High Zone MapperHigh Zone and Low Zone Mapper — Quick Manual (Short-Term Trading)
Author: hkpress | Script date: 2025-10-26
This indicator draws: PDH/PDL (Prior Day High/Low), PWH/PWL (Prior Week High/Low), ORH/ORL (Opening Range High/Low), IDH/IDL (Intraday High/Low), plus a shaded Opening Range box.
I built this script after watching an interview on TradingLion with a Hong Kong trader who uses prior-day, opening-range, intraday, and prior-week levels to plan entries and exits. The approach is especially useful for traders who run tight stops (about 1.5%–3%) while aiming to size up into bigger positions. (Youtube: www.youtube.com)
1) Quick Start (15-minute default)
Timeframe: use 1–15m for scalps, 5–30m for intraday.
Opening Range (OR — Opening Range): default 15 minutes.
Turn on “Show OR lines while opening range builds” if you want to see ORH/ORL during the first 15 minutes.
Session mode:
Stocks → Use Trading Session = ON (RTH — Regular Trading Hours, e.g., 09:30–16:00).
Crypto/24h → Use Trading Session = OFF (day-start mode).
Visuals: enable PDH/PDL, PWH/PWL, ORH/ORL, IDH/IDL, and the Opening Range box (fill).
2) What each line means
PDH/PDL (Prior Day High/Low): Yesterday’s extremes; frequent reaction zones.
PWH/PWL (Prior Week High/Low): Last week’s extremes; stronger “fences.”
ORH/ORL (Opening Range High/Low): High/low of the first 15 minutes by default; key breakout compass.
IDH/IDL (Intraday High/Low): Today’s high/low so far; confirms momentum after a break.
3) Short-Term Playbook (step-by-step)
A. Before the open
Note where price is vs PDH/PDL and PWH/PWL to set a bias.
Above PDH and pushing up → bullish lean.
Below PDL and slipping → bearish lean.
B. First 15 minutes (Opening Range forms)
Let the Opening Range box print (ORH top, ORL bottom).
Think of this box as the day’s first “battlefield.”
C. Breakout entries
Long: Clean break above ORH (preferably with momentum/volume).
Stop: just below ORH (aggressive) or below ORL (conservative).
Targets: step up through PDH → PWH.
Short: Clean break below ORL.
Stop: just above ORL (aggressive) or above ORH (conservative).
Targets: step down through PDL → PWL.
D. Retest entries (missed the first move?)
After a break, wait for a retest of ORH/ORL from the other side.
Enter on rejection/continuation; place stop on the opposite side of the retested level.
E. Momentum confirmation
New IDH (Intraday High) after an ORH break = trend strengthening (consider add/hold).
New IDL after an ORL break = downtrend strengthening.
Trail stops below higher lows (long) or above lower highs (short).
F. Range mode (no break yet)
If price stays inside the box, fade edges: buy near ORL, sell near ORH, until a decisive break.
4) Risk rules (keep it simple)
Aim for R:R (Risk-to-Reward) ≥ 1:2.
Set a daily max loss (e.g., 1–2R) and respect it.
Invalidation: if price breaks and then re-enters the box and holds, exit—don’t argue.
5) Quick example
The 15-minute OR prints: ORL = 100, ORH = 105.
Price breaks 106 with momentum → Long.
Stop 104.8 (below ORH) or 99.8 (below ORL).
Targets: PDH, then PWH. Trail as IDH keeps making new highs.
6) Handy tweaks
Noisy/news days: widen to 30-minute OR to reduce whipsaws.
Strong trend open: tighten to 5–10-minute OR to engage earlier.
Choppy session: stick to box-edge fades or stand aside after two failed breaks.
7) Built-in alerts to consider
“Break Above ORH / Below ORL” → entry triggers.
“New IDH / New IDL” → momentum confirms; tighten stops or scale.
“Break Above PDH / Below PDL / Above PWH / Below PWL” → target hits or bigger trend shifts.
8) Troubleshooting
No lines? Switch to an intraday timeframe (1–60m).
ORH/ORL missing? Turn ON Show OR lines while opening range builds.
Session mismatch? Use correct RTH hours, or turn session OFF for 24h symbols.
Abbreviation cheat-sheet
OR (Opening Range), ORH/ORL (Opening Range High/Low)
PDH/PDL (Prior Day High/Low)
PWH/PWL (Prior Week High/Low)
IDH/IDL (Intraday High/Low)
RTH (Regular Trading Hours), R:R (Risk-to-Reward)
Indikatoren und Strategien
GARCH Range PredictorThis was inspired by deltatrendtrading's video on GARCH models to predict daily trading ranges and identify favorable trading conditions. Based on advanced volatility forecasting techniques, it predicts whether a trading day's true range will exceed a threshold, helping traders decide when to trade or skip a session.
Key Features
GARCH(1,1) Volatility Modeling: Uses log-transformed true ranges with exponential moving average centering
Forward-Looking Predictions: Makes predictions at session start before the day unfolds
Dynamic or Static Thresholds: Choose between fixed dollar thresholds or adaptive 20-day averages
Accuracy Tracking: Monitors prediction accuracy with overall and recent (20-day) hit rates
Visual Session Boxes: Colors trading sessions green (trade) or red (skip) based on predictions
Real-Time Statistics: Displays current predictions, thresholds, and performance metrics
How It Works
Data Transformation: Log-transforms daily true ranges and centers them using an EMA
Variance Modeling: Updates GARCH variance using: σ²ₜ = ω + α(residual²) + β(σ²ₜ₋₁)
Prediction Generation: Back-transforms log predictions to dollar values
Signal Generation: Compares predictions to threshold to generate trade/skip signals
Performance Tracking: Validates predictions against actual outcomes
Parameters
GARCH Parameters (ω, α, β): Control volatility persistence and mean reversion
EMA Period: Smoothing period for log range centering
Threshold Settings: Static dollar amount or dynamic multiplier of recent averages
Session Time: Define regular trading hours for analysis
Best Use Cases
Breakout and momentum strategies that perform better on high-range days
Risk management by avoiding low-volatility sessions
Futures day trading (optimized for MNQ/NQ detection)
Any strategy where daily range impacts profitability
Important Notes
Requires 5+ sessions for initialization and warm-up
Accuracy depends heavily on proper parameter tuning for your specific instrument
Default parameters may need adjustment for different markets
Monitor the hit rate to validate effectiveness on your timeframe
EMA 9 + VWAP Bands Crossover With Buy Sell SignalsEMA 9 + VWAP Bands Crossover With Buy Sell Signal. Includes alerts
Fibonacci Retracement MTF/LOG 2WEEK KKKKFibonacci retracment should be used to create a line of lines to justify the rest of indicators to reduce stress in indicators because we should not shout
SC_Reversal Confirmation 30 minutes by Claude (Version 1)📉 When to Use
Use this setup when the stock is in a downtrend and a bullish reversal is anticipated.
🔍 Recommended Usage This model is designed for pullback phases, where the asset is declining and a reversal is expected. It helps filter out weak signals and waits for technical confirmation before triggering an entry.
✅ Entry Signal Green triangles appear only when all reversal conditions are fully met. Entry may occur slightly after the bottom, but with a reduced likelihood of false signals.
📊 Suggested Settings Apply on a 30-minute chart using a 100-period Exponential Moving Average (EMA) based on close. Recommended for Cobalt Chart 0.
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EMA HeatmapEMA Heatmap — Indicator Description
The EMA Order Heatmap is a visual trend-structure tool designed to show whether the market is currently trending bullish, trending bearish, or moving through a neutral consolidation phase. It evaluates the alignment of multiple exponential moving averages (EMAs) at three different structural layers: short-term daily, medium-term daily, and weekly macro trend. This creates a quick and intuitive picture of how well price movement is organized across timeframes.
Each layer of the heatmap is scored from bearish to bullish based on how the EMAs are stacked relative to each other. When EMAs are in a fully bullish configuration, the row displays a bright green or lime color. Fully bearish alignment is shown in red. Yellow tones appear when the EMAs are mixed or compressing, indicating uncertainty, trend exhaustion, or a change in market character. The three rows combined offer a concise view of whether strength or weakness is isolated to one timeframe or broad across the market.
This indicator is best used as a trend filter before making trading decisions. Traders may find more consistent setups when the majority of the heatmap supports the direction of their trade. Green-dominant conditions suggest a trending bullish environment where long trades can be favored. Red-dominant conditions indicate bearish momentum and stronger potential for short opportunities. When yellow becomes more prominent, the market may be transitioning, ranging, or gearing up for a breakout, making timing more challenging and risk higher.
• Helps quickly identify directional bias
• Highlights when trends strengthen, weaken, or turn
• Provides insight into whether momentum is supported by higher timeframes
• Encourages traders to avoid fighting market structure
It is important to recognize the limitations. EMAs are lagging indicators, so the heatmap may confirm a trend after the initial move is underway, especially during fast reversals. In sideways or low-volume environments, the structure can shift frequently, reducing clarity. This tool does not generate entry or exit signals on its own and should be paired with price action, momentum studies, or support and resistance analysis for precise trade execution.
The EMA Order Heatmap offers a clean and reliable way to stay aligned with the broader market environment and avoid lower-quality trades in indecisive conditions. It supports more disciplined decision-making by helping traders focus on setups that match the prevailing structural trend.
Fibonacci Retracement MTF/LOG 3 WEEK KKKKA Fibonacci arc trading strategy uses circular arcs drawn at Fibonacci retracement levels (38.2%, 50%, 61.8%) to identify potential support and resistance zones, often intersecting with a trend line. This strategy helps traders anticipate price reversals or pullbacks, and it should be used in conjunction with other indicators
Relative Valuation OscillatorThis is a Relative Valuation Oscillator (RVO) this is attempt of replication OTC Valuation - a sophisticated multi-asset comparison indicator designed to measure whether the current asset is overvalued or undervalued relative to up to three reference assets.
Overview
The RVO compares the current chart's asset against reference assets (default: 30-Year Treasury Bonds, Gold, and US Dollar Index) to determine relative strength and valuation extremes. It outputs normalized oscillator values ranging from -100 (undervalued) to +100 (overvalued).
Key Features
Multiple Calculation Methods
The indicator offers 5 different calculation approaches:
Simple Ratio - Normalized ratio deviation from average
Percentage Difference - Percentage change comparison
Ratio Z-Score - Standard deviation-based comparison
Rate of Change Comparison - Momentum differential analysis (default)
Normalized Ratio - Min-max normalized ratio
Configurable Reference Assets
Asset 1: Default ZB (30-Year Treasury Bond Futures) - tracks interest rate sensitivity
Asset 2: Default GC (Gold Futures) - tracks safe-haven and inflation dynamics
Asset 3: Default DXY (US Dollar Index) - tracks currency strength
Each asset can be enabled/disabled independently
Fully customizable symbols
Visual Components
Multiple oscillator lines - One for each active reference asset (color-coded)
Average line - Combined signal from all active assets
Overbought/Oversold zones - Configurable threshold levels (default: ±80)
Zero line - Neutral valuation reference
Background coloring - Visual zones for extreme conditions
Signal line - Optional smoothed average
Entry markers - Long/short signals at key reversals
Signal Generation
Crossover alerts - When crossing overbought/oversold levels
Entry signals - Reversals from extreme zones
Divergence detection - Bullish/bearish divergences between price and oscillator
Zero-line crosses - Trend strength changes
Customization Options
Lookback period (10-500): Controls statistical calculation window
Normalization period (50-1000): Determines scaling sensitivity
Smoothing toggle: Optional EMA/SMA smoothing with adjustable period
Visual customization: Colors, levels, and display options
Information Table
Real-time dashboard showing:
Average oscillator value
Current status (Overvalued/Undervalued/Neutral)
Current asset price
Individual values for each active reference asset
Use Cases
Mean reversion trading - Identify extreme relative valuations for reversal trades
Sector rotation - Compare assets within similar categories
Hedging strategies - Understand correlation dynamics
Multi-asset analysis - Simultaneously compare against bonds, commodities, and currencies
Divergence trading - Spot price/oscillator divergences
Trading Strategy Applications
Long signals: When oscillator crosses above oversold level (asset recovering from undervaluation)
Short signals: When oscillator crosses below overbought level (asset declining from overvaluation)
Confirmation: Use multiple reference assets for stronger signals
Risk management: Avoid trading when all assets show neutral readings
This indicator is particularly useful for traders who want to incorporate inter-market analysis and relative strength concepts into their trading decisions, especially in OTC (Over-The-Counter) and futures markets.
BullishBuzz ORB – CALL/PUT with Chart Alerts (Final)⚙️ The Bullish BuzzBot System
1️⃣ Data Feeds (Input Layer)
BuzzBot connects to live market data through TradingView’s chart engine (or via API for more advanced builds).
It continuously pulls:
Price data (open, high, low, close per bar)
Volume
RSI, MACD, VWAP, EMA 9/21 values
Timestamps & bar intervals (1m, 5m, 15m)
That’s the raw fuel — the same data you’d use for charting.
2️⃣ Indicator Engine (Signal Layer)
This is where the logic lives — it calculates conditions in real time.
BuzzBot checks for patterns like:
EMA 9/21 Cross: detects momentum shift
VWAP Reclaim or Reject: confirms intraday bias
RSI < 50 or > 70: momentum confirmation
MACD Cross: trend continuation signal
Volume > 2x average: validates conviction
WaveTrend RBF What it does
WT-RBF extracts a “wave” of momentum by subtracting a fast Gaussian-weighted smoother from a slow one, then robust-normalizes that wave with a median/MAD proxy to produce a z-score (z). A short EMA of z forms the signal line. Optional dynamic thresholds use the MAD of z itself so overbought/oversold levels adapt to volatility regimes.
How it’s built:
Radial (Gaussian) smoothers
Causal, exponentially-decaying weights over the last radius bars using σ (sigma) to control spread.
fast = rbf_smooth(src, fastR, fastSig)
slow = rbf_smooth(src, slowR, slowSig)
wave = fast − slow (band-pass)
Robust normalization
A two-stage EMA approximates the median; MAD is estimated from EMA of absolute deviations and scaled by 1.4826 to be stdev-comparable.
z = (wave − center) / MAD
Thresholds
Dynamic OB/OS: ±2.5 × MAD(z) (or fixed levels when disabled)
Reading the indicator
Bull Cross: z crosses above sig → momentum turning up.
Bear Cross: z crosses below sig → momentum turning down.
Exits / Bias flips: zero-line crosses (below 0 → exit long bias; above 0 → exit short bias).
Overbought/Oversold: z > +thrOB or z < thrOS. With dynamics on, the bands widen/narrow with recent noise; with dynamics off, static guides at ±2 / ±2.5 are shown.
Core Inputs
Source: Price series to analyze.
Fast Radius / Fast Sigma (defaults 6 / 2.5): Shorter radius/smaller σ = snappier, higher-freq.
Slow Radius / Slow Sigma (defaults 14 / 5.0): Larger radius/σ = smoother, lower-freq baseline.
Normalization
Robust Z-Score Window (default 200): Lookback for median/MAD proxy (stability vs responsiveness).
Small ε for MAD: Floor to avoid division by zero.
Signal & Thresholds
Dynamic Thresholds (MAD-based) (on by default): Adaptive OB/OS; toggle off to use fixed guides.
Visuals
Shade OB/OS Regions: Background highlights when z is beyond thresholds.
Show Zero Line: Midline reference.
(“Plot Cross Markers” input is present for future use.)
LogNormalLibrary "LogNormal"
A collection of functions used to model skewed distributions as log-normal.
Prices are commonly modeled using log-normal distributions (ie. Black-Scholes) because they exhibit multiplicative changes with long tails; skewed exponential growth and high variance. This approach is particularly useful for understanding price behavior and estimating risk, assuming continuously compounding returns are normally distributed.
Because log space analysis is not as direct as using math.log(price) , this library extends the Error Functions library to make working with log-normally distributed data as simple as possible.
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QUICK START
Import library into your project
Initialize model with a mean and standard deviation
Pass model params between methods to compute various properties
var LogNorm model = LN.init(arr.avg(), arr.stdev()) // Assumes the library is imported as LN
var mode = model.mode()
Outputs from the model can be adjusted to better fit the data.
var Quantile data = arr.quantiles()
var more_accurate_mode = mode.fit(model, data) // Fits value from model to data
Inputs to the model can also be adjusted to better fit the data.
datum = 123.45
model_equivalent_datum = datum.fit(data, model) // Fits value from data to the model
area_from_zero_to_datum = model.cdf(model_equivalent_datum)
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TYPES
There are two requisite UDTs: LogNorm and Quantile . They are used to pass parameters between functions and are set automatically (see Type Management ).
LogNorm
Object for log space parameters and linear space quantiles .
Fields:
mu (float) : Log space mu ( µ ).
sigma (float) : Log space sigma ( σ ).
variance (float) : Log space variance ( σ² ).
quantiles (Quantile) : Linear space quantiles.
Quantile
Object for linear quantiles, most similar to a seven-number summary .
Fields:
Q0 (float) : Smallest Value
LW (float) : Lower Whisker Endpoint
LC (float) : Lower Whisker Crosshatch
Q1 (float) : First Quartile
Q2 (float) : Second Quartile
Q3 (float) : Third Quartile
UC (float) : Upper Whisker Crosshatch
UW (float) : Upper Whisker Endpoint
Q4 (float) : Largest Value
IQR (float) : Interquartile Range
MH (float) : Midhinge
TM (float) : Trimean
MR (float) : Mid-Range
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TYPE MANAGEMENT
These functions reliably initialize and update the UDTs. Because parameterization is interdependent, avoid setting the LogNorm and Quantile fields directly .
init(mean, stdev, variance)
Initializes a LogNorm object.
Parameters:
mean (float) : Linearly measured mean.
stdev (float) : Linearly measured standard deviation.
variance (float) : Linearly measured variance.
Returns: LogNorm Object
set(ln, mean, stdev, variance)
Transforms linear measurements into log space parameters for a LogNorm object.
Parameters:
ln (LogNorm) : Object containing log space parameters.
mean (float) : Linearly measured mean.
stdev (float) : Linearly measured standard deviation.
variance (float) : Linearly measured variance.
Returns: LogNorm Object
quantiles(arr)
Gets empirical quantiles from an array of floats.
Parameters:
arr (array) : Float array object.
Returns: Quantile Object
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DESCRIPTIVE STATISTICS
Using only the initialized LogNorm parameters, these functions compute a model's central tendency and standardized moments.
mean(ln)
Computes the linear mean from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
median(ln)
Computes the linear median from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
mode(ln)
Computes the linear mode from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
variance(ln)
Computes the linear variance from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
skewness(ln)
Computes the linear skewness from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
kurtosis(ln, excess)
Computes the linear kurtosis from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
excess (bool) : Excess Kurtosis (true) or regular Kurtosis (false).
Returns: Between 0 and ∞
hyper_skewness(ln)
Computes the linear hyper skewness from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
Returns: Between 0 and ∞
hyper_kurtosis(ln, excess)
Computes the linear hyper kurtosis from log space parameters.
Parameters:
ln (LogNorm) : Object containing log space parameters.
excess (bool) : Excess Hyper Kurtosis (true) or regular Hyper Kurtosis (false).
Returns: Between 0 and ∞
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DISTRIBUTION FUNCTIONS
These wrap Gaussian functions to make working with model space more direct. Because they are contained within a log-normal library, they describe estimations relative to a log-normal curve, even though they fundamentally measure a Gaussian curve.
pdf(ln, x, empirical_quantiles)
A Probability Density Function estimates the probability density . For clarity, density is not a probability .
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate for which a density will be estimated.
empirical_quantiles (Quantile) : Quantiles as observed in the data (optional).
Returns: Between 0 and ∞
cdf(ln, x, precise)
A Cumulative Distribution Function estimates the area under a Log-Normal curve between Zero and a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
ccdf(ln, x, precise)
A Complementary Cumulative Distribution Function estimates the area under a Log-Normal curve between a linear X coordinate and Infinity.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
cdfinv(ln, a, precise)
An Inverse Cumulative Distribution Function reverses the Log-Normal cdf() by estimating the linear X coordinate from an area.
Parameters:
ln (LogNorm) : Object of log space parameters.
a (float) : Normalized area .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
ccdfinv(ln, a, precise)
An Inverse Complementary Cumulative Distribution Function reverses the Log-Normal ccdf() by estimating the linear X coordinate from an area.
Parameters:
ln (LogNorm) : Object of log space parameters.
a (float) : Normalized area .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
cdfab(ln, x1, x2, precise)
A Cumulative Distribution Function from A to B estimates the area under a Log-Normal curve between two linear X coordinates (A and B).
Parameters:
ln (LogNorm) : Object of log space parameters.
x1 (float) : First linear X coordinate .
x2 (float) : Second linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
ott(ln, x, precise)
A One-Tailed Test transforms a linear X coordinate into an absolute Z Score before estimating the area under a Log-Normal curve between Z and Infinity.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 0.5
ttt(ln, x, precise)
A Two-Tailed Test transforms a linear X coordinate into symmetrical ± Z Scores before estimating the area under a Log-Normal curve from Zero to -Z, and +Z to Infinity.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
ottinv(ln, a, precise)
An Inverse One-Tailed Test reverses the Log-Normal ott() by estimating a linear X coordinate for the right tail from an area.
Parameters:
ln (LogNorm) : Object of log space parameters.
a (float) : Half a normalized area .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
tttinv(ln, a, precise)
An Inverse Two-Tailed Test reverses the Log-Normal ttt() by estimating two linear X coordinates from an area.
Parameters:
ln (LogNorm) : Object of log space parameters.
a (float) : Normalized area .
precise (bool) : Double precision (true) or single precision (false).
Returns: Linear space tuple :
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UNCERTAINTY
Model-based measures of uncertainty, information, and risk.
sterr(sample_size, fisher_info)
The standard error of a sample statistic.
Parameters:
sample_size (float) : Number of observations.
fisher_info (float) : Fisher information.
Returns: Between 0 and ∞
surprisal(p, base)
Quantifies the information content of a single event.
Parameters:
p (float) : Probability of the event .
base (float) : Logarithmic base (optional).
Returns: Between 0 and ∞
entropy(ln, base)
Computes the differential entropy (average surprisal).
Parameters:
ln (LogNorm) : Object of log space parameters.
base (float) : Logarithmic base (optional).
Returns: Between 0 and ∞
perplexity(ln, base)
Computes the average number of distinguishable outcomes from the entropy.
Parameters:
ln (LogNorm)
base (float) : Logarithmic base used for Entropy (optional).
Returns: Between 0 and ∞
value_at_risk(ln, p, precise)
Estimates a risk threshold under normal market conditions for a given confidence level.
Parameters:
ln (LogNorm) : Object of log space parameters.
p (float) : Probability threshold, aka. the confidence level .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
value_at_risk_inv(ln, value_at_risk, precise)
Reverses the value_at_risk() by estimating the confidence level from the risk threshold.
Parameters:
ln (LogNorm) : Object of log space parameters.
value_at_risk (float) : Value at Risk.
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
conditional_value_at_risk(ln, p, precise)
Estimates the average loss beyond a confidence level, aka. expected shortfall.
Parameters:
ln (LogNorm) : Object of log space parameters.
p (float) : Probability threshold, aka. the confidence level .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
conditional_value_at_risk_inv(ln, conditional_value_at_risk, precise)
Reverses the conditional_value_at_risk() by estimating the confidence level of an average loss.
Parameters:
ln (LogNorm) : Object of log space parameters.
conditional_value_at_risk (float) : Conditional Value at Risk.
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and 1
partial_expectation(ln, x, precise)
Estimates the partial expectation of a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and µ
partial_expectation_inv(ln, partial_expectation, precise)
Reverses the partial_expectation() by estimating a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
partial_expectation (float) : Partial Expectation .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
conditional_expectation(ln, x, precise)
Estimates the conditional expectation of a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between X and ∞
conditional_expectation_inv(ln, conditional_expectation, precise)
Reverses the conditional_expectation by estimating a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
conditional_expectation (float) : Conditional Expectation .
precise (bool) : Double precision (true) or single precision (false).
Returns: Between 0 and ∞
fisher(ln, log)
Computes the Fisher Information Matrix for the distribution, not a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
log (bool) : Sets if the matrix should be in log (true) or linear (false) space.
Returns: FIM for the distribution
fisher(ln, x, log)
Computes the Fisher Information Matrix for a linear X coordinate, not the distribution itself.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
log (bool) : Sets if the matrix should be in log (true) or linear (false) space.
Returns: FIM for the linear X coordinate
confidence_interval(ln, x, sample_size, confidence, precise)
Estimates a confidence interval for a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate .
sample_size (float) : Number of observations.
confidence (float) : Confidence level .
precise (bool) : Double precision (true) or single precision (false).
Returns: CI for the linear X coordinate
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CURVE FITTING
An overloaded function that helps transform values between spaces. The primary function uses quantiles, and the overloads wrap the primary function to make working with LogNorm more direct.
fit(x, a, b)
Transforms X coordinate between spaces A and B.
Parameters:
x (float) : Linear X coordinate from space A .
a (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.
b (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.
Returns: Adjusted X coordinate
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EXPORTED HELPERS
Small utilities to simplify extensibility.
z_score(ln, x)
Converts a linear X coordinate into a Z Score.
Parameters:
ln (LogNorm) : Object of log space parameters.
x (float) : Linear X coordinate.
Returns: Between -∞ and +∞
x_coord(ln, z)
Converts a Z Score into a linear X coordinate.
Parameters:
ln (LogNorm) : Object of log space parameters.
z (float) : Standard normal Z Score.
Returns: Between 0 and ∞
iget(arr, index)
Gets an interpolated value of a pseudo -element (fictional element between real array elements). Useful for quantile mapping.
Parameters:
arr (array) : Float array object.
index (float) : Index of the pseudo element.
Returns: Interpolated value of the arrays pseudo element.
NASDAQ Trading System with PivotsThis TradingView indicator, designed for the 30-minute NASDAQ (^IXIC) chart, guides QQQ options trading using a trend-following strategy. It plots a 20-period SMA (blue) and a 100-period SMA (red), with an optional 250-period SMA (orange) inspired by rauItrades' NASDAQ SMA outfit. A bullish crossover (20 SMA > 100 SMA) triggers a green "BUY" triangle below the bar, signaling a potential long position in QQQ, while a bearish crossunder (20 SMA < 100 SMA) shows a red "SELL" triangle above, indicating a short or exit. The background colors green (bullish) or red (bearish) for trend bias. Orange circles (recent highs) and purple circles (recent lows) mark support/resistance levels using 5-bar pivot points.
Svopex Session Highlighter# Session Highlighter
## Description
**Session Highlighter** is a powerful Pine Script indicator designed to visually identify and mark specific trading hours on your chart. This tool helps traders focus on their preferred trading sessions by highlighting the background during active hours and marking the session start with customizable visual markers.
## Key Features
- **📊 Session Background Highlighting**: Automatically shades the chart background during your defined trading hours (default: 7:00 - 23:00)
- **🎯 Smart Session Start Marker**: Places a marker on the last candle before session start, intelligently adapting to your timeframe:
- 1 Hour chart: Marker at 6:00
- 15 Minute chart: Marker at 6:45
- 5 Minute chart: Marker at 6:55
- 1 Minute chart: Marker at 6:59
- **🌍 Timezone Support**: Choose from multiple timezones (Europe/Prague, Europe/London, America/New_York, UTC)
- **🎨 5 Marker Styles**: Customize your session start indicator:
- Triangle
- Circle
- Diamond
- Label with time text
- Vertical line
- **⚙️ Fully Customizable**: Adjust start/end hours, timezone, and marker style through simple settings
## Settings
- **Start Hour**: Set your session start time (0-23)
- **End Hour**: Set your session end time (0-23)
- **Timezone**: Select your trading timezone
- **Marker Style**: Choose your preferred visual marker
## Use Cases
- Identify London/New York trading sessions
- Mark Asian session hours
- Highlight your personal trading windows
- Avoid trading during off-hours
- Perfect for day traders and scalpers
## Installation
1. Copy the Pine Script code
2. Open TradingView Pine Editor
3. Paste the code and click "Add to Chart"
4. Configure settings to match your trading schedule
XAUUSD Family Scalping (5min)🟡 XAUUSD Family Scalping 5-Min — Momentum Precision Indicator
Overview
This indicator is built for XAUUSD (Gold) on the 5-minute timeframe and is designed for short-term momentum scalping.
It helps traders identify early reversal zones, confirm momentum direction, and detect exhaustion points during high-volatility market moves.
Core Concept
The indicator measures momentum strength and price acceleration using a smoothed oscillator.
It features two adjustable thresholds:
Overbought level: 58
Oversold level: -58
When the momentum line crosses above or below these zones, it signals potential trend continuation or reversal opportunities.
Features
Detects short-term momentum shifts on XAUUSD 5M.
Works with EMA-based trend confirmation (optional).
Adaptive smoothing reduces noise and false reversals.
Highlights overbought/oversold areas visually.
Can be combined with price action or other oscillators for confluence.
Usage
Instrument: XAUUSD (Gold)
Best timeframe: 5-minute (scalping setup)
Use case: Detecting momentum exhaustion and reversal entries.
Sessions: London & New York recommended.
Disclaimer
This indicator is for market analysis and educational purposes.
No indicator guarantees profit — use proper risk management and test before live trading.
Flux AI PullBack System (Hybrid Pro)Flux AI PullBack System (Hybrid Pro)
//Session-Aware | Adaptive Confluence | Grace Confirm Logic//
Overview:
The Flux AI PullBack System (Hybrid Pro v5) is an adaptive, session-aware pullback indicator designed to identify high-probability continuation setups within trending markets. It automatically adjusts between “Classic” and “Enhanced” logic modes based on volatility, volume, and ATR slope, allowing it to perform seamlessly across different market sessions (Asian, London, and New York).
Core Features:
Hybrid Auto Mode — Dynamically switches between Classic (fast-moving) and Enhanced (strict) modes.
Session-Aware Context — Optimized for intraday trading in ES, NQ, and SPY.
Grace Confirmation Logic — Validates pullbacks with a follow-through condition to reduce noise.
Adaptive EMA Zone (38/62) — Highlights pullback areas with dynamic aqua fill and transparency linked to trend strength.
Noise Suppression Filter — Prevents false pullbacks during EMA crossovers or unstable transitions.
Weighted Confluence Model — Combines trend, ATR, volume, and swing structure for confirmation strength.
Pine v6 Compliant Alerts — Constant-string safe, ready for webhooks and automation.
Visual Elements:
Aqua EMA Zone: Displays the “breathing” pullback band (tightens during volatility spikes).
PB↑ / PB↓ Markers: Confirmed pullbacks with subtle transparency and fixed label size.
Bar Highlights: Yellow for pullbacks; ice-blue for confirmed continuation.
Use Cases
Perfect for:
Intraday trend traders
0DTE SPX / ES scalpers
Futures traders (NQ, MNQ, MES)
Algorithmic strategy builders using webhooks
Recommended Timeframes:
1–15 minute charts (scalping / intraday)
Higher timeframes for swing confirmations.
Attribution:
This open-source script was inspired by Chris Moody’s “CM Slingshot System” and JustUncleL’s Pullback Tools, but it was built from scratch using AI-assisted code refinement (ChatGPT).
All logic and enhancements are original, not derived from proprietary software.
License: MIT (Open Source)
© 2025 Ken Anderson — You may modify, use, or redistribute with credit.
Keywords:
Pullback, Reversal, AI Trading, EMA Zone, Session Aware, Futures Trading, SPX, ES, NQ, ATR Filter, Volume Confirmation, Flux System, Pine Script v6, Non-Repainting, Adaptive Trading Indicator.
ChartWise Pro 9/30 Complete Indicator Description Added!
This comprehensive description covers:
📊 Core Signal System - MA Crossover Strategy (9/30)
📦 Order Blocks (OB) - Institutional zones
📈 Fair Value Gaps (FVG) - Price imbalances
🔝 Double Top/Bottom - Reversal patterns
📍 Support & Resistance - Key levels with beeping
🚩 Swing Flags - Green/Red for highs/lows
📐 Chart Patterns - Channels, wedges, triangles
🔄 CHOCH - Change of Character
🧠 Smart Features - Auto-cleanup, sound alerts, toggles
🌐 Markets - Crypto, Futures, Forex, Options
🔌 Platforms - TradingView, Sierra, Rithmic, etc.
This description is now live on your About page! 📚
Smart Money Concept: FVG Block Filter Smart Money Concept: FVG Block Filter (FVG Block Range vs N Range) with Candle Highlighter
Summary:
Smart Money Concept (SMC): An advanced indicator designed to visualize and filter Fair Value Gaps (FVG) blocks based on their size (Range) compared to the preceding N Range candle movement. It also includes a customizable Candle Highlighter function that marks the specific candle responsible for creating the FVG. The indicator allows full color customization for both blocks and the highlighter, and features clean, label-free charts by default.
Key Features:
FVG Block Detection: Automatically identifies and groups sequential FVG imbalances to form consolidated FVG blocks.
FVG Block Filtering (N Range): Filters blocks based on a user-defined rule, comparing the block's size (Range) to the range of the preceding N candles (e.g., requiring the FVG block to be larger than the range of the previous 6 candles).
Customizable Candle Highlighter: Marks the central candle (B) within the FVG structure (A-B-C) to highlight the source of the price imbalance. Highlighter colors are fully adjustable via inputs.
Visualization Control: Labels are turned OFF by default to keep the chart clean but can be easily enabled via the indicator settings.
Full Color Customization: Allows independent customization of Bullish and Bearish FVG Block colors, Block Transparency, and Bullish/Bearish Highlighter colors.
Keywords:
Smart Money Concept, SMC, Fair Value Gap, FVG, Imbalance, Block Filter, Candle Highlighter, Range.
Fixed Dollar Risk LinesFixed Dollar Risk Lines is a utility indicator that converts a user-defined dollar risk into price distance and plots risk lines above and below the current price for popular futures contracts. It helps you place stops or entries at a consistent dollar risk per trade, regardless of the market’s tick value or tick size.
What it does:
-You choose a dollar amount to risk (e.g., $100) and a futures contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC).
The script automatically:
-Looks up the contract’s tick value and tick size
-Converts your dollar risk into number of ticks
-Converts ticks into price distance
Plots:
-Long Risk line below current price
-Short Risk line above current price
-Optional labels show exact price levels and an information table summarizes your settings.
Key features
-Consistent dollar risk across instruments
-Supports major futures contracts with built‑in tick values and sizes
-Toggle Long and Short risk lines independently
-Customizable line width and colors (lines and labels)
-Right‑axis price level display for quick reading
-Compact info table with contract, risk, and computed prices
Typical use
-Long setups: use the green line as a stop level below entry to match your chosen dollar risk.
-Short setups: use the red line as a stop level above entry to match your chosen dollar risk.
-Quickly compare how the same dollar risk translates to distance on different contracts.
Inputs
-Risk Amount (USD)
-Futures Contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC)
-Show Long/Short lines (toggles)
-Line Width
-Colors for lines and labels
Notes
-Designed for futures symbols that match the listed contracts’ tick specs. If your symbol has different tick value/size than the defaults, results will differ.
-Intended for educational/informational use; not financial advice.
-This tool streamlines risk placement so you can focus on execution while keeping dollar risk consistent across markets.
DG Market Structure (Inspired By Deadcat)MS Indicator taken from Deadcat and enhanced a little bit
I added CHoCH and BOS to better tell the story of why price is moving a certain way. Also made a lot more of the values Input based for testing.
I tried to add in retracement values on the MTF chart but I don't think the math is right, maybe someone can figure out the math.
S&P Trading System with PivotsThe S&P Trading System with Pivots is a TradingView indicator designed for the 30-minute SPX chart to guide SPY options trading. It uses a trend-following strategy with:
10 SMA and 50 SMA: Plots a 10-period (blue) and 50-period (red) Simple Moving Average. A bullish crossover (10 SMA > 50 SMA) signals a potential buy (green triangle below bar), while a bearish crossunder (10 SMA < 50 SMA) signals a sell or exit (red triangle above bar).
Trend Bias: Colors the background green (bullish) or red (bearish) based on SMA positions.
Pivot Points: Marks recent highs (orange circles) and lows (purple circles) as potential resistance and support levels, using a 5-bar lookback period.
Simple BOS ScannerThis is a Break of Structure Scanner
It checks whenever there is a break of structure and can be used on the Screener screen
Turtle/Donchian Screener — Recency & CloseAtBuyTurtle strategy, donchian channels. For Pine screener with for example buysignals and sellsignals.
Liquidity Grab + RSI Divergence═══════════════════════════════════════════════════════════════
LIQUIDITY GRAB + RSI DIVERGENCE INDICATOR
═══════════════════════════════════════════════════════════════
📌 OVERVIEW
This indicator identifies high-probability reversals by combining:
• Liquidity sweeps (stop hunts)
• RSI divergence confirmation
• Filters false breakouts automatically
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🟢 BUY SIGNAL (Green Triangle Up)
REQUIRES BOTH CONDITIONS:
1. Liquidity Grab Below Previous Low
• Price breaks BELOW recent low
• Candle CLOSES ABOVE that low
• Traps sellers who shorted the breakdown
2. Bullish RSI Divergence
• Price: Lower Low (LL)
• RSI: Higher Low (HL)
• Shows weakening downward momentum
➜ Result: Potential bullish reversal
═══════════════════════════════════════════════════════════════
🔴 SELL SIGNAL (Red Triangle Down)
REQUIRES BOTH CONDITIONS:
1. Liquidity Grab Above Previous High
• Price breaks ABOVE recent high
• Candle CLOSES BELOW that high
• Traps buyers who bought the breakout
2. Bearish RSI Divergence
• Price: Higher High (HH)
• RSI: Lower High (LH)
• Shows weakening upward momentum
➜ Result: Potential bearish reversal
═══════════════════════════════════════════════════════════════
📊 VISUAL INDICATORS
Main Signals:
🔺 Large Green Triangle = BUY (Liq Grab + Bullish Div)
🔻 Large Red Triangle = SELL (Liq Grab + Bearish Div)
Reference Levels:
━ Red Line = Previous High Level
━ Green Line = Previous Low Level
Additional Markers (Optional):
○ Small Green Circle = Liquidity grab low only
○ Small Red Circle = Liquidity grab high only
✕ Small Blue Cross = Bullish divergence only
✕ Small Orange Cross = Bearish divergence only
═══════════════════════════════════════════════════════════════
⚙️ SETTINGS
1. Lookback Period (Default: 20)
• Range: 5-100
• Sets how far back to identify previous highs/lows
• Higher = fewer but stronger levels
• Lower = more frequent but weaker levels
2. RSI Length (Default: 14)
• Range: 5-50
• Standard RSI calculation period
• 14 is industry standard
3. RSI Divergence Lookback (Default: 5)
• Range: 3-20
• Controls pivot point sensitivity
• Higher = fewer divergence signals
• Lower = more divergence signals
4. Show Labels (Default: ON)
• Toggle BUY/SELL text labels
• Disable for cleaner chart view
═══════════════════════════════════════════════════════════════
💡 HOW TO USE
Step 1: WAIT FOR CONFIRMATION
• Only trade LARGE TRIANGLE signals
• Ignore small circles/crosses alone
Step 2: CHECK TIMEFRAME
• Best on: 15min, 1H, 4H, Daily
• Avoid: 1min, 5min (too noisy)
Step 3: CONFIRM CONTEXT
• Check overall market trend
• Identify key support/resistance
• Look for confluence with price action
Step 4: ENTRY & RISK MANAGEMENT
• Enter on signal candle close or pullback
• Stop loss below/above the liquidity grab wick
• Target: Previous swing high/low or key levels
• Risk/Reward: Minimum 1:2 ratio
Step 5: SET ALERTS
• Create alert for "BUY Signal"
• Create alert for "SELL Signal"
• Never miss opportunities
═══════════════════════════════════════════════════════════════
✅ BEST PRACTICES
DO:
✓ Use on multiple timeframes for confluence
✓ Combine with support/resistance zones
✓ Wait for both conditions (liq grab + divergence)
✓ Practice on demo account first
✓ Use proper position sizing
DON'T:
✗ Trade every small circle/cross
✗ Use on very low timeframes (<15min)
✗ Ignore overall market context
✗ Trade without stop loss
✗ Risk more than 1-2% per trade
═══════════════════════════════════════════════════════════════
⚠️ IMPORTANT NOTES
• This is a CONFIRMATION tool, not a holy grail
• No indicator is 100% accurate
• Combine with your trading strategy
• Backtest on your preferred instruments
• Adjust parameters for your trading style
• Higher timeframes = more reliable signals
• Always use risk management
═══════════════════════════════════════════════════════════════
🔔 ALERTS INCLUDED
Two alert conditions are built-in:
1. "BUY Signal" - Liquidity Grab + Bullish RSI Divergence
2. "SELL Signal" - Liquidity Grab + Bearish RSI Divergence
═══════════════════════════════════════════════════════════════
📈 RECOMMENDED SETTINGS BY TIMEFRAME
5-15 Min Charts:
• Lookback: 10-15
• RSI Length: 14
• RSI Div Lookback: 3-5
1H-4H Charts:
• Lookback: 20-30
• RSI Length: 14
• RSI Div Lookback: 5-7
Daily Charts:
• Lookback: 30-50
• RSI Length: 14
• RSI Div Lookback: 7-10
═══════════════════════════════════════════════════════════════
Good luck and trade safe! 🚀
