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PGS - Pareto-Gaussian Skew [Zofesu]PGS - Pareto-Gaussian Skew is a trend gravity indicator built on two mathematical principles: the Gaussian distribution for detecting statistically significant price moves, and the Pareto principle for isolating the minority of moves that drive the majority of directional displacement. The result is a single adaptive line that acts as a gravitational center — pulling toward institutional price displacement while filtering out the noise that makes standard moving averages lag or whipsaw. ───────────────────────────────────── 01 — What is PGS? ───────────────────────────────────── PGS plots a gravity line that tracks where price is being pulled by significant institutional moves. Unlike a standard moving average, it does not react to all price movement equally. It reacts only when price moves beyond one standard deviation from its mean — the threshold where statistically normal noise ends and directional displacement begins. Below that threshold, the line stays anchored to the mean. Above it, the Pareto Skew Factor shifts the gravity line toward the extreme, capturing the 20% of moves that drive 80% of the trend. ───────────────────────────────────── 02 — How it works ───────────────────────────────────── The engine runs in three steps: Step 1 — Mean and standard deviation SMA and standard deviation are calculated over the lookback window (default 100 bars). This defines the Gaussian baseline — the statistical center of recent price behavior. Step 2 — Extreme detection The distance between current close and the mean is measured. If that distance exceeds 1x standard deviation, the move is classified as extreme. Moves within 1 standard deviation are treated as noise and ignored. Step 3 — Pareto displacement Extreme moves are multiplied by the Pareto Skew Factor (default 0.8). This skewed value is then smoothed with an EMA over a quarter of the lookback period and added back to the mean — producing the final gravity line. Formula: gravity = SMA(close, n) + EMA(d × extreme × skew, n/4) where d = close − SMA(close, n), extreme = 1 if |d| > StDev else 0 ───────────────────────────────────── 03 — Visuals ───────────────────────────────────── Blue line — Pareto-Gaussian gravity line Tension Cloud — fill between price and the gravity line. Green fill = price above gravity line (bullish tension). Pink fill = price below gravity line (bearish tension). Note: Price Reference and PGS Reference appear in the indicator's plot list but are invisible on the chart. They are internal anchors required by Pine Script's fill() function and serve no visual or analytical purpose for the user. ───────────────────────────────────── 04 — Settings ───────────────────────────────────── Smith's Memory (Lookback) — default 100 Number of candles used for the Gaussian baseline. Higher = slower, more stable line. Lower = faster, more reactive. Pareto Skew Factor — default 0.8 Weight applied to extreme moves. Higher = gravity line shifts more aggressively toward institutional displacement. Lower = more conservative, stays closer to the mean. ───────────────────────────────────── 05 — How To Use ───────────────────────────────────── Step 1 — Read the Tension Cloud Green fill = price is above the gravity line. Bullish context — look for longs or hold existing positions. Pink fill = price is below the gravity line. Bearish context — look for shorts or avoid longs. Step 2 — Watch for gravity line crossings Price crossing the gravity line from below = potential bullish shift. Price crossing from above = potential bearish shift. Step 3 — Use as dynamic support and resistance In trending markets the gravity line acts as a dynamic S/R level. Price pulling back to the line in a green cloud = potential long entry zone. Step 4 — Combine with higher timeframe context PGS works best as a trend context filter alongside entry tools. It defines the direction — your entry indicator defines the moment. Works on all asset classes: Indices, Forex, Gold, Oil, Crypto. Best timeframes: H1, H4, D1.
Pine Script® indicator
by Zofesu
Trend filter v2 [Jamallo]Intro Trend Filter v2 is the evolution of the original Trend Filter, replacing the Kalman Filter baseline and WMA-scaled bands with a kernel-smoothed Vervoort Stop and percentile-ranked deviation bands. It also draws conceptual lineage from the ENW Kernel Bands indicator, inheriting the composite kernel and dual-Vervoort architecture — but rethinking both what the kernel is applied to and how bands are sized. Breakdown A Line — Vervoort Stop A classic trailing stop built on Heikin Ashi smoothed price and ATR. Color-coded by a trend state machine: bullish when price is above a rising A line, bearish when below a falling one, neutral otherwise. This is the signal line. K Line — Kernel-Smoothed Vervoort Stop Unlike ENW where the composite kernel is applied directly to raw price, v2 feeds a second Vervoort Stop output into the kernel. This means the K line is trend-filtered before smoothing even begins, producing a slower, more structurally stable baseline that is less reactive to noise. Signal Line — Independent Vervoort Stop A third Vervoort Stop with its own ATR settings, used as an additional trend reference. Percentile Bands The key structural departure from both v1 and ENW. Instead of scaling bands by smoothed volatility × a fixed multiplier, v2 ranks upside and downside deviations from K separately using `percentile_nearest_rank` over a configurable lookback. END v2 replaces the Kalman with a kernel-smoothed Vervoort Stop and swaps fixed-deviation bands for percentile-ranked asymmetric bands — a fundamental rethink of both the baseline and the band sizing at every level.
Pine Script® indicator
by Jamallo22
2
Exponential Nadaraya Watson kernel regression [Jamallo](2025) Intro Nadaraya-Watson (N-W) kernel regression is a non-parametric smoothing technique that estimates the underlying trend of a price series without assuming any fixed model shape (like a straight line or curve). Unlike a simple moving average which weights bars equally, or an EMA which applies a fixed exponential decay, N-W regression derives its curve by computing a weighted average of all prices within a lookback window — where the weights are determined by a kernel function. The most common kernel used is the Gaussian kernel, which assigns weights in a bell-curve shape — prices closer to the center of the window receive higher weight, prices at the edges receive lower weight. Most N-W implementations use a pure symmetric Gaussian kernel, meaning every bar within the lookback window is weighted purely by its distance from the center, with no preference for recency. This indicator uses a hybrid Exponential Nadaraya-Watson (ENW) kernel that combines two weighting forces simultaneously: Gaussian spatial weight — bell-curve weighting centered on the window, same as standard N-W Exponential time-decay weight — progressively heavier weighting on recent bars, similar to how an EMA behaves Both weights are multiplied together for each bar, meaning a price bar must be both spatially central and recent to receive maximum influence. In practice this shifts the effective weight peak toward the recent end of the window, making the K Line more responsive to current price action than standard N-W. Breakdown K Line — ENW Kernel Regression The central baseline of the indicator. A smooth adaptive curve derived from the hybrid ENW kernel applied to closing prices. Bandwidth is controlled via the Kernel Length and Alpha inputs — higher alpha increases the recency bias, lower alpha brings behavior closer to standard N-W. Volatility Bands (Inner & Outer) Rather than using ATR or standard deviation for band width, this indicator measures the absolute deviation of price from the K Line and smooths that deviation through the same ENW kernel. This means bands are fully adaptive — they expand and contract organically based on how far price has been straying from the regression curve, not a fixed statistical formula. Inner and outer bands are independently scaled via deviation multipliers. A Line — Vervoort ATR Stop A trailing stop built on the HLC4 price source with an ATR-based loss distance. Flips direction on a close beyond the stop level. Serves as the primary trend bias line — when above the K Line the fill turns bullish, when below it turns bearish. Cross signals (triangles) are plotted whenever the A Line crosses the K Line, marking potential trend shifts. Signal Line — Vervoort ATR Stop (Secondary) A second independent Vervoort trailing stop running on its own ATR period and multiplier settings. Typically configured looser than the A Line — wider multiplier, longer or equal period — so it acts as a slower confirmation layer. Useful for filtering noise on the A Line crosses: an A Line cross that also aligns with the Signal Line's bias carries more weight than one that doesn't. ------- Nadaraya-Watson kernel regression concept — E. Nadaraya (1964), G.S. Watson (1964) Vervoort ATR Stop — Sylvain Vervoort
Pine Script® indicator
by Jamallo22
5
Kalman Regime [Jamallo](2025) Intro Kalman Regime filters price through two stages — a Gaussian kernel weighted average followed by an adaptive Kalman filter — to produce a smooth, noise-resistant baseline. ATR is used throughout to make the system volatility-aware. Breakdown The Gaussian kernel pre-smooths price using a bell-shaped weight profile that blends recency bias (recent bars matter more) with center localization (edge bars contribute less). This pre-smoothed value is then passed into an adaptive Kalman filter, which recursively estimates the "true" price state by balancing how much to trust the new measurement versus the prior state — with that balance dynamically scaled by current ATR. The resulting baseline drives regime detection through a two-tier state machine. A strong signal fires when price breaks the ATR envelope and the baseline slope confirms direction. A weaker signal fires when price crosses the baseline and slope exceeds an ATR-scaled threshold. This dual-gate structure reduces whipsaws without adding lag. END A two-stage price filter combining a Gaussian kernel pre-smoother with an adaptive Kalman filter, using ATR envelopes and slope confirmation for regime detection.
Pine Script® indicator
by Jamallo22
MFI Distribution [UAlgo]MFI Distribution is a statistics focused Money Flow Index indicator that combines a live MFI oscillator with a forward projected distribution histogram and normality diagnostics. Instead of only showing the current MFI line, the script collects a rolling history of MFI values, studies their distribution over a configurable lookback period, and visualizes the result as a histogram in the oscillator pane. The indicator is designed for traders who want to understand how MFI behaves as a distribution , not only where it is on the current bar. It provides a compact statistical framework that helps answer questions such as: Is MFI clustering around a narrow regime or spread across the full range Is the recent MFI behavior skewed toward strong buying or selling pressure Does the MFI sample look approximately normal, or is it fat tailed / asymmetric How extreme is the current reading relative to the recent distribution To support this, the script includes: A live MFI line with dynamic gradient coloring Overbought and oversold visual zones with gradient fills A histogram of MFI frequency distribution over the selected lookback An optional Gaussian curve overlay for visual comparison A statistical dashboard with mean, standard deviation, skewness, kurtosis, and Jarque Bera normality test results The histogram is drawn into the future area of the pane, so it does not interfere with the live MFI trace while still remaining visually aligned to the 0 to 100 oscillator scale. Educational tool only. Not financial advice. 🔹 Features 🔸 1) Live MFI Oscillator with Regime Aware Coloring The script plots a standard MFI line and colors it dynamically based on level behavior: High MFI values transition toward red tones Low MFI values transition toward green tones Mid range values remain purple This makes the oscillator easier to read at a glance, especially during sustained overbought or oversold conditions. 🔸 2) Overbought and Oversold Zone Visualization The indicator includes standard MFI reference levels at 80 and 20, then adds gradient fills that become visible when the MFI pushes into extreme zones. This provides better visual emphasis for momentum extremes without cluttering the chart. 🔸 3) Rolling MFI History Collection for Statistical Analysis The script maintains an internal array of the latest MFI values up to the user defined lookback length. This rolling sample is used to compute all distribution statistics and histogram frequencies on the last bar. This design keeps the indicator responsive while ensuring the displayed distribution reflects the most recent market behavior. 🔸 4) Forward Projected MFI Distribution Histogram A histogram is drawn in the oscillator pane using bins over the fixed MFI range from 0 to 100. Each bin counts how many MFI observations fall inside that interval during the lookback window. The histogram is projected to the right of current price action in the pane, giving users a clean distribution panel without covering the live MFI line. 🔸 5) Configurable Histogram Resolution and Width Users can control: Lookback period used for distribution analysis Number of histogram bins Visual width of the histogram in future bars This makes the tool flexible for both high level regime reading and finer distribution inspection. 🔸 6) Optional Gaussian Curve Overlay When enabled, the script overlays a theoretical normal distribution curve using the sample mean and sample standard deviation. The curve is normalized to the histogram height so users can visually compare the empirical MFI distribution against a bell curve shape. This is useful for quickly spotting asymmetry, multimodal clustering, or fat tail behavior. 🔸 7) Full Statistical Summary Dashboard A built in dashboard table displays: Mean Standard Deviation Skewness Kurtosis (excess kurtosis) Jarque Bera statistic Pass / Fail normality status (95% threshold logic) This turns the indicator into a compact quantitative diagnostic panel, not only a visual oscillator. 🔸 8) Jarque Bera Normality Test Classification The script evaluates whether the MFI sample is approximately normal using a Jarque Bera style test and a fixed chi square threshold (95% confidence, 2 degrees of freedom). It then marks the result as PASS (Normal) or FAIL (Non Normal). This helps traders distinguish between more stable oscillator regimes and structurally distorted ones. 🔸 9) Histogram Color Theme Based on Normality Result The histogram automatically changes style depending on the test result: Teal themed histogram when normality test passes Red themed histogram when normality test fails This creates an immediate visual signal of distribution quality without needing to read the dashboard first. 🔸 10) Last Bar Only Heavy Processing for Efficiency Statistical calculations, histogram drawing, and dashboard refresh are performed only on the last bar. This reduces object churn and improves performance while preserving real time utility. 🔸 11) Object Based Drawing Management The script uses custom types to organize logic: DistributionStats for statistical values and normality output HistoDrawer for histogram bars, curve lines, and labels This makes the code structured and easier to extend with future features such as percentiles, z scores, or alternate tests. 🔹 Calculations 1) MFI Calculation The script computes Money Flow Index from a user selected source and length: mfi_val = ta.mfi(mfi_src, mfi_len) This value is plotted in the oscillator pane and colored dynamically according to level. 2) Rolling History Buffer for Distribution Sampling Each valid MFI value is pushed into a rolling array used for statistical analysis: if not na(mfi_val) mfi_history.push(mfi_val) if mfi_history.size() > lookback mfi_history.shift() This ensures the sample size is capped at the selected lookback and continuously refreshed with recent values. 3) Sample Variance and Standard Deviation The script computes sample variance using the classic n minus 1 denominator: sum_sq_diff / (n - 1) Standard deviation is then calculated as: stats.stdev := math.sqrt(variance_val) Using sample variance is appropriate here because the lookback window is treated as a sample of recent market behavior. 4) Sample Skewness Calculation Skewness is computed from standardized deviations and corrected for sample size: (n * sum_cube_diff) / ((n - 1) * (n - 2)) Interpretation: Positive skew suggests more mass on lower values with a right tail toward high MFI prints Negative skew suggests more mass on higher values with a left tail toward low MFI prints 5) Excess Kurtosis Calculation The script calculates excess kurtosis , where a normal distribution is centered around 0: float term1 = (n * (n + 1) * sum_quad_diff) / ((n - 1) * (n - 2) * (n - 3)) float term2 = (3 * math.pow(n - 1, 2)) / ((n - 2) * (n - 3)) term1 - term2 Interpretation: Positive excess kurtosis suggests heavier tails or more peaked behavior Negative excess kurtosis suggests flatter distribution behavior 6) Jarque Bera Normality Test The script uses skewness and excess kurtosis to compute the Jarque Bera statistic: stats.jb_stat := (n / 6.0) * (math.pow(stats.skew, 2) + 0.25 * math.pow(stats.kurt, 2)) Then it compares the result against a 95 percent chi square critical value (2 degrees of freedom): stats.is_normal := stats.jb_stat < 5.991 Important note: The code defines a jb_p_value field in the stats type, but this version does not explicitly calculate or display a p value. The pass / fail logic is threshold based. 7) Fixed MFI Range Histogram Binning (0 to 100) The histogram always bins data over the full MFI range: float min_val = 0.0 float max_val = 100.0 float bin_size = (max_val - min_val) / bins Each MFI value is mapped to a bin index: int bin_idx = math.floor(val / bin_size) The index is clamped so values at boundaries stay valid: if bin_idx >= bins bin_idx := bins - 1 if bin_idx < 0 bin_idx := 0 This makes the histogram consistent across symbols and timeframes. 8) Frequency Counting and Peak Detection For each binned MFI observation, the script increments a frequency counter and tracks the highest bin count: int new_count = frequencies.get(bin_idx) + 1 frequencies.set(bin_idx, new_count) if new_count > max_freq max_freq := new_count The maximum frequency is later used to normalize histogram bar heights. 9) Histogram Rendering Geometry The histogram is drawn as boxes in the oscillator pane, projected to the right of the last bar: int start_bar = bar_index + 5 float base_y = 10.0 float available_height = 80.0 This effectively uses the MFI pane vertical range from about 10 to 90 for histogram height visualization, keeping it aligned with the oscillator scale. Each bin is mapped to x coordinates using its MFI interval and the user selected histogram width: int x_left = start_bar + math.round((bin_val_start / 100.0) * chart_width_bars) int x_right = start_bar + math.round((bin_val_end / 100.0) * chart_width_bars) Each frequency is mapped to a vertical height using: float bar_height_val = (freq / max_freq) * available_height 10) Histogram Color Logic from Normality Result The histogram color theme is selected from the Jarque Bera pass / fail result: Teal palette when stats.is_normal is true Red palette when stats.is_normal is false This creates a direct link between statistical classification and visual presentation. 11) Gaussian Curve Overlay Calculation The script defines a normal probability density function: (1.0 / (sigma * math.sqrt(2.0 * math.pi))) * math.exp(-0.5 * math.pow((x - mu) / sigma, 2)) For curve plotting: It samples points across the histogram width Maps each x position back to an MFI value from 0 to 100 Computes the PDF at that MFI value using the sample mean and standard deviation Scales the PDF by the theoretical peak so the curve fits the histogram height Key normalization idea: float pdf_peak = normal_pdf(stats.mean, stats.mean, stats.stdev) float current_y = base_y + (pdf_val / pdf_peak) * available_height This makes the Gaussian curve visually comparable to the empirical histogram, even though one is a density and the other is raw frequency. 12) Dashboard Table Metrics On the last bar, the script updates a table with the computed statistics: Mean StdDev Skewness Kurtosis Jarque Bera statistic and normality classification The result cell color changes based on normality: Green for PASS (Normal) Red for FAIL (Non Normal) This gives traders a compact quantitative summary next to the visual distribution. 13) Overbought and Oversold Gradient Fills The script adds gradient fills that appear when MFI moves beyond standard thresholds: fill(plot_mfi, p_ob, 100, 80, ...) fill(plot_mfi, p_os, 20, 0, ...) This helps contextualize whether the current MFI reading is extreme while the histogram and dashboard describe the broader behavior of the recent MFI sample.
Pine Script® indicator
by UAlgo
Gaussian Volume Profile [LuxAlgo]The Gaussian Volume Profile indicator is a sophisticated volume analysis tool that uses the Levenberg-Marquardt optimization algorithm to fit a Sum of Gaussians model to historical volume distribution. This approach transcends traditional discrete volume profiles by providing a continuous, noise-reduced representation of liquidity clusters, allowing for the precise identification of high-volume nodes and their respective price boundaries. 🔶 USAGE The indicator projects a lateral volume density map to the right of the current price action. Users can utilize this tool to identify "fair value" zones where the Gaussian peaks are most concentrated. Unlike standard profiles that show jagged horizontal bars, this tool provides a smooth "fit" line that highlights the true center of gravity for volume at specific price levels. 🔹 Identifying High-Volume Nodes The script automatically detects local maxima (peaks) within the fitted Gaussian model. These peaks represent the most significant price levels where the highest density of trading occurred. Horizontal dashed lines are drawn at these apexes, color-coded to match the specific Gaussian component that is most dominant at that price. 🔹 Zone Width and Volatility By observing the width (standard deviation) of the individual Gaussian components (the dotted curves), traders can gauge the "breadth" of a value area. A narrow, sharp peak suggests a very specific price level of agreement, while a wide, shallow curve indicates a broad range where volume was distributed less precisely. 🔶 DETAILS This tool represents a scientific advancement over regular Volume Profiles by applying a Gaussian Density model to market data: Noise Reduction: Discrete profiles are often "noisy," with small volume gaps between price ticks. The Sum of Gaussians model acts as a sophisticated filter, smoothing out insignificant variances to reveal the underlying structural liquidity. Levenberg-Marquardt Optimization: The script utilizes the LM algorithm, a standard in non-linear least squares problems, to iteratively refine the fit of multiple Gaussian pulses. This ensures the model converges on the most mathematically accurate representation of the volume data. Precise Liquidity Centers: While a standard profile bin might be several ticks wide, the Gaussian apex provides a mathematically derived "center" ($\mu$) for liquidity, often offering more precise support and resistance levels. Continuous Distribution: Because it models volume as a continuous function, it can estimate volume density between discrete price bins, providing a more fluid view of market interest. The visual output combines a lateral histogram with a bold Gaussian density curve, color-coded components, and auto-detected peak levels for a comprehensive view of institutional interest. 🔶 SETTINGS 🔹 Profile Settings Lookback Window: The number of historical bars used to calculate the volume profile distribution. Number of Bins: Determines the vertical resolution of the profile. More bins provide more detail but require more computation. 🔹 Gaussian Settings Max Potential Peaks: The maximum number of Gaussian components (nodes) the algorithm will attempt to fit to the data. Max Iterations: Controls how many times the LM optimizer refines the fit. Higher values improve accuracy but may impact performance. Initial Lambda: The damping factor for the optimization algorithm, affecting the early steps of the fitting process. 🔹 Visuals Histogram Resolution: The maximum horizontal length of the projected histogram and fit line, measured in bar widths. Highlight Window Range: Toggles a visual background box covering the historical lookback area for context. Highlight Detected Peaks: Detects local maxima in the final fit and draws horizontal dashed levels at those price points. Fit Color: Sets the static color for the main density curve. Auto: When enabled, the fit color automatically adapts to your chart's foreground color (e.g., white on dark backgrounds).
Pine Script® indicator
by LuxAlgo
1
Adaptive Nadaraya-Watson (Non Repainting) [Metrify]To understand this implementation of the Nadaraya-Watson estimator, we have to look at the core equation governing non-parametric regression. This script aren't trying to average prices; we are trying to find the probability density of where price should be relative to its recent history. 1. The Kernel Physics (Bandwidth Modulation) In standard kernel regression, you have a bandwidth parameter (h). This controls the "smoothness" of the curve. If h is too low, the curve jitters with every tick of noise. If h is too high, it acts like a sluggish SMA. A static h fails because market volatility is dynamic. When the market explodes (high volatility), a tight bandwidth generates false signals. When the market sleeps, a wide bandwidth misses the micro-trends. It try solving this by making h a function of the Asset's volatility ratio: heff​=h×max(0.5,min(SMA(ATR20​,100)ATR20​​,2.0)) If the current ATR(20) is double the long-term average (100), the bandwidth doubles. This forces the estimator to "zoom out" during chaos, effectively ignoring noise that would otherwise look like a reversal. vol_ratio = use_vol ? vol_raw / (vol_base == 0 ? 1 : vol_base) : 1.0 vol_mod = math.max(0.5, math.min(vol_ratio, 2.0)) h_eff = h_val * vol_mod 2. The Gaussian Loop (Endpoint Estimation) Standard Nadaraya-Watson scripts repaint because they calculate the regression over a full window centered on the bar. To make this usable for live trading, we must calculate the Endpoint Estimate. We iterate backward from the current bar (i=0) to the lookback limit. For every historical price Xi​, we calculate a weight wi​ based on how far away it is in time (distance). The weight is derived from the Gaussian Kernel function: wi​=exp(−2heff2​i2​) Price data closer to the current bar (i=0) gets a weight near 1.0. Data further away (i=50) decays exponentially toward 0. for i = 0 to lookback by 1 float dist = float(i) float w = math.exp(-math.pow(dist, 2) / (2 * math.pow(h_eff, 2))) num := num + w * src den := den + w 3. Statistical Deviation (MAE vs. StDev) Most Bollinger Band-style indicators use Standard Deviation (Root Mean Square). The problem with StDev is that it squares the errors, which heavily penalizes large outliers. In crypto or volatile forex pairs, one wick can blow out the bands for 20 bars. This one use Mean Absolute Error (MAE) instead. MAE=N1​∑∣Price−y^​∣ MAE is linear. It measures the average distance price strays from the kernel estimate without squaring the penalty. This creates "tighter" bands that adhere closer to price action during normal trend behavior but don't expand ridiculously during a flash crash. Pine Script float error = math.abs(src - y_hat) float mae = ta.sma(error, lookback) We project two sets of bands: Inner Band (Balanced): The "Noise Zone". Price inside here is considered random walk. Outer Band (Precision): The "Exhaustion Zone". Price reaching here is statistically unlikely (2.8x MAE). Input & Visual Summary Kernel Physics: h_val: The base smoothness. Lower (e.g., 6) = faster, noisier. Higher (e.g., 10) = slower, smoother. use_vol: Keep this TRUE. It prevents the bands from being too tight during news events. Envelope Statistics: mult_in / mult_out: These are your risk settings. 1.5/2.8 is a standard deviation-like setting suited for MAE.
Pine Script® indicator
by Metrify
RSI Distribution [Kodexius]RSI Distribution is a statistics driven visualization companion for the classic RSI oscillator. In addition to plotting RSI itself, it continuously builds a rolling sample of recent RSI values and projects their distribution as a forward drawn histogram, so you can see where RSI has spent most of its time over the selected lookback window. The indicator is designed to add context to oscillator readings. Instead of only treating RSI as a single point estimate that is either “high” or “low”, you can evaluate the current RSI level relative to its own recent history. This makes it easier to recognize when the market is operating inside a familiar regime, and when RSI is pushing into rarer tail conditions that tend to appear during momentum bursts, exhaustion, or volatility expansion. To complement the histogram, the script can optionally overlay a Gaussian curve fitted to the sample mean and standard deviation. It also runs a Jarque Bera normality check, based on skewness and excess kurtosis, and surfaces the result both visually and in a compact dashboard. On the oscillator panel itself, RSI is presented with a clean gradient line and standard overbought and oversold references, with fills that become more visible when RSI meaningfully extends beyond key thresholds. 🔹 Features 1. Distribution Histogram of Recent RSI Values The script stores the last N RSI values in an internal sample and uses that rolling window to compute a frequency distribution across a user selected number of bins. The histogram is drawn into the future by a configurable width in bars, which keeps it readable and prevents it from colliding with the active RSI plot. The result is a compact visual summary of where RSI clusters most often, whether it is spending more time near the center, or shifting toward higher or lower regimes. 2. Gaussian Overlay for Shape Intuition If enabled, a fitted bell curve is drawn on top of the histogram using the sample mean and standard deviation. This overlay is not intended as a direct trading signal. Its purpose is to provide a fast visual comparator between the empirical RSI distribution and a theoretical normal shape. When the histogram diverges strongly from the curve, you can quickly spot skew, heavy tails, or regime changes that often occur when market structure or volatility conditions shift. 3. Jarque Bera Normality Check With Clear PASS/FAIL Feedback The script computes skewness and excess kurtosis from the RSI sample, then forms the Jarque Bera statistic and compares it to a fixed 95% critical value. When the distribution is closer to normal under this test, the status is marked as PASS, otherwise it is marked as FAIL. This result is displayed in the dashboard and can also influence the histogram styling, giving immediate feedback about whether the recent RSI behavior resembles a bell shaped distribution or a more distorted, regime driven profile. Jarque Bera is a goodness of fit test that evaluates whether a dataset looks consistent with a normal distribution by checking two shape properties: skewness (asymmetry) and kurtosis (tail heaviness, expressed here as excess kurtosis where a perfect normal has 0). Under the null hypothesis of normality, skewness should be near 0 and excess kurtosis should be near 0. The test combines deviations in both into a single statistic, which is then compared to a chi square threshold. A PASS in this script means the sample does not show strong evidence against normality at the chosen threshold, while a FAIL means the sample is meaningfully skewed, heavy tailed, or both. In practical trading terms, a FAIL often suggests RSI is behaving in a regime where extremes and asymmetry are more common, which is typical during strong trends, volatility expansions, or one sided market pressure. It is still a statistical diagnostic, not a prediction tool, and results can vary with lookback length and market conditions. 4. Integrated Stats Dashboard A compact table in the top right summarizes key distribution moments and the normality result: Mean, StdDev, Skewness, Kurtosis, and the JB statistic with PASS/FAIL text. Skewness is color coded by sign to quickly distinguish right skew (more time at higher RSI) versus left skew (more time at lower RSI), which can be helpful when diagnosing trend bias and momentum persistence. 5. RSI Visual Quality and Context Zones RSI is plotted with a gradient color scheme and standard overbought and oversold reference lines. The overbought and oversold areas are filled with a smart gradient so visual emphasis increases when RSI meaningfully extends beyond the 70 and 30 regions, improving readability without overwhelming the panel. 🔹 Calculations This section summarizes the main calculations and transformations used internally. 1. RSI Series RSI is computed from the selected source and length using the standard RSI function: rsi_val = ta.rsi(rsi_src, rsi_len) 2. Rolling Sample Collection A float array stores recent RSI values. Each bar appends the newest RSI, and if the array exceeds the configured lookback, the oldest value is removed. Conceptually: rsi_history.push(rsi_val) if rsi_history.size() > lookback rsi_history.shift() This maintains a fixed size window that represents the most recent RSI behavior. 3. Mean, Variance, and Standard Deviation The script computes the sample mean across the array. Variance is computed as sample variance using (n - 1) in the denominator, and standard deviation is the square root of that variance. These values serve both the dashboard display and the Gaussian overlay parameters. 4. Skewness and Excess Kurtosis Skewness is calculated from the standardized third central moment with a small sample correction. Kurtosis is computed as excess kurtosis (kurtosis minus 3), so the normal baseline is 0. These two metrics summarize asymmetry and tail heaviness, which are the core ingredients for the Jarque Bera statistic. 5. Jarque Bera Statistic and Decision Rule Using skewness S and excess kurtosis K, the Jarque Bera statistic is computed as: JB = (n / 6.0) * (S^2 + 0.25 * K^2) Normality is flagged using a fixed critical value: is_normal = JB < 5.991 This produces a simple PASS/FAIL classification suitable for fast chart interpretation. 6. Histogram Binning and Scaling The RSI domain is treated as 0 to 100 and divided into a configurable number of bins. Bin size is: bin_size = 100.0 / bins Each RSI sample maps to a bin index via floor(rsi / bin_size), with clamping to ensure the index stays within valid bounds. The script counts occurrences per bin, tracks the maximum frequency, and normalizes each bar height by freq/max_freq so the histogram remains visually stable and comparable as the window updates. 7. Gaussian Curve Overlay (Optional) The Gaussian overlay uses the normal probability density function with mu as the sample mean and sigma as the sample standard deviation: normal_pdf(x) = (1 / (sigma * sqrt(2*pi))) * exp(-0.5 * ((x - mu)/sigma)^2) For drawing, the script samples x across the histogram width, evaluates the PDF, and normalizes it relative to its peak so the curve fits within the same visual height scale as the histogram.
Pine Script® indicator
by UnknownUnicorn113699460
Half Causal EstimatorOverview The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output. Core Concept Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component). This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages. How It Works The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window. This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate. The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits. Understanding Kernel Smoothing At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness. The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points. Applicable Data Sources The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next). Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach. Practical Applications The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness. When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns. Technical Implementation The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day. For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter. This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques. Configuration Options The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width. These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument. Limitations The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns. Conclusion The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Pine Script® indicator
by The_Peaceful_Lizard
14
Truly Iterative Gaussian ChannelOVERVIEW The Truly Iterative Gaussian Channel is a robust channeling system that integrates a Gaussian smoothing kernel with a rolling standard deviation to create dynamically adaptive upper and lower boundaries around price. This indicator provides a smooth, yet responsive representation of price movements while minimizing lag and dynamically adjusting channel width to reflect real-time market volatility. Its versatility makes it effective across various timeframes and trading styles, offering significant potential for experimentation and integration into advanced trading systems. TRADING USES The Gaussian indicator can be used for multiple trading strategies. Trend following relies on the middle Gaussian line to gauge trend direction: prices above this line indicate bullish momentum, while prices below signal bearish momentum. The upper and lower boundaries act as dynamic support and resistance levels, offering breakout or pullback entry opportunities. Mean reversion focuses on identifying reversal setups when price approaches or breaches the outer boundaries, aiming for a return to the Gaussian centerline. Volatility filtering helps assess market conditions, with narrow channels indicating low volatility or consolidation and suggesting fewer trading opportunities or an impending breakout. Adaptive risk management uses channel width to adjust for market volatility, with wider channels signaling higher risk and tighter channels indicating lower volatility and potentially safer entry points. THEORY Gaussian kernel smoothing, derived from the Gaussian normal distribution, is a cornerstone of probability and statistics, valued for its ability to reduce noise while preserving critical signal features. In this indicator, it ensures price movements are smoothed with precision, minimizing distortion while maintaining responsiveness to market dynamics. The rolling standard deviation complements this by dynamically measuring price dispersion from the mean, enabling the channel to adapt in real time to changing market conditions. This combination leverages the mathematical correctness of both tools to balance smoothness and adaptability. An iterative framework processes data efficiently, bar by bar, without recalculating historical value to ensure reliability and preventing repainting to create a mathematically grounded channel system suitable for a wide range of market environments. The Gaussian channel excels at filtering noise while remaining responsive to price action, providing traders with a dependable tool for identifying trends, reversals, and volatility shifts with consistency and precision. CALIBRATION Calibration of the Gaussian channel involves adjusting its length to modify sensitivity and adaptability based on trading style. Shorter lengths (e.g., 50-100) are ideal for intraday traders seeking quick responses to price fluctuations. Medium lengths (e.g., 150-200) cater to swing traders aiming to capture broader market trends. Longer lengths (e.g., 250-400+) are better suited for positional traders focusing on long-term price movements and stability. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices.
Pine Script® indicator
by SensitiveSuit
1
Volatility Gaussian Bands [BigBeluga]The Volatility Gaussian Bands indicator is a cutting-edge tool designed to analyze market trends and volatility with high precision. By applying a Gaussian filter to smooth price data and implementing dynamic bands based on market volatility, this indicator provides clear signals for trend direction, strength, and potential reversals. With updated volatility calculations, it enhances the accuracy of trend detection, making it a powerful addition to any trader's toolkit. ⮁ KEY FEATURES & USAGE ● Gaussian Filter Trend Bands: The Gaussian Filter forms the foundation of this indicator by smoothing price data to reveal the underlying trend. The trend is visualized through upper and lower bands that adjust dynamically based on market volatility. These bands provide clear visual cues for traders: a crossover above the upper band indicates a potential uptrend, while a cross below the lower band signals a potential downtrend. This feature allows traders to identify trends with greater accuracy and act accordingly. ● Dynamic Trend Strength Gauges: The indicator includes trend strength gauges positioned at the top and bottom of the chart. These gauges dynamically measure the strength of the uptrend and downtrend, based on the middle Gaussian line. Even if the trend is downward, a rising midline will cause the upward trend strength gauge to show an increase, offering a nuanced view of the market’s momentum. Weakening of the trend: ● Fast Trend Change Indicators: Triangles with a "+" symbol appear on the chart to signal rapid changes in trend direction. These indicators are particularly useful when the trend changes swiftly while the midline continues to grow in its previous direction. For instance, during a downtrend, if the trend suddenly shifts upward while the midline is still declining, a triangle with a "+" will indicate this quick reversal. This feature is crucial for traders looking to capitalize on rapid market movements. ● Retest Signals: Retest signals, displayed as triangles, highlight potential areas where the price may retest the Gaussian line during a trend. These signals provide an additional layer of analysis, helping traders confirm trend continuations or identify possible reversals. The retest signals can be customized based on the trader’s preferences. ⮁ CUSTOMIZATION ● Length Adjustment: The length of the Gaussian filter can be customized to control the sensitivity of trend detection. Shorter lengths make the indicator more responsive, while longer lengths offer a smoother, more stable trend line. ● Volatility Calculation Mode: Traders can select from different modes (AVG, MEDIAN, MODE) to calculate the Gaussian filter, allowing for flexibility in how trends are detected and analyzed. ● Retest Signals Toggle: Enable or disable the retest signals based on your trading strategy. This toggle allows traders to choose whether they want these additional signals to appear on the chart, providing more control over the information displayed during their analysis. ⮁ CONCLUSION The Volatility Gaussian Bands indicator is a versatile and powerful tool for traders focused on trend and volatility analysis. By combining Gaussian-filtered trend lines with dynamic volatility bands, trend strength gauges, and rapid trend change indicators, this tool provides a comprehensive view of market conditions. Whether you are following established trends or looking to catch early reversals, the Volatility Gaussian Bands offers the precision and adaptability needed to enhance your trading strategy.
Pine Script® indicator
by BigBeluga
20
Gaussian Filter [BigBeluga]The Gaussian Filter - BigBeluga indicator is a trend-following tool that uses a Gaussian filter to smooth price data and identify directional shifts in the market. It provides dynamic signals for entering and exiting trades based on trend changes, helping traders stay aligned with the market's momentum. What sets this indicator apart is its ability to display precise entry and exit points with real-time tracking of percentage price changes, making it ideal for trend-based strategies. SP500: NIFTY50: 🔵 KEY FEATURES & USAGE ◉ Gaussian Filter Trend Line: //@function GaussianFilter is used for smoothing, reducing noise, and computing derivatives of data. //@param src (float) The source data (e.g., close price) to be smoothed. //@param params (GaussianFilterParams) Gaussian filter parameters that include length and sigma. //@returns (float) The smoothed value from the Gaussian filter. gaussian_filter(float src, params) => var float weights = array.new_float(params.length) // Array to store Gaussian weights total = 0.0 pi = math.pi for i = 0 to params.length - 1 weight = math.exp(-0.5 * math.pow((i - params.length / 2) / params.sigma, 2.0)) / math.sqrt(params.sigma * 2.0 * pi) weights.set(i, weight) total := total + weight for i = 0 to params.length - 1 weights.set(i, weights.get(i) / total) sum = 0.0 for i = 0 to params.length - 1 sum := sum + src * weights.get(i) sum The core functionality of the Gaussian Filter line is to show trend direction. When the trend line increases four times consecutively, it indicates an uptrend signal. Similarly, if it decreases four times in a row, it signals a downtrend. The smoothness of the filter helps traders stay on the right side of the market by filtering out noise and emphasizing the dominant trend direction. ◉ Entry and Exit Levels with Real-Time Price and Performance Data: Each time the indicator detects a trend change, it plots an entry or exit level on the chart. For an uptrend, an entry level is marked, and for a downtrend, an exit level is plotted. These levels display the price at the time of the signal. While the trend is ongoing, the indicator tracks the percentage change in price from the initial entry or exit signal to the current bar, updating in real-time. When a trend concludes, it displays the total percentage change from the entry or exit point to the trend's end. This feature provides valuable insights into how much the price has moved during each trend phase and allows traders to monitor the performance of each trade. ◉ Color-Coded Candlestick Representation with Trend Shift Alerts: In addition to coloring the candlesticks based on the trend direction, the indicator also uses gray candles to highlight potential early trend shifts. For example, if the Gaussian Filter detects a downtrend but the price moves above the filter line, the candles turn gray, signaling a possible reversal or shift in momentum. Similarly, in an uptrend, if the price moves below the Gaussian Filter line, the candles turn gray as an early indication of potential bearish momentum. This visual cue helps traders stay alert to possible faster shifts in market direction, allowing for quicker decision-making. 🔵 CUSTOMIZATION Length and Sigma for Gaussian Filter: Adjust the length and sigma parameters to control how the Gaussian Filter smooths the price data. A longer length provides smoother trend lines, while adjusting sigma can fine-tune the level of smoothing applied. Levels Display and Candle Coloring: You can toggle the visibility of entry and exit levels as well as enable or disable the dynamic coloring of candlesticks based on the trend direction. The additional gray color setting provides an extra layer of information, allowing you to spot potential trend reversals early. 🔵 CONCLUSION The Gaussian Filter indicator is a powerful tool for identifying and following market trends. By providing clear entry and exit signals, along with real-time tracking of price changes, it gives traders a structured way to manage trades and monitor performance. The color-coded candles, including gray to highlight possible trend shifts, add another dimension to visualizing market dynamics. The added flexibility of customizing colors and trend levels makes it a versatile indicator suitable for both trend-following and reversal strategies.
Pine Script® indicator
by BigBeluga
20
Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function. The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel. This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility. Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.
Pine Script® indicator
by Traders_Endeavors
LOWESS (Locally Weighted Scatterplot Smoothing) [ChartPrime]LOWESS (Locally Weighted Scatterplot Smoothing) ⯁ OVERVIEW The LOWESS (Locally Weighted Scatterplot Smoothing) [ ChartPrime ] indicator is an advanced technical analysis tool that combines LOWESS smoothing with a Modified Adaptive Gaussian Moving Average. This indicator provides traders with a sophisticated method for trend analysis, pivot point identification, and breakout detection. ◆ KEY FEATURES LOWESS Smoothing: Implements Locally Weighted Scatterplot Smoothing for trend analysis. Modified Adaptive Gaussian Moving Average: Incorporates a volatility-adapted Gaussian MA for enhanced trend detection. Pivot Point Identification: Detects and visualizes significant pivot highs and lows. Breakout Detection: Tracks and optionally displays the count of consecutive breakouts. Gaussian Scatterplot: Offers a unique visualization of price movements using randomly colored points. Customizable Parameters: Allows users to adjust calculation length, pivot detection, and visualization options. ◆ FUNCTIONALITY DETAILS ⬥ LOWESS Calculation: Utilizes a weighted local regression to smooth price data. Adapts to local trends, reducing noise while preserving important price movements. ⬥ Modified Adaptive Gaussian Moving Average: Combines Gaussian weighting with volatility adaptation using ATR and standard deviation. Smooths the Gaussian MA using LOWESS for enhanced trend visualization. ⬥ Pivot Point Detection and Visualization: Identifies pivot highs and lows using customizable left and right bar counts. Draws lines and labels to mark broke pivot points on the chart. ⬥ Breakout Tracking: Monitors price crossovers of pivot lines to detect breakouts. Optionally displays and updates the count of consecutive breakouts. ◆ USAGE Trend Analysis: Use the color and direction of the smoothed Gaussian MA line to identify overall trend direction. Breakout Trading: Monitor breakouts from pivot levels and their persistence using the breakout count feature. Volatility Assessment: The spread of the Gaussian scatterplot can provide insights into market volatility. ⯁ USER INPUTS Length: Sets the lookback period for LOWESS and Gaussian MA calculations (default: 30). Pivot Length: Determines the number of bars to the left for pivot calculation (default: 5). Count Breaks: Toggle to show the count of consecutive breakouts (default: false). Gaussian Scatterplot: Toggle to display the Gaussian MA as a scatterplot (default: true). ⯁ TECHNICAL NOTES Implements a custom LOWESS function for efficient local regression smoothing. Uses a modified Gaussian MA calculation that adapts to market volatility. Employs Pine Script's line and label drawing capabilities for clear pivot point visualization. Utilizes random color generation for the Gaussian scatterplot to enhance visual distinction between different time periods. The LOWESS (Locally Weighted Scatterplot Smoothing) indicator offers traders a sophisticated tool for trend analysis and breakout detection. By combining advanced smoothing techniques with pivot point analysis, it provides a comprehensive view of market dynamics. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Pine Script® indicator
by ChartPrime
21
Normalised Gaussian MACD Heikin Ashi [AlgoAlpha]🌟🚀Introducing the Normalised Gaussian MACD Heikin Ashi by AlgoAlpha ! Elevate your trading game with this multipurpose indicator, crafted to pinpoint trend continuation opportunities while highlighting volatility and oversold/overbought conditions. Whether you're embarking on your trading journey or you're a seasoned market navigator, this tool is equipped with intuitive visual cues to amplify your decision-making prowess and enrich your market analysis toolkit. Let's dive into the key features, utilization strategies, and the innovative logic underpinning this indispensable trading asset. Key Features: 🔧 Enhanced Customization : Tailor your experience with adjustable parameters including Fast Length, Slow Length, Source, Macd Smoothing Length, Signal Smoothing, and more. 🖌️ Visual Enhancements : Opt for Heikin Ashi Candles display and choose to show or hide MACD and Signal lines for a clutter-free chart. 🌈 Color Customization : Personalize your chart with selectable primary and secondary up and down colors to suit your visual preferences. 🔔 Advanced Alert System : Stay ahead with comprehensive alert conditions for market movements, including trend reversals, bullish and bearish swings. How to Use: Configure the Inputs : Start by customizing the indicator’s settings to match your trading style. Adjust the length parameters, source selection, and smoothing lengths to fine-tune the indicator’s sensitivity. Interpret the Candles and Colors : Keep an eye on the Heikin Ashi Candles (if enabled) and the color shifts within the MACD Line Candles and Histogram. These visual cues are pivotal for identifying market trends. Analyze with Flexibility : Make use of the option to display or hide the MACD and Signal lines based on your analysis requirements. This can help in focusing on the essential information without overcrowding your chart. Utilize Alerts for Timely Decisions : Leverage the extensive alert system to get notified about potential market movements. These alerts can help you capture the right moment to enter or exit trades. Basic Logic: The Normalised Gaussian MACD Heikin Ashi by AlgoAlpha integrates Gaussian filters to elevate the traditional MACD indicator's efficiency, providing a more detailed analysis of market trends and momentum. This sophisticated approach reduces noise and enhances signal speed, which is crucial for identifying momentum trading opportunities. Gaussian Filter Implementation : The core innovation lies in applying a Gaussian filter to the input price series. This mathematical technique smooths the price data, significantly reducing market noise and making trend signals clearer and more reliable. The Gaussian filter calculates a smoothed value for each data point by weighting nearby data points, with the weights decreasing as the distance from the current data point increases. Refined MACD Calculation : The Gaussian MACD is derived from the difference between two Gaussian smoothed moving averages (fast and slow), which are then normalized to account for market volatility. This normalization process involves dividing the difference by a measure of market range (such as the high minus the low), and multiplying by a factor (usually 100) to scale the indicator appropriately. 🔑 This script is a versatile tool designed to aid in the identification of momentum and reversals, helping traders to make informed decisions based on technical analysis. Its customization options allow for a tailored analysis experience, fitting the unique needs and strategies of each trader.
Pine Script® indicator
by AlgoAlpha
6
RSI in Candlestick MODEDescription: The "RSI Bar" indicator is a versatile tool designed to enhance your technical analysis on trading charts. This Pine Script™ code calculates the Relative Strength Index (RSI) for open, close, high, and low prices, and represents the results as bars on the chart. The bars are color-coded based on whether the closing RSI is higher or lower than the opening RSI. Additionally, the indicator incorporates advanced features such as Pareto analysis and Gaussian smoothing. The Pareto analysis helps identify significant lows and highs in the RSI, providing insights into potential trend reversals. The Gaussian smoothing further refines the analysis, contributing to a more accurate representation of the average RSI trend. Key Features: RSI calculation for open, close, high, and low prices. Color-coded bars for easy visualization of RSI trends. Pareto analysis to highlight key RSI levels indicating potential reversals. Gaussian smoothing for improved trend analysis and visualization. Heiken-Ashi
Pine Script® indicator
by shaklerner1
3
Kernel Regression RibbonKernel Regression Ribbon is a flexible, visually pleasing trend identification tool. Plotting 8 different kernel regressions of different types and parameters allows the user to see where levels of support and resistance are being tested, retested and broken. What’s Kernel Regression? A statistical method for estimating the best fitting curve for a dataset, in this case, a time/price chart. How’s Kernel Regression different from a Moving Average? A Moving Average is basically a simple form of Kernel Regression, in that it uses a fixed (Retangular) Kernel function. In an MA, all data points are weighted equally over its length. However, a Kernel function reacts more to data points that are closer to the current point. This means it will adapt more quickly to changes in data than an MA. Due to this adaptability, Kernel functions often form part of Machine Learning. Using this indicator: Explore the default Regular mode first to get a feel for the inputs, which are more numerous than for MAs. Try out different settings, filters and intervals to get the best out of each kernel. Not all parameters are available for each KR. There are info tips to explain this in the menu, but I’ve also included handy, optional labels on the chart for each KR as a more accessible guide. Once you know your way round the Regular mode, check out the Presets and start changing the parameters of each kernel to your liking in the “User KR1, KR2, … “ mode. Each kernel type has its strong and weak points. Blending different kernels is where this indicator comes into its own. Give your charts a funky shine! This indicator does NOT repaint. This script acknowledges, and hopefully showcases, the great work of @veryfid Kernel Regression Toolkit.
Pine Script® indicator
by alphaXiom
7
Strategy Gaussian Anomaly DerivativeConcept behind this Strategy : Considering a normal "buy/sell" situation, an asset would be bought in average at the median price following a Gaussian like concept. A higher or lower average trend would significate that the current perceived value is respectively higher or lower than the current median price, which mean that the buyers are evaluating the price underpriced or overpriced. This behaviour would be even more relevent depending on its derivative evolution. Therefore, this Strategy setup is based on this Gaussian like concept anomaly of average close positionning compare to high-low average derivative, such as the derivative of the following ploted basic signal : 1-(high+low)/(2*close). This Strategy can actually be used like a trend change and continuation strength indicator aswell. In the Setup Signal part : You can define the filtering of the basis signal "1-(high+low)/(2*close)" on EMA or SMA as you wish. You can define the corresponding period and the threathold as a mutiply of the average 1/3 of all time value of the basis signal. You can define the SMA filtering period of the Derivative signal and the corresponding threathold on the same mutiply of the average 1/3 of all time value of the derivative. In the Setup Strategy part : You can set up your strategy assesment based on Long and/or Short. You can also define the considered period. The most successful tuned strategies I did were based on the derivative indicator with periods on the basis signal and the derivative under 30, can be 1 to 3 of te derivative and 7 to 21 for the basis signal. The threathold depends on the asset volatility aswell, 1 is usually the most efficient but 0 to 10 can be relevent depending on the situation I met. You can find an example of tuning for this strategy based on Kering's case hereafter. I hoping that you will enjoy using this Strategy, don't hesitate to comment, to question, to correct or complete it ! I would be very curious about similar famous approaches that would have already been made. Thank to you !
Pine Script® strategy
by Koamind
5
ALMA Smoothed Gaussian Moving AverageThis indicator is an altered version of the Gaussian Moving Average (GMA) (Credit to author: © LeafAlgo ). The GMA applies weights to the prices, giving more importance to the values closer to the current period and gradually diminishing the significance of older prices. The ALMA Smoothed Gaussian Moving Average (ASGMA) applies an ALMA smoothing to its price data to minimize lag and provide a more accurate representation of the underlying trend by dynamically adapting to changing market conditions. The Arnaud Legoux Moving Average (ALMA) is a specialized smoothing technique that adjusts the weights of the moving average based on market volatility. Its calculation uses Wavelet Transform techniques which enables this type of smoothing to capture both high-frequency and low-frequency components of a signal or data. The rationale for this mashup between ALMA and Gaussian filtering is to smooth the moving average line over the smoothed price data and produce stronger trend signals. ASGMA serves as a trend-following indicator, identifying both bullish and bearish trends. It provides buy and sell signals indicated by "B" and "S" labels plotted alongside the price data. Additionally, the ASGMA's Exponential Moving Average (EMA) line alternates between green and red, indicating bullish and bearish momentum, respectively. The ASGMA also incorporates two popular momentum indicators, the Relative Strength Index (RSI) and the Chande Momentum Oscillator (CMO). The inclusion of these indicators aims to enhance trend identification and reversal signals. For a strong buy signal, all three indicators (RSI, CMO, and ASGMA) must indicate bullish conditions, resulting in a vertical green line. Conversely, a vertical red line is plotted when all indicators indicate bearish conditions, representing a strong sell signal. The ASGMA, with its unique combination of smoothing techniques and indicator amalgamation, provides traders and investors with powerful analytical tools. It can be applied in trend-following strategies using the regular buy and sell signals generated by labels and the EMA line. Alternatively, the vertical lines offer stronger buy and sell signals. These features aid in identifying potential entry and exit points, thereby enhancing trading decisions and market analysis. However, it is important to remember that the future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
Pine Script® indicator
by profitprotrading
41
Adaptive Gaussian Moving AverageThe Adaptive Gaussian Moving Average (AGMA) is a versatile technical indicator that combines the concept of a Gaussian Moving Average (GMA) with adaptive parameters based on market volatility. The indicator aims to provide a smoothed trend line that dynamically adjusts to different market conditions, offering a more responsive analysis of price movements. Calculation: The AGMA is calculated by applying a weighted moving average based on a Gaussian distribution. The length parameter determines the number of bars considered for the calculation. The adaptive parameter enables or disables the adaptive feature. When adaptive is true, the sigma value, which represents the standard deviation, is dynamically calculated using the standard deviation of the closing prices over the volatilityPeriod. When adaptive is false, a user-defined fixed value for sigma can be input. Interpretation: The AGMA generates a smoothed line that follows the trend of the price action. When the AGMA line is rising, it suggests an uptrend, while a declining line indicates a downtrend. The adaptive feature allows the indicator to adjust its sensitivity based on market volatility, making it more responsive during periods of high volatility and less sensitive during low volatility conditions. Potential Uses in Strategies: -- Trend Identification : Traders can use the AGMA to identify the direction of the prevailing trend. Buying opportunities may arise when the price is above the AGMA line during an uptrend, while selling opportunities may be considered when the price is below the AGMA line during a downtrend. -- Trend Confirmation : The AGMA can be used in conjunction with other technical indicators or trend-following strategies to confirm the strength and sustainability of a trend. A strong and steady AGMA line can provide additional confidence in the prevailing trend. -- Volatility-Based Strategies : Traders can utilize the adaptive feature of the AGMA to build volatility-based strategies. By adjusting the sigma value based on market volatility, the indicator can dynamically adapt to changing market conditions, potentially improving the accuracy of entry and exit signals. Limitations: -- Lagging Indicator : Like other moving averages, the AGMA is a lagging indicator that relies on historical price data. It may not provide timely signals during rapidly changing market conditions or sharp price reversals. -- Whipsaw in Sideways Markets : During periods of low volatility or when the market is moving sideways, the AGMA may generate false signals or exhibit frequent crossovers around the price, leading to whipsaw trades. -- Subjectivity of Parameters : The choice of length, adaptive parameters, and volatility period requires careful consideration and customization based on individual preferences and trading strategies. Traders need to adjust these parameters to suit the specific market and timeframe they are trading. Overall, the Adaptive Gaussian Moving Average can be a valuable tool in trend identification and confirmation, especially when combined with other technical analysis techniques. However, traders should exercise caution, conduct thorough analysis, and consider the indicator's limitations when incorporating it into their trading strategies.
Pine Script® indicator
by LeafAlgo
Adaptive Weighted Moving Average (AWMA)The script is a technical analysis indicator that calculates a weighted moving average of a given data series. The weighted moving average is calculated using a custom weighting scheme that adjusts the weights based on the volatility of the market, as measured by the average true range (ATR) indicator. The resulting weighted moving average is smoothed using a Gaussian moving average, and the resulting smoothed moving average is plotted on the chart. To use this script, the user can customize several input parameters, including the length of the moving average, the primary and secondary weights to use in the custom weighting scheme, the ATR length, and the smoothing parameter for the Gaussian moving average. With these inputs, the script calculates and plots the weighted moving average on the chart, allowing the user to analyze the trend and behavior of the data series using a custom weighted moving average.
Pine Script® indicator
by The_Peaceful_Lizard
MTF MA Ribbon and Bands + BB, Gaussian F. and R. VWAP with StDev█ Multi Timeframe Moving Average Ribbon and Bands + Bollinger Bands, Gaussian Filter and Rolling Volume Weighted Average Price with Standard Deviation Bands Up to 9 moving averages can be independently applied. The length , type and timeframe of each moving average are configurable . The lines, colors and background fill are customizable too. This script can also display: Moving Average Bands Bollinger Bands Gaussian Filter Rolling VWAP and Standard Deviation Bands Types of Moving Averages: Simple Moving Average (SMA) Exponential Moving Average (EMA) Smoothed Moving Average (SMMA) Weighted Moving Average (WMA) Volume Weighted Moving Average (VWMA) Least Squares Moving Average (LSMA) Hull Moving Average (HMA) Arnaud Legoux Moving Average (ALMA) █ Moving Average Moving Averages are price based, lagging (or reactive) indicators that display the average price of a security over a set period of time. A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance. █ Bollinger Bands Bollinger Bands consist of a band of three lines which are plotted in relation to security prices. The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (the type of trend line and period can be changed by the trader, a 20 day moving average is by far the most popular). The SMA then serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price. █ Gaussian Filter Gaussian filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components. █ Rolling VWAP The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day. Such VWAPs cannot be used on daily, weekly or monthly charts. Instead, this rolling VWAP uses a time period that automatically adjusts to the chart's timeframe. You can thus use the rolling VWAP on any chart that includes ​volume information in its data feed. Because the rolling VWAP uses a moving window, it does not exhibit the jumpiness of VWAP plots that reset. Made with the help from scripts of: adam24x, VishvaP, loxx and pmk07.
Pine Script® indicator
by Yatagarasu_
3
Nadaraya-Watson CombineThis is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue. In addition, I've added a plot of the actual values of the current barstate of the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at each time update. It is interesting to compare the trajectory of the end points of the Estimator and Envelope to the smoothing function at each time update. Due to the kernel smoothing at each update the history is lost at each update (repaint). I've added a feature to allow adjustment to the kernel smoothing algorithm as suggested by thomsonraja (line 59). The settings and usage are repeated from Lux Algo below. Settings Window Size: Determines the number of recent price observations to be used to fit the Nadaraya-Watson Estimator. Bandwidth: Controls the degree of smoothness of the envelopes , with higher values returning smoother results. Mult: Controls the envelope width. Src: Input source of the indicator. Kernel power: See line 59, adjusts the exponential power (powh) as suggested by thomsonraja Kernel denominator: See line 59, adjusts the denominator (den) as suggested by thomsonraja Usage This tool outlines extremes made by the prices within the selected window size. This is achieved by estimating the underlying trend in the price using kernel smoothing, calculating the mean absolute deviations from it, and adding/subtracting it from the estimated underlying trend. I repeat Lux Algo's caution: 'we do not recommend this tool to be used alone or solely for real time applications.'
Pine Script® indicator
by capam
19