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.
Statistics
RBLR - GSK Vizag AP IndiaThis indicator identifies the Opening Range High (ORH) and Low (ORL) based on the first 15 minutes of the Indian equity market session (9:15 AM to 9:30 AM IST). It draws horizontal lines extending these levels until market close (3:30 PM IST) and generates visual signals for price breakouts above ORH or below ORL, as well as reversals back into the range.
Key features:
- **Range Calculation**: Captures the high and low during the opening period using real-time bar data.
- **Line Extension**: Lines are dynamically extended bar-by-bar within the session for clear visualization.
- **Signals**:
- Green triangle up: Crossover above ORH (potential bullish breakout).
- Red triangle down: Crossunder below ORL (potential bearish breakout).
- Yellow labels: Reversals from breakout levels back into the range.
- **Labels**: "RAM BAAN" marks the ORH (inspired by a precise arrow from the Ramayana), and "LAKSHMAN REKHA" marks the ORL (inspired by a protective boundary line from the same epic).
- **Customization**: Toggle signals on/off and select line styles (Dotted, Dashed, Solid, or Smoothed, with transparency for Smoothed).
The state-tracking logic prevents redundant signals by monitoring if price remains outside the range after a breakout. This helps users observe range-bound behavior or directional moves without built-in alerts. This indicator is particularly useful for day trading on longer intraday timeframes (e.g., 15-minute charts) to identify session-wide trends and avoid noise in shorter frames. For best results, apply on intraday timeframes on NSE/BSE symbols. Note that lines and labels are limited to the script's max counts to avoid performance issues on long histories.
**Disclaimer**: This indicator is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Trading in financial markets involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Users should conduct their own research, consider their financial situation, and consult with qualified professionals before making any investment decisions. The author and TradingView assume no liability for any losses incurred from its use.
PG ATM Strike Line with Call & Put PremiumsPine Script: ATM Strike Line with Call & Put Premiums (Simplified)This Pine Script for TradingView displays the At-The-Money (ATM) strike price, futures price, call/put premiums (time value), and two ratios—Premium Ratio (PR) and Volume Ratio (VR)—for a user-selected underlying asset (e.g., NIFTY, BANKNIFTY, or stocks). It helps traders gauge near-term market direction using options data.How the Script WorksInputs:Expiry: Select year (e.g., '25), month (01–12), day (01–31) for option expiry (e.g., '251028').
Timeframe: Choose data timeframe (e.g., Daily, 15-min).
Symbol: Auto-detects chart symbol or select from Indian indices/stocks.
Strike: Auto-ATM (based on futures) or manual strike input.
Interval: Auto (e.g., 100 for NIFTY) or custom strike interval.
Colors: Customizable for ATM line, labels (Futures Price, CPR, PPR, VR, PR).
Calculations:Futures Price (FP): Fetches front-month futures price (e.g., NSE:NIFTY1!).
ATM Strike: Rounds futures price to nearest strike interval.
Option Data: Retrieves Last Traded Price (LTP) and volume for ATM call/put options (e.g., NSE:NIFTY251028C24200).
Call Premium (CPR): Call LTP minus intrinsic value (max(0, FP - Strike)).
Put Premium (PPR): Put LTP minus intrinsic value (max(0, Strike - FP)).
Premium Ratio (PR): PPR / CPR.
Volume Ratio (VR): Put Volume / Call Volume.
Visuals:Draws ATM strike line on chart.
Displays labels: FP (futures price), CPR (call premium), PPR (put premium), VR, PR.
VR/PR labels: Red (≥ 1.25, bearish), Green (≤ 0.75, bullish), Gray (0.75–1.25, neutral).
Updates on last confirmed bar to avoid redraws.
Using PR and VR for Market DirectionPremium Ratio (PR):PR ≥ 1.25 (Red): High put premiums suggest bearish sentiment (expect price drop).
PR ≤ 0.75 (Green): High call premiums suggest bullish sentiment (expect price rise).
0.75 < PR < 1.25 (Gray): Neutral, no clear direction.
Use: High PR favors bearish trades (e.g., buy puts); low PR favors bullish trades (e.g., buy calls).
Volume Ratio (VR):VR ≥ 1.25 (Red): High put volume indicates bearish activity.
VR ≤ 0.75 (Green): High call volume indicates bullish activity.
0.75 < VR < 1.25 (Gray): Neutral trading activity.
Use: High VR suggests bearish moves; low VR suggests bullish moves.
Combined Signals:High PR & VR: Strong bearish signal; consider put buying or call selling.
Low PR & VR: Strong bullish signal; consider call buying or put selling.
Mixed/Neutral: Use price action or support/resistance for confirmation.
Tips:Combine with technical analysis (e.g., trends, levels).
Match timeframe to trading horizon (e.g., 15-min for intraday).
Monitor FP for context; check volatility or news for accuracy.
ExampleNIFTY: FP = 24,237.50, ATM = 24,200, CPR = 120.25, PPR = 180.50, PR = 1.50 (Red), VR = 1.30 (Red).
Insight: High PR/VR suggests bearish bias; consider bearish trades if price nears resistance.
Action: Buy puts or exit longs, confirm with price action.
Conclusion: This script provides a concise tool for options traders, showing ATM strike, premiums, and PR/VR ratios. High PR/VR (≥ 1.25) signals bearish sentiment, low PR/VR (≤ 0.75) signals bullish sentiment, and neutral (0.75–1.25) suggests indecision. Combine with technical analysis for robust trading decisions in the Indian options market.
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.
Liquidity Stress Index (SOFR - IORB)How to use:
> +10 bps — TIGHT
−5 +10 bps — NEUTRAL
< −5 bps — LOOSE
Trading Lot & Margin Calculator
# 💹 Trading Lot & Margin Calculator - Professional Risk Management Tool
## 🎯 Overview
A comprehensive, all-in-one calculator dashboard that helps traders determine optimal position sizes, calculate margin requirements, and manage risk effectively across multiple asset classes. This indicator displays directly on your chart as a customizable table, providing real-time calculations based on current market prices.
## ✨ Key Features
### 📊 Three Powerful Calculation Modes:
**1. Calculate Lot Size (Risk-Based Position Sizing)**
- Input your risk percentage and stop loss in pips
- Automatically calculates the optimal lot size for your risk tolerance
- Respects margin limitations (configurable margin % cap)
- Ensures positions don't exceed minimum lot size (0.01)
- Perfect for risk management and proper position sizing
**2. Calculate Margin Cost**
- Input desired lot size
- See exactly how much margin is required
- Shows percentage of deposit used
- Displays free margin remaining
- Warns when insufficient funds
**3. Margin to Lots**
- Specify a fixed margin amount you want to use
- Calculator shows how many lots/contracts you can buy
- Ideal for traders with fixed margin budgets
## 🤖 Auto-Detection of Instruments
The calculator **automatically detects** what you're trading and adjusts calculations accordingly:
### ✅ Fully Supported:
- **💱 Forex Pairs** - All majors, minors, exotics (EURUSD, GBPJPY, etc.)
- Standard lot: 100,000 units
- JPY pairs: 0.01 pip size, others: 0.0001
- **🛢️ Commodities** - Gold, Silver, Oil
- XAUUSD (Gold): 100 oz per lot
- XAGUSD (Silver): 5,000 oz per lot
- Oil (WTI/Brent): 1,000 barrels per lot
- **📈 Indices** - US500, NAS100, US30, DAX, etc.
- Correct contract sizes per point
- **📊 Stocks** - All individual stocks
- 1 lot = 1 share
- Direct share calculations
### ⚠️ Known Limitation:
- **₿ Crypto calculations may not work properly** on all crypto pairs. Use manual contract size if needed.
## 📋 Dashboard Information Displayed:
- 🎯 Optimal/Requested Lot Size
- 💰 Margin Required
- 📊 Margin % of Deposit
- 💵 Free Margin Remaining
- 💎 Position Value
- 📈 Pip/Point Value
- ⚠️ Safety Warnings (insufficient funds, high risk, etc.)
- 🔍 Detected Instrument Type
- 📦 Contract Size
## ⚙️ Customizable Settings:
**Account Settings:**
- Account Deposit
- Leverage (1:1 to 1:1000)
- Max Margin % of Deposit (default 5% for safety)
**Risk Management:**
- Risk Percentage (for lot size calculation)
- Stop Loss in Pips
- Lot Amount (for margin cost calculation)
- Margin to Use (for margin-to-lots calculation)
**Display Options:**
- Show/Hide Dashboard
- Position: Top/Middle/Bottom, Left/Right
- Auto-detect instrument ON/OFF
- Manual contract size override
## 🎨 Professional Design
- Clean, modern table interface
- Color-coded warnings (red = danger, yellow = caution, green = safe)
- Large, readable text
- Minimal screen space usage
- Non-intrusive overlay
## 💡 Use Cases:
1. **Day Traders** - Quick position sizing based on account risk
2. **Swing Traders** - Calculate optimal positions for longer-term setups
3. **Risk Managers** - Ensure positions stay within margin limits
4. **Beginners** - Learn proper position sizing and risk management
5. **Multi-Asset Traders** - Seamlessly switch between forex, commodities, indices, and stocks
## ⚠️ Important Notes:
- ✅ Works on all timeframes
- ✅ Updates in real-time with price changes
- ✅ Minimum lot size enforced (0.01)
- ✅ Margin calculations use current chart price
- ⚠️ **Crypto calculations may be inaccurate** - verify with your broker
- 📌 Always verify calculations with your broker's specifications
- 📌 Contract sizes may vary by broker
## 🚀 How to Use:
1. Add indicator to any chart
2. Click settings ⚙️ icon
3. Enter your account details (deposit, leverage)
4. Choose calculation mode
5. Input your parameters
6. View optimal lot size and margin requirements on dashboard
## 📈 Perfect For:
- Forex traders managing multiple currency pairs
- Commodity traders (Gold, Silver, Oil)
- Index traders (S&P 500, NASDAQ, etc.)
- Stock traders
- Anyone who wants professional risk management
## 🛡️ Risk Management Features:
- Configurable margin % cap prevents over-leveraging
- Risk-based position sizing protects your account
- Warnings for high risk, insufficient funds, margin limitations
- Prevents positions below minimum lot size
---
**Trade smarter, not harder. Calculate before you trade!** 📊💪
---
## Version Notes:
- Pine Script v6
- Overlay mode for chart display
- No external dependencies
- Lightweight and fast
**Disclaimer:** This calculator is for educational and informational purposes only. Always verify calculations with your broker and trade at your own risk. Past performance does not guarantee future results.
---
Broad Market for Crypto + index# Broad Market Indicator for Crypto
## Overview
The Broad Market Indicator for Crypto helps traders assess the strength and divergence of individual cryptocurrency assets relative to the overall market. By comparing price deviations across multiple assets, this indicator reveals whether a specific coin is moving in sync with or diverging from the broader crypto market trend.
## How It Works
This indicator calculates percentage deviations from simple moving averages (SMA) for both individual assets and an equal-weighted market index. The core methodology:
1. **Deviation Calculation**: For each asset, the indicator measures how far the current price has moved from its SMA over a specified lookback period (default: 24 hours). The deviation is expressed as a percentage: `(Current Price - SMA) / SMA × 100`
2. **Market Index Construction**: An equal-weighted index is built from selected cryptocurrencies (up to 15 assets). The default composition includes major crypto assets: BTC, ETH, BNB, SOL, XRP, ADA, AVAX, LINK, DOGE, and TRX.
3. **Comparative Analysis**: The indicator displays both the current instrument's deviation and the market index deviation on the same panel, making it easy to spot relative strength or weakness.
## Key Features
- **Customizable Asset Selection**: Choose up to 15 different cryptocurrencies to include in your market index
- **Flexible Configuration**: Toggle individual assets on/off for display and index calculation
- **Current Instrument Tracking**: Automatically plots the deviation of whatever chart you're viewing
- **Visual Clarity**: Color-coded lines for easy differentiation between assets, with the market index shown as a filled area
- **Adjustable Lookback Period**: Modify the SMA period to match your trading timeframe
## How to Use
### Identifying Market Divergences
- When the current instrument deviates significantly above the index, it shows relative strength
- When it deviates below, it indicates relative weakness
- Assets clustering around zero suggest neutral market conditions
### Trend Confirmation
- If both the index and your asset are rising together (positive deviation), it confirms a broad market uptrend
- Divergence between asset and index can signal unique fundamental factors or early trend changes
### Entry/Exit Signals
- Extreme deviations from the index may indicate overbought/oversold conditions relative to the market
- Convergence back toward the index line can signal mean reversion opportunities
## Settings
- **Lookback Period**: Adjust the SMA calculation period (default: 24 hours)
- **Asset Configuration**: Select which cryptocurrencies to monitor and include in the index
- **Display Options**: Show/hide individual assets, current instrument, and market index
- **Color Customization**: Personalize colors for better visual analysis
## Best Practices
- Use on higher timeframes (4H, Daily) for more reliable signals
- Combine with volume analysis for confirmation
- Consider fundamental news when assets show extreme divergence
- Adjust the asset basket to match your trading focus (DeFi, L1s, memecoins, etc.)
## Technical Notes
- The indicator uses `request.security()` to fetch data from multiple symbols
- Deviations are calculated independently for each asset
- The zero line represents perfect alignment with the moving average
- Index calculation automatically adjusts based on active assets
## Default Assets
1. BTC (Bitcoin) - BINANCE:BTCUSDT
2. ETH (Ethereum) - BINANCE:ETHUSDT
3. BNB (Binance Coin) - BINANCE:BNBUSDT
4. SOL (Solana) - BINANCE:SOLUSDT
5. XRP (Ripple) - BINANCE:XRPUSDT
6. ADA (Cardano) - BINANCE:ADAUSDT
7. AVAX (Avalanche) - BINANCE:AVAXUSDT
8. LINK (Chainlink) - BINANCE:LINKUSDT
9. DOGE (Dogecoin) - BINANCE:DOGEUSDT
10. TRX (Tron) - BINANCE:TRXUSDT
Additional slots (11-15) are available for custom asset selection.
---
This indicator is particularly useful for cryptocurrency traders seeking to understand market breadth and identify opportunities where specific assets are diverging from overall market sentiment.
天干地支标注（当前视窗范围 + 居中标签）🇨🇳 中文说明
天干地支标注（自动匹配周期）
本指标会根据图表的时间周期（年、月、日、小时、分钟）自动计算并在每根 K 线上方显示对应的天干地支。
• 自动识别图表周期（年/月/日/时/分）
• 仅显示当前视窗内的柱子，性能高、不卡顿
• 可自定义每隔 N 根显示一次（默认每根）
• 支持居中矩形标签（label.style_label_center），清晰易读
• 无需区分暗黑/亮色主题，自动兼容所有图表样式
可作为金融时间序列与中国传统历法（干支纪时）结合的参考工具，
在时间周期研究、风水、气运周期、江恩时间分析等领域有辅助价值。
⸻
🇬🇧 English Description (for international visibility)
Heavenly Stems & Earthly Branches Marker (Auto-Adaptive Version)
This indicator automatically calculates and displays the corresponding Chinese Heavenly Stems and Earthly Branches (Ganzhi) for each candlestick, based on the chart’s timeframe (Year, Month, Day, Hour, or Minute).
• Auto-detects chart timeframe
• Draws only within the current visible window (optimized performance)
• Adjustable display interval (e.g., show every N bars)
• Uses centered label style for clarity
• Compatible with both dark and light themes
Useful for combining Chinese calendar cycles with financial time analysis, time-cycle studies, or Gann-style timing models.
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
Asia Range Next-Session Hit-Rate (Trend + Double + Conditional)code that calculates hit-rate of asia low and high. How often we gonna hit Asia High/Low in downtrend/uptrend. Hit-rate of hitting both High and low and other hitrates
Next-Day Wick Revisit Stats (Daily)This script calculates hit-rate of revisiting bullish/bearish wicks in next day.
Prev-Day POC Revisit (Daily check, Daily POC)This script is calculating hit-rate from previous session POC.
Digital Credit: Yields, Spreads & Regime
TN Preferreds is a yield-centric dashboard for bitcoin backed preferreds that overlays effective yields. It builds credit/benchmark spread series, a simple regime model (Risk-On / Cautious / Risk-Off), and a compact table that surfaces price, yield, target, upside and diagnostics—so you can quickly judge relative value and risk conditions.
What it does:
Plots effective yields for STRF/STRC/STRK/STRD (+ CNLTN toggle).
Pulls IG (FRED:BAMLC0A0CMEY), HY (FRED:BAMLH0A0HYM2EY) and US10Y as references.
Computes Credit Spreads vs US10Y and Benchmark Spreads (F−IG, C−IG, K−IG−1%, D−HY) with EMAs/SMA for context.
STRC monthly rate input: set 12 monthly percentages; the current month auto-applies to compute the dividend.
Targets & upside: yield-parity targets for each series + % move to target
Leader logic: picks the series with the strongest SMA-based spread improvement and estimates a leader target price.
Risk regime: EMA-based deltas across spreads define Risk-On / Cautious / Risk-Off; optional background + last-bar label.
Table view (bottom-right): price, eff. yield, target, upside, CS, BS, BS-EMA, BS-Diff, leader stats, regime deltas.
Notes:
Designed for overlay on any chart (format = percent, right scale). Works best with a yield based basis like US10Y
• FRED series must be available on your TradingView plan/region.
Educational tool, not investment advice. Always validate assumptions (dividends, conversion terms, required spreads).
Liquidity ToolkitKey Points:
Liquidity Toolkit is your liquidity companion for monitoring and anticipating price action.
Liquidity Toolkit combined the power of the Liquidity Status indicator with the potency of Price Triggers.
Liquidity Status indicates if the current current liquidity environment is bullish or bearish.
Price triggers highlight price levels where supports, resistances, and trend-changes are likely to occur.
Together, they create a comprehensive and actionable view of the market.
Summary
The Liquidity Toolkit (TK) is designed as a one-stop-shop indicator by combining novel liquidity metrics with traditional and impactful price measurements. In combination, TK grants unparalleled views of the market through effective yet simple displays.
The TK indicator contains two separate by synergistic algorithms: the Liquidity Status algorithm, which measures liquidity to determine if outlooks are bearish or bullish; and the Price Triggers algorithm which analyzes price-action to determine points of support and resistances.
Example 1 :
Example 2 :
Example 3 :
Details
Liquidity Status
Liquidity Status (LS) measures liquidity and produces either `Bullish` or `Bearish` indications depending on the current liquidity status.
Bullish indications indicate that the overall flow of liquidity is supportive of bullish price and bearish indications indicate that the overall flow of liquidity is supportive of bearish price action.
LS is displayed in two ways:
Candle-Coloring: if candles are green, liquidity status is bullish and if candles are red, liquidity status is bearish.
Text Display: Bearish and/or Bullish is displayed via text as well.
Price Triggers
Price Triggers (PT) measure price action and report their findings on several timeframes:
1-Minute
5-Minute
60-Minute
1-Day
1-Week
TK graphs the PTs based on the chart interval – only the higher PTs are display (i.e.: On the 1-Hour chart, the 5-, and 1-Min PTs will not be displayed).
Example 4
In additional to showing price-levels of support and resistance, Price Triggers also display the relative strength of these supports and resistances by displaying the Trigger Strengths. These represent areas of influence.
Opportunities often arise when PTs squeeze each other, often forcing spot to make a large move – as can be seen below:
Example 5
Frequently Asked Questions
How can I get access to the Liquidity Toolkit?
Please see the Author’s Instructions section at the top of the page for more details and information.
How can I get additional information on the indicators used?
Please see the Author’s Instructions section at the top of the page for more details and information.
I added the Liquidity Toolkit but I do not see all of the PT lines – where are they?
Depending on the chart interval, not all PT lines will be displayed. Those lower than the chart’s timeframe are hidden for clarity.
I added Liquidity Toolkit but the chart’s candles are not being filled by LS.
The chart will try to color over LS’ candles if you do not disable them. To disable, go to the Chart Settings then to Symbol and de-select Body, Borders and Wick.
Inflection Nexus - SPAInflection Nexus - SPA: Self-Adapting Trend Reversal System
Overview
Inflection Nexus - SPA (Shadow Portfolio Adaptation) is an adaptive trend-following indicator that automatically optimizes its parameters in real-time through a unique shadow testing methodology. Unlike traditional static indicators that use fixed ATR periods and multipliers, this system continuously evaluates multiple parameter combinations in the background and dynamically adjusts to current market conditions without manual intervention.
What Makes This Original
The core innovation is the Shadow Portfolio Adaptation (SPA) engine, which runs parallel virtual portfolios in the background to test different ATR period and multiplier combinations. The system tracks the performance of these shadow portfolios over rolling windows and automatically switches to the best-performing parameter set. This creates a self-improving indicator that adapts to changing volatility regimes, trending vs. ranging markets, and shifting market dynamics without requiring user reconfiguration.
This is not simply a combination of existing indicators. The SPA engine is a novel approach that transforms the traditional Supertrend methodology from a static tool into an adaptive system with built-in machine learning principles.
Core Components and How They Work Together
1. Adaptive Supertrend Foundation
The base trend detection uses an ATR-based Supertrend calculation with your chosen source (default: hlcc4 for smoothness). Rather than using fixed parameters, the system starts with your configured ATR Period and Multiplier as baseline values.
2. Shadow Portfolio Adaptation Engine
This is where the innovation happens. The system simultaneously tests multiple parameter variations in the background:
- Creates shadow portfolios with different ATR periods (spanning from your base period minus a range to plus a range)
- Tests different ATR multipliers for each period
- Each shadow portfolio tracks virtual trade performance over a configurable lookback window
- Calculates a confidence score based on win rate, profit factor, and trade frequency
- Automatically switches to the best-performing parameter combination
- Implements smooth transitions to prevent whipsaw from parameter changes
The adaptation happens continuously, allowing the system to shift from tight, responsive settings during low volatility to wider, more conservative settings during high volatility periods.
3. Signal Generation Logic
The system offers two complementary signal modes:
Reversal Mode (default): Identifies potential trend exhaustion points. A sell signal requires price to make a new structural high while the trend is bullish, then flip bearish. This captures the exact moment a trend runs out of momentum. The "Require New High/Low During Trend" filter ensures signals only occur at genuine extremes, not mid-range noise.
Breakout Mode (optional): Identifies trend continuation. Signals occur when price breaks to new highs/lows in the direction of the current trend, confirming momentum rather than reversing it.
4. Multi-Layer Confirmation Filters
Each signal passes through optional quality filters:
- RSI Momentum Filter : Ensures buy signals occur after RSI has been oversold (preventing buying into exhaustion) and sell signals occur after RSI has been overbought
- Volume Spike Confirmation : Requires increased volume relative to recent average, confirming conviction behind the move
- Major Level Filter : Ensures signals only occur after significant price moves (measured in ATR multiples), filtering out minor fluctuations
5. Risk Management Integration
The dashboard displays real-time metrics including:
- Current regime classification (Trending, Volatile, Ranging)
- Shadow portfolio performance tracking
- Adaptive confidence scores
- Parameter evolution log
- Market heat map showing probability zones
How to Use This Indicator
Setup:
1. Apply the indicator to your chart
2. Start with default settings for your first session
3. The SPA engine requires a warm-up period (controlled by "Learning Window") to gather sufficient data - expect optimal adaptation after 100-200 bars
4. Enable the dashboard to monitor the adaptation process and current market regime
Signal Interpretation:
- Long signals (green triangles below price): Enter long when the system detects a potential bullish reversal or breakout
- Short signals (red triangles above price): Enter short when the system detects a potential bearish reversal or breakout
- Dashboard color coding : Green regime = favorable for trend-following, Yellow = volatile (use caution), Red = choppy (consider reducing position size)
Best Practices:
- Use Reversal Mode in swing trading environments where you want to catch major turning points
- Use Breakout Mode in strong trending markets where you want confirmation entries
- Enable both modes for comprehensive coverage, but filter by the regime indicator
- The "Min Bars Between Signals" setting prevents over-trading - start at 10-12 bars for most timeframes
- Pay attention to the "Map Heat" metric - higher active cells indicate more defined market structure
Parameter Optimization:
The system is designed to self-optimize, but you can guide it:
- Sensitivity : Lower values (15-25) for intraday scalping, higher values (40-60) for swing trading
- ATR Period : Your baseline starting point - the SPA engine will explore around this value
- Multiplier : Your baseline band width - the engine tests variations of this
- Learning Window : How many bars of data the shadow portfolios evaluate (200-500 for most markets)
- Adaptation Frequency : How often the system checks for better parameters (30-50 bars balances responsiveness and stability)
Dashboard Insights:
The three-panel dashboard provides real-time intelligence:
- Panel A shows current signal state, trend direction, and overall market regime
- Panel B displays shadow portfolio statistics, confidence scores, and the adaptation log
- The regime classification helps you understand if current market conditions favor trending strategies or if you should reduce exposure
Calculation Methodology
The system operates in three phases:
Phase 1 - Base Calculation:
- Calculates ATR using your specified period and method (RMA for smoothness)
- Identifies structural highs/lows using the sensitivity parameter
- Computes initial Supertrend bands: Price ± (ATR × Multiplier)
Phase 2 - Shadow Testing:
- Creates a grid of parameter combinations (ATR periods from base-5 to base+15, multipliers from base-0.5 to base+1.0)
- For each combination, simulates trade entries and exits over the learning window
- Tracks metrics: win rate, profit factor, max drawdown, trade count
- Calculates a confidence score using weighted metrics (win rate × 0.4 + profit factor × 0.3 + normalized trade frequency × 0.3)
Phase 3 - Adaptive Selection:
- Every N bars (adaptation frequency), ranks all shadow portfolios by confidence score
- Selects the highest-scoring parameter set
- Implements parameter change with transition smoothing to prevent signal disruption
- Logs the change and updates the dashboard
This creates a continuous feedback loop where the indicator learns from recent market behavior and adjusts its sensitivity accordingly.
Ideal Market Conditions
Best Performance:
- Markets with clear swing structure (forex majors, liquid stocks, major indices)
- Timeframes from 5-minute to daily (indicator adapts across timeframes)
- Trending markets with periodic consolidations (where reversals are meaningful)
Challenging Conditions:
- Extremely low liquidity assets (insufficient price action for adaptation)
- Very low timeframes (1-minute or below) where noise dominates
- Markets in deep consolidation for extended periods (the system will reduce signal frequency appropriately)
Technical Notes
- The indicator uses lookback functions with a 5000-bar maximum, ensuring sufficient historical context
- Shadow portfolios are lightweight - they don't execute actual trades, only track hypothetical P&L
- The confidence-based selection prevents the system from chasing random variations
- The minimum bars between signals prevents over-fitting to short-term fluctuations
- All calculations are performed on closed bars to prevent repainting
Recommended Settings by Trading Style
Day Trading (5-15 min charts):
- Sensitivity: 20-30
- ATR Period: 14-20
- Multiplier: 1.2-1.5
- Min Bars Between Signals: 8-12
- Enable RSI Filter: Yes
Swing Trading (1H-4H charts):
- Sensitivity: 30-50
- ATR Period: 20-30
- Multiplier: 1.5-2.0
- Min Bars Between Signals: 10-15
- Enable Major Levels Only: Optional
Position Trading (Daily charts):
- Sensitivity: 50-80
- ATR Period: 30-40
- Multiplier: 2.0-2.5
- Min Bars Between Signals: 5-10
- Enable Breakout Mode: Consider
The SPA engine will refine these starting points automatically based on actual market performance.
Important Disclaimers
This indicator is a technical analysis tool designed to identify potential trend changes and continuation points. It should not be used as a standalone trading system. Always combine with proper risk management, position sizing, and additional confirmation methods. Past performance of the adaptation engine does not guarantee future results. The shadow portfolio system is designed to improve parameter selection, but no indicator can predict market movements with certainty.
— Dskyz, Trade with insight. Trade with anticipation.
Quantum Market Harmonics [QMH]# Quantum Market Harmonics - TradingView Script Description
## 📊 OVERVIEW
Quantum Market Harmonics (QMH) is a comprehensive multi-dimensional trading indicator that combines four independent analytical frameworks to generate high-probability trading signals with quantifiable confidence scores. Unlike simple indicator combinations that display multiple tools side-by-side, QMH synthesizes temporal analysis, inter-market correlations, behavioral psychology, and statistical probabilities into a unified confidence scoring system that requires agreement across all dimensions before generating a confirmed signal.
---
## 🎯 WHAT MAKES THIS SCRIPT ORIGINAL
### The Core Innovation: Weighted Confidence Scoring
Most indicators provide binary signals (buy/sell) or display multiple indicators separately, leaving traders to interpret conflicting information. QMH's originality lies in its weighted confidence scoring system that:
1. **Combines Four Independent Methods** - Each framework (described below) operates independently and contributes points to an overall confidence score
2. **Requires Multi-Dimensional Agreement** - Signals only fire when multiple frameworks align, dramatically reducing false positives
3. **Quantifies Signal Strength** - Every signal includes a numerical confidence rating (0-100%), allowing traders to filter by quality
4. **Adapts to Market Conditions** - Different market regimes activate different component combinations
### Why This Combination is Useful
Traditional approaches suffer from:
- **Single-dimension bias**: RSI shows oversold, but trend is still down
- **Conflicting signals**: MACD says buy, but volume is weak
- **No prioritization**: All signals treated equally regardless of strength
QMH solves these problems by requiring multiple independent confirmations and weighting each component's contribution to the final signal. This multi-dimensional approach mirrors how professional traders analyze markets - not relying on one indicator, but waiting for multiple pieces of evidence to align.
---
## 🔬 THE FOUR ANALYTICAL FRAMEWORKS
### 1. Temporal Fractal Resonance (TFR)
**What It Does:**
Analyzes trend alignment across four different timeframes simultaneously (15-minute, 1-hour, 4-hour, and daily) to identify periods of multi-timeframe synchronization.
**How It Works:**
- Uses `request.security()` with `lookahead=barmerge.lookahead_off` to retrieve confirmed price data from each timeframe
- Calculates "fractal strength" for each timeframe using this formula:
```
Fractal Strength = (Rate of Change / Standard Deviation) × 100
```
This creates a momentum-to-volatility ratio that measures trend strength relative to noise
- Computes a Resonance Index when all four timeframes show the same directional bias
- The index averages the absolute strength values when all timeframes align
**Why This Method:**
Fractal Market Hypothesis suggests that price patterns repeat across different time scales. When trends align from short-term (15m) to long-term (Daily), the probability of trend continuation increases substantially. The momentum/volatility ratio filters out low-conviction moves where volatility dominates direction.
**Contribution to Confidence Score:**
- TFR Bullish = +25 points
- TFR Bearish = +25 points (to bearish confidence)
- No alignment = 0 points
---
### 2. Cross-Asset Quantum Entanglement (CAQE)
**What It Does:**
Analyzes correlation patterns between the current asset and three reference markets (Bitcoin, US Dollar Index, and Volatility Index) to identify both normal correlation behavior and anomalous breakdowns that often precede significant moves.
**How It Works:**
- Retrieves price data from BTC (BINANCE:BTCUSDT), DXY (TVC:DXY), and VIX (TVC:VIX) using confirmed bars
- Calculates Pearson correlation coefficient between the main asset and each reference:
```
Correlation = Covariance(X,Y) / (StdDev(X) × StdDev(Y))
```
- Computes an Intermarket Pressure Index by weighting each reference asset's momentum by its correlation strength:
```
Pressure = (Corr₁ × ROC₁ + Corr₂ × ROC₂ + Corr₃ × ROC₃) / 3
```
- Detects "correlation breakdowns" when average correlation drops below 0.3
**Why This Method:**
Markets don't operate in isolation. Inter-market analysis (developed by John Murphy) recognizes that:
- Crypto assets often correlate with Bitcoin
- Risk assets inversely correlate with VIX (fear gauge)
- Dollar strength affects commodity and crypto prices
When these normal correlations break down, it signals potential regime changes. The term "quantum" reflects the interconnected nature of these relationships - like quantum entanglement where distant particles influence each other.
**Contribution to Confidence Score:**
- CAQE Bullish (positive pressure, stable correlations) = +25 points
- CAQE Bearish (negative pressure, stable correlations) = +25 points (to bearish)
- Correlation breakdown = Warning marker (potential reversal zone)
---
### 3. Adaptive Market Psychology Matrix (AMPM)
**What It Does:**
Classifies the current market emotional state into six distinct categories by analyzing the interaction between momentum (RSI), volume behavior, and volatility acceleration (ATR change).
**How It Works:**
The system evaluates three metrics:
1. **RSI (14-period)**: Measures overbought/oversold conditions
2. **Volume Analysis**: Compares current volume to 20-period average
3. **ATR Rate of Change**: Detects volatility acceleration
Based on these inputs, the market is classified into:
- **Euphoria**: RSI > 80, volume spike present, volatility rising (extreme bullish emotion)
- **Greed**: RSI > 70, normal volume (moderate bullish emotion)
- **Neutral**: RSI 40-60, declining volatility (balanced state)
- **Fear**: RSI 40-60, low volatility (uncertainty without panic)
- **Panic**: RSI < 30, volume spike present, volatility rising (extreme bearish emotion)
- **Despair**: RSI < 20, normal volume (capitulation phase)
**Why This Method:**
Behavioral finance principles (Kahneman, Tversky) show that markets follow predictable emotional cycles. Extreme psychological states often mark reversal points because:
- At Euphoria/Greed peaks, everyone bullish has already bought (no buyers left)
- At Panic/Despair bottoms, everyone bearish has already sold (no sellers left)
AMPM provides contrarian signals at these extremes while respecting trends during Fear and Greed intermediate states.
**Contribution to Confidence Score:**
- Psychology Bullish (Panic/Despair + RSI < 35) = +15 points
- Psychology Bearish (Euphoria/Greed + RSI > 65) = +15 points
- Neutral states = 0 points
---
### 4. Time-Decay Probability Zones (TDPZ)
**What It Does:**
Creates dynamic support and resistance zones based on statistical probability distributions that adapt to changing market volatility, similar to Bollinger Bands but with enhancements for trend environments.
**How It Works:**
- Calculates a 20-period Simple Moving Average as the basis line
- Computes standard deviation of price over the same period
- Creates four probability zones:
- **Extreme Upper**: Basis + 2.5 standard deviations (≈99% probability boundary)
- **Upper Zone**: Basis + 1.5 standard deviations
- **Lower Zone**: Basis - 1.5 standard deviations
- **Extreme Lower**: Basis - 2.5 standard deviations (≈99% probability boundary)
- Dynamically adjusts zone width based on ATR (Average True Range):
```
Adjusted Upper = Upper Zone + (ATR × adjustment_factor)
Adjusted Lower = Lower Zone - (ATR × adjustment_factor)
```
- The adjustment factor increases during high volatility, widening the zones
**Why This Method:**
Traditional support/resistance levels are static and don't account for volatility regimes. TDPZ zones are probability-based and mean-reverting:
- Price has ≈99% probability of staying within extreme zones in normal conditions
- Touches to extreme zones represent statistical outliers (high-probability reversal opportunities)
- Zone expansion/contraction reflects volatility regime changes
- ATR adjustment prevents false signals during unusual volatility
The "time-decay" concept refers to mean reversion - the further price moves from the basis, the higher the probability of eventual return.
**Contribution to Confidence Score:**
- Price in Lower Extreme Zone = +15 points (bullish reversal probability)
- Price in Upper Extreme Zone = +15 points (bearish reversal probability)
- Price near basis = 0 points
---
## 🎯 HOW THE CONFIDENCE SCORING SYSTEM WORKS
### Signal Generation Formula
QMH calculates separate Bullish and Bearish confidence scores each bar:
**Bullish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bullish: 25 points (if all 4 timeframes aligned bullish)
+ CAQE Bullish: 25 points (if intermarket pressure positive)
+ AMPM Bullish: 15 points (if Panic/Despair contrarian signal)
+ TDPZ Bullish: 15 points (if price in lower probability zones)
─────────
Maximum Possible: 100 points
```
**Bearish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bearish: 25 points (if all 4 timeframes aligned bearish)
+ CAQE Bearish: 25 points (if intermarket pressure negative)
+ AMPM Bearish: 15 points (if Euphoria/Greed contrarian signal)
+ TDPZ Bearish: 15 points (if price in upper probability zones)
─────────
Maximum Possible: 100 points
```
### Confirmed Signal Requirements
A **QBUY** (Quantum Buy) signal generates when:
1. Bullish Confidence ≥ User-defined threshold (default 60%)
2. Bullish Confidence > Bearish Confidence
3. No active sell signal present
A **QSELL** (Quantum Sell) signal generates when:
1. Bearish Confidence ≥ User-defined threshold (default 60%)
2. Bearish Confidence > Bullish Confidence
3. No active buy signal present
### Why This Approach Is Different
**Example Comparison:**
Traditional RSI Strategy:
- RSI < 30 → Buy signal
- Result: May buy into falling knife if trend remains bearish
QMH Approach:
- RSI < 30 → Psychology shows Panic (+15 points)
- But requires additional confirmation:
- Are all timeframes also showing bullish reversal? (+25 points)
- Is intermarket pressure turning positive? (+25 points)
- Is price at a statistical extreme? (+15 points)
- Only when total ≥ 60 points does a QBUY signal fire
This multi-layer confirmation dramatically reduces false signals while maintaining sensitivity to genuine opportunities.
---
## 🚫 NO REPAINT GUARANTEE
**QMH is designed to be 100% repaint-free**, which is critical for honest backtesting and reliable live trading.
### Technical Implementation:
1. **All Multi-Timeframe Data Uses Confirmed Bars**
```pinescript
tf1_close = request.security(syminfo.tickerid, "15", close , lookahead=barmerge.lookahead_off)
```
Using `close ` instead of `close ` ensures we only reference the previous confirmed bar, not the current forming bar.
2. **Lookahead Prevention**
```pinescript
lookahead=barmerge.lookahead_off
```
This parameter prevents the function from accessing future data that wouldn't be available in real-time.
3. **Signal Timing**
Signals appear on the bar AFTER all conditions are met, not retroactively on the bar where conditions first appeared.
### What This Means for Users:
- **Backtest Accuracy**: Historical signals match exactly what you would have seen in real-time
- **No Disappearing Signals**: Once a signal appears, it stays (though price may move against it)
- **Honest Performance**: Results reflect true predictive power, not hindsight optimization
- **Live Trading Reliability**: Alerts fire at the same time signals appear on the chart
The dashboard displays "✓ NO REPAINT" to confirm this guarantee.
---
## 📖 HOW TO USE THIS INDICATOR
### Basic Trading Strategy
**For Trend Followers:**
1. **Wait for Signal Confirmation**
- QBUY label appears below a bar = Confirmed bullish entry opportunity
- QSELL label appears above a bar = Confirmed bearish entry opportunity
2. **Check Confidence Score**
- 60-70%: Moderate confidence (consider smaller position size)
- 70-85%: High confidence (standard position size)
- 85-100%: Very high confidence (consider larger position size)
3. **Enter Trade**
- Long entry: Market or limit order near signal bar
- Short entry: Market or limit order near signal bar
4. **Set Targets Using Probability Zones**
- Long trades: Target the adjusted upper zone (lime line)
- Short trades: Target the adjusted lower zone (red line)
- Alternatively, target the basis line (yellow) for conservative exits
5. **Set Stop Loss**
- Long trades: Below recent swing low minus 1 ATR
- Short trades: Above recent swing high plus 1 ATR
**For Mean Reversion Traders:**
1. **Wait for Extreme Zones**
- Price touches extreme lower zone (dotted red line below)
- Price touches extreme upper zone (dotted lime line above)
2. **Confirm with Psychology**
- At lower extreme: Look for Panic or Despair state
- At upper extreme: Look for Euphoria or Greed state
3. **Wait for Confidence Build**
- Monitor dashboard until confidence exceeds threshold
- Requires patience - extreme touches don't always reverse immediately
4. **Enter Reversal**
- Target: Return to basis line (yellow SMA 20)
- Stop: Beyond the extreme zone
**For Position Traders (Longer Timeframes):**
1. **Use Daily Timeframe**
- Set chart to daily for longer-term signals
- Signals will be less frequent but higher quality
2. **Require High Confidence**
- Filter setting: Min Confidence Score 80%+
- Only take the strongest multi-dimensional setups
3. **Confirm with Resonance Background**
- Green tinted background = All timeframes bullish aligned
- Red tinted background = All timeframes bearish aligned
- Only enter when background tint matches signal direction
4. **Hold for Major Targets**
- Long trades: Hold until extreme upper zone or opposite signal
- Short trades: Hold until extreme lower zone or opposite signal
---
## 📊 DASHBOARD INTERPRETATION
The QMH Dashboard (top-right corner) provides real-time market analysis across all four dimensions:
### Dashboard Elements:
1. **✓ NO REPAINT**
- Green confirmation that signals don't repaint
- Always visible to remind users of signal integrity
2. **SIGNAL: BULL/BEAR XX%**
- Shows dominant direction (whichever confidence is higher)
- Displays current confidence percentage
- Background color intensity reflects confidence level
3. **Psychology: **
- Current market emotional state
- Color coded:
- Orange = Euphoria (extreme bullish emotion)
- Yellow = Greed (moderate bullish emotion)
- Gray = Neutral (balanced state)
- Purple = Fear (uncertainty)
- Red = Panic (extreme bearish emotion)
- Dark red = Despair (capitulation)
4. **Resonance: **
- Multi-timeframe alignment strength
- Positive = All timeframes bullish aligned
- Negative = All timeframes bearish aligned
- Near zero = Timeframes not synchronized
- Emoji indicator: 🔥 (bullish resonance) ❄️ (bearish resonance)
5. **Intermarket: **
- Cross-asset pressure measurement
- Positive = BTC/DXY/VIX correlations supporting upside
- Negative = Correlations supporting downside
- Warning ⚠️ if correlation breakdown detected
6. **RSI: **
- Current RSI(14) reading
- Background colors: Red (>70 overbought), Green (<30 oversold)
- Status: OB (overbought), OS (oversold), or • (neutral)
7. **Status: READY BUY / READY SELL / WAIT**
- Quick trade readiness indicator
- READY BUY: Confidence ≥ threshold, bias bullish
- READY SELL: Confidence ≥ threshold, bias bearish
- WAIT: Confidence below threshold
### How to Use Dashboard:
**Before Entering a Trade:**
- Verify Status shows READY (not WAIT)
- Check that Resonance matches signal direction
- Confirm Psychology isn't contradicting (e.g., buying during Euphoria)
- Note Intermarket value - breakdowns (⚠️) suggest caution
**During a Trade:**
- Monitor Psychology shifts (e.g., from Fear to Greed in a long)
- Watch for Resonance changes that could signal exit
- Check for Intermarket breakdown warnings
---
## ⚙️ CUSTOMIZATION SETTINGS
### TFR Settings (Temporal Fractal Resonance)
- **Enable/Disable**: Turn TFR analysis on/off
- **Fractal Sensitivity** (5-50, default 14):
- Lower values = More responsive to short-term changes
- Higher values = More stable, slower to react
- Recommendation: 14 for balanced, 7 for scalping, 21 for position trading
### CAQE Settings (Cross-Asset Quantum Entanglement)
- **Enable/Disable**: Turn CAQE analysis on/off
- **Asset 1** (default BTC): Reference asset for correlation analysis
- **Asset 2** (default DXY): Second reference asset
- **Asset 3** (default VIX): Third reference asset
- **Correlation Length** (10-100, default 20):
- Lower values = More sensitive to recent correlation changes
- Higher values = More stable correlation measurements
- Recommendation: 20 for most assets, 50 for less volatile markets
### Psychology Settings (Adaptive Market Psychology Matrix)
- **Enable/Disable**: Turn AMPM analysis on/off
- **Volume Spike Threshold** (1.0-5.0x, default 2.0):
- Lower values = Detect smaller volume increases as spikes
- Higher values = Only flag major volume surges
- Recommendation: 2.0 for stocks, 1.5 for crypto
### Probability Settings (Time-Decay Probability Zones)
- **Enable/Disable**: Turn TDPZ visualization on/off
- **Probability Lookback** (20-200, default 50):
- Lower values = Zones adapt faster to recent price action
- Higher values = Zones based on longer statistical history
- Recommendation: 50 for most uses, 100 for position trading
### Filter Settings
- **Min Confidence Score** (40-95%, default 60%):
- Lower threshold = More signals, more false positives
- Higher threshold = Fewer signals, higher quality
- Recommendation: 60% for active trading, 75% for selective trading
### Visual Settings
- **Show Entry Signals**: Toggle QBUY/QSELL labels on chart
- **Show Probability Zones**: Toggle zone visualization
- **Show Psychology State**: Toggle dashboard display
---
## 🔔 ALERT CONFIGURATION
QMH includes four alert conditions that can be configured via TradingView's alert system:
### Available Alerts:
1. **Quantum Buy Signal**
- Fires when: Confirmed QBUY signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications
2. **Quantum Sell Signal**
- Fires when: Confirmed QSELL signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications or exit warnings
3. **Market Panic**
- Fires when: Psychology state reaches Panic
- Use for: Contrarian opportunity alerts
4. **Market Euphoria**
- Fires when: Psychology state reaches Euphoria
- Use for: Reversal warning alerts
### How to Set Alerts:
1. Right-click on chart → "Add Alert"
2. Condition: Select "Quantum Market Harmonics"
3. Choose alert type from dropdown
4. Configure expiration, frequency, and notification method
5. Create alert
**Recommendation**: Set alerts for Quantum Buy/Sell signals with "Once Per Bar Close" frequency to avoid intra-bar false triggers.
---
## 💡 BEST PRACTICES
### For All Users:
1. **Backtest First**
- Test on your specific market and timeframe before live trading
- Different assets may perform better with different confidence thresholds
- Verify that the No Repaint guarantee works as described
2. **Paper Trade**
- Practice with signals on a demo account first
- Understand typical signal frequency for your timeframe
- Get comfortable with the dashboard interpretation
3. **Risk Management**
- Never risk more than 1-2% of capital per trade
- Use proper stop losses (not just mental stops)
- Position size based on confidence score (larger size at higher confidence)
4. **Consider Context**
- QMH signals work best in clear trends or at extremes
- During tight consolidation, false signals increase
- Major news events can invalidate technical signals
### Optimal Use Cases:
**QMH Works Best When:**
- ✅ Markets are trending (up or down)
- ✅ Volatility is normal to elevated
- ✅ Price reaches probability zone extremes
- ✅ Multiple timeframes align
- ✅ Clear inter-market relationships exist
**QMH Is Less Effective When:**
- ❌ Extremely low volatility (zones contract too much)
- ❌ Sideways choppy markets (conflicting timeframes)
- ❌ Flash crashes or news events (correlations break down)
- ❌ Very illiquid assets (irregular price action)
### Session Considerations:
- **24/7 Markets (Crypto)**: Works on all sessions, but signals may be more reliable during high-volume periods (US/European trading hours)
- **Forex**: Best during London/New York overlap when volume is highest
- **Stocks**: Most reliable during regular trading hours (not pre-market/after-hours)
---
## ⚠️ LIMITATIONS AND RISKS
### This Indicator Cannot:
- **Predict Black Swan Events**: Sudden unexpected events invalidate technical analysis
- **Guarantee Profits**: No indicator is 100% accurate; losses will occur
- **Replace Risk Management**: Always use stop losses and proper position sizing
- **Account for Fundamental Changes**: Company news, economic data, etc. can override technical signals
- **Work in All Market Conditions**: Less effective during extreme low volatility or major news events
### Known Limitations:
1. **Multi-Timeframe Lag**: Uses confirmed bars (`close `), so signals appear one bar after conditions met
2. **Correlation Dependency**: CAQE requires sufficient history; may be less reliable on newly listed assets
3. **Computational Load**: Multiple `request.security()` calls may cause slower performance on older devices
4. **Repaint of Dashboard**: Dashboard updates every bar (by design), but signals themselves don't repaint
### Risk Warnings:
- Past performance doesn't guarantee future results
- Backtesting results may not reflect actual trading results due to slippage, commissions, and execution delays
- Different markets and timeframes may produce different results
- The indicator should be used as a tool, not as a standalone trading system
- Always combine with your own analysis, risk management, and trading plan
---
## 🎓 EDUCATIONAL CONCEPTS
This indicator synthesizes several established financial theories and technical analysis concepts:
### Academic Foundations:
1. **Fractal Market Hypothesis** (Edgar Peters)
- Markets exhibit self-similar patterns across time scales
- Implemented via multi-timeframe resonance analysis
2. **Behavioral Finance** (Kahneman & Tversky)
- Investor psychology drives market inefficiencies
- Implemented via market psychology state classification
3. **Intermarket Analysis** (John Murphy)
- Asset classes correlate and influence each other predictably
- Implemented via cross-asset correlation monitoring
4. **Mean Reversion** (Statistical Arbitrage)
- Prices tend to revert to statistical norms
- Implemented via probability zones and standard deviation bands
5. **Multi-Timeframe Analysis** (Technical Analysis Standard)
- Higher timeframe trends dominate lower timeframe noise
- Implemented via fractal resonance scoring
### Learning Resources:
To better understand the concepts behind QMH:
- Read "Intermarket Analysis" by John Murphy (for CAQE concepts)
- Study "Thinking, Fast and Slow" by Daniel Kahneman (for psychology concepts)
- Review "Fractal Market Analysis" by Edgar Peters (for TFR concepts)
- Learn about Bollinger Bands (for TDPZ foundation)
---
## 🔄 VERSION HISTORY AND UPDATES
**Current Version: 1.0**
This is the initial public release. Future updates will be published using TradingView's Update feature (not as separate publications). Planned improvements may include:
- Additional reference assets for CAQE
- Optional machine learning-based weight optimization
- Customizable psychology state definitions
- Alternative probability zone calculations
- Performance metrics tracking
Check the "Updates" tab on the script page for version history.
---
## 📞 SUPPORT AND FEEDBACK
### How to Get Help:
1. **Read This Description First**: Most questions are answered in the detailed sections above
2. **Check Comments**: Other users may have asked similar questions
3. **Post Comments**: For general questions visible to the community
4. **Use TradingView Messaging**: For private inquiries (if available)
### Providing Useful Feedback:
When reporting issues or suggesting improvements:
- Specify your asset, timeframe, and settings
- Include a screenshot if relevant
- Describe expected vs. actual behavior
- Check if issue persists with default settings
### Continuous Improvement:
This indicator will evolve based on user feedback and market testing. Constructive suggestions for improvements are always welcome.
---
## ⚖️ DISCLAIMER
This indicator is provided for **educational and informational purposes only**. It does **not constitute financial advice, investment advice, trading advice, or any other type of advice**.
**Important Disclaimers:**
- You should **not** rely solely on this indicator to make trading decisions
- Always conduct your own research and due diligence
- Past performance is not indicative of future results
- Trading and investing involve substantial risk of loss
- Only trade with capital you can afford to lose
- Consider consulting with a licensed financial advisor before trading
- The author is not responsible for any trading losses incurred using this indicator
**By using this indicator, you acknowledge:**
- You understand the risks of trading
- You take full responsibility for your trading decisions
- You will use proper risk management techniques
- You will not hold the author liable for any losses
---
## 🙏 ACKNOWLEDGMENTS
This indicator builds upon the collective knowledge of the technical analysis and trading community. While the specific implementation and combination are original, the underlying concepts draw from:
- The Pine Script community on TradingView
- Academic research in behavioral finance and market microstructure
- Classical technical analysis methods developed over decades
- Open-source indicators that demonstrate best practices in Pine Script coding
Special thanks to TradingView for providing the platform and Pine Script language that make indicators like this possible.
---
## 📚 ADDITIONAL RESOURCES
**Pine Script Documentation:**
- Official Pine Script Manual: www.tradingview.com
**Related Concepts to Study:**
- Multi-timeframe analysis techniques
- Correlation analysis in financial markets
- Behavioral finance principles
- Mean reversion strategies
- Bollinger Bands methodology
**Recommended TradingView Tools:**
- Strategy Tester: To backtest signal performance
- Bar Replay: To see how signals develop in real-time
- Alert System: To receive notifications of new signals
---
**Thank you for using Quantum Market Harmonics. Trade safely and responsibly.**
Average Daily Range [Blaz]Version 1.0 – Published October 2025: Initial release
1. Overview & Purpose
The Average Daily Range is an advanced volatility assessment tool designed to give traders a clear, real-time view of the market's expected daily movement. It calculates the average range between daily highs and lows over a user-defined historical period and projects this average onto the current trading session.
By visualising the potential high and low boundaries for the day, this indicator assists in setting realistic profit targets, managing risk effectively, and identifying when price action is becoming overextended relative to its recent volatility profile. It is an essential tool for day traders and swing traders across all markets, including Forex, Stocks, Crypto, Futures, and Commodities.
2. Core Functionality & Key Features
The indicator provides a dynamic, multi-faceted analysis of daily volatility:
Historical ADR Calculation: Automatically computes the Average Daily Range based on the specified number of previous trading days (configurable from 1 to 20).
Real-Time Range Tracking: Monitors and displays the current day's live price range as it develops.
Percentage Used Metric: Shows the percentage of the historical ADR that the current day's range has already consumed, providing an immediate gauge of remaining volatility potential.
Remaining Range Projection: Visually highlights the potential upward and downward movement remaining to meet the average range, displayed as semi-transparent areas on the chart.
Daily Open Reference: Plots customisable vertical separation lines and horizontal price lines at the daily open to clearly anchor the current session's price action.
3. Visual Components & Analytical Insights
A fully configured Average Daily Range setup displays several key analytical components that work together to provide a comprehensive volatility overview.
3.1. Information Table
A highly customizable data table provides a concise summary of all critical metrics at a glance:
Historical Ranges: Displays the individual daily ranges for the selected lookback period.
ADR Value: The calculated average range.
Today's Range: The live, developing range for the current session.
% Used: A colour-coded percentage (turning orange upon exceeding 100% and red upon exceeding 150%) showing how much of the average volatility has been consumed.
3.2. Visual Range Projections
Remaining Range Zones: When the current day's range is below the historical average, semi-transparent zones extend from the current day's extreme high and low, illustrating the additional movement required to reach the ADR. This provides an instant visual cue for potential target zones.
Daily Open Markers: Clean, customisable lines mark the start of each trading day (vertical line) and the daily open price (horizontal line), helping to contextualise intraday price moves.
4. Input Parameters and Settings
4.1. General Settings
Lookback: Set the number of days used to calculate the Average Daily Range (1-20).
Set Alert: Configure alerts to be notified when the current day's range consumes a significant portion (e.g., 100% or more) of the historical ADR.
4.2. Table Customization
Visibility & Style: Toggle the table and historical data on/off. Fully customise the header and body colours, text colours, border style, and font sizes.
Placement & Orientation: Precisely position the table anywhere on the chart (Top/Bottom/Centre, Left/Right) and choose between Horizontal or Vertical layout to best suit your chart layout.
4.3. Visual Style Controls
Remaining Range: Toggle the projection zones on/off and customise their colour and transparency.
Daily Open Markers: Independently control the visibility, colour, style, and width of the daily separation line and the open price line.
5. Protected Logic & Original Design
The Average Daily Range indicator incorporates proprietary logic for efficiently tracking intraday extremes, managing historical data arrays, and dynamically rendering visual elements. The closed-source nature of this tool protects the author's original code structure and optimisation techniques, particularly the real-time area fill projection logic for the remaining daily range and the dynamic table management system. This ensures the indicator remains performant and reliable while being freely accessible to the entire TradingView community.
6. Disclaimer & Terms of Use
This indicator, titled Average Daily Range , has been independently developed by the author. The code and its structural logic are original and were written entirely from scratch to reflect a unique and efficient approach to volatility analysis. The internal mechanics were written from scratch and are not based on any publicly available script or third-party code.
This tool is provided solely for educational and informational purposes. It is not intended as financial advice, investment guidance, or a specific recommendation to buy or sell any financial instrument. The indicator is designed to assist with technical analysis based on volatility but does not guarantee accuracy or profitability.
Trading financial markets involves significant risk, including the possibility of loss of capital. By using this indicator, you acknowledge and accept that you are solely responsible for any decisions you make and for all trading outcomes. No part of this script should be considered a signal or assurance of success in the market.
Momentum Master v1Momentum Master v1 - Advanced Multi-Filter Confluence Trading System
### Technical Methodology
Multi-timeframe EMA crossover system with institutional flow analysis, proprietary Fair Value Gap (FVG) retracement detection, and Point of Control (POC) proximity filtering.
The script combines six distinct confirmation filters: customizable EMA crossover signals (3/21 default), RSI momentum analysis (14-period), proprietary FVG retracement algorithm with 200-bar lookback, multi-timeframe POC proximity calculation (Volume/Session/Daily/Weekly), institutional order block detection with retest confirmation, and adaptive ATR-based risk management with real-time confidence scoring.
### Unique Features
1. **Proprietary FVG Retracement Algorithm** - Advanced institutional flow analysis with 200-bar lookback and 20% ATR tolerance
2. **Multi-Timeframe POC Confluence System** - Combines 4 different POC calculations (Volume Profile 30-bar, Session, Daily, Weekly) for key level analysis
3. **Adaptive Confidence Scoring System** - Dynamic risk management based on signal quality (0-100%) with real-time performance tracking
4. **Advanced Performance Analytics** - Real-time win/loss statistics for each TP level with up to 500 individual trade verification labels
5. **Professional Risk Management** - Six-level take-profit system (2:1, 4:1, 6:1, 8:1, 10:1, 12:1) with ATR-based stops
### How It Works
**Entry Requirements:** Fast EMA (3) crosses above Slow EMA (21) + RSI < 70 + volume > 1.1x average + FVG retracement confirmation + POC proximity within 2.0x ATR + order block direction alignment.
**Risk Management:** ATR-based stop loss placement with 1.0x multiplier. Six take-profit levels at 2:1, 4:1, 6:1, 8:1, 10:1, and 12:1 risk/reward ratios.
**Performance Tracking:** Real-time win/loss statistics with up to 500 individual trade labels for verification, confidence scoring system, and comprehensive performance analytics for each TP level.
### Value Proposition
This script combines 6 different institutional flow analysis techniques that would require multiple free scripts to replicate. The proprietary FVG retracement algorithm, multi-timeframe POC analysis, and adaptive confidence scoring system are not available in any single free script.
### Use Cases
- **Best Timeframes:** 5-minute for scalping, 15-minute for swing trades
- **Suitable Markets:** Forex major pairs, Crypto, major indices
- **Market Conditions:** Trending markets with high volume sessions
### Access Instructions
To request access to this invite-only script:
- **Contact:** with your TradingView username
- **Requirements:** Include your TradingView username and brief trading experience
- **Process:** I will review requests within 24 hours and grant access to qualified traders
---
### Technical Justification for Indicator Combination
**Why These Indicators Work Together:**
This script combines 6 distinct analysis methods because each serves a specific purpose in the signal generation pipeline. The EMA crossover provides trend direction, RSI prevents entries in extreme zones, volume confirms institutional participation, FVG retracement validates institutional flow, POC proximity ensures key level alignment, and order block detection confirms institutional context.
**Component Integration Logic:**
- **EMA Crossover (3/21 customizable):** Primary trend detection mechanism with multiple speed options (Standard 9/21, Fast 7/17, Slow 13/26, Custom)
- **RSI Filter (14-period):** Momentum validation to avoid extreme overbought/oversold entries
- **Volume Analysis (1.1x threshold):** Institutional participation confirmation with trend analysis
- **FVG Retracement (200-bar lookback):** Validates price action within institutional flow zones
- **Multi-Timeframe POC (Volume/Session/Daily/Weekly):** Ensures confluence with key price levels
- **Order Block Detection:** Confirms institutional accumulation/distribution context
### Detailed Functionality Explanation
**What This Script Does:**
Generates high-probability momentum scalping signals with multiple take-profit levels and adaptive risk management. The script analyzes price action across multiple timeframes to identify optimal entry points where institutional flow, key levels, and momentum align.
**How It Works:**
1. **Signal Generation:** EMA crossover triggers base signal
2. **Filter Validation:** Six confirmation filters validate signal quality
3. **Confidence Scoring:** Dynamic scoring system rates signal strength (0-100%)
4. **Risk Management:** ATR-based stops with adaptive sizing based on confidence
5. **Profit Taking:** Six-level TP system with fixed risk/reward ratios
6. **Performance Tracking:** Real-time win/loss statistics with trade verification
**How to Use It:**
- **Timeframes:** 5m for scalping, 15m for swing trades, 1h for position entries
- **Markets:** Forex majors, Crypto, major indices
- **Setup:** Apply to chart, configure filters, set risk parameters, monitor confidence scores
- **Entry:** Wait for all filters to align, enter on signal confirmation
- **Exit:** Use ATR-based stops and multiple TP levels
### Originality and Unique Features
**Proprietary Algorithms:**
1. **Advanced FVG Retracement Detection:** 200-bar lookback with 20% ATR tolerance - not available in free scripts
2. **Multi-Timeframe POC Confluence:** Combines 4 different POC calculations with proximity filtering
3. **Adaptive Confidence Scoring:** Dynamic risk adjustment based on signal quality with real-time tracking
4. **Institutional Order Block Analysis:** Advanced detection with directional alignment filtering
5. **Performance Verification System:** Up to 500 individual trade labels for backtesting verification
6. **Advanced Performance Analytics:** Real-time win/loss statistics for each TP level with comprehensive reporting
### Value Justification
**Why This Is Worth Using:**
- **Institutional-Grade Analysis:** Combines techniques used by professional traders
- **Proprietary Algorithms:** FVG retracement and confidence scoring not available elsewhere
- **Comprehensive Confluence:** 6 different analysis methods in one unified system
- **Professional Risk Management:** Multi-level TP system with adaptive stops
- **Real-Time Performance Tracking:** Live win/loss statistics and confidence monitoring
- **Trade Verification:** Individual trade labels for backtesting and performance analysis
- **Advanced Analytics:** Detailed performance statistics for each take-profit level
- **Time-Saving:** All analysis tools in one script vs. 6+ separate indicators
- **High Accuracy:** Multiple confluence filters reduce false signals significantly
**Technical Specifications:**
- **Pine Script Version:** 6
- **Max Bars Back:** 5000 (for historical analysis)
- **Max Labels:** 500 (for performance optimization and trade verification)
- **Memory Usage:** Optimized for real-time performance
- **Compatibility:** Works on all TradingView timeframes and instruments
- **Alert System:** Built-in alert conditions for long/short entries
- **Visual Elements:** Professional chart display with customizable colors and styles
**No External Dependencies:**
This script operates entirely within TradingView's platform with no external links, contact information, or promotional content. All analysis is performed using built-in Pine Script functions and proprietary algorithms.
VOLUME PROFILE WITH FOOTPRINT AND IMBALANCEVOLUME PROFILE WITH FOOTPRINT AND IMBALANCE
A professional-grade market structure analysis tool that combines three powerful trading concepts into one comprehensive indicator: Volume Profile, Footprint Charts, and Imbalance detection. This script provides optimum-level market analysis for trading.
KEY FEATURES
1. Multi-Day Volume Profile
Customizable Row Density: Adjust price level granularity for precise volume distribution analysis
Point of Control (POC): Automatically identifies the price level with highest traded volume
Value Area Calculation: Highlights the price range containing 70% of the day's volume (customizable percentage)
Value Area High (VAH) & Low (VAL): Clear demarcation of institutional acceptance zones
Horizontal Volume Bars: Visual representation of buying vs. selling pressure at each price level
Color-Coded Volume: Distinguishes between value area volume and outlier volume for better visual clarity
2. Previous Day Reference Levels
Previous Day High/Low (PDH/PDL): Critical support/resistance levels from prior session
Previous Day POC: Yesterday's highest volume node - often acts as magnetic price level
Previous Day VAH/VAL: Prior session's value boundaries for gap analysis and mean reversion setups
All previous day levels extend into current session with customizable colors and line styles
3. Virgin Point of Control (VPOC)
Untouched POC Identification: Automatically tracks POC levels that haven't been revisited by price
Real-time Validation: Monitors whether subsequent price action has tested each historical POC
Multi-Day Tracking: Maintains VPOC levels across multiple sessions until filled
High-Probability Targets: Virgin POCs often act as magnets for future price action
4. Footprint Zone Analysis
Footprint Zone Detection: Identifies price levels touched only once during the session
Automated Ribbon Consolidation: Groups consecutive Footprint Zone into visual zones
Price Range Sensitivity: Automatically adjusts granularity based on instrument price
Historical Persistence: Consolidates previous day's footprint zones for multi-day context
Auction Failure Zones: Footprint Zone often indicate areas of poor liquidity and potential reversal points
5. Three-Candle Imbalance Detection
Bullish Imbalance
Bearish Imbalance
Visual Markers: Clear circular indicators on all three candles forming the imbalance
Customizable Colors: Separate colors for bullish and bearish imbalances
Gap Validation: Ensures meaningful price displacement before flagging imbalance
Kernel Market Dynamics🔍 Kernel Market Dynamics Pro - Advanced Distribution Divergence Detection System
OVERVIEW
Kernel Market Dynamics Pro (KMD Pro) is a revolutionary market regime detection system that employs Maximum Mean Discrepancy (MMD) - a cutting-edge statistical technique from machine learning - to identify when market behavior diverges from its recent historical distribution patterns. The system transforms complex statistical divergence analysis into actionable trading signals through kernel density estimation, regime classification algorithms, and multi-dimensional visualization frameworks that reveal hidden market transitions before traditional indicators can detect them.
WHAT MAKES IT ORIGINAL
While conventional indicators measure price or momentum divergence, KMD Pro analyzes distribution divergence - detecting when the statistical properties of market returns fundamentally shift from their baseline state. This approach, borrowed from high-frequency trading and quantitative finance, uses kernel methods to map market data into high-dimensional feature spaces where regime changes become mathematically detectable. The system is the first TradingView implementation to combine MMD with real-time regime visualization, making institutional-grade statistical arbitrage techniques accessible to retail traders.
HOW IT WORKS (Technical Methodology)
1. KERNEL DENSITY ESTIMATION ENGINE
Maximum Mean Discrepancy (MMD) Calculation:
The core innovation - measures distance between probability distributions:
• Maps return distributions to Reproducing Kernel Hilbert Space (RKHS)
• Computes empirical mean embeddings for reference and test windows
• Calculates supremum of mean differences across all RKHS functions
• MMD = ||μ_P - μ_Q||_H where H is the RKHS induced by kernel k
Three Kernel Functions Available:
RBF (Radial Basis Function) Kernel:
• k(x,y) = exp(-||x-y||²/2σ²)
• Gaussian kernel with smooth, infinite-dimensional feature mapping
• Bandwidth σ controls sensitivity (0.5-10.0 user configurable)
• Optimal for normally distributed returns
• Default choice providing balanced sensitivity
Laplacian Kernel:
• k(x,y) = exp(-|x-y|/σ)
• Exponential decay with heavier tails than RBF
• More sensitive to outliers and sudden moves
• Ideal for volatile, news-driven markets
• Faster regime shift detection at cost of more false positives
Cauchy Kernel:
• k(x,y) = 1/(1 + ||x-y||²/σ²)
• Heavy-tailed distribution from statistical physics
• Robust to extreme values and fat-tail events
• Best for cryptocurrency and emerging markets
• Most stable signals with fewer whipsaws
Implementation Details:
• Reference window: 30-300 bars of baseline distribution
• Test window: 10-100 bars of recent distribution
• Double-sum kernel matrix computation with O(m*n) complexity
• EMA smoothing (period 3) reduces noise in raw MMD
• Real-time updates every bar with incremental calculation
2. REGIME DETECTION FRAMEWORK
Three-State Regime Classification:
STABLE Regime (MMD < threshold):
• Market follows historical distribution patterns
• Mean-reverting behavior dominates
• Low probability of breakouts
• Reduced position sizing recommended
• Visual: Subtle background coloring
SHIFTING Regime (threshold < MMD < 2×threshold):
• Distribution divergence detected
• Transition period with directional bias emerging
• Optimal entry zone for trend-following
• Increased volatility expected
• Visual: Yellow/orange zone highlighting
EXTREME Regime (MMD > 2×threshold):
• Severe distribution anomaly
• Black swan or structural break potential
• Maximum caution required
• Consider hedging or exit
• Visual: Red/magenta warning zones
Adaptive Threshold System:
• Base threshold: 0.05-1.0 (default 0.15)
• Volatility adjustment: ±30% based on ATR ratio
• Regime persistence: 20-bar minimum for stability
• Cooldown periods prevent signal clustering
3. DIRECTIONAL BIAS DETERMINATION
Multi-Factor Direction Analysis:
Distribution Mean Comparison:
• Recent mean = SMA(normalized_returns, test_window)
• Reference mean = SMA(normalized_returns, reference_window)
• Direction = sign(recent_mean - reference_mean)
Momentum Confluence:
• Price momentum = close - close
• Volume momentum = volume/SMA(volume, reference_window)
• Weighted composite direction score
Trend Alignment:
• Fast EMA vs Slow EMA positioning
• Slope analysis of regression line
• Multi-timeframe bias confirmation (optional)
4. SIGNAL GENERATION ARCHITECTURE
Entry Signal Logic:
Stage 1 - Regime Shift Detection:
• MMD crosses above threshold
• Sustained for minimum 2 bars
• No signals within cooldown period
Stage 2 - Direction Confirmation:
• Distribution mean aligns with momentum
• Volume ratio > 1.0 (optional)
• Price above/below VWAP (optional)
Stage 3 - Risk Assessment:
• Calculate ATR-based stop distance
• Verify risk/reward ratio > 1.5
• Check for nearby support/resistance
Stage 4 - Signal Generation:
• Long: Regime shift + bullish direction
• Short: Regime shift + bearish direction
• Extreme: MMD > 2×threshold warning
5. PROBABILITY CLOUD VISUALIZATION
Adaptive Confidence Intervals:
• Standard deviation multiplier = 1 + MMD × 3
• Inner band: ±0.5 ATR × multiplier (68% probability)
• Outer band: ±1.0 ATR × multiplier (95% probability)
• Width expands with divergence magnitude
• Real-time adjustment every bar
Interpretation:
• Narrow cloud: Low uncertainty, stable regime
• Wide cloud: High uncertainty, shifting regime
• Asymmetric cloud: Directional bias present
6. MOMENTUM FLOW VECTORS
Three-Style Momentum Visualization:
Flow Arrows:
• Length proportional to momentum strength
• Width indicates confidence (1-3 pixels)
• Angle shows rate of change
• Frequency: Every 5 bars or on events
Gradient Bars:
• Vertical lines from price
• Height = momentum/ATR ratio
• Opacity based on strength
• Continuous flow indication
Momentum Ribbon:
• Envelope around price action
• Expands in momentum direction
• Color intensity shows strength
7. SIGNAL CONNECTION SYSTEM
Relationship Mapping:
• Links consecutive signals with lines
• Solid lines: Same direction (continuation)
• Dotted lines: Opposite direction (reversal)
• Maximum 10 connections maintained
• Distance limit: 100 bars
Purpose:
• Identifies signal clusters
• Shows trend development
• Reveals regime persistence
• Confirms directional bias
8. REGIME ZONE MAPPING
Unified Zone Visualization:
• Main zones: Full regime periods (entry to exit)
• Emphasis zones: Specific trigger points
• Historical memory: Last 20 regime shifts
• Color gradient based on intensity
• Border style indicates zone type
Zone Analytics:
• Duration tracking
• Maximum excursion
• Retest probability
• Support/resistance conversion
9. DYNAMIC RISK MANAGEMENT
ATR-Based Position Sizing:
• Stop loss: 1.0 × ATR from entry
• Target 1: 2.0 × ATR (2R)
• Target 2: 4.0 × ATR (4R)
• Volatility-adjusted scaling
Visual Target System:
• Entry pointer lines
• Target boxes with prices
• Stop boxes with invalidation
• Real-time P&L tracking
10. PROFESSIONAL DASHBOARD
Real-Time Metrics Display:
Primary Metrics:
• Current MMD value and threshold
• Risk level (MMD/threshold ratio)
• Velocity (rate of change)
• Acceleration (second derivative)
Signal Information:
• Active signal type and entry
• Stop loss and targets
• Current P&L percentage
• Bars since signal
Market Metrics:
• Directional bias (BULL/BEAR)
• Confidence percentage
• Win rate statistics
• Signal count tracking
Visual Design:
• Four position options
• Three size modes
• Five color themes
• Gauge visualizations
• Status banners
11. MMD INFO PANEL
Floating Statistics:
• Compact 3×4 table
• MMD vs threshold comparison
• Velocity with direction arrows
• Current bias indication
• Always-visible reference
FIVE COLOR THEMES
Quantum: Cyan/Magenta/Yellow - Modern, high contrast, optimal visibility
Matrix: Green/Red - Classic terminal aesthetic, traditional
Fire: Orange/Gold/Red - Warm spectrum, energetic feel
Aurora: Northern lights palette - Unique, beautiful gradients
Nebula: Deep space colors - Purple/Blue, futuristic
HOW TO USE
Step 1: Select Your Kernel
• RBF for normal markets (stocks, forex majors)
• Laplacian for volatile markets (small-caps, news-driven)
• Cauchy for fat-tail markets (crypto, emerging markets)
Step 2: Configure Bandwidth
• 0.5-2.0: Scalping (high sensitivity)
• 2.0-5.0: Day trading (balanced)
• 5.0-10.0: Swing trading (smooth signals)
Step 3: Set Analysis Windows
• Reference: 3-5× your holding period
• Test: Reference ÷ 3 approximately
• Adjust based on timeframe
Step 4: Calibrate Threshold
• Start with 0.15 default
• Increase if too many signals
• Decrease for earlier detection
Step 5: Enable Visuals
• Probability Cloud for volatility assessment
• Momentum Flow for direction confirmation
• Regime Zones for historical context
• Signal Connections for trend visualization
Step 6: Monitor Dashboard
• Check MMD vs threshold
• Verify regime state
• Confirm directional bias
• Review confidence metrics
Step 7: Execute Signals
• Wait for triangle markers
• Verify regime shift confirmed
• Check risk/reward setup
• Enter at close or next open
Step 8: Manage Position
• Place stop at calculated level
• Scale out at Target 1 (2R)
• Trail remainder to Target 2 (4R)
• Exit if regime reverses
OPTIMIZATION GUIDE
By Market Type:
Forex Majors:
• Kernel: RBF
• Bandwidth: 2.0-3.0
• Windows: 100/30
• Threshold: 0.15
Stock Indices:
• Kernel: RBF
• Bandwidth: 3.0-4.0
• Windows: 150/50
• Threshold: 0.20
Cryptocurrencies:
• Kernel: Cauchy
• Bandwidth: 2.5-3.5
• Windows: 100/30
• Threshold: 0.10-0.15
Commodities:
• Kernel: Laplacian
• Bandwidth: 2.0-3.0
• Windows: 200/60
• Threshold: 0.15-0.25
By Timeframe:
Scalping (1-5m):
• Test Window: 10-20
• Reference: 50-100
• Bandwidth: 1.0-2.0
• Cooldown: 5-10 bars
Day Trading (15m-1H):
• Test Window: 30-50
• Reference: 100-150
• Bandwidth: 2.0-3.0
• Cooldown: 10-20 bars
Swing Trading (4H-Daily):
• Test Window: 50-100
• Reference: 200-300
• Bandwidth: 3.0-5.0
• Cooldown: 20-50 bars
ADVANCED FEATURES
Multi-Timeframe Capability:
• HTF MMD calculation via security()
• Regime alignment across timeframes
• Fractal analysis support
Statistical Arbitrage Mode:
• Pair trading applications
• Spread divergence detection
• Cointegration breaks
Machine Learning Integration:
• Export signals for ML training
• Regime labels for classification
• Feature extraction support
PERFORMANCE METRICS
Computational Complexity:
• MMD calculation: O(m×n) where m,n are window sizes
• Memory usage: O(m+n) for kernel matrices
• Update frequency: Every bar (real-time)
• Optimization: Incremental updates where possible
Typical Signal Frequency:
• Conservative settings: 2-5 signals/week
• Balanced settings: 5-10 signals/week
• Aggressive settings: 10-20 signals/week
Win Rate Expectations:
• Trend following mode: 40-50% wins, 2:1 reward/risk
• Mean reversion mode: 60-70% wins, 1:1 reward/risk
• Depends heavily on market conditions
IMPORTANT DISCLAIMERS
• This indicator detects statistical divergence, not future price direction
• MMD measures distribution distance, not predictive probability
• Past regime shifts do not guarantee future performance
• Kernel methods are descriptive statistics, not AI predictions
• Requires minimum 100 bars historical data for stability
• Performance varies significantly across market conditions
• Not suitable for illiquid or heavily manipulated markets
• Always use proper risk management and position sizing
• Backtest thoroughly on your specific instruments
• This is an analysis tool, not a complete trading system
THEORETICAL FOUNDATION
The Maximum Mean Discrepancy was introduced by Gretton et al. (2012) as a kernel-based statistical test for comparing distributions. In financial markets, we adapt this technique to detect when return distributions shift, indicating potential regime changes. The mathematical rigor of MMD provides a robust, non-parametric approach to identifying market transitions without assuming specific distribution shapes.
SUPPORT & UPDATES
• Questions or configuration help via TradingView messaging
• Bug reports addressed within 48 hours
• Feature requests considered for monthly updates
• Video tutorials available on request
• Join our community for strategy discussions
FINAL NOTES
KMD Pro represents a paradigm shift in technical analysis - moving from price-based indicators to distribution-based detection. By measuring statistical divergence rather than price divergence, the system identifies regime changes that precede traditional breakouts. This anticipatory capability, combined with comprehensive visualization and risk management, provides traders with an institutional-grade toolkit for navigating modern market dynamics.
Remember: The edge comes not from the indicator alone, but from understanding when market distributions diverge from their normal state and positioning accordingly. Use KMD Pro as part of a complete trading strategy that includes fundamental analysis, risk management, and market context.
ICOptimizerLibrary "ICOptimizer"
Library for IC-based parameter optimization
findOptimalParam(testParams, icValues, currentParam, smoothing)
Find optimal parameter from array of IC values
Parameters:
testParams (array) : Array of parameter values being tested
icValues (array) : Array of IC values for each parameter (same size as testParams)
currentParam (float) : Current parameter value (for smoothing)
smoothing (simple float) : Smoothing factor (0-1, e.g., 0.2 means 20% new, 80% old)
Returns: New parameter value, its IC, and array index
adaptiveParamWithStarvation(opt, testParams, icValues, smoothing, starvationThreshold, starvationJumpSize)
Adaptive parameter selection with starvation handling
Parameters:
opt (ICOptimizer) : ICOptimizer object
testParams (array) : Array of parameter values
icValues (array) : Array of IC values for each parameter
smoothing (simple float) : Normal smoothing factor
starvationThreshold (simple int) : Number of updates before triggering starvation mode
starvationJumpSize (simple float) : Jump size when in starvation (as fraction of range)
Returns: Updated parameter and IC
detectAndAdjustDomination(longCount, shortCount, currentLongLevel, currentShortLevel, dominationRatio, jumpSize, minLevel, maxLevel)
Detect signal imbalance and adjust parameters
Parameters:
longCount (int) : Number of long signals in period
shortCount (int) : Number of short signals in period
currentLongLevel (float) : Current long threshold
currentShortLevel (float) : Current short threshold
dominationRatio (simple int) : Ratio threshold (e.g., 4 = 4:1 imbalance)
jumpSize (simple float) : Size of adjustment
minLevel (simple float) : Minimum allowed level
maxLevel (simple float) : Maximum allowed level
Returns:
calcIC(signals, returns, lookback)
Parameters:
signals (float)
returns (float)
lookback (simple int)
classifyIC(currentIC, icWindow, goodPercentile, badPercentile)
Parameters:
currentIC (float)
icWindow (simple int)
goodPercentile (simple int)
badPercentile (simple int)
evaluateSignal(signal, forwardReturn)
Parameters:
signal (float)
forwardReturn (float)
updateOptimizerState(opt, signal, forwardReturn, currentIC, metaICPeriod)
Parameters:
opt (ICOptimizer)
signal (float)
forwardReturn (float)
currentIC (float)
metaICPeriod (simple int)
calcSuccessRate(successful, total)
Parameters:
successful (int)
total (int)
createICStatsTable(opt, paramName, normalSuccess, normalTotal)
Parameters:
opt (ICOptimizer)
paramName (string)
normalSuccess (int)
normalTotal (int)
initOptimizer(initialParam)
Parameters:
initialParam (float)
ICOptimizer
Fields:
currentParam (series float)
currentIC (series float)
metaIC (series float)
totalSignals (series int)
successfulSignals (series int)
goodICSignals (series int)
goodICSuccess (series int)
nonBadICSignals (series int)
nonBadICSuccess (series int)
goodICThreshold (series float)
badICThreshold (series float)
updateCounter (series int)
IC optimiser libLibrary "IC optimiser lib"
Library for IC-based parameter optimization
findOptimalParam(testParams, icValues, currentParam, smoothing)
Find optimal parameter from array of IC values
Parameters:
testParams (array) : Array of parameter values being tested
icValues (array) : Array of IC values for each parameter (same size as testParams)
currentParam (float) : Current parameter value (for smoothing)
smoothing (simple float) : Smoothing factor (0-1, e.g., 0.2 means 20% new, 80% old)
Returns: New parameter value, its IC, and array index
adaptiveParamWithStarvation(opt, testParams, icValues, smoothing, starvationThreshold, starvationJumpSize)
Adaptive parameter selection with starvation handling
Parameters:
opt (ICOptimizer) : ICOptimizer object
testParams (array) : Array of parameter values
icValues (array) : Array of IC values for each parameter
smoothing (simple float) : Normal smoothing factor
starvationThreshold (simple int) : Number of updates before triggering starvation mode
starvationJumpSize (simple float) : Jump size when in starvation (as fraction of range)
Returns: Updated parameter and IC
detectAndAdjustDomination(longCount, shortCount, currentLongLevel, currentShortLevel, dominationRatio, jumpSize, minLevel, maxLevel)
Detect signal imbalance and adjust parameters
Parameters:
longCount (int) : Number of long signals in period
shortCount (int) : Number of short signals in period
currentLongLevel (float) : Current long threshold
currentShortLevel (float) : Current short threshold
dominationRatio (simple int) : Ratio threshold (e.g., 4 = 4:1 imbalance)
jumpSize (simple float) : Size of adjustment
minLevel (simple float) : Minimum allowed level
maxLevel (simple float) : Maximum allowed level
Returns:
calcIC(signals, returns, lookback)
Parameters:
signals (float)
returns (float)
lookback (simple int)
classifyIC(currentIC, icWindow, goodPercentile, badPercentile)
Parameters:
currentIC (float)
icWindow (simple int)
goodPercentile (simple int)
badPercentile (simple int)
evaluateSignal(signal, forwardReturn)
Parameters:
signal (float)
forwardReturn (float)
updateOptimizerState(opt, signal, forwardReturn, currentIC, metaICPeriod)
Parameters:
opt (ICOptimizer)
signal (float)
forwardReturn (float)
currentIC (float)
metaICPeriod (simple int)
calcSuccessRate(successful, total)
Parameters:
successful (int)
total (int)
createICStatsTable(opt, paramName, normalSuccess, normalTotal)
Parameters:
opt (ICOptimizer)
paramName (string)
normalSuccess (int)
normalTotal (int)
initOptimizer(initialParam)
Parameters:
initialParam (float)
ICOptimizer
Fields:
currentParam (series float)
currentIC (series float)
metaIC (series float)
totalSignals (series int)
successfulSignals (series int)
goodICSignals (series int)
goodICSuccess (series int)
nonBadICSignals (series int)
nonBadICSuccess (series int)
goodICThreshold (series float)
badICThreshold (series float)
updateCounter (series int)
Momentum Master v1Momentum Master v1 - Advanced Multi-Filter Confluence Trading System
### Technical Methodology
Multi-timeframe EMA crossover system with institutional flow analysis, proprietary Fair Value Gap (FVG) retracement detection, and Point of Control (POC) proximity filtering.
The script combines six distinct confirmation filters: 3/21 EMA crossover signals, RSI momentum analysis (14-period), proprietary FVG retracement algorithm with 200-bar lookback, multi-timeframe POC proximity calculation (Volume/Session/Daily/Weekly), institutional order block detection with retest confirmation, and adaptive ATR-based risk management.
### Unique Features
1. Proprietary FVG Retracement Algorithm - Institutional Flow Analysis
2. Multi-Timeframe POC Proximity Filtering - Key Level Analysis
3. Adaptive Confidence Scoring System - Dynamic Risk Management
### How It Works
Long entries require: Fast EMA (3) crosses above Slow EMA (21) + RSI < 70 + volume > 1.1x average + FVG retracement confirmation + POC proximity within 2.0x ATR + order block direction alignment.
Uses ATR-based stop loss placement with 1.0x multiplier. Take profit levels at 2:1, 4:1, 6:1, 8:1, 10:1, and 12:1 risk/reward ratios.
### Value Proposition
This script combines 6 different institutional flow analysis techniques that would require multiple free scripts to replicate. The proprietary FVG retracement algorithm, multi-timeframe POC analysis, and adaptive confidence scoring system are not available in any single free script.
### Use Cases
Best timeframes: 5-minute for scalping, 15-minute for swing trades
Suitable markets: Forex major pairs, Crypto, major indices
Market conditions: Trending markets with high volume sessions
### Access Instructions
To request access to this invite-only script:
Contact: with your TradingView username
Requirements: Include your TradingView username and brief trading experience
Process: I will review requests within 24 hours and grant access to qualified traders
2 days ago
Release Notes
Momentum Master v1 - Multi-Filter EMA Crossover with Institutional Flow Analysis
### Technical Methodology
The script uses a 3/21 EMA crossover system combined with six confirmation filters: RSI momentum analysis (14-period), proprietary Fair Value Gap (FVG) retracement detection with 200-bar lookback, multi-timeframe Point of Control (POC) proximity calculation, institutional order block detection with retest confirmation, volume analysis (1.1x average threshold), and adaptive ATR-based risk management (14-period ATR with 1.0x multiplier).
### Unique Features
1. Proprietary FVG Retracement Algorithm - Tracks whether price retraces into recent Fair Value Gaps before generating signals, using 200-bar lookback with 20% ATR tolerance for retest confirmation
2. Multi-Timeframe POC Analysis - Combines Volume Profile POC (30-bar), Session POC (previous session HLC/3), Daily POC (previous day HLC/3), and Weekly POC (previous week HLC/3) with 2.0x ATR proximity filtering
3. Adaptive Confidence Scoring - Proprietary algorithm scores signal confidence 0-100% based on filter confluence, adjusting stop loss distance (0.9x to 1.2x ATR) based on signal quality
### How It Works
Long entries require: Fast EMA (3) crosses above Slow EMA (21) + RSI < 70 + volume > 1.1x average + FVG retracement confirmation within 15 bars + POC proximity within 2.0x ATR + order block direction alignment. Optional filters include ADX > 20 for trending markets and divergence confirmation.
Exit strategy uses ATR-based stop loss (1.0x multiplier) with take profit levels at 2:1, 4:1, 6:1, 8:1, 10:1, and 12:1 risk/reward ratios. Multiple concurrent trades allowed with 5-bar cooldown between entries.
### Value Proposition
This script combines 6 different institutional flow analysis techniques that would require multiple free scripts to replicate. The proprietary FVG retracement algorithm, multi-timeframe POC analysis, and adaptive confidence scoring system are not available in any single free script. Most free scripts only provide basic EMA crossover signals without institutional context.
### Use Cases
Best timeframes: 5-minute for scalping, 15-minute for swing trades, 1-hour for position entries
Suitable markets: Forex major pairs (EUR/USD, GBP/USD), Crypto (BTC/USD, ETH/USD), major indices (S&P 500, NASDAQ)
Market conditions: Trending markets with ADX > 20, high volume sessions (London/NY overlap)
### Access Instructions
To request access to this invite-only script:
Contact: with your TradingView username
Requirements: Include your TradingView username and brief trading experience
Process: I will review requests within 24 hours and grant access to qualified traders
