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Dobromir Popov
189
balance_trading_signals.py
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189
balance_trading_signals.py
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#!/usr/bin/env python3
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"""
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Balance Trading Signals - Analyze and fix SHORT signal bias
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This script analyzes the trading signals from the orchestrator and adjusts
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the model weights to balance BUY and SELL signals.
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"""
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import os
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import sys
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import logging
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import json
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from pathlib import Path
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from datetime import datetime
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# Add project root to path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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from core.config import get_config, setup_logging
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from core.orchestrator import TradingOrchestrator
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from core.data_provider import DataProvider
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# Setup logging
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setup_logging()
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logger = logging.getLogger(__name__)
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def analyze_trading_signals():
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"""Analyze trading signals from the orchestrator"""
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logger.info("Analyzing trading signals...")
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# Initialize components
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider, enhanced_rl_training=True)
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# Get recent decisions
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symbols = orchestrator.symbols
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all_decisions = {}
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for symbol in symbols:
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decisions = orchestrator.get_recent_decisions(symbol)
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all_decisions[symbol] = decisions
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# Count actions
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action_counts = {'BUY': 0, 'SELL': 0, 'HOLD': 0}
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for decision in decisions:
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action_counts[decision.action] += 1
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total_decisions = sum(action_counts.values())
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if total_decisions > 0:
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buy_percent = action_counts['BUY'] / total_decisions * 100
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sell_percent = action_counts['SELL'] / total_decisions * 100
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hold_percent = action_counts['HOLD'] / total_decisions * 100
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logger.info(f"Symbol: {symbol}")
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logger.info(f" Total decisions: {total_decisions}")
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logger.info(f" BUY: {action_counts['BUY']} ({buy_percent:.1f}%)")
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logger.info(f" SELL: {action_counts['SELL']} ({sell_percent:.1f}%)")
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logger.info(f" HOLD: {action_counts['HOLD']} ({hold_percent:.1f}%)")
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# Check for bias
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if sell_percent > buy_percent * 2: # If SELL signals are more than twice BUY signals
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logger.warning(f" SELL bias detected: {sell_percent:.1f}% vs {buy_percent:.1f}%")
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# Adjust model weights to balance signals
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logger.info(" Adjusting model weights to balance signals...")
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# Get current model weights
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model_weights = orchestrator.model_weights
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logger.info(f" Current model weights: {model_weights}")
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# Identify models with SELL bias
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model_predictions = {}
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for model_name in model_weights:
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model_predictions[model_name] = {'BUY': 0, 'SELL': 0, 'HOLD': 0}
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# Analyze recent decisions to identify biased models
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for decision in decisions:
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reasoning = decision.reasoning
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if 'models_used' in reasoning:
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for model_name in reasoning['models_used']:
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if model_name in model_predictions:
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model_predictions[model_name][decision.action] += 1
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# Calculate bias for each model
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model_bias = {}
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for model_name, actions in model_predictions.items():
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total = sum(actions.values())
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if total > 0:
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buy_pct = actions['BUY'] / total * 100
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sell_pct = actions['SELL'] / total * 100
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# Calculate bias score (-100 to 100, negative = SELL bias, positive = BUY bias)
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bias_score = buy_pct - sell_pct
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model_bias[model_name] = bias_score
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logger.info(f" Model {model_name}: Bias score = {bias_score:.1f} (BUY: {buy_pct:.1f}%, SELL: {sell_pct:.1f}%)")
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# Adjust weights based on bias
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adjusted_weights = {}
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for model_name, weight in model_weights.items():
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if model_name in model_bias:
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bias = model_bias[model_name]
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# If model has strong SELL bias, reduce its weight
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if bias < -30: # Strong SELL bias
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adjusted_weights[model_name] = max(0.05, weight * 0.7) # Reduce weight by 30%
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logger.info(f" Reducing weight of {model_name} from {weight:.2f} to {adjusted_weights[model_name]:.2f} due to SELL bias")
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# If model has BUY bias, increase its weight to balance
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elif bias > 10: # BUY bias
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adjusted_weights[model_name] = min(0.5, weight * 1.3) # Increase weight by 30%
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logger.info(f" Increasing weight of {model_name} from {weight:.2f} to {adjusted_weights[model_name]:.2f} to balance SELL bias")
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else:
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adjusted_weights[model_name] = weight
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else:
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adjusted_weights[model_name] = weight
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# Save adjusted weights
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save_adjusted_weights(adjusted_weights)
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logger.info(f" Adjusted weights: {adjusted_weights}")
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logger.info(" Weights saved to 'adjusted_model_weights.json'")
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# Recommend next steps
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logger.info("\nRecommended actions:")
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logger.info("1. Update the model weights in the orchestrator")
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logger.info("2. Monitor trading signals for balance")
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logger.info("3. Consider retraining models with balanced data")
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def save_adjusted_weights(weights):
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"""Save adjusted weights to a file"""
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output = {
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'timestamp': datetime.now().isoformat(),
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'weights': weights,
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'notes': 'Adjusted to balance BUY/SELL signals'
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}
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with open('adjusted_model_weights.json', 'w') as f:
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json.dump(output, f, indent=2)
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def apply_balanced_weights():
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"""Apply balanced weights to the orchestrator"""
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try:
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# Check if weights file exists
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if not os.path.exists('adjusted_model_weights.json'):
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logger.error("Adjusted weights file not found. Run analyze_trading_signals() first.")
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return False
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# Load adjusted weights
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with open('adjusted_model_weights.json', 'r') as f:
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data = json.load(f)
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weights = data.get('weights', {})
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if not weights:
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logger.error("No weights found in the file.")
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return False
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logger.info(f"Loaded adjusted weights: {weights}")
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# Initialize components
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider, enhanced_rl_training=True)
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# Apply weights
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for model_name, weight in weights.items():
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if model_name in orchestrator.model_weights:
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orchestrator.model_weights[model_name] = weight
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# Save updated weights
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orchestrator._save_orchestrator_state()
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logger.info("Applied balanced weights to orchestrator.")
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logger.info("Restart the trading system for changes to take effect.")
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return True
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except Exception as e:
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logger.error(f"Error applying balanced weights: {e}")
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return False
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if __name__ == "__main__":
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logger.info("=" * 70)
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logger.info("TRADING SIGNAL BALANCE ANALYZER")
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logger.info("=" * 70)
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if len(sys.argv) > 1 and sys.argv[1] == 'apply':
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apply_balanced_weights()
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else:
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analyze_trading_signals()
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