Rainbow Moving Averages (v5 safe)Rainbow Moving Averages — plots multiple moving averages of different lengths in a rainbow colour scheme to visualise market trend strength and direction. The spread and alignment of the lines help identify trend changes and momentum shifts.
Chỉ báo và chiến lược
Micro cycle0-Minute Quarter Cycle Indicator (Q90-Final)
This indicator plots vertical lines marking the four quarters (Q1,Q2,Q3,Q4) of a continuous 90-minute cycle.
It is designed for traders who utilize time-based cycles for market analysis and entry/exit timing.
So you can easy identify the cycles off the micro cycles Q1,Q2,Q3 and Q4
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
HV Spike Strategy (HVP + OR Breakout + Reversal + TP/SL Modes)Here is a script that I tried to make it simple, although it has several parameters, I will try to explain, here we go:
Logic: Open Range Breakout: otherwise knows as First Candle Rule, usually used for the first candle in the opening of a market session, in my strategy there is an option to use it even for Crypto that operate 24/7, how to do that? Simply by detecting Volatility from the HVP (Historical Volatility Percentile). Then the ORB logic kicks in and the first candle with high volatility gives the ranges for the trades. The proper HVP Activation Threshold has to be selected for each currency pair/index/crypto in order to have maximum profit.
Enter a trade: when the price goes 100% above/below the First Candle Rule Range. That way it is filtering fake breakouts. Also if the price reverses back into the range the strategy takes the opposite trade.
Exit a trade: SL/TP By percentage or ATR, selection in the input menu.
My intention is to avoid using lagging indicators or guessing of Price Action, purely Bull/Bear indication by the first candle.
I hope you find this helpful! Wishing all successful Trades!
ATR Trailing Stop with Entry Date & First-Day MultiplierATR based trailing stop based on a X post of Aksel Kibar.
TrendFlowTrendFlow is a visual trend analysis tool that helps identify changes in market direction and momentum.
It uses three exponential moving averages (EMA 21, EMA 50, and EMA 200) to define short-, medium-, and long-term trend structure.
A dynamic color fill highlights the relationship between the 21 and 50 EMAs:
When EMA 21 is above EMA 50, a green fill indicates upward momentum.
When EMA 21 is below EMA 50, a red fill shows downward momentum.
The EMA 200 line adapts its color based on its position relative to the shorter EMAs:
Red when above both EMAs (strong upward structure)
Green when below both EMAs (strong downward structure)
Gray when between them (neutral or consolidation phase)
This setup provides a clear visual framework for observing trend flow and directional bias over multiple timeframes.
Developed by The Volume Hub Fintech and Strategy Development
Corpus Bollinger BandsThis is a copy of the build-in indicator, but as addition, it shows the distance between upper and lower band in percentage.
Buyside & Sellside Liquidity The Buyside & Sellside Liquidity Indicator is an advanced Smart Money Concepts (SMC) tool that automatically detects and visualizes liquidity zones and liquidity voids (imbalances) directly on the chart.
🟢 Function and meaning:
1. Buyside Liquidity (green):
Highlights price zones above current price where short traders’ stop-loss orders are likely resting.
When price sweeps these areas, it often indicates a liquidity grab or stop hunt.
👉 These zones are labeled with 💵💰 emojis for a clear visual cue where smart money collects liquidity.
2. Sellside Liquidity (red):
Highlights zones below the current price where long traders’ stop-losses are likely placed.
Once breached, these often signal a potential reversal upward.
👉 The 💵💰🪙 emojis make these liquidity targets visually intuitive on the chart.
3. Liquidity Voids (bright areas):
Indicate inefficient price areas, where the market moved too quickly without filling orders.
These zones are often revisited later as the market seeks balance (fair value).
👉 Shown as light shaded boxes with 💰 emojis to emphasize imbalance regions.
💡 Usage:
• Helps spot smart money manipulation and stop hunts.
• Marks potential reversal or breakout zones.
• Great for traders applying SMC, ICT, or Fair Value Gap strategies.
✨ Highlight:
Dollar and money bag emojis (💵💰🪙💸) are integrated directly into chart labels to create a clear and visually engaging representation of liquidity areas.
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.
NOVA Breakout Signals v2.2 (TF M30)A clean, rules-based breakout signal tool for 30-minute charts.
It detects Dow swing breakouts and filters them with RSI, MACD and Volume so you only see the higher-quality entries. The script does not place trades and does not calculate SL/TP – it only prints clear LONG/SHORT labels at the entry price.
⸻
How it works
1. Timeframe enforcement – Signals are generated only on M30. On other timeframes the script shows a notice and stays silent.
2. Breakout engine (Dow swings) – The last confirmed swing high/low (pivots) is tracked.
• Breakout Up: bar closes above the last swing high by a small buffer.
• Breakout Down: bar closes below the last swing low by a small buffer.
3. Quality filters (all must be true):
• RSI (default length 30):
• Long: RSI > threshold and rising.
• Short: RSI < threshold and falling.
• MACD (12/26/9):
• Long: histogram > 0 and line > signal.
• Short: histogram < 0 and line < signal.
• Volume: current volume > SMA(volume, 20) × multiplier.
4. Debounce / anti-spam
• Cooldown of 4 hours (8 M30 bars) after any signal.
• Minimum price distance from the previous signal to avoid clustered labels.
Signals appear once the bar closes (barstate.isconfirmed). No swing lines are drawn to keep the chart clean; only entry labels are shown.
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Inputs (key)
• RSI length & thresholds for Long/Short confirmation.
• MACD uses 12/26/9 (fixed).
• Volume multiplier (relative to SMA 20).
• Breakout buffer %, Cooldown hours, Min distance %.
• Show labels (on/off).
⸻
Usage tips
• Start with gold/major FX/indices on M30; use “Once per bar close” if you attach alerts.
• Increase the breakout buffer and volume multiplier in choppy markets.
• Tighten RSI thresholds (e.g., 55/45) if you want fewer but stronger signals.
⸻
Notes & limitations
• Pivots confirm after a few bars by definition; signals themselves are printed only on confirmed bar close and do not repaint once shown.
• This is a signal indicator, not investment advice. Always manage risk.
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter
A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.
If you would like to see my original Kalman Filter, please find it here:
Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:
Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.
How it works
Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
Noise parameters :
Process Noise controls agility — higher values make the filter more responsive but also more volatile.
Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.
Use cases
Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
Parameter guidance
Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking.
VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
Interpretation
When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO
The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions.
It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open.
This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance.
Key Features
Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance.
Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities.
Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York).
Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities.
Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times.
Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis.
Intended Use
ADSR PRO is a statistical reference indicator.
It does not generate buy/sell signals or predictive forecasts.
Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis.
Credits
Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools.
Risk Warning
This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument.
Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose.
Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed.
The analysis presented does not take into account your objectives, financial situation, or risk tolerance.
Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional.
Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured.
You are solely responsible for all trading decisions, including position sizing and risk management.
No external links, promotions, or contact details are provided, in line with TradingView House Rules.
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 levels MTF 2WEEK 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
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
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
TI65**TI65 (Trend Intensity 65)** is a technical indicator designed to measure the strength and momentum of a trend over two distinct periods. It compares a short-term 7-period simple moving average (SMA) with a long-term 65-period SMA, producing a ratio that helps traders identify shifts in market momentum and trend direction.
- When the **TI65 value is greater than 1**, it indicates that the short-term moving average is above the long-term average, suggesting increasing momentum and a potentially bullish trend.
- When the **TI65 value drops below 1**, it signals weakening short-term momentum relative to the longer-term trend, often interpreted as a bearish or consolidating phase.
This indicator can be applied to both price and volume data, making it useful for identifying periods of strong volume surges or price movements. By observing changes in the TI65 ratio, traders can pinpoint low-risk entry points for trend-following strategies and quickly recognize periods of market transition.
TI65 is commonly used by momentum and breakout traders for screening strong candidates and confirming the sustainability of ongoing trends. It is simple, effective, and easily implemented via custom scripts on popular platforms like TradingView.
Dow Jones Trading System with PivotsThis TradingView indicator, tailored for the 30-minute Dow Jones (^DJI) chart, supports DIA options trading with a trend-following approach. It features a 30-period SMA (blue) and a 60-period SMA (red), with an optional 90-period SMA (orange) drawn from rauItrades' Dow SMA outfit. A bullish crossover (30 SMA > 60 SMA) displays a green "BUY" triangle below the bar for potential DIA longs, while a bearish crossunder (30 SMA < 60 SMA) shows a red "SELL" triangle above for shorts or exits. The background turns green (bullish) or red (bearish) to indicate trend bias. Pivot points highlight recent highs (orange circles) and lows (purple circles) for support/resistance, using a 5-bar lookback. Alerts notify for crossovers.
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.
Volume TI65**TI65 (Trend Intensity 65)** is a technical indicator designed to measure the strength and momentum of a trend over two distinct periods. It compares a short-term 7-period simple moving average (SMA) with a long-term 65-period SMA, producing a ratio that helps traders identify shifts in market momentum and trend direction.
- When the **TI65 value is greater than 1**, it indicates that the short-term moving average is above the long-term average, suggesting increasing momentum and a potentially bullish trend.
- When the **TI65 value drops below 1**, it signals weakening short-term momentum relative to the longer-term trend, often interpreted as a bearish or consolidating phase.
This indicator can be applied to both price and volume data, making it useful for identifying periods of strong volume surges or price movements. By observing changes in the TI65 ratio, traders can pinpoint low-risk entry points for trend-following strategies and quickly recognize periods of market transition.
TI65 is commonly used by momentum and breakout traders for screening strong candidates and confirming the sustainability of ongoing trends. It is simple, effective, and easily implemented via custom scripts on popular platforms like TradingView.
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.
