Cyber Mondayลดสูงสุดถึง 80%

อินดิเคเตอร์และกลยุทธ์

ADX Forecast Colorful [DiFlip]ADX Forecast Colorful Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis. ⯁ Real-Time ADX Forecasting For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions. ⯁ Highly Customizable The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system. ⯁ Innovative and Science-Based This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is: y = β₀ + β₁x + ε Where: y  = predicted value (e.g., future ADX) x  = explanatory variable (e.g., bar index or time) β₀ = intercept β₁ = slope (rate of change) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends. ⯁ Least Squares Estimation To minimize the error, the regression coefficients are calculated as: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: Σ = summation x̄ and ȳ = means of x and y i ranges from 1 to n (number of data points) These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity. ⯁ Linear Regression in Machine Learning Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool. ⯁ Visual Interpretation Imagine an ADX time series like this: Time → ADX  → The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals. ⯁ Summary of Scientific Concepts Linear Regression: Models variable relationships with a straight line. Least Squares: Minimizes prediction errors for best fit. Time-Series Forecasting: Predicts future values using historical data. Supervised Learning: Trains models to predict outcomes from inputs. Statistical Smoothing: Reduces noise and highlights underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Based on rigorous statistical theory. Unprecedented: First public ADX using least-squares forecast modeling. Smart: Uses machine learning logic. Forward-Looking: Generates predictive, not just reactive, signals. Customizable: Flexible for any strategy or timeframe. ⯁ Conclusion By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What is the ADX? The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ How to use the ADX? The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: Strong Trend: When the ADX is above 25, indicating a strong trend. Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. Neutral Zone: Between 20 and 25, where the trend strength is unclear. ⯁ Entry Conditions Each condition below is fully configurable and can be combined to build precise trading logic. 📈 BUY 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 📉 SELL 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 🤖 Automation All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems. ⯁ Unique Features Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill" Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill"
อินดิเคเตอร์ Pine Script®
โดย DiFlip
Daily TQQQ Trend Strategy (Ultra-Discreet Text Signals)✅ TradingView Description (Professional + Clean) Daily TQQQ Trend Strategy (Ultra-Discreet Text Signals) This indicator provides clean, minimalistic trend-following signals designed for traders who want confirmation without cluttering the chart. Instead of using arrows, boxes, or colored shapes, this script prints tiny text labels (“Buy – trend strong” / “Sell – trend weakening”) directly on the price chart. These messages are intentionally discreet so they do not interfere with existing indicators, automated systems, or visually busy setups. 🔍 How It Works The indicator analyzes the market using three well-established components: 1. Trend Direction (EMA 8 & EMA 20) • Buy condition: price above both EMAs • Sell condition: price below both EMAs 2. Momentum Confirmation (MACD) • Buy: MACD line > Signal line • Sell: MACD line < Signal line 3. Strength Filter (RSI 14) • Buy: RSI above 50 (bullish strength) • Sell: RSI below 50 (weakening momentum) Only when all conditions align does the indicator print a discreet buy or sell label. 🧭 Signal Types Buy – trend strong Appears below the candle when overall trend, momentum, and strength all turn bullish. Sell – trend weakening Appears above the candle when trend and momentum show weakness and downside pressure increases.
อินดิเคเตอร์ Pine Script®
โดย basantd
Auto 5-Wave Fixed Channel + Wave 5 Top / Wave 2-ABC BottomAuto 5-Wave Fixed Channel + Wave 5 Top / Wave 2-ABC Bottom by Ron999 1. What this indicator does This tool automatically hunts for bullish 5-wave impulse structures and then: Labels the waves: W1, W2, W3, W4, W5 Draws a fixed “acceleration” channel based on the wave structure Projects a Wave-5 target zone using a 1.618 extension Marks the Wave-2 level as an ABC correction target Triggers optional alerts when: A new Wave-5 top completes An ABC bottom forms back near the Wave-2 low It’s designed as a mechanical, rule-based approximation of Elliott 5-wave impulses – built for traders who like the idea of wave structure but want something objective and programmable. 2. How the wave logic works The script continuously scans for pivot highs and lows using a user-defined Pivot Length. It only keeps the last 5 alternating pivots (high → low → high → low → high). When those last 5 pivots form this pattern: Pivot 1 → High (W1) Pivot 2 → Low (W2) Pivot 3 → High (W3) Pivot 4 → Low (W4) Pivot 5 → High (W5) …the indicator treats this as a bullish 5-wave impulse. When such a structure is detected, it “locks in” the wave prices and bars and draws the channels and labels. Note: Pivots are only confirmed after Pivot Length bars, so swings are slightly delayed by design (standard pivot logic). 3. Channels & levels Once a valid bullish 5-wave structure is found, the script builds three key pieces: a) Base Acceleration Channel (Blue) Anchored from Wave-2 low toward Wave-3 high. This forms a rising acceleration channel that represents the impulse leg. The channel extends to the right, so you can see how price interacts with it after W3–W5. b) Wave-5 Target Line (Red, dashed) Uses the height from Wave-2 low to Wave-3 high. Projects a 1.618 extension of that height above Wave-3. This line acts as a potential Wave-5 exhaustion zone (take-profit / reversal watch area). c) Wave-2 / ABC Bottom Level (Green, dotted) Horizontal line drawn at the Wave-2 low. This acts as a retest / corrective target for the ABC correction after the impulse completes. When price later revisits this area (within a tolerance), the script can mark it as a potential ABC bottom. 4. Labels & signals If labels are enabled: W1, W2, W3, W4, W5 are plotted directly on their corresponding pivot bars. When an ABC-style retest is detected near the Wave-2 level, an “ABC” label is printed at that low. Wave-5 Top Event Triggered when a new valid bullish 5-wave structure is completed. The last pivot high in the pattern is flagged as Wave-5. ABC Bottom Event After a Wave-5 impulse, the script watches for new low pivots. If a new low forms within ABC Bottom Proximity (%) of the Wave-2 price, it is treated as an ABC bottom near Wave-2 and marked on the chart. 5. Inputs & customization Show Fixed Channels Toggle all channel drawing on/off. Label Waves Toggle plotting of W1–W5 and ABC labels. Alerts: Wave-5 Top & ABC Bottom Master switch for enabling the script’s alert conditions. Pivot Length Controls how “swingy” the detection is. Smaller values → more frequent, smaller waves Larger values → fewer, larger structural waves ABC Bottom Proximity (%) Allowed percentage distance between the ABC low and the Wave-2 price. Example: 5% means any ABC low within ±5% of Wave-2 is considered valid. 6. Alerts (how to use them) The script exposes two alertcondition() events: Wave-5 Top (Bullish Impulse) Fires when a new 5-wave bullish structure completes. Use this to watch for potential exhaustion tops or to tighten stops. ABC Bottom near Wave-2 Low Fires when an ABC-style correction prints a low near the Wave-2 level. Use this to stalk potential end-of-correction entries in the direction of the original impulse. On TradingView, add an alert to the script and choose the desired condition from the dropdown. 7. How to use it in your trading This tool is best used as a structural context layer, not a standalone system: Identify bullish impulsive trends when a Wave-5 structure completes. Use the Wave-5 target line as a potential area for: Scaling out Watching for exhaustion / divergences / reversal patterns Use the Wave-2/ABC level and ABC Bottom signal: To look for end of correction entries back in the trend direction To align with your own confluence (support/resistance, volume, RSI, etc.) It works well on crypto, FX, indices, and stocks, especially on higher timeframes where structure is cleaner. 8. Limitations & notes This is a mechanical approximation of Elliott 5-wave theory — it will not match every analyst’s discretionary count. Pivots are confirmed after Pivot Length bars, so signals are not instant; they’re based on completed swings. The indicator currently focuses on bullish impulses (upward 5-wave structures). As always, this is not financial advice. Combine it with your own strategy, risk management, and confirmation tools. Created & coded by: Ron999 Built for traders who want wave structure + fixed channels, without the subjective Elliott argument on every chart. files.catbox.moe
อินดิเคเตอร์ Pine Script®
โดย Ron999
AliceTears GridAliceTears Grid is a customizable Mean Reversion system designed to capitalize on market volatility during specific trading sessions. Unlike standard grid bots that place blind limit orders, this strategy establishes a daily or session-based "Baseline" and looks for price over-extensions to fade the move back to the mean. This strategy is best suited for ranging markets (sideways accumulation) or specific forex sessions (e.g., Asian Session or NY/London overlap) where price tends to revert to the opening price. 🛠 How It Works 1. The Baseline & Grid Generation At the start of every session (or the daily open), the script records the Open price. It then projects visual grid lines above and below this price based on your Step % input. Example: If the Open is $100 and Step is 1%, lines are drawn at $101, $102, $99, $98, etc. 2. Entry Logic: Reversal Mode This script features a "Reversal Mode" (enabled by default) to filter out "falling knives." Standard Grid: Buys immediately when price touches the line. AliceTears Logic: Waits for the price to breach a grid level and then close back inside towards the mean. This confirms a potential rejection of that level before entering. 3. Exit Logic Target Profit: The primary target is the previous grid level (Mean Reversion). Trailing Stop: If the price continues moving in your favor, a trailing stop activates to maximize the run. Stop Loss: A manual percentage-based stop loss is available to prevent deep drawdowns in trending markets. ⚙️ Key Features Visual Grid: Automatically draws entry levels on the chart for the current session, helping you visualize where the "math" is waiting for price. Timezone & Session Control: Includes a custom Timezone Offset tool. You can trade specific hours (e.g., 09:30–16:00) regardless of your chart's UTC setting. Grid Management: Independent logic for Long and Short grids with pyramiding capabilities. Safety Filters: Options to force-close trades at the end of the session to avoid overnight gaps. ⚠️ Risk Warning Please Read Before Using: This is a Counter-Trend / Grid Strategy. Pros: High win rate in sideways/ranging markets. Cons: In strong trending markets (parabolic pumps or crashes), this strategy will add to losing positions ("catch a falling knife"). Recommendation: Always use the Stop Loss and Date Filter inputs. Do not run this on highly volatile assets without strict risk management parameters. Settings Guide Entry Reversal Mode: Keep checked for safer entries. Uncheck for aggressive limit-order style execution. Grid Step (%): The distance between lines. For Forex, use lower values (0.1% - 0.5%). For Crypto, use higher values (1.0% - 3.0%). UTC Offset: Adjust this to align the Session Hours with your target market (e.g., -5 for New York). This script is open source. Feel free to use it for educational purposes or modify it to fit your trading style.
กลยุทธ์ Pine Script®
โดย AliceTears
My script//@version=6 indicator("ISIN demo") // Define inputs for two symbols to compare. string symbol1Input = input.symbol("NASDAQ:AAPL", "Symbol 1") string symbol2Input = input.symbol("GETTEX:APC", "Symbol 2") if barstate.islastconfirmedhistory // Retrieve ISIN strings for `symbol1Input` and `symbol2Input`. var string isin1 = request.security(symbol1Input, "", syminfo.isin) var string isin2 = request.security(symbol2Input, "", syminfo.isin) // Log the retrieved ISIN codes. log.info("Symbol 1 ISIN: " + isin1) log.info("Symbol 2 ISIN: " + isin2) // Log an error message if one of the symbols does not have ISIN information. if isin1 == "" or isin2 == "" log.error("ISIN information is not available for both symbols.") // If both symbols do have ISIN information, log a message to confirm whether both refer to the same security. else if isin1 == isin2 log.info("Both symbols refer to the same security.") else log.info("The two symbols refer to different securities.")
อินดิเคเตอร์ Pine Script®
โดย zlavreniukk
Manual Pivot Plotter//================================================================================ //📌 Manual Pivot Plotter (P, R1–R3, S1–S3) //📈 Pine Script v6 // //This script allows the user to manually input Pivot levels (P), Resistance levels //(R1, R2, R3), and Support levels (S1, S2, S3). Each line starts at the beginning //of the new trading day (detected at 00:00 UTC+8) and extends only a limited //distance into the future (default: 3 bars). // //Features: //✔ Manual pivot, support, and resistance level inputs //✔ Lines refresh automatically at each new day (00:00 UTC+8) //✔ Lines extend only a few bars ahead (not full chart) //✔ Clean label placement slightly below line and near line end //✔ No repainting, memory-safe line handling //✔ Smooth intraday updates when values are edited // //This tool is ideal for traders who manually calculate or import pivot levels and //prefer clean, minimal, non-intrusive visual levels on the chart. //================================================================================
อินดิเคเตอร์ Pine Script®
โดย Raj7rader
inyerneck Quiet Bottom Hunter v1.5 — VERIFIED SIGNALSQuiet Bottom Hunter v1.5 — 85%+ Rebound Setup Designed for new traders who want the highest-probability, lowest-stress small-cap entries. Triggers only when ALL of these line up: • –20% to –80% from 90-day high (slow bleed, not crash) • Volume ≤80% of 50-day average (dry, no panic selling left) • RSI(14) ≤35 (deep oversold) • 2+ consecutive green or flat days at the low (quiet bottom confirmed) Fires roughly 1–3 times per month on most small caps (<$2B). Backtested 2024–2025: 85% win rate, avg +32% rebound, max DD ~11%. Tiny green “QB” arrow = entry signal. Use 10–20% position size. Works best on daily charts. Public script — code visible. use on 1 day or 4 hr chart. mid term swings, NOT day trades No spam. No chasing. Just big, calm rebounds.
กลยุทธ์ Pine Script®
โดย oogabooger
KAMA Flip strategyI built this strategy because I wanted something that doesn’t overcomplicate trading. No 20 indicators, no guessing, no “maybe I should close here.” Just a clear momentum flip, a defined stop, and a defined take profit. (for me on 1D BTC chart it works best with 6% stoploss and 3% takeprofit, lookback should be 40, everything else standard) The idea is simple: when momentum shifts, I want to be on the right side of it. KAMA is good for this because it speeds up when the market moves and slows down when it doesn’t. I normalize it so it becomes a clean zero-line oscillator. Above zero means momentum is turning up. Below zero means it’s turning down. That’s the entire entry logic. A flip is a flip. The exit logic is just as simple: one stop loss, one take profit, both fixed percentages from the entry. The position closes 100% at the target or the stop. No scaling in, no scaling out, no trailing. It’s straightforward and easy to analyze because every trade has the exact same structure. I originally made this for BTC on the daily chart, but nothing stops you from trying it on other charts. If you want it only to go long, only to go short, or take both sides, you can set that. All the KAMA parameters are open so you can play with how reactive the signal is. The visuals and SL/TP lines can be turned on or off depending on how clean you want your chart. This isn’t financial advice. It’s just a system I like because it’s simple, objective, and does exactly what it’s supposed to do. Test it, adjust it, break it, rebuild it — do whatever fits your own approach.
กลยุทธ์ Pine Script®
โดย SystemsOverFeelings
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel. █ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values. █ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical  Volatility  Rank, and Historical  Volatility  Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models. Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias. Pros:   • Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator. Cons:   • The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.   • Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.   • Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute   deviation makes the historical volatility more stable with extreme values. Parkinson Hv Estimator:   • Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.) Garman-Klass Estimator:   • Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.   • The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility. Rogers-Satchell Estimator:   • Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.   • The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.   • Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility. Garman-Klass Yang-Zhang extension:   • Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.   • The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies. Yang-Zhang Estimator:   • The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.   • This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.   • 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV. EWMA Estimator: • EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.   • EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.   • RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.   • However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH. Adjusted Mean Absolute Deviation Estimator:   • This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.   • It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.   • The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.   • In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility. Adjusted Median Absolute Deviation Estimator:   • This is another estimator that does not use standard deviation to measure volatility.   • Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.   • The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators. █ FEATURES   • You can select the volatility estimator models in the Volatility Model input   • Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.   • Alpha is used to adjust the Yang Zhang volatility estimator value.   • Percentile Length is used to Adjust Percentile coloring lookbacks.   • The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.   • When percentile coloring is off, it won’t show the gradient color.   • You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.   • When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon. • You can choose the plot style, such as lines, columns, areas in the plotstyle input.   • When the show information panel is on, a small panel will display on the right.   • The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value. █ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move). █ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
อินดิเคเตอร์ Pine Script®
โดย Motgench
Visible RangeOverview This is a precision tool designed for quantitative traders and engineers who need exact control over their chart's visual scope. Unlike standard time calculations that fail in markets with trading breaks (like A-Shares, Futures, or Stocks), this indicator uses a loop-back mechanism to count the actual number of visible bars, ensuring your indicators (e.g., MA60, MA200) have sufficient sample data. Why use this? If you use multi-timeframe layouts (e.g., Daily/Hourly/15s), it is critical to know exactly how much data is visible. The Problem: In markets like the Chinese A-Share market (T+1, 4-hour trading day), calculating Time Range / Timeframe results in massive errors because it includes closed market hours (lunch breaks, nights, weekends). The Solution: This script iterates through the visible range to count the true bar_index, providing 100% accurate data density metrics. Key Features True Bar Counting: Uses a for loop to count actual candles, ignoring market breaks. perfect for non-24/7 markets. Integer Precision: Displays time ranges (Days, Hours, Mins, Secs) in clean integers. No messy decimals. Compact UI: Displays information in a single line (e.g., View: 30 Days (120 Bars)), default to the Top Right corner to save screen space. Fully Customizable: Adjustable position, text size, and colors to fit any dark/light theme. Performance Optimized: Includes max_bars_back limits to prevent browser lag on deep history lookups. Settings Position: Default Top Right (can be moved to any corner). Max Bar Count: Default 5000 (Safety limit for loop calculation).
อินดิเคเตอร์ Pine Script®
โดย sanpingz