DafePatternsLib

DafePatternLib: The Adaptive Pattern Recognition Engine [DAFE]

DafePatternLib is not a static pattern library. It is an adaptive recognition engine. It doesn't just find patterns; it tracks, weights, and filters them based on their performance in the live market.

█ CHAPTER 1: THE PHILOSOPHY — BEYOND STATIC RULES, INTO DYNAMIC LEARNING
For decades, chart pattern analysis has been trapped in a rigid paradigm. An indicator coded to find a "Bullish Engulfing" will signal that pattern with the same confidence every time, regardless of whether it has been failing consistently for weeks. It has no memory and no ability to adapt.

DafePatternLib was created to change this. It is built on a performance-based reinforcement framework. This library is not just a collection of detection functions; it is a self-weighting logic system. It tracks outcomes. It remembers what works. Over time, it amplifies the signals of high-probability patterns and filters out those that are failing in the current market regime.

This is not a black box. It is an open-source, observable learning system. It strengthens and weakens its own internal weights based on positive and negative feedback, evolving into a tool adapted to the specific asset you are trading.

█ CHAPTER 2: CORE INNOVATIONS
This library introduces several advanced concepts for algorithmic analysis:

Reinforcement Learning Engine: The core of the system. Every high-confidence pattern is logged into "Active Memory." The library tracks the outcome against projected stops and targets. If successful, the weight for that pattern category is strengthened. If it fails, the weight is reduced. This creates a continuous feedback loop.

Adaptation Rate (Plasticity): You have direct control over the engine's "plasticity"—its ability to learn. High plasticity allows fast adaptation to new conditions; lower plasticity creates a stable, long-term model.

Dynamic Volatility Scaling (DVS): Markets breathe. DVS is a proprietary function that calculates a real-time volatility scalar by comparing current ATR to historical averages. This scalar automatically adjusts lookback periods and sensitivities. In high volatility, engines look for larger structures; in low volatility, they tighten focus.

Smart Confidence Score: The output is not a simple "true/false." Every pattern includes two scores:
• Raw Confidence: Static confidence based on the pattern's textbook definition.
• Net Confidence: The adaptive score (Raw Confidence × Learned Bias). A performing pattern sees its confidence boosted; a failing pattern gets penalized.

Intelligent Filtering: If the learned bias for a category (e.g., "Candle") drops below a threshold (e.g., 0.8), the library automatically filters those signals, treating them as low-probability noise until performance improves.

█ CHAPTER 3: ANATOMY OF THE LOGIC — HOW IT THINKS

The LogicWeights (The Core)
The central data structure holding the system's "memory." It stores a floating-point weight or "bias" for each of the five major categories (Candle, Harmonic, Structure, Geometry, VSA). Initialized at 1.0 (neutral).

update_core() (The Learning Process)
The heart of the reinforcement loop. When a pattern resolves (win/loss), this function applies a positive or negative adjustment to the corresponding category weight. Weights are constrained between 0.5 (distrust) and 2.0 (trust).

manage_memory() (Short-Term Memory)
Maintains an array of active signals. On every bar, it checks if targets or stops have been hit. Resolved patterns trigger update_core(). Unresolved patterns eventually expire, applying a minor penalty to discourage stagnation.

scan_pattern_universe() (Master Controller)
The main exported function. On every bar, it:
Calculates the Dynamic Volatility Scalar (DVS).
Runs all pattern detection engines (VSA, Geometry, Candles, etc.) adapted to DVS.
Identifies the single best pattern based on raw confidence.
Passes it to memory for tracking.
Applies the learned bias to calculate Net Confidence.
Returns the final, adaptively weighted PatternResult.

█ CHAPTER 4: DEVELOPER INTEGRATION GUIDE
Designed for simplicity and power.

1. Import the Library:

import DskyzInvestments/DafePatternLib/1 as pattern

2. Call the Scanner:
The library handles DVS, scanning, memory, and learning internally.

pattern.PatternResult signal = pattern.scan_pattern_universe()

3. Use the Result:

if signal.is_active
label.new(bar_index, signal.entry, "Conf: " + str.tostring(signal.net_confidence, "#") + "%")

With just these lines, you integrate a self-weighting, multi-pattern recognition engine.

█ INPUTS TEMPLATE (COPY INTO YOUR SCRIPT)

// ═══════════════════════════════════════════════════════════
// INPUT GROUPS
// ═══════════════════════════════════════════════════════════
string G_AI_ENGINE = "══════════ 🧠 LOGIC ENGINE ══════════"
string G_AI_PATTERNS = "══════════ 🔬 PATTERN SELECTION ══════════"
string G_AI_VISUALS = "══════════ 🎨 VISUALS & SIGNALS ══════════"
string G_AI_DASH = "══════════ 📋 LOGIC STATE DASHBOARD ══════════"
string G_AI_ALERTS = "══════════ 🔔 ALERTS ══════════"

// ═══════════════════════════════════════════════════════════
// LOGIC ENGINE CONTROLS
// ═══════════════════════════════════════════════════════════
bool i_enable_ai = input.bool(true, "✨ Enable Adaptive Engine", group = G_AI_ENGINE,
tooltip="Master switch to enable the pattern recognition and learning system.")

float i_plasticity = input.float(0.03, "Adaptation Rate", minval=0.01, maxval=0.1, step=0.01, group = G_AI_ENGINE,
tooltip="Controls adaptation speed.\n• Low (0.01-0.02): Stable learning.\n• Medium (0.03-0.05): Balanced.\n• High (0.06+): Fast adaptation.")

float i_filter_threshold = input.float(0.8, "Adaptive Filter Threshold", minval=0.5, maxval=1.0, step=0.05, group = G_AI_ENGINE,
tooltip="Hide signals from categories with a learned bias below this value.")

// ═══════════════════════════════════════════════════════════
// PATTERN SELECTION
// ═══════════════════════════════════════════════════════════
bool i_scan_candles = input.bool(true, "🕯️ Candlestick Patterns", group = G_AI_PATTERNS, inline="row1")
bool i_scan_vsa = input.bool(true, "📦 Volume Spread Analysis", group = G_AI_PATTERNS, inline="row1")
bool i_scan_geometry = input.bool(true, "📐 Geometric Patterns", group = G_AI_PATTERNS, inline="row2")
bool i_scan_structure = input.bool(true, "📈 Market Structure (SMC)", group = G_AI_PATTERNS, inline="row2")
bool i_scan_harmonic = input.bool(false, "🦋 Harmonic Setups", group = G_AI_PATTERNS, inline="row3")

// ═══════════════════════════════════════════════════════════
// VISUALS & DASHBOARD
// ═══════════════════════════════════════════════════════════
bool i_show_signals = input.bool(true, "Show Signals", group = G_AI_VISUALS)
bool i_show_dashboard = input.bool(true, "Show Logic Dashboard", group = G_AI_DASH)

█ DEVELOPMENT PHILOSOPHY
DafePatternLib was born from a vision to bring dynamic logic to technical analysis. We believe an indicator should not be a static tool, but an intelligent partner that adapts. This library is an open-source framework empowering developers to build the next generation of smart indicators.

█ DISCLAIMER
• LIBRARY FOR DEVELOPERS: This script produces no visual output on its own. It is an engine for developers.
• ADAPTIVE, NOT PREDICTIVE: Reinforcement learning optimizes based on recent history. It is a statistical edge, not a crystal ball.
• RISK WARNING: Patterns and confidence scores are for informational purposes.

"The key to trading success is emotional discipline. If intelligence were the key, there would be a lot more people making money trading." — Victor Sperandeo

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