Commencez

Statisticalprobability

Structural Sequence Pressure [JOAT]Structural Sequence Pressure Introduction Structural Sequence Pressure is a market-structure breakout indicator built around the idea that not all pivot levels carry the same weight. A level formed after one isolated pivot is ordinary. A level formed after repeated higher lows or lower highs represents structural persistence. This script quantifies that persistence, plots the resulting support and resistance levels, and highlights when those levels break. The indicator is designed for traders who want clean structure without overfitting. It tracks sequence depth, manages active levels, and focuses attention on breaks that matter more because the market spent time constructing them. Why This Indicator Exists Sequence-Based Structure: Measures how many pivots formed in the same directional progression Level Significance Filter: Helps distinguish weak levels from structurally reinforced ones Live Break Detection: Signals when meaningful support or resistance gives way Clutter Control: Caps active lines and labels to keep charts readable Statistical Feedback: Shows the most recent sequence pressure behavior directly in the dashboard Core Components Explained 1. Confirmed Pivot Detection pivHigh = ta.pivothigh(high, leftBars, rightBars) pivLow = ta.pivotlow(low, leftBars, rightBars) The script only reacts to confirmed pivots. This means levels are built from validated swing points rather than from intrabar noise, which keeps the tool stable and non-repainting after pivot confirmation. 2. Upward and Downward Sequence Counting When a new pivot low forms higher than the previous pivot low, the upward sequence count increases. When a new pivot high forms lower than the previous pivot high, the downward sequence count increases. If the relationship fails, the sequence resets to one. Higher lows: Build upward structural pressure Lower highs: Build downward structural pressure Longer sequences: Represent stronger structural continuity 3. Active Level Engine Every confirmed pivot creates a horizontal level that extends right. Support levels come from pivot lows. Resistance levels come from pivot highs. Each level stores: Its price Whether it is support or resistance Its sequence depth Its creation time Levels are trimmed by age and capped by quantity so the chart stays clean. 4. Break Logic brokeSupport = close < supportLevel brokeResistance = close > resistanceLevel Only levels with sequence depth greater than or equal to the selected threshold can trigger signals. This creates a cleaner breakout map that emphasizes structurally meaningful failures and expansions. 5. Chart Hygiene Controls The script stores and prunes pivot labels so they do not accumulate indefinitely. Broken or expired levels are removed from active management, and broken levels can optionally be deleted immediately for a cleaner chart. Visual Elements Support Lines: Teal dashed levels extending from higher-low pivots Resistance Lines: Red dashed levels extending from lower-high pivots Pivot Labels: Compact U/D sequence tags showing progression depth Break Markers: Triangles on bullish resistance breaks and bearish support breaks Dashboard: Up sequence, down sequence, total breaks, last break depths, and active level count Input Parameters Pivot Left / Right Bars: Confirmation strength for structural swings Minimum Sequence: Required sequence depth before a break can trigger Max Active Levels: Hard cap for line management and chart cleanliness Max Level Age: Removes stale structure from consideration Delete Broken Levels: Optional cleanup for a more minimal chart How to Use This Indicator Step 1: Identify whether the market is building higher lows or lower highs. Step 2: Focus on levels with deeper sequence counts. Step 3: Treat bullish signals as upside breaks of resistance sequences. Step 4: Treat bearish signals as downside breaks of support sequences. Step 5: Use the level count and recent break depths to judge whether structure is compressing or resolving. Best Practices Use larger pivot settings on volatile instruments to reduce noise Increase minimum sequence length when markets are choppy Pair with trend or participation tools to separate continuation from exhaustion Respect old levels less than fresh ones unless they were built with deep sequence pressure Use the dashboard to calibrate structure sensitivity instrument by instrument Indicator Limitations Pivot-based tools confirm after the fact by design Sequence strength measures persistence, not certainty Very low pivot settings can create too many structural levels Very high pivot settings can delay signals Breakouts can still fail, especially in range-bound conditions Technical Implementation Built in Pine Script v6 using: Confirmed pivot detection Directional sequence counting Array-managed line storage Label retention caps for chart cleanliness Age-based level cleanup Non-repainting break logic on confirmed bars Originality Statement This indicator is original in its focus on sequence depth as the source of structural weighting. Many support and resistance tools draw levels; this one grades their importance by the persistence of the swing process that created them, then filters breakout logic through that structural pressure. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Structural breaks can fail, reverse, or whipsaw. Always use independent analysis, stops, and appropriate risk management. -Made with passion by officialjackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
Deviation Lens [JOAT]Deviation Lens Introduction Deviation Lens is an open-source multi-dimensional statistical displacement tool that applies Z-Score analysis simultaneously to three market dimensions: price level, close-to-close price change, and volume. Rather than using arbitrary overbought/oversold thresholds derived from historical maxima and minima, Deviation Lens computes exactly how many standard deviations each dimension is from its recent rolling mean. This provides a precise, adaptive, distribution-aware measure of how statistically extreme current market conditions are. The core insight is that markets are mean-reverting systems over short time horizons. Statistical extremes — conditions where price, momentum, or volume are far from their recent averages — represent transient states. The further from the mean, the greater the statistical probability that conditions will normalize. Deviation Lens quantifies this probability directly, from 0% (at the mean) to 99.7% (at three standard deviations), and displays it as a live reversal probability for every bar. Core Concepts 1. Three-Dimensional Z-Score Calculation Three independent Z-Scores are computed on every bar: The Price Z-Score measures how far the current close is from the rolling mean close in standard deviation units. This captures whether the current price level is statistically cheap or expensive relative to recent history. The Change Z-Score measures how far the current bar's close-to-close price change is from the rolling mean change — quantifying momentum extremity rather than price level extremity. The Volume Z-Score measures how far the current volume is from the rolling mean volume. High-volume Z-Score values identify bars where unusual institutional participation is statistically evident: priceZ = priceStd > 0 ? (close - priceMean) / priceStd : 0.0 changeZ = changeStd > 0 ? (chg - changeMean) / changeStd : 0.0 volumeZ = volStd > 0 ? (volume - volMean) / volStd : 0.0 2. Reversal Probability Mapping The absolute Z-Score is mapped to a reversal probability percentage based on the properties of the normal distribution. A Z-Score of 1.0 corresponds to 68.3% of values lying within one standard deviation — meaning only 31.7% of readings exceed this level, implying a 68.3% probability of mean reversion. A Z-Score of 2.0 corresponds to 95.4%, and 3.0 to 99.7%: calcRevProb(float z) => float absZ = math.abs(z) absZ >= 3.0 ? 99.7 : absZ >= 2.5 ? 98.8 : absZ >= 2.0 ? 95.4 : absZ >= 1.5 ? 86.6 : absZ >= 1.0 ? 68.3 : absZ >= 0.5 ? 38.3 : 0.0 This probability is displayed in the dashboard alongside the live Z-Score value, giving the trader both the raw statistical reading and its corresponding reversal likelihood. 3. Composite Z-Score and Zone Classification The three individual Z-Scores are combined into a composite score using configurable weights for each dimension. The composite is then classified into a zone: EXTREME (above the configurable extreme threshold), ELEVATED, NEUTRAL, or the opposing directional equivalents. Zone classification determines the dashboard color coding and alert triggers: composite = (priceZ * wPrice + changeZ * wChange + volumeZ * wVolume) / totalWeight 4. Divergence and Hidden Divergence Detection Deviation Lens monitors for two divergence conditions. Standard divergence occurs when the Z-Score direction disagrees with the price direction — price makes a higher high but the Z-Score makes a lower high (bearish divergence), or price makes a lower low but the Z-Score makes a higher low (bullish divergence). Hidden divergence occurs when the Z-Score makes an extreme move while price action is relatively contained — a potential continuation pattern. Divergence events are labeled directly on the chart with bold, clearly sized labels: bullDiv = close > close and priceZ < priceZ // Price up, Z down = bull div bearDiv = close < close and priceZ > priceZ // Price down, Z up = bear div Labels: BULL DIV, BEAR DIV (size.small), H.BULL, H.BEAR (size.tiny for hidden divergence). 5. Multi-Dimensional Dashboard The institutional dashboard presents all three Z-Scores, the composite Z-Score, current zone classification, reversal probability, and divergence status simultaneously. The layout is designed so the most actionable information — Zone and Rev. Probability — is displayed at the largest text size, with supporting metrics at smaller sizes. Features Three independent Z-Scores: Price level, price change (momentum), and volume — each computed on its own rolling mean and standard deviation Configurable Z-Score weights: The composite score uses adjustable per-dimension weights allowing emphasis on price, momentum, or volume depending on trading context Live reversal probability: Probability percentage mapped directly from the Z-Score using normal distribution properties (68.3% at 1σ through 99.7% at 3σ) Zone classification: Composite Z-Score classified as Extreme, Elevated, or Neutral in both directions with color-coded dashboard display Divergence labels (BULL DIV / BEAR DIV): Z-Score vs price direction disagreement labeled on-chart at size.small Hidden divergence labels (H.BULL / H.BEAR): Z-Score extreme with contained price action labeled at size.tiny Configurable extreme and elevated thresholds: Both Z-Score thresholds independently adjustable Institutional dashboard (top right): 14-row table with Price Z, Change Z, Volume Z, Composite Z, Zone, Reversal Probability, and divergence status Adaptive thresholds: All calculations normalize to the rolling lookback period, adapting to current instrument and timeframe volatility Alerts: Separate alertconditions for extreme bull and extreme bear composite Z-Score readings Input Parameters Z-Score Settings: Z-Score Length: Rolling window for all three Z-Score calculations (default: 20) Extreme Threshold: Z-Score magnitude classified as Extreme zone (default: 2.0) Elevated Threshold: Z-Score magnitude classified as Elevated zone (default: 1.0) Dimension Weights: Price Weight: Relative weight of the price Z-Score in composite (default: 1.0) Change Weight: Relative weight of the momentum Z-Score in composite (default: 1.0) Volume Weight: Relative weight of the volume Z-Score in composite (default: 0.5) Divergence: Divergence Lookback: Bars back for divergence comparison (default: 5) Show Divergence Labels toggle Display: Show Dashboard toggle Bull and Bear color inputs How to Use This Indicator Step 1: Read the Composite Zone The Zone row in the dashboard shows the current composite Z-Score classification. EXTREME readings at the top of the scale indicate the highest statistical probability of mean reversion. NEUTRAL readings indicate current conditions are close to the mean and have low statistical directional edge from this tool alone. Step 2: Check Reversal Probability The Rev. Probability row translates the Z-Score magnitude directly into a percentage. A reading above 95% means the current composite Z-Score is in the outer 5% of its historical distribution — a statistical extreme that has preceded mean reversion 95% of the time in the measured period. Step 3: Assess Each Dimension Independently The three individual Z-Score rows reveal which dimension is driving the composite. A high composite driven entirely by volume Z-Score is a different setup than one driven by price Z-Score. Understanding which dimension is extreme helps filter entries: a price Z-Score extreme without supporting momentum or volume Z-Score extremes may be a lower-conviction reading. Step 4: React to Divergence Labels BULL DIV and BEAR DIV labels appear when Z-Score momentum diverges from price direction. These signal that the statistical driver of a move is weakening even as price continues. H.BULL and H.BEAR hidden divergence labels flag potential continuation setups where Z-Score is extreme but price is not. Step 5: Combine with Structural Context Deviation Lens produces the highest value when its extreme readings coincide with a structural confluence point — an order block, session low, or structure level. A 99.7% reversal probability at a tested support zone is a higher-conviction setup than the same reading in open air. Indicator Limitations All Z-Scores are computed relative to the rolling lookback window. The lookback defines what "normal" means. A very short lookback will produce extreme readings frequently; a very long lookback will rarely reach the extreme threshold. Calibration to the instrument and timeframe is required The reversal probability percentages are derived from the normal distribution assumption. Price change and volume distributions are not perfectly normal — they exhibit fat tails and skew. The probabilities are approximations, not precise statistical guarantees The composite Z-Score uses equal weights by default. Changing dimension weights significantly alters which market conditions produce extreme readings. Weight adjustments should be based on the specific instrument's characteristics Divergence detection uses a simple lookback comparison, not a peak-detection algorithm. In choppy markets, divergence labels may appear frequently without providing actionable signals Originality Statement Deviation Lens is original in its simultaneous, weighted multi-dimensional Z-Score framework that maps composite statistical extremity directly to a reversal probability percentage. This indicator is published because: Applying Z-Score analysis to three independent market dimensions simultaneously — price level, momentum (close-to-close change), and volume — rather than a single oscillator provides a richer statistical picture of current market extremity than any single-dimension Z-Score tool The direct mapping of Z-Score magnitude to reversal probability percentages using normal distribution properties gives traders an immediately interpretable statistic rather than a raw number requiring subjective interpretation The composite weighted Z-Score system, where each dimension's contribution to the overall reading is configurable, allows the indicator to be tuned toward price-mean-reversion strategies, momentum exhaustion strategies, or volume anomaly detection depending on the trader's methodology The combined detection of standard divergence and hidden divergence between the Z-Score and price direction provides trend continuation and reversal signals from the same framework 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. Z-Score readings and reversal probability percentages are statistical tools based on historical distributions and do not guarantee any future price behavior. The normal distribution assumption applied to price and volume data is an approximation. Always use proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
SuperTrend Take-Profit Dimensions [AlgoAlpha]🟠 OVERVIEW A multi-dimensional take-profit aid that scores how typical the current bar looks compared to past SuperTrend pivots, so you can tell when a trend has reached favorable exit conditions. The indicator runs a standard SuperTrend and records every confirmed zigzag pivot that occurs during a matching-direction run. Tops go into a bull pool , bottoms into a bear pool . Each pivot is stored as a set of readings across several independent axes, such as relative volume , time of day , and price position inside the recent range . On every bar, the current reading on each axis is compared to that historical pool. A blended score from 0 to 100 tells you how closely the current conditions resemble where past pivots in the same direction have clustered. The idea is to give trend followers a data-backed sense of when to start tightening up, rather than guessing an exit or using a fixed R-multiple. The three built-in axes were chosen deliberately to be as uncorrelated as possible , each describing a different dimension of market context: volume (relative volume percentile), time (time of day), and price (position in recent range). Correlated inputs would double-count the same information and distort the blended score; picking axes that describe genuinely different aspects of the market means each one contributes independent evidence, and the score reflects how many distinct dimensions are currently in agreement. 🟠 CONCEPTS SuperTrend — An ATR-based trailing stop that flips between bullish and bearish states. Controls which pool of historical pivots the script reads from. Pivot pool — A rolling store of confirmed zigzag pivots, split by direction. Bull pool holds pivot highs that printed during bullish SuperTrend runs; bear pool holds pivot lows from bearish runs. Capped at 2000 entries per side . Context axis — A 0–100 value measured at the pivot bar. The script ships with three built-ins ( relative volume percentile , time of day , position in recent range ) and one optional user-plugged signal. Axis independence — The three built-in axes cover volume , time , and price respectively, chosen so each describes a structurally different part of the market. Low correlation between axes keeps the blended score from being dominated by any single factor. Conditional histogram — For each active axis, the script walks its pool and keeps only pivots whose bins on every other active axis match the current bar. The survivors are binned to form a histogram. Axis score — For one axis, the count of pivots in the current bar's bin divided by the count in the histogram's tallest bin, scaled to 0–100 . 100 means the current context sits in the densest part of past pivots. Blended favourability score — Arithmetic mean of the active per-axis scores. This is what the gauge and table display. Density-match scoring — The score measures how common the current context is among past pivots. It is not a forward probability and makes no claim about what happens next. 🟠 FEATURES Right-side context profiles — Stacked mini histograms render to the right of price, one per active axis. • Bar heights show how pivots in each axis's conditional pool distribute across bins. • A dashed vertical line marks the current bar's bin on that axis, so you can see at a glance where today sits against history. • Bar hue tracks the active SuperTrend direction. Favourability gauge — A vertical gradient table in the bottom-right showing the blended score, with a chevron marking the current level. Green at the top, red at the bottom. Favourability breakdown table — A two-column readout of each active axis's individual score out of 100, plus a final row that classifies the blended score as Good , Neutral , or Bad . Position and text size are configurable. Bar coloring — Bars fade from neutral grey toward the opposing trend colour as the blended score rises toward 100, so the chart itself signals when the context is stretched. Take-profit markers — Small orange markers print above or below the bar when the blended score hits 100 for the active SuperTrend direction. Timeframe guard — The time-of-day axis disables automatically on daily and higher timeframes, where the reading has no meaning, and a banner explains this so the blended score stays honest. Multi-dimensional scoring engine — Four independent axes feed into a single score, each conditioned on all the others. • Three built-in axes can be toggled on or off individually. • A fourth axis accepts any plot via source input , provided the series stays within 0–100 on all loaded bars. • An on-chart warning prints if the custom signal leaves that range, and the axis is ignored until it is corrected. Deliberately uncorrelated built-in axes — Volume ( relative volume percentile ), time ( time of day ), and price ( position in recent range ) cover three structurally different facets of market context. Keeping the axes independent means each one adds new information to the blend rather than reinforcing the others. Alert conditions — Six alerts are included: SuperTrend bullish flip, SuperTrend bearish flip, score peak match, and crossovers into the Good , Neutral , and Bad bands. 🟠 HOW TO USE Add the script to an intraday chart on a liquid instrument and let it run long enough to populate the pools. More history means more stable conditional histograms. Let SuperTrend define the active regime. The script only scores in the direction of the current trend; bar coloring and take-profit markers respect that regime. Read the gauge and breakdown table together. The gauge shows the blended level; the table shows which individual axes are pulling it up or down. Use the right-side profiles as a sanity check. If the dashed current-bin marker is sitting on or near the tallest bar across most axes, the current context closely resembles past pivot contexts in that direction. Treat high scores as a cue to tighten management, not as reversal signals. A reading of 100 means conditions match where pivots have historically clustered, not that the trend is guaranteed to end. Adjust the zigzag pivot length to control how strict the pool is. Lower values admit more pivots ( bigger, noisier sample ); higher values keep only firmer pivots ( smaller, cleaner sample ). Plug your own signal into the custom axis to test whether an existing 0–100 oscillator adds useful conditioning, such as an RSI or a normalised momentum reading. For best results, pick a signal that is not strongly correlated with the three built-ins, so the custom axis adds a new dimension rather than re-stating an existing one. Enable only the alerts that fit your workflow. The band-crossover alerts fire once per transition , not on every bar inside a band. 🟠 LIMITATIONS The pool holds every confirmed pivot during a matching-direction run, not only pivots that ended the trend. Intermediate pullbacks sit alongside genuine terminal pivots. Raising the zigzag pivot length filters the pool further if you want a cleaner sample. On strongly trending symbols the pool is dominated by pullback pivots rather than true terminal exits, because strong trends have many small pullbacks and only one final top or bottom. On choppy symbols the ratio is more balanced. Read the score with this in mind. The blended score is a density-match measure, not a forward probability . A high reading means today's context is common among past pivots of this direction. It does not predict that the trend is about to end. The time-of-day axis has no meaning on daily and higher timeframes and is disabled automatically on those timeframes. A warning banner confirms when this is active. The custom axis requires a source already scaled to 0–100 on every loaded bar. Values outside that range disable the axis and surface a warning. Toggling the custom axis on a live chart starts the range check from the current bar; reload the chart to validate against full loaded history . Pools are capped at 2000 entries per direction , with the oldest entries dropped first. On very long intraday histories the effective lookback is symbol- and timeframe-dependent. All scoring uses data up to and including the confirmation bar of each pivot; pivots themselves are detected with the standard zigzag confirmation lag, meaning the scoring population on any given bar reflects pivots confirmed at least zzLen bars earlier. 🟠 CONCLUSION SuperTrend Take-Profit Dimensions combines a standard SuperTrend with a rolling pool of historical pivot contexts and scores the current bar against that pool across up to four independent axes spanning volume, time, and price. The output is a blended 0–100 favourability reading , a per-axis breakdown, and a set of context profiles that show where past pivots have clustered. It gives trend followers a structured, data-backed way to judge when the current context matches where trends have historically given back profit, without pretending to predict the next bar.
Indicateur Pine Script®
par AlgoAlpha
10
Statistical Zone Engine [JOAT]Statistical Zone Engine Introduction Statistical Zone Engine is an open-source overlay indicator that builds pivot-cluster support and resistance zones with walk-forward statistical scoring. Each zone is backed by a full expected value computation: the indicator counts historical touches and bounces from the zone's price range over a configurable lookback, computes a win rate, and derives an EV score in units of R. Zones are tiered into four strength categories — Weak, Moderate, Strong, and Institutional — based on their live touch count, with border thickness and fill opacity scaling proportionally to the EV and tier. Labels display R:R, win rate, EV, and touch count, all updated live each bar. The core problem this indicator solves is that conventional support and resistance drawing tools are entirely qualitative — the trader decides what is significant by eye. The SZE replaces that subjective judgment with a quantitative framework: zone strength is computed from actual price behavior over the lookback window, not from the visual prominence of the swing. A zone that has been tested eight times with seven bounces carries an objectively different statistical weight from one that was tested twice with one bounce, and the SZE communicates that difference through its tier system, border rendering, and live EV label. Cluster merging prevents adjacent pivots at nearly the same price from spawning overlapping zones that would misrepresent true strength. Core Concepts 1. Pivot Cluster Zones The indicator uses ta.pivothigh and ta.pivotlow with a configurable swing length. When a new pivot high is confirmed and no existing resistance zone is within ATR * clusterTol of the pivot price, a new zone is created. The cluster merge check prevents nearby pivots from generating duplicate zones at the same structural level — if a zone already exists within the tolerance radius, no new zone is spawned. This means zones represent genuinely distinct price levels, not just the most recent pivot above an existing zone. 2. Walk-Forward Expected Value Computation For each new zone, the indicator scans the prior lookback bars and counts every bar where the high-low range overlapped with the zone. For each touch, it checks whether the close exited the far side of the zone — if so, it counts as a bounce. Win rate = bounces / touches. EV = winRate * tpRR - (1 - winRate) * slRR. A positive EV means the zone has historically resolved in the bounce direction more often than not, weighted by the configured R:R ratio. 3. Four-Tier Strength System Zone tier is determined by live touch count: Weak: 1-2 touches — thin border (width 1), low opacity fill Moderate: 3 touches — medium border (width 1), moderate opacity fill Strong: 4-5 touches — thicker border (width 2), more opaque fill Institutional: 6+ touches — widest border (width 3), most opaque fill Both the border width and the border transparency scale with tier, producing a visual system where the most historically significant zones dominate the chart. The fill opacity also scales with EV — zones with positive EV are more opaque, zones with negative EV are more transparent. 4. Live Label Updates Each zone carries a label at its right edge displaying: type (RES/SUP), tier name, touch count, win rate percentage, and EV in R units. The label is recalculated and updated every bar when price is inside the zone, ensuring the statistics reflect current behavior. The label text color also scales with tier — more significant zones use brighter text. 5. Sweep Detection When price closes fully through a zone boundary — above the top for resistance, below the bottom for support — the zone is marked as mitigated. If volume exceeds 1.4x the SMA(20) at the mitigation bar, a BREAK label fires above or below the zone. The total sweep count accumulates in the dashboard. After a break, zone fill fades to near-transparent, clearly communicating that the level has been closed through. Features Pivot-Cluster Zone Detection: Swing-pivot based zone creation with ATR-cluster merge deduplication — nearby pivots do not spawn overlapping zones Walk-Forward EV Computation: Historical touch/bounce counting over configurable lookback produces win rate and R-unit EV scores for each zone Four-Tier Strength System: Weak / Moderate / Strong / Institutional tiers based on touch count — border width and opacity scale with tier EV-Scaled Fill Opacity: Positive EV zones are more opaque, negative EV zones are more transparent — fill intensity communicates statistical quality Live Label Updates: Type, tier, touch count, win rate %, and EV in R units update every bar when price is inside the zone Sweep Detection with Volume Filter: BREAK label fires on zone close-through when volume exceeds 1.4x SMA(20) Post-Break Zone Fade: Broken zones fade visually, clearly delineating active versus mitigated levels Proximity Markers: Diamond plotchar fires when price first enters a zone neighborhood Min Touches Filter: Only zones with at least the configured minimum historical touches are displayed, eliminating freshly-formed single-touch zones Zone Trim Management: Oldest zones are removed when arrays exceed the maximum zone count, keeping memory bounded 9-Row Dashboard: Active resistance and support zone counts, near-zone states, total sweep count, TP and SL R:R ratios, ATR 4 Alertconditions: Zone entry for resistance and support, new zone creation for both sides Input Parameters Zone Detection: Swing Length: Pivot confirmation lookback period — higher values detect fewer, more significant pivots (default 10) Cluster ATR Tolerance: Pivots within ATR * this of an existing zone are merged rather than spawning a new zone (default 0.4) Zone ATR Width: Half the zone height as an ATR multiple — controls vertical thickness (default 0.35) Max Active Zones: Maximum concurrent zones per direction before oldest are trimmed (default 12) Min Touches To Show: Minimum historical touches required to display a zone (default 2) Statistics: EV Lookback (bars): Historical bar window for touch/bounce counting (default 200) TP R:R Ratio: Take-profit distance in R units used for EV calculation (default 2.0) SL R:R Ratio: Stop-loss distance in R units used for EV calculation (default 1.0) Visuals: Toggles for zone labels, sweep labels, and dashboard Resistance Color (default orange #f97316), Support Color (default sky blue #38bdf8) How to Use This Indicator Primary Setup — Statistical Zone Entry: Look for Institutional or Strong zones with positive EV — these are the levels with the longest bounce history weighted by your R:R parameters. When price enters a zone, the live label shows the current win rate. Enter at the zone edge with a stop beyond the far edge and a target at your TP R:R ratio from entry. EV as a Selection Filter: Multiple zones may be on the chart simultaneously. Prioritize zones with positive EV labels (e.g., EV: 1.25R) over zones with negative EV. A zone with 4 touches and 75% win rate at 2R:1R produces an EV of +1.25R per trade — objectively worth trading. A zone with 3 touches and 33% win rate at the same R:R produces EV of -0.33R — not worth trading regardless of how prominent it looks. Using the Sweep Count: The total sweep count on the dashboard accumulates every time a zone break is detected with high-volume momentum. Rising sweep counts in one direction indicate the market is consistently breaking through levels on that side — a sign of trending pressure rather than range behavior. Adjust bias accordingly. Cluster Merge and Fresh Zones: When a new pivot forms near an existing zone and is merged rather than spawning a new zone, the existing zone's historical statistics remain unchanged. A fresh zone with no historical data will show EV close to 0 — treat these as unproven until more touches accumulate. Indicator Limitations EV computation scans up to the full lookback on every qualifying pivot — on very long lookback settings and active pivot instruments, this can increase calculation time The walk-forward EV uses the same zone size (ATR * width at creation time) for historical counting. If ATR changes significantly between creation time and the historical scan, the touch count may include bars where the equivalent zone boundaries would have been different Cluster merging uses the current ATR at detection time. In periods of sharply rising or falling ATR, two zones that appear to merge at one ATR level may have been distinct at a different level, potentially underrepresenting zone density The touch count displayed on the label is the live count updated each bar. The historical bounce count used for EV is computed at creation time and is not re-scanned dynamically — the label win rate reflects creation-time statistics The minimum touches filter removes zones with fewer historical touches than the threshold. On fresh instruments or small lookbacks, most zones may be filtered out, especially on less-traded timeframes Originality Statement This indicator is original in its walk-forward EV scoring framework, four-tier visual strength system driven by live touch counts, and the cluster merging deduplication approach. The publication is justified because: Walk-forward EV computation in R units provides a quantitative quality signal not found in standard support/resistance tools — each zone is backed by a historically derived expected value, enabling objective zone selection The four-tier visual system (border width and opacity scaling with tier and EV) embeds the statistical quality directly into the zone appearance, eliminating the need to read labels to gauge significance ATR-cluster merge deduplication prevents pivot-dense markets from generating overlapping zones at the same structural level, producing a cleaner, more meaningful map than raw pivot-based zone tools Live label updates during zone interaction show the evolving win rate and EV as each new touch is counted, providing real-time statistical feedback not present in static zone indicators The post-break fade combined with the total sweep count dashboard provides a structural memory of how many levels have been invalidated, enabling a directional bias gauge derived from zone lifecycle data Disclaimer This indicator is provided for educational and informational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument. Past performance of any pattern or signal does not guarantee future results. All trading involves substantial risk. Always use proper risk management and conduct your own independent analysis. — Made with passion by officialjackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
Crucible Convergence Engine [JOAT]Crucible Convergence Engine Introduction The Crucible Convergence Engine is an open-source multi-module convergence strategy that requires alignment across five independent analytical engines before entering a trade. It fuses a Regime Arbiter (market state classification), Directional Helix (trend direction), Pressure Reactor (volume-weighted momentum), Deviation Lattice (statistical band filter), and Fortress Grid (dynamic S/R levels) into a unified convergence scoring system. Entries only fire when all required modules agree — regime confirms a trending state, trend direction aligns, momentum confirms, price is not at a statistical extreme, and volume exceeds its gate threshold. Exits are managed through ATR-based stops and targets, an optional trailing shield, regime flip detection, lattice extreme reversal, and rail interaction exits. This strategy exists because most trading systems rely on one or two confirmation layers. A moving average crossover with an RSI filter, for example, still enters trades in ranging markets, against macro trends, or at statistical extremes. CCE addresses this by requiring convergence across five fundamentally different analytical dimensions before committing capital. The trade-off is fewer trades — but each trade has multi-dimensional confirmation behind it. Module Architecture Module 1: Regime Arbiter The Regime Arbiter classifies the market into four states using ATR percentile ranking, custom directional movement scoring, and EMA trend alignment: Kinetic Ascent: ATR percentile above the kinetic threshold, positive directional bias, fast EMA above slow EMA Kinetic Descent: Same volatility conditions but with negative directional bias Turbulence: ATR percentile above the turbulence threshold — high volatility without clear direction Equilibrium: Low volatility, no strong directional bias — ranging market A two-bar confirmation filter prevents single-bar regime flicker. When the Regime Gate is active (default), the strategy only enters trades during Kinetic Ascent or Kinetic Descent — it sits out during Turbulence and Equilibrium, avoiding the choppy conditions that destroy most trend-following systems. Module 2: Directional Helix A fast and slow moving average (EMA or SMA, configurable) determine trend direction. The strategy requires the helix to agree with the regime — a long entry needs both the regime in Kinetic Ascent AND the fast MA above the slow MA. Module 3: Pressure Reactor Volume-weighted momentum is calculated using the same logarithmic volume impact function found in the Ferrum Pressure Gauge indicator: float vwM = pChg * math.log(1 + vR * momVolSens) float mF = ta.ema(vwM, momFast) float mS = ta.ema(vwM, momSlow) float mIdx = ta.ema(mF - mS, 5) The Pressure Reactor must confirm the trade direction — bullish momentum for longs, bearish momentum for shorts. This ensures that volume-weighted price action supports the entry, not just trend direction. Module 4: Deviation Lattice A statistical band system (mean +/- standard deviation * multiplier) acts as an extreme filter. The strategy will NOT enter a long if price is already at or above the upper band (overbought), and will NOT enter a short if price is at or below the lower band (oversold). This prevents chasing extended moves that are statistically likely to revert. Module 5: Volume Gate A simple but effective filter requiring short-term volume to exceed a configurable multiple of average volume (default 1.1x). This ensures entries occur during periods of meaningful market participation, not during thin, unreliable conditions. Convergence Scoring Each module contributes a weighted score to the overall convergence percentage: Regime Arbiter: 25 points (trending state confirmed) Directional Helix: 25 points (trend direction aligned) Pressure Reactor: 25 points (momentum confirmed) Deviation Lattice: 15 points (not at statistical extreme) Volume Gate: 10 points (sufficient market participation) The convergence score is classified as FULL LOCK (90%+), STRONG (70%+), PARTIAL (50%+), or WEAK (below 50%). The dashboard displays this score in real-time so you can see how close the market is to triggering an entry even before it fires. Entry conditions require ALL modules to align simultaneously. Entries are edge-triggered — they fire only on the transition from non-convergent to convergent, preventing re-entry on the same signal. Risk Architecture ATR Shield (Stop Loss): Initial stop placed at entry price minus ATR * Shield Multiple (default 2.0x ATR). This adapts stop distance to current volatility. ATR Objective (Take Profit): Target placed at entry price plus ATR * Objective Multiple (default 3.0x ATR). The default 1:1.5 risk-reward ratio (2.0 stop vs 3.0 target) provides positive expectancy even with moderate win rates. Trailing Shield: When enabled, an ATR-based trailing stop ratchets in the direction of the trade. For longs, the trail is set at close minus ATR * Trail Multiple, and it only moves up, never down. This locks in profits during extended moves. Regime Flip Exit: If the Regime Arbiter flips to the opposite state (e.g., from Kinetic Ascent to Kinetic Descent while in a long), the position is closed immediately. This is a structural exit — the market environment that justified the entry no longer exists. Lattice Extreme Exit: If price reaches the opposite statistical extreme (upper band for longs, lower band for shorts), the position is closed. This captures profits at statistically extended levels. Rail Interaction Exit: If price enters the proximity zone of the opposing Fortress Grid rail (ceiling for longs, floor for shorts), the position is closed. This respects dynamic support/resistance levels. Strategy Default Properties These are the default settings used in the strategy's Properties dialog: Initial Capital: TradingView default Order Size: 10% of equity per trade (percent_of_equity) Pyramiding: 0 (no stacking — one position at a time) Commission: Not set by default — users should configure realistic commission for their instrument Slippage: Not set by default — users should add realistic slippage for their instrument Margin: margin_long=0, margin_short=0 (v5-equivalent behavior) Calc on Every Tick: false (confirmed bars only) Process Orders on Close: true Important: Users should set realistic commission AND slippage values in the strategy Properties before evaluating backtest results. The default results without commission/slippage will overstate performance. A commission of 0.04-0.1% per side and 1-3 ticks of slippage is reasonable for most liquid instruments. Command Panel (Dashboard) A 13-row monospace dashboard displays the complete strategy state: SCORE: Convergence classification with percentage (FULL LOCK / STRONG / PARTIAL / WEAK) REGIME: Current market state (Kinetic Ascent, Kinetic Descent, Turbulence, Equilibrium) HELIX: Trend direction (Ascent / Descent) PRESSURE: Momentum direction (Ascent / Descent) LATTICE: Band filter state (Clear / Ceiling Hit / Floor Hit) Z-SCORE: Current statistical deviation from mean VOL GATE: Volume gate status with current ratio (Open / Closed) POSITION: Current trade status (Long / Short / Flat) AGE: Bars since entry SHIELD: Current ATR-based stop distance TRAIL: Current trailing stop price (if active) DIR BIAS: Raw directional movement bias score Input Parameters Regime Arbiter: Dispersion Epoch / Rank Horizon / Kinetic Threshold / Turbulence Threshold / Regime Gate Active Directional Helix: Lead Filament / Anchor Filament / Filament Type (EMA or SMA) Pressure Reactor: Ignition Cycle / Sustain Cycle / Flux Epoch / Flux Amplifier Deviation Lattice: Lattice Depth / Sigma Aperture / Lattice Extreme Exit toggle Fortress Grid: Grid Anchor / Grid Increment / Proximity Radius / Rail Interaction Exit toggle Risk Architecture: Shield Multiple (stop) / Objective Multiple (target) / Risk Epoch (ATR period) / Trailing Shield toggle / Trail Multiple Volume Gate: Require Volume Confirmation / Volume Gate Threshold How to Use This Strategy Start by setting realistic commission and slippage in the strategy Properties before evaluating any backtest results. Adjust the Grid Increment in the Fortress Grid module to match your instrument (500-1000 for BTC, 50-100 for stocks, etc.). Monitor the Convergence Score in the dashboard — it shows how close the market is to triggering an entry. STRONG readings (70%+) that haven't yet reached FULL LOCK often precede entries by a few bars. The Regime Gate is the most impactful filter. Disabling it will produce more trades but in lower-quality market conditions. Keep it enabled unless you have a specific reason to trade ranging/volatile markets. Experiment with the Shield and Objective multiples to find the risk-reward ratio that matches your trading style. Higher Objective multiples produce fewer but larger winners; lower multiples produce more frequent but smaller wins. The Trailing Shield is most valuable in trending markets where moves extend beyond the initial target. In choppy markets, it may give back profits. Consider disabling it if the instrument tends to mean-revert quickly. Limitations and Honest Assessment Multi-module convergence produces fewer trades. On some instruments and timeframes, the strategy may go extended periods without a signal. This is by design — it prioritizes quality over quantity. Backtest results are hypothetical and do not account for real-world execution challenges including partial fills, requotes, and market impact. The strategy uses process_orders_on_close=true, which means orders execute at the close of the signal bar. In live trading, you would need to enter at the open of the next bar, which introduces slippage. Past performance shown in backtests does not guarantee future results. Market conditions change, and strategies that worked historically may underperform in different regimes. The default settings are not optimized for any specific instrument or timeframe. Users should test across multiple datasets and adjust parameters to their specific use case. The Regime Arbiter and all other modules use lagging indicators. Entries will always occur after a trend has begun, not at the exact bottom or top. No strategy works in all market conditions. CCE is designed for trending markets and will underperform during extended ranging or highly volatile periods. Originality Statement This strategy is original in its five-module convergence architecture. While individual components (ATR regime classification, MA crossovers, volume-weighted momentum, statistical bands, EMA-derived levels) are established concepts, CCE is justified because: The five-module convergence scoring system requires alignment across fundamentally different analytical dimensions (volatility regime, trend, momentum, statistics, structure) before entering — a more rigorous entry filter than typical dual-confirmation systems. The weighted convergence score provides a quantified readiness metric that communicates how close the market is to a valid entry, even when not all conditions are met. Four distinct exit mechanisms (ATR stop/target, trailing shield, regime flip, lattice extreme, rail interaction) provide layered risk management that adapts to different exit scenarios. The Regime Arbiter gate prevents trading during Turbulence and Equilibrium states, addressing the primary failure mode of trend-following strategies. Edge-triggered entries with two-bar regime confirmation prevent re-entry on the same signal and eliminate single-bar flicker. The comprehensive 13-row dashboard provides full transparency into every module's state, the convergence score, and the current risk parameters. Disclaimer This strategy 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. Backtest results are hypothetical, do not represent actual trading, and do not guarantee future performance. Past results in no way guarantee future results. Commission, slippage, and other real-world costs will reduce actual performance below what backtests show. Always use proper risk management, including position sizing appropriate for your account and risk tolerance. Never risk more than you can afford to lose. The author is not responsible for any losses incurred from using this strategy. -Made with passion by officialjackofalltrades
Stratégie Pine Script®
par officialjackofalltrades
Caldera Deviation Cloud [JOAT]Caldera Deviation Cloud Introduction The Caldera Deviation Cloud is an open-source statistical deviation band system that fuses anchored VWAP with Z-score adaptive band widths, Keltner ATR blending, and higher-timeframe volatility expansion into a unified probability envelope overlay. Instead of using a single method to calculate band width, CDC triple-blends VWAP standard deviation, statistical standard deviation, and ATR-based Keltner width — whichever produces the widest reading dominates, ensuring the bands never underestimate true market dispersion. The result is a layered cloud with inner (1-sigma, ~68% probability) and outer (2-sigma, ~95% probability) envelopes that adapt to both local and macro volatility conditions. What makes this indicator distinct from standard Bollinger Bands or VWAP bands is the fusion approach: it does not rely on a single deviation method. It also reverse-engineers historical Z-score reversal points into dynamic "fossil" support and resistance levels — price zones where statistical extremes have historically triggered reversals. Core Engine: Adaptive Deviation Fusion The band width calculation blends three independent deviation measurements: float baseAdapt = na(vwapStdev) or vwapStdev <= 0 ? statDev : math.max(vwapStdev, statDev * 0.5) float keltBlend = useKelt ? math.max(baseAdapt, keltW * 0.6) : baseAdapt float adaptDev = keltBlend * macroMult VWAP Standard Deviation: Derived from the anchored VWAP calculation (session, weekly, monthly, or quarterly reset). This captures volume-weighted price dispersion around the institutional fair value line. Statistical Standard Deviation: Classic standard deviation of closing prices over a configurable lookback (default 100 bars). This provides a pure statistical measure of price dispersion. Keltner Thermal Envelope: ATR-based width (EMA of close with ATR multiplier) that captures range-based volatility. When enabled, this prevents the bands from being too narrow during periods where price moves are large but close-to-close deviation is small. The wider of these three measurements is used as the base deviation, then multiplied by a macro volatility factor derived from the higher timeframe. Probability Lattice (Z-Score Engine) The Z-score engine computes how many standard deviations price is from its statistical mean, then smooths the result with VWMA for visual clarity: Z-Score: (close - SMA) / StdDev, smoothed with VWMA Mean Reversion Velocity: The rate of change of the Z-score, classified as EXPANDING (moving away from mean), CONTRACTING (returning toward mean), or STALLED Dynamic State: SHELL BREACH OB/OS (beyond historical reversal averages), ELEVATED/DEPRESSED (beyond 1 sigma), or EQUILIBRIUM (near mean) Reversal Archaeology (Fossil Levels) This is one of CDC's most distinctive features. The indicator detects Z-score pivot highs and pivot lows, filters them by a minimum threshold (default 1.5 sigma), and accumulates them into rolling arrays. The average of these historical reversal Z-scores is then reverse-engineered back into price levels: Fossil Resistance = VWMA(Mean + AvgTopReversalZ * StdDev) Fossil Support = VWMA(Mean + AvgBotReversalZ * StdDev) These "fossil levels" represent the price zones where, on average, the market has historically found statistical extremes significant enough to trigger reversals. They shift dynamically as new reversal data accumulates and old data rolls off. Macro Volatility Lens (HTF Expansion) When enabled, the indicator fetches standard deviation data from a higher timeframe (default 60-minute) and compares it to its own EMA. When macro volatility exceeds its average, the bands widen proportionally: macroMult = 1 + htfFactor * max(0, (htfStdev - htfAvgDev) / htfAvgDev) This prevents the bands from being too tight during periods of elevated macro uncertainty, even if the local timeframe appears calm. The security calls use lookahead=off to prevent repainting. Visual Elements Equilibrium Spine: The VWMA-smoothed center line (VWAP or statistical mean), plotted as a prominent purple line representing fair value. Core Envelope (Inner Bands): 1-sigma bands representing the ~68% probability zone. Color shifts dynamically based on price position within the cloud using color.from_gradient. Shell Envelope (Outer Bands): 2-sigma bands representing the ~95% probability zone. Price beyond these levels is statistically extreme. Nebula Gradient: A 10-layer gradient fill system creates a smooth visual transition from the spine outward through the core and shell envelopes. Upper layers use distribution (bearish) tones, lower layers use accumulation (bullish) tones. Fossil Levels: Cross-style plots marking the reverse-engineered support and resistance from Z-score reversal history. Signal Architecture CDC generates four signal types, all confirmed-bar only: SHELL BREACH: Price exceeds the outer (2-sigma) envelope — a statistically extreme event. Upper breach suggests distribution extreme, lower breach suggests accumulation extreme. Tooltip includes the sigma multiplier and current Z-score. CORE DRIFT: Price enters the zone between the inner and outer envelopes — elevated deviation but not yet extreme. This serves as an early warning before a potential shell breach. Command Panel (Dashboard) A 10-row monospace dashboard displays: LATTICE: Current smoothed Z-score value STATE: Statistical classification (Shell Breach OB/OS, Elevated, Depressed, Equilibrium) REV VEL: Mean reversion velocity direction (Expanding, Contracting, Stalled) SPINE: Current center line (VWAP/mean) price APERTURE: Current adaptive deviation width MACRO: HTF volatility multiplier (1.0x = normal, >1.1x = elevated macro vol) POSITION: Price location within the cloud (Upper Shell, Upper Core, Neutral, Lower Core, Lower Shell) FOSSIL R / FOSSIL S: Average Z-score at which historical reversals have occurred (resistance and support) Input Parameters Probability Lattice: Lattice Depth: Z-score lookback window (default 100) Lattice Damper: VWMA smoothing on raw Z-score (default 14) Sigma Core: Inner band multiplier, ~68% probability (default 1.0) Sigma Shell: Outer band multiplier, ~95% probability (default 2.0) Anchor Nexus: Volume Epoch: VWAP reset period — Session, Weekly, Monthly, or Quarterly Keltner Fusion: Enable Thermal Envelope: Toggle ATR-based width blending (default on) Thermal EMA / ATR Scale: Keltner channel parameters Macro Volatility Lens: Enable Horizon Expansion: Toggle HTF volatility widening (default on) Horizon Timeframe / Blend Factor: HTF parameters Reversal Archaeology: Fossil Depth: Rolling array size for reversal history (default 25) Fossil Threshold: Minimum Z-score magnitude for valid reversal (default 1.5) How to Use This Indicator Use the cloud as a probability envelope — price spending time near the outer shell is statistically unusual and often precedes mean reversion. Watch SHELL BREACH signals at the outer bands for potential reversal setups, especially when the Reversion Velocity shows CONTRACTING (Z-score returning toward mean). Fossil levels provide dynamic support/resistance derived from statistical history — they shift as new reversal data accumulates, making them adaptive rather than static. The MACRO multiplier in the dashboard warns when higher-timeframe volatility is elevated — wider bands during these periods reflect genuine uncertainty, not just noise. CORE DRIFT signals serve as early warnings — price entering the core-to-shell zone may continue to the shell or reverse. Use them as alerts to pay attention, not as standalone trade signals. The Equilibrium Spine (center line) acts as a dynamic fair value reference — extended moves away from it tend to revert over time. Limitations Statistical bands assume roughly normal price distributions, which markets frequently violate. Fat tails and gap events can exceed even the outer shell without warning. Z-score mean reversion is a tendency, not a guarantee — price can remain at statistical extremes for extended periods, especially during strong trends. Fossil levels are based on historical reversal averages and may not predict future reversal points accurately. They provide context, not certainty. The VWAP anchor resets at each period boundary (session, week, etc.), which can cause discontinuities in the center line and bands. Higher-timeframe volatility expansion depends on the selected HTF — different choices produce different macro multipliers. The indicator does not generate directional buy/sell signals — it provides statistical context for your own decision-making. Originality Statement This indicator is original in its triple-blend adaptive deviation approach. While VWAP bands, Bollinger Bands, and Keltner Channels are established concepts individually, CDC is justified because: The triple-blend deviation fusion (VWAP stdev + statistical stdev + Keltner ATR) ensures bands never underestimate dispersion regardless of which volatility measure is dominant. Reversal Archaeology reverse-engineers Z-score pivot history into dynamic price levels — a technique not found in standard deviation band indicators. The Macro Volatility Lens integrates higher-timeframe volatility directly into band width calculation, providing macro-aware probability envelopes. The 10-layer nebula gradient fill creates a visual probability density that communicates statistical significance through color intensity. Mean Reversion Velocity tracking provides directional context for Z-score movement, helping distinguish between expanding extremes and contracting reversals. The comprehensive dashboard presents statistical state, reversion dynamics, and fossil levels simultaneously. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Statistical deviation bands describe historical price distribution patterns but do not predict future price movement. Extreme Z-scores do not guarantee reversals. 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 tool. -Made with passion by officialjackofalltrades
Indicateur Pine Script®
par officialjackofalltrades
NQ Session Probability MapThis Pine Script indicator is a session-based probability analysis tool for NQ futures trading that helps traders identify high-probability trading opportunities by analyzing historical patterns across different market sessions. Core Functionality: The indicator tracks and visualizes three major trading sessions (Asian, London, and NY) and monitors how price interacts with key session levels. It uses historical data to calculate success probabilities for different trading scenarios during specific hours of the day. Key Features: Session Tracking - Automatically identifies and marks different trading sessions with their respective high/low levels Probability Heatmap - Color-codes the chart background based on historical success rates for the current hour, helping traders quickly identify favorable trading windows Level Monitoring - Tracks important price levels from previous sessions and specific hours, showing retracement probabilities Pattern Recognition - Identifies certain price behaviors and displays their historical success rates, with special emphasis on high-probability setups Gap Analysis - Detects price gaps at session openings and analyzes how they combine with other patterns to create enhanced opportunities Visual Statistics - Provides a comprehensive table showing success rates across different sessions and setup types, plus real-time information about current market conditions What Makes It Valuable: The indicator synthesizes multiple time-based patterns and displays probability percentages based on historical performance. This allows traders to make more informed decisions about when to trade and which setups to prioritize, without revealing the specific methodology behind the probabilities.
Indicateur Pine Script®
par givanne1995
Linear Regression Channel With Pearson's R (Multi Sigma & MTF)This indicator applies multi‑sigma linear regression across multiple institutional time horizons to quantify the line of best fit in equities and index markets. By combining multi‑timeframe presets with statistically derived deviation bands, it highlights trend structure, volatility expansion, and regime transitions with clarity. What’s New in This Update The original version of the indicator produced a linear regression channel with multiple deviation bands. However, the statistical values it displayed were not mathematically valid. The value labeled “r” was not Pearson’s correlation coefficient and could not be used to derive R² or any formal regression diagnostics. This update introduces a fully correct statistical engine based on ordinary least squares (OLS). NEW STATISTICAL OUTPUTS • True Pearson’s r • True R² (coefficient of determination) • RSS (Residual Sum of Squares) • TSS (Total Sum of Squares) These values are mathematically valid, bounded, and directly tied to the regression line. KEY IMPROVEMENTS • Correct OLS intercept (removes the erroneous +slope term) • Proper predicted values using ŷ = b₀ + b₁x • Correct centering around the actual mean of the data • Removal of correlation logic from the deviation engine • Clean separation between statistical computation and volatility computation • Regression channel visuals remain identical, but the underlying math is now fully accurate These changes ensure that r and R² reflect true trend strength and model fit, enabling more reliable interpretation of long‑term and short‑term trend regimes. CORE FEATURES (UNCHANGED) • Auto‑Multi‑Timeframe presets aligned with institutional trend horizons • Multi‑Sigma bands (+/‑1σ, +/‑2σ, +/‑3σ) for volatility structure and statistical extremes • True least‑squares regression recalculated each bar • Deviation mode toggle (Standard Deviation vs. Max Deviation) • Full documentation and institutional use‑case examples available on GitHub More information can be found here: github.com
Indicateur Pine Script®
par followmytradingjournal
Linear Regression Channel with Multi Sigma and Multi Time FrameThis indicator applies multi-sigma linear regression across multiple institutional time horizons to quantify the line of best fit in equities and index markets. By combining multi-timeframe presets with statistically derived deviation bands, it highlights trend structure, volatility expansion, and regime transitions with clarity. Features Auto-Multi-Timeframe presets map directly to institutional trend horizons (daily, weekly, monthly) for accurate regime detection. Multi-Sigma bands (+/-1, +/-2, +/-3) reveal volatility structure, trend strength, and statistical extremes. The regression line uses a true least-squares calculation, recalculated each bar for precise trend alignment. Deviation mode allows switching between standard deviation and max deviation to support different volatility models. A linked PDF on GitHub provides full documentation, derivations, and institutional use-case examples. More Information Can Be Found Here: github.com
Indicateur Pine Script®
par followmytradingjournal
Luxy Super-Duper SuperTrend Predictor Engine and Buy/Sell signalA professional trend-following grading system that analyzes historical trend patterns to provide statistical duration estimates using advanced similarity matching and k-nearest neighbors analysis. Combines adaptive Supertrend with intelligent duration statistics, multi-timeframe confluence, volume confirmation, and quality scoring to identify high-probability setups with data-driven target ranges across all timeframes. Note: All duration estimates are statistical calculations based on historical data, not guarantees of future performance. WHAT MAKES THIS DIFFERENT Unlike traditional SuperTrend indicators that only tell you trend direction, this system answers the critical question: "What is the typical duration for trends like this?" The Statistical Analysis Engine: • Analyzes your chart's last 15+ completed SuperTrend trends (bullish and bearish separately) • Uses k-nearest neighbors similarity matching to find historically similar setups • Calculates statistical duration estimates based on current market conditions • Learns from estimation errors and adapts over time (Advanced mode) • Displays visual duration analysis box showing median, average, and range estimates • Tracks Statistical accuracy with backtest statistics Complete Trading System: • Statistical trend duration analysis with three intelligence levels • Adaptive Supertrend with dynamic ATR-based bands • Multi-timeframe confluence analysis (6 timeframes: 5M to 1W) • Volume confirmation with spike detection and momentum tracking • Quality scoring system (0-70 points) rating each setup • One-click preset optimization for all trading styles • Anti-repaint guarantee on all signals and duration estimates METHODOLOGY CREDITS This indicator's approach is inspired by proven trading methodologies from respected market educators: • Mark Minervini - Volatility Contraction Pattern (VCP) and pullback entry techniques • William O'Neil - Volume confirmation principles and institutional buying patterns (CANSLIM methodology) • Dan Zanger - Volatility expansion entries and momentum breakout strategies Important: These are educational references only. This indicator does not guarantee any specific trading results. Always conduct your own analysis and risk management. KEY FEATURES 1. TREND DURATION ANALYSIS SYSTEM - The Core Innovation The statistical analysis engine is what sets this indicator apart from standard SuperTrend systems. It doesn't just identify trend changes - it provides statistical analysis of potential duration. How It Works: Step 1: Historical Tracking • Automatically records every completed SuperTrend trend (duration in bars) • Maintains separate databases for bullish trends and bearish trends • Stores up to 15 most recent trends of each type • Captures market conditions at each trend flip: volume ratio, ATR ratio, quality score, price distance from SuperTrend, proximity to support/resistance Step 2: Similarity Matching (k-Nearest Neighbors) • When new trend begins, system compares current conditions to ALL historical flips • Calculates similarity score based on: - Volume similarity (30% weight) - Is volume behaving similarly? - Volatility similarity (30% weight) - Is ATR/volatility similar? - Quality similarity (20% weight) - Is setup strength comparable? - Distance similarity (10% weight) - Is price distance from ST similar? - Support/Resistance proximity (10% weight) - Similar structural context? • Selects the 15 MOST SIMILAR historical trends (not just all trends) • This is like asking: "When conditions looked like this before, how long did trends last?" Step 3: Statistical Analysis • Calculates median duration (most common outcome) • Calculates average duration (mean of similar trends) • Determines realistic range (min to max of similar trends) • Applies exponential weighting (recent trends weighted more heavily) • Outputs confidence-weighted statistical estimate Step 4: Advanced Intelligence (Advanced Mode Only) The Advanced mode applies five sophisticated multipliers to refine estimates: A) Market Structure Multiplier (±30%): • Detects nearby support/resistance levels using pivot detection • If flip occurs NEAR a key level: Estimate adjusted -30% (expect bounce/rejection) • If flip occurs in open space: Estimate adjusted +30% (clear path for continuation) • Uses configurable lookback period and ATR-based proximity threshold B) Asset Type Multiplier (±40%): • Adjusts duration estimates based on asset volatility characteristics • Small Cap / Biotech: +40% (explosive, extended moves) • Tech Growth: +20% (momentum-driven, longer trends) • Blue Chip / Large Cap: 0% (baseline, steady trends) • Dividend / Value: -20% (slower, grinding trends) • Cyclical: Variable based on macro regime • Crypto / High Volatility: +30% (parabolic potential) C) Flip Strength Multiplier (±20%): • Analyzes the QUALITY of the trend flip itself • Strong flip (high volume + expanding ATR + quality score 60+): +20% • Weak flip (low volume + contracting ATR + quality score under 40): -20% • Logic: Historical data shows that powerful flips tend to be followed by longer trends D) Error Learning Multiplier (±15%): • Tracks Statistical accuracy over last 10 completed trends • Calculates error ratio: (estimated duration / Actual Duration) • If system consistently over-estimates: Apply -15% correction • If system consistently under-estimates: Apply +15% correction • Learns and adapts to current market regime E) Regime Detection Multiplier (±20%): • Analyzes last 3 trends of SAME TYPE (bull-to-bull or bear-to-bear) • Compares recent trend durations to historical average • If recent trends 20%+ longer than average: +20% adjustment (trending regime detected) • If recent trends 20%+ shorter than average: -20% adjustment (choppy regime detected) • Detects whether market is in trending or mean-reversion mode Three analysis modes: SIMPLE MODE - Basic Statistics • Uses raw median of similar trends only • No multipliers, no adjustments • Best for: Beginners, clean trending markets • Fastest calculations, minimal complexity STANDARD MODE - Full Statistical Analysis • Similarity matching with k-nearest neighbors • Exponential weighting of recent trends • Median, average, and range calculations • Best for: Most traders, general market conditions • Balance of accuracy and simplicity ADVANCED MODE - Statistics + Intelligence • Everything in Standard mode PLUS • All 5 advanced multipliers (structure, asset type, flip strength, learning, regime) • Highest Statistical accuracy in testing • Best for: Experienced traders, volatile/complex markets • Maximum intelligence, most adaptive Visual Duration Analysis Box: When a new trend begins (SuperTrend flip), a box appears on your chart showing: • Analysis Mode (Simple / Standard / Advanced) • Number of historical trends analyzed • Median expected duration (most likely outcome) • Average expected duration (mean of similar trends) • Range (minimum to maximum from similar trends) • Advanced multipliers breakdown (Advanced mode only) • Backtest accuracy statistics (if available) The box extends from the flip bar to the estimated endpoint based on historical data, giving you a visual target for trend duration. Box updates in real-time as trend progresses. Backtest & Accuracy Tracking: • System backtests its own duration estimates using historical data • Shows accuracy metrics: how well duration estimates matched actual durations • Tracks last 10 completed duration estimates separately • Displays statistics in dashboard and duration analysis boxes • Helps you understand statistical reliability on your specific symbol/timeframe Anti-Repaint Guarantee: • duration analysis boxes only appear AFTER bar close (barstate.isconfirmed) • Historical duration estimates never disappear or change • What you see in history is exactly what you would have seen real-time • No future data leakage, no lookahead bias 2. INTELLIGENT PRESET CONFIGURATIONS - One-Click Optimization Unlike indicators that require tedious parameter tweaking, this system includes professionally optimized presets for every trading style. Select your approach from the dropdown and ALL parameters auto-configure. "AUTO (DETECT FROM TF)" - RECOMMENDED The smartest option: automatically selects optimal settings based on your chart timeframe. • 1m-5m charts → Scalping preset (ATR: 7, Mult: 2.0) • 15m-1h charts → Day Trading preset (ATR: 10, Mult: 2.5) • 2h-4h-D charts → Swing Trading preset (ATR: 14, Mult: 3.0) • W-M charts → Position Trading preset (ATR: 21, Mult: 4.0) Benefits: • Zero configuration - works immediately • Always matched to your timeframe • Switch timeframe = automatic adjustment • Perfect for traders who use multiple timeframes "SCALPING (1-5M)" - Ultra-Fast Signals Optimized for: 1-5 minute charts, high-frequency trading, quick profits Target holding period: Minutes to 1-2 hours maximum Best markets: High-volume stocks, major crypto pairs, active futures Parameter Configuration: • Supertrend: ATR 7, Multiplier 2.0 (very sensitive) • Volume: MA 10, High 1.8x, Spike 3.0x (catches quick surges) • Volume Momentum: AUTO-DISABLED (too restrictive for fast scalping) • Quality minimum: 40 points (accepts more setups) • Duration Analysis: Uses last 15 trends with heavy recent weighting Trading Logic: Speed over precision. Short ATR period and low multiplier create highly responsive SuperTrend. Volume momentum filter disabled to avoid missing fast moves. Quality threshold relaxed to catch more opportunities in rapid market conditions. Signals per session: 5-15 typically Hold time: Minutes to couple hours Best for: Active traders with fast execution "DAY TRADING (15M-1H)" - Balanced Approach Optimized for: 15-minute to 1-hour charts, intraday moves, session-based trading Target holding period: 30 minutes to 8 hours (within trading day) Best markets: Large-cap stocks, major indices, established crypto Parameter Configuration: • Supertrend: ATR 10, Multiplier 2.5 (balanced) • Volume: MA 20, High 1.5x, Spike 2.5x (standard detection) • Volume Momentum: 5/20 periods (confirms intraday strength) • Quality minimum: 50 points (good setups preferred) • Duration Analysis: Balanced weighting of recent vs historical Trading Logic: The most balanced configuration. ATR 10 with multiplier 2.5 provides steady trend following that avoids noise while catching meaningful moves. Volume momentum confirms institutional participation without being overly restrictive. Signals per session: 2-5 typically Hold time: 30 minutes to full day Best for: Part-time and full-time active traders "SWING TRADING (4H-D)" - Trend Stability Optimized for: 4-hour to Daily charts, multi-day holds, trend continuation Target holding period: 2-15 days typically Best markets: Growth stocks, sector ETFs, trending crypto, commodity futures Parameter Configuration: • Supertrend: ATR 14, Multiplier 3.0 (stable) • Volume: MA 30, High 1.3x, Spike 2.2x (accumulation focus) • Volume Momentum: 10/30 periods (trend stability) • Quality minimum: 60 points (high-quality setups only) • Duration Analysis: Favors consistent historical patterns Trading Logic: Designed for substantial trend moves while filtering short-term noise. Higher ATR period and multiplier create stable SuperTrend that won't flip on minor corrections. Stricter quality requirements ensure only strongest setups generate signals. Signals per week: 2-5 typically Hold time: Days to couple weeks Best for: Part-time traders, swing style "POSITION TRADING (D-W)" - Long-Term Trends Optimized for: Daily to Weekly charts, major trend changes, portfolio allocation Target holding period: Weeks to months Best markets: Blue-chip stocks, major indices, established cryptocurrencies Parameter Configuration: • Supertrend: ATR 21, Multiplier 4.0 (very stable) • Volume: MA 50, High 1.2x, Spike 2.0x (long-term accumulation) • Volume Momentum: 20/50 periods (major trend confirmation) • Quality minimum: 70 points (excellent setups only) • Duration Analysis: Heavy emphasis on multi-year historical data Trading Logic: Conservative approach focusing on major trend changes. Extended ATR period and high multiplier create SuperTrend that only flips on significant reversals. Very strict quality filters ensure signals represent genuine long-term opportunities. Signals per month: 1-2 typically Hold time: Weeks to months Best for: Long-term investors, set-and-forget approach "CUSTOM" - Advanced Configuration Purpose: Complete manual control for experienced traders Use when: You understand the parameters and want specific optimization Best for: Testing new approaches, unusual market conditions, specific instruments Full control over: • All SuperTrend parameters • Volume thresholds and momentum periods • Quality scoring weights • analysis mode and multipliers • Advanced features tuning Preset Comparison Quick Reference: Chart Timeframe: Scalping (1M-5M) | Day Trading (15M-1H) | Swing (4H-D) | Position (D-W) Signals Frequency: Very High | High | Medium | Low Hold Duration: Minutes | Hours | Days | Weeks-Months Quality Threshold: 40 pts | 50 pts | 60 pts | 70 pts ATR Sensitivity: Highest | Medium | Lower | Lowest Time Investment: Highest | High | Medium | Lowest Experience Level: Expert | Advanced | Intermediate | Beginner+ 3. QUALITY SCORING SYSTEM (0-70 Points) Every signal is rated in real-time across three dimensions: Volume Confirmation (0-30 points): • Volume Spike (2.5x+ average): 30 points • High Volume (1.5x+ average): 20 points • Above Average (1.0x+ average): 10 points • Below Average: 0 points Volatility Assessment (0-30 points): • Expanding ATR (1.2x+ average): 30 points • Rising ATR (1.0-1.2x average): 15 points • Contracting/Stable ATR: 0 points Volume Momentum (0-10 points): • Strong Momentum (1.2x+ ratio): 10 points • Rising Momentum (1.0-1.2x ratio): 5 points • Weak/Neutral Momentum: 0 points Score Interpretation: 60-70 points - EXCELLENT: • All factors aligned • High conviction setup • Maximum position size (within risk limits) • Primary trading opportunities 45-59 points - STRONG: • Multiple confirmations present • Above-average setup quality • Standard position size • Good trading opportunities 30-44 points - GOOD: • Basic confirmations met • Acceptable setup quality • Reduced position size • Wait for additional confirmation or trade smaller Below 30 points - WEAK: • Minimal confirmations • Low probability setup • Consider passing • Only for aggressive traders in strong trends Only signals meeting your minimum quality threshold (configurable per preset) generate alerts and labels. 4. MULTI-TIMEFRAME CONFLUENCE ANALYSIS The system can simultaneously analyze trend alignment across 6 timeframes (optional feature): Timeframes analyzed: • 5-minute (scalping context) • 15-minute (intraday momentum) • 1-hour (day trading bias) • 4-hour (swing context) • Daily (primary trend) • Weekly (macro trend) Confluence Interpretation: • 5-6/6 aligned - Very strong multi-timeframe agreement (highest confidence) • 3-4/6 aligned - Moderate agreement (standard setup) • 1-2/6 aligned - Weak agreement (caution advised) Dashboard shows real-time alignment count with color-coding. Higher confluence typically correlates with longer, stronger trends. 5. VOLUME MOMENTUM FILTER - Institutional Money Flow Unlike traditional volume indicators that just measure size, Volume Momentum tracks the RATE OF CHANGE in volume: How it works: • Compares short-term volume average (fast period) to long-term average (slow period) • Ratio above 1.0 = Volume accelerating (money flowing IN) • Ratio above 1.2 = Strong acceleration (institutional participation likely) • Ratio below 0.8 = Volume decelerating (money flowing OUT) Why it matters: • Confirms trend with actual money flow, not just price • Leading indicator (volume often leads price) • Catches accumulation/distribution before breakouts • More intuitive than complex mathematical filters Integration with signals: • Optional filter - can be enabled/disabled per preset • When enabled: Only signals with rising volume momentum fire • AUTO-DISABLED in Scalping mode (too restrictive for fast trading) • Configurable fast/slow periods per trading style 6. ADAPTIVE SUPERTREND MULTIPLIER Traditional SuperTrend uses fixed ATR multiplier. This system dynamically adjusts the multiplier (0.8x to 1.2x base) based on: • Trend Strength: Price correlation over lookback period • Volume Weight: Current volume relative to average Benefits: • Tighter bands in calm markets (less premature exits) • Wider bands in volatile conditions (avoids whipsaws) • Better adaptation to biotech, small-cap, and crypto volatility • Optional - can be disabled for classic constant multiplier 7. VISUAL GRADIENT RIBBON 26-layer exponential gradient fill between price and SuperTrend line provides instant visual trend strength assessment: Color System: • Green shades - Bullish trend + volume confirmation (strongest) • Blue shades - Bullish trend, normal volume • Orange shades - Bearish trend + volume confirmation • Red shades - Bearish trend (weakest) Opacity varies based on: • Distance from SuperTrend (farther = more opaque) • Volume intensity (higher volume = stronger color) The ribbon provides at-a-glance trend strength without cluttering your chart. Can be toggled on/off. 8. INTELLIGENT ALERT SYSTEM Two-tier alert architecture for flexibility: Automatic Alerts: • Fire automatically on BUY and SELL signals • Include full context: quality score, volume state, volume momentum • One alert per bar close (alert.freq_once_per_bar_close) • Message format: "BUY: Supertrend bullish + Quality: 65/70 | Volume: HIGH | Vol Momentum: STRONG (1.35x)" Customizable Alert Conditions: • Appear in TradingView's "Create Alert" dialog • Three options: BUY Signal Only, SELL Signal Only, ANY Signal (BUY or SELL) • Use TradingView placeholders: {{ticker}}, {{interval}}, {{close}}, {{time}} • Fully customizable message templates All alerts use barstate.isconfirmed - Zero repaint guarantee. 9. ANTI-REPAINT ARCHITECTURE Every component guaranteed non-repainting: • Entry signals: Only appear after bar close • duration analysis boxes: Created only on confirmed SuperTrend flips • Informative labels: Wait for bar confirmation • Alerts: Fire once per closed bar • Multi-timeframe data: Uses lookahead=barmerge.lookahead_off What you see in history is exactly what you would have seen in real-time. No disappearing signals, no changed duration estimates. HOW TO USE THE INDICATOR QUICK START - 3 Steps to Trading: Step 1: Select Your Trading Style Open indicator settings → "Quick Setup" section → Trading Style Preset dropdown Options: • Auto (Detect from TF) - RECOMMENDED: Automatically configures based on your chart timeframe • Scalping (1-5m) - For 1-5 minute charts, ultra-fast signals • Day Trading (15m-1h) - For 15m-1h charts, balanced approach • Swing Trading (4h-D) - For 4h-Daily charts, trend stability • Position Trading (D-W) - For Daily-Weekly charts, long-term trends • Custom - Manual configuration (advanced users only) Choose "Auto" and you're done - all parameters optimize automatically. Step 2: Understand the Signals BUY Signal (Green Triangle Below Price): • SuperTrend flipped bullish • Quality score meets minimum threshold (varies by preset) • Volume confirmation present (if filter enabled) • Volume momentum rising (if filter enabled) • duration analysis box shows expected trend duration SELL Signal (Red Triangle Above Price): • SuperTrend flipped bearish • Quality score meets minimum threshold • Volume confirmation present (if filter enabled) • Volume momentum rising (if filter enabled) • duration analysis box shows expected trend duration Duration Analysis Box: • Appears at SuperTrend flip (start of new trend) • Shows median, average, and range duration estimates • Extends to estimated endpoint based on historical data visually • Updates mode-specific intelligence (Simple/Standard/Advanced) Step 3: Use the Dashboard for Context Dashboard (top-right corner) shows real-time metrics: • Row 1 - Quality Score: Current setup rating (0-70) • Row 2 - SuperTrend: Direction and current level • Row 3 - Volume: Status (Spike/High/Normal/Low) with color • Row 4 - Volatility: State (Expanding/Rising/Stable/Contracting) • Row 5 - Volume Momentum: Ratio and trend • Row 6 - Duration Statistics: Accuracy metrics and track record Every cell has detailed tooltip - hover for full explanations. SIGNAL INTERPRETATION BY QUALITY SCORE: Excellent Setup (60-70 points): • Quality Score: 60-70 • Volume: Spike or High • Volatility: Expanding • Volume Momentum: Strong (1.2x+) • MTF Confluence (if enabled): 5-6/6 • Action: Primary trade - maximum position size (within risk limits) • Statistical reliability: Highest - duration estimates most accurate Strong Setup (45-59 points): • Quality Score: 45-59 • Volume: High or Above Average • Volatility: Rising • Volume Momentum: Rising (1.0-1.2x) • MTF Confluence (if enabled): 3-4/6 • Action: Standard trade - normal position size • Statistical reliability: Good - duration estimates reliable Good Setup (30-44 points): • Quality Score: 30-44 • Volume: Above Average • Volatility: Stable or Rising • Volume Momentum: Neutral to Rising • MTF Confluence (if enabled): 3-4/6 • Action: Cautious trade - reduced position size, wait for additional confirmation • Statistical reliability: Moderate - duration estimates less certain Weak Setup (Below 30 points): • Quality Score: Below 30 • Volume: Low or Normal • Volatility: Contracting or Stable • Volume Momentum: Weak • MTF Confluence (if enabled): 1-2/6 • Action: Pass or wait for improvement • Statistical reliability: Low - duration estimates unreliable USING duration analysis boxES FOR TRADE MANAGEMENT: Entry Timing: • Enter on SuperTrend flip (signal bar close) • duration analysis box appears simultaneously • Note the median duration - this is your expected hold time Profit Targets: • Conservative: Use MEDIAN duration as profit target (50% probability) • Moderate: Use AVERAGE duration (mean of similar trends) • Aggressive: Aim for MAX duration from range (best historical outcome) Position Management: • Scale out at median duration (take partial profits) • Trail stop as trend extends beyond median • Full exit at average duration or SuperTrend flip (whichever comes first) • Re-evaluate if trend exceeds estimated range analysis mode Selection: • Simple: Clean trending markets, beginners, minimal complexity • Standard: Most markets, most traders (recommended default) • Advanced: Volatile markets, complex instruments, experienced traders seeking highest accuracy Asset Type Configuration (Advanced Mode): If using Advanced analysis mode, configure Asset Type for optimal accuracy: • Small Cap: Stocks under $2B market cap, low liquidity • Biotech / Speculative: Clinical-stage pharma, penny stocks, high-risk • Blue Chip / Large Cap: S&P 500, mega-cap tech, stable large companies • Tech Growth: High-growth tech (TSLA, NVDA, growth SaaS) • Dividend / Value: Dividend aristocrats, value stocks, utilities • Cyclical: Energy, materials, industrials (macro-driven) • Crypto / High Volatility: Bitcoin, altcoins, highly volatile assets Correct asset type selection improves Statistical accuracy by 15-20%. RISK MANAGEMENT GUIDELINES: 1. Stop Loss Placement: Long positions: • Place stop below recent swing low OR • Place stop below SuperTrend level (whichever is tighter) • Use 1-2 ATR distance as guideline • Recommended: SuperTrend level (built-in volatility adjustment) Short positions: • Place stop above recent swing high OR • Place stop above SuperTrend level (whichever is tighter) • Use 1-2 ATR distance as guideline • Recommended: SuperTrend level 2. Position Sizing by Quality Score: • Excellent (60-70): Maximum position size (2% risk per trade) • Strong (45-59): Standard position size (1.5% risk per trade) • Good (30-44): Reduced position size (1% risk per trade) • Weak (Below 30): Pass or micro position (0.5% risk - learning trades only) 3. Exit Strategy Options: Option A - Statistical Duration-Based Exit: • Exit at median estimated duration (conservative) • Exit at average estimated duration (moderate) • Trail stop beyond average duration (aggressive) Option B - Signal-Based Exit: • Exit on opposite signal (SELL after BUY, or vice versa) • Exit on SuperTrend flip (trend reversal) • Exit if quality score drops below 30 mid-trend Option C - Hybrid (Recommended): • Take 50% profit at median estimated duration • Trail stop on remaining 50% using SuperTrend as trailing level • Full exit on SuperTrend flip or quality collapse 4. Trade Filtering: For higher win-rate (fewer trades, better quality): • Increase minimum quality score (try 60 for swing, 50 for day trading) • Enable volume momentum filter (ensure institutional participation) • Require higher MTF confluence (5-6/6 alignment) • Use Advanced analysis mode with appropriate asset type For more opportunities (more trades, lower quality threshold): • Decrease minimum quality score (40 for day trading, 35 for scalping) • Disable volume momentum filter • Lower MTF confluence requirement • Use Simple or Standard analysis mode SETTINGS OVERVIEW Quick Setup Section: • Trading Style Preset: Auto / Scalping / Day Trading / Swing / Position / Custom Dashboard & Display: • Show Dashboard (ON/OFF) • Dashboard Position (9 options: Top/Middle/Bottom + Left/Center/Right) • Text Size (Auto/Tiny/Small/Normal/Large/Huge) • Show Ribbon Fill (ON/OFF) • Show SuperTrend Line (ON/OFF) • Bullish Color (default: Green) • Bearish Color (default: Red) • Show Entry Labels - BUY/SELL signals (ON/OFF) • Show Info Labels - Volume events (ON/OFF) • Label Size (Auto/Tiny/Small/Normal/Large/Huge) Supertrend Configuration: • ATR Length (default varies by preset: 7-21) • ATR Multiplier Base (default varies by preset: 2.0-4.0) • Use Adaptive Multiplier (ON/OFF) - Dynamic 0.8x-1.2x adjustment • Smoothing Factor (0.0-0.5) - EMA smoothing applied to bands • Neutral Bars After Flip (0-10) - Hide ST immediately after flip Volume Momentum: • Enable Volume Momentum Filter (ON/OFF) • Fast Period (default varies by preset: 3-20) • Slow Period (default varies by preset: 10-50) Volume Analysis: • Volume MA Length (default varies by preset: 10-50) • High Volume Threshold (default: 1.5x) • Spike Threshold (default: 2.5x) • Low Volume Threshold (default: 0.7x) Quality Filters: • Minimum Quality Score (0-70, varies by preset) • Require Volume Confirmation (ON/OFF) Trend Duration Analysis: • Show Duration Analysis (ON/OFF) - Display duration analysis boxes • analysis mode - Simple / Standard / Advanced • Asset Type - 7 options (Small Cap, Biotech, Blue Chip, Tech Growth, Dividend, Cyclical, Crypto) • Use Exponential Weighting (ON/OFF) - Recent trends weighted more • Decay Factor (0.5-0.99) - How much more recent trends matter • Structure Lookback (3-30) - Pivot detection period for support/resistance • Proximity Threshold (xATR) - How close to level qualifies as "near" • Enable Error Learning (ON/OFF) - System learns from estimation errors • Memory Depth (3-20) - How many past errors to remember Box Visual Settings: • duration analysis box Border Color • duration analysis box Background Color • duration analysis box Text Color • duration analysis box Border Width • duration analysis box Transparency Multi-Timeframe (Optional Feature): • Enable MTF Confluence (ON/OFF) • Minimum Alignment Required (0-6) • Individual timeframe enable/disable toggles • Custom timeframe selection options All preset configurations override manual inputs except when "Custom" is selected. ADVANCED FEATURES 1. Scalpel Mode (Optional) Advanced pullback entry system that waits for healthy retracements within established trends before signaling entry: • Monitors price distance from SuperTrend levels • Requires pullback to configurable range (default: 30-50%) • Ensures trend remains intact before entry signal • Reduces whipsaw and false breakouts • Inspired by Mark Minervini's VCP pullback entries Best for: Swing traders and day traders seeking precision entries Scalpers: Consider disabling for faster entries 2. Error Learning System (Advanced analysis mode Only) The system learns from its own estimation errors: • Tracks last 10-20 completed duration estimates (configurable memory depth) • Calculates error ratio for each: estimated duration / Actual Duration • If system consistently over-estimates: Applies negative correction (-15%) • If system consistently under-estimates: Applies positive correction (+15%) • Adapts to current market regime automatically This self-correction mechanism improves accuracy over time as the system gathers more data on your specific symbol and timeframe. 3. Regime Detection (Advanced analysis mode Only) Automatically detects whether market is in trending or choppy regime: • Compares last 3 trends to historical average • Recent trends 20%+ longer → Trending regime (+20% to estimates) • Recent trends 20%+ shorter → Choppy regime (-20% to estimates) • Applied separately to bullish and bearish trends Helps duration estimates adapt to changing market conditions without manual intervention. 4. Exponential Weighting Option to weight recent trends more heavily than distant history: • Default decay factor: 0.9 • Recent trends get higher weight in statistical calculations • Older trends gradually decay in importance • Rationale: Recent market behavior more relevant than old data • Can be disabled for equal weighting 5. Backtest Statistics System backtests its own duration estimates using historical data: • Walks through past trends chronologically • Calculates what duration estimate WOULD have been at each flip • Compares to actual duration that occurred • Displays accuracy metrics in duration analysis boxes and dashboard • Helps assess statistical reliability on your specific chart Note: Backtest uses only data available AT THE TIME of each historical flip (no lookahead bias). TECHNICAL SPECIFICATIONS • Pine Script Version: v6 • Indicator Type: Overlay (draws on price chart) • Max Boxes: 500 (for duration analysis box storage) • Max Bars Back: 5000 (for comprehensive historical analysis) • Security Calls: 1 (for MTF if enabled - optimized) • Repainting: NO - All signals and duration estimates confirmed on bar close • Lookahead Bias: NO - All HTF data properly offset, all duration estimates use only historical data • Real-time Updates: YES - Dashboard and quality scores update live • Alert Capable: YES - Both automatic alerts and customizable alert conditions • Multi-Symbol: Works on stocks, crypto, forex, futures, indices Performance Optimization: • Conditional calculations (duration analysis can be disabled to reduce load) • Efficient array management (circular buffers for trend storage) • Streamlined gradient rendering (26 layers, can be toggled off) • Smart label cooldown system (prevents label spam) • Optimized similarity matching (analyzes only relevant trends) Data Requirements: • Minimum 50-100 bars for initial duration analysis (builds historical database) • Optimal: 500+ bars for robust statistical analysis • Longer history = more accurate duration estimates • Works on any timeframe from 1 minute to monthly KNOWN LIMITATIONS • Trending Markets Only: Performs best in clear trends. May generate false signals in choppy/sideways markets (use quality score filtering and regime detection to mitigate) • Lagging Nature: Like all trend-following systems, signals occur AFTER trend establishment, not at exact tops/bottoms. Use duration analysis boxes to set realistic profit targets. • Initial Learning Period: Duration analysis system requires 10-15 completed trends to build reliable historical database. Early duration estimates less accurate (first few weeks on new symbol/timeframe). • Visual Load: 26-layer gradient ribbon may slow performance on older devices. Disable ribbon if experiencing lag. • Statistical accuracy Variables: Duration estimates are statistical estimates, not guarantees. Accuracy varies by: - Market regime (trending vs choppy) - Asset volatility characteristics - Quality of historical pattern matches - Timeframe traded (higher TF = more reliable) • Not Best Suitable For: - Ultra-short-term scalping (sub-1-minute charts) - Mean-reversion strategies (designed for trend-following) - Range-bound trading (requires trending conditions) - News-driven spikes (estimates based on technical patterns, not fundamentals) FREQUENTLY ASKED QUESTIONS Q: Does this indicator repaint? A: Absolutely not. All signals, duration analysis boxes, labels, and alerts use barstate.isconfirmed checks. They only appear after the bar closes. What you see in history is exactly what you would have seen in real-time. Zero repaint guarantee. Q: How accurate are the trend duration estimates? A: Accuracy varies by mode, market conditions, and historical data quality: • Simple mode: 60-70% accuracy (within ±20% of actual duration) • Standard mode: 70-80% accuracy (within ±20% of actual duration) • Advanced mode: 75-85% accuracy (within ±20% of actual duration) Best accuracy achieved on: • Higher timeframes (4H, Daily, Weekly) • Trending markets (not choppy/sideways) • Assets with consistent behavior (Blue Chip, Large Cap) • After 20+ historical trends analyzed (builds robust database) Remember: All duration estimates are statistical calculations based on historical patterns, not guarantees. Q: Which analysis mode should I use? A: • Simple: Beginners, clean trending markets, want minimal complexity • Standard: Most traders, general market conditions (RECOMMENDED DEFAULT) • Advanced: Experienced traders, volatile/complex markets (biotech, small-cap, crypto), seeking maximum accuracy Advanced mode requires correct Asset Type configuration for optimal results. Q: What's the difference between the trading style presets? A: Each preset optimizes ALL parameters for a specific trading approach: • Scalping: Ultra-sensitive (ATR 7, Mult 2.0), more signals, shorter holds • Day Trading: Balanced (ATR 10, Mult 2.5), moderate signals, intraday holds • Swing Trading: Stable (ATR 14, Mult 3.0), fewer signals, multi-day holds • Position Trading: Very stable (ATR 21, Mult 4.0), rare signals, week/month holds Auto mode automatically selects based on your chart timeframe. Q: Should I use Auto mode or manually select a preset? A: Auto mode is recommended for most traders. It automatically matches settings to your timeframe and re-optimizes if you switch charts. Only use manual preset selection if: • You want scalping settings on a 15m chart (overriding auto-detection) • You want swing settings on a 1h chart (more conservative than auto would give) • You're testing different approaches on same timeframe Q: Can I use this for scalping and day trading? A: Absolutely! The preset system is specifically designed for all trading styles: • Select "Scalping (1-5m)" for 1-5 minute charts • Select "Day Trading (15m-1h)" for 15m-1h charts • Or use "Auto" mode and it configures automatically Volume momentum filter is auto-disabled in Scalping mode for faster signals. Q: What is Volume Momentum and why does it matter? A: Volume Momentum compares short-term volume (fast MA) to long-term volume (slow MA). It answers: "Is money flowing into this asset faster now than historically?" Why it matters: • Volume often leads price (early warning system) • Confirms institutional participation (smart money) • No lag like price-based indicators • More intuitive than complex mathematical filters When the ratio is above 1.2, you have strong evidence that institutions are accumulating (bullish) or distributing (bearish). Q: How do I set up alerts? A: Two options: Option 1 - Automatic Alerts: 1. Right-click on chart → Add Alert 2. Condition: Select this indicator 3. Choose "Any alert() function call" 4. Configure notification method (app, email, webhook) 5. You'll receive detailed alerts on every BUY and SELL signal Option 2 - Customizable Alert Conditions: 1. Right-click on chart → Add Alert 2. Condition: Select this indicator 3. You'll see three options in dropdown: - "BUY Signal" (long signals only) - "SELL Signal" (short signals only) - "ANY Signal" (both BUY and SELL) 4. Choose desired option and customize message template 5. Uses TradingView placeholders: {{ticker}}, {{close}}, {{time}}, etc. All alerts fire only on confirmed bar close (no repaint). Q: What is Scalpel Mode and should I use it? A: Scalpel Mode waits for healthy pullbacks within established trends before signaling entry. It reduces whipsaws and improves entry timing. Recommended ON for: • Swing traders (want precision entries on pullbacks) • Day traders (willing to wait for better prices) • Risk-averse traders (prefer fewer but higher-quality entries) Recommended OFF for: • Scalpers (need immediate entries, can't wait for pullbacks) • Momentum traders (want to enter on breakout, not pullback) • Aggressive traders (prefer more opportunities over precision) Q: Why do some duration estimates show wider ranges than others? A: Range width reflects historical trend variability: • Narrow range: Similar historical trends had consistent durations (high confidence) • Wide range: Similar historical trends had varying durations (lower confidence) Wide ranges often occur: • Early in analysis (fewer historical trends to learn from) • In volatile/choppy markets (inconsistent trend behavior) • On lower timeframes (more noise, less consistency) The median and average still provide useful targets even when range is wide. Q: Can I customize the dashboard position and appearance? A: Yes! Dashboard settings include: • Position: 9 options (Top/Middle/Bottom + Left/Center/Right) • Text Size: Auto, Tiny, Small, Normal, Large, Huge • Show/Hide: Toggle entire dashboard on/off Choose position that doesn't overlap important price action on your specific chart. Q: Which timeframe should I trade on? A: Depends on your trading style and time availability: • 1-5 minute: Active scalping, requires constant monitoring • 15m-1h: Day trading, check few times per session • 4h-Daily: Swing trading, check once or twice daily • Daily-Weekly: Position trading, check weekly General principle: Higher timeframes produce: • Fewer signals (less frequent) • Higher quality setups (stronger confirmations) • More reliable duration estimates (better statistical data) • Less noise (clearer trends) Start with Daily chart if new to trading. Move to lower timeframes as you gain experience. Q: Does this work on all markets (stocks, crypto, forex)? A: Yes, it works on all markets with trending characteristics: Excellent for: • Stocks (especially growth and momentum names) • Crypto (BTC, ETH, major altcoins) • Futures (indices, commodities) • Forex majors (EUR/USD, GBP/USD, etc.) Best results on: • Trending markets (not range-bound) • Liquid instruments (tight spreads, good fills) • Volatile assets (clear trend development) Less effective on: • Range-bound/sideways markets • Ultra-low volatility instruments • Illiquid small-caps (use caution) Configure Asset Type (in Advanced analysis mode) to match your instrument for best accuracy. Q: How many signals should I expect per day/week? A: Highly variable based on: By Timeframe: • 1-5 minute: 5-15 signals per session • 15m-1h: 2-5 signals per day • 4h-Daily: 2-5 signals per week • Daily-Weekly: 1-2 signals per month By Market Volatility: • High volatility = more SuperTrend flips = more signals • Low volatility = fewer flips = fewer signals By Quality Filter: • Higher threshold (60-70) = fewer but better signals • Lower threshold (30-40) = more signals, lower quality By Volume Momentum Filter: • Enabled = Fewer signals (only volume-confirmed) • Disabled = More signals (all SuperTrend flips) Adjust quality threshold and filters to match your desired signal frequency. Q: What's the difference between entry labels and info labels? A: Entry Labels (BUY/SELL): • Your primary trading signals • Based on SuperTrend flip + all confirmations (quality, volume, momentum) • Include quality score and confirmation icons • These are actionable entry points Info Labels (Volume Spike): • Additional market context • Show volume events that may support or contradict trend • 8-bar cooldown to prevent spam • NOT necessarily entry points - contextual information only Control separately: Can show entry labels without info labels (recommended for clean charts). Q: Can I combine this with other indicators? A: Absolutely! This works well with: • RSI: For divergences and overbought/oversold conditions • Support/Resistance: Confluence with key levels • Fibonacci Retracements: Pullback targets in Scalpel Mode • Price Action Patterns: Flags, pennants, cup-and-handle • MACD: Additional momentum confirmation • Bollinger Bands: Volatility context This indicator provides trend direction and duration estimates - complement with other tools for entry refinement and additional confluence. Q: Why did I get a low-quality signal? Can I filter them out? A: Yes! Increase the Minimum Quality Score in settings. If you're seeing signals with quality below your preference: • Day Trading: Set minimum to 50 • Swing Trading: Set minimum to 60 • Position Trading: Set minimum to 70 Only signals meeting the threshold will appear. This reduces frequency but improves win-rate. Q: How do I interpret the MTF Confluence count? A: Shows how many of 6 timeframes agree with current trend: • 6/6 aligned: Perfect agreement (extremely rare, highest confidence) • 5/6 aligned: Very strong alignment (high confidence) • 4/6 aligned: Good alignment (standard quality setup) • 3/6 aligned: Moderate alignment (acceptable) • 2/6 aligned: Weak alignment (caution) • 1/6 aligned: Very weak (likely counter-trend) Higher confluence typically correlates with longer, stronger trends. However, MTF analysis is optional - you can disable it and rely solely on quality scoring. Q: Is this suitable for beginners? A: Yes, but requires foundational knowledge: You should understand: • Basic trend-following concepts (higher highs, higher lows) • Risk management principles (position sizing, stop losses) • How to read candlestick charts • What volume and volatility mean Beginner-friendly features: • Auto preset mode (zero configuration) • Quality scoring (tells you signal strength) • Dashboard tooltips (hover for explanations) • duration analysis boxes (visual profit targets) Recommended for beginners: 1. Start with "Auto" or "Swing Trading" preset on Daily chart 2. Use Standard Analysis Mode (not Advanced) 3. Set minimum quality to 60 (fewer but better signals) 4. Paper trade first for 2-4 weeks 5. Study methodology references (Minervini, O'Neil, Zanger) Q: What is the Asset Type setting and why does it matter? A: Asset Type (in Advanced analysis mode) adjusts duration estimates based on volatility characteristics: • Small Cap: Explosive moves, extended trends (+30-40%) • Biotech / Speculative: Parabolic potential, news-driven (+40%) • Blue Chip / Large Cap: Baseline, steady trends (0% adjustment) • Tech Growth: Momentum-driven, longer trends (+20%) • Dividend / Value: Slower, grinding trends (-20%) • Cyclical: Macro-driven, variable (±10%) • Crypto / High Volatility: Parabolic potential (+30%) Correct configuration improves Statistical accuracy by 15-20%. Using Blue Chip settings on a biotech stock may underestimate trend length (you'll exit too early). Q: Can I backtest this indicator? A: Yes! TradingView's Strategy Tester works with this indicator's signals. To backtest: 1. Note the entry conditions (SuperTrend flip + quality threshold + filters) 2. Create a strategy script using same logic 3. Run Strategy Tester on historical data Additionally, the indicator includes BUILT-IN duration estimate validation: • System backtests its own duration estimates • Shows accuracy metrics in dashboard and duration analysis boxes • Helps assess reliability on your specific symbol/timeframe Q: Why does Volume Momentum auto-disable in Scalping mode? A: Scalping requires ultra-fast entries to catch quick moves. Volume Momentum filter adds friction by requiring volume confirmation before signaling, which can cause missed opportunities in rapid scalping. Scalping preset is optimized for speed and frequency - the filter is counterproductive for that style. It remains enabled for Day Trading, Swing Trading, and Position Trading presets where patience improves results. You can manually enable it in Custom mode if desired. Q: How much historical data do I need for accurate duration estimates? A: Minimum: 50-100 bars (indicator will function but duration estimates less reliable) Recommended: 500+ bars (robust statistical database) Optimal: 1000+ bars (maximum Statistical accuracy) More history = more completed trends = better pattern matching = more accurate duration estimates. New symbols or newly-switched timeframes will have lower Statistical accuracy initially. Allow 2-4 weeks for the system to build historical database. IMPORTANT DISCLAIMERS No Guarantee of Profit: This indicator is an educational tool and does not guarantee any specific trading results. All trading involves substantial risk of loss. Duration estimates are statistical calculations based on historical patterns and are not guarantees of future performance. Past Performance: Historical backtest results and Statistical accuracy statistics do not guarantee future performance. Market conditions change constantly. What worked historically may not work in current or future markets. Not Financial Advice: This indicator provides technical analysis signals and statistical duration estimates only. It is not financial, investment, or trading advice. Always consult with a qualified financial advisor before making investment decisions. Risk Warning: Trading stocks, options, futures, forex, and cryptocurrencies involves significant risk. You can lose all of your invested capital. Never trade with money you cannot afford to lose. Only risk capital you can lose without affecting your lifestyle. Testing Required: Always test this indicator on a demo account or with paper trading before risking real capital. Understand how it works in different market conditions. Verify Statistical accuracy on your specific instruments and timeframes before trusting it with real money. User Responsibility: You are solely responsible for your trading decisions. The developer assumes no liability for trading losses, incorrect duration estimates, software errors, or any other damages incurred while using this indicator. Statistical Estimation Limitations: Trend Duration estimates are statistical estimates based on historical pattern matching. They are NOT guarantees. Actual trend durations may differ significantly from duration estimates due to unforeseen news events, market regime changes, or lack of historical precedent for current conditions. CREDITS & ACKNOWLEDGMENTS Methodology Inspiration: • Mark Minervini - Volatility Contraction Pattern (VCP) concepts and pullback entry techniques • William O'Neil - Volume analysis principles and CANSLIM institutional buying patterns • Dan Zanger - Momentum breakout strategies and volatility expansion entries Technical Components: • SuperTrend calculation - Classic ATR-based trend indicator (public domain) • Statistical analysis - Standard median, average, range calculations • k-Nearest Neighbors - Classic machine learning similarity matching concept • Multi-timeframe analysis - Standard request.security implementation in Pine Script For questions, feedback, or support, please comment below or send a private message. Happy Trading!
Indicateur Pine Script®
par orenluxy
43
Z-Score Regime DetectorThe Z-Score Regime Detector is a statistical market regime indicator that helps identify bullish and bearish market conditions based on normalized momentum of three core metrics: - Price (Close) - Volume - Market Capitalization (via CRYPTOCAP:TOTAL) Each metric is standardized using the Z-score over a user-defined period, allowing comparison of relative extremes across time. This removes raw value biases and reveals underlying momentum structure. 📊 How it Works - Z-Score: Measures how far a current value deviates from its average in terms of standard deviations. - A Bullish Regime is identified when both price and market cap Z-scores are above the volume Z-score. - A Bearish Regime occurs when price and market cap Z-scores fall below volume Z-score. Bias Signal: - Bullish Bias = Price Z-score > Market Cap Z-score - Bearish Bias = Market Cap Z-score > Price Z-score This provides a statistically consistent framework to assess whether the market is flowing with strength or stress. ✅ Why This Might Be Effective - Normalizing the data via Z-scores allows comparison of diverse metrics on a common scale. - Using market cap offers broader insight than price alone, especially for crypto. - Volume as a reference threshold helps identify accumulation/distribution regimes. - Simple regime logic makes it suitable for trend confirmation, filtering, or position biasing in systems. ⚠️ Disclaimer This script is for educational purposes only and should not be considered financial advice. Always perform your own research and risk management. Past performance is not indicative of future results. Use at your own discretion.
Indicateur Pine Script®
par RWCS_LTD
Central Limit Theorem Reversion IndicatorDear TV community, let me introduce you to the first-ever Central Limit Theorem indicator on TradingView. The Central Limit Theorem is used in statistics and it can be quite useful in quant trading and understanding market behaviors. In short, the CLT states: "When you take repeated samples from any population and calculate their averages, those averages will form a normal (bell curve) distribution—no matter what the original data looks like." In this CLT indicator, I use statistical theory to identify high-probability mean reversion opportunities in the markets. It calculates statistical confidence bands and z-scores to identify when price movements deviate significantly from their expected distribution, signaling potential reversion opportunities with quantifiable probability levels. Mathematical Foundation The Central Limit Theorem (CLT) says that when you average many data points together, those averages will form a predictable bell-curve pattern, even if the original data is completely random and unpredictable (which often is in the markets). This works no matter what you're measuring, and it gets more reliable as you use more data points. Why using it for trading? Individual price movements seem random and chaotic, but when we look at the average of many price movements, we can actually predict how they should behave statistically. This lets us spot when prices have moved "too far" from what's normal—and those extreme moves tend to snap back (mean reversion). Key Formula: Z = (X̄ - μ) / (σ / √n) Where: - X̄ = Sample mean (average return over n periods) - μ = Population mean (long-term expected return) - σ = Population standard deviation (volatility) - n = Sample size - σ/√n = Standard error of the mean How I Apply CLT Step 1: Calculate Returns Measures how much price changed from one bar to the next (using logarithms for better statistical properties) Step 2: Average Recent Returns Takes the average of the last n returns (e.g., last 100 bars). This is your "sample mean." Step 3: Find What's "Normal" Looks at historical data to determine: a) What the typical average return should be (the long-term mean) and b) How volatile the market usually is (standard deviation) Step 4: Calculate Standard Error Determines how much sample averages naturally vary. Larger samples = smaller expected variation. Step 5: Calculate Z-Score Measures how unusual the current situation is. Step 6: Draw Confidence Bands Converts these statistical boundaries into actual price levels on your chart, showing where price is statistically expected to stay 95% and 99% of the time. Interpretation & Usage The Z-Score: The z-score tells you how statistically unusual the current price deviation is: |Z| < 1.0 → Normal behavior, no action |Z| = 1.0 to 1.96 → Moderate deviation, watch closely |Z| = 1.96 to 2.58 → Significant deviation (95%+), consider entry |Z| > 2.58 → Extreme deviation (99%+), high probability setup The Confidence Bands - Upper Red Bands: 95% and 99% overbought zones → Expect mean reversion downward as the price is not likely to cross these lines. - Center Gray Line: Statistical expectation (fair value) - Lower Blue Bands: 95% and 99% oversold zones → Expect mean reversion upward Trading Logic: - When price exceeds the upper 95% band (z-score > +1.96), there's only a 5% probability this is random noise → Strong sell/short signal - When price falls below the lower 95% band (z-score < -1.96), there's a 95% statistical expectation of upward reversion → Strong buy/long signal Background Gradient The background color provides real-time visual feedback: - Blue shades: Oversold conditions, expect upward reversion - Red shades: Overbought conditions, expect downward reversion - Intensity: Darker colors indicate stronger statistical significance Trading Strategy Examples Hypothetically, this is how the indicator could be used: - Long: Z-score < -1.96 (below 95% confidence band) - Short: Z-score > +1.96 (above 95% confidence band) - Take profit when price returns to center line (Z ≈ 0) Input Parameters Sample Size (n) - Default: 100 Lookback Period (m) - Default: 100 You can also create alerts based on the indicator. Final notes: - The indicator uses logarithmic returns for better statistical properties - Converts statistical bands back to price space for practical use - Adaptive volatility: Bands automatically widen in high volatility, narrow in low volatility - No repainting: yay! All calculations use historical data only Feedback is more than welcome! Henri
Indicateur Pine Script®
par HenriqueCentieiro
2
GARCH Range PredictorThis was inspired by deltatrendtrading's video on GARCH models to predict daily trading ranges and identify favorable trading conditions. Based on advanced volatility forecasting techniques, it predicts whether a trading day's true range will exceed a threshold, helping traders decide when to trade or skip a session. Key Features GARCH(1,1) Volatility Modeling: Uses log-transformed true ranges with exponential moving average centering Forward-Looking Predictions: Makes predictions at session start before the day unfolds Dynamic or Static Thresholds: Choose between fixed dollar thresholds or adaptive 20-day averages Accuracy Tracking: Monitors prediction accuracy with overall and recent (20-day) hit rates Visual Session Boxes: Colors trading sessions green (trade) or red (skip) based on predictions Real-Time Statistics: Displays current predictions, thresholds, and performance metrics How It Works Data Transformation: Log-transforms daily true ranges and centers them using an EMA Variance Modeling: Updates GARCH variance using: σ²ₜ = ω + α(residual²) + β(σ²ₜ₋₁) Prediction Generation: Back-transforms log predictions to dollar values Signal Generation: Compares predictions to threshold to generate trade/skip signals Performance Tracking: Validates predictions against actual outcomes Parameters GARCH Parameters (ω, α, β): Control volatility persistence and mean reversion EMA Period: Smoothing period for log range centering Threshold Settings: Static dollar amount or dynamic multiplier of recent averages Session Time: Define regular trading hours for analysis Best Use Cases Breakout and momentum strategies that perform better on high-range days Risk management by avoiding low-volatility sessions Futures day trading (optimized for MNQ/NQ detection) Any strategy where daily range impacts profitability Important Notes Requires 5+ sessions for initialization and warm-up Accuracy depends heavily on proper parameter tuning for your specific instrument Default parameters may need adjustment for different markets Monitor the hit rate to validate effectiveness on your timeframe
Indicateur Pine Script®
par yundore
Markov 3D Trend AnalyzerMarkov 3D Trend Analyzer 🔹 What Is a Markov State? A Markov chain models systems as states with probabilities of transitioning from one state to another. The key property is memorylessness: the next state depends only on the current state, not the full past history. In financial markets, this allows us to study how conditions tend to persist or flip — for example, whether a green candle is more likely to be followed by another green or by a red. 🔹 How This Indicator Uses It The Markov 3D Trend Analyzer tracks three independent Markov chains: Direction Chain (short-term): Probability that a green/red candle continues or reverses. Volatility Chain (mid-term): Probability of volatility staying Low/Medium/High or transitioning between them. Momentum Chain (structural): Probability of momentum (Bullish, Neutral, Bearish) persisting or flipping. Each chain is updated dynamically using exponentially weighted probabilities (EMA), which balance the law of large numbers (stability) with adaptivity to new market conditions. The indicator then classifies each chain’s dominant state and combines them into an actionable summary at the bottom of the table (e.g. “📈 Bullish breakout,” “⚠️ Choppy bearish fakeouts,” “⏳ Trend squeeze / possible reversal”). 🔹 Settings Direction Lookback / Volatility Lookback / Momentum Lookback Control the rolling window length (sample size) for each chain. Larger = smoother but slower to adapt. EMA Weight Adjusts how much weight is given to recent transitions vs. older history. Lower values adapt faster, higher values stabilize. Table Position Choose where the table is displayed on your chart. Table Size Adjust the font size for readability. 🔹 How To Consider Using Contextual tool: Use the summary row to understand the current market condition (trending, mean-reverting, expanding, compressing, continuation, fakeout risk). Complementary filter: Combine with your existing strategies to confirm or filter signals. For example: 📈 If your breakout strategy fires and the summary says Bullish breakout, that’s confirmation. ⚠️ If it says Choppy fakeouts, be cautious of traps. Visualization aid: The table lets you see how probabilities shift across direction, volatility, and momentum simultaneously. ⚠️ This indicator is not a signal generator. It is designed to help interpret market states probabilistically. Always use in conjunction with broader analysis and risk management. 🔹 Disclaimer This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security, cryptocurrency, or instrument. Trading involves risk, and past probabilities or behaviors do not guarantee future outcomes. Always conduct your own research and use proper risk management.
Indicateur Pine Script®
par RWCS_LTD
HTF Current/Average RangeThe "HTF(Higher Timeframe) Current/Average Range" indicator calculates and displays the current and average price ranges across multiple timeframes, including daily, weekly, monthly, 4 hour, and user-defined custom timeframes. Users can customize the lookback period, table size, timeframe, and font color; with the indicator efficiently updating on the final bar to optimize performance. When the current range surpasses the average range for a given timeframe, the corresponding table cell is highlighted in green, indicating potential maximum price expansion and signaling the possibility of an impending retracement or consolidation. For day trading strategies, the daily average range can serve as a guide, allowing traders to hold positions until the current daily range approaches or meets the average range, at which point exiting the trade may be considered. For scalping strategies, the 15min and 5min average range can be utilized to determine optimal holding periods for fast trades. Other strategies: Intraday Trading - 1h and 4h Average Range Swing Trading - Monthly Average Range Short-term Trading - Weekly Average Range Also using these statistics in accordance with Power 3 ICT concepts, will assist in holding trades to their statistical average range of the chosen HTF candle. CODE The core functionality lies in the data retrieval and table population sections. The request.security function (e.g., = request.security(syminfo.tickerid, "D", , lookahead = barmerge.lookahead_off)) retrieves high and low prices from specified timeframes without lookahead bias, ensuring accurate historical data. These values are used to compute current ranges and average ranges (ta.sma(high - low, avgLength)), which are then displayed in a dynamically generated table starting at (if barstate.islast) using table.new, with conditional green highlighting when the current range is greater than average range, providing a clear visual cue for volatility analysis.
Indicateur Pine Script®
par Eightgent7
Price Statistical Strategy-Z Score V 1.01 Price Statistical Strategy – Z Score V 1.01 Overview A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”. This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included. Core Methodology The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits. Signal Conditions Entry Condition: A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met. Exit Condition: The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow. Signal Gapping: A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise. Momentum Filter: Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles. Z-Score Function The Z-Score is calculated as: Z = (Close - SMA(Close, N)) / STDEV(Close, N) Where N is the base period selected by the user. Input Parameters Enable Smoothed Z-Score Strategy Enables or disables the Z-Score strategy logic. When disabled, no trades are executed. Z-Score Base Period Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes. Short-Term Smoothing Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility. Long-Term Smoothing Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic. Bars gap between identical signals Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals. Trade Visualization Table A table positioned at the bottom-right displays live PnL for open trades: Entry Price Unrealized PnL % Text colors adapt based on whether unrealized profit is positive, negative, or neutral. Technical Notes This strategy uses only close prices — no trend indicators or volume components are applied. All calculations are based on simple moving averages and standard deviation over user-defined windows. Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Stratégie Pine Script®
par ata_sabanci
Bitcoin Monthly Seasonality [Alpha Extract]The Bitcoin Monthly Seasonality indicator analyzes historical Bitcoin price performance across different months of the year, enabling traders to identify seasonal patterns and potential trading opportunities. This tool helps traders: Visualize which months historically perform best and worst for Bitcoin. Track average returns and win rates for each month of the year. Identify seasonal patterns to enhance trading strategies. Compare cumulative or individual monthly performance. 🔶 CALCULATION The indicator processes historical Bitcoin price data to calculate monthly performance metrics Monthly Return Calculation Inputs: Monthly open and close prices. User-defined lookback period (1-15 years). Return Types: Percentage: (monthEndPrice / monthStartPrice - 1) × 100 Price: monthEndPrice - monthStartPrice Statistical Measures Monthly Averages: ◦ Average return for each month calculated from historical data. Win Rate: ◦ Percentage of positive returns for each month. Best/Worst Detection: ◦ Identifies months with highest and lowest average returns. Cumulative Option Standard View: Shows discrete monthly performance. Cumulative View: Shows compounding effect of consecutive months. Example Calculation (Pine Script): monthReturn = returnType == "Percentage" ? (monthEndPrice / monthStartPrice - 1) * 100 : monthEndPrice - monthStartPrice calcWinRate(arr) => winCount = 0 totalCount = array.size(arr) if totalCount > 0 for i = 0 to totalCount - 1 if array.get(arr, i) > 0 winCount += 1 (winCount / totalCount) * 100 else 0.0 🔶 DETAILS Visual Features Monthly Performance Bars: ◦ Color-coded bars (teal for positive, red for negative returns). ◦ Special highlighting for best (yellow) and worst (fuchsia) months. Optional Trend Line: ◦ Shows continuous performance across months. Monthly Axis Labels: ◦ Clear month names for easy reference. Statistics Table: ◦ Comprehensive view of monthly performance metrics. ◦ Color-coded rows based on performance. Interpretation Strong Positive Months: Historically bullish periods for Bitcoin. Strong Negative Months: Historically bearish periods for Bitcoin. Win Rate Analysis: Higher win rates indicate more consistently positive months. Pattern Recognition: Identify recurring seasonal patterns across years. Best/Worst Identification: Quickly spot the historically strongest and weakest months. 🔶 EXAMPLES The indicator helps identify key seasonal patterns Bullish Seasons: Visualize historically strong months where Bitcoin tends to perform well, allowing traders to align long positions with favorable seasonality. Bearish Seasons: Identify historically weak months where Bitcoin tends to underperform, helping traders avoid unfavorable periods or consider short positions. Seasonal Strategy Development: Create trading strategies that capitalize on recurring monthly patterns, such as entering positions in historically strong months and reducing exposure during weak months. Year-to-Year Comparison: Assess how current year performance compares to historical seasonal patterns to identify anomalies or confirmation of trends. 🔶 SETTINGS Customization Options Lookback Period: Adjust the number of years (1-15) used for historical analysis. Return Type: Choose between percentage returns or absolute price changes. Cumulative Option: Toggle between discrete monthly performance or cumulative effect. Visual Style Options: Bar Display: Enable/disable and customize colors for positive/negative bars, Line Display: Enable/disable and customize colors for trend line, Axes Display: Show/hide reference axes. Visual Enhancement: Best/Worst Month Highlighting: Toggle special highlighting of extreme months, Custom highlight colors for best and worst performing months. The Bitcoin Monthly Seasonality indicator provides traders with valuable insights into Bitcoin's historical performance patterns throughout the year, helping to identify potentially favorable and unfavorable trading periods based on seasonal tendencies.
Indicateur Pine Script®
par AlphaExtract
5
Statistical Trailing Stop [LuxAlgo]The Statistical Trailing Stop tool offers traders a way to lock in profits in trending markets with four statistical levels based on the log-normal distribution of volatility. The indicator also features a dashboard with statistics of all detected signals. 🔶 USAGE The tool works out of the box, traders can adjust the data used with two parameters: data & distribution length. By default, the tool takes volatility measures of groups of 10 candles, and statistical measures of the last 100 of these groups then traders can adjust the base level to use as trailing, the larger the level, the more resistant the tool will be to moves against the trend. 🔹 Base Levels Traders can choose up to 4 different levels of trailing, all based on the statistical distribution of volatility. As we can see in the chart above, each higher level is more resistant to market movements, so level 0 is the most reactive and level 3 the least. It is up to the trader to determine the best level for each underlying, time frame and market conditions. 🔹 Dashboard The tool provides a dashboard with the statistics of all trades, making it very easy to assess the performance of the parameters used for any given market. As we can see on the chart, all Daily BTC signals with default parameters but different base levels, level 2 is the best performing of all four, giving a positive expectation of $2435 per trade, taking into account all long and short trades. Of note are the long trades with a win rate of 76.47% and a risk-to-reward of 3.34, giving a positive expectation of $4839 per trade, with winners having an average duration of 210 days and losers 32 days. This, compared to short trades with negative expectation, speaks to the uptrend bias of this particular market. 🔶 SETTINGS Data Length: Select how many bars to use per data point Distribution Length: Select how many data points the distribution will have Base Level: Choose between 4 different trailing levels 🔹 Dashboard Show Statistics: Enable/disable dashboard Position: Select dashboard position Size: Select dashboard size
Indicateur Pine Script®
par LuxAlgo
20
Multi-Timeframe Anchored VWAP Valuation# Multi-Timeframe Anchored VWAP Valuation ## Overview This indicator provides a unique perspective on potential price valuation by comparing the current price to the Volume Weighted Average Price (VWAP) anchored to the start of multiple timeframes: Weekly, Monthly, Quarterly, and Yearly. It synthesizes these comparisons into a single oscillator value, helping traders gauge if the current price is potentially extended relative to significant volume-weighted levels. ## Core Concept & Calculation 1. **Anchored VWAP:** The script calculates the VWAP separately for the current Week, Month, Quarter (3 Months), and Year (12 Months), starting the calculation from the first bar of each period. 2. **Price Deviation:** It measures how far the current `close` price is from each of these anchored VWAPs. This distance is measured in terms of standard deviations calculated *within* that specific anchor period (e.g., how many weekly standard deviations the price is away from the weekly VWAP). 3. **Deviation Score (Multiplier):** Based on this standard deviation distance, a score is assigned. The further the price is from the VWAP (in terms of standard deviations), the higher the absolute score. The indicator uses linear interpolation to determine scores between the standard deviation levels (defaulted at 1, 2, and 3 standard deviations corresponding to scores of +/-2, +/-3, +/-4, with a score of 1 at the VWAP). 4. **Timeframe Weighting:** Longer timeframes are considered more significant. The deviation scores are multiplied by fixed scalars: Weekly (x1), Monthly (x2), Quarterly (x3), Yearly (x4). 5. **Final Valuation Metric:** The weighted scores from all four timeframes are summed up to produce the final oscillator value plotted in the indicator pane. ## How to Interpret and Use * **Histogram (Indicator Pane):** * The main output is the histogram representing the `Final Valuation Metric`. * **Positive Values:** Suggest the price is generally trading above its volume-weighted averages across the timeframes, potentially indicating strength or relative "overvaluation." * **Negative Values:** Suggest the price is generally trading below its volume-weighted averages, potentially indicating weakness or relative "undervaluation." * **Values Near Zero:** Indicate the price is relatively close to its volume-weighted averages. * **Histogram Color:** * The color of the histogram bars provides context based on the metric's *own recent history*. * **Green (Positive Color):** The metric is currently *above* its recent average plus a standard deviation band (dynamic upper threshold). This highlights potentially significant "overvalued" readings relative to its normal range. * **Red (Negative Color):** The metric is currently *below* its recent average minus a standard deviation band (dynamic lower threshold). This highlights potentially significant "undervalued" readings relative to its normal range. * **Gray (Neutral Color):** The metric is within its typical recent range (between the dynamic upper and lower thresholds). * **Orange Line:** Plots the moving average of the `Final Valuation Metric` itself (based on the "Threshold Lookback Period"), serving as the centerline for the dynamic thresholds. * **On-Chart Table:** * Provides a detailed breakdown for transparency. * Shows the calculated VWAP, the raw deviation multiplier score, and the final weighted (adjusted) metric for each individual timeframe (W, M, Q, Y). * Displays the current price, the final combined metric value, and a textual interpretation ("Overvalued", "Undervalued", "Neutral") based on the dynamic thresholds. ## Potential Use Cases * Identifying potential exhaustion points when the indicator reaches statistically high (green) or low (red) levels relative to its recent history. * Assessing whether price trends are supported by underlying volume-weighted average prices across multiple timeframes. * Can be used alongside other technical analysis tools for confirmation. ## Settings * **Calculation Settings:** * `STDEV Level 1`: Adjusts the 1st standard deviation level (default 1.0). * `STDEV Level 2`: Adjusts the 2nd standard deviation level (default 2.0). * `STDEV Level 3`: Adjusts the 3rd standard deviation level (default 3.0). * **Interpretation Settings:** * `Threshold Lookback Period`: Defines the number of bars used to calculate the average and standard deviation of the final metric for dynamic thresholds (default 200). * `Threshold StDev Multiplier`: Controls how many standard deviations above/below the metric's average are used to set the "Overvalued"/"Undervalued" thresholds (default 1.0). * **Table Settings:** Customize the position and colors of the data table displayed on the chart. ## Important Considerations * This indicator measures price deviation relative to *anchored* VWAPs and its *own historical range*. It is not a standalone trading system. * The interpretation of "Overvalued" and "Undervalued" is relative to the indicator's logic and calculations; it does not guarantee future price movement. * Like all indicators, past performance is not indicative of future results. Use this tool as part of a comprehensive analysis and risk management strategy. * The anchored VWAP and Standard Deviation values reset at the beginning of each respective period (Week, Month, Quarter, Year).
Indicateur Pine Script®
par Sickist
4
Elastic Volume-Weighted Student-T TensionOverview The Elastic Volume-Weighted Student-T Tension Bands indicator dynamically adapts to market conditions using an advanced statistical model based on the Student-T distribution. Unlike traditional Bollinger Bands or Keltner Channels, this indicator leverages elastic volume-weighted averaging to compute real-time dispersion and location parameters, making it highly responsive to volatility changes while maintaining robustness against price fluctuations. This methodology is inspired by incremental calculation techniques for weighted mean and variance, as outlined in the paper by Tony Finch: 📄 "Incremental Calculation of Weighted Mean and Variance" . Key Features ✅ Adaptive Volatility Estimation – Uses an exponentially weighted Student-T model to dynamically adjust band width. ✅ Volume-Weighted Mean & Dispersion – Incorporates real-time volume weighting, ensuring a more accurate representation of market sentiment. ✅ High-Timeframe Volume Normalization – Provides an option to smooth volume impact by referencing a higher timeframe’s cumulative volume, reducing noise from high-variability bars. ✅ Customizable Tension Parameters – Configurable standard deviation multipliers (σ) allow for fine-tuned volatility sensitivity. ✅ %B-Like Oscillator for Relative Price Positioning – The main indicator is in form of a dedicated oscillator pane that normalizes price position within the sigma ranges, helping identify overbought/oversold conditions and potential momentum shifts. ✅ Robust Statistical Foundation – Utilizes kurtosis-based degree-of-freedom estimation, enhancing responsiveness across different market conditions. How It Works Volume-Weighted Elastic Mean (eμ) – Computes a dynamic mean price using an elastic weighted moving average approach, influenced by trade volume, if not volume detected in series, study takes true range as replacement. Dispersion (eσ) via Student-T Distribution – Instead of assuming a fixed normal distribution, the bands adapt to heavy-tailed distributions using kurtosis-driven degrees of freedom. Incremental Calculation of Variance – The indicator applies Tony Finch’s incremental method for computing weighted variance instead of arithmetic sum's of fixed bar window or arrays, improving efficiency and numerical stability. Tension Calculation – There are 2 dispersion custom "zones" that are computed based on the weighted mean and dynamically adjusted standard student-t deviation. %B-Like Oscillator Calculation – The oscillator normalizes the price within the band structure, with values between 0 and 1: * 0.00 → Price is at the lower band (-2σ). * 0.50 → Price is at the volume-weighted mean (eμ). * 1.00 → Price is at the upper band (+2σ). * Readings above 1.00 or below 0.00 suggest extreme movements or possible breakouts. Recommended Usage For scalping in lower timeframes, it is recommended to use the fixed α Decay Factor, it is in raw format for better control, but you can easily make a like of transformation to N-bar size window like in EMA-1 bar dividing 2 / decayFactor or like an RMA dividing 1 / decayFactor. The HTF selector catch quite well Higher Time Frame analysis, for example using a Daily chart and using as HTF the 200-day timeframe, weekly or monthly. Suitable for trend confirmation, breakout detection, and mean reversion plays. The %B-like oscillator helps gauge momentum strength and detect divergences in price action if user prefer a clean chart without bands, this thanks to pineScript v6 force overlay feature. Ideal for markets with volume-driven momentum shifts (e.g., futures, forex, crypto). Customization Parameters Fixed α Decay Factor – Controls the rate of volume weighting influence for an approximation EWMA approach instead of using sum of series or arrays, making the code lightweight & computing fast O(1). HTF Volume Smoothing – Instead of a fixed denominator for computing α , a volume sum of the last 2 higher timeframe closed candles are used as denominator for our α weight factor. This is useful to review mayor trends like in daily, weekly, monthly. Tension Multipliers (±σ) – Adjusts sensitivity to dispersion sigma parameter (volatility). Oscillator Zone Fills – Visual cues for price positioning within the cloud range. Posible Interpretations As market within indicators relay on each individual edge, this are just some key ideas to glimpse how the indicator could be interpreted by the user: 📌 Price inside bands – Market is considered somehow "stable"; price is like resting from tension or "charging batteries" for volume spike moves. 📌 Price breaking outer bands – Potential breakout or extreme movement; watch for reversals or continuation from strong moves. Market is already in tension or generating it. 📌 Narrowing Bands – Decreasing volatility; expect contraction before expansion. 📌 Widening Bands – Increased volatility; prepare for high probability pull-back moves, specially to the center location of the bands (the mean) or the other side of them. 📌 Oscillator is just the interpretation of the price normalized across the Student-T distribution fitting "curve" using the location parameter, our Elastic Volume weighted mean (eμ) fixed at 0.5 value. Final Thoughts The Elastic Volume-Weighted Student-T Tension indicator provides a powerful, volume-sensitive alternative to traditional volatility bands. By integrating real-time volume analysis with an adaptive statistical model, incremental variance computation, in a relative price oscillator that can be overlayed in the chart as bands, it offers traders an edge in identifying momentum shifts, trend strength, and breakout potential. Think of the distribution as a relative "tension" rubber band in which price never leave so far alone. DISCLAIMER: The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets. The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s). WARNING NOTICE! THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
Indicateur Pine Script®
par xel_arjona
26
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication.. A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques? B : Machine Learning, no that's gonna be really heavy bro! A : Not really, because we use Naive Bayes. B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data. ---------------------------------------------------------------------------------------------------------------------- Hello, everyone! After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!. What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by. The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias. To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window. One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup. Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe. Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets. Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis. In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor! Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Indicateur Pine Script®
par sigmaCEO
1
Smart Market Bias [PhenLabs]📊 Smart Market Bias Indicator (SMBI) Version: PineScript™ v6 Description The Smart Market Bias Indicator (SMBI) is an advanced technical analysis tool that combines multiple statistical approaches to determine market direction and strength. It utilizes complexity analysis, information theory (Kullback Leibler divergence), and traditional technical indicators to provide a comprehensive market bias assessment. The indicator features adaptive parameters based on timeframe and trading style, with real-time visualization through a sophisticated dashboard. 🔧 Components Complexity Analysis: Measures price movement patterns and trend strength KL Divergence: Statistical comparison of price distributions Technical Overlays: RSI and Bollinger Bands integration Filter System: Volume and trend validation Visual Dashboard: Dynamic color-coded display of all components Simultaneous current timeframe + higher time frame analysis 🚨Important Explanation Feature🚨 By hovering over each individual cell in this comprehensive dashboard, you will get a thorough and in depth explanation of what each cells is showing you Visualization HTF Visualization 📌 Usage Guidelines Based on your own trading style you should alter the timeframe length that you would like to be analyzing with your dashboard The longer the term of the position you are planning on entering the higher timeframe you should have your dashboard set to Bias Interpretation: Values > 50% indicate bullish bias Values < 50% indicate bearish bias Neutral zone: 45-55% suggests consolidation ✅ Best Practices: Use appropriate timeframe preset for your trading style Monitor all components for convergence/divergence Consider filter strength for signal validation Use color intensity as confidence indicator ⚠️ Limitations Requires sufficient historical data for accurate calculations Higher computational complexity on lower timeframes May lag during extremely volatile conditions Best performance during regular market hours What Makes This Unique Multi-Component Analysis: Combines complexity theory, statistical analysis, and traditional technical indicators Adaptive Parameters: Automatically optimizes settings based on timeframe Triple-Layer Filtering: Uses trend, volume, and minimum strength thresholds Visual Confidence System: Color intensity indicates signal strength Multi-Timeframe Capabilities: Allowing the trader to analyze not only their current time frame but also the higher timeframe bias 🔧 How It Works The indicator processes market data through four main components: Complexity Score (40% weight): Analyzes price returns and pattern complexity Kullback Leibler Divergence (30% weight): Compares current and historical price distributions RSI Analysis (20% weight): Momentum and oversold/overbought conditions Bollinger Band Position (10% weight): Price position relative to volatility Underlying Method Maintains rolling windows of price data for multiple calculations Applies custom normalization using hyperbolic tangent function Weights component scores based on reliability and importance Generates final bias percentage with confidence visualization 💡 Note: For optimal results, use in conjunction with price action analysis and consider multiple timeframe confirmation. The indicator performs best when all components show alignment.
Indicateur Pine Script®
par PhenLabs
3
Trend Reversal Probability [Algoalpha]Introducing Trend Reversal Probability by AlgoAlpha – a powerful indicator that estimates the likelihood of trend reversals based on an advanced custom oscillator and duration-based statistics. Designed for traders who want to stay ahead of potential market shifts, this indicator provides actionable insights into trend momentum and reversal probabilities. Key Features : 🔧 Custom Oscillator Calculation: Combines a dual SMA strategy with a proprietary RSI-like calculation to detect market direction and strength. 📊 Probability Levels & Visualization: Plots average signal durations and their statistical deviations (±1, ±2, ±3 SD) on the chart for clear visual guidance. 🎨 Dynamic Color Customization: Choose your preferred colors for upward and downward trends, ensuring a personalized chart view. 📈 Signal Duration Metrics: Tracks and displays signal durations with columns representing key percentages (80%, 60%, 40%, and 20%). 🔔 Alerts for High Probability Events: Set alerts for significant reversal probabilities (above 84% and 98% or below 14%) to capture key trading moments. How to Use : Add the Indicator: Add Trend Reversal Probability to your favorites by clicking the star icon. Market Analysis: Use the plotted probability levels (average duration and ±SD bands) to identify overextended trends and potential reversals. Use the color of the duration counter to identify the current trend. Leverage Alerts: Enable alerts to stay informed of high or extreme reversal probabilities without constant chart monitoring. How It Works : The indicator begins by calculating a custom oscillator using short and long simple moving averages (SMA) of the midpoint price. A proprietary RSI-like formula then transforms these values to estimate trend direction and momentum. The duration between trend reversals is tracked and averaged, with standard deviations plotted to provide probabilistic guidance on trend longevity. Additionally, the indicator incorporates a cumulative probability function to estimate the likelihood of a trend reversal, displaying the result in a data table for easy reference. When probability levels cross key thresholds, alerts are triggered, helping traders take timely action.
Indicateur Pine Script®
par AlgoAlpha
16