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Trend Cloud with Institutional Volume Filter [wjdtks255]■ Overview: High-Conviction Trend Identification The Trend Cloud with Institutional Volume Filter is a specialized trend-following system that solves the "whipsaw" problem of traditional volatility indicators. It integrates ATR-based volatility bands with a strict volume conviction filter. ■ The "Conviction" Logic Many trend indicators fail during low-volume consolidation. This script introduces an Institutional Volume Filter that mandates a signal must be accompanied by volume exceeding its 20-period SMA. This ensures that "BUY" or "SELL" labels only appear when there is significant market participation. ■ Visual Dashboard Components Momentum Cloud: A dynamic color-coded zone that illustrates the strength and direction of the current trend regime. Institutional Labels: Clear "BUY" and "SELL" markers provided in a label format for instant readability.
Indicador Pine Script®
por wjdtks255
RBM - Range Body MeasureRBM – Range Body Measure A structural strength indicator for intraday trend filtering and noise avoidance This indicator measures the quality of price movement using the Range Body Measure (RBM), a composite metric derived from normalized range expansion and candle body efficiency. RBM helps identify whether price action is clean and directional, or weak and choppy. 🔍 What RBM Measures RBM is built from two key structural components: Range Expansion EMA of (High − Low), normalized by ATR Captures how meaningfully price is moving relative to volatility Body Efficiency EMA of (Candle Body / Total Range) Measures how directional each candle truly is RBM combines these to produce a smooth structural score typically between 0 and 1. Low RBM = weak structure, chop, noise High RBM = strong directional structure, trend-friendly movement 🎯 Trend & Chop Zones Two adjustable thresholds define structural zones: 🔴 Chop Zone Highlighted only when RBM < Chop Threshold Only the area below the threshold is shaded Indicates low-quality, non-directional movement Useful to filter out no-trade periods 🟢 Trend Zone Highlighted only when RBM > Trend Threshold Only the area above the threshold is shaded Indicates strong structure suitable for directional trades The middle region is intentionally left unshaded to keep the chart clean. 🛡️ Built-In Safety Trend threshold automatically adjusted to be ≥ Chop threshold Chop shading disabled if threshold ≤ 0 Trend shading disabled if threshold ≥ 1 This ensures stable, predictable behavior regardless of user settings. 📌 Intended Use Cases Filtering low-quality market structure before entering trades Validating directional setups for CE/PE options or index futures Avoiding chop-heavy intraday periods Enhancing systems based on VWAP, MACD histogram Building multi-layer conviction models for trend trading
Indicador Pine Script®
por gokul4trading
Volume Edge Pro [wjdtks255]■ Technical Methodology: Volumetric Institutional Tracking The Volume Edge Pro is an advanced analytical engine designed to detect institutional "footprints" through specific volume anomalies. Unlike standard volume indicators, this tool utilizes a proprietary lookback algorithm to identify Pocket Pivots (PPV) and Bearish Pivots (BPV). ■ Key Analytical Components Bullish Pocket Pivot (Blue): Identifies aggressive institutional accumulation where current up-day volume exceeds the maximum down-day volume within a 10-day lookback period. Bearish Pivot (Purple): Detects "Short Whale" activities where selling pressure eclipses the strongest buying volume of the previous 10 days. Regime Filtering: Integrated with a Volume Moving Average to differentiate between regular market flow and high-conviction institutional entries. ■ Strategic Utility This script is essential for trend-following and breakout traders who need to confirm price movements with institutional volume validation. It provides a clear visual regime: Blue for Bullish Institutional flow, and Purple for Bearish Institutional flow.
Indicador Pine Script®
por wjdtks255
Mean Deviation Trend [BackQuant]Mean Deviation Trend Overview Mean Deviation Trend is a structure-based trend and regime indicator that measures directional pressure as the market’s sustained deviation from a moving “mean,” then uses that pressure to drive an adaptive band , dynamic coloring, and a level engine that marks deviation peak extremes after momentum fades. Most trend tools start with direction, for example slope or MA cross, then try to estimate strength later. This script does the reverse: It first quantifies how far price is displaced from a central mean in volatility-adjusted units . It then smooths and accumulates that deviation to determine trend direction and conviction . Finally it converts conviction into a band that tightens when pressure is strong and widens when pressure is weak. The result is a single framework that blends: A mean anchor (EMA). A signed deviation engine normalized by ATR. A conviction score based on sustained deviation. An adaptive band that behaves like dynamic support/resistance. A “deviation peak” level system that plants levels at extremes after the push fades. Optional glow, fills, candle coloring, and flip markers. Core concept: deviation from mean as trend fuel A trend is not just “price up” or “price down.” A trend is a persistent imbalance where price spends time displaced from fair value and keeps re-asserting that displacement. This indicator treats the mean as a moving fair value proxy, and it measures how aggressively price is departing from it. Key idea: If price stays above the mean and that displacement is sustained, bullish pressure is dominant. If price stays below the mean and that displacement is sustained, bearish pressure is dominant. If price keeps snapping back and deviation cannot sustain, regime is weak and uncertainty is high. This is why the script doesn’t rely on a single moment like a cross. It cares about persistence . Mean anchor (the “center of gravity”) The mean is defined as an EMA of close: mean = EMA(close, meanLen) Why EMA: It responds faster than SMA to regime changes. It provides a stable anchor without overreacting to single bars. The mean line is not just a moving average here, it is the reference line that deviation is measured against. Everything downstream depends on the mean being a consistent “center.” Volatility normalization (why ATR is essential here) Raw distance from mean is meaningless across volatility regimes. A $200 deviation on BTC might be noise one week and huge another week. To fix this, the script normalizes deviation by ATR: atr = ATR(14) rawDev = (close - mean) / atr Interpretation: rawDev is “how many ATR units price is away from the mean.” This makes deviation comparable across timeframes and volatility states. This is critical because it turns the indicator into a dimensionless pressure metric rather than a price-distance tool. Deviation smoothing (instantaneous pressure vs noisy pressure) Instantaneous deviation can spike on one candle and mean nothing. So the script applies EMA smoothing to raw deviation: devSmooth = EMA(rawDev, devLen) What this does: Reduces single-bar spikes. Keeps the sign and general magnitude of displacement. Creates a cleaner “pressure line” that responds but does not jitter. This is the first stage of filtering: “Are we meaningfully deviating, or just wicking?” Deviation accumulation (turning pressure into conviction) This is the part that makes the indicator behave like a trend conviction model rather than a simple oscillator. The script computes: cumDev = SMA(devSmooth, devAccum) Even though it’s coded as an SMA, conceptually it behaves like a rolling accumulation of the deviation signal: If devSmooth stays positive for multiple bars, cumDev rises and stays positive. If devSmooth stays negative for multiple bars, cumDev drops and stays negative. If devSmooth flips sign repeatedly, cumDev compresses toward zero. This is the key “persistence detector.” It converts short-term deviation into a medium-term conviction read. Trend direction and flips Trend direction is derived purely from the sign of cumulative deviation: tDir = cumDev > 0 ? +1 : -1 flip = tDir != tDir Interpretation: Bull regime means the market’s sustained deviation is above the mean (pressure up). Bear regime means sustained deviation is below the mean (pressure down). A flip marks a regime transition where the sustained bias changes sign. This is intentionally simple because all the complexity is in how cumDev is built. Measuring conviction: devNorm (adaptive strength scale) The script measures absolute conviction: devAbs = abs(cumDev) Then it normalizes it relative to a rolling peak: devHigh = highest(devAbs, 80) devNorm = devHigh > 0 ? min(devAbs / devHigh, 1) : 0 Meaning: devNorm is a 0..1 strength scale. 0 means current conviction is tiny relative to recent extremes. 1 means conviction is at the strongest level seen in the last ~80 bars. This is not a z-score, it’s a “relative-to-recent-peak” normalization. That matters because it makes the band behavior adapt to each instrument’s recent character, not a fixed threshold system. Adaptive band logic (tight when confident, wide when uncertain) The band is built to behave differently depending on conviction. When conviction is strong, the band should hug price and act like a close structural guide. When conviction is weak, the band should widen and stop pretending it is precise. This is done by interpolating between two ATR multipliers: bandTight = ATR multiplier when devNorm is high bandWide = ATR multiplier when devNorm is low bandMult = bandWide - devNorm * (bandWide - bandTight) bandW = atr * bandMult Interpretation: devNorm near 1 → bandMult approaches bandTight → band width shrinks. devNorm near 0 → bandMult approaches bandWide → band width expands. So the band width is not arbitrary. It is a direct function of trend conviction. Active band placement (trend-aware support/resistance) The “active band” is placed on the opposite side of the mean depending on direction: If bullish: activeBand = mean - bandW If bearish: activeBand = mean + bandW So in bullish regimes, the band behaves like a dynamic support zone beneath the mean. In bearish regimes, it behaves like dynamic resistance above the mean. Then it is smoothed: activeBand = EMA(activeBand, 3) This prevents the band from stepping too harshly when ATR shifts. Outer band (secondary structure reference) A second band is created at half width on the opposite side: bull: outerBand = mean + bandW * 0.5 bear: outerBand = mean - bandW * 0.5 Then smoothed again. This outer line is not the main “stop band,” it is more of an additional structure marker to show where the mean plus/minus partial deviation zone sits. It can help visually gauge whether price is extended relative to the mean structure while still in the same regime. Color system (strength-aware gradient) The trend color is not binary. It is strength-weighted: If bullish, devNorm drives a gradient from a faint bull tint to full bull. If bearish, devNorm drives a gradient from a faint bear tint to full bear. This gives you an immediate read: Bright strong color = conviction high. Faded color = conviction low, regime fragile. It also ties into the glow and fill so the whole visual language matches the same underlying “pressure” variable. Deviation peak level engine (how the script plants levels) This indicator includes a separate mechanism that marks important extremes after a strong deviation push fades. The idea is: When trend pressure peaks and then collapses, the extreme price printed at peak deviation often becomes a reaction level later. This is similar in spirit to: exhaustion extremes, climactic deviation points, distribution/accumulation turning zones, but the script formalizes it using the deviation engine. 1) Track the strongest deviation peak The script stores a running peak: peakDev: maximum devAbs seen since last reset peakPrice: the extreme price at that peak (high for bull, low for bear) peakDir: direction at peak peakBar: bar index of peak When devAbs prints a new high, it updates those values. 2) Define “fade” (momentum has cooled) A fade event triggers when: peakDev is meaningfully large (peakDev > 0.3) current devAbs drops below a fraction of the peak: devAbs < peakDev * fadeThr fadeThr is the key user control. Lower fadeThr requires a deeper drop from peak before planting a level. What “fade” means in practice: A strong push happened (deviation expanded). That push is no longer active (deviation contracted). So the extreme created during the push is now “locked in” as a candidate level. 3) Plant a level at the extreme When faded: A dashed horizontal line is created at peakPrice. The line is projected forward (bar_index + 60). It is stored in an array with direction and retest state. It also respects maxLvls by deleting the oldest levels to avoid clutter. 4) Maintain levels and delete invalid ones Each bar, levels are checked: If price breaks far beyond the level (by about 2 ATR in the wrong direction), the level is deleted. That “broken” rule is a pragmatic invalidation filter. If price rips through a former deviation extreme by a large margin, the level is no longer acting like a meaningful reaction zone. 5) Detect retests and mark them A retest is detected when: close is within ~0.25 ATR of the level, and two bars ago price was not near it (distance > 0.5 ATR), and the level hasn’t already been marked as retested. When that happens: A diamond marker is printed (◆) above or below depending on approach. The level is flagged as retested so it won’t spam markers. So levels are not just static drawings. They have state: naked vs retested, and they get culled if invalidated. Glow system (volatility-scaled aesthetic, strength-scaled intensity) Glow is not random decoration here. Its width scales with devNorm: glowMult = 0.4 + devNorm * 1.2 glowW = atr * 0.08 * glowMult So in strong trends: Glow band expands. The mean core visually “radiates” more. In weak trends: Glow shrinks and becomes less prominent. The glow is built using multiple invisible plots above and below the mean, then layered fills with different transparencies. It creates a soft gradient aura around the mean that encodes strength. Band fill and line break behavior The active band is plotted with plot.style_linebr and forced to break on flips: bandBrk = flip ? na : activeBand This prevents the band from drawing a misleading connecting line across a regime change. It visually resets when direction flips, which matters because the band swaps sides of the mean when regime changes. Fill is drawn between: the active band line and hl2 (mid-price reference) So you get a shaded zone that reflects the current regime color and strength. Candles and flip labels Candles can be colored by the same strength-weighted regime color, which makes the entire chart consistent. On flips: Bull flip prints ▲ at the low. Bear flip prints ▼ at the high. These are regime markers, not “entry signals” by default. They simply identify when the cumulative deviation sign changed. How to read this indicator in practice 1) Regime and conviction Direction comes from cumDev sign. Conviction comes from devNorm intensity. Bright color + stable band on one side means strong sustained pressure. Faded color + widening band means weak sustained pressure and higher uncertainty. 2) Using the active band as structure In a bullish regime, activeBand is below mean and can behave like: dynamic support, risk boundary, trend “line in the sand.” In bearish regime, it flips above mean and acts like dynamic resistance. Because the band widens when conviction is low, it naturally tells you “do not treat this as a tight stop zone when the trend is weak.” 3) Using deviation peak levels Peak levels represent exhaustion extremes after a strong deviation impulse faded: If price returns to a naked level, that area can act as a reaction zone. Once retested, the script marks it and treats it as less “special.” If price breaks it by a wide margin, the script removes it as invalid. This level engine is best viewed as “structural memory of deviation events,” not generic support/resistance. 4) Extreme deviation alert devNorm > 0.85 means the current sustained deviation is near the strongest seen recently. That’s useful for: identifying trend climax states, detecting when continuation is strong but risk of snapback rises, flagging conditions where mean reversion pressure is building. It does not guarantee reversal, it flags “stretch.” Inputs and what they actually change Mean Length (meanLen) Controls the anchor responsiveness: Lower = mean follows price more closely, deviation shrinks, more frequent flips. Higher = mean is slower, deviation grows, trend regimes last longer. Deviation Smoothing (devLen) Controls how noisy the deviation signal is: Lower = faster response, more jitter. Higher = smoother pressure, slower flips. Deviation Accumulation (devAccum) Controls persistence requirement: Lower = trend conviction reacts quickly but can whipsaw. Higher = requires sustained deviation, fewer flips, more confirmation. Band Tight / Band Wide These define the band behavior range: bandTight: how close the band gets when conviction is strong. bandWide: how far it drifts when conviction is weak. If you want the band to behave more like a stop guide, reduce bandWide. If you want it to act more like a regime boundary, increase bandWide. Fade Threshold + Max Levels These shape the level engine: fadeThr lower = requires bigger cooling before planting levels (fewer, more meaningful). fadeThr higher = plants levels earlier (more levels, more noise). maxLvls controls clutter and historical depth. Alerts (what they represent) Dev Bull / Dev Bear: regime flips, cumulative deviation changed sign. Dev Faded: a deviation peak cooled enough to plant a level. Extreme Dev: sustained deviation is near local maximum, stretch condition. Summary Mean Deviation Trend models trend as sustained, volatility-normalized displacement from a mean rather than simple direction. It smooths and accumulates signed deviation to extract regime and conviction, then converts that conviction into an adaptive ATR band that tightens when pressure is strong and widens when pressure is weak. On top of that, it tracks deviation peak extremes and plants forward levels only after deviation fades, creating a structured map of “where trend impulses peaked” and how price reacts when those zones are revisited.
Indicador Pine Script®
por BackQuant
Legendary Linda Bradford Raschke ORB Turtle Soup Hybrid Linda Bradford Raschke ORB and Turtle Soup Hybrid Framework: ✔ Opening Range Breakout (ORB) ✔ Turtle Soup (false breakout reversal) ✔ 3/10 Oscillator momentum module ✔ Short-term swing structure inspired by LBR 30 ✔ Dynamic exits (no fixed R targets) Strategy Architecture Overview: 1️⃣ Opening Range Module (ORB) ✔ Define opening range using first N bars of session ✔ Break above/below range ✔ Confirm with momentum (3/10 oscillator) ✔ Trend bias filter 2️⃣ Turtle Soup Module Classic Raschke concept: ✔ Break prior swing high/low ✔ Immediate failure back inside range ✔ Enter reversal in direction of failure ✔ Momentum confirmation required 3️⃣ 3/10 Oscillator Module - Classic Raschke momentum tool: ✔ 3 EMA minus 10 EMA ✔ Smoothed with 16-period SMA ✔ Used for: * Momentum confirmation * Exit on momentum loss 4️⃣ LBR 30-Inspired Swing Filter ✔ Inspired by her LBR 30 concept: * 30-period EMA defines bias * Pullbacks to 30 EMA trigger continuation * Structure-based trailing exit Recommended Instrument & Timeframe: ✔ Primary Test: * Volatility 75 Index (or any high-beta instrument) * 5 Minute timeframe ✔ Secondary: * 15 Minute for swing stability This is more consistent with Raschke’s short-term swing style than 1-minute scalping.
Estrategia Pine Script®
por uzair2join
Parkinson Range Oscillator [BackQuant]Parkinson Range Oscillator Overview Parkinson Range Oscillator is a volatility regime indicator built around the Parkinson volatility estimator , a high-low based variance model originally proposed as a more statistically efficient alternative to close-to-close volatility. Instead of measuring volatility from closing returns, this script measures volatility from the intrabar price range using ln(H/L), then converts it into a normalized oscillator (z-score) so you can identify volatility expansion vs compression relative to the asset’s own history. The indicator is designed to answer questions like: Is volatility currently elevated or suppressed relative to its baseline? Is volatility expanding (risk rising) or compressing (coiling)? How extreme is the current vol state in percentile terms? How does range-based vol compare to a more common ATR-based vol read? It plots: A Parkinson-based volatility z-score oscillator with gradient fills. A signal line (EMA) for expansion/compression transitions. An ATR-based z-score for context comparison. A dashboard with current vol %, z-score, percentile rank, regime label, and ATR z-score. Where Parkinson volatility comes from (origin and intuition) The Parkinson estimator comes from academic finance and the study of volatility estimation. The key insight is simple: The daily high and low contain more information about variability than the close alone. Close-to-close volatility only uses one price per bar (the close), throwing away intrabar information. The high-low range captures the realized dispersion inside the bar, so under ideal assumptions it can estimate variance more efficiently. The Parkinson model is derived assuming: Price follows a continuous-time diffusion process (often framed like geometric Brownian motion). No drift matters for the variance estimate over the interval. No jumps and no microstructure distortions (idealized). Even though real markets violate these assumptions (gaps, jumps, wicks from order flow), the estimator remains useful because: Range is still a strong proxy for realized volatility. It reacts to intrabar expansion earlier than close-based methods. It is less dependent on where the bar closes. Core Parkinson formula (what the script implements) Parkinson variance for a window of n bars is: Var = (1 / (4 * n * ln(2))) * Σ This script computes it in the common rolling form: logHL2 = (ln(high/low))² parkVar = SMA(logHL2, n) / (4 * ln(2)) parkVol = sqrt(parkVar) * 100 Key details: ln(H/L) makes the range scale-invariant (percent-like), so it behaves more consistently across price levels. Squaring gives variance contribution. The 1/(4 ln 2) constant comes from the expected distribution of high-low range under a Brownian diffusion. sqrt converts variance to standard deviation (volatility). *100 expresses it as a percentage for readability. So parkVol is a “range-based realized volatility proxy” in percent terms. Why range-based volatility behaves differently than ATR ATR measures average true range, which is a linear range magnitude measure (high-low plus gaps). Parkinson uses ln(H/L) which is: Log-scaled (closer to a return-based measure). More directly tied to variance estimation theory. In practice: ATR can be driven by gaps and absolute range. Parkinson is driven by proportional range and tends to emphasize how wide the bar is relative to its price level. Parkinson often reacts sharply when wicks expand even if closes are stable. Normalization into an oscillator (making it comparable through time) Raw volatility values are hard to interpret across regimes because every market has different “normal.” This script normalizes Parkinson volatility against its own rolling baseline using a z-score: parkMA = SMA(parkVol, baselineLen) parkSD = stdev(parkVol, baselineLen) osc = (parkVol - parkMA) / parkSD Interpretation: osc = 0 means current vol is at its baseline average. osc = +1 means 1 standard deviation above normal (high vol). osc = -1 means 1 standard deviation below normal (compressed). osc > +2 flags extreme expansion states. This is the core output. It turns “volatility” into “volatility regime” in standardized units. Signal line and expansion/compression transitions The oscillator is smoothed with an EMA to create a signal line: signal = EMA(osc, signalLen) Then transitions are defined as: Expansion cross: crossover(osc, signal) and osc > 0 Compression cross: crossunder(osc, signal) and osc < 0 Why the extra osc > 0 and osc < 0 conditions: It prevents treating small oscillations around zero as meaningful. It forces expansion signals to occur in above-average volatility territory. It forces compression signals to occur in below-average volatility territory. So signals are regime-confirming, not constant cross spam. Percentile rank (how extreme is vol relative to the past) In addition to the z-score, the script computes the percentile rank of the raw Parkinson volatility: pctRank = percentrank(parkVol, pctRankLookback) Interpretation: pctRank near 90–100 means current vol is among the highest levels seen in that lookback. pctRank near 0–10 means it is among the lowest (compression). Z-score tells you “how many SDs from mean.” Percentile tells you “how rare is this state historically.” Those are different but complementary. ATR comparison line (context, not the main engine) The indicator also computes an ATR-based volatility proxy and normalizes it in the same way: atrVol = ATR(n) / close * 100 atrOsc = zscore(atrVol, baselineLen) This gives you a direct visual comparison: If Parkinson oscillator is high but ATR oscillator isn’t, range expansion may be happening in a way ATR is not emphasizing (or vice versa). If both agree, you have stronger confirmation of a true volatility regime shift. ATR is included as a “common benchmark,” not as the primary signal. Regime classification (human-readable state mapping) The script labels regimes from osc: osc > 2.0 → EXTREME osc > 1.0 → HIGH osc > 0.0 → ABOVE AVG osc > -1.0 → BELOW AVG else → COMPRESSED This is a practical mapping for dashboards and quick reads. It is not pretending that 2.0 is a universal constant, it is just a standardized “rare expansion” threshold. Coloring follows the same logic: More positive = more “expansion” coloring (bearCol). More negative = more “compression” coloring (bullCol). Note: the color naming is semantic here: “Low Vol / Compression” is bullCol because compression often precedes trend expansion opportunities. “High Vol / Expansion” is bearCol because high vol often implies risk, disorder, liquidation, or unstable conditions. You can interpret those however you prefer, the tool is measuring volatility regime, not directional bias. Plot design (why the oscillator is split into positive/negative) The oscillator is split into two series: oscPos = osc if osc > 0 else na oscNeg = osc if osc < 0 else na This is purely for visuals: Positive region is drawn with expansion color and expansion gradient fill to zero. Negative region is drawn with compression color and compression gradient fill to zero. This makes it obvious at a glance which side of “normal volatility” you’re on. How to interpret the indicator correctly 1) The oscillator is volatility regime, not price direction High osc does not mean price will go down. It means the market is moving violently relative to its baseline. That can occur in: Selloffs, liquidations, panic. Breakouts and momentum expansions. News-driven repricing. Low osc does not mean price will go up. It means the market is quiet relative to baseline: Ranges, coils, low realized movement. Slow grind trends with suppressed pullbacks. Pre-breakout compressions. 2) Compression regimes are often “setup states” When osc is deeply negative (compressed), it often indicates that realized movement has collapsed. In many markets this precedes: Breakouts (vol expansion from compression). Trend acceleration. Mean reversion bursts. But compression can also persist. This is why the script includes signal crosses and percentile rank to judge when compression is shifting. 3) Expansion regimes are often “risk states” When osc is positive and rising, the environment is more chaotic: Stops are more likely to be hit. Mean reversion can get violent. Trend continuation can be strong but timing becomes harder. In those regimes, the tool can be used to: Reduce leverage. Widen stops (if your system supports it). Switch to volatility-aware sizing. Wait for stabilization if you trade mean reversion. 4) Use percentile rank to identify “rare” volatility Two markets can both show osc = +1, but one might be at the 95th percentile and the other at the 70th depending on distribution shape. Percentile tells you whether the current vol is truly rare in that lookback. Cross dots (how to treat them) ExpansionCross and CompressionCross are not buy/sell signals. They are “volatility phase change” markers: ExpansionCross: vol regime moving up, above baseline, acceleration risk increases. CompressionCross: vol regime moving down, below baseline, quieting environment. These are useful for: Strategy toggles (trend mode vs chop mode). Sizing changes. Timing filters (avoid entries during extreme expansion if your edge hates noise). Dashboard (what it gives you at a glance) The table summarizes everything that matters without you needing to interpret plots manually: Parkinson Vol %: current raw range-based volatility level. Z-Score: current standardized regime reading. Percentile: rarity of current vol in the lookback. Regime: discrete label based on z-score thresholds. ATR Z-Score: comparison metric in standardized units. The dashboard is positioned and sized via inputs so it can fit different chart layouts. Parameter tuning guidance Parkinson Length Controls how quickly the raw Parkinson vol responds: Shorter = more reactive to immediate range changes. Longer = smoother volatility estimate, less noisy. Baseline Length Controls what “normal” means: Long baseline (like 100) creates stable regime definitions. Short baseline makes z-scores jump around and can overreact. Signal Length Controls how quickly you detect regime turning points: Short signal = more crosses, earlier detection, more noise. Long signal = fewer crosses, later detection, cleaner regime shifts. Percentile Lookback Controls rarity context: 252 approximates one trading year on daily charts. On intraday, it becomes “252 bars,” so adjust to match your horizon. Limitations and what to watch for Parkinson assumes continuous diffusion. Jumps and gaps can distort it. Wicks caused by illiquidity can inflate ln(H/L) and produce false “expansion.” Z-score assumes the baseline distribution is reasonably stable. If volatility distribution shifts structurally, your z-scores can be biased until baseline catches up. Percentile rank is lookback-dependent. Different lookbacks can change “rarity” classification materially. Summary Parkinson Range Oscillator converts a statistically grounded high-low volatility estimator into a regime oscillator by z-scoring Parkinson volatility against its own rolling baseline. It highlights expansion vs compression states with clear gradients, flags volatility phase changes via oscillator-signal crosses, ranks current volatility by percentile for rarity context, and overlays an ATR-based z-score for comparison. This makes it a practical tool for volatility-aware trading, regime filtering, sizing adjustments, and identifying compression-to-expansion transitions.
Indicador Pine Script®
por BackQuant
Globus Capitas EMA 9 & EMA 14 RibbonGLOBUS CAPITAS EMA 9 & EMA 14 RIBBON INDICATOR The EMA 9 & 14 Ribbon Indicator is a precision built trend visualization tool designed for modern intraday traders and scalpers who demand clarity speed and structure in fast moving markets. This indicator leverages the power of two exponential moving averages EMA 9 and EMA 14 and converts their interaction into a dynamic color ribbon allowing traders to instantly identify trend direction momentum shifts and potential trade bias without lag or clutter. HOW THE INDICATOR WORKS At its core the indicator tracks the relationship between EMA 9 Fast EMA reacts quickly to price changes EMA 14 Slow EMA provides short term trend stability Instead of relying only on line crossovers the indicator fills the area between both EMAs with a contextual ribbon making trend conditions visually obvious at a glance. RIBBON COLOR LOGIC SMART VISUAL FEEDBACK Green Ribbon Bullish Bias EMA 9 crosses and stays above EMA 14 Indicates increasing buying momentum Favors long positions and buy side setups Red Ribbon Bearish Bias EMA 14 crosses and stays above EMA 9 Indicates increasing selling pressure Favors short positions and sell side setups Neutral Zone Flat Market EMAs are tightly compressed Signals low momentum and consolidation Best avoided unless confirmed by volume or structure WHY THIS INDICATOR STANDS OUT 1 Instant trend recognition no guesswork no lag 2 Clean and minimal design ideal for scalping charts 3 Psychology friendly reduces emotional decision making 4 Works across all markets indices gold forex crypto options buying options selling 5 Works with all minute timeframes This ribbon structure allows traders to stay aligned with price flow instead of reacting late to cross signals. BEST USE CASES Intraday trading and scalping Trend continuation entries Pullback trades in strong trends Directional bias confirmation Trade filtering with price action or structure Pro Tip Combine this indicator with key support and resistance levels liquidity zones or VWAP for higher probability setups. TRADING PHILOSOPHY BEHIND IT This indicator is built around one core principle Trade what the market is doing not what you think it should do The EMA Ribbon removes noise and bias helping traders Follow trends objectively Avoid counter trend emotional trades Protect capital during choppy conditions IMPORTANT NOTES This is not a standalone system Best used as a trend filter not a signal generator Avoid trading during major news events without confirmation DISCLAIMER This indicator is provided strictly for educational purposes. It does not offer buy or sell recommendations. Trading involves risk always manage your position size use stop losses and consult a certified financial advisor if needed. Globus Capitas is not responsible for any profit or loss. FINAL WORD The Globus Capitas EMA 9 & 14 Ribbon is a clarity tool for serious traders designed to keep you aligned with momentum disciplined with entries and focused on capital protection. Trade smart. Stay aligned. Trade what you see. Follow the trend. Protect capital. Let the chart lead.
Indicador Pine Script®
por globuscapitas
5
Harmonic Confluence Wave Detector [JOAT]Harmonic Confluence Wave Detector Introduction The Harmonic Confluence Wave Detector is an open-source oscillator-based indicator that combines WaveTrend, Money Flow Index (MFI), RSI, MACD, and Stochastic RSI into a unified momentum analysis system. This mashup creates a multi-layered oscillator framework designed to identify momentum shifts, overbought/oversold conditions, and divergence patterns across multiple timeframes and calculation methods. The indicator addresses a common trading challenge: single oscillators can give conflicting or premature signals. By synthesizing five different momentum calculations that use distinct mathematical approaches, this tool provides confluence-based signals that occur when multiple momentum indicators align, significantly reducing false signals compared to using any single oscillator alone. Chart showing WaveTrend oscillator, MACD histogram, and multi-signal system on 1H timeframe Why This Mashup Exists This indicator combines five oscillators that complement each other through different calculation methodologies: WaveTrend: Smoothed momentum oscillator based on price deviation from exponential moving average Money Flow Index (MFI): Volume-weighted RSI showing buying/selling pressure RSI: Classic momentum oscillator measuring speed and magnitude of price changes MACD: Trend-following momentum indicator showing relationship between two EMAs Stochastic RSI: Stochastic calculation applied to RSI for enhanced sensitivity Each oscillator has unique strengths: WaveTrend excels at identifying wave-like momentum cycles, MFI incorporates volume for institutional flow analysis, RSI provides reliable overbought/oversold readings, MACD shows trend strength and direction, and Stochastic RSI catches early momentum shifts. Together, they create a comprehensive momentum picture that no single oscillator can provide. The mashup is justified because these oscillators use fundamentally different calculations (price-based, volume-weighted, moving average convergence, stochastic) that respond to different market conditions. When they align, it indicates genuine momentum shift rather than noise. Core Components Explained 1. WaveTrend Oscillator (Primary Signal Generator) WaveTrend is the primary oscillator, calculated using this methodology: // Calculate exponential average of HLC3 esa = ta.ema(hlc3, channelLength) // Calculate deviation d = ta.ema(abs(hlc3 - esa), channelLength) // Calculate channel index ci = (hlc3 - esa) / (0.015 * d) // Apply smoothing to create WaveTrend 1 wt1 = ta.ema(ci, averageLength) // Create WaveTrend 2 as simple moving average of WT1 wt2 = ta.sma(wt1, 4) WaveTrend oscillates around zero, with: Values above +60: Overbought zone Values above +80: Extreme overbought Values below -60: Oversold zone Values below -80: Extreme oversold Crossovers between WT1 and WT2: Momentum shift signals The indicator plots WT1 and WT2 as lines with dynamic coloring based on momentum direction and strength. 2. Money Flow Index (MFI) - Volume-Weighted Momentum MFI calculation incorporates both price and volume: // Calculate typical price typicalPrice = (high + low + close) / 3 // Calculate raw money flow rawMoneyFlow = typicalPrice * volume // Separate positive and negative money flow positiveFlow = close > close ? rawMoneyFlow : 0 negativeFlow = close < close ? rawMoneyFlow : 0 // Sum over MFI period positiveSum = sum(positiveFlow, mfiLength) negativeSum = sum(negativeFlow, mfiLength) // Calculate MFI mfi = 100 - (100 / (1 + positiveSum / negativeSum)) MFI ranges from 0-100, with readings above 80 indicating buying pressure and below 20 indicating selling pressure. The indicator plots MFI as a line and uses it for confluence scoring. 3. RSI - Classic Momentum Oscillator Standard RSI calculation over 14 periods (configurable): RSI > 70: Overbought RSI < 30: Oversold RSI > 65 with other bearish signals: Potential reversal RSI < 35 with other bullish signals: Potential reversal RSI provides reliable baseline momentum readings and is used for divergence detection. 4. MACD - Trend Momentum Indicator MACD uses standard 12/26/9 settings: = ta.macd(close, 12, 26, 9) The indicator displays MACD histogram with enhanced width (linewidth 8) for visibility. Histogram color changes based on: Green: Positive and increasing (bullish momentum) Light green: Positive but decreasing (weakening bulls) Red: Negative and decreasing (bearish momentum) Light red: Negative but increasing (weakening bears) MACD histogram provides visual confirmation of momentum strength and direction. 5. Stochastic RSI - Enhanced Sensitivity Stochastic calculation applied to RSI values: stochRSI = ta.stoch(rsi, rsi, rsi, 14) Stochastic RSI oscillates between 0-100 and is more sensitive than regular RSI, catching momentum shifts earlier. The indicator plots both K and D lines for crossover analysis. Example showing all oscillators with divergence markers and signal labels Multi-Signal System The indicator generates six tiers of signals based on confluence strength: BUY Signals: BUY: WT1 crosses above WT2 in oversold zone (WT1 < -40) STRONG BUY: BUY + volume above average + MACD histogram positive MEGA BUY: STRONG BUY + WT1 < -60 (extreme oversold) + RSI < 35 ULTRA BUY: MEGA BUY + MFI < 30 + Stoch RSI oversold + bullish divergence SELL Signals: SELL: WT1 crosses below WT2 in overbought zone (WT1 > 40) STRONG SELL: SELL + volume above average + MACD histogram negative MEGA SELL: STRONG SELL + WT1 > 60 (extreme overbought) + RSI > 65 ULTRA SELL: MEGA SELL + MFI > 70 + Stoch RSI overbought + bearish divergence Signal labels appear on chart with size proportional to signal strength (tiny for BUY/SELL, normal for ULTRA). Divergence Detection System The indicator detects divergences across multiple oscillators: RSI Divergence: Bullish: Price makes lower low, RSI makes higher low Bearish: Price makes higher high, RSI makes lower high WaveTrend Divergence: Bullish: Price makes lower low, WT1 makes higher low Bearish: Price makes higher high, WT1 makes lower high MACD Divergence: Bullish: Price makes lower low, MACD histogram makes higher low Bearish: Price makes higher high, MACD histogram makes lower high Divergences are marked with bright orange/yellow "D" labels (color.rgb(255, 200, 0)) with black text for maximum visibility. When multiple oscillators show divergence simultaneously, it signals strong momentum exhaustion and potential reversal. Confluence Scoring System The indicator calculates a real-time confluence score (0-100) by evaluating: Confluence Components: - WaveTrend Position: Up to 25 points (extreme zones add more weight) - WaveTrend Momentum: Up to 15 points (WT1-WT2 relationship) - RSI Level: Up to 15 points (extreme readings add weight) - MFI Level: Up to 15 points (volume pressure confirmation) - MACD Histogram: Up to 15 points (trend momentum) - Stochastic RSI: Up to 10 points (early momentum detection) - Divergence Presence: Up to 5 points (any divergence detected) The dashboard displays the current confluence score with color coding: Green (80-100): Strong bullish confluence Light green (60-79): Moderate bullish confluence Yellow (40-59): Neutral/mixed signals Light red (20-39): Moderate bearish confluence Red (0-19): Strong bearish confluence Visual Elements WaveTrend Lines: WT1 (blue) and WT2 (orange) with dynamic coloring Overbought/Oversold Zones: Horizontal lines at +60/-60 and +80/-80 Zero Line: Reference line at 0 MACD Histogram: Large bars (linewidth 8) with gradient coloring MFI Line: Purple line showing volume-weighted momentum RSI Line: Green line with overbought/oversold reference levels Stochastic RSI: K (blue) and D (red) lines Signal Labels: BUY/SELL markers with size based on signal strength Divergence Labels: Bright orange "D" markers at divergence points Dashboard: Top-right table showing confluence score and oscillator readings Chart demonstrating signal hierarchy from BUY to ULTRA BUY with divergence markers How Components Work Together The mashup creates a layered momentum analysis: Layer 1 - Primary Momentum: WaveTrend identifies wave cycles and crossover signals Layer 2 - Volume Confirmation: MFI validates moves with volume-weighted pressure Layer 3 - Baseline Momentum: RSI provides reliable overbought/oversold context Layer 4 - Trend Strength: MACD histogram shows underlying trend momentum Layer 5 - Early Detection: Stochastic RSI catches momentum shifts before other oscillators Layer 6 - Exhaustion Signals: Divergences across oscillators indicate momentum exhaustion Example scenario: WT1 crosses above WT2 in oversold zone (Layer 1), MFI shows buying pressure increasing (Layer 2), RSI is below 35 (Layer 3), MACD histogram turns positive (Layer 4), Stochastic RSI crosses up (Layer 5), and RSI shows bullish divergence (Layer 6). This generates an ULTRA BUY signal with 90+ confluence score. Input Parameters WaveTrend Settings: Channel Length: Period for EMA calculation (default: 10) Average Length: Smoothing period for WT1 (default: 21) Overbought Level: Upper threshold (default: 60) Oversold Level: Lower threshold (default: -60) Extreme OB Level: Extreme upper threshold (default: 80) Extreme OS Level: Extreme lower threshold (default: -80) Oscillator Settings: RSI Length: Period for RSI calculation (default: 14) MFI Length: Period for MFI calculation (default: 14) MACD Fast: Fast EMA period (default: 12) MACD Slow: Slow EMA period (default: 26) MACD Signal: Signal line period (default: 9) Stochastic RSI Length: Period for Stoch RSI (default: 14) Signal Settings: Show Signals: Toggle signal labels (default: enabled) Show Divergences: Toggle divergence markers (default: enabled) Volume Confirmation: Require volume for STRONG signals (default: enabled) Min Confluence for Signals: Minimum score to display signals (default: 60) Display Options: Show Dashboard: Toggle confluence score table (default: enabled) Show MACD Histogram: Toggle MACD display (default: enabled) Show MFI Line: Toggle MFI display (default: enabled) Show RSI Line: Toggle RSI display (default: enabled) Show Stochastic RSI: Toggle Stoch RSI display (default: enabled) Color Theme: Choose between multiple color schemes How to Use This Indicator Step 1: Monitor WaveTrend Oscillator Watch for WT1/WT2 crossovers in extreme zones. Crossovers in oversold zone (< -60) suggest bullish reversals, crossovers in overbought zone (> 60) suggest bearish reversals. Step 2: Check Confluence Score Review the dashboard. Scores above 70 indicate strong momentum alignment. Higher scores generally produce more reliable signals. Step 3: Identify Signal Strength Pay attention to signal labels. ULTRA signals have highest probability but occur less frequently. STRONG signals offer good balance between frequency and reliability. Step 4: Look for Divergences Divergence markers indicate momentum exhaustion. When divergences appear with extreme oscillator readings, reversal probability increases significantly. Step 5: Confirm with MACD Histogram Check MACD histogram direction and strength. Large histogram bars confirm strong momentum, shrinking bars suggest momentum loss. Step 6: Validate with Volume (MFI) Ensure MFI supports the move. Bullish signals with rising MFI are stronger, bearish signals with falling MFI are stronger. Best Practices Use on 15-minute to 4-hour timeframes for optimal signal quality Wait for STRONG or MEGA signals rather than acting on every BUY/SELL Divergences work best when combined with extreme oscillator readings Multiple oscillator divergences (RSI + WT + MACD) are most reliable Use confluence score as filter - avoid signals below 60 score MACD histogram size indicates momentum strength - larger bars = stronger moves MFI divergence from price often precedes reversals (volume leads price) Combine with price action and support/resistance for best results Indicator Limitations Oscillators can remain overbought/oversold longer than expected in strong trends Divergences can persist for multiple bars before reversal occurs Multiple signals in choppy markets can lead to whipsaws Confluence score is mathematical calculation, not prediction of future movement ULTRA signals are rare - waiting only for these may miss opportunities Volume data quality varies across markets and can affect MFI reliability Stochastic RSI is very sensitive and can generate premature signals No indicator combination eliminates false signals entirely Requires understanding of oscillator behavior for effective interpretation Technical Implementation Built with Pine Script v6 using: Custom WaveTrend calculation with dual-line system Proper MFI formula with volume-weighted money flow Multi-oscillator divergence detection with pivot analysis Confluence scoring algorithm with weighted components Enhanced MACD histogram visualization (linewidth 8) Dynamic color gradients for momentum visualization Anti-overlap logic for signal labels Real-time dashboard with oscillator readings The code is fully open-source and can be modified to adjust oscillator weights, signal thresholds, and visual preferences. Originality Statement This indicator is original in its multi-oscillator integration approach. While individual components (WaveTrend, MFI, RSI, MACD, Stochastic RSI) are established oscillators, this mashup is justified because: It combines five oscillators using fundamentally different calculation methods The tiered signal system (BUY to ULTRA) provides graduated confidence levels Multi-oscillator divergence detection catches momentum exhaustion across different timeframes Confluence scoring quantifies momentum alignment across all oscillators Volume integration through MFI adds institutional flow perspective Enhanced visualization (large MACD histogram, bright divergence markers) improves usability Each oscillator contributes unique information: WaveTrend provides wave-cycle analysis, MFI incorporates volume, RSI offers reliable baseline, MACD shows trend strength, and Stochastic RSI catches early shifts. The mashup's value lies in identifying when these different momentum calculations align, significantly reducing false signals compared to any single oscillator. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors. Oscillator-based indicators are lagging tools that analyze past price data. They do not predict future price movement. Overbought conditions can persist in strong uptrends, and oversold conditions can persist in strong downtrends. Divergences can continue for extended periods before reversals occur. The confluence score is a mathematical calculation, not a guarantee of trade success. High confluence scores do not ensure profitable trades. Past signal performance does not guarantee future results. Market conditions change, and oscillator behavior varies across different market regimes. Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool. -Made with passion by officialjackofalltrades
Indicador Pine Script®
por officialjackofalltrades
Apex Quantum Terminal Ultra.Description The Apex Quantum Terminal Ultra is a sophisticated, multi-layered quantitative signaling system designed for the modern trader. Moving beyond simple lagging indicators, this terminal utilizes a 5-Layer Heuristic Engine to calculate real-time trade probabilities based on trend alignment, momentum confluence, institutional volume, volatility bias, and higher-timeframe (MTF) trends. What sets this terminal apart is its Realistic Probability Model. Unlike standard "100% accurate" claims, this script uses a weighted decay algorithm that requires deep confluence to trigger high-conviction signals, reflecting the non-linear nature of financial markets. When a signal is generated, the terminal provides a Trade Narrative via interactive tooltips, explaining the market psychology and technical reasoning behind the move. How It Works The engine evaluates five distinct technical layers: Zero-Lag Alpha: A high-speed trend-following baseline. Momentum Confluence: Unpacked MACD and RSI synchronization. Institutional Volume: Detection of RVol spikes (Smart Money participation). Volatility Bias: Bollinger-based positioning for expansion detection. Macro Alignment: Direct integration with Daily timeframe bias. Instructions Signals: Look for BUY or SELL labels on the chart. The percentage shown represents the system's confidence level based on layer confluence. Trade Logic: Hover your mouse over any signal label to see the Trade Narrative. This popup will provide you with the technical specs, the verdict, and suggested Stop Loss (SL) and Take Profit (TP) levels. Quant Alert Header: A dynamic banner will appear at the top center of your chart only when a "High Confluence" (70%+) event is detected, signaling a premium trade opportunity. Settings: * Adjust Signal Alpha to change the sensitivity of the trend detection. Modify the Min Confidence % to filter out lower-probability noise. Set your preferred Risk:Reward Target to automatically calculate TP levels.
Indicador Pine Script®
por Bmillytookprof
LBR Cinderella Electric Curfew Session Capital ControlLinda Bradford Raschke's Cinderella Electric Curfew – Session Capital Control Overview This strategy demonstrates structured session-based trading with capital protection rules and volatility-adjusted risk management. The core idea is simple: Trade only during a defined exchange session, and automatically close all open positions once the session ends — enforcing a strict “capital curfew.” The script includes configurable session templates for major global markets and applies risk-based position sizing using ATR-derived stop distance. Core Features: Session-Based Trading Users can select a predefined exchange session (e.g., New York, London, Frankfurt, Tokyo, Hong Kong, Sydney). Trades are only allowed during the selected session window. Electric Curfew Logic All open positions are automatically closed when the session ends (with optional grace period in minutes). This prevents overnight exposure and reduces gap risk. Risk-Based Position Sizing Position size is calculated as: Risk Amount ÷ Stop Distance Where: Risk Amount = % of current equity Stop Distance = ATR × Multiplier This ensures consistent capital allocation regardless of volatility regime. ATR-Based Stops & Targets Optional stop-loss and take-profit levels are volatility-adjusted. Example Signal Logic A simple SMA crossover is included as a placeholder. Users are encouraged to replace it with their own entry logic. Intended Use This strategy is designed for: • Intraday traders • Session-based systems • Traders avoiding overnight risk • Prop-style capital discipline models The session control framework can be integrated into any strategy logic. Past performance does not guarantee future results.
Estrategia Pine Script®
por uzair2join
LBR Aberration Trend Following StrategyLinda Bradford Raschke's Aberration Trend Following Strategy Overview This strategy implements a directional Bollinger Band breakout model with volatility normalization and trend alignment. Instead of trading every band breakout, the system applies: • Trend bias using EMA • ATR-based volatility regime filtering • Volatility-adjusted stop-loss placement • Fixed risk-to-reward targeting Core Logic: [Bollinger Band Expansion: Entries trigger when price breaks beyond the outer band during acceptable volatility conditions. Trend Confirmation: Long trades require price above the trend EMA. Short trades require price below the trend EMA. Volatility Filter: ATR is compared against its rolling percentile median to avoid extremely low or extreme volatility environments. Risk Management: Stops are ATR-based and targets are defined using configurable risk-reward ratios. This strategy is designed for trending environments and performs best during volatility expansion phases.
Estrategia Pine Script®
por uzair2join
LBR's Game Theory EMA Strategy - Alternate Game Theory EMA Strategy Overview: This strategy combines EMA crossover structure with a normalized momentum “utility edge” model to filter low-conviction trades. Instead of trading every EMA crossover, the script evaluates whether directional momentum statistically favors buyers or sellers before entering. Core Logic: EMA Structure Shift: A fast EMA crossing a slow EMA signals a possible momentum transition. Trend Alignment: Trades are only taken in the direction of a higher timeframe trend (Trend EMA). Utility Edge Filter: RSI is normalized around 50 to estimate directional advantage: • RSI > 55 → Buyers show advantage • RSI < 45 → Sellers show advantage • Between 45–55 → Considered equilibrium zone (no trade) Optional ADX Filter Trades can be restricted to environments with expanding trend strength. Risk Management Default Properties: • Initial Capital: 10,000 • Risk per trade: 2% of equity • Risk–Reward Ratio: 1.5:1 • Commission: 0.05% • Slippage: 1 tick • Max Trades per Day: 5 • Daily Loss Limit: 2% Position sizing is volatility-adjusted: Position Size = (2% Equity) ÷ ATR Stop Distance Stops and targets expand or contract automatically based on market volatility. Intended Use Designed for trending instruments. Users are encouraged to test on long historical datasets to ensure sufficient trade sample size. This strategy does not predict price direction. It reacts to structural and momentum shifts. Past performance does not guarantee future results.
Estrategia Pine Script®
por uzair2join
LBR's Game Theory - EMA StrategyLinda Bradford Raschke's Game Theory EMA Strategy: Overview: This strategy combines EMA crossover structure with a momentum-based utility edge model inspired by basic game theory concepts. The objective is to participate only when directional momentum suggests one side of the market has a measurable advantage. Core Logic: Structure Shift: A fast EMA crossing a slow EMA signals a potential momentum transition. Trend Alignment: Trades are only taken in the direction of a higher timeframe trend (Trend EMA). Utility Edge Model: Momentum is normalized using RSI to approximate directional “utility.” When RSI is above 50, buyer utility is positive. When RSI is below 50, seller utility is positive. If the difference between buyers and sellers is too small (equilibrium zone), trades are avoided. Trend Strength Filter: Optional ADX filter ensures trades occur in expanding trend conditions. Risk Management: Default Properties: • Initial Capital: 10,000 • Risk per trade: 2% of equity • Risk–Reward: 1.8 : 1 • Commission: 0.05% • Slippage: 1 tick • No pyramiding Position size is dynamically calculated: Position Size = (2% Equity) ÷ Stop Distance Stops and targets are ATR-based to adapt to volatility. Intended Use: Best suited for: • Trending instruments (Forex majors, Indices, Gold, Crypto) • Multi-year datasets for proper statistical evaluation This strategy reacts to structural and momentum shifts. It does not predict future price movement. Past performance does not guarantee future results.
Estrategia Pine Script®
por uzair2join
Automatic Trendline [Metrify]Metrify Automatic Trendlines is an auto-drawing support/resistance channel built around pivot clustering + scoring, not “connect two perfect points”. The script continuously collects swing pivots (high/low) over a configurable lookback window, then searches for the best single support line and the best single resistance line that behave like a human-drawn trendline: multiple interactions, controlled slope, limited break-throughs, and (most importantly) still relevant to the current price. (configurable in "Max Relevance Distance" input) The fundamental problem with algorithmic trendlines is subjectivity. To solve this mathematically, we treat trendlines as a statistical regression problem with specific constraints. We do not use linear regression on all candles, instead, we use a brute-force iterative approach on specific "Pivot Points." The logic operates on a simple premise: Generate every possible line between past swing points, validate them against price history, score them based on fit, and render only the winner. The Calculation Engine (f_find_best_line) This function contains the primary computational load. It performs a nested loop operation: Outer Loop (newer): Iterates through recent pivots. Inner Loop (older): Iterates through older pivots to form a candidate line segment. For every pair of pivots (P1​,P2​), we calculate the slope (m) and the y-intercept concept. This gives us a tentative trendline equation: y=mx+c The Scoring Matrix We assign a score to each candidate line based on weighted heuristics: Touch Count (touches * 2.8): The primary driver. More touches = higher statistical significance. Recency (recency * 1.2): Lines originating closer to the current price action are weighted higher. Tightness (avgErr): We calculate the average distance of all touches from the line. A "tighter" fit (lower error) increases the score. Penalties: violations * 2.2: False breaks heavily penalize the score. barBreakRatio * 2.0: If the line cuts through candle bodies (even if pivots are fine). The line with the highest localBest score is returned as the dominant trendline. What you can use it for? This is a structure visualizer that tries to keep a clean, current S/R channel on screen with volatility-aware rules. It’s not a signal generator, it doesn’t predict breakouts, and it won’t always draw something, if the market is messy and no line survives the filters, it will show none instead of hallucinating geometry. If you need more lines (multiple concurrent channels), that’s a different design tradeoff (and usually becomes clutter + false confidence fast).
Indicador Pine Script®
por Metrify
2
Linda Bradford Raschke One Minute Gold Taylor Session Sweep Linda Bradford Raschke Gold Taylor Session Sweep Strategy This strategy is based on the idea of liquidity sweeps around the Asian session range, combined with optional trend filtering. The core concept: • Track the Asian session high and low • Wait for price to sweep liquidity during the London session • Enter when price reclaims the swept level • Use configurable Risk:Reward targeting • Optional EMA trend filter to align with higher timeframe direction 🔎 How It Works: The script records the Asian session range (high & low). During the London session: A buy setup forms when price sweeps below the Asian low and closes back above it. A sell setup forms when price sweeps above the Asian high and closes back below it. Stop loss is placed beyond the swept level. Take profit is calculated using a configurable Risk-Reward ratio. Optional 200 EMA filter can be enabled to trade with trend direction. ⚙️ Inputs: Asian, London, and NY session times (fully adjustable) Risk-Reward Ratio EMA Length Enable/Disable Trend Filter 📌 Best Used On: XAUUSD (Gold) Indices Major Forex pairs Lower timeframes (1m–15m) ⚠️ Notes: Market conditions vary, and results depend on settings, timeframe, and asset traded. Always forward test before using in live environments.
Estrategia Pine Script®
por uzair2join
All in one Dynamic Trade SessionsAll in one Dynamic Trade Sessions: Trend Filter: Uses the 50 EMA to determine the main direction (Buy only above, Sell only below). Momentum Entry (from MACD-RSI): Uses the MACD (12, 26, 9) for the entry crossover signal. Confirmation (from ---Momentum): Uses RSI (Length 7) to ensure the move has strength. Exits (from Breakout Detector): Uses ATR (14) to automatically calculate your Stop Loss and Take Profit levels. All in one Dynamic Trade Sessions identifies "Trade Sessions" dynamically. A "Session" starts when volume and momentum kick in (Green/Red background) and ends when the market goes into a chop (Grey background). How V5 Works (The "Breakdown" Logic) Session Start: When the Grey Zone ends (RSI breaks out of 45-55) AND Volume spikes. The background turns Blue. Session End: When the Grey Zone returns (RSI falls back between 45-55). The background turns Grey.
Indicador Pine Script®
por nio1101
2
Quant Seasonality ProQuant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze precisly historical data. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation. Hope you enjoy it
Indicador Pine Script®
por dinisfranco2006
1
Seasonality (100% reliable for the W)Quant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze historical data precisly. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation.
Indicador Pine Script®
por dinisfranco2006
TRADING BITE Supply Demand Marker V2.1This Indicator Automatically identifies key supply and demand candles and highlights potential reversal zones. Integrated volume analysis validates market moves, helping traders make more informed entry and exit decisions. Perfect for spotting high-probability trades and understanding market structure at a glance. Features: Highlights Supply & Demand zones automatically Marks key reversal candles Volume-based validation for stronger signals Easy-to-read visual alerts for trading decisions Disclaimer / No Liability Notice: This indicator is provided for educational and informational purposes only. It does not guarantee profits or predict future market movements. Trading financial instruments involves substantial risk of loss, and you should only trade with money you can afford to lose. By using this indicator, you acknowledge that you assume full responsibility for any trading decisions made based on its signals. The developer accepts no liability for any losses, damages, or financial consequences that may result from using this tool. Always perform your own analysis and consider consulting a licensed financial advisor before making trading decisions. Past performance is not indicative of future results.
Indicador Pine Script®
por Trading_bite