WickPressureKernel

Wick Pressure Kernel (WPK) [DAFE]: The Physics-ML Fusion Engine
This is not a candlestick pattern library. This is a physics and machine learning engine that decodes the institutional battle hidden inside every wick. It is the definitive toolkit for analyzing microstructure and quantifying order flow pressure.

█ CHAPTER 1: THE PHILOSOPHY - BEYOND THE WICK, INTO THE PHYSICS
A candlestick wick is not just a line on a chart. It is a fossil record of a battle. It is the remnant of a fierce conflict between aggressive market orders and passive limit orders. A long upper wick is not just "bearish"; it is the evidence of a failed auction, a footprint left by a wall of institutional supply that absorbed and rejected aggressive buying. Traditional candle analysis gives these events simple, unchanging names. It is a dead language.

The Wick Pressure Kernel (WPK) was created to translate this dead language into the living, dynamic language of physics and machine learning. It deconstructs the candle into its core components and subjects them to a rigorous, multi-layered analysis. It calculates the Kinetic Force of the candle, estimates the institutional Delta hidden within it, tracks the Siege Decay of the levels it tests, and uses a Bayesian Reinforcement Learning system to predict the probable outcome.

This library does not just identify patterns; it quantifies the underlying forces that create them. It is designed for the trader who is no longer content with subjective interpretation and demands a quantitative, data-driven edge in reading the story of every single bar.

█ CHAPTER 2: THE PHYSICS-ML FUSION - THE SIX LAYERS OF ANALYSIS
The WPK's unparalleled intelligence comes from its six-stage analytical pipeline. Each layer builds upon the last, transforming a simple candle into a rich, multi-dimensional data object.

LAYER 1 - DELTA PHYSICS: At its core, the WPK uses a proprietary model to estimate the institutional order flow (Delta) within a single candle. It analyzes the relationship between the wicks, the body, and the total range to approximate the net buying or selling pressure that occurred during the bar's formation.

LAYER 2 - SIEGE ANALYSIS: This is a revolutionary concept for measuring structural integrity. Every time a price level is tested by a wick, its "Siege Decay" score is updated. A fresh, untested level has a score of 1.0. A level that has been tested multiple times without breaking has a decayed score (e.g., 0.5), indicating it is weakening and likely to fail.

LAYER 3 - MAGNETISM ENGINE: Not all wicks are created equal. This engine calculates the probability that a wick will be "filled" (mean reversion). It understands that long wicks in a weak, choppy trend are likely to be filled, while wicks in a strong, trending market are more likely to represent valid rejection.

LAYER 4 - REGIME ML: The WPK is context-aware. It uses a suite of advanced statistical tools—Shannon Entropy (disorder), Detrended Fluctuation Analysis (trend vs. mean-reversion), and the Hurst Exponent (persistence)—to classify the market's current "personality" into one of six regimes (e.g., "Bull Trend," "Bear Range," "Choppy").

LAYER 5 - THOMPSON SAMPLING (REINFORCEMENT LEARNING): This is the AI brain. The library uses a Bayesian Multi-Armed Bandit algorithm called Thompson Sampling. It maintains a set of "Learning Agents," each specializing in a different type of wick pattern (e.g., Rejection, Absorption). Based on the real-time performance of these patterns, the AI continuously updates the win probability for each agent, learning which strategies are most effective in the current market.

LAYER 6 - CONTEXTUAL ROUTING: The final layer of intelligence. The WPK analyzes the wick, determines its pattern type, and then routes it to the specialist Learning Agent for a probability assessment, all while considering the current market regime.

█ CHAPTER 3: A DEEP DIVE INTO THE WPK's CORE ENGINES
[u]THE analyze_wick() FUNCTION (THE MASTER ANALYZER)[/u]
This is the primary function of the library. You feed it a bar, and it returns a complete WickAnalysis object, a rich data structure containing over 15 distinct metrics that tell the full story of that candle:
Anomaly Score: A Z-Score that tells you how statistically rare the wick's size is compared to recent history. A score > 2.0 is a significant outlier.
Kinetic Force: A physics-based metric that combines the bar's range and its relative volume to quantify the "impact energy" of the candle.
Estimated Delta & Delta Ratio: The raw institutional flow estimate and its ratio compared to the recent average.
Siege Decay & Test Count: The current structural integrity of the level the wick tested, and how many times it has been hit.
Magnet Score: The calculated probability (0-100%) that this specific wick will be filled.
Pattern & Context: The classified pattern name (e.g., "Dragonfly/Hammer," "Liquidity Trap") and its tactical context (e.g., "reversal," "trend_continuation").
Win Probability: The final, ML-predicted success rate for this pattern, as determined by the Thompson Sampling engine.

THE scan_clusters() FUNCTION (LIQUIDITY ZONE DETECTION)
Wicks rarely happen in isolation. This powerful function scans the recent price history and identifies areas where multiple high-pressure wicks have occurred at similar price levels. It groups these events into dynamic "Pressure Clusters," which function as high-probability supply and demand zones. Each cluster is tracked with its own set of metrics, including its total strength, age, and siege decay.

[u]THE detect_regime() FUNCTION (THE CONTEXT ENGINE)[/u]
This function is the AI's "eyes." It uses advanced statistical methods to understand the market's personality. Is it trending cleanly? Is it mean-reverting? Is it completely random and chaotic? The output of this function is fed into the rest of the WPK, allowing the analysis to adapt intelligently to the current environment.

█ CHAPTER 4: A GUIDE FOR DEVELOPERS - INTEGRATING THE KERNEL
The Wick Pressure Kernel is designed as a powerful, low-level engine for developers to build sophisticated trading tools.
Import the Library:
import YourUsername/WickPressureKernel/1 as wpk
Initialize the AI: The learning agents must be stored in a var variable to retain their memory.
var array<wpk.LearningAgent> agents = wpk.create_learning_agents()
Analyze the Market: On every bar, run the core analysis functions.
wpk.RegimeState regime = wpk.detect_regime(100)
wpk.WickAnalysis wick_data = wpk.analyze_wick(0, regime, agents)
float final_probability = wpk.get_probability(wick_data, regime)
Apply Your Logic: Use the rich data from the wick_data object and the final_probability to build your custom signal logic and filters.
bool my_buy_signal = wick_data.pattern == "Dragonfly/Hammer" and final_probability > 65
Provide Feedback (The Learning Step): After a trade based on a pattern is complete, you must tell the AI the outcome. This is how it learns.
int agent_id = wpk.get_agent_by_pattern(wick_data)
agents := wpk.update_learning_agent(agents, agent_id, 1.0) // +1.0 for a win, -1.0 for a loss
By following this loop, you are not just running an indicator; you are actively training a specialized AI to master the specific asset you are trading.

█ DEVELOPMENT PHILOSOPHY
The Wick Pressure Kernel was born from the conviction that the future of technical analysis lies in the fusion of quantitative physics and machine learning. It is an attempt to move beyond subjective pattern naming and into the realm of objective, measurable forces. This library is for the serious developer and the quantitative trader who is not satisfied with simple signals, but who seeks to understand the deep, complex, and often violent auction process that creates every candle on the chart.

The WPK is designed to be a tool for that patience, providing the deep contextual understanding needed to wait for moments of true, statistically-backed institutional pressure.

█ A NOTE TO USERS & DISCLAIMER
THIS IS A LIBRARY FOR DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be imported and used by other indicator developers to build their own tools.
THE AI IS A PROBABILISTIC GUIDE: The reinforcement learning system provides a powerful statistical edge, but it does not predict the future with certainty. All trading involves substantial risk.
LEARNING REQUIRES DATA: The Thompson Sampling engine becomes more intelligent over time as it processes more outcomes. Its initial predictions will be less reliable than its predictions after hundreds of trades.
**Please be aware that this is a library script and has no visual output on its own. The charts, signals, and dashboards shown in the images were created with a separate demonstration indicator that utilizes this library's powerful pattern recognition and learning engine.

"The stock market is a device for transferring money from the impatient to the patient."
— Warren Buffett

Taking you to school. - Dskyz, Create with DAFE