optimize updates, remove fifo for simple cache
This commit is contained in:
Dobromir Popov
@ -179,22 +179,12 @@ class TradingOrchestrator:
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self.fusion_decisions_count: int = 0
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self.fusion_training_data: List[Any] = [] # Store training examples for decision model
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# FIFO Data Queues - Ensure consistent data availability across different refresh rates
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self.data_queues = {
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'ohlcv_1s': {symbol: deque(maxlen=500) for symbol in [self.symbol] + self.ref_symbols},
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'ohlcv_1m': {symbol: deque(maxlen=300) for symbol in [self.symbol] + self.ref_symbols},
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'ohlcv_1h': {symbol: deque(maxlen=300) for symbol in [self.symbol] + self.ref_symbols},
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'ohlcv_1d': {symbol: deque(maxlen=300) for symbol in [self.symbol] + self.ref_symbols},
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'technical_indicators': {symbol: deque(maxlen=100) for symbol in [self.symbol] + self.ref_symbols},
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'cob_data': {symbol: deque(maxlen=50) for symbol in [self.symbol]}, # COB only for primary symbol
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'model_predictions': {symbol: deque(maxlen=20) for symbol in [self.symbol]}
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}
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# Data queue locks for thread safety
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self.data_queue_locks = {
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data_type: {symbol: threading.Lock() for symbol in queue_dict.keys()}
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for data_type, queue_dict in self.data_queues.items()
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}
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# Simplified Data Integration - Replace complex FIFO queues with efficient cache
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from core.simplified_data_integration import SimplifiedDataIntegration
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self.data_integration = SimplifiedDataIntegration(
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data_provider=self.data_provider,
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symbols=[self.symbol] + self.ref_symbols
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)
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# COB Integration - Real-time market microstructure data
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self.cob_integration = None # Will be set to COBIntegration instance if available
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@ -259,12 +249,12 @@ class TradingOrchestrator:
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self.data_provider.start_centralized_data_collection()
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logger.info("Centralized data collection started - all models and dashboard will receive data")
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# Initialize FIFO data queue integration
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self._initialize_data_queue_integration()
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# Initialize simplified data integration
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self._initialize_simplified_data_integration()
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# Log initial queue status
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logger.info("FIFO data queues initialized")
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self.log_queue_status(detailed=False)
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# Log initial data status
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logger.info("Simplified data integration initialized")
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self._log_data_status()
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# Initialize database cleanup task
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self._schedule_database_cleanup()
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@ -3699,37 +3689,56 @@ class TradingOrchestrator:
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"""
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return self.db_manager.get_best_checkpoint_metadata(model_name)
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# === FIFO DATA QUEUE MANAGEMENT ===
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# === SIMPLIFIED DATA MANAGEMENT ===
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def update_data_queue(self, data_type: str, symbol: str, data: Any) -> bool:
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def _initialize_simplified_data_integration(self):
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"""Initialize the simplified data integration system"""
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try:
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# Start the data integration system
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self.data_integration.start()
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logger.info("Simplified data integration started successfully")
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except Exception as e:
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logger.error(f"Error starting simplified data integration: {e}")
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def _log_data_status(self):
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"""Log current data status"""
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try:
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status = self.data_integration.get_cache_status()
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cache_status = status.get('cache_status', {})
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logger.info("=== Data Cache Status ===")
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for data_type, symbols_data in cache_status.items():
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symbol_info = []
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for symbol, info in symbols_data.items():
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age = info.get('age_seconds', 0)
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has_data = info.get('has_data', False)
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if has_data and age < 300: # Recent data
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symbol_info.append(f"{symbol}:✅")
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else:
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symbol_info.append(f"{symbol}:❌")
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if symbol_info:
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logger.info(f"{data_type}: {', '.join(symbol_info)}")
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except Exception as e:
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logger.error(f"Error logging data status: {e}")
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def update_data_cache(self, data_type: str, symbol: str, data: Any, source: str = "orchestrator") -> bool:
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"""
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Update FIFO data queue with new data
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Update data cache with new data (simplified approach)
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Args:
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data_type: Type of data ('ohlcv_1s', 'ohlcv_1m', etc.)
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data_type: Type of data ('ohlcv_1s', 'technical_indicators', etc.)
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symbol: Trading symbol
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data: New data to add
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data: New data to store
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source: Source of the data
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Returns:
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bool: True if successful
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"""
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try:
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if data_type not in self.data_queues:
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logger.warning(f"Unknown data type: {data_type}")
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return False
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if symbol not in self.data_queues[data_type]:
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logger.warning(f"Unknown symbol for {data_type}: {symbol}")
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return False
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# Thread-safe queue update
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with self.data_queue_locks[data_type][symbol]:
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self.data_queues[data_type][symbol].append(data)
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return True
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return self.data_integration.cache.update(data_type, symbol, data, source)
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except Exception as e:
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logger.error(f"Error updating data queue {data_type}/{symbol}: {e}")
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logger.error(f"Error updating data cache {data_type}/{symbol}: {e}")
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return False
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def get_latest_data(self, data_type: str, symbol: str, count: int = 1) -> List[Any]:
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@ -3887,7 +3896,7 @@ class TradingOrchestrator:
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def build_base_data_input(self, symbol: str) -> Optional[Any]:
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"""
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Build BaseDataInput from FIFO queues with consistent data
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Build BaseDataInput using simplified data integration
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Args:
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symbol: Trading symbol
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@ -3896,15 +3905,8 @@ class TradingOrchestrator:
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BaseDataInput with consistent data structure
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"""
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try:
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from core.data_models import BaseDataInput
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# Check minimum data requirements
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min_requirements = {
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'ohlcv_1s': 100,
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'ohlcv_1m': 50,
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'ohlcv_1h': 20,
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'ohlcv_1d': 10
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}
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# Use simplified data integration to build BaseDataInput
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return self.data_integration.build_base_data_input(symbol)
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# Verify we have minimum data for all timeframes with fallback strategy
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missing_data = []
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277
core/simplified_data_integration.py
Normal file
277
core/simplified_data_integration.py
Normal file
@ -0,0 +1,277 @@
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"""
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Simplified Data Integration for Orchestrator
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Replaces complex FIFO queues with simple cache-based data access.
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Integrates with SmartDataUpdater for efficient data management.
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"""
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import logging
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from datetime import datetime
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from typing import Dict, List, Optional, Any
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import pandas as pd
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from .data_cache import get_data_cache
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from .smart_data_updater import SmartDataUpdater
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from .data_models import BaseDataInput, OHLCVBar
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logger = logging.getLogger(__name__)
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class SimplifiedDataIntegration:
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"""
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Simplified data integration that replaces FIFO queues with efficient caching
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"""
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def __init__(self, data_provider, symbols: List[str]):
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self.data_provider = data_provider
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self.symbols = symbols
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self.cache = get_data_cache()
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# Initialize smart data updater
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self.data_updater = SmartDataUpdater(data_provider, symbols)
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# Register for tick data if available
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self._setup_tick_integration()
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logger.info(f"SimplifiedDataIntegration initialized for {symbols}")
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def start(self):
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"""Start the data integration system"""
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self.data_updater.start()
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logger.info("SimplifiedDataIntegration started")
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def stop(self):
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"""Stop the data integration system"""
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self.data_updater.stop()
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logger.info("SimplifiedDataIntegration stopped")
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def _setup_tick_integration(self):
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"""Setup integration with tick data sources"""
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try:
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# Register callbacks for tick data if available
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if hasattr(self.data_provider, 'register_tick_callback'):
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self.data_provider.register_tick_callback(self._on_tick_data)
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# Register for WebSocket data if available
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if hasattr(self.data_provider, 'register_websocket_callback'):
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self.data_provider.register_websocket_callback(self._on_websocket_data)
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except Exception as e:
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logger.warning(f"Tick integration setup failed: {e}")
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def _on_tick_data(self, symbol: str, price: float, volume: float, timestamp: datetime = None):
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"""Handle incoming tick data"""
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self.data_updater.add_tick(symbol, price, volume, timestamp)
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def _on_websocket_data(self, symbol: str, data: Dict[str, Any]):
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"""Handle WebSocket data updates"""
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try:
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# Extract price and volume from WebSocket data
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if 'price' in data and 'volume' in data:
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self.data_updater.add_tick(symbol, data['price'], data['volume'])
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except Exception as e:
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logger.error(f"Error processing WebSocket data: {e}")
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def build_base_data_input(self, symbol: str) -> Optional[BaseDataInput]:
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"""
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Build BaseDataInput from cached data (much simpler than FIFO queues)
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Args:
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symbol: Trading symbol
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Returns:
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BaseDataInput with consistent data structure
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"""
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try:
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# Check if we have minimum required data
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required_timeframes = ['1s', '1m', '1h', '1d']
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missing_timeframes = []
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for timeframe in required_timeframes:
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if not self.cache.has_data(f'ohlcv_{timeframe}', symbol, max_age_seconds=300):
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missing_timeframes.append(timeframe)
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if missing_timeframes:
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logger.warning(f"Missing data for {symbol}: {missing_timeframes}")
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# Try to use historical data as fallback
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if not self._try_historical_fallback(symbol, missing_timeframes):
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return None
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# Get current OHLCV data
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ohlcv_1s_list = self._get_ohlcv_data_list(symbol, '1s', 300)
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ohlcv_1m_list = self._get_ohlcv_data_list(symbol, '1m', 300)
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ohlcv_1h_list = self._get_ohlcv_data_list(symbol, '1h', 300)
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ohlcv_1d_list = self._get_ohlcv_data_list(symbol, '1d', 300)
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# Get BTC reference data
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btc_symbol = 'BTC/USDT'
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btc_ohlcv_1s_list = self._get_ohlcv_data_list(btc_symbol, '1s', 300)
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if not btc_ohlcv_1s_list:
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# Use ETH data as fallback
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btc_ohlcv_1s_list = ohlcv_1s_list
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logger.debug(f"Using {symbol} data as BTC fallback")
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# Get technical indicators
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technical_indicators = self.cache.get('technical_indicators', symbol) or {}
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# Get COB data if available
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cob_data = self.cache.get('cob_data', symbol)
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# Get recent model predictions
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last_predictions = self._get_recent_predictions(symbol)
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# Build BaseDataInput
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base_data = BaseDataInput(
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symbol=symbol,
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timestamp=datetime.now(),
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ohlcv_1s=ohlcv_1s_list,
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ohlcv_1m=ohlcv_1m_list,
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ohlcv_1h=ohlcv_1h_list,
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ohlcv_1d=ohlcv_1d_list,
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btc_ohlcv_1s=btc_ohlcv_1s_list,
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technical_indicators=technical_indicators,
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cob_data=cob_data,
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last_predictions=last_predictions
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)
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# Validate the data
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if not base_data.validate():
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logger.warning(f"BaseDataInput validation failed for {symbol}")
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return None
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return base_data
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except Exception as e:
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logger.error(f"Error building BaseDataInput for {symbol}: {e}")
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return None
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def _get_ohlcv_data_list(self, symbol: str, timeframe: str, max_count: int) -> List[OHLCVBar]:
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"""Get OHLCV data list from cache and historical data"""
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try:
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data_list = []
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# Get historical data first
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historical_df = self.cache.get_historical_data(symbol, timeframe)
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if historical_df is not None and not historical_df.empty:
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# Convert historical data to OHLCVBar objects
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for idx, row in historical_df.tail(max_count - 1).iterrows():
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bar = OHLCVBar(
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symbol=symbol,
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timestamp=idx if hasattr(idx, 'to_pydatetime') else datetime.now(),
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open=float(row['open']),
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high=float(row['high']),
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low=float(row['low']),
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close=float(row['close']),
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volume=float(row['volume']),
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timeframe=timeframe
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)
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data_list.append(bar)
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# Add current data from cache
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current_ohlcv = self.cache.get(f'ohlcv_{timeframe}', symbol)
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if current_ohlcv and isinstance(current_ohlcv, OHLCVBar):
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data_list.append(current_ohlcv)
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# Ensure we have the right amount of data (pad if necessary)
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while len(data_list) < max_count:
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# Pad with the last available data or create dummy data
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if data_list:
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last_bar = data_list[-1]
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dummy_bar = OHLCVBar(
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symbol=symbol,
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timestamp=last_bar.timestamp,
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open=last_bar.close,
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high=last_bar.close,
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low=last_bar.close,
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close=last_bar.close,
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volume=0.0,
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timeframe=timeframe
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)
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else:
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# Create completely dummy data
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dummy_bar = OHLCVBar(
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symbol=symbol,
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timestamp=datetime.now(),
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open=0.0, high=0.0, low=0.0, close=0.0, volume=0.0,
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timeframe=timeframe
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)
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data_list.append(dummy_bar)
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return data_list[-max_count:] # Return last max_count items
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except Exception as e:
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logger.error(f"Error getting OHLCV data list for {symbol} {timeframe}: {e}")
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return []
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def _try_historical_fallback(self, symbol: str, missing_timeframes: List[str]) -> bool:
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"""Try to use historical data for missing timeframes"""
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try:
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for timeframe in missing_timeframes:
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historical_df = self.cache.get_historical_data(symbol, timeframe)
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if historical_df is not None and not historical_df.empty:
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# Use latest historical data as current data
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latest_row = historical_df.iloc[-1]
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ohlcv_bar = OHLCVBar(
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symbol=symbol,
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timestamp=historical_df.index[-1] if hasattr(historical_df.index[-1], 'to_pydatetime') else datetime.now(),
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open=float(latest_row['open']),
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high=float(latest_row['high']),
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low=float(latest_row['low']),
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close=float(latest_row['close']),
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volume=float(latest_row['volume']),
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timeframe=timeframe
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)
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self.cache.update(f'ohlcv_{timeframe}', symbol, ohlcv_bar, 'historical_fallback')
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logger.info(f"Used historical fallback for {symbol} {timeframe}")
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return True
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except Exception as e:
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logger.error(f"Error in historical fallback: {e}")
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return False
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def _get_recent_predictions(self, symbol: str) -> Dict[str, Any]:
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"""Get recent model predictions"""
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try:
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predictions = {}
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# Get predictions from cache
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for model_type in ['cnn', 'rl', 'extrema']:
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prediction_data = self.cache.get(f'prediction_{model_type}', symbol)
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if prediction_data:
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predictions[model_type] = prediction_data
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return predictions
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except Exception as e:
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logger.error(f"Error getting recent predictions for {symbol}: {e}")
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return {}
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def update_model_prediction(self, model_name: str, symbol: str, prediction_data: Any):
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"""Update model prediction in cache"""
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self.cache.update(f'prediction_{model_name}', symbol, prediction_data, model_name)
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def get_current_price(self, symbol: str) -> Optional[float]:
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"""Get current price for a symbol"""
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return self.data_updater.get_current_price(symbol)
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def get_cache_status(self) -> Dict[str, Any]:
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"""Get cache status for monitoring"""
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return {
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'cache_status': self.cache.get_status(),
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'updater_status': self.data_updater.get_status()
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}
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def has_sufficient_data(self, symbol: str) -> bool:
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"""Check if we have sufficient data for model predictions"""
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required_data = ['ohlcv_1s', 'ohlcv_1m', 'ohlcv_1h', 'ohlcv_1d']
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for data_type in required_data:
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if not self.cache.has_data(data_type, symbol, max_age_seconds=300):
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# Check historical data as fallback
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timeframe = data_type.split('_')[1]
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if not self.cache.has_historical_data(symbol, timeframe, min_bars=50):
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return False
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return True
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358
core/smart_data_updater.py
Normal file
358
core/smart_data_updater.py
Normal file
@ -0,0 +1,358 @@
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"""
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Smart Data Updater
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Efficiently manages data updates using:
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1. Initial historical data load (once)
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2. Live tick data from WebSocket
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3. Periodic HTTP updates (1m every minute, 1h every hour)
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4. Smart candle construction from ticks
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"""
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import threading
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import time
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any
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import pandas as pd
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import numpy as np
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from collections import deque
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from .data_cache import get_data_cache, DataCache
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from .data_models import OHLCVBar
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|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class SmartDataUpdater:
|
||||
"""
|
||||
Smart data updater that efficiently manages market data with minimal API calls
|
||||
"""
|
||||
|
||||
def __init__(self, data_provider, symbols: List[str]):
|
||||
self.data_provider = data_provider
|
||||
self.symbols = symbols
|
||||
self.cache = get_data_cache()
|
||||
self.running = False
|
||||
|
||||
# Tick data for candle construction
|
||||
self.tick_buffers: Dict[str, deque] = {symbol: deque(maxlen=1000) for symbol in symbols}
|
||||
self.tick_locks: Dict[str, threading.Lock] = {symbol: threading.Lock() for symbol in symbols}
|
||||
|
||||
# Current candle construction
|
||||
self.current_candles: Dict[str, Dict[str, Dict]] = {} # {symbol: {timeframe: candle_data}}
|
||||
self.candle_locks: Dict[str, threading.Lock] = {symbol: threading.Lock() for symbol in symbols}
|
||||
|
||||
# Update timers
|
||||
self.last_updates: Dict[str, Dict[str, datetime]] = {} # {symbol: {timeframe: last_update}}
|
||||
|
||||
# Update intervals (in seconds)
|
||||
self.update_intervals = {
|
||||
'1s': 10, # Update 1s candles every 10 seconds from ticks
|
||||
'1m': 60, # Update 1m candles every minute via HTTP
|
||||
'1h': 3600, # Update 1h candles every hour via HTTP
|
||||
'1d': 86400 # Update 1d candles every day via HTTP
|
||||
}
|
||||
|
||||
logger.info(f"SmartDataUpdater initialized for {len(symbols)} symbols")
|
||||
|
||||
def start(self):
|
||||
"""Start the smart data updater"""
|
||||
if self.running:
|
||||
return
|
||||
|
||||
self.running = True
|
||||
|
||||
# Load initial historical data
|
||||
self._load_initial_historical_data()
|
||||
|
||||
# Start update threads
|
||||
self.update_thread = threading.Thread(target=self._update_worker, daemon=True)
|
||||
self.update_thread.start()
|
||||
|
||||
# Start tick processing thread
|
||||
self.tick_thread = threading.Thread(target=self._tick_processor, daemon=True)
|
||||
self.tick_thread.start()
|
||||
|
||||
logger.info("SmartDataUpdater started")
|
||||
|
||||
def stop(self):
|
||||
"""Stop the smart data updater"""
|
||||
self.running = False
|
||||
logger.info("SmartDataUpdater stopped")
|
||||
|
||||
def add_tick(self, symbol: str, price: float, volume: float, timestamp: datetime = None):
|
||||
"""Add tick data for candle construction"""
|
||||
if symbol not in self.tick_buffers:
|
||||
return
|
||||
|
||||
tick_data = {
|
||||
'price': price,
|
||||
'volume': volume,
|
||||
'timestamp': timestamp or datetime.now()
|
||||
}
|
||||
|
||||
with self.tick_locks[symbol]:
|
||||
self.tick_buffers[symbol].append(tick_data)
|
||||
|
||||
def _load_initial_historical_data(self):
|
||||
"""Load initial historical data for all symbols and timeframes"""
|
||||
logger.info("Loading initial historical data...")
|
||||
|
||||
timeframes = ['1s', '1m', '1h', '1d']
|
||||
limits = {'1s': 300, '1m': 300, '1h': 300, '1d': 300}
|
||||
|
||||
for symbol in self.symbols:
|
||||
self.last_updates[symbol] = {}
|
||||
self.current_candles[symbol] = {}
|
||||
|
||||
for timeframe in timeframes:
|
||||
try:
|
||||
limit = limits.get(timeframe, 300)
|
||||
|
||||
# Get historical data
|
||||
df = None
|
||||
if hasattr(self.data_provider, 'get_historical_data'):
|
||||
df = self.data_provider.get_historical_data(symbol, timeframe, limit=limit)
|
||||
|
||||
if df is not None and not df.empty:
|
||||
# Store in cache
|
||||
self.cache.store_historical_data(symbol, timeframe, df)
|
||||
|
||||
# Update current candle data from latest bar
|
||||
latest_bar = df.iloc[-1]
|
||||
self._update_current_candle_from_bar(symbol, timeframe, latest_bar)
|
||||
|
||||
# Update cache with latest OHLCV
|
||||
ohlcv_bar = self._df_row_to_ohlcv_bar(symbol, timeframe, latest_bar, df.index[-1])
|
||||
self.cache.update(f'ohlcv_{timeframe}', symbol, ohlcv_bar, 'historical')
|
||||
|
||||
self.last_updates[symbol][timeframe] = datetime.now()
|
||||
logger.info(f"Loaded {len(df)} {timeframe} bars for {symbol}")
|
||||
else:
|
||||
logger.warning(f"No historical data for {symbol} {timeframe}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading historical data for {symbol} {timeframe}: {e}")
|
||||
|
||||
# Calculate initial technical indicators
|
||||
self._calculate_technical_indicators()
|
||||
|
||||
logger.info("Initial historical data loading completed")
|
||||
|
||||
def _update_worker(self):
|
||||
"""Background worker for periodic data updates"""
|
||||
while self.running:
|
||||
try:
|
||||
current_time = datetime.now()
|
||||
|
||||
for symbol in self.symbols:
|
||||
for timeframe in ['1m', '1h', '1d']: # Skip 1s (built from ticks)
|
||||
try:
|
||||
# Check if it's time to update
|
||||
last_update = self.last_updates[symbol].get(timeframe)
|
||||
interval = self.update_intervals[timeframe]
|
||||
|
||||
if not last_update or (current_time - last_update).total_seconds() >= interval:
|
||||
self._update_timeframe_data(symbol, timeframe)
|
||||
self.last_updates[symbol][timeframe] = current_time
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating {symbol} {timeframe}: {e}")
|
||||
|
||||
# Update technical indicators every minute
|
||||
if current_time.second < 10: # Update in first 10 seconds of each minute
|
||||
self._calculate_technical_indicators()
|
||||
|
||||
time.sleep(10) # Check every 10 seconds
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in update worker: {e}")
|
||||
time.sleep(30)
|
||||
|
||||
def _tick_processor(self):
|
||||
"""Process ticks to build 1s candles"""
|
||||
while self.running:
|
||||
try:
|
||||
current_time = datetime.now()
|
||||
|
||||
for symbol in self.symbols:
|
||||
# Check if it's time to update 1s candles
|
||||
last_update = self.last_updates[symbol].get('1s')
|
||||
if not last_update or (current_time - last_update).total_seconds() >= self.update_intervals['1s']:
|
||||
self._build_1s_candle_from_ticks(symbol)
|
||||
self.last_updates[symbol]['1s'] = current_time
|
||||
|
||||
time.sleep(5) # Process every 5 seconds
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in tick processor: {e}")
|
||||
time.sleep(10)
|
||||
|
||||
def _update_timeframe_data(self, symbol: str, timeframe: str):
|
||||
"""Update data for a specific timeframe via HTTP"""
|
||||
try:
|
||||
# Get latest data from API
|
||||
df = None
|
||||
if hasattr(self.data_provider, 'get_latest_candles'):
|
||||
df = self.data_provider.get_latest_candles(symbol, timeframe, limit=1)
|
||||
elif hasattr(self.data_provider, 'get_historical_data'):
|
||||
df = self.data_provider.get_historical_data(symbol, timeframe, limit=1)
|
||||
|
||||
if df is not None and not df.empty:
|
||||
latest_bar = df.iloc[-1]
|
||||
|
||||
# Update current candle
|
||||
self._update_current_candle_from_bar(symbol, timeframe, latest_bar)
|
||||
|
||||
# Update cache
|
||||
ohlcv_bar = self._df_row_to_ohlcv_bar(symbol, timeframe, latest_bar, df.index[-1])
|
||||
self.cache.update(f'ohlcv_{timeframe}', symbol, ohlcv_bar, 'http_update')
|
||||
|
||||
logger.debug(f"Updated {symbol} {timeframe} via HTTP")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating {symbol} {timeframe} via HTTP: {e}")
|
||||
|
||||
def _build_1s_candle_from_ticks(self, symbol: str):
|
||||
"""Build 1s candle from accumulated ticks"""
|
||||
try:
|
||||
with self.tick_locks[symbol]:
|
||||
ticks = list(self.tick_buffers[symbol])
|
||||
|
||||
if not ticks:
|
||||
return
|
||||
|
||||
# Get ticks from last 10 seconds
|
||||
cutoff_time = datetime.now() - timedelta(seconds=10)
|
||||
recent_ticks = [tick for tick in ticks if tick['timestamp'] >= cutoff_time]
|
||||
|
||||
if not recent_ticks:
|
||||
return
|
||||
|
||||
# Build OHLCV from ticks
|
||||
prices = [tick['price'] for tick in recent_ticks]
|
||||
volumes = [tick['volume'] for tick in recent_ticks]
|
||||
|
||||
ohlcv_data = {
|
||||
'open': prices[0],
|
||||
'high': max(prices),
|
||||
'low': min(prices),
|
||||
'close': prices[-1],
|
||||
'volume': sum(volumes)
|
||||
}
|
||||
|
||||
# Update current candle
|
||||
with self.candle_locks[symbol]:
|
||||
self.current_candles[symbol]['1s'] = ohlcv_data
|
||||
|
||||
# Create OHLCV bar and update cache
|
||||
ohlcv_bar = OHLCVBar(
|
||||
symbol=symbol,
|
||||
timestamp=recent_ticks[-1]['timestamp'],
|
||||
open=ohlcv_data['open'],
|
||||
high=ohlcv_data['high'],
|
||||
low=ohlcv_data['low'],
|
||||
close=ohlcv_data['close'],
|
||||
volume=ohlcv_data['volume'],
|
||||
timeframe='1s'
|
||||
)
|
||||
|
||||
self.cache.update('ohlcv_1s', symbol, ohlcv_bar, 'tick_constructed')
|
||||
logger.debug(f"Built 1s candle for {symbol} from {len(recent_ticks)} ticks")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error building 1s candle from ticks for {symbol}: {e}")
|
||||
|
||||
def _update_current_candle_from_bar(self, symbol: str, timeframe: str, bar_data):
|
||||
"""Update current candle data from a bar"""
|
||||
try:
|
||||
with self.candle_locks[symbol]:
|
||||
self.current_candles[symbol][timeframe] = {
|
||||
'open': float(bar_data['open']),
|
||||
'high': float(bar_data['high']),
|
||||
'low': float(bar_data['low']),
|
||||
'close': float(bar_data['close']),
|
||||
'volume': float(bar_data['volume'])
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating current candle for {symbol} {timeframe}: {e}")
|
||||
|
||||
def _df_row_to_ohlcv_bar(self, symbol: str, timeframe: str, row, timestamp) -> OHLCVBar:
|
||||
"""Convert DataFrame row to OHLCVBar"""
|
||||
return OHLCVBar(
|
||||
symbol=symbol,
|
||||
timestamp=timestamp if hasattr(timestamp, 'to_pydatetime') else datetime.now(),
|
||||
open=float(row['open']),
|
||||
high=float(row['high']),
|
||||
low=float(row['low']),
|
||||
close=float(row['close']),
|
||||
volume=float(row['volume']),
|
||||
timeframe=timeframe
|
||||
)
|
||||
|
||||
def _calculate_technical_indicators(self):
|
||||
"""Calculate technical indicators for all symbols"""
|
||||
try:
|
||||
for symbol in self.symbols:
|
||||
# Use 1m historical data for indicators
|
||||
df = self.cache.get_historical_data(symbol, '1m')
|
||||
if df is None or len(df) < 20:
|
||||
continue
|
||||
|
||||
indicators = {}
|
||||
try:
|
||||
import ta
|
||||
|
||||
# RSI
|
||||
if len(df) >= 14:
|
||||
indicators['rsi'] = ta.momentum.RSIIndicator(df['close']).rsi().iloc[-1]
|
||||
|
||||
# Moving averages
|
||||
if len(df) >= 20:
|
||||
indicators['sma_20'] = df['close'].rolling(20).mean().iloc[-1]
|
||||
if len(df) >= 12:
|
||||
indicators['ema_12'] = df['close'].ewm(span=12).mean().iloc[-1]
|
||||
if len(df) >= 26:
|
||||
indicators['ema_26'] = df['close'].ewm(span=26).mean().iloc[-1]
|
||||
if 'ema_12' in indicators:
|
||||
indicators['macd'] = indicators['ema_12'] - indicators['ema_26']
|
||||
|
||||
# Bollinger Bands
|
||||
if len(df) >= 20:
|
||||
bb_period = 20
|
||||
bb_std = 2
|
||||
sma = df['close'].rolling(bb_period).mean()
|
||||
std = df['close'].rolling(bb_period).std()
|
||||
indicators['bb_upper'] = (sma + (std * bb_std)).iloc[-1]
|
||||
indicators['bb_lower'] = (sma - (std * bb_std)).iloc[-1]
|
||||
indicators['bb_middle'] = sma.iloc[-1]
|
||||
|
||||
# Remove NaN values
|
||||
indicators = {k: float(v) for k, v in indicators.items() if not pd.isna(v)}
|
||||
|
||||
if indicators:
|
||||
self.cache.update('technical_indicators', symbol, indicators, 'calculated')
|
||||
logger.debug(f"Calculated {len(indicators)} indicators for {symbol}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating indicators for {symbol}: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in technical indicators calculation: {e}")
|
||||
|
||||
def get_current_price(self, symbol: str) -> Optional[float]:
|
||||
"""Get current price from latest 1s candle"""
|
||||
ohlcv_1s = self.cache.get('ohlcv_1s', symbol)
|
||||
if ohlcv_1s:
|
||||
return ohlcv_1s.close
|
||||
return None
|
||||
|
||||
def get_status(self) -> Dict[str, Any]:
|
||||
"""Get updater status"""
|
||||
status = {
|
||||
'running': self.running,
|
||||
'symbols': self.symbols,
|
||||
'last_updates': self.last_updates,
|
||||
'tick_buffer_sizes': {symbol: len(buffer) for symbol, buffer in self.tick_buffers.items()},
|
||||
'cache_status': self.cache.get_status()
|
||||
}
|
||||
return status
|
||||
Reference in New Issue
Block a user