of the code, all is intended as an example on how to code in Pine Script a specific indicator from a very good specification in form of white paper If you wanna learn Pine Script form scratch just start HERE In case you have any kind of problem with Pine Script please use some of the awesome resources at our disposal: USRMAN , REFMAN , AWESOMENESS , MAGIC █  FEATURES Waindrops are a different way of seeing the volume and price plotted in a chart, its a volume profile indicator where you can see the volume of each price level plotted as a vertical histogram for each half of a custom period. By default the period is 60 so it plots an independent volume profile each 30m You can think of each waindrop as an user defined candlestick or bar with four key values:  • high of the period  • low of the period  • left vwap (volume weighted average price of the first half period)  • right vwap (volume weighted average price of the second half period) The waindrop can have 3 different colors (configurable by the user):  • GREEN: when the right vwap is higher than the left vwap (bullish sentiment )  • RED: when the right vwap is lower than the left vwap (bearish sentiment )  • BLUE: when the right vwap is equal than the left vwap ( neutral sentiment ) KEY FEATURES  • Help menu  • Custom periods  • Central bars  • Left/Right VWAPs  • Custom central bars and vwaps: color and pixels  • Highly configurable volume histogram: execution window, ticks, pixels, color, update frequency and fine tuning the neutral meaning  • Volume labels with custom size and color  • Tracking price dot to be able to see the current price when you hide your default candlesticks or bars █  SETTINGS Click here or set any impar period to see the HELP INFO : show the HELP INFO, if it is activated the indicator will not plot PERIOD SIZE (max 2880 min) : waindrop size in minutes, default 60, max 2880 to allow the first half of a 48H period as a full session volume profile BARS : show the central and vwap bars, default true    Central bars : show the central bars, default true    VWAP bars : show the left and right vwap bars, default true    Bars pixels : width of the bars in pixels, default 2    Bars color mode : bars color behavior    • BARS : gets the color from the 'Bars color' option on the settings panel    • HISTOGRAM : gets the color from the Bearish/Bullish/Neutral Histogram color options from the settings panel    Bars color : color for the central and vwap bars, default white HISTOGRAM show the volume histogram, default true    Execution window (x24H) : last 24H periods where the volume funcionality will be plotted, default 5    Ticks per bar (max 50) : width in ticks of each histogram bar, default 2    Updates per period : number of times the histogram will update    • ONE : update at the last bar of the period    • TWO : update at the last bar of each half period    • FOUR : slice the period in 4 quarters and updates at the last bar of each of them    • EACH BAR : updates at the close of each bar    Pixels per bar : width in pixels of each histogram bar, default 4    Neutral Treshold (ticks) : delta in ticks between left and right vwaps to identify a waindrop as neutral, default 0    Bearish Histogram color : histogram color when right vwap is lower than left vwap, default red    Bullish Histogram color : histogram color when right vwap is higher than left vwap, default green    Neutral Histogram color : histogram color when the delta between right and left vwaps is equal or lower than the Neutral treshold, default blue VOLUME LABELS : show volume labels    Volume labels color : color for the volume labels, default white    Volume Labels size : text size for the volume labels, choose between AUTO, TINY, SMALL, NORMAL or LARGE, default TINY TRACK PRICE : show a yellow ball tracking the last price, default true █  LIMITS This indicator only works on intraday charts (minutes only) up to 12H (720m), the lower chart timeframe you can use is 1m This indicator needs price, time and volume to work, it will not work on an index (there is no volume), the execution will not be allowed The histogram (volume profile) can be plotted on 24H sessions as limit but you can plot several 24H sessions █  ERRORS AND PERFORMANCE Depending on the choosed settings, the script performance will be highly affected and it will experience errors Two of the more common errors it can throw are:  • Calculation takes too long to execute  • Loop takes too long The indicator performance is highly related to the underlying volatility (tick wise), the script takes each candlestick or bar and for each tick in it stores the price and volume, if the ticker in your chart has thousands and thousands of ticks per bar the indicator will throw an error for sure, it can not calculate in time such amount of ticks. What all of that means? Simply put, this will throw error on the BITCOIN pair BTCUSD (high volatility with tick size 0.01) because it has too many ticks per bar, but lucky you it will work just fine on the futures contract BTC1! (tick size 5) because it has a lot less ticks per bar There are some options you can fine tune to boost the script performance, the more demanding option in terms of resources consumption is Updates per period , by default is maxed out so lowering this setting will improve the performance in a high way. If you wanna know more about how to improve the script performance, read the HELP INFO accessible from the settings panel █  HOW-TO SETUP The basic parameters to adjust are Period size , Ticks per bar and Pixels per bar  • Period size is the main setting, defines the waindrop size, to get a better looking histogram set bigger period and smaller chart timeframe  • Ticks per bar is the tricky one, adjust it differently for each underlying (ticker) volatility wise, for some you will need a low value, for others a high one.    To get a more accurate histogram set it as lower as you can (min value is 1)  • Pixels per bar allows you to adjust the width of each histogram bar, with it you can adjust the blank space between them or allow overlaping You must play with these three parameters until you obtain the desired histogram: smoother, sharper, etc... These are some of the different kind of charts you can setup thru the settings:  • Balanced Waindrops (default): charts with waindrops where the two halfs are of same size.    This is the default chart, just select a period (30m, 60m, 120m, 240m, pick your poison), adjust the histogram ticks and pixels and watch  • Unbalanced Waindrops: chart with waindrops where the two halfs are of different sizes.    Do you trade futures and want to plot a waindrop with the first half for the overnight session and the second half for the cash session? you got it;    just adjust the period to 1860 for any CME ticker (like ES1! for example) adjust the histogram ticks and pixels and watch  • Full Session Volume Profile: chart with waindrops where only the first half plots.    Do you use Volume profile to analize the market? Lucky you, now you can trick this one to plot it, just try a period of 780 on SPY, 2760 on ES1!, or 2880 on EURUSD    remember to adjust the histogram ticks and pixels for each underlying  • Only Bars: charts with only central and vwap bars plotted, simply deactivate the histogram and volume labels  • Only Histogram: charts with only the histogram plotted (volume profile charts), simply deactivate the bars and volume labels  • Only Volume: charts with only the raw volume numbers plotted, simply deactivate the bars and histogram If you wanna know more about custom full session periods for different asset classes, read the HELP INFO accessible from the settings panel EXAMPLES Full Session Volume Profile on MES 5m chart: Full Session Unbalanced Waindrop on MNQ 2m chart (left side Overnight session, right side Cash Session): The following examples will have the exact same charts but on four different tickers representing a futures contract, a forex pair, an etf and a stock. We are doing this to be able to see the different parameters we need for plotting the same kind of chart on different assets The chart composition is as follows:  • Left side: Volume Labels chart (period 10)  • Upper Right side: Waindrops (period 60)  • Lower Right side: Full Session Volume Profile The first example will specify the main parameters, the rest of the charts will have only the differences MES :  • Left: Period size: 10, Bars: uncheck, Histogram: uncheck, Execution window: 1, Ticks per bar: 2, Updates per period: EACH BAR,    Pixels per bar: 4, Volume labels: check, Track price: check  • Upper Right: Period size: 60, Bars: check, Bars color mode: HISTOGRAM, Histogram: check, Execution window: 2, Ticks per bar: 2,    Updates per period: EACH BAR, Pixels per bar: 4, Volume labels: uncheck, Track price: check  • Lower Right: Period size: 2760, Bars: uncheck, Histogram: check, Execution window: 1, Ticks per bar: 1, Updates per period: EACH BAR,    Pixels per bar: 2, Volume labels: uncheck, Track price: check EURUSD :  • Upper Right: Ticks per bar: 10  • Lower Right: Period size: 2880, Ticks per bar: 1, Pixels per bar: 1 SPY :  • Left: Ticks per bar: 3  • Upper Right: Ticks per bar: 5, Pixels per bar: 3  • Lower Right: Period size: 780, Ticks per bar: 2, Pixels per bar: 2 AAPL :  • Left: Ticks per bar: 2  • Upper Right: Ticks per bar: 6, Pixels per bar: 3  • Lower Right: Period size: 780, Ticks per bar: 1, Pixels per bar: 2 █  THANKS TO PineCoders for all they do, all the tools and help they provide and their involvement in making a better community scarf for the idea of coding a waindrops like indicator, I did not know something like that existed at all All the Pine Coders, Pine Pros and Pine Wizards, people who share their work and knowledge for the sake of it and helping others, I'm very grateful indeed I'm learning at each step of the way from you all, thanks for this awesome community; Opensource and shared knowledge: this is the way! (said with canned voice from inside my helmet :D) █  NOTE This description was formatted following THIS guidelines ═════════════════════════════════════════════════════════════════════════ I sincerely hope you enjoy reading and using this work as much as I enjoyed developing it :D GOOD LUCK AND HAPPY TRADING!
אינדיקטור Pine Script®
מאת ‎makit0‎
108
Dynamically Adjustable Moving AverageIntroduction The Dynamically Adjustable Moving Average (AMA) is an adaptive moving average proposed by Jacinta Chan Phooi M’ng (1) originally provided to forecast Asian Tiger's futures markets. AMA adjust to market condition in order to avoid whipsaw trades as well as entering the trending market earlier. This moving average showed better results than classical methods (SMA20, EMA20, MAC, MACD, KAMA, OptSMA) using a classical crossover/under strategy in Asian Tiger's futures from 2014 to 2015. Dynamically Adjustable Moving Average AMA adjust to market condition using a non-exponential method, which in itself is not common, AMA is described as follow : 1/v * sum(close,v) where v = σ/√σ σ is the price standard deviation. v is defined as the Efficacy Ratio (not be confounded with the Efficiency Ratio) . As you can see v determine the moving average period, you could resume the formula in pine with sma(close,v) but in pine its not possible to use the function sma with variables for length, however you can derive sma using cumulation. sma ≈ d/length where d = c - c_length and c = cum(close) So a moving average can be expressed as the difference of the cumulated price by the cumulated price length period back, this difference is then divided by length. The length period of the indicator should be short since rounded version of v tend to become less variables thus providing less adaptive results. AMA in Forex Market In 2014/2015 Major Forex currencies where more persistent than Asian Tiger's Futures (2) , also most traded currency pairs tend to have a strong long-term positive autocorrelation so AMA could have in theory provided good results if we only focus on the long term dependency. AMA has been tested with ASEAN-5 Currencies (3) and still showed good results, however forex is still a tricky market, also there is zero proof that switching to a long term moving average during ranging market avoid whipsaw trades (if you have a paper who prove it please pm me) . Conclusion An interesting indicator, however the idea behind it is far from being optimal, so far most adaptive methods tend to focus more in adapting themselves to market complexity than volatility. An interesting approach would have been to determine the validity of a signal by checking the efficacy ratio at time t . Backtesting could be a good way to see if the indicator is still performing well. References (1) J.C.P. M’ng, Dynamically adjustable moving average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets, Physica A (2018), doi.org (2) www.researchgate.net (3) www.ncbi.nlm.nih.gov
אינדיקטור Pine Script®
מאת ‎alexgrover‎
15
KD-NewAutoTrade for Future Trading - Heikin Ashi candles The KD-NewAutoTrade strategy is a dynamic trend-following indicator designed for scalping and swing trading across crypto, forex, and index futures. It combines the precision of EMA crossovers, RSI momentum, and ADX trend strength to deliver clear Buy/Sell signals with high reliability. 🔹 Core Logic EMA Fast & Slow Crossover – Identifies short-term and long-term trend shifts. RSI Confirmation – Filters out false signals by requiring RSI to cross custom Buy/Sell thresholds. ADX Filter – Ensures trades only trigger when market trend strength exceeds your chosen ADX minimum. 🔹 Key Features Visual Buy/Sell triangles directly on the chart. Customizable inputs for EMA, RSI, and ADX lengths. Works efficiently on all timeframes and all markets (Crypto, Indices, Stocks, Commodities). Optional background highlights for active trade zones. Alert conditions for both BUY and SELL setups – ready to use in automated strategies or alert bots. 🔹 Recommended Usage Use Heikin Ashi candles Works best on 1M - 5M timeframes. Combine with volume or higher-timeframe trend confirmation for stronger signals.
אינדיקטור Pine Script®
מאת ‎krish16887‎
NQ YM Correlogram Meter TypeOverview This indicator provides a real-time, visual "meter" of the correlation between the Nasdaq 100 (NQ) and the Dow Jones (YM) futures. It is designed as a clean, non-intrusive dashboard panel that displays only the current correlation value, making it an ideal companion for pairs traders who need to see the live relationship at a glance. Unlike a traditional oscillator that plots historical data, this tool focuses exclusively on the "right now" to aid in immediate trade decisions. Key Features Real-Time Correlation Meter: A single vertical bar displays the current correlation, visually mapping the -1.0 to +1.0 range. Clear Visual Cues: The bar's color gradient (from red for negative correlation to green for positive correlation) and fill level provide an instant understanding of the market relationship. Precise Value Display: The exact numerical correlation (e.g., 0.85 or -0.50) is shown clearly at the bottom of the meter. Contextual Y-Axis: Static labels (+1.0, +0.5, 0.0, -0.5) provide quick reference points for the meter's reading. Dashboard Panel: Renders cleanly as an overlay table on the right side of your chart, saving screen real estate. How to Use This indicator is best used as a confirmation tool alongside a primary pairs trading strategy or a historical correlation oscillator. High Green Bar (near +1.0): Indicates a strong positive correlation. NQ and YM are moving in sync. Bar near 0.0: Indicates little to no linear relationship. Low Red Bar (near -1.0): Indicates a strong negative (inverse) correlation. NQ and YM are moving in opposite directions. For a pairs trader, this meter provides an instant check to confirm if the two assets are in their expected state of correlation at the moment of execution. Settings & Customization Correlation Period: Set the lookback length for the correlation calculation. Symbols: Fully customizable, though it defaults to YM1! and NQ1!. Panel Appearance: Adjust the Table Size (Small/Large) and Chart Theme (Light/Dark). Text Size: Independently control the font size for the numerical Value Text and the Y-Axis Labels to perfectly fit your display.
אינדיקטור Pine Script®
מאת ‎coldFish52‎
NQ YM Correlation 1 min dataOverview This indicator plots the correlation between Nasdaq 100 (NQ) and Dow Jones (YM) futures. It is specifically designed to act as an "Engine RPM" gauge for pairs traders who trade divergence or spread breakouts—not mean reversion. To ensure consistent readings, this indicator always calculates using a 1-minute timeframe data, regardless of the chart timeframe you are currently viewing. The core idea is: High Correlation (Blue Zone): "Low RPM" or "Engine Idle." NQ and YM are moving together. The spread is flat. This is a no-trade zone. Low Correlation (Red Zone): "High RPM" or "Engine Hot." NQ and YM are diverging. The spread is moving. This is the primary trade zone.
אינדיקטור Pine Script®
מאת ‎coldFish52‎
Ehlers Phasor Analysis (PHASOR)# PHASOR: Phasor Analysis (Ehlers) ## Overview and Purpose The Phasor Analysis indicator, developed by John Ehlers, represents an advanced cycle analysis tool that identifies the phase of the dominant cycle component in a time series through complex signal processing techniques. This sophisticated indicator uses correlation-based methods to determine the real and imaginary components of the signal, converting them to a continuous phase angle that reveals market cycle progression. Unlike traditional oscillators, the Phasor provides unwrapped phase measurements that accumulate continuously, offering unique insights into market timing and cycle behavior. ## Core Concepts * **Complex Signal Analysis** — Uses real and imaginary components to determine cycle phase * **Correlation-Based Detection** — Employs Ehlers' correlation method for robust phase estimation * **Unwrapped Phase Tracking** — Provides continuous phase accumulation without discontinuities * **Anti-Regression Logic** — Prevents phase angle from moving backward under specific conditions Market Applications: * **Cycle Timing** — Precise identification of cycle peaks and troughs * **Market Regime Analysis** — Distinguishes between trending and cycling market conditions * **Turning Point Detection** — Advanced warning system for potential market reversals ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|----------------| | Period | 28 | Fixed cycle period for correlation analysis | Match to expected dominant cycle length | | Source | Close | Price series for phase calculation | Use typical price or other smoothed series | | Show Derived Period | false | Display calculated period from phase rate | Enable for adaptive period analysis | | Show Trend State | false | Display trend/cycle state variable | Enable for regime identification | ## Calculation and Mathematical Foundation **Technical Formula:** **Stage 1: Correlation Analysis** For period $n$ and source $x_t$: Real component correlation with cosine wave: $$R = \frac{n \sum x_t \cos\left(\frac{2\pi t}{n}\right) - \sum x_t \sum \cos\left(\frac{2\pi t}{n}\right)}{\sqrt{D_{cos}}}$$ Imaginary component correlation with negative sine wave: $$I = \frac{n \sum x_t \left(-\sin\left(\frac{2\pi t}{n}\right)\right) - \sum x_t \sum \left(-\sin\left(\frac{2\pi t}{n}\right)\right)}{\sqrt{D_{sin}}}$$ where $D_{cos}$ and $D_{sin}$ are normalization denominators. **Stage 2: Phase Angle Conversion** $$\theta_{raw} = \begin{cases} 90° - \arctan\left(\frac{I}{R}\right) \cdot \frac{180°}{\pi} & \text{if } R \neq 0 \\ 0° & \text{if } R = 0, I > 0 \\ 180° & \text{if } R = 0, I \leq 0 \end{cases}$$ **Stage 3: Phase Unwrapping** $$\theta_{unwrapped}(t) = \theta_{unwrapped}(t-1) + \Delta\theta$$ where $\Delta\theta$ is the normalized phase difference. **Stage 4: Ehlers' Anti-Regression Condition** $$\theta_{final}(t) = \begin{cases} \theta_{final}(t-1) & \text{if regression conditions met} \\ \theta_{unwrapped}(t) & \text{otherwise} \end{cases}$$ **Derived Calculations:** Derived Period: $P_{derived} = \frac{360°}{\Delta\theta_{final}}$ (clamped to ) Trend State: $$S_{trend} = \begin{cases} 1 & \text{if } \Delta\theta \leq 6° \text{ and } |\theta| \geq 90° \\ -1 & \text{if } \Delta\theta \leq 6° \text{ and } |\theta| < 90° \\ 0 & \text{if } \Delta\theta > 6° \end{cases}$$ > 🔍 **Technical Note:** The correlation-based approach provides robust phase estimation even in noisy market conditions, while the unwrapping mechanism ensures continuous phase tracking across cycle boundaries. ## Interpretation Details * **Phasor Angle (Primary Output):** - **+90°**: Potential cycle peak region - **0°**: Mid-cycle ascending phase - **-90°**: Potential cycle trough region - **±180°**: Mid-cycle descending phase * **Phase Progression:** - Continuous upward movement → Normal cycle progression - Phase stalling → Potential cycle extension or trend development - Rapid phase changes → Cycle compression or volatility spike * **Derived Period Analysis:** - Period < 10 → High-frequency cycle dominance - Period 15-40 → Typical swing trading cycles - Period > 50 → Trending market conditions * **Trend State Variable:** - **+1**: Long trend conditions (slow phase change in extreme zones) - **-1**: Short trend or consolidation (slow phase change in neutral zones) - **0**: Active cycling (normal phase change rate) ## Applications * **Cycle-Based Trading:** - Enter long positions near -90° crossings (cycle troughs) - Enter short positions near +90° crossings (cycle peaks) - Exit positions during mid-cycle phases (0°, ±180°) * **Market Timing:** - Use phase acceleration for early trend detection - Monitor derived period for cycle length changes - Combine with trend state for regime-appropriate strategies * **Risk Management:** - Adjust position sizes based on cycle clarity (derived period stability) - Implement different risk parameters for trending vs. cycling regimes - Use phase velocity for stop-loss placement timing ## Limitations and Considerations * **Parameter Sensitivity:** - Fixed period assumption may not match actual market cycles - Requires cycle period optimization for different markets and timeframes - Performance degrades when multiple cycles interfere * **Computational Complexity:** - Correlation calculations over full period windows - Multiple mathematical transformations increase processing requirements - Real-time implementation requires efficient algorithms * **Market Conditions:** - Most effective in markets with clear cyclical behavior - May provide false signals during strong trending periods - Requires sufficient historical data for correlation analysis Complementary Indicators: * MESA Adaptive Moving Average (cycle-based smoothing) * Dominant Cycle Period indicators * Detrended Price Oscillator (cycle identification) ## References 1. Ehlers, J.F. "Cycle Analytics for Traders." Wiley, 2013. 2. Ehlers, J.F. "Cybernetic Analysis for Stocks and Futures." Wiley, 2004.
אינדיקטור Pine Script®
מאת ‎mihakralj‎
Stock Fundamental Overlay [DarwinDarma]Stock Fundamental Overlay Stock Fundamental Overlay is a comprehensive valuation indicator that displays multiple fundamental analysis metrics directly on your price chart. Key Features: • Graham Number - Benjamin Graham's intrinsic value formula • Book Value Per Share (BVPS) - Net asset value baseline • DCF Valuation - Discounted Cash Flow analysis (non-financial stocks) • DDM Valuation - Dividend Discount Model (dividend-paying stocks) • Visual Value Zones - Color-coded undervalued/overvalued regions • Real-time Fundamental Table - Live metrics and valuations • Price vs Graham Comparison - Quick valuation assessment • Built-in Alerts - Notification when price crosses key levels Valuation Models: • Graham Number: √(22.5 × EPS × BVPS) • DCF: Customizable discount rate, growth rate, and forecast period • DDM: Gordon Growth Model for dividend analysis Visual Elements: • Plot lines for BVPS, Graham Number, and DCF values • Shaded value zone between BVPS and Graham Number • Background coloring: Deep value (below BVPS), Undervalued (below Graham), Overvalued (>1.5x Graham) • Dynamic table showing all metrics with theme-aware text colors Special Handling: • Financial sector detection - DCF disabled for banks/financials where FCF metrics are distorted • Automatic light/dark theme adaptation • TTM (Trailing Twelve Months) data for current metrics How to Use - Value Investing Approach: 1. Identifying Undervalued Stocks: • Look for price trading BELOW the Graham Number (green zone) - potential value opportunity • Deep value: Price below BVPS indicates trading below net asset value • Check "Price vs Graham" % in table - negative values suggest undervaluation • Compare multiple models: When price is below Graham, DCF, and BVPS simultaneously, stronger buy signal 2. Margin of Safety: • Benjamin Graham recommended buying at 2/3 of intrinsic value (33% margin of safety) • Monitor the gap between current price and valuation lines • Larger gaps = greater margin of safety = lower downside risk • Use the shaded "Value Zone" as your target buying range 3. Setting Alerts: • "Price Below Graham Number" - Notifies when stock enters value territory • "Price Below Book Value" - Extreme value alert for deep value hunters • "Price Below DCF Value" - Cash flow-based value signal • Set alerts on watchlist stocks to catch value opportunities 4. Customizing for Your Strategy: • Conservative investors: Use lower growth rates (3-4%) and higher discount rates (12-15%) • Growth-value investors: Adjust growth rate (6-8%) for quality compounders • Dividend investors: Focus on DDM value and Div/Share metrics • Adjust forecast years based on business predictability (stable = 10 years, cyclical = 5 years) 5. Red Flags to Avoid: • Negative EPS or FCF (red values in table) - proceed with caution • Financial sector stocks - Use DDM and Graham, ignore DCF • Price far above Graham (>1.5x) with red background = overvalued territory • No fundamental data = "N/A" in table - stock may lack reporting or be too small • Stock persistently below BVPS for extended periods - potential value trap or business in distress • Price significantly above ALL models (BVPS, Graham, DCF) - sentiment-driven, lacks intrinsic value foundation (fragile) ⚠️ Important Value Investing Warnings: • Value Trap Alert: A stock staying below BVPS for months/years may signal fundamental deterioration, asset impairments, or dying industry - not just "cheap." Investigate WHY it's cheap before buying • Sentiment Bubble Risk: When price trades far above BVPS, Graham Number, AND DCF simultaneously, the stock has no intrinsic value basis. Examples: commodity stocks during boom cycles (gold miners in gold rallies), meme stocks, hype-driven sectors. These are highly fragile and vulnerable to mean reversion • Cyclical Trap: Commodity/cyclical stocks can appear "cheap" at peak earnings (low P/E, high FCF) but are actually expensive. Normalize earnings across the cycle before valuing • Quality Matters: Some excellent businesses (asset-light, high ROIC) naturally trade above book value. Don't avoid quality - adjust expectations for business model 6. Monitoring Positions: • Watch for price approaching or exceeding Graham Number - consider taking profits • Track EPS and FCF trends quarter-to-quarter in the table • If fundamentals deteriorate (falling BVPS, negative FCF), reassess thesis • Use background colors for quick visual check: green = hold/buy, red = overvalued Perfect for: Value investors seeking multi-model fundamental analysis, long-term investors comparing intrinsic value to market price, dividend investors evaluating yield stocks, and fundamental traders looking for entry/exit signals. Note: Only works with stocks that have financial data available. Not applicable to crypto, forex, or futures. This indicator provides analysis tools; always conduct thorough research and due diligence before investing.
אינדיקטור Pine Script®
מאת ‎DarwinDarma‎
Indian Gold Festival Dates HistoricalIndian Gold Festival Dates (1975-2025) Marks 8 major Indian festivals associated with gold buying over 50 years of historical data. Essential for analyzing seasonal patterns and cultural demand cycles in gold markets. Festivals Included: Dhanteras (Gold) - Most auspicious gold buying day Diwali (Orange) - Festival of Lights Akshaya Tritiya (Green) - "Never-ending" prosperity Dussehra (Red) - Victory and success Makar Sankranti (Cyan) - Solar new year Gudi Padwa (Magenta) - Hindu New Year (Maharashtra) Ugadi (Purple) - Hindu New Year (South India) Navratri (Yellow) - 9-day festival Features: ✓ 408 exact historical dates (1975-2025) ✓ Color-coded vertical lines for easy identification ✓ Toggle individual festivals on/off ✓ Adjustable line width and labels ✓ Works on all timeframes (best on daily/weekly) Perfect for traders analyzing gold seasonality, Indian market sentiment, and cultural demand patterns. Use on XAUUSD, GC1!, or Indian gold futures.
אינדיקטור Pine Script®
מאת ‎firstlast69‎
Name of tickerDescription: This indicator displays the instrument’s ticker symbol and the current chart timeframe at the top center of the chart. Features: • Shows the ticker (e.g., BTCUSDT, AAPL, etc.). • Displays the current timeframe (1m, 5m, 1H, 1D, etc.). • Positioned at the top center of the chart for easy reference. • Transparent background for minimal interference with price action. • Lightweight and simple, no extra settings required. Usage: • Works with any instrument: stocks, crypto, futures. • Useful for traders who want to always see the ticker and timeframe while analyzing the chart. Settings: • Text size can be adjusted in the script (text_size). • Text and background colors can be customized (text_color, bgcolor).
אינדיקטור Pine Script®
מאת ‎Delonex‎
Real Time UVXY Spike Level TrackerKey Features Real Time All-Time Low Tracking: Continuously updates the ATL using daily timeframe data. Multiple Spike Levels: Displays +20%, +50%, +75%, and +100% levels above the ATL. Real-Time Spike Percentage: Shows current distance from ATL in an easy-to-read table. Understanding the Chart Lines Red Line (ATL): The all-time low baseline. This is your reference point for measuring volatility spikes. Yellow Line (+20%): First level of moderate volatility increase. Minor market stress or routine volatility expansion. Blue Line (+50%): Significant volatility event. Indicates elevated market concern or technical dislocation. Purple Line (+75%): Major volatility spike. Typically coincides with substantial market selloffs or uncertainty. Fuchsia Line (+100%): Extreme volatility event. Rare occurrences associated with market crashes, black swan events, or severe panic. The Data Table Displays: Current Spike %: Real-time percentage showing how far price is above the ATL (highlighted in green) Level Column: Each spike threshold level Price Column: Exact price at each level for quick reference Understanding UVXY spike levels is valuable for several reasons: Market Timing & Entry/Exit Points UVXY typically experiences extreme spikes during market panics or crashes. Knowing historical spike levels helps you: Identify extreme fear levels - When UVXY hits unusually high levels, it often signals peak panic and potential market bottoms Avoid chasing volatility - Understanding what constitutes an "extreme" spike prevents buying in after the move is already exhausted Mean Reversion Trading UVXY has a strong tendency to decay over time due to its leveraged structure and the contango in VIX futures. Spike levels matter because: High probability reversals - When UVXY reaches extreme levels (say 2-3x normal), there's historically been a high probability of reversion Risk/reward assessment - You can better evaluate whether a short position or volatility-selling strategy makes sense Leveraged ETF enthusiasts and volatility traders often use specific spike percentages as triggers to open short positions. For example, some traders might short when UVXY spikes 5-50%+ in a week or reaches certain percentage thresholds, betting on the inevitable decay back down
אינדיקטור Pine Script®
מאת ‎CIVolatility‎
Smart Weekly Lines — Clean & Scroll-Proof (Pine v6)Because your chart deserves structure. Elegant weekly dividers that stay aligned, scroll smoothly, and project future weeks using your wished UTC offset. Smart Weekly Lines draws precise, full-height vertical lines marking each new week — perfectly aligned to your local UTC offset. It stays clean, smooth, and consistent no matter how far you scroll. Features • Accurate weekly boundaries based on your local UTC offset (supports half-hour zones like India +5.5) • Clean, full-height lines that never cut off with zoom or scroll • Adjustable color, opacity, width, and style (solid, dashed, dotted) • Future week projection for planning and alignment • Optional visibility: show only on Daily and Intraday charts Works with any market — stocks, crypto, forex, or futures. Built for traders who value clarity, structure, and precision. Developed collaboratively with the assistance of ChatGPT under my direction and testing.
אינדיקטור Pine Script®
מאת ‎pgianpy‎
CloudShiftCloudShift + Bollinger Bands This version of CloudShift now includes fully optimized Bollinger Bands with all three dynamic lines: Upper Band: Highlights expansion during volatility spikes. Lower Band: Identifies compression and accumulation zones. Centerline (Basis): A smooth reference of the moving average, providing better visual balance and directional context. The bands are drawn with thin, clean lime lines, designed to integrate perfectly with the cloud logic — keeping your chart minimalist yet powerful. This update enhances the CloudShift indicator by providing a clear visual framework of market volatility and structure without altering its original logic. Recommended for use on: NASDAQ, S&P 500, and other high-volatility futures. Recommended timeframe: 5–15 minutes.
אינדיקטור Pine Script®
מאת ‎THEGREATNAPLESTRADERX‎
Info Panel (RSI, ADX, Volume,EMA, Delta)📊 Info Panel PRO — All-in-One Trader Dashboard Simplify market analysis at a glance. This powerful indicator displays key market metrics in a compact, customizable table directly overlaid on your chart — ideal for day trading, scalping, and swing trading strategies. 🔍 What’s Included: ✅ RSI (Relative Strength Index) — Measures overbought/oversold conditions. ✅ ADX (Average Directional Index) — Gauges trend strength (>25 = strong trend). ✅ Price vs 200 EMA on 4H timeframe — Strategic support/resistance level for multi-timeframe context. ✅ Current Bar Volume — Color-coded to reflect bullish/bearish sentiment. ✅ Volume Delta — Net buying/selling pressure on your chosen timeframe (default: 1 minute). ✅ CVD (Cumulative Volume Delta) — Daily running total of delta, resets each new trading day. ⚙️ Fully Customizable Settings: Adjustable lengths for RSI, ADX, and EMA. Select delta calculation timeframe — lower = more granular (e.g., “1” for 1-minute precision). Table position: top/bottom left/right corners. Color themes: Customize bullish, bearish, and neutral colors to match your style. 💡 Who Is This For? Scalpers & Day Traders needing real-time market context without clutter. Swing & Position Traders monitoring higher-timeframe structure and momentum. Order Flow & Volume Analysts tracking buyer/seller imbalance via delta and CVD. Beginners learning to read markets through consolidated, intuitive indicators. 🎯 Key Benefits: ✅ Clean, minimalist UI — stays out of your way while delivering critical data. ✅ Auto-formatting for large numbers (K, M, B) — easy readability. ✅ Visual cues (arrows, color coding) for instant decision-making. ✅ Works across all markets: Forex, Stocks, Crypto, Futures. 📌 How to Use: Add the indicator to your chart. Tweak settings to fit your trading style. Monitor real-time updates — all essential metrics visible in one place. Combine with other strategies (price action, S/R, VWAP) for signal confirmation. 📌 Pro Tip: For maximum edge, pair Info Panel PRO with liquidity zones, VWAP, or Market Profile tools. 📈 Trade smarter — let the market speak to you in clear, actionable terms. Author: Version: 1.0 Language: Pine Script v5 Overlay: Yes (draws directly on price chart) 😄 “If this indicator were a person, they’d be called ‘The One Who Knows Everything… But Never Gives Unsolicited Advice.’ …Unlike your ‘friend’ who yells ‘BUY!’ five minutes before the market crashes.” “A good trader isn’t the one who predicts the market. It’s the one who has everything on their chart — coffee optional. …Want the next indicator? Comment ‘YES’ below — and I’ll build you ‘Smart Alert PRO’ or ‘Volume Sniper’ next.” P.S. If this script saves even ONE trade — hit 👍. If it saves TWO — comment “THANK YOU” 🙏 If it saves THREE — expect “Volume Heatmap PRO” next week 😉🔥
אינדיקטור Pine Script®
מאת ‎Avefar1‎
ORB Breakout Traffic Signal (5/15/30)ORB Breakout Traffic Signal (5/15/30) This indicator visualizes Opening Range Breakouts (ORB) for the first 5, 15, and 30 minutes of the US regular trading session (09:30–16:00 ET). It provides a compact, easy-to-read traffic signal table on your chart to show whether price is breaking out, breaking down, or consolidating inside the range. 🔑 Features Auto-anchors at 09:30 ET (converted to your local time automatically). Tracks ORB High/Low for: 5-minute window (09:30–09:34) 15-minute window (09:30–09:44) 30-minute window (09:30–09:59) Displays results in a compact table: ↑ (green) → price has broken above the ORB high ↓ (red) → price has broken below the ORB low • (gray) → price remains inside the ORB range (optional; can be disabled) Customizable: Toggle which ORBs to show (5m, 15m, 30m) Choose table position (top/bottom left/right) Adjustable text size Option to plot the ORB High/Low lines on your chart 📌 Usage Designed for intraday traders watching US equities/ETFs/futures. Works best on 1-minute or 5-minute charts with Extended Hours turned OFF (so the session starts exactly at 09:30 ET). Helps you quickly spot early breakouts (5m), mid-session trends (15m), or confirmed directional moves (30m). ⚠️ Notes Signals only update during the RTH session Outside market hours, the last locked ORB and signal remain displayed until the next open. This tool is for analysis/visualization only; not a buy/sell signal. Always combine with your own trading strategy and risk management. 👉 Perfect for traders who want a quick visual confirmation of whether price is breaking out of the opening range or stuck inside it.
אינדיקטור Pine Script®
מאת ‎GYSTrading‎
Position Size CalculatorPosition Size Calculator This open-source Pine Script® indicator helps traders manage risk by calculating position size, margin, and risk/reward based on account size, leverage, entry, stop-loss, and take-profit. It features a customizable table and optional chart lines/labels for clear trade planning across stocks, forex, crypto, and futures. What It Does - Position Size: Computes units to trade based on risk percentage and stop-loss distance, capped by leverage. - Margin: Calculates initial margin in base currency and USD, with account size percentage. - Risk/Reward: Shows risk-reward ratio, percentage price movements, and USD gains/losses. - Visualization: Displays results in a table and optional chart lines/labels with customizable styles. How It Works - Precision: Adjusts price formatting using syminfo.mintick for accuracy across assets. - Calculations: Position size = accountSize * (riskPercent / 100) / |entry - stoploss|, capped by accountSize * leverage / entry. Margin = positionSize / leverage. Risk-reward = |takeprofit - entry| / |stoploss - entry|. - Display: Table shows metrics; optional lines/labels plot entry, stop-loss, and take-profit with percentage and USD details. How to Use - Set Inputs: 1- Account Size (USD): Your capital (e.g., 1000). 2- % Risk per Trade: Risk tolerance (e.g., 1%). 3- Leverage: Broker leverage (e.g., 1x, 10x). 4- Entry, Stop Loss, Take Profit: Trade prices. 5- Show Lines and Labels: Enable chart overlays. - Customize: Adjust table position, colors, and line styles (Solid, Dashed, Dotted). - View Results: Table shows position size, margin, and risk/reward. Chart lines/labels (if enabled) display prices, percentages, and USD outcomes. - Apply: Use metrics for trade execution; modify code for custom features. Notes - Ensure valid inputs (entry ≠ stop-loss, both positive) to avoid “N/A”. - Open-source: Inspect or extend the code for your needs. - Contact the author via TradingView for feedback.
אינדיקטור Pine Script®
מאת ‎traderharo‎
NQ FVG + MSS ChecklistThe NQ FVG + MSS Quick Checklist is a visual trading HUD for Nasdaq 100 (NQ) futures. It helps traders quickly track key setup elements: session & previous day levels, 5M FVG, retests, 1M MSS, and 1M FVG inside MSS. Each step can be manually ticked, and a Trade Score shows setup strength at a glance. The checklist table sits on top of all chart elements for easy reference without interfering with your analysis. Features: Step-by-step NQ trading checklist Manual inputs with visual ✅/❌ Trade Score for quick setup confirmation Table overlay always on top of the chart
אינדיקטור Pine Script®
מאת ‎FXNotes‎
2
NQ FVG + MSS ChecklistThe NQ FVG + MSS Quick Checklist is a simple yet powerful visual tool for traders focusing on the Nasdaq 100 (NQ) futures. It provides a step-by-step checklist to assess trade setups based on key market concepts like Fair Value Gaps (FVG), Market Structure Shifts (MSS), session highs/lows, and previous day levels. This indicator helps you quickly see which elements of your trading plan are met before entering a trade. Each checklist item can be manually toggled, and a cumulative Trade Score provides a quick visual guide to setup strength. Key Features: Step-by-step checklist for NQ trading setups Track levels: Session highs/lows & Previous Day High/Low Spot 5M FVG and Retests Identify MSS on 1M and find 1M FVG inside MSS Manual SL & TP guidance Trade Score for quick setup strength assessment Fully visible table overlay on top of the chart How to Use: Mark session & previous day levels Observe reaction at key levels (Sweep or Continue) Identify 5M FVG and any retests Spot 1M MSS and 1M FVG inside MSS Set SL/TP based on FVG extremes and next session levels Check the cumulative Trade Score for setup confirmation Note: This indicator is manual input-based, letting traders tick off items as they analyze the chart, making it a lightweight trading checklist HUD that stays on top of all chart elements.
אינדיקטור Pine Script®
מאת ‎FXNotes‎
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH Introduction: The Genesis of Financial Resilience In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952. Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather." The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable. HISTORICAL DEVELOPMENT AND EVOLUTION The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients. Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance. The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios. The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification. Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting. The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing. The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years. The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions. Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets. In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors. The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts. Understanding the Four Economic Seasons The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks. This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously. Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework. The Original All Weather Allocation: Simplicity Masquerading as Sophistication The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities. The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility. The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits. The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns. Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001). Historical Performance: The Evidence Speaks Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios. During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance. More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time. The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly. However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money." Implementing the All Weather Strategy: A Practical Guide The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies. To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes. For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error. At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges. The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management. For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations. The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance. Understanding the Indicator's Functionality The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software. The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets. DETAILED INDICATOR COMPONENTS AND FUNCTIONS Portfolio Configuration Module The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period. The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs. Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures. The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures. Visual Display System The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table. Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit. Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention. Advanced Label System The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol. "Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail. "Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required. Professional Color Themes Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts. "Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations. Real-Time Portfolio Tracking The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift. Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors. Performance Analytics Suite The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance. Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality. Data Quality Monitoring Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations. When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions. Risk Management and Behavioral Considerations Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns. Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s. Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times. Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management. Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities. Capital Requirements and Cost Analysis Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities. ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually. Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees. Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually. For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000. Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth. Alternative Implementations and Customizations While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities. Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies. Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts. Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management. Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error. Conclusion: Embracing the Long Game The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence. The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth. Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance. The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation. For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons. As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds. STEP-BY-STEP INDICATOR SETUP GUIDE Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance. Initial Setup Process Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart. Portfolio Configuration Settings Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision. Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking. Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection. Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading. Display Configuration Options Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users. Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions. Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities. Advanced Label Customization Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods. Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF. Appearance Customization Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making. Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme. Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility. Troubleshooting Common Setup Issues If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components. When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels. If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization. COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING PRIMARY SOURCES AND RAY DALIO WORKS Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster. Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates. Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster. BRIDGEWATER ASSOCIATES RESEARCH PAPERS Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research. Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations. Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research. ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187. Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39. Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127. Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53. Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46. BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press. Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons. MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43. PRACTICAL IMPLEMENTATION AND ETF ANALYSIS Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons. Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427. Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427. ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons. Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press. Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education. RISK MANAGEMENT AND ALTERNATIVE STRATEGIES Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House. Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House. Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12. CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59. Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press. Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar. PERIODICALS AND ONGOING RESEARCH Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy RECOMMENDED READING SEQUENCE For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky. Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes. Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
אינדיקטור Pine Script®
מאת ‎EdgeTools‎
Signal Stack MeterWhat it is A lightweight “go or no‑go” meter that combines your manual read of Structure, Location, and Momentum with automatic context from volatility and macro timing. It surfaces a single, tradeable answer on the chart: OK to engage or Standby. Why traders like it You keep your discretion and nuance, and the meter adds guardrails. It prevents good trade ideas from being executed in the wrong conditions. What it measures Manual buckets you set each day: Structure, Location, Momentum from 0 to 2 Volatility from VIX, term structure, ATR 5 over 60, and session gaps Time windows for CPI, NFP, and FOMC with ET inputs and an exchange‑offset Total score and a simple gate: threshold plus a “strong bucket” rule you choose How to use in 30 seconds Pick a preset for your market. Set Structure, Location, Momentum to 0, 1, or 2. Leave defaults for the auto metrics while you get a feel. Read the header. When it says OK to engage, you have both your read and the context. Defaults we recommend OK threshold: 5 Strong bucket rule: Either Structure or Location equals 2 VIX triggers: 22 and 1.25× the 20‑SMA Term mode: Diff at 0.00 tolerance. Ratio mode at 1.00+ is available ATR 5/60 defense: 1.25. Offense cue: 0.85 or lower ATR smoothing: 1 Gap mode: RTH with 0.60× ATR5 wild gap. ON wild range at 0.80× ATR5 CPI window 08:25 to 08:40 ET. FOMC window 13:50 to 14:30 ET ET to exchange offset: −60 for CME index futures. Set to 0 for NYSE symbols like SPY Alert cadence: Once per RTH session. Snooze first 30 minutes optional New since the last description Parity with Defense Mode for presets, sessions, ratio vs diff term mode, ATR smoothing, RTH‑key cadence, and snooze options Event windows in ET with a simple offset to your exchange time Alternate row backgrounds and full color control for readability Exposed series for automation: EngageOK(1=yes) plus TotalScore Debug toggle to see ATR ratio, term, and gap measurements directly Notes Dynamic alerts require “Any alert() function call”. The meter is designed to sit opposite Defense Mode on the chart. Use the position input to avoid overlap.
אינדיקטור Pine Script®
מאת ‎Uplift32‎
逆勢布林+RSI策略 for SOL可以直接套用到 SOLUSDT, SOLPERP, 或其他 SOL 合約。 在策略回測介面中選擇 5min 或 15min 看策略表現。 若要調整停利%或 RSI 數值，改變 rsi < 25 與 (shortEntryPrice - close) / shortEntryPrice >= 0.035 即可。 This can be directly applied to SOLUSDT, SOLPERP, or other SOL futures. In the strategy backtesting interface, select 5-minute or 15-minute periods to view strategy performance. To adjust the take-profit percentage or RSI value, set RSI < 25 and (shortEntryPrice - close) / shortEntryPrice >= 0.035.
אסטרטגיית Pine Script®
מאת ‎ray36730‎
HMA Trend Line (Croc Signal Line)HMA Trend Line (Croc Signal Line) — The Ultimate Hull Moving Average Trend Indicator Full English description here: What is the HMA Trend Line (Croc Signal Line)? The HMA Trend Line (Croc Signal Line) is a powerful, adaptive trend indicator for TradingView, based on the Hull Moving Average (HMA). This indicator is designed to help traders identify real market trends with less lag and reduced noise compared to traditional moving averages like SMA (Simple Moving Average) and EMA (Exponential Moving Average). Why use the HMA Trend Line? + Faster Trend Detection: The Hull Moving Average (HMA) responds more quickly to price action, giving you earlier buy and sell signals. + Smoother and Cleaner: It provides a visually clean trend line that avoids the choppiness of classic EMAs and SMAs. + Reduced Lag: The HMA Trend Line follows the market closer, helping you avoid late entries or exits and spot trend reversals sooner. + Dynamic Support and Resistance: Use the line as a dynamic support or resistance to manage trades and identify pullbacks or breakouts. What does “Croc Signal Line” mean? The “Croc” in Croc Signal Line stands for: + Clean + Responsive + Optimized + Curve This highlights the unique advantage of this indicator: a curve that is both fast-reacting and smooth, helping traders focus on real trends and filter out market noise. How does the Hull Moving Average (HMA) work? The HMA was developed by Alan Hull and uses weighted moving averages and a unique calculation to deliver both responsiveness and smoothness. Unlike standard moving averages, the HMA reacts faster to new price moves and avoids false signals in ranging or volatile markets. How to use the HMA Trend Line (Croc Signal Line) on TradingView? + Watch for price crossing above the trend line for potential bullish signals, and below for bearish signals. + Use on any timeframe: from 1-minute scalping to daily, weekly, or even monthly charts. + Works with all asset classes: Forex, stocks, indices, cryptocurrencies, commodities, and futures. + Combine with other indicators (like Stochastics, RSI, or volume) for confirmation and to build your unique trading strategy. + Adjust the Signal Line Period for your market and style: shorter periods for faster markets, longer for smoother trends. Who should use this indicator? + Day traders, swing traders, and long-term investors looking for reliable, actionable trend signals. + Anyone seeking a cleaner, more responsive alternative to the classic moving averages. + Traders who want a simple, visually clear way to filter out market noise and see real price direction. Disclaimer: This indicator is for educational and study purposes only. Please perform your own backtesting and analysis before using it in live trading. This script does not constitute financial advice. Use at your own risk. --------
אינדיקטור Pine Script®
מאת ‎mesuon‎
Chaikin Oscillator Multi-Timeframe BiasOverview Chaikin Oscillator Multi-Timeframe Bias is an indicator designed to help traders align with institutional buying and selling activity by analyzing Chaikin Oscillator signals across two timeframes—a higher timeframe (HTF) for trend bias and a lower timeframe (LTF) for timing. This dual-confirmation model helps traders avoid false breakouts and trade in sync with market momentum and accumulation or distribution dynamics. Core Concepts The Chaikin Oscillator measures the momentum of accumulation and distribution based on price and volume. Institutional traders typically accumulate slowly and steadily, and the Chaikin Oscillator helps reveal this pattern. Multi-timeframe analysis confirms whether short-term price action supports the longer-term trend. This indicator applies a smoothing EMA to each Chaikin Oscillator to help confirm direction and reduce noise. How to Use the Indicator Start by selecting your timeframes. The higher timeframe, set by default to Daily, establishes the broader directional bias. The lower timeframe, defaulted to 30 minutes, identifies short-term momentum confirmation. The indicator displays one of five labels: CALL Bias, CALL Wait, PUT Bias, PUT Wait, or NEUTRAL. CALL Bias means both HTF and LTF are bullish, signaling a potential opportunity for long or call trades. CALL Wait indicates that the HTF is bullish, but the LTF hasn’t confirmed yet. PUT Bias signals bearish alignment in both HTF and LTF, while PUT Wait indicates HTF is bearish and LTF has not yet confirmed. NEUTRAL means there is no alignment between timeframes and directional trades are not advised. Interpretation When the Chaikin Oscillator is above zero and also above its EMA, this indicates bullish momentum and accumulation. When the oscillator is below zero and below its EMA, it suggests bearish momentum and distribution. Bias labels identify when both timeframes are aligned for a higher-probability directional setup. When a “Wait” label appears, it means one timeframe has confirmed bias but the other has not, suggesting the trader should monitor closely but delay entry. Notes This indicator includes alerts for both CALL and PUT bias confirmation when both timeframes are aligned. It works on all asset classes, including stocks, ETFs, cryptocurrencies, and futures. Timeframes are fully customizable, and users may explore combinations such as 1D and 1H, or 4H and 15M depending on their strategy. For best results, consider pairing this tool with volume, volatility, or price action analysis.
אינדיקטור Pine Script®
מאת ‎scottivey‎
BG Ichimoku Tenkan MTFBG Ichimoku Tenkan MTF: Your Multi-Timeframe Trend Compass Elevate your Ichimoku analysis with the BG Ichimoku Tenkan MTF indicator. This powerful tool provides a comprehensive view of the Tenkan-sen (Conversion Line) across multiple timeframes, helping you identify trends and potential shifts with greater clarity. It's ideal for all markets, including stocks, cryptocurrencies, Forex, and futures. Key Features: Main Tenkan-sen Plot: Visualize the Tenkan-sen for your active chart timeframe with adjustable color. Multi-Timeframe Table: A dynamic table displays the Tenkan-sen's relationship to price (🔼 for above, 🔽 for below) and its current value for up to 7 timeframes. Continuous MTF Lines: Plot the Tenkan-sen from higher timeframes directly on your current chart, providing clear support/resistance levels and trend confluence. Fully Customizable Colors: Personalize the color for each individual timeframe in the table and for its corresponding MTF line, ensuring a clean and intuitive visual experience. You can also adjust the main Tenkan-sen color and the MTF line offset. Gain a deeper understanding of market dynamics by analyzing the Tenkan-sen across different time scales, all in one intuitive indicator. We created this indicator to help you better navigate the markets. Thank you for using it, and we hope it brings you value. Enjoy it in your daily analysis! Bab
אינדיקטור Pine Script®
מאת ‎babtrader24‎
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