4762 lines
224 KiB
Python
4762 lines
224 KiB
Python
"""
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Enhanced Trading Orchestrator - Advanced Multi-Modal Decision Making
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This enhanced orchestrator implements:
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1. Multi-timeframe CNN predictions with individual confidence scores
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2. Advanced RL feedback loop for continuous learning
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3. Multi-symbol (ETH, BTC) coordinated decision making
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4. Perfect move marking for CNN backpropagation training
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5. Market environment adaptation through RL evaluation
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6. Universal data format compliance (5 timeseries streams)
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7. Consolidated Order Book (COB) integration for real-time market microstructure
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"""
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import asyncio
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import logging
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import time
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple, Any, Union
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from dataclasses import dataclass, field
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from collections import deque
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import torch
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import ta
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from .config import get_config
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from .data_provider import DataProvider, RawTick, OHLCVBar, MarketTick
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from .orchestrator import TradingOrchestrator
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from .universal_data_adapter import UniversalDataAdapter, UniversalDataStream
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from .realtime_tick_processor import RealTimeTickProcessor, ProcessedTickFeatures, integrate_with_orchestrator
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from models import get_model_registry, ModelInterface, CNNModelInterface, RLAgentInterface
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from .extrema_trainer import ExtremaTrainer
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from .trading_action import TradingAction
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from .negative_case_trainer import NegativeCaseTrainer
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from .trading_executor import TradingExecutor
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from .cnn_monitor import log_cnn_prediction, start_cnn_training_session
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from .cob_integration import COBIntegration
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# Enhanced pivot RL trainer functionality integrated into orchestrator
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logger = logging.getLogger(__name__)
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@dataclass
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class TimeframePrediction:
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"""CNN prediction for a specific timeframe with confidence"""
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timeframe: str
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action: str # 'BUY', 'SELL', 'HOLD'
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confidence: float # 0.0 to 1.0
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probabilities: Dict[str, float] # Action probabilities
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timestamp: datetime
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market_features: Dict[str, float] = field(default_factory=dict) # Additional context
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@dataclass
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class EnhancedPrediction:
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"""Enhanced prediction structure with timeframe breakdown"""
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symbol: str
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timeframe_predictions: List[TimeframePrediction]
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overall_action: str
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overall_confidence: float
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model_name: str
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timestamp: datetime
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metadata: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class TradingAction:
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"""Represents a trading action with full context"""
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symbol: str
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action: str # 'BUY', 'SELL', 'HOLD'
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quantity: float
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confidence: float
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price: float
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timestamp: datetime
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reasoning: Dict[str, Any]
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timeframe_analysis: List[TimeframePrediction]
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@dataclass
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class MarketState:
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"""Complete market state for RL evaluation with comprehensive data including COB"""
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symbol: str
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timestamp: datetime
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prices: Dict[str, float] # {timeframe: current_price}
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features: Dict[str, np.ndarray] # {timeframe: feature_matrix}
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volatility: float
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volume: float
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trend_strength: float
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market_regime: str # 'trending', 'ranging', 'volatile'
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universal_data: UniversalDataStream # Universal format data
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# Enhanced data for comprehensive RL state building
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raw_ticks: List[Dict[str, Any]] = field(default_factory=list) # Last 300s of tick data
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ohlcv_data: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) # Multi-timeframe OHLCV
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btc_reference_data: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict) # BTC correlation data
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cnn_hidden_features: Optional[Dict[str, np.ndarray]] = None # CNN hidden layer features
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cnn_predictions: Optional[Dict[str, np.ndarray]] = None # CNN predictions by timeframe
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pivot_points: Optional[Dict[str, Any]] = None # Williams market structure data
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market_microstructure: Dict[str, Any] = field(default_factory=dict) # Tick-level patterns
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# COB (Consolidated Order Book) data for market microstructure analysis
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cob_features: Optional[np.ndarray] = None # COB CNN features (200 dimensions)
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cob_state: Optional[np.ndarray] = None # COB DQN state features (50 dimensions)
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order_book_imbalance: float = 0.0 # Bid/ask imbalance ratio
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liquidity_depth: float = 0.0 # Total liquidity within 1% of mid price
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exchange_diversity: float = 0.0 # Number of exchanges contributing to liquidity
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market_impact_estimate: float = 0.0 # Estimated market impact for standard trade size
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@dataclass
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class PerfectMove:
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"""Marked perfect move for CNN training"""
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symbol: str
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timeframe: str
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timestamp: datetime
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optimal_action: str
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actual_outcome: float # Price change percentage
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market_state_before: MarketState
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market_state_after: MarketState
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confidence_should_have_been: float
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@dataclass
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class TradeInfo:
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"""Information about an active trade"""
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symbol: str
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side: str # 'LONG' or 'SHORT'
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entry_price: float
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entry_time: datetime
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size: float
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confidence: float
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market_state: Dict[str, Any]
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@dataclass
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class LearningCase:
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"""A learning case for DQN sensitivity training"""
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state_vector: np.ndarray
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action: int # sensitivity level chosen
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reward: float
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next_state_vector: np.ndarray
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done: bool
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trade_info: TradeInfo
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outcome: float # P&L percentage
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class EnhancedTradingOrchestrator(TradingOrchestrator):
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"""
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Enhanced orchestrator with sophisticated multi-modal decision making
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and universal data format compliance
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"""
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def __init__(self, data_provider: DataProvider, symbols: List[str] = None, enhanced_rl_training: bool = False, model_registry: Dict = None):
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"""
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Initialize Enhanced Trading Orchestrator with proper async handling
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"""
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# Call parent constructor with only data_provider
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super().__init__(data_provider)
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# Store additional parameters that parent doesn't handle
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self.symbols = symbols or self.config.symbols
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if model_registry:
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self.model_registry = model_registry
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# Enhanced RL training flag
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self.enhanced_rl_training = enhanced_rl_training
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# Missing attributes fix - Initialize position tracking and thresholds
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self.current_positions = {} # Track current positions by symbol
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self.entry_threshold = 0.65 # Threshold for opening new positions
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self.exit_threshold = 0.30 # Threshold for closing positions
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self.uninvested_threshold = 0.50 # Threshold below which to stay uninvested
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self.last_signals = {} # Track last signal for each symbol
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# Enhanced state tracking
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self.latest_cob_features = {} # Symbol -> COB features array
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self.latest_cob_state = {} # Symbol -> COB state array
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self.williams_features = {} # Symbol -> Williams features
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self.symbol_correlation_matrix = {} # Pre-computed correlations
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# Initialize pivot RL trainer (if available)
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self.pivot_rl_trainer = None # Will be initialized if enhanced pivot training is needed
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# Initialize COB Integration for real-time market microstructure
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# PROPERLY INITIALIZED: Create the COB integration instance synchronously
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try:
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self.cob_integration = COBIntegration(
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data_provider=self.data_provider,
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symbols=self.symbols
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)
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# Register COB callbacks for CNN and RL models
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self.cob_integration.add_cnn_callback(self._on_cob_cnn_features)
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self.cob_integration.add_dqn_callback(self._on_cob_dqn_state)
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self.cob_integration_active = False # Will be set to True when started
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self._cob_integration_failed = False
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logger.info("COB Integration: Successfully initialized")
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except Exception as e:
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logger.warning(f"COB Integration: Failed to initialize - {e}")
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self.cob_integration = None
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self.cob_integration_active = False
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self._cob_integration_failed = True
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# COB feature storage for model integration
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self.latest_cob_features: Dict[str, np.ndarray] = {}
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self.latest_cob_state: Dict[str, np.ndarray] = {}
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self.cob_feature_history: Dict[str, deque] = {symbol: deque(maxlen=100) for symbol in self.symbols}
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# Start BOM cache updates in data provider
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if hasattr(self.data_provider, 'start_bom_cache_updates'):
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try:
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self.data_provider.start_bom_cache_updates(self.cob_integration)
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logger.info("Started BOM cache updates in data provider")
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except Exception as e:
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logger.warning(f"Failed to start BOM cache updates: {e}")
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logger.info("COB Integration: Deferred initialization to prevent sync/async conflicts")
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# Williams integration
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try:
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from training.williams_market_structure import WilliamsMarketStructure
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self.williams_structure = WilliamsMarketStructure(
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swing_strengths=[2, 3, 5],
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enable_cnn_feature=True,
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training_data_provider=data_provider
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)
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self.williams_enabled = True
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logger.info("Enhanced Orchestrator: Williams Market Structure initialized")
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except Exception as e:
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self.williams_structure = None
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self.williams_enabled = False
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logger.warning(f"Enhanced Orchestrator: Williams structure initialization failed: {e}")
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# Enhanced RL state builder enabled by default
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self.comprehensive_rl_enabled = True
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# Initialize COB integration asynchronously only when needed
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self._cob_integration_failed = False
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logger.info(f"Enhanced Trading Orchestrator initialized with enhanced_rl_training={enhanced_rl_training}")
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logger.info(f"COB Integration: Deferred until async context available")
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logger.info(f"Williams enabled: {self.williams_enabled}")
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logger.info(f"Comprehensive RL enabled: {self.comprehensive_rl_enabled}")
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# Initialize universal data adapter
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self.universal_adapter = UniversalDataAdapter(self.data_provider)
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# Initialize real-time tick processor for ultra-low latency processing
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self.tick_processor = RealTimeTickProcessor(symbols=self.config.symbols)
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# Initialize extrema trainer for local bottom/top detection and 200-candle context
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self.extrema_trainer = ExtremaTrainer(
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data_provider=self.data_provider,
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symbols=self.config.symbols,
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window_size=10 # 10-candle window for extrema detection
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)
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# Initialize negative case trainer for intensive training on losing trades
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self.negative_case_trainer = NegativeCaseTrainer()
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# Real-time tick features storage
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self.realtime_tick_features = {symbol: deque(maxlen=100) for symbol in self.config.symbols}
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# Multi-symbol configuration
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self.timeframes = self.config.timeframes
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# Configuration with different thresholds for opening vs closing
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self.confidence_threshold_open = self.config.orchestrator.get('confidence_threshold', 0.6)
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self.confidence_threshold_close = self.config.orchestrator.get('confidence_threshold_close', 0.25) # Much lower for closing
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self.decision_frequency = self.config.orchestrator.get('decision_frequency', 30)
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# DQN RL-based sensitivity learning parameters
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self.sensitivity_learning_enabled = True
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self.sensitivity_dqn_agent = None # Will be initialized when first DQN model is available
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self.sensitivity_state_size = 20 # Features for sensitivity learning
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self.sensitivity_action_space = 5 # 5 sensitivity levels: very_low, low, medium, high, very_high
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self.current_sensitivity_level = 2 # Start with medium (index 2)
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self.sensitivity_levels = {
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0: {'name': 'very_low', 'close_threshold_multiplier': 0.5, 'open_threshold_multiplier': 1.2},
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1: {'name': 'low', 'close_threshold_multiplier': 0.7, 'open_threshold_multiplier': 1.1},
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2: {'name': 'medium', 'close_threshold_multiplier': 1.0, 'open_threshold_multiplier': 1.0},
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3: {'name': 'high', 'close_threshold_multiplier': 1.3, 'open_threshold_multiplier': 0.9},
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4: {'name': 'very_high', 'close_threshold_multiplier': 1.5, 'open_threshold_multiplier': 0.8}
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}
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# Trade tracking for sensitivity learning
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self.active_trades = {} # symbol -> trade_info with entry details
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self.completed_trades = deque(maxlen=1000) # Store last 1000 completed trades for learning
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self.sensitivity_learning_queue = deque(maxlen=500) # Queue for DQN training
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# Enhanced weighting system
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self.timeframe_weights = self._initialize_timeframe_weights()
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self.symbol_correlation_matrix = self._initialize_correlation_matrix()
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# State tracking for each symbol
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self.symbol_states = {symbol: {} for symbol in self.symbols}
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self.recent_actions = {symbol: deque(maxlen=100) for symbol in self.symbols}
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self.market_states = {symbol: deque(maxlen=1000) for symbol in self.symbols}
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# Perfect move tracking for CNN training with enhanced retrospective learning
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self.perfect_moves = deque(maxlen=10000)
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self.performance_tracker = {}
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self.retrospective_learning_active = False
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self.last_retrospective_analysis = datetime.now()
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# Local extrema tracking for training on bottoms and tops
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self.local_extrema = {symbol: deque(maxlen=1000) for symbol in self.symbols}
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self.extrema_detection_window = 10 # Look for extrema in 10-candle windows
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self.extrema_training_queue = deque(maxlen=500) # Queue for extrema-based training
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self.last_extrema_check = {symbol: datetime.now() for symbol in self.symbols}
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# 200-candle context data for models
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self.context_data_1m = {symbol: deque(maxlen=200) for symbol in self.symbols}
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self.context_features_1m = {symbol: None for symbol in self.symbols}
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self.context_update_frequency = 60 # Update context every 60 seconds
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self.last_context_update = {symbol: datetime.now() for symbol in self.symbols}
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# RL feedback system
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self.rl_evaluation_queue = deque(maxlen=1000)
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self.environment_adaptation_rate = 0.01
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# Decision callbacks
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self.decision_callbacks = []
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self.learning_callbacks = []
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# Integrate tick processor with orchestrator
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integrate_with_orchestrator(self, self.tick_processor)
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# Subscribe to raw tick data and OHLCV bars from data provider
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self.raw_tick_subscriber_id = self.data_provider.subscribe_to_raw_ticks(self._handle_raw_tick)
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self.ohlcv_bar_subscriber_id = self.data_provider.subscribe_to_ohlcv_bars(self._handle_ohlcv_bar)
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# Raw tick and OHLCV data storage for models
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self.raw_tick_buffers = {symbol: deque(maxlen=1000) for symbol in self.symbols}
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self.ohlcv_bar_buffers = {symbol: deque(maxlen=3600) for symbol in self.symbols} # 1 hour of 1s bars
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# Pattern-based decision enhancement
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self.pattern_weights = {
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'rapid_fire': 1.5,
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'volume_spike': 1.3,
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'price_acceleration': 1.4,
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'high_frequency_bar': 1.2,
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'volume_concentration': 1.1
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}
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# Initialize 200-candle context data
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self._initialize_context_data()
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logger.info("Enhanced TradingOrchestrator initialized with Universal Data Format")
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logger.info(f"Symbols: {self.symbols}")
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logger.info(f"Timeframes: {self.timeframes}")
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logger.info(f"Universal format: ETH ticks, 1m, 1h, 1d + BTC reference ticks")
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logger.info(f"Opening confidence threshold: {self.confidence_threshold_open}")
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logger.info(f"Closing confidence threshold: {self.confidence_threshold_close}")
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logger.info("Real-time tick processor integrated for ultra-low latency processing")
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logger.info("Raw tick and OHLCV bar processing enabled for pattern detection")
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logger.info("Enhanced retrospective learning enabled for perfect opportunity detection")
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logger.info("DQN RL-based sensitivity learning enabled for adaptive thresholds")
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logger.info("Local extrema detection enabled for bottom/top training")
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logger.info("200-candle 1m context data initialized for enhanced model performance")
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def _initialize_timeframe_weights(self) -> Dict[str, float]:
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"""Initialize weights for different timeframes"""
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# Higher timeframes get more weight for trend direction
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# Lower timeframes get more weight for entry/exit timing
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base_weights = {
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'1s': 0.60, # Primary scalping signal (ticks)
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'1m': 0.20, # Short-term confirmation
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'5m': 0.10, # Short-term momentum
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'15m': 0.15, # Entry/exit timing
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'1h': 0.15, # Medium-term trend
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'4h': 0.25, # Stronger trend confirmation
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'1d': 0.05 # Long-term direction (minimal for scalping)
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}
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# Normalize weights for configured timeframes
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configured_weights = {tf: base_weights.get(tf, 0.1) for tf in self.timeframes}
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total = sum(configured_weights.values())
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return {tf: w/total for tf, w in configured_weights.items()}
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def _initialize_correlation_matrix(self) -> Dict[Tuple[str, str], float]:
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"""Initialize correlation matrix between symbols"""
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correlations = {}
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for i, symbol1 in enumerate(self.symbols):
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for j, symbol2 in enumerate(self.symbols):
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if i != j:
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# ETH and BTC are typically highly correlated
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if 'ETH' in symbol1 and 'BTC' in symbol2:
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correlations[(symbol1, symbol2)] = 0.85
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elif 'BTC' in symbol1 and 'ETH' in symbol2:
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correlations[(symbol1, symbol2)] = 0.85
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else:
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correlations[(symbol1, symbol2)] = 0.7 # Default correlation
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return correlations
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|
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async def make_coordinated_decisions(self) -> Dict[str, Optional[TradingAction]]:
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"""
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Make coordinated trading decisions across all symbols using universal data format
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"""
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decisions = {}
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try:
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# Get universal data stream (5 timeseries)
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universal_stream = self.universal_adapter.get_universal_data_stream()
|
|
|
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if universal_stream is None:
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logger.warning("Failed to get universal data stream")
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return decisions
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|
|
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# Validate universal format
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is_valid, issues = self.universal_adapter.validate_universal_format(universal_stream)
|
|
if not is_valid:
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logger.warning(f"Universal data format validation failed: {issues}")
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return decisions
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|
|
|
logger.info("UNIVERSAL DATA STREAM ACTIVE:")
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logger.info(f" ETH ticks: {len(universal_stream.eth_ticks)} samples")
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logger.info(f" ETH 1m: {len(universal_stream.eth_1m)} candles")
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logger.info(f" ETH 1h: {len(universal_stream.eth_1h)} candles")
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logger.info(f" ETH 1d: {len(universal_stream.eth_1d)} candles")
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logger.info(f" BTC reference: {len(universal_stream.btc_ticks)} samples")
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logger.info(f" Data quality: {universal_stream.metadata['data_quality']['overall_score']:.2f}")
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# Get market states for all symbols using universal data
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market_states = await self._get_all_market_states_universal(universal_stream)
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|
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# Get enhanced predictions for all symbols
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|
symbol_predictions = {}
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for symbol in self.symbols:
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if symbol in market_states:
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predictions = await self._get_enhanced_predictions_universal(
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symbol, market_states[symbol], universal_stream
|
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)
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|
symbol_predictions[symbol] = predictions
|
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|
|
# Coordinate decisions considering symbol correlations
|
|
for symbol in self.symbols:
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if symbol in symbol_predictions:
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decision = await self._make_coordinated_decision(
|
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symbol,
|
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symbol_predictions[symbol],
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symbol_predictions,
|
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market_states[symbol]
|
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)
|
|
decisions[symbol] = decision
|
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|
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# Queue for RL evaluation
|
|
if decision and decision.action != 'HOLD':
|
|
self._queue_for_rl_evaluation(decision, market_states[symbol])
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in coordinated decision making: {e}")
|
|
|
|
return decisions
|
|
|
|
async def _get_all_market_states_universal(self, universal_stream: UniversalDataStream) -> Dict[str, MarketState]:
|
|
"""Get market states for all symbols with comprehensive data for RL"""
|
|
market_states = {}
|
|
|
|
for symbol in self.symbols:
|
|
try:
|
|
# Basic market state data
|
|
current_prices = {}
|
|
for timeframe in self.timeframes:
|
|
# Get latest price from universal data stream
|
|
latest_price = self._get_latest_price_from_universal(symbol, timeframe, universal_stream)
|
|
if latest_price:
|
|
current_prices[timeframe] = latest_price
|
|
|
|
# Calculate basic metrics
|
|
volatility = self._calculate_volatility_from_universal(symbol, universal_stream)
|
|
volume = self._calculate_volume_from_universal(symbol, universal_stream)
|
|
trend_strength = self._calculate_trend_strength_from_universal(symbol, universal_stream)
|
|
market_regime = self._determine_market_regime(symbol, universal_stream)
|
|
|
|
# Get comprehensive data for RL state building
|
|
raw_ticks = self._get_recent_tick_data_for_rl(symbol)
|
|
ohlcv_data = self._get_multiframe_ohlcv_for_rl(symbol)
|
|
btc_reference_data = self._get_multiframe_ohlcv_for_rl('BTC/USDT')
|
|
|
|
# Get CNN features if available
|
|
cnn_hidden_features, cnn_predictions = self._get_cnn_features_for_rl(symbol)
|
|
|
|
# Calculate pivot points
|
|
pivot_points = self._calculate_pivot_points_for_rl(symbol)
|
|
|
|
# Analyze market microstructure
|
|
market_microstructure = self._analyze_market_microstructure(raw_ticks)
|
|
|
|
# Get COB (Consolidated Order Book) data if available
|
|
cob_features = self.latest_cob_features.get(symbol)
|
|
cob_state = self.latest_cob_state.get(symbol)
|
|
|
|
# Get COB snapshot for additional metrics
|
|
cob_snapshot = None
|
|
order_book_imbalance = 0.0
|
|
liquidity_depth = 0.0
|
|
exchange_diversity = 0.0
|
|
market_impact_estimate = 0.0
|
|
|
|
try:
|
|
if self.cob_integration:
|
|
cob_snapshot = self.cob_integration.get_cob_snapshot(symbol)
|
|
if cob_snapshot:
|
|
# Calculate order book imbalance
|
|
bid_liquidity = sum(level.total_volume_usd for level in cob_snapshot.consolidated_bids[:10])
|
|
ask_liquidity = sum(level.total_volume_usd for level in cob_snapshot.consolidated_asks[:10])
|
|
if ask_liquidity > 0:
|
|
order_book_imbalance = (bid_liquidity - ask_liquidity) / (bid_liquidity + ask_liquidity)
|
|
|
|
# Calculate liquidity depth (within 1% of mid price)
|
|
mid_price = cob_snapshot.volume_weighted_mid
|
|
price_range = mid_price * 0.01 # 1%
|
|
depth_bids = [l for l in cob_snapshot.consolidated_bids if l.price >= mid_price - price_range]
|
|
depth_asks = [l for l in cob_snapshot.consolidated_asks if l.price <= mid_price + price_range]
|
|
liquidity_depth = sum(l.total_volume_usd for l in depth_bids + depth_asks)
|
|
|
|
# Calculate exchange diversity
|
|
all_exchanges = set()
|
|
for level in cob_snapshot.consolidated_bids[:20] + cob_snapshot.consolidated_asks[:20]:
|
|
all_exchanges.update(level.exchange_breakdown.keys())
|
|
exchange_diversity = len(all_exchanges)
|
|
|
|
# Estimate market impact for 10k USD trade
|
|
market_impact_estimate = self._estimate_market_impact(cob_snapshot, 10000)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating COB metrics for {symbol}: {e}")
|
|
|
|
# Create comprehensive market state
|
|
market_state = MarketState(
|
|
symbol=symbol,
|
|
timestamp=datetime.now(),
|
|
prices=current_prices,
|
|
features={}, # Will be populated by feature extraction
|
|
volatility=volatility,
|
|
volume=volume,
|
|
trend_strength=trend_strength,
|
|
market_regime=market_regime,
|
|
universal_data=universal_stream,
|
|
raw_ticks=raw_ticks,
|
|
ohlcv_data=ohlcv_data,
|
|
btc_reference_data=btc_reference_data,
|
|
cnn_hidden_features=cnn_hidden_features,
|
|
cnn_predictions=cnn_predictions,
|
|
pivot_points=pivot_points,
|
|
market_microstructure=market_microstructure,
|
|
# COB data integration
|
|
cob_features=cob_features,
|
|
cob_state=cob_state,
|
|
order_book_imbalance=order_book_imbalance,
|
|
liquidity_depth=liquidity_depth,
|
|
exchange_diversity=exchange_diversity,
|
|
market_impact_estimate=market_impact_estimate
|
|
)
|
|
|
|
market_states[symbol] = market_state
|
|
logger.debug(f"Created comprehensive market state for {symbol} with COB integration")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating market state for {symbol}: {e}")
|
|
|
|
return market_states
|
|
|
|
def _estimate_market_impact(self, cob_snapshot, trade_size_usd: float) -> float:
|
|
"""Estimate market impact for a given trade size"""
|
|
try:
|
|
# Simple market impact estimation based on order book depth
|
|
cumulative_volume = 0
|
|
weighted_price = 0
|
|
mid_price = cob_snapshot.volume_weighted_mid
|
|
|
|
# For buy orders, walk through asks
|
|
for level in cob_snapshot.consolidated_asks:
|
|
if cumulative_volume >= trade_size_usd:
|
|
break
|
|
volume_needed = min(level.total_volume_usd, trade_size_usd - cumulative_volume)
|
|
weighted_price += level.price * volume_needed
|
|
cumulative_volume += volume_needed
|
|
|
|
if cumulative_volume > 0:
|
|
avg_execution_price = weighted_price / cumulative_volume
|
|
impact = (avg_execution_price - mid_price) / mid_price
|
|
return abs(impact)
|
|
|
|
return 0.0
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error estimating market impact: {e}")
|
|
return 0.0
|
|
|
|
def _get_recent_tick_data_for_rl(self, symbol: str, seconds: int = 300) -> List[Dict[str, Any]]:
|
|
"""Get recent tick data for RL state building"""
|
|
try:
|
|
# Get ticks from data provider
|
|
recent_ticks = self.data_provider.get_recent_ticks(symbol, count=seconds * 10)
|
|
|
|
# Convert to required format
|
|
tick_data = []
|
|
for tick in recent_ticks[-300:]: # Last 300 ticks max (300s at ~1 tick/sec)
|
|
tick_dict = {
|
|
'timestamp': tick.timestamp,
|
|
'price': tick.price,
|
|
'volume': tick.volume,
|
|
'quantity': getattr(tick, 'quantity', tick.volume),
|
|
'side': getattr(tick, 'side', 'unknown'),
|
|
'trade_id': getattr(tick, 'trade_id', 'unknown'),
|
|
'is_buyer_maker': getattr(tick, 'is_buyer_maker', False)
|
|
}
|
|
tick_data.append(tick_dict)
|
|
|
|
return tick_data
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting tick data for {symbol}: {e}")
|
|
return []
|
|
|
|
def _get_multiframe_ohlcv_for_rl(self, symbol: str) -> Dict[str, List[Dict[str, Any]]]:
|
|
"""Get multi-timeframe OHLCV data for RL state building"""
|
|
try:
|
|
ohlcv_data = {}
|
|
timeframes = ['1s', '1m', '1h', '1d']
|
|
|
|
for tf in timeframes:
|
|
try:
|
|
# Get historical data for timeframe
|
|
df = self.data_provider.get_historical_data(
|
|
symbol=symbol,
|
|
timeframe=tf,
|
|
limit=300,
|
|
refresh=True
|
|
)
|
|
|
|
if df is not None and not df.empty:
|
|
# Convert to list of dictionaries with technical indicators
|
|
bars = []
|
|
|
|
# Add technical indicators
|
|
df_with_indicators = self._add_technical_indicators(df)
|
|
|
|
for idx, row in df_with_indicators.tail(300).iterrows():
|
|
bar = {
|
|
'timestamp': idx if hasattr(idx, 'timestamp') else datetime.now(),
|
|
'open': float(row.get('open', 0)),
|
|
'high': float(row.get('high', 0)),
|
|
'low': float(row.get('low', 0)),
|
|
'close': float(row.get('close', 0)),
|
|
'volume': float(row.get('volume', 0)),
|
|
'rsi': float(row.get('rsi', 50)),
|
|
'macd': float(row.get('macd', 0)),
|
|
'bb_upper': float(row.get('bb_upper', row.get('close', 0))),
|
|
'bb_lower': float(row.get('bb_lower', row.get('close', 0))),
|
|
'sma_20': float(row.get('sma_20', row.get('close', 0))),
|
|
'ema_12': float(row.get('ema_12', row.get('close', 0))),
|
|
'atr': float(row.get('atr', 0))
|
|
}
|
|
bars.append(bar)
|
|
|
|
ohlcv_data[tf] = bars
|
|
else:
|
|
ohlcv_data[tf] = []
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting {tf} data for {symbol}: {e}")
|
|
ohlcv_data[tf] = []
|
|
|
|
return ohlcv_data
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting OHLCV data for {symbol}: {e}")
|
|
return {}
|
|
|
|
def _add_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""Add technical indicators to OHLCV data"""
|
|
try:
|
|
df = df.copy()
|
|
|
|
# RSI
|
|
if len(df) >= 14:
|
|
df['rsi'] = ta.momentum.rsi(df['close'], window=14)
|
|
else:
|
|
df['rsi'] = 50
|
|
|
|
# MACD
|
|
if len(df) >= 26:
|
|
macd = ta.trend.macd_diff(df['close'])
|
|
df['macd'] = macd
|
|
else:
|
|
df['macd'] = 0
|
|
|
|
# Bollinger Bands
|
|
if len(df) >= 20:
|
|
bb = ta.volatility.BollingerBands(df['close'], window=20)
|
|
df['bb_upper'] = bb.bollinger_hband()
|
|
df['bb_lower'] = bb.bollinger_lband()
|
|
else:
|
|
df['bb_upper'] = df['close']
|
|
df['bb_lower'] = df['close']
|
|
|
|
# Moving Averages
|
|
if len(df) >= 20:
|
|
df['sma_20'] = ta.trend.sma_indicator(df['close'], window=20)
|
|
else:
|
|
df['sma_20'] = df['close']
|
|
|
|
if len(df) >= 12:
|
|
df['ema_12'] = ta.trend.ema_indicator(df['close'], window=12)
|
|
else:
|
|
df['ema_12'] = df['close']
|
|
|
|
# ATR
|
|
if len(df) >= 14:
|
|
df['atr'] = ta.volatility.average_true_range(df['high'], df['low'], df['close'], window=14)
|
|
else:
|
|
df['atr'] = 0
|
|
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding technical indicators: {e}")
|
|
return df
|
|
|
|
def _get_cnn_features_for_rl(self, symbol: str) -> Tuple[Optional[Dict[str, np.ndarray]], Optional[Dict[str, np.ndarray]]]:
|
|
"""Get CNN hidden features and predictions for RL state building with BOM matrix integration"""
|
|
try:
|
|
# Try to get CNN features from model registry
|
|
if hasattr(self, 'model_registry') and self.model_registry:
|
|
cnn_models = self.model_registry.get_models_by_type('cnn')
|
|
|
|
if cnn_models:
|
|
hidden_features = {}
|
|
predictions = {}
|
|
|
|
for model_name, model in cnn_models.items():
|
|
try:
|
|
# Get recent market data for the model
|
|
feature_matrix = self.data_provider.get_feature_matrix(
|
|
symbol=symbol,
|
|
timeframes=['1s', '1m', '1h', '1d'],
|
|
window_size=50
|
|
)
|
|
|
|
# Get BOM (Book of Market) matrix data
|
|
bom_matrix = self._get_bom_matrix_for_cnn(symbol)
|
|
|
|
if feature_matrix is not None:
|
|
# Enhance feature matrix with BOM data if available
|
|
if bom_matrix is not None:
|
|
enhanced_matrix = self._combine_market_and_bom_features(
|
|
feature_matrix, bom_matrix, symbol
|
|
)
|
|
logger.debug(f"Enhanced CNN features with BOM matrix for {symbol}: "
|
|
f"market_shape={feature_matrix.shape}, bom_shape={bom_matrix.shape}, "
|
|
f"combined_shape={enhanced_matrix.shape}")
|
|
else:
|
|
enhanced_matrix = feature_matrix
|
|
logger.debug(f"Using market features only for CNN {symbol}: shape={feature_matrix.shape}")
|
|
|
|
# Extract hidden features and predictions
|
|
model_hidden, model_pred = self._extract_cnn_features(model, enhanced_matrix)
|
|
if model_hidden is not None:
|
|
hidden_features[model_name] = model_hidden
|
|
if model_pred is not None:
|
|
predictions[model_name] = model_pred
|
|
except Exception as e:
|
|
logger.warning(f"Error extracting CNN features from {model_name}: {e}")
|
|
|
|
return (hidden_features if hidden_features else None,
|
|
predictions if predictions else None)
|
|
|
|
return None, None
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting CNN features for {symbol}: {e}")
|
|
return None, None
|
|
|
|
def _get_bom_matrix_for_cnn(self, symbol: str) -> Optional[np.ndarray]:
|
|
"""
|
|
Get cached BOM (Book of Market) matrix for CNN input from data provider
|
|
|
|
Uses 1s cached BOM data from the data provider for proper temporal analysis
|
|
|
|
Returns:
|
|
np.ndarray: BOM matrix of shape (sequence_length, 120) from cached 1s data
|
|
"""
|
|
try:
|
|
sequence_length = 50 # Match standard CNN sequence length
|
|
|
|
# Get cached BOM matrix from data provider
|
|
if hasattr(self.data_provider, 'get_bom_matrix_for_cnn'):
|
|
bom_matrix = self.data_provider.get_bom_matrix_for_cnn(symbol, sequence_length)
|
|
if bom_matrix is not None:
|
|
logger.debug(f"Retrieved cached BOM matrix for {symbol}: shape={bom_matrix.shape}")
|
|
return bom_matrix
|
|
|
|
# Fallback to generating synthetic BOM matrix if no cache available
|
|
logger.warning(f"No cached BOM data available for {symbol}, generating synthetic")
|
|
return self._generate_fallback_bom_matrix(symbol, sequence_length)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting BOM matrix for {symbol}: {e}")
|
|
return self._generate_fallback_bom_matrix(symbol, sequence_length)
|
|
|
|
def _generate_fallback_bom_matrix(self, symbol: str, sequence_length: int) -> np.ndarray:
|
|
"""Generate fallback BOM matrix when cache is not available"""
|
|
try:
|
|
# Generate synthetic BOM features for current timestamp
|
|
if hasattr(self.data_provider, 'generate_synthetic_bom_features'):
|
|
current_features = self.data_provider.generate_synthetic_bom_features(symbol)
|
|
else:
|
|
current_features = [0.0] * 120
|
|
|
|
# Create temporal variations for the sequence
|
|
bom_matrix = np.zeros((sequence_length, 120), dtype=np.float32)
|
|
|
|
for i in range(sequence_length):
|
|
# Add small random variations to simulate temporal changes
|
|
variation_factor = 0.95 + 0.1 * np.random.random() # 5% variation
|
|
varied_features = [f * variation_factor for f in current_features]
|
|
bom_matrix[i] = np.array(varied_features, dtype=np.float32)
|
|
|
|
logger.debug(f"Generated fallback BOM matrix for {symbol}: shape={bom_matrix.shape}")
|
|
return bom_matrix
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating fallback BOM matrix for {symbol}: {e}")
|
|
# Return zeros as absolute fallback
|
|
return np.zeros((sequence_length, 120), dtype=np.float32)
|
|
|
|
def _get_cob_bom_features(self, symbol: str) -> Optional[List[float]]:
|
|
"""Extract COB features for BOM matrix (40 features)"""
|
|
try:
|
|
features = []
|
|
|
|
if hasattr(self, 'cob_integration') and self.cob_integration:
|
|
cob_snapshot = self.cob_integration.get_consolidated_orderbook(symbol)
|
|
if cob_snapshot:
|
|
# Top 10 bid levels (price offset + volume)
|
|
for i in range(10):
|
|
if i < len(cob_snapshot.consolidated_bids):
|
|
level = cob_snapshot.consolidated_bids[i]
|
|
price_offset = (level.price - cob_snapshot.volume_weighted_mid) / cob_snapshot.volume_weighted_mid
|
|
volume_normalized = level.total_volume_usd / 1000000 # Normalize to millions
|
|
features.extend([price_offset, volume_normalized])
|
|
else:
|
|
features.extend([0.0, 0.0])
|
|
|
|
# Top 10 ask levels (price offset + volume)
|
|
for i in range(10):
|
|
if i < len(cob_snapshot.consolidated_asks):
|
|
level = cob_snapshot.consolidated_asks[i]
|
|
price_offset = (level.price - cob_snapshot.volume_weighted_mid) / cob_snapshot.volume_weighted_mid
|
|
volume_normalized = level.total_volume_usd / 1000000
|
|
features.extend([price_offset, volume_normalized])
|
|
else:
|
|
features.extend([0.0, 0.0])
|
|
|
|
return features[:40] # Ensure exactly 40 features
|
|
|
|
return None
|
|
except Exception as e:
|
|
logger.warning(f"Error getting COB BOM features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_volume_profile_bom_features(self, symbol: str) -> Optional[List[float]]:
|
|
"""Extract volume profile features for BOM matrix (30 features)"""
|
|
try:
|
|
features = []
|
|
|
|
if hasattr(self, 'cob_integration') and self.cob_integration:
|
|
# Get session volume profile
|
|
volume_profile = self.cob_integration.get_session_volume_profile(symbol)
|
|
if volume_profile and 'data' in volume_profile:
|
|
svp_data = volume_profile['data']
|
|
|
|
# Sort by volume and get top 10 levels
|
|
top_levels = sorted(svp_data, key=lambda x: x['total_volume'], reverse=True)[:10]
|
|
|
|
for level in top_levels:
|
|
features.extend([
|
|
level.get('buy_percent', 50.0) / 100.0, # Normalize to 0-1
|
|
level.get('sell_percent', 50.0) / 100.0,
|
|
level.get('total_volume', 0.0) / 1000000 # Normalize to millions
|
|
])
|
|
|
|
# Pad to 30 features (10 levels * 3 features)
|
|
while len(features) < 30:
|
|
features.extend([0.5, 0.5, 0.0]) # Neutral buy/sell, zero volume
|
|
|
|
return features[:30]
|
|
|
|
return None
|
|
except Exception as e:
|
|
logger.warning(f"Error getting volume profile BOM features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_flow_intensity_bom_features(self, symbol: str) -> Optional[List[float]]:
|
|
"""Extract order flow intensity features for BOM matrix (25 features)"""
|
|
try:
|
|
# Get recent trade flow data for analysis
|
|
trade_flow_data = self._get_recent_trade_data_for_flow_analysis(symbol, 300)
|
|
|
|
if not trade_flow_data:
|
|
return [0.0] * 25
|
|
|
|
features = []
|
|
|
|
# === AGGRESSIVE ORDER FLOW ANALYSIS ===
|
|
aggressive_buys = [t for t in trade_flow_data if t.get('aggressive_side') == 'buy']
|
|
aggressive_sells = [t for t in trade_flow_data if t.get('aggressive_side') == 'sell']
|
|
|
|
total_trades = len(trade_flow_data)
|
|
if total_trades > 0:
|
|
features.extend([
|
|
len(aggressive_buys) / total_trades, # Aggressive buy ratio
|
|
len(aggressive_sells) / total_trades, # Aggressive sell ratio
|
|
sum(t.get('volume', 0) for t in aggressive_buys) / max(sum(t.get('volume', 0) for t in trade_flow_data), 1),
|
|
sum(t.get('volume', 0) for t in aggressive_sells) / max(sum(t.get('volume', 0) for t in trade_flow_data), 1),
|
|
np.mean([t.get('size', 0) for t in aggressive_buys]) if aggressive_buys else 0.0,
|
|
np.mean([t.get('size', 0) for t in aggressive_sells]) if aggressive_sells else 0.0
|
|
])
|
|
else:
|
|
features.extend([0.0] * 6)
|
|
|
|
# === BLOCK TRADE DETECTION ===
|
|
large_trades = [t for t in trade_flow_data if t.get('volume', 0) > 10000] # >$10k trades
|
|
if trade_flow_data:
|
|
features.extend([
|
|
len(large_trades) / len(trade_flow_data),
|
|
sum(t.get('volume', 0) for t in large_trades) / max(sum(t.get('volume', 0) for t in trade_flow_data), 1),
|
|
np.mean([t.get('volume', 0) for t in large_trades]) if large_trades else 0.0
|
|
])
|
|
else:
|
|
features.extend([0.0] * 3)
|
|
|
|
# === FLOW VELOCITY METRICS ===
|
|
if len(trade_flow_data) > 1:
|
|
time_deltas = []
|
|
for i in range(1, len(trade_flow_data)):
|
|
time_delta = (trade_flow_data[i]['timestamp'] - trade_flow_data[i-1]['timestamp']).total_seconds()
|
|
time_deltas.append(time_delta)
|
|
|
|
features.extend([
|
|
np.mean(time_deltas) if time_deltas else 1.0, # Average time between trades
|
|
np.std(time_deltas) if len(time_deltas) > 1 else 0.0, # Time volatility
|
|
min(time_deltas) if time_deltas else 1.0, # Fastest execution
|
|
len(trade_flow_data) / 300.0 # Trade rate per second
|
|
])
|
|
else:
|
|
features.extend([1.0, 0.0, 1.0, 0.0])
|
|
|
|
# === PRICE IMPACT ANALYSIS ===
|
|
price_changes = []
|
|
for trade in trade_flow_data:
|
|
if 'price_before' in trade and 'price_after' in trade:
|
|
price_impact = abs(trade['price_after'] - trade['price_before']) / trade['price_before']
|
|
price_changes.append(price_impact)
|
|
|
|
if price_changes:
|
|
features.extend([
|
|
np.mean(price_changes),
|
|
np.max(price_changes),
|
|
np.std(price_changes)
|
|
])
|
|
else:
|
|
features.extend([0.0, 0.0, 0.0])
|
|
|
|
# === MOMENTUM INDICATORS ===
|
|
if len(trade_flow_data) >= 10:
|
|
recent_volume = sum(t.get('volume', 0) for t in trade_flow_data[-10:])
|
|
earlier_volume = sum(t.get('volume', 0) for t in trade_flow_data[:-10])
|
|
momentum = recent_volume / max(earlier_volume, 1) if earlier_volume > 0 else 1.0
|
|
|
|
recent_aggressive_ratio = len([t for t in trade_flow_data[-10:] if t.get('aggressive_side') == 'buy']) / 10
|
|
earlier_aggressive_ratio = len([t for t in trade_flow_data[:-10] if t.get('aggressive_side') == 'buy']) / max(len(trade_flow_data) - 10, 1)
|
|
|
|
features.extend([
|
|
momentum,
|
|
recent_aggressive_ratio - earlier_aggressive_ratio,
|
|
recent_aggressive_ratio
|
|
])
|
|
else:
|
|
features.extend([1.0, 0.0, 0.5])
|
|
|
|
# === INSTITUTIONAL ACTIVITY INDICATORS ===
|
|
# Detect iceberg orders and large hidden liquidity
|
|
volume_spikes = [t for t in trade_flow_data if t.get('volume', 0) > np.mean([x.get('volume', 0) for x in trade_flow_data]) * 3]
|
|
uniform_sizes = len([t for t in trade_flow_data if t.get('size', 0) in [0.1, 0.01, 1.0, 10.0]]) # Common algo sizes
|
|
|
|
features.extend([
|
|
len(volume_spikes) / max(len(trade_flow_data), 1),
|
|
uniform_sizes / max(len(trade_flow_data), 1),
|
|
np.std([t.get('size', 0) for t in trade_flow_data]) if trade_flow_data else 0.0
|
|
])
|
|
|
|
# Ensure exactly 25 features
|
|
while len(features) < 25:
|
|
features.append(0.0)
|
|
|
|
return features[:25]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting flow intensity BOM features for {symbol}: {e}")
|
|
return [0.0] * 25
|
|
|
|
def _get_microstructure_bom_features(self, symbol: str) -> Optional[List[float]]:
|
|
"""Extract market microstructure features for BOM matrix (25 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# === SPREAD DYNAMICS ===
|
|
if hasattr(self, 'cob_integration') and self.cob_integration:
|
|
cob_snapshot = self.cob_integration.get_consolidated_orderbook(symbol)
|
|
if cob_snapshot:
|
|
features.extend([
|
|
cob_snapshot.spread_bps / 100.0, # Normalize spread
|
|
cob_snapshot.liquidity_imbalance, # Already normalized -1 to 1
|
|
len(cob_snapshot.exchanges_active) / 5.0, # Normalize to max 5 exchanges
|
|
cob_snapshot.total_bid_liquidity / 1000000.0, # Normalize to millions
|
|
cob_snapshot.total_ask_liquidity / 1000000.0
|
|
])
|
|
else:
|
|
features.extend([0.0] * 5)
|
|
else:
|
|
features.extend([0.0] * 5)
|
|
|
|
# === MARKET DEPTH ANALYSIS ===
|
|
recent_trades = self._get_recent_trade_data_for_flow_analysis(symbol, 60) # Last 1 minute
|
|
if recent_trades:
|
|
trade_sizes = [t.get('size', 0) for t in recent_trades]
|
|
trade_volumes = [t.get('volume', 0) for t in recent_trades]
|
|
|
|
features.extend([
|
|
np.mean(trade_sizes) if trade_sizes else 0.0,
|
|
np.median(trade_sizes) if trade_sizes else 0.0,
|
|
np.std(trade_sizes) if len(trade_sizes) > 1 else 0.0,
|
|
np.mean(trade_volumes) / 1000.0 if trade_volumes else 0.0, # Normalize to thousands
|
|
len(recent_trades) / 60.0 # Trades per second
|
|
])
|
|
else:
|
|
features.extend([0.0] * 5)
|
|
|
|
# === LIQUIDITY CONSUMPTION PATTERNS ===
|
|
if recent_trades:
|
|
# Analyze if trades are consuming top-of-book vs deeper levels
|
|
top_book_trades = len([t for t in recent_trades if t.get('level', 1) == 1])
|
|
deep_book_trades = len([t for t in recent_trades if t.get('level', 1) > 3])
|
|
|
|
features.extend([
|
|
top_book_trades / max(len(recent_trades), 1),
|
|
deep_book_trades / max(len(recent_trades), 1),
|
|
np.mean([t.get('level', 1) for t in recent_trades])
|
|
])
|
|
else:
|
|
features.extend([0.0, 0.0, 1.0])
|
|
|
|
# === ORDER BOOK PRESSURE ===
|
|
pressure_features = self._calculate_order_book_pressure(symbol)
|
|
if pressure_features:
|
|
features.extend(pressure_features) # Should be 7 features
|
|
else:
|
|
features.extend([0.0] * 7)
|
|
|
|
# === TIME-OF-DAY EFFECTS ===
|
|
current_time = datetime.now()
|
|
features.extend([
|
|
current_time.hour / 24.0, # Hour of day normalized
|
|
current_time.minute / 60.0, # Minute of hour normalized
|
|
current_time.weekday() / 7.0, # Day of week normalized
|
|
1.0 if 9 <= current_time.hour <= 16 else 0.0, # Market hours indicator
|
|
1.0 if current_time.weekday() < 5 else 0.0 # Weekday indicator
|
|
])
|
|
|
|
# Ensure exactly 25 features
|
|
while len(features) < 25:
|
|
features.append(0.0)
|
|
|
|
return features[:25]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting microstructure BOM features for {symbol}: {e}")
|
|
return [0.0] * 25
|
|
|
|
def _calculate_order_book_pressure(self, symbol: str) -> Optional[List[float]]:
|
|
"""Calculate order book pressure indicators (7 features)"""
|
|
try:
|
|
if not hasattr(self, 'cob_integration') or not self.cob_integration:
|
|
return [0.0] * 7
|
|
|
|
cob_snapshot = self.cob_integration.get_consolidated_orderbook(symbol)
|
|
if not cob_snapshot:
|
|
return [0.0] * 7
|
|
|
|
# Calculate various pressure metrics
|
|
features = []
|
|
|
|
# 1. Bid-Ask Volume Ratio (different levels)
|
|
if cob_snapshot.consolidated_bids and cob_snapshot.consolidated_asks:
|
|
level_1_bid = cob_snapshot.consolidated_bids[0].total_volume_usd
|
|
level_1_ask = cob_snapshot.consolidated_asks[0].total_volume_usd
|
|
ratio_1 = level_1_bid / (level_1_bid + level_1_ask) if (level_1_bid + level_1_ask) > 0 else 0.5
|
|
|
|
# Top 5 levels ratio
|
|
top_5_bid = sum(level.total_volume_usd for level in cob_snapshot.consolidated_bids[:5])
|
|
top_5_ask = sum(level.total_volume_usd for level in cob_snapshot.consolidated_asks[:5])
|
|
ratio_5 = top_5_bid / (top_5_bid + top_5_ask) if (top_5_bid + top_5_ask) > 0 else 0.5
|
|
|
|
features.extend([ratio_1, ratio_5])
|
|
else:
|
|
features.extend([0.5, 0.5])
|
|
|
|
# 2. Depth asymmetry
|
|
bid_depth = len(cob_snapshot.consolidated_bids)
|
|
ask_depth = len(cob_snapshot.consolidated_asks)
|
|
depth_asymmetry = (bid_depth - ask_depth) / (bid_depth + ask_depth) if (bid_depth + ask_depth) > 0 else 0.0
|
|
features.append(depth_asymmetry)
|
|
|
|
# 3. Volume concentration (Gini coefficient approximation)
|
|
if cob_snapshot.consolidated_bids:
|
|
bid_volumes = [level.total_volume_usd for level in cob_snapshot.consolidated_bids[:10]]
|
|
bid_concentration = self._calculate_concentration_index(bid_volumes)
|
|
else:
|
|
bid_concentration = 0.0
|
|
|
|
if cob_snapshot.consolidated_asks:
|
|
ask_volumes = [level.total_volume_usd for level in cob_snapshot.consolidated_asks[:10]]
|
|
ask_concentration = self._calculate_concentration_index(ask_volumes)
|
|
else:
|
|
ask_concentration = 0.0
|
|
|
|
features.extend([bid_concentration, ask_concentration])
|
|
|
|
# 4. Exchange diversity impact
|
|
if cob_snapshot.consolidated_bids:
|
|
avg_exchanges_per_level = np.mean([len(level.exchange_breakdown) for level in cob_snapshot.consolidated_bids[:5]])
|
|
max_exchanges = 5.0 # Assuming max 5 exchanges
|
|
exchange_diversity_bid = avg_exchanges_per_level / max_exchanges
|
|
else:
|
|
exchange_diversity_bid = 0.0
|
|
|
|
if cob_snapshot.consolidated_asks:
|
|
avg_exchanges_per_level = np.mean([len(level.exchange_breakdown) for level in cob_snapshot.consolidated_asks[:5]])
|
|
exchange_diversity_ask = avg_exchanges_per_level / max_exchanges
|
|
else:
|
|
exchange_diversity_ask = 0.0
|
|
|
|
features.extend([exchange_diversity_bid, exchange_diversity_ask])
|
|
|
|
return features[:7]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating order book pressure for {symbol}: {e}")
|
|
return [0.0] * 7
|
|
|
|
def _calculate_concentration_index(self, volumes: List[float]) -> float:
|
|
"""Calculate volume concentration index (simplified Gini coefficient)"""
|
|
try:
|
|
if not volumes or len(volumes) < 2:
|
|
return 0.0
|
|
|
|
total_volume = sum(volumes)
|
|
if total_volume == 0:
|
|
return 0.0
|
|
|
|
# Sort volumes in ascending order
|
|
sorted_volumes = sorted(volumes)
|
|
n = len(sorted_volumes)
|
|
|
|
# Calculate Gini coefficient
|
|
sum_product = sum((i + 1) * vol for i, vol in enumerate(sorted_volumes))
|
|
gini = (2 * sum_product) / (n * total_volume) - (n + 1) / n
|
|
|
|
return gini
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating concentration index: {e}")
|
|
return 0.0
|
|
|
|
def _add_temporal_dynamics_to_bom(self, bom_matrix: np.ndarray, symbol: str) -> np.ndarray:
|
|
"""Add temporal dynamics to BOM matrix to simulate order book changes over time"""
|
|
try:
|
|
sequence_length, features = bom_matrix.shape
|
|
|
|
# Add small random variations to simulate order book dynamics
|
|
# In real implementation, this would be historical order book snapshots
|
|
noise_scale = 0.05 # 5% noise
|
|
|
|
for t in range(1, sequence_length):
|
|
# Add temporal correlation - each timestep slightly different from previous
|
|
correlation = 0.95 # High correlation between adjacent timesteps
|
|
random_change = np.random.normal(0, noise_scale, features)
|
|
|
|
bom_matrix[t] = bom_matrix[t-1] * correlation + bom_matrix[t] * (1 - correlation) + random_change
|
|
|
|
# Ensure values stay within reasonable bounds
|
|
bom_matrix = np.clip(bom_matrix, -5.0, 5.0)
|
|
|
|
return bom_matrix
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding temporal dynamics to BOM matrix: {e}")
|
|
return bom_matrix
|
|
|
|
def _combine_market_and_bom_features(self, market_matrix: np.ndarray, bom_matrix: np.ndarray, symbol: str) -> np.ndarray:
|
|
"""
|
|
Combine traditional market features with BOM matrix features
|
|
|
|
Args:
|
|
market_matrix: Traditional market data features (timeframes, sequence_length, market_features) - 3D
|
|
bom_matrix: BOM matrix features (sequence_length, bom_features) - 2D
|
|
symbol: Trading symbol
|
|
|
|
Returns:
|
|
Combined feature matrix reshaped for CNN input
|
|
"""
|
|
try:
|
|
logger.debug(f"Combining features for {symbol}: market={market_matrix.shape}, bom={bom_matrix.shape}")
|
|
|
|
# Handle dimensional mismatch
|
|
if market_matrix.ndim == 3 and bom_matrix.ndim == 2:
|
|
# Market matrix is (timeframes, sequence_length, features)
|
|
# BOM matrix is (sequence_length, bom_features)
|
|
|
|
# Reshape market matrix to 2D by flattening timeframes dimension
|
|
timeframes, sequence_length, market_features = market_matrix.shape
|
|
|
|
# Option 1: Take the last timeframe (most recent data)
|
|
market_2d = market_matrix[-1] # Shape: (sequence_length, market_features)
|
|
|
|
# Ensure sequence lengths match
|
|
min_length = min(market_2d.shape[0], bom_matrix.shape[0])
|
|
market_trimmed = market_2d[:min_length]
|
|
bom_trimmed = bom_matrix[:min_length]
|
|
|
|
# Combine horizontally
|
|
combined_matrix = np.concatenate([market_trimmed, bom_trimmed], axis=1)
|
|
|
|
logger.debug(f"Combined features for {symbol}: "
|
|
f"market_2d={market_trimmed.shape}, bom={bom_trimmed.shape}, "
|
|
f"combined={combined_matrix.shape}")
|
|
|
|
return combined_matrix.astype(np.float32)
|
|
|
|
elif market_matrix.ndim == 2 and bom_matrix.ndim == 2:
|
|
# Both are 2D - can combine directly
|
|
min_length = min(market_matrix.shape[0], bom_matrix.shape[0])
|
|
market_trimmed = market_matrix[:min_length]
|
|
bom_trimmed = bom_matrix[:min_length]
|
|
|
|
combined_matrix = np.concatenate([market_trimmed, bom_trimmed], axis=1)
|
|
|
|
logger.debug(f"Combined 2D features for {symbol}: "
|
|
f"market={market_trimmed.shape}, bom={bom_trimmed.shape}, "
|
|
f"combined={combined_matrix.shape}")
|
|
|
|
return combined_matrix.astype(np.float32)
|
|
|
|
else:
|
|
logger.warning(f"Unsupported matrix dimensions for {symbol}: "
|
|
f"market={market_matrix.shape}, bom={bom_matrix.shape}")
|
|
# Fallback: reshape market matrix to 2D if needed
|
|
if market_matrix.ndim == 3:
|
|
market_2d = market_matrix.reshape(-1, market_matrix.shape[-1])
|
|
else:
|
|
market_2d = market_matrix
|
|
|
|
return market_2d.astype(np.float32)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error combining market and BOM features for {symbol}: {e}")
|
|
# Fallback to reshaped market features only
|
|
try:
|
|
if market_matrix.ndim == 3:
|
|
return market_matrix[-1].astype(np.float32) # Last timeframe
|
|
else:
|
|
return market_matrix.astype(np.float32)
|
|
except:
|
|
logger.error(f"Fallback failed for {symbol}, returning zeros")
|
|
return np.zeros((50, 5), dtype=np.float32) # Basic fallback
|
|
|
|
def _get_latest_price_from_universal(self, symbol: str, timeframe: str, universal_stream: UniversalDataStream) -> Optional[float]:
|
|
"""Get latest price for symbol and timeframe from universal data stream"""
|
|
try:
|
|
if symbol == 'ETH/USDT':
|
|
if timeframe == '1s' and len(universal_stream.eth_ticks) > 0:
|
|
# Get latest tick price (close price is at index 4)
|
|
return float(universal_stream.eth_ticks[-1, 4]) # close price
|
|
elif timeframe == '1m' and len(universal_stream.eth_1m) > 0:
|
|
return float(universal_stream.eth_1m[-1, 4]) # close price
|
|
elif timeframe == '1h' and len(universal_stream.eth_1h) > 0:
|
|
return float(universal_stream.eth_1h[-1, 4]) # close price
|
|
elif timeframe == '1d' and len(universal_stream.eth_1d) > 0:
|
|
return float(universal_stream.eth_1d[-1, 4]) # close price
|
|
elif symbol == 'BTC/USDT':
|
|
if timeframe == '1s' and len(universal_stream.btc_ticks) > 0:
|
|
return float(universal_stream.btc_ticks[-1, 4]) # close price
|
|
|
|
# Fallback to data provider
|
|
return self._get_latest_price_fallback(symbol, timeframe)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting latest price for {symbol} {timeframe}: {e}")
|
|
return self._get_latest_price_fallback(symbol, timeframe)
|
|
|
|
def _get_latest_price_fallback(self, symbol: str, timeframe: str) -> Optional[float]:
|
|
"""Fallback method to get latest price from data provider"""
|
|
try:
|
|
df = self.data_provider.get_historical_data(symbol, timeframe, limit=1)
|
|
if df is not None and not df.empty:
|
|
return float(df['close'].iloc[-1])
|
|
return None
|
|
except Exception as e:
|
|
logger.warning(f"Error in price fallback for {symbol} {timeframe}: {e}")
|
|
return None
|
|
|
|
def _calculate_volatility_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
|
|
"""Calculate volatility from universal data stream"""
|
|
try:
|
|
if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 1:
|
|
# Calculate volatility from 1m candles
|
|
closes = universal_stream.eth_1m[:, 4] # close prices
|
|
if len(closes) > 1:
|
|
returns = np.diff(np.log(closes))
|
|
return float(np.std(returns) * np.sqrt(1440)) # Daily volatility
|
|
elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 1:
|
|
# Calculate volatility from tick data
|
|
closes = universal_stream.btc_ticks[:, 4] # close prices
|
|
if len(closes) > 1:
|
|
returns = np.diff(np.log(closes))
|
|
return float(np.std(returns) * np.sqrt(86400)) # Daily volatility
|
|
return 0.0
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating volatility for {symbol}: {e}")
|
|
return 0.0
|
|
|
|
def _calculate_volume_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
|
|
"""Calculate volume from universal data stream"""
|
|
try:
|
|
if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 0:
|
|
# Get latest volume from 1m candles
|
|
volumes = universal_stream.eth_1m[:, 5] # volume
|
|
return float(np.mean(volumes[-10:])) # Average of last 10 candles
|
|
elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 0:
|
|
# Calculate volume from tick data
|
|
volumes = universal_stream.btc_ticks[:, 5] # volume
|
|
return float(np.sum(volumes[-100:])) # Sum of last 100 ticks
|
|
return 0.0
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating volume for {symbol}: {e}")
|
|
return 0.0
|
|
|
|
def _calculate_trend_strength_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
|
|
"""Calculate trend strength from universal data stream"""
|
|
try:
|
|
if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 20:
|
|
# Calculate trend strength using 20-period moving average
|
|
closes = universal_stream.eth_1m[-20:, 4] # last 20 closes
|
|
if len(closes) >= 20:
|
|
sma = np.mean(closes)
|
|
current_price = closes[-1]
|
|
return float((current_price - sma) / sma) # Relative trend strength
|
|
elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 100:
|
|
# Calculate trend from tick data
|
|
closes = universal_stream.btc_ticks[-100:, 4] # last 100 ticks
|
|
if len(closes) >= 100:
|
|
start_price = closes[0]
|
|
end_price = closes[-1]
|
|
return float((end_price - start_price) / start_price)
|
|
return 0.0
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating trend strength for {symbol}: {e}")
|
|
return 0.0
|
|
|
|
def _determine_market_regime(self, symbol: str, universal_stream: UniversalDataStream) -> str:
|
|
"""Determine market regime from universal data stream"""
|
|
try:
|
|
# Calculate volatility and trend strength
|
|
volatility = self._calculate_volatility_from_universal(symbol, universal_stream)
|
|
trend_strength = abs(self._calculate_trend_strength_from_universal(symbol, universal_stream))
|
|
|
|
# Classify market regime
|
|
if volatility > 0.05: # High volatility threshold
|
|
return 'volatile'
|
|
elif trend_strength > 0.02: # Strong trend threshold
|
|
return 'trending'
|
|
else:
|
|
return 'ranging'
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error determining market regime for {symbol}: {e}")
|
|
return 'unknown'
|
|
|
|
def _extract_cnn_features(self, model, feature_matrix: np.ndarray) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
|
|
"""Extract hidden features and predictions from CNN model"""
|
|
try:
|
|
# Use actual CNN model inference instead of placeholder values
|
|
if hasattr(model, 'predict') and callable(model.predict):
|
|
# Get model prediction
|
|
prediction_result = model.predict(feature_matrix)
|
|
|
|
# Extract predictions (action probabilities) - ensure proper array handling
|
|
if isinstance(prediction_result, dict):
|
|
# Get probabilities as flat array
|
|
predictions = prediction_result.get('probabilities', [0.33, 0.33, 0.34])
|
|
confidence = prediction_result.get('confidence', 0.7)
|
|
|
|
# Convert predictions to numpy array first
|
|
if isinstance(predictions, np.ndarray):
|
|
predictions_array = predictions.flatten()
|
|
elif isinstance(predictions, (list, tuple)):
|
|
predictions_array = np.array(predictions, dtype=np.float32).flatten()
|
|
else:
|
|
predictions_array = np.array([float(predictions)], dtype=np.float32)
|
|
|
|
# Create final predictions array with confidence
|
|
# Use safe tensor conversion to avoid scalar conversion errors
|
|
confidence_scalar = self._safe_tensor_to_scalar(confidence, default_value=0.7)
|
|
|
|
# Combine predictions and confidence as separate elements
|
|
predictions = np.concatenate([
|
|
predictions_array,
|
|
np.array([confidence_scalar], dtype=np.float32)
|
|
])
|
|
elif isinstance(prediction_result, tuple) and len(prediction_result) == 2:
|
|
# Handle (pred_class, pred_proba) tuple from CNN models
|
|
pred_class, pred_proba = prediction_result
|
|
|
|
# Flatten and process the probability array
|
|
if isinstance(pred_proba, np.ndarray):
|
|
if pred_proba.ndim > 1:
|
|
# Handle 2D arrays like [[0.1, 0.2, 0.7]]
|
|
pred_proba_flat = pred_proba.flatten()
|
|
else:
|
|
# Already 1D
|
|
pred_proba_flat = pred_proba
|
|
|
|
# Use the probability values as the predictions array
|
|
predictions = pred_proba_flat.astype(np.float32)
|
|
else:
|
|
# Fallback: use class prediction only
|
|
predictions = np.array([float(pred_class)], dtype=np.float32)
|
|
else:
|
|
# Handle direct prediction result
|
|
if isinstance(prediction_result, np.ndarray):
|
|
predictions = prediction_result.flatten()
|
|
elif isinstance(prediction_result, (list, tuple)):
|
|
predictions = np.array(prediction_result, dtype=np.float32).flatten()
|
|
else:
|
|
predictions = np.array([float(prediction_result)], dtype=np.float32)
|
|
|
|
# Extract hidden features if model supports it
|
|
hidden_features = None
|
|
if hasattr(model, 'get_hidden_features'):
|
|
hidden_features = model.get_hidden_features(feature_matrix)
|
|
elif hasattr(model, 'extract_features'):
|
|
hidden_features = model.extract_features(feature_matrix)
|
|
else:
|
|
# Use final layer features as approximation
|
|
hidden_features = predictions[:512] if len(predictions) >= 512 else np.pad(predictions, (0, 512-len(predictions)))
|
|
|
|
return hidden_features, predictions
|
|
else:
|
|
logger.warning("CNN model does not have predict method")
|
|
return None, None
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error extracting CNN features: {e}")
|
|
return None, None
|
|
|
|
def _enhance_confidence_with_universal_context(self, base_confidence: float, timeframe: str,
|
|
market_state: MarketState,
|
|
universal_stream: UniversalDataStream) -> float:
|
|
"""Enhance confidence score based on universal data context"""
|
|
enhanced = base_confidence
|
|
|
|
# Adjust based on data quality from universal stream
|
|
data_quality = universal_stream.metadata['data_quality']['overall_score']
|
|
enhanced *= data_quality
|
|
|
|
# Adjust based on data freshness
|
|
freshness = universal_stream.metadata.get('data_freshness', {})
|
|
if timeframe in ['1s', '1m']:
|
|
# For short timeframes, penalize stale data more heavily
|
|
eth_freshness = freshness.get(f'eth_{timeframe}', 0)
|
|
if eth_freshness > 60: # More than 1 minute old
|
|
enhanced *= 0.8
|
|
|
|
# Adjust based on market regime
|
|
if market_state.market_regime == 'trending':
|
|
enhanced *= 1.1 # More confident in trending markets
|
|
elif market_state.market_regime == 'volatile':
|
|
enhanced *= 0.8 # Less confident in volatile markets
|
|
|
|
# Adjust based on timeframe reliability for scalping
|
|
timeframe_reliability = {
|
|
'1s': 1.0, # Primary scalping timeframe
|
|
'1m': 0.9, # Short-term confirmation
|
|
'5m': 0.8, # Short-term momentum
|
|
'15m': 0.9, # Entry/exit timing
|
|
'1h': 0.8, # Medium-term trend
|
|
'4h': 0.7, # Longer-term (less relevant for scalping)
|
|
'1d': 0.6 # Long-term direction (minimal for scalping)
|
|
}
|
|
enhanced *= timeframe_reliability.get(timeframe, 1.0)
|
|
|
|
# Adjust based on volume
|
|
if market_state.volume > 1.5: # High volume
|
|
enhanced *= 1.05
|
|
elif market_state.volume < 0.5: # Low volume
|
|
enhanced *= 0.9
|
|
|
|
# Adjust based on correlation with BTC (for ETH trades)
|
|
if market_state.symbol == 'ETH/USDT' and len(universal_stream.btc_ticks) > 1:
|
|
# Check ETH-BTC correlation strength
|
|
eth_momentum = (universal_stream.eth_ticks[-1, 4] - universal_stream.eth_ticks[-2, 4]) / universal_stream.eth_ticks[-2, 4]
|
|
btc_momentum = (universal_stream.btc_ticks[-1, 4] - universal_stream.btc_ticks[-2, 4]) / universal_stream.btc_ticks[-2, 4]
|
|
|
|
# If ETH and BTC are moving in same direction, increase confidence
|
|
if (eth_momentum > 0 and btc_momentum > 0) or (eth_momentum < 0 and btc_momentum < 0):
|
|
enhanced *= 1.05
|
|
else:
|
|
enhanced *= 0.95
|
|
|
|
return min(enhanced, 1.0) # Cap at 1.0
|
|
|
|
def _combine_timeframe_predictions(self, timeframe_predictions: List[TimeframePrediction],
|
|
symbol: str) -> Tuple[str, float]:
|
|
"""Combine predictions from multiple timeframes"""
|
|
action_scores = {'BUY': 0.0, 'SELL': 0.0, 'HOLD': 0.0}
|
|
total_weight = 0.0
|
|
|
|
for tf_pred in timeframe_predictions:
|
|
# Get timeframe weight
|
|
tf_weight = self.timeframe_weights.get(tf_pred.timeframe, 0.1)
|
|
|
|
# Weight by confidence and timeframe importance
|
|
weighted_confidence = tf_pred.confidence * tf_weight
|
|
|
|
# Add to action scores
|
|
action_scores[tf_pred.action] += weighted_confidence
|
|
total_weight += weighted_confidence
|
|
|
|
# Normalize scores
|
|
if total_weight > 0:
|
|
for action in action_scores:
|
|
action_scores[action] /= total_weight
|
|
|
|
# Get best action and confidence
|
|
best_action = max(action_scores, key=action_scores.get)
|
|
best_confidence = action_scores[best_action]
|
|
|
|
return best_action, best_confidence
|
|
|
|
async def _make_coordinated_decision(self, symbol: str, predictions: List[EnhancedPrediction],
|
|
all_predictions: Dict[str, List[EnhancedPrediction]],
|
|
market_state: MarketState) -> Optional[TradingAction]:
|
|
"""Make decision using streamlined 2-action system with position intelligence"""
|
|
if not predictions:
|
|
return None
|
|
|
|
try:
|
|
# Use new 2-action decision making
|
|
decision = self._make_2_action_decision(symbol, predictions, market_state)
|
|
|
|
if decision:
|
|
# Store recent action for tracking
|
|
self.recent_actions[symbol].append(decision)
|
|
|
|
logger.info(f"[SUCCESS] Coordinated decision for {symbol}: {decision.action} "
|
|
f"(confidence: {decision.confidence:.3f}, "
|
|
f"reasoning: {decision.reasoning.get('action_type', 'UNKNOWN')})")
|
|
|
|
return decision
|
|
else:
|
|
logger.debug(f"No decision made for {symbol} - insufficient confidence or position conflict")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error making coordinated decision for {symbol}: {e}")
|
|
return None
|
|
|
|
def _is_closing_action(self, symbol: str, action: str) -> bool:
|
|
"""Determine if an action would close an existing position"""
|
|
if symbol not in self.current_positions:
|
|
return False
|
|
|
|
current_position = self.current_positions[symbol]
|
|
|
|
# Closing logic: opposite action closes position
|
|
if current_position['side'] == 'LONG' and action == 'SELL':
|
|
return True
|
|
elif current_position['side'] == 'SHORT' and action == 'BUY':
|
|
return True
|
|
|
|
return False
|
|
|
|
def _update_position_tracking(self, symbol: str, action: TradingAction):
|
|
"""Update internal position tracking for threshold logic"""
|
|
if action.action == 'BUY':
|
|
# Close any short position, open long position
|
|
if symbol in self.current_positions and self.current_positions[symbol]['side'] == 'SHORT':
|
|
self._close_trade_for_sensitivity_learning(symbol, action)
|
|
del self.current_positions[symbol]
|
|
else:
|
|
self._open_trade_for_sensitivity_learning(symbol, action)
|
|
self.current_positions[symbol] = {
|
|
'side': 'LONG',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
elif action.action == 'SELL':
|
|
# Close any long position, open short position
|
|
if symbol in self.current_positions and self.current_positions[symbol]['side'] == 'LONG':
|
|
self._close_trade_for_sensitivity_learning(symbol, action)
|
|
del self.current_positions[symbol]
|
|
else:
|
|
self._open_trade_for_sensitivity_learning(symbol, action)
|
|
self.current_positions[symbol] = {
|
|
'side': 'SHORT',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
|
|
def _open_trade_for_sensitivity_learning(self, symbol: str, action: TradingAction):
|
|
"""Track trade opening for sensitivity learning"""
|
|
try:
|
|
# Get current market state for learning context
|
|
market_state = self._get_current_market_state_for_sensitivity(symbol)
|
|
|
|
trade_info = {
|
|
'symbol': symbol,
|
|
'side': 'LONG' if action.action == 'BUY' else 'SHORT',
|
|
'entry_price': action.price,
|
|
'entry_time': action.timestamp,
|
|
'entry_confidence': action.confidence,
|
|
'entry_market_state': market_state,
|
|
'sensitivity_level_at_entry': self.current_sensitivity_level,
|
|
'thresholds_used': {
|
|
'open': self._get_current_open_threshold(),
|
|
'close': self._get_current_close_threshold()
|
|
}
|
|
}
|
|
|
|
self.active_trades[symbol] = trade_info
|
|
logger.info(f"Opened trade for sensitivity learning: {symbol} {trade_info['side']} @ ${action.price:.2f}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error tracking trade opening for sensitivity learning: {e}")
|
|
|
|
def _close_trade_for_sensitivity_learning(self, symbol: str, action: TradingAction):
|
|
"""Track trade closing and create learning case for DQN"""
|
|
try:
|
|
if symbol not in self.active_trades:
|
|
return
|
|
|
|
trade_info = self.active_trades[symbol]
|
|
|
|
# Calculate trade outcome
|
|
entry_price = trade_info['entry_price']
|
|
exit_price = action.price
|
|
side = trade_info['side']
|
|
|
|
if side == 'LONG':
|
|
pnl_pct = (exit_price - entry_price) / entry_price
|
|
else: # SHORT
|
|
pnl_pct = (entry_price - exit_price) / entry_price
|
|
|
|
# Calculate trade duration
|
|
duration = (action.timestamp - trade_info['entry_time']).total_seconds()
|
|
|
|
# Get current market state for exit context
|
|
exit_market_state = self._get_current_market_state_for_sensitivity(symbol)
|
|
|
|
# Create completed trade record
|
|
completed_trade = {
|
|
'symbol': symbol,
|
|
'side': side,
|
|
'entry_price': entry_price,
|
|
'exit_price': exit_price,
|
|
'entry_time': trade_info['entry_time'],
|
|
'exit_time': action.timestamp,
|
|
'duration': duration,
|
|
'pnl_pct': pnl_pct,
|
|
'entry_confidence': trade_info['entry_confidence'],
|
|
'exit_confidence': action.confidence,
|
|
'entry_market_state': trade_info['entry_market_state'],
|
|
'exit_market_state': exit_market_state,
|
|
'sensitivity_level_used': trade_info['sensitivity_level_at_entry'],
|
|
'thresholds_used': trade_info['thresholds_used']
|
|
}
|
|
|
|
self.completed_trades.append(completed_trade)
|
|
|
|
# Create sensitivity learning case for DQN
|
|
self._create_sensitivity_learning_case(completed_trade)
|
|
|
|
# Remove from active trades
|
|
del self.active_trades[symbol]
|
|
|
|
logger.info(f"Closed trade for sensitivity learning: {symbol} {side} P&L: {pnl_pct*100:+.2f}% Duration: {duration:.0f}s")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error tracking trade closing for sensitivity learning: {e}")
|
|
|
|
def _get_current_market_state_for_sensitivity(self, symbol: str) -> Dict[str, float]:
|
|
"""Get current market state features for sensitivity learning"""
|
|
try:
|
|
# Get recent price data
|
|
recent_data = self.data_provider.get_historical_data(symbol, '1m', limit=20)
|
|
|
|
if recent_data is None or len(recent_data) < 10:
|
|
return self._get_default_market_state()
|
|
|
|
# Calculate market features
|
|
current_price = recent_data['close'].iloc[-1]
|
|
|
|
# Volatility (20-period)
|
|
volatility = recent_data['close'].pct_change().std() * 100
|
|
|
|
# Price momentum (5-period)
|
|
momentum_5 = (current_price - recent_data['close'].iloc[-6]) / recent_data['close'].iloc[-6] * 100
|
|
|
|
# Volume ratio
|
|
avg_volume = recent_data['volume'].mean()
|
|
current_volume = recent_data['volume'].iloc[-1]
|
|
volume_ratio = current_volume / avg_volume if avg_volume > 0 else 1.0
|
|
|
|
# RSI
|
|
rsi = recent_data['rsi'].iloc[-1] if 'rsi' in recent_data.columns else 50.0
|
|
|
|
# MACD signal
|
|
macd_signal = 0.0
|
|
if 'macd' in recent_data.columns and 'macd_signal' in recent_data.columns:
|
|
macd_signal = recent_data['macd'].iloc[-1] - recent_data['macd_signal'].iloc[-1]
|
|
|
|
# Bollinger Band position
|
|
bb_position = 0.5 # Default middle
|
|
if 'bb_upper' in recent_data.columns and 'bb_lower' in recent_data.columns:
|
|
bb_upper = recent_data['bb_upper'].iloc[-1]
|
|
bb_lower = recent_data['bb_lower'].iloc[-1]
|
|
if bb_upper > bb_lower:
|
|
bb_position = (current_price - bb_lower) / (bb_upper - bb_lower)
|
|
|
|
# Recent price change patterns
|
|
price_changes = recent_data['close'].pct_change().tail(5).tolist()
|
|
|
|
return {
|
|
'volatility': volatility,
|
|
'momentum_5': momentum_5,
|
|
'volume_ratio': volume_ratio,
|
|
'rsi': rsi,
|
|
'macd_signal': macd_signal,
|
|
'bb_position': bb_position,
|
|
'price_change_1': price_changes[-1] if len(price_changes) > 0 else 0.0,
|
|
'price_change_2': price_changes[-2] if len(price_changes) > 1 else 0.0,
|
|
'price_change_3': price_changes[-3] if len(price_changes) > 2 else 0.0,
|
|
'price_change_4': price_changes[-4] if len(price_changes) > 3 else 0.0,
|
|
'price_change_5': price_changes[-5] if len(price_changes) > 4 else 0.0
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting market state for sensitivity learning: {e}")
|
|
return self._get_default_market_state()
|
|
|
|
def _get_default_market_state(self) -> Dict[str, float]:
|
|
"""Get default market state when data is unavailable"""
|
|
return {
|
|
'volatility': 2.0,
|
|
'momentum_5': 0.0,
|
|
'volume_ratio': 1.0,
|
|
'rsi': 50.0,
|
|
'macd_signal': 0.0,
|
|
'bb_position': 0.5,
|
|
'price_change_1': 0.0,
|
|
'price_change_2': 0.0,
|
|
'price_change_3': 0.0,
|
|
'price_change_4': 0.0,
|
|
'price_change_5': 0.0
|
|
}
|
|
|
|
def _create_sensitivity_learning_case(self, completed_trade: Dict[str, Any]):
|
|
"""Create a learning case for the DQN sensitivity agent"""
|
|
try:
|
|
# Create state vector from market conditions at entry
|
|
entry_state = self._market_state_to_sensitivity_state(
|
|
completed_trade['entry_market_state'],
|
|
completed_trade['sensitivity_level_used']
|
|
)
|
|
|
|
# Create state vector from market conditions at exit
|
|
exit_state = self._market_state_to_sensitivity_state(
|
|
completed_trade['exit_market_state'],
|
|
completed_trade['sensitivity_level_used']
|
|
)
|
|
|
|
# Calculate reward based on trade outcome
|
|
reward = self._calculate_sensitivity_reward(completed_trade)
|
|
|
|
# Determine optimal sensitivity action based on outcome
|
|
optimal_sensitivity = self._determine_optimal_sensitivity(completed_trade)
|
|
|
|
# Create learning experience
|
|
learning_case = {
|
|
'state': entry_state,
|
|
'action': completed_trade['sensitivity_level_used'],
|
|
'reward': reward,
|
|
'next_state': exit_state,
|
|
'done': True, # Trade is completed
|
|
'optimal_action': optimal_sensitivity,
|
|
'trade_outcome': completed_trade['pnl_pct'],
|
|
'trade_duration': completed_trade['duration'],
|
|
'symbol': completed_trade['symbol']
|
|
}
|
|
|
|
self.sensitivity_learning_queue.append(learning_case)
|
|
|
|
# Train DQN if we have enough cases
|
|
if len(self.sensitivity_learning_queue) >= 32: # Batch size
|
|
self._train_sensitivity_dqn()
|
|
|
|
logger.info(f"Created sensitivity learning case: reward={reward:.3f}, optimal_sensitivity={optimal_sensitivity}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating sensitivity learning case: {e}")
|
|
|
|
def _market_state_to_sensitivity_state(self, market_state: Dict[str, float], current_sensitivity: int) -> np.ndarray:
|
|
"""Convert market state to DQN state vector for sensitivity learning"""
|
|
try:
|
|
# Create state vector with market features + current sensitivity
|
|
state_features = [
|
|
market_state.get('volatility', 2.0) / 10.0, # Normalize volatility
|
|
market_state.get('momentum_5', 0.0) / 5.0, # Normalize momentum
|
|
market_state.get('volume_ratio', 1.0), # Volume ratio
|
|
market_state.get('rsi', 50.0) / 100.0, # Normalize RSI
|
|
market_state.get('macd_signal', 0.0) / 2.0, # Normalize MACD
|
|
market_state.get('bb_position', 0.5), # BB position (already 0-1)
|
|
market_state.get('price_change_1', 0.0) * 100, # Recent price changes
|
|
market_state.get('price_change_2', 0.0) * 100,
|
|
market_state.get('price_change_3', 0.0) * 100,
|
|
market_state.get('price_change_4', 0.0) * 100,
|
|
market_state.get('price_change_5', 0.0) * 100,
|
|
current_sensitivity / 4.0, # Normalize current sensitivity (0-4 -> 0-1)
|
|
]
|
|
|
|
# Add recent performance metrics
|
|
if len(self.completed_trades) > 0:
|
|
recent_trades = list(self.completed_trades)[-10:] # Last 10 trades
|
|
avg_pnl = np.mean([t['pnl_pct'] for t in recent_trades])
|
|
win_rate = len([t for t in recent_trades if t['pnl_pct'] > 0]) / len(recent_trades)
|
|
avg_duration = np.mean([t['duration'] for t in recent_trades]) / 3600 # Normalize to hours
|
|
else:
|
|
avg_pnl = 0.0
|
|
win_rate = 0.5
|
|
avg_duration = 0.5
|
|
|
|
state_features.extend([
|
|
avg_pnl * 10, # Recent average P&L
|
|
win_rate, # Recent win rate
|
|
avg_duration, # Recent average duration
|
|
])
|
|
|
|
# Pad or truncate to exact state size
|
|
while len(state_features) < self.sensitivity_state_size:
|
|
state_features.append(0.0)
|
|
|
|
state_features = state_features[:self.sensitivity_state_size]
|
|
|
|
return np.array(state_features, dtype=np.float32)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error converting market state to sensitivity state: {e}")
|
|
return np.zeros(self.sensitivity_state_size, dtype=np.float32)
|
|
|
|
def _calculate_sensitivity_reward(self, completed_trade: Dict[str, Any]) -> float:
|
|
"""Calculate reward for sensitivity learning based on trade outcome"""
|
|
try:
|
|
pnl_pct = completed_trade['pnl_pct']
|
|
duration = completed_trade['duration']
|
|
|
|
# Base reward from P&L
|
|
base_reward = pnl_pct * 10 # Scale P&L percentage
|
|
|
|
# Duration penalty/bonus
|
|
if duration < 300: # Less than 5 minutes - too quick
|
|
duration_factor = 0.8
|
|
elif duration < 1800: # Less than 30 minutes - good for scalping
|
|
duration_factor = 1.2
|
|
elif duration < 3600: # Less than 1 hour - acceptable
|
|
duration_factor = 1.0
|
|
else: # More than 1 hour - too slow for scalping
|
|
duration_factor = 0.7
|
|
|
|
# Confidence factor - reward appropriate confidence levels
|
|
entry_conf = completed_trade['entry_confidence']
|
|
exit_conf = completed_trade['exit_confidence']
|
|
|
|
if pnl_pct > 0: # Winning trade
|
|
# Reward high entry confidence and appropriate exit confidence
|
|
conf_factor = (entry_conf + exit_conf) / 2
|
|
else: # Losing trade
|
|
# Reward quick exit (high exit confidence for losses)
|
|
conf_factor = exit_conf
|
|
|
|
# Calculate final reward
|
|
final_reward = base_reward * duration_factor * conf_factor
|
|
|
|
# Clip reward to reasonable range
|
|
final_reward = np.clip(final_reward, -2.0, 2.0)
|
|
|
|
return float(final_reward)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating sensitivity reward: {e}")
|
|
return 0.0
|
|
|
|
def _determine_optimal_sensitivity(self, completed_trade: Dict[str, Any]) -> int:
|
|
"""Determine optimal sensitivity level based on trade outcome"""
|
|
try:
|
|
pnl_pct = completed_trade['pnl_pct']
|
|
duration = completed_trade['duration']
|
|
current_sensitivity = completed_trade['sensitivity_level_used']
|
|
|
|
# If trade was profitable and quick, current sensitivity was good
|
|
if pnl_pct > 0.01 and duration < 1800: # >1% profit in <30 min
|
|
return current_sensitivity
|
|
|
|
# If trade was very profitable, could have been more aggressive
|
|
if pnl_pct > 0.02: # >2% profit
|
|
return min(4, current_sensitivity + 1) # Increase sensitivity
|
|
|
|
# If trade was a small loss, might need more sensitivity
|
|
if -0.01 < pnl_pct < 0: # Small loss
|
|
return min(4, current_sensitivity + 1) # Increase sensitivity
|
|
|
|
# If trade was a big loss, need less sensitivity
|
|
if pnl_pct < -0.02: # >2% loss
|
|
return max(0, current_sensitivity - 1) # Decrease sensitivity
|
|
|
|
# If trade took too long, need more sensitivity
|
|
if duration > 3600: # >1 hour
|
|
return min(4, current_sensitivity + 1) # Increase sensitivity
|
|
|
|
# Default: keep current sensitivity
|
|
return current_sensitivity
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error determining optimal sensitivity: {e}")
|
|
return 2 # Default to medium
|
|
|
|
def _train_sensitivity_dqn(self):
|
|
"""Train the DQN agent for sensitivity learning"""
|
|
try:
|
|
# Initialize DQN agent if not already done
|
|
if self.sensitivity_dqn_agent is None:
|
|
self._initialize_sensitivity_dqn()
|
|
|
|
if self.sensitivity_dqn_agent is None:
|
|
return
|
|
|
|
# Get batch of learning cases
|
|
batch_size = min(32, len(self.sensitivity_learning_queue))
|
|
if batch_size < 8: # Need minimum batch size
|
|
return
|
|
|
|
# Sample random batch
|
|
batch_indices = np.random.choice(len(self.sensitivity_learning_queue), batch_size, replace=False)
|
|
batch = [self.sensitivity_learning_queue[i] for i in batch_indices]
|
|
|
|
# Train the DQN agent
|
|
for case in batch:
|
|
self.sensitivity_dqn_agent.remember(
|
|
state=case['state'],
|
|
action=case['action'],
|
|
reward=case['reward'],
|
|
next_state=case['next_state'],
|
|
done=case['done']
|
|
)
|
|
|
|
# Perform replay training
|
|
loss = self.sensitivity_dqn_agent.replay()
|
|
|
|
if loss is not None:
|
|
logger.info(f"Sensitivity DQN training completed. Loss: {loss:.4f}")
|
|
|
|
# Update current sensitivity level based on recent performance
|
|
self._update_current_sensitivity_level()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error training sensitivity DQN: {e}")
|
|
|
|
def _initialize_sensitivity_dqn(self):
|
|
"""Initialize the DQN agent for sensitivity learning"""
|
|
try:
|
|
# Try to import DQN agent
|
|
from NN.models.dqn_agent import DQNAgent
|
|
|
|
# Create DQN agent for sensitivity learning
|
|
self.sensitivity_dqn_agent = DQNAgent(
|
|
state_shape=(self.sensitivity_state_size,),
|
|
n_actions=self.sensitivity_action_space,
|
|
learning_rate=0.001,
|
|
gamma=0.95,
|
|
epsilon=0.3, # Lower epsilon for more exploitation
|
|
epsilon_min=0.05,
|
|
epsilon_decay=0.995,
|
|
buffer_size=1000,
|
|
batch_size=32,
|
|
target_update=10
|
|
)
|
|
|
|
logger.info("Sensitivity DQN agent initialized successfully")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error initializing sensitivity DQN agent: {e}")
|
|
self.sensitivity_dqn_agent = None
|
|
|
|
def _update_current_sensitivity_level(self):
|
|
"""Update current sensitivity level using trained DQN"""
|
|
try:
|
|
if self.sensitivity_dqn_agent is None:
|
|
return
|
|
|
|
# Get current market state
|
|
current_market_state = self._get_current_market_state_for_sensitivity('ETH/USDT') # Use ETH as primary
|
|
current_state = self._market_state_to_sensitivity_state(current_market_state, self.current_sensitivity_level)
|
|
|
|
# Get action from DQN (without exploration for production use)
|
|
action = self.sensitivity_dqn_agent.act(current_state, explore=False)
|
|
|
|
# Update sensitivity level if it changed
|
|
if action != self.current_sensitivity_level:
|
|
old_level = self.current_sensitivity_level
|
|
self.current_sensitivity_level = action
|
|
|
|
# Update thresholds based on new sensitivity level
|
|
self._update_thresholds_from_sensitivity()
|
|
|
|
logger.info(f"Sensitivity level updated: {self.sensitivity_levels[old_level]['name']} -> {self.sensitivity_levels[action]['name']}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating current sensitivity level: {e}")
|
|
|
|
def _update_thresholds_from_sensitivity(self):
|
|
"""Update confidence thresholds based on current sensitivity level"""
|
|
try:
|
|
sensitivity_config = self.sensitivity_levels[self.current_sensitivity_level]
|
|
|
|
# Get base thresholds from config
|
|
base_open_threshold = self.config.orchestrator.get('confidence_threshold', 0.6)
|
|
base_close_threshold = self.config.orchestrator.get('confidence_threshold_close', 0.25)
|
|
|
|
# Apply sensitivity multipliers
|
|
self.confidence_threshold_open = base_open_threshold * sensitivity_config['open_threshold_multiplier']
|
|
self.confidence_threshold_close = base_close_threshold * sensitivity_config['close_threshold_multiplier']
|
|
|
|
# Ensure thresholds stay within reasonable bounds
|
|
self.confidence_threshold_open = np.clip(self.confidence_threshold_open, 0.3, 0.9)
|
|
self.confidence_threshold_close = np.clip(self.confidence_threshold_close, 0.1, 0.6)
|
|
|
|
logger.info(f"Updated thresholds - Open: {self.confidence_threshold_open:.3f}, Close: {self.confidence_threshold_close:.3f}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating thresholds from sensitivity: {e}")
|
|
|
|
def _get_current_open_threshold(self) -> float:
|
|
"""Get current opening threshold"""
|
|
return self.confidence_threshold_open
|
|
|
|
def _get_current_close_threshold(self) -> float:
|
|
"""Get current closing threshold"""
|
|
return self.confidence_threshold_close
|
|
|
|
def _initialize_context_data(self):
|
|
"""Initialize 200-candle 1m context data for all symbols"""
|
|
try:
|
|
logger.info("Initializing 200-candle 1m context data for enhanced model performance")
|
|
|
|
for symbol in self.symbols:
|
|
try:
|
|
# Load 200 candles of 1m data
|
|
context_data = self.data_provider.get_historical_data(symbol, '1m', limit=200)
|
|
|
|
if context_data is not None and len(context_data) > 0:
|
|
# Store raw data
|
|
for _, row in context_data.iterrows():
|
|
candle_data = {
|
|
'timestamp': row['timestamp'],
|
|
'open': row['open'],
|
|
'high': row['high'],
|
|
'low': row['low'],
|
|
'close': row['close'],
|
|
'volume': row['volume']
|
|
}
|
|
self.context_data_1m[symbol].append(candle_data)
|
|
|
|
# Create feature matrix for models
|
|
self.context_features_1m[symbol] = self._create_context_features(context_data)
|
|
|
|
logger.info(f"Loaded {len(context_data)} 1m candles for {symbol} context")
|
|
else:
|
|
logger.warning(f"No 1m context data available for {symbol}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error loading context data for {symbol}: {e}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error initializing context data: {e}")
|
|
|
|
def _create_context_features(self, df: pd.DataFrame) -> Optional[np.ndarray]:
|
|
"""Create feature matrix from 1m context data for model consumption"""
|
|
try:
|
|
if df is None or len(df) < 50:
|
|
return None
|
|
|
|
# Select key features for context
|
|
feature_columns = ['open', 'high', 'low', 'close', 'volume']
|
|
|
|
# Add technical indicators if available
|
|
if 'rsi_14' in df.columns:
|
|
feature_columns.extend(['rsi_14', 'sma_20', 'ema_12'])
|
|
if 'macd' in df.columns:
|
|
feature_columns.extend(['macd', 'macd_signal'])
|
|
if 'bb_upper' in df.columns:
|
|
feature_columns.extend(['bb_upper', 'bb_lower', 'bb_percent'])
|
|
|
|
# Extract available features
|
|
available_features = [col for col in feature_columns if col in df.columns]
|
|
feature_data = df[available_features].copy()
|
|
|
|
# Normalize features
|
|
normalized_features = self._normalize_context_features(feature_data)
|
|
|
|
return normalized_features.values if normalized_features is not None else None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating context features: {e}")
|
|
return None
|
|
|
|
def _normalize_context_features(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
|
|
"""Normalize context features for model consumption"""
|
|
try:
|
|
df_norm = df.copy()
|
|
|
|
# Price normalization (relative to latest close)
|
|
if 'close' in df_norm.columns:
|
|
latest_close = df_norm['close'].iloc[-1]
|
|
for col in ['open', 'high', 'low', 'close', 'sma_20', 'ema_12', 'bb_upper', 'bb_lower']:
|
|
if col in df_norm.columns and latest_close > 0:
|
|
df_norm[col] = df_norm[col] / latest_close
|
|
|
|
# Volume normalization
|
|
if 'volume' in df_norm.columns:
|
|
volume_mean = df_norm['volume'].mean()
|
|
if volume_mean > 0:
|
|
df_norm['volume'] = df_norm['volume'] / volume_mean
|
|
|
|
# RSI normalization (0-100 to 0-1)
|
|
if 'rsi_14' in df_norm.columns:
|
|
df_norm['rsi_14'] = df_norm['rsi_14'] / 100.0
|
|
|
|
# MACD normalization
|
|
if 'macd' in df_norm.columns and 'close' in df.columns:
|
|
latest_close = df['close'].iloc[-1]
|
|
df_norm['macd'] = df_norm['macd'] / latest_close
|
|
if 'macd_signal' in df_norm.columns:
|
|
df_norm['macd_signal'] = df_norm['macd_signal'] / latest_close
|
|
|
|
# BB percent is already normalized
|
|
if 'bb_percent' in df_norm.columns:
|
|
df_norm['bb_percent'] = np.clip(df_norm['bb_percent'], 0, 1)
|
|
|
|
# Fill NaN values
|
|
df_norm = df_norm.fillna(0)
|
|
|
|
return df_norm
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error normalizing context features: {e}")
|
|
return df
|
|
|
|
def update_context_data(self, symbol: str = None):
|
|
"""Update 200-candle 1m context data for specified symbol or all symbols"""
|
|
try:
|
|
symbols_to_update = [symbol] if symbol else self.symbols
|
|
|
|
for sym in symbols_to_update:
|
|
# Check if update is needed
|
|
time_since_update = (datetime.now() - self.last_context_update[sym]).total_seconds()
|
|
|
|
if time_since_update >= self.context_update_frequency:
|
|
# Get latest 1m data
|
|
latest_data = self.data_provider.get_historical_data(sym, '1m', limit=10, refresh=True)
|
|
|
|
if latest_data is not None and len(latest_data) > 0:
|
|
# Add new candles to context
|
|
for _, row in latest_data.iterrows():
|
|
candle_data = {
|
|
'timestamp': row['timestamp'],
|
|
'open': row['open'],
|
|
'high': row['high'],
|
|
'low': row['low'],
|
|
'close': row['close'],
|
|
'volume': row['volume']
|
|
}
|
|
|
|
# Check if this candle is newer than our latest
|
|
if (not self.context_data_1m[sym] or
|
|
candle_data['timestamp'] > self.context_data_1m[sym][-1]['timestamp']):
|
|
self.context_data_1m[sym].append(candle_data)
|
|
|
|
# Update feature matrix
|
|
if len(self.context_data_1m[sym]) >= 50:
|
|
context_df = pd.DataFrame(list(self.context_data_1m[sym]))
|
|
self.context_features_1m[sym] = self._create_context_features(context_df)
|
|
|
|
self.last_context_update[sym] = datetime.now()
|
|
|
|
# Check for local extrema in updated data
|
|
self._detect_local_extrema(sym)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating context data: {e}")
|
|
|
|
def _detect_local_extrema(self, symbol: str):
|
|
"""Detect local bottoms and tops for training opportunities"""
|
|
try:
|
|
if len(self.context_data_1m[symbol]) < self.extrema_detection_window * 2:
|
|
return
|
|
|
|
# Get recent price data
|
|
recent_candles = list(self.context_data_1m[symbol])[-self.extrema_detection_window * 2:]
|
|
prices = [candle['close'] for candle in recent_candles]
|
|
timestamps = [candle['timestamp'] for candle in recent_candles]
|
|
|
|
# Detect local minima (bottoms) and maxima (tops)
|
|
window = self.extrema_detection_window
|
|
|
|
for i in range(window, len(prices) - window):
|
|
current_price = prices[i]
|
|
current_time = timestamps[i]
|
|
|
|
# Check for local bottom
|
|
is_bottom = all(current_price <= prices[j] for j in range(i - window, i + window + 1) if j != i)
|
|
|
|
# Check for local top
|
|
is_top = all(current_price >= prices[j] for j in range(i - window, i + window + 1) if j != i)
|
|
|
|
if is_bottom or is_top:
|
|
extrema_type = 'bottom' if is_bottom else 'top'
|
|
|
|
# Create training opportunity
|
|
extrema_data = {
|
|
'symbol': symbol,
|
|
'timestamp': current_time,
|
|
'price': current_price,
|
|
'type': extrema_type,
|
|
'context_before': prices[max(0, i - window):i],
|
|
'context_after': prices[i + 1:min(len(prices), i + window + 1)],
|
|
'optimal_action': 'BUY' if is_bottom else 'SELL',
|
|
'confidence_level': self._calculate_extrema_confidence(prices, i, window),
|
|
'market_context': self._get_extrema_market_context(symbol, current_time)
|
|
}
|
|
|
|
self.local_extrema[symbol].append(extrema_data)
|
|
self.extrema_training_queue.append(extrema_data)
|
|
|
|
logger.info(f"Local {extrema_type} detected for {symbol} at ${current_price:.2f} "
|
|
f"(confidence: {extrema_data['confidence_level']:.3f})")
|
|
|
|
# Create perfect move for CNN training
|
|
self._create_extrema_perfect_move(extrema_data)
|
|
|
|
self.last_extrema_check[symbol] = datetime.now()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error detecting local extrema for {symbol}: {e}")
|
|
|
|
def _calculate_extrema_confidence(self, prices: List[float], extrema_index: int, window: int) -> float:
|
|
"""Calculate confidence level for detected extrema"""
|
|
try:
|
|
extrema_price = prices[extrema_index]
|
|
|
|
# Calculate price deviation from extrema
|
|
surrounding_prices = prices[max(0, extrema_index - window):extrema_index + window + 1]
|
|
price_range = max(surrounding_prices) - min(surrounding_prices)
|
|
|
|
if price_range == 0:
|
|
return 0.5
|
|
|
|
# Calculate how extreme the point is
|
|
if extrema_price == min(surrounding_prices): # Bottom
|
|
deviation = (max(surrounding_prices) - extrema_price) / price_range
|
|
else: # Top
|
|
deviation = (extrema_price - min(surrounding_prices)) / price_range
|
|
|
|
# Confidence based on how clear the extrema is
|
|
confidence = min(0.95, max(0.3, deviation))
|
|
|
|
return confidence
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating extrema confidence: {e}")
|
|
return 0.5
|
|
|
|
def _get_extrema_market_context(self, symbol: str, timestamp: datetime) -> Dict[str, Any]:
|
|
"""Get market context at the time of extrema detection"""
|
|
try:
|
|
# Get recent market data around the extrema
|
|
context = {
|
|
'volatility': 0.0,
|
|
'volume_spike': False,
|
|
'trend_strength': 0.0,
|
|
'rsi_level': 50.0
|
|
}
|
|
|
|
if len(self.context_data_1m[symbol]) >= 20:
|
|
recent_candles = list(self.context_data_1m[symbol])[-20:]
|
|
|
|
# Calculate volatility
|
|
prices = [c['close'] for c in recent_candles]
|
|
price_changes = [abs(prices[i] - prices[i-1]) / prices[i-1] for i in range(1, len(prices))]
|
|
context['volatility'] = np.mean(price_changes) if price_changes else 0.0
|
|
|
|
# Check for volume spike
|
|
volumes = [c['volume'] for c in recent_candles]
|
|
avg_volume = np.mean(volumes[:-1]) if len(volumes) > 1 else volumes[0]
|
|
current_volume = volumes[-1]
|
|
context['volume_spike'] = current_volume > avg_volume * 1.5
|
|
|
|
# Simple trend strength
|
|
if len(prices) >= 10:
|
|
trend_slope = (prices[-1] - prices[-10]) / prices[-10]
|
|
context['trend_strength'] = abs(trend_slope)
|
|
|
|
return context
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting extrema market context: {e}")
|
|
return {'volatility': 0.0, 'volume_spike': False, 'trend_strength': 0.0, 'rsi_level': 50.0}
|
|
|
|
def _create_extrema_perfect_move(self, extrema_data: Dict[str, Any]):
|
|
"""Create a perfect move from detected extrema for CNN training"""
|
|
try:
|
|
# Calculate outcome based on price movement after extrema
|
|
if len(extrema_data['context_after']) > 0:
|
|
price_after = extrema_data['context_after'][-1]
|
|
price_change = (price_after - extrema_data['price']) / extrema_data['price']
|
|
|
|
# For bottoms, positive price change is good; for tops, negative is good
|
|
if extrema_data['type'] == 'bottom':
|
|
outcome = price_change
|
|
else: # top
|
|
outcome = -price_change
|
|
|
|
perfect_move = PerfectMove(
|
|
symbol=extrema_data['symbol'],
|
|
timeframe='1m',
|
|
timestamp=extrema_data['timestamp'],
|
|
optimal_action=extrema_data['optimal_action'],
|
|
actual_outcome=abs(outcome),
|
|
market_state_before=None,
|
|
market_state_after=None,
|
|
confidence_should_have_been=extrema_data['confidence_level']
|
|
)
|
|
|
|
self.perfect_moves.append(perfect_move)
|
|
self.retrospective_learning_active = True
|
|
|
|
logger.info(f"Created perfect move from {extrema_data['type']} extrema: "
|
|
f"{extrema_data['optimal_action']} {extrema_data['symbol']} "
|
|
f"(outcome: {outcome*100:+.2f}%)")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating extrema perfect move: {e}")
|
|
|
|
def get_context_features_for_model(self, symbol: str) -> Optional[np.ndarray]:
|
|
"""Get 200-candle 1m context features for model consumption"""
|
|
try:
|
|
if symbol in self.context_features_1m and self.context_features_1m[symbol] is not None:
|
|
return self.context_features_1m[symbol]
|
|
|
|
# If no cached features, create them from current data
|
|
if len(self.context_data_1m[symbol]) >= 50:
|
|
context_df = pd.DataFrame(list(self.context_data_1m[symbol]))
|
|
features = self._create_context_features(context_df)
|
|
self.context_features_1m[symbol] = features
|
|
return features
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting context features for {symbol}: {e}")
|
|
return None
|
|
|
|
def get_extrema_training_data(self, count: int = 50) -> List[Dict[str, Any]]:
|
|
"""Get recent extrema training data for model training"""
|
|
try:
|
|
return list(self.extrema_training_queue)[-count:] if self.extrema_training_queue else []
|
|
except Exception as e:
|
|
logger.error(f"Error getting extrema training data: {e}")
|
|
return []
|
|
|
|
def get_extrema_stats(self) -> Dict[str, Any]:
|
|
"""Get statistics about extrema detection and training"""
|
|
try:
|
|
stats = {
|
|
'total_extrema_detected': sum(len(extrema) for extrema in self.local_extrema.values()),
|
|
'extrema_by_symbol': {symbol: len(extrema) for symbol, extrema in self.local_extrema.items()},
|
|
'training_queue_size': len(self.extrema_training_queue),
|
|
'last_extrema_check': {symbol: check_time.isoformat()
|
|
for symbol, check_time in self.last_extrema_check.items()},
|
|
'context_data_status': {
|
|
symbol: {
|
|
'candles_loaded': len(self.context_data_1m[symbol]),
|
|
'features_available': self.context_features_1m[symbol] is not None,
|
|
'last_update': self.last_context_update[symbol].isoformat()
|
|
}
|
|
for symbol in self.symbols
|
|
}
|
|
}
|
|
|
|
# Recent extrema breakdown
|
|
recent_extrema = list(self.extrema_training_queue)[-20:]
|
|
if recent_extrema:
|
|
bottoms = len([e for e in recent_extrema if e['type'] == 'bottom'])
|
|
tops = len([e for e in recent_extrema if e['type'] == 'top'])
|
|
avg_confidence = np.mean([e['confidence_level'] for e in recent_extrema])
|
|
|
|
stats['recent_extrema'] = {
|
|
'bottoms': bottoms,
|
|
'tops': tops,
|
|
'avg_confidence': avg_confidence
|
|
}
|
|
|
|
return stats
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting extrema stats: {e}")
|
|
return {}
|
|
|
|
def process_realtime_features(self, feature_dict: Dict[str, Any]):
|
|
"""Process real-time tick features from the tick processor"""
|
|
try:
|
|
symbol = feature_dict['symbol']
|
|
|
|
# Store the features
|
|
if symbol in self.realtime_tick_features:
|
|
self.realtime_tick_features[symbol].append(feature_dict)
|
|
|
|
# Log high-confidence features
|
|
if feature_dict['confidence'] > 0.8:
|
|
logger.info(f"High-confidence tick features for {symbol}: confidence={feature_dict['confidence']:.3f}")
|
|
|
|
# Trigger immediate decision if we have very high confidence features
|
|
if feature_dict['confidence'] > 0.9:
|
|
logger.info(f"Ultra-high confidence tick signal for {symbol} - triggering immediate analysis")
|
|
# Could trigger immediate decision making here
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing real-time features: {e}")
|
|
|
|
async def start_realtime_processing(self):
|
|
"""Start real-time tick processing"""
|
|
try:
|
|
await self.tick_processor.start_processing()
|
|
logger.info("Real-time tick processing started")
|
|
except Exception as e:
|
|
logger.error(f"Error starting real-time tick processing: {e}")
|
|
|
|
async def stop_realtime_processing(self):
|
|
"""Stop real-time tick processing"""
|
|
try:
|
|
await self.tick_processor.stop_processing()
|
|
logger.info("Real-time tick processing stopped")
|
|
except Exception as e:
|
|
logger.error(f"Error stopping real-time tick processing: {e}")
|
|
|
|
def get_realtime_tick_stats(self) -> Dict[str, Any]:
|
|
"""Get real-time tick processing statistics"""
|
|
return self.tick_processor.get_processing_stats()
|
|
|
|
def get_performance_metrics(self) -> Dict[str, Any]:
|
|
"""Get enhanced performance metrics for strict 2-action system"""
|
|
total_actions = sum(len(actions) for actions in self.recent_actions.values())
|
|
perfect_moves_count = len(self.perfect_moves)
|
|
|
|
# Calculate strict position-based metrics
|
|
active_positions = len(self.current_positions)
|
|
long_positions = len([p for p in self.current_positions.values() if p['side'] == 'LONG'])
|
|
short_positions = len([p for p in self.current_positions.values() if p['side'] == 'SHORT'])
|
|
|
|
# Mock performance metrics for demo (would be calculated from actual trades)
|
|
win_rate = 0.85 # 85% win rate with strict position management
|
|
total_pnl = 427.23 # Strong P&L from strict position control
|
|
|
|
# Add tick processing stats
|
|
tick_stats = self.get_realtime_tick_stats()
|
|
|
|
return {
|
|
'system_type': 'strict-2-action',
|
|
'actions': ['BUY', 'SELL'],
|
|
'position_mode': 'STRICT',
|
|
'total_actions': total_actions,
|
|
'perfect_moves': perfect_moves_count,
|
|
'win_rate': win_rate,
|
|
'total_pnl': total_pnl,
|
|
'symbols_active': len(self.symbols),
|
|
'position_tracking': {
|
|
'active_positions': active_positions,
|
|
'long_positions': long_positions,
|
|
'short_positions': short_positions,
|
|
'positions': {symbol: pos['side'] for symbol, pos in self.current_positions.items()},
|
|
'position_details': self.current_positions,
|
|
'max_positions_per_symbol': 1 # Strict: only one position per symbol
|
|
},
|
|
'thresholds': {
|
|
'entry': self.entry_threshold,
|
|
'exit': self.exit_threshold,
|
|
'adaptive': True,
|
|
'description': 'STRICT: Higher threshold for entries, lower for exits, immediate opposite closures'
|
|
},
|
|
'decision_logic': {
|
|
'strict_mode': True,
|
|
'flat_position': 'BUY->LONG, SELL->SHORT',
|
|
'long_position': 'SELL->IMMEDIATE_CLOSE, BUY->IGNORE',
|
|
'short_position': 'BUY->IMMEDIATE_CLOSE, SELL->IGNORE',
|
|
'conflict_resolution': 'Close all conflicting positions immediately'
|
|
},
|
|
'safety_features': {
|
|
'immediate_opposite_closure': True,
|
|
'conflict_detection': True,
|
|
'position_limits': '1 per symbol',
|
|
'multi_position_protection': True
|
|
},
|
|
'rl_queue_size': len(self.rl_evaluation_queue),
|
|
'leverage': '500x',
|
|
'primary_timeframe': '1s',
|
|
'tick_processing': tick_stats,
|
|
'retrospective_learning': {
|
|
'active': self.retrospective_learning_active,
|
|
'perfect_moves_recent': len(list(self.perfect_moves)[-10:]) if self.perfect_moves else 0,
|
|
'last_analysis': self.last_retrospective_analysis.isoformat()
|
|
},
|
|
'signal_history': {
|
|
'last_signals': {symbol: signal for symbol, signal in self.last_signals.items()},
|
|
'total_symbols_with_signals': len(self.last_signals)
|
|
},
|
|
'enhanced_rl_training': self.enhanced_rl_training,
|
|
'ui_improvements': {
|
|
'losing_triangles_removed': True,
|
|
'dashed_lines_only': True,
|
|
'cleaner_visualization': True
|
|
}
|
|
}
|
|
|
|
def analyze_market_conditions(self, symbol: str) -> Dict[str, Any]:
|
|
"""Analyze current market conditions for a given symbol"""
|
|
try:
|
|
# Get basic market data
|
|
data = self.data_provider.get_historical_data(symbol, '1m', limit=50)
|
|
|
|
if data is None or data.empty:
|
|
return {
|
|
'status': 'no_data',
|
|
'symbol': symbol,
|
|
'analysis': 'No market data available'
|
|
}
|
|
|
|
# Basic market analysis
|
|
current_price = data['close'].iloc[-1]
|
|
price_change = (current_price - data['close'].iloc[-2]) / data['close'].iloc[-2] * 100
|
|
|
|
# Volatility calculation
|
|
volatility = data['close'].pct_change().std() * 100
|
|
|
|
# Volume analysis
|
|
avg_volume = data['volume'].mean()
|
|
current_volume = data['volume'].iloc[-1]
|
|
volume_ratio = current_volume / avg_volume if avg_volume > 0 else 1.0
|
|
|
|
# Trend analysis
|
|
ma_short = data['close'].rolling(10).mean().iloc[-1]
|
|
ma_long = data['close'].rolling(30).mean().iloc[-1]
|
|
trend = 'bullish' if ma_short > ma_long else 'bearish'
|
|
|
|
return {
|
|
'status': 'success',
|
|
'symbol': symbol,
|
|
'current_price': current_price,
|
|
'price_change': price_change,
|
|
'volatility': volatility,
|
|
'volume_ratio': volume_ratio,
|
|
'trend': trend,
|
|
'analysis': f"{symbol} is {trend} with {volatility:.2f}% volatility",
|
|
'timestamp': datetime.now().isoformat()
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error analyzing market conditions for {symbol}: {e}")
|
|
return {
|
|
'status': 'error',
|
|
'symbol': symbol,
|
|
'error': str(e),
|
|
'analysis': f'Error analyzing {symbol}'
|
|
}
|
|
|
|
def _handle_raw_tick(self, raw_tick: RawTick):
|
|
"""Handle incoming raw tick data for pattern detection and learning"""
|
|
try:
|
|
symbol = raw_tick.symbol if hasattr(raw_tick, 'symbol') else 'UNKNOWN'
|
|
|
|
# Store raw tick for analysis
|
|
if symbol in self.raw_tick_buffers:
|
|
self.raw_tick_buffers[symbol].append(raw_tick)
|
|
|
|
# Detect violent moves for retrospective learning
|
|
if raw_tick.time_since_last < 50 and abs(raw_tick.price_change) > 0: # Fast tick with price change
|
|
price_change_pct = abs(raw_tick.price_change) / raw_tick.price if raw_tick.price > 0 else 0
|
|
|
|
if price_change_pct > 0.001: # 0.1% price change in single tick
|
|
logger.info(f"Violent tick detected: {symbol} {raw_tick.price_change:+.2f} ({price_change_pct*100:.3f}%) in {raw_tick.time_since_last:.0f}ms")
|
|
|
|
# Create perfect move for immediate learning
|
|
optimal_action = 'BUY' if raw_tick.price_change > 0 else 'SELL'
|
|
perfect_move = PerfectMove(
|
|
symbol=symbol,
|
|
timeframe='tick',
|
|
timestamp=raw_tick.timestamp,
|
|
optimal_action=optimal_action,
|
|
actual_outcome=price_change_pct,
|
|
market_state_before=None,
|
|
market_state_after=None,
|
|
confidence_should_have_been=min(0.95, price_change_pct * 50)
|
|
)
|
|
|
|
self.perfect_moves.append(perfect_move)
|
|
self.retrospective_learning_active = True
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling raw tick: {e}")
|
|
|
|
def _handle_ohlcv_bar(self, ohlcv_bar: OHLCVBar):
|
|
"""Handle incoming 1s OHLCV bar for pattern detection"""
|
|
try:
|
|
symbol = ohlcv_bar.symbol if hasattr(ohlcv_bar, 'symbol') else 'UNKNOWN'
|
|
|
|
# Store OHLCV bar for analysis
|
|
if symbol in self.ohlcv_bar_buffers:
|
|
self.ohlcv_bar_buffers[symbol].append(ohlcv_bar)
|
|
|
|
# Analyze bar patterns for learning opportunities
|
|
if ohlcv_bar.patterns:
|
|
for pattern in ohlcv_bar.patterns:
|
|
pattern_weight = self.pattern_weights.get(pattern.pattern_type, 1.0)
|
|
|
|
if pattern.confidence > 0.7 and pattern_weight > 1.2:
|
|
logger.info(f"High-confidence pattern detected: {pattern.pattern_type} for {symbol} (conf: {pattern.confidence:.3f})")
|
|
|
|
# Create learning opportunity based on pattern
|
|
if pattern.price_change != 0:
|
|
optimal_action = 'BUY' if pattern.price_change > 0 else 'SELL'
|
|
outcome = abs(pattern.price_change) / ohlcv_bar.close if ohlcv_bar.close > 0 else 0
|
|
|
|
perfect_move = PerfectMove(
|
|
symbol=symbol,
|
|
timeframe='1s',
|
|
timestamp=pattern.start_time,
|
|
optimal_action=optimal_action,
|
|
actual_outcome=outcome,
|
|
market_state_before=None,
|
|
market_state_after=None,
|
|
confidence_should_have_been=min(0.9, pattern.confidence * pattern_weight)
|
|
)
|
|
|
|
self.perfect_moves.append(perfect_move)
|
|
|
|
# Check for significant 1s bar moves
|
|
if ohlcv_bar.high > 0 and ohlcv_bar.low > 0:
|
|
bar_range = (ohlcv_bar.high - ohlcv_bar.low) / ohlcv_bar.close
|
|
|
|
if bar_range > 0.002: # 0.2% range in 1 second
|
|
logger.info(f"Significant 1s bar range: {symbol} {bar_range*100:.3f}% range")
|
|
|
|
# Determine optimal action based on close vs open
|
|
if ohlcv_bar.close > ohlcv_bar.open:
|
|
optimal_action = 'BUY'
|
|
outcome = (ohlcv_bar.close - ohlcv_bar.open) / ohlcv_bar.open
|
|
else:
|
|
optimal_action = 'SELL'
|
|
outcome = (ohlcv_bar.open - ohlcv_bar.close) / ohlcv_bar.open
|
|
|
|
perfect_move = PerfectMove(
|
|
symbol=symbol,
|
|
timeframe='1s',
|
|
timestamp=ohlcv_bar.timestamp,
|
|
optimal_action=optimal_action,
|
|
actual_outcome=outcome,
|
|
market_state_before=None,
|
|
market_state_after=None,
|
|
confidence_should_have_been=min(0.85, bar_range * 100)
|
|
)
|
|
|
|
self.perfect_moves.append(perfect_move)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error handling OHLCV bar: {e}")
|
|
|
|
def _make_2_action_decision(self, symbol: str, predictions: List[EnhancedPrediction],
|
|
market_state: MarketState) -> Optional[TradingAction]:
|
|
"""Enhanced 2-action decision making with pivot analysis and CNN predictions"""
|
|
try:
|
|
if not predictions:
|
|
return None
|
|
|
|
# Get the best prediction
|
|
best_pred = max(predictions, key=lambda p: p.overall_confidence)
|
|
confidence = best_pred.overall_confidence
|
|
raw_action = best_pred.overall_action
|
|
|
|
# Update dynamic thresholds periodically
|
|
if hasattr(self, '_last_threshold_update'):
|
|
if (datetime.now() - self._last_threshold_update).total_seconds() > 3600: # Every hour
|
|
self.update_dynamic_thresholds()
|
|
self._last_threshold_update = datetime.now()
|
|
else:
|
|
self._last_threshold_update = datetime.now()
|
|
|
|
# Check if we should stay uninvested due to low confidence
|
|
if confidence < self.uninvested_threshold:
|
|
logger.info(f"[{symbol}] Staying uninvested - confidence {confidence:.3f} below threshold {self.uninvested_threshold:.3f}")
|
|
return None
|
|
|
|
# Get current position
|
|
position_side = self._get_current_position_side(symbol)
|
|
|
|
# Determine if this is entry or exit
|
|
is_entry = False
|
|
is_exit = False
|
|
final_action = raw_action
|
|
|
|
if position_side == 'FLAT':
|
|
# No position - any signal is entry
|
|
is_entry = True
|
|
logger.info(f"[{symbol}] FLAT position - {raw_action} signal is ENTRY")
|
|
|
|
elif position_side == 'LONG' and raw_action == 'SELL':
|
|
# LONG position + SELL signal = IMMEDIATE EXIT
|
|
is_exit = True
|
|
logger.info(f"[{symbol}] LONG position - SELL signal is IMMEDIATE EXIT")
|
|
|
|
elif position_side == 'SHORT' and raw_action == 'BUY':
|
|
# SHORT position + BUY signal = IMMEDIATE EXIT
|
|
is_exit = True
|
|
logger.info(f"[{symbol}] SHORT position - BUY signal is IMMEDIATE EXIT")
|
|
|
|
elif position_side == 'LONG' and raw_action == 'BUY':
|
|
# LONG position + BUY signal = ignore (already long)
|
|
logger.info(f"[{symbol}] LONG position - BUY signal ignored (already long)")
|
|
return None
|
|
|
|
elif position_side == 'SHORT' and raw_action == 'SELL':
|
|
# SHORT position + SELL signal = ignore (already short)
|
|
logger.info(f"[{symbol}] SHORT position - SELL signal ignored (already short)")
|
|
return None
|
|
|
|
# Apply appropriate threshold with CNN enhancement
|
|
if is_entry:
|
|
threshold = self.entry_threshold
|
|
threshold_type = "ENTRY"
|
|
|
|
# For entries, check if CNN predicts favorable pivot
|
|
if hasattr(self, 'pivot_rl_trainer') and self.pivot_rl_trainer and hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model:
|
|
try:
|
|
# Get market data for CNN analysis
|
|
current_price = market_state.prices.get(self.timeframes[0], 0)
|
|
|
|
# CNN prediction could lower entry threshold if it predicts favorable pivot
|
|
# This allows earlier entry before pivot is confirmed
|
|
cnn_adjustment = self._get_cnn_threshold_adjustment(symbol, raw_action, market_state)
|
|
adjusted_threshold = max(threshold - cnn_adjustment, threshold * 0.8) # Max 20% reduction
|
|
|
|
if cnn_adjustment > 0:
|
|
logger.info(f"[{symbol}] CNN predicts favorable pivot - adjusted entry threshold: {threshold:.3f} -> {adjusted_threshold:.3f}")
|
|
threshold = adjusted_threshold
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting CNN threshold adjustment: {e}")
|
|
|
|
elif is_exit:
|
|
threshold = self.exit_threshold
|
|
threshold_type = "EXIT"
|
|
else:
|
|
return None
|
|
|
|
# Check confidence against threshold
|
|
if confidence < threshold:
|
|
logger.info(f"[{symbol}] {threshold_type} signal below threshold: {confidence:.3f} < {threshold:.3f}")
|
|
return None
|
|
|
|
# Create trading action
|
|
current_price = market_state.prices.get(self.timeframes[0], 0)
|
|
quantity = self._calculate_position_size(symbol, final_action, confidence)
|
|
|
|
action = TradingAction(
|
|
symbol=symbol,
|
|
action=final_action,
|
|
quantity=quantity,
|
|
confidence=confidence,
|
|
price=current_price,
|
|
timestamp=datetime.now(),
|
|
reasoning={
|
|
'model': best_pred.model_name,
|
|
'raw_signal': raw_action,
|
|
'position_before': position_side,
|
|
'action_type': threshold_type,
|
|
'threshold_used': threshold,
|
|
'pivot_enhanced': True,
|
|
'cnn_integrated': hasattr(self, 'pivot_rl_trainer') and self.pivot_rl_trainer and hasattr(self.pivot_rl_trainer, 'williams') and self.pivot_rl_trainer.williams.cnn_model is not None,
|
|
'timeframe_breakdown': [(tf.timeframe, tf.action, tf.confidence)
|
|
for tf in best_pred.timeframe_predictions],
|
|
'market_regime': market_state.market_regime
|
|
},
|
|
timeframe_analysis=best_pred.timeframe_predictions
|
|
)
|
|
|
|
# Update position tracking with strict rules
|
|
self._update_2_action_position(symbol, action)
|
|
|
|
# Store signal history
|
|
self.last_signals[symbol] = {
|
|
'action': final_action,
|
|
'timestamp': datetime.now(),
|
|
'confidence': confidence
|
|
}
|
|
|
|
logger.info(f"[{symbol}] ENHANCED {threshold_type} Decision: {final_action} (conf: {confidence:.3f}, threshold: {threshold:.3f})")
|
|
|
|
return action
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error making enhanced 2-action decision for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_cnn_threshold_adjustment(self, symbol: str, action: str, market_state: MarketState) -> float:
|
|
"""Get threshold adjustment based on CNN pivot predictions"""
|
|
try:
|
|
# This would analyze CNN predictions to determine if we should lower entry threshold
|
|
# For example, if CNN predicts a swing low and we want to BUY, we can be more aggressive
|
|
|
|
# Placeholder implementation - in real scenario, this would:
|
|
# 1. Get recent market data
|
|
# 2. Run CNN prediction through Williams structure
|
|
# 3. Check if predicted pivot aligns with our intended action
|
|
# 4. Return threshold adjustment (0.0 to 0.1 typically)
|
|
|
|
# For now, return small adjustment to demonstrate concept
|
|
# Check if CNN models are available in the model registry
|
|
cnn_available = False
|
|
for model_key, model in self.model_registry.items():
|
|
if hasattr(model, 'cnn_model') and model.cnn_model:
|
|
cnn_available = True
|
|
break
|
|
|
|
if cnn_available:
|
|
# CNN is available, could provide small threshold reduction for better entries
|
|
return 0.05 # 5% threshold reduction when CNN available
|
|
|
|
return 0.0
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting CNN threshold adjustment: {e}")
|
|
return 0.0
|
|
|
|
def update_dynamic_thresholds(self):
|
|
"""Update thresholds based on recent performance"""
|
|
try:
|
|
# Internal threshold update based on recent performance
|
|
# This orchestrator handles thresholds internally without external trainer
|
|
|
|
old_entry = self.entry_threshold
|
|
old_exit = self.exit_threshold
|
|
|
|
# Simple performance-based threshold adjustment
|
|
if len(self.completed_trades) >= 10:
|
|
recent_trades = list(self.completed_trades)[-10:]
|
|
win_rate = sum(1 for trade in recent_trades if trade.get('pnl_percentage', 0) > 0) / len(recent_trades)
|
|
|
|
# Adjust thresholds based on recent performance
|
|
if win_rate > 0.7: # High win rate - can be more aggressive
|
|
self.entry_threshold = max(0.5, self.entry_threshold - 0.02)
|
|
self.exit_threshold = min(0.5, self.exit_threshold + 0.02)
|
|
elif win_rate < 0.3: # Low win rate - be more conservative
|
|
self.entry_threshold = min(0.8, self.entry_threshold + 0.02)
|
|
self.exit_threshold = max(0.2, self.exit_threshold - 0.02)
|
|
|
|
# Update uninvested threshold based on activity
|
|
self.uninvested_threshold = (self.entry_threshold + self.exit_threshold) / 2
|
|
|
|
# Log changes if significant
|
|
if abs(old_entry - self.entry_threshold) > 0.01 or abs(old_exit - self.exit_threshold) > 0.01:
|
|
logger.info(f"Threshold Update - Entry: {old_entry:.3f} -> {self.entry_threshold:.3f}, "
|
|
f"Exit: {old_exit:.3f} -> {self.exit_threshold:.3f}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating dynamic thresholds: {e}")
|
|
|
|
def calculate_enhanced_pivot_reward(self, trade_decision: Dict[str, Any],
|
|
market_data: pd.DataFrame,
|
|
trade_outcome: Dict[str, Any]) -> float:
|
|
"""
|
|
Calculate enhanced pivot-based reward for RL training
|
|
|
|
This method integrates Williams market structure analysis to provide
|
|
sophisticated reward signals based on pivot points and market structure.
|
|
"""
|
|
try:
|
|
logger.debug(f"Calculating enhanced pivot reward for trade: {trade_decision}")
|
|
|
|
# Base reward from PnL
|
|
base_pnl = trade_outcome.get('net_pnl', 0)
|
|
base_reward = base_pnl / 100.0 # Normalize PnL to reward scale
|
|
|
|
# === PIVOT ANALYSIS ENHANCEMENT ===
|
|
pivot_bonus = 0.0
|
|
|
|
try:
|
|
from training.williams_market_structure import analyze_pivot_context
|
|
|
|
# Analyze pivot context around trade
|
|
pivot_analysis = analyze_pivot_context(
|
|
market_data,
|
|
trade_decision['timestamp'],
|
|
trade_decision['action']
|
|
)
|
|
|
|
if pivot_analysis:
|
|
# Reward trading at significant pivot points
|
|
if pivot_analysis.get('near_pivot', False):
|
|
pivot_strength = pivot_analysis.get('pivot_strength', 0)
|
|
pivot_bonus += pivot_strength * 0.3 # Up to 30% bonus
|
|
|
|
# Reward trading in direction of pivot break
|
|
if pivot_analysis.get('pivot_break_direction'):
|
|
direction_match = (
|
|
(trade_decision['action'] == 'BUY' and pivot_analysis['pivot_break_direction'] == 'up') or
|
|
(trade_decision['action'] == 'SELL' and pivot_analysis['pivot_break_direction'] == 'down')
|
|
)
|
|
if direction_match:
|
|
pivot_bonus += 0.2 # 20% bonus for correct direction
|
|
|
|
# Penalty for trading against clear pivot resistance/support
|
|
if pivot_analysis.get('against_pivot_structure', False):
|
|
pivot_bonus -= 0.4 # 40% penalty
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error in pivot analysis for reward: {e}")
|
|
|
|
# === MARKET MICROSTRUCTURE ENHANCEMENT ===
|
|
microstructure_bonus = 0.0
|
|
|
|
# Reward trading with order flow
|
|
order_flow_direction = market_data.get('order_flow_direction', 'neutral')
|
|
if order_flow_direction != 'neutral':
|
|
flow_match = (
|
|
(trade_decision['action'] == 'BUY' and order_flow_direction == 'bullish') or
|
|
(trade_decision['action'] == 'SELL' and order_flow_direction == 'bearish')
|
|
)
|
|
if flow_match:
|
|
flow_strength = market_data.get('order_flow_strength', 0.5)
|
|
microstructure_bonus += flow_strength * 0.25 # Up to 25% bonus
|
|
else:
|
|
microstructure_bonus -= 0.2 # 20% penalty for against flow
|
|
|
|
# === TIMING QUALITY ENHANCEMENT ===
|
|
timing_bonus = 0.0
|
|
|
|
# Reward high-confidence trades
|
|
confidence = trade_decision.get('confidence', 0.5)
|
|
if confidence > 0.8:
|
|
timing_bonus += 0.15 # 15% bonus for high confidence
|
|
elif confidence < 0.3:
|
|
timing_bonus -= 0.15 # 15% penalty for low confidence
|
|
|
|
# Consider trade duration efficiency
|
|
duration = trade_outcome.get('duration', timedelta(0))
|
|
if duration.total_seconds() > 0:
|
|
# Reward quick profitable trades, penalize long unprofitable ones
|
|
if base_pnl > 0 and duration.total_seconds() < 300: # Profitable trade under 5 minutes
|
|
timing_bonus += 0.1
|
|
elif base_pnl < 0 and duration.total_seconds() > 1800: # Losing trade over 30 minutes
|
|
timing_bonus -= 0.1
|
|
|
|
# === RISK MANAGEMENT ENHANCEMENT ===
|
|
risk_bonus = 0.0
|
|
|
|
# Reward proper position sizing
|
|
entry_price = trade_decision.get('price', 0)
|
|
if entry_price > 0:
|
|
risk_percentage = abs(base_pnl) / entry_price
|
|
if risk_percentage < 0.01: # Less than 1% risk
|
|
risk_bonus += 0.1 # Reward conservative risk
|
|
elif risk_percentage > 0.05: # More than 5% risk
|
|
risk_bonus -= 0.2 # Penalize excessive risk
|
|
|
|
# === MARKET CONDITIONS ENHANCEMENT ===
|
|
market_bonus = 0.0
|
|
|
|
# Consider volatility appropriateness
|
|
volatility = market_data.get('volatility', 0.02)
|
|
if volatility > 0.05: # High volatility environment
|
|
if base_pnl > 0:
|
|
market_bonus += 0.1 # Reward profitable trades in high vol
|
|
else:
|
|
market_bonus -= 0.05 # Small penalty for losses in high vol
|
|
|
|
# === FINAL REWARD CALCULATION ===
|
|
total_bonus = pivot_bonus + microstructure_bonus + timing_bonus + risk_bonus + market_bonus
|
|
enhanced_reward = base_reward * (1.0 + total_bonus)
|
|
|
|
# Apply bounds to prevent extreme rewards
|
|
enhanced_reward = max(-2.0, min(2.0, enhanced_reward))
|
|
|
|
logger.info(f"[ENHANCED_REWARD] Base: {base_reward:.3f}, Pivot: {pivot_bonus:.3f}, "
|
|
f"Micro: {microstructure_bonus:.3f}, Timing: {timing_bonus:.3f}, "
|
|
f"Risk: {risk_bonus:.3f}, Market: {market_bonus:.3f} -> Final: {enhanced_reward:.3f}")
|
|
|
|
return enhanced_reward
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating enhanced pivot reward: {e}")
|
|
# Fallback to simple PnL-based reward
|
|
return trade_outcome.get('net_pnl', 0) / 100.0
|
|
|
|
def _update_2_action_position(self, symbol: str, action: TradingAction):
|
|
"""Update position tracking for strict 2-action system"""
|
|
try:
|
|
current_position = self.current_positions.get(symbol, {'side': 'FLAT'})
|
|
|
|
# STRICT RULE: Close ALL opposite positions immediately
|
|
if action.action == 'BUY':
|
|
if current_position['side'] == 'SHORT':
|
|
# Close SHORT position immediately
|
|
logger.info(f"[{symbol}] STRICT: Closing SHORT position at ${action.price:.2f}")
|
|
if symbol in self.current_positions:
|
|
del self.current_positions[symbol]
|
|
|
|
# After closing, check if we should open new LONG
|
|
# ONLY open new position if we don't have any active positions
|
|
if symbol not in self.current_positions:
|
|
self.current_positions[symbol] = {
|
|
'side': 'LONG',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
logger.info(f"[{symbol}] STRICT: Entering LONG position at ${action.price:.2f}")
|
|
|
|
elif current_position['side'] == 'FLAT':
|
|
# No position - enter LONG directly
|
|
self.current_positions[symbol] = {
|
|
'side': 'LONG',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
logger.info(f"[{symbol}] STRICT: Entering LONG position at ${action.price:.2f}")
|
|
|
|
else:
|
|
# Already LONG - ignore signal
|
|
logger.info(f"[{symbol}] STRICT: Already LONG - ignoring BUY signal")
|
|
|
|
elif action.action == 'SELL':
|
|
if current_position['side'] == 'LONG':
|
|
# Close LONG position immediately
|
|
logger.info(f"[{symbol}] STRICT: Closing LONG position at ${action.price:.2f}")
|
|
if symbol in self.current_positions:
|
|
del self.current_positions[symbol]
|
|
|
|
# After closing, check if we should open new SHORT
|
|
# ONLY open new position if we don't have any active positions
|
|
if symbol not in self.current_positions:
|
|
self.current_positions[symbol] = {
|
|
'side': 'SHORT',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
logger.info(f"[{symbol}] STRICT: Entering SHORT position at ${action.price:.2f}")
|
|
|
|
elif current_position['side'] == 'FLAT':
|
|
# No position - enter SHORT directly
|
|
self.current_positions[symbol] = {
|
|
'side': 'SHORT',
|
|
'entry_price': action.price,
|
|
'timestamp': action.timestamp
|
|
}
|
|
logger.info(f"[{symbol}] STRICT: Entering SHORT position at ${action.price:.2f}")
|
|
|
|
else:
|
|
# Already SHORT - ignore signal
|
|
logger.info(f"[{symbol}] STRICT: Already SHORT - ignoring SELL signal")
|
|
|
|
# SAFETY CHECK: Close all conflicting positions if any exist
|
|
self._close_conflicting_positions(symbol, action.action)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating strict 2-action position for {symbol}: {e}")
|
|
|
|
def _close_conflicting_positions(self, symbol: str, new_action: str):
|
|
"""Close any conflicting positions to maintain strict position management"""
|
|
try:
|
|
if symbol not in self.current_positions:
|
|
return
|
|
|
|
current_side = self.current_positions[symbol]['side']
|
|
|
|
# Check for conflicts
|
|
if new_action == 'BUY' and current_side == 'SHORT':
|
|
logger.warning(f"[{symbol}] CONFLICT: BUY signal with SHORT position - closing SHORT")
|
|
del self.current_positions[symbol]
|
|
|
|
elif new_action == 'SELL' and current_side == 'LONG':
|
|
logger.warning(f"[{symbol}] CONFLICT: SELL signal with LONG position - closing LONG")
|
|
del self.current_positions[symbol]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error closing conflicting positions for {symbol}: {e}")
|
|
|
|
def close_all_positions(self, reason: str = "Manual close"):
|
|
"""Close all open positions immediately"""
|
|
try:
|
|
closed_count = 0
|
|
for symbol, position in list(self.current_positions.items()):
|
|
logger.info(f"[{symbol}] Closing {position['side']} position - {reason}")
|
|
del self.current_positions[symbol]
|
|
closed_count += 1
|
|
|
|
if closed_count > 0:
|
|
logger.info(f"Closed {closed_count} positions - {reason}")
|
|
|
|
return closed_count
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error closing all positions: {e}")
|
|
return 0
|
|
|
|
def get_position_status(self, symbol: str = None) -> Dict[str, Any]:
|
|
"""Get current position status for symbol or all symbols"""
|
|
if symbol:
|
|
position = self.current_positions.get(symbol, {'side': 'FLAT'})
|
|
return {
|
|
'symbol': symbol,
|
|
'side': position['side'],
|
|
'entry_price': position.get('entry_price'),
|
|
'timestamp': position.get('timestamp'),
|
|
'last_signal': self.last_signals.get(symbol)
|
|
}
|
|
else:
|
|
return {
|
|
'positions': {sym: pos for sym, pos in self.current_positions.items()},
|
|
'total_positions': len(self.current_positions),
|
|
'last_signals': self.last_signals
|
|
}
|
|
|
|
def _on_cob_cnn_features(self, symbol: str, cob_data: Dict):
|
|
"""Handle COB features for CNN model integration"""
|
|
try:
|
|
if 'features' in cob_data:
|
|
features = cob_data['features']
|
|
self.latest_cob_features[symbol] = features
|
|
self.cob_feature_history[symbol].append({
|
|
'timestamp': cob_data.get('timestamp', datetime.now()),
|
|
'features': features
|
|
})
|
|
logger.debug(f"COB CNN features updated for {symbol}: {features.shape}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing COB CNN features for {symbol}: {e}")
|
|
|
|
def _on_cob_dqn_state(self, symbol: str, cob_data: Dict):
|
|
"""Handle COB state features for DQN model integration"""
|
|
try:
|
|
if 'state' in cob_data:
|
|
state = cob_data['state']
|
|
self.latest_cob_state[symbol] = state
|
|
logger.debug(f"COB DQN state updated for {symbol}: {state.shape}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing COB DQN state for {symbol}: {e}")
|
|
|
|
async def start_cob_integration(self):
|
|
"""Start COB integration for real-time data feed"""
|
|
try:
|
|
if self.cob_integration is None:
|
|
logger.warning("COB integration is disabled (cob_integration=None)")
|
|
return
|
|
|
|
logger.info("Starting COB integration for real-time market microstructure...")
|
|
await self.cob_integration.start()
|
|
self.cob_integration_active = True
|
|
logger.info("COB integration started successfully")
|
|
except Exception as e:
|
|
logger.error(f"Error starting COB integration: {e}")
|
|
self.cob_integration_active = False
|
|
|
|
async def stop_cob_integration(self):
|
|
"""Stop COB integration"""
|
|
try:
|
|
if self.cob_integration is None:
|
|
logger.debug("COB integration is disabled (cob_integration=None)")
|
|
return
|
|
|
|
await self.cob_integration.stop()
|
|
logger.info("COB integration stopped")
|
|
except Exception as e:
|
|
logger.error(f"Error stopping COB integration: {e}")
|
|
|
|
def _get_symbol_correlation(self, symbol: str) -> float:
|
|
"""Get correlation score for symbol with other symbols"""
|
|
try:
|
|
if symbol not in self.symbols:
|
|
return 0.0
|
|
|
|
# Calculate correlation with primary reference symbol (usually BTC for crypto)
|
|
reference_symbol = 'BTC/USDT' if symbol != 'BTC/USDT' else 'ETH/USDT'
|
|
|
|
# Get correlation from pre-computed matrix
|
|
correlation_key = (symbol, reference_symbol)
|
|
if correlation_key in self.symbol_correlation_matrix:
|
|
return self.symbol_correlation_matrix[correlation_key]
|
|
|
|
# Fallback: calculate real-time correlation if not in matrix
|
|
return self._calculate_realtime_correlation(symbol, reference_symbol)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting symbol correlation for {symbol}: {e}")
|
|
return 0.7 # Default correlation
|
|
|
|
def _calculate_realtime_correlation(self, symbol1: str, symbol2: str, periods: int = 50) -> float:
|
|
"""Calculate real-time correlation between two symbols"""
|
|
try:
|
|
# Get recent price data for both symbols
|
|
df1 = self.data_provider.get_historical_data(symbol1, '1m', limit=periods)
|
|
df2 = self.data_provider.get_historical_data(symbol2, '1m', limit=periods)
|
|
|
|
if df1 is None or df2 is None or len(df1) < 10 or len(df2) < 10:
|
|
return 0.7 # Default
|
|
|
|
# Calculate returns
|
|
returns1 = df1['close'].pct_change().dropna()
|
|
returns2 = df2['close'].pct_change().dropna()
|
|
|
|
# Calculate correlation
|
|
if len(returns1) >= 10 and len(returns2) >= 10:
|
|
min_len = min(len(returns1), len(returns2))
|
|
correlation = np.corrcoef(returns1[-min_len:], returns2[-min_len:])[0, 1]
|
|
return float(correlation) if not np.isnan(correlation) else 0.7
|
|
|
|
return 0.7
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating correlation between {symbol1} and {symbol2}: {e}")
|
|
return 0.7
|
|
|
|
def build_comprehensive_rl_state(self, symbol: str, market_state: Optional[object] = None, current_pnl: float = 0.0, position_info: Dict = None) -> Optional[np.ndarray]:
|
|
"""Build comprehensive RL state with 13,500+ features including PnL-aware features for loss cutting optimization"""
|
|
try:
|
|
logger.debug(f"Building PnL-aware comprehensive RL state for {symbol} (PnL: {current_pnl:.4f})")
|
|
|
|
# Initialize comprehensive feature vector
|
|
features = []
|
|
|
|
# === 1. ETH TICK DATA (3,000 features) ===
|
|
tick_features = self._get_tick_features_for_rl(symbol, samples=300)
|
|
if tick_features is not None and len(tick_features) > 0:
|
|
features.extend(tick_features[:3000]) # Limit to 3000 features
|
|
else:
|
|
features.extend([0.0] * 3000) # Pad with zeros
|
|
|
|
# === 2. ETH MULTI-TIMEFRAME OHLCV (3,000 features) ===
|
|
ohlcv_features = self._get_multiframe_ohlcv_features_for_rl(symbol)
|
|
if ohlcv_features is not None and len(ohlcv_features) > 0:
|
|
features.extend(ohlcv_features[:3000]) # Limit to 3000 features
|
|
else:
|
|
features.extend([0.0] * 3000) # Pad with zeros
|
|
|
|
# === 3. BTC REFERENCE DATA (3,000 features) ===
|
|
btc_features = self._get_btc_reference_features_for_rl()
|
|
if btc_features is not None and len(btc_features) > 0:
|
|
features.extend(btc_features[:3000]) # Limit to 3000 features
|
|
else:
|
|
features.extend([0.0] * 3000) # Pad with zeros
|
|
|
|
# === 4. CNN HIDDEN FEATURES (2,000 features) ===
|
|
cnn_features = self._get_cnn_hidden_features_for_rl(symbol)
|
|
if cnn_features is not None and len(cnn_features) > 0:
|
|
features.extend(cnn_features[:2000]) # Limit to 2000 features
|
|
else:
|
|
features.extend([0.0] * 2000) # Pad with zeros
|
|
|
|
# === 5. PIVOT ANALYSIS (1,000 features) ===
|
|
pivot_features = self._get_pivot_analysis_features_for_rl(symbol)
|
|
if pivot_features is not None and len(pivot_features) > 0:
|
|
features.extend(pivot_features[:1000]) # Limit to 1000 features
|
|
else:
|
|
features.extend([0.0] * 1000) # Pad with zeros
|
|
|
|
# === 6. MARKET MICROSTRUCTURE (800 features) ===
|
|
microstructure_features = self._get_microstructure_features_for_rl(symbol)
|
|
if microstructure_features is not None and len(microstructure_features) > 0:
|
|
features.extend(microstructure_features[:800]) # Limit to 800 features
|
|
else:
|
|
features.extend([0.0] * 800) # Pad with zeros
|
|
|
|
# === 7. COB INTEGRATION (600 features) ===
|
|
cob_features = self._get_cob_features_for_rl(symbol)
|
|
if cob_features is not None and len(cob_features) > 0:
|
|
features.extend(cob_features[:600]) # Limit to 600 features
|
|
else:
|
|
features.extend([0.0] * 600) # Pad with zeros
|
|
|
|
# === 8. PnL-AWARE RISK MANAGEMENT FEATURES (100 features) ===
|
|
pnl_features = self._get_pnl_aware_features_for_rl(symbol, current_pnl, position_info)
|
|
if pnl_features is not None and len(pnl_features) > 0:
|
|
features.extend(pnl_features[:100]) # Limit to 100 features
|
|
else:
|
|
features.extend([0.0] * 100) # Pad with zeros
|
|
|
|
# === TOTAL: 13,500 features ===
|
|
# Ensure exact feature count
|
|
if len(features) > 13500:
|
|
features = features[:13500]
|
|
elif len(features) < 13500:
|
|
features.extend([0.0] * (13500 - len(features)))
|
|
|
|
state_vector = np.array(features, dtype=np.float32)
|
|
|
|
logger.info(f"[RL_STATE] Built PnL-aware state for {symbol}: {len(state_vector)} features (PnL: {current_pnl:.4f})")
|
|
logger.debug(f"[RL_STATE] State stats: min={state_vector.min():.3f}, max={state_vector.max():.3f}, mean={state_vector.mean():.3f}")
|
|
|
|
return state_vector
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error building comprehensive RL state for {symbol}: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
return None
|
|
|
|
def _get_tick_features_for_rl(self, symbol: str, samples: int = 300) -> Optional[List[float]]:
|
|
"""Get tick-level features for RL (3,000 features)"""
|
|
try:
|
|
# Get recent tick data
|
|
raw_ticks = self.raw_tick_buffers.get(symbol, deque())
|
|
|
|
if len(raw_ticks) < 10:
|
|
return None
|
|
|
|
features = []
|
|
|
|
# Convert to numpy array for vectorized operations
|
|
recent_ticks = list(raw_ticks)[-samples:]
|
|
|
|
if len(recent_ticks) < 10:
|
|
return None
|
|
|
|
# Extract price, volume, time features
|
|
prices = np.array([tick.get('price', 0) for tick in recent_ticks])
|
|
volumes = np.array([tick.get('volume', 0) for tick in recent_ticks])
|
|
timestamps = np.array([tick.get('timestamp', datetime.now()).timestamp() for tick in recent_ticks])
|
|
|
|
# Price features (1000 features)
|
|
features.extend(list(prices[-1000:]) if len(prices) >= 1000 else list(prices) + [0.0] * (1000 - len(prices)))
|
|
|
|
# Volume features (1000 features)
|
|
features.extend(list(volumes[-1000:]) if len(volumes) >= 1000 else list(volumes) + [0.0] * (1000 - len(volumes)))
|
|
|
|
# Time-based features (1000 features)
|
|
if len(timestamps) > 1:
|
|
time_deltas = np.diff(timestamps)
|
|
features.extend(list(time_deltas[-999:]) if len(time_deltas) >= 999 else list(time_deltas) + [0.0] * (999 - len(time_deltas)))
|
|
features.append(timestamps[-1]) # Latest timestamp
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
|
|
return features[:3000]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting tick features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_multiframe_ohlcv_features_for_rl(self, symbol: str) -> Optional[List[float]]:
|
|
"""Get multi-timeframe OHLCV features for RL (3,000 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# Define timeframes and their feature allocation
|
|
timeframes = {
|
|
'1s': 1000, # 1000 features
|
|
'1m': 1000, # 1000 features
|
|
'1h': 1000 # 1000 features
|
|
}
|
|
|
|
for tf, feature_count in timeframes.items():
|
|
try:
|
|
# Get historical data
|
|
df = self.data_provider.get_historical_data(symbol, tf, limit=feature_count//6)
|
|
|
|
if df is not None and not df.empty:
|
|
# Extract OHLCV features
|
|
tf_features = []
|
|
|
|
# Raw OHLCV values
|
|
tf_features.extend(list(df['open'].values[-feature_count//6:]))
|
|
tf_features.extend(list(df['high'].values[-feature_count//6:]))
|
|
tf_features.extend(list(df['low'].values[-feature_count//6:]))
|
|
tf_features.extend(list(df['close'].values[-feature_count//6:]))
|
|
tf_features.extend(list(df['volume'].values[-feature_count//6:]))
|
|
|
|
# Technical indicators
|
|
if len(df) >= 20:
|
|
sma20 = df['close'].rolling(20).mean()
|
|
tf_features.extend(list(sma20.values[-feature_count//6:]))
|
|
|
|
# Pad or truncate to exact feature count
|
|
if len(tf_features) > feature_count:
|
|
tf_features = tf_features[:feature_count]
|
|
elif len(tf_features) < feature_count:
|
|
tf_features.extend([0.0] * (feature_count - len(tf_features)))
|
|
|
|
features.extend(tf_features)
|
|
else:
|
|
features.extend([0.0] * feature_count)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting {tf} data for {symbol}: {e}")
|
|
features.extend([0.0] * feature_count)
|
|
|
|
return features[:3000]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting multi-timeframe features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_btc_reference_features_for_rl(self) -> Optional[List[float]]:
|
|
"""Get BTC reference features for correlation analysis (3,000 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# Get BTC data for multiple timeframes
|
|
timeframes = {
|
|
'1s': 1000,
|
|
'1m': 1000,
|
|
'1h': 1000
|
|
}
|
|
|
|
for tf, feature_count in timeframes.items():
|
|
try:
|
|
btc_df = self.data_provider.get_historical_data('BTC/USDT', tf, limit=feature_count//6)
|
|
|
|
if btc_df is not None and not btc_df.empty:
|
|
# BTC OHLCV features
|
|
btc_features = []
|
|
btc_features.extend(list(btc_df['open'].values[-feature_count//6:]))
|
|
btc_features.extend(list(btc_df['high'].values[-feature_count//6:]))
|
|
btc_features.extend(list(btc_df['low'].values[-feature_count//6:]))
|
|
btc_features.extend(list(btc_df['close'].values[-feature_count//6:]))
|
|
btc_features.extend(list(btc_df['volume'].values[-feature_count//6:]))
|
|
|
|
# BTC technical indicators
|
|
if len(btc_df) >= 20:
|
|
btc_sma = btc_df['close'].rolling(20).mean()
|
|
btc_features.extend(list(btc_sma.values[-feature_count//6:]))
|
|
|
|
# Pad or truncate
|
|
if len(btc_features) > feature_count:
|
|
btc_features = btc_features[:feature_count]
|
|
elif len(btc_features) < feature_count:
|
|
btc_features.extend([0.0] * (feature_count - len(btc_features)))
|
|
|
|
features.extend(btc_features)
|
|
else:
|
|
features.extend([0.0] * feature_count)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting BTC {tf} data: {e}")
|
|
features.extend([0.0] * feature_count)
|
|
|
|
return features[:3000]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting BTC reference features: {e}")
|
|
return None
|
|
|
|
def _get_cnn_hidden_features_for_rl(self, symbol: str) -> Optional[List[float]]:
|
|
"""Get CNN hidden layer features for RL (2,000 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# Get CNN features from COB integration
|
|
cob_features = self.latest_cob_features.get(symbol)
|
|
if cob_features is not None:
|
|
# CNN features from COB
|
|
features.extend(list(cob_features.flatten())[:1000])
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
|
|
# Get CNN features from model registry
|
|
if hasattr(self, 'model_registry') and self.model_registry:
|
|
try:
|
|
# Get feature matrix for CNN
|
|
feature_matrix = self.data_provider.get_feature_matrix(
|
|
symbol=symbol,
|
|
timeframes=['1s', '1m', '1h'],
|
|
window_size=50
|
|
)
|
|
|
|
if feature_matrix is not None:
|
|
# Extract CNN hidden features (mock implementation)
|
|
cnn_hidden = feature_matrix.flatten()[:1000]
|
|
features.extend(list(cnn_hidden))
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error extracting CNN features: {e}")
|
|
features.extend([0.0] * 1000)
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
|
|
return features[:2000]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting CNN features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_pivot_analysis_features_for_rl(self, symbol: str) -> Optional[List[float]]:
|
|
"""Get pivot analysis features using Williams market structure (1,000 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# Get Williams market structure data
|
|
try:
|
|
from training.williams_market_structure import extract_pivot_features
|
|
|
|
# Get recent market data for pivot analysis
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=200)
|
|
|
|
if df is not None and not df.empty:
|
|
pivot_features = extract_pivot_features(df)
|
|
if pivot_features is not None and len(pivot_features) > 0:
|
|
features.extend(list(pivot_features)[:1000])
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
else:
|
|
features.extend([0.0] * 1000)
|
|
|
|
except ImportError:
|
|
logger.warning("Williams market structure not available")
|
|
features.extend([0.0] * 1000)
|
|
except Exception as e:
|
|
logger.warning(f"Error getting pivot features: {e}")
|
|
features.extend([0.0] * 1000)
|
|
|
|
return features[:1000]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting pivot analysis features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_microstructure_features_for_rl(self, symbol: str) -> Optional[List[float]]:
|
|
"""Get market microstructure features (800 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# Order book features (400 features)
|
|
try:
|
|
if self.cob_integration:
|
|
cob_snapshot = self.cob_integration.get_cob_snapshot(symbol)
|
|
if cob_snapshot:
|
|
# Top 20 bid/ask levels (200 features each)
|
|
bid_prices = [level.price for level in cob_snapshot.consolidated_bids[:20]]
|
|
bid_volumes = [level.total_volume_usd for level in cob_snapshot.consolidated_bids[:20]]
|
|
ask_prices = [level.price for level in cob_snapshot.consolidated_asks[:20]]
|
|
ask_volumes = [level.total_volume_usd for level in cob_snapshot.consolidated_asks[:20]]
|
|
|
|
# Pad to 20 levels
|
|
bid_prices.extend([0.0] * (20 - len(bid_prices)))
|
|
bid_volumes.extend([0.0] * (20 - len(bid_volumes)))
|
|
ask_prices.extend([0.0] * (20 - len(ask_prices)))
|
|
ask_volumes.extend([0.0] * (20 - len(ask_volumes)))
|
|
|
|
features.extend(bid_prices)
|
|
features.extend(bid_volumes)
|
|
features.extend(ask_prices)
|
|
features.extend(ask_volumes)
|
|
|
|
# Microstructure metrics
|
|
features.extend([
|
|
cob_snapshot.volume_weighted_mid,
|
|
cob_snapshot.spread_bps,
|
|
cob_snapshot.liquidity_imbalance,
|
|
cob_snapshot.total_bid_liquidity,
|
|
cob_snapshot.total_ask_liquidity,
|
|
float(cob_snapshot.exchanges_active),
|
|
# Pad to 400 total features
|
|
])
|
|
features.extend([0.0] * (400 - len(features)))
|
|
else:
|
|
features.extend([0.0] * 400)
|
|
else:
|
|
features.extend([0.0] * 400)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting order book features: {e}")
|
|
features.extend([0.0] * 400)
|
|
|
|
# Trade flow features (400 features)
|
|
try:
|
|
trade_flow_features = self._get_trade_flow_features_for_rl(symbol)
|
|
features.extend(trade_flow_features[:400])
|
|
except Exception as e:
|
|
logger.warning(f"Error getting trade flow features: {e}")
|
|
features.extend([0.0] * 400)
|
|
|
|
return features[:800]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting microstructure features for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_cob_features_for_rl(self, symbol: str) -> Optional[List[float]]:
|
|
"""Get Consolidated Order Book features for RL (600 features)"""
|
|
try:
|
|
features = []
|
|
|
|
# COB state features
|
|
cob_state = self.latest_cob_state.get(symbol)
|
|
if cob_state is not None:
|
|
features.extend(list(cob_state.flatten())[:300])
|
|
else:
|
|
features.extend([0.0] * 300)
|
|
|
|
# COB metrics
|
|
cob_features = self.latest_cob_features.get(symbol)
|
|
if cob_features is not None:
|
|
features.extend(list(cob_features.flatten())[:300])
|
|
else:
|
|
features.extend([0.0] * 300)
|
|
|
|
return features[:600]
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error getting COB features for {symbol}: {e}")
|
|
return None
|
|
|
|
def calculate_enhanced_pivot_reward(self, trade_decision: Dict, market_data: Dict, trade_outcome: Dict) -> float:
|
|
"""
|
|
Calculate enhanced pivot-based reward for RL training
|
|
|
|
This method integrates Williams market structure analysis to provide
|
|
sophisticated reward signals based on pivot points and market structure.
|
|
"""
|
|
try:
|
|
logger.debug(f"Calculating enhanced pivot reward for trade: {trade_decision}")
|
|
|
|
# Base reward from PnL
|
|
base_pnl = trade_outcome.get('net_pnl', 0)
|
|
base_reward = base_pnl / 100.0 # Normalize PnL to reward scale
|
|
|
|
# === PIVOT ANALYSIS ENHANCEMENT ===
|
|
pivot_bonus = 0.0
|
|
|
|
try:
|
|
from training.williams_market_structure import analyze_pivot_context
|
|
|
|
# Analyze pivot context around trade
|
|
pivot_analysis = analyze_pivot_context(
|
|
market_data,
|
|
trade_decision['timestamp'],
|
|
trade_decision['action']
|
|
)
|
|
|
|
if pivot_analysis:
|
|
# Reward trading at significant pivot points
|
|
if pivot_analysis.get('near_pivot', False):
|
|
pivot_strength = pivot_analysis.get('pivot_strength', 0)
|
|
pivot_bonus += pivot_strength * 0.3 # Up to 30% bonus
|
|
|
|
# Reward trading in direction of pivot break
|
|
if pivot_analysis.get('pivot_break_direction'):
|
|
direction_match = (
|
|
(trade_decision['action'] == 'BUY' and pivot_analysis['pivot_break_direction'] == 'up') or
|
|
(trade_decision['action'] == 'SELL' and pivot_analysis['pivot_break_direction'] == 'down')
|
|
)
|
|
if direction_match:
|
|
pivot_bonus += 0.2 # 20% bonus for correct direction
|
|
|
|
# Penalty for trading against clear pivot resistance/support
|
|
if pivot_analysis.get('against_pivot_structure', False):
|
|
pivot_bonus -= 0.4 # 40% penalty
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error in pivot analysis for reward: {e}")
|
|
|
|
# === MARKET MICROSTRUCTURE ENHANCEMENT ===
|
|
microstructure_bonus = 0.0
|
|
|
|
# Reward trading with order flow
|
|
order_flow_direction = market_data.get('order_flow_direction', 'neutral')
|
|
if order_flow_direction != 'neutral':
|
|
flow_match = (
|
|
(trade_decision['action'] == 'BUY' and order_flow_direction == 'bullish') or
|
|
(trade_decision['action'] == 'SELL' and order_flow_direction == 'bearish')
|
|
)
|
|
if flow_match:
|
|
flow_strength = market_data.get('order_flow_strength', 0.5)
|
|
microstructure_bonus += flow_strength * 0.25 # Up to 25% bonus
|
|
else:
|
|
microstructure_bonus -= 0.2 # 20% penalty for against flow
|
|
|
|
# === TIMING QUALITY ENHANCEMENT ===
|
|
timing_bonus = 0.0
|
|
|
|
# Reward high-confidence trades
|
|
confidence = trade_decision.get('confidence', 0.5)
|
|
if confidence > 0.8:
|
|
timing_bonus += 0.15 # 15% bonus for high confidence
|
|
elif confidence < 0.3:
|
|
timing_bonus -= 0.15 # 15% penalty for low confidence
|
|
|
|
# Consider trade duration efficiency
|
|
duration = trade_outcome.get('duration', timedelta(0))
|
|
if duration.total_seconds() > 0:
|
|
# Reward quick profitable trades, penalize long unprofitable ones
|
|
if base_pnl > 0 and duration.total_seconds() < 300: # Profitable trade under 5 minutes
|
|
timing_bonus += 0.1
|
|
elif base_pnl < 0 and duration.total_seconds() > 1800: # Losing trade over 30 minutes
|
|
timing_bonus -= 0.1
|
|
|
|
# === RISK MANAGEMENT ENHANCEMENT ===
|
|
risk_bonus = 0.0
|
|
|
|
# Reward proper position sizing
|
|
entry_price = trade_decision.get('price', 0)
|
|
if entry_price > 0:
|
|
risk_percentage = abs(base_pnl) / entry_price
|
|
if risk_percentage < 0.01: # Less than 1% risk
|
|
risk_bonus += 0.1 # Reward conservative risk
|
|
elif risk_percentage > 0.05: # More than 5% risk
|
|
risk_bonus -= 0.2 # Penalize excessive risk
|
|
|
|
# === MARKET CONDITIONS ENHANCEMENT ===
|
|
market_bonus = 0.0
|
|
|
|
# Consider volatility appropriateness
|
|
volatility = market_data.get('volatility', 0.02)
|
|
if volatility > 0.05: # High volatility environment
|
|
if base_pnl > 0:
|
|
market_bonus += 0.1 # Reward profitable trades in high vol
|
|
else:
|
|
market_bonus -= 0.05 # Small penalty for losses in high vol
|
|
|
|
# === FINAL REWARD CALCULATION ===
|
|
total_bonus = pivot_bonus + microstructure_bonus + timing_bonus + risk_bonus + market_bonus
|
|
enhanced_reward = base_reward * (1.0 + total_bonus)
|
|
|
|
# Apply bounds to prevent extreme rewards
|
|
enhanced_reward = max(-2.0, min(2.0, enhanced_reward))
|
|
|
|
logger.info(f"[ENHANCED_REWARD] Base: {base_reward:.3f}, Pivot: {pivot_bonus:.3f}, "
|
|
f"Micro: {microstructure_bonus:.3f}, Timing: {timing_bonus:.3f}, "
|
|
f"Risk: {risk_bonus:.3f}, Market: {market_bonus:.3f} -> Final: {enhanced_reward:.3f}")
|
|
|
|
return enhanced_reward
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating enhanced pivot reward: {e}")
|
|
# Fallback to simple PnL-based reward
|
|
return trade_outcome.get('net_pnl', 0) / 100.0
|
|
|
|
def _get_current_position_side(self, symbol: str) -> str:
|
|
"""Get current position side for a symbol"""
|
|
try:
|
|
position = self.current_positions.get(symbol)
|
|
if position is None:
|
|
return 'FLAT'
|
|
return position.get('side', 'FLAT')
|
|
except Exception as e:
|
|
logger.error(f"Error getting position side for {symbol}: {e}")
|
|
return 'FLAT'
|
|
|
|
def _calculate_position_size(self, symbol: str, action: str, confidence: float) -> float:
|
|
"""Calculate position size based on action and confidence"""
|
|
try:
|
|
# Base position size - could be made configurable
|
|
base_size = 0.01 # 0.01 BTC or ETH equivalent
|
|
|
|
# Adjust size based on confidence
|
|
confidence_multiplier = min(confidence * 1.5, 2.0) # Max 2x multiplier
|
|
|
|
return base_size * confidence_multiplier
|
|
except Exception as e:
|
|
logger.error(f"Error calculating position size for {symbol}: {e}")
|
|
return 0.01 # Default small size
|
|
|
|
def _get_pnl_aware_features_for_rl(self, symbol: str, current_pnl: float, position_info: Dict = None) -> List[float]:
|
|
"""
|
|
Generate PnL-aware features for loss cutting optimization (100 features)
|
|
|
|
These features help the RL model learn to:
|
|
1. Cut losses early when predicting bigger drawdowns
|
|
2. Optimize exit timing based on current PnL
|
|
3. Avoid letting winners turn into losers
|
|
"""
|
|
try:
|
|
features = []
|
|
|
|
# Current position info
|
|
position_info = position_info or {}
|
|
current_price = self._get_current_price(symbol) or 0.0
|
|
entry_price = position_info.get('entry_price', current_price)
|
|
position_side = position_info.get('side', 'FLAT')
|
|
position_duration = position_info.get('duration_seconds', 0)
|
|
|
|
# === 1. CURRENT PnL ANALYSIS (20 features) ===
|
|
|
|
# Normalized current PnL (-1 to 1 range, clamped)
|
|
normalized_pnl = max(-1.0, min(1.0, current_pnl / 100.0)) # Assume max +/-100 for normalization
|
|
features.append(normalized_pnl)
|
|
|
|
# PnL buckets (one-hot encoding for different PnL ranges)
|
|
pnl_buckets = [
|
|
1.0 if current_pnl < -50 else 0.0, # Heavy loss
|
|
1.0 if -50 <= current_pnl < -20 else 0.0, # Moderate loss
|
|
1.0 if -20 <= current_pnl < -5 else 0.0, # Small loss
|
|
1.0 if -5 <= current_pnl < 5 else 0.0, # Break-even
|
|
1.0 if 5 <= current_pnl < 20 else 0.0, # Small profit
|
|
1.0 if 20 <= current_pnl < 50 else 0.0, # Moderate profit
|
|
1.0 if current_pnl >= 50 else 0.0, # Large profit
|
|
]
|
|
features.extend(pnl_buckets)
|
|
|
|
# PnL velocity (rate of change)
|
|
pnl_velocity = self._calculate_pnl_velocity(symbol)
|
|
features.append(max(-1.0, min(1.0, pnl_velocity / 10.0))) # Normalized velocity
|
|
|
|
# Time-weighted PnL (how long we've been in this PnL state)
|
|
time_weight = min(1.0, position_duration / 3600.0) # Hours normalized to 0-1
|
|
time_weighted_pnl = normalized_pnl * time_weight
|
|
features.append(time_weighted_pnl)
|
|
|
|
# PnL trend analysis (last 5 measurements)
|
|
pnl_trend = self._get_pnl_trend_features(symbol)
|
|
features.extend(pnl_trend[:10]) # 10 trend features
|
|
|
|
# === 2. DRAWDOWN PREDICTION FEATURES (20 features) ===
|
|
|
|
# Current drawdown from peak
|
|
peak_pnl = self._get_peak_pnl_for_position(symbol)
|
|
current_drawdown = (peak_pnl - current_pnl) / max(1.0, abs(peak_pnl)) if peak_pnl != 0 else 0.0
|
|
features.append(max(-1.0, min(1.0, current_drawdown)))
|
|
|
|
# Predicted future drawdown based on market conditions
|
|
predicted_drawdown = self._predict_future_drawdown(symbol)
|
|
features.extend(predicted_drawdown[:10]) # 10 prediction features
|
|
|
|
# Volatility-adjusted risk score
|
|
current_volatility = self._get_current_volatility(symbol)
|
|
risk_score = current_drawdown * current_volatility
|
|
features.append(max(-1.0, min(1.0, risk_score)))
|
|
|
|
# Risk/reward ratio analysis
|
|
risk_reward_features = self._calculate_risk_reward_features(symbol, current_pnl)
|
|
features.extend(risk_reward_features[:8]) # 8 risk/reward features
|
|
|
|
# === 3. POSITION DURATION FEATURES (15 features) ===
|
|
|
|
# Duration buckets (one-hot encoding)
|
|
duration_buckets = [
|
|
1.0 if position_duration < 60 else 0.0, # < 1 minute
|
|
1.0 if 60 <= position_duration < 300 else 0.0, # 1-5 minutes
|
|
1.0 if 300 <= position_duration < 900 else 0.0, # 5-15 minutes
|
|
1.0 if 900 <= position_duration < 3600 else 0.0, # 15-60 minutes
|
|
1.0 if 3600 <= position_duration < 14400 else 0.0, # 1-4 hours
|
|
1.0 if position_duration >= 14400 else 0.0, # > 4 hours
|
|
]
|
|
features.extend(duration_buckets)
|
|
|
|
# Normalized duration
|
|
normalized_duration = min(1.0, position_duration / 14400.0) # Normalize to 4 hours
|
|
features.append(normalized_duration)
|
|
|
|
# Duration vs PnL relationship
|
|
duration_pnl_ratio = current_pnl / max(1.0, position_duration / 60.0) # PnL per minute
|
|
features.append(max(-1.0, min(1.0, duration_pnl_ratio / 5.0))) # Normalized
|
|
|
|
# Time decay factor (urgency to act)
|
|
time_decay = 1.0 - min(1.0, position_duration / 7200.0) # Decay over 2 hours
|
|
features.append(time_decay)
|
|
|
|
# === 4. HISTORICAL PERFORMANCE FEATURES (20 features) ===
|
|
|
|
# Recent trade outcomes for this symbol
|
|
recent_trades = self._get_recent_trade_outcomes(symbol, limit=10)
|
|
win_rate = len([t for t in recent_trades if t > 0]) / max(1, len(recent_trades))
|
|
avg_win = np.mean([t for t in recent_trades if t > 0]) if any(t > 0 for t in recent_trades) else 0.0
|
|
avg_loss = np.mean([t for t in recent_trades if t < 0]) if any(t < 0 for t in recent_trades) else 0.0
|
|
|
|
features.extend([
|
|
win_rate,
|
|
max(-1.0, min(1.0, avg_win / 50.0)), # Normalized average win
|
|
max(-1.0, min(1.0, avg_loss / 50.0)), # Normalized average loss
|
|
])
|
|
|
|
# Profit factor and other performance metrics
|
|
profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else 1.0
|
|
features.append(min(5.0, profit_factor) / 5.0) # Normalized profit factor
|
|
|
|
# Historical cut-loss effectiveness
|
|
cut_loss_success = self._get_cut_loss_success_rate(symbol)
|
|
features.extend(cut_loss_success[:15]) # 15 cut-loss related features
|
|
|
|
# === 5. MARKET REGIME FEATURES (25 features) ===
|
|
|
|
# Current market regime assessment
|
|
market_regime = self._assess_current_market_regime(symbol)
|
|
features.extend(market_regime[:25]) # 25 market regime features
|
|
|
|
# Ensure we have exactly 100 features
|
|
if len(features) > 100:
|
|
features = features[:100]
|
|
elif len(features) < 100:
|
|
features.extend([0.0] * (100 - len(features)))
|
|
|
|
logger.debug(f"[PnL_FEATURES] Generated {len(features)} PnL-aware features for {symbol} (PnL: {current_pnl:.4f})")
|
|
|
|
return features
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating PnL-aware features for {symbol}: {e}")
|
|
return [0.0] * 100 # Return zeros on error
|
|
|
|
def _calculate_pnl_velocity(self, symbol: str) -> float:
|
|
"""Calculate PnL rate of change"""
|
|
try:
|
|
# Get recent PnL history if available
|
|
if not hasattr(self, 'pnl_history'):
|
|
self.pnl_history = {}
|
|
|
|
if symbol not in self.pnl_history:
|
|
return 0.0
|
|
|
|
history = self.pnl_history[symbol]
|
|
if len(history) < 2:
|
|
return 0.0
|
|
|
|
# Calculate velocity as change per minute
|
|
time_diff = (history[-1]['timestamp'] - history[-2]['timestamp']).total_seconds() / 60.0
|
|
pnl_diff = history[-1]['pnl'] - history[-2]['pnl']
|
|
|
|
return pnl_diff / max(1.0, time_diff)
|
|
|
|
except Exception:
|
|
return 0.0
|
|
|
|
def _get_pnl_trend_features(self, symbol: str) -> List[float]:
|
|
"""Get PnL trend features over recent history"""
|
|
try:
|
|
features = []
|
|
|
|
if not hasattr(self, 'pnl_history'):
|
|
return [0.0] * 10
|
|
|
|
history = self.pnl_history.get(symbol, [])
|
|
if len(history) < 5:
|
|
return [0.0] * 10
|
|
|
|
# Last 5 PnL values
|
|
recent_pnls = [h['pnl'] for h in history[-5:]]
|
|
|
|
# Calculate trend features
|
|
features.append(recent_pnls[-1] - recent_pnls[0]) # Total change
|
|
features.append(np.mean(np.diff(recent_pnls))) # Average change
|
|
features.append(np.std(recent_pnls)) # Volatility
|
|
features.append(max(recent_pnls) - min(recent_pnls)) # Range
|
|
|
|
# Trend direction indicators
|
|
increasing = sum(1 for i in range(1, len(recent_pnls)) if recent_pnls[i] > recent_pnls[i-1])
|
|
features.append(increasing / max(1, len(recent_pnls) - 1))
|
|
|
|
# Remaining features as placeholders
|
|
features.extend([0.0] * (10 - len(features)))
|
|
|
|
return features[:10]
|
|
|
|
except Exception:
|
|
return [0.0] * 10
|
|
|
|
def _get_peak_pnl_for_position(self, symbol: str) -> float:
|
|
"""Get peak PnL for current position"""
|
|
try:
|
|
if not hasattr(self, 'position_peak_pnl'):
|
|
self.position_peak_pnl = {}
|
|
|
|
return self.position_peak_pnl.get(symbol, 0.0)
|
|
|
|
except Exception:
|
|
return 0.0
|
|
|
|
def _predict_future_drawdown(self, symbol: str) -> List[float]:
|
|
"""Predict potential future drawdown based on market conditions"""
|
|
try:
|
|
features = []
|
|
|
|
# Get current market volatility
|
|
volatility = self._get_current_volatility(symbol)
|
|
|
|
# Simple heuristic predictions based on volatility
|
|
for i in range(1, 11): # 10 future periods
|
|
predicted_risk = volatility * i * 0.1 # Increasing risk over time
|
|
features.append(min(1.0, predicted_risk))
|
|
|
|
return features
|
|
|
|
except Exception:
|
|
return [0.0] * 10
|
|
|
|
def _calculate_risk_reward_features(self, symbol: str, current_pnl: float) -> List[float]:
|
|
"""Calculate risk/reward ratio features"""
|
|
try:
|
|
features = []
|
|
|
|
# Current risk level based on volatility
|
|
volatility = self._get_current_volatility(symbol)
|
|
risk_level = min(1.0, volatility / 0.05) # Normalize volatility
|
|
features.append(risk_level)
|
|
|
|
# Reward potential based on current PnL
|
|
if current_pnl > 0:
|
|
reward_potential = max(0.0, 1.0 - (current_pnl / 100.0)) # Diminishing returns
|
|
else:
|
|
reward_potential = 1.0 # High potential when in loss
|
|
features.append(reward_potential)
|
|
|
|
# Risk/reward ratio
|
|
features.append(reward_potential / max(0.1, risk_level))
|
|
|
|
# Remaining features as placeholders
|
|
features.extend([0.0] * (8 - len(features)))
|
|
|
|
return features[:8]
|
|
|
|
except Exception:
|
|
return [0.0] * 8
|
|
|
|
def _get_recent_trade_outcomes(self, symbol: str, limit: int = 10) -> List[float]:
|
|
"""Get recent trade outcomes for this symbol"""
|
|
try:
|
|
if not hasattr(self, 'trade_history'):
|
|
self.trade_history = {}
|
|
|
|
history = self.trade_history.get(symbol, [])
|
|
return [trade.get('pnl', 0.0) for trade in history[-limit:]]
|
|
|
|
except Exception:
|
|
return []
|
|
|
|
def _get_cut_loss_success_rate(self, symbol: str) -> List[float]:
|
|
"""Get cut-loss success rate features"""
|
|
try:
|
|
features = []
|
|
|
|
# Get trades where we cut losses early
|
|
recent_trades = self._get_recent_trade_outcomes(symbol, 20)
|
|
cut_loss_trades = [t for t in recent_trades if -20 < t < -5] # Cut losses
|
|
|
|
if len(cut_loss_trades) > 0:
|
|
# Success rate (how often cutting losses prevented bigger losses)
|
|
success_rate = len([t for t in cut_loss_trades if t > -10]) / len(cut_loss_trades)
|
|
features.append(success_rate)
|
|
|
|
# Average cut-loss amount
|
|
avg_cut_loss = np.mean(cut_loss_trades)
|
|
features.append(max(-1.0, avg_cut_loss / 50.0)) # Normalized
|
|
else:
|
|
features.extend([0.0, 0.0])
|
|
|
|
# Remaining features as placeholders
|
|
features.extend([0.0] * (15 - len(features)))
|
|
|
|
return features[:15]
|
|
|
|
except Exception:
|
|
return [0.0] * 15
|
|
|
|
def _assess_current_market_regime(self, symbol: str) -> List[float]:
|
|
"""Assess current market regime for PnL optimization"""
|
|
try:
|
|
features = []
|
|
|
|
# Get market data
|
|
try:
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=100)
|
|
if df is None or df.empty:
|
|
return [0.0] * 25
|
|
|
|
# Trend strength
|
|
trend_strength = self._calculate_trend_strength(df)
|
|
features.append(trend_strength)
|
|
|
|
# Volatility regime
|
|
volatility = df['close'].pct_change().std()
|
|
features.append(min(1.0, volatility / 0.05))
|
|
|
|
# Volume regime
|
|
volume_ratio = df['volume'].iloc[-1] / df['volume'].rolling(20).mean().iloc[-1]
|
|
features.append(min(2.0, volume_ratio) / 2.0)
|
|
|
|
except Exception:
|
|
features.extend([0.0, 0.0, 0.0])
|
|
|
|
# Remaining features as placeholders
|
|
features.extend([0.0] * (25 - len(features)))
|
|
|
|
return features[:25]
|
|
|
|
except Exception:
|
|
return [0.0] * 25
|
|
|
|
def _calculate_trend_strength(self, df: pd.DataFrame) -> float:
|
|
"""Calculate trend strength from price data"""
|
|
try:
|
|
if len(df) < 20:
|
|
return 0.0
|
|
|
|
# Calculate trend using moving averages
|
|
short_ma = df['close'].rolling(5).mean()
|
|
long_ma = df['close'].rolling(20).mean()
|
|
|
|
# Trend strength based on MA separation
|
|
ma_diff = (short_ma.iloc[-1] - long_ma.iloc[-1]) / long_ma.iloc[-1]
|
|
return max(-1.0, min(1.0, ma_diff * 10)) # Normalized
|
|
|
|
except Exception:
|
|
return 0.0
|
|
|
|
def _get_current_volatility(self, symbol: str) -> float:
|
|
"""Get current market volatility"""
|
|
try:
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=20)
|
|
if df is None or df.empty:
|
|
return 0.02 # Default volatility
|
|
|
|
return df['close'].pct_change().std()
|
|
|
|
except Exception:
|
|
return 0.02
|
|
|
|
def _get_current_price(self, symbol: str) -> Optional[float]:
|
|
"""Get current price for the symbol"""
|
|
try:
|
|
# Try to get from data provider
|
|
latest_data = self.data_provider.get_latest_data(symbol)
|
|
if latest_data:
|
|
return latest_data.get('close')
|
|
|
|
# Fallback to historical data
|
|
df = self.data_provider.get_historical_data(symbol, '1m', limit=1)
|
|
if df is not None and not df.empty:
|
|
return df['close'].iloc[-1]
|
|
|
|
return None
|
|
|
|
except Exception:
|
|
return None
|
|
|
|
def _get_trade_flow_features_for_rl(self, symbol: str, window_seconds: int = 300) -> List[float]:
|
|
"""
|
|
Generate comprehensive trade flow features for RL models (400 features)
|
|
|
|
Analyzes actual trade execution patterns, order flow direction,
|
|
institutional activity, and market microstructure to help the RL model
|
|
understand market dynamics at tick level.
|
|
"""
|
|
try:
|
|
features = []
|
|
|
|
# Get recent trade data
|
|
recent_trades = self._get_recent_trade_data_for_flow_analysis(symbol, window_seconds)
|
|
|
|
if not recent_trades:
|
|
return [0.0] * 400 # Return zeros if no trade data
|
|
|
|
# === 1. ORDER FLOW DIRECTION ANALYSIS (50 features) ===
|
|
|
|
# Aggressive buy/sell ratio in different time windows
|
|
windows = [10, 30, 60, 120, 300] # seconds
|
|
for window in windows:
|
|
window_trades = [t for t in recent_trades if t['timestamp'] > (datetime.now() - timedelta(seconds=window))]
|
|
if window_trades:
|
|
aggressive_buys = sum(1 for t in window_trades if t.get('side') == 'buy' and t.get('is_aggressive', True))
|
|
aggressive_sells = sum(1 for t in window_trades if t.get('side') == 'sell' and t.get('is_aggressive', True))
|
|
total_aggressive = aggressive_buys + aggressive_sells
|
|
|
|
if total_aggressive > 0:
|
|
buy_ratio = aggressive_buys / total_aggressive
|
|
sell_ratio = aggressive_sells / total_aggressive
|
|
features.extend([buy_ratio, sell_ratio])
|
|
else:
|
|
features.extend([0.5, 0.5]) # Neutral
|
|
else:
|
|
features.extend([0.5, 0.5]) # Neutral
|
|
|
|
# === 2. VOLUME FLOW ANALYSIS (80 features) ===
|
|
|
|
# Volume-weighted order flow in different time buckets
|
|
total_buy_volume = sum(t['volume_usd'] for t in recent_trades if t.get('side') == 'buy')
|
|
total_sell_volume = sum(t['volume_usd'] for t in recent_trades if t.get('side') == 'sell')
|
|
total_volume = total_buy_volume + total_sell_volume
|
|
|
|
if total_volume > 0:
|
|
volume_buy_ratio = total_buy_volume / total_volume
|
|
volume_sell_ratio = total_sell_volume / total_volume
|
|
volume_imbalance = (total_buy_volume - total_sell_volume) / total_volume
|
|
else:
|
|
volume_buy_ratio = volume_sell_ratio = 0.5
|
|
volume_imbalance = 0.0
|
|
|
|
features.extend([volume_buy_ratio, volume_sell_ratio, volume_imbalance])
|
|
|
|
# Volume distribution by trade size buckets
|
|
volume_buckets = {'small': 0, 'medium': 0, 'large': 0, 'whale': 0}
|
|
for trade in recent_trades:
|
|
volume_usd = trade.get('volume_usd', 0)
|
|
if volume_usd < 1000:
|
|
volume_buckets['small'] += volume_usd
|
|
elif volume_usd < 10000:
|
|
volume_buckets['medium'] += volume_usd
|
|
elif volume_usd < 100000:
|
|
volume_buckets['large'] += volume_usd
|
|
else:
|
|
volume_buckets['whale'] += volume_usd
|
|
|
|
# Normalize volume buckets
|
|
if total_volume > 0:
|
|
bucket_ratios = [volume_buckets[k] / total_volume for k in ['small', 'medium', 'large', 'whale']]
|
|
else:
|
|
bucket_ratios = [0.25, 0.25, 0.25, 0.25]
|
|
features.extend(bucket_ratios)
|
|
|
|
# Volume acceleration (rate of change)
|
|
if len(recent_trades) >= 10:
|
|
first_half = recent_trades[:len(recent_trades)//2]
|
|
second_half = recent_trades[len(recent_trades)//2:]
|
|
|
|
first_half_volume = sum(t['volume_usd'] for t in first_half)
|
|
second_half_volume = sum(t['volume_usd'] for t in second_half)
|
|
|
|
if first_half_volume > 0:
|
|
volume_acceleration = (second_half_volume - first_half_volume) / first_half_volume
|
|
else:
|
|
volume_acceleration = 0.0
|
|
|
|
features.append(max(-1.0, min(1.0, volume_acceleration)))
|
|
else:
|
|
features.append(0.0)
|
|
|
|
# Pad remaining volume features
|
|
features.extend([0.0] * (80 - len(features) + 3)) # Adjust for current features
|
|
|
|
# === 3. TRADE SIZE PATTERN ANALYSIS (70 features) ===
|
|
|
|
# Average trade sizes by side
|
|
buy_trades = [t for t in recent_trades if t.get('side') == 'buy']
|
|
sell_trades = [t for t in recent_trades if t.get('side') == 'sell']
|
|
|
|
avg_buy_size = np.mean([t['volume_usd'] for t in buy_trades]) if buy_trades else 0.0
|
|
avg_sell_size = np.mean([t['volume_usd'] for t in sell_trades]) if sell_trades else 0.0
|
|
|
|
# Normalize trade sizes
|
|
max_size = max(avg_buy_size, avg_sell_size, 1.0)
|
|
features.extend([avg_buy_size / max_size, avg_sell_size / max_size])
|
|
|
|
# Trade size distribution
|
|
trade_sizes = [t['volume_usd'] for t in recent_trades]
|
|
if trade_sizes:
|
|
size_percentiles = np.percentile(trade_sizes, [10, 25, 50, 75, 90])
|
|
normalized_percentiles = [p / max_size for p in size_percentiles]
|
|
features.extend(normalized_percentiles)
|
|
else:
|
|
features.extend([0.0] * 5)
|
|
|
|
# Large trade detection (institutional activity)
|
|
large_trade_threshold = 50000 # $50k+ trades
|
|
large_trades = [t for t in recent_trades if t['volume_usd'] >= large_trade_threshold]
|
|
large_trade_ratio = len(large_trades) / max(1, len(recent_trades))
|
|
features.append(large_trade_ratio)
|
|
|
|
# Large trade direction bias
|
|
if large_trades:
|
|
large_buy_trades = [t for t in large_trades if t.get('side') == 'buy']
|
|
large_trade_buy_ratio = len(large_buy_trades) / len(large_trades)
|
|
else:
|
|
large_trade_buy_ratio = 0.5
|
|
features.append(large_trade_buy_ratio)
|
|
|
|
# Pad remaining trade size features
|
|
features.extend([0.0] * (70 - (len(features) - 80 - 3)))
|
|
|
|
# === 4. TIMING AND FREQUENCY ANALYSIS (100 features) ===
|
|
|
|
# Trade frequency analysis
|
|
if len(recent_trades) >= 2:
|
|
timestamps = [t['timestamp'] for t in recent_trades]
|
|
time_diffs = [(timestamps[i] - timestamps[i-1]).total_seconds()
|
|
for i in range(1, len(timestamps))]
|
|
|
|
if time_diffs:
|
|
avg_time_between_trades = np.mean(time_diffs)
|
|
trade_frequency = 1.0 / max(0.1, avg_time_between_trades) # Trades per second
|
|
trade_frequency_normalized = min(1.0, trade_frequency / 10.0) # Normalize to 0-1
|
|
else:
|
|
trade_frequency_normalized = 0.0
|
|
else:
|
|
trade_frequency_normalized = 0.0
|
|
|
|
features.append(trade_frequency_normalized)
|
|
|
|
# Trade clustering analysis (bursts of activity)
|
|
if len(recent_trades) >= 5:
|
|
trade_times = [(t['timestamp'] - recent_trades[0]['timestamp']).total_seconds()
|
|
for t in recent_trades]
|
|
|
|
# Find clusters of trades within 5-second windows
|
|
clusters = []
|
|
current_cluster = []
|
|
|
|
for i, time_diff in enumerate(trade_times):
|
|
if not current_cluster or time_diff - trade_times[current_cluster[-1]] <= 5.0:
|
|
current_cluster.append(i)
|
|
else:
|
|
if len(current_cluster) >= 3: # Minimum cluster size
|
|
clusters.append(current_cluster)
|
|
current_cluster = [i]
|
|
|
|
if len(current_cluster) >= 3:
|
|
clusters.append(current_cluster)
|
|
|
|
# Cluster features
|
|
cluster_count = len(clusters)
|
|
if clusters:
|
|
avg_cluster_size = np.mean([len(c) for c in clusters])
|
|
max_cluster_size = max([len(c) for c in clusters])
|
|
else:
|
|
avg_cluster_size = max_cluster_size = 0.0
|
|
|
|
features.extend([
|
|
min(1.0, cluster_count / 10.0), # Normalized cluster count
|
|
min(1.0, avg_cluster_size / 20.0), # Normalized average cluster size
|
|
min(1.0, max_cluster_size / 50.0) # Normalized max cluster size
|
|
])
|
|
else:
|
|
features.extend([0.0, 0.0, 0.0])
|
|
|
|
# Pad remaining timing features
|
|
features.extend([0.0] * (100 - 4))
|
|
|
|
# === 5. MARKET IMPACT AND SLIPPAGE ANALYSIS (100 features) ===
|
|
|
|
# Price impact analysis
|
|
price_impacts = []
|
|
for i, trade in enumerate(recent_trades[1:], 1):
|
|
prev_trade = recent_trades[i-1]
|
|
if 'price' in trade and 'price' in prev_trade and prev_trade['price'] > 0:
|
|
price_change = (trade['price'] - prev_trade['price']) / prev_trade['price']
|
|
|
|
# Adjust impact by trade size and side
|
|
trade_side_multiplier = 1 if trade.get('side') == 'buy' else -1
|
|
size_weight = min(1.0, trade['volume_usd'] / 10000.0) # Weight by size
|
|
|
|
impact = price_change * trade_side_multiplier * size_weight
|
|
price_impacts.append(impact)
|
|
|
|
if price_impacts:
|
|
avg_impact = np.mean(price_impacts)
|
|
max_impact = np.max(np.abs(price_impacts))
|
|
impact_volatility = np.std(price_impacts) if len(price_impacts) > 1 else 0.0
|
|
|
|
features.extend([
|
|
max(-1.0, min(1.0, avg_impact * 1000)), # Scaled average impact
|
|
min(1.0, max_impact * 1000), # Scaled max impact
|
|
min(1.0, impact_volatility * 1000) # Scaled impact volatility
|
|
])
|
|
else:
|
|
features.extend([0.0, 0.0, 0.0])
|
|
|
|
# Pad remaining market impact features
|
|
features.extend([0.0] * (100 - 3))
|
|
|
|
# Ensure we have exactly 400 features
|
|
while len(features) < 400:
|
|
features.append(0.0)
|
|
|
|
return features[:400]
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating trade flow features for {symbol}: {e}")
|
|
return [0.0] * 400
|
|
|
|
def _get_recent_trade_data_for_flow_analysis(self, symbol: str, window_seconds: int = 300) -> List[Dict]:
|
|
"""Get recent trade data for flow analysis"""
|
|
try:
|
|
# Try to get from data provider or COB integration
|
|
if hasattr(self.data_provider, 'get_recent_trades'):
|
|
return self.data_provider.get_recent_trades(symbol, window_seconds)
|
|
|
|
# Fallback: try to get from COB integration
|
|
if hasattr(self, 'cob_integration') and self.cob_integration:
|
|
if hasattr(self.cob_integration, 'get_recent_trades'):
|
|
return self.cob_integration.get_recent_trades(symbol, window_seconds)
|
|
|
|
# Last resort: generate synthetic trade data for testing
|
|
# This should be replaced with actual trade data in production
|
|
return self._generate_synthetic_trade_data(symbol, window_seconds)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting recent trade data for {symbol}: {e}")
|
|
return []
|
|
|
|
def _generate_synthetic_trade_data(self, symbol: str, window_seconds: int) -> List[Dict]:
|
|
"""Generate synthetic trade data for testing (should be replaced with real data)"""
|
|
try:
|
|
import random
|
|
|
|
current_price = self._get_current_price(symbol) or 2500.0
|
|
trades = []
|
|
|
|
# Generate some synthetic trades
|
|
base_time = datetime.now() - timedelta(seconds=window_seconds)
|
|
|
|
for i in range(random.randint(50, 200)): # Random number of trades
|
|
timestamp = base_time + timedelta(seconds=i * (window_seconds / 100))
|
|
|
|
# Random price movement
|
|
price_change = random.uniform(-0.001, 0.001) # ±0.1% max
|
|
price = current_price * (1 + price_change)
|
|
|
|
# Random trade details
|
|
side = random.choice(['buy', 'sell'])
|
|
volume = random.uniform(0.001, 1.0) # Random volume
|
|
volume_usd = price * volume
|
|
|
|
trades.append({
|
|
'timestamp': timestamp,
|
|
'price': price,
|
|
'volume': volume,
|
|
'volume_usd': volume_usd,
|
|
'side': side,
|
|
'is_aggressive': random.choice([True, False])
|
|
})
|
|
|
|
return sorted(trades, key=lambda x: x['timestamp'])
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating synthetic trade data: {e}")
|
|
return []
|
|
|
|
def _analyze_market_microstructure(self, raw_ticks: List[Dict[str, Any]]) -> Dict[str, Any]:
|
|
"""
|
|
Analyze market microstructure from raw tick data
|
|
|
|
Returns comprehensive microstructure analysis including:
|
|
- Bid-ask spread patterns
|
|
- Order book pressure
|
|
- Trade clustering
|
|
- Volume profile analysis
|
|
"""
|
|
try:
|
|
if not raw_ticks:
|
|
return {
|
|
'spread_analysis': {'avg_spread_bps': 0.0, 'spread_volatility': 0.0},
|
|
'order_book_pressure': {'bid_pressure': 0.0, 'ask_pressure': 0.0},
|
|
'trade_clustering': {'cluster_count': 0, 'avg_cluster_size': 0.0},
|
|
'volume_profile': {'total_volume': 0.0, 'volume_imbalance': 0.0},
|
|
'market_regime': 'unknown'
|
|
}
|
|
|
|
# === SPREAD ANALYSIS ===
|
|
spreads = []
|
|
for tick in raw_ticks:
|
|
if 'bid' in tick and 'ask' in tick and tick['bid'] > 0 and tick['ask'] > 0:
|
|
spread_bps = ((tick['ask'] - tick['bid']) / tick['bid']) * 10000
|
|
spreads.append(spread_bps)
|
|
|
|
if spreads:
|
|
avg_spread_bps = np.mean(spreads)
|
|
spread_volatility = np.std(spreads) if len(spreads) > 1 else 0.0
|
|
else:
|
|
avg_spread_bps = spread_volatility = 0.0
|
|
|
|
# === ORDER BOOK PRESSURE ===
|
|
bid_volumes = []
|
|
ask_volumes = []
|
|
|
|
for tick in raw_ticks:
|
|
if 'bid_volume' in tick:
|
|
bid_volumes.append(tick['bid_volume'])
|
|
if 'ask_volume' in tick:
|
|
ask_volumes.append(tick['ask_volume'])
|
|
|
|
if bid_volumes and ask_volumes:
|
|
total_bid_volume = sum(bid_volumes)
|
|
total_ask_volume = sum(ask_volumes)
|
|
total_volume = total_bid_volume + total_ask_volume
|
|
|
|
if total_volume > 0:
|
|
bid_pressure = total_bid_volume / total_volume
|
|
ask_pressure = total_ask_volume / total_volume
|
|
else:
|
|
bid_pressure = ask_pressure = 0.5
|
|
else:
|
|
bid_pressure = ask_pressure = 0.5
|
|
|
|
# === TRADE CLUSTERING ===
|
|
# Analyze clustering of price movements
|
|
price_changes = []
|
|
for i in range(1, len(raw_ticks)):
|
|
if 'price' in raw_ticks[i] and 'price' in raw_ticks[i-1]:
|
|
if raw_ticks[i-1]['price'] > 0:
|
|
change = (raw_ticks[i]['price'] - raw_ticks[i-1]['price']) / raw_ticks[i-1]['price']
|
|
price_changes.append(change)
|
|
|
|
# Simple clustering based on consecutive movements in same direction
|
|
clusters = []
|
|
current_cluster = []
|
|
current_direction = None
|
|
|
|
for change in price_changes:
|
|
direction = 'up' if change > 0 else 'down' if change < 0 else 'flat'
|
|
|
|
if direction == current_direction or current_direction is None:
|
|
current_cluster.append(change)
|
|
current_direction = direction
|
|
else:
|
|
if len(current_cluster) >= 2: # Minimum cluster size
|
|
clusters.append(current_cluster)
|
|
current_cluster = [change]
|
|
current_direction = direction
|
|
|
|
if len(current_cluster) >= 2:
|
|
clusters.append(current_cluster)
|
|
|
|
cluster_count = len(clusters)
|
|
avg_cluster_size = np.mean([len(c) for c in clusters]) if clusters else 0.0
|
|
|
|
# === VOLUME PROFILE ===
|
|
total_volume = sum(tick.get('volume', 0) for tick in raw_ticks)
|
|
|
|
# Calculate volume imbalance (more detailed analysis)
|
|
buy_volume = sum(tick.get('volume', 0) for tick in raw_ticks
|
|
if tick.get('side') == 'buy' or tick.get('price', 0) > tick.get('prev_price', 0))
|
|
sell_volume = total_volume - buy_volume
|
|
|
|
if total_volume > 0:
|
|
volume_imbalance = (buy_volume - sell_volume) / total_volume
|
|
else:
|
|
volume_imbalance = 0.0
|
|
|
|
# === MARKET REGIME DETECTION ===
|
|
if len(price_changes) > 10:
|
|
price_volatility = np.std(price_changes)
|
|
price_trend = np.mean(price_changes)
|
|
|
|
if abs(price_trend) > 2 * price_volatility:
|
|
market_regime = 'trending'
|
|
elif price_volatility > 0.001: # High volatility threshold
|
|
market_regime = 'volatile'
|
|
else:
|
|
market_regime = 'ranging'
|
|
else:
|
|
market_regime = 'unknown'
|
|
|
|
return {
|
|
'spread_analysis': {
|
|
'avg_spread_bps': avg_spread_bps,
|
|
'spread_volatility': spread_volatility
|
|
},
|
|
'order_book_pressure': {
|
|
'bid_pressure': bid_pressure,
|
|
'ask_pressure': ask_pressure
|
|
},
|
|
'trade_clustering': {
|
|
'cluster_count': cluster_count,
|
|
'avg_cluster_size': avg_cluster_size
|
|
},
|
|
'volume_profile': {
|
|
'total_volume': total_volume,
|
|
'volume_imbalance': volume_imbalance
|
|
},
|
|
'market_regime': market_regime
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error analyzing market microstructure: {e}")
|
|
return {
|
|
'spread_analysis': {'avg_spread_bps': 0.0, 'spread_volatility': 0.0},
|
|
'order_book_pressure': {'bid_pressure': 0.0, 'ask_pressure': 0.0},
|
|
'trade_clustering': {'cluster_count': 0, 'avg_cluster_size': 0.0},
|
|
'volume_profile': {'total_volume': 0.0, 'volume_imbalance': 0.0},
|
|
'market_regime': 'unknown'
|
|
}
|
|
|
|
def _calculate_pivot_points_for_rl(self, symbol: str) -> Optional[Dict[str, Any]]:
|
|
"""
|
|
Calculate pivot points for RL feature enhancement
|
|
|
|
Returns pivot point analysis including support/resistance levels,
|
|
pivot strength, and market structure context for the RL model.
|
|
"""
|
|
try:
|
|
# Get recent price data for pivot calculation
|
|
if hasattr(self.data_provider, 'get_recent_ohlcv'):
|
|
recent_data = self.data_provider.get_recent_ohlcv(symbol, '1h', 50)
|
|
else:
|
|
# Fallback to basic price data
|
|
return self._get_basic_pivot_analysis(symbol)
|
|
|
|
if not recent_data or len(recent_data) < 3:
|
|
return None
|
|
|
|
# Convert to DataFrame for easier analysis
|
|
import pandas as pd
|
|
df = pd.DataFrame(recent_data)
|
|
|
|
# Calculate standard pivot points (yesterday's H, L, C)
|
|
if len(df) >= 2:
|
|
prev_high = df['high'].iloc[-2]
|
|
prev_low = df['low'].iloc[-2]
|
|
prev_close = df['close'].iloc[-2]
|
|
|
|
# Standard pivot calculations
|
|
pivot = (prev_high + prev_low + prev_close) / 3
|
|
r1 = (2 * pivot) - prev_low # Resistance 1
|
|
s1 = (2 * pivot) - prev_high # Support 1
|
|
r2 = pivot + (prev_high - prev_low) # Resistance 2
|
|
s2 = pivot - (prev_high - prev_low) # Support 2
|
|
|
|
# Current price context
|
|
current_price = df['close'].iloc[-1]
|
|
|
|
# Calculate pivot strength and position
|
|
price_to_pivot_ratio = current_price / pivot if pivot > 0 else 1.0
|
|
|
|
# Determine current market structure
|
|
if current_price > r1:
|
|
market_bias = 'bullish'
|
|
nearest_level = r2
|
|
level_type = 'resistance'
|
|
elif current_price < s1:
|
|
market_bias = 'bearish'
|
|
nearest_level = s2
|
|
level_type = 'support'
|
|
else:
|
|
market_bias = 'neutral'
|
|
if current_price > pivot:
|
|
nearest_level = r1
|
|
level_type = 'resistance'
|
|
else:
|
|
nearest_level = s1
|
|
level_type = 'support'
|
|
|
|
# Calculate distance to nearest level
|
|
distance_to_level = abs(current_price - nearest_level) / current_price if current_price > 0 else 0.0
|
|
|
|
# Volume-weighted pivot strength
|
|
volume_strength = 1.0
|
|
if 'volume' in df.columns:
|
|
recent_volume = df['volume'].tail(5).mean()
|
|
historical_volume = df['volume'].mean()
|
|
volume_strength = min(2.0, recent_volume / max(1.0, historical_volume))
|
|
|
|
return {
|
|
'pivot_point': pivot,
|
|
'resistance_1': r1,
|
|
'resistance_2': r2,
|
|
'support_1': s1,
|
|
'support_2': s2,
|
|
'current_price': current_price,
|
|
'market_bias': market_bias,
|
|
'nearest_level': nearest_level,
|
|
'level_type': level_type,
|
|
'distance_to_level': distance_to_level,
|
|
'price_to_pivot_ratio': price_to_pivot_ratio,
|
|
'volume_strength': volume_strength,
|
|
'pivot_strength': min(1.0, volume_strength * (1.0 - distance_to_level))
|
|
}
|
|
|
|
else:
|
|
return self._get_basic_pivot_analysis(symbol)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating pivot points for {symbol}: {e}")
|
|
return self._get_basic_pivot_analysis(symbol)
|
|
|
|
def _get_basic_pivot_analysis(self, symbol: str) -> Dict[str, Any]:
|
|
"""Fallback basic pivot analysis when detailed data is unavailable"""
|
|
try:
|
|
current_price = self._get_current_price(symbol) or 2500.0
|
|
|
|
# Create basic pivot structure
|
|
return {
|
|
'pivot_point': current_price,
|
|
'resistance_1': current_price * 1.01,
|
|
'resistance_2': current_price * 1.02,
|
|
'support_1': current_price * 0.99,
|
|
'support_2': current_price * 0.98,
|
|
'current_price': current_price,
|
|
'market_bias': 'neutral',
|
|
'nearest_level': current_price * 1.01,
|
|
'level_type': 'resistance',
|
|
'distance_to_level': 0.01,
|
|
'price_to_pivot_ratio': 1.0,
|
|
'volume_strength': 1.0,
|
|
'pivot_strength': 0.5
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Error in basic pivot analysis for {symbol}: {e}")
|
|
return {
|
|
'pivot_point': 2500.0,
|
|
'resistance_1': 2525.0,
|
|
'resistance_2': 2550.0,
|
|
'support_1': 2475.0,
|
|
'support_2': 2450.0,
|
|
'current_price': 2500.0,
|
|
'market_bias': 'neutral',
|
|
'nearest_level': 2525.0,
|
|
'level_type': 'resistance',
|
|
'distance_to_level': 0.01,
|
|
'price_to_pivot_ratio': 1.0,
|
|
'volume_strength': 1.0,
|
|
'pivot_strength': 0.5
|
|
}
|
|
|
|
# Helper function to safely extract scalar values from tensors
|
|
def _safe_tensor_to_scalar(self, tensor_value, default_value: float = 0.7) -> float:
|
|
"""
|
|
Safely convert tensor/array values to Python scalar floats
|
|
|
|
Args:
|
|
tensor_value: Input tensor, array, or scalar value
|
|
default_value: Default value to return if conversion fails
|
|
|
|
Returns:
|
|
Python float scalar value
|
|
"""
|
|
try:
|
|
if hasattr(tensor_value, 'item'):
|
|
# PyTorch tensor - handle different shapes
|
|
if tensor_value.numel() == 1:
|
|
return float(tensor_value.item())
|
|
else:
|
|
return float(tensor_value.flatten()[0].item())
|
|
elif isinstance(tensor_value, np.ndarray):
|
|
# NumPy array - handle different shapes
|
|
if tensor_value.ndim == 0:
|
|
return float(tensor_value.item())
|
|
elif tensor_value.size == 1:
|
|
return float(tensor_value.flatten()[0])
|
|
else:
|
|
return float(tensor_value.flat[0])
|
|
else:
|
|
# Already a scalar value
|
|
return float(tensor_value)
|
|
except Exception as e:
|
|
logger.warning(f"Error converting tensor to scalar, using default {default_value}: {e}")
|
|
return default_value |