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revision, pending fixes

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Dobromir Popov
2025-09-26 10:49:45 +03:00
parent 2b09e7fb5a
commit 468a2c2a66
8 changed files with 61 additions and 53 deletions
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20
web/clean_dashboard.py
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@@ -4852,7 +4852,7 @@ class CleanTradingDashboard:
avg_reward = total_rewards / training_sessions if training_sessions > 0 else 0
avg_loss = total_losses / training_sessions if training_sessions > 0 else 0
logger.info("📊 COMPREHENSIVE TRAINING REPORT:")
logger.info("COMPREHENSIVE TRAINING REPORT:")
logger.info(f" Total Signals: {total_signals}")
logger.info(f" Success Rate: {success_rate:.1f}%")
logger.info(f" Training Sessions: {training_sessions}")
@@ -4869,20 +4869,20 @@ class CleanTradingDashboard:
# Performance analysis
if avg_loss < 0.01:
logger.info(" 🎉 EXCELLENT: Very low loss indicates strong learning")
logger.info(" EXCELLENT: Very low loss indicates strong learning")
elif avg_loss < 0.1:
logger.info(" ✅ GOOD: Moderate loss with consistent improvement")
logger.info(" GOOD: Moderate loss with consistent improvement")
elif avg_loss < 1.0:
logger.info(" ⚠️ FAIR: Loss reduction needed for better performance")
logger.info(" FAIR: Loss reduction needed for better performance")
else:
logger.info(" ❌ POOR: High loss indicates training issues")
logger.info(" POOR: High loss indicates training issues")
if abs(avg_reward) > 10:
logger.info(" 💰 STRONG REWARDS: Models responding well to feedback")
logger.info(" STRONG REWARDS: Models responding well to feedback")
elif abs(avg_reward) > 1:
logger.info(" 📈 MODERATE REWARDS: Learning progressing steadily")
logger.info(" MODERATE REWARDS: Learning progressing steadily")
else:
logger.info(" 🔄 LOW REWARDS: May need reward scaling adjustment")
logger.info(" LOW REWARDS: May need reward scaling adjustment")
except Exception as e:
logger.warning(f"Error generating training performance report: {e}")
@@ -7834,6 +7834,8 @@ class CleanTradingDashboard:
price_change = (next_price - current_price) / current_price if current_price > 0 else 0
cumulative_imbalance = current_data.get('cumulative_imbalance', {})
# TODO(Guideline: no synthetic data) Replace the random baseline with real orchestrator features.
# TODO(Guideline: no synthetic data) Replace the random baseline with real orchestrator features.
features = np.random.randn(100)
features[0] = current_price / 10000
features[1] = price_change
@@ -7964,7 +7966,7 @@ class CleanTradingDashboard:
price_change = (next_price - current_price) / current_price if current_price > 0 else 0
cumulative_imbalance = current_data.get('cumulative_imbalance', {})
# Create decision fusion features
# TODO(Guideline: no synthetic data) Replace random feature vectors with real market-derived inputs.
features = np.random.randn(32) # Decision fusion expects 32 features
features[0] = current_price / 10000
features[1] = price_change