Inizia

Indicatori e strategie

Buyside & Sellside Liquidity The Buyside & Sellside Liquidity Indicator is an advanced Smart Money Concepts (SMC) tool that automatically detects and visualizes liquidity zones and liquidity voids (imbalances) directly on the chart. 🟢 Function and meaning: 1. Buyside Liquidity (green): Highlights price zones above current price where short traders’ stop-loss orders are likely resting. When price sweeps these areas, it often indicates a liquidity grab or stop hunt. 👉 These zones are labeled with 💵💰 emojis for a clear visual cue where smart money collects liquidity. 2. Sellside Liquidity (red): Highlights zones below the current price where long traders’ stop-losses are likely placed. Once breached, these often signal a potential reversal upward. 👉 The 💵💰🪙 emojis make these liquidity targets visually intuitive on the chart. 3. Liquidity Voids (bright areas): Indicate inefficient price areas, where the market moved too quickly without filling orders. These zones are often revisited later as the market seeks balance (fair value). 👉 Shown as light shaded boxes with 💰 emojis to emphasize imbalance regions. 💡 Usage: • Helps spot smart money manipulation and stop hunts. • Marks potential reversal or breakout zones. • Great for traders applying SMC, ICT, or Fair Value Gap strategies. ✨ Highlight: Dollar and money bag emojis (💵💰🪙💸) are integrated directly into chart labels to create a clear and visually engaging representation of liquidity areas.
Indicatore Pine Script®
di morris220768
1
LogNormalLibrary "LogNormal" A collection of functions used to model skewed distributions as log-normal. Prices are commonly modeled using log-normal distributions (ie. Black-Scholes) because they exhibit multiplicative changes with long tails; skewed exponential growth and high variance. This approach is particularly useful for understanding price behavior and estimating risk, assuming continuously compounding returns are normally distributed. Because log space analysis is not as direct as using math.log(price) , this library extends the Error Functions library to make working with log-normally distributed data as simple as possible. - - - QUICK START Import library into your project Initialize model with a mean and standard deviation Pass model params between methods to compute various properties var LogNorm model = LN.init(arr.avg(), arr.stdev()) // Assumes the library is imported as LN var mode = model.mode() Outputs from the model can be adjusted to better fit the data. var Quantile data = arr.quantiles() var more_accurate_mode = mode.fit(model, data) // Fits value from model to data Inputs to the model can also be adjusted to better fit the data. datum = 123.45 model_equivalent_datum = datum.fit(data, model) // Fits value from data to the model area_from_zero_to_datum = model.cdf(model_equivalent_datum) - - - TYPES There are two requisite UDTs: LogNorm and Quantile . They are used to pass parameters between functions and are set automatically (see Type Management ). LogNorm   Object for log space parameters and linear space quantiles .   Fields:      mu (float) : Log space mu ( µ ).      sigma (float) : Log space sigma ( σ ).      variance (float) : Log space variance ( σ² ).      quantiles (Quantile) : Linear space quantiles. Quantile   Object for linear quantiles, most similar to a seven-number summary .   Fields:      Q0 (float) : Smallest Value      LW (float) : Lower Whisker Endpoint      LC (float) : Lower Whisker Crosshatch      Q1 (float) : First Quartile      Q2 (float) : Second Quartile      Q3 (float) : Third Quartile      UC (float) : Upper Whisker Crosshatch      UW (float) : Upper Whisker Endpoint      Q4 (float) : Largest Value      IQR (float) : Interquartile Range      MH (float) : Midhinge      TM (float) : Trimean      MR (float) : Mid-Range - - - TYPE MANAGEMENT These functions reliably initialize and update the UDTs. Because parameterization is interdependent, avoid setting the LogNorm and Quantile fields directly . init(mean, stdev, variance)   Initializes a LogNorm object.   Parameters:      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object set(ln, mean, stdev, variance)   Transforms linear measurements into log space parameters for a LogNorm object.   Parameters:      ln (LogNorm) : Object containing log space parameters.      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object quantiles(arr)   Gets empirical quantiles from an array of floats.   Parameters:      arr (array) : Float array object.   Returns: Quantile Object - - - DESCRIPTIVE STATISTICS Using only the initialized LogNorm parameters, these functions compute a model's central tendency and standardized moments. mean(ln)   Computes the linear mean from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ median(ln)   Computes the linear median from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ mode(ln)   Computes the linear mode from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ variance(ln)   Computes the linear variance from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ skewness(ln)   Computes the linear skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ kurtosis(ln, excess)   Computes the linear kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Kurtosis (true) or regular Kurtosis (false).   Returns: Between 0 and ∞ hyper_skewness(ln)   Computes the linear hyper skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ hyper_kurtosis(ln, excess)   Computes the linear hyper kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Hyper Kurtosis (true) or regular Hyper Kurtosis (false).   Returns: Between 0 and ∞ - - - DISTRIBUTION FUNCTIONS These wrap Gaussian functions to make working with model space more direct. Because they are contained within a log-normal library, they describe estimations relative to a log-normal curve, even though they fundamentally measure a Gaussian curve. pdf(ln, x, empirical_quantiles)   A Probability Density Function estimates the probability density . For clarity, density is not a probability .   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate for which a density will be estimated.      empirical_quantiles (Quantile) : Quantiles as observed in the data (optional).   Returns: Between 0 and ∞ cdf(ln, x, precise)   A Cumulative Distribution Function estimates the area under a Log-Normal curve between Zero and a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ccdf(ln, x, precise)   A Complementary Cumulative Distribution Function estimates the area under a Log-Normal curve between a linear X coordinate and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 cdfinv(ln, a, precise)   An Inverse Cumulative Distribution Function reverses the Log-Normal cdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ ccdfinv(ln, a, precise)   An Inverse Complementary Cumulative Distribution Function reverses the Log-Normal ccdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ cdfab(ln, x1, x2, precise)   A Cumulative Distribution Function from A to B estimates the area under a Log-Normal curve between two linear X coordinates (A and B).   Parameters:      ln (LogNorm) : Object of log space parameters.      x1 (float) : First linear X coordinate .      x2 (float) : Second linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ott(ln, x, precise)   A One-Tailed Test transforms a linear X coordinate into an absolute Z Score before estimating the area under a Log-Normal curve between Z and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 0.5 ttt(ln, x, precise)    A Two-Tailed Test transforms a linear X coordinate into symmetrical ± Z Scores before estimating the area under a Log-Normal curve from Zero to -Z, and +Z to Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ottinv(ln, a, precise)   An Inverse One-Tailed Test reverses the Log-Normal ott() by estimating a linear X coordinate for the right tail from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Half a normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ tttinv(ln, a, precise)   An Inverse Two-Tailed Test reverses the Log-Normal ttt() by estimating two linear X coordinates from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Linear space tuple : - - - UNCERTAINTY Model-based measures of uncertainty, information, and risk. sterr(sample_size, fisher_info)   The standard error of a sample statistic.   Parameters:      sample_size (float) : Number of observations.      fisher_info (float) : Fisher information.   Returns: Between 0 and ∞ surprisal(p, base)   Quantifies the information content of a single event.   Parameters:      p (float) : Probability of the event .      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ entropy(ln, base)   Computes the differential entropy (average surprisal).   Parameters:      ln (LogNorm) : Object of log space parameters.      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ perplexity(ln, base)   Computes the average number of distinguishable outcomes from the entropy.     Parameters:      ln (LogNorm)      base (float) : Logarithmic base used for Entropy (optional).   Returns: Between 0 and ∞ value_at_risk(ln, p, precise)   Estimates a risk threshold under normal market conditions for a given confidence level.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ value_at_risk_inv(ln, value_at_risk, precise)   Reverses the value_at_risk() by estimating the confidence level from the risk threshold.   Parameters:      ln (LogNorm) : Object of log space parameters.      value_at_risk (float) : Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 conditional_value_at_risk(ln, p, precise)   Estimates the average loss beyond a confidence level, aka. expected shortfall.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_value_at_risk_inv(ln, conditional_value_at_risk, precise)   Reverses the conditional_value_at_risk() by estimating the confidence level of an average loss.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_value_at_risk (float) : Conditional Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 partial_expectation(ln, x, precise)   Estimates the partial expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and µ partial_expectation_inv(ln, partial_expectation, precise)   Reverses the partial_expectation() by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      partial_expectation (float) : Partial Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_expectation(ln, x, precise)   Estimates the conditional expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between X and ∞ conditional_expectation_inv(ln, conditional_expectation, precise)   Reverses the conditional_expectation by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_expectation (float) : Conditional Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ fisher(ln, log)   Computes the Fisher Information Matrix for the distribution, not a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the distribution fisher(ln, x, log)   Computes the Fisher Information Matrix for a linear X coordinate, not the distribution itself.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the linear X coordinate confidence_interval(ln, x, sample_size, confidence, precise)   Estimates a confidence interval for a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      sample_size (float) : Number of observations.      confidence (float) : Confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: CI for the linear X coordinate - - - CURVE FITTING An overloaded function that helps transform values between spaces. The primary function uses quantiles, and the overloads wrap the primary function to make working with LogNorm more direct. fit(x, a, b)   Transforms X coordinate between spaces A and B.   Parameters:      x (float) : Linear X coordinate from space A .      a (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.      b (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.   Returns: Adjusted X coordinate - - - EXPORTED HELPERS Small utilities to simplify extensibility. z_score(ln, x)   Converts a linear X coordinate into a Z Score.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate.   Returns: Between -∞ and +∞ x_coord(ln, z)   Converts a Z Score into a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      z (float) : Standard normal Z Score.   Returns: Between 0 and ∞ iget(arr, index)   Gets an interpolated value of a pseudo -element (fictional element between real array elements). Useful for quantile mapping.   Parameters:      arr (array) : Float array object.      index (float) : Index of the pseudo element.   Returns: Interpolated value of the arrays pseudo element.
Libreria Pine Script®
di liquid-trader
NOVA Breakout Signals v2.2 (TF M30)A clean, rules-based breakout signal tool for 30-minute charts. It detects Dow swing breakouts and filters them with RSI, MACD and Volume so you only see the higher-quality entries. The script does not place trades and does not calculate SL/TP – it only prints clear LONG/SHORT labels at the entry price. ⸻ How it works 1. Timeframe enforcement – Signals are generated only on M30. On other timeframes the script shows a notice and stays silent. 2. Breakout engine (Dow swings) – The last confirmed swing high/low (pivots) is tracked. • Breakout Up: bar closes above the last swing high by a small buffer. • Breakout Down: bar closes below the last swing low by a small buffer. 3. Quality filters (all must be true): • RSI (default length 30): • Long: RSI > threshold and rising. • Short: RSI < threshold and falling. • MACD (12/26/9): • Long: histogram > 0 and line > signal. • Short: histogram < 0 and line < signal. • Volume: current volume > SMA(volume, 20) × multiplier. 4. Debounce / anti-spam • Cooldown of 4 hours (8 M30 bars) after any signal. • Minimum price distance from the previous signal to avoid clustered labels. Signals appear once the bar closes (barstate.isconfirmed). No swing lines are drawn to keep the chart clean; only entry labels are shown. ⸻ Inputs (key) • RSI length & thresholds for Long/Short confirmation. • MACD uses 12/26/9 (fixed). • Volume multiplier (relative to SMA 20). • Breakout buffer %, Cooldown hours, Min distance %. • Show labels (on/off). ⸻ Usage tips • Start with gold/major FX/indices on M30; use “Once per bar close” if you attach alerts. • Increase the breakout buffer and volume multiplier in choppy markets. • Tighten RSI thresholds (e.g., 55/45) if you want fewer but stronger signals. ⸻ Notes & limitations • Pivots confirm after a few bars by definition; signals themselves are printed only on confirmed bar close and do not repaint once shown. • This is a signal indicator, not investment advice. Always manage risk.
Indicatore Pine Script®
di kenny_nguyen
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution. If you would like to see my original Kalman Filter, please find it here: Concept overview The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory: Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price). VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred. By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters. How it works Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement. Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state. Noise parameters : Process Noise controls agility — higher values make the filter more responsive but also more volatile. Measurement Noise controls smoothness — higher values make it steadier but slower to adapt. Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes. Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow. Why this matters Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves. Use cases Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions. Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow. Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness. Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value. Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices. Parameter guidance Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping. Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking. VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics. Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines. Interpretation When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value. When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value. Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance. Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control. Summary The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Indicatore Pine Script®
di BackQuant
4
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions. It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open. This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance. Key Features Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance. Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities. Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York). Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities. Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times. Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis. Intended Use ADSR PRO is a statistical reference indicator. It does not generate buy/sell signals or predictive forecasts. Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis. Credits Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools. Risk Warning This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument. Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose. Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed. The analysis presented does not take into account your objectives, financial situation, or risk tolerance. Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional. Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured. You are solely responsible for all trading decisions, including position sizing and risk management. No external links, promotions, or contact details are provided, in line with TradingView House Rules.
Indicatore Pine Script®
di CapitalizeLabs
High Zone MapperHigh Zone and Low Zone Mapper — Quick Manual (Short-Term Trading) Author: hkpress | Script date: 2025-10-26 This indicator draws: PDH/PDL (Prior Day High/Low), PWH/PWL (Prior Week High/Low), ORH/ORL (Opening Range High/Low), IDH/IDL (Intraday High/Low), plus a shaded Opening Range box. I built this script after watching an interview on TradingLion with a Hong Kong trader who uses prior-day, opening-range, intraday, and prior-week levels to plan entries and exits. The approach is especially useful for traders who run tight stops (about 1.5%–3%) while aiming to size up into bigger positions. (Youtube: www.youtube.com) 1) Quick Start (15-minute default) Timeframe: use 1–15m for scalps, 5–30m for intraday. Opening Range (OR — Opening Range): default 15 minutes. Turn on “Show OR lines while opening range builds” if you want to see ORH/ORL during the first 15 minutes. Session mode: Stocks → Use Trading Session = ON (RTH — Regular Trading Hours, e.g., 09:30–16:00). Crypto/24h → Use Trading Session = OFF (day-start mode). Visuals: enable PDH/PDL, PWH/PWL, ORH/ORL, IDH/IDL, and the Opening Range box (fill). 2) What each line means PDH/PDL (Prior Day High/Low): Yesterday’s extremes; frequent reaction zones. PWH/PWL (Prior Week High/Low): Last week’s extremes; stronger “fences.” ORH/ORL (Opening Range High/Low): High/low of the first 15 minutes by default; key breakout compass. IDH/IDL (Intraday High/Low): Today’s high/low so far; confirms momentum after a break. 3) Short-Term Playbook (step-by-step) A. Before the open Note where price is vs PDH/PDL and PWH/PWL to set a bias. Above PDH and pushing up → bullish lean. Below PDL and slipping → bearish lean. B. First 15 minutes (Opening Range forms) Let the Opening Range box print (ORH top, ORL bottom). Think of this box as the day’s first “battlefield.” C. Breakout entries Long: Clean break above ORH (preferably with momentum/volume). Stop: just below ORH (aggressive) or below ORL (conservative). Targets: step up through PDH → PWH. Short: Clean break below ORL. Stop: just above ORL (aggressive) or above ORH (conservative). Targets: step down through PDL → PWL. D. Retest entries (missed the first move?) After a break, wait for a retest of ORH/ORL from the other side. Enter on rejection/continuation; place stop on the opposite side of the retested level. E. Momentum confirmation New IDH (Intraday High) after an ORH break = trend strengthening (consider add/hold). New IDL after an ORL break = downtrend strengthening. Trail stops below higher lows (long) or above lower highs (short). F. Range mode (no break yet) If price stays inside the box, fade edges: buy near ORL, sell near ORH, until a decisive break. 4) Risk rules (keep it simple) Aim for R:R (Risk-to-Reward) ≥ 1:2. Set a daily max loss (e.g., 1–2R) and respect it. Invalidation: if price breaks and then re-enters the box and holds, exit—don’t argue. 5) Quick example The 15-minute OR prints: ORL = 100, ORH = 105. Price breaks 106 with momentum → Long. Stop 104.8 (below ORH) or 99.8 (below ORL). Targets: PDH, then PWH. Trail as IDH keeps making new highs. 6) Handy tweaks Noisy/news days: widen to 30-minute OR to reduce whipsaws. Strong trend open: tighten to 5–10-minute OR to engage earlier. Choppy session: stick to box-edge fades or stand aside after two failed breaks. 7) Built-in alerts to consider “Break Above ORH / Below ORL” → entry triggers. “New IDH / New IDL” → momentum confirms; tighten stops or scale. “Break Above PDH / Below PDL / Above PWH / Below PWL” → target hits or bigger trend shifts. 8) Troubleshooting No lines? Switch to an intraday timeframe (1–60m). ORH/ORL missing? Turn ON Show OR lines while opening range builds. Session mismatch? Use correct RTH hours, or turn session OFF for 24h symbols. Abbreviation cheat-sheet OR (Opening Range), ORH/ORL (Opening Range High/Low) PDH/PDL (Prior Day High/Low) PWH/PWL (Prior Week High/Low) IDH/IDL (Intraday High/Low) RTH (Regular Trading Hours), R:R (Risk-to-Reward)
Indicatore Pine Script®
di hkpress
EMA HeatmapEMA Heatmap — Indicator Description The EMA Order Heatmap is a visual trend-structure tool designed to show whether the market is currently trending bullish, trending bearish, or moving through a neutral consolidation phase. It evaluates the alignment of multiple exponential moving averages (EMAs) at three different structural layers: short-term daily, medium-term daily, and weekly macro trend. This creates a quick and intuitive picture of how well price movement is organized across timeframes. Each layer of the heatmap is scored from bearish to bullish based on how the EMAs are stacked relative to each other. When EMAs are in a fully bullish configuration, the row displays a bright green or lime color. Fully bearish alignment is shown in red. Yellow tones appear when the EMAs are mixed or compressing, indicating uncertainty, trend exhaustion, or a change in market character. The three rows combined offer a concise view of whether strength or weakness is isolated to one timeframe or broad across the market. This indicator is best used as a trend filter before making trading decisions. Traders may find more consistent setups when the majority of the heatmap supports the direction of their trade. Green-dominant conditions suggest a trending bullish environment where long trades can be favored. Red-dominant conditions indicate bearish momentum and stronger potential for short opportunities. When yellow becomes more prominent, the market may be transitioning, ranging, or gearing up for a breakout, making timing more challenging and risk higher. • Helps quickly identify directional bias • Highlights when trends strengthen, weaken, or turn • Provides insight into whether momentum is supported by higher timeframes • Encourages traders to avoid fighting market structure It is important to recognize the limitations. EMAs are lagging indicators, so the heatmap may confirm a trend after the initial move is underway, especially during fast reversals. In sideways or low-volume environments, the structure can shift frequently, reducing clarity. This tool does not generate entry or exit signals on its own and should be paired with price action, momentum studies, or support and resistance analysis for precise trade execution. The EMA Order Heatmap offers a clean and reliable way to stay aligned with the broader market environment and avoid lower-quality trades in indecisive conditions. It supports more disciplined decision-making by helping traders focus on setups that match the prevailing structural trend.
Indicatore Pine Script®
di Scatman_Jeff
TI65**TI65 (Trend Intensity 65)** is a technical indicator designed to measure the strength and momentum of a trend over two distinct periods. It compares a short-term 7-period simple moving average (SMA) with a long-term 65-period SMA, producing a ratio that helps traders identify shifts in market momentum and trend direction. - When the **TI65 value is greater than 1**, it indicates that the short-term moving average is above the long-term average, suggesting increasing momentum and a potentially bullish trend. - When the **TI65 value drops below 1**, it signals weakening short-term momentum relative to the longer-term trend, often interpreted as a bearish or consolidating phase. This indicator can be applied to both price and volume data, making it useful for identifying periods of strong volume surges or price movements. By observing changes in the TI65 ratio, traders can pinpoint low-risk entry points for trend-following strategies and quickly recognize periods of market transition. TI65 is commonly used by momentum and breakout traders for screening strong candidates and confirming the sustainability of ongoing trends. It is simple, effective, and easily implemented via custom scripts on popular platforms like TradingView.
Indicatore Pine Script®
di echanxyz
Dow Jones Trading System with PivotsThis TradingView indicator, tailored for the 30-minute Dow Jones (^DJI) chart, supports DIA options trading with a trend-following approach. It features a 30-period SMA (blue) and a 60-period SMA (red), with an optional 90-period SMA (orange) drawn from rauItrades' Dow SMA outfit. A bullish crossover (30 SMA > 60 SMA) displays a green "BUY" triangle below the bar for potential DIA longs, while a bearish crossunder (30 SMA < 60 SMA) shows a red "SELL" triangle above for shorts or exits. The background turns green (bullish) or red (bearish) to indicate trend bias. Pivot points highlight recent highs (orange circles) and lows (purple circles) for support/resistance, using a 5-bar lookback. Alerts notify for crossovers.
Indicatore Pine Script®
di DarkMoneyMusic
NASDAQ Trading System with PivotsThis TradingView indicator, designed for the 30-minute NASDAQ (^IXIC) chart, guides QQQ options trading using a trend-following strategy. It plots a 20-period SMA (blue) and a 100-period SMA (red), with an optional 250-period SMA (orange) inspired by rauItrades' NASDAQ SMA outfit. A bullish crossover (20 SMA > 100 SMA) triggers a green "BUY" triangle below the bar, signaling a potential long position in QQQ, while a bearish crossunder (20 SMA < 100 SMA) shows a red "SELL" triangle above, indicating a short or exit. The background colors green (bullish) or red (bearish) for trend bias. Orange circles (recent highs) and purple circles (recent lows) mark support/resistance levels using 5-bar pivot points.
Indicatore Pine Script®
di DarkMoneyMusic
Volume TI65**TI65 (Trend Intensity 65)** is a technical indicator designed to measure the strength and momentum of a trend over two distinct periods. It compares a short-term 7-period simple moving average (SMA) with a long-term 65-period SMA, producing a ratio that helps traders identify shifts in market momentum and trend direction. - When the **TI65 value is greater than 1**, it indicates that the short-term moving average is above the long-term average, suggesting increasing momentum and a potentially bullish trend. - When the **TI65 value drops below 1**, it signals weakening short-term momentum relative to the longer-term trend, often interpreted as a bearish or consolidating phase. This indicator can be applied to both price and volume data, making it useful for identifying periods of strong volume surges or price movements. By observing changes in the TI65 ratio, traders can pinpoint low-risk entry points for trend-following strategies and quickly recognize periods of market transition. TI65 is commonly used by momentum and breakout traders for screening strong candidates and confirming the sustainability of ongoing trends. It is simple, effective, and easily implemented via custom scripts on popular platforms like TradingView.
Indicatore Pine Script®
di echanxyz
S&P Trading System with PivotsThe S&P Trading System with Pivots is a TradingView indicator designed for the 30-minute SPX chart to guide SPY options trading. It uses a trend-following strategy with: 10 SMA and 50 SMA: Plots a 10-period (blue) and 50-period (red) Simple Moving Average. A bullish crossover (10 SMA > 50 SMA) signals a potential buy (green triangle below bar), while a bearish crossunder (10 SMA < 50 SMA) signals a sell or exit (red triangle above bar). Trend Bias: Colors the background green (bullish) or red (bearish) based on SMA positions. Pivot Points: Marks recent highs (orange circles) and lows (purple circles) as potential resistance and support levels, using a 5-bar lookback period.
Indicatore Pine Script®
di DarkMoneyMusic
WaveTrend RBF What it does WT-RBF extracts a “wave” of momentum by subtracting a fast Gaussian-weighted smoother from a slow one, then robust-normalizes that wave with a median/MAD proxy to produce a z-score (z). A short EMA of z forms the signal line. Optional dynamic thresholds use the MAD of z itself so overbought/oversold levels adapt to volatility regimes. How it’s built: Radial (Gaussian) smoothers Causal, exponentially-decaying weights over the last radius bars using σ (sigma) to control spread. fast = rbf_smooth(src, fastR, fastSig) slow = rbf_smooth(src, slowR, slowSig) wave = fast − slow (band-pass) Robust normalization A two-stage EMA approximates the median; MAD is estimated from EMA of absolute deviations and scaled by 1.4826 to be stdev-comparable. z = (wave − center) / MAD Thresholds Dynamic OB/OS: ±2.5 × MAD(z) (or fixed levels when disabled) Reading the indicator Bull Cross: z crosses above sig → momentum turning up. Bear Cross: z crosses below sig → momentum turning down. Exits / Bias flips: zero-line crosses (below 0 → exit long bias; above 0 → exit short bias). Overbought/Oversold: z > +thrOB or z < thrOS. With dynamics on, the bands widen/narrow with recent noise; with dynamics off, static guides at ±2 / ±2.5 are shown. Core Inputs Source: Price series to analyze. Fast Radius / Fast Sigma (defaults 6 / 2.5): Shorter radius/smaller σ = snappier, higher-freq. Slow Radius / Slow Sigma (defaults 14 / 5.0): Larger radius/σ = smoother, lower-freq baseline. Normalization Robust Z-Score Window (default 200): Lookback for median/MAD proxy (stability vs responsiveness). Small ε for MAD: Floor to avoid division by zero. Signal & Thresholds Dynamic Thresholds (MAD-based) (on by default): Adaptive OB/OS; toggle off to use fixed guides. Visuals Shade OB/OS Regions: Background highlights when z is beyond thresholds. Show Zero Line: Midline reference. (“Plot Cross Markers” input is present for future use.)
Indicatore Pine Script®
di Cmo22
Rolling Performance Metrics TableRolling Performance Metrics Table A clean, customizable table overlay that displays rolling performance metrics across multiple time periods. Perfect for quickly assessing price momentum and performance trends at a glance. FEATURES: - Displays performance across 5 time periods: 1 Week, 3 Month, 6 Month, 1 Year, and 2 Year - Shows historical price at the start of each period - Calculates both absolute price change and percentage change - Color-coded results: Green for positive performance, Red for negative performance - Fully transparent design with no background or borders - text floats cleanly over your chart - Customizable table position (9 placement options) DISPLAY COLUMNS: 1. Period - The lookback timeframe 2. Price - The historical price at the start of the period 3. Change (Value) - Absolute price change from the period start 4. Change (%) - Percentage return over the period CUSTOMIZATION: - Adjust the number of bars for each period (default: 1 Week = 5 bars, 3 Month = 63 bars, 6 Month = 126 bars, 1 Year = 252 bars, 2 Year = 504 bars) - Choose from 9 table positions: Top, Middle, Bottom combined with Left, Center, Right - Default position: Middle Left USAGE: Perfect for traders who want to quickly assess momentum across multiple timeframes. The transparent overlay design ensures minimal obstruction of chart analysis while providing critical performance data at a glance. NOTE: - The table only appears on the last bar of your chart - Customize bar counts in settings to match your specific timeframe needs (e.g., daily vs hourly charts) - "N/A" appears when historical data is insufficient for the selected period
Indicatore Pine Script®
di bobmcallan
TTM Squeeze Pro - IntradayTTM Squeeze Pro – Intraday (AI MTF Edition) Design Rationale This indicator is built to help traders identify when markets are consolidating, when volatility is building (squeeze), and when a breakout or trend is starting — all across multiple timeframes. The design combines three powerful ideas: Volatility Compression & Expansion (TTM Squeeze Logic): By comparing Bollinger Bands (BB) and Keltner Channels (KC), the indicator detects when volatility contracts (BB inside KC). These moments often precede explosive moves. White dots on the BB basis line mark these “squeeze” periods. Trend Strength & Direction (ADX System): The ADX (Average Directional Index) measures how strong a trend is. ADX rising above the threshold → trending market. ADX falling below the threshold → consolidation. The system classifies each bar as Trending Up, Trending Down, Consolidating, or Neutral, depending on ADX and momentum direction. Multi-Timeframe (MTF) Alignment: The same logic is applied to several timeframes (1m, 3m, 5m, 15m, 30m, 1h). A compact table at the top-right shows each timeframe’s trend and squeeze strength. This helps traders see whether short-term and higher timeframes are aligned, improving trade confidence and timing. The AI Enhancer automatically adjusts all parameters (ADX, BB, KC lengths, and thresholds) depending on the current chart timeframe, keeping signals consistent between scalping and swing trading setups. Trend and squeeze strengths are normalized on a 1–9 scale, giving users a quick numerical sense of trend power and squeeze intensity. The design emphasizes clarity, speed, and adaptability — critical for intraday trading decisions. How to Use Identify a Squeeze Setup: Look for white dots on the chart — this marks low volatility and potential energy buildup. Wait for Breakout Confirmation: When the white dots disappear, volatility expands. Check the MTF table — if multiple timeframes show green (uptrend) or red (downtrend) in the “TR” column, momentum is aligning. Enter the Trade: Go long if breakout happens above BB basis and most timeframes show green. Go short if breakout happens below BB basis and most timeframes show red. Exit or Manage Position: When new white dots appear → volatility contracting again → consider exiting or tightening stops. If MTF colors become mixed → trend losing strength. In Summary The TTM Squeeze Pro – Intraday AI MTF Indicator blends volatility analysis, trend strength, momentum, and multi-timeframe alignment into one adaptive tool. Its design aims to simplify complex market behavior into a visual, data-backed format — enabling traders to catch high-probability breakout trends early and avoid false moves during low-volatility phases.
Indicatore Pine Script®
di Just-a-Trader