Forward-Test Experiment Runner #39
Description
Overview
Build a live paper trading system for real-time validation of LLM-generated trading signals. This system provides the final validation step by testing the complete pipeline in live market conditions without financial risk.
Core Components
1. Real-Time Data Integration
class LiveDataManager:
def __init__(self, data_sources):
self.sources = data_sources
self.current_data = {}
def get_live_market_data(self):
\"\"\"Fetch current market conditions for analysis.\"\"\"
return {
'spy_price': self.get_current_price('SPY'),
'spy_options_chain': self.get_live_options_data('SPY'),
'vix_level': self.get_current_vix(),
'market_hours': self.get_market_status(),
'timestamp': datetime.now()
}
def calculate_live_gex(self, options_data):
\"\"\"Real-time GEX calculation from live data.\"\"\"
gex_calc = GEXCalculator()
return gex_calc.calculate_daily_gex_metrics(
options_data,
self.current_data['spy_price']
)2. Pattern Recognition Pipeline
- Live GEX Monitoring: Continuous calculation of GEX metrics
- Pattern Detection: Real-time identification of trading patterns
- Context Assembly: Current market regime and technical conditions
- LLM Signal Generation: Generate trading recommendations
- Confidence Scoring: Assess signal quality and conviction
3. Paper Trading Engine
class PaperTradingSystem:
def __init__(self, initial_capital=100000):
self.capital = initial_capital
self.positions = {}
self.trade_history = []
self.performance_metrics = {}
def execute_signal(self, signal, current_price, timestamp):
\"\"\"Execute paper trade based on LLM signal.\"\"\"
trade = {
'timestamp': timestamp,
'signal': signal,
'entry_price': current_price,
'position_size': self.calculate_position_size(signal),
'stop_loss': signal.get('stop_loss'),
'target': signal.get('target'),
'rationale': signal.get('explanation')
}
self.positions[signal['id']] = trade
self.log_trade(trade)
return trade
def monitor_positions(self, current_market_data):
\"\"\"Monitor open positions and manage exits.\"\"\"
for position_id, position in self.positions.items():
# Check stop loss and target levels
if self.should_close_position(position, current_market_data):
self.close_position(position_id, current_market_data)4. Performance Tracking System
- Real-Time Metrics: P&L, win rate, Sharpe ratio tracking
- Signal Quality: Track LLM prediction accuracy
- Pattern Performance: Success rates by pattern type
- Risk Metrics: Drawdown monitoring and risk management
- Comparison Tracking: Performance vs baseline strategies
Implementation Strategy
Phase 1: Infrastructure Setup
- Market Data Feeds: Establish reliable real-time data sources
- GEX Calculation: Adapt existing engine for live data
- Pattern Recognition: Integrate trained LLM system
- Paper Trading Framework: Build execution and tracking systems
Phase 2: Signal Generation Pipeline
class LiveSignalGenerator:
def __init__(self, llm_pipeline, pattern_detector):
self.llm = llm_pipeline
self.detector = pattern_detector
async def generate_trading_signals(self):
\"\"\"Main signal generation loop.\"\"\"
while self.market_is_open():
# Get current market data
market_data = self.data_manager.get_live_market_data()
# Calculate current GEX profile
gex_profile = self.calculate_live_gex(market_data['options_chain'])
# Detect active patterns
active_patterns = self.detector.identify_patterns(gex_profile)
if active_patterns:
# Generate LLM analysis
signal = await self.llm.analyze_patterns(
patterns=active_patterns,
market_context=market_data,
gex_profile=gex_profile
)
if signal['confidence'] > self.min_confidence_threshold:
await self.execute_signal(signal)
# Wait for next analysis cycle
await asyncio.sleep(self.analysis_interval)Phase 3: Validation Framework
- Performance Monitoring: Continuous tracking of all metrics
- Pattern Analysis: Which patterns work in live conditions
- LLM Calibration: How well does confidence match outcomes
- Market Regime Adaptation: Performance across different conditions
Phase 4: Reporting and Analysis
- Daily Reports: Summary of signals, trades, and performance
- Pattern Effectiveness: Success rates by pattern and conditions
- LLM Performance: Accuracy and calibration analysis
- Comparison Analysis: Live vs historical backtesting results
Risk Management
1. Position Sizing
- Fixed Fractional: Risk fixed percentage of capital per trade
- Kelly Criterion: Optimal sizing based on edge and win rate
- Volatility Adjustment: Size based on current market volatility
- Maximum Risk: Hard limits on position size and total exposure
2. Stop Loss Management
def calculate_stop_loss(self, signal, current_gex):
\"\"\"Dynamic stop loss based on GEX levels.\"\"\"
if signal['pattern_type'] == 'gamma_flip':
# Stop if price moves significantly away from flip point
stop_distance = abs(current_gex['gamma_flip'] - signal['entry_price']) * 0.5
elif signal['pattern_type'] == 'negative_gamma_extreme':
# Wider stops in high gamma environments
stop_distance = signal['entry_price'] * 0.02 # 2%
else:
stop_distance = signal['entry_price'] * 0.015 # 1.5% default
return signal['entry_price'] - stop_distance if signal['direction'] == 'long' else signal['entry_price'] + stop_distance3. Drawdown Protection
- Maximum Drawdown: Halt trading if losses exceed threshold
- Consecutive Losses: Reduce position sizes after losing streaks
- Market Conditions: Adjust risk during high volatility periods
- Emergency Stop: Manual override capability
Validation Metrics
1. Trading Performance
- Total Return: Absolute and risk-adjusted returns
- Win Rate: Percentage of profitable trades
- Profit Factor: Ratio of gross profits to gross losses
- Maximum Drawdown: Worst peak-to-trough decline
- Sharpe Ratio: Risk-adjusted return measurement
2. LLM Signal Quality
- Prediction Accuracy: Direction prediction success rate
- Confidence Calibration: Does stated confidence match outcomes
- Pattern Recognition: Success rate by pattern type
- False Positive Rate: Frequency of incorrect signals
3. Market Adaptation
- Regime Performance: Success across different market conditions
- Volatility Adjustment: Performance in high/low vol environments
- Time-of-Day Effects: Intraday performance variations
- News Event Response: Handling of unexpected market events
Success Criteria
- Real-time data pipeline operational with <5 second latency
- Live GEX calculation matching historical accuracy
- LLM signal generation working in real-time
- Paper trading system tracking all positions and performance
- Risk management system preventing excessive losses
- Performance tracking and reporting operational
- 30+ days of live forward testing completed
- Comprehensive performance analysis vs baseline strategies
Dependencies
- Final Integration: Requires all previous systems operational
- After: LLM Few-Shot Training Pipeline (LLM Few-Shot Training Pipeline #38)
- After: Walk-Forward Backtesting Framework (Walk-Forward Backtesting Framework with No-Lookahead Validation #8)
- Requires: Multi-Agent LLM Orchestration (Multi-Agent LLM Orchestration System #20)
- Integrates With: Baseline Comparison System (Baseline Comparison System #35)
Priority: High
Final validation step for complete system integration.
Risk Considerations
- No Real Money: Strict paper trading only during validation
- Market Hours: Operate only during regular trading hours
- Data Quality: Robust handling of bad or delayed data
- System Failures: Graceful degradation and error handling
🤖 Generated with Claude Code
Co-Authored-By: Claude noreply@anthropic.com