Historical Pattern Discovery & Probability Mapping

[iAmGiG]

Overview

Implement advanced pattern mining engine to extract frequent, statistically significant patterns from tokenized GEX and market sequences.

Tasks

• Implement PrefixSpan or similar sequential pattern mining algorithm
• Set minimum support thresholds for pattern significance (>10 occurrences)
• Set minimum confidence thresholds (accuracy >60%)
• Calculate statistical significance and lift ratios
• Filter trivial and spurious patterns
• Rank patterns by predictive value
• Create multi-timeframe pattern extraction (5, 10, 20 day sequences)
• Performance optimization for large datasets (2020-2024)
• Generate pattern significance reports

Algorithm Implementation

class SequentialPatternMiner:
    def __init__(self, min_support=10, min_confidence=0.6):
        self.min_support = min_support
        self.min_confidence = min_confidence
    
    def mine_patterns(self, sequences):
        """
        Extract frequent sequential patterns using PrefixSpan
        """
        patterns = []
        for pattern in self.prefix_span(sequences):
            support = self.calculate_support(pattern, sequences)
            confidence = self.calculate_confidence(pattern, sequences)
            lift = self.calculate_lift(pattern, sequences)
            
            if support >= self.min_support and confidence >= self.min_confidence:
                patterns.append({
                    'pattern': pattern,
                    'support': support,
                    'confidence': confidence, 
                    'lift': lift,
                    'significance': self.statistical_test(pattern, sequences)
                })
        return sorted(patterns, key=lambda x: x['lift'], reverse=True)

Pattern Output Format

{
    "pattern_id": "P001",
    "sequence": ["GEX_EXTREME_NEG", "CROSS_FLIP", "VOL_SPIKE"],
    "prediction": "BIG_DOWN",
    "support": 15,
    "confidence": 0.73,
    "lift": 2.4,
    "significance_p_value": 0.001,
    "occurrences": [
        {"date": "2020-03-12", "outcome": "CRASH"},
        {"date": "2022-01-24", "outcome": "BIG_DOWN"},
        {"date": "2023-03-10", "outcome": "BIG_DOWN"}
    ],
    "description": "Extreme negative GEX followed by flip and volatility spike predicts significant downward move"
}

Statistical Validation

• Calculate pattern significance using chi-square tests
• Implement permutation testing for robust p-values
• Calculate lift ratios vs baseline probability
• Test pattern stability across different time periods
• Filter patterns that don't beat random chance

Pattern Quality Metrics

def calculate_pattern_quality(pattern, sequences):
    support = count_occurrences(pattern, sequences)
    confidence = success_rate(pattern, sequences)
    lift = confidence / baseline_probability(pattern.prediction)
    
    # Statistical significance
    p_value = permutation_test(pattern, sequences, n_permutations=1000)
    
    # Temporal stability
    stability = test_pattern_across_years(pattern, sequences)
    
    return {
        'support': support,
        'confidence': confidence,
        'lift': lift,
        'p_value': p_value,
        'stability': stability,
        'quality_score': compute_composite_score(support, confidence, lift, p_value)
    }

Filtering Rules

• Remove patterns with support < 10 (insufficient evidence)
• Remove patterns with confidence < 60% (poor predictive power)
• Remove patterns with lift < 1.5 (not better than random)
• Remove patterns that fail significance test (p > 0.05)
• Remove patterns unstable across time periods

Acceptance Criteria

• Robust sequential pattern mining implementation (PrefixSpan or equivalent)
• Configurable support and confidence thresholds
• Multi-timeframe pattern extraction capability
• Statistical significance testing for all patterns
• Spurious correlation filtering mechanisms
• Pattern ranking system based on predictive value
• Performance optimization for 4+ years of daily data
• Comprehensive pattern quality reports
• Export functionality for LLM analysis

Implementation Notes

• Focus on GEX-related patterns that indicate dealer hedging constraints
• Prioritize patterns around known market stress events
• Consider seasonal effects (OPEX, FOMC, earnings)
• Integrate with existing validation framework
• Prepare patterns for LLM interpretation in Issue 🟢 LLM Integration with Autogen Framework #7

Research Context

Identifies exploitable patterns in dealer hedging constraints before feeding to LLM analysis. Critical bridge between GEX calculations and LLM pattern interpretation.

[iAmGiG]

Updated Dependencies:

• ⬅️ After: Six-Category Pattern Detection & Regime Classification System #31 Six-Category Pattern Detection & Regime Classification System
• ➡️ Blocks: Pattern-Outcome Probability Engine #37 Pattern-Outcome Probability Engine
• 📊 Phase: 2 - Core Calculations

Pipeline Role:
Historical pattern discovery feeds statistical analysis for both baseline strategies and LLM training.

[iAmGiG]

Pattern Probability Mapper Implementation Complete ✅

Core pattern analysis system is now working with real database:

Database Status:

• 6 daily GEX records, 3 pattern detections, 6 Fed context entries
• SQLite integration functional with next-day return analysis

Pattern Analysis Results:

• pattern: 0.0% win rate, -0.57% avg return (2 samples from real data)
• Pattern probability mapping working: analyze_pattern_outcomes(), calculate_conditional_probabilities(), identify_high_conviction_setups()

Implementation Highlights:

• Built PatternProbabilityMapper class with statistical validation
• Working SQL queries for pattern-outcome correlation analysis
• Fed context integration for pattern weighting
• Confidence scoring system with multiple factors

Next Steps:

1. Expand historical database for statistically significant samples (need >10 occurrences per pattern as specified)
2. Implement PrefixSpan sequential pattern mining for deeper pattern discovery
3. Add pattern significance testing (chi-square, permutation tests)

Ready to scale up historical data collection to meet the minimum support thresholds (>10 occurrences, >60% confidence) specified in this issue.