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Forecasting

Physics of PricePhysics of Price is a non-repainting kinematic reversal and volatility overlay. It models price as a physical object with position, velocity, and acceleration, then builds adaptive bands and a short-term predictive “ghost cone” to highlight where reversals are statistically more likely. CONCEPT Instead of using only moving averages, the core engine tracks a smoothed price (position), trend speed (velocity), and change in trend speed (acceleration). Standard deviation of the model error defines probabilistic bands around this kinematic centerline. When price stretches too far away and snaps back, the move is treated as a potential exhaustion event. CORE COMPONENTS – Kinematic centerline (Alpha–Beta–Gamma style filter) that bends with trend instead of lagging like a simple MA. – Inner and outer bands based on the standard deviation of residuals between price and the kinematic model. – Regime filter using R² and band width to avoid signals in chaotic or ultra-wide regimes. – Optional RSI “hook” filter that waits for momentum to actually turn instead of buying into a falling RSI. – Optional divergence add-on using kinematic velocity, so a marginal new price extreme with weaker velocity is recognized as a possible exhaustion pattern. REVERSAL EVENTS AND SCORING Raw events are detected when price wicks through the outer band and closes back inside (band hit with snap). These are plotted as diamonds and treated as candidates, not automatic trades. Each event is then scored from 0 to 100 using several factors: – How far price overshot the outer band. – How strongly it snapped back inside. – Whether an RSI hook is present (if enabled). – Regime quality from the kinematic model. – Basic kinematic safety to avoid the most aggressive “knife-catch” situations. – Optional divergence bonus when price makes a new extreme but velocity does not. Only events with a score above the chosen threshold become confirmed signals (triangles labeled PHYSICS REV). GHOST CONE (PREDICTIVE BAND) On the latest bar, the script projects a short-horizon “ghost cone” into the future using position, velocity, and a damped acceleration term. This creates a curved predictive band that visualizes a plausible short-term path and range, rather than a simple straight line. The cone is meant as context for trade management and risk, not as a hard target. FILTERS AND OPTIONS – Regime filter (R² and band width) can be tightened or relaxed depending on how selective you want the engine to be. – RSI and volume filters can be toggled on for extra confirmation or off to see the raw kinematic behavior. – An optional trend baseline (EMA) can be enabled to bias or restrict reversals relative to a higher-timeframe trend. – Dynamic cooldown scales with volatility so the script does not spam signals in fast environments. HOW TO USE Physics of Price is primarily a mean-reversion and exhaustion tool. It works best in markets that respect ranges, swings, and two-sided order flow. Confirmed PHYSICS REV signals near the outer bands, with decent model health and a clean RSI hook, are the core use case. The bands and ghost cone can also be used as a context overlay alongside your own entries, exits, and risk framework. This is an indicator, not a complete trading system. It does not use lookahead or higher-timeframe security calls and is designed for “once per bar close” alerts. Always combine it with your own risk management and confluence.
Pine Script® göstergesi
Finntech1 tarafından
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"
Pine Script® göstergesi
DiFlip tarafından
Cycle Forecast + MACD Divergence (Kombi v6 FULL)This indicator merges two powerful analytical models: 🔮 1. Dominant Cycle Forecasting The script automatically identifies major structural market cycles by detecting significant swing highs and lows. It then fits a sinusoidal wave (amplitude, phase, and period) to the dominant cycle and projects it into the future. Features: Automatically extracts large, dominant cycles (no noise, no small swings) Smooth sinusoidal historical cycle visualization Future cycle projection for 1–2 upcoming cycle periods Dynamic amplitude and phase alignment based on market structure Helps anticipate cycle tops and bottoms for long-term timing 📉 2. MACD Divergence Detection Full divergence detection engine using MACD or MACD Histogram. Detects: Bullish Divergence Price ↓ while MACD (or Histogram) ↑ → Possible trend reversal upward Bearish Divergence Price ↑ while MACD (or Histogram) ↓ → Possible trend reversal downward Features: Pivot-based divergence confirmation (no repaint) Choice of MACD Line or Histogram as divergence source Labels + connecting divergence lines Works across all markets and timeframes ⚙️ Smart Auto-Pivot System The indicator optionally adjusts pivot sensitivity based on timeframe: Weekly → tighter pivots Daily → medium pivots Intraday → wider pivots Ensures stable, meaningful divergence signals even on higher timeframes. 🎯 Use cases Identify upcoming cycle highs/lows Spot major trend reversals early Improve swing entries with MACD divergences near cycle turns Combine forecasting with momentum exhaustion Suitable for crypto, stocks, indices, forex & commodities 🧠 Why this indicator is powerful This tool blends time-based cycle forecasting with momentum-based divergence signals, giving you a unique perspective of where the market is likely to turn. Cycles reveal when a move may occur. Divergences reveal why a move may occur. Combined, they offer highly effective market timing.
Pine Script® göstergesi
PythonWay tarafından
SSL ST Strategy – Accuracy Enhanced v2.0 (Parser Safe)This strategy is built to identify high-probability trend breakouts using a combination of SSL Channel, Baseline, Hull / EMA signals, and Candle-based confirmations. The goal is to filter noise, avoid false breakouts, and enter only when the trend is truly shifting. This strategy identifies high-probability trend breakouts using SSL Channel, Baseline, Hull/EMA, and candle confirmations. 1. SSL shows trend shift when price breaks high/low levels. 2. Baseline filters direction (price above = buy bias, below = sell bias). 3. Hull/EMA gives early momentum confirmation. 4. Candle breakout ensures real momentum (breaks previous high/low). 5. Optional filters: ATR, reversal logic, continuation entries. 6. Exits occur on SSL flip, baseline cross, or weakness Disclaimer This strategy is provided strictly for educational and informational purposes only. It does not guarantee any profit, nor does it protect against losses of any kind. Financial markets are inherently unpredictable, and any market movement can only be assumed or estimated with a probability that is never guaranteed and can often be no better than a 50/50 chance. By using this strategy, you acknowledge that all trading decisions are made solely at your own risk. I am not liable for any profits, losses, or financial consequences incurred by anyone using or relying on this strategy. Always perform your own research, manage your risk responsibly, and consult with a qualified financial advisor before trading.
Pine Script® stratejisi
vinodkummarnair tarafından
Prime-Time × Vortex (3/6/9) — Ace (clean v3)1️⃣ Prime-Time Index (PT) A bar becomes Prime-Time when the count satisfies the formula: 4·n − 3 is a perfect square This generates the sequence: 1, 3, 7, 13, 21, 31, 43, 57, 73, 91, … These are time windows where price is more likely to form: Shifts in market structure Impulses Reversals Liquidity expansions These PT bars are drawn as small circles above the candle. If labels are enabled, the counter value (n) is also shown. 2️⃣ Vortex 3/6/9 Digital-Root Timing Every bar also has a digital root, calculated from the counter: If n → digitalRoot(n) = 3, 6, or 9, the bar is considered a Vortex bar. These moments often align with: Swing highs / swing lows Micro shifts Mini-reversals Minor liquidity grabs When a Prime-Time bar is also a 3/6/9 bar → high-probability timing. These bars are highlighted in green by default. 3️⃣ Filters & Display You can customize: Anchor time → when counting begins Reset daily → restart counter each new trading day Show only 3/6/9 → hides normal PT hits Label offset → distance above the candle Color themes This makes the indicator usable on: 1Min 5Min 15Min 1H Any timeframe you want 4️⃣ How To Apply It in Trading Use it as a time confluence tool, not a signal generator. ✔ Best ways to use: Look for MSS, sweeps, OB retests, FVG reactions when they occur on or near a Prime-Time or 3/6/9 bar Expect volatility increases after PT bars Use 3/6/9 hits to anticipate internal turning points Combine with: Session High/Low Killzones (London, NYO, PM) Purge Protocol MMXM Execution ✔ Example: If price sweeps a level and prints a 3/6/9 vortex bar inside a PT window → you have a very strong timing alignment for reversal. 5️⃣ Simple Summary Feature Meaning Prime-Time Hit (PT) Major time window where price often shifts 3/6/9 Vortex Bar Micro-timing for internal swings PT + 3/6/9 together High-probability timing for entries Reset Daily Perfect for intraday models like NYO & London Anchor Time Defines the entire cycle structure
Pine Script® göstergesi
TIMEANDPRICEPR tarafından
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise. █ OVERVIEW We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning). █ Detrend Fluctuation Analysis Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent. The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:  • Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.  • According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).  • Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned. █ Calculation DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series. Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.) We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression. We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale. As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement. The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window. Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements. The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases. If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation). If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation. If the slope = 0.5, It means the series follows a random walk. █ FEATURES  • Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.  • When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)  • “show filter” option applies a smoothing moving average to smooth the exponent.  • Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2. • For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues. The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot. When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use. █ How to Use Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.  • In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.  • When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.  • Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa. However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant. So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency. The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right. An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future. However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal. The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead. Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4). Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Pine Script® göstergesi
Motgench tarafından
@Aladdin's Trading Web – Command CenterThe indicator uses standard Pine Script functionality including z-score normalization, standard deviation calculations, percentage change measurements, and request.security calls for multiple predefined symbols. There are no proprietary algorithms, external data feeds, or restricted calculation methods that would require protecting the source code. Description: The @Aladdin's Trading Web – Command Center indicator provides a composite market regime assessment through a weighted combination of multiple intermarket relationships. The indicator calculates normalized z-scores across several key market components including banks, volatility, the US dollar, credit spreads, interest rates, and alternative assets. Each component is standardized using z-score methodology over a user-defined lookback period and combined according to configurable weighting parameters. The resulting composite measure provides a normalized assessment of the prevailing market environment, with the option to invert rate relationships for specific market regime conditions. The indicator focuses on capturing the synchronized behavior across these interconnected market segments to provide a unified view of systemic market conditions.
Pine Script® göstergesi
Timmy741 tarafından
Fast Autocorrelation Estimator█ Overview: The Fast ACF and PACF Estimation indicator efficiently calculates the autocorrelation function (ACF) and partial autocorrelation function (PACF) using an online implementation. It helps traders identify patterns and relationships in financial time series data, enabling them to optimize their trading strategies and make better-informed decisions in the markets. █ Concepts: Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. This indicator displays autocorrelation based on lag number. The autocorrelation is not displayed based over time on the x-axis. It's based on the lag number which ranges from 1 to 30. The calculations can be done with "Log Returns", "Absolute Log Returns" or "Original Source" (the price of the asset displayed on the chart). When calculating autocorrelation, the resulting value will range from +1 to -1, in line with the traditional correlation statistic. An autocorrelation of +1 represents a perfect correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of -1, on the other hand, represents a perfect inverse correlation (an increase seen in one time series results in a proportionate decrease in the other time series). Lag number indicates which historical data point is autocorrelated. For example, if lag 3 shows significant autocorrelation, it means current data is influenced by the data three bars ago. The Fast Online Estimation of ACF and PACF Indicator is a powerful tool for analyzing the linear relationship between a time series and its lagged values in TradingView. The indicator implements an online estimation of the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) up to 30 lags, providing a real-time assessment of the underlying dependencies in your time series data. The Autocorrelation Function (ACF) measures the linear relationship between a time series and its lagged values, capturing both direct and indirect dependencies. The Partial Autocorrelation Function (PACF) isolates the direct dependency between the time series and a specific lag while removing the effect of any indirect dependencies. This distinction is crucial in understanding the underlying relationships in time series data and making more informed decisions based on those relationships. For example, let's consider a time series with three variables: A, B, and C. Suppose that A has a direct relationship with B, B has a direct relationship with C, but A and C do not have a direct relationship. The ACF between A and C will capture the indirect relationship between them through B, while the PACF will show no significant relationship between A and C, as it accounts for the indirect dependency through B. Meaning that when ACF is significant at for lag 5, the dependency detected could be caused by an observation that came in between, and PACF accounts for that. This indicator leverages the Fast Moments algorithm to efficiently calculate autocorrelations, making it ideal for analyzing large datasets or real-time data streams. By using the Fast Moments algorithm, the indicator can quickly update ACF and PACF values as new data points arrive, reducing the computational load and ensuring timely analysis. The PACF is derived from the ACF using the Durbin-Levinson algorithm, which helps in isolating the direct dependency between a time series and its lagged values, excluding the influence of other intermediate lags. █ How to Use the Indicator: Interpreting autocorrelation values can provide valuable insights into the market behavior and potential trading strategies. When applying autocorrelation to log returns, and a specific lag shows a high positive autocorrelation, it suggests that the time series tends to move in the same direction over that lag period. In this case, a trader might consider using a momentum-based strategy to capitalize on the continuation of the current trend. On the other hand, if a specific lag shows a high negative autocorrelation, it indicates that the time series tends to reverse its direction over that lag period. In this situation, a trader might consider using a mean-reversion strategy to take advantage of the expected reversal in the market. ACF of log returns: Absolute returns are often used to as a measure of volatility. There is usually significant positive autocorrelation in absolute returns. We will often see an exponential decay of autocorrelation in volatility. This means that current volatility is dependent on historical volatility and the effect slowly dies off as the lag increases. This effect shows the property of "volatility clustering". Which means large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes. ACF of absolute log returns: Autocorrelation in price is always significantly positive and has an exponential decay. This predictably positive and relatively large value makes the autocorrelation of price (not returns) generally less useful. ACF of price: █ Significance: The significance of a correlation metric tells us whether we should pay attention to it. In this script, we use 95% confidence interval bands that adjust to the size of the sample. If the observed correlation at a specific lag falls within the confidence interval, we consider it not significant and the data to be random or IID (identically and independently distributed). This means that we can't confidently say that the correlation reflects a real relationship, rather than just random chance. However, if the correlation is outside of the confidence interval, we can state with 95% confidence that there is an association between the lagged values. In other words, the correlation is likely to reflect a meaningful relationship between the variables, rather than a coincidence. A significant difference in either ACF or PACF can provide insights into the underlying structure of the time series data and suggest potential strategies for traders. By understanding these complex patterns, traders can better tailor their strategies to capitalize on the observed dependencies in the data, which can lead to improved decision-making in the financial markets. Significant ACF but not significant PACF: This might indicate the presence of a moving average (MA) component in the time series. A moving average component is a pattern where the current value of the time series is influenced by a weighted average of past values. In this case, the ACF would show significant correlations over several lags, while the PACF would show significance only at the first few lags and then quickly decay. Significant PACF but not significant ACF: This might indicate the presence of an autoregressive (AR) component in the time series. An autoregressive component is a pattern where the current value of the time series is influenced by a linear combination of past values at specific lags. Often we find both significant ACF and PACF, in that scenario simply and AR or MA model might not be sufficient and a more complex model such as ARMA or ARIMA can be used. █ Features: Source selection: User can choose either 'Log Returns' , 'Absolute Returns' or 'Original Source' for the input data. Autocorrelation Selection: User can choose either 'ACF' or 'PACF' for the plot selection. Plot Selection: User can choose either 'Autocorrelarrogram' or 'Historical Autocorrelation' for plotting the historical autocorrelation at a specified lag. Max Lag: User can select the maximum number of lags to plot. Precision: User can set the number of decimal points to display in the plot.
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Motgench tarafından
Support Line [by rukich]🟠 OVERVIEW The indicator displays a floating line that acts as a support level. It's important to remember that any support level can be broken. 🟠 COMPONENTS The indicator is based on the percentage difference between the closes of the n-th bar back and the current bar. The resulting percentage is smoothed to remove noise. The indicator is displayed as a green-red line (the colors don’t carry meaning — they are used just for visual variety). When the price touches the support level, the bar background turns green. For convenience, there is a label on the right side of the indicator showing the current value of the line. 🟠 HOW TO USE The indicator includes several settings that can be adjusted, though optimal defaults are provided. Settings: Timeframe — specifies which timeframe’s data is used to calculate the line. Candles back — specifies how many bars back from the current one are used. The indicator should be used according to general support-zone logic. Since no support zone guarantees a price bounce, the optimal approach is to confirm the reaction after the price touches the line. Example of use: In the current example, the Timeframe in the indicator settings is set to 1 hour, and the currently open chart is 5 minutes. This means that on the 5-minute chart we see a 1-hour line. After the price touches the support line, you need to see a confirmation of the reaction to understand whether the support zone is holding the price. In the examples, reaction confirmation is shown through: the formation of an M5 shift and the invalidation of an FVG M5- (the latter is more risky than the M5 shift): 🟠 CONCLUSION The indicator shows a floating support zone, and when tested, you should confirm the reaction on a lower timeframe.
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rukich tarafından
ES-VIX Expected Daily MoveThis indicator calculates the expected daily price movement for ES futures based on current volatility levels as measured by the VIX (CBOE Volatility Index). Formula: Expected Daily Move = (ES Price × VIX Price) / √252 / 100 The calculation converts the annualized VIX volatility into an expected daily move by dividing by the square root of 252 (the approximate number of trading days per year). Features: Real-time calculation using current ES futures price and VIX level Histogram visualization in a separate pane for easy trend analysis Information table displaying: Current ES futures price Current VIX level Expected daily move in points Expected daily move as a percentage
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alanjeremiebarbe tarafından
ES-VIX Daily Price Bands - Inner bandsES-VIX Daily Price Bands This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets. Formulas: Upper Band = Daily Low + (ES Price × VIX ÷ √252 ÷ 100) Lower Band = Daily High - (ES Price × VIX ÷ √252 ÷ 100) The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's extremes. Features: Real-time band calculation that updates throughout the trading session Upper band (green) extends from the current day's low Lower band (red) contracts from the current day's high Inner upper band (green) at 50% of expected move Inner lower band (red) at 50% of expected move Shaded zone between bands for visual clarity Information table displaying: Current ES price and VIX level Running daily high and low Current upper and lower band values
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alanjeremiebarbe tarafından
Daily ATR Dashboard - NIRALADaily ATR Dashboard: Volatility at a Glance What is this? The "Daily ATR Dashboard" is a simple, non-intrusive utility tool designed for intraday traders. It places a clean information table in the top-right corner of your chart, displaying the Daily Average True Range (DATR) for the current session and the previous two days. Why is it useful? Understanding daily volatility is crucial for setting realistic targets and stop-losses. Know the Range: Instantly see how much the instrument typically moves in a day. Context: Compare today's volatility with yesterday's and the day before to gauge if the market is expanding (becoming more volatile) or contracting (consolidating). Clean Charts: Instead of plotting a messy ATR line indicator below your price action, this dashboard gives you the raw data you need without cluttering your workspace. Features: Real-Time Data: The "Today" row updates in real-time as the current daily candle develops. Historical Context: Automatically fetches and displays the final DATR values for the previous two sessions ("Yesterday" and "Day Before"). Highlighted Current Day: The current day's data is highlighted in yellow for immediate visibility. Customizable: You can adjust the ATR length (default is 14) and the text size to fit your screen perfectly. How to Read It: Today: The current volatility of the ongoing daily session. Yesterday / Day Before: The finalized volatility of past sessions. Tip: If "Today's" ATR is significantly lower than the previous days, expect potential expansion or a breakout soon. If it is significantly higher, the market may be overextended. Settings: DATR Length: The lookback period for the ATR calculation (Default: 14). Text Size: Adjust the size of the table text (Tiny, Small, Normal, Large).
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aknoshutters tarafından
XAUUSD 9/1 and 6/4 ZONE LINE (Buy zone and SELL zone)When trading the XAUUSD pair, I noticed that gold often reverses from price levels ending with the digits 9/1 and 6/4. Because of this pattern, I began drawing lines based on these price endings and integrating them into my trading strategy. When combined with other trading methods, these levels provided strong and consistent results. Feel free to try it yourself — just make sure to analyze the market carefully before entering any trade!
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usarov1987_21 tarafından
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions. ⯁ Real-Time RSI Forecasting For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions. ⯁ Highly Customizable The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy. ⯁ Innovative and Science-Based This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is: y = β₀ + β₁x + ε where: y  = predicted variable (e.g., future RSI value) x  = explanatory variable (e.g., bar index or time) β₀ = intercept (value of y when x = 0) β₁ = slope (rate of change of y relative to x) 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 RSI projection based on recent trends. ⯁ Least Squares Estimation To minimize the error between predicted and observed values, we use the formulas: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables. ⯁ Linear Regression in Machine Learning Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension. ⯁ Visual Interpretation Imagine a time series of RSI values like this: Time → RSI    → The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior. ⯁ Summary of Scientific Concepts Used Linear Regression: Models relationships between variables using a straight line. Least Squares: Minimizes squared prediction errors for optimal fit. Time-Series Forecasting: Predicts future values from historical patterns. Supervised Learning: Predictive modeling based on known output values. Statistical Smoothing: Reduces noise to highlight underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Built on statistical and mathematical theory. First of its kind: The first public RSI with least-squares predictive modeling. Intelligent: Incorporates machine-learning logic into RSI interpretation. Forward-looking: Generates predictive, not just reactive, signals. Customizable: Exceptionally flexible for any strategic framework. ⯁ Conclusion By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems. ⯁ 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 RSI? The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength. ⯁ How RSI Works RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones: Overbought: RSI above 70 may signal an overbought market. Oversold: RSI below 30 may signal an oversold market. Neutral Zone: RSI between 30 and 70, indicating no extreme condition. These zones help identify potential price reversals and confirm trend strength. ⯁ Entry Conditions All conditions below are fully customizable and allow detailed control over entry signal creation. 📈 BUY 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 📉 SELL 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 🤖 Automation All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies. ⯁ Unique Features Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill
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DiFlip tarafından
LazyTradeLazyTrade is a clean, high-confidence trend-following indicator built on TradingView’s non-repainting SuperTrend V6 engine. It adds intelligent RSI confirmation, profit-tracking labels, trend-flip markers, and optional background shading to highlight momentum shifts. Designed for intraday and swing traders who want fast, reliable signals without chart clutter. Features: • Non-repainting Buy/Sell signals • Smart RSI confirmation (Aggressive / Standard / Conservative) • Auto P&L between opposite signals • Trend-flip circles and transparent background zones • Clean visual structure optimized for daily and leveraged ETF trading A simple, intuitive tool that keeps you aligned with the dominant trend—no noise, no over-complication.
Pine Script® göstergesi
dmandrey tarafından
Exhaustion Zone [by rukich]🟠 OVERVIEW The indicator shows asset exhaustion — an area of interest where potential buying opportunities can be considered. 🟠 COMPONENTS The indicator is based on a combination of fundamental tools designed to properly react to price movement and volatility. It is displayed on the chart as a green line. When the price touches the indicator line, the candle lights up and is highlighted in green. 🟠 HOW TO USE The best timeframes for using the indicator: 1D and 3D. Since the indicator is used on higher timeframes, the price rarely reaches the indicator line, but it often shows a strong reaction when it does, which suggests that the indicator can be used for investment purposes. Since the zone suggests potential buying opportunities, it’s best to act from the zone only when a reaction is confirmed. Confirmation may include a candle close beyond nearby fractals or the invalidation of the nearest resistance zone. 🟠 CONCLUSION The indicator highlights an area of interest where, upon confirmation of a reaction, buying opportunities may be considered.
Pine Script® göstergesi
rukich tarafından
2
Smart Adaptive Double Patterns [The_lurker]Smart Adaptive Double Patterns This is an advanced technical indicator that combines two of the strongest and most renowned classical price reversal patterns: ✅ Double Bottom Pattern — a bullish reversal pattern that forms after a downtrend ✅ Double Top Pattern — a bearish reversal pattern that forms after an uptrend The indicator does not merely detect patterns — it provides a fully integrated, intelligent system that includes: ✅ Precise quality scoring for each pattern using 5 technical criteria ✅ Automatic price target calculation at three levels (Conservative, Balanced, Aggressive) ✅ Multi-layer dynamic filtering to avoid false signals ✅ Live pattern tracking from formation to target achievement or failure ✅ Comprehensive alert system covering all possible trading scenarios 🎯 Why Is This Indicator Unique? 1️⃣ High Detection Accuracy Unlike traditional indicators that rely on simple rules, this one applies 5 strict structural conditions to confirm pattern validity: A clear trend must precede the pattern High symmetry between the two bottoms or two tops No break of critical price levels during formation Logical spacing between key points Technical confirmation from ADX, ATR, and Volume 2️⃣ Advanced Quality Scoring System Each pattern is scored out of 100 based on 5 weighted criteria: Symmetry (30%): How closely the two bottoms or tops match Trend Strength (20%): Strength of the prior trend Volume Behavior (20%): Trading activity at critical points Pattern Depth (15%): Vertical distance between neckline and bottom/top Structural Integrity (15%): Full compliance with structural rules 3️⃣ Smart Target Management Separate targets for bullish (Double Bottom) and bearish (Double Top) patterns Separate projections for success and failure cases Multiple options: Conservative (0.618) / Balanced (1.0) / Aggressive (1.618) Live tracking with dynamic moving lines 4️⃣ Professional Failure Handling Failed patterns are not ignored — they are turned into counter-trend opportunities: Failed Double Bottom → triggers a bearish signal with downside targets Failed Double Top → triggers a bullish signal with upside targets Automatic color change for clear visual distinction 5️⃣ Full Customization Flexibility Enable/disable each pattern independently 22+ adjustable settings Unique colors for each pattern and quality level Full bilingual support (Arabic / English) 📐 Pattern Details 🟦 Double Bottom Pattern Sequence of points: 🔹 Point 1: Peak marking the start of a strong downtrend 🔹 Point 2 (Bottom 1): First low — first key bounce 🔹 Point 3: Intermediate high — forms the neckline (resistance) 🔹 Point 4 (Bottom 2): Second low — should closely match Bottom 1 🔹 Point 5: Breakout point — pattern confirmation Mandatory Conditions: ✅ Clear downtrend before Point 2 ✅ Bottoms 2 & 4 nearly identical (≤1.5% difference by default) ✅ Point 3 higher than both bottoms ✅ Neither bottom is broken during formation ✅ Sufficient time between points (≥10 candles by default) ✅ Success Scenario → Price breaks above the neckline (Point 3) → Point 5 is plotted at breakout candle → Dashed vertical line drawn from Point 5 to target → Horizontal dashed line tracks price toward target → Dashboard shows: Pattern Type | Quality | Rating | Target | Status → When target hits: line turns green + ✅ appears 🎯 Target Calculation Pattern Height = Point 3 − Point 4 • Conservative: Point 3 + (Height × 0.618 × Quality Factor) • Balanced: Point 3 + (Height × 1.0 × Quality Factor) • Aggressive: Point 3 + (Height × 1.618 × Quality Factor) ❌ Failure Scenario → Price breaks below both Bottom 1 or Bottom 2 before neckline breakout Visual Changes: All lines turn red Red ✖ appears at breakdown candle Neckline stops expanding Red dashed vertical line from breakdown point to bearish target Red horizontal tracking line follows price Dashboard updates to: ⚠ Failed Bottom – Bearish → Shows new bearish target → Indicates target mode for failure case → Status: Bearish Reversal → Fully red display 🟥 Double Top Pattern Sequence of points: 🔹 Point 1: Trough marking the start of a strong uptrend 🔹 Point 2 (Top 1): First peak — first key resistance 🔹 Point 3: Intermediate low — forms the neckline (support) 🔹 Point 4 (Top 2): Second peak — should closely match Top 1 🔹 Point 5: Breakdown point — pattern confirmation Mandatory Conditions: ✅ Clear uptrend before Point 2 ✅ Tops 2 & 4 nearly identical (≤1.5% difference by default) ✅ Point 3 lower than both tops ✅ Neither top is breached during formation ✅ Sufficient time between points (≥10 candles by default) ✅ Success Scenario → Price breaks below the neckline (Point 3) → Point 5 is plotted at breakdown candle → Dashed vertical line drawn to target → Horizontal tracking line moves with price → Dashboard updates accordingly → Green line + ✅ on hit 🎯 Target Calculation Pattern Height = Point 4 − Point 3 • Conservative: Point 3 − (Height × 0.618 × Quality Factor) • Balanced: Point 3 − (Height × 1.0 × Quality Factor) • Aggressive: Point 3 − (Height × 1.618 × Quality Factor) ❌ Failure Scenario → Price breaks above either Top 1 or Top 2 before neckline breakdown Visual Changes: All lines turn cyan (light blue) Cyan ✖ appears at breakout candle Neckline stops expanding Cyan dashed vertical line to bullish target Cyan horizontal tracking line follows price Dashboard updates to: ⚠ Failed Top – Bullish → Shows new bullish target → Indicates target mode for failure case → Status: Bullish Reversal → Fully cyan display 🎯 Upside Target (after Double Top failure) Max Top = max(Point 2, Point 4) Height = Max Top − Point 3 • Conservative: Max Top + (Height × 0.618) • Balanced: Max Top + (Height × 1.0) • Aggressive: Max Top + (Height × 1.618) 📊 Quality Scoring System (0–100) 1️⃣ Symmetry (30%) Measures price match between the two bottoms or two tops. High score (25–30): Near-perfect symmetry → very strong pattern Medium (15–24): Good match → reliable signal Low (5–14): Weak symmetry → use caution Zero: No symmetry → invalid pattern 2️⃣ Trend Strength (20%) Uses ADX and DI indicators. 20 pts: Strong trend confirmed (e.g., ADX ≥ 20 + correct DI alignment) 10 pts: Trend filter disabled 6 pts: Weak or sideways trend 3️⃣ Volume Behavior (20%) Declining volume on second touch is a positive sign (shows exhaustion). 15–20 pts: Clear volume drop → strong signal 10 pts: Neutral volume 6 pts: Rising volume → higher risk of continuation 4️⃣ Pattern Depth (15%) Deeper patterns = stronger reversals. 12–15 pts: Deep → high reversal power 8–11 pts: Medium → acceptable <8 pts: Shallow → weak signal 5️⃣ Structural Integrity (15%) Checks logical structure (e.g., Point 1 > Point 3 in Double Bottom). 12–15 pts: Ideal structure 8–11 pts: Minor flaws <8 pts: Poor setup 📈 Final Quality Rating & Colors • 85–100 → ⭐ Excellent  → Double Bottom: Cyan #00BCD4  → Double Top: Light Red #FF5252 • 75–84 → ✨ Very Good • 65–74 → ✓ Good • 60–64 → ○ Acceptable  → All use Amber #FFC107 • <60 → ❌ Rejected (not shown)  → Gray #9E9E9E 🔧 Dynamic Filters 1️⃣ ATR Filter (Volatility Check) Rejects patterns in abnormally high volatility periods. → If current ATR > 1.8 × 50-period ATR MA → pattern rejected ✅ Recommended for crypto, small caps ❌ Optional for calm markets (gold, bonds) 2️⃣ ADX Filter (Trend Confirmation) Ensures a real trend exists before the pattern. → If ADX < 14 (70% of default 20) → pattern rejected ✅ Strongly recommended (keep ON) 3️⃣ Volume Filter (Behavior Validation) Not used to reject patterns, but strongly affects quality score. ✅ Best for liquid markets (Forex majors, large stocks) ❌ Optional for illiquid assets ⚙️ Key Settings Explained 🔘 General Settings • Language: Arabic / English • Show Previous Patterns: Yes / No  → “No” keeps chart clean; “Yes” for historical review 🔘 Pattern Selection • Enable Double Bottom: ✅ / ❌ • Enable Double Top: ✅ / ❌ → Use combinations:  ✅✅ → Full reversal scanning  ✅❌ → Long setups only  ❌✅ → Short setups only  ❌❌ → Indicator OFF 🔘 Detection Parameters • Pivots Left (1–20): Higher = more reliable, fewer patterns • Pivots Right (1–20): Lower = faster signals • Min Width (5–100): Min candles between Bottom/Top 1 & 2 • Tolerance % (0.1%–5%): Max allowed price difference • Min Arm (5–50): Min candles between pivot & neckline • Min Trend (5–50): Min candles in prior trend • Trend Lookback (50–500): How far back to detect trend start • Extension Multiplier (1.0–5.0): How long to wait for breakout 🔘 Quality Settings • Min Quality Score (0–100):  → Conservative: 75–85  → Balanced: 60–70  → Flexible: 50–55 • Custom Weights: Adjust based on market (e.g., increase Volume weight in Forex) 🔘 Target Settings • Bottom Bullish Target: Conservative / Balanced / Aggressive • Bottom Bearish Target: (used on failure) • Top Bearish Target: Conservative / Balanced / Aggressive • Top Bullish Target: (used on failure) 🔘 Visual Settings • Label Size: Small / Normal / Large / Huge • Pattern Colors: Fully customizable • Table: Show/Hide + Size (Small/Normal/Large) + Position (Top-Right / Top-Left / Bottom-Right / Bottom-Left) • Fill Transparency: 70%–95% (default: 85%) 🔔 Alert System (8 Independent Alerts) 📌 Double Bottom Alerts Bullish Breakout → “Double Bottom Breakout – Bullish!” Bullish Target Hit → “Bullish Target Achieved!” Failure (Bearish) → “Double Bottom Failed – Bearish!” Bearish Target Hit → “Bearish Target Achieved (Failure)!” 📌 Double Top Alerts Bearish Breakdown → “Double Top Breakdown – Bearish!” Bearish Target Hit → “Bearish Target Achieved!” Failure (Bullish) → “Double Top Failed – Bullish!” Bullish Target Hit → “Bullish Target Achieved (Failure)!” Each alert can be enabled/disabled independently and supports pop-ups, emails, or webhooks. ⚠️ Disclaimer: This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses.
Pine Script® göstergesi
The_lurker tarafından
4