wip
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Dobromir Popov
395
core/model_output_manager.py
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395
core/model_output_manager.py
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"""
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Model Output Manager
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This module provides extensible model output storage and management for the multi-modal trading system.
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Supports CNN, RL, LSTM, Transformer, and future model types with cross-model feeding capabilities.
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"""
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import logging
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import json
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import pickle
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any, Union
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from collections import deque, defaultdict
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from threading import Lock
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from pathlib import Path
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from .data_models import ModelOutput, create_model_output
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logger = logging.getLogger(__name__)
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class ModelOutputManager:
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"""
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Extensible model output storage and management system
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Features:
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- Standardized ModelOutput storage for all model types
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- Cross-model feeding with hidden states
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- Historical output tracking
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- Metadata management
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- Persistence and recovery
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- Performance analytics
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"""
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def __init__(self, cache_dir: str = "cache/model_outputs", max_history: int = 1000):
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"""
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Initialize the model output manager
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Args:
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cache_dir: Directory for persistent storage
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max_history: Maximum number of outputs to keep in memory per model
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"""
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self.cache_dir = Path(cache_dir)
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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self.max_history = max_history
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# In-memory storage
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self.current_outputs: Dict[str, Dict[str, ModelOutput]] = defaultdict(dict) # {symbol: {model_name: ModelOutput}}
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self.output_history: Dict[str, Dict[str, deque]] = defaultdict(lambda: defaultdict(lambda: deque(maxlen=max_history))) # {symbol: {model_name: deque}}
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self.cross_model_states: Dict[str, Dict[str, Dict[str, Any]]] = defaultdict(lambda: defaultdict(dict)) # {symbol: {model_name: hidden_states}}
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# Metadata tracking
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self.model_metadata: Dict[str, Dict[str, Any]] = defaultdict(dict) # {model_name: metadata}
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self.performance_stats: Dict[str, Dict[str, Any]] = defaultdict(lambda: defaultdict(dict)) # {symbol: {model_name: stats}}
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# Thread safety
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self.storage_lock = Lock()
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# Supported model types
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self.supported_model_types = {
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'cnn', 'rl', 'lstm', 'transformer', 'orchestrator',
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'ensemble', 'hybrid', 'custom' # Extensible for future types
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}
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logger.info(f"ModelOutputManager initialized with cache dir: {self.cache_dir}")
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logger.info(f"Supported model types: {self.supported_model_types}")
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def store_output(self, model_output: ModelOutput) -> bool:
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"""
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Store model output with full extensibility support
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Args:
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model_output: ModelOutput from any model type
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Returns:
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bool: True if stored successfully, False otherwise
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"""
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try:
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with self.storage_lock:
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symbol = model_output.symbol
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model_name = model_output.model_name
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model_type = model_output.model_type
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# Validate model type (extensible)
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if model_type not in self.supported_model_types:
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logger.warning(f"Unknown model type '{model_type}' - adding to supported types")
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self.supported_model_types.add(model_type)
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# Store current output
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self.current_outputs[symbol][model_name] = model_output
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# Add to history
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self.output_history[symbol][model_name].append(model_output)
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# Store cross-model states if available
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if model_output.hidden_states:
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self.cross_model_states[symbol][model_name] = model_output.hidden_states
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# Update model metadata
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self._update_model_metadata(model_name, model_type, model_output.metadata)
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# Update performance statistics
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self._update_performance_stats(symbol, model_name, model_output)
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# Persist to disk (async to avoid blocking)
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self._persist_output_async(model_output)
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logger.debug(f"Stored output from {model_name} ({model_type}) for {symbol}")
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return True
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except Exception as e:
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logger.error(f"Error storing model output: {e}")
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return False
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def get_current_output(self, symbol: str, model_name: str) -> Optional[ModelOutput]:
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"""
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Get the current (latest) output from a specific model
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Args:
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symbol: Trading symbol
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model_name: Name of the model
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Returns:
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ModelOutput: Latest output from the model, or None if not available
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"""
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try:
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return self.current_outputs.get(symbol, {}).get(model_name)
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except Exception as e:
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logger.error(f"Error getting current output for {model_name}: {e}")
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return None
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def get_all_current_outputs(self, symbol: str) -> Dict[str, ModelOutput]:
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"""
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Get all current outputs for a symbol (for cross-model feeding)
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Args:
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symbol: Trading symbol
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Returns:
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Dict[str, ModelOutput]: Dictionary of current outputs by model name
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"""
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try:
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return dict(self.current_outputs.get(symbol, {}))
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except Exception as e:
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logger.error(f"Error getting all current outputs for {symbol}: {e}")
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return {}
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def get_output_history(self, symbol: str, model_name: str, count: int = 10) -> List[ModelOutput]:
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"""
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Get historical outputs from a model
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Args:
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symbol: Trading symbol
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model_name: Name of the model
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count: Number of historical outputs to retrieve
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Returns:
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List[ModelOutput]: List of historical outputs (most recent first)
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"""
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try:
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history = self.output_history.get(symbol, {}).get(model_name, deque())
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return list(history)[-count:][::-1] # Most recent first
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except Exception as e:
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logger.error(f"Error getting output history for {model_name}: {e}")
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return []
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def get_cross_model_states(self, symbol: str, requesting_model: str) -> Dict[str, Dict[str, Any]]:
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"""
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Get hidden states from other models for cross-model feeding
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Args:
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symbol: Trading symbol
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requesting_model: Name of the model requesting the states
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Returns:
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Dict[str, Dict[str, Any]]: Hidden states from other models
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"""
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try:
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all_states = self.cross_model_states.get(symbol, {})
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# Return states from all models except the requesting one
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return {model_name: states for model_name, states in all_states.items()
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if model_name != requesting_model}
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except Exception as e:
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logger.error(f"Error getting cross-model states for {requesting_model}: {e}")
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return {}
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def get_model_types_active(self, symbol: str) -> List[str]:
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"""
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Get list of active model types for a symbol
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Args:
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symbol: Trading symbol
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Returns:
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List[str]: List of active model types
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"""
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try:
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current_outputs = self.current_outputs.get(symbol, {})
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return [output.model_type for output in current_outputs.values()]
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except Exception as e:
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logger.error(f"Error getting active model types for {symbol}: {e}")
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return []
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def get_consensus_prediction(self, symbol: str, confidence_threshold: float = 0.5) -> Optional[Dict[str, Any]]:
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"""
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Get consensus prediction from all active models
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Args:
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symbol: Trading symbol
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confidence_threshold: Minimum confidence threshold for inclusion
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Returns:
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Dict containing consensus prediction or None
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"""
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try:
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current_outputs = self.current_outputs.get(symbol, {})
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if not current_outputs:
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return None
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# Filter by confidence threshold
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high_confidence_outputs = [
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output for output in current_outputs.values()
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if output.confidence >= confidence_threshold
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]
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if not high_confidence_outputs:
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return None
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# Calculate consensus
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buy_votes = sum(1 for output in high_confidence_outputs
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if output.predictions.get('action') == 'BUY')
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sell_votes = sum(1 for output in high_confidence_outputs
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if output.predictions.get('action') == 'SELL')
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hold_votes = sum(1 for output in high_confidence_outputs
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if output.predictions.get('action') == 'HOLD')
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total_votes = len(high_confidence_outputs)
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avg_confidence = sum(output.confidence for output in high_confidence_outputs) / total_votes
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# Determine consensus action
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if buy_votes > sell_votes and buy_votes > hold_votes:
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consensus_action = 'BUY'
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elif sell_votes > buy_votes and sell_votes > hold_votes:
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consensus_action = 'SELL'
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else:
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consensus_action = 'HOLD'
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return {
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'action': consensus_action,
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'confidence': avg_confidence,
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'votes': {'BUY': buy_votes, 'SELL': sell_votes, 'HOLD': hold_votes},
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'total_models': total_votes,
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'model_types': [output.model_type for output in high_confidence_outputs]
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}
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except Exception as e:
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logger.error(f"Error calculating consensus prediction for {symbol}: {e}")
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return None
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def _update_model_metadata(self, model_name: str, model_type: str, metadata: Dict[str, Any]):
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"""Update metadata for a model"""
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try:
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if model_name not in self.model_metadata:
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self.model_metadata[model_name] = {
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'model_type': model_type,
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'first_seen': datetime.now(),
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'total_predictions': 0,
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'custom_metadata': {}
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}
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self.model_metadata[model_name]['total_predictions'] += 1
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self.model_metadata[model_name]['last_seen'] = datetime.now()
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# Merge custom metadata
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if metadata:
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self.model_metadata[model_name]['custom_metadata'].update(metadata)
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except Exception as e:
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logger.error(f"Error updating model metadata: {e}")
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def _update_performance_stats(self, symbol: str, model_name: str, model_output: ModelOutput):
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"""Update performance statistics for a model"""
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try:
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stats = self.performance_stats[symbol][model_name]
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if 'prediction_count' not in stats:
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stats['prediction_count'] = 0
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stats['confidence_sum'] = 0.0
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stats['action_counts'] = {'BUY': 0, 'SELL': 0, 'HOLD': 0}
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stats['first_prediction'] = model_output.timestamp
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stats['prediction_count'] += 1
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stats['confidence_sum'] += model_output.confidence
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stats['avg_confidence'] = stats['confidence_sum'] / stats['prediction_count']
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stats['last_prediction'] = model_output.timestamp
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action = model_output.predictions.get('action', 'HOLD')
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if action in stats['action_counts']:
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stats['action_counts'][action] += 1
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except Exception as e:
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logger.error(f"Error updating performance stats: {e}")
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def _persist_output_async(self, model_output: ModelOutput):
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"""Persist model output to disk (simplified version)"""
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try:
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# Create filename based on model and timestamp
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timestamp_str = model_output.timestamp.strftime("%Y%m%d_%H%M%S")
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filename = f"{model_output.model_name}_{model_output.symbol.replace('/', '_')}_{timestamp_str}.json"
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filepath = self.cache_dir / filename
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# Convert to JSON-serializable format
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output_dict = {
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'model_type': model_output.model_type,
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'model_name': model_output.model_name,
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'symbol': model_output.symbol,
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'timestamp': model_output.timestamp.isoformat(),
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'confidence': model_output.confidence,
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'predictions': model_output.predictions,
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'metadata': model_output.metadata
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}
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# Save to file (in a real implementation, this would be async)
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with open(filepath, 'w') as f:
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json.dump(output_dict, f, indent=2)
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except Exception as e:
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logger.error(f"Error persisting model output: {e}")
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def get_performance_summary(self, symbol: str) -> Dict[str, Any]:
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"""
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Get performance summary for all models for a symbol
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Args:
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symbol: Trading symbol
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Returns:
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Dict containing performance summary
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"""
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try:
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summary = {
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'symbol': symbol,
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'active_models': len(self.current_outputs.get(symbol, {})),
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'model_stats': {}
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}
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for model_name, stats in self.performance_stats.get(symbol, {}).items():
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summary['model_stats'][model_name] = {
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'predictions': stats.get('prediction_count', 0),
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'avg_confidence': round(stats.get('avg_confidence', 0.0), 3),
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'action_distribution': stats.get('action_counts', {}),
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'model_type': self.model_metadata.get(model_name, {}).get('model_type', 'unknown')
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}
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return summary
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except Exception as e:
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logger.error(f"Error getting performance summary: {e}")
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return {'symbol': symbol, 'error': str(e)}
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def cleanup_old_outputs(self, max_age_hours: int = 24):
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"""
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Clean up old outputs to manage memory usage
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Args:
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max_age_hours: Maximum age of outputs to keep in hours
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"""
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try:
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cutoff_time = datetime.now() - timedelta(hours=max_age_hours)
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with self.storage_lock:
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for symbol in self.output_history:
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for model_name in self.output_history[symbol]:
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history = self.output_history[symbol][model_name]
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# Remove old outputs
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while history and history[0].timestamp < cutoff_time:
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history.popleft()
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logger.info(f"Cleaned up outputs older than {max_age_hours} hours")
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except Exception as e:
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logger.error(f"Error cleaning up old outputs: {e}")
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def add_custom_model_type(self, model_type: str):
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"""
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Add support for a new custom model type
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Args:
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model_type: Name of the new model type
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"""
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self.supported_model_types.add(model_type)
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logger.info(f"Added support for custom model type: {model_type}")
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def get_supported_model_types(self) -> List[str]:
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"""Get list of all supported model types"""
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return list(self.supported_model_types)
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