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behaviour/agressiveness sliders, fix cob data using provider

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Dobromir Popov
2025-07-07 01:37:04 +03:00
parent 9101448e78
commit c2c0e12a4b
4 changed files with 380 additions and 35 deletions
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282
core/orchestrator.py
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@ -67,6 +67,10 @@ class TradingDecision:
timestamp: datetime
reasoning: Dict[str, Any] # Why this decision was made
memory_usage: Dict[str, int] # Memory usage of models
# NEW: Aggressiveness parameters
entry_aggressiveness: float = 0.5 # 0.0 = conservative, 1.0 = very aggressive
exit_aggressiveness: float = 0.5 # 0.0 = conservative, 1.0 = very aggressive
current_position_pnl: float = 0.0 # Current open position P&L for RL feedback
class TradingOrchestrator:
"""
@ -90,6 +94,14 @@ class TradingOrchestrator:
self.decision_frequency = self.config.orchestrator.get('decision_frequency', 30)
self.symbols = self.config.get('symbols', ['ETH/USDT', 'BTC/USDT']) # Enhanced to support multiple symbols
# NEW: Aggressiveness parameters
self.entry_aggressiveness = self.config.orchestrator.get('entry_aggressiveness', 0.5) # 0.0 = conservative, 1.0 = very aggressive
self.exit_aggressiveness = self.config.orchestrator.get('exit_aggressiveness', 0.5) # 0.0 = conservative, 1.0 = very aggressive
# Position tracking for P&L feedback
self.current_positions: Dict[str, Dict] = {} # {symbol: {side, size, entry_price, entry_time, pnl}}
self.trading_executor = None # Will be set by dashboard or external system
# Dynamic weights (will be adapted based on performance)
self.model_weights: Dict[str, float] = {} # {model_name: weight}
self._initialize_default_weights()
@ -1483,7 +1495,7 @@ class TradingOrchestrator:
def _combine_predictions(self, symbol: str, price: float,
predictions: List[Prediction],
timestamp: datetime) -> TradingDecision:
"""Combine all predictions into a final decision"""
"""Combine all predictions into a final decision with aggressiveness and P&L feedback"""
try:
reasoning = {
'predictions': len(predictions),
@ -1491,6 +1503,9 @@ class TradingOrchestrator:
'models_used': [pred.model_name for pred in predictions]
}
# Get current position P&L for feedback
current_position_pnl = self._get_current_position_pnl(symbol, price)
# Initialize action scores
action_scores = {'BUY': 0.0, 'SELL': 0.0, 'HOLD': 0.0}
total_weight = 0.0
@ -1516,10 +1531,35 @@ class TradingOrchestrator:
best_action = max(action_scores, key=action_scores.get)
best_confidence = action_scores[best_action]
# Apply confidence threshold
if best_confidence < self.confidence_threshold:
best_action = 'HOLD'
reasoning['threshold_applied'] = True
# Calculate aggressiveness-adjusted thresholds
entry_threshold, exit_threshold = self._calculate_aggressiveness_thresholds(
current_position_pnl, symbol
)
# Apply aggressiveness-based confidence thresholds
if best_action in ['BUY', 'SELL']:
# For entry signals, use entry aggressiveness
if not self._has_open_position(symbol):
if best_confidence < entry_threshold:
best_action = 'HOLD'
reasoning['entry_threshold_applied'] = True
reasoning['entry_threshold'] = entry_threshold
# For exit signals, use exit aggressiveness
else:
if best_confidence < exit_threshold:
best_action = 'HOLD'
reasoning['exit_threshold_applied'] = True
reasoning['exit_threshold'] = exit_threshold
else:
# Standard threshold for HOLD
if best_confidence < self.confidence_threshold:
best_action = 'HOLD'
reasoning['threshold_applied'] = True
# Add P&L-based decision adjustment
best_action, best_confidence = self._apply_pnl_feedback(
best_action, best_confidence, current_position_pnl, symbol, reasoning
)
# Get memory usage stats
try:
@ -1527,6 +1567,10 @@ class TradingOrchestrator:
except Exception:
memory_usage = {}
# Calculate dynamic aggressiveness based on recent performance
entry_aggressiveness = self._calculate_dynamic_entry_aggressiveness(symbol)
exit_aggressiveness = self._calculate_dynamic_exit_aggressiveness(symbol, current_position_pnl)
# Create final decision
decision = TradingDecision(
action=best_action,
@ -1535,12 +1579,15 @@ class TradingOrchestrator:
price=price,
timestamp=timestamp,
reasoning=reasoning,
memory_usage=memory_usage.get('models', {}) if memory_usage else {}
memory_usage=memory_usage.get('models', {}) if memory_usage else {},
entry_aggressiveness=entry_aggressiveness,
exit_aggressiveness=exit_aggressiveness,
current_position_pnl=current_position_pnl
)
logger.info(f"Decision for {symbol}: {best_action} (confidence: {best_confidence:.3f})")
if memory_usage and 'total_used_mb' in memory_usage:
logger.debug(f"Memory usage: {memory_usage['total_used_mb']:.1f}MB / {memory_usage['total_limit_mb']:.1f}MB")
logger.info(f"Decision for {symbol}: {best_action} (confidence: {best_confidence:.3f}, "
f"entry_agg: {entry_aggressiveness:.2f}, exit_agg: {exit_aggressiveness:.2f}, "
f"pnl: ${current_position_pnl:.2f})")
return decision
@ -1554,7 +1601,10 @@ class TradingOrchestrator:
price=price,
timestamp=timestamp,
reasoning={'error': str(e)},
memory_usage={}
memory_usage={},
entry_aggressiveness=0.5,
exit_aggressiveness=0.5,
current_position_pnl=0.0
)
def _get_timeframe_weight(self, timeframe: str) -> float:
@ -1918,12 +1968,75 @@ class TradingOrchestrator:
logger.warning(f"Microstructure features fallback: {e}")
comprehensive_features.extend([0.0] * 100)
# Final validation - now includes COB features (13,400 + 400 = 13,800)
# === NEW: P&L FEEDBACK AND AGGRESSIVENESS FEATURES (50) ===
try:
current_price = self._get_current_price(symbol) or 3500.0
current_pnl = self._get_current_position_pnl(symbol, current_price)
# P&L feedback features (25)
pnl_features = [
current_pnl, # Current P&L
max(-1.0, min(1.0, current_pnl / 100.0)), # Normalized P&L (-1 to 1)
1.0 if current_pnl > 0 else 0.0, # Is profitable
1.0 if current_pnl < -10.0 else 0.0, # Is losing significantly
1.0 if current_pnl > 20.0 else 0.0, # Is winning significantly
1.0 if self._has_open_position(symbol) else 0.0, # Has open position
]
# Recent performance features (10)
recent_decisions = self.get_recent_decisions(symbol, limit=10)
if recent_decisions:
win_rate = sum(1 for d in recent_decisions if d.reasoning.get('was_profitable', False)) / len(recent_decisions)
avg_confidence = sum(d.confidence for d in recent_decisions) / len(recent_decisions)
recent_pnl_changes = [d.current_position_pnl for d in recent_decisions if hasattr(d, 'current_position_pnl')]
avg_recent_pnl = sum(recent_pnl_changes) / len(recent_pnl_changes) if recent_pnl_changes else 0.0
else:
win_rate = 0.5
avg_confidence = 0.5
avg_recent_pnl = 0.0
pnl_features.extend([
win_rate,
avg_confidence,
max(-1.0, min(1.0, avg_recent_pnl / 50.0)), # Normalized recent P&L
len(recent_decisions) / 10.0, # Decision frequency
])
# Aggressiveness features (15)
entry_agg = getattr(self, 'entry_aggressiveness', 0.5)
exit_agg = getattr(self, 'exit_aggressiveness', 0.5)
aggressiveness_features = [
entry_agg,
exit_agg,
entry_agg * 2.0 - 1.0, # Scaled entry aggressiveness (-1 to 1)
exit_agg * 2.0 - 1.0, # Scaled exit aggressiveness (-1 to 1)
entry_agg * exit_agg, # Combined aggressiveness
abs(entry_agg - exit_agg), # Aggressiveness difference
1.0 if entry_agg > 0.7 else 0.0, # Is very aggressive entry
1.0 if exit_agg > 0.7 else 0.0, # Is very aggressive exit
1.0 if entry_agg < 0.3 else 0.0, # Is very conservative entry
1.0 if exit_agg < 0.3 else 0.0, # Is very conservative exit
]
# Pad to 50 features total
all_feedback_features = pnl_features + aggressiveness_features
while len(all_feedback_features) < 50:
all_feedback_features.append(0.0)
comprehensive_features.extend(all_feedback_features[:50])
logger.debug("P&L feedback and aggressiveness features: 50 added")
except Exception as e:
logger.warning(f"P&L feedback features fallback: {e}")
comprehensive_features.extend([0.0] * 50)
# Final validation - now includes P&L feedback (13,400 + 400 + 50 = 13,850)
total_features = len(comprehensive_features)
expected_features = 13800 # Updated to include 400 COB features
expected_features = 13850 # Updated to include P&L feedback features
if total_features >= expected_features - 100: # Allow small tolerance
# logger.info(f"TRAINING: Comprehensive RL state built successfully: {total_features} features (including COB)")
# logger.info(f"TRAINING: Comprehensive RL state built successfully: {total_features} features (including P&L feedback)")
return comprehensive_features
else:
logger.warning(f"⚠️ Comprehensive RL state incomplete: {total_features} features (expected {expected_features}+)")
@ -2651,4 +2764,145 @@ class TradingOrchestrator:
return None
except Exception as e:
logger.error(f"Error getting universal data for {model_type}: {e}")
return None
return None
def _get_current_position_pnl(self, symbol: str, current_price: float) -> float:
"""Get current position P&L for the symbol"""
try:
if self.trading_executor and hasattr(self.trading_executor, 'get_current_position'):
position = self.trading_executor.get_current_position(symbol)
if position:
entry_price = position.get('price', 0)
size = position.get('size', 0)
side = position.get('side', 'LONG')
if entry_price and size > 0:
if side.upper() == 'LONG':
pnl = (current_price - entry_price) * size
else: # SHORT
pnl = (entry_price - current_price) * size
return pnl
return 0.0
except Exception as e:
logger.debug(f"Error getting position P&L for {symbol}: {e}")
return 0.0
def _has_open_position(self, symbol: str) -> bool:
"""Check if there's an open position for the symbol"""
try:
if self.trading_executor and hasattr(self.trading_executor, 'get_current_position'):
position = self.trading_executor.get_current_position(symbol)
return position is not None and position.get('size', 0) > 0
return False
except Exception:
return False
def _calculate_aggressiveness_thresholds(self, current_pnl: float, symbol: str) -> tuple:
"""Calculate confidence thresholds based on aggressiveness settings"""
# Base thresholds
base_entry_threshold = self.confidence_threshold
base_exit_threshold = self.confidence_threshold_close
# Get aggressiveness settings (could be from config or adaptive)
entry_agg = getattr(self, 'entry_aggressiveness', 0.5)
exit_agg = getattr(self, 'exit_aggressiveness', 0.5)
# Adjust thresholds based on aggressiveness
# More aggressive = lower threshold (more trades)
# Less aggressive = higher threshold (fewer, higher quality trades)
entry_threshold = base_entry_threshold * (1.5 - entry_agg) # 0.5 agg = 1.0x, 1.0 agg = 0.5x
exit_threshold = base_exit_threshold * (1.5 - exit_agg)
# Ensure minimum thresholds
entry_threshold = max(0.05, entry_threshold)
exit_threshold = max(0.02, exit_threshold)
return entry_threshold, exit_threshold
def _apply_pnl_feedback(self, action: str, confidence: float, current_pnl: float,
symbol: str, reasoning: dict) -> tuple:
"""Apply P&L-based feedback to decision making"""
try:
# If we have a losing position, be more aggressive about cutting losses
if current_pnl < -10.0: # Losing more than $10
if action == 'SELL' and self._has_open_position(symbol):
# Boost confidence for exit signals when losing
confidence = min(1.0, confidence * 1.2)
reasoning['pnl_loss_cut_boost'] = True
elif action == 'BUY':
# Reduce confidence for new entries when losing
confidence *= 0.8
reasoning['pnl_loss_entry_reduction'] = True
# If we have a winning position, be more conservative about exits
elif current_pnl > 5.0: # Winning more than $5
if action == 'SELL' and self._has_open_position(symbol):
# Reduce confidence for exit signals when winning (let profits run)
confidence *= 0.9
reasoning['pnl_profit_hold'] = True
elif action == 'BUY':
# Slightly boost confidence for entries when on a winning streak
confidence = min(1.0, confidence * 1.05)
reasoning['pnl_winning_streak_boost'] = True
reasoning['current_pnl'] = current_pnl
return action, confidence
except Exception as e:
logger.debug(f"Error applying P&L feedback: {e}")
return action, confidence
def _calculate_dynamic_entry_aggressiveness(self, symbol: str) -> float:
"""Calculate dynamic entry aggressiveness based on recent performance"""
try:
# Start with base aggressiveness
base_agg = getattr(self, 'entry_aggressiveness', 0.5)
# Get recent decisions for this symbol
recent_decisions = self.get_recent_decisions(symbol, limit=10)
if len(recent_decisions) < 3:
return base_agg
# Calculate win rate
winning_decisions = sum(1 for d in recent_decisions
if d.reasoning.get('was_profitable', False))
win_rate = winning_decisions / len(recent_decisions)
# Adjust aggressiveness based on performance
if win_rate > 0.7: # High win rate - be more aggressive
return min(1.0, base_agg + 0.2)
elif win_rate < 0.3: # Low win rate - be more conservative
return max(0.1, base_agg - 0.2)
else:
return base_agg
except Exception as e:
logger.debug(f"Error calculating dynamic entry aggressiveness: {e}")
return 0.5
def _calculate_dynamic_exit_aggressiveness(self, symbol: str, current_pnl: float) -> float:
"""Calculate dynamic exit aggressiveness based on P&L and market conditions"""
try:
# Start with base aggressiveness
base_agg = getattr(self, 'exit_aggressiveness', 0.5)
# Adjust based on current P&L
if current_pnl < -20.0: # Large loss - be very aggressive about cutting
return min(1.0, base_agg + 0.3)
elif current_pnl < -5.0: # Small loss - be more aggressive
return min(1.0, base_agg + 0.1)
elif current_pnl > 20.0: # Large profit - be less aggressive (let it run)
return max(0.1, base_agg - 0.2)
elif current_pnl > 5.0: # Small profit - slightly less aggressive
return max(0.2, base_agg - 0.1)
else:
return base_agg
except Exception as e:
logger.debug(f"Error calculating dynamic exit aggressiveness: {e}")
return 0.5
def set_trading_executor(self, trading_executor):
"""Set the trading executor for position tracking"""
self.trading_executor = trading_executor
logger.info("Trading executor set for position tracking and P&L feedback")