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Machine Learning BBPct [BackQuant]Machine Learning BBPct What this is (in one line) A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system. Background: %B in plain terms Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes. Why add (simplified) KNN? Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas: • Feature vector . Each bar is summarized by up to five normalized features:   – %B itself (normalized)   – Band width (volatility proxy)   – Price momentum (ROC)   – Volume momentum (ROC of volume)   – Price position within the bands • Distance metric . Euclidean distance ranks the most similar recent bars. • Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance. • Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence. This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration. How the script is organized (by input groups) 1) BBPct Settings • Price Source – Which price to evaluate (%B is computed from this). • Calculation Period – Lookback for SMA basis and standard deviation. • Multiplier – Standard deviation width (e.g., 2.0). • Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B. 2) Thresholds • Overbought/Oversold – Default 0.8 / 0.2 (inside ). • Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions. 3) KNN Machine Learning • Enable KNN – Switch between pure %B and hybrid. • K (neighbors) – How many historical analogs to blend (default 8). • Historical Period – Size of the search window for neighbors. • ML Weight – Blend between raw %B and KNN estimate. • Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows. 4) Filtering • Method – None, Adaptive, Kalman-style (first-order), or Hull smoothing. • Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate. 5) Performance Tracking • Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest). • Early Entry Lookback – Horizon for forecasting a potential threshold cross. • Profit Target / Stop Loss – Used only by the internal win-rate heuristic. 6) Self-Optimization • Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes). • Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is. • Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3). 7) UI Settings • Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays. • Signal Line Width, Candle Painting, Colors – Visual preferences. Step-by-step logic A) Compute %B Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev. %B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB . B) Build the feature vector All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5). C) Find nearest neighbors For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K . D) Predict and blend Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient. E) Adaptive thresholds Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes. F) Early entry heuristic A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself. G) Informational win-rate The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest. Outputs and visual language • ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering. • Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline. • Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes. • ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7. • Early arrows – Optional small triangles for approaching OB/OS. • Candle painting – Light green above the midline, light red below (optional). • Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed. Signal classification (informational) The indicator does not fire trade commands; it labels state: • STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds. • BUY / SELL – finalBB beyond adaptive OS/OB. • EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence. • NEUTRAL – between adaptive bands. Alerts (what you can automate) • Entering adaptive OB/OS and extreme OB/OS. • Midline cross (0.5). • Overfitting detected (frequent parameter flipping). • Early signals when early confidence > 0.7. These are purely descriptive triggers around the indicator’s state. Practical interpretation • Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B. • Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts. • Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag. • Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools. • Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop. Methodological notes • Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that. • Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts. • Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias. • Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA. • Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator. • Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative. Limitations and cautions • Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks). • Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare. • Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail. • Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest. • Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives. Tuning guide • Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength. • Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest. • Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges. • Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes. How to use it responsibly Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation. Summary Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
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BackQuant tarafından
VSA - The Volume HUDVSA Volume HUD: Your At-a-Glance Volume Dashboard Tired of cluttered charts with multiple indicators taking up screen space? The VSA Volume HUD is a clean, powerful, and fully customisable Heads-Up Display that puts all the critical volume and price action data you need into one compact box, right on your chart. Designed for traders who rely on Volume Spread Analysis (VSA), this tool helps you instantly gauge the strength, conviction, and context behind every price move as it happens. Key Features This indicator isn't just about showing the current volume; it provides a comprehensive, real-time analysis of the market's activity. Real-time VSA Dashboard: A persistent on-screen table that updates with every tick, giving you instant feedback without needing to look away from the price. The HUD is fully draggable (hold Ctrl/Cmd + click and drag) to place it anywhere you like. Essential Volume Metrics: Current Volume: Displayed in a clean, abbreviated format (e.g., 1.25M for millions, 54.3K for thousands). % Change (vs. Previous Bar): Instantly see if volume is expanding or contracting. Vs Short-Term Average: Compare the current bar's volume to a moving average to spot unusual spikes. Volume Velocity: Measures the rate of change in volume over a short period, helping you spot acceleration or deceleration in market interest. Relative Volume (RVOL): See how the current volume compares to the average for that specific time of day, perfect for identifying abnormally high or low activity. Price Action & Volatility Context: Range vs. ATR: Quickly determine if the current bar's volatility is expanding or contracting compared to the recent average. Price vs. VWAP: See how far the current price has deviated from the session's Volume-Weighted Average Price, a key level for institutional traders. Deep Customization is Key Tailor the HUD to perfectly match your trading style and chart aesthetic. Display & Layout: Compact Mode: Remove the metric labels for a sleek, minimalist view that saves screen space. Bar Meters: Enable optional visual bars next to key metrics for a quick, graphical representation of strength. Total Control: Toggle every single metric on or off to build the exact dashboard you need. Adjust text size, position, and background opacity with ease. Smart Coloring & Visual Alerts: Advanced VSA Coloring: This isn't just about up/down candles. The script intelligently colors volume based on confluence. It highlights increasing volume on a strong up-bar (bullish confirmation) or increasing volume on a down-bar (potential climax or distribution), giving you a deeper VSA context. High Volume Highlight: Make standout bars impossible to miss! The entire HUD background can change color automatically when volume surges past a custom threshold (e.g., over 150% of the average), instantly drawing your attention to critical moments. Full Color Customization: Change every color to match your chart's theme, including separate colors for bullish/bearish moves, the background, and the border. How to Use It The VSA Volume HUD is a powerful confirmation tool. Use it to: Confirm Breakouts: Look for a spike in Volume vs. Average and RVOL as price breaks a key level. Spot Exhaustion: Notice high volume on a narrow-range candle after a long trend, visible through the Range/ATR metric. Gauge Conviction: Use the Advanced Coloring to see if volume is supporting the price move (e.g., green volume on a green candle) or diverging from it.
Pine Script® göstergesi
MrPhipps tarafından
Gemini All-in-OneDescription The Gemini AIO (All-in-One) is a comprehensive overlay indicator designed for swing and position traders. It merges three distinct and powerful trading strategies into a single, cohesive tool to identify high-probability setups in stocks that are in confirmed uptrends. What the Indicator Does: Combines Three Strategies: Integrates a multi-scanner breakout system, a mean-reversion model, and a multi-year breakout tool into one indicator. Main Modules Signals Module: 1. Features six unique scanner signals (CS1-CS6) to identify a variety of bullish consolidation patterns. 2. Includes a full trade management framework with RVC (Red Volume Candle), PBP (Post Breakout Pivot Entry), and ISL (Initial Stop Loss) levels. 3. Identifies powerful Episodic Pivot (EP) and EP Entry (EPE) signals for stocks showing exceptional strength. Reversal Module: 1. A mean-reversion strategy that primarily uses Bollinger Bands to find oversold conditions. 2. Provides a three-stage signal process: RA (Reversal Setup), Entry 1, and Entry 2 to time entries from a potential bottom. Multi-Year Breakout (MYBO) Module: 1. Automatically identifies and plots historical, multi-year resistance and support levels. 2. Generates a clear signal when the price breaks out above these significant long-term levels. Advanced Alerts: Features a highly customizable alert system that can be timed to trigger either on the bar's close or at a specific time of day (e.g., 2:30 PM IST), allowing for end-of-day style notifications. How to Best Use It: This indicator is most powerful when used with a systematic, rules-based approach. The core principle is to use long-term moving averages to define the trend and then use the indicator's signals to time entries within that trend. The Foundation (Trend Filter): The most important rule is to only consider long setups on stocks where the 150-day SMA is above the 200-day EMA, and the 150-day SMA is sloping upwards. This keeps you aligned with the primary uptrend. Strategy 1: The Momentum Breakout (PBP Entry) 1. Confirm the stock meets the primary trend filter rules. 2. Wait for an AIO setup signal (Super, Pls Buy, etc.) to draw a PBP line. 3. Enter when the price crosses above the PBP line or wait for a pull back after the price has crossed the PBP line. Strategy 2: The Mean Reversion (RA Entry) 1. Confirm the stock meets the primary trend filter rules. 2. Wait for an "RA" (Reversal Setup) signal to appear on the chart. 3. Enter on the "ENTRY 1" (Risky Entry) or "ENTRY 2" signal (Safer Entry) or wait for a pull back after "ENTRY 1" or "ENTRY 2" signal. Strategy 3: Multi-Year Breakout (MYBO) : 1. A breakout triangle (orange or fuchsia) appears below the candle, signaling a close above the "Recent High" (Orange) or "Older High" (Fuchsia). 2. Recent High refers to the highest price the stock has reached in last 12 months. Breaking above the "Recent High" is a sign of strong current demand. 3. Older High refers to the highest price the stock reached in a more distant, historical period - the period between 5 years ago and 1 year ago. Breaking above the "Older High" is a sign of VERY strong demand as it has broken a historic high. 4. Wait for a breakout triangle to appear on the chart. 5. Enter on the high of the candle marked with a breakout triangle or wait for a pull back after that signal. Customize Your View: Use the "Inputs" tab to enable/disable the modules you want to focus on and configure the alerts you want to receive. Use the "Style" tab to hide any visual elements you don't need to keep your chart clean.
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sushantthatte tarafından
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach. This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits. 🔎 Features Four Filter Types 1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings. Real world application for low pass filter - Used in power supplies to provide a clean, stable power level. 2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility. Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity. 3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility. Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to. 4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action. Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else Configurable Parameters • Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter. • Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay). • Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart. • Source Selection – Apply filters to close, open, high, low, or custom sources. Histograms for Comparison • BS–LP Histogram – Shows distance between Band Stop and Low Pass filters. • BP–HP Histogram – Highlights differences between Band Pass and High Pass filters. Histograms give the visualization of a pseudo-MACD style indicator Visual & Informational Aids • Customizable colors for each filter line. • Optional zero-line for histogram reference. • On-chart info table summarizing active filters, cutoff settings, histograms, and filter order. 📊 Use Cases Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction. Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns. Noise Suppression – Deploy Band Stop to remove specific choppy frequencies. Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
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TheUltimator5 tarafından
Nasdaq Sentiment DashboardBuilds a composite sentiment state — RISK-ON / NEUTRAL / RISK-OFF — using three legs: Volatility: CBOE VXN vs its moving average and absolute thresholds (risk-on when low & below MA; risk-off when high & above MA). Breadth (quality of participation): QQEW/QQQ ratio vs its MA (equal-weight beating cap-weight = healthier breadth). Advance/Decline (intraday breadth): advdec.nq vs its MA, with a magnitude filter (ignores tiny A/D days). How it works Pulls each series on your chosen signal timeframe (default Daily). Creates binary signals per leg: Vol: volOn if VXN < MA and < vxnLower; volOff if VXN > MA and > vxnUpper. Breadth: brOn if QQEW/QQQ is above its MA by a deadband; brOff if below. A/D: adOn if A/D > MA and above adMin; adOff if below MA and < -adMin. Scores each leg (+1 on, −1 off, 0 neutral) → sums to −3…+3. State rule (default): RISK-ON if score ≥ +2, RISK-OFF if ≤ −2, else NEUTRAL (i.e., need 2 of 3 to agree). Detects flips (changes in state) and provides alert conditions that fire only on the flip bar. What you see Lines for VXN & MA, QQEW/QQQ & MA, A/D & MA. Background color shows current composite state. Triangle markers on the flip bar (up for ON, down for OFF). A top-right table summarizing state, each leg vs its MA, and the composite score. How to tune Vol thresholds: vxnLower / vxnUpper. Breadth whipsaw control: deadbandBps around the ratio’s MA. A/D sensitivity: adMin and adMaLen. Stricter regime: require all 3 to agree by changing the state line to score == 3 / -3.
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NH_JM90 tarafından
Scalp Sense AI# Scalp Sense AI (No Repaint) **Adaptive trend & reversal detector with an AI-driven score, multi-timeframe confirmations, robust volume filters, and a purpose-built Scalping Mode.** Signals are generated **only on bar close** (no repaint), include structured alert payloads for webhooks, and come with optional ATR-based TP/SL visualization for study and validation. --- ## What it is (in one paragraph) **Scalp Sense AI** combines classic market structure (DI/ADX, EMA, SMA, Keltner, ATR) with a continuous **AI Score** that fuses RSI normalization, EMA distance (in ATR units), and DI edge into a single, volatility-aware signal. It adaptively gates **trend** and **reversal** entries, applies **HTF confirmation** without lookahead, and enforces **guard rails** (e.g., strong-trend reversal blocking) unless a high-confidence AI override and volume confirmation are present. **Scalping Mode** compresses reaction times and adds micro price-action cues (wick rejections, micro-EMA crosses, small engulfing) to surface more—but disciplined—opportunities. --- ## Non-Repainting Design * All signals, markers, state, and alerts are computed **after bar close** using `barstate.isconfirmed`. * HTF data are requested with `lookahead_off`. * No “future-peeking” constructs are used. * Result: signals do **not** change after the candle closes. --- ## How the engine works (pipeline overview) 1. **Base metrics** * **RSI**, **EMA**, **ATR** (+ ATR SMA for regime/volatility), **SMA long & short**, **Keltner** (EMA ± ATR×mult). * **Manual DI/ADX** for fine control (DM+, DM−, true range smoothing). 2. **Volatility regime** * Compares ATR to its SMA and scales thresholds by √(ATR/ATR\_SMA) → robust “high\_vol” gating. 3. **Volume & flow** * **Volume Z-score**, **OBV slope**, and **MFI** (all computed manually) to confirm impulses and filter weak reversals. 4. **Higher-Timeframe confirmation (optional)** * Imports HTF **PDI/MDI/ADX** and **SMA** (no lookahead) to require alignment when enabled. 5. **AI Score** * Weighted fusion of **RSI (normalized around 0)**, **EMA distance (in ATR)**, and **DI edge**. * Smoothed; then its **mean (μ)** and **volatility (σ)** are estimated to form **adaptive bands** (hi/lo), with optional **hysteresis**. * **Debounce** (M in N bars) avoids flicker; **bias state** persists until truly invalidated. 6. **Signal logic** * **Trend entries** require AI bias + trend confirmations (DI/ADX/SMA, HTF if enabled), volatility OK, and **anti-breakout** filter. * **Reversal entries** come in **core**, **early**, and **scalp** flavors (progressively more frequent), guarded by strong-trend blocks that an **AI+volume+ADX-cooling override** can bypass. 7. **Scalping Mode** * Adaptive parameter contraction (shorter lengths), gentler guards, micro-patterns (wick/engulf/micro-EMA cross), and reduced cooldown to increase high-quality opportunities. 8. **Cooldown & state** * One signal per side after a configurable spacing in bars; internal “last direction” avoids clustering. 9. **Visualization & alerts** * **Triangles** for trend, **circles** for reversals (offset by ATR to avoid overlap). * **Single-line alert payload** (BUY/SELL, reason, AI, volZ, ADX) ready for webhooks. --- ## Signals & visualization * **Trend Long/Short** → triangle markers (above/below) when: * AI bias aligns with trend confirmations (DI edge, ADX above threshold, price vs long SMA, optional HTF alignment). * Volatility regime agrees; **anti-breakout** prevents entries exactly at lookback highs/lows. * **Reversal Long/Short** → circular markers when: * **Core**: AI near “loose” band, OBV/MFI/volZ supportive, ADX cooling, DI spread relaxed, PA confirms (crosses/div). * **Early**: anticipatory patterns (Keltner exhaustion, simple RSI “quasi-divergence”). * **Scalp**: micro-EMA cross, wick rejection, mini-engulfing, with relaxed guards but AI/volume still in the loop. * **Markers appear only on the bar that actually emitted the signal** (no repaint); offsets use ATR so shapes don’t overlap. --- ## Alerts (ready for webhooks) Enable “**Any alert() function call**” and you’ll receive compact, single-line payloads once per bar: ``` action=BUY;reason=reversal-early;ai=0.1375;volZ=0.82;adx=27.5 action=SELL;reason=trend;ai=-0.2210;volZ=0.43;adx=31.9 ``` * `action`: BUY / SELL * `reason`: `trend` | `reversal-core` | `reversal-early` | `reversal-scalp` * `ai`: current smoothed AI Score at signal bar * `volZ`: volume Z-score * `adx`: current ADX --- ## Inputs (exhaustive) ### 1) Core Inputs * **RSI Length (Base)** (`rsi_length_base`, int) Base RSI lookback. Shorter = more reactive; longer = smoother. * **RSI Overbought Threshold** (`rsi_overbought`, int) Informational for context; RSI is used normalized in the AI fusion. * **RSI Oversold Threshold** (`rsi_oversold`, int) Informational; complements visual context. * **EMA Length (Base)** (`ema_length_base`, int) Primary adaptive mean; also used for Keltner mid and distance metric. * **ATR Length (Base)** (`atr_length_base`, int) Volatility unit for Keltner, SL/TP (debug), and regime detection. * **ATR SMA Length** (`atr_sma_len`, int) Smooth baseline for ATR regime; supports “high\_vol” logic. * **ATR Multiplier Base** (`atr_mult_base`, float) Scales volatility gating (sqrt-scaled); higher = tighter high-vol requirement. * **Disable Volatility Filter** (`disable_volatility_check`, bool) Bypass volatility gating if true. * **Price Change Period (bars)** (`price_change_period_base`, int) Simple momentum check (+/−% over N bars) used in trend validation. * **Base Cooldown Bars Between Signals** (`signal_cooldown_base`, int ≥ 0) Minimum bars to wait between signals (per side). * **Trend Confirmation Bars** (`trend_confirm_bars`, int ≥ 1) Require persistence above/below long SMA for this many bars. * **Use Higher Timeframe Confirmation** (`use_higher_tf`, bool) Turn on/off HTF alignment (no repaint). * **Higher Timeframe for Confirmation** (`higher_tf`, timeframe) E.g., “60” to confirm M15 with H1; used for HTF PDI/MDI/ADX and SMA. * **TP as ATR Multiple** (`tp_atr_mult`, float) For **visual debug** only (drawn after entries); not an order manager. * **SL as ATR Multiple** (`sl_atr_mult`, float) For visual debug only. * **Enable Scalping Mode** (`scalping_mode`, bool) Compresses lengths/thresholds, unlocks micro-PA modules, reduces cooldown. * **Show Debug Lines** (`show_debug`, bool) Plots AI bands, DI/ADX, EMA/SMA, Keltner, vol metrics, and TP/SL (debug). ### 2) AI Score & Thresholds * **AI Score Smooth Len** (`ai_len`, int) EMA smoothing over the raw fusion. * **AI Volatility Window** (`ai_sigma_len`, int) Window to estimate AI mean (μ) and standard deviation (σ). * **K High (sigma)** (`ai_k_hi`, float) Upper band width (σ multiplier) for strong threshold. * **K Low (sigma)** (`ai_k_lo`, float) Lower band width (σ multiplier) for loose threshold. * **Debounce Window (bars)** (`ai_debounce_m`, int ≥ 1) Rolling window length used by the confirm counter. * **Min Bars>Thr in Window** (`ai_debounce_n`, int ≥ 1) Minimum confirmations inside the debounce window to validate a state. * **Use Hysteresis Thresholds** (`ai_hysteresis`, bool) Requires crossing back past a looser band to exit bias → fewer whipsaws. * **Weight DI Edge (0–1)** (`ai_weight_di`, float) Importance of DI edge within the fusion. * **Weight EMA Dist (0–1)** (`ai_weight_ema`, float) Importance of EMA distance (in ATR units). * **Weight RSI Norm (0–1)** (`ai_weight_rsi`, float) Importance of normalized RSI. * **Sensitivity (0–1)** (`sensitivity`, float) Contracts/expands bands (higher = more sensitive). ### 3) Volume Filters * **Volume MA Length** (`vol_ma_len`, int) Baseline for volume Z-score. * **Volume Z-Score Window** (`vol_z_len`, int) Std-dev window for Z-score; larger = fewer volume “spikes”. * **Reversal: Min Volume Z for confirm** (`vol_rev_min_z`, float) Minimum Z required to validate reversals (adaptively relaxed in scalping). * **OBV Slope Lookback** (`obv_slope_len`, int) Rising/falling OBV over this window supports bull/bear confirmations. * **MFI Length** (`mfi_len`, int) Money Flow Index lookback (manual calculation). ### 4) Filters (Breakout / ADX / Reversal) * **Enable Breakout Filter** (`enable_breakout_fil`, bool) Avoid trend entries at lookback highs/lows. * **Breakout Lookback Bars** (`breakout_lookback`, int ≥ 1) Window for the anti-breakout guard. * **Base ADX Length** (`adx_length_base`, int) Lookback for DI/ADX smoothing (also adapted in Scalping Mode). * **Base ADX Threshold** (`adx_threshold_base`, float) Minimum ADX to validate trend context (scaled in Scalping Mode). * **Enable Reversal Filter** (`enable_rev_filter`, bool) Master switch for reversal logic. * **Max ADX for Reversal** (`rev_adx_max`, float) Hard cap: above this ADX, reversals are blocked (unless overridden by AI if allowed in Guards). ### 5) Reversal Guard (regime protection & overrides) * **Strong Trend: ADX add-above Thr** (`guard_adx_add`, float) Extra ADX above `adx_threshold` to mark “strong” trend. * **Strong Trend: min DI spread** (`guard_spread_min`, float) Minimum DI separation to consider a trend “dominant”. * **Require ADX drop from window max (%)** (`guard_adx_drop_min_pct`, float 0–1) ADX must drop at least this fraction from its window maximum to consider “cooling”. * **Regime Window (bars)** (`guard_regime_len`, int ≥ 10) Window over which ADX max is measured for the “cooling” check. * **EMA Slope Lookback** (`guard_slope_len`, int ≥ 2) EMA slope horizon used alongside Keltner for strong-trend identification. * **Keltner Mult (ATR)** (`guard_kc_mult`, float) Keltner width for strong trend bands and exhaustion checks. * **HTF Reversal Block Mode** (`htf_block_mode`, string: `Off` | `On` | `AI-controlled`) * `Off`: never block by HTF. * `On`: block reversals whenever HTF is strong. * `AI-controlled`: block **unless** AI+volume+ADX-cooling override criteria are met. * **AI-controlled: allow AI override** (`ai_htf_override`, bool) Enables the override mechanism in `AI-controlled` mode. * **AI override multiplier (vs band\_hi)** (`ai_override_mult`, float) Strength needed beyond the high band to count as “strong AI”. * **AI override: min bars beyond strong thr** (`ai_override_min_bars`, int ≥ 1) Debounce on the override itself. ### 6) Markers * **Reversal Circle ATR Offset** (`rev_marker_offset_atr`, float ≥ 0) Vertical offset for reversal circles; trend triangles use a separate (internal) offset. ### 7) Scalping Mode Tuning * **Reversal aggressiveness (0–1)** (`scalp_rev_aggr`, float) Higher = looser guards and stronger AI sensitivity. * **Wick: body multiple (bull/bear)** (`scalp_wick_body_mult`, float) Wick must be at least this multiple of body to count as rejection. * **Wick: ATR multiple (min)** (`scalp_wick_atr_mult`, float) Minimal wick length in ATR units. * **Micro EMA factor (vs EMA base)** (`scalp_ema_fast_factor`, float 0.2–0.9) Fast EMA length = base EMA × factor (rounded/int). * **Relax breakout filter in scalping** (`scalp_breakout_relax`, bool) Lets more trend entries through in scalping context. ### 8) ICT-style SMA (bases) * **ICT SMA Long Length (Base)** (`sma_long_len_base`, int) Long-term baseline for regime/trend. * **ICT SMA Short1 Length (Base)** (`sma_short1_len_base`, int) Short baseline for price-action crosses. * **ICT SMA Short2 Length (Base)** (`sma_short2_len_base`, int) Companion short baseline used in PA cross checks. > **Adaptive “effective” values:** When **Scalping Mode** is ON, the script internally shortens multiple lengths (RSI/EMA/ATR/ADX/μσ windows, SMAs) and gently relaxes guards (ADX drop %, DI spread, volume Z, override thresholds), reduces cooldown/confirm bars, and optionally relaxes the breakout filter—so you get **more frequent but still curated** signals. --- ## Plots & debug (optional) * DI+/DI−, ADX (curr + HTF), EMA, long SMA, Keltner up/down (when strong), AI Score, AI mean, AI bands (hi/lo; low plots only when hysteresis is on), Volume MA and Z-score, and ATR-based TP/SL guide (after entries). * These are **study aids**; the indicator does not manage trades. --- ## Recommended use * **Timeframes**: * Scalping Mode: M1–M15. * Standard Mode: M15–H1 (or higher). * **Markets**: Designed for liquid FX, indices, metals, and large-cap crypto. * **Chart type**: Standard candles recommended (Heikin-Ashi alters inputs and hence signals). * **Alerts**: Use “Any alert() function call”. Parse the key/value payloads server-side. --- ## Good to know * **Why some alerts don’t draw shapes retroactively**: markers are drawn **only on** the bar that emitted the signal (no repaint by design). * **Why a reversal didn’t fire**: strong-trend guards + HTF block may have been active; check ADX, DI spread, Keltner position, EMA slope, and whether AI override criteria were met. * **Too many / too few signals**: tune **Scalping Mode**, `signal_cooldown_base`, AI bands (`ai_k_hi/lo`, `sensitivity`), volume Z (`vol_rev_min_z`), and guards (`rev_adx_max`, `guard_*`). --- ## Disclaimer This is an **indicator**, not a strategy or an execution system. It does not place, modify, or manage orders. Markets carry risk—validate on historical data and demo before any live decisions. No performance claims are made. --- ### Version **Scalp Sense AI v11.5** — Adaptive AI bands with hysteresis/debounce, HTF no-lookahead confirmations, guarded reversal logic with AI override, full volume suite (Z, OBV slope, MFI), anti-breakout filter, and a dedicated Scalping Mode with micro-PA cues.
Pine Script® göstergesi
Algo_Systems tarafından
3
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management. Strategic Design and Methodological Basis The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution. Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation: Manual Calculation: Provides a transparent and deterministic computation of HA values. ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts. Regular Candles: Allows for direct comparison with standard OHLC price action. This multi-methodological approach to trend smoothing is critical for robust signal generation.   Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (   Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses. EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.   Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation. Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation. Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment. Applicability The Mutanabby_AI | ATR+ strategy is particularly suited for: Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.   Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach. Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models. Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments. Implementation and Evaluation To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns. I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Pine Script® stratejisi
Mutanabby_AI tarafından
The Barking Rat LiteMomentum & FVG Reversion Strategy The Barking Rat Lite is a disciplined, short-term mean-reversion strategy that combines RSI momentum filtering, EMA bands, and Fair Value Gap (FVG) detection to identify short-term reversal points. Designed for practical use on volatile markets, it focuses on precise entries and ATR-based take profit management to balance opportunity and risk. Core Concept This strategy seeks potential reversals when short-term price action shows exhaustion outside an EMA band, confirmed by momentum and FVG signals: EMA Bands: Parameters used: A 20-period EMA (fast) and 100-period EMA (slow). Why chosen: - The 20 EMA is sensitive to short-term moves and reflects immediate momentum. - The 100 EMA provides a slower, structural anchor. When price trades outside both bands, it often signals overextension relative to both short-term and medium-term trends. Application in strategy: - Long entries are only considered when price dips below both EMAs, identifying potential undervaluation. - Short entries are only considered when price rises above both EMAs, identifying potential overvaluation. This dual-band filter avoids counter-trend signals that would occur if only a single EMA was used, making entries more selective.. Fair Value Gap Detection (FVG): Parameters used: The script checks for dislocations using a 12-bar lookback (i.e. comparing current highs/lows with values 12 candles back). Why chosen: - A 12-bar displacement highlights significant inefficiencies in price structure while filtering out micro-gaps that appear every few bars in high-volatility markets. - By aligning FVG signals with candle direction (bullish = close > open, bearish = close < open), the strategy avoids random gaps and instead targets ones that suggest exhaustion. Application in strategy: - Bullish FVGs form when earlier lows sit above current highs, hinting at downward over-extension. - Bearish FVGs form when earlier highs sit below current lows, hinting at upward over-extension. This gives the strategy a structural filter beyond simple oscillators, ensuring signals have price-dislocation context. RSI Momentum Filter: Parameters used: 14-period RSI with thresholds of 80 (overbought) and 20 (oversold). Why chosen: - RSI(14) is a widely recognized momentum measure that balances responsiveness with stability. - The thresholds are intentionally extreme (80/20 vs. the more common 70/30), so the strategy only engages at genuine exhaustion points rather than frequent minor corrections. Application in strategy: - Longs trigger when RSI < 20, suggesting oversold exhaustion. - Shorts trigger when RSI > 80, suggesting overbought exhaustion. This ensures entries are not just technically valid but also backed by momentum extremes, raising conviction. ATR-Based Take Profit: Parameters used: 14-period ATR, with a default multiplier of 4. Why chosen: - ATR(14) reflects the prevailing volatility environment without reacting too much to outliers. - A multiplier of 4 is a pragmatic compromise: wide enough to let trades breathe in volatile conditions, but tight enough to enforce disciplined exits before mean reversion fades. Application in strategy: - At entry, a fixed target is set = Entry Price ± (ATR × 4). - This target scales automatically with volatility: narrower in calm periods, wider in explosive markets. By avoiding discretionary exits, the system maintains rule-based discipline. Visual Signals on Chart Blue “▲” below candle: Potential long entry Orange/Yellow “▼” above candle: Potential short entry Green “✔️”: Trade closed at ATR take profit Blue (20 EMA) & Orange (100 EMA) lines: Dynamic channel reference ⚙️Strategy report properties Position size: 25% equity per trade Initial capital: 10,000.00 USDT Pyramiding: 10 entries per direction Slippage: 2 ticks Commission: 0.055% per side Backtest timeframe: 1-minute Backtest instrument: HYPEUSDT Backtesting range: Jul 28, 2025 — Aug 17, 2025 Note on Sample Size: You’ll notice the report displays fewer than the ideal 100 trades in the strategy report above. This is intentional. The goal of the script is to isolate high-quality, short-term reversal opportunities while filtering out low-conviction setups. This means that the Barking Rat Lite strategy is very selective, filtering out over 90% of market noise. The brief timeframe shown in the strategy report here illustrates its filtering logic over a short window — not its full capabilities. As a result, even on lower timeframes like the 1-minute chart, signals are deliberately sparse — each one must pass all criteria before triggering. For a larger dataset: Once the strategy is applied to your chart, users are encouraged to expand the lookback range or apply the strategy to other volatile pairs to view a full sample. 💡Why 25% Equity Per Trade? While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why: Backtests using this sizing show manageable drawdowns even under volatile periods. The strategy generates a sizeable number of trades, reducing reliance on a single outcome. Combined with conservative filters, the 25% setting offers a balance between aggression and control. Users are strongly encouraged to customize this to suit their risk profile. What makes Barking Rat Lite valuable Combines multiple layers of confirmation: EMA bands + FVG + RSI Adaptive to volatility: ATR-based exits scale with market conditions Clear, actionable visuals: Easy to monitor and manage trades
Pine Script® stratejisi
TheBarkingRat tarafından
2
Globex Overnight Futures ORB with FIB's by TenAMTrader📌 Globex Overnight Futures ORB with FIB’s – by TenAMTrader This indicator is designed for futures traders who want to track the Globex Overnight Opening Range (ORB) and apply Fibonacci projections to anticipate potential support/resistance zones. It’s especially useful for traders who follow overnight sessions (such as ES, NQ, CL) and want to map out key levels before the U.S. regular session begins. ⚙️ How It Works Primary Range (ORB): You define a start and end time (default set to 18:00 – 18:15 EST). During this period, the script tracks the session high, low, and midpoint. Opening Range Plots: High Line (green) Low Line (red) Midpoint Line (yellow) A shaded cloud between High–Mid and Mid–Low for easy visualization. Fibonacci Projections: Once the ORB is complete, the script calculates a full suite of Fibonacci retracements and extensions (e.g., 0.236, 0.382, 0.618, 1.0, 1.618, 2.0). Standard key levels (0.618, 0.786, 1.0, etc.) are always shown if enabled. Optional extended levels (1.236, 1.382, 1.5, 2.0, etc.) can be toggled on/off. "Between Range" fibs (such as 0.382 and 0.618 inside the ORB) are also available for traders who like intra-range precision. 🔧 User Settings Time Inputs: Choose your ORB start/end time. Color Controls: Customize high, low, midpoint, and fib line colors. Display Toggles: Turn on/off High, Low, Midpoint lines and Fibonacci projections. Fib Extensions Toggle: Decide whether to show only major fibs or all extensions. Alerts (Optional): Alerts can be set for crossing the ORB High, Low, or Midpoint. 📊 Practical Use Cases Breakout Traders: Use the ORB high/low as breakout triggers. Mean Reversion Traders: Watch for rejections near fib extension levels. Overnight Futures Monitoring: Track Globex behavior to prepare for RTH open. Risk Management: ORB and Fib levels make for natural stop/target placement zones. ⚠️ Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading recommendations. Trading futures involves substantial risk of loss and may not be suitable for all investors. Always do your own due diligence and consult with a licensed financial professional before making trading decisions.
Pine Script® göstergesi
TenAM_Trader tarafından
Volume Spike Detector - by TenAMTrader📌 Volume Spike Detector – by TenAMTrader This indicator is designed to help traders quickly identify unusual surges in trading volume relative to recent activity. High-volume spikes can often signal strong buying or selling pressure, potential trend reversals, or breakout setups. ⚙️ How It Works The script calculates the average trading volume over a user-defined period (default: 21 bars). It then sets a spike threshold, which is that average volume plus a percentage buffer (default: 25%). Whenever the current bar’s volume exceeds this threshold, a 💰 label is plotted below the candle. If alerts are enabled, you’ll also receive a real-time alert whenever a spike occurs. 🔧 User Settings Spike Ratio % → Adjust how much higher than average volume must be to qualify as a spike. Trading Period → Set the lookback period used to calculate the average volume. Enable Alert → Turn alerts on/off. 📊 Practical Use Cases Breakout Trading: Volume spikes often confirm breakouts from consolidation zones. Reversal Signals: A sudden surge in volume may precede a trend reversal. News & Events: Spot unusual activity during earnings, economic releases, or unexpected events. ⚠️ Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading recommendations. Past performance is not indicative of future results. Always do your own research and consult with a licensed financial professional before making any trading decisions.
Pine Script® göstergesi
TenAM_Trader tarafından
Intelligent Trading SuiteIntelligent Trading Suite “One script to rule them all.” Overview The Intelligent Trading Suite is a professional-grade decision system built in Pine Script. It is a unified engine—not a bundle of indicators—that combines adaptive pattern recognition, historical memory, and multi-context intelligence into one framework. Using a deep historical pattern database and integrating session dynamics, market calendars, holiday effects, and economic events, it filters noise and adapts to conditions. Core emphasis: precise pattern detection with target-price projection that remains stable as new candles print (mitigates target drift) and early detection of forming geometric patterns and divergences/hidden divergences. Core Features All-Timeframe Operation: Works across every TradingView timeframe—from 1m to 1W—without performance drift. Pattern Recognition with Targets: Detects triangles, wedges, cup & handle, flags, and H&S; projects targets and stabilizes them against common drift as price evolves. Early Signal Engine: Flags forming patterns and divergences before completion and notifies when prerequisites align. Historical Pattern Intelligence: Stores and compares thousands of prior states (Hull, VWAP, RSI, MACD, SMA, CVD) to weight current conditions and calibrate confidence. Context & Regime Awareness: Adjusts for volatility regimes, liquidity sessions, day-of-week bias, holidays, and macro events. Unified Signal & Confidence: Fuses all streams into a single Overall signal with calibrated confidence levels (Weak / Neutral / Strong). Visualization & Alerts Compact Ultimate Intelligence Table showing each analytical pillar, plus the Overall signal, and an option to show them on the chart as well. Alerts on table for: new pattern detection, divergence events, volatility shifts, and trend reversals. Important Notes -Free plan runtime: TradingView Free accounts may hit platform limits. Fix: Open settings → switch Mode from Paid to Free → runs within Free limits. -Heavy computation: The script is calculation- and data-intensive; initial runs can take time. If a rare runtime error occurs, simply reload the page and continue. Attributions Hull Moving Average (Alan Hull) VWAP (Volume Weighted Average Price) RSI (Relative Strength Index, J. Welles Wilder Jr.) MACD (Moving Average Convergence Divergence, Gerald Appel) Black Flag ATR bands (Jose Azcarate) Proprietary enhancements, target-stabilization logic, and the nuclear intelligence architecture are original research for this suite. Compliance Educational and analytical use only No financial advice Ad-free; aligned with TradingView House Rules Proper attribution included Access To get access, please read the Author’s instructions on the script’s page.
Pine Script® göstergesi
FutureisinPast tarafından
2
Bollinger Bands % | QuantEdgeB📊 Introducing Bollinger Bands % (BB%) by QuantEdgeB 🛠️ Overview BB% | QuantEdgeB is a volatility-aware momentum tool that maps price within a Bollinger envelope onto a normalized scale. By letting you choose the base moving average (SMA, EMA, DEMA, TEMA, HMA, ALMA, EHMA, THMA, RMA, WMA, VWMA, T3, LSMA) and even Heikin-Ashi sources, it adapts to your style while keeping readings consistent across symbols and timeframes. Clear thresholds and color-coded visuals make it easy to spot emerging strength, fading moves, and potential mean-reversions. ✨ Key Features • 🔹 Flexible Baseline Pick from 12 MA types (plus Heikin-Ashi source option) to tailor responsiveness and smoothness. • 🔹 Normalized Positioning Price is expressed as a percentage of the band range, yielding an intuitive 0–100 style read (can exceed in extreme trends). • 🔹 Actionable Thresholds Default Long 55 / Short 45 levels provide simple, objective triggers. • 🔹 Visual Clarity Color-coded candles, shaded OB/OS zones, and adaptive color themes speed up decision-making. • 🔹 Ready-to-Alert Built-in alerts for long/short transitions. 📐 How It Works 1️⃣ Band Construction A moving average (your choice) defines the midline; volatility (standard deviation) builds upper/lower bands. 2️⃣ Normalization The indicator measures where price sits between the lower and upper band, scaling that into a bounded oscillator (BB%). 3️⃣ Signal Logic • ✅ Long when BB% rises above 55 (strength toward the top of the envelope). • ❌ Short when BB% falls below 45 (weakness toward the bottom). 4️⃣ OB/OS Context Shaded regions above/below typical ranges highlight exhaustion and potential snap-backs. ⚙️ Custom Settings • Base MA Type: SMA, EMA, DEMA, TEMA, HMA, ALMA, EHMA, THMA, RMA, WMA, VWMA, T3, LSMA • Source Mode: Classic price or Heikin-Ashi (close/open/high/hlc3) • Base Length: default 40 • Band Width: standard deviation-based (2× SD by default) • Long / Short Thresholds: defaults 55 / 45 • Color Mode: Alpha, MultiEdge, TradingSuite, Premium, Fundamental, Classic, Warm, Cold, Strategy • Candles & Labels: optional candle coloring and signal markers 👥 Ideal For ✅ Trend Followers — Ride strength as price compresses near the upper band. ✅ Swing/Mean-Reversion Traders — Fade extremes when BB% stretches into OB/OS zones. ✅ Multi-Timeframe Analysts — Compare band position consistently across periods. ✅ System Builders — Use BB% as a normalized feature for strategies and filters. 📌 Conclusion BB% | QuantEdgeB delivers a clean, normalized read of price versus its volatility envelope—adaptable via rich MA/source options and easy to automate with thresholds and alerts. 🔹 Key Takeaways: 1️⃣ Normalized view of price inside the volatility bands 2️⃣ Flexible baseline (12+ MA choices) and Heikin-Ashi support 3️⃣ Straightforward 55/45 triggers with clear visual context 📌 Disclaimer: Past performance is not indicative of future results. No strategy guarantees success. 📌 Strategic Advice: Always backtest, tune parameters, and align with your risk profile before live trading.
Pine Script® göstergesi
QuantEdgeB tarafından
PRC-VIDYA | QuantEdgeBIntroducing PRC-VIDYA by QuantEdgeB Overview The PRC-VIDYA(Volatility–Indexed Dynamic Average) is a sophisticated trading indicator developed for traders looking to capitalize on trend shifts with enhanced filtering mechanisms. It blends an Endpoint VIDYA filter—an adaptive, volatility-scaled moving average with percentile-based thresholds and a median-absolute-deviation buffer to craft a dynamic entry/exit envelope. Price thrusts beyond the upper or lower band generate crisp long/short signals, complete with colored fills, candle tinting, alerts and optional backtest stats ____ Key Features 🔹VIDYA(Volatility–Indexed Dynamic Average): - Adaptive Moving Average that adjusts its responsiveness based on market volatility. - Uses a dynamic smoothing constant based on standard deviations. - Allows for better trend detection compared to static moving averages. 🔹2. Percentile Rank-Based Dynamic Levels: - Identifies overbought (75th percentile) and oversold (25th percentile) zones. - Dynamically adjusts based on historical data, making it robust across different market conditions. 🔹3. Median Absolute Deviation (MAD) Filtering: - An advanced volatility filter that refines entry and exit points. - Reduces noise by filtering out weak signals, focusing only on meaningful trend shifts. - Uses two multipliers (long and short) to fine-tune sensitivity. 🔹4. Signal Generation: - 📈Long Signal: Triggered when price closes above the upper dynamic threshold. - 📉Short Signal: Triggered when price closes below the lower dynamic threshold. - Uses color-coded candles to visually indicate trend shifts. - Optional signal labels can be enabled for clear entry/exit indications. 🔹5. Customizable Visualization: - Multiple color themes to match user preferences. - Ability to overlay signals on price charts. - Alerts available for long & short crossovers. _____ How It Works 1. The script calculates VIDYA based on a user-defined period. 2. It computes the 75th and 25th percentile ranks of the moving average. 3. Median Absolute Deviation (MAD) Filtering is applied to reduce false breakouts. 4. A buy (long) or sell (short) signal is triggered when price crosses the respective filtered percentile levels. 5. Alerts and labels can be used to notify traders of new signals. _____ Behavior across Crypto Majors BTC ETH SOL Note: Past behaviour is not indicative of future results. Always conduct thorough testing and risk management before making trading decisions. _____ Best Use Cases 📌 Trend Confirmation – Use VIDYA to confirm if a trend is strengthening or weakening. 📌 Noise Reduction – MAD filtering prevents reacting to minor fluctuations, focusing on stronger trend shifts. 📌 Multi-Timeframe Scalability – Works across multiple timeframes (1H, 4H, Daily, etc.), depending on the trader’s strategy. 🧬 Default Settings • Endpoint VIDYA Mode: “Mid” (9 bar, 24 bar hist) • Percentile Length: 21 bars • Upper/Lower Percentiles: 75% / 25% • MAD Window: 21 bars • Upper/Lower MAD Multipliers: 1.8 / 0.9 • Visuals: Candle coloring on, labels off, “Strategy” palette • Backtest Table: off by default _____ 📌 Conclusion PRC-VIDYA fuses a volatility-aware adaptive average with percentile boundaries and a robust deviation buffer, yielding a self-adjusting channel that captures genuine breakouts and breakdowns. Its clear regime coloring, alerts and optional backtest table make it a turnkey solution for traders who want signals that breathe with the market. 🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets. 🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Pine Script® göstergesi
QuantEdgeB tarafından
Marcius Studio® - Cross-Asset Correlator™Cross-Asset Correlator™ — a pair-trading strategy that identifies correlation breakdowns between two assets and captures profit opportunities from market inefficiencies. The strategy enters trades when the correlation drops below a set threshold and closes positions once correlation recovers. The main concept is to exploit temporary divergence between two assets by going long the stronger one and short the weaker one, aiming to profit when their correlation reverts. Important : This script illustrates asset correlation concepts for educational purposes only. It's not for live trading—requires adjustments and offers no performance guarantees. Always apply risk management. TradingView Limitation By default, TradingView’s built-in Strategy interface does not support backtesting with two different assets . To overcome this, the script is implemented as an indicator with a fully custom backtesting engine that calculates PnL, trades, and performance statistics directly on the chart. Idea Markets move in clusters : altcoins follow BTC, memecoins track Solana, L2 projects mirror Ethereum. But correlations aren’t perfect—temporary divergences create pricing inefficiencies. The logic: When an asset lags or overshoots its usual correlation, it’s a mispricing opportunity. Trade the reversion: buy undervalued divergence, sell overextended convergence. The market eventually corrects, but the inefficiency window allows profit before realignment. OKX Signal Bot Integration This script includes a built-in interface for OKX Signal Bot . It can generate structured JSON alerts (ENTER / EXIT, long / short) and directly manage trades on OKX exchange . This allows seamless automation of correlation-based strategies without manual order execution. Note : The OKX Signal Bot (for demo use only) assists with alerts & trade management but does not ensure profits. You are fully responsible for your trades—always apply risk management. Strategy Parameters Symbol 1 / Symbol 2 : trading instruments to be analyzed. SMA Period : smoothing period for price averages. Correlation Period : number of bars used to calculate correlation coefficient. Upper Correlation Threshold : level above which trades are closed. Lower Correlation Threshold : level below which new trades are opened. percentage_investment (%) : allocation per entry signal (used for OKX integration). Example Settings OKX:FARTCOINUSDT.P / OKX:PENGUUSDT.P Timeframe : 1H SMA Period : 60 Correlation Period : 25 Upper Threshold : 0.9 Lower Threshold : 0.1 percentage_investment : 10% How the Code Works Retrieves closing prices of two selected assets. Calculates correlation coefficient and moving averages. When correlation breaks below the lower threshold, the script opens a pair trade (long/short depending on SMA relation). When correlation recovers above the upper threshold, all open trades are closed. Real-time alerts are generated in JSON format for OKX bots (ENTER/EXIT signals). Built-in backtesting engine tracks PnL, trades, and statistics (7d / 30d / total). Visual labels mark entries, exits, and PnL results directly on the chart. Disclaimer Trading involves risk — always do your own research (DYOR) and seek professional financial advice. We are not responsible for any potential financial losses.
Pine Script® göstergesi
marcius-studio tarafından
Monthly Expected Move (IV + Realized)What it does Overlays 1-month expected move bands on price using both forward-looking options data and backward-looking realized movement: IV30 band — from your pasted 30-day implied vol (%) Straddle band — from your pasted ATM ~30-DTE call+put total HV band — from Historical Volatility computed on-chart ATR band — from ATR% extrapolated to ~1 trading month Use it to quickly answer: “How much could this stock move in ~1 month?” and “Is the market now pricing more/less movement than we’ve actually been getting?” Inputs (quick) Implied (forward-looking) Use IV30 (%) — paste annualized IV30 from your options platform. Use ATM 30-DTE Straddle — paste Call+Put total (per share) at the ATM strike, ~30 DTE. Realized (backward-looking) HV lookback (days) — default 21 (≈1 trading month). ATR length — default 14. Note: TradingView can’t fetch option data automatically. Paste the IV30 % or the straddle total you read from your broker (use Mark/mid prices). How it’s calculated IV band (±%) = IV30 × √(21/252) (annualized → ~1-month). Straddle band (±%) = (ATM Call + Put) / Spot to that expiry (≈30 DTE). HV band (±%) = stdev(log returns, N) × √252 × √(21/252). ATR band (±%) = (ATR(len)/Close) × √21. All bands are plotted as upper/lower envelopes around price, plus an on-chart readout of each ±% for quick scanning. How to use it (at a glance) IV/Straddle bands wider than HV/ATR → market expects bigger movement than recent actuals (possible catalyst/expansion). All bands narrow → likely a low-mover; look elsewhere if you want action. HV > IV → realized swings exceed current pricing (mean-reversion or vol bleed often follows). Pro tips For ATM straddle: pick the expiry closest to ~30 DTE, use the ATM strike (closest to spot), and add Call Mark + Put Mark (per share). If the exact ATM strike isn’t quoted, average the two neighboring strikes. The simple straddle/spot heuristic can read slightly below the IV-derived 1σ; that’s normal. Keep the chart on daily timeframe—the math assumes trading-day conventions (~252/yr, ~21/mo).
Pine Script® göstergesi
rixman14 tarafından
Volume Profile Grid [Alpha Extract]A sophisticated volume distribution analysis system that transforms market activity into institutional-grade visual profiles, revealing hidden support/resistance zones and market participant behavior. Utilizing advanced price level segmentation, bullish/bearish volume separation, and dynamic range analysis, the Volume Profile Grid delivers comprehensive market structure insights with Point of Control (POC) identification, Value Area boundaries, and volume delta analysis. The system features intelligent visualization modes, real-time sentiment analysis, and flexible range selection to provide traders with clear, actionable volume-based market context. 🔶 Dynamic Range Analysis Engine Implements dual-mode range selection with visible chart analysis and fixed period lookback, automatically adjusting to current market view or analyzing specified historical periods. The system intelligently calculates optimal bar counts while maintaining performance through configurable maximum limits, ensuring responsive profile generation across all timeframes with institutional-grade precision. // Dynamic period calculation with intelligent caching get_analysis_period() => if i_use_visible_range chart_start_time = chart.left_visible_bar_time current_time = last_bar_time time_span = current_time - chart_start_time tf_seconds = timeframe.in_seconds() estimated_bars = time_span / (tf_seconds * 1000) range_bars = math.floor(estimated_bars) final_bars = math.min(range_bars, i_max_visible_bars) math.max(final_bars, 50) // Minimum threshold else math.max(i_periods, 50) 🔶 Advanced Bull/Bear Volume Separation Employs sophisticated candle classification algorithms to separate bullish and bearish volume at each price level, with weighted distribution based on bar intersection ratios. The system analyzes open/close relationships to determine volume direction, applying proportional allocation for doji patterns and ensuring accurate representation of buying versus selling pressure across the entire price spectrum. 🔶 Multi-Mode Volume Visualization Features three distinct display modes for bull/bear volume representation: Split mode creates mirrored profiles from a central axis, Side by Side mode displays sequential bull/bear segments, and Stacked mode separates volumes vertically. Each mode offers unique insights into market participant behavior with customizable width, thickness, and color parameters for optimal visual clarity. // Bull/Bear volume calculation with weighted distribution for bar_offset = 0 to actual_periods - 1 bar_high = high bar_low = low bar_volume = volume // Calculate intersection weight weight = math.min(bar_high, next_level) - math.max(bar_low, current_level) weight := weight / (bar_high - bar_low) weighted_volume = bar_volume * weight // Classify volume direction if bar_close > bar_open level_bull_volume += weighted_volume else if bar_close < bar_open level_bear_volume += weighted_volume else // Doji handling level_bull_volume += weighted_volume * 0.5 level_bear_volume += weighted_volume * 0.5 🔶 Point of Control & Value Area Detection Implements institutional-standard POC identification by locating the price level with maximum volume accumulation, providing critical support/resistance zones. The Value Area calculation uses sophisticated sorting algorithms to identify the price range containing 70% of trading volume, revealing the market's accepted value zone where institutional participants concentrate their activity. 🔶 Volume Delta Analysis System Incorporates real-time volume delta calculation with configurable dominance thresholds to identify significant bull/bear imbalances. The system visually highlights price levels where buying or selling pressure exceeds threshold percentages, providing immediate insight into directional volume flow and potential reversal zones through color-coded delta indicators. // Value Area calculation using 70% volume accumulation total_volume_sum = array.sum(total_volumes) target_volume = total_volume_sum * 0.70 // Sort volumes to find highest activity zones for i = 0 to array.size(sorted_volumes) - 2 for j = i + 1 to array.size(sorted_volumes) - 1 if array.get(sorted_volumes, j) > array.get(sorted_volumes, i) // Swap and track indices for value area boundaries // Accumulate until 70% threshold reached for i = 0 to array.size(sorted_indices) - 1 accumulated_volume += vol array.push(va_levels, array.get(volume_levels, idx)) if accumulated_volume >= target_volume break ❓How It Works 🔶 Weighted Volume Distribution Implements proportional volume allocation based on the percentage of each bar that intersects with price levels. When a bar spans multiple levels, volume is distributed proportionally based on the intersection ratio, ensuring precise representation of trading activity across the entire price spectrum without double-counting or volume loss. 🔶 Real-Time Profile Generation Profiles regenerate on each bar close when in visible range mode, automatically adapting to chart zoom and scroll actions. The system maintains optimal performance through intelligent caching mechanisms and selective line updates, ensuring smooth operation even with maximum resolution settings and extended analysis periods. 🔶 Market Sentiment Analysis Features comprehensive volume analysis table displaying total volume metrics, bullish/bearish percentages, and overall market sentiment classification. The system calculates volume dominance ratios in real-time, providing immediate insight into whether buyers or sellers control the current price structure with percentage-based sentiment thresholds. 🔶 Visual Profile Mapping Provides multi-layered visual feedback through colored volume bars, POC line highlighting, Value Area boundaries, and optional delta indicators. The system supports profile mirroring for alternative perspectives, line extension for future reference, and customizable label positioning with detailed price information at critical levels. Why Choose Volume Profile Grid The Volume Profile Grid represents the evolution of volume analysis tools, combining traditional volume profile concepts with modern visualization techniques and intelligent analysis algorithms. By integrating dynamic range selection, sophisticated bull/bear separation, and multi-mode visualization with POC/Value Area detection, it provides traders with institutional-quality market structure analysis that adapts to any trading style. The comprehensive delta analysis and sentiment monitoring system eliminates guesswork while the flexible visualization options ensure optimal clarity across all market conditions, making it an essential tool for traders seeking to understand true market dynamics through volume-based price discovery.
Pine Script® göstergesi
AlphaExtract tarafından
2
VIX > 20/25 HighlightThis indicator tracks the CBOE Volatility Index (VIX) and highlights when volatility exceeds critical thresholds. Plots the VIX with dashed reference lines at 20 and 25. Background turns orange when the VIX is above 20. Background turns bright red when the VIX is above 25. Includes alert conditions to notify you when the VIX crosses above 20 or 25. Use this tool to quickly visualize periods of elevated market stress and manage risk accordingly.
Pine Script® göstergesi
cfbagnoud tarafından
Candle Body Size AlertThis indicator monitors the body size of each candle (close minus open, ignoring wicks) and compares it to a user-defined threshold measured in ticks. If the candle body exceeds the threshold, the indicator triggers an alert condition at the close of the candle. Features: 1. Adjustable threshold in ticks (default: 4000) 2. Adjustable timeframe (or use chart timeframe) 3. Alerts only at candle close (no intrabar signals) Use Case: Designed for traders who want to be notified when unusually large candles form, helping to identify strong momentum moves or volatility spikes.
Pine Script® göstergesi
ibrahiimH tarafından