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Momentum Oscillators

Volume Weighted Median Oscillator [AlgoAlpha]🟠 OVERVIEW This script measures price movement relative to a volume-weighted median instead of a simple average. It builds an oscillator that reflects where price is positioned compared to the level where most trading activity is concentrated. This helps reduce the effect of outliers and highlights more balanced price zones. The output combines a momentum histogram and a smoothed oscillator line, both normalized by recent price range. It also includes a signal line and visual zones to show when price is stretched relative to its volume-based center. 🟠 CONCEPTS Volume-Weighted Median — The price level where half of the traded volume occurred above and half below within the lookback window. Normalized Momentum — The difference between current price and the weighted median, divided by an average price range to keep values comparable across markets. Median Trend Deviation — The distance between the median and its longer-term EMA, used to define the main oscillator direction. Volume Normalization — Current volume divided by its moving average to highlight periods of higher activity. Signal Line — An EMA of the oscillator used to track short-term direction changes. 🟠 FEATURES Dual Output Structure — Combines histogram and oscillator line. • Histogram shows short-term momentum shifts. • Oscillator line reflects broader deviation from median trend. Optional Smoothing — Applies WMA smoothing to reduce noise in both outputs. Dynamic Ribbon — Visual fill between oscillator and signal line. • Changes color based on direction. • Intensity reflects strength of separation. Overbought and Oversold Zones — Gradient fills beyond defined thresholds. • Expands visually as extremes increase. • Helps identify stretched conditions. Volume-Aware Zero Line — Brightness increases when current volume exceeds its average. Volume-Weighted Median Core — Uses sorted price and volume arrays to compute a true weighted median. • Reduces sensitivity to spikes compared to mean-based indicators. • Anchors calculations to where trading activity is concentrated. Built-in Alerts — Includes conditions for crosses, zone entries, and ribbon flips. 🟠 HOW TO USE Watch the histogram crossing above or below zero — shows short-term momentum shifts relative to the median. Use the main oscillator crossing zero — indicates broader directional bias change. Track crosses between the oscillator and its signal line — helps identify early trend shifts. Observe entries into extreme zones — signals that price is stretched relative to its volume center. Pay attention to ribbon color changes — confirms alignment between momentum and trend. Check zero line brightness — higher brightness suggests stronger participation behind moves. 🟠 CONCLUSION Volume Weighted Median Oscillator combines a volume-based median, normalized momentum, and trend deviation into one view. It focuses on where trading activity is concentrated instead of simple averages. This gives a clearer read on price imbalance and helps traders judge when moves are supported or stretched.
Pine Script® indicator
by AlgoAlpha
Quantum Momentum Wave [Pineify]Quantum Momentum Wave Quantum Momentum Wave is a signed peak-momentum oscillator paired with a Kaufman-style adaptive filter. Instead of measuring the net change from one point to another, the wave scans the full lookback window and returns the largest absolute price displacement with its original sign. The wave is linearly smoothed and then tracked by an adaptive signal line, so entry markers fire on context-filtered reversals rather than every momentum flip. Key Features Signed peak-momentum engine that isolates the strongest move inside the lookback Linear regression smoothing to tame the step-change behavior of raw peak readings Adaptive signal line that speeds up when the wave is stretched and slows down in noise Zone-filtered BUY and SELL markers that only fire when the wave is past zero in the opposite direction Gradient fill between the wave and zero — bullish above, bearish below Alerts for reversal setups and for zero-line regime shifts How It Works The core function is a QMW scan. On each bar it walks the last Momentum Lookback bars, computes price minus each historical bar, and keeps the comparison that produced the largest absolute change. It returns that value with its original sign. So a -6 reading means the single strongest move inside the window was a 6-unit drop, even if the bar-to-bar change right now is small. Raw peak readings step when a new candle takes over as the dominant move, so the output is passed through linear regression over the Signal Smoothing length. LR fits a straight line through the recent raw values and returns its endpoint, preserving signed direction while cutting short-term jitter. The adaptive signal runs a Kaufman-style update on top of the smoothed wave. Its coefficient is the ratio of the wave's absolute value to the sum of its recent absolute one-bar changes. When momentum is clearly extended and directional, the ratio is high and the signal follows quickly. When the wave oscillates near zero, the ratio collapses and the line nearly freezes. How the Components Work Together Three layers combine into one output. The peak scan answers how hard has price moved inside the window, not just where it ended. Linear regression answers what is the underlying direction of the peak reading after filtering out one-bar swaps between competing peak bars. The adaptive signal answers when has that direction actually shifted by providing a responsive reference line. Entries layer a location filter on top. A crossover only qualifies when the wave is on the opposite side of zero — buys look for the wave turning up while stretched below zero, sells for the wave rolling over while stretched above it. Crosses near the midline are ignored because they tend to be low-conviction flips without prior displacement, so the setup is biased toward reversals from extended states rather than trend-chasing entries. Trading Ideas and Insights BUY triangles below zero can flag exhaustion of a downside push and a possible rotation back toward the mean SELL triangles above zero can flag fading upside strength after an extended rally Zero-line crosses act as broader regime markers; alerts on those can frame intraday bias During strong trends the wave may stay on one side of zero for many bars. Counter-trend BUY or SELL labels in that state often need extra confirmation from price structure or higher-timeframe context Signals are context aids, not standalone trade instructions. Past reactions at these conditions do not guarantee future ones. Unique Aspects The momentum calculation returns the peak signed displacement within the lookback, not a simple close-minus-close value. The reading stays elevated while the dominant move is still inside the window, which tracks the strongest displacement in memory rather than just endpoint delta The adaptive signal uses a non-standard variation of Kaufman's efficiency ratio — absolute wave value divided by total absolute wave travel — tuned for oscillator input rather than raw price Entry markers require both a crossover and a zone match. No label prints for a crossover that happens in the neutral zone around zero, which filters out the weakest setups How to Use Add the indicator to the chart and start with the defaults. The thicker line is the Quantum Wave; the fainter line is the Adaptive Signal Read the wave color and the gradient fill to see which side of zero the wave is on and how stretched it is Watch for BUY triangles when the wave is clearly below zero and turning up through the signal line Watch for SELL triangles when the wave is clearly above zero and turning down through the signal line Configure alerts for buy, sell, or zero-cross conditions if you want notifications outside the chart Customization Source Data (default: close) — Price series used for the peak momentum scan Momentum Lookback (default: 14) — Window size for the peak displacement scan. Higher values capture broader swings; lower values react faster Signal Smoothing (default: 9) — Linear regression length applied to the raw wave. Higher values produce a cleaner line with more lag Bullish Color / Bearish Color — Wave color in positive and negative territory, and the BUY/SELL marker colors Adaptive Signal Color — Color of the adaptive reference line; a subtle contrast against the wave usually reads cleanest Limitations Linear regression smoothing introduces a small amount of lag. On fast reversals the wave can reach the crossover a few bars after price has already moved Because the adaptive ratio uses the wave's own value, very small wave readings near zero make the signal line nearly freeze. This is deliberate but means the reference reacts slowly while momentum is mild Mean-reversion style BUY and SELL labels can occur repeatedly during strong trends. Pair them with price structure or higher-timeframe bias instead of using them in isolation Conclusion Quantum Momentum Wave is for traders who want an oscillator that reflects the strongest recent move rather than the endpoint delta. Its value comes from the combination of peak-momentum measurement, linear regression smoothing, and an adaptive reference line that only produces labels when the wave is both stretched past zero and visibly turning back.
Pine Script® indicator
by Pineify
Ultimate Scalping Tool 2.0 [BullByte]Ultimate Scalping Tool 2.0 What This Tool Was Built For UST 2.0 is a flux oscillator designed to help scalpers and intraday traders read market state more clearly. It sits in the lower pane of your chart and shows you whether the current market environment is trending cleanly, compressing before a move, chopping in a range, or losing momentum after an extension. The core idea is simple. Four internal measurements feed into one oscillator. Each measurement looks at a different part of market behavior: how cleanly price is traveling, whether momentum is meaningful given current volatility, what volatility itself is doing, and whether participation is building or fading. These four readings are weighted differently depending on what the market is doing right now. During a trend, directional measurements get more weight. During compression, volatility and flow get more weight. During a range, momentum and participation matter most. The output is displayed as a candle on the oscillator pane. The candle color, the regime label, the confluence histogram, and the structural overlays all update on every bar to give you a current read on conditions. This is not a signal generator. It is a state monitor. You still need to decide when and how to act. --- Why Version 2.0 Exists Separately Ultimate Scalping Tool Version 1.0 is still published. It uses a different internal structure and outputs labeled signals like Strong Buy and Pullback Sell. Many traders built their workflows around that approach, and changing it would have broken their setups. Keeping both versions available lets traders choose the workflow that fits how they trade. Version 2.0 shifts away from signal labels toward market state classification. The four internal measurements are different. The adaptive weighting engine is different. The visual output is different. The confluence scoring, multi-timeframe context, and oscillator-native zones are all new. The two scripts serve related but different purposes, which is why they exist side by side rather than as an update that replaces the first one. The continuity between them is the Flux State Candle concept. That visualization approach worked well enough in version 1.0 that it became the foundation for how version 2.0 displays its output. --- One Engine, Not a Collection of Indicators This is worth addressing directly because it is easy to look at a script that mentions RSI, ATR, and Bollinger Bands and assume it is just wrapping existing indicators together. That is not what is happening here. RSI does not appear on the chart. ATR does not appear on the chart. Bollinger Bands do not appear on the chart. None of the four internal measurements produce a visible output of their own. They are inputs into a calculation that produces one single output: the Flux Oscillator value. That value is then displayed as a candle. The reason RSI is used inside the Adaptive Momentum subsystem is not because RSI is a good indicator to show. It is because RSI gives a normalized momentum reading on a known scale, which makes it easier to combine mathematically with the other three measurements on equal footing. The period is not fixed. It changes at runtime based on current volatility, which standard RSI does not do. The output is scaled before it enters the weighting engine. What comes out the other side is not RSI. It is one weighted component of a composite reading. The same logic applies to ATR inside the Volatility Regime subsystem and to bar-structure calculations inside the Flow Proxy. These are raw materials, not finished products. They feed a pipeline that produces something different from any of them individually. The four subsystems were chosen because they answer four different questions: is price traveling efficiently, is momentum meaningful right now, is volatility expanding or contracting, and is participation building or fading. Those four questions together give a more complete picture of market state than any one of them alone. That is the design intent, and it is why the adaptive weighting engine matters. Different market conditions make different questions more relevant. The engine adjusts accordingly. --- The Four Internal Measurements The oscillator is built on four subsystems. Each one measures something the others do not. Directional Efficiency looks at how cleanly price is moving. A market that travels 100 points in a straight line has high efficiency. A market that gyrates up and down while netting only 20 points forward has low efficiency. This helps you see whether a trending move is organized or grinding. For scalping, clean efficient moves are easier to work with than choppy grinders. Adaptive Momentum uses RSI internally but selects the period based on current volatility. When volatility is high, it uses a shorter period to stay responsive. When volatility is low, it uses a longer period to reduce noise. The reading is then adjusted so that momentum in a high-volatility environment is not overstated and momentum in a quiet environment is not understated. Volatility Regime combines three volatility signals: where ATR sits within its recent range, whether short-window ATR is rising or falling relative to long-window ATR, and where Bollinger Band width sits within its range. Positive values mean volatility is expanding. Negative values mean it is contracting. Deep negative values below -0.20 usually mean the market is compressing hard, which often precedes directional expansion. Flow Proxy estimates directional participation from bar structure and relative volume. It approximates buying versus selling pressure using close position within range, price velocity weighted by volume, and absorption detection for high-volume bars with small bodies. This is not true order flow because that requires tick data. It is a proxy built from OHLCV data, and it behaves like one. These four readings are combined using weights that shift based on what the market is doing. You get one oscillator value per bar. That value is displayed as a candle, classified into a regime state, scored for confluence, and optionally blended with a higher-timeframe context reading. --- How the Regime Classification Works The script classifies every bar into one of four states: TREND UP, TREND DOWN, COIL, or RANGE. When efficiency is strong, momentum is aligned, and volatility is not compressing, the regime is TREND . When efficiency is low, volatility is compressing hard, and participation is building, the regime is COIL . When none of those conditions are met, the regime is RANGE . The regime label appears near the bottom of the pane on the last bar. The background tint changes subtly based on the regime. This classification affects how the four measurements are weighted before they combine into the oscillator value. --- The Confluence Histogram The gold histogram at the bottom of the pane shows the confluence score from 0 to 100. The score measures how aligned six independent factors are: regime matches oscillator direction, oscillator magnitude is strong, the four internal measurements agree on direction, the higher timeframe aligns with the current one, flow quality is clean without absorption, and volatility context supports the current structure. A low score means conditions are mixed or weak. A high score means the environment is organized and several factors are confirming the same picture. The histogram grows taller as the score rises. When the bar is short and dark amber, confluence is low. When the bar is tall and bright gold, confluence is high. This is useful for filtering. If you are watching for a breakout from a consolidation and confluence is at 80, the setup has more environmental support than the same breakout pattern with confluence at 30. The score does not tell you to enter or exit. It tells you whether the broader conditions are organized or scattered. --- How to Read the Flux State Candle The candle is built from the oscillator value, not from price. Green candles mean the oscillator is above its moving average. Red candles mean it is below. Violet candles mean a volume absorption bar was detected , which is a specific condition where high volume produced a small-bodied bar. The border brightness increases when the oscillator is further from its moving average. A bright border means conviction. A dim border means the reading is close to neutral. When the candle is consistently green and the cloud below it is stacked with the fast line above the slow line, the oscillator trend is internally aligned in a bullish direction. When the candle flips to red and the cloud inverts, the oscillator trend is bearish. When the cloud is mixed and candles are alternating color frequently, the oscillator is in a transition or range state. --- Oscillator-Native Zones and Trendlines The red and green-cyan lines you see on the oscillator pane are support and resistance zones, but they are zones on the oscillator itself, not on price. They mark levels where the oscillator has repeatedly turned. These are built from confirmed pivot highs and lows. When the oscillator approaches a red resistance zone that has been touched five times, you know the flux reading is approaching a level where it has reversed before. If price is near a key price level at the same time and confluence is high, that convergence can add weight to a reversal setup you are already watching on the price chart. The cyan and orange trendlines work the same way. They connect confirmed swing points on the oscillator. A cyan ascending trendline from oscillator swing lows tells you the oscillator structure is building higher. An orange descending trendline from oscillator swing highs tells you the oscillator structure is weakening lower. These lines appear after the swing points are confirmed, not before. All of these structural tools use confirmed pivot logic, which means they appear a few bars after the actual turning point. This is intentional. Unconfirmed pivots fail frequently. Waiting for confirmation removes most of the false starts. When you see a zone or trendline appear or update, it is showing you structure that has been validated by subsequent price action, not structure that might form. Reading the oscillator structure in practice looks like this. Two red resistance lines sitting around +25 and +30 on the oscillator, both labelled R MAJO R, tell you that every time flux has pushed up into that band it has been rejected. That ceiling has held six and seven touches respectively. Until flux breaks and closes above those two red lines, the upside is capped on the oscillator. Below there is an S ZONE 3T sitting near -15. That is the floor that has caught the most recent bounce. On the trendline side, three cyan ascending lines rising from the lower left are bull trendlines built from oscillator swing lows. The lowest at TL 52% is the broadest support. The middle at TL 66% is tighter. The top at TL 74% is the steepest and most recent. If flux bounces off the TL 74% line, that is the most important touch right now. An orange descending line pressing down from above is the bear trendline. When flux is squeezed between a rising cyan trendline and a falling orange trendline, that is a classic wedge compression on the oscillator and it often precedes a directional expansion once one side breaks. --- Divergence Labels When the oscillator and price stop agreeing on direction, the divergence engine marks it on the chart. There are four labels. Each one means something specific. DIV+ appears below the oscillator at a swing low. It means price made a lower low on the price chart, but the oscillator made a higher low at the same point. The downward move is losing internal support. This is regular bullish divergence and it tends to appear near potential exhaustion of a downward move. DIV- appears above the oscillator at a swing high. It means price made a higher high on the price chart, but the oscillator made a lower high at the same point. The upward move is losing internal support. This is regular bearish divergence and it tends to appear near potential exhaustion of an upward move. H+ appears as a small circle at a swing low. It means price made a higher low, a normal pullback in an uptrend, but the oscillator made a lower low. The oscillator dipped deeper than price did. This is hidden bullish divergence. It often appears mid-trend during a pullback and can suggest the trend is likely to continue. H- appears as a small circle at a swing high. It means price made a lower high, a normal pullback in a downtrend, but the oscillator made a higher high. The oscillator pushed higher than price did. This is hidden bearish divergence. It often appears mid-trend during a bounce and can suggest the downtrend is likely to continue. All four labels appear a few bars after the actual swing point because the pivot must be confirmed by subsequent bars before the label is placed. When you see a label appear, it is sitting on a past bar where the swing has already been validated. Nothing is marked in advance. The label was not there before, and it will not move or disappear after it appears. --- How This Fits Into a Price Action Workflow Here is one example of how the tool can layer into a real trade setup. You are watching a 5-minute chart. Price has been consolidating in a tight range for the last hour. On the main price chart, you see a clean horizontal level being tested multiple times. That is your setup area. You are waiting for a breakout. You glance at the oscillator pane. The regime label says COIL. The flux candles are small and alternating color near the zero line. The confluence histogram is rising and just reached 65. VRI in the dashboard shows -0.25, which is hard compression. COFP is positive and building. This tells you the oscillator agrees with what you are seeing on price: the market is compressing. Volatility is contracting. Participation is starting to build in one direction. Confluence is improving, meaning the internal measurements are starting to agree. You wait. Price breaks the consolidation upward on increased volume. At the same moment, the flux candle turns bright green with a strong border. The regime label flips to TREND UP. The confluence histogram jumps to 78. The cloud stacks bullish. The dashboard shows all four internal measurements turned positive. You already know where your entry is from the price chart. The oscillator is not telling you to buy. The oscillator is telling you the conditions support the breakout you are watching. Efficiency turned positive, meaning price is traveling cleanly. Momentum confirmed. Volatility started expanding. Flow is directional. The environment is organized. You take the trade based on your price-action plan. You manage the trade from price structure. But while you are in the trade, you glance at the oscillator occasionally. If the flux candle stays green and confluence stays high, conditions remain supportive. If the candle turns red and confluence drops, you tighten your management because the internal read is weakening. A real chart example of what COIL into expansion looks like: Gold on the 3-minute chart shows a full cycle, a base building through the early session, a strong impulsive rally, then a controlled rollover where the oscillator hugs just above zero with the regime label reading COIL. The oscillator has compressed hard after the post-peak selloff, and the cyan ascending bull trendline is now acting as the floor of the oscillator structure. This is the compression phase the tool is designed to identify before the next directional move develops. --- What This Tool Does Not Do This script does not tell you where to enter or exit. It does not produce buy and sell signals. The flux candle, the regime label, the confluence score, and the structural overlays all describe conditions, not actions. The zones and trendlines on the oscillator are not price levels. They are oscillator levels. They tell you where the oscillator has turned before, not where price will turn next. The HTF oscillator is a simplified version of the current-timeframe calculation. It uses fixed parameters and reduced components for cross-timeframe stability. It is useful for context, but it is not identical to the full engine running on the higher timeframe. Pivot-based elements appear after confirmation, which means they lag the actual pivot by the confirmation period . This is intentional. The tradeoff is fewer false signals at the cost of slightly delayed information. Confluence measures environmental alignment, not trade direction. A high score means conditions are organized. A low score means they are not. Neither tells you to buy or sell. --- Recommended Timeframes and Settings This tool works best on intraday timeframes from 1 minute to 1 hour. For active scalping, use 1-minute to 5-minute charts with the HTF set to 15 minutes. For slightly slower intraday swings, use 15-minute to 60-minute charts with the HTF set to 4 hours or daily. The multi-timeframe panel should show timeframes that are meaningfully different from your chart timeframe. If you are on 5 minutes, set the panel to 15 minutes, 60 minutes, and 240 minutes. If the panel shows timeframes that are too close together, the readings will be nearly identical and provide no additional information. The default settings are balanced for general use. If the oscillator is too noisy, increase the DER length or the AMVS base period. If the oscillator is too slow, decrease them. If zones and trendlines are appearing too frequently, increase the minimum touch requirements. If they are not appearing enough, decrease them. The adaptive weighting setting should generally stay on unless you have a specific reason to use fixed manual weights. The adaptive behavior is part of how the tool adjusts to different market conditions. --- Understanding the Limitations No indicator can predict the future . This tool shows you the current state of several internal measurements and how aligned they are. It does not know what will happen next. Markets can shift from organized to chaotic in seconds. A high confluence reading can drop to low confluence on the next bar if one of the internal measurements changes direction. That is normal. The tool is reactive, not predictive . The S/R zones and trendlines are based on past oscillator behavior. Just because the oscillator turned at a zone five times before does not mean it will turn there again. Past structure informs context but does not guarantee repetition. Divergence suggests momentum quality is changing. It does not guarantee reversal. Many divergences resolve with price continuing in the original direction after a brief pause or consolidation. Volume absorption detection is based on relative volume and body size. It identifies a specific bar condition but does not tell you how the market will respond to that condition. --- A Note on Expectations This script was built to give scalpers and intraday traders more clarity about market state. It was not built to replace chart reading, price action analysis, or trade management discipline. If you are looking for a tool that tells you exactly when to enter and exit, this is not that tool. If you are looking for a tool that helps you see whether the current environment is organized or messy, whether internal measurements are aligned or conflicting, and whether higher-timeframe context supports or opposes your intended direction, this can help with that. The best results come when the oscillator is used as confirmation for setups you are already identifying from price structure, not as a standalone signal generator. The flux candle, regime label, confluence score, and structural overlays are all context layers. They work best when layered on top of solid price action principles, not in place of them. --- Disclaimer This script is published for educational purposes. Trading involves substantial risk of loss. No indicator or tool can eliminate that risk. Past performance of any indicator does not predict future results. The author is not responsible for any trading decisions made using this script. Always apply your own risk management, test thoroughly on demo or simulation before live use, and never risk more than you can afford to lose. - BullByte
Pine Script® indicator
by BullByte
4
Equilibrium Momentum Shift + Divegence [BigBeluga]🔵 OVERVIEW Equilibrium Momentum Shift is a range-based momentum oscillator designed to measure how far price has deviated from its current equilibrium. Instead of focusing purely on trend direction or overbought/oversold conditions, this indicator evaluates price relative to the midpoint of its recent range and quantifies the strength of the shift away from that balance. By combining normalized range deviation, smoothing techniques, and nonlinear compression, the indicator provides a clear view of when markets transition from equilibrium into directional momentum, now featuring Normal Divergence detection to spot potential trend reversals. 🔵 CONCEPT Equilibrium Midpoint — The midpoint between the highest high and lowest low over the selected range length represents the equilibrium price. Deviation Measurement — The indicator measures how far the current price has moved away from this midpoint. Range Normalization — Deviations are normalized relative to the size of the current range, allowing the oscillator to remain consistent across different volatility conditions. Momentum Compression — A hyperbolic tangent function compresses extreme values, stabilizing the oscillator and preventing runaway signals during large trends. Divergence Identification — Automatically identifies discrepancies between price action and the oscillator to highlight weakening momentum in established trends. 🔵 HOW IT WORKS 1️⃣ Equilibrium Range Calculation The indicator calculates the highest high and lowest low over the user-defined range length. The midpoint between these two levels forms the equilibrium line. This midline represents the center of balance for recent price activity. 2️⃣ Price Deviation Measurement The distance between the current close and the equilibrium midpoint is calculated. This deviation is then smoothed using a double EMA structure to reduce noise. The smoothed value is normalized relative to half of the current range size. 3️⃣ Nonlinear Oscillator Transformation A hyperbolic tangent function compresses normalized deviations into a stable range between -1 and +1. This transformation prevents extreme outliers and creates a more interpretable oscillator. 4️⃣ Momentum Histogram A signal line is generated using EMA smoothing. The difference between the oscillator and the signal line forms a histogram. The histogram behaves similarly to a MACD-style momentum indicator: Expanding bars indicate strengthening momentum. Contracting bars indicate weakening momentum. 5️⃣ Normal Divergence Logic Bullish Divergence: Occurs when price makes a Lower Low , but the Equilibrium Oscillator makes a Higher Low . This suggests that despite the price drop, the selling pressure relative to equilibrium is fading. Bearish Divergence: Occurs when price makes a Higher High , but the Equilibrium Oscillator makes a Lower High . This indicates that the buyers' ability to push price away from the midpoint is losing strength. 🔵 KEY FEATURES Equilibrium midpoint plotted directly on the chart. Range-normalized momentum oscillator. Hyperbolic tangent compression to stabilize signals. MACD-style histogram for momentum acceleration detection. Automatic Normal Divergence labels to spot exhaustion. Gradient-colored oscillator line reflecting directional bias. Dashboard displaying real-time momentum metrics. 🔵 DASHBOARD METRICS Shift — Current oscillator value showing how far price has moved from equilibrium. State — Market regime derived from oscillator thresholds: Bullish Bearish Neutral Range Position — Location of price inside the current range expressed as a percentage. Pressure — Magnitude of momentum deviation from equilibrium. 🔵 HOW TO USE Use the equilibrium midline as a dynamic balance reference. When the oscillator moves above zero, bullish momentum dominates. When the oscillator moves below zero, bearish momentum dominates. Trading Divergences: Watch for divergence labels when price is at historical range extremes. A bullish divergence near the "Lowest Low" of the range suggests a high-probability mean-reversion trade back toward equilibrium. Histogram expansions highlight momentum acceleration. Histogram contraction can signal potential momentum exhaustion. 🔵 INTERPRETING MOMENTUM SHIFTS Oscillator near zero → Market is balanced around equilibrium. Oscillator above 0.2 → Bullish momentum phase. Oscillator below -0.2 → Bearish momentum phase. Divergence Label + Oscillator Flatline → High probability of a trend reversal or deep pullback. Rapid oscillator expansion → Strong directional pressure. Oscillator flattening → Momentum compression or consolidation. 🔵 CONCLUSION Equilibrium Momentum Shift offers a structured way to analyze how price behaves relative to its recent balance point. By measuring normalized deviations from equilibrium and visualizing momentum shifts with a smoothed oscillator, histogram, and integrated divergence analysis , the indicator helps traders identify when markets transition from balance into directional movement. This makes it especially useful for spotting early momentum expansions, trend continuation signals, and potential exhaustion points where price is likely to snap back to its equilibrium midpoint.
Pine Script® indicator
by BigBeluga
10
AI Predictive Flow (Zeiierman)█ Overview AI Predictive Flow (Zeiierman) is a pattern-based oscillator that estimates future price direction by comparing the current market state to similar historical conditions. Instead of relying on traditional indicators like momentum or moving averages alone, the script builds a multi-feature representation of price behavior and uses a k-Nearest Neighbors (kNN) model to identify past patterns that closely resemble the present. From those matches, it derives an expected forward return, which is then transformed into a smooth oscillator and a predicted trend regime. The result is a forward-looking signal that reflects a data-driven expectation based on similar past patterns, not just current price movement. █ How It Works ⚪ Feature Extraction (Market State Model) The script converts price into a compact feature set that describes the current market state. It uses four core features: Short-term return Momentum RSI bias EMA spread These are created inside the feature function: feat(shift, mode) => c = close c1 = close cm = close ef = ta.ema(close, fLen) es = ta.ema(close, sLen) r = ta.rsi(close, rsiLn) float v = 0.0 if mode == 1 v := c1 != 0 ? math.log(c / c1) : 0.0 else if mode == 2 v := cm != 0 ? (c - cm) / cm : 0.0 else if mode == 3 v := (r - 50.0) / 50.0 else v := c != 0 ? (ef - es) / c : 0.0 v Each feature captures a different dimension of price behavior: return measures immediate movement momentum measures directional displacement RSI bias measures internal pressure EMA spread measures trend structure These values are then stacked across multiple bars to form the pattern used for comparison. ⚪ Pattern Memory (Historical Pattern Library) The script stores rolling sequences of each feature into separate matrices so the current market state can be compared against past states. That process is built here: pushFeat(mat, mode) => vals = array.new(tot, 0.0) for i = 0 to tot - 1 array.set(vals, tot - 1 - i, feat(i, mode)) cur = array.slice(vals, tot - len, tot) old = array.slice(vals, 0, len) matrix out = matrix.new(1, len, 0.0) for i = 0 to len - 1 matrix.set(out, 0, i, array.get(cur, i)) hist = array.new(len, 0.0) for i = 0 to len - 1 array.set(hist, i, array.get(old, i)) if mat.rows() >= mem mat.remove_row(0) mat.add_row(mat.rows(), hist) out This creates: a current feature row a rolling history of prior feature patterns So rather than comparing single-bar values, the model compares multi-bar pattern structure. ⚪ Pattern Matching Engine (kNN Distance Model) Once the current feature pattern is built, it is compared to all stored historical patterns. Distance is measured feature-by-feature across the full pattern length: getDist(matrix a1, matrix a2, matrix a3, matrix a4, matrix b1, matrix b2, matrix b3, matrix b4) => out = array.new(b1.rows(), 0.0) for i = 0 to b1.rows() - 1 s = 0.0 d1 = a1.diff(b1.submatrix(i, i + 1)).row(0) d2 = a2.diff(b2.submatrix(i, i + 1)).row(0) d3 = a3.diff(b3.submatrix(i, i + 1)).row(0) d4 = a4.diff(b4.submatrix(i, i + 1)).row(0) for j = 0 to len - 1 s += math.pow(d1.get(j), 2) * 0.25 + math.pow(d2.get(j), 2) * 0.25 + math.pow(d3.get(j), 2) * 0.25 + math.pow(d4.get(j), 2) * 0.25 out.set(i, math.sqrt(s)) out This produces a similarity score for every stored pattern. A smaller distance means the past setup looked more like the present one. ⚪ Prediction Model (kNN Forward Expectation) After the distances are ranked, the script selects the nearest neighbors and averages their future outcomes. The kNN model is implemented here: knn(dist, n) => ix = dist.sort_indices() useN = math.min(n, ix.size()) sumD = 0.0 avg = 0.0 for i = 0 to useN - 1 sumD += dist.get(ix.get(i)) if useN > 0 for i = 0 to useN - 1 d = dist.get(ix.get(i)) w = useN > 1 ? (sumD != 0 ? (1 - d / sumD) : 1.0) : 1.0 avg += Y.get(ix.get(i)) * w avg The forward return used for comparison is defined here: y := math.log(base) - math.log(base ) This represents the forward return following each historical pattern. The result is a weighted expectation of future movement, not just a reading of current trend. ⚪ Predictive Oscillator The raw kNN prediction is smoothed and transformed into the main oscillator and signal line. pred_ = ta.ema(pred, smth) if not na(pred) predSm := smth > 1 ? pred_ : pred osc = ta.ema(predSm, oscLn) sig = ta.ema(osc, sigLn) hist = osc - sig This creates: Oscillator = smoothed expected return Signal line = secondary smoothing for crossover confirmation Histogram = distance between oscillator and signal ⚪ Predicted Trend Regime Beyond the oscillator, the script also builds a broader trend regime using the predicted price path. First, the raw prediction is converted into a projected price line: predLine := base + base * (math.exp(pred) - 1) Then a regime band is created using ATR: hiRef = predLine + bandM * atr loRef = predLine - bandM * atr if ta.highest(hiRef, regLn) == hiRef trendUp := true if ta.lowest(loRef, regLn) == loRef trendUp := false This background state represents: bullish predicted regime when the projected path is pressing into new highs bearish predicted regime when the projected path is pressing into new lows So the background is not showing the raw price trend. It is showing the model’s predicted regime bias. █ How to Use ⚪ Read the Oscillator Above 0 → bullish expectation Below 0 → bearish expectation Near 0 → neutral/low conviction Far from 0 → strong directional push Use crossovers for entry timing: Bullish crossover → potential upward continuation Bearish crossover → potential downward continuation ⚪ Use the Predicted Trend Regime The background highlights the model’s broader directional bias: Green → predicted bullish regime Red → predicted bearish regime Regime shifts often indicate: early trend transitions continuation confirmation structural changes in expectation ⚪ Combine Signals Best use comes from alignment: Oscillator above zero + bullish regime + signal → strong continuation bias Oscillator below zero + bearish regime + signal → strong downside bias Divergence between the two → caution / mixed signals █ Settings Pattern Length – Controls how many bars define the current pattern. Higher values capture more structure, lower values increase responsiveness. Memory Size – Number of historical patterns stored for comparison. Larger values improve context but increase computation. Neighbors (k) – Number of closest matches used in prediction. Lower values are more reactive, higher values are smoother. Prediction Smoothing – EMA smoothing applied to the raw prediction. Reduces noise at the cost of lag. Signal Length – Smoothing of the signal line used for crossover signals. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script® indicator
by Zeiierman
4
KDJ L2: Advanced Momentum & Reversal StrategyThis strategy utilizes an optimized KDJ oscillator to identify high-probability momentum shifts. By refining the traditional stochastic approach, it captures the "sweet spot" of trend reversals and breakout continuations, filtering out market noise. Its edge lies in the sensitivity of the J-line, allowing for timely entries before the crowd catches on Long Entry: Triggered when the J-line crosses above the BuyAlertLimit. Exit Logic: Positions are closed either when the J-line crosses below the SellAlertLimit or when the J-line crosses below the K-line (suggesting momentum exhaustion) while above the 50-level. I built this tool to make trading more systematic. Your feedback is the engine of its evolution! Please share your backtest results in the comments—let me know which pairs and timeframes work best for you. If you have ideas for adding trailing stops or new filters, drop a comment below. Together, we can make this the most robust KDJ tool on the platform Disclaimer Trading involves significant risk of loss. This script is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always perform your own due diligence before trading with real capital
Pine Script® strategy
by MyStrategyHub
Quant Edge Ribbon PRO🟦 Quant Edge Ribbon PRO is a multi-kernel divergence ribbon indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). A primary kernel and eleven longer-bandwidth kernels form a dual-ribbon visualization driven by kernel regression mathematics, an integer trend score in , a theme-aware rendering pipeline, four user-configurable reference levels, and a PRO dashboard. All twelve kernels route through the unified library dispatcher, so the user may select any of the eight kernel families and any of the three filter modes from a single configuration panel. 🟦 HOW IT WORKS Quant Edge Ribbon PRO calls the KernelLens library's unified dispatcher (`kl.estimate`) twelve times per bar — once for the primary kernel and once for each of the eleven outer kernels: ``` primary = kl.estimate(type, src, ℓ, α, period, phase, filter) long00 = kl.estimate(type, src, ℓ + 1·s, α, period, phase, filter) long01 = kl.estimate(type, src, ℓ + 2·s, α, period, phase, filter) ... long10 = kl.estimate(type, src, ℓ + 11·s, α, period, phase, filter) ``` where ℓ is the Primary Bandwidth and s is the Bandwidth Step. All twelve kernels share the same kernel type, filter, shape α, period, and phase — only the bandwidth differs. This guarantees the ribbon behaves as a coherent spectrum of kernel scales rather than a mixture of unrelated signals. The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, division-by-zero guards, and input validation internally. Quant Edge Ribbon PRO does not reimplement any kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Quant Edge Ribbon PRO imports the published KernelLens library and uses the following export: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on the user's Kernel Type dropdown. Called twelve times per bar, once for the primary kernel and once for each of the eleven outer kernels. | Every regression computation — kernel weight evaluation, NA-safe summation, bandwidth-aware loop termination — is delegated to the library. The indicator itself contains zero kernel math; it only orchestrates twelve library calls and aggregates their outputs into the trend score. 🟦 THE TWELVE-KERNEL RIBBON ARCHITECTURE **Primary kernel (the shortest, the anchor)** — A single kernel at bandwidth ℓ that serves two roles: (1) it is the reference baseline for the trend score calculation, and (2) it is plotted as a dedicated highlighted anchor line when the "Highlight Primary Kernel" toggle is ON. **Eleven outer kernels (progressively wider)** — Kernels at bandwidths ℓ+1·s, ℓ+2·s, …, ℓ+11·s, where s is the Bandwidth Step (default: 1). Each outer kernel is plotted as a line on the main chart, all eleven sharing a single score-driven gradient color — so the entire outer ribbon shifts between the theme's bull and bear hues as the trend score moves between −11 and +11. **Eleven inner ribbon plots (lagged primary snapshots)** — The primary kernel plotted at eleven time-lag offsets (0, 1, 2, …, 10 bars). The resulting visual is a gently flowing shadow that makes expansion and contraction of the outer ribbon easier to perceive. Opacity is user-controlled (default: 40 %). 🟦 INTEGER TREND SCORE The trend score is a signed integer in , computed on every bar by eleven pairwise comparisons between the primary kernel (at progressive lag offsets) and the eleven outer kernels: ``` score = 0 for i in 0..10: if primary < long_i: score += 1 else: score -= 1 ``` **Semantic interpretation** — Each comparison pairs an older snapshot of the shortest kernel against a current snapshot of a progressively wider kernel. In an uptrend, past primary values are lower while current wider-kernel values have caught up above them — the inequality resolves positive on most pairs and the score climbs toward +11. The symmetric argument drives the score toward −11 in a downtrend. **Score parity** — Because the score is a sum of eleven ±1 terms (score = 2k − 11, k ∈ ), it is always odd. Reachable values: { −11, −9, −7, −5, −3, −1, 1, 3, 5, 7, 9, 11 }. The score is never exactly zero. 🟦 STRENGTH CATEGORIZATION The absolute score is bucketed into four bands, each with a matched label glyph used throughout the dashboard and the last-bar signal label: | Score Range | Strength | Label | |---|---|---| | \|score\| = 1 | NEUTRAL | ▰▱▱▱ NEUTRAL | | \|score\| ∈ {3, 5} | WEAK | ▰▰▱▱ WEAK BULL / WEAK BEAR | | \|score\| = 7 | STRONG | ▰▰▰▱ STRONG BULL / STRONG BEAR | | \|score\| ∈ {9, 11} | TRIPLE | ▰▰▰▰ TRIPLE BULL / TRIPLE BEAR | The bull / bear suffix is driven by the sign of the score. The progress-bar glyphs (▰▱) give an instant at-a-glance read of confluence intensity without needing to parse the numeric value. 🟦 NON-REPAINTING BEHAVIOR Quant Edge Ribbon PRO inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. A single Phase input (default: 2) shifts every one of the twelve kernel centers into the past by that many bars. - Phase = 0 — live estimate, flickers on the current bar (real-time only; history is immutable) - Phase = 1 — 1-bar lag, non-repainting once the bar is confirmed - Phase = 2 — recommended balance between freshness and stability (default) - Phase = 3+ — extra margin against erratic ticks, higher lag Historical repainting never occurs at any phase value. The library contains no `request.security` calls, no lookahead, and no array rotation that could leak future data. Every historical bar's plotted value is final once confirmed. 🟦 VISUAL PIPELINE **Outer Ribbon Gradient** — All eleven outer kernels are plotted with a single shared color driven by the trend score via `color.from_gradient(score, -11, 11, thBear, thBull)`. As the score walks across its range, the entire ribbon shifts continuously between the active theme's bearish and bullish hues — producing a smooth visual feedback loop between the math and the palette. **Inner Ribbon Shadow Trail** — Eleven lag-shifted primary snapshots (primary through primary ) drawn in the theme's accent hue with user-controlled opacity. On a trending chart the trail visually expands; on a reversing chart it contracts. Adjust opacity from 0 (invisible) to 100 (fully opaque) — default 40 balances presence and subtlety. **Primary Kernel Anchor Line** — The primary kernel plotted as a dedicated bold line in the theme's accent color at 80 % opacity, distinct from the shadow trail. Provides a clear centerline amid the ribbon flow. Toggleable. **Oscillator Subplot** — The smoothed trend score plotted in a dedicated subplot with a score-gradient vertical fill between the score line and the zero line. Opacity is user-controlled. An optional bold score line (up to 4 px wide) overlays the fill for sharp numeric reading. **Last-Bar Trend Label** — A right-anchored label at the current bar in the oscillator pane. Format: `▲ TRIPLE BULL 11 / 11` (bull) or `▼ WEAK BEAR −3 / 11` (bear). The label is deleted and redrawn on every bar, so only one instance is ever present on the chart. 🟦 OSCILLATOR STYLES The oscillator subplot ships with two visual presets, selectable from the Oscillator Style dropdown: | Style | Plot Style | Fill | Best For | |---|---|---|---| | Classic Gradient | `plot.style_line` (smooth curve) | Continuous vertical gradient from score to zero | Trend flow, slope momentum | | Stepline | `plot.style_stepline` (staircase) | Stepped gradient mirroring the discrete score plateaus | Signal / threshold trading, discrete level crossings | **Classic Gradient** produces a smooth curve traced through the smoothed score values, with the gradient fill flowing continuously between the score line and the zero line. This is the default and suits traders who read trend direction through slope and curvature. **Stepline** renders each bar as a horizontal plateau joined to the next bar by a vertical edge. Because the raw score is always an odd integer in { −11, −9, …, 9, 11 }, the staircase visualization honors the score's true discrete nature — making it easier to identify exact threshold crossings (e.g. the moment the score enters the ±9 extreme zone). The fill inherits the same stepline style, so the entire oscillator pane stays geometrically consistent. Both styles share the same opacity controls, score line toggle, and line width setting — only the geometry of the score line and its fill changes between them. 🟦 THEME SYSTEM Ten cohesive color palettes tuned to the Quant Edge Ribbon PRO optical brand. One selection drives every visual component — outer ribbon gradient, inner ribbon accent, oscillator fill, reference lines, signal label, dashboard accents — all sharing the same bull / bear / accent color axes: | Theme | Bull | Bear | |---|---|---| | Prism | Forest green | Crimson red | | Focus | Cyan steel | Deep orange | | Solar | Warm amber | Indigo red | | Frost | Sky blue | Soft lavender | | Laser | Neon lime | Hot crimson | | Aurora | Bright gold | Scarlet | | Plasma | Electric aqua | Magenta | | Bloom | Mint green | Hot pink | | Eclipse | Deep navy | Dark crimson | | Carbon | Near-black | Silver grey | The oscillator's zero line uses Pine's `chart.fg_color` so it auto-adapts to the actual chart background (white on dark charts, black on light charts) — independent of the Dashboard's Display Mode setting. 🟦 REFERENCE LEVELS Four user-configurable horizontal reference lines mark the score's structural thresholds inside the oscillator subplot: | Level | Style | Meaning | |---|---|---| | +11 / −11 | Dotted | Ceiling / floor — the mathematical maximum (every comparison aligned) | | +9 / −9 | Dashed | Extreme zone — nine or more of the eleven comparisons agree on direction | Each pair (±11 and ±9) has an independent opacity input (0–100 %). A master toggle (Show Reference Levels) collapses all four lines to fully transparent in a single branch — useful for minimalist layouts. An additional `showOsc` gate hides them automatically when the oscillator itself is disabled. 🟦 PRO DASHBOARD A 2-column, 12-row theme-aware status panel that updates only on the last bar (zero historical overhead). Supports Dark and Light display modes, six docking positions, and four text sizes. Renders via `force_overlay = true` on the main price chart. | Row | Label | Content | |---|---|---| | Header | Q-EDGE PRO | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Selected kernel type | | Divider | RIBBON | — | | Primary ℓ | Primary ℓ | Primary bandwidth value | | Span | Span | ℓ → ℓ + 11·s | | Filter | Filter | No Filter / Smooth / Zero Lag | | Divider | SCORE | — | | Score | Score | ▲/▼ + integer score + " / 11" (bull/bear colored) | | Bull / Bear | Bull / Bear | Bull comparison count / bear comparison count | | Strength | Strength | ▰-bar + NEUTRAL / WEAK / STRONG / TRIPLE label | | Primary | Primary | Primary kernel value in chart mintick format | **Bull / Bear breakdown** — The eleven pairwise comparisons split into bulls (resolved +1) and bears (resolved −1). Always sums to 11, so this row gives a direct visual of how many kernel scales agree with the net direction. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bullish Flip | Score crosses from ≤ 0 into positive territory | | Bearish Flip | Score crosses from ≥ 0 into negative territory | | Extreme Bullish | Score reaches +9 or higher (first entry into the zone) | | Extreme Bearish | Score reaches −9 or lower (first entry into the zone) | | Full Confluence Up | Score hits +11 — every outer kernel aligned bullishly | | Full Confluence Down | Score hits −11 — every outer kernel aligned bearishly | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Quant Edge Ribbon PRO: "` for easy parsing in downstream automation. 🟦 RECOMMENDED PRESETS | Style | Primary ℓ | Bandwidth Step s | Phase | Filter | Chart | |---|---|---|---|---|---| | Scalper | 8–16 | 1 | 1 | No Filter | 1m–5m | | Day Trader | 16–32 | 1–2 | 2 | Smooth | 15m–1h | | Swing | 25–50 | 1–2 | 2 | Smooth | 4h–1D | | Position | 50–120 | 2–3 | 3 | Smooth | 1D–1W | **Bandwidth Step tuning** — Step = 1 produces a tight ribbon where adjacent outer kernels sit visually close together. Steps of 2–4 spread the eleven outer kernels across a broader spectrum of scales, making expansion / contraction easier to read at a glance. Step 5–6 is reserved for very wide ribbons where each line represents a distinctly different time scale. 🟦 COMPATIBILITY - Pine Script v6 - All exchanges, all asset classes (crypto, forex, equities, commodities, indices) - All timeframes (1 minute through Monthly) - Both Dark and Light chart themes — visual elements auto-adapt via `chart.fg_color` - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression math is delegated to the published library - **Plot budget** — 11 outer + 11 inner + 1 primary + 2 oscillator plots + 1 fill + 4 hlines = well under Pine's 64-plot limit - **Table** — Single `var table` created once on `barstate.islast` with `force_overlay = true`; dashboard renders on the main chart pane, zero historical overhead - **No persistent drawing objects** — no `box.new`, `line.new`, no `array.new`; the single trend label is deleted and recreated every bar so only one instance is ever present - **Opacity convention** — every user-facing opacity input follows `0 = invisible, 100 = fully opaque`; conversion to Pine's native transparency is centralized in a single helper function (`f_opac`) - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead, no future-bar leakage at any phase value - **Chart background adaptive** — the oscillator's zero line uses `chart.fg_color`, so it always renders with high contrast regardless of the user's chart color scheme 🟦 DISCLAIMER Quant Edge Ribbon PRO is a technical analysis indicator built on the KernelLens Nadaraya–Watson regression library. It is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. The integer trend score is a geometric summary of kernel alignments — not a forecast — and should always be combined with broader context: higher-timeframe structure, volatility regime, liquidity, news, and risk management. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor. Responsibility for any trading decisions rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of Quant Edge Ribbon PRO or the underlying KernelLens library.
Pine Script® indicator
by a_jabbaroff
P3 Power Price & Money Flow Extremes Don’t Trade the Move. Understand It. P3 Power is a simple tool built to help you recognize when the market is in an extreme condition. It combines two components: • %R (blue) → measures how far price has stretched • MFI (green) → shows whether money is actually behind the move When both align, something important is happening. That doesn’t mean buy or sell — it means pay attention. HOW I USE IT This is not an entry indicator. It is a decision filter. I use it to answer: • Is price stretched? • Is money confirming the move? • Is this expansion or exhaustion? When both are at extremes, the market is in a high-energy state. From there, I rely on structure and location. USING WITH VWAP This is where it becomes powerful. • Extreme + far from VWAP → often reversion • Extreme + holding VWAP bands → often continuation • Extreme + VWAP rejection → strong reaction potential P3 Power tells you when something matters. VWAP helps decide what to do. MULTI-TIMEFRAME USE You can run %R and MFI on different timeframes. Examples to test: • Same timeframe → precise timing • Lower TF %R + higher TF MFI → early signal + confirmation • Higher TF MFI → larger exhaustion zones This is where the tool opens up — but it requires backtesting. IMPORTANT This indicator does NOT provide entry or exit signals. If you trade every extreme, you will get run over. Extremes can mean: • reversal • continuation • or nothing Use this with structure, VWAP, and your own rules. This tool is built to help you understand conditions — not chase signals.
Pine Script® indicator
by lacaze1102
Haar Wavelet RSI [Jamallo]Author's Note This is the oscillator companion to the Haar Wavelet Range Filter. Both indicators share the same MODWT Haar wavelet foundation, creating a synergistic effect when used together — trend and momentum derived from the same mathematical decomposition of price. Introduction The Haar Wavelet RSI is a momentum oscillator that replaces the standard price-difference input of a traditional RSI with wavelet detail coefficients derived from a Maximal Overlap Discrete Wavelet Transform (MODWT) with a Haar basis. Instead of measuring bar-to-bar price change, it measures momentum at a specific frequency scale — filtering out the noise above and below that scale before the RSI calculation even begins. How It Works MODWT Haar Wavelet Decomposition The script decomposes hl2 through up to 5 wavelet levels using a shift-invariant Haar transform. At each level, the detail coefficients capture the local price delta at that specific scale — Level 1 is 2-bar momentum, Level 3 is 8-bar momentum, Level 5 is 32-bar momentum. These detail coefficients become the input to the RSI in place of raw price change, meaning the oscillator is measuring directional energy at a single isolated frequency rather than the full noisy price series. Wavelet RSI Calculation The selected detail coefficient is split into its upward and downward components, smoothed with Wilder's RMA, and fed into the standard RSI formula. The math is identical to a conventional RSI — the only difference is what it is measuring. Because the detail coefficients are already frequency-isolated, the resulting RSI is inherently cleaner than applying RSI to raw price. Adaptive Deadband Step-Hold The wavelet RSI output is then passed through an adaptive deadband filter. The threshold is self-derived from the RSI's own recent volatility — a short rolling average of absolute RSI changes — scaled by the deadband multiplier. The RSI only updates when it departs from its held value by more than this threshold. Below the threshold it holds flat. This eliminates the continuous micro-oscillation that makes standard RSI difficult to read on noisy bars, producing a stepped line that moves with conviction or not at all. Closing This is not a standard RSI with cosmetic changes. The wavelet decomposition fundamentally changes what the oscillator is measuring — momentum at a defined frequency scale rather than raw bar-to-bar noise. The decomposition level is the most important setting: lower levels suit faster timeframes and shorter momentum cycles, higher levels filter more aggressively and are better suited to swing-level momentum reads.
Pine Script® indicator
by Jamallo22
Delta Pressure Gauge [JOAT]Delta Pressure Gauge Introduction Delta Pressure Gauge is a pane-based oscillator that constructs a volume-weighted directional wave from bar-by-bar delta estimation, normalized using a rolling maximum to ensure consistent scaling across all instruments and timeframes. The oscillator measures the pressure imbalance between buying volume and selling volume, smoothed into a wave that reveals accumulation and distribution phases with high visual clarity. The indicator includes a money flow pressure line, a cumulative windowed delta cloud, divergence detection, and crossover signal dots. Traditional volume indicators — OBV, CMF, MFI — measure volume flows using raw or price-weighted calculations that are difficult to compare across instruments or timeframes because their absolute values depend on the asset's volume profile. Delta Pressure Gauge normalizes everything to a -1 to +1 scale using a rolling maximum, producing readings that are immediately interpretable regardless of whether the asset trades 100 shares or 100 million. The wave design provides a visual rhythm that makes accumulation and distribution phases recognizable at a glance. Core Concepts 1. Body-Quality Weighted Bar Delta Each bar contributes a delta value based on direction (bullish = +volume, bearish = -volume) multiplied by the bar's body quality ratio (body size divided by total range). A full-body bar contributes 100% of its volume to delta. A doji bar with no body contributes 0%. This filtering reduces the noise contribution of indecision bars that add volume without directional information. body_qual = math.abs(close - open) / math.max(high - low, syminfo.mintick) bar_delta = bar_dir * volume * body_qual 2. Rolling Maximum Normalization The raw wave EMA is normalized by dividing by the rolling maximum absolute value over the normalization window. Unlike percentile-based normalization, rolling maximum works reliably from the first bar, requires no minimum warmup period, and produces values that are always within the -1 to +1 range: norm_ref = ta.highest(math.abs(raw_wave), i_norm) wt1 = raw_wave / math.max(nz(norm_ref, 1.0), 1.0) 3. Windowed Cumulative Delta Rather than using an all-time cumulative delta (which grows without bound and becomes dominated by early bars), the cumulative component uses a 30-bar rolling sum. This produces a medium-term delta bias that reflects the recent directional commitment of volume participants. 4. Money Flow Pressure Line A separate money flow calculation weights volume by the ratio of price movement to range: (close - open) / range × volume. This captures the efficiency of price movement relative to its volume cost — high-momentum bars have larger weights than range-bound bars. 5. Divergence Detection Bullish divergence is detected when the delta wave makes a higher low while price makes a lower low. Bearish divergence is the mirror. Detection uses confirmed pivot points on the wave with persistent previous-pivot storage, avoiding any ta.valuewhen type compatibility issues. Divergence lines are rendered directly on the oscillator pane. Features Wave Oscillator: Gradient area fill between wave and zero, color-coded by direction and intensity Signal Line: Smoothed signal with direction-colored rendering Histogram: Four-state colored momentum bars showing wave-signal separation and its rate of change Crossover Dots: Large circles with glow rings at every wave/signal crossover Zero-Line Cross Dots: Small markers when wave crosses the zero line Overbought/Oversold Extreme Dots: Markers at extreme readings Divergence Triangles and Lines: Yellow markers and connecting lines when divergence is detected Cumulative Delta Cloud: Area fill showing 30-bar rolling delta direction Money Flow Line: Purple secondary line for cross-confirmation Volume Surge Markers: Cross markers when volume exceeds 2x average 12-Row Dashboard: Pressure state, wave values, histogram, signals, cumulative delta, money flow, volume ratio, divergence state Input Parameters Wave Channel Length: Fast EMA for wave construction (default: 10) Wave Average Length: Signal line smoothing period (default: 21) Rolling Norm Window: Window for rolling maximum normalization (default: 100) Overbought/Oversold levels: Four configurable threshold lines Divergence pivot lookback settings How to Use This Indicator Crossover Dots as Momentum Shifts When the wave crosses above the signal line (green dot), buying pressure is accelerating relative to the smoothed baseline. This confirms a momentum pickup. The opposite for bearish crosses. These signals are strongest when they occur near or below the oversold line. Zero-Line Confirmation The wave crossing zero from below indicates that aggregate buying pressure over the wave window has turned net positive. This is a regime confirmation, not an entry signal in isolation, but it supports bullish bias when aligned with price structure. Divergence at Extremes Divergence is most meaningful when the wave is at or near an overbought or oversold extreme. A bullish divergence from the oversold zone (yellow triangle pointing up) suggests the distribution of buying pressure is shifting despite continued price weakness. Cumulative Delta Direction The blue-purple cloud shows whether the 30-bar rolling delta is net positive or negative. When the wave crosses bullishly and the cumulative delta is also positive, both the momentum and the persistent pressure agree. Limitations This indicator uses close-open direction to estimate bar delta. True bid-ask volume data (available only through specialized data providers) would be more precise. On instruments with significant wick activity (doji bars), this estimation introduces noise Normalization by rolling maximum means a single extreme bar sets the scale for the entire norm window. One unusually large volume bar will compress all surrounding readings Divergence detection requires enough bars for pivot confirmation. The pivot right-side lookback introduces a lag in divergence signals This indicator measures volume pressure proxies, not actual institutional activity. Large volume does not always reflect institutional intent Originality Statement The body-quality weighting applied before delta smoothing is a deliberate design choice that reduces doji noise in a way that raw-volume or typical-price approaches do not. The rolling maximum normalization (rather than percentile or z-score) was chosen specifically because it operates reliably from the first bar without a warmup cliff, making the indicator immediately usable on limited datasets. The combination of a wave oscillator, cumulative delta cloud, and money flow line on a single pane provides three independent perspectives on the same underlying volume pressure question. Disclaimer This indicator is for educational and informational purposes only. Volume pressure readings are estimates derived from OHLCV data. They do not represent actual order flow or institutional positioning. Past divergence patterns do not predict future price reactions. Always apply appropriate risk management. -Made with passion by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
Fluid Momentum OscillatorFluid momentum oscillator complete description & user manual Fluid momentum oscillator visualizes momentum behavior and market sentiment across multiple lookback periods. It is designed as an analytical tool to support interpretation of market conditions and does not replace decision-making. Nothing in this indicator has been copied; any script errors have been fixed with Quant.AI and Claude.AI. Feel free to get ideas from the script and possibly adapt it to your own ideas or strategies. 1. Overview Fluid momentum oscillator is a high-resolution momentum indicator that maps sentiment across 20 simultaneous lookback periods, forming a continuous ribbon that reflects momentum strength and direction. Built on rsi processed through zero lag ema smoothing, the oscillator responds early to shifts in momentum compared to standard indicators. Each strand represents a different perspective of momentum. together they provide a structured view of expansion, compression, and transition. 2. Core components 2.1 Rsi — relative strength index Calculated across 20 different lookback periods. each strand starts from the base length and increases using the defined step, creating a range from fast to slow momentum perspectives. Values are normalized around a zero midline on a −50 to +50 scale. 2.2 Zlema — zero lag ema smoothing Applied to each rsi strand to reduce lag. this method incorporates recent change before smoothing, allowing faster response to shifts. 2.3 Trend detection — fast vs slow groups The ribbon is divided into: - fast group (strands 1–5) - slow group (strands 16–20) When fast leads slow, momentum is bullish. When fast lags slow, momentum is bearish. When the spread compresses below the twist threshold, the oscillator enters a neutral or transition state. 2.4 Momentum histogram Shows the difference between fast and slow averages near the midline. Wide bars indicate strong separation and directional momentum. Narrow bars indicate compression or transition. 2.5 Ribbon slope Measures the rate of change of the fast group average. Rising slope indicates increasing momentum. Falling slope indicates decreasing momentum. Flat slope indicates consolidation or transition. 2.6 Divergence detection Compares price pivots with fast average pivots over a defined window. Bullish divergence occurs when price makes a lower low while momentum holds higher. Bearish divergence occurs when price makes a higher high while momentum weakens. These conditions can highlight potential shifts beneath price structure. 3. Presets Default | swing — 4h / daily Rsi base 10 | zlema 5 | step 2 | twist 1.5 Balanced configuration for general trend conditions. Fast | scalping — 1m to 15m Rsi base 7 | zlema 3 | step 2 | twist 1.0 Shorter lookbacks for faster response. Smooth | position — daily / weekly Rsi base 14 | zlema 8 | step 3 | twist 2.5 Longer periods with reduced sensitivity. 4. Visual guide ribbon expanding upward — bullish momentum increasing ribbon expanding downward — bearish momentum increasing ribbon compressing — low momentum or transition background grey — compression state green flash — bullish expansion red flash — bearish expansion divergence highlights: - warm tones — bullish divergence - red tones — bearish divergence histogram — momentum strength relative to midline slope line — acceleration or deceleration 5. Usage — lower timeframes recommended: - use fast preset - monitor ribbon direction and compression - observe slope for acceleration or fading typical observations: - compression before expansion - slope flattening before reversals - divergence forming before price reacts 6. Usage — higher timeframes recommended: - default preset for intraday higher timeframes - smooth preset for daily and above reading structure: - sustained expansion indicates stable momentum - compression during narrowing price action indicates transition - divergence on higher timeframes carries more weight 7. Dashboard reference momentum — bullish / bearish / neutral fast avg — average of fastest strands slow avg — average of slowest strands spread — distance between fast and slow twist — compression state zone — relative position vs extremes slope — acceleration state divergence — active or none signal — last detected condition 8. Alerts available conditions: - bullish state change - bearish state change - compression state - bullish divergence - bearish divergence - any state change alerts include ticker and timeframe context. 9. Disclaimer This indicator is provided for educational purposes only. All outputs are based on historical price calculations and do not guarantee outcomes. Trading involves risk. Past performance does not indicate future results. Use at your own discretion.
Pine Script® indicator
by Anonycryptous
Market Structure Navigator [JOAT]Market Structure Navigator Introduction Market structure is one of the most widely discussed concepts in technical analysis, yet most tools that attempt to visualize it reduce swing highs and swing lows to single price points. In practice, price rarely reverses at a precise tick level — it reacts within a zone , which may span several ATR units depending on the instrument and timeframe. This distinction matters: treating a level as a point rather than a zone leads to false breaks being mistaken for genuine structural shifts. The Market Structure Navigator addresses this by modeling every significant swing high and swing low as an ATR-based zone with measurable thickness. It tracks two layers of structure simultaneously — external (major) pivots that define the larger-degree trend, and internal (minor) pivots that provide context within that trend. Each zone passes through a three-state lifecycle, and structural breaks are automatically classified as either Break of Structure (BOS) or Change of Character (CHoCH), giving you an immediate read on whether a break confirms the existing trend or signals a potential reversal. This is an overlay indicator — all elements are drawn directly on the price chart. --- Core Concepts Dual-Layer Structure: External and Internal The indicator identifies two categories of structural pivots using TradingView's built-in pivot functions: External structure (major): Pivots requiring a configurable number of bars on each side (default 20). These are the significant swing highs and lows that define the broader trend context. They update less frequently and represent the higher-degree market structure. Internal structure (minor): Pivots requiring fewer bars on each side (default 7). These fire more frequently and capture the sub-swings that occur within the larger structural moves. This dual-layer approach mirrors how institutional analysis treats structure: external levels define the direction of the larger trend; internal levels provide precision context for entries and exits within that trend. Zone Geometry and ATR Thickness Rather than marking a pivot as a single horizontal line, each pivot is rendered as a rectangular zone. The zone's top and bottom are calculated as: zoneTop = pivotPrice + (ATR * zoneAtrMult) zoneBottom = pivotPrice - (ATR * zoneAtrMult) The default ATR multiplier is 0.5, meaning the zone extends half an ATR above and below the pivot price. This thickness is dynamic — it adjusts to the current volatility of the instrument rather than using a fixed pip or point value. Zones extend to the right as new bars form, keeping them visible as price approaches. Three-State Zone Lifecycle Every zone passes through three possible states: Active (State 0): The zone has formed and not yet been tested. It extends to the right as bars pass, representing untested support or resistance. This is the most significant state — an active zone has not had its interest absorbed. Swept (State 1): Price has reached the midpoint of the zone (configurable as either a wick touch or a close through the midpoint). A swept zone has been tested and shows that some activity occurred at that level. It may have weakened but is not yet confirmed as broken. Broken (State 2): Price has closed decisively beyond the zone boundary (above the top for a resistance zone, below the bottom for a support zone). A broken zone stops extending. This state marks the structural failure of that level. This lifecycle gives actionable information that a single horizontal line cannot. An active zone is very different from a swept zone even if they appear at the same price level. BOS and CHoCH Classification When price breaks through the last significant external high or low, the indicator determines whether the break is: Break of Structure (BOS): The break occurs in the same direction as the current trend. A bullish BOS is a higher high that extends a confirmed uptrend; a bearish BOS is a lower low extending a downtrend. BOS signals trend continuation. Change of Character (CHoCH): The break occurs against the current trend direction. A CHoCH in a downtrend means price has broken above the last significant swing high — a structural signal that the trend may be reversing. CHoCH signals a potential regime shift, not a confirmed reversal. Both external and internal structural breaks are tracked separately. An internal CHoCH during an external downtrend, for example, may represent a short-term countertrend move within a larger bearish structure — context that a single-layer tool would miss. Momentum Normalization Momentum is calculated as the normalized price change: momentum = priceChange / stdev(close, lookback) This expresses the size of recent price movement in terms of its own standard deviation, making the momentum reading comparable across different instruments and volatility regimes. Target Arrows When an internal structural break occurs, the indicator draws a directional arrow label pointing toward the next significant external level. This provides a visual read on where price may be targeting within the larger structural context. --- Features Dual-layer structure detection: external major pivots and internal minor pivots tracked independently ATR-dynamic zone thickness — zones adapt to current instrument volatility Three-state zone lifecycle: Active, Swept, Broken — each visually distinct Automatic BOS vs. CHoCH classification for both external and internal breaks Configurable sweep mode: wick-based (high/low touches midpoint) or close-based (close through midpoint) Equal high/low detection with configurable ATR tolerance to flag double tops/bottoms Target arrows on internal breaks pointing toward the next external level Dashboard table showing trend state, momentum, active zone counts, last known structural levels, and last break type Full visual toggle controls for all overlay elements Maximum zone count cap per type to maintain chart performance --- Dashboard Table A table displayed at the top-right of the chart provides a live summary of structural conditions: Ext Trend: Current external trend direction (Bullish / Bearish) Int Trend: Current internal trend direction Momentum: Normalised momentum value Ext Zones Active: Number of currently active external zones Int Zones Active: Number of currently active internal zones Last Ext High / Low: Price level of the most recent significant external pivot high and low Last Break: The most recent break type (eBOS, eCHoCH, iBOS, iCHoCH) --- Color System External high zones: red External low zones: white Internal high zones: teal Internal low zones: blue BOS labels: green CHoCH labels: red This color separation allows you to immediately distinguish between the two structural layers without reading labels. --- Input Parameters External Pivot Length (default 20) — bars required on each side for a major pivot to confirm Internal Pivot Length (default 7) — bars required on each side for a minor pivot to confirm ATR Length (default 14) — ATR period used for zone thickness calculation Zone ATR Multiplier (default 0.5) — controls how wide each zone is relative to ATR Equal H/L Tolerance (default 0.3 ATR) — how close two pivots must be to be considered equal highs or lows Sweep Mode (Wick / Close) — whether zone sweeps are triggered by wick touch or candle close through the midpoint Max Zones Per Type (default 40) — caps the number of active zones drawn per category to preserve chart performance BOS Lookback (default 30) — bars looked back when checking for structural breaks Visual Toggles — individual controls for zones, BOS/CHoCH labels, target arrows, and the dashboard table --- How to Use Apply to any chart. The indicator will begin plotting zones as enough bars form to confirm pivots at the chosen lengths. Allow sufficient historical bars to load so that the external pivots have time to confirm — with extLen set to 20, a pivot requires 20 bars after the swing point before it is officially plotted. Read the external layer first. The external trend (eBOS/eCHoCH sequence) tells you the larger-degree direction. Trade in alignment with this unless you have strong reason to fade it. Use the internal layer for timing. Internal CHoCH events within an external uptrend can signal pullback entries; internal BOS events in the direction of the external trend can confirm continuation. Active zones are the most significant. A first-touch of an active zone is generally more meaningful than a return to a swept or broken zone. When price approaches a zone, check the sweep mode setting and monitor whether price is reaching the midpoint (sweep) or closing through the boundary (break). The target arrow on an internal break shows the next external level — this is a reference, not a guaranteed target. --- Limitations Pivot detection is inherently lagging. A pivot at bar N is only confirmed after N + extLen (or intLen) additional bars have formed. This means zones appear after the fact — the exact pivot price was in the past by the time the zone is drawn. With longer pivot lengths, the indicator requires more historical bars to display meaningful structure. On short lookback windows or when first applied to a chart, the initial display may show few or no zones until sufficient pivot confirmation has accumulated. ATR-based zone thickness means zone width changes with volatility. During high-volatility periods, zones become wider and may overlap. During compressed volatility, zones are narrower. Adjust the ATR multiplier if zones are too wide or too narrow for your preferred trading style. The BOS/CHoCH classification is based on the direction of the previous break relative to the current one. In choppy, range-bound markets, rapid BOS/CHoCH alternation is possible and does not necessarily indicate a trend — it may simply reflect noise. Zone lifecycle states reflect price behavior relative to the zone midpoint and boundary. They do not predict whether a swept zone will hold or whether a broken zone will become a new support/resistance level. Maximum zone count limits (default 40 per type) are necessary to maintain chart performance. On instruments or timeframes with many pivot formations, older zones will be removed as new ones are added. No indicator can classify market structure with certainty in real time. CHoCH signals a potential reversal; it does not confirm one. Always apply additional judgment. --- Originality Statement The distinguishing design decisions in this indicator are: zone-based pivot modeling, the three-state lifecycle, and the integrated dual-layer structure system. Most structure tools mark a swing high or low as a horizontal line at a single price. This indicator models each pivot as a zone with ATR-derived thickness, acknowledging that price reactions do not occur at a tick but within a range that varies with the instrument's current volatility. A 0.5 ATR zone around a pivot is a more honest representation of where liquidity and interest are likely to cluster. The three-state lifecycle (Active → Swept → Broken) adds information that static horizontal lines cannot convey. An active zone that has never been tested is categorically different from one that has seen a wick test — and both are different from a zone that has been fully broken. Treating these three states identically discards relevant context. The dual-layer architecture (external major + internal minor) is not simply running the same algorithm twice at different sensitivities. The external layer provides the trend framework; the internal layer provides the tactical sub-structure within it. The BOS/CHoCH classification connects these two layers — an internal CHoCH is interpreted in the context of the external trend direction, not in isolation. --- Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any security. All trading involves risk, including the possible loss of principal. Past indicator performance does not guarantee future results. Always conduct your own research and consult a qualified financial professional before making any trading decisions. -Made with passion by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
Aura Trend Cloud Navigator [Pineify]Aura Trend Cloud Navigator This indicator builds a trend-following system around a Trend Regularity Adaptive Moving Average — an adaptive MA that adjusts its responsiveness based on how frequently price makes new period highs or lows. When the market trends, the baseline tracks price closely; when it ranges, the baseline goes nearly flat, cutting down on false crossover signals. An ATR-based volatility cloud wraps around the baseline to frame the trend's expected range. Key Features Adaptive baseline that self-adjusts speed — fast in trends, nearly frozen in ranges Dynamic ATR cloud that expands and contracts with real-time volatility Gradient-filled cloud with automatic color shifts between bullish and bearish states Crossover-based buy/sell signals filtered by the adaptive regime detection Bar coloring for quick visual trend identification How It Works The core is a recursive adaptive moving average controlled by a trend coefficient derived from market structure: On each bar, the script checks whether a new highest high or lowest low has been established over the lookback period (default 99 bars). If either condition is met, a binary signal of 1 is recorded; otherwise 0. The average of this binary signal over the same period is computed via SMA, then squared. Squaring creates a nonlinear response — the coefficient drops sharply when trend activity fades and rises steeply when price consistently prints new extremes. This squared value becomes the smoothing factor in a recursive formula: baseline = baseline + coefficient × (close − baseline) . A high coefficient pulls the baseline toward price; a near-zero coefficient locks it in place. An Average True Range envelope is then added above and below the baseline, scaled by a user-defined multiplier. This forms the cloud — a corridor representing expected price movement around the trend. How the Components Work Together The adaptive baseline and the ATR cloud serve complementary roles. The baseline handles trend direction and generates entry signals, but because it adapts to market regime, it sidesteps the main weakness of fixed-period moving averages: constant whipsawing during consolidation. The cloud adds a volatility dimension on top of that directional read. When price stays inside the cloud, it is oscillating within the normal range of the current trend. Price pushing beyond the cloud edges may signal overextension or trend acceleration. In short, the baseline answers "which direction" while the cloud answers "how far is normal." Trading Ideas and Insights When the baseline shifts color and a triangle signal fires, treat it as a potential trend change. Confirmation improves if the crossover happens near or beyond the opposite cloud edge, suggesting genuine momentum rather than a range poke. In a bullish regime, price hugging the upper cloud boundary suggests strong follow-through. A pullback toward the baseline that holds can offer a continuation entry — especially if the baseline remains clearly sloped upward. Watch for the baseline going flat while the cloud tightens — this is the indicator explicitly telling you to stand aside. Crossovers that occur while the baseline is flat carry less conviction than those during a clearly sloped baseline, since the adaptive mechanism has detected a low-regularity environment. Unique Aspects The trend regularity approach counts how often new period extremes occur rather than measuring price direction or momentum directly. Most adaptive MAs rely on efficiency ratios or volatility scaling; this one responds to the structural behavior of the market itself — whether it is consistently making progress or stalling out. Squaring the trend coefficient means the MA doesn't merely slow down in ranges — it practically stops. This creates clear visual separation between trending and non-trending regimes that is immediately readable on the chart. The gradient cloud gives volatility context without the visual clutter of multi-line band systems. One glance tells you both direction and how stretched the current move is. How to Use Add the indicator to your chart. The thick colored line is the adaptive baseline — green for bullish, red for bearish. Triangle markers appear at crossover points: upward green triangles below bars for potential long entries, downward red triangles above bars for potential short entries or exits. Use the cloud as context. Price within the cloud is behaving normally for the trend. Moves beyond the cloud edges are stretched and may revert or signal acceleration. On higher timeframes (4H, Daily), the default 99-period length works well as a medium-term trend filter. On lower timeframes, consider reducing the length for faster adaptation — though this increases sensitivity to noise. Customization Trend Filter Length (default: 99) — How many bars are used to assess trend regularity. Higher values produce a smoother, slower baseline that only reacts to sustained directional moves. Lower values increase sensitivity but also raise whipsaw risk in choppy markets. Cloud Width / ATR Multiplier (default: 2.0) — Scales the ATR-based cloud. Wider values capture more price action within the corridor; tighter values make cloud-edge touches more frequent and potentially more meaningful as overextension signals. ATR Length (default: 14) — Period for the Average True Range calculation. The standard 14 suits most instruments. Increase it for smoother cloud behavior on volatile assets. Conclusion The Aura Trend Cloud Navigator provides a regime-adaptive trend filter that goes quiet when the market goes quiet. The TRAMA baseline combined with an ATR cloud delivers both directional guidance and volatility context in a single overlay. It suits swing and position traders looking for a trend filter that avoids generating noise during consolidation — though the adaptive smoothing does introduce lag at trend reversals, so pairing it with a momentum oscillator for timing may improve entries on faster-moving instruments.
Pine Script® indicator
by Pineify
RSI Transition Speed: Oversold to Overbought Session CounterDescription: Most traders watch where the RSI is. This indicator measures how fast it gets there. RSI Transition Speed tracks the number of bars (sessions) it takes for the RSI to travel from oversold territory to overbought territory — and vice versa. Transition velocity is a proxy for momentum quality: a fast OS→OB crossing signals explosive buying pressure, while a slow one may indicate a weak, unconvincing recovery. How it works The indicator starts a counter the moment RSI exits the oversold zone (default: below 30) and stops it when RSI enters the overbought zone (default: above 70). The same logic applies in reverse for OB→OS transitions. If RSI re-enters the origin zone before completing the crossing, the counter resets — only clean, directional transitions are counted. What you get RSI pane with dynamic color coding: red in oversold, green in overbought, purple in neutral Live histogram showing the current in-progress transition bar count in real time Labels on each completed transition showing the exact number of bars elapsed Stats table with Count, Last, Average, Min, and Max transition speed for both directions (OS→OB and OB→OS) Live status row in the table showing the active transition counter Inputs RSI Length (default: 14) RSI Source (default: Close) Oversold Level (default: 30) Overbought Level (default: 70) Show/hide labels Table position Use cases Identify whether the current rally or selloff has momentum behind it Compare historical transition speeds to contextualize the current move Combine with volume or price action to confirm fast transitions as high-conviction signals Filter out slow, grinding RSI crossings from high-velocity ones in systematic strategies Works on any asset and any timeframe.
Pine Script® indicator
by pcingari
MULTIVITAMIN1. What is MULTIVITAMIN? MULTIVITAMIN is a custom-built, open-source algorithmic scalping indicator designed for TradingView. Its primary goal is to capture high-probability momentum trades by combining pure Price Action (PA) mechanics with advanced trend and volatility filters, specifically engineered to avoid "chop" (ranging/flat markets) and deliver healthy, actionable signals. 2. What does it cover? (Core Modules) Price Action Engine: The core of the system. It detects real-time structural market shifts by identifying Engulfing patterns, Order Blocks (OB) with volume confirmation, and Fair Value Gaps (FVG). Trend & Momentum Filters: Utilizes a multi-timeframe Hull Moving Average (HMA) for signal smoothing, a 50-period EMA for macro trend direction, and an ADX filter to ensure the market has sufficient momentum before entering a trade. Pro Walls (Anti-Chop System): Features advanced, optional defensive mechanisms: an ATR-based volatility filter (ensuring candle size is larger than average market noise) and an EMA slope filter (ensuring the trend line is actually pointing up or down, not moving sideways). Volume Supported Breakout (BKR): An optional hybrid feature that looks for extreme volume spikes (compared to a 20-period SMA) combined with local high/low breakouts. Lifecycle Management: A built-in state machine (Idle, Long, Short) that tracks the trade's status, preventing overlapping entries and managing exits based on user-defined criteria. 3. What is its operational logic? Step 1: Scanning & Setup: The script continuously monitors price action for base entry conditions. Step 2: The Gauntlet (Filtering): A setup must pass through all active user filters (EMA, ADX, Volatility, etc.) before being validated. Step 3: State Execution: If all conditions align, the internal bot state shifts from 0 (Idle) to 1 or -1, plotting an ENTER shape on the chart and triggering an external webhook alert. Step 4: The Exit: The bot remains in this state until a valid exit condition (like a reverse PA signal) is met. 4. ⚠️ CRITICAL: How to Set Up Alerts (The Anti-Repainting Rule) ⚠️ If you are using the "Exit on Reverse Signal" (Ters Sinyalde Çık) feature in the settings, you MUST configure all your TradingView alerts (both ENTER and EXIT alerts) to trigger "Once Per Bar Close". Why? If you set them to "Once Per Bar" (anlık tetikleme), intra-bar price fluctuations (wicks) will trigger premature exit/entry orders to your exchange before the candle officially closes, leading to repainting on the chart and ghost orders in your account. Always wait for the candle to close to confirm the structural shift!
Pine Script® indicator
by lisabyco
1
Average Price History CandleThe Average Price History Candle indicator (APHC) is my own invention and is entirely new. It's calculated from a time period, specified by a number of bars, hours or days. It's basically one candle calculated from the average price history, with the high and low being that of the specified time period. The open is the average (SMA) of the first half of the time period. The close is the average (SMA) of the second half of the time period. The plot is the size of the candle body expressed as a percentage of the whole price range, with positive being bullish and negative bearish. It generally resembles a sine wave more or less, as the momentume shifts from bullish to bearish and back again. Because it's fairly consistant and predictable, it gives a good indication where the market momentum is going next. It also seems to work quite well with divergences. Using a time period of a very few bars, say 6 or 8, it behaves more like an RSI or Stochastic. But to be honest, I've not had much chance to explore all the different ways in which this indicator could be used to provide useful trading information. Please leave ideas in the comments if you find ways in which you find it useful. Thanks.
Pine Script® indicator
by JustinTimeTunes
Spectra Inflection [JOAT]Spectra Inflection Introduction Spectra Inflection is an advanced open-source momentum oscillator that replaces conventional RSI with a Laguerre-domain filter, applies Jurik Moving Average (JMA) adaptive smoothing, and overlays a Zero-Lag EMA (ZEMA) signal line to produce a momentum reading with substantially less lag and noise than standard oscillators. The indicator then layers on Schmitt trigger state transitions, dynamic VWMA bands, gradient histogram rendering, momentum divergence detection, velocity and acceleration tracking, squeeze detection, exhaustion signals, and a comprehensive 16-row dashboard — all in a single pane. This indicator exists because traditional momentum oscillators like RSI suffer from two fundamental problems: lag and noise. Lag causes late entries and exits. Noise causes false signals in choppy markets. Spectra Inflection addresses both by combining a Laguerre filter (which compresses price history into a shorter effective window without losing smoothness) with JMA adaptive smoothing (which tracks fast moves closely while filtering out chop). The result is a momentum curve that responds to genuine trend shifts quickly while remaining stable during consolidation. Core Concepts 1. Laguerre RSI Core The Laguerre filter is a four-element recursive filter originally developed by John Ehlers. Unlike a standard RSI that uses a fixed lookback window, the Laguerre filter uses a damping factor (alpha) to create an exponentially-weighted cascade of four internal registers (L0 through L3). This produces a smoother, more responsive oscillator: float gamma = 1.0 - alpha L0 := alpha * close + gamma * nz(L0 ) L1 := -gamma * L0 + nz(L0 ) + gamma * nz(L1 ) L2 := -gamma * L1 + nz(L1 ) + gamma * nz(L2 ) L3 := -gamma * L2 + nz(L2 ) + gamma * nz(L3 ) The cumulative up/down movements across all four registers are then computed to derive an RSI-like value scaled 0-100. Lower alpha values produce smoother output (more filtering), while higher values produce faster response. The default alpha of 0.07 provides a balance between responsiveness and noise rejection. 2. JMA Adaptive Smoothing The raw Laguerre RSI output is then passed through a Jurik Moving Average, which is a proprietary-class adaptive filter. JMA uses a volatility-tracking mechanism to adjust its smoothing dynamically: when the input is volatile, JMA tracks more closely; when the input is stable, JMA smooths more aggressively. This means the momentum line hugs genuine reversals tightly while filtering out noise during consolidation. The JMA implementation uses three parameters: period (smoothing length), phase (lead/lag adjustment), and power (responsiveness curve). 3. ZEMA Signal Line A Zero-Lag EMA is calculated on the JMA-smoothed momentum line. ZEMA works by computing two EMAs and extrapolating the difference to cancel out the inherent lag: ema1 = ta.ema(src, len) ema2 = ta.ema(ema1, len) zema = ema1 + (ema1 - ema2) Crossovers between the momentum line and the ZEMA signal line generate potential entry and exit signals. The indicator scores each crossover based on the angle of approach, distance from the midline, and volume context to produce a "cross quality" rating. 4. Schmitt Trigger State Machine Rather than using simple threshold crossings (which produce whipsaws), the indicator uses a Schmitt trigger — a hysteresis-based state machine where the entry threshold differs from the exit threshold. For example, the momentum line must cross above 62 to enter a bullish state, but must drop below 55 to exit it. This prevents rapid flip-flopping in choppy conditions and produces cleaner, more tradeable state transitions. 5. Dynamic VWMA Bands Volume-Weighted Moving Average bands are calculated around the momentum line. These bands expand when volume is high (indicating conviction) and contract when volume is low (indicating indecision). Price touching or exceeding the bands while momentum is extended signals potential exhaustion or continuation depending on the volume context. Features Gradient Histogram: A color-gradient histogram below the momentum line shows the distance from the midline (50). Colors shift smoothly from muted near the center to vivid at extremes, providing instant visual feedback on momentum intensity without cluttering the chart Neon Glow Rendering: The main momentum line uses a multi-layer plot technique where progressively wider, more transparent copies of the line are stacked to create a subtle glow effect that intensifies with momentum strength Momentum Divergence Detection: The indicator detects both regular and hidden divergences using fractal pivot anchoring. When price makes a new high but the Laguerre RSI makes a lower high (bearish divergence), or price makes a new low but the oscillator makes a higher low (bullish divergence), the indicator draws divergence lines and labels Velocity and Acceleration Tracking: First and second derivatives of the momentum line are calculated and smoothed. Velocity shows the rate of momentum change; acceleration shows whether momentum is speeding up or slowing down. These are displayed in the dashboard OB/OS Exhaustion Detection: When momentum reaches extreme overbought or oversold levels with declining velocity, the indicator flags potential exhaustion points where reversals are more likely Cross Quality Scoring: Each momentum/signal crossover is scored 0-100 based on the angle of the cross, distance from the midline, and whether volume confirms the move. Higher scores indicate higher-conviction crosses Band Squeeze Detection: When VWMA bands contract below a threshold, the indicator identifies a "squeeze" condition — compressed momentum that often precedes a sharp expansion move Midline Conviction Signals: Crosses of the 50 midline are tracked with volume confirmation to identify shifts in the underlying momentum bias Momentum Regime Classification: The dashboard classifies the current momentum state as Trending Bull, Trending Bear, Ranging, or Transitional based on the composite of all sub-systems 16-Row Dashboard: A comprehensive real-time table displays Laguerre RSI, JMA momentum, ZEMA signal, state, velocity, acceleration, cross quality, band width, squeeze status, divergence history, regime classification, and more Input Parameters Laguerre Core: Alpha: Damping factor for the Laguerre filter (default: 0.07). Lower = smoother, higher = faster JMA Smoothing: Period: JMA smoothing length (default: 8) Phase: Lead/lag adjustment from -100 to +100 (default: -50) Power: Responsiveness curve (default: 0.6) Signal Line: ZEMA Length: Period for the zero-lag signal line (default: 13) State Thresholds: Bull Entry/Exit: Schmitt trigger thresholds for bullish state (default: 62/55) Bear Entry/Exit: Schmitt trigger thresholds for bearish state (default: 38/45) VWMA Bands: Band Length: VWMA calculation period (default: 20) Band Width: Multiplier for band distance (default: 1.5) Visuals: Toggles for histogram, glow, divergence lines, bar coloring, background zones, squeeze markers, and dashboard How to Use This Indicator Step 1: Identify the Momentum Regime Check the dashboard's regime classification. In trending regimes, look for pullback entries in the direction of the trend. In ranging regimes, look for mean-reversion setups at the VWMA band extremes. Step 2: Wait for Schmitt Trigger State Transitions Rather than acting on every oscillator wiggle, wait for the Schmitt trigger to confirm a state change. A transition from neutral to bullish (momentum crossing above the bull threshold with hysteresis) is a higher-conviction signal than a simple RSI crossing 50. Step 3: Confirm with Cross Quality When a momentum/signal crossover occurs, check the cross quality score. Scores above 60 indicate strong, angled crosses with volume confirmation. Scores below 30 suggest weak, flat crosses that are more likely to fail. Step 4: Watch for Divergences Divergences between price and the Laguerre RSI often precede reversals. Regular divergences signal potential trend changes; hidden divergences signal trend continuation. Use these in conjunction with the regime classification for context. Step 5: Monitor Squeeze and Exhaustion Band squeezes indicate compressed momentum — prepare for a breakout. Exhaustion signals at OB/OS extremes with declining velocity suggest the current move is losing steam. Indicator Limitations Like all momentum oscillators, this indicator is a lagging derivative of price. It confirms moves rather than predicting them The Laguerre filter's alpha parameter significantly affects behavior — values that work well on one timeframe or instrument may need adjustment for others Divergence detection uses fractal pivots which require a right-bar confirmation delay (default 5 bars). Divergences are identified after the fact, not in real-time The Schmitt trigger prevents whipsaws but also delays state transitions. In fast-moving markets, the state change may come after a significant portion of the move has already occurred Volume-based features (VWMA bands, cross quality scoring) work best on instruments with reliable volume data. On forex or instruments with synthetic volume, these features may be less meaningful This is a momentum tool, not a complete trading system. It should be combined with trend structure, support/resistance, and risk management for actual trading decisions Originality Statement This indicator is original in its synthesis of multiple advanced signal processing techniques into a unified momentum analysis system. While individual components (Laguerre filters, JMA smoothing, ZEMA, Schmitt triggers) are established concepts in technical analysis and signal processing, this indicator is justified because: The Laguerre-to-JMA-to-ZEMA processing chain creates a momentum signal with properties not achievable by any single technique alone — the Laguerre provides the raw momentum extraction, JMA provides adaptive noise filtering, and ZEMA provides a lag-compensated reference The Schmitt trigger state machine replaces simple threshold crossings with hysteresis-based transitions, substantially reducing false signals in choppy conditions Cross quality scoring provides a quantitative measure of signal conviction that is not available in standard oscillator implementations The integration of velocity, acceleration, exhaustion detection, squeeze detection, and divergence analysis into a single coherent pane eliminates the need for multiple separate indicators Dynamic VWMA bands provide volume-contextual overbought/oversold boundaries rather than fixed levels 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. Past performance of any indicator does not guarantee future results. The momentum readings, state classifications, and signals displayed are mathematical calculations based on historical price data — they do not predict future price movement. Always use proper risk management and conduct your own analysis before making trading decisions. The author is not responsible for any losses incurred from using this indicator. -Made with passion by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
Trend Energy Filter 🚀 Trend Energy Filter Introducing a new dimension in trend analysis: Trend Energy. In automotive systems (like ECU sensor data), we use hysteresis and noise-gates to prevent "jitter" from triggering false responses. This script applies that same logic to Momentum (Trend Energy): ENERGY: Measures the "Engine Load" of the trend by calculating the distance from a long-term SMA. NOISE FILTER: Uses an ATR-based threshold. The Energy value only updates if the change is significant, effectively filtering out the "market static" that causes false signals. EXHAUSTION: Detects when the "Fuel" is running out by identifying peaks in energy and subsequent cooling. While this type of signal processing is often hidden inside expensive commercial "black box" tools, this script provides it as a transparent, open-source engineering solution. While standard indicators look at price in isolation, the Trend Energy Filter analyzes the "tension" between price and its long-term baseline (SMA). By focusing on the Surface Area of this tension, we gain a visual representation of market conviction that has been largely overlooked by traditional technical analysis. The Innovation of the "Energy Surface" Most traders view moving averages as simple static lines. The Trend Energy Filter reimagines the gap between price and the SMA as a dynamic surface. Volumetric Visualization: Instead of thin lines, the indicator uses a neon-glow "Surface" with vertical gradients. This represents the total "Energy" currently held by the trend. Volatility-Adjusted Noise Filter: Unlike standard oscillators that whipsaw during consolidation, this script utilizes a state-persistent ATR filter. It only updates the "Surface" when market energy moves significantly, effectively silencing the noise of minor price fluctuations. XAUUSD 15min Peak-Based Exhaustion Logic: By tracking the Highest energy peaks over a lookback period, the script identifies when a trend's "batteries" are running low—turning the surface blue when momentum begins to stall. How to Trade with the Energy Surface: 1. Entering the Energy Flow (Momentum Resumption) Watch for the "Surface" to break above its previous peak. When the color shifts from the "Exhaustion Blue" back to a vibrant Bullish Green or Bearish Red, it signals that the market has finished its rest and is ready to expand the surface area again. NAS100 15min Signal: Momentum Increasing alert. 2. Spotting the Blow-Off (Exhaustion Detection) When the "Surface" is high but begins to contract (falling below its recent high), the trend is becoming "over-extended" or "exhausted." This is the ideal time to take profits or tighten trailing stops. Visual: The surface turns blue (#5b9cf6) while still at high levels. NAS100 15 min 3. The Squeeze (Energy Compression) When the Energy Surface is exceptionally low and the Noise Filter prevents it from fluctuating, the market is in a "coiled spring" state. A sudden expansion of the surface from a flat baseline often precedes a massive directional breakout. NAS100 15min 4. Baseline Context Green Surface: Price is above the 200 SMA (Bullish Energy). Red Surface: Price is below the 200 SMA (Bearish Energy). Blue Surface: Trend is pausing or mean-reverting (Exhaustion).
Pine Script® indicator
by TheRealHawk
Artemis Oscillator PROArtemis Oscillator PRO is a momentum oscillator built on a custom Directional Range Momentum (DRM) engine — an original algorithm that measures signed range impulse, not price distance. Unlike RSI or Stochastic, DRM scores the direction and force of range expansion relative to its own history, normalized to 0–100. The result is an oscillator that reacts cleanly to structural momentum shifts without the lag inherent in traditional smoothed-ratio oscillators. The indicator integrates five analytical layers — oscillator core, volume pressure, momentum state scoring, divergence detection, and KNN machine learning bias — each operating independently and rendered on a single, clean oscillator panel. 🟦 HOW THE CORE ENGINE WORKS **Directional Range Momentum (DRM)** Each bar, the engine measures the current highest high and lowest low over the lookback period. It then assigns a signed range force: - If the range is expanding to the upside → positive force (full range span) - If the range is expanding to the downside → negative force (negative full span) - If range is stable → raw tick delta (neutral) This signed force is split into two smoothed components: - `sUp` — smoothed directed force (can be positive or negative) - `sAbs` — smoothed absolute force (always positive, represents total activity) The oscillator value is: `(sUp / sAbs) * 50 + 50` This produces a 0–100 scale where 50 is neutral, above 50 means upward range pressure dominates, and below 50 means downward range pressure dominates. Division by the absolute component normalizes the output regardless of asset volatility. **Signal Line** A second independently configurable smoothing pass over the oscillator value. Crossovers between the oscillator and signal line mark momentum regime changes. **Smoothing Methods** Four algorithms available for both oscillator and signal line: - RMA — Wilder's Moving Average (default, most stable, minimal overreaction) - EMA — Exponential, faster response - SMA — Simple, equal weight across all bars - TMA — Double-smoothed SMA, maximum smoothness 🟦 VOLUME PRESSURE **Dual MFI Architecture** Volume Pressure uses two Money Flow Index calculations simultaneously: - Fast MFI: `Period / 1.33` (shorter window, more reactive) - Slow MFI: `Period * 1.33` (longer window, structural bias) These are blended 50/50, then passed through a power-curve compression function (`x^0.75`) that reduces extreme readings while preserving directional accuracy. The result is mapped to 0–100 and rendered as fill areas anchored at the midline: - Above 50: Inflow fill (buying pressure dominates) - Below 50: Outflow fill (selling pressure dominates) The MFI price source is configurable: HLC3 (default), Close, HL2, or OHLC4. 🟦 MOMENTUM STATE SCORING A composite score from 0 to 3 is calculated each bar using three independent binary conditions: | Condition | +1 Point | |---|---| | Oscillator > Signal Line | Momentum direction | | Oscillator > 50 | Oscillator position | | Volume Pressure > 50 | Volume confirmation | **Score interpretation:** - Score 3 → Full overbought state fill (all three conditions bull-aligned) - Score 2 → Partial overbought fill (two conditions) - Score 1 → Partial oversold fill (one condition) - Score 0 → Full oversold state fill (all three conditions bear-aligned) Fills render inside the oscillator panel at adjustable opacity. The scoring is continuous — it updates every bar without look-ahead. 🟦 MOMENTUM EXHAUSTION Exhaustion labels (`✦ OB` / `✦ OS`) fire when the oscillator is inside a zone but weakening consecutively. Specifically: ✦ OB — oscillator is at or above the OB threshold AND has closed lower for N consecutive bars ✦ OS — oscillator is at or below the OS threshold AND has closed higher for N consecutive bars N (Confirmation Bars) is configurable from 1 to 4. Higher values produce fewer but more structurally confirmed signals. This is not a predictive signal — it marks the process of momentum fading, not the reversal itself. 🟦 DIVERGENCE DETECTION Pivots are calculated using `ta.pivothigh` and `ta.pivotlow` with an arm of `Period / 2`. All divergence results appear `Period / 2` bars late — this is standard Pine Script pivot behavior, not a bug. **Four divergence types:** | Type | Price | Oscillator | Signal | |---|---|---|---| | Regular Bull (D▲) | Lower Low | Higher Low | Potential reversal up | | Regular Bear (D▼) | Higher High | Lower High | Potential reversal down | | Hidden Bull (H▲) | Higher Low | Lower Low | Uptrend continuation | | Hidden Bear (H▼) | Lower High | Higher High | Downtrend continuation | Regular divergence uses solid lines. Hidden divergence uses dashed lines. Labels use clean symbol-only style with no background box. **Smart Divergence Filter (AI)** An optional pre-filter that rejects low-quality divergence signals before they render. Three conditions must pass (each configurable independently): 1. Minimum oscillator swing between the two pivot points (default: 5 units) 2. Minimum price swing as a percentage of recent range (default: 0.3%) 3. Volume Pressure confirmation — VP must agree with divergence direction When the filter is OFF, all detected divergences render. When ON, only structurally significant divergences pass. The filter applies identically to both chart rendering and alert conditions — no mismatch between visual and alert signals. 🟦 KNN BIAS PANEL **What KNN Does** K-Nearest Neighbors (KNN) is a non-parametric machine learning algorithm. Each bar, the indicator stores the current market state as a point in 3-dimensional feature space. When making a prediction, it finds the K historically most similar states and votes on the likely outcome. **The Three Features** | Feature | Calculation | Signal | |---|---|---| | O (Oscillator) | `oscVal / 100` | O+ = above 50 (bull zone) | | V (Volume) | `vpMid / 100` | V+ = above 50 (inflow) | | S (Signal gap) | `(oscVal - sigVal + 100) / 200` | S+ = oscillator above trigger | **ATR-Normalized Output Label** The classification target uses ATR-adjusted thresholds rather than a raw `close > close ` comparison. A bar is labeled bullish only if close moved more than `0.1 × ATR(14)` upward, bearish if it moved more than `0.1 × ATR(14)` downward, otherwise the previous label is carried forward. This eliminates noise-driven label flips caused by micro price movements. **Distance Calculation** Euclidean distance in 3D feature space: ``` d = sqrt((kf1_now - kf1_hist)^2 + (kf2_now - kf2_hist)^2 + (kf3_now - kf3_hist)^2) ``` The K nearest historical points vote. If majority voted bullish → BULL bias. Result thresholds: ≥60% bull vote = BULL, ≤40% = BEAR, between = NEUTRAL. **Panel Display** | Row | Left | Right | |---|---|---| | Header | KNN AI | Direction (▲ BULL / ▼ BEAR / ◈ NEUTRAL) | | Row 1 | Confidence | Percentage (0–100%) | | Row 2 | Progress bar (█░) | Bars in training memory | | Row 3 | O± V± S± feature states | K= neighbor count | Panel renders with monospace font, theme-aware backgrounds, and a single accent color derived from the active theme. 🟦 COLOR THEMES Eight complete palettes, each defining five independently tuned colors (bull, bear, signal, VP buy, VP sell): | Theme | Character | Bull Color | Bear Color | |---|---|---|---| | Aurora | Dark mode, high contrast | Cyan | Violet | | Ember | Dark mode, warm | Amber | Steel Blue | | Cyber | Dark mode, neon | Lime Green | Crimson | | Arctic | Dark mode, cool | Sky Blue | Soft Purple | | Solar | Dark mode, warm gold | Yellow Gold | Salmon | | Obsidian | Dark mode, pro trader | Emerald | Scarlet | | Classic | Light mode | Navy Blue | Crimson | | Mono | Light mode, minimal | Near Black | Gray | All five color roles (bull/bear/signal/VP buy/VP sell) change simultaneously when the theme changes. No per-color manual adjustment required. 🟦 ALERT SYSTEM Twelve alert conditions, all using `alert.freq_once_per_bar`: | Alert | Condition | |---|---| | Bullish Cross | Oscillator crosses above signal while below midline | | Bearish Cross | Oscillator crosses below signal while above midline | | Exit Oversold | Oscillator crosses above OS level | | Exit Overbought | Oscillator crosses below OB level | | Regular Bull Divergence | Price LL + oscillator HL (+ Smart Filter if enabled) | | Regular Bear Divergence | Price HH + oscillator LH (+ Smart Filter if enabled) | | Hidden Bull Divergence | Price HL + oscillator LL (+ Smart Filter if enabled) | | Hidden Bear Divergence | Price LH + oscillator HH (+ Smart Filter if enabled) | | VP Inflow | Volume Pressure crosses above 50 | | VP Outflow | Volume Pressure crosses below 50 | | Exhaustion OB | Oscillator weakening N bars while above OB level | | Exhaustion OS | Oscillator strengthening N bars while below OS level | Divergence alerts respect the Smart Divergence Filter — if the filter is ON and a divergence is rejected visually, the alert will also not fire. 🟦 SETTINGS REFERENCE **Oscillator** - Period — DRM lookback window. Also controls divergence pivot arm (Period / 2). Default: 14 - Smoothing — EMA / SMA / RMA / TMA. Default: RMA - Source — Price input for the range engine. Default: Close - Adaptive Color — Oscillator line shifts between bull/bear colors based on position. Default: ON **Signal Line** - Period — Signal smoothing window. Default: 7 - Smoothing — EMA / SMA / RMA / TMA. Default: EMA - Show Cross Dots — Dot on signal line at each crossover. Default: ON **Zones & Fills** - Overbought level — Default: 80 - Oversold level — Default: 20 - Zone fill opacity — Configurable per zone. Default: 20 **Volume Pressure** - Show Volume Pressure — Toggle. Default: ON - MFI Source — HLC3 / Close / HL2 / OHLC4. Default: HLC3 - Opacity — Default: 18 **Momentum State** - Show Momentum State — Toggle. Default: ON - OB Opacity / OS Opacity — Default: 35 each **Divergence** - Regular / Hidden — Independent toggles and opacity controls - Label Size — Tiny / Small / Normal / Large. Default: Tiny - Smart Divergence Filter — ON/OFF. Default: OFF - Min Oscillator Swing — Default: 5.0 - Min Price Swing % — Default: 0.3% - Require VP Confirmation — Default: ON **Momentum Exhaustion** - Show Labels — Toggle. Default: ON - Confirmation Bars — 1 to 4. Default: 4 **KNN Bias** - Show KNN Panel — Toggle. Default: ON - Neighbors (K) — 3 to 10. Default: 5 - Training Window — 50 to 300 bars. Default: 100 - Panel Position — Four corners. Default: Bottom Right - Panel Text Size — Tiny / Small / Normal / Large. Default: Small 🟦 COMPATIBILITY Works on all asset classes and all timeframes in TradingView Pine Script v6. - Crypto: Spot, futures, perpetual contracts - Forex: All pairs - Equities: Stocks, ETFs, indices - Commodities: Metals, energy, agriculture - Timeframes: 1m through Monthly The DRM engine normalizes by its own absolute smoothed component, making it volatility-agnostic across assets and timeframes without manual recalibration. 🟦 TECHNICAL NOTES - Pine Script v6 - max_lines_count = 500`, `max_labels_count = 500` (divergence drawings) - No repainting — all values calculated on bar close. Pivot-based divergence results appear `Period / 2` bars late by design - KNN training updates only after `bar_index > 1` to avoid cold-start artifacts - Volume Pressure fill anchors use `color = na` (not `display.none`) to maintain Pine v6 `fill()` compatibility - OB/OS state fills render outside the 0–100 oscillator range (104–110 and −10 to −4) to avoid visual overlap 🟦 DISCLAIMER This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and apply proper risk management.
Pine Script® indicator
by a_jabbaroff
13
Fractal Velocity Accelerator [JOAT]Fractal Velocity Accelerator Introduction The Fractal Velocity Accelerator is an advanced open-source momentum indicator that combines fractal efficiency measurement, adaptive Laguerre filtering, and Gaussian smoothing to create a multi-dimensional momentum oscillator with institutional-grade signal generation. This indicator transforms raw price data into a sophisticated momentum measurement system that reveals not just momentum direction and strength, but also velocity, acceleration, and regime characteristics. Unlike traditional momentum indicators that simply measure rate of change, this system analyzes the efficiency of price movement through fractal mathematics, applies adaptive lag reduction through Laguerre transforms, and smooths data using 4th-order Gauss filters. The result is a momentum oscillator that responds quickly to genuine momentum shifts while filtering out noise and false signals. Why This Indicator Exists This indicator addresses fundamental limitations in traditional momentum analysis by introducing fractal efficiency concepts and adaptive filtering: 4th-Order Gauss Filter: Ultra-smooth OHLC data processing that eliminates noise while preserving genuine price movements Fractal Efficiency Engine: Logarithmic path efficiency measurement that quantifies how directly price moves from point A to point B Adaptive Laguerre Transform: Dynamic lag reduction that adjusts based on fractal efficiency, responding faster during efficient moves Percentile-Based Bands: Self-adjusting overbought/oversold zones that adapt to each instrument's unique momentum characteristics Velocity and Acceleration Tracking: First and second derivative calculations that identify momentum shifts before they're obvious Momentum Regime Classification: Seven-level regime system from Extreme Bearish to Extreme Bullish with confidence measurements Divergence Detection: Fractal-based divergence scanner that identifies price-momentum asymmetries Each component provides unique intelligence about momentum dynamics. Gauss filtering ensures clean data, fractal efficiency measures directional clarity, Laguerre adaptation reduces lag, percentile bands provide context, velocity/acceleration track changes, regime classification guides strategy, and divergences reveal hidden shifts. Core Components Explained 1. 4th-Order Gauss Filter System The indicator applies a sophisticated Gaussian filter to all OHLC data: w = (2.0 * math.pi / gaussLength) beta = (1 - math.cos(w)) / (math.pow(1.414, 2.0 / betaDev) - 1) alpha = (-beta + math.sqrt(beta * beta + 2 * beta)) Gc := math.pow(alpha, 4) * close + 4 * (1.0 - alpha) * nz(Gc ) - 6 * math.pow(1 - alpha, 2) * nz(Gc ) + 4 * math.pow(1 - alpha, 3) * nz(Gc ) - math.pow(1 - alpha, 4) * nz(Gc ) This 4th-order filter provides exceptional smoothing while maintaining responsiveness. The filter uses four previous values with specific weightings that create a bell curve response, eliminating high-frequency noise while preserving genuine price movements. The beta deviation parameter (default 2.0) controls filter aggressiveness. Higher values create more smoothing but add lag. Lower values maintain responsiveness but allow more noise. The default balances these tradeoffs optimally for most instruments. 2. Fractal Efficiency Calculation Fractal efficiency measures how efficiently price moves by comparing net displacement to total path length: sumRange = math.sum((math.max(Gh, nz(Gc )) - math.min(Gl, nz(Gc ))), fractalLength) totalRange = ta.highest(Gh, fractalLength) - ta.lowest(Gl, fractalLength) fractalGamma = if totalRange > 0 math.log(sumRange / totalRange) / math.log(fractalLength) else 0.0 fractalEfficiency = math.max(0, math.min(1, (fractalGamma + 1) / 2)) The calculation uses logarithmic scaling to measure path complexity. When price moves in a straight line (high efficiency), the ratio approaches 1.0. When price moves erratically (low efficiency), the ratio approaches 0.0. Fractal efficiency is normalized to 0-1 range where: - 1.0 = Perfect efficiency (straight line movement) - 0.7-1.0 = High efficiency (strong trending) - 0.4-0.7 = Moderate efficiency (developing trend) - 0.0-0.4 = Low efficiency (choppy/ranging) This measurement is crucial because it determines how aggressively the Laguerre filter adapts. 3. Adaptive Laguerre Transform The Laguerre filter applies adaptive lag reduction based on fractal efficiency: gamma = laguerreGamma * (1 - fractalEfficiency) + 0.1 * fractalEfficiency L0 := (1 - gamma) * Gc + gamma * nz(L0 ) L1 := -gamma * L0 + nz(L0 ) + gamma * nz(L1 ) L2 := -gamma * L1 + nz(L1 ) + gamma * nz(L2 ) L3 := -gamma * L2 + nz(L2 ) + gamma * nz(L3 ) cu = (L0 > L1 ? L0 - L1 : 0) + (L1 > L2 ? L1 - L2 : 0) + (L2 > L3 ? L2 - L3 : 0) cd = (L0 < L1 ? L1 - L0 : 0) + (L1 < L2 ? L2 - L1 : 0) + (L2 < L3 ? L3 - L2 : 0) laguerreRSI = cu + cd != 0 ? 100 * (cu / (cu + cd)) : 50 The Laguerre transform creates four cascading filters (L0-L3) that progressively smooth the data. The gamma parameter controls lag - lower gamma means less lag but more noise, higher gamma means more lag but smoother output. The adaptive component adjusts gamma based on fractal efficiency: - High efficiency (trending): Gamma decreases toward 0.1, reducing lag for fast response - Low efficiency (choppy): Gamma increases toward laguerreGamma setting, adding smoothing to filter noise The cu (count up) and cd (count down) calculations measure upward vs downward movement across the four Laguerre levels, creating an RSI-like oscillator that's far more responsive than traditional RSI. 4. Fractal Momentum Oscillator The final momentum value combines Laguerre RSI with fractal efficiency: rawMomentum = (laguerreRSI - 50) * (1 + fractalEfficiency) momentumEMA = ta.ema(rawMomentum, 5) fractalMomentum = math.max(-100, math.min(100, momentumEMA)) This calculation: 1. Centers Laguerre RSI around zero by subtracting 50 2. Amplifies the signal by (1 + fractalEfficiency), giving more weight to efficient moves 3. Smooths with 5-period EMA to reduce jitter 4. Bounds the result to -100 to +100 range The efficiency amplification is key - during high-efficiency trending moves, momentum readings become more extreme, providing clear signals. During low-efficiency choppy moves, momentum readings stay muted, preventing false signals. 5. Velocity and Acceleration Tracking The indicator calculates first and second derivatives of momentum: momentumVelocity = ta.change(fractalMomentum, 1) momentumAcceleration = ta.change(momentumVelocity, 1) velocityEMA = ta.ema(momentumVelocity, 3) Velocity (first derivative) shows the rate of momentum change. Positive velocity means momentum is increasing, negative velocity means momentum is decreasing. Acceleration (second derivative) shows the rate of velocity change. Positive acceleration means velocity is increasing (momentum gaining speed). Negative acceleration means velocity is decreasing (momentum losing speed). These metrics provide early warning of momentum shifts: - Positive momentum + positive velocity + positive acceleration = Strong bullish momentum building - Positive momentum + positive velocity + negative acceleration = Bullish momentum slowing (potential top) - Positive momentum + negative velocity = Bullish momentum fading (reversal warning) 6. Momentum Regime Classification The indicator classifies momentum into seven regimes: Extreme Bullish: Momentum > threshold (default 60), very strong upward pressure Strong Bullish: Momentum 40-60, solid upward pressure Weak Bullish: Momentum 20-40, mild upward pressure Neutral: Momentum -20 to +20, balanced conditions Weak Bearish: Momentum -40 to -20, mild downward pressure Strong Bearish: Momentum -60 to -40, solid downward pressure Extreme Bearish: Momentum < -threshold, very strong downward pressure Each regime includes confidence measurement equal to the absolute momentum value. Higher confidence indicates stronger regime conviction. 7. Adaptive Band System The indicator uses percentile-based bands that adapt to each instrument: momentumPercentile = ta.percentrank(fractalMomentum, bandLength) dynamicOB = ta.percentile_linear_interpolation(fractalMomentum, bandLength, obLevel) dynamicOS = ta.percentile_linear_interpolation(fractalMomentum, bandLength, 100 - obLevel) These bands automatically adjust to the instrument's typical momentum range. An instrument that frequently reaches ±80 will have wider bands than one that typically stays within ±40. This prevents false overbought/oversold signals on volatile instruments and ensures sensitivity on stable instruments. 8. Fractal Divergence Detection The indicator detects divergences using fractal pivot analysis: momentumHigh = ta.pivothigh(fractalMomentum, divLookback, divLookback) momentumLow = ta.pivotlow(fractalMomentum, divLookback, divLookback) bullishDiv := lastPrice < prevPrice and lastMomentum > prevMomentum and lastMomentum < 0 bearishDiv := lastPrice > prevPrice and lastMomentum < prevMomentum and lastMomentum > 0 Regular divergences signal potential reversals: - Bullish: Price makes lower low, momentum makes higher low (selling pressure weakening) - Bearish: Price makes higher high, momentum makes lower high (buying pressure weakening) Hidden divergences signal trend continuation: - Hidden Bullish: Price makes higher low, momentum makes lower low (trend resumption after pullback) - Hidden Bearish: Price makes lower high, momentum makes higher high (downtrend resumption after bounce) Visual Elements Multi-Layer Momentum Line: Three overlaid plots (white underlay, gradient middle, solid core) creating depth and visibility Velocity Histogram: Histogram showing momentum velocity scaled 10x for visibility Adaptive Bands: Dynamic overbought/oversold lines that adjust to instrument characteristics Zone Fills: Gradient fills between bands and zero line showing bullish/bearish zones Reference Lines: Horizontal lines at extreme (±60), strong (±40), and weak (±20) levels Regime Background: Subtle background coloring showing current momentum regime Divergence Labels: Text labels marking regular and hidden divergences Reversal Signals: Labels marking extreme momentum reversals Velocity Signals: Small labels marking velocity acceleration/deceleration Comprehensive Dashboard: 14-row intelligence panel showing momentum value, regime, velocity, acceleration, efficiency, Laguerre RSI, trend strength, consistency, adaptive bands, and divergence status The dashboard provides complete momentum intelligence with color-coded metrics and status indicators. Input Parameters Signal Architecture: Extreme Momentum Reversals: Toggle high-confidence exhaustion signals (default enabled) Fractal Divergence Detection: Toggle price-momentum asymmetry detection (default enabled) Velocity Acceleration Alerts: Toggle momentum acceleration warnings (default enabled) Extreme Momentum Threshold: Score required for extreme classification (40-90, default 60) Gauss Filter: Gauss Filter Length: Smoothing period (5-100, default 20) Beta Deviation: Filter aggressiveness (0.5-5.0, default 2.0) Fractal Engine: Fractal Efficiency Length: Efficiency calculation period (10-200, default 50) Laguerre Transform: Laguerre Gamma: Base lag parameter (0.1-0.99, default 0.7) Adaptive Bands: Band Percentile Length: Percentile calculation period (20-500, default 100) Overbought Level: Upper band percentile (50-95, default 75) Oversold Level: Lower band percentile (5-50, default 25) Divergence: Enable Divergence Scanner: Toggle divergence detection (default enabled) Divergence Lookback: Pivot detection period (3-20, default 5) Visualization: Momentum Intelligence Panel: Toggle dashboard (default enabled) Momentum Regime Zones: Toggle background coloring (default enabled) Velocity Histogram: Toggle velocity display (default enabled) Dashboard Scale: Small/Normal/Large sizing (default Normal) Colors: All colors fully customizable including bullish momentum (neon cyan), bearish momentum (neon pink), extreme bullish (neon green), extreme bearish (neon red), neutral (gold), and divergence (neon purple). How to Use This Indicator Step 1: Assess Momentum Value and Direction Check dashboard "MOMENTUM" value and direction. Positive values indicate bullish momentum, negative indicate bearish. Values above 60 or below -60 suggest extreme conditions that may precede reversals or strong continuations. Step 2: Identify Current Regime Review "REGIME" classification and confidence percentage. Extreme regimes with high confidence (>80%) indicate strong momentum that typically continues. Weak regimes suggest transitional conditions. Step 3: Monitor Velocity and Acceleration Check "VELOCITY" and "ACCEL" metrics. Positive velocity with positive acceleration suggests momentum is building. Negative acceleration while momentum is still positive warns of potential momentum exhaustion. Step 4: Evaluate Fractal Efficiency Review "EFFICIENCY" percentage. High efficiency (>70%) confirms that momentum is backed by clean, directional price movement. Low efficiency (<40%) suggests choppy conditions where momentum signals may be less reliable. Step 5: Check Adaptive Bands Monitor "OB LEVEL" and "OS LEVEL" showing dynamic overbought/oversold thresholds. When momentum exceeds these levels, watch for reversal signals or continuation acceleration. Step 6: Watch for Divergences Check "DIVERGENCE" status and look for divergence labels. Regular divergences at extreme momentum levels often precede significant reversals. Hidden divergences in established trends suggest continuation after pullbacks. Step 7: Identify Extreme Reversals Watch for "EXTREME REVERSAL" labels when momentum crosses from extreme territory. These high-confidence signals often mark major turning points or trend acceleration phases. Step 8: Track Velocity Acceleration Monitor velocity acceleration labels. "VELOCITY ACCEL" signals indicate momentum is gaining speed, often marking optimal entry timing in early trend phases. Best Practices Extreme momentum reversals (>60 or <-60) are most reliable when confirmed by velocity deceleration High fractal efficiency (>70%) validates momentum signals as backed by clean price action Divergences at extreme momentum levels offer highest-probability reversal setups Velocity acceleration signals work best in early trend phases, less reliable in mature trends Adaptive bands automatically adjust to instrument volatility - respect them as dynamic thresholds Momentum regime transitions provide clear strategy adjustment points Combine momentum analysis with price action for optimal entry timing Laguerre RSI above 70 or below 30 confirms extreme momentum readings Trend strength above 60 indicates strong momentum persistence Trend consistency above 70 confirms momentum is directionally stable Hidden divergences in strong trends (momentum >40 or <-40) suggest continuation opportunities Neutral regime (-20 to +20) suggests range-bound conditions unsuitable for momentum strategies Indicator Limitations Momentum indicators are lagging by nature - they confirm trends rather than predict them Extreme momentum can persist longer than expected during strong trends Fractal efficiency requires sufficient price history - may be unreliable on newly listed instruments Gauss filter adds smoothing which inherently introduces some lag Adaptive bands require adequate history for percentile calculations Divergences can persist for extended periods before price responds The indicator works best on liquid instruments with consistent price action Very low timeframes may produce excessive noise despite filtering Velocity and acceleration are sensitive to sudden price spikes Regime classification is probabilistic, not deterministic The indicator shows momentum dynamics but cannot predict duration Technical Implementation Built with Pine Script v6 using: 4th-order Gaussian filter with customizable beta deviation Logarithmic fractal efficiency calculation using path complexity measurement Adaptive Laguerre transform with four cascading filter levels Fractal momentum oscillator combining Laguerre RSI with efficiency amplification First and second derivative calculations for velocity and acceleration Seven-level momentum regime classification with confidence measurement Percentile-based adaptive bands using linear interpolation Fractal pivot-based divergence detection system Multi-layer gradient visualization with depth effects Comprehensive dashboard with 14 metrics and color-coded indicators Alert system for reversals, divergences, and velocity signals The code is fully open-source with extensive comments explaining fractal mathematics and adaptive filtering concepts. Originality Statement This indicator is original in its integration of fractal efficiency with adaptive momentum measurement. While individual components exist, this indicator is justified because: It combines 4th-order Gauss filtering with fractal efficiency and Laguerre transforms in a unified system The adaptive Laguerre gamma adjustment based on fractal efficiency is a novel approach to lag reduction Fractal momentum amplification using efficiency multiplier creates regime-aware momentum measurement Velocity and acceleration tracking provides multi-dimensional momentum analysis Seven-level regime classification with confidence measurement guides strategy selection Percentile-based adaptive bands automatically adjust to each instrument's characteristics Fractal pivot-based divergence detection identifies asymmetries with statistical precision The comprehensive dashboard synthesizes 14 distinct metrics into unified momentum intelligence Multi-layer visualization with gradient effects provides exceptional clarity Integration of efficiency, velocity, acceleration, and regime creates layered confirmation Each component contributes unique intelligence: Gauss filtering ensures clean data, fractal efficiency measures directional quality, Laguerre adaptation reduces lag, momentum oscillator quantifies strength, velocity tracks changes, acceleration identifies inflections, regime classification guides strategy, bands provide context, and divergences reveal hidden shifts. The indicator's value lies in combining these complementary perspectives into a cohesive, adaptive momentum system. 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. Momentum analysis is a tool for understanding price dynamics, not a crystal ball for predicting future movement. Extreme momentum readings do not guarantee reversals. Divergences do not guarantee price response. Past momentum patterns do not guarantee future patterns. Market conditions change, and strategies that worked historically may not work in the future. The metrics displayed are mathematical calculations based on current market data, not predictions of future price movement. Momentum readings, divergences, and regime classifications do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions. 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
Pine Script® indicator
by officialjackofalltrades
Confluence Ledger [JOAT]Confluence Ledger Introduction The Confluence Ledger is an advanced open-source multi-timeframe confluence scoring engine that evaluates eight independent analytical dimensions across five configurable timeframes, producing a unified directional bias score from 0 to 100. It answers the question every trader asks: "Do the timeframes agree, and how strongly?" Rather than checking multiple indicators on multiple charts, this single tool synthesizes trend alignment, momentum phase, volatility state, market structure, volume conviction, RSI regime, VWAP bias, and ATR expansion into one actionable number. The indicator overlays on the price chart with gradient-colored candles, confluence pressure zones, regime transition lines, divergence detection boxes, and institutional confluence signals - all backed by a compact 12-row dashboard that displays every metric in real-time. Why This Indicator Exists Multi-timeframe analysis is widely recognized as essential for high-probability trading, but executing it manually is tedious and error-prone. A trader might check the daily trend, the 4H momentum, the 1H structure, and the 15m entry — but doing this across eight different analytical dimensions is impractical without automation. The Confluence Ledger automates this entire process by: Scoring eight distinct analytical dimensions on each of five timeframes, producing 40 individual data points per bar Weighting higher timeframes more heavily (Daily gets 2x the weight of the 5-minute chart), reflecting the institutional reality that higher timeframe trends dominate Mapping the weighted aggregate to a 0-100 scale where 50 is perfectly neutral, above 60 is bullish, and below 40 is bearish Adding institutional features that analyze the score itself: pressure zones where extreme confluence persisted, regime transitions where the bias flipped, and divergences between price and the confluence score The Eight Scoring Dimensions Each dimension returns a score from -1 (maximum bearish) to +1 (maximum bullish). Here is what each measures and why it matters: 1. Trend Alignment Combines EMA slope direction with price position relative to the adaptive moving average. If price is above a rising EMA, the trend score is +1. If price is below a falling EMA, it is -1. Mixed conditions produce intermediate scores. This captures the most fundamental question: is the trend up or down? 2. Momentum Phase A composite of three normalized oscillators — Bollinger %B, CCI, and ROC. Each is scored independently and averaged. This measures whether momentum is bullish, bearish, or neutral, using three different mathematical approaches to reduce the chance of a single oscillator giving a misleading signal. 3. Volatility State Measures Bollinger Band width relative to its 50-bar average. Expanding volatility scores positive (in the direction of price), compressing volatility scores near zero. This dimension captures whether the market is in expansion (trending) or compression (range-bound). 4. Structure Bias Uses an oscillator-based swing detection method to track whether the market is making higher highs/higher lows (bullish structure, score +1) or lower highs/lower lows (bearish structure, score -1). This is the structural backbone of Smart Money analysis. 5. Volume Conviction Calculates current volume relative to the 20-bar average and weights it by candle direction. A bullish candle on 2x average volume scores strongly positive. A bearish candle on low volume scores weakly negative. This measures whether volume confirms the directional move. 6. RSI Regime RSI position relative to 50 provides the base score, with additional weight for extreme readings (above 70 or below 30). This captures overbought/oversold conditions and the general momentum regime. 7. VWAP Bias Price distance from VWAP normalized by ATR. When price is significantly above VWAP, institutional flow is net bullish. Below VWAP, net bearish. The ATR normalization ensures the score adapts to the instrument's volatility. 8. ATR Expansion The rate of change of ATR itself, weighted by candle direction. When ATR is expanding in the direction of price, it confirms the move has volatility behind it. Contracting ATR suggests the move is losing energy. Multi-Timeframe Aggregation All eight dimensions are calculated on each of five timeframes (default: 5m, 15m, 1H, 4H, Daily) using request.security(). The per-timeframe scores are then weighted: TF1 (5m): weight 1.0 TF2 (15m): weight 1.2 TF3 (1H): weight 1.5 TF4 (4H): weight 1.8 TF5 (Daily): weight 2.0 This weighting reflects the institutional principle that higher timeframe trends are more significant. A strong daily bias overrides conflicting 5-minute noise. The weighted aggregate is mapped from to : float confluence_score = math.round((raw_agg + 1.0) / 2.0 * 100) Score interpretation: 80-100: EXTREME LONG — near-unanimous multi-TF bullish agreement 70-79: STRONG LONG — clear bullish bias across most timeframes 60-69: LEAN LONG — moderate bullish tilt 41-59: NEUTRAL — no clear directional consensus 31-40: LEAN SHORT — moderate bearish tilt 21-30: STRONG SHORT — clear bearish bias 0-20: EXTREME SHORT — near-unanimous bearish agreement Institutional Analytics Engine Beyond the core score, the indicator calculates several advanced metrics: TF Agreement: Counts how many of the five timeframes are bullish vs bearish. When 4+ timeframes agree, a "Full Alignment" signal fires — these are the highest-conviction directional setups. Score Velocity: The rate of change of the confluence score itself. "ACCEL UP" means the score is rising and accelerating. "FALLING" means directional conviction is weakening. This is the first derivative of confluence — it tells you whether agreement is building or fading. Conviction Meter: Measures how tightly aligned the five timeframe scores are using standard deviation. Low variance (high conviction) means all timeframes agree closely. High variance (low conviction) means timeframes are giving conflicting signals. Cross-TF Momentum Divergence: Compares the average of lower timeframes (TF1+TF2) against higher timeframes (TF4+TF5). When lower TFs are leading (diverging bullish while higher TFs lag), it can signal an early trend change. When upper TFs are leading, the higher timeframe trend is asserting dominance. HTF Dominance: Identifies which higher timeframe is currently driving the overall bias the most. This tells you whether the daily, 4H, or 1H is the primary force behind the score. Dimension Consensus: Averages each of the eight dimensions across all five timeframes to find which dimension is the strongest driver. If "TREND" is the strongest dimension, the trend alignment across timeframes is the primary force. If "VOLUME" is strongest, volume conviction is driving the bias. Chart Features 1. Confluence Pressure Zones When the confluence score stays extreme (above 70 or below 30) for a configurable minimum number of bars (default 5), the indicator draws a dashed box marking the price range during that period. These "pressure zones" represent areas where sustained multi-timeframe agreement created institutional accumulation or distribution. They often act as future support/resistance. 2. Regime Transition Lines When the confluence score crosses from bullish to bearish territory (or vice versa), a labeled dashed line is drawn at the transition price. These lines show the exact price where the multi-timeframe consensus shifted — they act as institutional support/resistance levels that are derived from confluence rather than price structure. 3. Confluence Divergence Detector When price makes a new 20-bar high but the confluence score is declining (or price makes a new low but the score is rising), the indicator marks a confluence divergence. This is a unique concept — it detects divergence between a multi-timeframe composite score and price action, which is fundamentally different from single-oscillator divergence. 4. Institutional Confluence Signals Multi-condition filtered signals that fire when confluence score exceeds thresholds, velocity confirms, and a cooldown period has elapsed. These are the highest-conviction signals the indicator produces. 5. Gradient Confluence Candles Candles are colored on a gradient from the bearish color (score near 0) to the bullish color (score near 100). This creates an instant visual read of confluence strength on every candle. Input Parameters Timeframes: TF 1 through TF 5 (defaults: 5m, 15m, 60m, 240m, Daily) — all configurable Scoring Parameters: MA Length (27), ATR Length (14), BB Length (20), BB Mult (2.0) CCI Length (23), ROC Length (50), RSI Length (14), Swing Length (10) Institutional Features: Gradient Confluence Candles, Confluence Pressure Zones, Regime Transition Lines Confluence Divergence Boxes, Institutional Confluence Signals Signal Cooldown (20 bars), Pressure Zone Min Bars (5) How to Use This Indicator Step 1: Read the Score The confluence score (0-100) is your primary directional gauge. Above 60 = bullish bias. Below 40 = bearish bias. 40-60 = no clear edge — consider staying flat or reducing position size. Step 2: Check TF Agreement The dashboard shows how many timeframes agree. 4/5 or 5/5 agreement in one direction is a high-conviction setup. 2B/3S or similar splits suggest conflicting signals — proceed with caution. Step 3: Monitor Score Velocity A score of 72 that is "ACCEL UP" is more bullish than a score of 72 that is "FALLING." Velocity tells you whether the consensus is strengthening or weakening. Step 4: Use Pressure Zones as S/R When price returns to a previous pressure zone, expect a reaction. These zones represent areas where sustained multi-timeframe agreement existed — institutional memory. Step 5: Watch for Confluence Divergences If price is making new highs but the confluence score is declining, the multi-timeframe consensus is not confirming the move. This is a warning sign that the advance may stall or reverse. Step 6: Trade Regime Transitions When the score crosses from bearish to bullish territory (or vice versa), the regime transition line marks the pivot price. These transitions often produce sustained directional moves. Limitations The indicator uses request.security() to fetch data from five timeframes. On very low timeframes (1m), the higher timeframe data updates less frequently, which can create lag in the score. The weighting system (higher TFs get more weight) is a design choice that works well for trend-following. Scalpers who trade against the higher timeframe trend may find the score misleading for their style. Confluence score is a composite of mathematical calculations. A score of 80 does not mean "80% chance of going up" — it means 80% of the weighted analytical dimensions agree on a bullish reading. The indicator evaluates current conditions, not future ones. A high confluence score can reverse quickly on unexpected news or institutional repositioning. VWAP calculations may behave differently on instruments without continuous trading sessions. Past confluence patterns do not guarantee future confluence patterns. Originality Statement This indicator is original in its systematic multi-dimensional, multi-timeframe confluence approach. While individual components (EMA trend, RSI, VWAP, etc.) are established concepts, this indicator is justified because: It evaluates eight independent analytical dimensions simultaneously — not just trend and momentum, but also volatility state, market structure, volume conviction, RSI regime, VWAP bias, and ATR expansion Each dimension is scored on five timeframes with weighted aggregation, producing 40 data points synthesized into a single actionable score The Confluence Pressure Zone concept — marking areas where extreme multi-TF agreement persisted — creates institutional S/R levels derived from confluence rather than price structure Regime Transition Lines mark the exact price where multi-timeframe consensus shifted, providing a unique form of dynamic support/resistance Confluence Divergence Detection compares a multi-TF composite score against price action — fundamentally different from single-oscillator divergence Score Velocity, Conviction Meter, Cross-TF Momentum Divergence, HTF Dominance, and Dimension Consensus provide meta-analysis of the confluence score itself The integration of all these features with gradient candle coloring and a comprehensive dashboard creates a unified confluence analysis system not available in any single existing indicator 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. The confluence score is a mathematical composite of current market conditions across multiple timeframes. It does not predict future price movement. High confluence does not guarantee profitable trades. Market conditions can change rapidly, and past confluence patterns do not guarantee future patterns. Always use proper risk management. Never risk more than you can afford to lose. The author is not responsible for any losses incurred from using this indicator. -Made with passion by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
Cadence Refracted Oscillator [JOAT]Cadence Refracted Oscillator Introduction The Cadence Refracted Oscillator is an open-source multi-layer momentum analysis tool built in Pine Script v6. It combines three distinct momentum methodologies — Spectral-Filtered RSI, Stochastic Momentum Index (SMI), and Cumulative Volume Delta (CVD) divergence detection — into a single composite oscillator displayed in a separate pane below the chart. The indicator produces a blended momentum reading (0-100), a gradient histogram, a signal line with crossover detection, Z-score extreme markers, and Wyckoff absorption alerts. It is designed for traders who want a deeper, noise-reduced view of momentum that goes beyond what a standard RSI or stochastic can provide. The key innovation is the spectral filtering stage. Instead of applying RSI directly to raw price, the indicator first passes price data through a Discrete Fourier Transform (DFT) to extract dominant frequency components, then applies RSI-weighted filtering to produce a cleaner, less noisy momentum signal. This filtered signal is then blended with the Stochastic Momentum Index to create a composite that captures both trend momentum and mean-reversion potential. Why This Indicator Exists Standard momentum oscillators have well-known limitations. RSI is noisy on lower timeframes and produces frequent false signals in choppy markets. Stochastic oscillators are fast but whipsaw-prone. Neither incorporates volume information. This indicator addresses these issues by layering three complementary approaches: Spectral-Filtered RSI: Applies a Discrete Fourier Transform to extract the dominant price cycle, then weights the filtered output by RSI distance from the midpoint. This removes high-frequency noise while preserving the meaningful momentum signal. The result is a smoother RSI that responds to genuine trend changes rather than random fluctuations. Stochastic Momentum Index: Measures where the close is relative to the midpoint of the recent high-low range, double-smoothed with configurable EMA periods. Unlike classic stochastic which measures close relative to the range boundaries, SMI measures distance from the center — making it more sensitive to directional momentum and less prone to ceiling/floor effects. CVD Divergence: Tracks Cumulative Volume Delta (buy volume minus sell volume) and compares it to price extremes. When price makes a new low but CVD is higher than its previous low, buying pressure is diverging from price — a bullish signal. The reverse applies for bearish divergences. This adds a volume-based confirmation layer that pure price-based oscillators lack. How the Spectral Filter Works The spectral filtering process uses a Discrete Fourier Transform — the same mathematical tool used in signal processing, audio analysis, and scientific computing — to decompose price data into frequency components: // Discrete Fourier Transform implementation // Decomposes price into frequency components // DC component (index 0) represents the dominant trend // Higher harmonics represent shorter-term oscillations The process works in four stages: Stage 1 — Short RSI Weighting: A short-period RSI is calculated and converted to an "absolute distance from 50" value. Bars where RSI is far from 50 (strong momentum) receive higher weight in the filter. Stage 2 — Forward DFT: Price data is transformed into the frequency domain using a configurable number of harmonics (default 3). The magnitude spectrum is extracted, and the DC component (the dominant low-frequency trend) becomes the filtered subject. Stage 3 — RSI-Weighted Smoothing: The filtered subject is smoothed using the RSI absolute distance as weights. This means the filter responds more to bars with strong momentum and less to bars with weak, indecisive momentum. Stage 4 — Final RSI: RSI is calculated on the filtered data with the main length (default 21). A divergence component (rate of change of the spectral RSI) is added to create the final Cadence RSI value. The result is an RSI-like oscillator that is significantly smoother than standard RSI while still being responsive to genuine trend changes. The Fourier harmonics parameter controls how many frequency components are retained — fewer harmonics produce a smoother signal, more harmonics preserve more detail. Stochastic Momentum Index Component The SMI component provides a complementary momentum perspective. While the spectral RSI focuses on trend momentum, the SMI captures where price sits within its recent range: The lookback period defines the range (highest high, lowest low) The distance from the midpoint of that range is double-smoothed with two EMA passes The range itself is also double-smoothed and halved to create the denominator The resulting value oscillates between -100 and +100, where positive values indicate price is above the range midpoint and negative values indicate it is below This is normalized to 0-100 for blending with the spectral RSI The SMI is particularly useful for detecting mean-reversion opportunities. When the spectral RSI shows a trend but the SMI is at an extreme, it suggests the trend may be overextended. Composite Blending The final composite oscillator blends the spectral RSI (60% weight) with the normalized SMI (40% weight). This weighting prioritizes the trend-following spectral RSI while incorporating the mean-reversion sensitivity of the SMI. The composite oscillates between 0 and 100, with 50 as the neutral midpoint. The histogram displays the difference from 50, making it easy to see momentum direction and intensity at a glance. Positive histogram bars indicate bullish momentum, negative bars indicate bearish momentum, and the gradient coloring intensifies with momentum strength. Signal Line and Crossovers An EMA-based signal line (default 9 periods) is applied to the composite. Crossovers between the composite and signal line provide timing signals: Bull Cross: Composite crosses above the signal line — momentum is accelerating upward Bear Cross: Composite crosses below the signal line — momentum is decelerating or reversing The distance between composite and signal line indicates momentum conviction — wide separation means strong momentum, tight convergence suggests a potential cross is forming Z-Score Extreme Detection The indicator calculates a Z-score of the composite value over a configurable lookback (default 50 bars). When the Z-score exceeds +2.0 or falls below -2.0, the momentum is at a statistical extreme — more than two standard deviations from the mean. These events are marked with square markers and indicate: Potential exhaustion of the current move High probability of mean reversion Possible climax buying or selling Z-score extremes are not automatic reversal signals — strong trends can sustain extremes for extended periods. They are best used as warnings to tighten stops or take partial profits. Wyckoff Absorption Detection The indicator detects Wyckoff absorption events — bars where volume is significantly above average (1.5x) but the price range is significantly below average (0.5x). This pattern indicates that large institutional orders are being filled without moving price, which often precedes a directional breakout. Absorption markers appear as circles at the midline. Visual Design The indicator uses a "Solar Flare" color theme — golds, ambers, magentas, and plasma purples on a dark background: Composite Line: Neon glow effect with three layered plots (outer glow at 85% transparency, mid glow at 65%, core line at full intensity). Color adapts to trend state — gold/amber for bullish, magenta/red for bearish, ash for neutral. Gradient Histogram: 10-level color gradient from bright gold (strong bull) through amber to magenta (strong bear). Rising momentum within a direction intensifies the color. Signal Line: Plasma purple with glow effect Zone Fills: Subtle fills between threshold lines — gold tint in the bull zone, magenta tint in the bear zone, ash in the neutral zone OB/OS Fills: When the composite enters overbought (>75) or oversold (<25) territory, a colored fill highlights the extreme SMI Reference: A thin blue line showing the normalized SMI for comparison Markers: Triangles for signal crossovers, diamonds for CVD divergences, squares for Z-score extremes, circles for absorption HUD Dashboard The real-time HUD displays 14 metrics: Composite value with color-coded bull/bear/neutral state Trend direction (Bullish/Bearish/Neutral) Z-Score value with classification (Extreme Bull/Bear, Strong, Normal) Momentum Percentile Rank (0-100%) SMI value Signal Line distance (Wide/Moderate/Tight) Volume Flow direction (Buying/Selling/Neutral) from CVD Spectral RSI component value Momentum Strength percentage with classification (Very Strong to Very Weak) Overbought/Oversold pressure state Divergence status (Bull Div/Bear Div/None) Current mode description (Bull Momentum/Bear Momentum/Consolidating) Input Parameters Spectral RSI: RSI Length: Main RSI period (default: 21) Source: Price source (default: close) Filter Length: Short RSI period for weighting (default: 12) Fourier Harmonics: Number of DFT components (default: 3). Lower = smoother, higher = more detail. Stochastic Momentum: SMI Lookback: Range period (default: 13) SMI Smooth 1/2: Double-smoothing EMA periods (default: 25/2) Signal Length: Signal line EMA period (default: 13) Volume Delta: Show CVD Divergence: Toggle divergence detection CVD Divergence Lookback: Period for comparing CVD extremes to price extremes (default: 14) Levels: Overbought/Oversold: Extreme thresholds (default: 75/25) Bull/Bear Threshold: Trend classification levels (default: 58/42) How to Use This Indicator Step 1: Read the Composite Direction Above 58 = bullish momentum. Below 42 = bearish momentum. Between = consolidation. The histogram makes this immediately visible. Step 2: Watch for Signal Crossovers Bull crosses (composite above signal) in the lower half of the range are potential long entries. Bear crosses in the upper half are potential short entries. Crosses near the midline are less significant. Step 3: Check for Divergences CVD divergences at price extremes are powerful reversal warnings. A bullish CVD divergence at an oversold composite reading is a high-probability long setup. Step 4: Monitor Z-Score Extremes Z-score beyond +/-2.0 warns of potential exhaustion. Consider tightening stops or taking partial profits when the Z-score reaches extreme levels. Step 5: Use Absorption as Early Warning Absorption events (high volume, small range) often precede breakouts. When absorption appears near a threshold level, be prepared for a directional move. Best Practices The spectral filter works best on timeframes with sufficient data — 5-minute and above is recommended Fewer Fourier harmonics (2-3) produce a smoother, more trend-following signal. More harmonics (5-8) produce a more responsive but noisier signal. The composite is most reliable when the spectral RSI and SMI agree. Divergence between the two components suggests mixed conditions. CVD divergences are most significant at overbought/oversold extremes Z-score extremes in trending markets can persist — do not blindly fade them The signal line crossover is a timing tool, not a standalone entry signal. Combine with price action and structure analysis. Absorption events are context-dependent — they are most meaningful near support/resistance levels Limitations The DFT calculation is computationally intensive. Very high harmonic counts may slow chart loading on lower timeframes with large datasets. The spectral filter introduces a small amount of lag compared to raw RSI. This is the tradeoff for noise reduction. CVD divergence detection uses a simple comparison of extremes over the lookback period. It may miss complex divergences or flag simple pullbacks as divergences. Buy/sell volume separation is estimated from candle direction, not true order flow data. The composite blending weights (60/40) are fixed. Different instruments or timeframes might benefit from different weights. Z-score extremes are relative to the lookback period. A Z-score of +2.0 over 50 bars may not be extreme over 200 bars. Like all oscillators, this indicator can remain at extremes during strong trends. It is not a contrarian tool by default. Technical Implementation Built with Pine Script v6 using: Custom Discrete Fourier Transform implementation (forward and inverse) with configurable harmonics RSI-weighted spectral filtering for noise reduction Double-smoothed Stochastic Momentum Index with normalization Cumulative Volume Delta tracking with divergence detection Z-score calculation for statistical extreme identification Wyckoff absorption detection (effort vs result) 10-level gradient histogram coloring function Multi-layer neon glow effect on composite and signal lines barstate.isconfirmed gating on all signal markers 10 alert conditions covering threshold crosses, divergences, signal crossovers, and Z-score extremes Originality Statement This indicator is original in its synthesis of spectral analysis with momentum oscillators and volume delta. While RSI, stochastic, and CVD are established concepts, this indicator is justified because: The Discrete Fourier Transform spectral filtering applied to RSI calculation is a novel approach that significantly reduces noise while preserving signal responsiveness The RSI-weighted filtering stage ensures the spectral filter responds more to high-momentum bars and less to noise, creating an adaptive smoothing mechanism Blending spectral RSI with SMI combines trend-following and mean-reversion perspectives into a single composite that captures both dimensions of momentum CVD divergence detection adds a volume-based confirmation layer that pure price-based oscillators cannot provide Z-score extreme detection provides statistical context for momentum readings, helping traders distinguish between normal momentum and genuine extremes Wyckoff absorption integration connects volume analysis with momentum analysis in a way that standard oscillators do not The Solar Flare theme with gradient histogram and neon glow provides immediate visual clarity about momentum direction and intensity 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. Momentum oscillators measure the rate and direction of price change — they do not predict future price movement. Overbought conditions can persist in strong uptrends, and oversold conditions can persist in strong downtrends. Signal crossovers and divergences are probabilistic, not deterministic. Past momentum patterns do not guarantee future behavior. Always use proper risk management and never risk more than you can afford to lose. The author is not responsible for any losses incurred from using this indicator. -Made by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
Stochastic Divergence (live) AAWhat it does: This script detects live regular bullish and bearish divergence between price and a smoothed Stochastic D line. It uses confirmed fractal swing highs and lows on price to define the first anchor point of the divergence, and those pivot settings can be adjusted in the UI. The first pivot is confirmed, but the second pivot is live, so the script can identify divergence as it is forming rather than waiting for a second confirmed swing. A bullish divergence is found when price makes a lower low while the Stochastic D makes a higher low. A bearish divergence is found when price makes a higher high while the Stochastic D makes a lower high. The oscillator used here is a double-smoothed Stochastic D built from TradingView’s built-in stochastic calculation. Instead of using the close as the source, it uses ohlc4, which helps reduce some of the wick-driven noise that can make divergence readings less consistent. If you want to compare or debug the signals against TradingView’s built-in Stochastic, make sure to change its source from close to ohlc4 so the readings line up properly. This script uses a double invalidation model. A divergence setup is cleared not only when the Stochastic D loses its side of the 50 line, but also when price breaks the structure of the latest anchor pivot bar in the opposite direction. That combination helps reset the setup faster, keeps stale divergence from lingering, and makes the active signals more structurally relevant. How to use: Use this script as a price-action context tool, not as a blind entry or exit trigger. Bullish divergences tend to matter most when they appear after an extended move down and line up with support, liquidity sweeps, equal lows, demand zones, or other signs of downside exhaustion. Bearish divergences tend to matter most when they appear after an extended move up and line up with resistance, equal highs, trapped breakouts, supply zones, or other signs of upside exhaustion. The Pivot Left and Pivot Right settings control how strict the first anchor pivot is, with higher values giving fewer but more confirmed structures and lower values making the script more reactive. The Min and Max Bar Distance settings control how far apart the anchor point and the live opposing price extreme can be, which helps filter out weak or overly stretched divergence structures. It works best when combined with structure, location, and real price behavior around an important level, rather than used in isolation. These are my own takes on widely used market calculations and are not meant to be a standalone trading system. If you found this useful, leave a like 🚀 and follow for more. Thanks, and good luck on your trading journey.
Pine Script® indicator
by Alex_Algo