NY Session First 15m Range ORB Strategy first 15m high&low NY session
let you know the high and low of first 15m and the first candle is sitck out of the line you can ride on the wave to make moeny no bul OANDA:XAUUSD SP:SPX
Volatilidade
Penguin TrendMeasures the volatility regime by comparing the upper Bollinger Band to the upper Keltner Channel and colors bars with a lightweight trend state. Supports SMA/EMA/WMA/RMA/HMA/VWMA/VWAP and a selectable calculation timeframe. Default settings preserve the original look and behavior.
Penguin Trend visualizes expansion vs. compression in price action by comparing two classic volatility envelopes. It computes:
Diff% = (UpperBB − UpperKC) / UpperKC × 100
* Diff > 0: Bollinger Bands are wider than Keltner Channels -> expansion / momentum regime.
* Diff < 0: BB narrower than KC -> compression / squeeze regime.
A white “Average Difference” line smooths Diff% (default: SMA(5)) to help spot regime shifts.
Trend coloring (kept from original):
Bars are colored only when Diff > 0 to emphasize expansion phases. A lightweight trend engine defines four states using a fast/slow MA bias and a short “thrust” MA applied to ohlc4:
* Green: Bullish bias and thrust > fast MA (healthy upside thrust).
* Red: Bearish bias and thrust < fast MA (healthy downside thrust).
* Yellow: Bullish bias but thrust ≤ fast MA (pullback/weakness).
* Blue: Bearish bias but thrust ≥ fast MA (bear rally/short squeeze).
Note: By default, Blue renders as Yellow to preserve the original visual style. Enable “Use true BLUE color” if you prefer Aqua for Blue.
How it works (under the hood):
* Bollinger Bands (BB): Basis = selected MA of src (default SMA(20)). Width = StdDev × Mult (default 2.0).
* Keltner Channels (KC): Basis = selected MA of src (default SMA(20)). Width = ATR(kcATR) × Mult (defaults 20 and 2.0).
* Diff%: Safe division guards against division-by-zero.
* MA engine: You can choose SMA / EMA / WMA / RMA / HMA / VWMA / VWAP for BB/KC bases, Diff smoothing, and the trend components (VWAP is session-anchored).
* Calculation timeframe: Set “Calculation timeframe” to compute all internals on a chosen TF via request.security() while viewing any chart TF.
Inputs (key ones):
* Calculation timeframe: Empty = use chart TF; if set (e.g., 60), all internals compute on that TF.
* BB: Length, StdDev Mult, MA Type.
* KC: Basis Length, ATR Length, Multiplier, MA Type.
* Smoothing: Average Length & MA Type for the “Average Difference” line.
* Trend Engine: Fast/Slow lengths & MA type; Signal (kept for completeness); Thrust length & MA type (defaults replicate original behavior).
* Display: Paint bars only when Diff > 0; optional Zero line; optional true Blue color.
How to use:
1. Regime changes: Watch Diff% or Average Diff crossing 0. Above zero favors momentum/continuation setups; below zero suggests compression and potential breakout conditions.
2. State confirmation: Use bar colors to qualify expansion: Green/Red indicate expansion aligned with trend thrust; Yellow/Blue flag weaker/contrarian thrust during expansion.
3. Multi-timeframe analysis: Run calculations on a higher TF (e.g., H1/H4) while trading a lower TF chart to smooth noise.
Alerts:
* Diff crosses above/below 0.
* Average Diff crosses above/below 0.
* State changes: GREEN / RED / YELLOW / BLUE.
Notes & limitations:
* VWAP is session-anchored and best on intraday data. If not applicable on the selected calculation TF, the script automatically falls back to EMA.
* Default parameters (SMA(20) for BB/KC, multipliers 2.0, SMA(5) smoothing, trend logic and bar painting) preserve the original appearance.
Release notes:
v6.0 — Rewritten in Pine v6 with structured inputs and guards. Multi-MA support (SMA/EMA/WMA/RMA/HMA/VWMA/VWAP). Calculation timeframe via request.security() for multi-TF workflows. Safe division; optional zero line; optional true Blue color. Original visuals and behavior preserved by default.
License / disclaimer:
© waranyu.trkm — MIT License. Educational use only; not financial advice.
Previous Days High & Low RTH Session by TenAM TraderPurpose:
This indicator plots the high and low levels of previous trading days’ Regular Trading Hours (RTH), helping traders identify key support and resistance zones based on historical price action.
How to Use / Strategy:
Designed as a super simple trading strategy:
Buy when price breaks above and confirms the previous day’s high.
Sell when price breaks below and confirms the previous day’s low.
Alerts notify you when price interacts with these levels, helping traders act on confirmed breakout opportunities rather than premature moves.
*Traders can also look for reversal opportunities if price breaks back through one of the levels.
Note: Make sure RTH (Regular Trading Hours) is turned on for the chart, as the indicator is based on RTH highs and lows.
Features:
Tracks previous days’ highs and lows.
Provides clear visual reference for support and resistance.
Simple, actionable strategy based on breakout confirmations and reversal plays.
Alerts for confirmed price breaks.
Disclaimer:
This indicator is for educational and informational purposes only. It does not provide financial advice. Trading involves risk, and past performance does not guarantee future results. Users trade at their own risk.
Volume Spikes + Daily VWAP SD BandsVolume Spikes + Daily VWAP SD Bands
This indicator combines volume spike detection to help traders identify potential absorption zones with daily VWAP and standard deviation bands , key price levels, continuation opportunities, and possible institutional bias.
Features:
Volume Spike Detection
Highlights candles with unusually high volume relative to a configurable SMA.
Optional filters:
Local highs/lows only (Only Use Valid Highs & Lows)
Candle shapes: Hammer / Shooter only
Candle color match: bullish spikes on green, bearish on red
Plots small circles above/below bars for bullish and bearish volume spikes.
Alerts available for both bullish and bearish spikes.
Interpretation: Volume spikes at local highs/lows can indicate absorption, where one side absorbs aggressive buying/selling pressure.
Daily VWAP
Calculates volume-weighted average price (VWAP) for the current day.
Optionally shows previous day’s VWAP for reference.
Plot lines are customizable with optional circles on lines for visual clarity.
Labels on the last bar show exact VWAP values.
Institutional Bias Insight: Price above both current and previous VWAPs may indicate bullish positioning; price below both VWAPs may indicate bearish positioning. Many professional traders consider this a clue to institutional bias, but it’s not guaranteed. Always confirm with volume, delta, or orderflow analysis.
Standard Deviation Bands
Optional x1 and x2 SD bands around the daily VWAP.
Visual fill between bands shows price volatility zones.
Can be used to identify potential support/resistance or absorption zones.
Use Case: Price bounces off first SD band may indicate continuation signals, especially when volume spikes occur at those levels.
Customizable Visuals
Colors for bullish and bearish volume spikes
VWAP and SD band colors and thickness
Optional circles and filled bands for better readability
Alerts
Bullish / Bearish Volume Spikes
Supports TradingView alert system for automated notifications
Advanced Use Cases:
Combine with Cumulative Delta or Orderflow tools to confirm true absorption zones.
Identify high-volume rejection candles signaling possible trend continuation.
Use VWAP positioning relative to price to assess potential institutional bias, keeping in mind it is probabilistic, not guaranteed.
Visualize intraday VWAP levels and volatility with SD bands for better trade timing.
Settings: Fully customizable, including volume multiplier, SMA length, session filter, candle shape, color options, and VWAP/SD display preferences.
Shock Detector: Price Jerk with Std-Dev BandsDetect sudden shocks in market behaviour
This indicator measures the jerk of price – the third derivative of price with respect to time (rate of change of acceleration). It highlights sudden accelerations and decelerations in price movement that are often invisible with standard momentum or volatility indicators.
Per-bar or time-scaled derivatives (choose whether calculations are based on bars or actual seconds).
Features
Log-price option for more stable readings across different price levels.
Optional smoothing with EMA to reduce noise.
Line or column view for flexible visualization.
Standard deviation bands (±1σ and ±2σ), centered either on zero or the rolling mean.
Auto window selection (1 day to 4 weeks), adaptive to chart timeframe.
Color-coded jerk: green for positive, red for negative.
Optional filled bands for easy visual context of normal vs. extreme jerk moves.
How to Use
Use jerk to identify sudden shifts in market dynamics, where price movement is not just changing direction but changing its acceleration.
Bands help highlight when jerk values are statistically unusual compared to recent history.
Combine with trend or momentum indicators for potential early warning of breakouts, reversals, or exhaustion.
Why it’s useful
Most indicators measure price, velocity (returns), or acceleration (momentum). This goes one step further to look at jerk, giving you a tool to spot “shock” movements in the market. By framing jerk within standard deviation bands, it’s easy to see whether current moves are ordinary or exceptional.
Developed with the assistance of ChatGPT (OpenAI).
NY ORB (30m) + ATR CheckNY Open strategy
First candle at 30min NY Open @ 9:30
Mark high/low of that candle (ORB)
Make sure ATR is within 25% deviation +/-
If ATR is in harmony with the price difference of the first candle high/low
You trade the first candle close that closes above the candle high/low (ORB)
ICT Macro Time Window NYThis script highlights the typical ICT “macro” algorithm activity windows on your chart. It marks 10 minutes before to 10 minutes after each full hour, based on New York time (NY). The display is restricted to the 00:00 – 16:00 NY time range.
Overlay on chart with semi-transparent background
Automatically adjusts to the chart timeframe
Customizable: window start/end minutes, hours, and background color
Ideal for traders following ICT concepts to visually identify high-probability algorithm activity periods.
Kitti-Playbook ATR Study R0
Date : Aug 22 2025
Kitti-Playbook ATR Study R0
This is used to study the operation of the ATR Trailing Stop on the Long side, starting from the calculation of True Range.
1) Studying True Range Calculation
1.1) Specify the Bar graph you want to analyze for True Range.
Enable "Show Selected Price Bar" to locate the desired bar.
1.2) Enable/disable "Display True Range" in the Settings.
True Range is calculated as:
TR = Max (|H - L|, |H - Cp|, |Cp - L|)
• Show True Range:
Each color on the bar represents the maximum range value selected:
◦ |H - L| = Green
◦ |H - Cp| = Yellow
◦ |Cp - L| = Blue
• Show True Range on Selected Price Bar:
An arrow points to the range, and its color represents the maximum value chosen:
◦ |H - L| = Green
◦ |H - Cp| = Yellow
◦ |Cp - L| = Blue
• Show True Range Information Table:
Displays the actual values of |H - L|, |H - Cp|, and |Cp - L| from the selected bar.
2) Studying Average True Range (ATR)
2.1) Set the ATR Length in Settings.
Default value: ATR Length = 14
2.2) Enable/disable "Display Average True Range (RMA)" in Settings:
• Show ATR
• Show ATR Length from Selected Price Bar
(An arrow will point backward equal to the ATR Length)
3) Studying ATR Trailing
3.1) Set the ATR Multiplier in Settings.
Default value: ATR Multiply = 3
3.2) Enable/disable "Display ATR Trailing" in Settings:
• Show High Line
• Show ATR Bands
• Show ATR Trailing
4) Studying ATR Trailing Exit
(Occurs when the Close price crosses below the ATR Trailing line)
Enable/disable "Display ATR Trailing" in Settings:
• Show Close Line
• Show Exit Points
(Exit points are marked by an orange diamond symbol above the price bar)
StdDev Supply/Demand Zone RefinerThis indicator uses standard deviation bands to identify statistically significant price extremes, then validates these levels through volume analysis and market structure. It employs a proprietary "Zone Refinement" technique that dynamically adjusts zones based on price interaction and volume concentration, creating increasingly precise support/resistance areas.
Key Features:
Statistical Extremes Detection: Identifies when price reaches 2+ standard deviations from mean
Volume-Weighted Zone Creation: Only creates zones at extremes with abnormal volume
Dynamic Zone Refinement: Automatically tightens zones based on touch points and volume nodes
Point of Control (POC) Identification: Finds the exact price with maximum volume within each zone
Volume Profile Visualization: Shows horizontal volume distribution to identify key liquidity levels
Multi-Factor Validation: Combines volume imbalance, zone strength, and touch count metrics
Unlike traditional support/resistance indicators that use arbitrary levels, this system:
Self-adjusts based on market volatility (standard deviation)
Refines zones through machine-learning-like feedback from price touches
Weights by volume to show where real money was positioned
Tracks zone decay - older, untested zones automatically fade
Overnight Gap Dominance Indicator (OGDI)The Overnight Gap Dominance Indicator (OGDI) measures the relative volatility of overnight price gaps versus intraday price movements for a given security, such as SPY or SPX. It uses a rolling standard deviation of absolute overnight percentage changes divided by the standard deviation of absolute intraday percentage changes over a customizable window. This helps traders identify periods where overnight gaps predominate, suggesting potential opportunities for strategies leveraging extended market moves.
Instructions
A
pply the indicator to your TradingView chart for the desired security (e.g., SPY or SPX).
Adjust the "Rolling Window" input to set the lookback period (default: 60 bars).
Modify the "1DTE Threshold" and "2DTE+ Threshold" inputs to tailor the levels at which you switch from 0DTE to 1DTE or multi-DTE strategies (default: 0.5 and 0.6).
Observe the OGDI line: values above the 1DTE threshold suggest favoring 1DTE strategies, while values above the 2DTE+ threshold indicate multi-DTE strategies may be more effective.
Use in conjunction with low VIX environments and uptrend legs for optimal results.
KAMA Trend Flip - SightLing LabsBuckle up, traders—this open-source KAMA Trend Flip indicator is your ticket to sniping trend reversals with a Kaufman Adaptive Moving Average (KAMA) that’s sharper than a Wall Street shark’s tooth. No voodoo, no fluff—just raw, volatility-adaptive math that dances with the market’s rhythm. It zips through trending rockets and chills in choppy waters, slashing false signals like a samurai. Not laggy like the others - this thing is the real deal!
Core Mechanics:
• Efficiency Ratio (ER): Reads the market’s pulse (0-1). High ER = turbo-charged MA, low ER = smooth operator.
• Adaptive Smoothing: Mixes fast (default power 2) and slow (default 30) constants to match market mood swings.
• Trend Signals: KAMA climbs = blue uptrend (bulls run wild). KAMA dips = yellow downtrend (bears take over). Flat = gray snooze-fest.
• Alerts: Instant pings on flips—“Trend Flip Up” for long plays, “Down” for shorts. Plug into bots for set-and-forget domination.
Why It Crushes:
• Smokes static MAs in volatile arenas (crypto, stocks, you name it). Backtests show 20-30% fewer fakeouts than SMA50.
• Visual Pop: Overlays price with bold blue/yellow signals. Slap it on BTC 1D to see trends light up like Times Square.
• Tweakable: Dial ER length (default 50) to your timeframe. Short for scalps, long for swing trades.
Example Settings in Action:
• 10s Chart (Hyper-Scalping): Set Source: Close, ER Length: 100, Fast Power: 1, Slow Power: 6. Catches micro-trends in crypto like a heat-seeking missile. Blue/yellow flips scream entry/exit on fast moves.
• 2m Chart (Quick Trades): Set Source: Close, ER Length: 14, Fast Power: 1, Slow Power: 6. Perfect for rapid trend shifts in stocks or forex. Signals align with momentum bursts—check historical flips for proof.
Deployment:
• Drop it on any chart. Backtest settings to match your asset’s volatility—tweak until it sings.
• Pair with RSI or volume spikes for killer confirmation. Pro move: Enter on flip + volume pop, exit on reverse.
• Strategy-Ready: Slap long/short logic on alerts to build a lean, mean trading machine.
Open source from SightLing Labs—grab it, hack it, profit from it. Share your tweaks in the comments and let’s outsmart the market together. Trade hard, win big!
Realized Volatility (StdDev of Returns, %)📌 Realized Volatility (StdDev of Returns, %)
This indicator measures realized volatility directly from price returns, instead of the common but misleading approach of calculating standard deviation around a moving average.
🔹 How it works:
Computes close-to-close log returns (the most common way volatility is measured in finance).
Calculates the standard deviation of these returns over a chosen lookback period (default = 200 bars).
Converts results into percentages for easier interpretation.
Provides three key volatility measures:
Daily Realized Vol (%) – raw standard deviation of returns.
Annualized Vol (%) – scaled by √250 trading days (market convention).
Horizon Vol (%) – volatility over a custom horizon (default = 5 days, i.e. weekly).
🔹 Why use this indicator?
Shows true realized volatility from historical returns.
More accurate than measuring deviation around a moving average.
Useful for traders analyzing risk, position sizing, and comparing realized vs implied volatility.
⚠️ Note:
It is best used on the Daily Chart!
By default, this uses log returns (which are additive and standard in quant finance).
If you prefer, you can easily switch to simple % returns in the code.
Volatility estimates depend on your chosen lookback length and may vary across timeframes.
Range Percent Histogram📌 Range Percent Histogram – Indicator Description
The Range Percent Histogram is a custom indicator that behaves like a traditional volume histogram, but instead of showing traded volume it displays the percentage range of each candle.
In other words, the height of each bar represents how much the price moved (in percentage terms) within that candle, from its low to its high.
🔧 What it shows
The indicator has two main components:
Component Description
Histogram Bars Columns plotted in red or green depending on the candle direction (green = bullish candle, red = bearish). The height of each bar = (high - low) / low * 100. That means a candle that moved, for example, 1 % from its lowest point to its highest point will show a bar with 1 % height.
Moving Average (optional) A 20-period Simple Moving Average applied directly to the bar values. It can be turned ON/OFF via a checkbox and helps you detect whether current range activity is above or below the average range of the past candles.
⚙️ How it works
Every time a new candle closes, the indicator calculates its range and converts it into a percentage.
This value is drawn as a column under the chart.
If the closing price is above the opening price → the bar is green (bullish range).
If the closing price is below the opening price → the bar is red (bearish range).
When the Show Moving Average option is enabled, a smooth line is plotted on top of the histogram representing the average percentage range of the last 20 candles.
📈 How to use it
This indicator is very helpful for detecting moments of range expansion or contraction.
One powerful way to use it is similar to a volume exhaustion / low-volume pattern:
Situation Interpretation
Consecutive bars with very low height Price is in a period of low volatility → possible accumulation or "pause" phase.
A sudden large bar after a series of small ones Indicates a strong pickup in volatility → often marks the start of a new impulse in the direction of the breakout.
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach.
This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits.
🔎 Features
Four Filter Types
1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings.
Real world application for low pass filter - Used in power supplies to provide a clean, stable power level.
2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility.
Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity.
3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility.
Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to.
4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action.
Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else
Configurable Parameters
• Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter.
• Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay).
• Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart.
• Source Selection – Apply filters to close, open, high, low, or custom sources.
Histograms for Comparison
• BS–LP Histogram – Shows distance between Band Stop and Low Pass filters.
• BP–HP Histogram – Highlights differences between Band Pass and High Pass filters.
Histograms give the visualization of a pseudo-MACD style indicator
Visual & Informational Aids
• Customizable colors for each filter line.
• Optional zero-line for histogram reference.
• On-chart info table summarizing active filters, cutoff settings, histograms, and filter order.
📊 Use Cases
Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction.
Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns.
Noise Suppression – Deploy Band Stop to remove specific choppy frequencies.
Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
ATR Stoploss 15m with EMA Trend 1H - Dotted Fixeduse this as a basic ATR stoploss. It uses 100 and 20 EMA on 1hr to determine trend.
Средний дневной ATR (по High–Low)Test v.1
we calculate in % the average ATR passed in 1 day (for 5 days)
Globex Overnight Futures ORB with FIB's by TenAMTrader📌 Globex Overnight Futures ORB with FIB’s – by TenAMTrader
This indicator is designed for futures traders who want to track the Globex Overnight Opening Range (ORB) and apply Fibonacci projections to anticipate potential support/resistance zones. It’s especially useful for traders who follow overnight sessions (such as ES, NQ, CL) and want to map out key levels before the U.S. regular session begins.
⚙️ How It Works
Primary Range (ORB):
You define a start and end time (default set to 18:00 – 18:15 EST). During this period, the script tracks the session high, low, and midpoint.
Opening Range Plots:
High Line (green)
Low Line (red)
Midpoint Line (yellow)
A shaded cloud between High–Mid and Mid–Low for easy visualization.
Fibonacci Projections:
Once the ORB is complete, the script calculates a full suite of Fibonacci retracements and extensions (e.g., 0.236, 0.382, 0.618, 1.0, 1.618, 2.0).
Standard key levels (0.618, 0.786, 1.0, etc.) are always shown if enabled.
Optional extended levels (1.236, 1.382, 1.5, 2.0, etc.) can be toggled on/off.
"Between Range" fibs (such as 0.382 and 0.618 inside the ORB) are also available for traders who like intra-range precision.
🔧 User Settings
Time Inputs: Choose your ORB start/end time.
Color Controls: Customize high, low, midpoint, and fib line colors.
Display Toggles: Turn on/off High, Low, Midpoint lines and Fibonacci projections.
Fib Extensions Toggle: Decide whether to show only major fibs or all extensions.
Alerts (Optional): Alerts can be set for crossing the ORB High, Low, or Midpoint.
📊 Practical Use Cases
Breakout Traders: Use the ORB high/low as breakout triggers.
Mean Reversion Traders: Watch for rejections near fib extension levels.
Overnight Futures Monitoring: Track Globex behavior to prepare for RTH open.
Risk Management: ORB and Fib levels make for natural stop/target placement zones.
⚠️ Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading recommendations. Trading futures involves substantial risk of loss and may not be suitable for all investors. Always do your own due diligence and consult with a licensed financial professional before making trading decisions.
Volume Spike Detector - by TenAMTrader📌 Volume Spike Detector – by TenAMTrader
This indicator is designed to help traders quickly identify unusual surges in trading volume relative to recent activity. High-volume spikes can often signal strong buying or selling pressure, potential trend reversals, or breakout setups.
⚙️ How It Works
The script calculates the average trading volume over a user-defined period (default: 21 bars).
It then sets a spike threshold, which is that average volume plus a percentage buffer (default: 25%).
Whenever the current bar’s volume exceeds this threshold, a 💰 label is plotted below the candle.
If alerts are enabled, you’ll also receive a real-time alert whenever a spike occurs.
🔧 User Settings
Spike Ratio % → Adjust how much higher than average volume must be to qualify as a spike.
Trading Period → Set the lookback period used to calculate the average volume.
Enable Alert → Turn alerts on/off.
📊 Practical Use Cases
Breakout Trading: Volume spikes often confirm breakouts from consolidation zones.
Reversal Signals: A sudden surge in volume may precede a trend reversal.
News & Events: Spot unusual activity during earnings, economic releases, or unexpected events.
⚠️ Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading recommendations. Past performance is not indicative of future results. Always do your own research and consult with a licensed financial professional before making any trading decisions.
Bollinger Bands % | QuantEdgeB📊 Introducing Bollinger Bands % (BB%) by QuantEdgeB
🛠️ Overview
BB% | QuantEdgeB is a volatility-aware momentum tool that maps price within a Bollinger envelope onto a normalized scale. By letting you choose the base moving average (SMA, EMA, DEMA, TEMA, HMA, ALMA, EHMA, THMA, RMA, WMA, VWMA, T3, LSMA) and even Heikin-Ashi sources, it adapts to your style while keeping readings consistent across symbols and timeframes. Clear thresholds and color-coded visuals make it easy to spot emerging strength, fading moves, and potential mean-reversions.
✨ Key Features
• 🔹 Flexible Baseline
Pick from 12 MA types (plus Heikin-Ashi source option) to tailor responsiveness and smoothness.
• 🔹 Normalized Positioning
Price is expressed as a percentage of the band range, yielding an intuitive 0–100 style read (can exceed in extreme trends).
• 🔹 Actionable Thresholds
Default Long 55 / Short 45 levels provide simple, objective triggers.
• 🔹 Visual Clarity
Color-coded candles, shaded OB/OS zones, and adaptive color themes speed up decision-making.
• 🔹 Ready-to-Alert
Built-in alerts for long/short transitions.
📐 How It Works
1️⃣ Band Construction
A moving average (your choice) defines the midline; volatility (standard deviation) builds upper/lower bands.
2️⃣ Normalization
The indicator measures where price sits between the lower and upper band, scaling that into a bounded oscillator (BB%).
3️⃣ Signal Logic
• ✅ Long when BB% rises above 55 (strength toward the top of the envelope).
• ❌ Short when BB% falls below 45 (weakness toward the bottom).
4️⃣ OB/OS Context
Shaded regions above/below typical ranges highlight exhaustion and potential snap-backs.
⚙️ Custom Settings
• Base MA Type: SMA, EMA, DEMA, TEMA, HMA, ALMA, EHMA, THMA, RMA, WMA, VWMA, T3, LSMA
• Source Mode: Classic price or Heikin-Ashi (close/open/high/hlc3)
• Base Length: default 40
• Band Width: standard deviation-based (2× SD by default)
• Long / Short Thresholds: defaults 55 / 45
• Color Mode: Alpha, MultiEdge, TradingSuite, Premium, Fundamental, Classic, Warm, Cold, Strategy
• Candles & Labels: optional candle coloring and signal markers
👥 Ideal For
✅ Trend Followers — Ride strength as price compresses near the upper band.
✅ Swing/Mean-Reversion Traders — Fade extremes when BB% stretches into OB/OS zones.
✅ Multi-Timeframe Analysts — Compare band position consistently across periods.
✅ System Builders — Use BB% as a normalized feature for strategies and filters.
📌 Conclusion
BB% | QuantEdgeB delivers a clean, normalized read of price versus its volatility envelope—adaptable via rich MA/source options and easy to automate with thresholds and alerts.
🔹 Key Takeaways:
1️⃣ Normalized view of price inside the volatility bands
2️⃣ Flexible baseline (12+ MA choices) and Heikin-Ashi support
3️⃣ Straightforward 55/45 triggers with clear visual context
📌 Disclaimer: Past performance is not indicative of future results. No strategy guarantees success.
📌 Strategic Advice: Always backtest, tune parameters, and align with your risk profile before live trading.
All-in-One EMA & BBThis script combines Bollinger Bands and multiple EMAs into one powerful tool. It includes:
1) Bollinger Bands with customizable MA type and colors.
2) EMA 21 on Daily and Weekly timeframes.
3) EMA 21, 50, 100, 200 on current chart timeframe.
4) Toggle options for each indicator for a clean, flexible view.
Ideal for traders seeking multi-timeframe trend analysis and volatility insights.
Monthly Expected Move (IV + Realized)What it does
Overlays 1-month expected move bands on price using both forward-looking options data and backward-looking realized movement:
IV30 band — from your pasted 30-day implied vol (%)
Straddle band — from your pasted ATM ~30-DTE call+put total
HV band — from Historical Volatility computed on-chart
ATR band — from ATR% extrapolated to ~1 trading month
Use it to quickly answer: “How much could this stock move in ~1 month?” and “Is the market now pricing more/less movement than we’ve actually been getting?”
Inputs (quick)
Implied (forward-looking)
Use IV30 (%) — paste annualized IV30 from your options platform.
Use ATM 30-DTE Straddle — paste Call+Put total (per share) at the ATM strike, ~30 DTE.
Realized (backward-looking)
HV lookback (days) — default 21 (≈1 trading month).
ATR length — default 14.
Note: TradingView can’t fetch option data automatically. Paste the IV30 % or the straddle total you read from your broker (use Mark/mid prices).
How it’s calculated
IV band (±%) = IV30 × √(21/252) (annualized → ~1-month).
Straddle band (±%) = (ATM Call + Put) / Spot to that expiry (≈30 DTE).
HV band (±%) = stdev(log returns, N) × √252 × √(21/252).
ATR band (±%) = (ATR(len)/Close) × √21.
All bands are plotted as upper/lower envelopes around price, plus an on-chart readout of each ±% for quick scanning.
How to use it (at a glance)
IV/Straddle bands wider than HV/ATR → market expects bigger movement than recent actuals (possible catalyst/expansion).
All bands narrow → likely a low-mover; look elsewhere if you want action.
HV > IV → realized swings exceed current pricing (mean-reversion or vol bleed often follows).
Pro tips
For ATM straddle: pick the expiry closest to ~30 DTE, use the ATM strike (closest to spot), and add Call Mark + Put Mark (per share). If the exact ATM strike isn’t quoted, average the two neighboring strikes.
The simple straddle/spot heuristic can read slightly below the IV-derived 1σ; that’s normal.
Keep the chart on daily timeframe—the math assumes trading-day conventions (~252/yr, ~21/mo).
Volume Profile Grid [Alpha Extract]A sophisticated volume distribution analysis system that transforms market activity into institutional-grade visual profiles, revealing hidden support/resistance zones and market participant behavior. Utilizing advanced price level segmentation, bullish/bearish volume separation, and dynamic range analysis, the Volume Profile Grid delivers comprehensive market structure insights with Point of Control (POC) identification, Value Area boundaries, and volume delta analysis. The system features intelligent visualization modes, real-time sentiment analysis, and flexible range selection to provide traders with clear, actionable volume-based market context.
🔶 Dynamic Range Analysis Engine
Implements dual-mode range selection with visible chart analysis and fixed period lookback, automatically adjusting to current market view or analyzing specified historical periods. The system intelligently calculates optimal bar counts while maintaining performance through configurable maximum limits, ensuring responsive profile generation across all timeframes with institutional-grade precision.
// Dynamic period calculation with intelligent caching
get_analysis_period() =>
if i_use_visible_range
chart_start_time = chart.left_visible_bar_time
current_time = last_bar_time
time_span = current_time - chart_start_time
tf_seconds = timeframe.in_seconds()
estimated_bars = time_span / (tf_seconds * 1000)
range_bars = math.floor(estimated_bars)
final_bars = math.min(range_bars, i_max_visible_bars)
math.max(final_bars, 50) // Minimum threshold
else
math.max(i_periods, 50)
🔶 Advanced Bull/Bear Volume Separation
Employs sophisticated candle classification algorithms to separate bullish and bearish volume at each price level, with weighted distribution based on bar intersection ratios. The system analyzes open/close relationships to determine volume direction, applying proportional allocation for doji patterns and ensuring accurate representation of buying versus selling pressure across the entire price spectrum.
🔶 Multi-Mode Volume Visualization
Features three distinct display modes for bull/bear volume representation: Split mode creates mirrored profiles from a central axis, Side by Side mode displays sequential bull/bear segments, and Stacked mode separates volumes vertically. Each mode offers unique insights into market participant behavior with customizable width, thickness, and color parameters for optimal visual clarity.
// Bull/Bear volume calculation with weighted distribution
for bar_offset = 0 to actual_periods - 1
bar_high = high
bar_low = low
bar_volume = volume
// Calculate intersection weight
weight = math.min(bar_high, next_level) - math.max(bar_low, current_level)
weight := weight / (bar_high - bar_low)
weighted_volume = bar_volume * weight
// Classify volume direction
if bar_close > bar_open
level_bull_volume += weighted_volume
else if bar_close < bar_open
level_bear_volume += weighted_volume
else // Doji handling
level_bull_volume += weighted_volume * 0.5
level_bear_volume += weighted_volume * 0.5
🔶 Point of Control & Value Area Detection
Implements institutional-standard POC identification by locating the price level with maximum volume accumulation, providing critical support/resistance zones. The Value Area calculation uses sophisticated sorting algorithms to identify the price range containing 70% of trading volume, revealing the market's accepted value zone where institutional participants concentrate their activity.
🔶 Volume Delta Analysis System
Incorporates real-time volume delta calculation with configurable dominance thresholds to identify significant bull/bear imbalances. The system visually highlights price levels where buying or selling pressure exceeds threshold percentages, providing immediate insight into directional volume flow and potential reversal zones through color-coded delta indicators.
// Value Area calculation using 70% volume accumulation
total_volume_sum = array.sum(total_volumes)
target_volume = total_volume_sum * 0.70
// Sort volumes to find highest activity zones
for i = 0 to array.size(sorted_volumes) - 2
for j = i + 1 to array.size(sorted_volumes) - 1
if array.get(sorted_volumes, j) > array.get(sorted_volumes, i)
// Swap and track indices for value area boundaries
// Accumulate until 70% threshold reached
for i = 0 to array.size(sorted_indices) - 1
accumulated_volume += vol
array.push(va_levels, array.get(volume_levels, idx))
if accumulated_volume >= target_volume
break
❓How It Works
🔶 Weighted Volume Distribution
Implements proportional volume allocation based on the percentage of each bar that intersects with price levels. When a bar spans multiple levels, volume is distributed proportionally based on the intersection ratio, ensuring precise representation of trading activity across the entire price spectrum without double-counting or volume loss.
🔶 Real-Time Profile Generation
Profiles regenerate on each bar close when in visible range mode, automatically adapting to chart zoom and scroll actions. The system maintains optimal performance through intelligent caching mechanisms and selective line updates, ensuring smooth operation even with maximum resolution settings and extended analysis periods.
🔶 Market Sentiment Analysis
Features comprehensive volume analysis table displaying total volume metrics, bullish/bearish percentages, and overall market sentiment classification. The system calculates volume dominance ratios in real-time, providing immediate insight into whether buyers or sellers control the current price structure with percentage-based sentiment thresholds.
🔶 Visual Profile Mapping
Provides multi-layered visual feedback through colored volume bars, POC line highlighting, Value Area boundaries, and optional delta indicators. The system supports profile mirroring for alternative perspectives, line extension for future reference, and customizable label positioning with detailed price information at critical levels.
Why Choose Volume Profile Grid
The Volume Profile Grid represents the evolution of volume analysis tools, combining traditional volume profile concepts with modern visualization techniques and intelligent analysis algorithms. By integrating dynamic range selection, sophisticated bull/bear separation, and multi-mode visualization with POC/Value Area detection, it provides traders with institutional-quality market structure analysis that adapts to any trading style. The comprehensive delta analysis and sentiment monitoring system eliminates guesswork while the flexible visualization options ensure optimal clarity across all market conditions, making it an essential tool for traders seeking to understand true market dynamics through volume-based price discovery.
