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price vector predictions

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This commit is contained in:
Dobromir Popov
2025-07-29 23:45:57 +03:00
parent 3fad2caeb8
commit 29382ac0db
2 changed files with 209 additions and 29 deletions
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236
core/orchestrator.py
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@ -2230,6 +2230,13 @@ class TradingOrchestrator:
# Add training samples for CNN predictions using sophisticated reward system
for prediction in predictions:
if "cnn" in prediction.model_name.lower():
# Extract price vector information if available
predicted_price_vector = None
if hasattr(prediction, 'price_direction') and prediction.price_direction:
predicted_price_vector = prediction.price_direction
elif hasattr(prediction, 'metadata') and prediction.metadata and 'price_direction' in prediction.metadata:
predicted_price_vector = prediction.metadata['price_direction']
# Calculate sophisticated reward using the new PnL penalty/reward system
sophisticated_reward, was_correct = self._calculate_sophisticated_reward(
predicted_action=prediction.action,
@ -2239,7 +2246,8 @@ class TradingOrchestrator:
has_price_prediction=False,
symbol=symbol,
has_position=has_position,
current_position_pnl=current_position_pnl
current_position_pnl=current_position_pnl,
predicted_price_vector=predicted_price_vector
)
# Create training record for the new training system
@ -3323,6 +3331,12 @@ class TradingOrchestrator:
# Calculate reward for logging
current_pnl = self._get_current_position_pnl(self.symbol)
# Extract price vector from prediction metadata if available
predicted_price_vector = None
if "price_direction" in prediction and prediction["price_direction"]:
predicted_price_vector = prediction["price_direction"]
reward, _ = self._calculate_sophisticated_reward(
predicted_action,
predicted_confidence,
@ -3331,6 +3345,7 @@ class TradingOrchestrator:
has_price_prediction=predicted_price is not None,
symbol=self.symbol,
current_position_pnl=current_pnl,
predicted_price_vector=predicted_price_vector,
)
# Enhanced logging with detailed information
@ -3420,6 +3435,12 @@ class TradingOrchestrator:
# Calculate sophisticated reward based on multiple factors
current_pnl = self._get_current_position_pnl(symbol)
# Extract price vector from prediction metadata if available
predicted_price_vector = None
if "price_direction" in prediction and prediction["price_direction"]:
predicted_price_vector = prediction["price_direction"]
reward, was_correct = self._calculate_sophisticated_reward(
predicted_action,
prediction_confidence,
@ -3429,6 +3450,7 @@ class TradingOrchestrator:
symbol, # Pass symbol for position lookup
None, # Let method determine position status
current_position_pnl=current_pnl,
predicted_price_vector=predicted_price_vector,
)
# Update model performance tracking
@ -3537,11 +3559,13 @@ class TradingOrchestrator:
symbol: str = None,
has_position: bool = None,
current_position_pnl: float = 0.0,
predicted_price_vector: dict = None,
) -> tuple[float, bool]:
"""
Calculate sophisticated reward based on prediction accuracy, confidence, and price movement magnitude
Now considers position status and current P&L when evaluating decisions
NOISE REDUCTION: Treats neutral/low-confidence signals as HOLD to reduce training noise
PRICE VECTOR BONUS: Rewards accurate price direction and magnitude predictions
Args:
predicted_action: The predicted action ('BUY', 'SELL', 'HOLD')
@ -3552,6 +3576,7 @@ class TradingOrchestrator:
symbol: Trading symbol (for position lookup)
has_position: Whether we currently have a position (if None, will be looked up)
current_position_pnl: Current unrealized P&L of open position (0.0 if no position)
predicted_price_vector: Dict with 'direction' (-1 to 1) and 'confidence' (0 to 1)
Returns:
tuple: (reward, was_correct)
@ -3563,9 +3588,12 @@ class TradingOrchestrator:
predicted_action = "HOLD"
logger.debug(f"Low confidence ({prediction_confidence:.2f}) - treating as HOLD for noise reduction")
# Base thresholds for determining correctness
movement_threshold = 0.15 # Increased from 0.1% to 0.15% for stronger signals
strong_movement_threshold = 0.5 # 0.5% for strong movements
# FEE-AWARE THRESHOLDS: Account for trading fees (0.05-0.06% per trade, ~0.12% round trip)
fee_cost = 0.12 # 0.12% round trip fee cost
movement_threshold = 0.15 # Minimum movement to be profitable after fees
strong_movement_threshold = 0.5 # Strong movements - good profit potential
rapid_movement_threshold = 1.0 # Rapid movements - excellent profit potential
massive_movement_threshold = 2.0 # Massive movements - extraordinary profit potential
# Determine current position status if not provided
if has_position is None and symbol:
@ -3581,20 +3609,49 @@ class TradingOrchestrator:
directional_accuracy = 0.0
if predicted_action == "BUY":
# BUY signals need stronger confirmation for higher rewards
# BUY signals need to overcome fee costs for profitability
was_correct = price_change_pct > movement_threshold
if price_change_pct > strong_movement_threshold:
directional_accuracy = price_change_pct * 2.0 # Bonus for strong moves
# ENHANCED FEE-AWARE REWARD STRUCTURE
if price_change_pct > massive_movement_threshold:
# Massive movements (2%+) - EXTRAORDINARY rewards for high confidence
directional_accuracy = price_change_pct * 5.0 # 5x multiplier for massive moves
if prediction_confidence > 0.8:
directional_accuracy *= 2.0 # Additional 2x for high confidence (10x total)
elif price_change_pct > rapid_movement_threshold:
# Rapid movements (1%+) - EXCELLENT rewards for high confidence
directional_accuracy = price_change_pct * 3.0 # 3x multiplier for rapid moves
if prediction_confidence > 0.7:
directional_accuracy *= 1.5 # Additional 1.5x for good confidence (4.5x total)
elif price_change_pct > strong_movement_threshold:
# Strong movements (0.5%+) - GOOD rewards
directional_accuracy = price_change_pct * 2.0 # 2x multiplier for strong moves
else:
directional_accuracy = max(0, price_change_pct) # Standard reward
# Small movements - minimal rewards (fees eat most profit)
directional_accuracy = max(0, (price_change_pct - fee_cost)) * 0.5 # Penalty for fee cost
elif predicted_action == "SELL":
# SELL signals need stronger confirmation for higher rewards
# SELL signals need to overcome fee costs for profitability
was_correct = price_change_pct < -movement_threshold
if price_change_pct < -strong_movement_threshold:
directional_accuracy = abs(price_change_pct) * 2.0 # Bonus for strong moves
# ENHANCED FEE-AWARE REWARD STRUCTURE (symmetric to BUY)
abs_change = abs(price_change_pct)
if abs_change > massive_movement_threshold:
# Massive movements (2%+) - EXTRAORDINARY rewards for high confidence
directional_accuracy = abs_change * 5.0 # 5x multiplier for massive moves
if prediction_confidence > 0.8:
directional_accuracy *= 2.0 # Additional 2x for high confidence (10x total)
elif abs_change > rapid_movement_threshold:
# Rapid movements (1%+) - EXCELLENT rewards for high confidence
directional_accuracy = abs_change * 3.0 # 3x multiplier for rapid moves
if prediction_confidence > 0.7:
directional_accuracy *= 1.5 # Additional 1.5x for good confidence (4.5x total)
elif abs_change > strong_movement_threshold:
# Strong movements (0.5%+) - GOOD rewards
directional_accuracy = abs_change * 2.0 # 2x multiplier for strong moves
else:
directional_accuracy = max(0, -price_change_pct) # Standard reward
# Small movements - minimal rewards (fees eat most profit)
directional_accuracy = max(0, (abs_change - fee_cost)) * 0.5 # Penalty for fee cost
elif predicted_action == "HOLD":
# HOLD evaluation with noise reduction - smaller rewards to reduce training noise
@ -3633,10 +3690,17 @@ class TradingOrchestrator:
was_correct = abs(price_change_pct) < movement_threshold
directional_accuracy = max(0, movement_threshold - abs(price_change_pct)) * 0.4 # Reduced reward
# Calculate magnitude-based multiplier (higher rewards for larger correct movements)
magnitude_multiplier = min(
abs(price_change_pct) / 2.0, 2.5 # Reduced from 3.0 to 2.5 to reduce noise
) # Cap at 2.5x for 5% moves
# Calculate FEE-AWARE magnitude-based multiplier (aggressive rewards for profitable movements)
abs_movement = abs(price_change_pct)
if abs_movement > massive_movement_threshold:
magnitude_multiplier = min(abs_movement / 1.0, 8.0) # Up to 8x for massive moves (8% = 8x)
elif abs_movement > rapid_movement_threshold:
magnitude_multiplier = min(abs_movement / 1.5, 4.0) # Up to 4x for rapid moves (6% = 4x)
elif abs_movement > strong_movement_threshold:
magnitude_multiplier = min(abs_movement / 2.0, 2.0) # Up to 2x for strong moves (4% = 2x)
else:
# Small movements get minimal multiplier due to fees
magnitude_multiplier = max(0.1, (abs_movement - fee_cost) / 2.0) # Penalty for fee cost
# Calculate confidence-based reward adjustment
if was_correct:
@ -3648,22 +3712,61 @@ class TradingOrchestrator:
directional_accuracy * magnitude_multiplier * confidence_multiplier
)
# Bonus for high-confidence correct predictions with large movements
if prediction_confidence > 0.8 and abs(price_change_pct) > 1.0:
base_reward *= 1.5 # 50% bonus for very confident + large movement
# ENHANCED HIGH-CONFIDENCE BONUSES for profitable movements
abs_movement = abs(price_change_pct)
# Extraordinary confidence bonus for massive movements
if prediction_confidence > 0.9 and abs_movement > massive_movement_threshold:
base_reward *= 3.0 # 300% bonus for ultra-confident massive moves
logger.info(f"ULTRA CONFIDENCE BONUS: {prediction_confidence:.2f} confidence + {abs_movement:.2f}% movement = 3x reward")
# Excellent confidence bonus for rapid movements
elif prediction_confidence > 0.8 and abs_movement > rapid_movement_threshold:
base_reward *= 2.0 # 200% bonus for very confident rapid moves
logger.info(f"HIGH CONFIDENCE BONUS: {prediction_confidence:.2f} confidence + {abs_movement:.2f}% movement = 2x reward")
# Good confidence bonus for strong movements
elif prediction_confidence > 0.7 and abs_movement > strong_movement_threshold:
base_reward *= 1.5 # 150% bonus for confident strong moves
logger.info(f"CONFIDENCE BONUS: {prediction_confidence:.2f} confidence + {abs_movement:.2f}% movement = 1.5x reward")
# Rapid movement detection bonus (speed matters for fees)
if time_diff_minutes < 5.0 and abs_movement > rapid_movement_threshold:
base_reward *= 1.3 # 30% bonus for rapid detection of big moves
logger.info(f"RAPID DETECTION BONUS: {abs_movement:.2f}% movement in {time_diff_minutes:.1f}m = 1.3x reward")
# PRICE VECTOR ACCURACY BONUS - Reward models for accurate price direction/magnitude predictions
if predicted_price_vector and isinstance(predicted_price_vector, dict):
vector_bonus = self._calculate_price_vector_bonus(
predicted_price_vector, price_change_pct, abs_movement, prediction_confidence
)
if vector_bonus > 0:
base_reward += vector_bonus
logger.info(f"PRICE VECTOR BONUS: +{vector_bonus:.3f} for accurate direction/magnitude prediction")
else:
# ENHANCED PENALTY SYSTEM: Discourage fee-losing trades
abs_movement = abs(price_change_pct)
# Penalize incorrect predictions more severely if they were confident
confidence_penalty = 0.5 + (
prediction_confidence * 1.5
) # Higher confidence = higher penalty
base_penalty = abs(price_change_pct) * confidence_penalty
confidence_penalty = 0.5 + (prediction_confidence * 1.5) # Higher confidence = higher penalty
base_penalty = abs_movement * confidence_penalty
# Extra penalty for very confident wrong predictions
if prediction_confidence > 0.8:
base_penalty *= (
2.0 # Double penalty for overconfident wrong predictions
)
# SEVERE penalties for confident wrong predictions on big moves
if prediction_confidence > 0.8 and abs_movement > rapid_movement_threshold:
base_penalty *= 5.0 # 5x penalty for very confident wrong on big moves
logger.warning(f"SEVERE PENALTY: {prediction_confidence:.2f} confidence wrong on {abs_movement:.2f}% movement = 5x penalty")
elif prediction_confidence > 0.7 and abs_movement > strong_movement_threshold:
base_penalty *= 3.0 # 3x penalty for confident wrong on strong moves
logger.warning(f"HIGH PENALTY: {prediction_confidence:.2f} confidence wrong on {abs_movement:.2f}% movement = 3x penalty")
elif prediction_confidence > 0.8:
base_penalty *= 2.0 # 2x penalty for overconfident wrong predictions
# ADDITIONAL penalty for predictions that would lose money to fees
if abs_movement < fee_cost and prediction_confidence > 0.5:
fee_loss_penalty = (fee_cost - abs_movement) * 2.0 # Penalty for fee-losing trades
base_penalty += fee_loss_penalty
logger.warning(f"FEE LOSS PENALTY: {abs_movement:.2f}% movement < {fee_cost:.2f}% fees = +{fee_loss_penalty:.3f} penalty")
base_reward = -base_penalty
@ -3706,6 +3809,78 @@ class TradingOrchestrator:
)
return (1.0 if simple_correct else -0.5, simple_correct)
def _calculate_price_vector_bonus(
self,
predicted_vector: dict,
actual_price_change_pct: float,
abs_movement: float,
prediction_confidence: float
) -> float:
"""
Calculate bonus reward for accurate price direction and magnitude predictions
Args:
predicted_vector: Dict with 'direction' (-1 to 1) and 'confidence' (0 to 1)
actual_price_change_pct: Actual price change percentage
abs_movement: Absolute value of price movement
prediction_confidence: Overall model confidence
Returns:
Bonus reward value (0 or positive)
"""
try:
predicted_direction = predicted_vector.get('direction', 0.0)
vector_confidence = predicted_vector.get('confidence', 0.0)
# Skip if vector prediction is too weak
if abs(predicted_direction) < 0.1 or vector_confidence < 0.3:
return 0.0
# Calculate direction accuracy
actual_direction = 1.0 if actual_price_change_pct > 0 else -1.0 if actual_price_change_pct < 0 else 0.0
direction_accuracy = 0.0
if actual_direction != 0.0: # Only if there was actual movement
# Check if predicted direction matches actual direction
if (predicted_direction > 0 and actual_direction > 0) or (predicted_direction < 0 and actual_direction < 0):
direction_accuracy = min(abs(predicted_direction), 1.0) # Stronger prediction = higher bonus
# MAGNITUDE ACCURACY BONUS
# Convert predicted direction to expected magnitude (scaled by confidence)
predicted_magnitude = abs(predicted_direction) * vector_confidence * 2.0 # Scale to ~2% max
magnitude_error = abs(predicted_magnitude - abs_movement)
# Bonus for accurate magnitude prediction (lower error = higher bonus)
if magnitude_error < 1.0: # Within 1% error
magnitude_accuracy = max(0, 1.0 - magnitude_error) # 0 to 1.0
# COMBINED BONUS CALCULATION
base_vector_bonus = direction_accuracy * magnitude_accuracy * vector_confidence
# Scale bonus based on movement size (bigger movements get bigger bonuses)
if abs_movement > 2.0: # Massive movements
scale_factor = 3.0
elif abs_movement > 1.0: # Rapid movements
scale_factor = 2.0
elif abs_movement > 0.5: # Strong movements
scale_factor = 1.5
else:
scale_factor = 1.0
final_bonus = base_vector_bonus * scale_factor * prediction_confidence
logger.debug(f"VECTOR ANALYSIS: pred_dir={predicted_direction:.3f}, actual_dir={actual_direction:.3f}, "
f"pred_mag={predicted_magnitude:.3f}, actual_mag={abs_movement:.3f}, "
f"dir_acc={direction_accuracy:.3f}, mag_acc={magnitude_accuracy:.3f}, bonus={final_bonus:.3f}")
return min(final_bonus, 2.0) # Cap bonus at 2.0
return 0.0
except Exception as e:
logger.error(f"Error calculating price vector bonus: {e}")
return 0.0
async def _train_model_on_outcome(
self,
record: Dict,
@ -3724,6 +3899,10 @@ class TradingOrchestrator:
if sophisticated_reward is None:
symbol = record.get("symbol", self.symbol)
current_pnl = self._get_current_position_pnl(symbol)
# Extract price vector from record if available
predicted_price_vector = record.get("price_direction") or record.get("predicted_price_vector")
sophisticated_reward, _ = self._calculate_sophisticated_reward(
record.get("action", "HOLD"),
record.get("confidence", 0.5),
@ -3732,6 +3911,7 @@ class TradingOrchestrator:
record.get("has_price_prediction", False),
symbol=symbol,
current_position_pnl=current_pnl,
predicted_price_vector=predicted_price_vector,
)
# Train decision fusion model if it's the model being evaluated

2
data/ui_state.json
View File

@ -25,5 +25,5 @@
"training_enabled": true
}
},
"timestamp": "2025-07-29T23:22:58.380697"
"timestamp": "2025-07-29T23:33:51.882579"
}