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Adaptive Centric Moving Average [LuxAlgo]The Adaptive Centric Moving Average indicator provides a dynamic smoothing tool that adjusts its reactivity based on where the price sits relative to its recent trading range midpoint. 🔶 USAGE The Adaptive Centric Moving Average (AMA) is designed to filter out noise during periods of consolidation while remaining highly responsive during trending moves. When the price is near the center of its recent high-low range, the indicator becomes flatter and less prone to "whipsaws." As price moves toward the extremes of its range, the indicator accelerates to catch the emerging trend. Users can utilize the AMA for trend identification and trailing stop-loss levels. The visual gradient fill between the source price and the AMA line helps traders quickly identify the current trend strength and the distance between price and the smoothed average. 🔶 DETAILS The core logic of the script relies on a normalized relative position (similar to a Stochastic calculation) to determine how far the price is from its range midpoint. 🔹 Adaptive Smoothing Logic The indicator calculates a smoothing factor (alpha) based on the absolute distance from the 50% level of the range. When price is at the midpoint (50%), the alpha is zero, causing the moving average to stay flat. As price moves toward the upper or lower boundaries (0% or 100%), the alpha increases, making the average more reactive. 🔹 The Centric Calculation Unlike standard moving averages that track the source price directly, this indicator centers its target around the range midpoint. The Attenuation Factor scales the distance between the source and the midpoint, while the Power Factor applies an exponent to the smoothing factor, allowing for non-linear reactivity. 🔶 SETTINGS 🔹 Price Settings Source: The price series used for calculations (default is Close). Length: The window size used for pre-smoothing the source and determining the highest highs and lowest lows for the range. 🔹 Adaptive Settings Attenuation Factor: Controls the intensity of the price input relative to the midpoint. Lower values increase reactivity, while higher values provide a more stable, base smoothing speed. Power Factor: Exponents the smoothing factor. Higher values make the moving average significantly flatter when the price is near the range midpoint, requiring stronger moves to trigger a reaction. 🔹 Colors AMA Color: The color of the main Adaptive Centric Moving Average line. Bullish Fill: The color used for the gradient fill when the price is above the AMA. Bearish Fill: The color used for the gradient fill when the price is below the AMA.
Indicateur Pine Script®
par LuxAlgo
1
HMM Enhanced: Regime ProbabilityHMM Enhanced: Regime Probability Most technical indicators tell you what price has already done. This one asks a different question entirely: what kind of market are we probably in right now? The HMM Enhanced: Regime Probability indicator applies a Bayesian Hidden Markov Model to three independently constructed market features, producing a real-time probability estimate for three market regimes — Bullish, Bearish, and Chop. It runs directly on the price chart as an overlay, colours your bars by the dominant regime and its confidence, and plots the adaptive trend engine's basis and trailing level so you can see the model's inputs on the chart. A compact dashboard shows the live probability split at all times. 1. What Is a Hidden Markov Model? A Hidden Markov Model is a statistical framework for reasoning about systems that switch between a small number of underlying states — states that can't be observed directly, but leave traces in the data we can observe. The "hidden" part is the key insight. In financial markets, the true regime — whether the market is in a trending, ranging, or volatile state — is never directly visible. We can't look at a price bar and read off "this is a bull regime bar" the way we'd read a closing price. What we can observe are things like price's distance from a trend reference, the slope of a moving average, and the strength of directional movement. An HMM uses these observable signals to continuously estimate the probability that the hidden state is X versus Y versus Z. The model has three core components: States — the hidden regimes (here: Bull, Bear, Chop). Emission distributions — for each state, a probability distribution over what the observable signals tend to look like when that state is active. In this indicator, those are Gaussian (normal) distributions parameterised by a mean and standard deviation for each of the three features. Transition matrix — the probability of moving from one regime to another between bars. This encodes the "stickiness" of regimes: a trending market doesn't instantly flip to choppy on a single bad bar. Each bar, the forward algorithm combines the previous regime probabilities (propagated through the transition matrix) with the likelihood of today's observations under each regime's emission model, and normalises to produce a fresh posterior probability for each state. This is sequential Bayesian inference: every bar is an update step. Baum-Welch and Why This Indicator Takes a Different Approach In a fully trained HMM, the emission parameters (the means and variances that define what each regime "looks like") would be estimated from historical data using the Baum-Welch algorithm — an Expectation-Maximisation procedure that iterates forward and backward passes over the full training set until the parameters converge to a locally optimal fit. Baum-Welch is the textbook solution, but it has a practical limitation in a live trading context: it requires a fixed training window, it's computationally heavy to run on every new bar, and it can produce a model that's well-fitted to its training period but struggles on new market conditions. This indicator uses a different approach: online dominant-state EM. The emission parameters start from carefully designed prior values (described below) and then update incrementally each bar — but only for whichever state currently has a posterior probability above 50%. This means the model adapts continuously to the instrument and timeframe you're trading, without the need for a training phase, without look-ahead, and without the risk of a single regime's parameters drifting toward another's. HMMs in Trading — What They Show and Why They're Useful Markets don't behave the same way all the time. A momentum strategy that performs brilliantly in a trending regime will bleed in chop; a mean-reversion strategy that thrives in ranging markets gets destroyed in a sustained trend. Regime detection is the discipline of figuring out which kind of market you're in before committing to a strategy. HMMs are particularly well-suited to this problem because: They're probabilistic, not binary. Instead of telling you "it's a bull market," they tell you "there's a 72% probability this is a bull regime." That distinction matters enormously for position sizing, risk management, and knowing when to wait. They have memory. The transition matrix means the model can't flip on a single bar. If it called Bull last bar with 80% confidence, it takes sustained evidence to shift that. The states are inferred, not defined by rules. There's no arbitrary threshold like "above the 200MA = bull." The model learns what each regime looks like in terms of the underlying feature distribution. 2. What This Indicator Does At its core, this indicator runs a three-state Gaussian HMM using three z-scored features derived from an adaptive trend engine. Each bar it outputs a probability for Bull, Bear, and Chop. The dominant state colours the bars, the probabilities populate the dashboard, and the trend engine's basis and trailing level are plotted directly on the price chart so the model is fully transparent. The Three Features All three features are z-scored against a rolling window before entering the model, so they sit on a common scale regardless of instrument or timeframe. Feature 1 — Trend Position: (close − trailing_level) / ATR14, then z-scored. The trailing level is a ratcheting ATR band around a dual-MA midline — it only moves in the trend's favour, which filters out noise. Price well above the level is strong bull evidence; well below is bear evidence; near the level suggests indecision or chop. Feature 2 — Basis Slope: The rate of change of the dual-MA midline, ATR-normalised and z-scored. This measures whether the trend reference itself is rising or falling, and how fast. A slow grind higher reads differently from a sharp spike, and both read differently from a flat midline. Feature 3 — ADX Strength: Z-scored ADX. ADX rises when the market is trending in either direction and falls when it's ranging. The bull and bear emission priors for this feature are identical (+0.5) because ADX is directionless — it only distinguishes trending from ranging. Chop gets a negative ADX prior (−0.5) to reflect that ranging markets have below-average directional strength. The Adaptive Trend Engine The trend basis is a dual-MA midline: the average of a fast MA and a slow MA (at 2× the fast length), smoothed using your choice of MA type. The trailing level sits at an ATR band below the basis during uptrends and above it during downtrends. With the Min/Max Ratchet enabled, the level can only move in the trend's favour — it won't retreat on a retracement bar, which significantly reduces whipsaw flips. The basis and trailing level are plotted on your price chart, coloured by the current dominant regime. Emission Parameter Design The prior means are set symmetrically so neither bull nor bear is favoured at initialisation: FeatureBullBearChopTrend Position+1.0−1.00.0Basis Slope+0.7−0.70.0ADX Strength+0.5+0.5−0.5 The standard deviations start at 1.0 and adapt via learning. Bull and Bear are exact mirrors — there is no directional bias baked into the model. Transition Matrix Structure The off-diagonal transition probabilities are asymmetric by design. When the model exits a Bull or Bear regime, 80% of the exit probability routes through Chop — not directly to the opposite regime. This reflects the realistic observation that markets rarely flip directly from trending up to trending down without a period of consolidation first. Exits from Chop are split 50/50 to Bull and Bear. 3. How This Differs From Other HMM Indicators on TradingView Several HMM indicators exist on TradingView. Here's how this one is different: vs. Static emission parameters: Most HMM indicators on TradingView use fixed emission parameters — the model's definition of what each regime "looks like" is set at publication and never changes. This indicator adapts its emission parameters online via dominant-state EM learning, so the model continuously calibrates to what Bull, Bear, and Chop actually look like on your specific instrument and timeframe. vs. Separate pane oscillators: The majority of HMM indicators on TradingView run as subplot oscillators showing regime probabilities in a separate pane. This indicator runs as a price chart overlay — the trend engine that generates the model's primary features is plotted directly on your bars, so the model's inputs are always visible alongside its outputs. vs. Momentum and volatility features: Several published HMM indicators use raw price returns, rate-of-change, or historical volatility as their observation features. This indicator uses a purpose-built adaptive trend engine instead — specifically the distance from a ratcheting trailing level and the slope of a dual-MA basis. These are more directly connected to trend structure than raw returns or volatility measures, and the ratcheting mechanism filters out pullback noise before the feature ever reaches the model. vs. Full Baum-Welch training: Some implementations use full Baum-Welch EM on a fixed lookback window, which requires a defined training period and can introduce look-ahead bias if not implemented carefully. This indicator uses only causal bar-by-bar forward updates — it never looks ahead and has no training window. vs. Volume-based features: Some indicators incorporate volume ratio as an observation feature. This indicator deliberately avoids volume — volume data is unreliable, inconsistent, or entirely absent on many instruments and timeframes (particularly forex and synthetic indices). All three features are built purely from price structure. The key differentiators in summary: Adaptive online learning — emission parameters update every bar on the dominant state, no training window required Purpose-built trend engine features — trend position and basis slope capture trend structure more precisely than raw returns Overlay on price chart — the trend engine is visible alongside the probability output Asymmetric transition matrix — regime exits route through chop before flipping, reducing false direct reversals Emission temperature control — softens the model's decisiveness to avoid overconfident calls on noisy data Adaptive MA basis combined with ADX to identify trend 4. Configuration & Inputs 1. Adaptive Trend Engine (big thanks to @Crak_Trading for his excellent Adaptive Trend indicator Source — Price input for the trend engine. Default is HLC3 (a noise-reduced proxy for the bar's "true price"). You can change this to Close, HL2, or any other source. Adaptive Length — The period for the fast MA. The slow MA is automatically set to 2× this value. The basis is the midpoint of the two. Shorter = more responsive, more noise. Longer = smoother, more lag. 20 is a balanced default for most timeframes. Smoothing — Period for the ATR band smoothing. Controls how quickly the band width responds to changes in volatility. Higher = slower, smoother bands. Band Multiplier — Width of the ATR band around the basis. Higher values require price to move further from the basis before a trend flip occurs, reducing whipsaw at the cost of more lag on genuine reversals. Band Type — How the band width is calculated. True Range (default) uses the average of the bar's true range, which is sensitive to gaps. ATR uses the standard 14-period ATR. StDev uses the rolling standard deviation of price, which responds faster to volatility spikes. MA Type — The moving average used for the basis calculation. Various options (EMA, SMA, WMA, HMA, RMA) are available for comparison. Use Min/Max Ratchet — When enabled, the trailing level can only move in the direction of the current trend. In an uptrend it can only rise; in a downtrend it can only fall. This prevents the level from retreating on retracement bars and significantly reduces false trend flips. 2. ADX Settings DMI Length — The lookback for the DI+ and DI− directional movement calculations. 14 is the standard Wilder setting. ADX Smoothing — Smoothing period for the ADX line itself. 14 is standard. Higher values produce a smoother, slower-reacting ADX. Z-Score Lookback — The rolling window used to z-score all three features. A longer window produces a more stable baseline and makes the z-scores less sensitive to recent volatility changes. 50 bars is a reasonable default; increase to 100+ for higher timeframes. 3. Transition Matrix P(Bull | Bull) — The probability that the market stays in a Bull regime from one bar to the next. Higher values make bull regimes stickier and slower to exit. 0.80 is the default, meaning an 80% chance of remaining bull each bar. The remaining 20% is split: 80% routes to Chop, 20% routes directly to Bear. P(Bear | Bear) — Same as above for the Bear regime. P(Chop | Chop) — The probability of remaining in Chop. Set lower by default (0.60) because choppy periods tend to resolve faster than trends persist. When Chop exits, the probability is split 50/50 to Bull or Bear. Note: Higher persistence values increase regime stability but slow the model's response to genuine regime changes. Lower values make it more reactive but noisier. 4. Adaptive Learning Enable Adaptive Learning — When on, the emission means and standard deviations update incrementally each bar for whichever regime currently has >50% posterior probability. Over time, the model calibrates to what Bull, Bear, and Chop actually look like on your instrument. When off, the parameters stay fixed at their initial prior values. Learning Rate — Controls the speed of adaptation. 0.05 (default) is slow and stable — good for most use cases. Values around 0.10–0.15 adapt faster but produce noisier parameter estimates. Values above 0.20 should be used with caution as they can cause the emission distributions to drift away from their priors quickly on anomalous bars. Min Emission Std Dev — A floor on the emission standard deviation. Prevents the Gaussian distributions from collapsing to near-zero width, which would make the model pathologically confident and brittle. 0.3 is a safe default. 5. HMM Settings Emission Temperature — A softening parameter applied to the raw emission likelihoods before the HMM update. At temperature 1.0, the likelihoods are used as-is, which can produce very sharp, confident regime calls. Higher values (default 2.0) flatten the differences between regimes' likelihoods, keeping probabilities in a more realistic 50–70% range for clear signals rather than snapping to 90%+. Think of it as a confidence dampener — useful on noisy instruments or timeframes where overconfident calls are common. 6. Visuals Show Basis / Show Trailing Level — Toggle the dual-MA midline and ratcheting trailing level plots on the price chart. Basis / Trail Line Width — Line thickness for each plot. Color Transition Speed — Controls the smoothing of bar colour transitions between regime changes. Lower values (closer to 0) produce very gradual colour transitions; higher values (closer to 1.0) make colour changes near-instant. 0.4 is the default for a natural-looking blend. Bull / Bear / Chop Weak & Strong colours — Each regime has a gradient from a "weak" colour (lower confidence) to a "strong" colour (high confidence). Bars are coloured by the dominant regime's probability within that gradient. The same colours are used for the basis line. Table Position — Where the dashboard appears on the chart. All eight TradingView anchor positions are available. Table Text Size — Four sizes: Tiny, Small (default), Normal, Large. The regime label row (BULLISH / BEARISH / CHOP) automatically renders one step larger than the body text for visual hierarchy. 5. The Dashboard — How to Read It The dashboard table shows the model's current state in real time. All values update on the close of each bar. Row: What It Shows REGIME: The dominant state: BULLISH, BEARISH, or CHOP. Coloured by the current bar colour.CONFIDENCEThe probability of the dominant state, followed by HIGH (>65%), MED (>50%), or LOW (≤50%). P(BULL): Probability the market is currently in a Bull regime, as a percentage. P(BEAR): Probability of Bear regime. P(CHOP): Probability of Chop regime.sig B/Br/CThe current emission standard deviations for Bull, Bear, and Chop respectively, followed by LRN (learning enabled) or FIX (fixed). How to Use the Confidence Tiers HIGH confidence (>65%) — The model has a strong view. The dominant regime's probability is well above the other two. This is when the signal is most actionable as a strategy filter. MED confidence (50–65%) — The model has a lean but not a strong conviction. One regime is leading but the others are not far behind. Use as context rather than a hard filter. LOW confidence (≤50%) — The three probabilities are close together. The model is genuinely uncertain. This is not a failure state — it's an honest reflection of ambiguous market conditions. Avoid relying on it as a filter until confidence improves. The sig B/Br/C Row This row shows the adaptive emission standard deviations for each regime. At startup, all three are 1.0. As the model learns: Values shrinking toward the floor (default 0.3) mean the model is seeing consistent, tight feature values in that regime — it has a precise picture of what it looks like. Values staying near 1.0 or above mean the regime is observing noisy, inconsistent feature values — the model has a broad, uncertain picture of it. If Bull and Bear sigmas drift far apart, it may indicate one regime has been much more prevalent than the other, and you may want to reset the indicator or consider whether your instrument is structurally one-sided on your chosen timeframe. As a Strategy Filter This indicator is not designed to generate buy and sell signals on its own. It is designed to tell you what kind of strategy is appropriate right now: BULL dominant (HIGH/MED): Favour trend-following long setups. Momentum indicators and breakout strategies are in their element. Ignore oversold signals from oscillators. BEAR dominant (HIGH/MED): Favour trend-following short setups or defensive positioning. Overbought oscillator signals can be ignored. CHOP dominant or LOW confidence: Trend-following strategies are likely to churn. Consider mean-reversion approaches, tighter stops, reduced position sizes, or standing aside entirely. Alerts Three alert conditions are included, all non-repainting (they trigger on confirmed bar closes only): HMM: Regime → BULLISH — The dominant regime has shifted to Bull. HMM: Regime → BEARISH — The dominant regime has shifted to Bear. HMM: Regime → CHOP — The dominant regime has shifted to Chop. This indicator is open-source. The code is fully commented and structured to be readable, developed and aid learning for all users
Indicateur Pine Script®
par lucymatos
3
MTF Screener MA Cross SystemMTF Screener MA Cross System Overview Multi-Timeframe Structural Analysis with Integrated Risk and Dashboard Modules MTF Screener MA Cross System is a multi-timeframe analytical framework that integrates moving average cross logic, higher timeframe context, risk modeling and multi-symbol monitoring within a unified interface. The script visualizes relationships between moving averages, structural alignment across symbols and internally calculated strategy metrics. It does not predict future price direction. It does not provide discretionary trade calls. It does not issue standalone buy or sell recommendations. All calculations are rule-based and derived from internal strategy logic. It is designed for research, observation and structured testing. 📷 Image – Complete System Layout Complete system view including screener, gradient structure, risk panel and performance dashboard. System Architecture The system is built around three moving averages: • Fast EMA • Medium EMA • Slow HMA Users can select between different structural cross relationships: • Fast vs Medium • Fast vs Slow • Medium vs Slow A higher timeframe filter is included to display broader structural context relative to the selected cross configuration. The script visualizes how these components interact under different parameter settings. 📷 Image – Cross Mode Comparison Fast Mode Medium Mode Slow Mode 📷 Image – Higher Timeframe Filter Example Higher timeframe filter provides structural context for rule-based entry conditions. Risk Modeling & Trade Management Two configurable risk models are available: • Percentage-based stop and target • ATR-based volatility-adjusted stop model Trade management modes include: • Fixed stop/target • Break-even adjustment • Partial position handling • ATR-based trailing logic These configurations allow comparative backtesting of different rule-based management approaches within the strategy framework. No configuration implies future performance or profitability. 📷 Image – Risk Model Comparison Comparison of stop placement under percentage-based risk mode. (Stop level reflects percent-based configuration.) Comparison of stop placement under ATR-based risk mode. (Stop level reflects ATR-based configuration.) 📷 Image – Management Mode Comparison Advanced (Break-even + Trailing) Example of break-even activation followed by ATR-based trailing stop adjustment under Advanced management mode. Basic Example of fixed stop and target under Basic management mode. Multi-Symbol Screener & Market Alignment The integrated screener supports monitoring up to 10 symbols with independent timeframe selection. Displayed metrics include: • Directional state based on selected cross configuration • Bars since last cross • Relative distance between moving averages • Daily percentage change An alignment score summarizes the distribution of directional states across enabled symbols. This score is descriptive and does not indicate or forecast market direction. 📷 Image – Screener, Alignment & Dashboard Overview Live Risk Monitoring Panel When a position is active, the system displays: • Open position change (%) • Initial risk exposure (%) • Distance to stop level • Risk/reward progression (%) • Composite normalized metric (normalized scale) Values are calculated from internal strategy data and visualized using a structured scale. These metrics reflect current strategy state and are informational. Strategy Performance Dashboard The dashboard summarizes historical backtest metrics, including: • Net result percentage • Win rate • Total trades • Profit factor • Maximum drawdown • Expectancy All values are derived from historical strategy calculations. Historical performance does not represent future performance. Gradient Wave Visualization A layered gradient engine visually represents the positional relationship between the three moving averages. Active cross zones and neutral structural regions are differentiated through a multi-step interpolation system. The visualization reflects internal calculations and does not forecast or anticipate price movement. 📷 Image – Gradient Wave Structure Layered gradient structure representing the relative positioning of the three moving averages. Customization 📷 Image – Gradient Color Layering Example Layered gradient fill between moving averages showing active and neutral structural color zones. The script allows configuration of: • Moving average lengths • Cross mode selection • Risk parameters • Trade management mode • Screener symbols and timeframes • Table position and size • Color settings All components are adjustable without modifying the core structure. How To Use Select a cross mode according to the structural relationship you wish to observe. Configure the higher timeframe filter if broader structural context is required. Adjust risk model and management settings for comparative testing. Enable screener symbols to monitor multi-instrument structural alignment. Observe displayed metrics and historical calculations during testing. The script operates strictly on predefined rules and does not replace independent analysis. Notes This script is provided for analytical and educational purposes. It does not predict market direction. It does not provide investment advice. It does not guarantee performance or profitability. Users are responsible for their own analysis, testing and risk management decisions. Disclaimer This script is a rule-based analytical tool. It is not a financial advisory service and does not provide personalized investment guidance. All strategy calculations are based on historical data and predefined logic. Market conditions may vary, and users should perform independent analysis before making trading decisions.
Stratégie Pine Script®
par yigdeli
6
Multi Timeframe Scalper StructureWhat It Does This indicator generates trend-filtered scalping entries by requiring three independent layers of confirmation before any signal appears. A higher-timeframe trend filter sets the allowed direction. Two separate EMA structure zones on the micro timeframe must both confirm momentum alignment. Only when all three agree does an entry dot print on the chart. It also includes a real-time signal matrix table that shows which conditions are currently met, an on-chart TP/SL line system for quick risk-reward visualization, and configurable alerts. How It Works The indicator operates on two timeframe layers: Macro layer — A moving average (selectable: EMA, SMA, WMA, or VWMA) is calculated on a higher timeframe you choose (default: 4H). If the closing price on that timeframe is above the MA, the macro trend is bullish. If below, bearish. This acts as a directional gate — no signal can fire against it. The chart background tints green or red to reflect this bias. Micro layer — Two pairs of EMAs run on the chart timeframe (or a custom micro timeframe), each forming a colored filled zone: Structure 2 (default periods: 16 and 30) — the wider zone. When its fast EMA is above its slow EMA, the zone fills green, confirming bullish intermediate momentum. Below fills red. Structure 1 (default periods: 8 and 16) — the tighter zone, used for precise entry timing. A crossover of its fast EMA above the slow EMA is the trigger event for a long entry. A crossunder triggers a short entry. Entry conditions: A long entry dot appears when: The macro layer is bullish (price above the higher-TF MA) Structure 2 is bullish (fast above slow) Structure 1 crosses bullish (fast crosses above slow) A short entry dot appears under the mirror conditions — macro bearish, both structures bearish, Structure 1 crosses down. Early reversal detection (optional) — When enabled, the indicator can also trigger a signal when Structure 2 flips from bearish to bullish (or vice versa), allowing entry before Structure 1 has crossed, as long as the macro confirms. This captures momentum shifts earlier. Non-repainting — All higher-timeframe data uses lookahead_off. The "Wait for Candle Close" option (on by default) ensures signals only appear after the candle is fully confirmed. How the Components Interact The macro layer decides which direction you are allowed to trade — it is the filter. Structure 2 confirms that intermediate momentum on the chart timeframe has already shifted in that same direction — it is the confirmation. Structure 1 provides the exact timing — its crossover is the trigger that places the entry dot. Each layer serves a distinct purpose: direction, confirmation, and timing. A signal must pass through all three before it appears, which is what reduces noise compared to a single crossover system. When early reversal detection is active, Structure 2 can also serve as an alternative trigger (its color flip), but only while the macro still confirms. This creates two possible trigger paths under the same directional filter. How to Use It Set your macro timeframe meaningfully above your chart timeframe. If you scalp on the 5-minute chart, a 1-hour or 4-hour macro works well. On the 1-minute, try 15 minutes or 1 hour. The background color tells you the macro bias instantly — green for bullish, red for bearish. Watch the two filled zones on your chart. When both turn green (or both red) and align with the background color, conditions are building toward a signal. Entry dots appear at the bottom of the chart for longs (green) and at the top for shorts (red). The signal matrix table (movable to any corner) shows real-time status of each layer: Macro Trend, Structure 2, Structure 1, and the combined result — LONG, SHORT, or WAIT. Use the TP/SL tool to project take-profit and stop-loss lines on the chart. Set the direction (Long or Short), optionally enter a manual entry price, and configure your percentages. Up to two partial TP levels can be displayed alongside the main TP. Set alerts using the built-in alert conditions ("LONG Signal" or "SHORT Signal") to be notified when a confirmed entry fires. Key Features Three-layer hierarchical filtering architecture: Each layer has a distinct role — directional gating (macro), momentum confirmation (Structure 2), and entry timing (Structure 1) — and all three must agree simultaneously. This multi-layer approach reduces noise compared to single crossover systems. Dual EMA structure zones: Two independent pairs of EMAs operating at different speeds provide both intermediate momentum confirmation and precise entry timing under a separate higher-timeframe umbrella. Early reversal detection: Optional second trigger path that reads when the wider Structure 2 zone changes polarity, allowing entries at the point where intermediate momentum shifts. Real-time signal matrix: Dashboard view showing which conditions are satisfied in real time, turning the indicator into a structured workflow rather than just a dot generator. Integrated TP/SL system: On-chart trade management with optional partial targets keeps risk management visible without needing a separate tool. Non-repainting design: All signals are confirmed and use proper lookahead settings to ensure historical accuracy matches real-time behavior. Suitability Markets: Any liquid market — forex pairs, crypto, stock indices, commodities, equities. Timeframes: Designed for lower chart timeframes (1m, 3m, 5m, 15m) with the macro set higher (1H, 4H). Can also be adapted for intraday swing setups using 15m–1H charts with a daily macro. Trading style: Trend-following momentum scalping — taking quick entries in the direction of the dominant trend when short-term momentum confirms the higher-timeframe bias. Entries target short directional bursts within established trends, not reversals or range-bound conditions. The built-in candle-close confirmation and the three-layer filter are designed for disciplined, fast-execution scalping where you enter with momentum and exit at predefined percentage targets. Disclaimer This indicator is a technical analysis tool, not financial advice. It does not guarantee profits and does not predict future price movements. No indicator can eliminate risk. Past behavior of signals does not ensure future results. Always use proper risk management and assess your own financial situation before entering any trade.
Indicateur Pine Script®
par TheScalpingAnt
Advanced Divergence Hunter [JOAT]Advanced Divergence Hunter Introduction The Advanced Divergence Hunter is an open-source multi-oscillator indicator that simultaneously tracks divergences across six different momentum indicators: RSI, MACD, Stochastic RSI, CCI, MFI, and Williams %R. This mashup creates a comprehensive divergence detection system designed to identify momentum exhaustion and potential reversals by analyzing when multiple oscillators simultaneously show divergence from price action. The indicator addresses a critical limitation of single-oscillator divergence detection: false signals. By requiring confluence across multiple oscillators using different calculation methods, this tool significantly reduces false divergence signals and highlights only the most reliable momentum exhaustion patterns that occur when price and momentum fundamentally disconnect across multiple measurement frameworks. Chart showing multiple divergence signals across oscillators with dashboard on 1H timeframe Why This Mashup Exists This indicator combines six oscillators that detect divergences using fundamentally different methodologies: RSI: Momentum oscillator based on average gains vs losses MACD: Trend-following momentum using EMA convergence/divergence Stochastic RSI: Stochastic calculation applied to RSI for enhanced sensitivity CCI (Commodity Channel Index): Measures deviation from statistical mean MFI (Money Flow Index): Volume-weighted RSI showing buying/selling pressure Williams %R: Momentum indicator measuring overbought/oversold using highest high/lowest low Each oscillator responds to different market dynamics: RSI tracks momentum speed, MACD shows trend strength changes, Stochastic RSI catches early shifts, CCI identifies statistical extremes, MFI incorporates volume, and Williams %R uses price extremes. When multiple oscillators show divergence simultaneously, it indicates genuine momentum exhaustion rather than noise from a single calculation method. The mashup is justified because these oscillators use distinct mathematical approaches (rate of change, moving average convergence, stochastic, statistical deviation, volume-weighted, price extremes) that respond to different aspects of price movement. Confluence across multiple methods provides significantly higher reliability than any single divergence signal. Example showing all six oscillators with divergence markers and alignment indicators Core Components Explained 1. RSI Divergence Detection Standard RSI calculation with pivot-based divergence logic: rsi = ta.rsi(close, 14) // Identify swing points pivotHigh = ta.pivothigh(rsi, 5, 5) pivotLow = ta.pivotlow(rsi, 5, 5) // Regular Bullish Divergence // Price: Lower Low, RSI: Higher Low bullDiv = priceLow < prevPriceLow AND rsiLow > prevRSILow // Regular Bearish Divergence // Price: Higher High, RSI: Lower High bearDiv = priceHigh > prevPriceHigh AND rsiHigh < prevRSIHigh 2. MACD Histogram Divergence MACD histogram divergences often lead price divergences: = ta.macd(close, 12, 26, 9) // Histogram divergence detection // Bullish: Price LL, Histogram HL // Bearish: Price HH, Histogram LH The indicator plots MACD histogram with enhanced visualization and tracks divergences separately from RSI. 3. Stochastic RSI Divergence More sensitive than regular RSI, catches early momentum shifts: stochRSI = ta.stoch(rsi, rsi, rsi, 14) // K and D line divergences // Often diverges before regular RSI Stochastic RSI K and D lines are plotted, and divergences on both lines are tracked independently. 4. CCI Divergence Detection CCI measures price deviation from statistical mean: cci = ta.cci(close, 20) // CCI divergences indicate statistical exhaustion // Bullish: Price LL, CCI HL // Bearish: Price HH, CCI LH CCI divergences are particularly reliable at extreme levels (> +100 or < -100). 5. MFI Divergence (Volume-Weighted) MFI incorporates volume, making divergences more significant: // Calculate typical price typicalPrice = (high + low + close) / 3 // Money flow with volume rawMoneyFlow = typicalPrice * volume // MFI calculation mfi = 100 - (100 / (1 + positiveFlow / negativeFlow)) // Volume divergences often lead price // Bullish: Price LL, MFI HL (buying pressure increasing) // Bearish: Price HH, MFI LH (selling pressure increasing) MFI divergences are weighted more heavily in the confluence system because they incorporate volume. 6. Williams %R Divergence Williams %R uses highest high and lowest low: williamsR = -100 * (ta.highest(high, 14) - close) / (ta.highest(high, 14) - ta.lowest(low, 14)) // Divergences at extreme levels (-80 to -100 or -20 to 0) // Bullish: Price LL, Williams %R HL // Bearish: Price HH, Williams %R LH Williams %R divergences are most reliable when oscillator is in extreme zones. Divergence Confluence System The indicator tracks divergences across all six oscillators and calculates confluence: Divergence Confluence Score: - Single oscillator divergence: 1 point - Two oscillators: 2 points - Three oscillators: 4 points - Four oscillators: 7 points - Five oscillators: 11 points - All six oscillators: 15 points (MEGA divergence) Divergence classification: Weak Divergence: 1-2 oscillators (score 1-2) Moderate Divergence: 3 oscillators (score 4) Strong Divergence: 4 oscillators (score 7) Very Strong Divergence: 5 oscillators (score 11) MEGA Divergence: All 6 oscillators (score 15) The dashboard displays which oscillators are showing divergence and the total confluence score. Oscillator Alignment Analysis Beyond divergences, the indicator tracks oscillator alignment: Alignment Score: - RSI in healthy range (40-60 bull, 60-40 bear): +1 - MACD histogram direction: +1 - Stochastic RSI position: +1 - CCI direction: +1 - MFI level: +1 - Williams %R position: +1 Total Alignment: 0-6 points Alignment interpretation: 5-6 aligned: Strong momentum consensus 3-4 aligned: Moderate momentum 0-2 aligned: Weak or conflicting momentum Enhanced Dashboard System The indicator features an 11-row dashboard showing: Row 1: Overall momentum direction (BULL/BEAR/NEUTRAL) Row 2: Divergence confluence score with color coding Row 3: RSI value and divergence status Row 4: MACD histogram status Row 5: Stochastic RSI K value Row 6: CCI value and status Row 7: MFI value and divergence status Row 8: Williams %R value Row 9: Momentum strength (0-100) Row 10: Oscillator alignment (X/6 aligned) Row 11: Active divergences count Dashboard showing divergence confluence with individual oscillator breakdown Visual Elements Oscillator Lines: All six oscillators plotted with distinct colors Divergence Labels: "DIV" markers at divergence points, sized by confluence Confluence Markers: Large diamond shapes for MEGA divergences (6/6) Background Zones: Color-coded backgrounds for extreme conditions Overbought/Oversold Lines: Reference levels for each oscillator Zero/Midpoint Lines: Centerline references Histogram Bars: MACD histogram with gradient coloring Dashboard: Comprehensive table with all oscillator readings How Components Work Together The mashup creates layered divergence analysis: Layer 1 - Individual Detection: Each oscillator independently detects divergences Layer 2 - Confluence Calculation: System counts how many oscillators show divergence Layer 3 - Weighting: Volume-based divergences (MFI) weighted more heavily Layer 4 - Alignment Check: Verifies overall oscillator consensus Layer 5 - Extreme Zones: Identifies when divergences occur at statistical extremes Layer 6 - Signal Generation: Produces graded signals based on confluence strength Example scenario: Price makes higher high, but RSI, MACD, Stochastic RSI, and MFI all make lower highs (4/6 divergence). CCI and Williams %R are in extreme overbought zones. Confluence score is 7 (Strong Divergence), and dashboard shows 4 active divergences. This signals high-probability bearish reversal setup. Input Parameters Oscillator Settings: RSI Length: Period for RSI (default: 14) MACD Settings: Fast 12, Slow 26, Signal 9 Stochastic RSI Length: Period for Stoch RSI (default: 14) CCI Length: Period for CCI (default: 20) MFI Length: Period for MFI (default: 14) Williams %R Length: Period for Williams %R (default: 14) Divergence Settings: Pivot Lookback: Bars for pivot detection (default: 5) Min Confluence: Minimum oscillators for signal (default: 3) Show All Divergences: Display single-oscillator divergences (default: disabled) Show Only Strong: Display only 4+ confluence (default: enabled) Display Options: Show Dashboard: Toggle dashboard (default: enabled) Show Oscillators: Toggle oscillator plots (default: enabled) Show Background Zones: Toggle extreme zone coloring (default: enabled) Dashboard Position: Top-right, bottom-right, etc. How to Use This Indicator Step 1: Monitor Divergence Confluence Watch the dashboard divergence score. Wait for 3+ oscillators showing divergence (score 4+) before considering reversal trades. Step 2: Check Oscillator Extremes Divergences are most reliable when oscillators are in extreme zones (RSI > 70 or < 30, MFI > 80 or < 20, etc.). Step 3: Verify Alignment Check oscillator alignment score. Low alignment (0-2) with high divergence confluence suggests strong reversal potential. Step 4: Identify MEGA Divergences When all 6 oscillators show divergence (MEGA), it signals extremely high probability reversal setup. These are rare but very reliable. Step 5: Confirm with Price Action Wait for price action confirmation (reversal candlestick patterns, trendline breaks) before entering trades based on divergences. Step 6: Use for Exit Signals If holding trend-following position and strong divergence appears, consider taking profits or tightening stops. Best Practices Use on 15-minute to 4-hour timeframes for optimal divergence reliability Wait for 3+ oscillator confluence before acting on divergence signals MEGA divergences (6/6) are rare but extremely reliable - don't ignore them MFI divergences are particularly significant because they incorporate volume Divergences in strong trends often lead to pullbacks, not full reversals Combine with support/resistance levels for precise entry timing Hidden divergences signal trend continuation, regular divergences signal reversal Multiple consecutive divergences increase reversal probability Use oscillator alignment to gauge overall momentum health Indicator Limitations Divergences can persist for extended periods before reversal occurs Strong trends can continue despite multiple oscillator divergences Pivot-based detection means divergences are confirmed with lag False divergences can occur in choppy, ranging markets MEGA divergences are rare - waiting only for these may miss opportunities Oscillator calculations vary in sensitivity - some may diverge prematurely Requires understanding of each oscillator's characteristics No divergence system eliminates false signals entirely Performance varies across different markets and volatility regimes Technical Implementation Built with Pine Script v6 using: Six independent oscillator calculations Pivot-based divergence detection for each oscillator Confluence scoring algorithm with weighted components Oscillator alignment tracking system Enhanced 11-row dashboard with real-time updates Dynamic background zones for extreme conditions Anti-overlap logic for divergence labels Gradient coloring for MACD histogram The code is fully open-source and can be modified to adjust oscillator parameters, confluence thresholds, and visual preferences. Originality Statement This indicator is original in its multi-oscillator divergence confluence approach. While individual oscillators (RSI, MACD, Stochastic RSI, CCI, MFI, Williams %R) are established tools, this mashup is justified because: It tracks divergences across six oscillators using fundamentally different calculations The confluence scoring system quantifies divergence strength across multiple methods Integration of volume-weighted divergence (MFI) with price-based oscillators Oscillator alignment analysis provides momentum consensus measurement Enhanced dashboard presents complex multi-oscillator data clearly MEGA divergence detection identifies extremely rare, high-probability setups Each oscillator contributes unique divergence information: RSI shows momentum speed divergence, MACD shows trend strength divergence, Stochastic RSI catches early divergences, CCI shows statistical divergence, MFI shows volume-weighted divergence, and Williams %R shows price extreme divergence. The mashup's value lies in identifying when multiple independent calculation methods simultaneously show momentum exhaustion, significantly reducing false signals. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors. Divergence indicators are analytical tools that identify potential momentum exhaustion, not guarantees of reversals. Divergences can persist for extended periods, and strong trends can continue despite multiple divergence signals. Past divergence performance does not guarantee future results. The confluence score is a mathematical calculation based on current oscillator readings, not a prediction of future price movement. High confluence scores do not ensure profitable trades. Market conditions change, and divergence patterns that worked historically may not work in the future. Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool. -Made with passion by officialjackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
Trend Strategy Flip📌 Trend Strategy Flip — Adaptive Trend‑Following System with Smart Flip Logic (Built on the L1 Proximal Filter concept by LuxAlgo) Trend Strategy Flip is an adaptive trend‑following system built on top of the L1 Proximal Filter, a concept originally introduced by LuxAlgo. This strategy expands the idea with multi‑mode entries, smart flip logic, breakeven automation, and a clean visual HUD for real‑time decision support. The result is a lightweight, responsive trend engine suitable for both discretionary and automated trading. 🔍 Core Concept The system uses the L1 Proximal Filter to extract a smooth, noise‑resistant trend line (z) and its velocity (v). This allows the strategy to detect: Confirmed trend reversals (Mode A) Early entries via price cross or velocity shift (Mode B1/B2) Next‑bar confirmation entries (Mode C) When the trend flips, the strategy can automatically reverse the position. ✨ Key Features ✔ Built on the L1 Proximal Filter (LuxAlgo concept) A robust, adaptive trend extraction method. ✔ Multiple Entry Modes Choose between confirmed, early, or next‑bar entries. ✔ Smart Flip Logic Automatically reverses positions on trend change. ✔ Breakeven Protection Dynamic breakeven activation with a semi‑transparent BE line. ✔ Compact HUD (Top‑Right Corner) Displays: Current position Entry price Live PnL (%) Last flip direction Breakeven status Bars since entry ✔ Optional Trend Background Soft diagonal gradient (lightweight imitation) for visual trend context. ✔ Clean Visual Markers Entry arrows, flip markers, trend line, and BE line. ✔ Alerts Included Ready for automation or webhook‑based execution. 🎯 Use Cases Trend‑following systems Reversal/flip strategies Automated execution via alerts Swing and intraday trading Strategy research and optimization ⚠️ Notes This strategy is based on the L1 Proximal Filter concept by LuxAlgo, extended with additional logic and visualization. For educational and research purposes only. Always test before using in live trading.
Stratégie Pine Script®
par olegask68
Futures Regime Break Strategy [KedArcQuant]Overview Futures Regime Engine v1.0 is a professional-grade, non-repainting trend regime strategy designed for Futures trading and option selling alignment. It identifies institutional directional bias using price structure, VWAP positioning, trend confirmation, and momentum validation. This engine is built specifically to detect when markets transition from neutral to directional regimes. It avoids chop conditions and focuses on high-probability trend continuation and reversal confirmation zones. The strategy works equally well for: * Futures trading (LONG and SHORT execution) * Option selling (PE SELL and CE SELL alignment) * Index futures such as NIFTY and BANKNIFTY * Liquid stock futures All signals are confirmed only on candle close. No repainting or forward-looking logic is used. Core Philosophy Markets operate in regimes, not random noise. Institutional participation creates directional bias which can be detected using objective price positioning relative to VWAP, trend structure, and momentum confirmation. This engine is designed to identify those regime shifts and allow traders to align with institutional directional flow. Why this is not a mashup This strategy is not a mashup of unrelated indicators. It uses a single integrated regime model based on four logically connected components: 1. Institutional bias via VWAP positioning 2. Trend confirmation via EMA directional alignment 3. Momentum validation via RSI threshold structure 4. Structural confirmation via breakout of recent price structure Additional filters such as regime strength, cooldown logic, and trend lock are not separate indicators. They are execution safeguards to prevent false entries and overtrading. All components work together as part of a unified regime detection framework. There is no indicator stacking, curve fitting, or arbitrary combination of unrelated tools. Key Features * Non repainting signals * Candle close confirmation only * Regime based directional detection * Trend continuation capture * Regime reversal detection * Chop avoidance logic * Futures and options compatible * Institutional bias aligned logic How to use for Futures trading LONG Futures signal appears when: * Price is above VWAP * Price is above trend EMA * Momentum confirms bullish regime * Price breaks recent structure high This indicates bullish institutional regime. SHORT Futures signal appears when: * Price is below VWAP * Price is below trend EMA * Momentum confirms bearish regime * Price breaks recent structure low This indicates bearish institutional regime. Entry execution: Enter at candle close when signal appears. Stop loss: LONG trade stop loss below signal candle low SHORT trade stop loss above signal candle high Target: Minimum 1.5 times stop loss distance How to use for option selling Futures signals directly map to option selling direction. LONG signal: Market regime is bullish Sell PUT option Recommended delta range: 0.20 to 0.30 delta Example: If NIFTY is at 25000 Sell 24800 PE or 24700 PE SHORT signal: Market regime is bearish Sell CALL option Recommended delta range: 0.20 to 0.30 delta Example: If NIFTY is at 25000 Sell 25200 CE or 25300 CE Avoid selling ATM options for safety. Input Configuration Guide Trend EMA Length: Controls trend sensitivity. Default 50 recommended. RSI Length: Momentum measurement period. Default 14 recommended. Structure Length: Determines breakout sensitivity. Default 10 recommended. ADX Strength Filter: Filters weak regime conditions. Helps avoid chop markets. VWAP Distance Filter: Prevents entering when price is overextended from institutional value. Cooldown Bars: Prevents repeated entries during same trend. Trend Lock: Prevents rapid signal flipping in noisy conditions. Default settings are optimized for 5 minute timeframe. Recommended Markets and Timeframes Best instruments: NIFTY futures BANKNIFTY futures Liquid stock futures Best timeframes: 5 minute for balanced trading 15 minute for safer swing trading 3 minute for aggressive intraday trading Expected behavior Strategy performs best in trending markets. During sideways or low volatility markets, signals will reduce significantly. This is intentional and helps avoid false trades. The strategy focuses on quality over quantity. FAQ Does this repaint No. All signals are confirmed at candle close and never change after appearing. Can this be used for option selling Yes. Futures signals can be directly used to sell PUT or CALL options based on regime direction. Does this work on all stocks Works best on liquid futures and index instruments. Can this be used for automation Yes. Logic can be mapped to automated execution platforms. Why are there fewer signals This is intentional. The strategy avoids low probability trades. Glossary VWAP Volume weighted average price. Represents institutional fair value. EMA Exponential moving average. Used to identify trend direction. Regime Market state indicating bullish, bearish, or neutral conditions. Momentum Measurement of strength behind price movement. Structure breakout Price breaking recent highs or lows indicating directional continuation. Delta Option sensitivity to price movement. Used to select strike distance.
Stratégie Pine Script®
par kedarcquant
Dynamic RSI Level + Divergence [Metrify]This script provides a customized approach to tracking RSI divergence by replacing static overbought and oversold levels with dynamic volatility bands. Standard oscillators often fail during strong trends by staying pinned above 70 or below 30, producing premature signals. To handle this, the indicator calculates a moving baseline for the RSI and wraps it in standard deviation bands. This allows the overbought and oversold boundaries to adapt to the current market situation. Does the divergence delayed? No. The divergence label is printed exactly at the bar where the condition becomes valid. There is no forward confirmation or repaint shifting. This honest approach may produce losing signals in strong trends, but it preserves the integrity of real-time detection. Divergence filters (optional, set this to 0 to avoid the filter): Min RSI Diff (Steepness) -> requires sufficient vertical gap between RSI pivots to avoid weak, flat divergences. Min Price Diff -> minimum price to move a meaningful distance (relative to ATR) between both pivots.
Indicateur Pine Script®
par Metrify
QEMA Signal System// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 // mozilla.org // © QEMA Signal System for MNQ/NQ Futures // QEMA = 4×EMA¹ − 6×EMA² + 4×EMA³ − EMA⁴ (single line, reduced lag) // Direction: Price > QEMA = LONG | Price < QEMA = SHORT // Backtested 330 days CME MNQ: 64.5% WR, PF 1.45, Sharpe 4.37 //@version=6 indicator("QEMA Signal System", shorttitle="QEMA", overlay=true, max_labels_count=500, max_lines_count=500) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 1: QEMA // ═══════════════════════════════════════════════════════════════════════════════ grpQEMA = "══ QEMA ══" qema_len = input.int(170, "QEMA Period", minval=2, maxval=500, group=grpQEMA, tooltip="Quadruple EMA period. Optimized: 200. Your default: 144. Both profitable.") ema_src = input.source(close, "Source", group=grpQEMA) show_qema = input.bool(true, "Show QEMA Line", group=grpQEMA) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 2: ADX // ═══════════════════════════════════════════════════════════════════════════════ grpADX = "══ ADX ══" adx_len = input.int(14, "ADX Period", minval=5, maxval=50, group=grpADX) adx_smooth = input.int(14, "ADX Smoothing", minval=5, maxval=50, group=grpADX) adx_threshold = input.int(20, "ADX Trend Threshold", minval=10, maxval=50, group=grpADX, tooltip="Optimized: 35") adx_strong = input.int(45, "ADX Strong Trend", minval=20, maxval=60, group=grpADX) require_adx = input.bool(true, "Require ADX Confirmation", group=grpADX) show_adx_bg = input.bool(true, "Show ADX Background", group=grpADX) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 3: VOLUME // ═══════════════════════════════════════════════════════════════════════════════ grpVOL = "══ Volume ══" vol_avg_len = input.int(20, "Volume Average Period", minval=5, maxval=100, group=grpVOL) vol_threshold = input.float(0.8, "Volume Spike Threshold", minval=0.5, maxval=5.0, step=0.1, group=grpVOL, tooltip="Optimized: 1.0x") vol_delta_mode = input.string("Candle Body", "Delta Estimation", options= , group=grpVOL) require_vol = input.bool(true, "Require Volume Confirmation", group=grpVOL) show_vol_dots = input.bool(true, "Show Volume Dots", group=grpVOL) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 4: ATR / SL / TP // ═══════════════════════════════════════════════════════════════════════════════ grpATR = "══ ATR / Risk ══" atr_len = input.int(14, "ATR Period", minval=5, maxval=50, group=grpATR, tooltip="Optimized: 14") atr_sl_mult = input.float(1.0, "ATR SL Multiplier", minval=0.5, maxval=5.0, step=0.1, group=grpATR, tooltip="Optimized: 3.5") atr_tp1_mult = input.float(2.0, "ATR TP1 Multiplier", minval=1.0, maxval=10.0, step=0.5, group=grpATR, tooltip="Optimized: 2.0") atr_tp2_mult = input.float(2.5, "ATR TP2 Multiplier", minval=1.5, maxval=15.0, step=0.5, group=grpATR, tooltip="Optimized: 2.5") show_sl_tp = input.bool(true, "Show SL/TP Lines", group=grpATR) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 5: SESSION // ═══════════════════════════════════════════════════════════════════════════════ grpSES = "══ Session ══" use_session = input.bool(true, "Enable Session Filter", group=grpSES) sess_start = input.int(930, "Session Start (HHMM)", minval=0, maxval=2359, group=grpSES) sess_end = input.int(1100, "Session End (HHMM)", minval=0, maxval=2359, group=grpSES) skip_first_min = input.int(10, "Skip First N Minutes", minval=0, maxval=30, group=grpSES, tooltip="Optimized: 5") sess_tz = input.string("America/New_York", "Timezone", options= , group=grpSES) show_session_bg = input.bool(true, "Highlight Active Session", group=grpSES) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 6: SIGNAL // ═══════════════════════════════════════════════════════════════════════════════ grpSIG = "══ Signal ══" cooldown_bars = input.int(5, "Signal Cooldown (bars)", minval=0, maxval=30, group=grpSIG, tooltip="Optimized: 2") show_labels = input.bool(true, "Show Signal Labels", group=grpSIG) alert_enabled = input.bool(true, "Enable Alerts", group=grpSIG) // ═══════════════════════════════════════════════════════════════════════════════ // ██ GROUP 7: COLORS // ═══════════════════════════════════════════════════════════════════════════════ grpVIS = "══ Colors ══" col_bull = input.color(#00E676, "Bullish", group=grpVIS) col_bear = input.color(#FF1744, "Bearish", group=grpVIS) col_neutral = input.color(#9E9E9E, "Neutral", group=grpVIS) col_qema = input.color(#FFD600, "QEMA Line", group=grpVIS) // ═══════════════════════════════════════════════════════════════════════════════ // ██ QEMA CALCULATION // ═══════════════════════════════════════════════════════════════════════════════ // Real Quadruple EMA: QEMA = 4×EMA¹ − 6×EMA² + 4×EMA³ − EMA⁴ ema1 = ta.ema(ema_src, qema_len) ema2 = ta.ema(ema1, qema_len) ema3 = ta.ema(ema2, qema_len) ema4 = ta.ema(ema3, qema_len) qema_val = 4 * ema1 - 6 * ema2 + 4 * ema3 - ema4 // ═══════════════════════════════════════════════════════════════════════════════ // ██ ADX CALCULATION // ═══════════════════════════════════════════════════════════════════════════════ tr_val = ta.tr(true) plus_dm = math.max(high - high , 0) minus_dm = math.max(low - low, 0) plus_dm := plus_dm > minus_dm ? plus_dm : 0.0 minus_dm := minus_dm > plus_dm ? minus_dm : 0.0 smoothed_tr = ta.rma(tr_val, adx_len) smoothed_plus_dm = ta.rma(plus_dm, adx_len) smoothed_minus_dm = ta.rma(minus_dm, adx_len) plus_di = smoothed_tr != 0 ? (smoothed_plus_dm / smoothed_tr) * 100 : 0 minus_di = smoothed_tr != 0 ? (smoothed_minus_dm / smoothed_tr) * 100 : 0 dx = (plus_di + minus_di) != 0 ? math.abs(plus_di - minus_di) / (plus_di + minus_di) * 100 : 0 adx_val = ta.rma(dx, adx_smooth) adx_trending = adx_val >= adx_threshold adx_strong_trend = adx_val >= adx_strong adx_bullish = plus_di > minus_di adx_bearish = minus_di > plus_di // ═══════════════════════════════════════════════════════════════════════════════ // ██ VOLUME CALCULATION // ═══════════════════════════════════════════════════════════════════════════════ vol_raw = volume > 0 ? volume : ta.tr(true) vol_avg = ta.sma(vol_raw, vol_avg_len) vol_spike = vol_raw > vol_avg * vol_threshold vol_relative = vol_avg != 0 ? vol_raw / vol_avg : 1.0 candle_body = close - open candle_range = high - low close_position = candle_range != 0 ? (close - low) / candle_range : 0.5 vol_delta = switch vol_delta_mode "Candle Body" => candle_range != 0 ? (candle_body > 0 ? vol_raw * (candle_body / candle_range) : -vol_raw * math.abs(candle_body / candle_range)) : 0 "Close Position" => vol_raw * (2 * close_position - 1) "Hybrid" => candle_range != 0 ? vol_raw * ((candle_body / candle_range) * 0.5 + (2 * close_position - 1) * 0.5) : 0 => candle_range != 0 ? vol_raw * (candle_body / candle_range) : 0 vol_delta_ema = ta.ema(vol_delta, vol_avg_len) vol_delta_bull = vol_delta > 0 and vol_delta > vol_delta_ema vol_delta_bear = vol_delta < 0 and vol_delta < vol_delta_ema // ═══════════════════════════════════════════════════════════════════════════════ // ██ ATR // ═══════════════════════════════════════════════════════════════════════════════ atr_val = ta.atr(atr_len) // ═══════════════════════════════════════════════════════════════════════════════ // ██ SESSION FILTER // ═══════════════════════════════════════════════════════════════════════════════ current_hour = hour(time, sess_tz) current_minute = minute(time, sess_tz) current_hhmm = current_hour * 100 + current_minute sess_start_hour = math.floor(sess_start / 100) sess_start_min = sess_start % 100 sess_start_total_min = sess_start_hour * 60 + sess_start_min current_total_min = current_hour * 60 + current_minute minutes_into_session = current_total_min - sess_start_total_min in_session = use_session ? (current_hhmm >= sess_start and current_hhmm <= sess_end and minutes_into_session >= skip_first_min) : true // ═══════════════════════════════════════════════════════════════════════════════ // ██ SIGNAL ENGINE // ══════════════════════════════════════════════════════════════════════════════ // // Direction comes ONLY from price vs QEMA: // Price > QEMA → LONG candidate // Price < QEMA → SHORT candidate // // ADX and Volume are optional confirmation gates. // // ═══════════════════════════════════════════════════════════════════════════════ // Direction gate: QEMA only bull_direction = close > qema_val bear_direction = close < qema_val // ADX gate adx_bull_ok = require_adx ? (adx_trending and adx_bullish) : true adx_bear_ok = require_adx ? (adx_trending and adx_bearish) : true // Volume gate vol_bull_ok = require_vol ? (vol_spike and close > open) : true vol_bear_ok = require_vol ? (vol_spike and close < open) : true // Cooldown var int bars_since_signal = 100 bars_since_signal += 1 // Final signals bull_signal = bull_direction and adx_bull_ok and vol_bull_ok and in_session and bars_since_signal >= cooldown_bars bear_signal = bear_direction and adx_bear_ok and vol_bear_ok and in_session and bars_since_signal >= cooldown_bars if bull_signal or bear_signal bars_since_signal := 0 // ═══════════════════════════════════════════════════════════════════════════════ // ██ SL / TP // ═══════════════════════════════════════════════════════════════════════════════ bull_sl = bull_signal ? close - atr_val * atr_sl_mult : na bull_tp1 = bull_signal ? close + atr_val * atr_tp1_mult : na bull_tp2 = bull_signal ? close + atr_val * atr_tp2_mult : na bear_sl = bear_signal ? close + atr_val * atr_sl_mult : na bear_tp1 = bear_signal ? close - atr_val * atr_tp1_mult : na bear_tp2 = bear_signal ? close - atr_val * atr_tp2_mult : na // ═══════════════════════════════════════════════════════════════════════════════ // ██ SIGNAL QUALITY (1-5 stars) // ═══════════════════════════════════════════════════════════════════════════════ signal_quality = 0 if bull_signal or bear_signal signal_quality += 1 // base signal_quality += adx_strong_trend ? 1 : 0 // strong ADX signal_quality += vol_relative > 1.5 ? 1 : 0 // big volume signal_quality += vol_relative > 2.0 ? 1 : 0 // huge volume signal_quality += (bull_signal and vol_delta_bull) or (bear_signal and vol_delta_bear) ? 1 : 0 // delta confirms quality_stars = switch signal_quality 1 => "★" 2 => "★★" 3 => "★★★" 4 => "★★★★" 5 => "★★★★★" => "" // ═══════════════════════════════════════════════════════════════════════════════ // ██ PLOTTING // ═══════════════════════════════════════════════════════════════════════════════ // QEMA line plot(show_qema ? qema_val : na, "QEMA", color=col_qema, linewidth=2) // Session background bgcolor(in_session and show_session_bg ? color.new(#2196F3, 95) : na, title="Active Session") // ADX background adx_bg = show_adx_bg ? (adx_strong_trend and adx_bullish ? color.new(col_bull, 93) : adx_strong_trend and adx_bearish ? color.new(col_bear, 93) : na) : na bgcolor(adx_bg, title="ADX Trend Background") // Volume dots plotshape(show_vol_dots and vol_spike and vol_delta > 0 ? low - atr_val * 0.3 : na, "Vol Bull", shape.circle, location.absolute, color.new(col_bull, 40), size=size.tiny) plotshape(show_vol_dots and vol_spike and vol_delta < 0 ? high + atr_val * 0.3 : na, "Vol Bear", shape.circle, location.absolute, color.new(col_bear, 40), size=size.tiny) // Signal arrows plotshape(bull_signal, "LONG", shape.triangleup, location.belowbar, col_bull, size=size.normal) plotshape(bear_signal, "SHORT", shape.triangledown, location.abovebar, col_bear, size=size.normal) // Signal labels if show_labels and bull_signal txt = "LONG " + quality_stars + " " txt += "SL: " + str.tostring(bull_sl, format.mintick) + " " txt += "TP1: " + str.tostring(bull_tp1, format.mintick) + " " txt += "TP2: " + str.tostring(bull_tp2, format.mintick) + " " txt += "ADX: " + str.tostring(adx_val, "#.0") + " | ATR: " + str.tostring(atr_val, "#.0") label.new(bar_index, low - atr_val * 0.8, txt, style=label.style_label_up, color=color.new(col_bull, 20), textcolor=color.white, size=size.small) if show_labels and bear_signal txt = "SHORT " + quality_stars + " " txt += "SL: " + str.tostring(bear_sl, format.mintick) + " " txt += "TP1: " + str.tostring(bear_tp1, format.mintick) + " " txt += "TP2: " + str.tostring(bear_tp2, format.mintick) + " " txt += "ADX: " + str.tostring(adx_val, "#.0") + " | ATR: " + str.tostring(atr_val, "#.0") label.new(bar_index, high + atr_val * 0.8, txt, style=label.style_label_down, color=color.new(col_bear, 20), textcolor=color.white, size=size.small) // SL/TP lines if show_sl_tp and bull_signal line.new(bar_index, bull_sl, bar_index + 10, bull_sl, color=col_bear, style=line.style_dashed, width=1) line.new(bar_index, bull_tp1, bar_index + 10, bull_tp1, color=col_bull, style=line.style_dotted, width=1) line.new(bar_index, bull_tp2, bar_index + 10, bull_tp2, color=col_bull, style=line.style_dotted, width=1) if show_sl_tp and bear_signal line.new(bar_index, bear_sl, bar_index + 10, bear_sl, color=col_bear, style=line.style_dashed, width=1) line.new(bar_index, bear_tp1, bar_index + 10, bear_tp1, color=col_bull, style=line.style_dotted, width=1) line.new(bar_index, bear_tp2, bar_index + 10, bear_tp2, color=col_bull, style=line.style_dotted, width=1) // ═══════════════════════════════════════════════════════════════════════════════ // ██ DASHBOARD // ═══════════════════════════════════════════════════════════════════════════════ var table dash = table.new(position.top_right, 2, 7, bgcolor=color.new(#1E1E1E, 10), border_color=color.new(#424242, 30), border_width=1, frame_color=color.new(#424242, 30), frame_width=1) if barstate.islast table.cell(dash, 0, 0, "QEMA", text_color=color.white, text_size=size.small, bgcolor=color.new(#2196F3, 20)) table.cell(dash, 1, 0, str.tostring(qema_len), text_color=color.white, text_size=size.small, bgcolor=color.new(#2196F3, 20)) // Price vs QEMA pos_col = close > qema_val ? col_bull : col_bear pos_txt = close > qema_val ? "ABOVE → Long bias" : "BELOW → Short bias" table.cell(dash, 0, 1, "Price vs QEMA", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 1, pos_txt, text_color=pos_col, text_size=size.tiny) // ADX adx_col = adx_strong_trend ? col_bull : adx_trending ? #FFB300 : col_neutral adx_dir = adx_bullish ? " +DI" : " -DI" table.cell(dash, 0, 2, "ADX", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 2, str.tostring(adx_val, "#.0") + adx_dir, text_color=adx_col, text_size=size.tiny) // ATR table.cell(dash, 0, 3, "ATR", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 3, str.tostring(atr_val, "#.1"), text_color=color.white, text_size=size.tiny) // Volume vol_col = vol_spike ? col_bull : col_neutral table.cell(dash, 0, 4, "Vol", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 4, str.tostring(vol_relative, "#.1") + "x" + (vol_delta > 0 ? " Buy" : " Sell"), text_color=vol_col, text_size=size.tiny) // Session sess_col = in_session ? col_bull : col_neutral table.cell(dash, 0, 5, "Session", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 5, in_session ? "ACTIVE" : "INACTIVE", text_color=sess_col, text_size=size.tiny) // Filters filter_txt = (require_adx ? "ADX " : "") + (require_vol ? "VOL" : "") table.cell(dash, 0, 6, "Filters", text_color=color.gray, text_size=size.tiny) table.cell(dash, 1, 6, filter_txt != "" ? filter_txt : "None", text_color=#FFB300, text_size=size.tiny) // ═══════════════════════════════════════════════════════════════════════════════ // ██ ALERTS // ═══════════════════════════════════════════════════════════════════════════════ alertcondition(bull_signal, title="QEMA LONG", message="🟢 LONG | Price above QEMA | {{close}}") alertcondition(bear_signal, title="QEMA SHORT", message="🔴 SHORT | Price below QEMA | {{close}}") alertcondition(vol_spike, title="Volume Spike", message="📊 Volume Spike") // ═══════════════════════════════════════════════════════════════════════════════ // ██ DATA WINDOW // ═══════════════════════════════════════════════════════════════════════════════ plot(qema_val, "QEMA", display=display.data_window) plot(close - qema_val, "Price-QEMA Distance", display=display.data_window) plot(adx_val, "ADX", display=display.data_window) plot(atr_val, "ATR", display=display.data_window) plot(vol_relative, "Vol Ratio", display=display.data_window) plot(vol_delta, "Vol Delta", display=display.data_window) plot(signal_quality, "Quality", display=display.data_window)
Indicateur Pine Script®
par martinrecalde777
Dynamic Sigmoid ATR-Normalized EMA Distance OscillatorDynamic Sigmoid ATR-Normalized EMA Distance Oscillator Overview This indicator is built to evaluate trend strength and trend sustainability, not to prioritize mean-reversion calls. It measures directional pressure relative to a long-term EMA, scales that pressure by current volatility, and maps the result into a stable 0-100 oscillator using a sigmoid transform. The key advantage is dynamic adaptation: center and regime bands are not static references only, they adjust to evolving oscillator behavior. This helps separate healthy trend continuation from weak, noisy movement. Mathematical Construction (No Code Description) 1) Trend baseline: A long-horizon exponential moving average defines structural direction. 2) Signed distance: Distance = Price - EMA Positive values imply price is structurally above baseline; negative values imply below-baseline pressure. 3) Volatility normalization: Normalized Distance = Distance / ATR This makes the signal scale-aware across different volatility environments. 4) Nonlinear compression: Sigmoid(x) = 1 / (1 + e^(-k*x)) The multiplier k controls response sharpness. The output is then scaled to 0-100. 5) Dynamic center: Center = EMA of oscillator values (adaptive midpoint). Optional fixed midpoint mode is available for a classic 50-line reference. 6) Dynamic regime bands: Upper Band = Center + (StdDev of oscillator * multiplier) Lower Band = Center - (StdDev of oscillator * multiplier) These bands expand/contract with oscillator volatility, making the framework regime-aware. How to Read It (Trend-Focused) - Oscillator above dynamic center: bullish pressure dominates current regime. - Oscillator below dynamic center: bearish pressure dominates current regime. - Persistent distance from center: trend continuation probability is generally stronger. - Oscillator flattening back toward center: trend sustainability may be weakening. Long and Short Signal Framework Long setup concept: 1) Oscillator crosses above dynamic center. 2) Oscillator remains above center for multiple bars (persistence confirmation). 3) Pullbacks that hold above center and re-expand upward can be treated as continuation entries. Short setup concept: 1) Oscillator crosses below dynamic center. 2) Oscillator remains below center for multiple bars. 3) Bounces that fail near center and rotate down can be treated as continuation entries. Strength confirmation: - Rising histogram above zero supports long continuation quality. - Falling histogram below zero supports short continuation quality. Example Use Cases (Educational) Example A - Long trend continuation: - Market transitions from neutral to bullish as oscillator crosses and holds above center. - Dynamic upper band begins rising, indicating expanding bullish regime capacity. - Dips in oscillator that stay above center suggest trend remains structurally intact. Example B - Short trend continuation: - Oscillator breaks below center and stays suppressed under it. - Lower band trends down while histogram remains negative. - Failed recoveries toward center often mark lower-risk continuation timing. Example C - Potential trend fatigue: - Price makes a new directional push, but oscillator fails to sustain distance from center. - Histogram contracts progressively. - This can indicate weakening impulse and the need for tighter risk control. Why Dynamic Components Matter - A fixed 50-level alone can be too rigid when regime characteristics shift. - Dynamic center adapts to the oscillator's local equilibrium. - Dynamic bands adapt to oscillator variance, helping contextualize what is truly "extended" in current conditions. Inputs (Configurable Parameters) - EMA Length: structural trend anchor sensitivity. - ATR Length: volatility normalization depth. - Sigmoid Multiplier: nonlinear response intensity. - Use Dynamic Center: adaptive center or fixed 50 reference. - Center Length: smoothness of adaptive center behavior. - Band Length: lookback horizon for oscillator variance. - Band Stdev Multiplier: adaptive band width. - Visual toggles: gradient and table display options. Risk and Implementation Notes - This is an analytical framework, not a guaranteed signal engine. - Choppy markets can still produce false transitions. - Parameter calibration should be done per symbol and timeframe with independent testing. - Position sizing, stop logic, and risk limits remain essential. Publishing and Compliance Notes - Educational and analytical content only; not financial advice. - No guaranteed returns, no performance promises, no misleading language. - Real-world outcomes vary by market conditions, execution, and risk management.
Indicateur Pine Script®
par hasanaksoy199264
Regime Switching [Pointalgo]Regime Switching Regime Switching is a market condition classifier designed to automatically detect whether price is in a Trending, Ranging (Mean Reversion), or Neutral/Noise environment. Instead of applying one strategy to all conditions, this script adapts its visual tools and signals based on the detected regime. How It Works: 1. ADX (Trend Strength) Measures directional strength. Higher values indicate stronger trends. 2. Efficiency Ratio (Kaufman Concept) Compares net price movement to total movement. High efficiency → directional move Low efficiency → choppy movement Using these two inputs, the script classifies the market into: TRENDING → Strong ADX + Efficient movement RANGING → Weak ADX + Inefficient movement NOISE → Transitional / unclear conditions Adaptive Visual System: The indicator automatically switches tools depending on the regime: Trending Mode Displays Donchian Channel (20-period highs/lows) Highlights breakout conditions Green background shading Ranging Mode Displays Bollinger Bands (20, 2 standard deviations) Highlights fade/reversal conditions Blue background shading Noise Mode 1.Neutral background 2.No active channel emphasis Signal Logic: Breakout above previous Donchian high Breakdown below previous Donchian low Range Mode: Buy when price crosses above lower Bollinger Band Sell when price crosses below upper Bollinger Band Signals are informational and intended for study and research. Dashboard: The built-in table shows: 1. Current detected regime 2. Efficiency value 3. State color coding This allows quick interpretation of market condition at a glance. Disclaimer: This script is for educational and research purposes only. It does not constitute financial advice or a trading recommendation. Always test and validate strategies before live trading.
Indicateur Pine Script®
par pointalgo
Multi-Timeframe Strength Scanner [JOAT]Multi-Timeframe Strength Scanner Introduction The Multi-Timeframe Strength Scanner is an open-source indicator that combines higher timeframe trend analysis with current timeframe momentum indicators to create a comprehensive market strength assessment system. This mashup integrates ADX (Average Directional Index), Donchian Channels, VWAP (Volume Weighted Average Price), RSI divergence detection, and multi-timeframe EMA analysis into a unified scanner that identifies when trend strength aligns across multiple timeframes. The indicator addresses a critical trading challenge: signals that look strong on one timeframe often fail because higher timeframes are moving in the opposite direction. By analyzing 15-minute, 1-hour, and 4-hour timeframes simultaneously while monitoring current timeframe momentum, this tool helps traders avoid counter-trend trades and identify high-probability setups where multiple timeframes align. Chart showing multi-timeframe alignment dashboard and strength indicators on 15M timeframe Why This Mashup Exists This indicator combines five analytical frameworks that address different aspects of trend strength: ADX Analysis: Measures trend strength regardless of direction using directional movement Donchian Channels: Identifies breakouts and trend continuation using price extremes VWAP: Shows institutional average price and volume-weighted fair value RSI Divergence: Detects momentum exhaustion at current timeframe swing points Multi-Timeframe EMAs: Confirms trend direction across 15M, 1H, and 4H timeframes Each component serves a specific purpose: ADX quantifies trend strength, Donchian Channels identify breakout momentum, VWAP reveals institutional positioning, RSI divergences warn of reversals, and multi-timeframe EMAs ensure directional alignment. Together, they create a strength scanner that filters out weak, counter-trend setups and highlights only those with multi-timeframe confirmation. The mashup is justified because these components use fundamentally different data (directional movement, price extremes, volume-weighted averages, momentum oscillators, moving averages) that respond to different market conditions. When they align, it indicates genuine trend strength rather than temporary momentum. Core Components Explained 1. ADX Trend Strength System ADX (Average Directional Index) measures trend strength on a scale of 0-100: = ta.dmi(adxLength, adxLength) // Trend strength classification strongTrend = adx > adxThreshold // Default: 20 veryStrongTrend = adx > 40 extremeTrend = adx > 60 // Direction determination bullishTrend = plus > minus bearishTrend = minus > plus ADX interpretation: ADX < 20: Weak trend or ranging market - avoid trend-following strategies ADX 20-40: Moderate trend strength - standard trend-following viable ADX 40-60: Strong trend - high-probability trend continuation ADX > 60: Extreme trend - potential exhaustion or very strong momentum The indicator plots ADX as a line with color coding: Green: Strong bullish trend (ADX > 20, +DI > -DI) Red: Strong bearish trend (ADX > 20, -DI > +DI) Gray: Weak trend or ranging (ADX < 20) 2. Donchian Channel Breakout System Donchian Channels track the highest high and lowest low over a specified period: donchianLength = 20 // Configurable upperChannel = ta.highest(high, donchianLength) lowerChannel = ta.lowest(low, donchianLength) midChannel = (upperChannel + lowerChannel) / 2 Breakout signals: Bullish Breakout: Close above upper channel = new 20-bar high Bearish Breakout: Close below lower channel = new 20-bar low Channel Position: Price near upper channel = bullish strength, near lower = bearish strength The indicator uses Donchian breakouts to confirm trend strength. When price breaks out of the channel with strong ADX, it signals high-momentum trend continuation. 3. VWAP Analysis VWAP (Volume Weighted Average Price) calculates the average price weighted by volume: vwap = ta.vwap(hlc3) // Position analysis aboveVWAP = close > vwap // Bullish positioning belowVWAP = close < vwap // Bearish positioning // Distance from VWAP vwapDistance = ((close - vwap) / vwap) * 100 VWAP significance: Institutional traders use VWAP as benchmark for execution quality Price above VWAP = buyers in control, institutions paying premium Price below VWAP = sellers in control, institutions getting discount Large distance from VWAP = potential mean reversion opportunity VWAP acts as dynamic support/resistance level The indicator plots VWAP with dynamic coloring based on price position and uses it for trend confirmation. 4. RSI Divergence Detection The indicator detects divergences using pivot-based analysis: rsi = ta.rsi(close, 14) // Identify swing points pivotHigh = ta.pivothigh(rsi, 5, 5) pivotLow = ta.pivotlow(rsi, 5, 5) // Compare current pivot with previous pivot bullishDivergence = price makes lower low AND rsi makes higher low bearishDivergence = price makes higher high AND rsi makes lower high Divergence types: Regular Bullish: Price LL, RSI HL - momentum improving, potential reversal up Regular Bearish: Price HH, RSI LH - momentum deteriorating, potential reversal down Hidden Bullish: Price HL, RSI LL - trend continuation signal in uptrend Hidden Bearish: Price LH, RSI HH - trend continuation signal in downtrend Divergences are marked with "DIV" labels and used to warn of potential trend exhaustion or continuation. 5. Multi-Timeframe EMA Analysis The indicator analyzes trend direction across three higher timeframes: // Request higher timeframe data htf15mEMA = request.security(syminfo.tickerid, "15", ta.ema(close, 21)) htf1hEMA = request.security(syminfo.tickerid, "60", ta.ema(close, 21)) htf4hEMA = request.security(syminfo.tickerid, "240", ta.ema(close, 21)) // Determine trend direction htf15mBullish = close > htf15mEMA htf1hBullish = close > htf1hEMA htf4hBullish = close > htf4hEMA // Count aligned timeframes bullishCount = (htf15mBullish ? 1 : 0) + (htf1hBullish ? 1 : 0) + (htf4hBullish ? 1 : 0) bearishCount = (!htf15mBullish ? 1 : 0) + (!htf1hBullish ? 1 : 0) + (!htf4hBullish ? 1 : 0) Alignment classification: STRONG BULL: All 3 timeframes bullish (3/3 alignment) BULL: 2 out of 3 timeframes bullish MIXED: Timeframes conflicting (1-1-1 or 2-1 split) BEAR: 2 out of 3 timeframes bearish STRONG BEAR: All 3 timeframes bearish (3/3 alignment) Example showing multi-timeframe alignment dashboard with all three timeframes bullish Strength Scoring System The indicator calculates a comprehensive strength score (0-100) by evaluating: Strength Score Components: - ADX Strength: Up to 25 points (ADX > 40 = 25, ADX > 20 = 15, ADX < 20 = 0) - ADX Direction: Up to 15 points (+DI > -DI = 15 for bull, -DI > +DI = 15 for bear) - Donchian Position: Up to 15 points (breakout = 15, near channel = 10, mid-channel = 5) - VWAP Position: Up to 15 points (above VWAP = 15 for bull, below = 15 for bear) - MTF Alignment: Up to 20 points (3/3 = 20, 2/3 = 13, 1/3 = 7) - RSI Level: Up to 10 points (healthy range = 10, extreme = 5, divergence = -5) Score interpretation: 80-100: Extremely strong trend - high-probability continuation 60-79: Strong trend - favorable for trend-following 40-59: Moderate trend - selective trend trades 20-39: Weak trend - caution, potential reversal 0-19: Very weak or counter-trend - avoid trend-following The dashboard displays the strength score with color coding and individual component breakdown. Visual Elements ADX Line: Main trend strength indicator with dynamic coloring +DI/-DI Lines: Directional movement indicators ADX Threshold: Horizontal line at 20 (configurable) Donchian Channels: Upper, middle, and lower channel lines VWAP Line: Volume-weighted average price with dynamic coloring Divergence Labels: "DIV" markers at RSI divergence points Strength Bars: Background coloring based on strength score Dashboard: Comprehensive table showing: - Current strength score - ADX value and direction - Donchian position - VWAP position - MTF alignment (15M, 1H, 4H status) - RSI level - Overall trend classification Chart showing strength dashboard with component breakdown and visual indicators How Components Work Together The mashup creates a layered strength analysis: Layer 1 - Trend Strength: ADX quantifies how strong the trend is Layer 2 - Breakout Momentum: Donchian Channels identify momentum surges Layer 3 - Institutional Positioning: VWAP shows where smart money is positioned Layer 4 - Momentum Health: RSI divergences warn of exhaustion Layer 5 - Multi-Timeframe Confirmation: HTF EMAs ensure directional alignment Layer 6 - Synthesis: Strength score combines all factors into actionable metric Example scenario: ADX is 45 (Layer 1), price breaks above Donchian upper channel (Layer 2), trading above VWAP (Layer 3), no RSI divergence (Layer 4), and all three higher timeframes are bullish (Layer 5). The strength score reaches 90 (Layer 6), signaling extremely strong bullish trend with high continuation probability. Input Parameters ADX Settings: ADX Length: Period for ADX calculation (default: 14) ADX Threshold: Minimum ADX for strong trend (default: 20) Show +DI/-DI: Toggle directional indicators (default: enabled) Donchian Settings: Donchian Length: Period for channel calculation (default: 20) Show Channels: Toggle channel display (default: enabled) Breakout Sensitivity: Threshold for breakout signals (default: close beyond channel) VWAP Settings: Show VWAP: Toggle VWAP line (default: enabled) VWAP Reset: Session, Week, Month, or Never (default: Daily) Distance Alert: Alert when price moves X% from VWAP (default: 2%) RSI Settings: RSI Length: Period for RSI calculation (default: 14) Show Divergences: Toggle divergence markers (default: enabled) Pivot Lookback: Bars for pivot detection (default: 5) Multi-Timeframe Settings: HTF 1: First higher timeframe (default: 15 minutes) HTF 2: Second higher timeframe (default: 1 hour) HTF 3: Third higher timeframe (default: 4 hours) EMA Length: Period for HTF EMAs (default: 21) Min Alignment: Minimum timeframes aligned for signal (default: 2/3) Display Options: Show Dashboard: Toggle strength score table (default: enabled) Show Strength Bars: Toggle background coloring (default: enabled) Dashboard Position: Top-right, top-left, bottom-right, bottom-left Color Theme: Choose between multiple color schemes How to Use This Indicator Step 1: Check Multi-Timeframe Alignment Review the dashboard MTF section. Look for 2/3 or 3/3 alignment in your intended trade direction. Avoid trades when timeframes are mixed or opposing. Step 2: Verify ADX Strength Ensure ADX is above 20 (preferably above 30) for trend-following trades. ADX below 20 suggests ranging market where trend strategies underperform. Step 3: Confirm Donchian Position Check if price is near or breaking through Donchian channels. Breakouts with strong ADX signal high-momentum moves. Step 4: Assess VWAP Position For long trades, prefer price above VWAP. For short trades, prefer price below VWAP. Large distances from VWAP may indicate overextension. Step 5: Check for Divergences Look for RSI divergence warnings. If divergence appears with extreme strength score, consider taking profits or tightening stops. Step 6: Review Strength Score Use the overall strength score as final filter. Scores above 70 indicate strong trend conditions favorable for trend-following. Scores below 40 suggest caution. Best Practices Use on 5-minute to 1-hour timeframes for optimal multi-timeframe analysis Wait for 2/3 or 3/3 MTF alignment before entering trend trades Strong ADX (> 30) with MTF alignment produces highest-probability setups Donchian breakouts with ADX > 25 often lead to sustained moves VWAP acts as dynamic support/resistance - use for entry refinement RSI divergences in strong trends often lead to pullbacks, not reversals Strength score above 80 suggests strong trend continuation potential Avoid trading when strength score is below 40 unless counter-trend trading Combine with price action and key levels for precise entries Indicator Limitations ADX is lagging indicator - trend strength confirmed after move has started Donchian breakouts can produce false signals in choppy markets VWAP resets daily, may not reflect longer-term institutional positioning Multi-timeframe analysis requires sufficient data history Strength score is mathematical calculation, not prediction of future movement Strong trends can reverse suddenly despite high strength scores Divergences can persist for extended periods in strong trends Higher timeframe data may repaint on lower timeframes Requires understanding of trend analysis concepts for effective use Technical Implementation Built with Pine Script v6 using: DMI/ADX calculation with directional indicators Donchian Channel calculation with breakout detection VWAP calculation with session reset options Pivot-based RSI divergence detection request.security() for multi-timeframe EMA analysis Comprehensive strength scoring algorithm Dynamic dashboard with component breakdown Background coloring based on strength levels The code is fully open-source and can be modified to adjust timeframes, thresholds, and scoring weights. Originality Statement This indicator is original in its multi-timeframe strength integration approach. While individual components (ADX, Donchian Channels, VWAP, RSI divergence, EMAs) are established tools, this mashup is justified because: It combines trend strength measurement with multi-timeframe directional confirmation The strength scoring system quantifies trend quality across multiple dimensions Multi-timeframe analysis prevents counter-trend trades on lower timeframes Integration of volume-weighted analysis (VWAP) with momentum indicators Divergence detection provides early warning within trend strength context Comprehensive dashboard presents complex multi-timeframe data clearly Each component contributes unique information: ADX measures trend strength, Donchian identifies breakout momentum, VWAP shows institutional positioning, RSI divergences warn of exhaustion, and MTF EMAs ensure alignment. The mashup's value lies in filtering out weak, counter-trend setups and highlighting only those with genuine multi-timeframe strength confirmation. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors. Trend strength indicators are lagging tools that confirm trends after they've begun. Strong trends can reverse suddenly, and high strength scores do not guarantee trend continuation. Multi-timeframe analysis does not eliminate the risk of losses. The strength score is a mathematical calculation based on current market data, not a prediction of future price movement. Past trend strength does not guarantee future performance. Market conditions change, and trends that appear strong can reverse without warning. Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool. -Made with passion by officialjackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
Ratio Between Two SymbolsThis indicator calculates and displays the price ratio between two symbols, allowing you to compare relative performance and strength. The chart shows the current symbol divided by a benchmark symbol (default: S&P 500), with an optional multiplier to scale the ratio for easier visualization. A moving average (default 20-period SMA) smooths the ratio to identify trends in relative performance. When the ratio is rising, the current symbol is outperforming the benchmark. When falling, it's underperforming. The moving average helps identify the overall trend direction and potential reversals in relative strength. Features: - Compare any two symbols in real-time - Adjustable moving average period - Customizable ratio multiplier for scaling - Optional label showing current ratio value - Adjustable decimal precision Useful for: - Relative strength analysis - Pair trading opportunities - Sector/stock rotation strategies - Comparing asset class performance
Indicateur Pine Script®
par northstarbadcharts
Globus Capitas EMA 9 & EMA 14 RibbonGLOBUS CAPITAS EMA 9 & EMA 14 RIBBON INDICATOR The EMA 9 & 14 Ribbon Indicator is a precision built trend visualization tool designed for modern intraday traders and scalpers who demand clarity speed and structure in fast moving markets. This indicator leverages the power of two exponential moving averages EMA 9 and EMA 14 and converts their interaction into a dynamic color ribbon allowing traders to instantly identify trend direction momentum shifts and potential trade bias without lag or clutter. HOW THE INDICATOR WORKS At its core the indicator tracks the relationship between EMA 9 Fast EMA reacts quickly to price changes EMA 14 Slow EMA provides short term trend stability Instead of relying only on line crossovers the indicator fills the area between both EMAs with a contextual ribbon making trend conditions visually obvious at a glance. RIBBON COLOR LOGIC SMART VISUAL FEEDBACK Green Ribbon Bullish Bias EMA 9 crosses and stays above EMA 14 Indicates increasing buying momentum Favors long positions and buy side setups Red Ribbon Bearish Bias EMA 14 crosses and stays above EMA 9 Indicates increasing selling pressure Favors short positions and sell side setups Neutral Zone Flat Market EMAs are tightly compressed Signals low momentum and consolidation Best avoided unless confirmed by volume or structure WHY THIS INDICATOR STANDS OUT 1 Instant trend recognition no guesswork no lag 2 Clean and minimal design ideal for scalping charts 3 Psychology friendly reduces emotional decision making 4 Works across all markets indices gold forex crypto options buying options selling 5 Works with all minute timeframes This ribbon structure allows traders to stay aligned with price flow instead of reacting late to cross signals. BEST USE CASES Intraday trading and scalping Trend continuation entries Pullback trades in strong trends Directional bias confirmation Trade filtering with price action or structure Pro Tip Combine this indicator with key support and resistance levels liquidity zones or VWAP for higher probability setups. TRADING PHILOSOPHY BEHIND IT This indicator is built around one core principle Trade what the market is doing not what you think it should do The EMA Ribbon removes noise and bias helping traders Follow trends objectively Avoid counter trend emotional trades Protect capital during choppy conditions IMPORTANT NOTES This is not a standalone system Best used as a trend filter not a signal generator Avoid trading during major news events without confirmation DISCLAIMER This indicator is provided strictly for educational purposes. It does not offer buy or sell recommendations. Trading involves risk always manage your position size use stop losses and consult a certified financial advisor if needed. Globus Capitas is not responsible for any profit or loss. FINAL WORD The Globus Capitas EMA 9 & 14 Ribbon is a clarity tool for serious traders designed to keep you aligned with momentum disciplined with entries and focused on capital protection. Trade smart. Stay aligned. Trade what you see. Follow the trend. Protect capital. Let the chart lead.
Indicateur Pine Script®
par globuscapitas
2
Block-Based Visual Logic Builder FrameworkThis strategy demonstrates a modular, block-based approach to strategy design in Pine Script. While TradingView does not support true drag-and-drop strategy builders, this script is structured to simulate a visual logic builder by separating trading logic into independent, configurable blocks. Each block represents a conceptual component (trend, momentum, volatility, session, and risk), allowing users to enable, disable, or modify individual modules for testing and experimentation. Visual Logic Builder Concept The strategy is organized into distinct logic blocks: Session Block - controls when trades are allowed Trend Block - defines directional bias Momentum Block - confirms directional strength Volatility Block - filters for sufficient market movement Risk Block - standardized ATR-based stop and target logic This structure mirrors how visual trading systems and drag-and-drop interfaces organize logic into reusable components. How It Works Market conditions are evaluated through separate logic blocks. Each block can be enabled or disabled using inputs. Final entry decisions are based on the combined output of active blocks. All exits are managed through a centralized risk block using ATR-based stops and fixed risk-reward. Purpose This script is intended as a structural template for building and testing modular trading systems. It emphasizes clarity, execution realism, and repeatable logic rather than optimization or signal density. Usage Notes Results depend on symbol, timeframe, volatility regime, and selected block settings. Users are encouraged to experiment with different combinations of blocks and validate behavior across multiple markets before relying on any configuration.
Stratégie Pine Script®
par AIScripts
Adaptive Volatility Trend Breakout (4H+ Strategy)Adaptive Volatility Trend Breakout (4H+ Strategy) Overview This strategy is a robust, trend-following system specifically engineered for higher timeframes ( 4H, Daily, and Weekly ). It aims to capture significant market moves by using dynamic, self-adjusting volatility bands based on Standard Deviation rather than fixed percentages. The strategy is designed to identify and ride major trends while remaining "neutral" during choppy, sideways price action. Core Logic The system revolves around three main components: Baseline (MA): Uses a Moving Average to define the core value area of the asset. Dynamic Volatility Bands: The script calculates the real-time percentage change of price over a historical lookback period. It then applies a Standard Deviation Multiplier to create a "Neutral Zone." Trend State Memory: A built-in filtering logic ensures the strategy only enters a trade when a definitive breakout occurs and stays in that trend until a reversal is confirmed by the opposite band. Key Features Optimized for 4H & Higher: Specifically tuned to ignore "intra-day noise," making it ideal for swing traders and trend followers. Minimal Parameter Risk: With only three main inputs (MA Length, Lookback, and Multiplier), the strategy is built to be simple and robust, reducing the risk of over-optimization. Volatility-Aware: The bands automatically expand during high-volatility events and contract during consolidation, helping to protect capital from premature entries during market "noise." Set-and-Forget Design: Built for long-term consistency, this strategy requires minimal manual intervention once the parameters are set for a specific asset. How to Use Timeframe: Apply the strategy to 4H charts or higher. Calibration: Adjust the Multiplier (StdDev) to fit the specific volatility of the asset you are trading. Execution: Entry occurs on a candle close outside the band. Exit occurs when the price crosses and closes beyond the opposite boundary.
Stratégie Pine Script®
par SpankyFPV
Triple EMA (7/25/100) Triple EMA (7 / 25 / 100) – Trend Alignment & Bias Tool This script plots three Exponential Moving Averages (7, 25, and 100) to help identify market bias and trend alignment. EMA 100 (Blue, thick) → Primary bias / higher timeframe control EMA 25 (Orange) → Intermediate trend direction EMA 7 (Yellow) → Short-term momentum How to use: Bullish alignment: 7 > 25 > 100 → Strong uptrend structure Bearish alignment: 7 < 25 < 100 → Strong downtrend structure Price above 100 EMA → Buyers in control Price below 100 EMA → Sellers in control This tool is best used for trend continuation setups and filtering trades in conjunction with structure, displacement, or Fair Value Gaps (FVGs). Designed for clean, bias-first decision making without indicator clutter.
Indicateur Pine Script®
par k57dqbbnnx