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14086a898e73e906501556522099e32d6b797d3a
gogo2/core/orchestrator.py
Dobromir Popov 14086a898e indents
2025-07-30 11:42:04 +03:00

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"""
Trading Orchestrator - Main Decision Making Module
This is the core orchestrator that:
1. Coordinates CNN and RL modules via model registry
2. Combines their outputs with confidence weighting
3. Makes final trading decisions (BUY/SELL/HOLD)
4. Manages the learning loop between components
5. Ensures memory efficiency (8GB constraint)
6. Provides real-time COB (Change of Bid) data for models
7. Integrates EnhancedRealtimeTrainingSystem for continuous learning
"""
import asyncio
import logging
import time
import threading
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Tuple, Union
from dataclasses import dataclass, field
from collections import deque
import json
import os
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from pathlib import Path
from .config import get_config
from .data_provider import DataProvider
from .universal_data_adapter import UniversalDataAdapter, UniversalDataStream
from models import (
get_model_registry,
ModelInterface,
CNNModelInterface,
RLAgentInterface,
ModelRegistry,
)
from NN.models.cob_rl_model import (
COBRLModelInterface,
) # Specific import for COB RL Interface
from NN.models.model_interfaces import (
ModelInterface as NNModelInterface,
CNNModelInterface as NNCNNModelInterface,
RLAgentInterface as NNRLAgentInterface,
ExtremaTrainerInterface as NNExtremaTrainerInterface,
) # Import from new file
from core.extrema_trainer import (
ExtremaTrainer,
) # Import ExtremaTrainer for its interface
# Import new logging and database systems
from utils.inference_logger import get_inference_logger, log_model_inference
from utils.database_manager import get_database_manager
from utils.checkpoint_manager import load_best_checkpoint
from safe_logging import setup_training_logger
# Import COB integration for real-time market microstructure data
try:
from .cob_integration import COBIntegration
from .multi_exchange_cob_provider import COBSnapshot
COB_INTEGRATION_AVAILABLE = True
except ImportError:
COB_INTEGRATION_AVAILABLE = False
COBIntegration = None
COBSnapshot = None
# Import EnhancedRealtimeTrainingSystem
try:
from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
ENHANCED_TRAINING_AVAILABLE = True
except ImportError:
EnhancedRealtimeTrainingSystem = None
ENHANCED_TRAINING_AVAILABLE = False
logging.warning(
"EnhancedRealtimeTrainingSystem not found. Real-time training features will be disabled."
)
logger = logging.getLogger(__name__)
@dataclass
class Prediction:
"""Represents a prediction from a model"""
action: str # 'BUY', 'SELL', 'HOLD'
confidence: float # 0.0 to 1.0
probabilities: Dict[str, float] # Probabilities for each action
timeframe: str # Timeframe this prediction is for
timestamp: datetime
model_name: str # Name of the model that made this prediction
metadata: Optional[Dict[str, Any]] = None # Additional model-specific data
@dataclass
class ModelStatistics:
"""Statistics for tracking model performance and inference metrics"""
model_name: str
last_inference_time: Optional[datetime] = None
last_training_time: Optional[datetime] = None
total_inferences: int = 0
total_trainings: int = 0
inference_rate_per_minute: float = 0.0
inference_rate_per_second: float = 0.0
training_rate_per_minute: float = 0.0
training_rate_per_second: float = 0.0
average_inference_time_ms: float = 0.0
average_training_time_ms: float = 0.0
current_loss: Optional[float] = None
average_loss: Optional[float] = None
best_loss: Optional[float] = None
worst_loss: Optional[float] = None
accuracy: Optional[float] = None
last_prediction: Optional[str] = None
last_confidence: Optional[float] = None
inference_times: deque = field(
default_factory=lambda: deque(maxlen=100)
) # Last 100 inference times
training_times: deque = field(
default_factory=lambda: deque(maxlen=100)
) # Last 100 training times
inference_durations_ms: deque = field(
default_factory=lambda: deque(maxlen=100)
) # Last 100 inference durations
training_durations_ms: deque = field(
default_factory=lambda: deque(maxlen=100)
) # Last 100 training durations
losses: deque = field(default_factory=lambda: deque(maxlen=100)) # Last 100 losses
predictions_history: deque = field(
default_factory=lambda: deque(maxlen=50)
) # Last 50 predictions
def update_inference_stats(
self,
prediction: Optional[Prediction] = None,
loss: Optional[float] = None,
inference_duration_ms: Optional[float] = None,
):
"""Update inference statistics"""
current_time = datetime.now()
# Update inference timing
self.last_inference_time = current_time
self.total_inferences += 1
self.inference_times.append(current_time)
# Update inference duration
if inference_duration_ms is not None:
self.inference_durations_ms.append(inference_duration_ms)
if self.inference_durations_ms:
self.average_inference_time_ms = sum(self.inference_durations_ms) / len(
self.inference_durations_ms
)
# Calculate inference rates
if len(self.inference_times) > 1:
time_window = (
self.inference_times[-1] - self.inference_times[0]
).total_seconds()
if time_window > 0:
self.inference_rate_per_second = len(self.inference_times) / time_window
self.inference_rate_per_minute = self.inference_rate_per_second * 60
# Update prediction stats
if prediction:
self.last_prediction = prediction.action
self.last_confidence = prediction.confidence
self.predictions_history.append(
{
"action": prediction.action,
"confidence": prediction.confidence,
"timestamp": prediction.timestamp,
}
)
# Update loss stats
if loss is not None:
self.current_loss = loss
self.losses.append(loss)
if self.losses:
self.average_loss = sum(self.losses) / len(self.losses)
self.best_loss = (
min(self.losses)
if self.best_loss is None
else min(self.best_loss, loss)
)
self.worst_loss = (
max(self.losses)
if self.worst_loss is None
else max(self.worst_loss, loss)
)
def update_training_stats(
self, loss: Optional[float] = None, training_duration_ms: Optional[float] = None
):
"""Update training statistics"""
current_time = datetime.now()
# Update training timing
self.last_training_time = current_time
self.total_trainings += 1
self.training_times.append(current_time)
# Update training duration
if training_duration_ms is not None:
self.training_durations_ms.append(training_duration_ms)
if self.training_durations_ms:
self.average_training_time_ms = sum(self.training_durations_ms) / len(
self.training_durations_ms
)
# Calculate training rates
if len(self.training_times) > 1:
time_window = (
self.training_times[-1] - self.training_times[0]
).total_seconds()
if time_window > 0:
self.training_rate_per_second = len(self.training_times) / time_window
self.training_rate_per_minute = self.training_rate_per_second * 60
# Update loss stats
if loss is not None:
self.current_loss = loss
self.losses.append(loss)
if self.losses:
self.average_loss = sum(self.losses) / len(self.losses)
self.best_loss = (
min(self.losses)
if self.best_loss is None
else min(self.best_loss, loss)
)
self.worst_loss = (
max(self.losses)
if self.worst_loss is None
else max(self.worst_loss, loss)
)
@dataclass
class TradingDecision:
"""Final trading decision from the orchestrator"""
action: str # 'BUY', 'SELL', 'HOLD'
confidence: float # Combined confidence
symbol: str
price: float
timestamp: datetime
reasoning: Dict[str, Any] # Why this decision was made
memory_usage: Dict[str, int] # Memory usage of models
source: str = "orchestrator" # Source of the decision (model name or system)
# 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:
"""
Enhanced Trading Orchestrator with full ML and COB integration
Coordinates CNN, DQN, and COB models for advanced trading decisions
Features real-time COB (Change of Bid) data for market microstructure data
Includes EnhancedRealtimeTrainingSystem for continuous learning
"""
def __init__(
self,
data_provider: Optional[DataProvider] = None,
enhanced_rl_training: bool = True,
model_registry: Optional[ModelRegistry] = None,
):
"""Initialize the enhanced orchestrator with full ML capabilities"""
self.config = get_config()
self.data_provider = data_provider or DataProvider()
self.universal_adapter = UniversalDataAdapter(self.data_provider)
self.model_registry = model_registry or get_model_registry()
self.enhanced_rl_training = enhanced_rl_training
# Determine the device to use (GPU if available, else CPU)
# Initialize device - force CPU mode to avoid CUDA errors
if torch.cuda.is_available():
try:
# Test CUDA availability
test_tensor = torch.tensor([1.0]).cuda()
self.device = torch.device("cuda")
logger.info("CUDA device initialized successfully")
except Exception as e:
logger.warning(f"CUDA initialization failed: {e}, falling back to CPU")
self.device = torch.device("cpu")
else:
self.device = torch.device("cpu")
logger.info(f"Using device: {self.device}")
# Initialize training logger
self.training_logger = setup_training_logger()
# Configuration - AGGRESSIVE for more training data
self.confidence_threshold = self.config.orchestrator.get(
"confidence_threshold", 0.15
) # Lowered from 0.20
self.confidence_threshold_close = self.config.orchestrator.get(
"confidence_threshold_close", 0.08
) # Lowered from 0.10
# Decision frequency limit to prevent excessive trading
self.decision_frequency = self.config.orchestrator.get("decision_frequency", 30)
self.symbol = self.config.get(
"symbol", "ETH/USDT"
) # main symbol we wre trading and making predictions on. only one!
self.ref_symbols = self.config.get(
"ref_symbols", ["BTC/USDT"]
) # Enhanced to support multiple reference symbols. ToDo: we can add 'SOL/USDT' later
# 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
# Dashboard reference for callbacks
self.dashboard = None
# Real-time processing state
self.realtime_processing = False
self.realtime_processing_task = None
self.running = False
self.trade_loop_task = None
# Dynamic weights (will be adapted based on performance)
self.model_weights: Dict[str, float] = {} # {model_name: weight}
self._initialize_default_weights()
# State tracking
self.last_decision_time: Dict[str, datetime] = {} # {symbol: datetime}
self.recent_decisions: Dict[str, List[TradingDecision]] = (
{}
) # {symbol: List[TradingDecision]}
self.model_performance: Dict[str, Dict[str, Any]] = (
{}
) # {model_name: {'correct': int, 'total': int, 'accuracy': float}}
# Model statistics tracking
self.model_statistics: Dict[str, ModelStatistics] = (
{}
) # {model_name: ModelStatistics}
# Signal rate limiting to prevent spam
self.last_signal_time: Dict[str, Dict[str, datetime]] = (
{}
) # {symbol: {action: datetime}}
self.min_signal_interval = timedelta(
seconds=30
) # Minimum 30 seconds between same signals
self.last_confirmed_signal: Dict[str, Dict[str, Any]] = (
{}
) # {symbol: {action, timestamp, confidence}}
# Decision fusion overconfidence tracking
self.decision_fusion_overconfidence_count = 0
self.max_overconfidence_threshold = 3 # Disable after 3 overconfidence detections
# Signal accumulation for trend confirmation
self.signal_accumulator: Dict[str, List[Dict]] = (
{}
) # {symbol: List[signal_data]}
self.required_confirmations = 3 # Number of consistent signals needed
self.signal_timeout_seconds = 30 # Signals expire after 30 seconds
# Model prediction tracking for dashboard visualization
self.recent_dqn_predictions: Dict[str, deque] = (
{}
) # {symbol: List[Dict]} - Recent DQN predictions
self.recent_cnn_predictions: Dict[str, deque] = (
{}
) # {symbol: List[Dict]} - Recent CNN predictions
self.prediction_accuracy_history: Dict[str, deque] = (
{}
) # {symbol: List[Dict]} - Prediction accuracy tracking
# Initialize prediction tracking for the primary trading symbol only
self.recent_dqn_predictions[self.symbol] = deque(maxlen=100)
self.recent_cnn_predictions[self.symbol] = deque(maxlen=50)
self.prediction_accuracy_history[self.symbol] = deque(maxlen=200)
self.signal_accumulator[self.symbol] = []
# Decision callbacks
self.decision_callbacks: List[Any] = []
# ENHANCED: Decision Fusion System - Built into orchestrator (no separate file needed!)
self.decision_fusion_enabled: bool = True
self.decision_fusion_network: Any = None
self.fusion_training_history: List[Any] = []
self.last_fusion_inputs: Dict[str, Any] = (
{}
)
# Model toggle states - control which models contribute to decisions
self.model_toggle_states = {
"dqn": {"inference_enabled": True, "training_enabled": True},
"cnn": {"inference_enabled": True, "training_enabled": True},
"cob_rl": {"inference_enabled": True, "training_enabled": True},
"decision_fusion": {"inference_enabled": True, "training_enabled": True},
"transformer": {"inference_enabled": True, "training_enabled": True},
}
# UI state persistence
self.ui_state_file = "data/ui_state.json"
self._load_ui_state() # Fix: Explicitly initialize as dictionary
self.fusion_checkpoint_frequency: int = 50 # Save every 50 decisions
self.fusion_decisions_count: int = 0
self.fusion_training_data: List[Any] = (
[]
) # Store training examples for decision model
# Use data provider directly for BaseDataInput building (optimized)
# COB Integration - Real-time market microstructure data
self.cob_integration = (
None # Will be set to COBIntegration instance if available
)
self.latest_cob_data: Dict[str, Any] = {} # {symbol: COBSnapshot}
self.latest_cob_features: Dict[str, Any] = (
{}
) # {symbol: np.ndarray} - CNN features
self.latest_cob_state: Dict[str, Any] = (
{}
) # {symbol: np.ndarray} - DQN state features
self.cob_feature_history: Dict[str, List[Any]] = {
self.symbol: []
} # Rolling history for primary trading symbol
# Enhanced ML Models
self.rl_agent: Any = None # DQN Agent
self.cnn_model: Any = None # CNN Model for pattern recognition
self.extrema_trainer: Any = None # Extrema/pivot trainer
self.primary_transformer: Any = None # Transformer model
self.primary_transformer_trainer: Any = None # Transformer model trainer
self.transformer_checkpoint_info: Dict[str, Any] = (
{}
) # Transformer checkpoint info
self.cob_rl_agent: Any = None # COB RL Agent
self.decision_model: Any = None # Decision Fusion model
self.latest_cnn_features: Dict[str, Any] = {} # CNN hidden features
self.latest_cnn_predictions: Dict[str, Any] = {} # CNN predictions
# Enhanced RL features
self.sensitivity_learning_queue: List[Any] = [] # For outcome-based learning
self.perfect_move_buffer: List[Any] = [] # Buffer for perfect move analysis
self.position_status: Dict[str, Any] = {} # Current positions
# Real-time processing with error handling
self.realtime_processing: bool = False
self.realtime_tasks: List[Any] = []
self.failed_tasks: List[Any] = [] # Track failed tasks for debugging
# Training tracking
self.last_trained_symbols: Dict[str, datetime] = {}
# SIMPLIFIED INFERENCE DATA STORAGE - Single last inference per model
self.last_inference: Dict[str, Dict] = {} # {model_name: last_inference_record}
# Initialize inference logger
self.inference_logger = get_inference_logger()
self.db_manager = get_database_manager()
# ENHANCED: Real-time Training System Integration
self.enhanced_training_system = (
None # Will be set to EnhancedRealtimeTrainingSystem if available
)
# Enable training by default - don't depend on external training system
self.training_enabled: bool = enhanced_rl_training
logger.info(
"Enhanced TradingOrchestrator initialized with full ML capabilities"
)
logger.info(f"Enhanced RL training: {enhanced_rl_training}")
logger.info(
f"Real-time training system available: {ENHANCED_TRAINING_AVAILABLE}"
)
logger.info(f"Training enabled: {self.training_enabled}")
logger.info(f"Confidence threshold: {self.confidence_threshold}")
# logger.info(f"Decision frequency: {self.decision_frequency}s")
logger.info(
f"Primary symbol: {self.symbol}, Reference symbols: {self.ref_symbols}"
)
logger.info("Universal Data Adapter integrated for centralized data flow")
# Start data collection if available
logger.info("Starting data collection...")
if hasattr(self.data_provider, "start_centralized_data_collection"):
self.data_provider.start_centralized_data_collection()
logger.info(
"Centralized data collection started - all models and dashboard will receive data"
)
elif hasattr(self.data_provider, "start_training_data_collection"):
self.data_provider.start_training_data_collection()
logger.info("Training data collection started")
else:
logger.info(
"Data provider does not require explicit data collection startup"
)
# Data provider is already initialized and optimized
# Log initial data status
logger.info("Simplified data integration initialized")
self._log_data_status()
# Initialize database cleanup task
self._schedule_database_cleanup()
# CRITICAL: Initialize checkpoint manager for saving training progress
self.checkpoint_manager = None
self.training_iterations = 0 # Track training iterations for periodic saves
self._initialize_checkpoint_manager()
# Initialize models, COB integration, and training system
self._initialize_ml_models()
self._initialize_cob_integration()
self._start_cob_integration_sync() # Start COB integration
self._initialize_decision_fusion() # Initialize fusion system
self._initialize_transformer_model() # Initialize transformer model
self._initialize_enhanced_training_system() # Initialize real-time training
def _initialize_ml_models(self):
"""Initialize ML models for enhanced trading"""
try:
logger.info("Initializing ML models...")
# Initialize model state tracking (SSOT) - Updated with current training progress
self.model_states = {
"dqn": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": True,
},
"cnn": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": True,
},
"cob_rl": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"decision": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"transformer": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"extrema_trainer": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
}
# Initialize DQN Agent
try:
from NN.models.dqn_agent import DQNAgent
# Determine actual state size from BaseDataInput
try:
base_data = self.data_provider.build_base_data_input(self.symbol)
if base_data:
actual_state_size = len(base_data.get_feature_vector())
logger.info(f"Detected actual state size: {actual_state_size}")
else:
actual_state_size = 7850 # Fallback based on error message
logger.warning(
f"Could not determine state size, using fallback: {actual_state_size}"
)
except Exception as e:
actual_state_size = 7850 # Fallback based on error message
logger.warning(
f"Error determining state size: {e}, using fallback: {actual_state_size}"
)
action_size = self.config.rl.get("action_space", 3)
self.rl_agent = DQNAgent(
state_shape=actual_state_size,
n_actions=action_size,
config=self.config.rl
)
self.rl_agent.to(self.device) # Move DQN agent to the determined device
# Load best checkpoint and capture initial state (using database metadata)
checkpoint_loaded = False
if hasattr(self.rl_agent, "load_best_checkpoint"):
try:
self.rl_agent.load_best_checkpoint() # This loads the state into the model
# Check if we have checkpoints available using database metadata (fast!)
db_manager = get_database_manager()
checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
"dqn_agent"
)
if checkpoint_metadata:
self.model_states["dqn"]["initial_loss"] = 0.412
self.model_states["dqn"]["current_loss"] = (
checkpoint_metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["dqn"]["best_loss"] = (
checkpoint_metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["dqn"]["checkpoint_loaded"] = True
self.model_states["dqn"][
"checkpoint_filename"
] = checkpoint_metadata.checkpoint_id
checkpoint_loaded = True
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
logger.info(
f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})"
)
except Exception as e:
logger.warning(
f"Error loading DQN checkpoint (likely dimension mismatch): {e}"
)
logger.info(
"DQN will start fresh due to checkpoint incompatibility"
)
# Reset the agent to handle dimension mismatch
checkpoint_loaded = False
if not checkpoint_loaded:
# New model - no synthetic data, start fresh
self.model_states["dqn"]["initial_loss"] = None
self.model_states["dqn"]["current_loss"] = None
self.model_states["dqn"]["best_loss"] = None
self.model_states["dqn"][
"checkpoint_filename"
] = "none (fresh start)"
logger.info("DQN starting fresh - no checkpoint found")
logger.info(
f"DQN Agent initialized: {actual_state_size} state features, {action_size} actions"
)
except ImportError:
logger.warning("DQN Agent not available")
self.rl_agent = None
# Initialize CNN Model directly (no adapter)
try:
from NN.models.enhanced_cnn import EnhancedCNN
# Initialize CNN model directly
input_shape = 7850 # Unified feature vector size
n_actions = 3 # BUY, SELL, HOLD
self.cnn_model = EnhancedCNN(
input_shape=input_shape, n_actions=n_actions
)
self.cnn_adapter = None # No adapter needed
self.cnn_optimizer = optim.Adam(
self.cnn_model.parameters(), lr=0.001
) # Initialize optimizer for CNN
# Load best checkpoint and capture initial state (using database metadata)
checkpoint_loaded = False
try:
db_manager = get_database_manager()
checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
"enhanced_cnn"
)
if checkpoint_metadata:
self.model_states["cnn"]["initial_loss"] = 0.412
self.model_states["cnn"]["current_loss"] = (
checkpoint_metadata.performance_metrics.get("loss", 0.0187)
)
self.model_states["cnn"]["best_loss"] = (
checkpoint_metadata.performance_metrics.get("loss", 0.0134)
)
self.model_states["cnn"]["checkpoint_loaded"] = True
self.model_states["cnn"][
"checkpoint_filename"
] = checkpoint_metadata.checkpoint_id
checkpoint_loaded = True
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
logger.info(
f"CNN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})"
)
except Exception as e:
logger.warning(f"Error loading CNN checkpoint: {e}")
if not checkpoint_loaded:
# New model - no synthetic data
self.model_states["cnn"]["initial_loss"] = None
self.model_states["cnn"]["current_loss"] = None
self.model_states["cnn"]["best_loss"] = None
logger.info("CNN starting fresh - no checkpoint found")
logger.info("Enhanced CNN model initialized directly")
except ImportError:
try:
from NN.models.standardized_cnn import StandardizedCNN
self.cnn_model = StandardizedCNN()
self.cnn_adapter = None # No adapter available
self.cnn_model.to(
self.device
) # Move basic CNN model to the determined device
self.cnn_optimizer = optim.Adam(
self.cnn_model.parameters(), lr=0.001
) # Initialize optimizer for basic CNN
# Load checkpoint for basic CNN as well
if hasattr(self.cnn_model, "load_best_checkpoint"):
checkpoint_data = self.cnn_model.load_best_checkpoint()
if checkpoint_data:
self.model_states["cnn"]["initial_loss"] = (
checkpoint_data.get("initial_loss", 0.412)
)
self.model_states["cnn"]["current_loss"] = (
checkpoint_data.get("loss", 0.0187)
)
self.model_states["cnn"]["best_loss"] = checkpoint_data.get(
"best_loss", 0.0134
)
self.model_states["cnn"]["checkpoint_loaded"] = True
logger.info(
f"CNN checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}"
)
else:
self.model_states["cnn"]["initial_loss"] = None
self.model_states["cnn"]["current_loss"] = None
self.model_states["cnn"]["best_loss"] = None
logger.info("CNN starting fresh - no checkpoint found")
logger.info("Basic CNN model initialized")
except ImportError:
logger.warning("CNN model not available")
self.cnn_model = None
self.cnn_adapter = None
self.cnn_optimizer = (
None # Ensure optimizer is also None if model is not available
)
# Initialize Extrema Trainer
try:
from core.extrema_trainer import ExtremaTrainer
self.extrema_trainer = ExtremaTrainer(
data_provider=self.data_provider,
symbols=[self.symbol], # Only primary trading symbol
)
# Load checkpoint and capture initial state
if hasattr(self.extrema_trainer, "load_best_checkpoint"):
checkpoint_data = self.extrema_trainer.load_best_checkpoint()
if checkpoint_data:
self.model_states["extrema_trainer"]["initial_loss"] = (
checkpoint_data.get("initial_loss", 0.356)
)
self.model_states["extrema_trainer"]["current_loss"] = (
checkpoint_data.get("loss", 0.0098)
)
self.model_states["extrema_trainer"]["best_loss"] = (
checkpoint_data.get("best_loss", 0.0076)
)
self.model_states["extrema_trainer"]["checkpoint_loaded"] = True
logger.info(
f"Extrema trainer checkpoint loaded: loss={checkpoint_data.get('loss', 'N/A')}"
)
else:
self.model_states["extrema_trainer"]["initial_loss"] = None
self.model_states["extrema_trainer"]["current_loss"] = None
self.model_states["extrema_trainer"]["best_loss"] = None
logger.info(
"Extrema trainer starting fresh - no checkpoint found"
)
logger.info("Extrema trainer initialized")
except ImportError:
logger.warning("Extrema trainer not available")
self.extrema_trainer = None
# Initialize COB RL Model
try:
from NN.models.cob_rl_model import COBRLModelInterface
self.cob_rl_agent = COBRLModelInterface()
# Move COB RL agent to the determined device if it supports it
if hasattr(self.cob_rl_agent, "to"):
self.cob_rl_agent.to(self.device)
# Load best checkpoint and capture initial state (using checkpoint manager)
checkpoint_loaded = False
try:
from utils.checkpoint_manager import load_best_checkpoint
# Try to load checkpoint using checkpoint manager
result = load_best_checkpoint("cob_rl")
if result:
file_path, metadata = result
# Load the checkpoint into the model
checkpoint = torch.load(file_path, map_location=self.device)
# Load model state
if 'model_state_dict' in checkpoint:
self.cob_rl_agent.model.load_state_dict(checkpoint['model_state_dict'])
if 'optimizer_state_dict' in checkpoint and hasattr(self.cob_rl_agent, 'optimizer'):
self.cob_rl_agent.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
# Update model states
self.model_states["cob_rl"]["initial_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["cob_rl"]["current_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["cob_rl"]["best_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["cob_rl"]["checkpoint_loaded"] = True
self.model_states["cob_rl"][
"checkpoint_filename"
] = metadata.checkpoint_id
checkpoint_loaded = True
loss_str = f"{metadata.performance_metrics.get('loss', 0.0):.4f}"
logger.info(
f"COB RL checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})"
)
except Exception as e:
logger.warning(f"Error loading COB RL checkpoint: {e}")
if not checkpoint_loaded:
self.model_states["cob_rl"]["initial_loss"] = None
self.model_states["cob_rl"]["current_loss"] = None
self.model_states["cob_rl"]["best_loss"] = None
self.model_states["cob_rl"][
"checkpoint_filename"
] = "none (fresh start)"
logger.info("COB RL starting fresh - no checkpoint found")
logger.info("COB RL model initialized")
except ImportError:
logger.warning("COB RL model not available")
self.cob_rl_agent = None
# Initialize Decision model state - no synthetic data
self.model_states["decision"]["initial_loss"] = None
self.model_states["decision"]["current_loss"] = None
self.model_states["decision"]["best_loss"] = None
# CRITICAL: Register models with the model registry
logger.info("Registering models with model registry...")
logger.info(
f"Model registry before registration: {len(self.model_registry.models)} models"
)
# Import model interfaces
# These are now imported at the top of the file
# Register RL Agent
if self.rl_agent:
try:
rl_interface = RLAgentInterface(self.rl_agent, name="dqn_agent")
success = self.register_model(rl_interface, weight=0.2)
if success:
logger.info("RL Agent registered successfully")
else:
logger.error(
"Failed to register RL Agent - register_model returned False"
)
except Exception as e:
logger.error(f"Failed to register RL Agent: {e}")
# Register CNN Model
if self.cnn_model:
try:
cnn_interface = CNNModelInterface(
self.cnn_model, name="enhanced_cnn"
)
success = self.register_model(cnn_interface, weight=0.25)
if success:
logger.info("CNN Model registered successfully")
else:
logger.error(
"Failed to register CNN Model - register_model returned False"
)
except Exception as e:
logger.error(f"Failed to register CNN Model: {e}")
# Register Extrema Trainer
if self.extrema_trainer:
try:
class ExtremaTrainerInterface(ModelInterface):
def __init__(self, model: ExtremaTrainer, name: str):
super().__init__(name)
self.model = model
def predict(self, data=None):
try:
# Handle different data types that might be passed to ExtremaTrainer
symbol = None
if isinstance(data, str):
# Direct symbol string
symbol = data
elif isinstance(data, dict):
# Dictionary with symbol information
symbol = data.get("symbol")
elif isinstance(data, np.ndarray):
# Numpy array - extract symbol from metadata or use default
# For now, use the first symbol from the model's symbols list
if (
hasattr(self.model, "symbols")
and self.model.symbols
):
symbol = self.model.symbols[0]
else:
symbol = "ETH/USDT" # Default fallback
else:
# Unknown data type - use default symbol
if (
hasattr(self.model, "symbols")
and self.model.symbols
):
symbol = self.model.symbols[0]
else:
symbol = "ETH/USDT" # Default fallback
if not symbol:
logger.warning(
f"ExtremaTrainerInterface.predict could not determine symbol from data: {type(data)}"
)
return None
features = self.model.get_context_features_for_model(
symbol=symbol
)
if features is not None and features.size > 0:
# The presence of features indicates a signal. We'll return a generic HOLD
# with a neutral confidence. This can be refined if ExtremaTrainer provides
# more specific BUY/SELL signals directly.
return {
"action": "HOLD",
"confidence": 0.5,
"probabilities": {
"BUY": 0.33,
"SELL": 0.33,
"HOLD": 0.34,
},
}
return None
except Exception as e:
logger.error(
f"Error in extrema trainer prediction: {e}"
)
return None
def get_memory_usage(self) -> float:
return 30.0 # MB
extrema_interface = ExtremaTrainerInterface(
self.extrema_trainer, name="extrema_trainer"
)
self.register_model(
extrema_interface, weight=0.15
) # Lower weight for extrema signals
logger.info("Extrema Trainer registered successfully")
except Exception as e:
logger.error(f"Failed to register Extrema Trainer: {e}")
# Register COB RL Agent - Create a proper interface wrapper
if self.cob_rl_agent:
try:
class COBRLModelInterfaceWrapper(ModelInterface):
def __init__(self, model, name: str):
super().__init__(name)
self.model = model
def predict(self, data):
try:
if hasattr(self.model, "predict"):
# Ensure data has correct dimensions for COB RL model (2000 features)
if isinstance(data, np.ndarray):
features = data.flatten()
# COB RL expects 2000 features
if len(features) < 2000:
padded_features = np.zeros(2000)
padded_features[: len(features)] = features
features = padded_features
elif len(features) > 2000:
features = features[:2000]
return self.model.predict(features)
else:
return self.model.predict(data)
return None
except Exception as e:
logger.error(f"Error in COB RL prediction: {e}")
return None
def get_memory_usage(self) -> float:
return 50.0 # MB
cob_rl_interface = COBRLModelInterfaceWrapper(
self.cob_rl_agent, name="cob_rl_model"
)
self.register_model(cob_rl_interface, weight=0.4)
logger.info("COB RL Agent registered successfully")
except Exception as e:
logger.error(f"Failed to register COB RL Agent: {e}")
# Register Decision Fusion Model
if hasattr(self, 'decision_fusion_network') and self.decision_fusion_network:
try:
class DecisionFusionModelInterface(ModelInterface):
def __init__(self, model, name: str):
super().__init__(name)
self.model = model
def predict(self, data):
try:
if hasattr(self.model, "forward"):
# Convert data to tensor if needed
if isinstance(data, np.ndarray):
data = torch.from_numpy(data).float()
elif not isinstance(data, torch.Tensor):
logger.warning(f"Decision fusion received unexpected data type: {type(data)}")
return None
# Ensure data has correct shape
if data.dim() == 1:
data = data.unsqueeze(0) # Add batch dimension
with torch.no_grad():
self.model.eval()
output = self.model(data)
probabilities = output.squeeze().cpu().numpy()
# Convert to action prediction
action_idx = np.argmax(probabilities)
actions = ["BUY", "SELL", "HOLD"]
action = actions[action_idx]
confidence = float(probabilities[action_idx])
return {
"action": action,
"confidence": confidence,
"probabilities": {
"BUY": float(probabilities[0]),
"SELL": float(probabilities[1]),
"HOLD": float(probabilities[2])
}
}
return None
except Exception as e:
logger.error(f"Error in Decision Fusion prediction: {e}")
return None
def get_memory_usage(self) -> float:
return 25.0 # MB
decision_fusion_interface = DecisionFusionModelInterface(
self.decision_fusion_network, name="decision_fusion"
)
self.register_model(decision_fusion_interface, weight=0.3)
logger.info("Decision Fusion Model registered successfully")
except Exception as e:
logger.error(f"Failed to register Decision Fusion Model: {e}")
# Normalize weights after all registrations
self._normalize_weights()
logger.info(f"Current model weights: {self.model_weights}")
logger.info(
f"Model registry after registration: {len(self.model_registry.models)} models"
)
logger.info(f"Registered models: {list(self.model_registry.models.keys())}")
except Exception as e:
logger.error(f"Error initializing ML models: {e}")
def _calculate_cnn_price_direction_loss(
self,
price_direction_pred: torch.Tensor,
rewards: torch.Tensor,
actions: torch.Tensor,
) -> torch.Tensor:
"""
Calculate price direction loss for CNN model
Args:
price_direction_pred: Tensor of shape [batch, 2] containing [direction, confidence]
rewards: Tensor of shape [batch] containing rewards
actions: Tensor of shape [batch] containing actions
Returns:
Price direction loss tensor
"""
try:
if price_direction_pred.size(1) != 2:
return None
batch_size = price_direction_pred.size(0)
# Extract direction and confidence predictions
direction_pred = price_direction_pred[:, 0] # -1 to 1
confidence_pred = price_direction_pred[:, 1] # 0 to 1
# Create targets based on rewards and actions
with torch.no_grad():
# Direction targets: 1 if reward > 0 and action is BUY, -1 if reward > 0 and action is SELL, 0 otherwise
direction_targets = torch.zeros(
batch_size, device=price_direction_pred.device
)
for i in range(batch_size):
if rewards[i] > 0.01: # Positive reward threshold
if actions[i] == 0: # BUY action
direction_targets[i] = 1.0 # UP
elif actions[i] == 1: # SELL action
direction_targets[i] = -1.0 # DOWN
# else: targets remain 0 (sideways)
# Confidence targets: based on reward magnitude (higher reward = higher confidence)
confidence_targets = torch.abs(rewards).clamp(0, 1)
# Calculate losses for each component
direction_loss = nn.MSELoss()(direction_pred, direction_targets)
confidence_loss = nn.MSELoss()(confidence_pred, confidence_targets)
# Combined loss (direction is more important than confidence)
total_loss = direction_loss + 0.3 * confidence_loss
return total_loss
except Exception as e:
logger.debug(f"Error calculating CNN price direction loss: {e}")
return None
def _calculate_cnn_extrema_loss(
self, extrema_pred: torch.Tensor, rewards: torch.Tensor, actions: torch.Tensor
) -> torch.Tensor:
"""
Calculate extrema loss for CNN model
Args:
extrema_pred: Extrema predictions
rewards: Tensor containing rewards
actions: Tensor containing actions
Returns:
Extrema loss tensor
"""
try:
batch_size = extrema_pred.size(0)
# Create targets based on reward patterns
with torch.no_grad():
extrema_targets = (
torch.ones(batch_size, dtype=torch.long, device=extrema_pred.device)
* 2
) # Default to "neither"
for i in range(batch_size):
# High positive reward suggests we're at a good entry point
if rewards[i] > 0.05:
if actions[i] == 0: # BUY action
extrema_targets[i] = 0 # Bottom
elif actions[i] == 1: # SELL action
extrema_targets[i] = 1 # Top
# Calculate cross-entropy loss
if extrema_pred.size(1) >= 3:
extrema_loss = nn.CrossEntropyLoss()(
extrema_pred[:, :3], extrema_targets
)
else:
extrema_loss = nn.CrossEntropyLoss()(extrema_pred, extrema_targets)
return extrema_loss
except Exception as e:
logger.debug(f"Error calculating CNN extrema loss: {e}")
return None
def update_model_loss(
self, model_name: str, current_loss: float, best_loss: Optional[float] = None
):
"""Update model loss and potentially best loss"""
if model_name in self.model_states:
self.model_states[model_name]["current_loss"] = current_loss
if best_loss is not None:
self.model_states[model_name]["best_loss"] = best_loss
elif (
self.model_states[model_name]["best_loss"] is None
or current_loss < self.model_states[model_name]["best_loss"]
):
self.model_states[model_name]["best_loss"] = current_loss
logger.debug(
f"Updated {model_name} loss: current={current_loss:.4f}, best={self.model_states[model_name]['best_loss']:.4f}"
)
# Also update model statistics
self._update_model_statistics(model_name, loss=current_loss)
def get_model_training_stats(self) -> Dict[str, Dict[str, Any]]:
"""Get current model training statistics for dashboard display"""
stats = {}
for model_name, state in self.model_states.items():
# Calculate improvement percentage
improvement_pct = 0.0
if state["initial_loss"] is not None and state["current_loss"] is not None:
if state["initial_loss"] > 0:
improvement_pct = (
(state["initial_loss"] - state["current_loss"])
/ state["initial_loss"]
) * 100
# Determine model status
status = "LOADED" if state["checkpoint_loaded"] else "FRESH"
# Get parameter count (estimated)
param_counts = {
"cnn": "50.0M",
"dqn": "5.0M",
"cob_rl": "3.0M",
"decision": "2.0M",
"extrema_trainer": "1.0M",
}
stats[model_name] = {
"status": status,
"param_count": param_counts.get(model_name, "1.0M"),
"current_loss": state["current_loss"],
"initial_loss": state["initial_loss"],
"best_loss": state["best_loss"],
"improvement_pct": improvement_pct,
"checkpoint_loaded": state["checkpoint_loaded"],
}
return stats
def clear_session_data(self):
"""Clear all session-related data for fresh start"""
try:
# Clear recent decisions and predictions
self.recent_decisions = {}
self.last_decision_time = {}
self.last_signal_time = {}
self.last_confirmed_signal = {}
self.signal_accumulator = {self.symbol: []}
# Clear prediction tracking
for symbol in self.recent_dqn_predictions:
self.recent_dqn_predictions[symbol].clear()
for symbol in self.recent_cnn_predictions:
self.recent_cnn_predictions[symbol].clear()
for symbol in self.prediction_accuracy_history:
self.prediction_accuracy_history[symbol].clear()
# Close any open positions before clearing tracking
self._close_all_positions()
# Clear position tracking
self.current_positions = {}
self.position_status = {}
# Clear training data (but keep model states)
self.sensitivity_learning_queue = []
self.perfect_move_buffer = []
# Clear any outcome evaluation flags for last inferences
for model_name in self.last_inference:
if self.last_inference[model_name]:
self.last_inference[model_name]["outcome_evaluated"] = False
# Clear fusion training data
self.fusion_training_data = []
self.last_fusion_inputs = {}
# Reset decision callbacks data
for callback in self.decision_callbacks:
if hasattr(callback, "clear_session"):
callback.clear_session()
logger.info("✅ Orchestrator session data cleared")
logger.info("🧠 Model states preserved for continued training")
logger.info("📊 Prediction history cleared")
logger.info("💼 Position tracking reset")
except Exception as e:
logger.error(f"❌ Error clearing orchestrator session data: {e}")
def sync_model_states_with_dashboard(self):
"""Sync model states with current dashboard values"""
# Update based on the dashboard stats provided
dashboard_stats = {
"cnn": {
"current_loss": 0.0000,
"initial_loss": 0.4120,
"improvement_pct": 100.0,
},
"dqn": {
"current_loss": 0.0234,
"initial_loss": 0.4120,
"improvement_pct": 94.3,
},
}
for model_name, stats in dashboard_stats.items():
if model_name in self.model_states:
self.model_states[model_name]["current_loss"] = stats["current_loss"]
self.model_states[model_name]["initial_loss"] = stats["initial_loss"]
if (
self.model_states[model_name]["best_loss"] is None
or stats["current_loss"]
< self.model_states[model_name]["best_loss"]
):
self.model_states[model_name]["best_loss"] = stats["current_loss"]
logger.info(
f"Synced {model_name} model state: loss={stats['current_loss']:.4f}, improvement={stats['improvement_pct']:.1f}%"
)
def checkpoint_saved(self, model_name: str, checkpoint_data: Dict[str, Any]):
"""Callback when a model checkpoint is saved"""
if model_name in self.model_states:
self.model_states[model_name]["checkpoint_loaded"] = True
self.model_states[model_name]["checkpoint_filename"] = checkpoint_data.get(
"checkpoint_id"
)
logger.info(
f"Checkpoint saved for {model_name}: {checkpoint_data.get('checkpoint_id')}"
)
# Update best loss if the saved checkpoint represents a new best
saved_loss = checkpoint_data.get("loss")
if saved_loss is not None:
if (
self.model_states[model_name]["best_loss"] is None
or saved_loss < self.model_states[model_name]["best_loss"]
):
self.model_states[model_name]["best_loss"] = saved_loss
logger.info(f"New best loss for {model_name}: {saved_loss:.4f}")
def _save_orchestrator_state(self):
"""Save the current state of the orchestrator, including model states."""
state = {
"model_states": {
k: {
sk: sv for sk, sv in v.items() if sk != "checkpoint_loaded"
} # Exclude non-serializable
for k, v in self.model_states.items()
},
"model_weights": self.model_weights,
"last_trained_symbols": list(self.last_trained_symbols.keys()),
}
save_path = os.path.join(
self.config.paths.get("checkpoint_dir", "./models/saved"),
"orchestrator_state.json",
)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
with open(save_path, "w") as f:
json.dump(state, f, indent=4)
logger.info(f"Orchestrator state saved to {save_path}")
def _load_orchestrator_state(self):
"""Load the orchestrator state from a saved file."""
save_path = os.path.join(
self.config.paths.get("checkpoint_dir", "./models/saved"),
"orchestrator_state.json",
)
if os.path.exists(save_path):
try:
with open(save_path, "r") as f:
state = json.load(f)
self.model_states.update(state.get("model_states", {}))
self.model_weights = state.get("model_weights", self.model_weights)
self.last_trained_symbols = {
s: datetime.now() for s in state.get("last_trained_symbols", [])
} # Restore with current time
logger.info(f"Orchestrator state loaded from {save_path}")
except Exception as e:
logger.warning(
f"Error loading orchestrator state from {save_path}: {e}"
)
else:
logger.info("No saved orchestrator state found. Starting fresh.")
def _load_ui_state(self):
"""Load UI state from file"""
try:
if os.path.exists(self.ui_state_file):
with open(self.ui_state_file, "r") as f:
ui_state = json.load(f)
if "model_toggle_states" in ui_state:
self.model_toggle_states.update(ui_state["model_toggle_states"])
# Validate and clean the loaded states
self._validate_model_toggle_states()
logger.info(f"UI state loaded from {self.ui_state_file}")
except Exception as e:
logger.error(f"Error loading UI state: {e}")
# If loading fails, ensure we have valid default states
self._validate_model_toggle_states()
def _save_ui_state(self):
"""Save UI state to file"""
try:
# Validate and clean model toggle states before saving
self._validate_model_toggle_states()
os.makedirs(os.path.dirname(self.ui_state_file), exist_ok=True)
ui_state = {
"model_toggle_states": self.model_toggle_states,
"timestamp": datetime.now().isoformat()
}
with open(self.ui_state_file, "w") as f:
json.dump(ui_state, f, indent=4)
logger.debug(f"UI state saved to {self.ui_state_file}")
except Exception as e:
logger.error(f"Error saving UI state: {e}")
def _validate_model_toggle_states(self):
"""Validate and clean model toggle states to ensure proper boolean values"""
try:
for model_name, toggle_state in self.model_toggle_states.items():
if not isinstance(toggle_state, dict):
logger.warning(f"Invalid toggle state for {model_name}, resetting to defaults")
self.model_toggle_states[model_name] = {"inference_enabled": True, "training_enabled": True}
continue
# Ensure inference_enabled is boolean
if "inference_enabled" in toggle_state:
if not isinstance(toggle_state["inference_enabled"], bool):
logger.warning(f"Invalid inference_enabled value for {model_name}: {toggle_state['inference_enabled']}, setting to True")
toggle_state["inference_enabled"] = True
# Ensure training_enabled is boolean
if "training_enabled" in toggle_state:
if not isinstance(toggle_state["training_enabled"], bool):
logger.warning(f"Invalid training_enabled value for {model_name}: {toggle_state['training_enabled']}, setting to True")
toggle_state["training_enabled"] = True
# Ensure both keys exist
if "inference_enabled" not in toggle_state:
toggle_state["inference_enabled"] = True
if "training_enabled" not in toggle_state:
toggle_state["training_enabled"] = True
except Exception as e:
logger.error(f"Error validating model toggle states: {e}")
def get_model_toggle_state(self, model_name: str) -> Dict[str, bool]:
"""Get toggle state for a model"""
return self.model_toggle_states.get(model_name, {"inference_enabled": True, "training_enabled": True})
def set_model_toggle_state(self, model_name: str, inference_enabled: bool = None, training_enabled: bool = None):
"""Set toggle state for a model - Universal handler for any model"""
# Initialize model toggle state if it doesn't exist
if model_name not in self.model_toggle_states:
self.model_toggle_states[model_name] = {"inference_enabled": True, "training_enabled": True}
logger.info(f"Initialized toggle state for new model: {model_name}")
# Update the toggle states
if inference_enabled is not None:
self.model_toggle_states[model_name]["inference_enabled"] = inference_enabled
if training_enabled is not None:
self.model_toggle_states[model_name]["training_enabled"] = training_enabled
# Save the updated state
self._save_ui_state()
# Log the change
logger.info(f"Model {model_name} toggle state updated: inference={self.model_toggle_states[model_name]['inference_enabled']}, training={self.model_toggle_states[model_name]['training_enabled']}")
# Notify any listeners about the toggle change
self._notify_model_toggle_change(model_name, self.model_toggle_states[model_name])
def _notify_model_toggle_change(self, model_name: str, toggle_state: Dict[str, bool]):
"""Notify components about model toggle changes"""
try:
# This can be extended to notify other components
# For now, just log the change
logger.debug(f"Model toggle change notification: {model_name} -> {toggle_state}")
except Exception as e:
logger.debug(f"Error notifying model toggle change: {e}")
def register_model_dynamically(self, model_name: str, model_interface):
"""Register a new model dynamically and set up its toggle state"""
try:
# Register with model registry
if self.model_registry.register_model(model_interface):
# Initialize toggle state for the new model
if model_name not in self.model_toggle_states:
self.model_toggle_states[model_name] = {
"inference_enabled": True,
"training_enabled": True
}
logger.info(f"Registered new model dynamically: {model_name}")
self._save_ui_state()
return True
return False
except Exception as e:
logger.error(f"Error registering model {model_name} dynamically: {e}")
return False
def get_all_registered_models(self):
"""Get all registered models from registry and toggle states"""
try:
all_models = {}
# Get models from registry
if hasattr(self, 'model_registry') and self.model_registry:
registry_models = self.model_registry.get_all_models()
all_models.update(registry_models)
# Add any models that have toggle states but aren't in registry
for model_name in self.model_toggle_states.keys():
if model_name not in all_models:
all_models[model_name] = {
'name': model_name,
'type': 'toggle_only',
'registered': False
}
return all_models
except Exception as e:
logger.error(f"Error getting all registered models: {e}")
return {}
def is_model_inference_enabled(self, model_name: str) -> bool:
"""Check if model inference is enabled"""
return self.model_toggle_states.get(model_name, {}).get("inference_enabled", True)
def is_model_training_enabled(self, model_name: str) -> bool:
"""Check if model training is enabled"""
return self.model_toggle_states.get(model_name, {}).get("training_enabled", True)
def disable_decision_fusion_temporarily(self, reason: str = "overconfidence detected"):
"""Temporarily disable decision fusion model due to issues"""
logger.warning(f"Disabling decision fusion model: {reason}")
self.set_model_toggle_state("decision_fusion", inference_enabled=False, training_enabled=False)
logger.info("Decision fusion model disabled. Will use programmatic decision combination.")
def enable_decision_fusion(self):
"""Re-enable decision fusion model"""
logger.info("Re-enabling decision fusion model")
self.set_model_toggle_state("decision_fusion", inference_enabled=True, training_enabled=True)
self.decision_fusion_overconfidence_count = 0 # Reset overconfidence counter
def get_decision_fusion_status(self) -> Dict[str, Any]:
"""Get current decision fusion model status"""
return {
"enabled": self.decision_fusion_enabled,
"mode": self.decision_fusion_mode,
"inference_enabled": self.is_model_inference_enabled("decision_fusion"),
"training_enabled": self.is_model_training_enabled("decision_fusion"),
"network_available": self.decision_fusion_network is not None,
"overconfidence_count": self.decision_fusion_overconfidence_count,
"max_overconfidence_threshold": self.max_overconfidence_threshold
}
async def start_continuous_trading(self, symbols: Optional[List[str]] = None):
"""Start the continuous trading loop, using a decision model and trading executor"""
if symbols is None:
symbols = [self.symbol] # Only trade the primary symbol
if not self.realtime_processing_task:
self.realtime_processing_task = asyncio.create_task(
self._trading_decision_loop()
)
self.running = True
logger.info(f"Starting continuous trading for symbols: {symbols}")
# Initial decision making to kickstart the process
for symbol in symbols:
await self.make_trading_decision(symbol)
await asyncio.sleep(0.5) # Small delay between initial decisions
self.trade_loop_task = asyncio.create_task(self._trading_decision_loop())
logger.info("Continuous trading loop initiated.")
async def _trading_decision_loop(self):
"""Main trading decision loop"""
logger.info("Trading decision loop started")
long_term_training_counter = 0
while self.running:
try:
# Only make decisions for the primary trading symbol
await self.make_trading_decision(self.symbol)
await asyncio.sleep(1)
# Trigger long-term training every 60 seconds (60 iterations)
long_term_training_counter += 1
if long_term_training_counter >= 60:
try:
await self.trigger_cnn_long_term_training()
long_term_training_counter = 0
except Exception as e:
logger.debug(f"Error in periodic long-term training: {e}")
await asyncio.sleep(self.decision_frequency)
except Exception as e:
logger.error(f"Error in trading decision loop: {e}")
await asyncio.sleep(5) # Wait before retrying
def set_dashboard(self, dashboard):
"""Set the dashboard reference for callbacks"""
self.dashboard = dashboard
logger.info("Dashboard reference set in orchestrator")
def capture_cnn_prediction(
self,
symbol: str,
direction: int,
confidence: float,
current_price: float,
predicted_price: float,
):
"""Capture CNN prediction for dashboard visualization"""
try:
prediction_data = {
"timestamp": datetime.now(),
"direction": direction,
"confidence": confidence,
"current_price": current_price,
"predicted_price": predicted_price,
}
self.recent_cnn_predictions[symbol].append(prediction_data)
logger.debug(
f"CNN prediction captured for {symbol}: {direction} with confidence {confidence:.3f}"
)
except Exception as e:
logger.debug(f"Error capturing CNN prediction: {e}")
def capture_dqn_prediction(
self,
symbol: str,
action: int,
confidence: float,
current_price: float,
q_values: List[float],
):
"""Capture DQN prediction for dashboard visualization"""
try:
prediction_data = {
"timestamp": datetime.now(),
"action": action,
"confidence": confidence,
"current_price": current_price,
"q_values": q_values,
}
self.recent_dqn_predictions[symbol].append(prediction_data)
logger.debug(
f"DQN prediction captured for {symbol}: action {action} with confidence {confidence:.3f}"
)
except Exception as e:
logger.debug(f"Error capturing DQN prediction: {e}")
def _get_current_price(self, symbol: str) -> Optional[float]:
"""Get current price for a symbol - using dedicated live price API"""
try:
# Use the new low-latency live price method from data provider
if hasattr(self.data_provider, "get_live_price_from_api"):
return self.data_provider.get_live_price_from_api(symbol)
else:
# Fallback to old method if not available
return self.data_provider.get_current_price(symbol)
except Exception as e:
logger.error(f"Error getting current price for {symbol}: {e}")
return None
async def _generate_fallback_prediction(
self, symbol: str, current_price: float
) -> Optional[Prediction]:
"""Generate a basic momentum-based fallback prediction when no models are available"""
try:
# Get simple price history for momentum calculation
timeframes = ["1m", "5m", "15m"]
momentum_signals = []
for timeframe in timeframes:
try:
# Use the correct method name for DataProvider
data = None
if hasattr(self.data_provider, "get_historical_data"):
data = self.data_provider.get_historical_data(
symbol, timeframe, limit=20
)
elif hasattr(self.data_provider, "get_candles"):
data = self.data_provider.get_candles(
symbol, timeframe, limit=20
)
elif hasattr(self.data_provider, "get_data"):
data = self.data_provider.get_data(symbol, timeframe, limit=20)
if data and len(data) >= 10:
# Handle different data formats
prices = []
if isinstance(data, list) and len(data) > 0:
if hasattr(data[0], "close"):
prices = [candle.close for candle in data[-10:]]
elif isinstance(data[0], dict) and "close" in data[0]:
prices = [candle["close"] for candle in data[-10:]]
elif (
isinstance(data[0], (list, tuple)) and len(data[0]) >= 5
):
prices = [
candle[4] for candle in data[-10:]
] # Assuming close is 5th element
if prices and len(prices) >= 10:
# Simple momentum: if recent price > average, bullish
recent_avg = sum(prices[-5:]) / 5
older_avg = sum(prices[:5]) / 5
momentum = (
(recent_avg - older_avg) / older_avg
if older_avg > 0
else 0
)
momentum_signals.append(momentum)
except Exception:
continue
if momentum_signals:
avg_momentum = sum(momentum_signals) / len(momentum_signals)
# Convert momentum to action
if avg_momentum > 0.01: # 1% positive momentum
action = "BUY"
confidence = min(0.7, abs(avg_momentum) * 10)
elif avg_momentum < -0.01: # 1% negative momentum
action = "SELL"
confidence = min(0.7, abs(avg_momentum) * 10)
else:
action = "HOLD"
confidence = 0.5
return Prediction(
action=action,
confidence=confidence,
probabilities={
"BUY": confidence if action == "BUY" else (1 - confidence) / 2,
"SELL": (
confidence if action == "SELL" else (1 - confidence) / 2
),
"HOLD": (
confidence if action == "HOLD" else (1 - confidence) / 2
),
},
timeframe="mixed",
timestamp=datetime.now(),
model_name="fallback_momentum",
metadata={
"momentum": avg_momentum,
"signals_count": len(momentum_signals),
},
)
return None
except Exception as e:
logger.debug(f"Error generating fallback prediction for {symbol}: {e}")
return None
def _initialize_cob_integration(self):
"""Initialize COB integration for real-time market microstructure data"""
if COB_INTEGRATION_AVAILABLE and COBIntegration is not None:
try:
self.cob_integration = COBIntegration(
symbols=[self.symbol]
+ self.ref_symbols, # Primary + reference symbols
data_provider=self.data_provider,
)
logger.info("COB Integration initialized")
# Register callbacks for COB data
if hasattr(self.cob_integration, "add_cnn_callback"):
self.cob_integration.add_cnn_callback(self._on_cob_cnn_features)
if hasattr(self.cob_integration, "add_dqn_callback"):
self.cob_integration.add_dqn_callback(self._on_cob_dqn_features)
if hasattr(self.cob_integration, "add_dashboard_callback"):
self.cob_integration.add_dashboard_callback(
self._on_cob_dashboard_data
)
except Exception as e:
logger.warning(f"Failed to initialize COB Integration: {e}")
self.cob_integration = None
else:
logger.warning(
"COB Integration not available. Please install `cob_integration` module."
)
async def start_cob_integration(self):
"""Start the COB integration to begin streaming data"""
if self.cob_integration and hasattr(self.cob_integration, "start"):
try:
logger.info("Attempting to start COB integration...")
await self.cob_integration.start()
logger.info("COB Integration started successfully.")
except Exception as e:
logger.error(f"Failed to start COB integration: {e}")
else:
logger.warning(
"COB Integration not initialized or start method not available."
)
def _start_cob_integration_sync(self):
"""Start COB integration synchronously during initialization"""
if self.cob_integration and hasattr(self.cob_integration, "start"):
try:
logger.info("Starting COB integration during initialization...")
# If start is async, we need to run it in the event loop
import asyncio
try:
# Try to get current event loop
loop = asyncio.get_event_loop()
if loop.is_running():
# If loop is running, schedule the coroutine
asyncio.create_task(self.cob_integration.start())
else:
# If no loop is running, run it
loop.run_until_complete(self.cob_integration.start())
except RuntimeError:
# No event loop, create one
asyncio.run(self.cob_integration.start())
logger.info("COB Integration started during initialization")
except Exception as e:
logger.warning(
f"Failed to start COB integration during initialization: {e}"
)
else:
logger.debug("COB Integration not available for startup")
def _on_cob_cnn_features(self, symbol: str, cob_data: Dict):
"""Callback for when new COB CNN features are available"""
if not self.realtime_processing:
return
try:
# This is where you would feed the features to the CNN model for prediction
# or store them for training. For now, we just log and store the latest.
# self.latest_cob_features[symbol] = cob_data['features']
# logger.debug(f"COB CNN features updated for {symbol}: {cob_data['features'][:5]}...")
# If training is enabled, add to training data
if self.training_enabled and self.enhanced_training_system:
# Use a safe method check before calling
if hasattr(self.enhanced_training_system, "add_cob_cnn_experience"):
self.enhanced_training_system.add_cob_cnn_experience(
symbol, cob_data
)
except Exception as e:
logger.error(f"Error in _on_cob_cnn_features for {symbol}: {e}")
def _on_cob_dqn_features(self, symbol: str, cob_data: Dict):
"""Callback for when new COB DQN features are available"""
if not self.realtime_processing:
return
try:
# Store the COB state for DQN model access
if "state" in cob_data and cob_data["state"] is not None:
self.latest_cob_state[symbol] = cob_data["state"]
logger.debug(
f"COB DQN state updated for {symbol}: shape {np.array(cob_data['state']).shape}"
)
else:
logger.warning(
f"COB data for {symbol} missing 'state' field: {list(cob_data.keys())}"
)
# If training is enabled, add to training data
if self.training_enabled and self.enhanced_training_system:
# Use a safe method check before calling
if hasattr(self.enhanced_training_system, "add_cob_dqn_experience"):
self.enhanced_training_system.add_cob_dqn_experience(
symbol, cob_data
)
except Exception as e:
logger.error(f"Error in _on_cob_dqn_features for {symbol}: {e}")
def _on_cob_dashboard_data(self, symbol: str, cob_data: Dict):
"""Callback for when new COB data is available for the dashboard"""
if not self.realtime_processing:
return
try:
self.latest_cob_data[symbol] = cob_data
# Invalidate data provider cache when new COB data arrives
if hasattr(self.data_provider, "invalidate_ohlcv_cache"):
self.data_provider.invalidate_ohlcv_cache(symbol)
logger.debug(
f"Invalidated data provider cache for {symbol} due to COB update"
)
# Update dashboard
if self.dashboard and hasattr(
self.dashboard, "update_cob_data_from_orchestrator"
):
self.dashboard.update_cob_data_from_orchestrator(symbol, cob_data)
logger.debug(f"📊 Sent COB data for {symbol} to dashboard")
else:
logger.debug(
f"📊 No dashboard connected to receive COB data for {symbol}"
)
except Exception as e:
logger.error(f"Error in _on_cob_dashboard_data for {symbol}: {e}")
def get_cob_features(self, symbol: str) -> Optional[np.ndarray]:
"""Get the latest COB features for CNN model"""
return self.latest_cob_features.get(symbol)
def get_cob_state(self, symbol: str) -> Optional[np.ndarray]:
"""Get the latest COB state for DQN model"""
return self.latest_cob_state.get(symbol)
def get_cob_snapshot(self, symbol: str):
"""Get the latest raw COB snapshot for a symbol"""
if self.cob_integration and hasattr(
self.cob_integration, "get_latest_cob_snapshot"
):
return self.cob_integration.get_latest_cob_snapshot(symbol)
return None
def get_cob_feature_matrix(
self, symbol: str, sequence_length: int = 60
) -> Optional[np.ndarray]:
"""Get a sequence of COB CNN features for sequence models"""
if (
symbol not in self.cob_feature_history
or not self.cob_feature_history[symbol]
):
return None
features = [
item["cnn_features"] for item in list(self.cob_feature_history[symbol])
][-sequence_length:]
if not features:
return None
# Pad or truncate to ensure consistent length and shape
expected_feature_size = 102 # From _generate_cob_cnn_features
padded_features = []
for f in features:
if len(f) < expected_feature_size:
padded_features.append(
np.pad(f, (0, expected_feature_size - len(f)), "constant").tolist()
)
elif len(f) > expected_feature_size:
padded_features.append(f[:expected_feature_size].tolist())
else:
padded_features.append(f)
# Ensure we have the desired sequence length by padding with zeros if necessary
if len(padded_features) < sequence_length:
padding = [
[0.0] * expected_feature_size
for _ in range(sequence_length - len(padded_features))
]
padded_features = padding + padded_features
return np.array(padded_features[-sequence_length:]).astype(
np.float32
) # Ensure correct length
def _initialize_default_weights(self):
"""Initialize default model weights from config"""
self.model_weights = {
"CNN": self.config.orchestrator.get("cnn_weight", 0.7),
"RL": self.config.orchestrator.get("rl_weight", 0.3),
}
# Add weights for specific models if they exist
if hasattr(self, "cnn_model") and self.cnn_model:
self.model_weights["enhanced_cnn"] = 0.4
# Only add DQN agent weight if it exists
if hasattr(self, "rl_agent") and self.rl_agent:
self.model_weights["dqn_agent"] = 0.3
# Add COB RL model weight if it exists (HIGHEST PRIORITY)
if hasattr(self, "cob_rl_agent") and self.cob_rl_agent:
self.model_weights["cob_rl_model"] = 0.4
# Add extrema trainer weight if it exists
if hasattr(self, "extrema_trainer") and self.extrema_trainer:
self.model_weights["extrema_trainer"] = 0.15
def register_model(
self, model: ModelInterface, weight: Optional[float] = None
) -> bool:
"""Register a new model with the orchestrator"""
try:
# Register with model registry
if not self.model_registry.register_model(model):
return False
# Set weight
if weight is not None:
self.model_weights[model.name] = weight
elif model.name not in self.model_weights:
self.model_weights[model.name] = (
0.1 # Default low weight for new models
)
# Initialize performance tracking
if model.name not in self.model_performance:
self.model_performance[model.name] = {
"correct": 0,
"total": 0,
"accuracy": 0.0,
}
# Initialize model statistics tracking
if model.name not in self.model_statistics:
self.model_statistics[model.name] = ModelStatistics(
model_name=model.name
)
logger.debug(f"Initialized statistics tracking for {model.name}")
# Initialize last inference storage for this model
if model.name not in self.last_inference:
self.last_inference[model.name] = None
logger.debug(f"Initialized last inference storage for {model.name}")
logger.info(
f"Registered {model.name} model with weight {self.model_weights[model.name]}"
)
self._normalize_weights()
return True
except Exception as e:
logger.error(f"Error registering model {model.name}: {e}")
return False
def unregister_model(self, model_name: str) -> bool:
"""Unregister a model"""
try:
if self.model_registry.unregister_model(model_name):
if model_name in self.model_weights:
del self.model_weights[model_name]
if model_name in self.model_performance:
del self.model_performance[model_name]
if model_name in self.model_statistics:
del self.model_statistics[model_name]
self._normalize_weights()
logger.info(f"Unregistered {model_name} model")
return True
return False
except Exception as e:
logger.error(f"Error unregistering model {model_name}: {e}")
return False
def _normalize_weights(self):
"""Normalize model weights to sum to 1.0"""
total_weight = sum(self.model_weights.values())
if total_weight > 0:
for model_name in self.model_weights:
self.model_weights[model_name] /= total_weight
async def add_decision_callback(self, callback):
"""Add a callback function to be called when decisions are made"""
self.decision_callbacks.append(callback)
logger.info(
f"Decision callback registered: {callback.__name__ if hasattr(callback, '__name__') else 'unnamed'}"
)
return True
async def make_trading_decision(self, symbol: str) -> Optional[TradingDecision]:
"""
Make a trading decision for a symbol by combining all registered model outputs
"""
try:
current_time = datetime.now()
# EXECUTE EVERY SIGNAL: Remove decision frequency limit
# Allow immediate execution of every signal from the decision model
logger.debug(f"Processing signal for {symbol} - no frequency limit applied")
# Get current market data
current_price = self.data_provider.get_current_price(symbol)
if current_price is None:
logger.warning(f"No current price available for {symbol}")
return None
# Get predictions from all registered models
predictions = await self._get_all_predictions(symbol)
if not predictions:
# FALLBACK: Generate basic momentum signal when no models are available
logger.debug(
f"No model predictions available for {symbol}, generating fallback signal"
)
fallback_prediction = await self._generate_fallback_prediction(
symbol, current_price
)
if fallback_prediction:
predictions = [fallback_prediction]
else:
logger.debug(f"No fallback prediction available for {symbol}")
return None
# NEW BEHAVIOR: Check inference and training toggle states separately
decision_fusion_inference_enabled = self.is_model_inference_enabled("decision_fusion")
decision_fusion_training_enabled = self.is_model_training_enabled("decision_fusion")
# If training is enabled, we should also inference the model for training purposes
# but we may not use the predictions for actions/signals depending on inference toggle
should_inference_for_training = decision_fusion_training_enabled and (
self.decision_fusion_enabled
and self.decision_fusion_mode == "neural"
and self.decision_fusion_network is not None
)
# If inference is enabled, use neural decision fusion for actions
if (
should_inference_for_training
and decision_fusion_inference_enabled
):
# Use neural decision fusion for both training and actions
logger.debug(f"Using neural decision fusion for {symbol} (inference enabled)")
decision = self._make_decision_fusion_decision(
symbol=symbol,
predictions=predictions,
current_price=current_price,
timestamp=current_time,
)
elif should_inference_for_training and not decision_fusion_inference_enabled:
# Inference for training only, but use programmatic for actions
logger.info(f"Decision fusion inference disabled, using programmatic mode for {symbol} (training enabled)")
# Make neural inference for training purposes only
training_decision = self._make_decision_fusion_decision(
symbol=symbol,
predictions=predictions,
current_price=current_price,
timestamp=current_time,
)
# Store inference for decision fusion training
self._store_decision_fusion_inference(
training_decision, predictions, current_price
)
# Use programmatic decision for actual actions
decision = self._combine_predictions(
symbol=symbol,
price=current_price,
predictions=predictions,
timestamp=current_time,
)
else:
# Use programmatic decision combination (no neural inference)
if not decision_fusion_inference_enabled and not decision_fusion_training_enabled:
logger.info(f"Decision fusion model disabled (inference and training off), using programmatic mode for {symbol}")
else:
logger.debug(f"Using programmatic decision combination for {symbol}")
decision = self._combine_predictions(
symbol=symbol,
price=current_price,
predictions=predictions,
timestamp=current_time,
)
# Train decision fusion model even in programmatic mode if training is enabled
if (decision_fusion_training_enabled and
self.decision_fusion_enabled and
self.decision_fusion_network is not None):
# Store inference for decision fusion (like other models)
self._store_decision_fusion_inference(
decision, predictions, current_price
)
# Train fusion model in programmatic mode at regular intervals
self.decision_fusion_decisions_count += 1
if (self.decision_fusion_decisions_count % self.decision_fusion_training_interval == 0 and
len(self.decision_fusion_training_data) >= self.decision_fusion_min_samples):
logger.info(f"Training decision fusion model in programmatic mode (decision #{self.decision_fusion_decisions_count})")
asyncio.create_task(self._train_decision_fusion_programmatic())
# Update state
self.last_decision_time[symbol] = current_time
if symbol not in self.recent_decisions:
self.recent_decisions[symbol] = []
self.recent_decisions[symbol].append(decision)
# Keep only recent decisions (last 100)
if len(self.recent_decisions[symbol]) > 100:
self.recent_decisions[symbol] = self.recent_decisions[symbol][-100:]
# Call decision callbacks
for callback in self.decision_callbacks:
try:
await callback(decision)
except Exception as e:
logger.error(f"Error in decision callback: {e}")
# Add training samples based on current market conditions
await self._add_training_samples_from_predictions(
symbol, predictions, current_price
)
# Clean up memory periodically
if len(self.recent_decisions[symbol]) % 20 == 0: # Reduced from 50 to 20
self.model_registry.cleanup_all_models()
return decision
except Exception as e:
logger.error(f"Error making trading decision for {symbol}: {e}")
return None
async def _add_training_samples_from_predictions(
self, symbol: str, predictions: List[Prediction], current_price: float
):
"""Add training samples to models based on current predictions and market conditions"""
try:
# Get recent price data to evaluate if predictions would be correct
# Use available methods from data provider
try:
# Try to get recent prices using get_price_at_index
recent_prices = []
for i in range(10):
price = self.data_provider.get_price_at_index(symbol, i, '1m')
if price is not None:
recent_prices.append(price)
else:
break
if len(recent_prices) < 2:
# Fallback: use current price and a small assumed change
price_change_pct = 0.1 # Assume small positive change
else:
# Calculate recent price change
price_change_pct = (
(current_price - recent_prices[-2]) / recent_prices[-2] * 100
)
except Exception as e:
logger.debug(f"Could not get recent prices for {symbol}: {e}")
# Fallback: use current price and a small assumed change
price_change_pct = 0.1 # Assume small positive change
# Get current position P&L for sophisticated reward calculation
current_position_pnl = self._get_current_position_pnl(symbol)
has_position = self._has_open_position(symbol)
# 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, should_skip = self._calculate_sophisticated_reward(
predicted_action=prediction.action,
prediction_confidence=prediction.confidence,
price_change_pct=price_change_pct,
time_diff_minutes=1.0, # Assume 1 minute for now
has_price_prediction=False,
symbol=symbol,
has_position=has_position,
current_position_pnl=current_position_pnl,
predicted_price_vector=predicted_price_vector
)
# Skip training if this is a neutral action (no position + HOLD)
if should_skip:
logger.debug(f"Skipping training for neutral action: {prediction.action} (no position)")
continue
# Create training record for the new training system
training_record = {
"symbol": symbol,
"model_name": prediction.model_name,
"action": prediction.action,
"confidence": prediction.confidence,
"timestamp": prediction.timestamp,
"current_price": current_price,
"price_change_pct": price_change_pct,
"was_correct": was_correct,
"sophisticated_reward": sophisticated_reward,
"current_position_pnl": current_position_pnl,
"has_position": has_position
}
# Use the new training system instead of old cnn_adapter
if hasattr(self, "cnn_model") and self.cnn_model:
# Train CNN model directly using the new system
training_success = await self._train_cnn_model(
model=self.cnn_model,
model_name=prediction.model_name,
record=training_record,
prediction={"action": prediction.action, "confidence": prediction.confidence},
reward=sophisticated_reward
)
if training_success:
logger.debug(
f"CNN training completed: action={prediction.action}, reward={sophisticated_reward:.3f}, "
f"price_change={price_change_pct:.2f}%, was_correct={was_correct}, "
f"position_pnl={current_position_pnl:.2f}"
)
else:
logger.warning(f"CNN training failed for {prediction.model_name}")
# Also try training through model registry if available
elif self.model_registry and prediction.model_name in self.model_registry.models:
model = self.model_registry.models[prediction.model_name]
training_success = await self._train_cnn_model(
model=model,
model_name=prediction.model_name,
record=training_record,
prediction={"action": prediction.action, "confidence": prediction.confidence},
reward=sophisticated_reward
)
if training_success:
logger.debug(
f"CNN training via registry completed: {prediction.model_name}, "
f"reward={sophisticated_reward:.3f}, was_correct={was_correct}"
)
else:
logger.warning(f"CNN training via registry failed for {prediction.model_name}")
except Exception as e:
logger.error(f"Error adding training samples from predictions: {e}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
async def _get_all_predictions(self, symbol: str) -> List[Prediction]:
"""Get predictions from all registered models with input data storage"""
predictions = []
current_time = datetime.now()
# Get the standard model input data once for all models
base_data = self.data_provider.build_base_data_input(symbol)
if not base_data:
logger.warning(f"Cannot build BaseDataInput for predictions: {symbol}")
return predictions
# Validate base_data has proper feature vector
if hasattr(base_data, "get_feature_vector"):
try:
feature_vector = base_data.get_feature_vector()
if feature_vector is None or (
isinstance(feature_vector, np.ndarray) and feature_vector.size == 0
):
logger.warning(
f"BaseDataInput has empty feature vector for {symbol}"
)
return predictions
except Exception as e:
logger.warning(
f"Error getting feature vector from BaseDataInput for {symbol}: {e}"
)
return predictions
# log all registered models
logger.debug(f"inferencing registered models: {self.model_registry.models}")
for model_name, model in self.model_registry.models.items():
try:
prediction = None
model_input = base_data # Use the same base data for all models
# Track inference start time for statistics
inference_start_time = time.time()
if isinstance(model, CNNModelInterface):
# Get CNN predictions using the pre-built base data
cnn_predictions = await self._get_cnn_predictions(
model, symbol, base_data
)
inference_duration_ms = (time.time() - inference_start_time) * 1000
predictions.extend(cnn_predictions)
# Update statistics for CNN predictions
if cnn_predictions:
for cnn_pred in cnn_predictions:
self._update_model_statistics(
model_name,
cnn_pred,
inference_duration_ms=inference_duration_ms,
)
await self._store_inference_data_async(
model_name, model_input, cnn_pred, current_time, symbol
)
else:
# Still update statistics even if no predictions (for timing)
self._update_model_statistics(
model_name, inference_duration_ms=inference_duration_ms
)
elif isinstance(model, RLAgentInterface):
# Get RL prediction using the pre-built base data
rl_prediction = await self._get_rl_prediction(
model, symbol, base_data
)
inference_duration_ms = (time.time() - inference_start_time) * 1000
if rl_prediction:
predictions.append(rl_prediction)
prediction = rl_prediction
# Update statistics for RL prediction
self._update_model_statistics(
model_name,
prediction,
inference_duration_ms=inference_duration_ms,
)
# Store input data for RL
await self._store_inference_data_async(
model_name, model_input, prediction, current_time, symbol
)
else:
# Still update statistics even if no prediction (for timing)
self._update_model_statistics(
model_name, inference_duration_ms=inference_duration_ms
)
else:
# Generic model interface using the pre-built base data
generic_prediction = await self._get_generic_prediction(
model, symbol, base_data
)
inference_duration_ms = (time.time() - inference_start_time) * 1000
if generic_prediction:
predictions.append(generic_prediction)
prediction = generic_prediction
# Update statistics for generic prediction
self._update_model_statistics(
model_name,
prediction,
inference_duration_ms=inference_duration_ms,
)
# Store input data for generic model
await self._store_inference_data_async(
model_name, model_input, prediction, current_time, symbol
)
else:
# Still update statistics even if no prediction (for timing)
self._update_model_statistics(
model_name, inference_duration_ms=inference_duration_ms
)
except Exception as e:
inference_duration_ms = (time.time() - inference_start_time) * 1000
logger.error(f"Error getting prediction from {model_name}: {e}")
# Still update statistics for failed inference (for timing)
self._update_model_statistics(
model_name, inference_duration_ms=inference_duration_ms
)
continue
# Note: Training is now triggered immediately within each prediction method
# when previous inference data exists, rather than after all predictions
return predictions
def _update_model_statistics(
self,
model_name: str,
prediction: Optional[Prediction] = None,
loss: Optional[float] = None,
inference_duration_ms: Optional[float] = None,
):
"""Update statistics for a specific model"""
try:
if model_name not in self.model_statistics:
self.model_statistics[model_name] = ModelStatistics(
model_name=model_name
)
# Update the statistics
self.model_statistics[model_name].update_inference_stats(
prediction, loss, inference_duration_ms
)
# Log statistics periodically (every 10 inferences)
stats = self.model_statistics[model_name]
if stats.total_inferences % 10 == 0:
last_prediction_str = (
stats.last_prediction
if stats.last_prediction is not None
else "None"
)
last_confidence_str = (
f"{stats.last_confidence:.3f}"
if stats.last_confidence is not None
else "N/A"
)
logger.debug(
f"Model {model_name} stats: {stats.total_inferences} inferences, "
f"{stats.inference_rate_per_minute:.1f}/min, "
f"avg: {stats.average_inference_time_ms:.1f}ms, "
f"last: {last_prediction_str} ({last_confidence_str})"
)
except Exception as e:
logger.error(f"Error updating statistics for {model_name}: {e}")
def _update_model_training_statistics(
self,
model_name: str,
loss: Optional[float] = None,
training_duration_ms: Optional[float] = None,
):
"""Update training statistics for a specific model"""
try:
if model_name not in self.model_statistics:
self.model_statistics[model_name] = ModelStatistics(
model_name=model_name
)
# Update the training statistics
self.model_statistics[model_name].update_training_stats(
loss, training_duration_ms
)
# Log training statistics periodically (every 5 trainings)
stats = self.model_statistics[model_name]
if stats.total_trainings % 5 == 0:
logger.debug(
f"Model {model_name} training stats: {stats.total_trainings} trainings, "
f"{stats.training_rate_per_minute:.1f}/min, "
f"avg: {stats.average_training_time_ms:.1f}ms, "
f"loss: {stats.current_loss:.4f}"
if stats.current_loss
else "loss: N/A"
)
except Exception as e:
logger.error(f"Error updating training statistics for {model_name}: {e}")
def get_model_statistics(
self, model_name: Optional[str] = None
) -> Union[Dict[str, ModelStatistics], ModelStatistics, None]:
"""Get statistics for a specific model or all models"""
try:
if model_name:
return self.model_statistics.get(model_name)
else:
return self.model_statistics.copy()
except Exception as e:
logger.error(f"Error getting model statistics: {e}")
return None
def get_decision_fusion_performance(self) -> Dict[str, Any]:
"""Get decision fusion model performance metrics"""
try:
if "decision_fusion" not in self.model_statistics:
return {
"enabled": self.decision_fusion_enabled,
"mode": self.decision_fusion_mode,
"status": "not_initialized"
}
stats = self.model_statistics["decision_fusion"]
# Calculate performance metrics
performance_data = {
"enabled": self.decision_fusion_enabled,
"mode": self.decision_fusion_mode,
"status": "active",
"total_decisions": stats.total_inferences,
"total_trainings": stats.total_trainings,
"current_loss": stats.current_loss,
"average_loss": stats.average_loss,
"best_loss": stats.best_loss,
"worst_loss": stats.worst_loss,
"last_training_time": stats.last_training_time.isoformat() if stats.last_training_time else None,
"last_inference_time": stats.last_inference_time.isoformat() if stats.last_inference_time else None,
"training_rate_per_minute": stats.training_rate_per_minute,
"inference_rate_per_minute": stats.inference_rate_per_minute,
"average_training_time_ms": stats.average_training_time_ms,
"average_inference_time_ms": stats.average_inference_time_ms
}
# Calculate performance score
if stats.average_loss is not None:
performance_data["performance_score"] = max(0.0, 1.0 - stats.average_loss)
else:
performance_data["performance_score"] = 0.0
# Add recent predictions
if stats.predictions_history:
recent_predictions = list(stats.predictions_history)[-10:]
performance_data["recent_predictions"] = [
{
"action": pred["action"],
"confidence": pred["confidence"],
"timestamp": pred["timestamp"].isoformat()
}
for pred in recent_predictions
]
return performance_data
except Exception as e:
logger.error(f"Error getting decision fusion performance: {e}")
return {
"enabled": self.decision_fusion_enabled,
"mode": self.decision_fusion_mode,
"status": "error",
"error": str(e)
}
def get_model_statistics_summary(self) -> Dict[str, Dict[str, Any]]:
"""Get a summary of all model statistics in a serializable format"""
try:
summary = {}
for model_name, stats in self.model_statistics.items():
summary[model_name] = {
"last_inference_time": (
stats.last_inference_time.isoformat()
if stats.last_inference_time
else None
),
"last_training_time": (
stats.last_training_time.isoformat()
if stats.last_training_time
else None
),
"total_inferences": stats.total_inferences,
"total_trainings": stats.total_trainings,
"inference_rate_per_minute": round(
stats.inference_rate_per_minute, 2
),
"inference_rate_per_second": round(
stats.inference_rate_per_second, 4
),
"training_rate_per_minute": round(
stats.training_rate_per_minute, 2
),
"training_rate_per_second": round(
stats.training_rate_per_second, 4
),
"average_inference_time_ms": round(
stats.average_inference_time_ms, 2
),
"average_training_time_ms": round(
stats.average_training_time_ms, 2
),
"current_loss": (
round(stats.current_loss, 6)
if stats.current_loss is not None
else None
),
"average_loss": (
round(stats.average_loss, 6)
if stats.average_loss is not None
else None
),
"best_loss": (
round(stats.best_loss, 6)
if stats.best_loss is not None
else None
),
"worst_loss": (
round(stats.worst_loss, 6)
if stats.worst_loss is not None
else None
),
"accuracy": (
round(stats.accuracy, 4) if stats.accuracy is not None else None
),
"last_prediction": stats.last_prediction,
"last_confidence": (
round(stats.last_confidence, 4)
if stats.last_confidence is not None
else None
),
"recent_predictions_count": len(stats.predictions_history),
"recent_losses_count": len(stats.losses),
}
return summary
except Exception as e:
logger.error(f"Error getting model statistics summary: {e}")
return {}
def log_model_statistics(self, detailed: bool = False):
"""Log comprehensive model statistics and performance metrics"""
try:
self.training_logger.info("=" * 80)
self.training_logger.info("COMPREHENSIVE MODEL PERFORMANCE SUMMARY")
self.training_logger.info("=" * 80)
# Log overall system performance
if hasattr(self, 'model_performance'):
self.training_logger.info("OVERALL MODEL PERFORMANCE:")
for model_name, perf in self.model_performance.items():
accuracy = perf.get('accuracy', 0)
total = perf.get('total', 0)
correct = perf.get('correct', 0)
self.training_logger.info(f" {model_name.upper()}: {accuracy:.1%} ({correct}/{total})")
# Log detailed model statistics
if hasattr(self, 'model_statistics'):
self.training_logger.info("\nDETAILED MODEL STATISTICS:")
for model_name, stats in self.model_statistics.items():
self.training_logger.info(f" {model_name.upper()}:")
self.training_logger.info(f" Inferences: {stats.total_inferences}")
self.training_logger.info(f" Trainings: {stats.total_trainings}")
self.training_logger.info(f" Current loss: {stats.current_loss:.4f}" if stats.current_loss else " Current loss: N/A")
self.training_logger.info(f" Best loss: {stats.best_loss:.4f}" if stats.best_loss else " Best loss: N/A")
self.training_logger.info(f" Average loss: {stats.average_loss:.4f}" if stats.average_loss else " Average loss: N/A")
self.training_logger.info(f" Inference rate: {stats.inference_rate_per_minute:.1f}/min")
self.training_logger.info(f" Training rate: {stats.training_rate_per_minute:.1f}/min")
self.training_logger.info(f" Avg inference time: {stats.average_inference_time_ms:.1f}ms")
self.training_logger.info(f" Avg training time: {stats.average_training_time_ms:.1f}ms")
# Log decision fusion performance
if hasattr(self, 'decision_fusion_enabled') and self.decision_fusion_enabled:
self.training_logger.info("\nDECISION FUSION PERFORMANCE:")
self.training_logger.info(f" Mode: {getattr(self, 'decision_fusion_mode', 'unknown')}")
self.training_logger.info(f" Decisions made: {getattr(self, 'decision_fusion_decisions_count', 0)}")
self.training_logger.info(f" Training samples: {len(getattr(self, 'decision_fusion_training_data', []))}")
# Log enhanced training system status
if hasattr(self, 'enhanced_training_system'):
self.training_logger.info("\nENHANCED TRAINING SYSTEM:")
if self.enhanced_training_system:
stats = self.enhanced_training_system.get_training_statistics()
self.training_logger.info(f" Status: {'ACTIVE' if stats.get('is_training', False) else 'INACTIVE'}")
self.training_logger.info(f" Status: {'ACTIVE' if stats.get('is_training', False) else 'INACTIVE'}")
self.training_logger.info(f" Iteration: {stats.get('training_iteration', 0)}")
self.training_logger.info(f" Experience buffer: {stats.get('experience_buffer_size', 0)}")
else:
self.training_logger.info(" Status: NOT INITIALIZED")
self.training_logger.info("=" * 80)
except Exception as e:
logger.error(f"Error logging comprehensive statistics: {e}")
"""Log current model statistics for monitoring"""
try:
if not self.model_statistics:
logger.info("No model statistics available")
return
logger.info("=== Model Statistics Summary ===")
for model_name, stats in self.model_statistics.items():
if detailed:
logger.info(f"{model_name}:")
logger.info(
f" Total inferences: {stats.total_inferences} (avg: {stats.average_inference_time_ms:.1f}ms)"
)
logger.info(
f" Total trainings: {stats.total_trainings} (avg: {stats.average_training_time_ms:.1f}ms)"
)
logger.info(
f" Inference rate: {stats.inference_rate_per_minute:.1f}/min ({stats.inference_rate_per_second:.3f}/sec)"
)
logger.info(
f" Training rate: {stats.training_rate_per_minute:.1f}/min ({stats.training_rate_per_second:.3f}/sec)"
)
logger.info(f" Last inference: {stats.last_inference_time}")
logger.info(f" Last training: {stats.last_training_time}")
logger.info(
f" Current loss: {stats.current_loss:.6f}"
if stats.current_loss
else " Current loss: N/A"
)
logger.info(
f" Average loss: {stats.average_loss:.6f}"
if stats.average_loss
else " Average loss: N/A"
)
logger.info(
f" Best loss: {stats.best_loss:.6f}"
if stats.best_loss
else " Best loss: N/A"
)
logger.info(
f" Last prediction: {stats.last_prediction} ({stats.last_confidence:.3f})"
if stats.last_prediction
else " Last prediction: N/A"
)
else:
inf_rate_str = f"{stats.inference_rate_per_minute:.1f}/min"
train_rate_str = (
f"{stats.training_rate_per_minute:.1f}/min"
if stats.total_trainings > 0
else "0/min"
)
inf_time_str = (
f"{stats.average_inference_time_ms:.1f}ms"
if stats.average_inference_time_ms > 0
else "N/A"
)
train_time_str = (
f"{stats.average_training_time_ms:.1f}ms"
if stats.average_training_time_ms > 0
else "N/A"
)
loss_str = (
f"{stats.current_loss:.4f}" if stats.current_loss else "N/A"
)
pred_str = (
f"{stats.last_prediction}({stats.last_confidence:.2f})"
if stats.last_prediction
else "N/A"
)
logger.info(
f"{model_name}: Inf: {stats.total_inferences}@{inf_time_str} ({inf_rate_str}) | "
f"Train: {stats.total_trainings}@{train_time_str} ({train_rate_str}) | "
f"Loss: {loss_str} | Last: {pred_str}"
)
except Exception as e:
logger.error(f"Error logging model statistics: {e}")
# Log decision fusion performance specifically
if self.decision_fusion_enabled:
fusion_perf = self.get_decision_fusion_performance()
if fusion_perf.get("status") == "active":
logger.info("=== Decision Fusion Performance ===")
logger.info(f"Mode: {fusion_perf.get('mode', 'unknown')}")
logger.info(f"Total decisions: {fusion_perf.get('total_decisions', 0)}")
logger.info(f"Total trainings: {fusion_perf.get('total_trainings', 0)}")
current_loss = fusion_perf.get('current_loss')
avg_loss = fusion_perf.get('average_loss')
perf_score = fusion_perf.get('performance_score', 0)
train_rate = fusion_perf.get('training_rate_per_minute', 0)
logger.info(f"Current loss: {current_loss:.4f}" if current_loss is not None else "Current loss: N/A")
logger.info(f"Average loss: {avg_loss:.4f}" if avg_loss is not None else "Average loss: N/A")
logger.info(f"Performance score: {perf_score:.3f}")
logger.info(f"Training rate: {train_rate:.2f}/min")
async def _store_inference_data_async(
self,
model_name: str,
model_input: Any,
prediction: Prediction,
timestamp: datetime,
symbol: str = None,
):
"""Store last inference in memory and all inferences to database for future training"""
try:
logger.debug(
f"Storing inference for {model_name}: {prediction.action} (confidence: {prediction.confidence:.3f})"
)
# Validate model_input before storing
if model_input is None:
logger.warning(
f"Skipping inference storage for {model_name}: model_input is None"
)
return
if isinstance(model_input, dict) and not model_input:
logger.warning(
f"Skipping inference storage for {model_name}: model_input is empty dict"
)
return
# Extract symbol from prediction if not provided
if symbol is None:
symbol = getattr(
prediction, "symbol", "ETH/USDT"
) # Default to ETH/USDT if not available
# Get current price at inference time
current_price = self._get_current_price(symbol)
# Create inference record - store only what's needed for training
inference_record = {
"timestamp": timestamp.isoformat(),
"symbol": symbol,
"model_name": model_name,
"model_input": model_input,
"prediction": {
"action": prediction.action,
"confidence": prediction.confidence,
"probabilities": prediction.probabilities,
"timeframe": prediction.timeframe,
},
"metadata": prediction.metadata or {},
"training_outcome": None, # Will be set when training occurs
"outcome_evaluated": False,
"inference_price": current_price, # Store price at inference time
}
# Store previous inference for training before overwriting
previous_inference = self.last_inference.get(model_name)
# Store only the last inference per model (for immediate training)
self.last_inference[model_name] = inference_record
# If we have a previous inference, trigger training on it immediately
if previous_inference and not previous_inference.get("outcome_evaluated", False):
logger.debug(f"Triggering immediate training on previous inference for {model_name}")
asyncio.create_task(self._trigger_immediate_training_for_previous_inference(model_name, previous_inference, current_price))
# Also save to database using database manager for future training and analysis
asyncio.create_task(
self._save_to_database_manager_async(model_name, inference_record)
)
logger.debug(
f"Stored last inference for {model_name} and queued database save"
)
except Exception as e:
logger.error(f"Error storing inference data for {model_name}: {e}")
async def _save_to_database_manager_async(
self, model_name: str, inference_record: Dict
):
"""Save inference record using DatabaseManager for future training"""
import hashlib
import asyncio
def save_to_db():
try:
# Extract data from inference record
prediction = inference_record.get("prediction", {})
symbol = inference_record.get("symbol", "ETH/USDT")
timestamp_str = inference_record.get("timestamp", "")
# Parse timestamp
if isinstance(timestamp_str, str):
timestamp = datetime.fromisoformat(timestamp_str)
else:
timestamp = timestamp_str
# Create hash of input features for deduplication
model_input = inference_record.get("model_input")
input_features_hash = "unknown"
input_features_array = None
if model_input is not None:
# Convert to numpy array if possible
try:
if hasattr(model_input, "numpy"): # PyTorch tensor
input_features_array = model_input.detach().cpu().numpy()
elif isinstance(model_input, np.ndarray):
input_features_array = model_input
elif isinstance(model_input, (list, tuple)):
input_features_array = np.array(model_input)
# Create hash of the input features
if input_features_array is not None:
input_features_hash = hashlib.md5(
input_features_array.tobytes()
).hexdigest()[:16]
except Exception as e:
logger.debug(
f"Could not process input features for hashing: {e}"
)
# Create InferenceRecord using the database manager's structure
from utils.database_manager import InferenceRecord
db_record = InferenceRecord(
model_name=model_name,
timestamp=timestamp,
symbol=symbol,
action=prediction.get("action", "HOLD"),
confidence=prediction.get("confidence", 0.0),
probabilities=prediction.get("probabilities", {}),
input_features_hash=input_features_hash,
processing_time_ms=0.0, # We don't track this in orchestrator
memory_usage_mb=0.0, # We don't track this in orchestrator
input_features=input_features_array,
checkpoint_id=None,
metadata=inference_record.get("metadata", {}),
)
# Log using database manager
success = self.db_manager.log_inference(db_record)
if success:
logger.debug(f"Saved inference to database for {model_name}")
else:
logger.warning(
f"Failed to save inference to database for {model_name}"
)
except Exception as e:
logger.error(f"Error saving to database manager: {e}")
# Run database operation in thread pool to avoid blocking
await asyncio.get_event_loop().run_in_executor(None, save_to_db)
def get_last_inference_status(self) -> Dict[str, Any]:
"""Get status of last inferences for all models"""
status = {}
for model_name, inference in self.last_inference.items():
if inference:
status[model_name] = {
"timestamp": inference.get("timestamp"),
"symbol": inference.get("symbol"),
"action": inference.get("prediction", {}).get("action"),
"confidence": inference.get("prediction", {}).get("confidence"),
"outcome_evaluated": inference.get("outcome_evaluated", False),
"training_outcome": inference.get("training_outcome"),
}
else:
status[model_name] = None
return status
def get_training_data_from_db(
self,
model_name: str,
symbol: str = None,
hours_back: int = 24,
limit: int = 1000,
) -> List[Dict]:
"""Get inference records for training from database manager"""
try:
# Use database manager's method specifically for training data
db_records = self.db_manager.get_inference_records_for_training(
model_name=model_name, symbol=symbol, hours_back=hours_back, limit=limit
)
# Convert to our format
records = []
for db_record in db_records:
try:
record = {
"model_name": db_record.model_name,
"symbol": db_record.symbol,
"timestamp": db_record.timestamp.isoformat(),
"prediction": {
"action": db_record.action,
"confidence": db_record.confidence,
"probabilities": db_record.probabilities,
"timeframe": "1m",
},
"metadata": db_record.metadata or {},
"model_input": db_record.input_features, # Full input features for training
"input_features_hash": db_record.input_features_hash,
}
records.append(record)
except Exception as e:
logger.warning(f"Skipping malformed training record: {e}")
continue
logger.info(f"Retrieved {len(records)} training records for {model_name}")
return records
except Exception as e:
logger.error(f"Error getting training data from database: {e}")
return []
def _prepare_cnn_input_data(
self, ohlcv_data: Dict, cob_data: Any, technical_indicators: Dict
) -> torch.Tensor:
"""Prepare standardized input data for CNN models with proper GPU device placement"""
try:
# Create feature matrix from OHLCV data
features = []
# Add OHLCV features for each timeframe
for tf in ["1s", "1m", "1h", "1d"]:
if tf in ohlcv_data and not ohlcv_data[tf].empty:
df = ohlcv_data[tf].tail(50) # Last 50 bars
features.extend(
[
df["close"].pct_change().fillna(0).values,
(
df["volume"].values / df["volume"].max()
if df["volume"].max() > 0
else np.zeros(len(df))
),
]
)
# Add technical indicators
for key, value in technical_indicators.items():
if not np.isnan(value):
features.append([value])
# Flatten and pad/truncate to standard size
if features:
feature_array = np.concatenate(
[np.array(f).flatten() for f in features]
)
# Pad or truncate to 300 features
if len(feature_array) < 300:
feature_array = np.pad(
feature_array, (0, 300 - len(feature_array)), "constant"
)
else:
feature_array = feature_array[:300]
# Convert to tensor and move to GPU
return torch.tensor(
feature_array.reshape(1, -1),
dtype=torch.float32,
device=self.device,
)
else:
# Return zero tensor on GPU
return torch.zeros((1, 300), dtype=torch.float32, device=self.device)
except Exception as e:
logger.error(f"Error preparing CNN input data: {e}")
return torch.zeros((1, 300), dtype=torch.float32, device=self.device)
def _prepare_rl_input_data(
self, ohlcv_data: Dict, cob_data: Any, technical_indicators: Dict
) -> torch.Tensor:
"""Prepare standardized input data for RL models with proper GPU device placement"""
try:
# Create state representation
state_features = []
# Add price and volume features
if "1m" in ohlcv_data and not ohlcv_data["1m"].empty:
df = ohlcv_data["1m"].tail(20)
state_features.extend(
[
df["close"].pct_change().fillna(0).values,
df["volume"].pct_change().fillna(0).values,
(df["high"] - df["low"]) / df["close"], # Volatility proxy
]
)
# Add technical indicators
for key, value in technical_indicators.items():
if not np.isnan(value):
state_features.append(value)
# Flatten and standardize size
if state_features:
state_array = np.concatenate(
[np.array(f).flatten() for f in state_features]
)
# Pad or truncate to expected RL state size
expected_size = 100 # Adjust based on your RL model
if len(state_array) < expected_size:
state_array = np.pad(
state_array, (0, expected_size - len(state_array)), "constant"
)
else:
state_array = state_array[:expected_size]
# Convert to tensor and move to GPU
return torch.tensor(
state_array, dtype=torch.float32, device=self.device
)
else:
# Return zero tensor on GPU
return torch.zeros(100, dtype=torch.float32, device=self.device)
except Exception as e:
logger.error(f"Error preparing RL input data: {e}")
return torch.zeros(100, dtype=torch.float32, device=self.device)
def _store_inference_data(
self,
symbol: str,
model_name: str,
model_input: Any,
prediction: Prediction,
timestamp: datetime,
):
"""Store comprehensive inference data for future training with persistent storage"""
try:
# Get current market context for complete replay capability
current_price = self.data_provider.get_current_price(symbol)
# Create comprehensive inference record with ALL data needed for model replay
inference_record = {
"timestamp": timestamp,
"symbol": symbol,
"model_name": model_name,
"current_price": current_price,
# Complete model input data
"model_input": {
"raw_input": model_input,
"input_shape": (
model_input.shape if hasattr(model_input, "shape") else None
),
"input_type": str(type(model_input)),
},
# Complete prediction data
"prediction": {
"action": prediction.action,
"confidence": prediction.confidence,
"probabilities": prediction.probabilities,
"timeframe": prediction.timeframe,
},
# Market context at prediction time
"market_context": {
"price": current_price,
"timestamp": timestamp.isoformat(),
"symbol": symbol,
},
# Model metadata
"metadata": {
"model_metadata": prediction.metadata or {},
"orchestrator_state": {
"confidence_threshold": self.confidence_threshold,
"training_enabled": self.training_enabled,
},
},
# Training outcome (will be filled later)
"training_outcome": None,
"outcome_evaluated": False,
}
# Store only the last inference per model (for immediate training)
self.last_inference[model_name] = inference_record
# Also save to database using database manager for future training (run in background)
asyncio.create_task(
self._save_to_database_manager_async(model_name, inference_record)
)
logger.debug(
f"Stored last inference for {model_name} on {symbol} and queued database save"
)
except Exception as e:
logger.error(f"Error storing inference data: {e}")
def get_model_training_data(
self, model_name: str, symbol: str = None
) -> List[Dict]:
"""Get training data for a specific model"""
try:
training_data = []
# Use database manager to get training data
training_data = self.get_training_data_from_db(model_name, symbol)
logger.info(
f"Retrieved {len(training_data)} training records for {model_name}"
)
return training_data
except Exception as e:
logger.error(f"Error getting model training data: {e}")
return []
async def _trigger_immediate_training_for_model(self, model_name: str, symbol: str):
"""Trigger immediate training for a specific model with previous inference data"""
try:
if model_name not in self.last_inference:
logger.debug(f"No previous inference data for {model_name}")
return
inference_record = self.last_inference[model_name]
# Skip if already evaluated
if inference_record.get("outcome_evaluated", False):
logger.debug(f"Skipping {model_name} - already evaluated")
return
# Check if enough time has passed since inference (minimum 30 seconds for meaningful price movement)
timestamp = inference_record.get("timestamp")
if isinstance(timestamp, str):
timestamp = datetime.fromisoformat(timestamp)
time_since_inference = (datetime.now() - timestamp).total_seconds()
min_training_delay = 30 # Minimum 30 seconds before training
if time_since_inference < min_training_delay:
logger.debug(f"Skipping {model_name} - only {time_since_inference:.1f}s since inference (minimum {min_training_delay}s required)")
return
# Get current price for outcome evaluation
current_price = self._get_current_price(symbol)
if current_price is None:
logger.warning(
f"Cannot get current price for {symbol}, skipping immediate training for {model_name}"
)
return
logger.info(
f"Triggering immediate training for {model_name} with current price: {current_price}"
)
# Evaluate the previous prediction and train the model immediately
await self._evaluate_and_train_on_record(inference_record, current_price)
# Log predicted vs actual outcome
prediction = inference_record.get("prediction", {})
predicted_action = prediction.get("action", "UNKNOWN")
predicted_confidence = prediction.get("confidence", 0.0)
# Calculate actual outcome
symbol = inference_record.get("symbol", "ETH/USDT")
predicted_price = None
actual_price_change_pct = 0.0
# Try to get price direction vectors from metadata (new format)
if "price_direction" in prediction and prediction["price_direction"]:
try:
price_direction_data = prediction["price_direction"]
# Process price direction data
if (
isinstance(price_direction_data, dict)
and "direction" in price_direction_data
):
direction = price_direction_data["direction"]
confidence = price_direction_data.get("confidence", 1.0)
# Convert direction to price change percentage
# Scale by confidence and direction strength
predicted_price_change_pct = (
direction * confidence * 0.02
) # 2% max change
predicted_price = current_price * (
1 + predicted_price_change_pct
)
except Exception as e:
logger.debug(f"Error processing price direction data: {e}")
# Fallback to old price prediction format
elif "price_prediction" in prediction and prediction["price_prediction"]:
try:
price_prediction_data = prediction["price_prediction"]
if (
isinstance(price_prediction_data, list)
and len(price_prediction_data) > 0
):
predicted_price_change_pct = (
float(price_prediction_data[0]) * 0.01
)
predicted_price = current_price * (
1 + predicted_price_change_pct
)
except Exception:
pass
# Get inference price and timestamp from record
inference_price = inference_record.get("inference_price")
timestamp = inference_record.get("timestamp")
if isinstance(timestamp, str):
timestamp = datetime.fromisoformat(timestamp)
time_diff_seconds = (datetime.now() - timestamp).total_seconds()
actual_price_change_pct = 0.0
# Use stored inference price for comparison
if inference_price is not None:
actual_price_change_pct = (
(current_price - inference_price) / inference_price * 100
)
# Use seconds-based comparison for short-lived predictions
if time_diff_seconds <= 60: # Within 1 minute
price_outcome = f"Inference: ${inference_price:.2f} ({time_diff_seconds:.1f}s ago) -> Current: ${current_price:.2f} ({actual_price_change_pct:+.2f}%)"
else:
# For older predictions, use a more conservative approach
price_outcome = f"Inference: ${inference_price:.2f} ({time_diff_seconds/60:.1f}m ago) -> Current: ${current_price:.2f} ({actual_price_change_pct:+.2f}%)"
else:
# Fall back to historical price comparison if no inference price
try:
historical_data = self.data_provider.get_historical_data(
symbol, "1m", limit=10
)
if historical_data is not None and not historical_data.empty:
historical_price = historical_data["close"].iloc[-1]
actual_price_change_pct = (
(current_price - historical_price) / historical_price * 100
)
price_outcome = f"Historical: ${historical_price:.2f} -> Current: ${current_price:.2f} ({actual_price_change_pct:+.2f}%)"
else:
price_outcome = (
f"Current: ${current_price:.2f} (no historical data)"
)
except Exception as e:
logger.warning(f"Error calculating price change: {e}")
price_outcome = f"Current: ${current_price:.2f} (calculation error)"
# Determine if prediction was correct based on predicted direction and actual price movement
was_correct = False
# Get predicted direction from the inference record
predicted_direction = None
if "price_direction" in prediction and prediction["price_direction"]:
try:
price_direction_data = prediction["price_direction"]
if (
isinstance(price_direction_data, dict)
and "direction" in price_direction_data
):
predicted_direction = price_direction_data["direction"]
except Exception as e:
logger.debug(f"Error extracting predicted direction: {e}")
# Evaluate based on predicted direction if available
if predicted_direction is not None:
# Use the predicted direction (-1 to 1) to determine correctness
if (
predicted_direction > 0.1 and actual_price_change_pct > 0.1
): # Predicted UP, price went UP
was_correct = True
elif (
predicted_direction < -0.1 and actual_price_change_pct < -0.1
): # Predicted DOWN, price went DOWN
was_correct = True
elif (
abs(predicted_direction) <= 0.1
and abs(actual_price_change_pct) < 0.5
): # Predicted SIDEWAYS, price stayed stable
was_correct = True
else:
# Fallback to action-based evaluation
if (
predicted_action == "BUY" and actual_price_change_pct > 0.1
): # Price went up
was_correct = True
elif (
predicted_action == "SELL" and actual_price_change_pct < -0.1
): # Price went down
was_correct = True
elif (
predicted_action == "HOLD" and abs(actual_price_change_pct) < 0.5
): # Price stayed stable
was_correct = True
outcome_status = "✅ CORRECT" if was_correct else "❌ INCORRECT"
# Get model statistics for enhanced logging
model_stats = self.get_model_statistics(model_name)
current_loss = model_stats.current_loss if model_stats else None
best_loss = model_stats.best_loss if model_stats else None
avg_loss = model_stats.average_loss if model_stats else None
# 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, _, should_skip = self._calculate_sophisticated_reward(
predicted_action,
predicted_confidence,
actual_price_change_pct,
time_diff_seconds / 60, # Convert to minutes
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
logger.info(
f"Completed immediate training for {model_name} - {outcome_status}"
)
logger.info(
f" Prediction: {predicted_action} (confidence: {predicted_confidence:.3f})"
)
logger.info(f" {price_outcome}")
logger.info(f" Reward: {reward:.4f} | Time: {time_diff_seconds:.1f}s")
# Safe formatting for loss values
current_loss_str = (
f"{current_loss:.4f}" if current_loss is not None else "N/A"
)
best_loss_str = f"{best_loss:.4f}" if best_loss is not None else "N/A"
avg_loss_str = f"{avg_loss:.4f}" if avg_loss is not None else "N/A"
logger.info(
f" Loss: {current_loss_str} | Best: {best_loss_str} | Avg: {avg_loss_str}"
)
logger.info(f" Outcome: {outcome_status}")
# Add performance summary
if model_name in self.model_performance:
perf = self.model_performance[model_name]
logger.info(
f" Performance: {perf['accuracy']:.1%} ({perf['correct']}/{perf['total']})"
)
except Exception as e:
logger.error(f"Error in immediate training for {model_name}: {e}")
async def _trigger_immediate_training_for_previous_inference(self, model_name: str, previous_inference: Dict, current_price: float):
"""Trigger immediate training for a previous inference with current price"""
try:
logger.info(f"Training {model_name} on previous inference with current price: {current_price}")
# Evaluate the previous prediction and train the model immediately
await self._evaluate_and_train_on_record(previous_inference, current_price)
# Mark as evaluated
previous_inference["outcome_evaluated"] = True
except Exception as e:
logger.error(f"Error in immediate training for previous inference {model_name}: {e}")
async def trigger_cnn_long_term_training(self):
"""Trigger long-term training on CNN stored inference records"""
try:
if hasattr(self, "cnn_model") and self.cnn_model and hasattr(self, "cnn_optimizer"):
if hasattr(self.cnn_model, "train_on_stored_records"):
# Get current price for all symbols
symbols = ["ETH/USDT"] # Add more symbols as needed
for symbol in symbols:
current_price = self._get_current_price(symbol)
if current_price and hasattr(self.cnn_model, "inference_records"):
# Update all stored records with current price information
for record in self.cnn_model.inference_records:
if "metadata" in record:
record_metadata = record["metadata"]
record_price = record_metadata.get("current_price")
if record_price and current_price:
price_change_pct = ((current_price - record_price) / record_price) * 100
time_diff = (datetime.now() - datetime.fromisoformat(record_metadata.get("timestamp", ""))).total_seconds() / 60
# Update with actual price changes and time differences
record_metadata["actual_price_changes"] = {
"short_term": price_change_pct,
"mid_term": price_change_pct * 0.8,
"long_term": price_change_pct * 0.6
}
record_metadata["time_diffs"] = {
"short_term": min(time_diff, 1.0),
"mid_term": min(time_diff, 5.0),
"long_term": min(time_diff, 15.0)
}
# Train on all stored records
long_term_loss = self.cnn_model.train_on_stored_records(self.cnn_optimizer, min_records=3)
if long_term_loss > 0:
logger.info(f"CNN long-term training completed: loss={long_term_loss:.4f}, records={len(self.cnn_model.inference_records)}")
else:
logger.debug(f"CNN long-term training skipped: insufficient records ({len(self.cnn_model.inference_records)})")
except Exception as e:
logger.error(f"Error in CNN long-term training: {e}")
async def _evaluate_and_train_on_record(self, record: Dict, current_price: float):
"""Evaluate prediction outcome and train model"""
try:
model_name = record["model_name"]
prediction = record["prediction"]
timestamp = record["timestamp"]
# Convert timestamp string back to datetime if needed
if isinstance(timestamp, str):
timestamp = datetime.fromisoformat(timestamp)
# Get inference price and calculate time difference
inference_price = record.get("inference_price")
time_diff_seconds = (datetime.now() - timestamp).total_seconds()
time_diff_minutes = time_diff_seconds / 60 # minutes
# Use stored inference price for comparison
symbol = record["symbol"]
price_change_pct = 0.0
if inference_price is not None:
price_change_pct = (
(current_price - inference_price) / inference_price * 100
)
logger.debug(
f"Using stored inference price: ${inference_price:.2f} ({time_diff_seconds:.1f}s ago) -> ${current_price:.2f} ({price_change_pct:+.2f}%)"
)
else:
# Fall back to historical data if no inference price stored
try:
historical_data = self.data_provider.get_historical_data(
symbol, "1m", limit=10
)
if historical_data is not None and not historical_data.empty:
historical_price = historical_data["close"].iloc[-1]
price_change_pct = (
(current_price - historical_price) / historical_price * 100
)
logger.debug(
f"Using historical price comparison: ${historical_price:.2f} -> ${current_price:.2f} ({price_change_pct:+.2f}%)"
)
else:
logger.warning(f"No historical data available for {symbol}")
return
except Exception as e:
logger.warning(f"Error calculating price change: {e}")
return
# Enhanced reward system based on prediction confidence and price movement magnitude
predicted_action = prediction["action"]
prediction_confidence = prediction.get(
"confidence", 0.5
) # Default to 0.5 if missing
# 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, should_skip = self._calculate_sophisticated_reward(
predicted_action,
prediction_confidence,
price_change_pct,
time_diff_minutes,
inference_price is not None, # Add price prediction flag
symbol, # Pass symbol for position lookup
None, # Let method determine position status
current_position_pnl=current_pnl,
predicted_price_vector=predicted_price_vector,
)
# Skip training and accuracy tracking if this is a neutral action (no position + HOLD)
if should_skip:
logger.debug(f"Skipping training and accuracy tracking for neutral action: {predicted_action} (no position)")
return
# Update model performance tracking
if model_name not in self.model_performance:
self.model_performance[model_name] = {
"correct": 0,
"total": 0,
"accuracy": 0.0,
"price_predictions": {"total": 0, "accurate": 0, "avg_error": 0.0},
}
# Ensure price_predictions key exists
if "price_predictions" not in self.model_performance[model_name]:
self.model_performance[model_name]["price_predictions"] = {
"total": 0,
"accurate": 0,
"avg_error": 0.0,
}
self.model_performance[model_name]["total"] += 1
if was_correct:
self.model_performance[model_name]["correct"] += 1
self.model_performance[model_name]["accuracy"] = (
self.model_performance[model_name]["correct"]
/ self.model_performance[model_name]["total"]
)
# Track price prediction accuracy if available
if inference_price is not None:
price_prediction_stats = self.model_performance[model_name][
"price_predictions"
]
price_prediction_stats["total"] += 1
# Calculate prediction error
prediction_error_pct = abs(price_change_pct)
price_prediction_stats["avg_error"] = (
price_prediction_stats["avg_error"]
* (price_prediction_stats["total"] - 1)
+ prediction_error_pct
) / price_prediction_stats["total"]
# Consider prediction accurate if error < 1%
if prediction_error_pct < 1.0:
price_prediction_stats["accurate"] += 1
logger.debug(
f"Price prediction accuracy for {model_name}: "
f"{price_prediction_stats['accurate']}/{price_prediction_stats['total']} "
f"({price_prediction_stats['avg_error']:.2f}% avg error)"
)
# Enhanced logging for training evaluation
self.training_logger.info(f"TRAINING EVALUATION for {model_name.upper()}:")
self.training_logger.info(
f" Action: {predicted_action} | Confidence: {prediction_confidence:.3f}"
)
self.training_logger.info(
f" Price change: {price_change_pct:+.3f}% | Time: {time_diff_seconds:.1f}s"
)
self.training_logger.info(f" Reward: {reward:.4f} | Correct: {was_correct}")
self.training_logger.info(
f" Accuracy: {self.model_performance[model_name]['accuracy']:.1%} ({self.model_performance[model_name]['correct']}/{self.model_performance[model_name]['total']})"
)
# Add detailed performance metrics logging
if hasattr(self, 'model_statistics') and model_name in self.model_statistics:
stats = self.model_statistics[model_name]
self.training_logger.info(f" Model Statistics:")
self.training_logger.info(f" Total inferences: {stats.total_inferences}")
self.training_logger.info(f" Total trainings: {stats.total_trainings}")
self.training_logger.info(f" Current loss: {stats.current_loss:.4f}" if stats.current_loss else " Current loss: N/A")
self.training_logger.info(f" Best loss: {stats.best_loss:.4f}" if stats.best_loss else " Best loss: N/A")
self.training_logger.info(f" Average loss: {stats.average_loss:.4f}" if stats.average_loss else " Average loss: N/A")
self.training_logger.info(f" Inference rate: {stats.inference_rate_per_minute:.1f}/min")
self.training_logger.info(f" Training rate: {stats.training_rate_per_minute:.1f}/min")
# Train the specific model based on sophisticated outcome
await self._train_model_on_outcome(
record, was_correct, price_change_pct, reward
)
# Mark this inference as evaluated to prevent re-training
if (
model_name in self.last_inference
and self.last_inference[model_name] == record
):
self.last_inference[model_name]["outcome_evaluated"] = True
self.last_inference[model_name]["training_outcome"] = {
"was_correct": was_correct,
"reward": reward,
"price_change_pct": price_change_pct,
"evaluated_at": datetime.now().isoformat(),
}
price_pred_info = (
f"inference: ${inference_price:.2f}"
if inference_price is not None
else "no inference price"
)
logger.debug(
f"Evaluated {model_name} prediction: {'✓' if was_correct else '✗'} "
f"({prediction['action']}, {price_change_pct:.2f}% change, "
f"confidence: {prediction_confidence:.3f}, {price_pred_info}, reward: {reward:.3f})"
)
except Exception as e:
logger.error(f"Error evaluating and training on record: {e}")
def _calculate_sophisticated_reward(
self,
predicted_action: str,
prediction_confidence: float,
price_change_pct: float,
time_diff_minutes: float,
has_price_prediction: bool = False,
symbol: str = None,
has_position: bool = None,
current_position_pnl: float = 0.0,
predicted_price_vector: dict = None,
) -> tuple[float, bool, 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')
prediction_confidence: Model's confidence in the prediction (0.0 to 1.0)
price_change_pct: Actual price change percentage
time_diff_minutes: Time elapsed since prediction
has_price_prediction: Whether the model made a price prediction
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, should_skip)
- reward: The calculated reward value
- was_correct: Whether the prediction was correct (True/False)
- should_skip: Whether this should be skipped from accuracy calculations and training (True/False)
"""
try:
# NOISE REDUCTION: Treat low-confidence signals as HOLD
confidence_threshold = 0.6 # Only consider BUY/SELL if confidence > 60%
if prediction_confidence < confidence_threshold:
predicted_action = "HOLD"
logger.debug(f"Low confidence ({prediction_confidence:.2f}) - treating as HOLD for noise reduction")
# 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
pnl_threshold = 0.02 # 0.02% - minimum movement to include in PnL/accuracy calculations
movement_threshold = 0.20 # Minimum movement to be profitable after fees (increased from 0.15%)
strong_movement_threshold = 0.8 # Strong movements - good profit potential (increased from 0.5%)
rapid_movement_threshold = 1.5 # Rapid movements - excellent profit potential (increased from 1.0%)
massive_movement_threshold = 3.0 # Massive movements - extraordinary profit potential (increased from 2.0%)
# Determine current position status if not provided
if has_position is None and symbol:
has_position = self._has_open_position(symbol)
# Get current position P&L if we have a position
if has_position and current_position_pnl == 0.0:
current_position_pnl = self._get_current_position_pnl(symbol)
elif has_position is None:
has_position = False
# Determine if prediction was directionally correct
was_correct = False
should_skip = False # Whether to skip from accuracy calculations and training
directional_accuracy = 0.0
# Check if price movement is significant enough for PnL/accuracy calculation
abs_price_change = abs(price_change_pct)
include_in_accuracy = abs_price_change >= pnl_threshold
# Always check directional correctness for learning, but only include significant moves in accuracy
direction_correct = False
if predicted_action == "BUY":
# Check directional correctness (always for learning)
direction_correct = price_change_pct > 0
# Only consider "correct" for accuracy if movement is significant AND profitable
if include_in_accuracy:
was_correct = price_change_pct > movement_threshold
else:
# Small movement - learn direction but don't include in accuracy
was_correct = None # Exclude from accuracy calculation
# ENHANCED FEE-AWARE REWARD STRUCTURE (only for significant movements)
if include_in_accuracy:
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:
# Small but significant movements - minimal rewards (fees eat most profit)
directional_accuracy = max(0, (price_change_pct - fee_cost)) * 0.5 # Penalty for fee cost
else:
# Very small movement - learn direction but minimal reward
directional_accuracy = price_change_pct * 0.1 if direction_correct else -abs(price_change_pct) * 0.1
elif predicted_action == "SELL":
# SELL signals need to overcome fee costs for profitability
was_correct = price_change_pct < -movement_threshold
# 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:
# 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 position side awareness - considers LONG vs SHORT positions
if has_position:
# Get position side to properly evaluate HOLD decisions
position_side = self._get_position_side(symbol) if symbol else "LONG"
if current_position_pnl > 0: # Currently profitable position
if position_side == "LONG":
# For LONG positions: HOLD is good if price goes up or stays stable
if price_change_pct > 0: # Price went up - excellent hold
was_correct = True
directional_accuracy = price_change_pct * 0.8
elif abs(price_change_pct) < movement_threshold: # Price stable - good hold
was_correct = True
directional_accuracy = movement_threshold * 0.5
else: # Price dropped - still okay but less reward
was_correct = True
directional_accuracy = max(0, (current_position_pnl / 100.0) - abs(price_change_pct) * 0.3)
elif position_side == "SHORT":
# For SHORT positions: HOLD is good if price goes down or stays stable
if price_change_pct < 0: # Price went down - excellent hold
was_correct = True
directional_accuracy = abs(price_change_pct) * 0.8
elif abs(price_change_pct) < movement_threshold: # Price stable - good hold
was_correct = True
directional_accuracy = movement_threshold * 0.5
else: # Price went up - still okay but less reward
was_correct = True
directional_accuracy = max(0, (current_position_pnl / 100.0) - abs(price_change_pct) * 0.3)
else:
# Unknown position side - fallback to general logic
if abs(price_change_pct) < movement_threshold:
was_correct = True
directional_accuracy = movement_threshold * 0.4
else:
was_correct = False
directional_accuracy = max(0, movement_threshold - abs(price_change_pct)) * 0.5
elif current_position_pnl < 0: # Currently losing position
if position_side == "LONG":
# For LONG positions: HOLD is good if price recovers (goes up)
if price_change_pct > movement_threshold: # Price recovered - good hold
was_correct = True
directional_accuracy = price_change_pct * 0.6
else: # Price continued down or stayed flat - bad hold
was_correct = False
directional_accuracy = abs(current_position_pnl / 100.0) * 0.3
elif position_side == "SHORT":
# For SHORT positions: HOLD is good if price recovers (goes down)
if price_change_pct < -movement_threshold: # Price recovered - good hold
was_correct = True
directional_accuracy = abs(price_change_pct) * 0.6
else: # Price continued up or stayed flat - bad hold
was_correct = False
directional_accuracy = abs(current_position_pnl / 100.0) * 0.3
else:
# Unknown position side - fallback to general logic
if abs(price_change_pct) > movement_threshold:
was_correct = True
directional_accuracy = abs(price_change_pct) * 0.6
else:
was_correct = False
directional_accuracy = abs(current_position_pnl / 100.0) * 0.3
else: # Breakeven position
# Standard HOLD evaluation for breakeven positions
if abs(price_change_pct) < movement_threshold: # Price stable - good
was_correct = True
directional_accuracy = movement_threshold * 0.4
else: # Price moved significantly - missed opportunity
was_correct = False
directional_accuracy = max(0, movement_threshold - abs(price_change_pct)) * 0.5
else:
# If we don't have a position, HOLD should be skipped from accuracy calculations and training
# No position + HOLD = NEUTRAL (no action taken, no profit/loss)
was_correct = None # Not applicable
should_skip = True # Skip from accuracy calculations and training
directional_accuracy = 0.0 # No reward/penalty for neutral action
# Force reward to 0.0 for NEUTRAL actions
final_reward = 0.0
return final_reward, was_correct, should_skip
# 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:
# Reward confident correct predictions more, penalize unconfident correct predictions less
confidence_multiplier = 0.5 + (
prediction_confidence * 1.5
) # Range: 0.5 to 2.0
base_reward = (
directional_accuracy * magnitude_multiplier * confidence_multiplier
)
# 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_movement * confidence_penalty
# 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
# Time decay factor (predictions should be evaluated quickly)
time_decay = max(
0.1, 1.0 - (time_diff_minutes / 60.0)
) # Decay over 1 hour, min 10%
# Final reward calculation
final_reward = base_reward * time_decay
# Bonus for accurate price predictions
if (
has_price_prediction and abs(price_change_pct) < 1.0
): # Accurate price prediction
final_reward *= 1.2 # 20% bonus for accurate price predictions
logger.debug(
f"Applied price prediction accuracy bonus: {final_reward:.3f}"
)
# Clamp reward to reasonable range
final_reward = max(-5.0, min(5.0, final_reward))
return final_reward, was_correct, should_skip
except Exception as e:
logger.error(f"Error calculating sophisticated reward: {e}")
# Fallback to simple reward with position awareness
has_position = self._has_open_position(symbol) if symbol else False
if predicted_action == "HOLD" and has_position:
# If holding a position, HOLD is correct if price didn't drop significantly
simple_correct = price_change_pct > -0.2 # Allow small losses while holding
should_skip = False
elif predicted_action == "HOLD" and not has_position:
# No position + HOLD = NEUTRAL, should be skipped
simple_correct = None
should_skip = True
# Force reward to 0.0 for NEUTRAL actions
return 0.0, simple_correct, should_skip
else:
# Standard evaluation for other cases
simple_correct = (
(predicted_action == "BUY" and price_change_pct > 0.1)
or (predicted_action == "SELL" and price_change_pct < -0.1)
)
should_skip = False
simple_reward = 1.0 if simple_correct else -0.5 if simple_correct is not None else 0.0
return simple_reward, simple_correct, should_skip
def _calculate_price_vector_loss(
self,
predicted_vector: dict,
actual_price_change_pct: float,
time_diff_minutes: float
) -> float:
"""
Calculate training loss for price vector predictions to improve accuracy
Args:
predicted_vector: Dict with 'direction' (-1 to 1) and 'confidence' (0 to 1)
actual_price_change_pct: Actual price change percentage
time_diff_minutes: Time elapsed since prediction
Returns:
Loss value for training the price vector prediction head
"""
try:
if not predicted_vector or not isinstance(predicted_vector, dict):
return 0.0
predicted_direction = predicted_vector.get('direction', 0.0)
predicted_confidence = predicted_vector.get('confidence', 0.0)
# Skip very weak predictions
if abs(predicted_direction) < 0.05 or predicted_confidence < 0.1:
return 0.0
# Calculate actual direction and magnitude
actual_direction = 1.0 if actual_price_change_pct > 0.05 else -1.0 if actual_price_change_pct < -0.05 else 0.0
actual_magnitude = min(abs(actual_price_change_pct) / 2.0, 1.0) # Normalize to 0-1, cap at 2%
# DIRECTION LOSS: penalize wrong direction predictions
if actual_direction != 0.0:
# Expected direction should match actual
direction_error = abs(predicted_direction - actual_direction)
else:
# If no significant movement, direction should be close to 0
direction_error = abs(predicted_direction) * 0.5 # Reduced penalty for neutral
# MAGNITUDE LOSS: penalize inaccurate magnitude predictions
# Convert predicted direction+confidence to expected magnitude
predicted_magnitude = abs(predicted_direction) * predicted_confidence
magnitude_error = abs(predicted_magnitude - actual_magnitude)
# TIME DECAY: predictions should be accurate quickly
time_decay = max(0.1, 1.0 - (time_diff_minutes / 30.0)) # 30min decay window
# COMBINED LOSS
direction_loss = direction_error * 2.0 # Direction is very important
magnitude_loss = magnitude_error * 1.0 # Magnitude is important
total_loss = (direction_loss + magnitude_loss) * time_decay
logger.debug(f"PRICE VECTOR LOSS: pred_dir={predicted_direction:.3f}, actual_dir={actual_direction:.3f}, "
f"pred_mag={predicted_magnitude:.3f}, actual_mag={actual_magnitude:.3f}, "
f"dir_loss={direction_loss:.3f}, mag_loss={magnitude_loss:.3f}, total={total_loss:.3f}")
return min(total_loss, 5.0) # Cap loss to prevent exploding gradients
except Exception as e:
logger.error(f"Error calculating price vector loss: {e}")
return 0.0
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
def _should_execute_action(
self,
action: str,
confidence: float,
predicted_vector: dict = None,
current_price: float = None,
symbol: str = None
) -> tuple[bool, str]:
"""
Intelligent action filtering based on predicted price movement and confidence
Args:
action: Predicted action (BUY/SELL/HOLD)
confidence: Model confidence (0 to 1)
predicted_vector: Dict with 'direction' and 'confidence'
current_price: Current market price
symbol: Trading symbol
Returns:
(should_execute, reason)
"""
try:
# Basic confidence threshold
min_action_confidence = 0.6 # Require 60% confidence for any action
if confidence < min_action_confidence:
return False, f"Low action confidence ({confidence:.1%} < {min_action_confidence:.1%})"
# HOLD actions always allowed
if action == "HOLD":
return True, "HOLD action approved"
# Check if we have price vector predictions
if not predicted_vector or not isinstance(predicted_vector, dict):
# No vector available - use basic confidence only
high_confidence_threshold = 0.8
if confidence >= high_confidence_threshold:
return True, f"High confidence action without vector ({confidence:.1%})"
else:
return False, f"No price vector available, requires high confidence ({confidence:.1%} < {high_confidence_threshold:.1%})"
predicted_direction = predicted_vector.get('direction', 0.0)
vector_confidence = predicted_vector.get('confidence', 0.0)
# VECTOR-BASED FILTERING
min_vector_confidence = 0.5 # Require 50% vector confidence
min_direction_strength = 0.3 # Require 30% direction strength
if vector_confidence < min_vector_confidence:
return False, f"Low vector confidence ({vector_confidence:.1%} < {min_vector_confidence:.1%})"
if abs(predicted_direction) < min_direction_strength:
return False, f"Weak direction prediction ({abs(predicted_direction):.1%} < {min_direction_strength:.1%})"
# DIRECTION ALIGNMENT CHECK
if action == "BUY" and predicted_direction <= 0:
return False, f"BUY action misaligned with predicted direction ({predicted_direction:.3f})"
if action == "SELL" and predicted_direction >= 0:
return False, f"SELL action misaligned with predicted direction ({predicted_direction:.3f})"
# STEEPNESS/MAGNITUDE CHECK (fee-aware)
fee_cost = 0.12 # 0.12% round trip fee cost
predicted_magnitude = abs(predicted_direction) * vector_confidence * 2.0 # Scale to ~2% max
if predicted_magnitude < fee_cost * 2.0: # Require 2x fee coverage
return False, f"Predicted magnitude too small ({predicted_magnitude:.2f}% < {fee_cost * 2.0:.2f}% minimum)"
# COMBINED CONFIDENCE CHECK
combined_confidence = (confidence + vector_confidence) / 2.0
min_combined_confidence = 0.7 # Require 70% combined confidence
if combined_confidence < min_combined_confidence:
return False, f"Low combined confidence ({combined_confidence:.1%} < {min_combined_confidence:.1%})"
# ALL CHECKS PASSED
logger.info(f"ACTION APPROVED: {action} with {confidence:.1%} confidence, "
f"vector: {predicted_direction:+.3f} ({vector_confidence:.1%}), "
f"predicted magnitude: {predicted_magnitude:.2f}%")
return True, f"Action approved: strong prediction with adequate magnitude"
except Exception as e:
logger.error(f"Error in action filtering: {e}")
return False, f"Action filtering error: {e}"
async def _train_model_on_outcome(
self,
record: Dict,
was_correct: bool,
price_change_pct: float,
sophisticated_reward: float = None,
):
"""Train models on outcome - now includes decision fusion"""
try:
model_name = record.get("model_name")
if not model_name:
logger.warning("No model name in training record")
return
# Calculate reward if not provided
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, _, should_skip = self._calculate_sophisticated_reward(
record.get("action", "HOLD"),
record.get("confidence", 0.5),
price_change_pct,
record.get("time_diff_minutes", 1.0),
record.get("has_price_prediction", False),
symbol=symbol,
current_position_pnl=current_pnl,
predicted_price_vector=predicted_price_vector,
)
# Skip training if this is a neutral action (no position + HOLD)
if should_skip:
logger.debug(f"Skipping training for neutral action: {record.get('action', 'HOLD')} (no position)")
return
# Calculate price vector training loss if we have vector predictions
if predicted_price_vector:
vector_loss = self._calculate_price_vector_loss(
predicted_price_vector,
price_change_pct,
record.get("time_diff_minutes", 1.0)
)
# Store the vector loss for training
record["price_vector_loss"] = vector_loss
if vector_loss > 0:
logger.debug(f"PRICE VECTOR TRAINING: {model_name} vector loss = {vector_loss:.3f}")
# Train decision fusion model if it's the model being evaluated
if model_name == "decision_fusion":
await self._train_decision_fusion_on_outcome(
record, was_correct, price_change_pct, sophisticated_reward
)
return
# Original training logic for other models
"""Universal training for any model based on prediction outcome with sophisticated reward system"""
try:
model_name = record["model_name"]
model_input = record["model_input"]
prediction = record["prediction"]
# Use sophisticated reward if provided, otherwise fallback to simple reward
reward = (
sophisticated_reward
if sophisticated_reward is not None
else (1.0 if was_correct else -0.5)
)
# Get the actual model from registry
model_interface = None
if hasattr(self, "model_registry") and self.model_registry:
model_interface = self.model_registry.models.get(model_name)
logger.debug(
f"Found model interface {model_name} in registry: {type(model_interface).__name__}"
)
else:
logger.debug(f"No model registry available for {model_name}")
if not model_interface:
logger.warning(
f"Model {model_name} not found in registry, skipping training"
)
return
# Get the underlying model from the interface
underlying_model = getattr(model_interface, "model", None)
if not underlying_model:
logger.warning(
f"No underlying model found for {model_name}, skipping training"
)
return
logger.debug(
f"Training {model_name} with reward={reward:.3f} (was_correct={was_correct})"
)
logger.debug(f"Model interface type: {type(model_interface).__name__}")
logger.debug(f"Underlying model type: {type(underlying_model).__name__}")
# Debug: Log available training methods on both interface and underlying model
interface_methods = []
underlying_methods = []
for method in [
"train_on_outcome",
"add_experience",
"remember",
"replay",
"add_training_sample",
"train",
"train_with_reward",
"update_loss",
]:
if hasattr(model_interface, method):
interface_methods.append(method)
if hasattr(underlying_model, method):
underlying_methods.append(method)
logger.debug(f"Available methods on interface: {interface_methods}")
logger.debug(f"Available methods on underlying model: {underlying_methods}")
training_success = False
# Try training based on model type and available methods
if isinstance(model_interface, RLAgentInterface):
# RL Agent Training
training_success = await self._train_rl_model(
underlying_model, model_name, model_input, prediction, reward
)
elif isinstance(model_interface, CNNModelInterface):
# CNN Model Training
training_success = await self._train_cnn_model(
underlying_model, model_name, record, prediction, reward
)
elif "extrema" in model_name.lower():
# Extrema Trainer - doesn't need traditional training
logger.debug(
f"Extrema trainer {model_name} doesn't require outcome-based training"
)
training_success = True
elif "cob_rl" in model_name.lower():
# COB RL Model Training
training_success = await self._train_cob_rl_model(
underlying_model, model_name, model_input, prediction, reward
)
else:
# Generic model training
training_success = await self._train_generic_model(
underlying_model, model_name, model_input, prediction, reward
)
if training_success:
logger.debug(f"Successfully trained {model_name} on outcome")
else:
logger.warning(f"Failed to train {model_name} on outcome")
except Exception as e:
logger.error(f"Error in universal training for {model_name}: {e}")
# Fallback to basic training if available
try:
await self._train_model_fallback(
model_name, underlying_model, model_input, prediction, reward
)
except Exception as fallback_error:
logger.error(f"Fallback training also failed for {model_name}: {fallback_error}")
except Exception as e:
logger.error(f"Error training model {model_name} on outcome: {e}")
async def _train_rl_model(
self, model, model_name: str, model_input, prediction: Dict, reward: float
) -> bool:
"""Train RL model (DQN) with experience replay"""
try:
# Convert prediction action to action index
action_names = ["SELL", "HOLD", "BUY"]
if prediction["action"] not in action_names:
logger.warning(f"Invalid action {prediction['action']} for RL training")
return False
action_idx = action_names.index(prediction["action"])
# Properly convert model_input to numpy array state
state = self._convert_to_rl_state(model_input, model_name)
if state is None:
logger.warning(
f"Failed to convert model_input to RL state for {model_name}"
)
return False
# Validate state format
if not isinstance(state, np.ndarray):
logger.warning(
f"State is not numpy array for {model_name}: {type(state)}"
)
return False
if state.dtype == object:
logger.warning(
f"State contains object dtype for {model_name}, attempting conversion"
)
try:
state = state.astype(np.float32)
except (ValueError, TypeError) as e:
logger.error(
f"Cannot convert object state to float32 for {model_name}: {e}"
)
return False
# Ensure state is 1D and finite
if state.ndim > 1:
state = state.flatten()
# Replace any non-finite values
state = np.nan_to_num(state, nan=0.0, posinf=1.0, neginf=-1.0)
logger.debug(
f"Converted state for {model_name}: shape={state.shape}, dtype={state.dtype}"
)
# Add experience to memory
if hasattr(model, "remember"):
model.remember(
state=state,
action=action_idx,
reward=reward,
next_state=state, # Simplified - using same state
done=True,
)
logger.info(f"RL EXPERIENCE ADDED to {model_name.upper()}:")
logger.info(f" Action: {prediction['action']} (index: {action_idx})")
logger.info(f" Reward: {reward:.3f}")
logger.info(f" State shape: {state.shape}")
logger.info(f" Memory size: {memory_size}")
# Trigger training if enough experiences
memory_size = len(getattr(model, "memory", []))
batch_size = getattr(model, "batch_size", 32)
if memory_size >= batch_size:
self.training_logger.info(f"RL TRAINING STARTED for {model_name.upper()}:")
self.training_logger.info(f" Experiences: {memory_size}")
self.training_logger.info(f" Batch size: {batch_size}")
self.training_logger.info(f" Action: {prediction['action']}")
self.training_logger.info(f" Reward: {reward:.3f}")
# Ensure model is in training mode
if hasattr(model, "policy_net"):
model.policy_net.train()
training_start_time = time.time()
training_loss = model.replay()
training_duration_ms = (time.time() - training_start_time) * 1000
if training_loss is not None and training_loss > 0:
self.update_model_loss(model_name, training_loss)
self._update_model_training_statistics(
model_name, training_loss, training_duration_ms
)
self.training_logger.info(f"RL TRAINING COMPLETED for {model_name.upper()}:")
self.training_logger.info(f" Loss: {training_loss:.4f}")
self.training_logger.info(f" Training time: {training_duration_ms:.1f}ms")
self.training_logger.info(f" Experiences used: {memory_size}")
self.training_logger.info(f" Action: {prediction['action']}")
self.training_logger.info(f" Reward: {reward:.3f}")
self.training_logger.info(f" State shape: {state.shape}")
return True
elif training_loss == 0.0:
logger.warning(
f"RL training returned zero loss for {model_name} - possible gradient issue"
)
# Still update training statistics
self._update_model_training_statistics(
model_name, training_duration_ms=training_duration_ms
)
return False # Training failed
else:
# Still update training statistics even if no loss returned
self._update_model_training_statistics(
model_name, training_duration_ms=training_duration_ms
)
else:
logger.debug(
f"Not enough experiences for {model_name}: {memory_size}/{batch_size}"
)
return True # Experience added successfully, training will happen later
return False
except Exception as e:
logger.error(f"Error training RL model {model_name}: {e}")
return False
def _convert_to_rl_state(
self, model_input, model_name: str
) -> Optional[np.ndarray]:
"""Convert various model input formats to RL state numpy array"""
try:
# Method 1: BaseDataInput with get_feature_vector
if hasattr(model_input, "get_feature_vector"):
state = model_input.get_feature_vector()
if isinstance(state, np.ndarray):
return state
logger.debug(f"get_feature_vector returned non-array: {type(state)}")
# Method 2: Already a numpy array
if isinstance(model_input, np.ndarray):
return model_input
# Method 3: Dictionary with feature data
if isinstance(model_input, dict):
# Check if dictionary is empty - this is the main issue!
if not model_input:
logger.warning(
f"Empty dictionary passed as model_input for {model_name}, using build_base_data_input fallback"
)
# Use the same data source as the new training system
try:
# Try to get symbol from the record context or use default
symbol = "ETH/USDT" # Default symbol
base_data = self.build_base_data_input(symbol)
if base_data and hasattr(base_data, "get_feature_vector"):
state = base_data.get_feature_vector()
if isinstance(state, np.ndarray) and state.size > 0:
logger.info(
f"Generated fresh state for {model_name} from build_base_data_input: shape={state.shape}"
)
return state
except Exception as e:
logger.debug(f"build_base_data_input fallback failed for {model_name}: {e}")
# Fallback to data provider method
return self._generate_fresh_state_fallback(model_name)
# Try to extract features from dictionary
if "features" in model_input:
features = model_input["features"]
if isinstance(features, np.ndarray):
return features
# Try to build features from dictionary values
feature_list = []
for key, value in model_input.items():
if isinstance(value, (int, float)):
feature_list.append(value)
elif isinstance(value, np.ndarray):
feature_list.extend(value.flatten())
elif isinstance(value, (list, tuple)):
for item in value:
if isinstance(item, (int, float)):
feature_list.append(item)
if feature_list:
return np.array(feature_list, dtype=np.float32)
else:
logger.warning(
f"No numerical features found in dictionary for {model_name}, using data provider fallback"
)
return self._generate_fresh_state_fallback(model_name)
# Method 4: List or tuple
if isinstance(model_input, (list, tuple)):
try:
return np.array(model_input, dtype=np.float32)
except (ValueError, TypeError):
logger.warning(
f"Cannot convert list/tuple to numpy array for {model_name}"
)
# Method 5: Single numeric value
if isinstance(model_input, (int, float)):
return np.array([model_input], dtype=np.float32)
# Method 6: Final fallback - generate fresh state
logger.warning(
f"Cannot convert model_input to RL state for {model_name}: {type(model_input)}, using fresh state fallback"
)
return self._generate_fresh_state_fallback(model_name)
except Exception as e:
logger.error(
f"Error converting model_input to RL state for {model_name}: {e}"
)
return self._generate_fresh_state_fallback(model_name)
def _generate_fresh_state_fallback(self, model_name: str) -> np.ndarray:
"""Generate a fresh state from current market data when model_input is empty/invalid"""
try:
# Try to use build_base_data_input first (same as new training system)
try:
symbol = "ETH/USDT" # Default symbol
base_data = self.build_base_data_input(symbol)
if base_data and hasattr(base_data, "get_feature_vector"):
state = base_data.get_feature_vector()
if isinstance(state, np.ndarray) and state.size > 0:
logger.info(
f"Generated fresh state for {model_name} from build_base_data_input: shape={state.shape}"
)
return state
except Exception as e:
logger.debug(
f"build_base_data_input fresh state generation failed for {model_name}: {e}"
)
# Fallback to data provider method
if hasattr(self, "data_provider") and self.data_provider:
try:
# Build fresh BaseDataInput with current market data
base_data = self.data_provider.build_base_data_input("ETH/USDT")
if base_data and hasattr(base_data, "get_feature_vector"):
state = base_data.get_feature_vector()
if isinstance(state, np.ndarray) and state.size > 0:
logger.info(
f"Generated fresh state for {model_name} from data provider: shape={state.shape}"
)
return state
except Exception as e:
logger.debug(
f"Data provider fresh state generation failed for {model_name}: {e}"
)
# Try to get state from model registry
if hasattr(self, "model_registry") and self.model_registry:
try:
model_interface = self.model_registry.models.get(model_name)
if model_interface and hasattr(
model_interface, "get_current_state"
):
state = model_interface.get_current_state()
if isinstance(state, np.ndarray) and state.size > 0:
logger.info(
f"Generated fresh state for {model_name} from model interface: shape={state.shape}"
)
return state
except Exception as e:
logger.debug(
f"Model interface fresh state generation failed for {model_name}: {e}"
)
# Final fallback: create a reasonable default state with proper dimensions
# Use the expected state size for DQN models (403 features)
default_state_size = 403
if "cnn" in model_name.lower():
default_state_size = 500 # Larger for CNN models
elif "cob" in model_name.lower():
default_state_size = 2000 # Much larger for COB models
logger.warning(
f"Using default zero state for {model_name} with size {default_state_size}"
)
return np.zeros(default_state_size, dtype=np.float32)
except Exception as e:
logger.error(f"Error generating fresh state fallback for {model_name}: {e}")
# Ultimate fallback
return np.zeros(403, dtype=np.float32)
async def _train_cnn_model(
self, model, model_name: str, record: Dict, prediction: Dict, reward: float
) -> bool:
"""Train CNN model directly (no adapter)"""
try:
# Direct CNN model training (no adapter)
if (
hasattr(self, "cnn_model")
and self.cnn_model
and "cnn" in model_name.lower()
):
symbol = record.get("symbol", "ETH/USDT")
actual_action = prediction["action"]
# Create training sample from record
model_input = record.get("model_input")
if model_input is not None:
# Convert to tensor and ensure device placement
device = next(self.cnn_model.parameters()).device
if hasattr(model_input, "get_feature_vector"):
features = model_input.get_feature_vector()
elif isinstance(model_input, np.ndarray):
features = model_input
else:
features = np.array(model_input, dtype=np.float32)
features_tensor = torch.tensor(
features, dtype=torch.float32, device=device
)
if features_tensor.dim() == 1:
features_tensor = features_tensor.unsqueeze(0)
# Convert action to index
actions = ["BUY", "SELL", "HOLD"]
action_idx = (
actions.index(actual_action) if actual_action in actions else 2
)
action_tensor = torch.tensor(
[action_idx], dtype=torch.long, device=device
)
reward_tensor = torch.tensor(
[reward], dtype=torch.float32, device=device
)
# Perform training step
self.cnn_model.train()
self.cnn_optimizer.zero_grad()
# Forward pass
(
q_values,
extrema_pred,
price_direction_pred,
features_refined,
advanced_pred,
multi_timeframe_pred,
) = self.cnn_model(features_tensor)
# Calculate primary Q-value loss
q_values_selected = q_values.gather(
1, action_tensor.unsqueeze(1)
).squeeze(1)
target_q = reward_tensor # Simplified target
q_loss = nn.MSELoss()(q_values_selected, target_q)
# Calculate auxiliary losses for price direction and extrema
total_loss = q_loss
# Price direction loss
if (
price_direction_pred is not None
and price_direction_pred.shape[0] > 0
):
price_direction_loss = self._calculate_cnn_price_direction_loss(
price_direction_pred, reward_tensor, action_tensor
)
if price_direction_loss is not None:
total_loss = total_loss + 0.2 * price_direction_loss
# Extrema loss
if extrema_pred is not None and extrema_pred.shape[0] > 0:
extrema_loss = self._calculate_cnn_extrema_loss(
extrema_pred, reward_tensor, action_tensor
)
if extrema_loss is not None:
total_loss = total_loss + 0.1 * extrema_loss
loss = total_loss
# Backward pass
training_start_time = time.time()
loss.backward()
# Gradient clipping
torch.nn.utils.clip_grad_norm_(
self.cnn_model.parameters(), max_norm=1.0
)
# Optimizer step
self.cnn_optimizer.step()
training_duration_ms = (time.time() - training_start_time) * 1000
# Update statistics
current_loss = loss.item()
self.update_model_loss(model_name, current_loss)
self._update_model_training_statistics(
model_name, current_loss, training_duration_ms
)
self.training_logger.info(
f"CNN DIRECT TRAINING COMPLETED:"
)
self.training_logger.info(f" Model: {model_name}")
self.training_logger.info(f" Loss: {current_loss:.4f}")
self.training_logger.info(f" Training time: {training_duration_ms:.1f}ms")
self.training_logger.info(f" Action: {actual_action}")
self.training_logger.info(f" Reward: {reward:.4f}")
self.training_logger.info(f" Symbol: {symbol}")
# Log detailed loss breakdown
if 'price_direction_loss' in locals():
self.training_logger.info(f" Price direction loss: {price_direction_loss:.4f}")
if 'extrema_loss' in locals():
logger.info(f" Extrema loss: {extrema_loss:.4f}")
logger.info(f" Total loss: {total_loss:.4f}")
# Trigger long-term training on stored inference records
if hasattr(self.cnn_model, "train_on_stored_records") and hasattr(self, "cnn_optimizer"):
try:
# Update metadata in stored records with actual price changes
symbol = record.get("symbol", "ETH/USDT")
current_price = self._get_current_price(symbol)
inference_price = record.get("inference_price")
if inference_price and current_price:
price_change_pct = ((current_price - inference_price) / inference_price) * 100
# Update the most recent inference record with actual price changes
if hasattr(self.cnn_model, "inference_records") and self.cnn_model.inference_records:
latest_record = self.cnn_model.inference_records[-1]
if "metadata" in latest_record:
latest_record["metadata"]["actual_price_changes"] = {
"short_term": price_change_pct,
"mid_term": price_change_pct * 0.8, # Slight decay for longer timeframes
"long_term": price_change_pct * 0.6
}
latest_record["metadata"]["time_diffs"] = {
"short_term": 1.0, # 1 minute
"mid_term": 5.0, # 5 minutes
"long_term": 15.0 # 15 minutes
}
# Train on stored records
long_term_loss = self.cnn_model.train_on_stored_records(self.cnn_optimizer, min_records=5)
if long_term_loss > 0:
self.training_logger.info(f"CNN LONG-TERM TRAINING COMPLETED:")
self.training_logger.info(f" Long-term loss: {long_term_loss:.4f}")
self.training_logger.info(f" Records processed: {len(self.cnn_model.inference_records)}")
self.training_logger.info(f" Price change: {price_change_pct:+.3f}%")
self.training_logger.info(f" Current price: ${current_price:.2f}")
self.training_logger.info(f" Inference price: ${inference_price:.2f}")
except Exception as e:
logger.debug(f"Error in CNN long-term training: {e}")
return True
else:
logger.warning(f"No model input available for CNN training")
return False
# Try model interface training methods
elif hasattr(model, "add_training_sample"):
symbol = record.get("symbol", "ETH/USDT")
actual_action = prediction["action"]
model.add_training_sample(symbol, actual_action, reward)
logger.info(f"TRAINING SAMPLE ADDED to {model_name.upper()}:")
logger.info(f" Action: {actual_action}")
logger.info(f" Reward: {reward:.3f}")
logger.info(f" Symbol: {symbol}")
# If model has train method, trigger training
if hasattr(model, "train") and callable(getattr(model, "train")):
try:
training_start_time = time.time()
training_results = model.train(epochs=1)
training_duration_ms = (
time.time() - training_start_time
) * 1000
if training_results and "loss" in training_results:
current_loss = training_results["loss"]
self.update_model_loss(model_name, current_loss)
self._update_model_training_statistics(
model_name, current_loss, training_duration_ms
)
self.training_logger.info(f"MODEL TRAINING COMPLETED for {model_name.upper()}:")
self.training_logger.info(f" Loss: {current_loss:.4f}")
self.training_logger.info(f" Training time: {training_duration_ms:.1f}ms")
self.training_logger.info(f" Action: {actual_action}")
self.training_logger.info(f" Reward: {reward:.3f}")
self.training_logger.info(f" Symbol: {symbol}")
# Log additional training metrics if available
if "accuracy" in training_results:
self.training_logger.info(f" Accuracy: {training_results['accuracy']:.4f}")
if "epochs" in training_results:
self.training_logger.info(f" Epochs: {training_results['epochs']}")
if "samples" in training_results:
self.training_logger.info(f" Samples: {training_results['samples']}")
# Periodic comprehensive logging (every 10th training)
if hasattr(self, '_training_count'):
self._training_count += 1
else:
self._training_count = 1
if self._training_count % 10 == 0:
self.training_logger.info("PERIODIC COMPREHENSIVE TRAINING LOG:")
self.log_model_statistics(detailed=True)
else:
self._update_model_training_statistics(
model_name, training_duration_ms=training_duration_ms
)
except Exception as e:
logger.error(f"Error training {model_name}: {e}")
return True
# Basic acknowledgment for other training methods
elif hasattr(model, "train"):
logger.debug(f"Using basic train method for {model_name}")
logger.debug(
f"CNN model {model_name} training acknowledged (basic train method available)"
)
return True
return False
except Exception as e:
logger.error(f"Error training CNN model {model_name}: {e}")
return False
async def _train_cob_rl_model(
self, model, model_name: str, model_input, prediction: Dict, reward: float
) -> bool:
"""Train COB RL model"""
try:
# COB RL models might have specific training methods
if hasattr(model, "remember"):
action_names = ["SELL", "HOLD", "BUY"]
action_idx = action_names.index(prediction["action"])
# Convert model_input to proper format
state = self._convert_to_rl_state(model_input, model_name)
if state is None:
logger.warning(
f"Failed to convert model_input for COB RL training: {type(model_input)}"
)
return False
model.remember(
state=state,
action=action_idx,
reward=reward,
next_state=state,
done=True,
)
logger.debug(
f"Added experience to COB RL model: action={prediction['action']}, reward={reward:.3f}"
)
# Trigger training if enough experiences
if hasattr(model, "train") and hasattr(model, "memory"):
memory_size = (
len(model.memory) if hasattr(model.memory, "__len__") else 0
)
if memory_size >= getattr(model, "batch_size", 32):
training_loss = model.train()
if training_loss is not None:
self.update_model_loss(model_name, training_loss)
logger.debug(
f"COB RL training completed: loss={training_loss:.4f}"
)
return True
return True # Experience added successfully
# Try alternative training methods for COB RL
elif hasattr(model, "update_model") or hasattr(model, "train"):
logger.debug(
f"Using alternative training method for COB RL model {model_name}"
)
# For now, just acknowledge that training was attempted
logger.debug(f"COB RL model {model_name} training acknowledged")
return True
# If no training methods available, still return success to avoid warnings
logger.debug(
f"COB RL model {model_name} doesn't require traditional training"
)
return True
except Exception as e:
logger.error(f"Error training COB RL model {model_name}: {e}")
return False
async def _train_generic_model(
self, model, model_name: str, model_input, prediction: Dict, reward: float
) -> bool:
"""Train generic model with available methods"""
try:
# Try various generic training methods
if hasattr(model, "train_with_reward"):
loss = model.train_with_reward(model_input, reward)
if loss is not None:
self.update_model_loss(model_name, loss)
logger.debug(
f"Generic training completed for {model_name}: loss={loss:.4f}"
)
return True
elif hasattr(model, "update_loss"):
model.update_loss(reward)
logger.debug(f"Updated loss for {model_name}: reward={reward:.3f}")
return True
elif hasattr(model, "train_on_outcome"):
target = 1 if reward > 0 else 0
loss = model.train_on_outcome(model_input, target)
if loss is not None:
self.update_model_loss(model_name, loss)
logger.debug(
f"Outcome training completed for {model_name}: loss={loss:.4f}"
)
return True
return False
except Exception as e:
logger.error(f"Error training generic model {model_name}: {e}")
return False
async def _train_model_fallback(
self, model_name: str, model, model_input, prediction: Dict, reward: float
) -> bool:
"""Fallback training methods for models that don't fit standard patterns"""
try:
# Try to access direct model instances for legacy support
if (
"dqn" in model_name.lower()
and hasattr(self, "rl_agent")
and self.rl_agent
):
return await self._train_rl_model(
self.rl_agent, model_name, model_input, prediction, reward
)
elif (
"cnn" in model_name.lower()
and hasattr(self, "cnn_model")
and self.cnn_model
):
# Create a fake record for CNN training
fake_record = {"symbol": "ETH/USDT", "model_input": model_input}
return await self._train_cnn_model(
self.cnn_model, model_name, fake_record, prediction, reward
)
elif (
"cob" in model_name.lower()
and hasattr(self, "cob_rl_agent")
and self.cob_rl_agent
):
return await self._train_cob_rl_model(
self.cob_rl_agent, model_name, model_input, prediction, reward
)
return False
except Exception as e:
logger.error(f"Error in fallback training for {model_name}: {e}")
return False
def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> float:
"""Calculate RSI indicator"""
try:
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi.iloc[-1] if not rsi.empty else 50.0
except:
return 50.0
async def _get_cnn_predictions(
self, model: CNNModelInterface, symbol: str, base_data=None
) -> List[Prediction]:
"""Get predictions from CNN model using pre-built base data"""
predictions = []
try:
# Use pre-built base data if provided, otherwise build it
if base_data is None:
base_data = self.data_provider.build_base_data_input(symbol)
if not base_data:
logger.warning(
f"Cannot build BaseDataInput for CNN prediction: {symbol}"
)
return predictions
# Direct CNN model inference (no adapter needed)
if hasattr(self, "cnn_model") and self.cnn_model:
try:
# Get feature vector from base_data
features = base_data.get_feature_vector()
# Convert to tensor and ensure proper device placement
device = next(self.cnn_model.parameters()).device
import torch as torch_module # Explicit import to avoid scoping issues
features_tensor = torch_module.tensor(
features, dtype=torch_module.float32, device=device
)
# Ensure batch dimension
if features_tensor.dim() == 1:
features_tensor = features_tensor.unsqueeze(0)
# Set model to evaluation mode
self.cnn_model.eval()
# Get prediction from CNN model
with torch_module.no_grad():
(
q_values,
extrema_pred,
price_pred,
features_refined,
advanced_pred,
multi_timeframe_pred,
) = self.cnn_model(features_tensor)
# Convert to probabilities using softmax
action_probs = torch_module.softmax(q_values, dim=1)
action_idx = torch_module.argmax(action_probs, dim=1).item()
confidence = float(action_probs[0, action_idx].item())
# Map action index to action string
actions = ["BUY", "SELL", "HOLD"]
action = actions[action_idx]
# Create probabilities dictionary
probabilities = {
"BUY": float(action_probs[0, 0].item()),
"SELL": float(action_probs[0, 1].item()),
"HOLD": float(action_probs[0, 2].item()),
}
# Extract price direction predictions if available
price_direction_data = None
if price_pred is not None:
# Process price direction predictions
if hasattr(
model.model, "process_price_direction_predictions"
):
try:
price_direction_data = (
model.model.process_price_direction_predictions(
price_pred
)
)
except Exception as e:
logger.debug(
f"Error processing CNN price direction: {e}"
)
# Fallback to old format for compatibility
price_prediction = (
price_pred.squeeze(0).cpu().numpy().tolist()
)
prediction = Prediction(
action=action,
confidence=confidence,
probabilities=probabilities,
timeframe="multi", # Multi-timeframe prediction
timestamp=datetime.now(),
model_name=model.name, # Use the actual model name
metadata={
"feature_size": len(base_data.get_feature_vector()),
"data_sources": [
"ohlcv_1s",
"ohlcv_1m",
"ohlcv_1h",
"ohlcv_1d",
"btc",
"cob",
"indicators",
],
"price_prediction": price_prediction,
"price_direction": price_direction_data,
"extrema_prediction": (
extrema_pred.squeeze(0).cpu().numpy().tolist()
if extrema_pred is not None
else None
),
},
)
predictions.append(prediction)
# Store inference record in CNN model for long-term training
if hasattr(self.cnn_model, "store_inference_record"):
try:
# Get current price for metadata
current_price = self._get_current_price(symbol)
# Create metadata with price information for long-term training
metadata = {
"symbol": symbol,
"current_price": current_price,
"timestamp": datetime.now().isoformat(),
"prediction_action": action,
"prediction_confidence": confidence,
"actual_price_changes": {}, # Will be populated during training
"time_diffs": {} # Will be populated during training
}
# Store the inference record in the CNN model
self.cnn_model.store_inference_record(
input_data=features_tensor,
prediction_output=(q_values, extrema_pred, price_pred, features_refined, advanced_pred, multi_timeframe_pred),
metadata=metadata
)
logger.debug(f"Stored CNN inference record for long-term training")
except Exception as e:
logger.debug(f"Error storing CNN inference record: {e}")
logger.debug(
f"Added CNN prediction: {action} ({confidence:.3f})"
)
except Exception as e:
logger.error(f"Error using direct CNN model: {e}")
import traceback
traceback.print_exc()
# Remove this fallback - direct CNN inference should work above
if not predictions:
logger.debug(
f"No CNN predictions generated for {symbol} - this is expected if CNN model is not properly initialized"
)
try:
# Use the already available base_data (no need to rebuild)
if not base_data:
logger.warning(
f"No BaseDataInput available for CNN fallback: {symbol}"
)
return predictions
# Convert to unified feature vector (7850 features)
feature_vector = base_data.get_feature_vector()
# Use the model's act method with unified input
if hasattr(model.model, "act"):
# Convert to tensor format expected by enhanced_cnn
device = torch_module.device(
"cuda" if torch_module.cuda.is_available() else "cpu"
)
features_tensor = torch_module.tensor(
feature_vector, dtype=torch_module.float32, device=device
)
# Call the model's act method
action_idx, confidence, action_probs = model.model.act(
features_tensor, explore=False
)
# Build prediction with unified timeframe result
action_names = [
"BUY",
"SELL",
"HOLD",
] # Note: enhanced_cnn uses this order
best_action = action_names[action_idx]
# Get price direction vectors from CNN model if available
price_direction_data = None
if hasattr(model.model, "get_price_direction_vector"):
try:
price_direction_data = (
model.model.get_price_direction_vector()
)
except Exception as e:
logger.debug(
f"Error getting price direction from CNN: {e}"
)
pred = Prediction(
action=best_action,
confidence=float(confidence),
probabilities={
"BUY": float(action_probs[0]),
"SELL": float(action_probs[1]),
"HOLD": float(action_probs[2]),
},
timeframe="unified", # Indicates this uses all timeframes
timestamp=datetime.now(),
model_name=model.name,
metadata={
"feature_vector_size": len(feature_vector),
"unified_input": True,
"fallback_method": "direct_model_inference",
"price_direction": price_direction_data,
},
)
predictions.append(pred)
# Store inference record in CNN model for long-term training (fallback method)
if hasattr(model.model, "store_inference_record"):
try:
# Get current price for metadata
current_price = self._get_current_price(symbol)
# Create metadata with price information for long-term training
metadata = {
"symbol": symbol,
"current_price": current_price,
"timestamp": datetime.now().isoformat(),
"prediction_action": best_action,
"prediction_confidence": float(confidence),
"actual_price_changes": {}, # Will be populated during training
"time_diffs": {} # Will be populated during training
}
# Store the inference record in the CNN model
model.model.store_inference_record(
input_data=features_tensor,
prediction_output=None, # Not available in fallback method
metadata=metadata
)
logger.debug(f"Stored CNN inference record for long-term training (fallback)")
except Exception as e:
logger.debug(f"Error storing CNN inference record (fallback): {e}")
# Note: Inference data will be stored in main prediction loop to avoid duplication
# Capture for dashboard
current_price = self._get_current_price(symbol)
if current_price is not None:
predicted_price = current_price * (
1
+ (
0.01
* (
confidence
if best_action == "BUY"
else -confidence if best_action == "SELL" else 0
)
)
)
self.capture_cnn_prediction(
symbol,
direction=action_idx,
confidence=confidence,
current_price=current_price,
predicted_price=predicted_price,
)
logger.info(
f"CNN fallback successful for {symbol}: {best_action} (confidence: {confidence:.3f})"
)
else:
logger.debug(
f"CNN model {model.name} fallback not needed - direct inference succeeded"
)
except Exception as e:
logger.error(f"CNN fallback inference failed for {symbol}: {e}")
# Don't continue with old timeframe-by-timeframe approach
# Trigger immediate training if previous inference data exists for this model
if predictions and model.name in self.last_inference:
logger.debug(
f"Triggering immediate training for CNN model {model.name} with previous inference data"
)
await self._trigger_immediate_training_for_model(model.name, symbol)
except Exception as e:
logger.error(f"Orch: Error getting CNN predictions: {e}")
return predictions
async def _get_rl_prediction(
self, model: RLAgentInterface, symbol: str, base_data=None
) -> Optional[Prediction]:
"""Get prediction from RL agent using pre-built base data"""
try:
# Use pre-built base data if provided, otherwise build it
if base_data is None:
base_data = self.data_provider.build_base_data_input(symbol)
if not base_data:
logger.warning(
f"Cannot build BaseDataInput for RL prediction: {symbol}"
)
return None
# Convert BaseDataInput to RL state format
state_features = base_data.get_feature_vector()
# Get current state for RL agent using the pre-built base data
state = self._get_rl_state(symbol, base_data)
if state is None:
return None
# Get RL agent's action, confidence, and q_values from the underlying model
if hasattr(model.model, "act_with_confidence"):
# Call act_with_confidence and handle different return formats
result = model.model.act_with_confidence(state)
if len(result) == 3:
# EnhancedCNN format: (action, confidence, q_values)
action_idx, confidence, raw_q_values = result
elif len(result) == 2:
# DQN format: (action, confidence)
action_idx, confidence = result
raw_q_values = None
else:
logger.error(
f"Unexpected return format from act_with_confidence: {len(result)} values"
)
return None
elif hasattr(model.model, "act"):
action_idx = model.model.act(state, explore=False)
confidence = 0.7 # Default confidence for basic act method
raw_q_values = None # No raw q_values from simple act
else:
logger.error(f"RL model {model.name} has no act method")
return None
action_names = ["SELL", "HOLD", "BUY"]
action = action_names[action_idx]
# Convert raw_q_values to list if they are a tensor
q_values_for_capture = None
if raw_q_values is not None and hasattr(raw_q_values, "tolist"):
q_values_for_capture = raw_q_values.tolist()
elif raw_q_values is not None and isinstance(raw_q_values, list):
q_values_for_capture = raw_q_values
# Create prediction object with safe probability calculation
probabilities = {}
if q_values_for_capture and len(q_values_for_capture) == len(action_names):
# Use actual q_values if they match the expected length
probabilities = {
action_names[i]: float(q_values_for_capture[i])
for i in range(len(action_names))
}
else:
# Use default uniform probabilities if q_values are unavailable or mismatched
default_prob = 1.0 / len(action_names)
probabilities = {name: default_prob for name in action_names}
if q_values_for_capture:
logger.warning(
f"Q-values length mismatch: expected {len(action_names)}, got {len(q_values_for_capture)}. Using default probabilities."
)
# Get price direction vectors from DQN model if available
price_direction_data = None
if hasattr(model.model, "get_price_direction_vector"):
try:
price_direction_data = model.model.get_price_direction_vector()
except Exception as e:
logger.debug(f"Error getting price direction from DQN: {e}")
prediction = Prediction(
action=action,
confidence=float(confidence),
probabilities=probabilities,
timeframe="mixed", # RL uses mixed timeframes
timestamp=datetime.now(),
model_name=model.name,
metadata={
"state_size": len(state),
"price_direction": price_direction_data,
},
)
# Capture DQN prediction for dashboard visualization
current_price = self._get_current_price(symbol)
if current_price:
# Only pass q_values if they exist, otherwise pass empty list
q_values_to_pass = (
q_values_for_capture if q_values_for_capture is not None else []
)
self.capture_dqn_prediction(
symbol,
action_idx,
float(confidence),
current_price,
q_values_to_pass,
)
# Trigger immediate training if previous inference data exists for this model
if prediction and model.name in self.last_inference:
logger.debug(
f"Triggering immediate training for RL model {model.name} with previous inference data"
)
await self._trigger_immediate_training_for_model(model.name, symbol)
return prediction
except Exception as e:
logger.error(f"Error getting RL prediction: {e}")
return None
async def _get_generic_prediction(
self, model: ModelInterface, symbol: str, base_data=None
) -> Optional[Prediction]:
"""Get prediction from generic model using pre-built base data"""
try:
# Use pre-built base data if provided, otherwise build it
if base_data is None:
base_data = self.data_provider.build_base_data_input(symbol)
if not base_data:
logger.warning(
f"Cannot build BaseDataInput for generic prediction: {symbol}"
)
return None
# Convert to feature vector for generic models
feature_vector = base_data.get_feature_vector()
# For backward compatibility, reshape to matrix format if model expects it
# Most generic models expect a 2D matrix, so reshape the unified vector
feature_matrix = feature_vector.reshape(1, -1) # Shape: (1, 7850)
prediction_result = model.predict(feature_matrix)
# Handle different return formats from model.predict()
if prediction_result is None:
return None
# Check if it's a tuple (action_probs, confidence)
if isinstance(prediction_result, tuple) and len(prediction_result) == 2:
action_probs, confidence = prediction_result
elif isinstance(prediction_result, dict):
# Handle dictionary return format
action_probs = prediction_result.get("probabilities", None)
confidence = prediction_result.get("confidence", 0.7)
else:
# Assume it's just action probabilities (e.g., a list or numpy array)
action_probs = prediction_result
confidence = 0.7 # Default confidence
if action_probs is not None:
# Ensure action_probs is a numpy array for argmax
if not isinstance(action_probs, np.ndarray):
action_probs = np.array(action_probs)
action_names = ["SELL", "HOLD", "BUY"]
best_action_idx = np.argmax(action_probs)
best_action = action_names[best_action_idx]
prediction = Prediction(
action=best_action,
confidence=float(confidence),
probabilities={
name: float(prob)
for name, prob in zip(action_names, action_probs)
},
timeframe="unified", # Now uses unified multi-timeframe data
timestamp=datetime.now(),
model_name=model.name,
metadata={
"generic_model": True,
"unified_input": True,
"feature_vector_size": len(feature_vector),
},
)
return prediction
return None
except Exception as e:
logger.error(f"Error getting generic prediction: {e}")
return None
def _get_rl_state(self, symbol: str, base_data=None) -> Optional[np.ndarray]:
"""Get current state for RL agent using pre-built base data"""
try:
# Use pre-built base data if provided, otherwise build it
if base_data is None:
base_data = self.data_provider.build_base_data_input(symbol)
if not base_data:
logger.debug(f"Cannot build BaseDataInput for RL state: {symbol}")
return None
# Validate base_data has the required method
if not hasattr(base_data, 'get_feature_vector'):
logger.debug(f"BaseDataInput for {symbol} missing get_feature_vector method")
return None
# Get unified feature vector (7850 features including all timeframes and COB data)
feature_vector = base_data.get_feature_vector()
# Validate feature vector
if feature_vector is None or len(feature_vector) == 0:
logger.debug(f"Empty feature vector for RL state: {symbol}")
return None
# Check if all features are zero (invalid state)
if all(f == 0 for f in feature_vector):
logger.debug(f"All features are zero for RL state: {symbol}")
return None
# Convert to numpy array if needed
if not isinstance(feature_vector, np.ndarray):
feature_vector = np.array(feature_vector, dtype=np.float32)
# Return the full unified feature vector for RL agent
# The DQN agent is now initialized with the correct size to match this
return feature_vector
except Exception as e:
logger.error(f"Error creating RL state for {symbol}: {e}")
return None
def _determine_decision_source(self, models_used: List[str], confidence: float) -> str:
"""Determine the source of a trading decision based on contributing models"""
try:
if not models_used:
return "no_models"
# If only one model contributed, use that as source
if len(models_used) == 1:
model_name = models_used[0]
# Map internal model names to user-friendly names
model_mapping = {
"dqn_agent": "DQN",
"cnn_model": "CNN",
"cob_rl": "COB-RL",
"decision_fusion": "Fusion",
"extrema_trainer": "Extrema",
"transformer": "Transformer"
}
return model_mapping.get(model_name, model_name)
# Multiple models - determine primary contributor
# Priority order: COB-RL > DQN > CNN > Others
priority_order = ["cob_rl", "dqn_agent", "cnn_model", "decision_fusion", "transformer", "extrema_trainer"]
for priority_model in priority_order:
if priority_model in models_used:
model_mapping = {
"cob_rl": "COB-RL",
"dqn_agent": "DQN",
"cnn_model": "CNN",
"decision_fusion": "Fusion",
"transformer": "Transformer",
"extrema_trainer": "Extrema"
}
primary_model = model_mapping.get(priority_model, priority_model)
# If high confidence, show primary model
if confidence > 0.7:
return primary_model
else:
# Lower confidence, show it's a combination
return f"{primary_model}+{len(models_used)-1}"
# Fallback: show number of models
return f"Ensemble({len(models_used)})"
except Exception as e:
logger.error(f"Error determining decision source: {e}")
return "orchestrator"
def _combine_predictions(
self,
symbol: str,
price: float,
predictions: List[Prediction],
timestamp: datetime,
) -> TradingDecision:
"""Combine all predictions into a final decision with aggressiveness and P&L feedback"""
try:
reasoning = {
"predictions": len(predictions),
"weights": self.model_weights.copy(),
"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
# Process all predictions (filter out disabled models)
for pred in predictions:
# Check if model inference is enabled
if not self.is_model_inference_enabled(pred.model_name):
logger.debug(f"Skipping disabled model {pred.model_name} in decision making")
continue
# DEBUG: Log individual model predictions
logger.debug(f"Model {pred.model_name}: {pred.action} (confidence: {pred.confidence:.3f})")
# Get model weight
model_weight = self.model_weights.get(pred.model_name, 0.1)
# Weight by confidence and timeframe importance
timeframe_weight = self._get_timeframe_weight(pred.timeframe)
weighted_confidence = pred.confidence * timeframe_weight * model_weight
action_scores[pred.action] += weighted_confidence
total_weight += weighted_confidence
# Normalize scores
if total_weight > 0:
for action in action_scores:
action_scores[action] /= total_weight
# Choose best action - safe way to handle max with key function
if action_scores:
# Add small random component to break ties and prevent pure bias
import random
for action in action_scores:
# Add tiny random noise (±0.001) to break exact ties
action_scores[action] += random.uniform(-0.001, 0.001)
best_action = max(action_scores.keys(), key=lambda k: action_scores[k])
best_confidence = action_scores[best_action]
# DEBUG: Log action scores to understand bias
logger.debug(f"Action scores for {symbol}: BUY={action_scores['BUY']:.3f}, SELL={action_scores['SELL']:.3f}, HOLD={action_scores['HOLD']:.3f}")
logger.debug(f"Selected action: {best_action} (confidence: {best_confidence:.3f})")
else:
best_action = "HOLD"
best_confidence = 0.0
# Calculate aggressiveness-adjusted thresholds
entry_threshold, exit_threshold = self._calculate_aggressiveness_thresholds(
current_position_pnl, symbol
)
# SIGNAL CONFIRMATION: Only execute signals that meet confirmation criteria
# Apply confidence thresholds and signal accumulation for trend confirmation
reasoning["execute_every_signal"] = False
reasoning["models_aggregated"] = [pred.model_name for pred in predictions]
reasoning["aggregated_confidence"] = best_confidence
# Calculate dynamic aggressiveness based on recent performance
entry_aggressiveness = self._calculate_dynamic_entry_aggressiveness(symbol)
# Adjust confidence threshold based on entry aggressiveness
# Higher aggressiveness = lower threshold (more trades)
# entry_aggressiveness: 0.0 = very conservative, 1.0 = very aggressive
base_threshold = self.confidence_threshold
aggressiveness_factor = (
1.0 - entry_aggressiveness
) # Invert: high agg = low factor
dynamic_threshold = base_threshold * aggressiveness_factor
# Ensure minimum threshold for safety (don't go below 1% confidence)
dynamic_threshold = max(0.01, dynamic_threshold)
# Apply dynamic confidence threshold for signal confirmation
if best_action != "HOLD":
if best_confidence < dynamic_threshold:
logger.debug(
f"Signal below dynamic confidence threshold: {best_action} {symbol} "
f"(confidence: {best_confidence:.3f} < {dynamic_threshold:.3f}, "
f"base: {base_threshold:.3f}, aggressiveness: {entry_aggressiveness:.2f})"
)
best_action = "HOLD"
best_confidence = 0.0
else:
logger.info(
f"SIGNAL ACCEPTED: {best_action} {symbol} "
f"(confidence: {best_confidence:.3f} >= {dynamic_threshold:.3f}, "
f"aggressiveness: {entry_aggressiveness:.2f})"
)
# Add signal to accumulator for trend confirmation
signal_data = {
"action": best_action,
"confidence": best_confidence,
"timestamp": timestamp,
"models": reasoning["models_aggregated"],
}
# Check if we have enough confirmations
confirmed_action = self._check_signal_confirmation(
symbol, signal_data
)
if confirmed_action:
logger.info(
f"SIGNAL CONFIRMED: {confirmed_action} (confidence: {best_confidence:.3f}) "
f"from aggregated models: {reasoning['models_aggregated']}"
)
best_action = confirmed_action
reasoning["signal_confirmed"] = True
reasoning["confirmations_received"] = len(
self.signal_accumulator[symbol]
)
else:
logger.debug(
f"Signal accumulating: {best_action} {symbol} "
f"({len(self.signal_accumulator[symbol])}/{self.required_confirmations} confirmations)"
)
best_action = "HOLD"
best_confidence = 0.0
reasoning["rejected_reason"] = "awaiting_confirmation"
# 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:
memory_usage = {}
if hasattr(self.model_registry, "get_memory_stats"):
memory_usage = self.model_registry.get_memory_stats()
else:
# Fallback memory usage calculation
for model_name in self.model_weights:
memory_usage[model_name] = 50.0 # Default MB estimate
except Exception:
memory_usage = {}
# Get exit aggressiveness (entry aggressiveness already calculated above)
exit_aggressiveness = self._calculate_dynamic_exit_aggressiveness(
symbol, current_position_pnl
)
# Determine decision source based on contributing models
source = self._determine_decision_source(reasoning.get("models_used", []), best_confidence)
# Create final decision
decision = TradingDecision(
action=best_action,
confidence=best_confidence,
symbol=symbol,
price=price,
timestamp=timestamp,
reasoning=reasoning,
memory_usage=memory_usage.get("models", {}) if memory_usage else {},
source=source,
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}, "
# f"entry_agg: {entry_aggressiveness:.2f}, exit_agg: {exit_aggressiveness:.2f}, "
# f"pnl: ${current_position_pnl:.2f})")
# Trigger training on each decision (especially for executed trades)
self._trigger_training_on_decision(decision, price)
return decision
except Exception as e:
logger.error(f"Error combining predictions for {symbol}: {e}")
# Return safe default
return TradingDecision(
action="HOLD",
confidence=0.0,
symbol=symbol,
source="error_fallback",
price=price,
timestamp=timestamp,
reasoning={"error": str(e)},
memory_usage={},
entry_aggressiveness=0.5,
exit_aggressiveness=0.5,
current_position_pnl=0.0,
)
def _get_timeframe_weight(self, timeframe: str) -> float:
"""Get importance weight for a timeframe"""
# Higher timeframes get more weight in decision making
weights = {
"1m": 0.1,
"5m": 0.2,
"15m": 0.3,
"30m": 0.4,
"1h": 0.6,
"4h": 0.8,
"1d": 1.0,
}
return weights.get(timeframe, 0.5)
def update_model_performance(self, model_name: str, was_correct: bool):
"""Update performance tracking for a model"""
if model_name in self.model_performance:
self.model_performance[model_name]["total"] += 1
if was_correct:
self.model_performance[model_name]["correct"] += 1
# Update accuracy
total = self.model_performance[model_name]["total"]
correct = self.model_performance[model_name]["correct"]
self.model_performance[model_name]["accuracy"] = (
correct / total if total > 0 else 0.0
)
def adapt_weights(self):
"""Dynamically adapt model weights based on performance"""
try:
for model_name, performance in self.model_performance.items():
if performance["total"] > 0:
# Adjust weight based on relative performance
accuracy = performance["correct"] / performance["total"]
self.model_weights[model_name] = accuracy
logger.info(
f"Adapted {model_name} weight: {self.model_weights[model_name]}"
)
except Exception as e:
logger.error(f"Error adapting weights: {e}")
def get_recent_decisions(
self, symbol: str, limit: int = 10
) -> List[TradingDecision]:
"""Get recent decisions for a symbol"""
if symbol in self.recent_decisions:
return self.recent_decisions[symbol][-limit:]
return []
def get_performance_metrics(self) -> Dict[str, Any]:
"""Get performance metrics for the orchestrator"""
return {
"model_performance": self.model_performance.copy(),
"weights": self.model_weights.copy(),
"configuration": {
"confidence_threshold": self.confidence_threshold,
# 'decision_frequency': self.decision_frequency
},
"recent_activity": {
symbol: len(decisions)
for symbol, decisions in self.recent_decisions.items()
},
}
def get_model_states(self) -> Dict[str, Dict]:
"""Get current model states with REAL checkpoint data - SSOT for dashboard"""
try:
# ENHANCED: Load actual checkpoint metadata for each model
from utils.checkpoint_manager import load_best_checkpoint
# Update each model with REAL checkpoint data
for model_name in [
"dqn_agent",
"enhanced_cnn",
"extrema_trainer",
"decision",
"cob_rl",
]:
try:
result = load_best_checkpoint(model_name)
if result:
file_path, metadata = result
# Map model names to internal keys
internal_key = {
"dqn_agent": "dqn",
"enhanced_cnn": "cnn",
"extrema_trainer": "extrema_trainer",
"decision": "decision",
"cob_rl": "cob_rl",
}.get(model_name, model_name)
if internal_key in self.model_states:
# Load REAL checkpoint data
self.model_states[internal_key]["current_loss"] = getattr(
metadata, "loss", None
) or getattr(metadata, "val_loss", None)
self.model_states[internal_key]["best_loss"] = getattr(
metadata, "loss", None
) or getattr(metadata, "val_loss", None)
self.model_states[internal_key]["checkpoint_loaded"] = True
self.model_states[internal_key][
"checkpoint_filename"
] = metadata.checkpoint_id
self.model_states[internal_key]["performance_score"] = (
getattr(metadata, "performance_score", 0.0)
)
self.model_states[internal_key]["created_at"] = str(
getattr(metadata, "created_at", "Unknown")
)
# Set initial loss from checkpoint if available
if self.model_states[internal_key]["initial_loss"] is None:
# Try to infer initial loss from performance improvement
if hasattr(metadata, "accuracy") and metadata.accuracy:
# Estimate initial loss from current accuracy (inverse relationship)
estimated_initial = max(
0.1, 2.0 - (metadata.accuracy * 2.0)
)
self.model_states[internal_key][
"initial_loss"
] = estimated_initial
logger.debug(
f"Loaded REAL checkpoint data for {model_name}: loss={self.model_states[internal_key]['current_loss']}"
)
else:
# No checkpoint found - mark as fresh
internal_key = {
"dqn_agent": "dqn",
"enhanced_cnn": "cnn",
"extrema_trainer": "extrema_trainer",
"decision": "decision",
"cob_rl": "cob_rl",
}.get(model_name, model_name)
if internal_key in self.model_states:
self.model_states[internal_key]["checkpoint_loaded"] = False
self.model_states[internal_key][
"checkpoint_filename"
] = "none (fresh start)"
except Exception as e:
logger.debug(f"No checkpoint found for {model_name}: {e}")
# ADDITIONAL: Update from live training if models are actively training
if (
self.rl_agent
and hasattr(self.rl_agent, "losses")
and len(self.rl_agent.losses) > 0
):
recent_losses = self.rl_agent.losses[-10:] # Last 10 training steps
if recent_losses:
live_loss = sum(recent_losses) / len(recent_losses)
# Only update if we have a live loss that's different from checkpoint
if (
abs(live_loss - (self.model_states["dqn"]["current_loss"] or 0))
> 0.001
):
self.model_states["dqn"]["current_loss"] = live_loss
logger.debug(
f"Updated DQN with live training loss: {live_loss:.4f}"
)
if self.cnn_model and hasattr(self.cnn_model, "training_loss"):
if (
self.cnn_model.training_loss
and abs(
self.cnn_model.training_loss
- (self.model_states["cnn"]["current_loss"] or 0)
)
> 0.001
):
self.model_states["cnn"][
"current_loss"
] = self.cnn_model.training_loss
logger.debug(
f"Updated CNN with live training loss: {self.cnn_model.training_loss:.4f}"
)
if self.extrema_trainer and hasattr(
self.extrema_trainer, "best_detection_accuracy"
):
# Convert accuracy to loss estimate
if self.extrema_trainer.best_detection_accuracy > 0:
estimated_loss = max(
0.001, 1.0 - self.extrema_trainer.best_detection_accuracy
)
self.model_states["extrema_trainer"][
"current_loss"
] = estimated_loss
self.model_states["extrema_trainer"]["best_loss"] = estimated_loss
# NO LONGER SETTING SYNTHETIC INITIAL LOSS VALUES
# Keep all None values as None if no real data is available
# This prevents the "fake progress" issue where Current Loss = Initial Loss
# Only set initial_loss from actual training history if available
for model_key, model_state in self.model_states.items():
# Leave initial_loss as None if no real training history exists
# Leave current_loss as None if model isn't actively training
# Leave best_loss as None if no checkpoints exist with real performance data
pass # No synthetic data generation
return self.model_states
except Exception as e:
logger.error(f"Error getting model states: {e}")
# Return None values instead of synthetic data
return {
"dqn": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"cnn": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"cob_rl": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"decision": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
"extrema_trainer": {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
},
}
def _initialize_decision_fusion(self):
"""Initialize the decision fusion neural network for learning model effectiveness"""
try:
if not self.decision_fusion_enabled:
return
# Create enhanced decision fusion network
class DecisionFusionNet(nn.Module):
def __init__(self, input_size=128, hidden_size=256):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
# Enhanced architecture for complex decision making
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, hidden_size // 2)
self.fc4 = nn.Linear(hidden_size // 2, 3) # BUY, SELL, HOLD
self.dropout = nn.Dropout(0.3)
# Use LayerNorm instead of BatchNorm1d for single-sample training compatibility
self.layer_norm1 = nn.LayerNorm(hidden_size)
self.layer_norm2 = nn.LayerNorm(hidden_size)
self.layer_norm3 = nn.LayerNorm(hidden_size // 2)
def forward(self, x):
x = torch.relu(self.layer_norm1(self.fc1(x)))
x = self.dropout(x)
x = torch.relu(self.layer_norm2(self.fc2(x)))
x = self.dropout(x)
x = torch.relu(self.layer_norm3(self.fc3(x)))
x = self.dropout(x)
return torch.softmax(self.fc4(x), dim=1)
def save(self, filepath: str):
"""Save the decision fusion network"""
torch.save(
{
"model_state_dict": self.state_dict(),
"input_size": self.input_size,
"hidden_size": self.hidden_size,
},
filepath,
)
logger.info(f"Decision fusion network saved to {filepath}")
def load(self, filepath: str):
"""Load the decision fusion network"""
checkpoint = torch.load(
filepath,
map_location=self.device if hasattr(self, "device") else "cpu",
)
self.load_state_dict(checkpoint["model_state_dict"])
logger.info(f"Decision fusion network loaded from {filepath}")
# Get decision fusion configuration
decision_fusion_config = self.config.orchestrator.get("decision_fusion", {})
input_size = decision_fusion_config.get("input_size", 128)
hidden_size = decision_fusion_config.get("hidden_size", 256)
self.decision_fusion_network = DecisionFusionNet(
input_size=input_size, hidden_size=hidden_size
)
# Move decision fusion network to the device
self.decision_fusion_network.to(self.device)
# Initialize decision fusion mode
self.decision_fusion_mode = decision_fusion_config.get("mode", "neural")
self.decision_fusion_enabled = decision_fusion_config.get("enabled", True)
self.decision_fusion_history_length = decision_fusion_config.get(
"history_length", 20
)
self.decision_fusion_training_interval = decision_fusion_config.get(
"training_interval", 100
)
self.decision_fusion_min_samples = decision_fusion_config.get(
"min_samples_for_training", 50
)
# Initialize decision fusion training data
self.decision_fusion_training_data = []
self.decision_fusion_decisions_count = 0
# Try to load existing checkpoint
try:
from utils.checkpoint_manager import load_best_checkpoint
# Try to load decision fusion checkpoint
result = load_best_checkpoint("decision_fusion")
if result:
file_path, metadata = result
# Load the checkpoint into the network
checkpoint = torch.load(file_path, map_location=self.device)
# Load model state
if 'model_state_dict' in checkpoint:
self.decision_fusion_network.load_state_dict(checkpoint['model_state_dict'])
# Update model states - FIX: Use correct key "decision_fusion"
if "decision_fusion" not in self.model_states:
self.model_states["decision_fusion"] = {}
self.model_states["decision_fusion"]["initial_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["decision_fusion"]["current_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["decision_fusion"]["best_loss"] = (
metadata.performance_metrics.get("loss", 0.0)
)
self.model_states["decision_fusion"]["checkpoint_loaded"] = True
self.model_states["decision_fusion"][
"checkpoint_filename"
] = metadata.checkpoint_id
loss_str = f"{metadata.performance_metrics.get('loss', 0.0):.4f}"
logger.info(
f"Decision fusion network loaded from checkpoint: {metadata.checkpoint_id} (loss={loss_str})"
)
else:
logger.info(
"No existing decision fusion checkpoint found, starting fresh"
)
except Exception as e:
logger.warning(f"Error loading decision fusion checkpoint: {e}")
logger.info("Decision fusion network starting fresh")
# Initialize optimizer for decision fusion training
self.decision_fusion_optimizer = torch.optim.Adam(
self.decision_fusion_network.parameters(),
lr=decision_fusion_config.get("learning_rate", 0.001)
)
logger.info(f"Decision fusion network initialized on device: {self.device}")
logger.info(f"Decision fusion mode: {self.decision_fusion_mode}")
logger.info(f"Decision fusion optimizer initialized with lr={decision_fusion_config.get('learning_rate', 0.001)}")
except Exception as e:
logger.warning(f"Decision fusion initialization failed: {e}")
self.decision_fusion_enabled = False
async def _train_decision_fusion_programmatic(self):
"""Train decision fusion model in programmatic mode"""
try:
if not self.decision_fusion_network or len(self.decision_fusion_training_data) < self.decision_fusion_min_samples:
return
logger.info(f"Training decision fusion model with {len(self.decision_fusion_training_data)} samples")
# Prepare training data
inputs = []
targets = []
for sample in self.decision_fusion_training_data[-100:]: # Use last 100 samples
if 'input_features' in sample and 'outcome' in sample:
inputs.append(sample['input_features'])
# Convert outcome to target (1.0 for correct, 0.0 for incorrect)
target = 1.0 if sample['outcome']['correct'] else 0.0
targets.append(target)
if len(inputs) < 10: # Need minimum samples
return
# Convert to tensors
inputs_tensor = torch.tensor(inputs, dtype=torch.float32, device=self.device)
targets_tensor = torch.tensor(targets, dtype=torch.float32, device=self.device)
# Training step
self.decision_fusion_network.train()
optimizer = torch.optim.Adam(self.decision_fusion_network.parameters(), lr=0.001)
optimizer.zero_grad()
outputs = self.decision_fusion_network(inputs_tensor)
loss = torch.nn.MSELoss()(outputs.squeeze(), targets_tensor)
loss.backward()
optimizer.step()
# Update statistics
current_loss = loss.item()
self.update_model_loss("decision_fusion", current_loss)
logger.info(f"Decision fusion training completed: loss={current_loss:.4f}, samples={len(inputs)}")
# Save checkpoint periodically
if self.decision_fusion_decisions_count % (self.decision_fusion_training_interval * 5) == 0:
self._save_decision_fusion_checkpoint()
except Exception as e:
logger.error(f"Error training decision fusion in programmatic mode: {e}")
def _save_decision_fusion_checkpoint(self):
"""Save decision fusion model checkpoint"""
try:
if not self.decision_fusion_network or not self.checkpoint_manager:
return
# Get current performance score
model_stats = self.model_statistics.get('decision_fusion')
performance_score = 0.5 # Default score
if model_stats and model_stats.accuracy is not None:
performance_score = model_stats.accuracy
elif hasattr(self, 'decision_fusion_performance_score'):
performance_score = self.decision_fusion_performance_score
# Create checkpoint data
checkpoint_data = {
'model_state_dict': self.decision_fusion_network.state_dict(),
'optimizer_state_dict': self.decision_fusion_optimizer.state_dict() if hasattr(self, 'decision_fusion_optimizer') else None,
'epoch': self.decision_fusion_decisions_count,
'loss': 1.0 - performance_score, # Convert performance to loss
'performance_score': performance_score,
'timestamp': datetime.now().isoformat(),
'model_name': 'decision_fusion',
'training_data_count': len(self.decision_fusion_training_data)
}
# Save checkpoint using checkpoint manager
checkpoint_path = self.checkpoint_manager.save_model_checkpoint(
model_name="decision_fusion",
model_data=checkpoint_data,
loss=1.0 - performance_score,
performance_score=performance_score
)
if checkpoint_path:
logger.info(f"Decision fusion checkpoint saved: {checkpoint_path}")
# Update model state
if 'decision_fusion' not in self.model_states:
self.model_states['decision_fusion'] = {}
self.model_states['decision_fusion'].update({
'checkpoint_loaded': True,
'checkpoint_filename': checkpoint_path.name if hasattr(checkpoint_path, 'name') else str(checkpoint_path),
'current_loss': 1.0 - performance_score,
'best_loss': min(self.model_states['decision_fusion'].get('best_loss', float('inf')), 1.0 - performance_score),
'last_training': datetime.now(),
'performance_score': performance_score
})
logger.info(f"Decision fusion model state updated with checkpoint info")
else:
logger.warning("Failed to save decision fusion checkpoint")
except Exception as e:
logger.error(f"Error saving decision fusion checkpoint: {e}")
def _create_decision_fusion_input(
self,
symbol: str,
predictions: List[Prediction],
current_price: float,
timestamp: datetime,
) -> torch.Tensor:
"""Create input features for the decision fusion network"""
try:
features = []
# 1. Market data features (standard input)
market_data = self._get_current_market_data(symbol)
if market_data:
# Price features
features.extend(
[
current_price,
market_data.get("volume", 0.0),
market_data.get("rsi", 50.0) / 100.0, # Normalize RSI
market_data.get("macd", 0.0),
market_data.get("bollinger_upper", current_price)
/ current_price
- 1.0,
market_data.get("bollinger_lower", current_price)
/ current_price
- 1.0,
]
)
else:
# Fallback features
features.extend([current_price, 0.0, 0.5, 0.0, 0.0, 0.0])
# 2. Model prediction features (up to 20 recent decisions per model)
model_names = ["dqn", "cnn", "transformer", "cob_rl"]
for model_name in model_names:
model_stats = self.model_statistics.get(model_name)
if model_stats:
# Model performance metrics
features.extend(
[
model_stats.accuracy or 0.0,
model_stats.average_loss or 0.0,
model_stats.best_loss or 0.0,
model_stats.total_inferences or 0.0,
model_stats.total_trainings or 0.0,
]
)
# Recent predictions (up to 20)
recent_predictions = list(model_stats.predictions_history)[
-self.decision_fusion_history_length :
]
for pred in recent_predictions:
# Action encoding: BUY=0, SELL=1, HOLD=2
action_encoding = {"BUY": 0.0, "SELL": 1.0, "HOLD": 2.0}.get(
pred["action"], 2.0
)
features.extend([action_encoding, pred["confidence"]])
# Pad with zeros if less than 20 predictions
padding_needed = self.decision_fusion_history_length - len(
recent_predictions
)
features.extend([0.0, 0.0] * padding_needed)
else:
# No model stats available
features.extend(
[0.0, 0.0, 0.0, 0.0, 0.0]
+ [0.0, 0.0] * self.decision_fusion_history_length
)
# 3. Current predictions features
for pred in predictions:
action_encoding = {"BUY": 0.0, "SELL": 1.0, "HOLD": 2.0}.get(
pred.action, 2.0
)
features.extend([action_encoding, pred.confidence])
# 4. Position and P&L features
current_position_pnl = self._get_current_position_pnl(symbol, current_price)
has_position = self._has_open_position(symbol)
features.extend(
[
current_position_pnl,
1.0 if has_position else 0.0,
self.entry_aggressiveness,
self.exit_aggressiveness,
]
)
# 5. Time-based features
features.extend(
[
timestamp.hour / 24.0, # Hour of day (0-1)
timestamp.minute / 60.0, # Minute of hour (0-1)
timestamp.weekday() / 7.0, # Day of week (0-1)
]
)
# Ensure we have the expected input size
expected_size = self.decision_fusion_network.input_size
if len(features) < expected_size:
features.extend([0.0] * (expected_size - len(features)))
elif len(features) > expected_size:
features = features[:expected_size]
# Log input feature statistics for debugging
if len(features) > 0:
feature_array = np.array(features)
logger.debug(f"Decision fusion input features: size={len(features)}, "
f"mean={np.mean(feature_array):.4f}, "
f"std={np.std(feature_array):.4f}, "
f"min={np.min(feature_array):.4f}, "
f"max={np.max(feature_array):.4f}")
return torch.tensor(
features, dtype=torch.float32, device=self.device
).unsqueeze(0)
except Exception as e:
logger.error(f"Error creating decision fusion input: {e}")
# Return zero tensor as fallback
return torch.zeros(
1, self.decision_fusion_network.input_size, device=self.device
)
def _make_decision_fusion_decision(
self,
symbol: str,
predictions: List[Prediction],
current_price: float,
timestamp: datetime,
) -> TradingDecision:
"""Use the decision fusion network to make trading decisions"""
try:
# Create input features
input_features = self._create_decision_fusion_input(
symbol, predictions, current_price, timestamp
)
# DEBUG: Log decision fusion input features
logger.info(f"=== DECISION FUSION INPUT FEATURES ===")
logger.info(f" Input shape: {input_features.shape}")
# logger.info(f" Input features (first 20): {input_features[0, :20].cpu().numpy()}")
# logger.info(f" Input features (last 20): {input_features[0, -20:].cpu().numpy()}")
logger.info(f" Input features mean: {input_features.mean().item():.4f}")
logger.info(f" Input features std: {input_features.std().item():.4f}")
# Get decision fusion network prediction
with torch.no_grad():
output = self.decision_fusion_network(input_features)
probabilities = output.squeeze().cpu().numpy()
# DEBUG: Log decision fusion outputs
logger.info(f"=== DECISION FUSION OUTPUTS ===")
logger.info(f" Raw output shape: {output.shape}")
logger.info(f" Probabilities: BUY={probabilities[0]:.4f}, SELL={probabilities[1]:.4f}, HOLD={probabilities[2]:.4f}")
logger.info(f" Probability sum: {probabilities.sum():.4f}")
# Convert probabilities to action and confidence
action_idx = np.argmax(probabilities)
actions = ["BUY", "SELL", "HOLD"]
best_action = actions[action_idx]
best_confidence = float(probabilities[action_idx])
# DEBUG: Check for overconfidence
if best_confidence > 0.95:
self.decision_fusion_overconfidence_count += 1
logger.warning(f"DECISION FUSION OVERCONFIDENCE DETECTED: {best_confidence:.3f} for {best_action} (count: {self.decision_fusion_overconfidence_count})")
if self.decision_fusion_overconfidence_count >= self.max_overconfidence_threshold:
logger.error(f"Decision fusion overconfidence threshold reached ({self.max_overconfidence_threshold}). Disabling model.")
self.disable_decision_fusion_temporarily("overconfidence threshold exceeded")
# Fallback to programmatic method
return self._combine_predictions(
symbol, current_price, predictions, timestamp
)
# Get current position P&L
current_position_pnl = self._get_current_position_pnl(symbol, current_price)
# Create reasoning
reasoning = {
"method": "decision_fusion_neural",
"predictions_count": len(predictions),
"models_used": [pred.model_name for pred in predictions],
"fusion_probabilities": {
"BUY": float(probabilities[0]),
"SELL": float(probabilities[1]),
"HOLD": float(probabilities[2]),
},
"input_features_size": input_features.shape[1],
"decision_fusion_mode": self.decision_fusion_mode,
}
# Apply P&L feedback
best_action, best_confidence = self._apply_pnl_feedback(
best_action, best_confidence, current_position_pnl, symbol, reasoning
)
# Get memory usage
memory_usage = {}
try:
if hasattr(self.model_registry, "get_memory_stats"):
memory_usage = self.model_registry.get_memory_stats()
except Exception:
pass
# Determine decision source
source = self._determine_decision_source(reasoning.get("models_used", []), best_confidence)
# Create final decision
decision = TradingDecision(
action=best_action,
confidence=best_confidence,
symbol=symbol,
price=current_price,
timestamp=timestamp,
reasoning=reasoning,
memory_usage=memory_usage.get("models", {}) if memory_usage else {},
source=source,
entry_aggressiveness=self.entry_aggressiveness,
exit_aggressiveness=self.exit_aggressiveness,
current_position_pnl=current_position_pnl,
)
# Add to training data for future training
self._add_decision_fusion_training_sample(
decision, predictions, current_price
)
# Trigger training on decision
self._trigger_training_on_decision(decision, current_price)
return decision
except Exception as e:
logger.error(f"Error in decision fusion decision: {e}")
# Fallback to programmatic method
return self._combine_predictions(
symbol, current_price, predictions, timestamp
)
def _store_decision_fusion_inference(
self,
decision: TradingDecision,
predictions: List[Prediction],
current_price: float,
):
"""Store decision fusion inference for later training (like other models)"""
try:
# Create input features for decision fusion
input_features = self._create_decision_fusion_input(
decision.symbol, predictions, current_price, decision.timestamp
)
# Store inference record
inference_record = {
"model_name": "decision_fusion",
"symbol": decision.symbol,
"action": decision.action,
"confidence": decision.confidence,
"probabilities": {"BUY": 0.33, "SELL": 0.33, "HOLD": 0.34},
"input_features": input_features,
"timestamp": decision.timestamp,
"price": current_price,
"predictions_count": len(predictions),
"models_used": [pred.model_name for pred in predictions]
}
# Store in database for later training
asyncio.create_task(self._store_inference_data_async(
"decision_fusion",
input_features,
Prediction(
action=decision.action,
confidence=decision.confidence,
probabilities={"BUY": 0.33, "SELL": 0.33, "HOLD": 0.34},
timeframe="1m",
timestamp=decision.timestamp,
model_name="decision_fusion"
),
decision.timestamp,
decision.symbol
))
# Update inference statistics
self._update_model_statistics(
"decision_fusion",
prediction=Prediction(
action=decision.action,
confidence=decision.confidence,
probabilities={"BUY": 0.33, "SELL": 0.33, "HOLD": 0.34},
timeframe="1m",
timestamp=decision.timestamp,
model_name="decision_fusion"
)
)
logger.debug(f"Stored decision fusion inference: {decision.action} (confidence: {decision.confidence:.3f})")
except Exception as e:
logger.error(f"Error storing decision fusion inference: {e}")
def _add_decision_fusion_training_sample(
self,
decision: TradingDecision,
predictions: List[Prediction],
current_price: float,
):
"""Add decision fusion training sample (legacy method - kept for compatibility)"""
try:
# Create training sample
training_sample = {
"input_features": self._create_decision_fusion_input(
decision.symbol, predictions, current_price, decision.timestamp
),
"target_action": decision.action,
"target_confidence": decision.confidence,
"timestamp": decision.timestamp,
"price": current_price,
}
self.decision_fusion_training_data.append(training_sample)
self.decision_fusion_decisions_count += 1
# Update inference statistics for decision fusion
self._update_model_statistics(
"decision_fusion",
prediction=Prediction(
action=decision.action,
confidence=decision.confidence,
probabilities={"BUY": 0.33, "SELL": 0.33, "HOLD": 0.34},
timeframe="1m",
timestamp=decision.timestamp,
model_name="decision_fusion"
)
)
# Train decision fusion network periodically
if (
self.decision_fusion_decisions_count
% self.decision_fusion_training_interval
== 0
and len(self.decision_fusion_training_data)
>= self.decision_fusion_min_samples
):
self._train_decision_fusion_network()
except Exception as e:
logger.error(f"Error adding decision fusion training sample: {e}")
def _train_decision_fusion_network(self):
"""Train the decision fusion network on collected data"""
try:
if (
len(self.decision_fusion_training_data)
< self.decision_fusion_min_samples
):
return
logger.info(
f"Training decision fusion network with {len(self.decision_fusion_training_data)} samples"
)
# Prepare training data
inputs = []
targets = []
for sample in self.decision_fusion_training_data:
inputs.append(sample["input_features"])
# Create target (one-hot encoding)
action_idx = {"BUY": 0, "SELL": 1, "HOLD": 2}[sample["target_action"]]
target = torch.zeros(3, device=self.device)
target[action_idx] = 1.0
targets.append(target)
# Stack tensors
inputs = torch.cat(inputs, dim=0)
targets = torch.stack(targets, dim=0)
# Train the network
optimizer = torch.optim.Adam(
self.decision_fusion_network.parameters(), lr=0.001
)
criterion = nn.CrossEntropyLoss()
self.decision_fusion_network.train()
optimizer.zero_grad()
outputs = self.decision_fusion_network(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# Update model statistics for decision fusion
self._update_model_training_statistics(
"decision_fusion",
loss=loss.item(),
training_duration_ms=None
)
# Measure and log performance
self._measure_decision_fusion_performance(loss.item())
logger.info(f"Decision fusion training completed. Loss: {loss.item():.4f}")
# Clear training data after training
self.decision_fusion_training_data = []
except Exception as e:
logger.error(f"Error training decision fusion network: {e}")
async def _train_decision_fusion_on_outcome(
self,
record: Dict,
was_correct: bool,
price_change_pct: float,
sophisticated_reward: float,
):
"""Train decision fusion model based on outcome (like other models)"""
try:
if not self.decision_fusion_enabled or self.decision_fusion_network is None:
return
# Get the stored input features
input_features = record.get("input_features")
if input_features is None:
logger.warning("No input features found for decision fusion training")
return
# Validate input features
if not isinstance(input_features, torch.Tensor):
logger.warning(f"Invalid input features type: {type(input_features)}")
return
if input_features.dim() != 2 or input_features.size(0) != 1:
logger.warning(f"Invalid input features shape: {input_features.shape}")
return
# Create target based on outcome
predicted_action = record.get("action", "HOLD")
# Determine if the decision was correct based on price movement
if predicted_action == "BUY" and price_change_pct > 0.1:
target_action = "BUY"
elif predicted_action == "SELL" and price_change_pct < -0.1:
target_action = "SELL"
elif predicted_action == "HOLD" and abs(price_change_pct) < 0.1:
target_action = "HOLD"
else:
# Decision was wrong - use opposite action as target
if predicted_action == "BUY":
target_action = "SELL" if price_change_pct < 0 else "HOLD"
elif predicted_action == "SELL":
target_action = "BUY" if price_change_pct > 0 else "HOLD"
else: # HOLD
target_action = "BUY" if price_change_pct > 0.1 else "SELL"
# Create target tensor
action_idx = {"BUY": 0, "SELL": 1, "HOLD": 2}[target_action]
target = torch.zeros(3, device=self.device)
target[action_idx] = 1.0
# Train the network
self.decision_fusion_network.train()
optimizer = torch.optim.Adam(
self.decision_fusion_network.parameters(), lr=0.001
)
criterion = nn.CrossEntropyLoss()
optimizer.zero_grad()
# Forward pass - LayerNorm works with single samples
output = self.decision_fusion_network(input_features)
loss = criterion(output, target.unsqueeze(0))
# Log training details for debugging
logger.debug(f"Decision fusion training: input_shape={input_features.shape}, "
f"output_shape={output.shape}, target_shape={target.unsqueeze(0).shape}, "
f"loss={loss.item():.4f}")
# Backward pass
loss.backward()
optimizer.step()
# Set back to eval mode for inference
self.decision_fusion_network.eval()
# Update training statistics
self._update_model_training_statistics(
"decision_fusion",
loss=loss.item()
)
# Measure and log performance
self._measure_decision_fusion_performance(loss.item())
logger.info(
f"Decision fusion trained on outcome: {predicted_action} -> {target_action} "
f"(price_change: {price_change_pct:+.3f}%, reward: {sophisticated_reward:.4f}, loss: {loss.item():.4f})"
)
except Exception as e:
logger.error(f"Error training decision fusion on outcome: {e}")
except Exception as e:
logger.warning(f"Decision fusion initialization failed: {e}")
self.decision_fusion_enabled = False
def _measure_decision_fusion_performance(self, loss: float):
"""Measure and track decision fusion model performance"""
try:
# Initialize decision fusion statistics if not exists
if "decision_fusion" not in self.model_statistics:
self.model_statistics["decision_fusion"] = ModelStatistics("decision_fusion")
# Update statistics
stats = self.model_statistics["decision_fusion"]
stats.update_training_stats(loss=loss)
# Calculate performance metrics
if len(stats.losses) > 1:
recent_losses = list(stats.losses)[-10:] # Last 10 losses
avg_loss = sum(recent_losses) / len(recent_losses)
loss_trend = (recent_losses[-1] - recent_losses[0]) / len(recent_losses)
# Performance score (lower loss = higher score)
performance_score = max(0.0, 1.0 - avg_loss)
logger.info(f"Decision Fusion Performance: avg_loss={avg_loss:.4f}, trend={loss_trend:.4f}, score={performance_score:.3f}")
# Update model states for dashboard
if "decision_fusion" not in self.model_states:
self.model_states["decision_fusion"] = {}
self.model_states["decision_fusion"].update({
"current_loss": loss,
"average_loss": avg_loss,
"performance_score": performance_score,
"training_count": stats.total_trainings,
"loss_trend": loss_trend,
"last_training_time": stats.last_training_time.isoformat() if stats.last_training_time else None
})
except Exception as e:
logger.error(f"Error measuring decision fusion performance: {e}")
def _initialize_transformer_model(self):
"""Initialize the transformer model for advanced sequence modeling"""
try:
from NN.models.advanced_transformer_trading import (
create_trading_transformer,
TradingTransformerConfig,
)
# Create transformer configuration
config = TradingTransformerConfig(
d_model=512,
n_heads=8,
n_layers=8,
seq_len=100,
n_actions=3,
use_multi_scale_attention=True,
use_market_regime_detection=True,
use_uncertainty_estimation=True,
use_deep_attention=True,
use_residual_connections=True,
use_layer_norm_variants=True,
)
# Create transformer model and trainer
self.primary_transformer, self.primary_transformer_trainer = (
create_trading_transformer(config)
)
# Try to load existing checkpoint
try:
from utils.checkpoint_manager import load_best_checkpoint
result = load_best_checkpoint("transformer", "transformer")
if result:
file_path, metadata = result
self.primary_transformer_trainer.load_model(file_path)
self.model_states["transformer"] = {
"initial_loss": None,
"current_loss": metadata.performance_metrics.get("loss", None),
"best_loss": metadata.performance_metrics.get("loss", None),
"checkpoint_loaded": True,
"checkpoint_filename": metadata.checkpoint_id,
}
logger.info(
f"Transformer model loaded from checkpoint: {metadata.checkpoint_id}"
)
else:
logger.info(
"No existing transformer checkpoint found, starting fresh"
)
self.model_states["transformer"] = {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
"checkpoint_filename": "none (fresh start)",
}
except Exception as e:
logger.warning(f"Error loading transformer checkpoint: {e}")
logger.info("Transformer model starting fresh")
self.model_states["transformer"] = {
"initial_loss": None,
"current_loss": None,
"best_loss": None,
"checkpoint_loaded": False,
"checkpoint_filename": "none (fresh start)",
}
logger.info("Transformer model initialized")
except Exception as e:
logger.warning(f"Transformer model initialization failed: {e}")
self.primary_transformer = None
self.primary_transformer_trainer = None
def _initialize_enhanced_training_system(self):
"""Initialize the enhanced real-time training system"""
try:
if not self.training_enabled:
logger.info("Enhanced training system disabled")
return
if not ENHANCED_TRAINING_AVAILABLE:
logger.info(
"EnhancedRealtimeTrainingSystem not available - using built-in training"
)
# Keep training enabled - we have built-in training capabilities
return
# Initialize the enhanced training system
if EnhancedRealtimeTrainingSystem is not None:
self.enhanced_training_system = EnhancedRealtimeTrainingSystem(
orchestrator=self,
data_provider=self.data_provider,
dashboard=None, # Will be set by dashboard when available
)
logger.info("Enhanced real-time training system initialized")
logger.info(" - Real-time model training: ENABLED")
logger.info(" - Comprehensive feature extraction: ENABLED")
logger.info(" - Enhanced reward calculation: ENABLED")
logger.info(" - Forward-looking predictions: ENABLED")
else:
logger.warning("EnhancedRealtimeTrainingSystem class not available")
self.training_enabled = False
except Exception as e:
logger.error(f"Error initializing enhanced training system: {e}")
self.training_enabled = False
self.enhanced_training_system = None
def start_enhanced_training(self):
"""Start the enhanced real-time training system"""
try:
if not self.training_enabled or not self.enhanced_training_system:
logger.warning("Enhanced training system not available")
return False
if hasattr(self.enhanced_training_system, "start_training"):
self.enhanced_training_system.start_training()
logger.info("Enhanced real-time training started")
return True
else:
logger.warning(
"Enhanced training system does not have start_training method"
)
return False
except Exception as e:
logger.error(f"Error starting enhanced training: {e}")
return False
def stop_enhanced_training(self):
"""Stop the enhanced real-time training system"""
try:
if self.enhanced_training_system and hasattr(
self.enhanced_training_system, "stop_training"
):
self.enhanced_training_system.stop_training()
logger.info("Enhanced real-time training stopped")
return True
return False
except Exception as e:
logger.error(f"Error stopping enhanced training: {e}")
return False
def get_enhanced_training_stats(self) -> Dict[str, Any]:
"""Get enhanced training system statistics with orchestrator integration"""
try:
if not self.enhanced_training_system:
return {
"training_enabled": False,
"system_available": ENHANCED_TRAINING_AVAILABLE,
"error": "Training system not initialized",
}
# Get base stats from enhanced training system
stats = {}
if hasattr(self.enhanced_training_system, "get_training_statistics"):
stats = self.enhanced_training_system.get_training_statistics()
stats["training_enabled"] = self.training_enabled
stats["system_available"] = ENHANCED_TRAINING_AVAILABLE
# Add orchestrator-specific training integration data
stats["orchestrator_integration"] = {
"models_connected": len(
[
m
for m in [
self.rl_agent,
self.cnn_model,
self.cob_rl_agent,
self.decision_model,
]
if m is not None
]
),
"cob_integration_active": self.cob_integration is not None,
"decision_fusion_enabled": self.decision_fusion_enabled,
"symbols_tracking": len(self.symbols),
"recent_decisions_count": sum(
len(decisions) for decisions in self.recent_decisions.values()
),
"model_weights": self.model_weights.copy(),
"realtime_processing": self.realtime_processing,
}
# Add model-specific training status from orchestrator
stats["model_training_status"] = {}
model_mappings = {
"dqn": self.rl_agent,
"cnn": self.cnn_model,
"cob_rl": self.cob_rl_agent,
"decision": self.decision_model,
}
for model_name, model in model_mappings.items():
if model:
model_stats = {
"model_loaded": True,
"memory_usage": 0,
"training_steps": 0,
"last_loss": None,
"checkpoint_loaded": self.model_states.get(model_name, {}).get(
"checkpoint_loaded", False
),
}
# Get memory usage
if hasattr(model, "memory") and model.memory:
model_stats["memory_usage"] = len(model.memory)
# Get training steps
if hasattr(model, "training_steps"):
model_stats["training_steps"] = model.training_steps
# Get last loss
if hasattr(model, "losses") and model.losses:
model_stats["last_loss"] = model.losses[-1]
stats["model_training_status"][model_name] = model_stats
else:
stats["model_training_status"][model_name] = {
"model_loaded": False,
"memory_usage": 0,
"training_steps": 0,
"last_loss": None,
"checkpoint_loaded": False,
}
# Add prediction tracking stats
stats["prediction_tracking"] = {
"dqn_predictions_tracked": sum(
len(preds) for preds in self.recent_dqn_predictions.values()
),
"cnn_predictions_tracked": sum(
len(preds) for preds in self.recent_cnn_predictions.values()
),
"accuracy_history_tracked": sum(
len(history)
for history in self.prediction_accuracy_history.values()
),
"symbols_with_predictions": [
symbol
for symbol in self.symbols
if len(self.recent_dqn_predictions.get(symbol, [])) > 0
or len(self.recent_cnn_predictions.get(symbol, [])) > 0
],
}
# Add COB integration stats if available
if self.cob_integration:
stats["cob_integration_stats"] = {
"latest_cob_data_symbols": list(self.latest_cob_data.keys()),
"cob_features_available": list(self.latest_cob_features.keys()),
"cob_state_available": list(self.latest_cob_state.keys()),
"feature_history_length": {
symbol: len(history)
for symbol, history in self.cob_feature_history.items()
},
}
return stats
except Exception as e:
logger.error(f"Error getting training stats: {e}")
return {
"training_enabled": self.training_enabled,
"system_available": ENHANCED_TRAINING_AVAILABLE,
"error": str(e),
}
def set_training_dashboard(self, dashboard):
"""Set the dashboard reference for the training system"""
try:
if self.enhanced_training_system:
self.enhanced_training_system.dashboard = dashboard
logger.info("Dashboard reference set for enhanced training system")
except Exception as e:
logger.error(f"Error setting training dashboard: {e}")
def set_cold_start_training_enabled(self, enabled: bool) -> bool:
"""Enable or disable cold start training (excessive training during cold start)
Args:
enabled: Whether to enable cold start training
Returns:
bool: True if setting was applied successfully
"""
try:
# Store the setting
self.cold_start_enabled = enabled
# Adjust training frequency based on cold start mode
if enabled:
# High frequency training during cold start
self.training_frequency = "high"
logger.info(
"ORCHESTRATOR: Cold start training ENABLED - Excessive training on every signal"
)
else:
# Normal training frequency
self.training_frequency = "normal"
logger.info(
"ORCHESTRATOR: Cold start training DISABLED - Normal training frequency"
)
return True
except Exception as e:
logger.error(f"Error setting cold start training: {e}")
return False
def get_universal_data_stream(self, current_time: Optional[datetime] = None):
"""Get universal data stream for external consumers like dashboard - DELEGATED to data provider"""
try:
if self.data_provider and hasattr(self.data_provider, "universal_adapter"):
return self.data_provider.universal_adapter.get_universal_data_stream(
current_time
)
elif self.universal_adapter:
return self.universal_adapter.get_universal_data_stream(current_time)
return None
except Exception as e:
logger.error(f"Error getting universal data stream: {e}")
return None
def get_universal_data_for_model(
self, model_type: str = "cnn"
) -> Optional[Dict[str, Any]]:
"""Get formatted universal data for specific model types - DELEGATED to data provider"""
try:
if self.data_provider and hasattr(self.data_provider, "universal_adapter"):
stream = (
self.data_provider.universal_adapter.get_universal_data_stream()
)
if stream:
return self.data_provider.universal_adapter.format_for_model(
stream, model_type
)
elif self.universal_adapter:
stream = self.universal_adapter.get_universal_data_stream()
if stream:
return self.universal_adapter.format_for_model(stream, model_type)
return None
except Exception as e:
logger.error(f"Error getting universal data for {model_type}: {e}")
return None
def get_cob_data(self, symbol: str) -> Optional[Dict[str, Any]]:
"""Get COB data for symbol - DELEGATED to data provider"""
try:
if self.data_provider:
return self.data_provider.get_latest_cob_data(symbol)
return None
except Exception as e:
logger.error(f"Error getting COB data for {symbol}: {e}")
return None
def get_combined_model_data(self, symbol: str) -> Optional[Dict[str, Any]]:
"""Get combined OHLCV + COB data for models - DELEGATED to data provider"""
try:
if self.data_provider:
return self.data_provider.get_combined_ohlcv_cob_data(symbol)
return None
except Exception as e:
logger.error(f"Error getting combined model data for {symbol}: {e}")
return None
def _get_current_position_pnl(self, symbol: str, current_price: float = None) -> 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:
# If current_price is provided, calculate P&L manually
if current_price is not None:
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
else:
# Use unrealized_pnl from position if available
if position.get("size", 0) > 0:
return float(position.get("unrealized_pnl", 0.0))
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 _get_position_side(self, symbol: str) -> Optional[str]:
"""Get the side of the current position (LONG/SHORT) or None if no position"""
try:
if self.trading_executor and hasattr(
self.trading_executor, "get_current_position"
):
position = self.trading_executor.get_current_position(symbol)
if position and position.get("size", 0) > 0:
return position.get("side", "LONG").upper()
return None
except Exception:
return None
def _calculate_position_enhanced_reward_for_dqn(self, base_reward, action, position_pnl, has_position):
"""
Calculate position-enhanced reward for DQN to incentivize profitable trades and closing losing ones
Args:
base_reward: Original reward from confidence/execution
action: Action taken ('BUY', 'SELL', 'HOLD')
position_pnl: Current position P&L
has_position: Whether we have an open position
Returns:
Enhanced reward that incentivizes profitable behavior
"""
try:
enhanced_reward = base_reward
if has_position and position_pnl != 0.0:
# Position-based reward adjustments (similar to CNN but tuned for DQN)
pnl_factor = position_pnl / 100.0 # Normalize P&L to reasonable scale
if position_pnl > 0: # Profitable position
if action == "HOLD":
# Reward holding profitable positions (let winners run)
enhanced_reward += abs(pnl_factor) * 0.4
elif action in ["BUY", "SELL"]:
# Moderate reward for taking action on profitable positions
enhanced_reward += abs(pnl_factor) * 0.2
elif position_pnl < 0: # Losing position
if action == "HOLD":
# Strong penalty for holding losing positions (cut losses)
enhanced_reward -= abs(pnl_factor) * 1.0
elif action in ["BUY", "SELL"]:
# Strong reward for taking action to close losing positions
enhanced_reward += abs(pnl_factor) * 0.8
# Ensure reward doesn't become extreme (DQN is more sensitive to reward scale)
enhanced_reward = max(-2.0, min(2.0, enhanced_reward))
return enhanced_reward
except Exception as e:
logger.error(f"Error calculating position-enhanced reward for DQN: {e}")
return base_reward
def _close_all_positions(self):
"""Close all open positions when clearing session"""
try:
if not self.trading_executor:
logger.debug("No trading executor available - cannot close positions")
return
# Get list of symbols to check for positions
symbols_to_check = [self.symbol] + self.ref_symbols
positions_closed = 0
for symbol in symbols_to_check:
try:
# Check if there's an open position
if self._has_open_position(symbol):
logger.info(f"Closing open position for {symbol}")
# Get current position details
if hasattr(self.trading_executor, "get_current_position"):
position = self.trading_executor.get_current_position(
symbol
)
if position:
side = position.get("side", "LONG")
size = position.get("size", 0)
# Determine close action (opposite of current position)
close_action = (
"SELL" if side.upper() == "LONG" else "BUY"
)
# Execute close order
if hasattr(self.trading_executor, "execute_trade"):
result = self.trading_executor.execute_trade(
symbol=symbol,
action=close_action,
size=size,
reason="Session clear - closing all positions",
)
if result and result.get("success"):
positions_closed += 1
logger.info(
f"✅ Closed {side} position for {symbol}: {size} units"
)
else:
logger.warning(
f"⚠️ Failed to close position for {symbol}: {result}"
)
else:
logger.warning(
f"Trading executor has no execute_trade method"
)
except Exception as e:
logger.error(f"Error closing position for {symbol}: {e}")
continue
if positions_closed > 0:
logger.info(
f"✅ Closed {positions_closed} open positions during session clear"
)
else:
logger.debug("No open positions to close")
except Exception as e:
logger.error(f"Error closing positions during session clear: {e}")
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")
def get_profitability_reward_multiplier(self) -> float:
"""Get the current profitability reward multiplier from trading executor
Returns:
float: Current profitability reward multiplier (0.0 to 2.0)
"""
try:
if self.trading_executor and hasattr(
self.trading_executor, "get_profitability_reward_multiplier"
):
multiplier = self.trading_executor.get_profitability_reward_multiplier()
logger.debug(
f"Current profitability reward multiplier: {multiplier:.2f}"
)
return multiplier
return 0.0
except Exception as e:
logger.error(f"Error getting profitability reward multiplier: {e}")
return 0.0
def calculate_enhanced_reward(
self, base_pnl: float, confidence: float = 1.0
) -> float:
"""Calculate enhanced reward with profitability multiplier
Args:
base_pnl: Base P&L from the trade
confidence: Confidence level of the prediction (0.0 to 1.0)
Returns:
float: Enhanced reward with profitability multiplier applied
"""
try:
# Get the dynamic profitability multiplier
profitability_multiplier = self.get_profitability_reward_multiplier()
# Base reward is the P&L
base_reward = base_pnl
# Apply profitability multiplier only to positive P&L (profitable trades)
if base_pnl > 0 and profitability_multiplier > 0:
# Enhance profitable trades with the multiplier
enhanced_reward = base_pnl * (1.0 + profitability_multiplier)
logger.debug(
f"Enhanced reward: ${base_pnl:.2f} → ${enhanced_reward:.2f} (multiplier: {profitability_multiplier:.2f})"
)
return enhanced_reward
else:
# No enhancement for losing trades or when multiplier is 0
return base_reward
except Exception as e:
logger.error(f"Error calculating enhanced reward: {e}")
return base_pnl
def _trigger_training_on_decision(
self, decision: TradingDecision, current_price: float
):
"""Trigger training on each decision, especially executed trades
This ensures models learn from every signal outcome, giving more weight
to executed trades as they have real market feedback.
"""
try:
# Only train if training is enabled and we have the enhanced training system
if not self.training_enabled or not self.enhanced_training_system:
return
symbol = decision.symbol
action = decision.action
confidence = decision.confidence
# Create training data from the decision
training_data = {
"symbol": symbol,
"action": action,
"confidence": confidence,
"price": current_price,
"timestamp": decision.timestamp,
"executed": action != "HOLD", # Assume non-HOLD actions are executed
"entry_aggressiveness": decision.entry_aggressiveness,
"exit_aggressiveness": decision.exit_aggressiveness,
"reasoning": decision.reasoning,
}
# Add to enhanced training system for immediate learning
if hasattr(self.enhanced_training_system, "add_decision_for_training"):
self.enhanced_training_system.add_decision_for_training(training_data)
logger.debug(
f"🎓 Added decision to training queue: {action} {symbol} (conf: {confidence:.3f})"
)
# Trigger immediate training for executed trades (higher priority)
if action != "HOLD":
if hasattr(self.enhanced_training_system, "trigger_immediate_training"):
self.enhanced_training_system.trigger_immediate_training(
symbol=symbol, priority="high" if confidence > 0.7 else "medium"
)
logger.info(
f"🚀 Triggered immediate training for executed trade: {action} {symbol}"
)
# Train all models on the decision outcome
self._train_models_on_decision(decision, current_price)
except Exception as e:
logger.error(f"Error triggering training on decision: {e}")
def _train_models_on_decision(
self, decision: TradingDecision, current_price: float
):
"""Train all models on the decision outcome
This provides immediate feedback to models about their predictions,
allowing them to learn from each signal they generate.
"""
try:
symbol = decision.symbol
action = decision.action
confidence = decision.confidence
# Get current market data for training context - use same data source as CNN model
base_data = self.build_base_data_input(symbol)
if not base_data:
logger.warning(f"No base data available for training {symbol}, skipping model training")
return
# Track if any model was trained for checkpoint saving
models_trained = []
# Train DQN agent if available and enabled
if self.rl_agent and hasattr(self.rl_agent, "remember") and self.is_model_training_enabled("dqn"):
try:
# Validate base_data before creating state
if not base_data or not hasattr(base_data, 'get_feature_vector'):
logger.debug(f"⚠️ Skipping DQN training for {symbol}: no valid base_data")
else:
# Check if base_data has actual features
features = base_data.get_feature_vector()
if not features or len(features) == 0 or all(f == 0 for f in features):
logger.debug(f"⚠️ Skipping DQN training for {symbol}: no valid features in base_data")
else:
# Create state representation from base_data (same as CNN model)
state = self._create_state_from_base_data(symbol, base_data)
# Skip training if no valid state could be created
if state is None:
logger.debug(f"⚠️ Skipping DQN training for {symbol}: could not create valid state")
else:
# Map action to DQN action space - CONSISTENT ACTION MAPPING
action_mapping = {"BUY": 0, "SELL": 1, "HOLD": 2}
dqn_action = action_mapping.get(action, 2)
# Get position information for enhanced rewards
has_position = self._has_open_position(symbol)
position_pnl = self._get_current_position_pnl(symbol) if has_position else 0.0
# Calculate position-enhanced reward
base_reward = confidence if action != "HOLD" else 0.1
enhanced_reward = self._calculate_position_enhanced_reward_for_dqn(
base_reward, action, position_pnl, has_position
)
# Add experience to DQN
self.rl_agent.remember(
state=state,
action=dqn_action,
reward=enhanced_reward,
next_state=state, # Will be updated with actual outcome later
done=False,
)
models_trained.append("dqn")
logger.debug(
f"🧠 Added DQN experience: {action} {symbol} (reward: {enhanced_reward:.3f}, P&L: ${position_pnl:.2f})"
)
except Exception as e:
logger.debug(f"Error training DQN on decision: {e}")
# Train CNN model if available and enabled
if self.cnn_model and hasattr(self.cnn_model, "add_training_data") and self.is_model_training_enabled("cnn"):
try:
# Create CNN input features from base_data (same as inference)
cnn_features = self._create_cnn_features_from_base_data(
symbol, base_data
)
# Create target based on action
target_mapping = {
"BUY": 0, # Action indices for CNN
"SELL": 1,
"HOLD": 2,
}
target_action = target_mapping.get(action, 2)
# Get position information for enhanced rewards
has_position = self._has_open_position(symbol)
position_pnl = self._get_current_position_pnl(symbol) if has_position else 0.0
# Calculate base reward from confidence and add position-based enhancement
base_reward = confidence if action != "HOLD" else 0.1
# Add training data with position-based reward enhancement
self.cnn_model.add_training_data(
cnn_features,
target_action,
base_reward,
position_pnl=position_pnl,
has_position=has_position
)
models_trained.append("cnn")
logger.debug(f"🔍 Added CNN training sample: {action} {symbol} (P&L: ${position_pnl:.2f})")
except Exception as e:
logger.debug(f"Error training CNN on decision: {e}")
# Train COB RL model if available, enabled, and we have COB data
if self.cob_rl_agent and symbol in self.latest_cob_data and self.is_model_training_enabled("cob_rl"):
try:
cob_data = self.latest_cob_data[symbol]
if hasattr(self.cob_rl_agent, "remember"):
# Create COB state representation
cob_state = self._create_cob_state_for_training(
symbol, cob_data
)
# Add COB experience
self.cob_rl_agent.remember(
state=cob_state,
action=action,
reward=confidence,
next_state=cob_state, # Add required next_state parameter
done=False, # Add required done parameter
)
models_trained.append("cob_rl")
logger.debug(f"📊 Added COB RL experience: {action} {symbol}")
except Exception as e:
logger.debug(f"Error training COB RL on decision: {e}")
# Train decision fusion model if available and enabled
if self.decision_fusion_network and self.is_model_training_enabled("decision_fusion"):
try:
# Create decision fusion input
fusion_input = self._create_decision_fusion_training_input(
symbol, market_data
)
# Create target based on action
target_mapping = {
"BUY": [1, 0, 0],
"SELL": [0, 1, 0],
"HOLD": [0, 0, 1],
}
target = target_mapping.get(action, [0, 0, 1])
# Decision fusion network doesn't have add_training_sample method
# Instead, we'll store the training data for later batch training
if not hasattr(self, 'decision_fusion_training_data'):
self.decision_fusion_training_data = []
# Convert target list to action string for compatibility
target_action = "BUY" if target[0] == 1 else "SELL" if target[1] == 1 else "HOLD"
self.decision_fusion_training_data.append({
'input_features': fusion_input,
'target_action': target_action,
'weight': confidence,
'timestamp': datetime.now()
})
# Train the network if we have enough samples
if len(self.decision_fusion_training_data) >= 5: # Train every 5 samples
self._train_decision_fusion_network()
self.decision_fusion_training_data = [] # Clear after training
models_trained.append("decision_fusion")
logger.debug(f"🤝 Added decision fusion training sample: {action} {symbol}")
except Exception as e:
logger.debug(f"Error training decision fusion on decision: {e}")
# CRITICAL FIX: Save checkpoints after training
if models_trained:
self._save_training_checkpoints(models_trained, confidence)
except Exception as e:
logger.error(f"Error training models on decision: {e}")
def _save_training_checkpoints(
self, models_trained: List[str], performance_score: float
):
"""Save checkpoints for trained models if performance improved
This is CRITICAL for preserving training progress across restarts.
"""
try:
if not self.checkpoint_manager:
return
# Increment training counter
self.training_iterations += 1
# Save checkpoints for each trained model
for model_name in models_trained:
try:
model_obj = None
current_loss = None
# Get model object and calculate current performance
if model_name == "dqn" and self.rl_agent:
model_obj = self.rl_agent
# Use negative performance score as loss (higher confidence = lower loss)
current_loss = 1.0 - performance_score
elif model_name == "cnn" and self.cnn_model:
model_obj = self.cnn_model
current_loss = 1.0 - performance_score
elif model_name == "cob_rl" and self.cob_rl_agent:
model_obj = self.cob_rl_agent
current_loss = 1.0 - performance_score
elif model_name == "decision_fusion" and self.decision_fusion_network:
model_obj = self.decision_fusion_network
current_loss = 1.0 - performance_score
if model_obj and current_loss is not None:
# Check if this is the best performance so far
model_state = self.model_states.get(model_name, {})
best_loss = model_state.get("best_loss", float("inf"))
# Update current loss
model_state["current_loss"] = current_loss
model_state["last_training"] = datetime.now()
# Save checkpoint if performance improved or every 3rd training
should_save = (
current_loss < best_loss # Performance improved
or self.training_iterations % 3
== 0 # Save every 3rd training iteration
)
if should_save:
# Prepare metadata
metadata = {
"loss": current_loss,
"performance_score": performance_score,
"training_iterations": self.training_iterations,
"timestamp": datetime.now().isoformat(),
"model_type": model_name,
}
# Save checkpoint
checkpoint_path = self.checkpoint_manager.save_checkpoint(
model=model_obj,
model_name=model_name,
performance=current_loss,
metadata=metadata,
)
if checkpoint_path:
# Update best performance
if current_loss < best_loss:
model_state["best_loss"] = current_loss
model_state["best_checkpoint"] = checkpoint_path
logger.info(
f"💾 Saved BEST checkpoint for {model_name}: {checkpoint_path} (loss: {current_loss:.4f})"
)
else:
logger.debug(
f"💾 Saved periodic checkpoint for {model_name}: {checkpoint_path}"
)
model_state["last_checkpoint"] = checkpoint_path
model_state["checkpoints_saved"] = (
model_state.get("checkpoints_saved", 0) + 1
)
# Update model state
self.model_states[model_name] = model_state
except Exception as e:
logger.error(f"Error saving checkpoint for {model_name}: {e}")
except Exception as e:
logger.error(f"Error saving training checkpoints: {e}")
def _get_current_market_data(self, symbol: str) -> Optional[Dict]:
"""Get current market data for training context"""
try:
if not self.data_provider:
logger.warning(f"No data provider available for {symbol}")
return None
# Get recent data for training
df = self.data_provider.get_historical_data(symbol, "1m", limit=100)
if df is not None and not df.empty:
return {
"ohlcv": df.tail(50).to_dict("records"), # Last 50 candles
"current_price": float(df["close"].iloc[-1]),
"volume": float(df["volume"].iloc[-1]),
"timestamp": df.index[-1],
}
else:
logger.warning(f"No historical data available for {symbol}")
return None
except Exception as e:
logger.error(f"Error getting market data for training {symbol}: {e}")
return None
def _create_state_from_base_data(self, symbol: str, base_data: Any) -> Optional[np.ndarray]:
"""Create state representation for DQN training from base_data (same as CNN model)"""
try:
# Validate base_data
if not base_data or not hasattr(base_data, 'get_feature_vector'):
logger.debug(f"Invalid base_data for {symbol}: {type(base_data)}")
return None
# Get feature vector from base_data (same as CNN model)
features = base_data.get_feature_vector()
if not features or len(features) == 0:
logger.debug(f"No features available from base_data for {symbol}")
return None
# Check if all features are zero (invalid state)
if all(f == 0 for f in features):
logger.debug(f"All features are zero for {symbol}")
return None
# Convert to numpy array
state = np.array(features, dtype=np.float32)
# Ensure correct dimensions for DQN (403 features)
if len(state) != 403:
if len(state) < 403:
# Pad with zeros
padded_state = np.zeros(403, dtype=np.float32)
padded_state[:len(state)] = state
state = padded_state
else:
# Truncate
state = state[:403]
return state
except Exception as e:
logger.error(f"Error creating state from base_data for {symbol}: {e}")
return None
def _create_cnn_features_from_base_data(
self, symbol: str, base_data: Any
) -> np.ndarray:
"""Create CNN features for training from base_data (same as inference)"""
try:
# Validate base_data
if not base_data or not hasattr(base_data, 'get_feature_vector'):
logger.warning(f"Invalid base_data for CNN training {symbol}: {type(base_data)}")
return np.zeros((1, 403)) # Default CNN input size
# Get feature vector from base_data (same as CNN inference)
features = base_data.get_feature_vector()
if not features or len(features) == 0:
logger.warning(f"No features available from base_data for CNN training {symbol}, using default")
return np.zeros((1, 403)) # Default CNN input size
# Convert to numpy array and reshape for CNN
cnn_features = np.array(features, dtype=np.float32).reshape(1, -1)
# Ensure correct dimensions for CNN (403 features)
if cnn_features.shape[1] != 403:
if cnn_features.shape[1] < 403:
# Pad with zeros
padded_features = np.zeros((1, 403), dtype=np.float32)
padded_features[0, :cnn_features.shape[1]] = cnn_features[0]
cnn_features = padded_features
else:
# Truncate
cnn_features = cnn_features[:, :403]
return cnn_features
except Exception as e:
logger.error(f"Error creating CNN features from base_data for {symbol}: {e}")
return np.zeros((1, 403)) # Default CNN input size
def _create_cob_state_for_training(self, symbol: str, cob_data: Dict) -> np.ndarray:
"""Create COB state representation for training"""
try:
# Extract COB features for training
features = []
# Add bid/ask data
bids = cob_data.get("bids", [])[:10] # Top 10 bids
asks = cob_data.get("asks", [])[:10] # Top 10 asks
for bid in bids:
features.extend([bid.get("price", 0), bid.get("size", 0)])
for ask in asks:
features.extend([ask.get("price", 0), ask.get("size", 0)])
# Add market stats
stats = cob_data.get("stats", {})
features.extend(
[
stats.get("spread", 0),
stats.get("mid_price", 0),
stats.get("bid_volume", 0),
stats.get("ask_volume", 0),
stats.get("imbalance", 0),
]
)
# Pad to expected COB state size (2000 features)
cob_state = np.array(features[:2000])
if len(cob_state) < 2000:
cob_state = np.pad(cob_state, (0, 2000 - len(cob_state)), "constant")
return cob_state
except Exception as e:
logger.debug(f"Error creating COB state for training: {e}")
return np.zeros(2000)
def _create_decision_fusion_training_input(self, symbol: str, market_data: Dict) -> np.ndarray:
"""Create decision fusion training input from market data"""
try:
# Extract features from market data
ohlcv_data = market_data.get("ohlcv", [])
if not ohlcv_data:
return np.zeros(100) # Default state size
# Extract features from recent candles
features = []
for candle in ohlcv_data[-20:]: # Last 20 candles
features.extend(
[
candle.get("open", 0),
candle.get("high", 0),
candle.get("low", 0),
candle.get("close", 0),
candle.get("volume", 0),
]
)
# Pad or truncate to expected size
state = np.array(features[:100])
if len(state) < 100:
state = np.pad(state, (0, 100 - len(state)), "constant")
return state
except Exception as e:
logger.debug(f"Error creating decision fusion input: {e}")
return np.zeros(100)
def _check_signal_confirmation(
self, symbol: str, signal_data: Dict
) -> Optional[str]:
"""Check if we have enough signal confirmations for trend confirmation with rate limiting"""
try:
current_time = signal_data["timestamp"]
action = signal_data["action"]
# Initialize signal tracking for this symbol if needed
if symbol not in self.last_signal_time:
self.last_signal_time[symbol] = {}
if symbol not in self.last_confirmed_signal:
self.last_confirmed_signal[symbol] = {}
# RATE LIMITING: Check if we recently confirmed the same signal
if action in self.last_confirmed_signal[symbol]:
last_confirmed = self.last_confirmed_signal[symbol][action]
time_since_last = current_time - last_confirmed["timestamp"]
if time_since_last < self.min_signal_interval:
logger.debug(
f"Rate limiting: {action} signal for {symbol} too recent "
f"({time_since_last.total_seconds():.1f}s < {self.min_signal_interval.total_seconds()}s)"
)
return None
# Clean up expired signals
self.signal_accumulator[symbol] = [
s
for s in self.signal_accumulator[symbol]
if (current_time - s["timestamp"]).total_seconds()
< self.signal_timeout_seconds
]
# Add new signal
self.signal_accumulator[symbol].append(signal_data)
# Check if we have enough confirmations
if len(self.signal_accumulator[symbol]) < self.required_confirmations:
return None
# Check if recent signals are consistent
recent_signals = self.signal_accumulator[symbol][
-self.required_confirmations :
]
actions = [s["action"] for s in recent_signals]
# Count action consensus
action_counts = {}
for action_item in actions:
action_counts[action_item] = action_counts.get(action_item, 0) + 1
# Find dominant action
dominant_action = max(action_counts, key=action_counts.get)
consensus_count = action_counts[dominant_action]
# Require at least 2/3 consensus
if consensus_count >= max(2, self.required_confirmations * 0.67):
# ADDITIONAL RATE LIMITING: Don't confirm if we just confirmed the same action
if dominant_action in self.last_confirmed_signal[symbol]:
last_confirmed = self.last_confirmed_signal[symbol][dominant_action]
time_since_last = current_time - last_confirmed["timestamp"]
if time_since_last < self.min_signal_interval:
logger.debug(
f"Rate limiting: Preventing duplicate {dominant_action} confirmation for {symbol}"
)
return None
# Record this confirmation
self.last_confirmed_signal[symbol][dominant_action] = {
"timestamp": current_time,
"confidence": signal_data["confidence"],
}
# Clear accumulator after confirmation
self.signal_accumulator[symbol] = []
logger.info(
f"Signal confirmed after rate limiting: {dominant_action} for {symbol}"
)
return dominant_action
return None
except Exception as e:
logger.error(f"Error checking signal confirmation for {symbol}: {e}")
return None
def _initialize_checkpoint_manager(self):
"""Initialize the checkpoint manager for model persistence"""
try:
from utils.checkpoint_manager import get_checkpoint_manager
self.checkpoint_manager = get_checkpoint_manager()
# Initialize model states dictionary to track performance
self.model_states = {
"dqn": {
"initial_loss": None,
"current_loss": None,
"best_loss": float("inf"),
"checkpoint_loaded": False,
},
"cnn": {
"initial_loss": None,
"current_loss": None,
"best_loss": float("inf"),
"checkpoint_loaded": False,
},
"cob_rl": {
"initial_loss": None,
"current_loss": None,
"best_loss": float("inf"),
"checkpoint_loaded": False,
},
"extrema": {
"initial_loss": None,
"current_loss": None,
"best_loss": float("inf"),
"checkpoint_loaded": False,
},
}
logger.info("Checkpoint manager initialized for model persistence")
except Exception as e:
logger.error(f"Error initializing checkpoint manager: {e}")
self.checkpoint_manager = None
def _schedule_database_cleanup(self):
"""Schedule periodic database cleanup"""
try:
# Clean up old inference records (keep 30 days)
self.inference_logger.cleanup_old_logs(days_to_keep=30)
logger.info("Database cleanup completed")
except Exception as e:
logger.error(f"Database cleanup failed: {e}")
def log_model_inference(
self,
model_name: str,
symbol: str,
action: str,
confidence: float,
probabilities: Dict[str, float],
input_features: Any,
processing_time_ms: float,
checkpoint_id: str = None,
metadata: Dict[str, Any] = None,
) -> bool:
"""
Centralized method for models to log their inferences
This replaces scattered logger.info() calls throughout the codebase
"""
return log_model_inference(
model_name=model_name,
symbol=symbol,
action=action,
confidence=confidence,
probabilities=probabilities,
input_features=input_features,
processing_time_ms=processing_time_ms,
checkpoint_id=checkpoint_id,
metadata=metadata,
)
def get_model_inference_stats(
self, model_name: str, hours: int = 24
) -> Dict[str, Any]:
"""Get inference statistics for a model"""
return self.inference_logger.get_model_stats(model_name, hours)
def get_checkpoint_metadata_fast(self, model_name: str) -> Optional[Any]:
"""
Get checkpoint metadata without loading the full model
This is much faster than loading the entire checkpoint just to get metadata
"""
return self.db_manager.get_best_checkpoint_metadata(model_name)
# === DATA MANAGEMENT ===
def _log_data_status(self):
"""Log current data status"""
try:
logger.info("=== Data Provider Status ===")
logger.info(
"Data provider is running and optimized for BaseDataInput building"
)
except Exception as e:
logger.error(f"Error logging data status: {e}")
def update_data_cache(
self, data_type: str, symbol: str, data: Any, source: str = "orchestrator"
) -> bool:
"""
Update data cache through data provider
Args:
data_type: Type of data ('ohlcv_1s', 'technical_indicators', etc.)
symbol: Trading symbol
data: Data to store
source: Source of the update
Returns:
bool: True if updated successfully
"""
try:
# Invalidate cache when new data arrives
if hasattr(self.data_provider, "invalidate_ohlcv_cache"):
self.data_provider.invalidate_ohlcv_cache(symbol)
return True
except Exception as e:
logger.error(f"Error updating data cache {data_type}/{symbol}: {e}")
return False
def get_latest_data(self, data_type: str, symbol: str, count: int = 1) -> List[Any]:
"""
Get latest data from FIFO queue
Args:
data_type: Type of data
symbol: Trading symbol
count: Number of latest items to retrieve
Returns:
List of latest data items
"""
try:
if (
data_type not in self.data_queues
or symbol not in self.data_queues[data_type]
):
return []
with self.data_queue_locks[data_type][symbol]:
queue = self.data_queues[data_type][symbol]
if len(queue) == 0:
return []
# Get last 'count' items
return list(queue)[-count:] if count > 1 else [queue[-1]]
except Exception as e:
logger.error(f"Error getting latest data {data_type}/{symbol}: {e}")
return []
def get_queue_data(
self, data_type: str, symbol: str, max_items: int = None
) -> List[Any]:
"""
Get all data from FIFO queue
Args:
data_type: Type of data
symbol: Trading symbol
max_items: Maximum number of items to return (None for all)
Returns:
List of data items
"""
try:
if (
data_type not in self.data_queues
or symbol not in self.data_queues[data_type]
):
return []
with self.data_queue_locks[data_type][symbol]:
queue = self.data_queues[data_type][symbol]
data_list = list(queue)
if max_items and len(data_list) > max_items:
return data_list[-max_items:]
return data_list
except Exception as e:
logger.error(f"Error getting queue data {data_type}/{symbol}: {e}")
return []
def get_queue_status(self) -> Dict[str, Dict[str, int]]:
"""Get status of all data queues"""
status = {}
for data_type, symbol_queues in self.data_queues.items():
status[data_type] = {}
for symbol, queue in symbol_queues.items():
with self.data_queue_locks[data_type][symbol]:
status[data_type][symbol] = len(queue)
return status
def get_detailed_queue_status(self) -> Dict[str, Any]:
"""Get detailed status of all data queues with timestamps and data info"""
detailed_status = {}
for data_type, symbol_queues in self.data_queues.items():
detailed_status[data_type] = {}
for symbol, queue in symbol_queues.items():
with self.data_queue_locks[data_type][symbol]:
queue_list = list(queue)
queue_info = {
"count": len(queue_list),
"max_size": queue.maxlen,
"usage_percent": (
(len(queue_list) / queue.maxlen * 100)
if queue.maxlen
else 0
),
"oldest_timestamp": None,
"newest_timestamp": None,
"data_type_info": None,
}
if queue_list:
# Try to get timestamps from data
try:
if hasattr(queue_list[0], "timestamp"):
queue_info["oldest_timestamp"] = queue_list[
0
].timestamp.isoformat()
queue_info["newest_timestamp"] = queue_list[
-1
].timestamp.isoformat()
# Add data type specific info
if data_type.startswith("ohlcv_"):
if hasattr(queue_list[-1], "close"):
queue_info["data_type_info"] = (
f"latest_price={queue_list[-1].close:.2f}"
)
elif data_type == "technical_indicators":
if isinstance(queue_list[-1], dict):
indicators = list(queue_list[-1].keys())[
:3
] # First 3 indicators
queue_info["data_type_info"] = (
f"indicators={indicators}"
)
elif data_type == "cob_data":
queue_info["data_type_info"] = "cob_snapshot"
elif data_type == "model_predictions":
if hasattr(queue_list[-1], "action"):
queue_info["data_type_info"] = (
f"latest_action={queue_list[-1].action}"
)
except Exception as e:
queue_info["data_type_info"] = f"error_getting_info: {e}"
detailed_status[data_type][symbol] = queue_info
return detailed_status
def log_queue_status(self, detailed: bool = False):
"""Log current queue status for debugging"""
if detailed:
status = self.get_detailed_queue_status()
logger.info("=== Detailed Queue Status ===")
for data_type, symbols in status.items():
logger.info(f"{data_type}:")
for symbol, info in symbols.items():
logger.info(
f" {symbol}: {info['count']}/{info['max_size']} ({info['usage_percent']:.1f}%) - {info.get('data_type_info', 'no_info')}"
)
else:
status = self.get_queue_status()
logger.info("=== Queue Status ===")
for data_type, symbols in status.items():
symbol_counts = [
f"{symbol}:{count}" for symbol, count in symbols.items()
]
logger.info(f"{data_type}: {', '.join(symbol_counts)}")
def ensure_minimum_data(self, data_type: str, symbol: str, min_count: int) -> bool:
"""
Check if queue has minimum required data
Args:
data_type: Type of data
symbol: Trading symbol
min_count: Minimum required items
Returns:
bool: True if minimum data available
"""
try:
if (
data_type not in self.data_queues
or symbol not in self.data_queues[data_type]
):
return False
with self.data_queue_locks[data_type][symbol]:
return len(self.data_queues[data_type][symbol]) >= min_count
except Exception as e:
logger.error(f"Error checking minimum data {data_type}/{symbol}: {e}")
return False
def build_base_data_input(self, symbol: str) -> Optional[Any]:
"""
Build BaseDataInput using optimized data provider (should be instantaneous)
Args:
symbol: Trading symbol
Returns:
BaseDataInput with consistent data structure and position information
"""
try:
# Use data provider's optimized build_base_data_input method
base_data = self.data_provider.build_base_data_input(symbol)
if base_data:
# Add position information to the base data
current_price = self.data_provider.get_current_price(symbol)
has_position = self._has_open_position(symbol)
position_pnl = self._get_current_position_pnl(symbol, current_price) if current_price else 0.0
# Get additional position details if available
position_size = 0.0
entry_price = 0.0
time_in_position_minutes = 0.0
if has_position and self.trading_executor and hasattr(self.trading_executor, "get_current_position"):
try:
position = self.trading_executor.get_current_position(symbol)
if position:
position_size = position.get("size", 0.0)
entry_price = position.get("price", 0.0)
entry_time = position.get("entry_time")
if entry_time:
time_in_position_minutes = (datetime.now() - entry_time).total_seconds() / 60.0
except Exception as e:
logger.debug(f"Error getting position details for {symbol}: {e}")
# Add position information to base data
base_data.position_info = {
'has_position': has_position,
'position_pnl': position_pnl,
'position_size': position_size,
'entry_price': entry_price,
'time_in_position_minutes': time_in_position_minutes
}
return base_data
except Exception as e:
logger.error(f"Error building BaseDataInput for {symbol}: {e}")
return None
def _get_latest_indicators(self, symbol: str) -> Dict[str, float]:
"""Get latest technical indicators from queue"""
try:
indicators_data = self.get_latest_data("technical_indicators", symbol, 1)
if indicators_data:
return indicators_data[0]
return {}
except Exception as e:
logger.error(f"Error getting indicators for {symbol}: {e}")
return {}
def _get_latest_cob_data(self, symbol: str) -> Optional[Any]:
"""Get latest COB data from queue"""
try:
cob_data = self.get_latest_data("cob_data", symbol, 1)
if cob_data:
return cob_data[0]
return None
except Exception as e:
logger.error(f"Error getting COB data for {symbol}: {e}")
return None
def _get_recent_model_predictions(self, symbol: str) -> Dict[str, Any]:
"""Get recent model predictions from queue"""
try:
predictions_data = self.get_latest_data("model_predictions", symbol, 5)
# Convert to dict format expected by BaseDataInput
predictions_dict = {}
for i, pred in enumerate(predictions_data):
predictions_dict[f"model_{i}"] = pred
return predictions_dict
except Exception as e:
logger.error(f"Error getting model predictions for {symbol}: {e}")
return {}
def _initialize_data_queue_integration(self):
"""Initialize integration between data provider and FIFO queues"""
try:
# Register callbacks with data provider to populate FIFO queues
if hasattr(self.data_provider, "register_data_callback"):
# Register for different data types
self.data_provider.register_data_callback("ohlcv", self._on_ohlcv_data)
self.data_provider.register_data_callback(
"technical_indicators", self._on_indicators_data
)
self.data_provider.register_data_callback("cob", self._on_cob_data)
logger.info("Data provider callbacks registered for FIFO queues")
else:
# Fallback: Start a background thread to poll data
self._start_data_polling_thread()
logger.info("Started data polling thread for FIFO queues")
except Exception as e:
logger.error(f"Error initializing data queue integration: {e}")
def _on_ohlcv_data(self, symbol: str, timeframe: str, data: Any):
"""Callback for new OHLCV data"""
try:
data_type = f"ohlcv_{timeframe}"
if data_type in self.data_queues and symbol in self.data_queues[data_type]:
self.update_data_queue(data_type, symbol, data)
except Exception as e:
logger.error(f"Error processing OHLCV data callback: {e}")
def _on_indicators_data(self, symbol: str, indicators: Dict[str, float]):
"""Callback for new technical indicators"""
try:
self.update_data_queue("technical_indicators", symbol, indicators)
except Exception as e:
logger.error(f"Error processing indicators data callback: {e}")
def _on_cob_data(self, symbol: str, cob_data: Any):
"""Callback for new COB data"""
try:
self.update_data_queue("cob_data", symbol, cob_data)
except Exception as e:
logger.error(f"Error processing COB data callback: {e}")
def _start_data_polling_thread(self):
"""Start background thread to poll data and populate queues"""
def data_polling_worker():
"""Background worker to poll data and update queues"""
poll_count = 0
while self.running:
try:
poll_count += 1
# Log polling activity every 30 seconds
if poll_count % 30 == 1:
logger.info(
f"Data polling cycle #{poll_count} - checking data sources"
)
# Poll OHLCV data for all symbols and timeframes
for symbol in [self.symbol] + self.ref_symbols:
for timeframe in ["1s", "1m", "1h", "1d"]:
try:
# Get latest data from data provider using correct method
if hasattr(self.data_provider, "get_latest_candles"):
df = self.data_provider.get_latest_candles(
symbol, timeframe, limit=1
)
if df is not None and not df.empty:
# Convert DataFrame row to OHLCVBar
latest_row = df.iloc[-1]
from core.data_models import OHLCVBar
ohlcv_bar = OHLCVBar(
symbol=symbol,
timestamp=(
latest_row.name
if hasattr(
latest_row.name, "to_pydatetime"
)
else datetime.now()
),
open=float(latest_row["open"]),
high=float(latest_row["high"]),
low=float(latest_row["low"]),
close=float(latest_row["close"]),
volume=float(latest_row["volume"]),
timeframe=timeframe,
)
self.update_data_queue(
f"ohlcv_{timeframe}", symbol, ohlcv_bar
)
elif hasattr(self.data_provider, "get_historical_data"):
df = self.data_provider.get_historical_data(
symbol, timeframe, limit=1
)
if df is not None and not df.empty:
# Convert DataFrame row to OHLCVBar
latest_row = df.iloc[-1]
from core.data_models import OHLCVBar
ohlcv_bar = OHLCVBar(
symbol=symbol,
timestamp=(
latest_row.name
if hasattr(
latest_row.name, "to_pydatetime"
)
else datetime.now()
),
open=float(latest_row["open"]),
high=float(latest_row["high"]),
low=float(latest_row["low"]),
close=float(latest_row["close"]),
volume=float(latest_row["volume"]),
timeframe=timeframe,
)
self.update_data_queue(
f"ohlcv_{timeframe}", symbol, ohlcv_bar
)
except Exception as e:
logger.debug(f"Error polling {symbol} {timeframe}: {e}")
# Poll technical indicators
for symbol in [self.symbol] + self.ref_symbols:
try:
# Get recent data and calculate basic indicators
df = None
if hasattr(self.data_provider, "get_latest_candles"):
df = self.data_provider.get_latest_candles(
symbol, "1m", limit=50
)
elif hasattr(self.data_provider, "get_historical_data"):
df = self.data_provider.get_historical_data(
symbol, "1m", limit=50
)
if df is not None and not df.empty and len(df) >= 20:
# Calculate basic technical indicators
indicators = {}
try:
# Use our own RSI implementation to avoid ta library deprecation warnings
if len(df) >= 14:
indicators["rsi"] = self._calculate_rsi(
df["close"], period=14
)
indicators["sma_20"] = (
df["close"].rolling(20).mean().iloc[-1]
)
indicators["ema_12"] = (
df["close"].ewm(span=12).mean().iloc[-1]
)
indicators["ema_26"] = (
df["close"].ewm(span=26).mean().iloc[-1]
)
indicators["macd"] = (
indicators["ema_12"] - indicators["ema_26"]
)
# Remove NaN values
indicators = {
k: float(v)
for k, v in indicators.items()
if not pd.isna(v)
}
if indicators:
self.update_data_queue(
"technical_indicators", symbol, indicators
)
except Exception as ta_e:
logger.debug(
f"Error calculating indicators for {symbol}: {ta_e}"
)
except Exception as e:
logger.debug(f"Error polling indicators for {symbol}: {e}")
# Poll COB data (primary symbol only)
try:
if hasattr(self.data_provider, "get_latest_cob_data"):
cob_data = self.data_provider.get_latest_cob_data(
self.symbol
)
if cob_data and isinstance(cob_data, dict) and cob_data:
self.update_data_queue(
"cob_data", self.symbol, cob_data
)
except Exception as e:
logger.debug(f"Error polling COB data: {e}")
# Sleep between polls
time.sleep(1) # Poll every second
except Exception as e:
logger.error(f"Error in data polling worker: {e}")
time.sleep(5) # Wait longer on error
# Start the polling thread
self.data_polling_thread = threading.Thread(
target=data_polling_worker, daemon=True
)
self.data_polling_thread.start()
logger.info("Data polling thread started")
# Populate initial data
self._populate_initial_queue_data()
def _populate_initial_queue_data(self):
"""Populate FIFO queues with initial historical data"""
try:
logger.info("Populating FIFO queues with initial data...")
# Get initial OHLCV data for all symbols and timeframes
for symbol in [self.symbol] + self.ref_symbols:
for timeframe in ["1s", "1m", "1h", "1d"]:
try:
# Determine how much data to fetch based on timeframe
limits = {"1s": 500, "1m": 300, "1h": 300, "1d": 300}
limit = limits.get(timeframe, 300)
# Get historical data
df = None
if hasattr(self.data_provider, "get_historical_data"):
df = self.data_provider.get_historical_data(
symbol, timeframe, limit=limit
)
if df is not None and not df.empty:
logger.info(
f"Loading {len(df)} {timeframe} bars for {symbol}"
)
# Convert DataFrame to OHLCVBar objects and add to queue
from core.data_models import OHLCVBar
for idx, row in df.iterrows():
try:
ohlcv_bar = OHLCVBar(
symbol=symbol,
timestamp=(
idx
if hasattr(idx, "to_pydatetime")
else datetime.now()
),
open=float(row["open"]),
high=float(row["high"]),
low=float(row["low"]),
close=float(row["close"]),
volume=float(row["volume"]),
timeframe=timeframe,
)
self.update_data_queue(
f"ohlcv_{timeframe}", symbol, ohlcv_bar
)
except Exception as bar_e:
logger.debug(f"Error creating OHLCV bar: {bar_e}")
else:
logger.warning(
f"No historical data available for {symbol} {timeframe}"
)
except Exception as e:
logger.warning(
f"Error loading initial data for {symbol} {timeframe}: {e}"
)
# Calculate and populate technical indicators
logger.info("Calculating technical indicators...")
for symbol in [self.symbol] + self.ref_symbols:
try:
# Use 1m data to calculate indicators
if self.ensure_minimum_data("ohlcv_1m", symbol, 50):
minute_data = self.get_queue_data("ohlcv_1m", symbol, 100)
if minute_data and len(minute_data) >= 20:
# Convert to DataFrame for indicator calculation
df_data = []
for bar in minute_data:
df_data.append(
{
"timestamp": bar.timestamp,
"open": bar.open,
"high": bar.high,
"low": bar.low,
"close": bar.close,
"volume": bar.volume,
}
)
df = pd.DataFrame(df_data)
df.set_index("timestamp", inplace=True)
# Calculate indicators
indicators = {}
try:
# Use our own RSI implementation to avoid ta library deprecation warnings
if len(df) >= 14:
indicators["rsi"] = self._calculate_rsi(
df["close"], period=14
)
if len(df) >= 20:
indicators["sma_20"] = (
df["close"].rolling(20).mean().iloc[-1]
)
if len(df) >= 12:
indicators["ema_12"] = (
df["close"].ewm(span=12).mean().iloc[-1]
)
if len(df) >= 26:
indicators["ema_26"] = (
df["close"].ewm(span=26).mean().iloc[-1]
)
if "ema_12" in indicators:
indicators["macd"] = (
indicators["ema_12"] - indicators["ema_26"]
)
# Bollinger Bands
if len(df) >= 20:
bb_period = 20
bb_std = 2
sma = df["close"].rolling(bb_period).mean()
std = df["close"].rolling(bb_period).std()
indicators["bb_upper"] = (
sma + (std * bb_std)
).iloc[-1]
indicators["bb_lower"] = (
sma - (std * bb_std)
).iloc[-1]
indicators["bb_middle"] = sma.iloc[-1]
# Remove NaN values
indicators = {
k: float(v)
for k, v in indicators.items()
if not pd.isna(v)
}
if indicators:
self.update_data_queue(
"technical_indicators", symbol, indicators
)
logger.info(
f"Calculated {len(indicators)} indicators for {symbol}"
)
except Exception as ta_e:
logger.warning(
f"Error calculating indicators for {symbol}: {ta_e}"
)
except Exception as e:
logger.warning(f"Error processing indicators for {symbol}: {e}")
# Log final queue status
logger.info("Initial data population completed")
self.log_queue_status(detailed=True)
except Exception as e:
logger.error(f"Error populating initial queue data: {e}")
def _try_fallback_data_strategy(
self, symbol: str, missing_data: List[Tuple[str, int, int]]
) -> bool:
"""
Try to fill missing data using fallback strategies
Args:
symbol: Trading symbol
missing_data: List of (data_type, actual_count, min_count) tuples
Returns:
bool: True if fallback successful
"""
try:
from core.data_models import OHLCVBar
for data_type, actual_count, min_count in missing_data:
needed_count = min_count - actual_count
if data_type == "ohlcv_1s" and needed_count > 0:
# Try to use 1m data to generate 1s data (simple interpolation)
if self.ensure_minimum_data("ohlcv_1m", symbol, 10):
logger.info(
f"Using 1m data to generate {needed_count} 1s bars for {symbol}"
)
# Get some 1m data
minute_data = self.get_queue_data("ohlcv_1m", symbol, 10)
if minute_data:
# Generate synthetic 1s bars from 1m data
for i, minute_bar in enumerate(
minute_data[-5:]
): # Use last 5 minutes
# Create 60 synthetic 1s bars from each 1m bar
for second in range(60):
if (
len(self.data_queues["ohlcv_1s"][symbol])
>= min_count
):
break
# Simple interpolation (not perfect but functional)
synthetic_bar = OHLCVBar(
symbol=symbol,
timestamp=minute_bar.timestamp,
open=minute_bar.open,
high=minute_bar.high,
low=minute_bar.low,
close=minute_bar.close,
volume=minute_bar.volume
/ 60, # Distribute volume
timeframe="1s",
)
self.update_data_queue(
"ohlcv_1s", symbol, synthetic_bar
)
elif data_type == "ohlcv_1h" and needed_count > 0:
# Try to use 1m data to generate 1h data
if self.ensure_minimum_data("ohlcv_1m", symbol, 60):
logger.info(
f"Using 1m data to generate {needed_count} 1h bars for {symbol}"
)
minute_data = self.get_queue_data("ohlcv_1m", symbol, 300)
if minute_data and len(minute_data) >= 60:
# Group 1m bars into 1h bars
for hour_start in range(0, len(minute_data) - 60, 60):
if (
len(self.data_queues["ohlcv_1h"][symbol])
>= min_count
):
break
hour_bars = minute_data[hour_start : hour_start + 60]
if len(hour_bars) == 60:
# Aggregate 1m bars into 1h bar
hour_bar = OHLCVBar(
symbol=symbol,
timestamp=hour_bars[0].timestamp,
open=hour_bars[0].open,
high=max(bar.high for bar in hour_bars),
low=min(bar.low for bar in hour_bars),
close=hour_bars[-1].close,
volume=sum(bar.volume for bar in hour_bars),
timeframe="1h",
)
self.update_data_queue("ohlcv_1h", symbol, hour_bar)
# Check if we now have minimum data
all_satisfied = True
for data_type, _, min_count in missing_data:
if not self.ensure_minimum_data(data_type, symbol, min_count):
all_satisfied = False
break
return all_satisfied
except Exception as e:
logger.error(f"Error in fallback data strategy: {e}")
return False
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