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Median

Median ATR SD OscillatorMedian ATR SD Oscillator Median ATR SD Oscillator is a trend-following volatility oscillator that measures the distance between price and two independent reference levels — an ATR band and a standard deviation band — both anchored to a percentile-based median. The asymmetric design uses different volatility measures for long and short signals, creating a natural bias toward staying in bullish trends longer while reacting quickly to breakdowns. The result is an area oscillator that expands above zero in a confirmed bullish state and contracts below zero in a bearish state, with a white EMA line as a momentum confluence filter. How It Works A percentile median is calculated from a configurable price source — giving a robust, noise-resistant central reference level that adapts to recent price behavior. Two independent volatility bands are then derived from this median: ATR Band — median plus ATR multiplied by a configurable factor. The short condition uses the ATR band — price must fall below it to confirm a bearish state SD Band — median plus the standard deviation of close. The long condition uses the standard deviation band — price must break above it to confirm a bullish state Once a directional state is confirmed, the oscillator measures the distance between price and its reference level. An EMA of this distance acts as a confluence filter — the final signal only confirms when the distance is not only positive or negative but also above or below its own EMA, ensuring momentum is genuinely building in that direction. Why This Approach Works Most oscillators use a single volatility measure for both long and short signals. The Median ATR SD Oscillator deliberately uses two different measures — ATR for shorts and SD for longs — because they capture different market dynamics and create a natural asymmetry between entries and exits. This asymmetric design reflects the structural reality of markets like crypto — price spends more time trending upward than downward. Longs require a statistically significant breakout above the SD band, while shorts only need price to fall back below the ATR band. The result is a system that stays in bullish trends longer while reacting quickly when momentum fades. Settings Median Source — Price source for the percentile median calculation (default: hl2) Median Length — Lookback period for the percentile median (default: 63) ATR Length — Lookback period for the ATR calculation (default: 4) ATR Factor — Controls the width of the ATR band (default: 1.0) SD Length — Lookback period for the standard deviation calculation (default: 29) Use EMA — Enables the EMA confluence filter (default: true) EMA Length — Lookback period for the EMA confluence filter (default: 35) Use Bar Coloring — Colors bars based on the current state (default: true) Color Background — Enables background coloring of the chart based on the current state (default: false) Background Transparency — Controls the transparency of the background color (default: 85) How to trade it Long — when the oscillator expands above zero and the area turns blue, a bullish state has been confirmed. This is the signal to look for long entries or to hold existing long positions Short / Cash — when the oscillator contracts below zero and the area turns red, the momentum has faded. This is the signal to exit longs, move to cash, or look for short entries depending on your strategy EMA line — when the area is above the white EMA line momentum is building, when it crosses below momentum is weakening Recommended Usage Best used on the 1D timeframe for clean and reliable signal generation Should not be used alone for trade entries — combine with an additional confirmation indicator for best results The asymmetric ATR/SD design makes this oscillator particularly well suited for bullish-biased markets like crypto Higher ATR Factor values make short exits more sensitive — lower values make them less reactive Higher SD Length values create more stable long signals — lower values make them more reactive to short-term price movements All signals are confirmed on bar close.
مؤشر Pine Script®
من ‎Unknownhodler‎
Descriptive Statistics [Median, Quartiles, Outliers]This indicator seeks to provide insight to traders by modeling market structure using widely accepted statistical methods applied to price data. It does not predict direction; instead, it describes how current price behaves relative to its historical distribution. It is built around non-parametric statistics, making it resistant to distortion from extreme price movements. What it shows? 1. Median (Q2): The central equilibrium level of price distribution. 2. Quartiles (Q1, Q3): Boundaries of the “normal” trading range. 3. Interquartile Range (IQR): Measures the width of the core market structure. 4. Outlier Bands (1.5 × IQR rule): Statistical extremes where price becomes unusual relative to recent behavior. How it works? The indicator collects price data either through: 1. Reset Mode: Builds a new distribution each session (Daily, Weekly, Monthly, or chart timeframe). 2. Length Mode: Uses a rolling window of the last N candles. All values are sorted to construct a real-time price distribution, from which median, quartiles, and outlier thresholds are derived. How to use it? 1. Price inside Q1–Q3 range → normal market conditions 2. Price near Median → equilibrium / fair value zone 3. Price outside Outlier bands → statistically extreme conditions (potential exhaustion, expansion zones or news driven events) 4. Large expansions between Q1 and Q3 → increased volatility and potential momentum in either direction Key concept? This tool does not forecast price. It provides a distribution map of market behavior, helping traders understand structure, deviation, and statistical positioning of price. ⚠️ Note This indicator is for educational and analytical purposes only and should not be used as standalone trading advice. Author: TUGUME WILLIAM MUTARA
مؤشر Pine Script®
من ‎mutaratugumewilliam‎
Dynamic Median Momentum Oscillator [AlgoAlpha]🟠 OVERVIEW This script provides a momentum oscillator that uses a median-based approach rather than traditional averages to find the center of price action. By calculating the distance between the current price and a rolling median (HLC3), it identifies how far the market has stretched from its historical equilibrium. The indicator is designed to filter out the noise typical of standard momentum tools, using a standardized range calculation to provide fixed overbought and oversold zones. It helps traders identify trend strength, potential exhaustion, and mean reversion opportunities across different market conditions. 🟠 CONCEPTS The core of this tool is the Dynamic Median basis, which uses a rolling median of the HLC3 price to establish a "fair value" line. Unlike a simple moving average, the median is less sensitive to extreme price spikes, making the resulting oscillator more robust against outliers. To ensure the oscillator remains readable across different assets, the raw difference between price and median is standardized by the average candle range (EMA of High-Low). This normalization allows for the use of fixed thresholds (e.g., +/- 200, 250, 300) regardless of the asset's price. The median sets the context for the baseline, while the smoothed MCD and its signal line provide the timing for entries and exits. 🟠 FEATURES Standardization feature to enable fixed overbought/oversold levels across any asset Multi-component display: Fast (histogram), Slow (lines), and Super Slow (filled zones) Reversion markers (triangles) indicating price returning from extreme levels 🟠 USAGE Setup : Add the script to your chart and choose your preferred Display Mode. Use "All" to see the full picture or "Slow" for a cleaner view of trend direction. Ensure "Standardize" is checked if you want to use the built-in overbought/oversold bands effectively. Read the chart : Look for the Smooth MCD (white line) crossing the Signal (orange line) for momentum shifts. Values above 0 indicate bullish momentum, while values below 0 indicate bearish momentum. Triangles appear at the top or bottom of the oscillator when price reaches extreme levels (300/-300) and begins to revert to the mean. Settings that matter : The Basis Length determines how much historical data defines the "center" of the market; longer lengths are better for higher timeframes. Smoothing Length controls the reactivity of the main white line—increase this if you find the oscillator is giving too many false signals in choppy markets.
مؤشر Pine Script®
من ‎AlgoAlpha‎
9
Adaptive Momentum RibbonWhat It Does Adaptive Momentum Ribbon (AMR) is a directional momentum tool that identifies shifts in price equilibrium and marks them directly on the price chart with entry labels and dynamic invalidation levels. It pairs a robust statistical oscillator with a Keltner Channel envelope to give traders both timing signals and volatility context in a single overlay. How It Works The indicator is built on three independent components working together: 1. Median Absolute Deviation (MAD) Z-Score Instead of using a standard deviation z-score (which is heavily influenced by outlier candles and spike wicks), AMR calculates a rolling median of price, then measures how far price has deviated from that median, normalized by the Median Absolute Deviation. MAD is a statistical measure of dispersion that is approximately 50% more resistant to outliers than standard deviation. The raw MAD value is scaled by 1.4826 to make it directly comparable to standard deviation under normal distribution assumptions. The result is a z-score that reflects genuine shifts in price positioning rather than reacting disproportionately to single volatile bars. 2. Butterworth Low-Pass Filter The raw z-score is then passed through a second-order Butterworth low-pass filter. Unlike cascaded EMAs (which introduce cumulative lag at each stage), the Butterworth filter is designed to have a maximally flat frequency response in the passband. This means it preserves the shape of genuine momentum moves while attenuating high-frequency noise. The filter's two-pole design provides a steeper roll-off than any single-pole smoothing method, delivering cleaner zero-line crossovers with less delay. 3. Keltner Channel Envelope A standard Keltner Channel (moving average +/- ATR multiple) provides volatility context. Unlike Bollinger Bands, which use standard deviation and tend to expand/contract sharply on individual outlier bars, Keltner Channels use Average True Range, producing smoother, more stable bands that account for gap behavior and true intrabar range. Signal Logic A bullish signal (upward label) fires when the filtered z-score crosses above zero, indicating that price has shifted above its rolling median by a statistically meaningful amount after smoothing. A bearish signal (downward label) fires when the filtered z-score crosses below zero. Each signal generates an invalidation level (dashed line), placed at the low (for bullish) or high (for bearish) of the signal bar, offset by a long-term volatility measure (100-period EMA of True Range). If price breaches this level, an X marker appears, indicating the signal's premise has been violated. The invalidation level extends forward in real time until it is either breached or replaced by a new signal in the opposite direction. How to Use It Trend Confirmation: Use the signal labels alongside the Keltner Channel. Bullish signals firing near the lower KC band suggest mean-reversion opportunities. Bullish signals near or above the midline suggest trend continuation. Invalidation as Risk Management: The dashed invalidation line can serve as a reference for stop-loss placement. When the X appears, the statistical basis for the entry no longer holds. Parameter Guidance: Momentum Period (default 20): Controls the lookback for the median and MAD calculation. Shorter periods react faster but produce more signals. Longer periods are smoother but slower. Butterworth Smoothing (default 3): Controls noise filtering. A value of 1 passes the raw z-score through with minimal filtering. Values of 3-5 work well for most timeframes. KC settings: Standard Keltner Channel parameters. The defaults (20 EMA, 14 ATR, 2x multiplier) are widely used and work across most instruments and timeframes. Limitations and Honest Caveats This indicator does not predict future price. It identifies statistical shifts in recent price positioning relative to a rolling median. These shifts may or may not lead to sustained moves. Like all zero-crossing oscillators, it will generate false signals during choppy, range-bound markets. The Keltner Channel can help filter these visually (signals near the midline in a flat channel are lower conviction). The invalidation levels are references, not guaranteed stop levels. Slippage and gaps can cause exits beyond these prices. The MAD z-score assumes sufficient data variation. In extremely low-volatility environments where price barely moves, the MAD can approach zero. A fallback to standard deviation is built in for this edge case, but signals during such periods should be treated with extra caution. No repainting. All signals fire on the confirmed close of the signal bar and reference the prior bar's high/low for level placement. No future data is accessed. Summary AMR combines outlier-robust statistics (MAD z-score), signal-processing-grade smoothing (Butterworth filter), and volatility-adaptive context (Keltner Channel) into a single chart overlay. It is designed for traders who want clean directional signals with built-in invalidation logic, without relying on indicators that overreact to spike wicks or require a separate oscillator pane.
مؤشر Pine Script®
من ‎ZakAlgoTrade‎
3
True Baseline Median SuperTrendTrue Baseline Median SuperTrend (TBM SuperTrend) | MisinkoMaster True Baseline Median SuperTrend is a volatility-adaptive trend indicator designed to refine traditional SuperTrend logic by introducing a volatility-filtered baseline and median-based smoothing techniques. Instead of relying on a fixed midpoint calculation, TBM SuperTrend dynamically constructs its baseline from structurally significant price observations, then applies layered median smoothing to reduce noise while preserving trend integrity. The result is a cleaner, more stable trend-following tool that reacts to meaningful shifts in volatility and directional pressure without excessive whipsaws. Core Philosophy Most SuperTrend-style indicators anchor their bands to a simple price midpoint and apply an ATR-based offset. While effective, this approach can be overly sensitive during volatile consolidations. TBM SuperTrend improves this structure by: • Building a volatility-qualified baseline • Filtering insignificant price movements • Applying median smoothing instead of simple averaging • Retaining ATR-based adaptive band distance This creates a trend structure that prioritizes meaningful price expansion over random noise. Key Features Volatility-qualified baseline construction Median-smoothed upper and lower bands ATR-based adaptive volatility envelope Dynamic trend state detection Automatic candle coloring Clear long and short transition labels Reduced whipsaw behavior compared to standard SuperTrend Works across intraday and higher timeframes Designed for trend continuation and breakout frameworks How It Works (Conceptual) The indicator operates in three structural layers: Volatility Measurement Market volatility is assessed using an ATR-based structure. Baseline Construction Instead of averaging all recent prices, the script filters price samples based on volatility conditions. Only structurally relevant bars contribute to the baseline calculation. This ensures that the baseline reflects meaningful movement rather than passive drift. Median Smoothing Both the volatility-adjusted bands and the baseline structure undergo median smoothing. Median smoothing is less sensitive to outliers than standard averaging, which helps stabilize the trend line during erratic price spikes. After the adaptive bands are constructed, price interaction with those bands determines directional bias: • Price closing above the upper threshold confirms bullish trend state • Price closing below the lower threshold confirms bearish trend state Internal implementation details remain proprietary in the protected version. Trend Logic Explained Bullish State When price maintains strength above the adaptive upper boundary, the indicator confirms a long bias. The trailing structure shifts beneath price, acting as dynamic support. Bearish State When price closes below the adaptive lower boundary, the indicator confirms a short bias. The trailing structure shifts above price, acting as dynamic resistance. State transitions occur only when decisive boundary breaks happen, helping reduce false flips. Visual Components Trend Lines Only the active directional band is displayed, reducing clutter and emphasizing current bias. Shaded Volatility Zone A filled region between price and the active band visually highlights trend dominance. Long / Short Labels Clear on-chart labels mark confirmed trend transitions. Candle Coloring Price candles automatically reflect current trend state for immediate visual recognition. Inputs Overview Source Defines the price series used for baseline construction. ATR Length Controls the volatility lookback period. True Baseline Length Determines the window used for constructing the volatility-qualified baseline. Factor Adjusts the volatility multiplier that expands or contracts the adaptive bands. Median Period Controls the median smoothing strength applied to the bands. Lower values increase responsiveness. Higher values improve stability and reduce noise. Why Median Smoothing Matters Traditional smoothing methods (like EMA or SMA) can be distorted by sharp price spikes. Median-based smoothing reduces the impact of extreme values, making TBM SuperTrend particularly effective in: • Crypto markets • High-volatility equities • News-driven instruments • Lower timeframe trading This improves structural consistency during sudden volatility expansions. Best Use Cases Trend-following systems Breakout confirmation Pullback entries within established trends Trailing stop framework Directional bias filtering Volatility-adaptive strategy design Parameter Tuning Guidance Shorter ATR Length → Faster adaptation → More sensitivity → Suitable for intraday trading Longer ATR Length → Smoother volatility structure → Better for swing trading Higher Factor → Wider bands → Fewer signals → Stronger trend confirmation Lower Factor → Tighter bands → Earlier entries → More reversals Longer Median Period → Smoother band structure → Reduced whipsaws Shorter Median Period → Faster reaction → More sensitivity to shifts Practical Strategy Integration Use TBM SuperTrend as: • Primary directional filter • Trailing stop mechanism • Confirmation layer for breakout systems • Bias alignment tool across multiple timeframes It performs best when combined with momentum confirmation or volume expansion tools. Summary True Baseline Median SuperTrend enhances traditional SuperTrend logic by introducing volatility-qualified baseline construction and median smoothing for structural stability. The result is a cleaner, more adaptive trend tool that prioritizes meaningful price movement while minimizing noise. It is well suited for traders seeking a disciplined, volatility-aware trend framework that remains robust across changing market conditions.
مؤشر Pine Script®
من ‎MisinkoMaster‎
12
Robust Scaled Dema | OquantOverview The Robust Scaled DEMA indicator is a tool designed for traders seeking to identify potential trend directions in financial markets. It combines the smoothing capabilities of a Double Exponential Moving Average (DEMA) with a robust scaling mechanism to normalize the data, making it more resilient to outliers and extreme price movements. This scaling helps in generating long and short signals based on predefined thresholds, visualized through color-coded plots and bars. The indicator aims to provide a balanced view of market momentum, reducing the impact of noise while highlighting significant shifts in price behavior. Key Factors/Components DEMA (Double Exponential Moving Average): Serves as the core smoothing component, reducing lag compared to simple averages by emphasizing recent price action more effectively. Robust Scaling Mechanism: Utilizes statistical measures like median and interquartile range to normalize the DEMA values, ensuring the indicator is less sensitive to extreme values or price spikes. Thresholds: User-defined upper and lower levels that trigger long or short signals when the scaled DEMA crosses them. Visual Elements: Includes plotted lines for the scaled DEMA and thresholds, plus color-coded candlestick bars for intuitive interpretation. Alerts: Built-in conditions for notifying users of potential entry points for long or short positions. How It Works The indicator starts by applying a DEMA to the chosen price source to create a smoothed representation of the market's direction. This smoothed value is then scaled using a robust statistical approach that accounts for the distribution of recent DEMA values, centering it around a median and adjusting for variability to minimize the influence of outliers. The resulting scaled metric is compared against user-set upper and lower thresholds: crossing above the upper suggests a bullish momentum (long signal), while dipping below the lower indicates bearish conditions (short signal). A state variable tracks these conditions to color the chart accordingly, helping traders visualize regime changes. Optional alerts fire on transitions. For Who Is Best/Recommended Use Cases This indicator is ideal for traders who employ trend-following or momentum-based strategies and need tools that perform well in non-normal market conditions, such as during high volatility or in assets prone to spikes. Use cases include identifying entry/exit points in trending environments, confirming breakouts, or integrating into multi-indicator systems for added confirmation. Quantitative traders or those backtesting strategies will appreciate its customizable parameters for optimization. Settings and Default Settings Source: The price data input for calculations, such as close, open, high, or low. Default: close. DEMA Length: Controls the period for the DEMA smoothing; shorter values increase responsiveness but may add noise, longer ones provide more lag but smoother signals. Default: 25. Robust Scaling Length: Defines the lookback period for the scaling statistics; affects how adaptive the normalization is to recent data distributions. Default: 40. Upper Threshold: The level above which a long signal is triggered; higher values make signals rarer but potentially more reliable. Default: 0.5. Lower Threshold: The level below which a short signal is triggered; lower values allow for more aggressive bearish detection. Default: 0. Conclusion The Robust Scaled DEMA offers an outlier-resistant alternative to traditional moving average indicators, empowering traders to navigate volatile markets. By blending exponential smoothing with statistical robustness, it provides actionable insights into trend shifts while minimizing false positives from extreme events.. ⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
مؤشر Pine Script®
من ‎oquant‎
2
Percentile Rank Oscillator (Price + VWMA)A statistical oscillator designed to identify potential market turning points using percentile-based price analytics and volume-weighted confirmation. What is PRO? Percentile Rank Oscillator measures how extreme current price behavior is relative to its own recent history. It calculates a rolling percentile rank of price midpoints and VWMA deviation (volume-weighted price drift). When price reaches historically rare levels – high or low percentiles – it may signal exhaustion and potential reversal conditions. How it works Takes midpoint of each candle ((H+L)/2) Ranks the current value vs previous N bars using rolling percentile rank Maps percentile to a normalized oscillator scale (-1..+1 or 0–100) Optionally evaluates VWMA deviation percentile for volume-confirmed signals Highlights extreme conditions and confluence zones Why percentile rank? Median-based percentiles ignore outliers and read the market statistically – not by fixed thresholds. Instead of guessing “overbought/oversold” values, the indicator adapts to current volatility and structure. Key features Rolling percentile rank of price action Optional VWMA-based percentile confirmation Adaptive, noise-robust structure User-selectable thresholds (default 95/5) Confluence highlighting for price + VWMA extremes Optional smoothing (RMA) Visual extreme zone fills for rapid signal recognition How to use High percentile values –> statistically extreme upward deviation (potential top) Low percentile values –> statistically extreme downward deviation (potential bottom) Price + VWMA confluence strengthens reversal context Best used as part of a broader trading framework (market structure, order flow, etc.) Tip: Look for percentile spikes at key HTF levels, after extended moves, or where liquidity sweeps occur. Strong moves into rare percentile territory may precede mean reversion. Suggested settings Default length: 100 bars Thresholds: 95 / 5 Smoothing: 1–3 (optional) Important note This tool does not predict direction or guarantee outcomes. It provides statistical context for price extremes to help traders frame probability and timing. Always combine with sound risk management and other tools.
مؤشر Pine Script®
من ‎exploretranspose‎
Statistical Price Deviation Index (MAD/VWMA)SPDI is a statistical oscillator designed to detect potential price reversal zones by measuring how far price deviates from its typical behavior within a defined rolling window. Instead of using momentum or moving averages like traditional indicators, SPDI applies robust statistics - a rolling median and Mean Absolute Deviation (MAD) - to calculate a normalized measure of price displacement. This normalization keeps the output bounded (from −1 to +1 by default), producing a stable and consistent oscillator that adapts to changing volatility conditions. The second line in SPDI uses a Volume-Weighted Moving Average (VWMA) instead of a simple price median. This creates a complementary oscillator showing statistically weighted deviations based on traded volume. When both oscillators align in their extremes, strong confluence reversal signals are generated. How It Works For each bar, SPDI calculates the median price of the last N bars (default 100). It then measures how far the current bar’s midpoint deviates from that rolling median. The Mean Absolute Deviation (MAD) of those distances defines a “normal” range of fluctuation. The deviation is normalized and compressed via a tanh mapping, keeping the oscillator in fixed boundaries (−1 to +1). The same logic is applied to the VWMA line to gauge volume-weighted deviations. How to Use The blue line (Price MAD) represents pure price deviation. The green line (VWMA Disp) shows the volume-weighted deviation. Overbought (red) zones indicate statistically extreme upward deviation -> potential short-term overextension. Oversold (green) zones indicate statistically extreme downward deviation -> potential rebound area. Confluence signals (both lines hitting the same extreme) often mark strong reversal points. Settings Tips Lookback length controls how much historical data defines “normal” behavior. Larger = smoother, smaller = more sensitive. Smoothing (RMA length) can reduce noise without changing the overall statistical logic. Output scale can be set to either −1..+1 or 0..100, depending on your visual preference. Alerts and color fills are fully customizable in the Style tab. Summary: SPDI transforms raw price and volume data into a statistically bounded deviation index. When both Price MAD and VWMA Disp reach joint extremes, it highlights probable market turning points - offering traders a clean, data-driven way to spot potential reversals ahead of time.
مؤشر Pine Script®
من ‎exploretranspose‎
2
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound. The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts. There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass. The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter. Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly. The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart. What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
مؤشر Pine Script®
من ‎The_Peaceful_Lizard‎
4
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it. QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn). You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems. The thresholds are: Basis: the model of the data (median in our case); Deviations: represent typical spread around basis, together form “value” in general sense; Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers; Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges. … The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/: First part: In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy. For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it. Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually. Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better. In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension. Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same: Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you. So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows. ... Second part: Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles. Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric. Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry. Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same. Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero. 1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias. Realizing stuff > understanding stuff Learning 2981234 human invented fields < realizing the same unified principles how the Universe works ∞
مؤشر Pine Script®
من ‎gorx1‎
3
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts. What this is A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks. Why Theil-Sen Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns. What it plots Filtered baseline that advances by a fraction of the robust slope each bar. Optional candle coloring by baseline slope sign for quick trend read. Alerts when the baseline slope turns up or down. How it behaves (high level) Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes. Takes the median of those slopes to get a robust estimate for the bar. Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line. Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive. Inputs and what they do Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother. Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier. Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves. Slope Cap Mode — optional guardrail on each bar’s slope: None — no cap. ATR — cap scales with recent true range. Percent — cap scales with price level. Points — fixed absolute cap in price points. ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size. UI Settings — show or hide the line, paint candles by slope, choose long and short colors. How to read it Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction. Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise. Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets. Use cases Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups. Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure. Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles. Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars. Tuning guidance Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves. Position trading — longer window, moderate response. ATR cap tends to scale well across cycles. Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities. Alerts included Theil-Sen Up Slope — baseline’s one-bar change crosses above zero. Theil-Sen Down Slope — baseline’s one-bar change crosses below zero. Strengths Robust to outliers through median-based slope estimation. Continuously advances with price rather than re-anchoring, which reduces lag at turns. User-selectable slope caps to tame shock bars without over-smoothing everything. Minimal visuals with optional candle painting for fast regime recognition. Notes This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
مؤشر Pine Script®
من ‎BackQuant‎
P/E Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Earnings (P/E) ratio in relation to Bollinger Bands and its historical median. - 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context. - 💡 Vertical green shading is applied when the P/E ratio drops below the lower Bollinger Band, which is calculated directly from the P/E ratio itself — not price. This condition often signals the ticker may be oversold. - 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk. - 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry. - ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017. - ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete. - ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence. - 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity. - 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada - 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
مؤشر Pine Script®
من ‎traceockinga‎
Interpolated Median Volatility LSMA | OttoThis indicator combines trend-following and volatility analysis by enhancing traditional LSMA with percentile-based linear interpolation applied to both the Least Squares Moving Average (LSMA) and standard deviation. Rather than relying on raw values, it uses the interpolated median (50th percentile) to smooth out noise while preserving sensitivity to significant price shifts. This approach produces a cleaner trend signal that remains responsive to real market changes, adapts to evolving volatility conditions, and improves the accuracy of breakout detection. Core Concept The indicator builds on these core components: LSMA (Least Squares Moving Average): A linear regression-based moving average that fits line using user selected source over user defined period. It offers a smoother and more reactive trend signal compared to standard moving averages. Standard Deviation shows how much price varies from the mean. In this indicator, it’s used to measure market volatility. Volatility Bands: Instead of traditional Bollinger-style bands, this script calculates custom upper and lower bands using percentile-based linear interpolation on both the LSMA and standard deviation. This method produces smoother bands that filter out noise while remaining adaptive to meaningful price movements, making them more aligned with real market behavior and helping reduce false signals. Percentile interpolation estimates a specific percentile (like the median — the 50th percentile) from a set of values — even when that percentile doesn't fall exactly on one data point. Instead of selecting a single nearest value, it calculates a smoothed value between nearby points. In this script, it’s used to find the median of past LSMA and standard deviation values, reducing the impact of outliers and smoothing the trend and volatility signals for more robust results. Signal Logic: A long signal is identified when close price goes above the upper band, and a short signal when close price goes below the lower band. ⚙️ Inputs Source: The price source used in calculations LSMA Length: Period for calculating LSMA Standard Deviation Length: Period for calculating volatility Percentile Length: Period used for interpolating percentile values of LSMA and standard deviation Multiplier: Controls the width of the bands by scaling the interpolated standard deviation 📈 Visual Output Colored LSMA Line: Changes color based on signal (green for bullish, purple for bearish) Upper & Lower Bands: Volatility bands calculated using interpolated values (green for bullish, purple for bearish) Bar Coloring: Price bars are colored to reflect signal state (green for bullish, purple for bearish) Optional Candlestick Overlay: Enhances visual context by coloring candles to match the signal state (green for bullish, purple for bearish) How to Use Add the indicator to your chart and look for signals when close price goes above or below the bands. Long Signal: close Price goes above the upper band Short Signal: close Price goes below the lower band 🔔 Alerts: This script supports alert conditions for long and short signals. You can set alerts based on band crossovers to be notified of potential entries/exits. ⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate strategies before applying them in live markets. Use at your own risk.
مؤشر Pine Script®
من ‎oquant‎
5
Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy. Core Concept: Relative Strength Index (RSI) The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points. To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter. Savitzky-Golay Filter: Smoothing with Precision The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result. In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements. Median Filter: Reducing Outliers After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends. Long and Short Signals The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels: Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity. Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position. These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator. Customization and Visualization Options The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs: Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices). RSI Period: Adjust the lookback period for the RSI calculation (default is 14). Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal. Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals. UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals. Visual Feedback: Color-Coded Signals and Thresholds To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend: Green Bars indicate long signals when momentum is bullish. Red Bars indicate short signals when momentum is bearish. Gray Bars indicate neutral or undecided conditions when no clear signal is present. In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds. Alerts for Automation For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity. Trading Applications This indicator is versatile and can be used in a variety of trading strategies: Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend. Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction. Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts. Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong. Final Thoughts The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions. As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system. Thus following all of the key points here are some sample backtests on the 1D Chart Disclaimer: Backtests are based off past results, and are not indicative of the future. INDEX:BTCUSD INDEX:ETHUSD BINANCE:SOLUSD
مؤشر Pine Script®
من ‎BackQuant‎
series_collectionLibrary "series_collection" A personal collection of commonly used series types like moving averages that are supported directly by the pinescript library ('ALMA', 'DEMA', 'EMA', 'HMA', 'RMA', 'SMA', 'SWMA', 'VWMA', 'WMA'), highest and lowest source, median and pivots. One single function (with overloads) that can be configured easily by the user input and can be used as a core piece of functionality for many user cases. This library was created to abstract away and re-use this commonly used functionality in my "Two MA Signal Indicator" script and the "Template Trailing Strategy" script. Both of them use the "two_ma_logic" for defining entry and exit signals. While this piece of work does not contain any novel mathematical expressions and just adds a convinient (and configurable) way to do things, I hope that might add value to other scripts as well and future projects. cust_series(length, seriesType, source)   cust_series - Calculate the custom series of the given source for the given length and type   Parameters:      length (simple int) : - The length of the custom series      seriesType (simple string) : - The type of the custom series      source (float) : - The source of the values   Returns: - The resulting value of the calculations of the custom series cust_series(length, seriesType, source)   cust_series - Calculate the custom series of the given source for the given length and type   Parameters:      length (simple float) : - The length of the custom series (ceiled)      seriesType (simple string) : - The type of the custom series      source (float) : - The source of the values   Returns: - The resulting value of the calculations of the custom series
مكتبة Pine Script®
من ‎jason5480‎
Standardized Median Proximity [AlgoAlpha]Introducing the Standardized Median Proximity by AlgoAlpha 🚀📊 – a dynamic tool designed to enhance your trading strategy by analyzing price fluctuations relative to the median value. This indicator is built to provide clear visual cues on the price deviation from its median, allowing for a nuanced understanding of market trends and potential reversals. 🔍 Key Features: 1. 📈 Median Tracking: At the core of this indicator is the calculation of the median price over a specified lookback period. By evaluating the current price against this median, the indicator provides a sense of whether the price is trending above or below its recent median value. medianValue = ta.median(priceSource, lookbackLength) 2. 🌡️ Normalization of Price Deviation: The deviation of the price from the median is normalized using standard deviation, ensuring that the indicator's readings are consistent and comparable across different time frames and instruments. standardDeviation = ta.stdev(priceDeviation, 45) normalizedValue = priceDeviation / (standardDeviation + standardDeviation) 3. 📌 Boundary Calculations: The indicator sets upper and lower boundaries based on the normalized values, helping to identify overbought and oversold conditions. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier 4. 🎨 Visual Appeal and Clarity: With carefully chosen colors, the plots provide an intuitive and clear representation of market states. Rising trends are indicated in a shade of green, while falling trends are shown in red. 5. 🚨 Alert Conditions: Stay ahead of market movements with customizable alerts for trend shifts and impulse signals, enabling timely decisions. alertcondition(ta.crossover(normalizedValue, 0), "Bullish Trend Shift", "Median Proximity Crossover Zero Line") 🔧 How to Use: - 🎯 Set your preferred lookback lengths and standard deviation multipliers to tailor the indicator to your trading style. - 💹 Utilize the boundary plots to understand potential overbought or oversold conditions. - 📈 Analyze the color-coded column plots for quick insights into the market's direction relative to the median. - ⏰ Set alerts to notify you of significant trend changes or conditions that match your trading criteria. Basic Logic Explained: - The indicator first calculates the median of the selected price source over your chosen lookback period. This median serves as a baseline for measuring price deviation. - It then standardizes this deviation by dividing it by the standard deviation of the price deviation over a 45-period lookback, creating a normalized value. - Upper and lower boundaries are computed using the exponential moving average (EMA) and standard deviation of these normalized values, adjusted by your selected multiplier. - Finally, color-coded plots provide a visual representation of these calculations, offering at-a-glance insights into market conditions. Remember, while this tool offers valuable insights, it's crucial to use it as part of a comprehensive trading strategy, complemented by other analysis and indicators. Happy trading! 🚀
مؤشر Pine Script®
من ‎AlgoAlpha‎
4
Median Proximity Percentile [AlgoAlpha]📊🚀 Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions. 🌟 Key Features: Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals. Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy. Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions. 🔍 Deep Dive into the Code: Choose your preferred price data source and define lookback lengths for median and EMA calculations. priceSource = input.source(close, "Source") and lookbackLength = input.int(21, minval = 1, title = "Lookback Length") Calculate median value, price deviation, and normalized value to analyze market position relative to the median. medianValue = ta.median(priceSource, lookbackLength) Determine upper and lower boundaries based on standard deviation and EMA. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier Compute the percentile value to track market position within these boundaries. percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50 Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification. emaValue = ta.hma(percentileValue, emaLookbackLength) Visualize trends with color-coded plots and characters for easy interpretation. plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na Set up advanced alerts to stay informed about significant market movements. // Alerts alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line") alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line") alertcondition(ta.crossunder(emaValue,emaValue ) and emaValue > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal") alertcondition(ta.crossunder(emaValue ,emaValue) and emaValue < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal") 🚨 Remember, the "Median Proximity Percentile " is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! 📈📉
مؤشر Pine Script®
من ‎AlgoAlpha‎
15
FlexiMA Variance Tracker [presentTrading]🔶 Introduction and How it is Different The FlexiMA Variance Tracker (FlexiMA-VT) represents a novel approach in technical analysis, distinctively standing out in the realm of financial market indicators. It leverages the concept of a variable Length Moving Average (MA) to create a versatile and dynamic oscillator. Unlike traditional oscillators that rely on a fixed-length MA, the FlexiMA-VT adapts to market conditions by varying the length of the MA, offering a more responsive and nuanced view of market trends. (*The achieved method took reference from SuperTrend Polyfactor Oscillator) This innovative design allows the FlexiMA-VT to capture a broader spectrum of market movements, making it highly effective in diverse trading environments. Whether in stable or volatile markets, its adaptability ensures consistent relevance, providing traders with deeper insights into potential market swings. The proposed oscillator accentuates several key aspects through a distinctive mesh of bars, which are derived from the differences between the price and a set of 20 Moving Averages, each altered by varying factors. The intensity of the mesh's colors serves as an indicator, with brighter hues signifying a greater convergence of Moving Average signals. Starting Length = 5 Starting Length = 40 🔶 Strategy, How it Works: Detailed Explanation 1. Core Concept: The FlexiMA-VT operates by comparing the price or an average value (indicator source) against a set of moving averages with varying lengths. These lengths are dynamically adjusted through a starting factor and multiple increment factors, ensuring a comprehensive analysis over different time scales. 2. Normalization and Standard Deviation Calculation: Once deviations are calculated, they undergo a normalization process, which can be set to 'None', 'Max-Min', or 'Absolute Sum'. This step is crucial as it standardizes the deviations, allowing for a consistent scale of comparison. The standard deviation of these normalized deviations is then calculated, offering insights into the market’s volatility and potential trend strength. 🔹Normalization 3. Median Value and Oscillator Creation: The median of the normalized deviations forms the core of the FlexiMA-VT oscillator. This median value provides a balanced central point, reflecting the consensus of various MA lengths. The standard deviation bands plotted around the median enhance the interpretative power of the oscillator, indicating potential overbought or oversold conditions. 4. Multi-Factor Analysis: The FlexiMA-VT uses multiple increment factors to generate a range of MAs, each factor representing a different scale of trend analysis. By averaging the results from these different scales, the FlexiMA-VT forms a more comprehensive and reliable oscillator. 🔹Consensus 5. Practical Application: Traders can use the FlexiMA-VT for various purposes, including identifying trend reversals, gauging market momentum, and determining overbought or oversold conditions. Its dynamic nature makes it adaptable to different trading strategies, from short-term scalping to long-term position trading. 🔶 Settings 1. Indicator Source (indicatorSource): Determines the base data for calculations, typically a price average (HLC3). 2. Indicator Length (indicatorLength): Sets the base length for Moving Averages, influencing initial calculations. 3. Starting Factor (startingFactor): Initial multiplier for MA length, impacting the starting point of analysis. 4. Increment Factors (incrementFactor_1, incrementFactor_2, incrementFactor_3): Modulate the rate of change in MA lengths, adding variability. 5. Normalization Method (normalizeMethod): Standardizes deviations, with methods like 'Max-Min' and 'Absolute Sum' for comparability.
مؤشر Pine Script®
من ‎PresentTrading‎
1
Trend Shift ProThe indicator is designed to identify shifts or changes in trends as blocks, the indicator's focus on analyzing the Median of Means, Interquartile Range, and Practical Significance for potential trend changes in the market using non parametric Cohen's D. The script is designed to operate on blocks of 21 bars. The key parts of the script related to this are the conditions inside the "if" statements: The bar_index % 21 == 0 condition checks if the current bar index is divisible by 21, meaning it's the beginning of a new block of 21 bars. This condition is used to reset and calculate new values at the start of each block. Therefore, signals or calculations related to the median of means (MoM), interquartile range (IQR), and Cohen's D are updated and calculated once every 21 bars. What this means is the frequency of signals is shown once every 21 bars. Price Movements of Blocks: Block-Based Analysis: This approach divides the price data into blocks or segments, often a fixed number of bars or candles. Each block represents a specific interval of time or price action. It involves No Smoothing: Unlike moving averages, block-based analysis does not apply any smoothing to the price data within each block. It directly examines the raw prices within each block. Let's break down the key concepts and how they are used for trading: Median of Means (MoM): The script calculates the median of the means of seven subgroups, each consisting of three bars in shuffled order. Each subgroup's mean is calculated based on the typical price (hlc3) of the bars within that subgroup. The median is then computed from these seven means, representing a central tendency measure. Note: The Median of Means provides a robust measure of central tendency, especially in situations where the dataset may have outliers or exhibit non-normal distribution characteristics. By calculating means within smaller subgroups, the method is less sensitive to extreme values that might unduly influence the overall average. This can make the Median of Means more robust than a simple mean or median when dealing with datasets that have heterogeneity or skewed distributions. Interquartile Range (IQR): The script calculates the IQR for each block of 21 bars. The IQR is a measure of statistical dispersion, representing the range between the first quartile (Q1) and the third quartile (Q3) of the data. Q1 and Q3 are calculated from the sorted array of closing prices of the 21 bars. Non-Parametric Cohen's D Calculation: Cohen's D is a measure of effect size, indicating the standardized difference between two means. In this script, a non-parametric version of Cohen's D is calculated, comparing the MoM values of the current block with the MoM values of the previous block. The calculation involves the MoM difference divided by the square root of the average squared IQR values. Practical Significance Threshold: The user can set a threshold for practical significance using the Threshold input. The script determines practical significance by comparing the calculated Cohen's D with this threshold. Plotting: The script plots the MoM values using both straight lines and circles, with the color of the circles indicating the direction of the MoM change (green for upward, red for downward, and blue for no change). Triangular shapes are plotted when the absolute value of Cohen's D is less than the practical significance threshold. Overall Purpose for Trading: The indicator is designed to help traders identify potential turning points or shifts in market sentiment. and use it as levels which needs to be crossed to have a new trend. Changes in MoM, especially when accompanied by practical significance as determined by Cohen's D, may signal the start of a new trend or a significant move in the market. Traders using this indicator would typically look for instances where the MoM values and associated practical significance suggest a high probability of a trend change, providing them with potential entry or exit signals. It's important for users to backtest and validate the indicator's effectiveness in different market conditions before relying on it for trading decisions.
مؤشر Pine Script®
من ‎federalTacos5392b‎
SuperTrend Polyfactor Oscillator [LuxAlgo]The SuperTrend Polyfactor Oscillator is an oscillator based on the popular SuperTrend indicator that aims to highlight information returned by a collection of SuperTrends with varying factors inputs. A general consensus is calculated from all this information, returning an indication of the current market sentiment. 🔶 USAGE Multiple elements are highlighted by the proposed oscillator. A mesh of bars is constructed from the difference between the price and a total of 20 SuperTrends with varying factors. Brighter colors of the mesh indicate a higher amount of aligned SuperTrends indications. The factor input of the SuperTrends is determined by the user from the Starting Factor setting which determines the factor of the shorter-term SuperTrend, and the Increment settings which control the step between each factor inputs. Using higher values for these settings will return information for longer-term term price variations. 🔹 Consensus From the collection of SuperTrends, a consensus is obtained. It is calculated as the median of all the differences between the price and the collection of SuperTrends. This consensus is highlighted in the script by a blue and orange line, with a blue color indicating an overall bullish market, and orange indicating a bearish market. Both elements can be used together to highlight retracements within a trend. If we see various red bars while the general consensus is bullish, we can interpret it as the presence of a retracement. 🔹 StDev Area The indicator includes an area constructed from the standard deviation of all the differences between the price and the collection of SuperTrends. This area can be useful to see if the market is overall trending or ranging, with a consensus over the area indicative of a trending market. 🔹 Normalization Users can decide to normalize the results and constrain them within a specific range, this can allow obtaining a lower degree of variations of the indicator outputs. Two methods are proposed "Absolute Sum", and "Max-Min". The "Absolute Sum" method will divide any output returned by the indicator by the absolute sum of all the differences between the price and SuperTrends. This will constrain all the indicator elements in a (1, -1) scale. The "Max-Min" method will apply min-max normalization to the indicator outputs (with the exception of the stdev area). This will constrain all the indicator elements in a (0, 1) scale. 🔶 SETTINGS Length: ATR Length of all calculated SuperTrends. Starting Factor: Factor input of the shorter-term SuperTrend. Increment: Step value between all SuperTrends factors. Normalize: Normalization method used to rescale the indicator output.
مؤشر Pine Script®
من ‎LuxAlgo‎
14
Median of Means Estimator Median of Means (MoM) is a measure of central tendency like mean (average) and median. However, it could be a better and robust estimator of central tendency when the data is not normal, asymmetric, have fat tails (like stock price data) and have outliers. The MoM can be used as a robust trend following tool and in other derived indicators. Median of means (MoM) is calculated as follows, the MoM estimator shuffles the "n" data points and then splits them into k groups of m data points (n= k*m). It then computes the Arithmetic Mean of each group (k). Finally, it calculate the median over the resulting k Arithmetic Means. This technique diminishes the effect that outliers have on the final estimation by splitting the data and only considering the median of the resulting sub-estimations. This preserves the overall trend despite the data shuffle. Below is an example to illustrate the advantages of MoM Set A Set B Set C 3 4 4 3 4 4 3 5 5 3 5 5 4 5 5 4 5 5 5 5 5 5 5 5 6 6 8 6 6 8 7 7 10 7 7 15 8 8 40 9 9 50 10 100 100 Median 5 5 5 Mean 5.5 12.1 17.9 MoM 5.7 6.0 17.3 For all three sets the median is the same, though set A and B are the same except for one outlier in set B (100) it skews the mean but the median is resilient. However, in set C the group has several high values despite that the median is not responsive and still give 5 as the central tendency of the group, but the median of means is a value of 17.3 which is very close to the group mean 17.9. In all three cases (set A, B and C) the MoM provides a better snapshot of the central tendency of the group. Note: The MoM is dependent on the way we split the data initially and the value might slightly vary when the randomization is done sevral time and the resulting value can give the confidence interval of the MoM estimator.
مؤشر Pine Script®
من ‎federalTacos5392b‎
2
Oscillator Profile IndicatorDescription: The Oscillator Profile Indicator (OPI) is designed to provide insights into market trends and potential reversal points by profiling the value distribution of an oscillator or the price chart over a specified lookback period. The OPI works by calculating the Point of Control (PoC) for the oscillator values or prices in the given lookback period. This PoC, essentially a median, is considered the fair value where most trading activities have happened. Along with this, OPI also calculates lower and upper boundaries by taking the specified percentile of the sorted distribution of values. These boundaries outline the value area within which a significant portion of trading activity has occurred. The main feature of the OPI is the interpretation of PoC movement and how it relates to general market trends. If the PoC moves above 0 on the oscillator, it's a potential indication that we are in a general uptrend. Conversely, if the PoC moves below 0, this can be a signal for a general downtrend. Usage: While OPI can be used on both price charts and oscillators, its effectiveness is more pronounced when used on oscillators. Applying this indicator to oscillators such as the Relative Strength Index (RSI) or the Moving Average Convergence Divergence (MACD) can provide useful insights. How to Read: PoC line: The line represents the median of the past 'n' periods. Its movement above or below 0 can be used to identify general uptrends or downtrends respectively. Upper and Lower Boundary lines: These lines represent the specified percentile of the value distribution in the lookback period. Colored Fills: The fills between the upper and lower boundary lines visually represent the value area. The color changes based on the relative position of the source value (price or oscillator value) to the PoC. Signals: An uptrend is indicated when the PoC moves above 0 on the oscillator, especially when coupled with an upward crossover of the source value through the PoC. A downtrend is signaled when the PoC drops below 0 on the oscillator, particularly when paired with a downward crossover of the source value through the PoC. (!) Note: Like all indicators, OPI should be used in conjunction with other technical analysis tools for the best results. It is also advisable to backtest this indicator with your strategy before using it in live trading.
مؤشر Pine Script®
من ‎mezor13‎
1