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QQQ Quant Power STRATEGY v13.3 (Ribbon + TQQQ Specs)1. The Quant Engine (Data Processing) Weighted Scoring: It assigns specific weights to stocks (e.g., NVDA gets 8.5% weight, TXN gets 1.0%). Z-Score Pressure: It calculates how "unusual" the current buying/selling pressure is compared to the average (Standard Deviation). Alignment Bonus: It boosts the "Conviction Score" if Mega Caps (Top 8) and Large Caps (Next 12) are moving in the same direction. 2. The Dashboard (Mission Control) The dashboard gives you an X-Ray view of the market: Main Status: Tells you if the market is BULLISH, BEARISH, or CHOP (Sit Out). Conviction %: A probability score (0-99%). Higher = Safer trade. Breadth: Counts how many of the top 20 stocks are above their EMA. Chop Logic: If Breadth is mixed (between 6 and 14 stocks above EMA), it declares "CHOP" and blocks trades. Mega/Large Net: Shows the net buying/selling pressure for each group. 3. Visuals Pressure Line: The line on the chart isn't just a Moving Average; it's the Net Pressure of the 20 stocks pushing price up or down. Conviction Ribbon: The squares at the bottom of the screen. 🟩 Green: High Probability Long (>77%). 🟥 Red: High Probability Short (>77%). ⬜ Gray: Low Conviction / Holding. 4. Strategy Logic (Automated Trading) Entry: Enters when the "Basket" of stocks is aligned (Bull/Bear Pressure) AND the Conviction Score is high (>77%). Exit: Closes the trade if Conviction drops (Signal fades) or hits a Hard Stop Loss. Time Filters: Includes strict trading windows (e.g., No trading during lunch 12-1pm, closes all positions on Friday). Summary This is a Market Breadth & Momentum Strategy. It assumes that QQQ cannot sustain a trend unless its underlying components (NVDA, AAPL, etc.) are pushing it. It filters out "fake moves" where QQQ moves but the components don't support it.
אסטרטגיית Pine Script®
מאת ‎CustomQuantLabs‎
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past. With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge. ⯁ Real-Time MACD Forecasting For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight. ⯁ Fully Customizable This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model. Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered. ⯁ With MACD Forecast Colorful, you can: • Detect MACD crossovers before they happen. • Anticipate trend reversals with greater precision. • React earlier than traditional indicators. • Gain a powerful edge in both discretionary and automated strategies. • This isn’t just smarter MACD — it’s predictive momentum intelligence. ⯁ Scientifically Powered by Linear Regression MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool. It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart. ⯁ Linear Regression: Technical Foundation Linear regression is a statistical method that models 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 variable (e.g., future MACD value) x  = independent variable (e.g., bar index) β₀ = intercept β₁ = slope ε   = random error (residual) The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum. ⯁ Least Squares Estimation The regression coefficients are computed with the following formulas: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: 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. ⯁ Regression in Machine Learning Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis. Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading. ⯁ Visual Interpretation Picture the MACD values over time like this: Time  → MACD → A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes. The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement. ⯁ Scientific Concepts Used Linear Regression: models the relationship between variables using a straight line. Least Squares Method: minimizes squared prediction errors for best-fit. Time-Series Forecasting: projects future data based on past patterns. Supervised Learning: predictive modeling using labeled inputs. Statistical Smoothing: filters noise to highlight trends. ⯁ Why This Indicator Is Revolutionary First open-source MACD with real-time predictive modeling. Scientifically grounded with linear regression logic. Automatable through TradingView alerts and bots. Smart signal generation using forecasted crossovers. Highly customizable with 22 buy/sell conditions. Enhanced visuals with background (bgcolor) and area fill (fill) support. This isn’t just an update — it’s the next evolution of MACD forecasting. ⯁ 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 MACD? The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram. ⯁ How to use the MACD? The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line. Here are the primary signals generated by the MACD: • Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal. • Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal. • Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal. • Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI. ⯁ How to use MACD forecast? The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities. Step 1 — Spot Predicted Crossovers: Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move. Step 2 — Confirm with Histogram Projection: Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions. Step 3 — Combine with Multi-Timeframe Analysis: Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast). Step 4 — Set Entry Conditions & Automation: Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts. Step 5 — Trade Ahead of the Market: By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders. 📈 BUY 🍟 Signal Validity: The signal will remain valid for X bars. 🍟 Signal Sequence: Configurable as AND or OR. 🍟 MACD > Signal Smoothing 🍟 MACD < Signal Smoothing 🍟 Histogram > 0 🍟 Histogram < 0 🍟 Histogram Positive 🍟 Histogram Negative 🍟 MACD > 0 🍟 MACD < 0 🍟 Signal > 0 🍟 Signal < 0 🍟 MACD > Histogram 🍟 MACD < Histogram 🍟 Signal > Histogram 🍟 Signal < Histogram 🍟 MACD (Crossover) Signal 🍟 MACD (Crossunder) Signal 🍟 MACD (Crossover) 0 🍟 MACD (Crossunder) 0 🍟 Signal (Crossover) 0 🍟 Signal (Crossunder) 0 🔮 MACD (Crossover) Signal Forecast 🔮 MACD (Crossunder) Signal Forecast 📉 SELL 🍟 Signal Validity: The signal will remain valid for X bars. 🍟 Signal Sequence: Configurable as AND or OR. 🍟 MACD > Signal Smoothing 🍟 MACD < Signal Smoothing 🍟 Histogram > 0 🍟 Histogram < 0 🍟 Histogram Positive 🍟 Histogram Negative 🍟 MACD > 0 🍟 MACD < 0 🍟 Signal > 0 🍟 Signal < 0 🍟 MACD > Histogram 🍟 MACD < Histogram 🍟 Signal > Histogram 🍟 Signal < Histogram 🍟 MACD (Crossover) Signal 🍟 MACD (Crossunder) Signal 🍟 MACD (Crossover) 0 🍟 MACD (Crossunder) 0 🍟 Signal (Crossover) 0 🍟 Signal (Crossunder) 0 🔮 MACD (Crossover) Signal Forecast 🔮 MACD (Crossunder) Signal Forecast 🤖 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: 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" 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"
אינדיקטור Pine Script®
מאת ‎DiFlip‎
Multi-Factor Trend Confluence Indicator (PTP V4)Disclaimer: This is a technical analysis tool for educational and informational purposes only. It does not constitute investment advice, financial solicitation, or a recommendation to buy or sell any security or instrument. Trading involves significant risk, and past performance is not indicative of future results. Use at your own risk. KEY Features and Strategic Methodology This is a comprehensive trend and confluence indicator built on multiple factors to identify potential pullbacks within an established trend. • Core Trend Filter: Uses a long-term EMA to confirm the overall market bias. • Fibonacci Pullback Logic: Identifies potential low-risk entry zones by calculating a 61.8% Fibonacci Retracement over a user-defined lookback period. • Multi-Factor Confluence: A signal is generated only when the price touches the Fib zone AND the following factors align (You can edit the script to adjust the confluence conditions.): o RSI is above 50. o Positive DI is above Negative DI (DMI Bullish Crossover). o Price is above the fast EMA. • Consecutive Signal Counter: Includes a unique counter that highlights bars where the confluence conditions have been met for a minimum number of consecutive candles (4 by default), aiding in the validation of strong momentum entries. • Moving Average Visualization: Plots and color-fills 10 WMA, 21 EMA, 42 EMA, and 200 EMA to provide a full market context and visualize momentum shifts. 1. Short-Term Momentum (WMA10 vs. EMA42 Fill) This fill area highlights immediate price acceleration and momentum shifts: • Green Fill (Bullish Momentum): WMA10 > EMA42. • Red Fill (Bearish Momentum): WMA10 < EMA42. 2. Long-Term Market Context (EMA200 vs. EMA42 Fill) This fill area defines the dominant backdrop of the market, essential for strategic positioning: • Green Fill (Bullish Context): EMA200 < EMA42. • Red Fill (Bearish Context): EMA200 > EMA42. EMA200 Line Coloration The EMA200 line color itself also provides a visual cue for the long-term context: • Red Line: When EMA200 > EMA42 (Bearish Context). • Green Line: When EMA200 < EMA42 (Bullish Context). Customization The indicator is highly customizable via the settings menu, allowing users to adjust lengths for EMA, RSI, DMI, Pivot Points, and the specific parameters for the Fibonacci Retracement Strategy (tolerance and candle limits).
אינדיקטור Pine Script®
מאת ‎pablo.corti‎
Donchian ForecastDonchian Forecast – multi-timeframe Donchian/ATR bias with ADX regime blending Donchian Forecast is a multi-timeframe bias tool that turns classic Donchian channels into a normalized trend/mean-reversion “forecast” and a single bias value in . It projects a short polyline path from the current price and shows how that path adapts when the market shifts from ranging to trending (via ADX). --- Concept 1. Donchian position → direction For each timeframe, the script measures where price sits inside its Donchian channel: -1 = near channel low 0 = middle +1 = near channel high This Donchian position is multiplied by ATR to create a **price delta** (how far the forecast moves from current price). 2. Local behavior: trend vs mean-reversion around Donchian The indicator treats the edges vs middle of the Donchian channel differently: * By default, edges behave more “trend-like”, middle more “mean-reverting”. * If you enable the reversed option, this logic flips (edges = mean-reverting, middle = trend- like). * This “local” behavior is controlled smoothly by the absolute Donchian position |pos| (not by hard zone switches). 3. Global ADX modulation (regime aware) ADX is mapped from your chosen low → high thresholds into a signed factor in : * ADX ≤ low → -1 (fully reversed behavior, more range/mean-reversion oriented) * ADX ≥ high → +1 (fully normal behavior, more trend oriented) * Values in between create a **smooth transition**. * This global factor can: * Keep the local behavior as is (trending regime), * Flip it (range regime), or * Neutralize it (indecisive regime). 4. Multi-timeframe aggregation (1x–12x chart timeframe) * The script repeats the same logic across 12 horizons: * 1x = chart timeframe * 2x..12x = multiples of the chart timeframe (e.g., 5m → 10m, 15m, …; 1h → 2h, 3h, …). * For each horizon it builds: * Donchian position * ATR-scaled delta (in price units) * Locally + globally blended delta (after Donchian + ADX logic). * These blended deltas are ATR-weighted and summed into a single bias in , which is then shown as Bias % in the on-chart table. --- ### What you see on the chart * Forecast polyline * Starting at the current close, the indicator draws a short chain of **up to 12 segments**: * Segment 1: from current price → 1x projection * Segment 2: 1x → 2x projection * … up to 12x. * Each segment is: * Green when its blended delta is ≥ 0 (upward bias) * Red when its blended delta is < 0 (downward bias) * This is not future price, but a synthetic path showing how the Donchian/ATR/ADX model “expects” price to drift across multiple horizons. * Bias table (top-center) * `Bias: X.Y%` * > 0% (green) → net upward bias across horizons * < 0% (red) → net downward bias * Magnitude (e.g., ±70–100%) ≈ strength of the directional skew. * `ADX:` current ADX value (from your DMI settings). * `ADXBlend:` the signed ADX factor in : * +1 ≈ fully “trend-interpretation” of Donchian behavior * 0 ≈ neutral / mixed regime * -1 ≈ fully “reversed/mean-reversion interpretation” --- Inputs & settings Core Donchian / ATR * Donchian Length – lookback for Donchian high/low on each horizon. * Price Source – input series used for position inside the Donchian channel (default: close). * ATR Length – ATR lookback for all horizons. * ATR Multiplier – scales the size of each forecast step in price units (higher = longer segments / more aggressive forecast). *Local behavior at high ADX * Reversed local blend at high ADX? * Off (default) – edges behave more trend-like, middle more mean-reverting. * On – flips that logic (edges more mean-reverting, middle more trend-like). * The actual effect is always modulated by the global ADX factor, so you can experiment with how the regime logic feels in different markets. Global ADX blending * DMI DI Length – period for the DI+ and DI- components. * ADX Smoothing – smoothing length for ADX. * ADX low (mean-rev zone) – below this level, the global factor pushes behavior toward reversal/range logic . * ADX high (trend zone) – above this level, the global factor pushes behavior toward **trend logic**. * Values between low and high create a smooth blend rather than a hard on/off switch. --- How to use it (examples) * Directional bias dashboard * Use the Bias % as a compact summary of multi-horizon Donchian/ATR/ADX conditions: * Consider only trades aligned with the sign of Bias (e.g., longs only when Bias > 0). * Use the magnitude to filter for **strong vs weak** directional contexts. * Regime-aware context * Watch ADX and ADXBlend: * High ADX & ADXBlend ≈ +1 → favor trend-continuation ideas. * Low ADX & ADXBlend ≈ -1 → favor range/mean-reversion ideas. * Around 0 → mixed/transition regimes; forecasts will be more muted. * Visual sanity check for systems * Overlay Donchian Forecast on your usual entries/exits to see: * When your system trades **with** the multi-TF Donchian bias. * When it trades **against** it (possible fade setups or no-trade zones). This script does not generate entry or exit signals by itself. It is a contextual/forecast tool meant to sit on top of your own trading logic. --- Notes * Works on most symbols and timeframes; higher-timeframe multiples are built from the chart timeframe. * The forecast line is a model-based projection, not a prediction or guarantee of future price. * Always combine this with your own risk management, testing, and judgement. This is for educational and analytical purposes only and is not financial advice.
אינדיקטור Pine Script®
מאת ‎Tradenometry‎
FPT - DCA ModelFPT - DCA Model is a simple but powerful tool to backtest a weekly “buy the dip” DCA plan with dynamic position sizing and partial profit-taking. 🔹 Core Idea - Invest a fixed amount every week (on Friday closes) - Buy more aggressively when price trades at a discount from its 52-week high - Take partial profits when price stretches too far above the daily EMA50 - Track the performance of your DCA plan vs a simple buy-and-hold from the same start date ⚙ How it works 1. Weekly DCA (on Daily timeframe) - On each Friday after the Start Date: - Add the “Weekly contribution” to the cash pool. - If the close is below the “Discount from 52W high” level: → FULL DCA: use the full weekly contribution + an extra booster from your stash (up to “Max extra stash used on dip”). → Marked on the chart with a small green triangle under the bar. - Otherwise: → HALF DCA: invest only 50% of the weekly contribution and keep the other 50% as stash (uninvested cash). → Marked with a small blue triangle under the bar. 2. 52-Week High Discount Logic - The script computes the 52-week high as the highest daily high of the last 252 trading days. - The “discount level” is: 52W high × (1 – Discount%). - When price is at or below this level, dips are treated as buying opportunities and the model allocates more. 3. Selling Logic (Partial Take Profit) - When the close is above the daily EMA50 by the selected percentage: → Sell the given “Sell portion of qty (%)” of your current holdings. → Marked with a small red triangle above the bar. - This behaves like a gradual profit-taking system: if price stays extended above EMA50, multiple partial sells can occur over time. 📊 Panel (top-right) The panel summarizes the state of your DCA plan: - Weeks: number of DCA weeks since Start Date - Total deposit: total money contributed (sum of all weekly contributions) - Shares qty: total number of shares accumulated - Avg price: volume-weighted average entry price - Shares value: current market value of all shares (qty × close) - Cash: uninvested cash (including saved stash) - Total equity: Shares value + Cash - DCA % PnL: performance of the DCA plan vs total deposits - Stock % since start: performance of the underlying asset since the Start Date ✅ Recommended Use - Timeframe: Daily (the DCA engine is designed to run on daily bars and Friday closes). - Works best on stocks, ETFs or indices where a 52-week high is a meaningful reference. - You can tune: - Weekly contribution - Discount from 52W high - Booster amount - EMA50 extension threshold and sell portion ⚠ Notes & Disclaimer - This script is a backtesting and educational tool. It does not place real orders. - Past performance does not guarantee future results. - Always combine DCA and risk management with your own research and judgment. Built by FPT (Funded Pips Trading) for long-term, rules-based DCA planning.
אינדיקטור Pine Script®
מאת ‎erendev‎
Fibonacci Projection with Volume & Delta Profile (Zeiierman)█ Overview Fibonacci Projection with Volume & Delta Profile (Zeiierman) blends classic Fibonacci swing analysis with modern volume-flow reading to create a unified, projection-based market framework. The indicator automatically detects the latest swing high and swing low, builds a complete Fibonacci structure, and then projects future extension targets with clear visual pathways. What makes this tool unique is the integration of two volume-based systems directly into the Fibonacci structure. A Fib-aligned Volume Profile shows how bullish and bearish volume accumulated inside the swing range, while a separate Delta Profile reveals the imbalance of buy–sell pressure inside each Fibonacci interval. Together, these elements transform the standard Fibonacci tool into a multi-dimensional structural and volume-flow map. █ How It Works The indicator first detects the most recent swing high and swing low using the Period setting. That swing defines the Fibonacci range, from which the script draws retracement levels (0.236–0.786) and builds a forward projection path using the chosen Projection Level and a 1.272 extension. Along this path, it draws projection lines, target boxes, and percentage labels that show how far each projected leg extends relative to the previous one. Inside the same swing range, the script builds a Fib-based Volume Profile by splitting price into rows and assigning each bar’s volume as bullish (close > open) or bearish (close ≤ open). On top of that, it calculates a Volume Delta Profile between each pair of fib levels, showing whether buyers or sellers dominated that band and how strong that imbalance was. █ How to Use This tool helps traders quickly understand market structure and where the price may be heading next. The projection engine shows the most likely future targets, highlights strong or weak legs in the move, and updates automatically whenever a new swing forms. This ensures you always see the most relevant and up-to-date projection path. The Fib Volume Profile shows where volume supported the move and where it did not. Thick bullish buckets reveal zones where buyers stepped in aggressively, often becoming retestable support. Thick bearish buckets highlight zones of resistance or rejection, particularly useful if projected levels align with prior liquidity. The Delta Profile adds a second dimension to volume reading by showing where buy–sell pressure was truly imbalanced. A projected Fibonacci target that aligns with a strong bullish delta, for example, may suggest continuation. A projection into a band dominated by bearish delta may warn of reversal or hesitation. █ Settings Period – bars used to determine swing high/low Projection Level – chosen Fib ratio for projection path ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
אינדיקטור Pine Script®
מאת ‎Zeiierman‎
23
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.
אינדיקטור Pine Script®
מאת ‎Finntech1‎
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).
אינדיקטור Pine Script®
מאת ‎aknoshutters‎
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.
אסטרטגיית Pine Script®
מאת ‎vinodkummarnair‎
1
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!
אינדיקטור Pine Script®
מאת ‎usarov1987_21‎
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
אינדיקטור Pine Script®
מאת ‎TIMEANDPRICEPR‎
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.
אינדיקטור Pine Script®
מאת ‎Motgench‎
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.
אינדיקטור Pine Script®
מאת ‎Motgench‎
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"
אינדיקטור Pine Script®
מאת ‎DiFlip‎
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.
אינדיקטור Pine Script®
מאת ‎rukich‎