LLM Few-Shot Training Pipeline

[iAmGiG]

Overview

Design and implement a few-shot learning pipeline that trains LLMs to recognize GEX patterns and generate trading signals. This system creates example libraries from historical data and builds context templates for optimal LLM performance.

Core Components

1. Example Library Builder

class FewShotExampleBuilder:
    def __init__(self, pattern_probability_engine, historical_db):
        self.pattern_engine = pattern_probability_engine
        self.db = historical_db
        
    def create_example_library(self, pattern_type, min_success_rate=0.6, max_examples=50):
        \"\"\"Build curated examples for each pattern type.\"\"\"
        # Get high-confidence successful examples
        successful_examples = self.get_successful_examples(pattern_type, min_success_rate)
        
        # Include some failure cases for contrast
        failure_examples = self.get_failure_examples(pattern_type, max_examples//5)
        
        # Format for LLM consumption
        formatted_examples = self.format_for_llm(successful_examples, failure_examples)
        
        return formatted_examples

2. Context Template System

• Market Context: Current regime, volatility environment, recent events
• GEX Profile: Total GEX, flip points, key levels, regime classification
• Technical Context: Price levels, momentum, support/resistance
• Options Flow: Unusual activity, large prints, put/call ratios
• Historical Context: Similar patterns and their outcomes

3. Prompt Engineering Framework

class PromptTemplate:
    def __init__(self, pattern_type):
        self.pattern_type = pattern_type
        self.base_template = self.load_base_template()
        
    def generate_analysis_prompt(self, market_data, examples):
        \"\"\"Create optimized prompt for pattern analysis.\"\"\"
        prompt = f\"\"\"
You are an expert options trader analyzing GEX (Gamma Exposure) patterns for trading opportunities.

CURRENT MARKET CONDITIONS:
{self.format_market_context(market_data)}

PATTERN TYPE: {self.pattern_type}

HISTORICAL EXAMPLES:
{self.format_examples(examples)}

ANALYSIS TASK:
1. Identify if the current pattern matches historical successful cases
2. Assess the strength of the pattern (1-10 scale)
3. Estimate probability of successful outcome based on examples
4. Provide specific entry/exit criteria
5. Identify key risk factors and invalidation levels

Respond with structured analysis focusing on actionable insights.
\"\"\"
        return prompt

4. Iterative Learning System

• Outcome Tracking: Monitor LLM predictions vs actual results
• Example Refinement: Update example library based on performance
• Prompt Optimization: A/B test different prompt structures
• Feedback Integration: Incorporate trading results into training

Pattern-Specific Training

1. Negative Gamma Extremes

• Example Selection: Days with GEX < -$2B and strong follow-through
• Context Emphasis: VIX levels, market positioning, catalyst events
• Success Criteria: Next-day moves >1% in predicted direction
• Failure Analysis: False signals and their characteristics

2. Gamma Flip Point Analysis

• Example Selection: Clean flip point approaches with clear outcomes
• Context Emphasis: Distance to flip, approach velocity, options flow
• Success Criteria: Reaction at flip point (bounce or break)
• Nuanced Analysis: Why some flips hold and others don't

3. Call Wall/Put Support Dynamics

• Example Selection: Clear breaks or holds at major levels
• Context Emphasis: Options concentration, delta exposure, time to expiry
• Success Criteria: Sustained moves beyond/holds at levels
• Market Microstructure: Why levels matter and when they fail

Implementation Strategy

Phase 1: Example Curation

• Data Mining: Extract best historical examples from pattern database
• Quality Scoring: Rank examples by clarity and outcome quality
• Diverse Selection: Include various market regimes and conditions
• Format Optimization: Structure data for optimal LLM consumption

Phase 2: Prompt Engineering

• Template Design: Create modular, reusable prompt structures
• Context Optimization: Test different context lengths and formats
• Chain-of-Thought: Implement reasoning steps for complex analysis
• Output Structuring: Standardize LLM response formats

Phase 3: Training Pipeline

class LLMTrainingPipeline:
    def __init__(self, llm_client, example_library):
        self.llm = llm_client
        self.examples = example_library
        
    def train_pattern_recognition(self, pattern_type):
        \"\"\"Train LLM on specific pattern type.\"\"\"
        examples = self.examples.get_examples(pattern_type)
        
        for batch in self.batch_examples(examples):
            # Generate training prompts
            prompts = self.create_training_prompts(batch)
            
            # Get LLM responses
            responses = self.llm.batch_generate(prompts)
            
            # Evaluate accuracy
            accuracy = self.evaluate_responses(responses, batch)
            
            # Update example weights based on performance
            self.update_example_weights(batch, accuracy)
            
        return self.generate_performance_report()

Phase 4: Performance Validation

• Backtesting: Test LLM signals on historical data
• Out-of-Sample: Validate on held-out time periods
• Regime Testing: Performance across different market conditions
• Comparison: LLM vs statistical baseline performance

Quality Assurance

1. Example Quality Control

• Historical Accuracy: Verify all historical data is correct
• Outcome Verification: Confirm pattern outcomes are properly labeled
• Bias Detection: Check for survivorship and selection biases
• Diversity Metrics: Ensure examples span different market conditions

2. LLM Response Validation

• Consistency Testing: Same inputs should produce similar outputs
• Reasoning Quality: Assess logical coherence of explanations
• Calibration: Check if confidence matches actual accuracy
• Hallucination Detection: Identify factually incorrect statements

3. Performance Monitoring

• Real-time Tracking: Monitor LLM signal accuracy
• Drift Detection: Identify when performance degrades
• Example Updating: Refresh examples with recent high-quality cases
• Continuous Improvement: Regular model fine-tuning

Success Criteria

• High-quality example library for each major pattern type
• Optimized prompt templates with proven effectiveness
• LLM achieving >65% accuracy on pattern recognition
• Structured output format for downstream systems
• Performance validation across multiple market regimes
• Iterative learning system operational
• Integration with real-time trading pipeline
• Comprehensive explanation generation for transparency

Dependencies

• After Issue Dynamic Prompt Generation System for Context-Aware LLM Analysis #32: Dynamic Prompt Generation System
• Requires: Pattern-Outcome Probability Engine (Pattern-Outcome Probability Engine #37)
• Requires: Historical GEX Database (Historical GEX Database Builder #36)
• Blocks: Forward-Test Experiment Runner
• Integrates With: Multi-Agent LLM Orchestration (Multi-Agent LLM Orchestration System #20)

Priority: High

Core component for LLM-based pattern recognition and signal generation.

Risk Management

• Hallucination Control: Strict fact-checking protocols
• Overfitting Prevention: Regular out-of-sample validation
• Bias Mitigation: Diverse example selection and testing
• Performance Monitoring: Continuous accuracy tracking

🤖 Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com

[iAmGiG]

Dependencies:

• ⬅️ After: Pattern-Outcome Probability Engine #37 Pattern-Outcome Probability Engine, Dynamic Prompt Generation System for Context-Aware LLM Analysis #32 Dynamic Prompt Generation, Multi-Agent LLM Orchestration System #20 Multi-Agent Orchestration
• ➡️ Blocks: Walk-Forward Backtesting Framework with No-Lookahead Validation #8 Walk-Forward Backtesting, Forward-Test Experiment Runner #39 Forward-Test Experiment Runner
• 📊 Phase: 4 - LLM Integration

Relationship in Pipeline:
Trains LLM on historical patterns to generate trading signals for backtesting and live testing.

[iAmGiG]

Closing: Few-Shot Training Pipeline is premature optimization. Need to first validate which patterns are real (mechanical) vs folklore (narrative) through Pattern Taxonomy framework. Training should come after we know which patterns actually work through obfuscation testing and economic validation.