3519 lines
157 KiB
Python
3519 lines
157 KiB
Python
"""
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Multi-Timeframe, Multi-Symbol Data Provider
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CRITICAL POLICY: NO SYNTHETIC DATA ALLOWED
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This module MUST ONLY use real market data from exchanges.
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NEVER use np.random.*, mock/fake/synthetic data, or placeholder values.
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If data is unavailable: return None/0/empty, log errors, raise exceptions.
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See: reports/REAL_MARKET_DATA_POLICY.md
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This module consolidates all data functionality including:
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- Historical data fetching from Binance API
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- Real-time data streaming via WebSocket
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- Multi-timeframe candle generation
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- Caching and data management
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- Technical indicators calculation
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- Williams Market Structure pivot points with monthly data analysis
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- Pivot-based feature normalization for improved model training
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- Centralized data distribution to multiple subscribers (AI models, dashboard, etc.)
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"""
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import asyncio
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import json
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import logging
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import os
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import time
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import uuid
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import websockets
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import requests
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import pandas as pd
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import numpy as np
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import pickle
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Any, Callable
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from dataclasses import dataclass, field
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import ta
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import warnings
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from threading import Thread, Lock
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from collections import deque
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import math
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# Suppress ta library deprecation warnings
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warnings.filterwarnings("ignore", category=FutureWarning, module="ta")
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# Import timezone utilities
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from utils.timezone_utils import (
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normalize_timestamp, normalize_dataframe_timestamps, normalize_dataframe_index,
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now_system, now_utc, to_sofia, UTC, SOFIA_TZ, log_timezone_info
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)
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from .config import get_config
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from .tick_aggregator import RealTimeTickAggregator, RawTick, OHLCVBar
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from .cnn_monitor import log_cnn_prediction
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from .williams_market_structure import WilliamsMarketStructure, PivotPoint, TrendLevel
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from .enhanced_cob_websocket import EnhancedCOBWebSocket, get_enhanced_cob_websocket
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from .huobi_cob_websocket import get_huobi_cob_websocket
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from .cob_integration import COBIntegration
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logger = logging.getLogger(__name__)
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@dataclass
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class PivotBounds:
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"""Pivot-based normalization bounds derived from Williams Market Structure"""
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symbol: str
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price_max: float
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price_min: float
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volume_max: float
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volume_min: float
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pivot_support_levels: List[float]
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pivot_resistance_levels: List[float]
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pivot_context: Dict[str, Any]
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created_timestamp: datetime
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data_period_start: datetime
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data_period_end: datetime
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total_candles_analyzed: int
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def get_price_range(self) -> float:
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"""Get price range for normalization"""
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return self.price_max - self.price_min
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def normalize_price(self, price: float) -> float:
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"""Normalize price using pivot bounds"""
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return (price - self.price_min) / self.get_price_range()
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def get_nearest_support_distance(self, current_price: float) -> float:
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"""Get distance to nearest support level (normalized)"""
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if not self.pivot_support_levels:
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return 0.5
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distances = [abs(current_price - s) for s in self.pivot_support_levels]
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return min(distances) / self.get_price_range()
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def get_nearest_resistance_distance(self, current_price: float) -> float:
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"""Get distance to nearest resistance level (normalized)"""
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if not self.pivot_resistance_levels:
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return 0.5
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distances = [abs(current_price - r) for r in self.pivot_resistance_levels]
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return min(distances) / self.get_price_range()
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@dataclass
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class SimplePivotLevel:
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"""Simple pivot level structure for fallback pivot detection"""
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swing_points: List[Any] = field(default_factory=list)
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support_levels: List[float] = field(default_factory=list)
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resistance_levels: List[float] = field(default_factory=list)
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@dataclass
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class MarketTick:
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"""Standardized market tick data structure"""
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symbol: str
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timestamp: datetime
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price: float
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volume: float
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quantity: float
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side: str # 'buy' or 'sell'
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trade_id: str
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is_buyer_maker: bool
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raw_data: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DataSubscriber:
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"""Data subscriber information"""
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subscriber_id: str
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callback: Callable[[MarketTick], None]
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symbols: List[str]
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active: bool = True
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last_update: datetime = field(default_factory=datetime.now)
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tick_count: int = 0
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subscriber_name: str = "unknown"
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class DataProvider:
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"""Unified data provider for historical and real-time market data with centralized distribution"""
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def __init__(self, symbols: List[str] = None, timeframes: List[str] = None):
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"""Initialize the data provider"""
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self.config = get_config()
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# Fixed symbols and timeframes for caching
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self.symbols = ['ETH/USDT', 'BTC/USDT']
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self.timeframes = ['1s', '1m', '1h', '1d']
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# Cache settings (initialize first)
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self.cache_enabled = True
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self.cache_dir = Path('cache')
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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# Data storage - cached OHLCV data (1500 candles each)
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self.cached_data = {} # {symbol: {timeframe: DataFrame}}
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self.real_time_data = {} # {symbol: {timeframe: deque}}
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self.current_prices = {} # {symbol: float}
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# Live price cache for low-latency price updates
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self.live_price_cache: Dict[str, Tuple[float, datetime]] = {}
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self.live_price_cache_ttl = timedelta(milliseconds=500)
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# Initialize cached data structure
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for symbol in self.symbols:
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self.cached_data[symbol] = {}
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for timeframe in self.timeframes:
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self.cached_data[symbol][timeframe] = pd.DataFrame()
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# Pivot-based normalization system
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self.pivot_bounds: Dict[str, PivotBounds] = {} # {symbol: PivotBounds}
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self.pivot_cache_dir = self.cache_dir / 'pivot_bounds'
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self.pivot_cache_dir.mkdir(parents=True, exist_ok=True)
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self.pivot_refresh_interval = timedelta(days=1) # Refresh pivot bounds daily
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self.monthly_data_cache_dir = self.cache_dir / 'monthly_1s_data'
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self.monthly_data_cache_dir.mkdir(parents=True, exist_ok=True)
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# Enhanced WebSocket integration
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self.enhanced_cob_websocket: Optional[EnhancedCOBWebSocket] = None
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self.websocket_tasks = {}
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# COB collection state guard to prevent duplicate starts
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self._cob_started: bool = False
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# Ensure COB collection is started so BaseDataInput includes real order book data
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try:
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self.start_cob_collection()
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except Exception as _cob_init_ex:
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logger.error(f"Failed to start COB collection at init: {_cob_init_ex}")
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self.is_streaming = False
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self.data_lock = Lock()
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# COB data from enhanced WebSocket
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self.cob_websocket_data: Dict[str, Dict] = {} # Latest COB data from WebSocket
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self.cob_websocket_status: Dict[str, str] = {} # WebSocket status per symbol
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# Subscriber management for centralized data distribution
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self.subscribers: Dict[str, DataSubscriber] = {}
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self.subscriber_lock = Lock()
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self.tick_buffers: Dict[str, deque] = {}
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self.buffer_size = 1000 # Keep last 1000 ticks per symbol
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# Initialize tick buffers
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for symbol in self.symbols:
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binance_symbol = symbol.replace('/', '').upper()
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self.tick_buffers[binance_symbol] = deque(maxlen=self.buffer_size)
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# BOM (Book of Market) data caching - 1s resolution for last 5 minutes
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self.bom_cache_duration = 300 # 5 minutes in seconds
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self.bom_feature_count = 120 # Number of BOM features per timestamp
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self.bom_data_cache: Dict[str, deque] = {} # {symbol: deque of (timestamp, bom_features)}
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# Initialize BOM cache for each symbol
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for symbol in self.symbols:
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# Store 300 seconds worth of 1s BOM data
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self.bom_data_cache[symbol] = deque(maxlen=self.bom_cache_duration)
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# Initialize tick aggregator for raw tick processing
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binance_symbols = [symbol.replace('/', '').upper() for symbol in self.symbols]
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self.tick_aggregator = RealTimeTickAggregator(symbols=binance_symbols)
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# Raw tick and OHLCV bar callbacks
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self.raw_tick_callbacks = []
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self.ohlcv_bar_callbacks = []
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# Performance tracking for subscribers
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self.distribution_stats = {
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'total_ticks_received': 0,
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'total_ticks_distributed': 0,
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'distribution_errors': 0,
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'last_tick_time': {},
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'ticks_per_symbol': {symbol.replace('/', '').upper(): 0 for symbol in self.symbols},
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'raw_ticks_processed': 0,
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'ohlcv_bars_created': 0,
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'patterns_detected': 0
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}
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# Data validation
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self.last_prices = {symbol.replace('/', '').upper(): 0.0 for symbol in self.symbols}
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self.price_change_threshold = 0.1 # 10% price change threshold for validation
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# Timeframe conversion
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self.timeframe_seconds = {
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'1s': 1, '1m': 60, '5m': 300, '15m': 900, '30m': 1800,
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'1h': 3600, '4h': 14400, '1d': 86400
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}
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# Williams Market Structure integration
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self.williams_structure: Dict[str, WilliamsMarketStructure] = {}
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for symbol in self.symbols:
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self.williams_structure[symbol] = WilliamsMarketStructure(min_pivot_distance=3)
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# Pivot point caching
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self.pivot_points_cache: Dict[str, Dict[int, TrendLevel]] = {} # {symbol: {level: TrendLevel}}
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self.last_pivot_calculation: Dict[str, datetime] = {}
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self.pivot_calculation_interval = timedelta(minutes=5) # Recalculate every 5 minutes
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# Auto-fix corrupted cache files on startup
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self._auto_fix_corrupted_cache()
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# Load existing pivot bounds from cache
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self._load_all_pivot_bounds()
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# COB (Consolidated Order Book) data system using WebSocket
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self.cob_integration: Optional[COBIntegration] = None
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# COB data storage - 30 minutes of raw ticks and 1s aggregated data
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self.cob_raw_ticks: Dict[str, deque] = {} # Raw COB ticks (30 min)
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self.cob_1s_aggregated: Dict[str, deque] = {} # 1s aggregated COB data with $1 buckets (30 min)
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# Initialize COB data structures
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for symbol in self.symbols:
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# Raw ticks: ~100 ticks/second * 30 minutes = ~180,000 ticks
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self.cob_raw_ticks[symbol] = deque(maxlen=180000)
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# 1s aggregated: 30 minutes = 1,800 seconds
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self.cob_1s_aggregated[symbol] = deque(maxlen=1800)
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# COB callbacks for data distribution
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self.cob_data_callbacks: List[Callable] = []
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self.cob_aggregated_callbacks: List[Callable] = []
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# Training data collection (simplified)
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self.training_data_cache: Dict[str, deque] = {}
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self.training_data_callbacks: List[Callable] = []
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self.model_prediction_callbacks: List[Callable] = []
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# Initialize training data cache
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for symbol in self.symbols:
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binance_symbol = symbol.replace('/', '').upper()
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self.training_data_cache[binance_symbol] = deque(maxlen=1000)
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# Data collection threads
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self.data_collection_active = False
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# COB data collection
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self.cob_collection_active = False
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self.cob_collection_thread = None
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# Training data collection
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self.training_data_collection_active = False
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self.training_data_thread = None
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# Price-level bucketing
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self.bucketed_cob_data: Dict[str, Dict] = {}
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self.bucket_sizes = [1, 10] # $1 and $10 buckets
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self.bucketed_cob_callbacks: Dict[int, List[Callable]] = {size: [] for size in self.bucket_sizes}
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# Automatic data maintenance
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self.data_maintenance_active = False
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self.data_maintenance_thread = None
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# Timeframe intervals in seconds for automatic updates
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self.timeframe_intervals = {
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'1s': 1,
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'1m': 60,
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'1h': 3600,
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'1d': 86400
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}
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logger.info(f"DataProvider initialized for symbols: {self.symbols}")
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logger.info(f"Timeframes: {self.timeframes}")
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logger.info("Automatic data maintenance enabled")
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logger.info("Centralized data distribution enabled")
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logger.info("Pivot-based normalization system enabled")
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logger.info("Williams Market Structure integration enabled")
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logger.info("COB and training data collection enabled")
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# Rate limiting
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self.last_request_time = {}
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self.request_interval = 0.5 # 500ms between requests to avoid rate limits
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self.retry_delay = 60 # 1 minute retry delay for 451 errors
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self.max_retries = 3
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# Start automatic data maintenance
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self.start_automatic_data_maintenance()
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# Start COB WebSocket integration
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self.start_cob_websocket_integration()
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def start_automatic_data_maintenance(self):
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"""Start automatic data maintenance system"""
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if self.data_maintenance_active:
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logger.warning("Data maintenance already active")
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return
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self.data_maintenance_active = True
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self.data_maintenance_thread = Thread(target=self._data_maintenance_worker, daemon=True)
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self.data_maintenance_thread.start()
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logger.info("Automatic data maintenance started")
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def stop_automatic_data_maintenance(self):
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"""Stop automatic data maintenance system"""
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self.data_maintenance_active = False
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if self.data_maintenance_thread and self.data_maintenance_thread.is_alive():
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self.data_maintenance_thread.join(timeout=5)
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logger.info("Automatic data maintenance stopped")
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def _data_maintenance_worker(self):
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"""Worker thread for automatic data maintenance"""
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logger.info("Data maintenance worker started")
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# Initial data load
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self._initial_data_load()
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# Track last update times for each symbol/timeframe
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last_updates = {}
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for symbol in self.symbols:
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last_updates[symbol] = {}
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for timeframe in self.timeframes:
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last_updates[symbol][timeframe] = 0
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while self.data_maintenance_active:
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try:
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current_time = time.time()
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# Check each symbol/timeframe for updates
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for symbol in self.symbols:
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for timeframe in self.timeframes:
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interval = self.timeframe_intervals[timeframe]
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half_interval = interval / 2
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# Update every half candle period
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if current_time - last_updates[symbol][timeframe] >= half_interval:
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self._update_cached_data(symbol, timeframe)
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last_updates[symbol][timeframe] = current_time
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# Sleep for 1 second before next check
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time.sleep(1)
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except Exception as e:
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logger.error(f"Error in data maintenance worker: {e}")
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time.sleep(10) # Wait longer on error
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def _initial_data_load(self):
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"""Load initial 1500 candles for each symbol/timeframe"""
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logger.info("Starting initial data load (1500 candles each)")
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for symbol in self.symbols:
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for timeframe in self.timeframes:
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try:
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logger.info(f"Loading initial data for {symbol} {timeframe}")
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df = self._fetch_from_binance(symbol, timeframe, 1500)
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if df is None or df.empty:
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logger.warning(f"Binance failed for {symbol} {timeframe}, trying MEXC")
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df = self._fetch_from_mexc(symbol, timeframe, 1500)
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if df is not None and not df.empty:
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# Ensure proper datetime index
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df = self._ensure_datetime_index(df)
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# Store in cached data
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self.cached_data[symbol][timeframe] = df
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logger.info(f"Loaded {len(df)} candles for {symbol} {timeframe}")
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else:
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logger.error(f"Failed to load initial data for {symbol} {timeframe}")
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# Rate limiting between requests
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time.sleep(0.5)
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except Exception as e:
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logger.error(f"Error loading initial data for {symbol} {timeframe}: {e}")
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logger.info("Initial data load completed")
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def _update_cached_data(self, symbol: str, timeframe: str):
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"""Update cached data by fetching last 2 candles"""
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try:
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# Fetch last 2 candles
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df = self._fetch_from_binance(symbol, timeframe, 2)
|
|
|
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if df is None or df.empty:
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df = self._fetch_from_mexc(symbol, timeframe, 2)
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|
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if df is not None and not df.empty:
|
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# Ensure proper datetime index
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df = self._ensure_datetime_index(df)
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|
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# Get existing cached data
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existing_df = self.cached_data[symbol][timeframe]
|
|
|
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if not existing_df.empty:
|
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# Merge new data with existing, avoiding duplicates
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combined_df = pd.concat([existing_df, df], ignore_index=False)
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combined_df = combined_df[~combined_df.index.duplicated(keep='last')]
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combined_df = combined_df.sort_index()
|
|
|
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# Keep only last 1500 candles
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self.cached_data[symbol][timeframe] = combined_df.tail(1500)
|
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else:
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self.cached_data[symbol][timeframe] = df
|
|
|
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logger.debug(f"Updated cached data for {symbol} {timeframe}: {len(self.cached_data[symbol][timeframe])} candles")
|
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|
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except Exception as e:
|
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logger.debug(f"Error updating cached data for {symbol} {timeframe}: {e}")
|
|
|
|
def start_cob_websocket_integration(self):
|
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"""Start COB WebSocket integration using COBIntegration class"""
|
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try:
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logger.info("Starting COB WebSocket integration")
|
|
|
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# Initialize COB integration
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self.cob_integration = COBIntegration(data_provider=self, symbols=self.symbols)
|
|
|
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# Add callback for COB data
|
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self.cob_integration.add_dashboard_callback(self._on_cob_websocket_update)
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|
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# Start COB integration in background thread
|
|
cob_thread = Thread(target=self._run_cob_integration, daemon=True)
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cob_thread.start()
|
|
|
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# Start 1s aggregation worker
|
|
aggregation_thread = Thread(target=self._cob_aggregation_worker, daemon=True)
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aggregation_thread.start()
|
|
|
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logger.info("COB WebSocket integration started")
|
|
|
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except Exception as e:
|
|
logger.error(f"Error starting COB WebSocket integration: {e}")
|
|
|
|
def _run_cob_integration(self):
|
|
"""Run COB integration in asyncio event loop"""
|
|
try:
|
|
# Create new event loop for this thread
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
|
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# Run COB integration
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|
loop.run_until_complete(self.cob_integration.start())
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error running COB integration: {e}")
|
|
|
|
def _on_cob_websocket_update(self, symbol: str, cob_data: Dict):
|
|
"""Handle COB updates from WebSocket"""
|
|
try:
|
|
# Extract the actual COB data from the wrapper
|
|
if 'data' in cob_data:
|
|
actual_data = cob_data['data']
|
|
else:
|
|
actual_data = cob_data
|
|
|
|
# Create raw tick entry
|
|
raw_tick = {
|
|
'symbol': symbol,
|
|
'timestamp': now_system(), # Use system timezone consistently
|
|
'bids': actual_data.get('bids', [])[:50], # Top 50 levels
|
|
'asks': actual_data.get('asks', [])[:50], # Top 50 levels
|
|
'stats': actual_data.get('stats', {}),
|
|
'source': 'websocket'
|
|
}
|
|
|
|
# Store raw tick
|
|
self.cob_raw_ticks[symbol].append(raw_tick)
|
|
|
|
# Distribute to raw COB callbacks
|
|
for callback in self.cob_data_callbacks:
|
|
try:
|
|
callback(symbol, raw_tick)
|
|
except Exception as e:
|
|
logger.error(f"Error in COB callback: {e}")
|
|
|
|
logger.debug(f"Processed COB WebSocket update for {symbol}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing COB WebSocket update for {symbol}: {e}")
|
|
|
|
def _cob_aggregation_worker(self):
|
|
"""Worker thread for 1s COB aggregation with $1 price buckets"""
|
|
logger.info("Starting COB 1s aggregation worker")
|
|
|
|
# Track last aggregation time for each symbol
|
|
last_aggregation = {symbol: 0 for symbol in self.symbols}
|
|
|
|
while True:
|
|
try:
|
|
current_time = time.time()
|
|
current_second = int(current_time)
|
|
|
|
# Process each symbol
|
|
for symbol in self.symbols:
|
|
# Aggregate every second
|
|
if current_second > last_aggregation[symbol]:
|
|
self._aggregate_cob_1s(symbol, current_second - 1)
|
|
last_aggregation[symbol] = current_second
|
|
|
|
# Sleep until next second boundary
|
|
sleep_time = 1.0 - (current_time % 1.0)
|
|
time.sleep(max(0.1, sleep_time))
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in COB aggregation worker: {e}")
|
|
time.sleep(1)
|
|
|
|
def _aggregate_cob_1s(self, symbol: str, target_second: int):
|
|
"""Aggregate COB data for 1 second with $1 price buckets and multi-timeframe imbalances"""
|
|
try:
|
|
# Get raw ticks for the target second
|
|
target_ticks = []
|
|
|
|
# FIXED: Create a copy of the deque to avoid mutation during iteration
|
|
if symbol in self.cob_raw_ticks:
|
|
# Create a safe copy of the deque to iterate over
|
|
ticks_copy = list(self.cob_raw_ticks[symbol])
|
|
|
|
for tick in ticks_copy:
|
|
tick_timestamp = tick['timestamp']
|
|
|
|
# Handle both datetime and float timestamps
|
|
if isinstance(tick_timestamp, datetime):
|
|
tick_time = tick_timestamp.timestamp()
|
|
else:
|
|
tick_time = float(tick_timestamp)
|
|
|
|
# Check if tick is in target second
|
|
if target_second <= tick_time < target_second + 1:
|
|
target_ticks.append(tick)
|
|
|
|
if not target_ticks:
|
|
return
|
|
|
|
# Aggregate the ticks with $1 price buckets
|
|
aggregated_data = self._create_1s_cob_aggregation(symbol, target_ticks, target_second)
|
|
|
|
# Add multi-timeframe imbalance calculations
|
|
aggregated_data = self._add_multi_timeframe_imbalances(symbol, aggregated_data, target_second)
|
|
|
|
# Store aggregated data
|
|
self.cob_1s_aggregated[symbol].append(aggregated_data)
|
|
|
|
# Distribute to aggregated COB callbacks
|
|
for callback in self.cob_aggregated_callbacks:
|
|
try:
|
|
callback(symbol, aggregated_data)
|
|
except Exception as e:
|
|
logger.error(f"Error in COB aggregated callback: {e}")
|
|
|
|
logger.debug(f"Aggregated {len(target_ticks)} COB ticks for {symbol} at second {target_second}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error aggregating COB 1s for {symbol}: {e}")
|
|
|
|
def _add_multi_timeframe_imbalances(self, symbol: str, aggregated_data: Dict, current_second: int) -> Dict:
|
|
"""Add COB-based order book imbalances with configurable price ranges"""
|
|
try:
|
|
# Get price range based on symbol
|
|
price_range = self._get_price_range_for_symbol(symbol)
|
|
|
|
# Get latest COB data for current imbalance calculation
|
|
latest_cob = self.get_latest_cob_data(symbol)
|
|
current_imbalance = 0.0
|
|
|
|
if latest_cob:
|
|
current_imbalance = self._calculate_cob_imbalance(latest_cob, price_range)
|
|
|
|
# Get historical COB data for timeframe calculations
|
|
# FIXED: Create a safe copy to avoid deque mutation during iteration
|
|
historical_cob_data = []
|
|
if symbol in self.cob_raw_ticks:
|
|
try:
|
|
historical_cob_data = list(self.cob_raw_ticks[symbol])
|
|
except Exception as e:
|
|
logger.debug(f"Error copying COB raw ticks for {symbol}: {e}")
|
|
historical_cob_data = []
|
|
|
|
# Calculate imbalances for different timeframes using COB data
|
|
imbalances = {
|
|
'imbalance_1s': current_imbalance, # Current COB imbalance
|
|
'imbalance_5s': self._calculate_timeframe_cob_imbalance(historical_cob_data, 5, price_range),
|
|
'imbalance_15s': self._calculate_timeframe_cob_imbalance(historical_cob_data, 15, price_range),
|
|
'imbalance_60s': self._calculate_timeframe_cob_imbalance(historical_cob_data, 60, price_range)
|
|
}
|
|
|
|
# Add volume-weighted imbalances within price range
|
|
volume_imbalances = {
|
|
'volume_imbalance_1s': current_imbalance,
|
|
'volume_imbalance_5s': self._calculate_volume_weighted_imbalance(historical_cob_data, 5, price_range),
|
|
'volume_imbalance_15s': self._calculate_volume_weighted_imbalance(historical_cob_data, 15, price_range),
|
|
'volume_imbalance_60s': self._calculate_volume_weighted_imbalance(historical_cob_data, 60, price_range)
|
|
}
|
|
|
|
# Combine all imbalance metrics
|
|
all_imbalances = {**imbalances, **volume_imbalances}
|
|
|
|
# Add to aggregated data
|
|
aggregated_data.update(all_imbalances)
|
|
|
|
# Also add to stats section for compatibility
|
|
if 'stats' not in aggregated_data:
|
|
aggregated_data['stats'] = {}
|
|
aggregated_data['stats'].update(all_imbalances)
|
|
|
|
# Add price range information for debugging
|
|
aggregated_data['stats']['price_range_used'] = price_range
|
|
|
|
logger.debug(f"COB imbalances for {symbol} (±${price_range}): {current_imbalance:.4f}")
|
|
|
|
return aggregated_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating COB-based imbalances for {symbol}: {e}")
|
|
# Return original data with default imbalances
|
|
default_imbalances = {
|
|
'imbalance_1s': 0.0, 'imbalance_5s': 0.0, 'imbalance_15s': 0.0, 'imbalance_60s': 0.0,
|
|
'volume_imbalance_1s': 0.0, 'volume_imbalance_5s': 0.0, 'volume_imbalance_15s': 0.0, 'volume_imbalance_60s': 0.0
|
|
}
|
|
aggregated_data.update(default_imbalances)
|
|
return aggregated_data
|
|
|
|
def _get_price_range_for_symbol(self, symbol: str) -> float:
|
|
"""Get configurable price range for order book imbalance calculation"""
|
|
# Configurable price ranges per symbol
|
|
price_ranges = {
|
|
'ETH/USDT': 5.0, # $5 range for ETH
|
|
'BTC/USDT': 50.0, # $50 range for BTC
|
|
}
|
|
|
|
return price_ranges.get(symbol, 10.0) # Default $10 range for other symbols
|
|
|
|
def get_current_cob_imbalance(self, symbol: str) -> Dict[str, float]:
|
|
"""Get current COB imbalance metrics for a symbol"""
|
|
try:
|
|
price_range = self._get_price_range_for_symbol(symbol)
|
|
latest_cob = self.get_latest_cob_data(symbol)
|
|
|
|
if not latest_cob:
|
|
return {
|
|
'imbalance': 0.0,
|
|
'price_range': price_range,
|
|
'mid_price': 0.0,
|
|
'bid_volume_in_range': 0.0,
|
|
'ask_volume_in_range': 0.0
|
|
}
|
|
|
|
# Calculate detailed imbalance info
|
|
bids = latest_cob.get('bids', [])
|
|
asks = latest_cob.get('asks', [])
|
|
|
|
if not bids or not asks:
|
|
return {'imbalance': 0.0, 'price_range': price_range, 'mid_price': 0.0}
|
|
|
|
# Calculate mid price with proper safety checks
|
|
try:
|
|
if not bids or not asks or len(bids) == 0 or len(asks) == 0:
|
|
return {'imbalance': 0.0, 'price_range': price_range, 'mid_price': 0.0}
|
|
|
|
best_bid = float(bids[0][0])
|
|
best_ask = float(asks[0][0])
|
|
mid_price = (best_bid + best_ask) / 2.0
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error calculating mid price for {symbol}: {e}")
|
|
return {'imbalance': 0.0, 'price_range': price_range, 'mid_price': 0.0, 'error': str(e)}
|
|
|
|
# Calculate volumes in range with safety checks
|
|
price_min = mid_price - price_range
|
|
price_max = mid_price + price_range
|
|
|
|
bid_volume_in_range = 0.0
|
|
ask_volume_in_range = 0.0
|
|
|
|
try:
|
|
for price, vol in bids:
|
|
price = float(price)
|
|
vol = float(vol)
|
|
if price_min <= price <= mid_price:
|
|
bid_volume_in_range += vol
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing bid volumes for {symbol}: {e}")
|
|
|
|
try:
|
|
for price, vol in asks:
|
|
price = float(price)
|
|
vol = float(vol)
|
|
if mid_price <= price <= price_max:
|
|
ask_volume_in_range += vol
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing ask volumes for {symbol}: {e}")
|
|
|
|
total_volume = bid_volume_in_range + ask_volume_in_range
|
|
imbalance = (bid_volume_in_range - ask_volume_in_range) / total_volume if total_volume > 0 else 0.0
|
|
|
|
return {
|
|
'imbalance': imbalance,
|
|
'price_range': price_range,
|
|
'mid_price': mid_price,
|
|
'bid_volume_in_range': bid_volume_in_range,
|
|
'ask_volume_in_range': ask_volume_in_range,
|
|
'total_volume_in_range': total_volume,
|
|
'best_bid': best_bid,
|
|
'best_ask': best_ask
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting current COB imbalance for {symbol}: {e}")
|
|
return {'imbalance': 0.0, 'price_range': price_range, 'error': str(e)}
|
|
|
|
def _calculate_cob_imbalance(self, cob_data: Any, price_range: float) -> float:
|
|
"""Calculate order book imbalance within specified price range around mid price.
|
|
Accepts dict snapshot or COBData-like objects (with bids/asks as list of [price, size]).
|
|
"""
|
|
try:
|
|
# Normalize input
|
|
if isinstance(cob_data, dict):
|
|
bids = cob_data.get('bids', [])
|
|
asks = cob_data.get('asks', [])
|
|
else:
|
|
# Try attribute access (COBData-like or snapshot objects)
|
|
bids = getattr(cob_data, 'bids', []) or []
|
|
asks = getattr(cob_data, 'asks', []) or []
|
|
|
|
if not bids or not asks:
|
|
return 0.0
|
|
|
|
# Calculate mid price with proper safety checks
|
|
try:
|
|
if not bids or not asks or len(bids) == 0 or len(asks) == 0:
|
|
return 0.0
|
|
|
|
best_bid = float(bids[0][0])
|
|
best_ask = float(asks[0][0])
|
|
|
|
if best_bid <= 0 or best_ask <= 0:
|
|
return 0.0
|
|
|
|
mid_price = (best_bid + best_ask) / 2.0
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error calculating mid price: {e}")
|
|
return 0.0
|
|
|
|
# Define price range around mid price
|
|
price_min = mid_price - price_range
|
|
price_max = mid_price + price_range
|
|
|
|
# Sum volumes within price range
|
|
bid_volume_in_range = 0.0
|
|
ask_volume_in_range = 0.0
|
|
|
|
# Sum bid volumes within range with safety checks
|
|
try:
|
|
for bid_price, bid_volume in bids:
|
|
bid_price = float(bid_price)
|
|
bid_volume = float(bid_volume)
|
|
if price_min <= bid_price <= mid_price:
|
|
bid_volume_in_range += bid_volume
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing bid volumes: {e}")
|
|
|
|
# Sum ask volumes within range with safety checks
|
|
try:
|
|
for ask_price, ask_volume in asks:
|
|
ask_price = float(ask_price)
|
|
ask_volume = float(ask_volume)
|
|
if mid_price <= ask_price <= price_max:
|
|
ask_volume_in_range += ask_volume
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing ask volumes: {e}")
|
|
|
|
# Calculate imbalance: (bid_volume - ask_volume) / (bid_volume + ask_volume)
|
|
total_volume = bid_volume_in_range + ask_volume_in_range
|
|
|
|
if total_volume > 0:
|
|
imbalance = (bid_volume_in_range - ask_volume_in_range) / total_volume
|
|
return imbalance
|
|
else:
|
|
return 0.0
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating COB imbalance: {e}")
|
|
return 0.0
|
|
|
|
def _calculate_timeframe_cob_imbalance(self, historical_cob_data: List[Dict], seconds: int, price_range: float) -> float:
|
|
"""Calculate average COB imbalance over specified timeframe"""
|
|
try:
|
|
if not historical_cob_data or len(historical_cob_data) == 0:
|
|
return 0.0
|
|
|
|
# Get recent data within timeframe (approximate by using last N ticks)
|
|
# Assuming ~100 ticks per second, so N = seconds * 100
|
|
max_ticks = seconds * 100
|
|
recent_ticks = historical_cob_data[-max_ticks:] if len(historical_cob_data) > max_ticks else historical_cob_data
|
|
|
|
if not recent_ticks:
|
|
return 0.0
|
|
|
|
# Calculate imbalance for each tick and average
|
|
imbalances = []
|
|
for tick in recent_ticks:
|
|
imbalance = self._calculate_cob_imbalance(tick, price_range)
|
|
imbalances.append(imbalance)
|
|
|
|
if imbalances:
|
|
return sum(imbalances) / len(imbalances)
|
|
else:
|
|
return 0.0
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating {seconds}s COB imbalance: {e}")
|
|
return 0.0
|
|
|
|
def _calculate_volume_weighted_imbalance(self, historical_cob_data: List[Dict], seconds: int, price_range: float) -> float:
|
|
"""Calculate volume-weighted average imbalance over timeframe"""
|
|
try:
|
|
if not historical_cob_data:
|
|
return 0.0
|
|
|
|
# Get recent data within timeframe
|
|
max_ticks = seconds * 100 # Approximate ticks per second
|
|
recent_ticks = historical_cob_data[-max_ticks:] if len(historical_cob_data) > max_ticks else historical_cob_data
|
|
|
|
if not recent_ticks:
|
|
return 0.0
|
|
|
|
total_weighted_imbalance = 0.0
|
|
total_volume = 0.0
|
|
|
|
for tick in recent_ticks:
|
|
imbalance = self._calculate_cob_imbalance(tick, price_range)
|
|
|
|
# Calculate total volume in range for weighting
|
|
bids = tick.get('bids', [])
|
|
asks = tick.get('asks', [])
|
|
|
|
if bids and asks and len(bids) > 0 and len(asks) > 0:
|
|
# Get mid price for this tick with proper safety checks
|
|
try:
|
|
best_bid = float(bids[0][0])
|
|
best_ask = float(asks[0][0])
|
|
mid_price = (best_bid + best_ask) / 2.0
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Skipping tick due to data format issue: {e}")
|
|
continue
|
|
|
|
# Calculate volume in range
|
|
price_min = mid_price - price_range
|
|
price_max = mid_price + price_range
|
|
|
|
tick_volume = 0.0
|
|
try:
|
|
for bid_price, bid_volume in bids:
|
|
bid_price = float(bid_price)
|
|
bid_volume = float(bid_volume)
|
|
if price_min <= bid_price <= mid_price:
|
|
tick_volume += bid_volume
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing bid volumes in weighted calculation: {e}")
|
|
|
|
try:
|
|
for ask_price, ask_volume in asks:
|
|
ask_price = float(ask_price)
|
|
ask_volume = float(ask_volume)
|
|
if mid_price <= ask_price <= price_max:
|
|
tick_volume += ask_volume
|
|
except (IndexError, KeyError, ValueError) as e:
|
|
logger.debug(f"Error processing ask volumes in weighted calculation: {e}")
|
|
|
|
if tick_volume > 0:
|
|
total_weighted_imbalance += imbalance * tick_volume
|
|
total_volume += tick_volume
|
|
|
|
if total_volume > 0:
|
|
return total_weighted_imbalance / total_volume
|
|
else:
|
|
return 0.0
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating volume-weighted {seconds}s imbalance: {e}")
|
|
return 0.0
|
|
|
|
|
|
|
|
def _create_1s_cob_aggregation(self, symbol: str, ticks: List[Dict], timestamp: int) -> Dict:
|
|
"""Create 1s aggregation with $1 price buckets"""
|
|
try:
|
|
if not ticks:
|
|
return {}
|
|
|
|
# Initialize buckets
|
|
bid_buckets = {} # {price_bucket: total_volume}
|
|
ask_buckets = {} # {price_bucket: total_volume}
|
|
|
|
# Statistics tracking
|
|
all_mid_prices = []
|
|
all_spreads = []
|
|
all_imbalances = []
|
|
total_bid_volume = 0
|
|
total_ask_volume = 0
|
|
|
|
# Process each tick
|
|
for tick in ticks:
|
|
stats = tick.get('stats', {})
|
|
bids = tick.get('bids', [])
|
|
asks = tick.get('asks', [])
|
|
|
|
# Track statistics
|
|
mid_price = stats.get('mid_price', 0)
|
|
if mid_price > 0:
|
|
all_mid_prices.append(mid_price)
|
|
|
|
spread = stats.get('spread_bps', 0)
|
|
if spread > 0:
|
|
all_spreads.append(spread)
|
|
|
|
imbalance = stats.get('imbalance', 0)
|
|
all_imbalances.append(imbalance)
|
|
|
|
# Process bids with $1 buckets
|
|
for bid in bids:
|
|
if isinstance(bid, dict):
|
|
price = bid.get('price', 0)
|
|
volume = bid.get('volume', 0)
|
|
elif isinstance(bid, list) and len(bid) >= 2:
|
|
price = float(bid[0])
|
|
volume = float(bid[1])
|
|
else:
|
|
continue
|
|
|
|
if price > 0 and volume > 0:
|
|
# Create $1 bucket (floor to nearest dollar)
|
|
bucket = math.floor(price)
|
|
if bucket not in bid_buckets:
|
|
bid_buckets[bucket] = 0
|
|
bid_buckets[bucket] += volume
|
|
total_bid_volume += volume
|
|
|
|
# Process asks with $1 buckets
|
|
for ask in asks:
|
|
if isinstance(ask, dict):
|
|
price = ask.get('price', 0)
|
|
volume = ask.get('volume', 0)
|
|
elif isinstance(ask, list) and len(ask) >= 2:
|
|
price = float(ask[0])
|
|
volume = float(ask[1])
|
|
else:
|
|
continue
|
|
|
|
if price > 0 and volume > 0:
|
|
# Create $1 bucket (floor to nearest dollar)
|
|
bucket = math.floor(price)
|
|
if bucket not in ask_buckets:
|
|
ask_buckets[bucket] = 0
|
|
ask_buckets[bucket] += volume
|
|
total_ask_volume += volume
|
|
|
|
# Calculate aggregated statistics
|
|
avg_mid_price = sum(all_mid_prices) / len(all_mid_prices) if all_mid_prices else 0
|
|
avg_spread = sum(all_spreads) / len(all_spreads) if all_spreads else 0
|
|
avg_imbalance = sum(all_imbalances) / len(all_imbalances) if all_imbalances else 0
|
|
|
|
# Calculate current imbalance from total volumes
|
|
total_volume = total_bid_volume + total_ask_volume
|
|
current_imbalance = (total_bid_volume - total_ask_volume) / total_volume if total_volume > 0 else 0
|
|
|
|
# Create aggregated data structure
|
|
aggregated = {
|
|
'symbol': symbol,
|
|
'timestamp': timestamp,
|
|
'tick_count': len(ticks),
|
|
'bucket_size_usd': 1.0, # $1 buckets
|
|
'bid_buckets': dict(sorted(bid_buckets.items(), reverse=True)[:50]), # Top 50 bid buckets
|
|
'ask_buckets': dict(sorted(ask_buckets.items())[:50]), # Top 50 ask buckets
|
|
'imbalance': current_imbalance, # Current 1s imbalance
|
|
'total_volume': total_volume,
|
|
'stats': {
|
|
'avg_mid_price': avg_mid_price,
|
|
'avg_spread_bps': avg_spread,
|
|
'avg_imbalance': avg_imbalance,
|
|
'current_imbalance': current_imbalance,
|
|
'total_bid_volume': total_bid_volume,
|
|
'total_ask_volume': total_ask_volume,
|
|
'total_volume': total_volume,
|
|
'bid_bucket_count': len(bid_buckets),
|
|
'ask_bucket_count': len(ask_buckets),
|
|
'price_range_usd': max(max(bid_buckets.keys()) if bid_buckets else 0,
|
|
max(ask_buckets.keys()) if ask_buckets else 0) -
|
|
min(min(bid_buckets.keys()) if bid_buckets else 0,
|
|
min(ask_buckets.keys()) if ask_buckets else 0)
|
|
}
|
|
}
|
|
|
|
return aggregated
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating 1s COB aggregation: {e}")
|
|
return {}
|
|
|
|
def _ensure_datetime_index(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""Ensure dataframe has proper datetime index"""
|
|
if df is None or df.empty:
|
|
return df
|
|
|
|
try:
|
|
# If we already have a proper DatetimeIndex, return as is
|
|
if isinstance(df.index, pd.DatetimeIndex):
|
|
return df
|
|
|
|
# If timestamp column exists, use it as index
|
|
if 'timestamp' in df.columns:
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True)
|
|
df.set_index('timestamp', inplace=True)
|
|
return df
|
|
|
|
# If we have a RangeIndex or other non-datetime index, create datetime index
|
|
if isinstance(df.index, pd.RangeIndex) or not isinstance(df.index, pd.DatetimeIndex):
|
|
# Use current UTC time and work backwards for realistic timestamps
|
|
from datetime import datetime, timedelta
|
|
end_time = datetime.utcnow()
|
|
start_time = end_time - timedelta(minutes=len(df))
|
|
df.index = pd.date_range(start=start_time, end=end_time, periods=len(df), tz='UTC')
|
|
logger.debug(f"Converted RangeIndex to DatetimeIndex for {len(df)} records")
|
|
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error ensuring datetime index: {e}")
|
|
return df
|
|
|
|
def get_price_range_over_period(self, symbol: str, start_time: datetime,
|
|
end_time: datetime, timeframe: str = '1m') -> Optional[Dict[str, float]]:
|
|
"""Get min/max price and other statistics over a specific time period"""
|
|
try:
|
|
# Get historical data for the period
|
|
data = self.get_historical_data(symbol, timeframe, limit=50000, refresh=False)
|
|
if data is None:
|
|
return None
|
|
|
|
# Filter data for the time range
|
|
data = data[(data.index >= start_time) & (data.index <= end_time)]
|
|
|
|
if len(data) == 0:
|
|
return None
|
|
|
|
# Calculate statistics
|
|
price_range = {
|
|
'min_price': float(data['low'].min()),
|
|
'max_price': float(data['high'].max()),
|
|
'open_price': float(data.iloc[0]['open']),
|
|
'close_price': float(data.iloc[-1]['close']),
|
|
'avg_price': float(data['close'].mean()),
|
|
'price_volatility': float(data['close'].std()),
|
|
'total_volume': float(data['volume'].sum()),
|
|
'data_points': len(data),
|
|
'time_range_seconds': (end_time - start_time).total_seconds()
|
|
}
|
|
|
|
return price_range
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting price range for {symbol}: {e}")
|
|
return None
|
|
|
|
def get_historical_data(self, symbol: str, timeframe: str, limit: int = 1000, refresh: bool = False) -> Optional[pd.DataFrame]:
|
|
"""Get historical OHLCV data.
|
|
- Prefer cached data for low latency.
|
|
- If cache is empty or refresh=True, fetch real data from exchanges.
|
|
- Never generate synthetic data.
|
|
"""
|
|
try:
|
|
# Serve from cache when available
|
|
if symbol in self.cached_data and timeframe in self.cached_data[symbol]:
|
|
cached_df = self.cached_data[symbol][timeframe]
|
|
if not cached_df.empty and not refresh:
|
|
return cached_df.tail(limit)
|
|
|
|
# Cache empty or refresh requested: fetch real data now
|
|
df = self._fetch_from_binance(symbol, timeframe, limit)
|
|
if (df is None or df.empty):
|
|
df = self._fetch_from_mexc(symbol, timeframe, limit)
|
|
|
|
if df is not None and not df.empty:
|
|
df = self._ensure_datetime_index(df)
|
|
# Store/merge into cache
|
|
if symbol not in self.cached_data:
|
|
self.cached_data[symbol] = {}
|
|
if timeframe not in self.cached_data[symbol] or self.cached_data[symbol][timeframe].empty:
|
|
self.cached_data[symbol][timeframe] = df.tail(1500)
|
|
else:
|
|
combined_df = pd.concat([self.cached_data[symbol][timeframe], df], ignore_index=False)
|
|
combined_df = combined_df[~combined_df.index.duplicated(keep='last')]
|
|
combined_df = combined_df.sort_index()
|
|
self.cached_data[symbol][timeframe] = combined_df.tail(1500)
|
|
logger.info(f"Cached {len(self.cached_data[symbol][timeframe])} candles for {symbol} {timeframe}")
|
|
return self.cached_data[symbol][timeframe].tail(limit)
|
|
|
|
logger.warning(f"No real data available for {symbol} {timeframe} at request time")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting historical data for {symbol} {timeframe}: {e}")
|
|
return None
|
|
|
|
|
|
|
|
def _fetch_from_mexc(self, symbol: str, timeframe: str, limit: int) -> Optional[pd.DataFrame]:
|
|
"""Fetch data from MEXC API (fallback data source when Binance is unavailable)"""
|
|
try:
|
|
# For 1s timeframe, generate from WebSocket tick data
|
|
if timeframe == '1s':
|
|
# logger.deta(f"Generating 1s candles from WebSocket ticks for {symbol}")
|
|
return self._generate_1s_candles_from_ticks(symbol, limit)
|
|
|
|
# Convert symbol format
|
|
mexc_symbol = symbol.replace('/', '').upper()
|
|
|
|
# Convert timeframe for MEXC (excluding 1s)
|
|
timeframe_map = {
|
|
'1m': '1m', '5m': '5m', '15m': '15m', '30m': '30m',
|
|
'1h': '1h', '4h': '4h', '1d': '1d'
|
|
}
|
|
mexc_timeframe = timeframe_map.get(timeframe)
|
|
|
|
if mexc_timeframe is None:
|
|
logger.warning(f"MEXC doesn't support timeframe {timeframe}, skipping {symbol}")
|
|
return None
|
|
|
|
# MEXC API request
|
|
url = "https://api.mexc.com/api/v3/klines"
|
|
params = {
|
|
'symbol': mexc_symbol,
|
|
'interval': mexc_timeframe,
|
|
'limit': limit
|
|
}
|
|
|
|
response = requests.get(url, params=params)
|
|
response.raise_for_status()
|
|
|
|
data = response.json()
|
|
|
|
# Convert to DataFrame (MEXC uses 8 columns vs Binance's 12)
|
|
df = pd.DataFrame(data, columns=[
|
|
'timestamp', 'open', 'high', 'low', 'close', 'volume',
|
|
'close_time', 'quote_volume'
|
|
])
|
|
|
|
# Process columns with proper timezone handling (MEXC returns UTC timestamps)
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
|
|
# Keep in UTC to match COB WebSocket data (no timezone conversion)
|
|
for col in ['open', 'high', 'low', 'close', 'volume']:
|
|
df[col] = df[col].astype(float)
|
|
|
|
# Keep only OHLCV columns
|
|
df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
|
|
df = df.sort_values('timestamp').reset_index(drop=True)
|
|
|
|
logger.info(f"MEXC: Fetched {len(df)} candles for {symbol} {timeframe}")
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"MEXC: Error fetching data: {e}")
|
|
return None
|
|
|
|
def _generate_1s_candles_from_ticks(self, symbol: str, limit: int = 1000) -> Optional[pd.DataFrame]:
|
|
"""Generate 1-second OHLCV candles from WebSocket tick data"""
|
|
try:
|
|
# Get recent ticks from COB data
|
|
recent_ticks = self.get_cob_raw_ticks(symbol, count=limit * 10) # Get more ticks than needed
|
|
|
|
if not recent_ticks:
|
|
logger.debug(f"No tick data available for {symbol}, cannot generate 1s candles")
|
|
return None
|
|
|
|
# Group ticks by second and create OHLCV candles
|
|
candles = []
|
|
current_second = None
|
|
current_candle = None
|
|
|
|
for tick in recent_ticks:
|
|
# Extract timestamp and price from tick
|
|
if isinstance(tick, dict):
|
|
timestamp = tick.get('timestamp')
|
|
# Prefer explicit price if available, fallback to stats.mid_price
|
|
stats = tick.get('stats', {}) if isinstance(tick.get('stats', {}), dict) else {}
|
|
price = tick.get('price')
|
|
if not price:
|
|
price = tick.get('mid_price') or stats.get('mid_price', 0)
|
|
# Strict: if still falsy or non-finite, skip
|
|
try:
|
|
price = float(price)
|
|
except Exception:
|
|
price = 0.0
|
|
# Volume: do not synthesize from other stats; use provided value or 0.0
|
|
volume = tick.get('volume')
|
|
try:
|
|
volume = float(volume) if volume is not None else 0.0
|
|
except Exception:
|
|
volume = 0.0
|
|
else:
|
|
continue
|
|
|
|
# Normalize timestamp; support seconds or milliseconds since epoch and tz-aware datetimes
|
|
if not timestamp or not price or price <= 0:
|
|
continue
|
|
|
|
# Convert timestamp to datetime if needed
|
|
if isinstance(timestamp, (int, float)):
|
|
import pytz
|
|
utc = pytz.UTC
|
|
# Handle ms epoch inputs by thresholding reasonable ranges
|
|
try:
|
|
# If timestamp looks like milliseconds (e.g., > 10^12), convert to seconds
|
|
if timestamp > 1e12:
|
|
tick_time = datetime.fromtimestamp(timestamp / 1000.0, tz=utc)
|
|
else:
|
|
tick_time = datetime.fromtimestamp(timestamp, tz=utc)
|
|
except Exception:
|
|
# Skip bad timestamps cleanly on Windows
|
|
continue
|
|
# Keep in UTC to match COB WebSocket data
|
|
elif isinstance(timestamp, datetime):
|
|
import pytz
|
|
utc = pytz.UTC
|
|
tick_time = timestamp
|
|
# If no timezone info, assume UTC and keep in UTC
|
|
if tick_time.tzinfo is None:
|
|
try:
|
|
tick_time = utc.localize(tick_time)
|
|
except Exception:
|
|
# Fallback: coerce via fromtimestamp using naive seconds
|
|
try:
|
|
tick_time = datetime.fromtimestamp(tick_time.timestamp(), tz=utc)
|
|
except Exception:
|
|
continue
|
|
# Keep in UTC (no timezone conversion)
|
|
else:
|
|
continue
|
|
|
|
# Round to second
|
|
tick_second = tick_time.replace(microsecond=0)
|
|
|
|
# Start new candle if second changed
|
|
if current_second != tick_second:
|
|
# Save previous candle if exists
|
|
if current_candle:
|
|
candles.append(current_candle)
|
|
|
|
# Start new candle
|
|
current_second = tick_second
|
|
current_candle = {
|
|
'timestamp': tick_second,
|
|
'open': price,
|
|
'high': price,
|
|
'low': price,
|
|
'close': price,
|
|
'volume': volume
|
|
}
|
|
else:
|
|
# Update current candle
|
|
current_candle['high'] = max(current_candle['high'], price)
|
|
current_candle['low'] = min(current_candle['low'], price)
|
|
current_candle['close'] = price
|
|
current_candle['volume'] += volume
|
|
|
|
# Add final candle
|
|
if current_candle:
|
|
candles.append(current_candle)
|
|
|
|
if not candles:
|
|
logger.debug(f"No valid candles generated for {symbol}")
|
|
return None
|
|
|
|
# Convert to DataFrame and normalize timestamps to UTC tz-aware
|
|
df = pd.DataFrame(candles)
|
|
if not df.empty and 'timestamp' in df.columns:
|
|
# Coerce to datetime with UTC; avoid .dt on non-datetimelike
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True, errors='coerce')
|
|
df = df.dropna(subset=['timestamp'])
|
|
|
|
df = df.sort_values('timestamp').reset_index(drop=True)
|
|
|
|
# Limit to requested number
|
|
if len(df) > limit:
|
|
df = df.tail(limit)
|
|
|
|
# logger.info(f"Generated {len(df)} 1s candles from {len(recent_ticks)} ticks for {symbol}")
|
|
return df
|
|
|
|
except Exception as e:
|
|
# Handle invalid argument or bad timestamps gracefully (Windows-safe)
|
|
try:
|
|
import errno
|
|
if hasattr(e, 'errno') and e.errno == errno.EINVAL:
|
|
logger.warning(f"Invalid argument while generating 1s candles for {symbol}; trimming tick buffer and falling back")
|
|
else:
|
|
logger.error(f"Error generating 1s candles from ticks for {symbol}: {e}")
|
|
except Exception:
|
|
logger.error(f"Error generating 1s candles from ticks for {symbol}: {e}")
|
|
# Always trim a small portion of tick buffer to recover from corrupt front entries
|
|
try:
|
|
if hasattr(self, 'cob_raw_ticks') and symbol in getattr(self, 'cob_raw_ticks', {}):
|
|
buf = self.cob_raw_ticks[symbol]
|
|
drop = max(1, min(50, len(buf)//10)) # drop up to 10% or 50 entries
|
|
for _ in range(drop):
|
|
buf.popleft()
|
|
except Exception:
|
|
pass
|
|
return None
|
|
|
|
def _fetch_from_binance(self, symbol: str, timeframe: str, limit: int) -> Optional[pd.DataFrame]:
|
|
"""Fetch data from Binance API with robust rate limiting and error handling"""
|
|
try:
|
|
from .api_rate_limiter import get_rate_limiter
|
|
|
|
# For 1s timeframe, try to generate from WebSocket ticks first
|
|
if timeframe == '1s':
|
|
# Attempt to generate from WebSocket ticks, but throttle attempts to avoid spam
|
|
if not hasattr(self, '_last_1s_generation_attempt'):
|
|
self._last_1s_generation_attempt = {}
|
|
now_ts = time.time()
|
|
last_attempt = self._last_1s_generation_attempt.get(symbol, 0)
|
|
generated_df = None
|
|
if now_ts - last_attempt >= 1.5:
|
|
self._last_1s_generation_attempt[symbol] = now_ts
|
|
generated_df = self._generate_1s_candles_from_ticks(symbol, limit)
|
|
if generated_df is not None and not generated_df.empty:
|
|
return generated_df
|
|
else:
|
|
logger.debug(f"Could not generate 1s candles from ticks for {symbol}; trying Binance API")
|
|
|
|
# Convert symbol format
|
|
binance_symbol = symbol.replace('/', '').upper()
|
|
|
|
# Convert timeframe (now includes 1s support)
|
|
timeframe_map = {
|
|
'1s': '1s', '1m': '1m', '5m': '5m', '15m': '15m', '30m': '30m',
|
|
'1h': '1h', '4h': '4h', '1d': '1d'
|
|
}
|
|
binance_timeframe = timeframe_map.get(timeframe, '1h')
|
|
|
|
# Use rate limiter for API requests
|
|
rate_limiter = get_rate_limiter()
|
|
|
|
# Check if we can make request
|
|
can_request, wait_time = rate_limiter.can_make_request('binance_api')
|
|
if not can_request:
|
|
logger.debug(f"Binance rate limited, waiting {wait_time:.1f}s for {symbol} {timeframe}")
|
|
if wait_time > 30: # If wait is too long, use fallback
|
|
return self._get_fallback_data(symbol, timeframe, limit)
|
|
time.sleep(min(wait_time, 5)) # Cap wait at 5 seconds
|
|
|
|
# API request with rate limiter
|
|
url = "https://api.binance.com/api/v3/klines"
|
|
params = {
|
|
'symbol': binance_symbol,
|
|
'interval': binance_timeframe,
|
|
'limit': limit
|
|
}
|
|
|
|
response = rate_limiter.make_request('binance_api', url, 'GET', params=params)
|
|
|
|
if response is None:
|
|
logger.warning(f"Binance API request failed for {symbol} {timeframe} - using fallback")
|
|
return self._get_fallback_data(symbol, timeframe, limit)
|
|
|
|
if response.status_code != 200:
|
|
logger.warning(f"Binance API returned {response.status_code} for {symbol} {timeframe}")
|
|
return self._get_fallback_data(symbol, timeframe, limit)
|
|
|
|
data = response.json()
|
|
|
|
# Convert to DataFrame
|
|
df = pd.DataFrame(data, columns=[
|
|
'timestamp', 'open', 'high', 'low', 'close', 'volume',
|
|
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
|
|
'taker_buy_quote', 'ignore'
|
|
])
|
|
|
|
# Process columns with proper timezone handling (Binance returns UTC timestamps)
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
|
|
# Keep in UTC to match COB WebSocket data (no timezone conversion)
|
|
# This prevents the 3-hour gap when appending live COB data
|
|
for col in ['open', 'high', 'low', 'close', 'volume']:
|
|
df[col] = df[col].astype(float)
|
|
|
|
# Keep only OHLCV columns
|
|
df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
|
|
df = df.sort_values('timestamp').reset_index(drop=True)
|
|
|
|
logger.info(f"Binance: Fetched {len(df)} candles for {symbol} {timeframe}")
|
|
return df
|
|
|
|
except Exception as e:
|
|
if "451" in str(e) or "Client Error" in str(e):
|
|
logger.warning(f"Binance API access blocked (451) for {symbol} {timeframe} - using fallback")
|
|
return self._get_fallback_data(symbol, timeframe, limit)
|
|
else:
|
|
logger.error(f"Error fetching from Binance API: {e}")
|
|
return self._get_fallback_data(symbol, timeframe, limit)
|
|
|
|
def _get_fallback_data(self, symbol: str, timeframe: str, limit: int) -> Optional[pd.DataFrame]:
|
|
"""Get fallback data when Binance API is unavailable - REAL DATA ONLY"""
|
|
try:
|
|
logger.info(f"FALLBACK: Attempting to get real cached data for {symbol} {timeframe}")
|
|
|
|
# For 1s timeframe, try generating from WebSocket ticks first
|
|
if timeframe == '1s':
|
|
# logger.info(f"FALLBACK: Attempting to generate 1s candles from WebSocket ticks for {symbol}")
|
|
generated_data = self._generate_1s_candles_from_ticks(symbol, limit)
|
|
if generated_data is not None and not generated_data.empty:
|
|
# logger.info(f"FALLBACK: Generated 1s candles from WebSocket ticks for {symbol}: {len(generated_data)} bars")
|
|
return generated_data
|
|
|
|
# ONLY try cached data
|
|
cached_data = self._load_from_cache(symbol, timeframe)
|
|
if cached_data is not None and not cached_data.empty:
|
|
# Limit to requested amount
|
|
limited_data = cached_data.tail(limit) if len(cached_data) > limit else cached_data
|
|
logger.info(f"FALLBACK: Using cached real data for {symbol} {timeframe}: {len(limited_data)} bars")
|
|
return limited_data
|
|
|
|
# Try MEXC as secondary real data source
|
|
mexc_data = self._fetch_from_mexc(symbol, timeframe, limit)
|
|
if mexc_data is not None and not mexc_data.empty:
|
|
logger.info(f"FALLBACK: Using MEXC real data for {symbol} {timeframe}: {len(mexc_data)} bars")
|
|
return mexc_data
|
|
|
|
# NO SYNTHETIC DATA - Return None if no real data available
|
|
logger.warning(f"FALLBACK: No real data available for {symbol} {timeframe} - waiting for real data")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting fallback data: {e}")
|
|
return None
|
|
|
|
def _add_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""Add comprehensive technical indicators AND pivot-based normalization context"""
|
|
try:
|
|
df = df.copy()
|
|
|
|
# Ensure we have enough data for indicators
|
|
if len(df) < 50:
|
|
logger.warning(f"Insufficient data for comprehensive indicators: {len(df)} rows")
|
|
return self._add_basic_indicators(df)
|
|
|
|
# === EXISTING TECHNICAL INDICATORS ===
|
|
# Moving averages (multiple timeframes)
|
|
df['sma_10'] = ta.trend.sma_indicator(df['close'], window=10)
|
|
df['sma_20'] = ta.trend.sma_indicator(df['close'], window=20)
|
|
df['sma_50'] = ta.trend.sma_indicator(df['close'], window=50)
|
|
df['ema_12'] = ta.trend.ema_indicator(df['close'], window=12)
|
|
df['ema_26'] = ta.trend.ema_indicator(df['close'], window=26)
|
|
df['ema_50'] = ta.trend.ema_indicator(df['close'], window=50)
|
|
|
|
# MACD family
|
|
macd = ta.trend.MACD(df['close'])
|
|
df['macd'] = macd.macd()
|
|
df['macd_signal'] = macd.macd_signal()
|
|
df['macd_histogram'] = macd.macd_diff()
|
|
|
|
# ADX (Average Directional Index)
|
|
adx = ta.trend.ADXIndicator(df['high'], df['low'], df['close'])
|
|
df['adx'] = adx.adx()
|
|
df['adx_pos'] = adx.adx_pos()
|
|
df['adx_neg'] = adx.adx_neg()
|
|
|
|
# Parabolic SAR
|
|
psar = ta.trend.PSARIndicator(df['high'], df['low'], df['close'])
|
|
df['psar'] = psar.psar()
|
|
|
|
# === MOMENTUM INDICATORS ===
|
|
# RSI (multiple periods) - using our own implementation to avoid ta library deprecation warnings
|
|
df['rsi_14'] = self._calculate_rsi(df['close'], period=14)
|
|
df['rsi_7'] = self._calculate_rsi(df['close'], period=7)
|
|
df['rsi_21'] = self._calculate_rsi(df['close'], period=21)
|
|
|
|
# Stochastic Oscillator
|
|
stoch = ta.momentum.StochasticOscillator(df['high'], df['low'], df['close'])
|
|
df['stoch_k'] = stoch.stoch()
|
|
df['stoch_d'] = stoch.stoch_signal()
|
|
|
|
# Williams %R
|
|
df['williams_r'] = ta.momentum.williams_r(df['high'], df['low'], df['close'])
|
|
|
|
# Ultimate Oscillator (instead of CCI which isn't available)
|
|
df['ultimate_osc'] = ta.momentum.ultimate_oscillator(df['high'], df['low'], df['close'])
|
|
|
|
# === VOLATILITY INDICATORS ===
|
|
# Bollinger Bands
|
|
bollinger = ta.volatility.BollingerBands(df['close'])
|
|
df['bb_upper'] = bollinger.bollinger_hband()
|
|
df['bb_lower'] = bollinger.bollinger_lband()
|
|
df['bb_middle'] = bollinger.bollinger_mavg()
|
|
df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']
|
|
df['bb_percent'] = (df['close'] - df['bb_lower']) / (df['bb_upper'] - df['bb_lower'])
|
|
|
|
# Average True Range
|
|
df['atr'] = ta.volatility.average_true_range(df['high'], df['low'], df['close'])
|
|
|
|
# Keltner Channels
|
|
keltner = ta.volatility.KeltnerChannel(df['high'], df['low'], df['close'])
|
|
df['keltner_upper'] = keltner.keltner_channel_hband()
|
|
df['keltner_lower'] = keltner.keltner_channel_lband()
|
|
df['keltner_middle'] = keltner.keltner_channel_mband()
|
|
|
|
# === VOLUME INDICATORS ===
|
|
# Volume moving averages
|
|
df['volume_sma_10'] = df['volume'].rolling(window=10).mean()
|
|
df['volume_sma_20'] = df['volume'].rolling(window=20).mean()
|
|
df['volume_sma_50'] = df['volume'].rolling(window=50).mean()
|
|
|
|
# On Balance Volume
|
|
df['obv'] = ta.volume.on_balance_volume(df['close'], df['volume'])
|
|
|
|
# Volume Price Trend
|
|
df['vpt'] = ta.volume.volume_price_trend(df['close'], df['volume'])
|
|
|
|
# Money Flow Index
|
|
df['mfi'] = ta.volume.money_flow_index(df['high'], df['low'], df['close'], df['volume'])
|
|
|
|
# Accumulation/Distribution Line
|
|
df['ad_line'] = ta.volume.acc_dist_index(df['high'], df['low'], df['close'], df['volume'])
|
|
|
|
# Volume Weighted Average Price (VWAP)
|
|
df['vwap'] = (df['close'] * df['volume']).cumsum() / df['volume'].cumsum()
|
|
|
|
# === PRICE ACTION INDICATORS ===
|
|
# Price position relative to range
|
|
df['price_position'] = (df['close'] - df['low']) / (df['high'] - df['low'])
|
|
|
|
# True Range (use ATR calculation for true range)
|
|
df['true_range'] = df['atr'] # ATR is based on true range, so use it directly
|
|
|
|
# Rate of Change
|
|
df['roc'] = ta.momentum.roc(df['close'], window=10)
|
|
|
|
# === CUSTOM INDICATORS ===
|
|
# Trend strength (combination of multiple trend indicators)
|
|
df['trend_strength'] = (
|
|
(df['close'] > df['sma_20']).astype(int) +
|
|
(df['sma_10'] > df['sma_20']).astype(int) +
|
|
(df['macd'] > df['macd_signal']).astype(int) +
|
|
(df['adx'] > 25).astype(int)
|
|
) / 4.0
|
|
|
|
# Momentum composite
|
|
df['momentum_composite'] = (
|
|
(df['rsi_14'] / 100) +
|
|
((df['stoch_k'] + 50) / 100) + # Normalize stoch_k
|
|
((df['williams_r'] + 50) / 100) # Normalize williams_r
|
|
) / 3.0
|
|
|
|
# Volatility regime
|
|
df['volatility_regime'] = (df['atr'] / df['close']).rolling(window=20).rank(pct=True)
|
|
|
|
# === WILLIAMS MARKET STRUCTURE PIVOT CONTEXT ===
|
|
# Check if we need to refresh pivot bounds for this symbol
|
|
symbol = self._extract_symbol_from_dataframe(df)
|
|
if symbol and self._should_refresh_pivot_bounds(symbol):
|
|
logger.info(f"Refreshing pivot bounds for {symbol}")
|
|
self._refresh_pivot_bounds_for_symbol(symbol)
|
|
|
|
# Add pivot-based context features
|
|
if symbol and symbol in self.pivot_bounds:
|
|
df = self._add_pivot_context_features(df, symbol)
|
|
|
|
# === FILL NaN VALUES ===
|
|
# Forward fill first, then backward fill, then zero fill
|
|
df = df.ffill().bfill().fillna(0)
|
|
|
|
logger.debug(f"Added technical indicators + pivot context for {len(df)} rows")
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error adding comprehensive technical indicators: {e}")
|
|
# Fallback to basic indicators
|
|
return self._add_basic_indicators(df)
|
|
|
|
# === WILLIAMS MARKET STRUCTURE PIVOT SYSTEM ===
|
|
|
|
def _collect_monthly_1m_data(self, symbol: str) -> Optional[pd.DataFrame]:
|
|
"""Collect 30 days of 1m candles with smart gap-filling cache system"""
|
|
try:
|
|
# Check for cached data and determine what we need to fetch
|
|
cached_data = self._load_monthly_data_from_cache(symbol)
|
|
|
|
import pytz
|
|
utc = pytz.UTC
|
|
|
|
end_time = datetime.utcnow().replace(tzinfo=utc)
|
|
start_time = end_time - timedelta(days=30)
|
|
|
|
if cached_data is not None and not cached_data.empty:
|
|
logger.info(f"Found cached monthly 1m data for {symbol}: {len(cached_data)} candles")
|
|
|
|
# Check cache data range
|
|
cache_start = cached_data['timestamp'].min()
|
|
cache_end = cached_data['timestamp'].max()
|
|
|
|
logger.info(f"Cache range: {cache_start} to {cache_end}")
|
|
|
|
# Remove data older than 30 days
|
|
cached_data = cached_data[cached_data['timestamp'] >= start_time]
|
|
|
|
# Check if we need to fill gaps
|
|
gap_start = cache_end + timedelta(minutes=1)
|
|
|
|
# Ensure gap_start has same timezone as end_time for comparison
|
|
if gap_start.tzinfo is None:
|
|
gap_start = SOFIA_TZ.localize(gap_start)
|
|
elif gap_start.tzinfo != SOFIA_TZ:
|
|
gap_start = gap_start.astimezone(SOFIA_TZ)
|
|
|
|
if gap_start < end_time:
|
|
# Need to fill gap from cache_end to now
|
|
logger.info(f"Filling gap from {gap_start} to {end_time}")
|
|
gap_data = self._fetch_1m_data_range(symbol, gap_start, end_time)
|
|
|
|
if gap_data is not None and not gap_data.empty:
|
|
# Combine cached data with gap data
|
|
monthly_df = pd.concat([cached_data, gap_data], ignore_index=True)
|
|
monthly_df = monthly_df.sort_values('timestamp').drop_duplicates(subset=['timestamp']).reset_index(drop=True)
|
|
logger.info(f"Combined cache + gap: {len(monthly_df)} total candles")
|
|
else:
|
|
monthly_df = cached_data
|
|
logger.info(f"Using cached data only: {len(monthly_df)} candles")
|
|
else:
|
|
monthly_df = cached_data
|
|
logger.info(f"Cache is up to date: {len(monthly_df)} candles")
|
|
else:
|
|
# No cache - fetch full 30 days
|
|
logger.info(f"No cache found, collecting full 30 days of 1m data for {symbol}")
|
|
monthly_df = self._fetch_1m_data_range(symbol, start_time, end_time)
|
|
|
|
if monthly_df is not None and not monthly_df.empty:
|
|
# Final cleanup: ensure exactly 30 days
|
|
monthly_df = monthly_df[monthly_df['timestamp'] >= start_time]
|
|
monthly_df = monthly_df.sort_values('timestamp').reset_index(drop=True)
|
|
|
|
logger.info(f"Final dataset: {len(monthly_df)} 1m candles for {symbol}")
|
|
|
|
# Update cache
|
|
self._save_monthly_data_to_cache(symbol, monthly_df)
|
|
|
|
return monthly_df
|
|
else:
|
|
logger.error(f"No monthly 1m data collected for {symbol}")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error collecting monthly 1m data for {symbol}: {e}")
|
|
return None
|
|
|
|
def _fetch_1s_batch_with_endtime(self, symbol: str, end_time: datetime, limit: int = 1000) -> Optional[pd.DataFrame]:
|
|
"""Fetch a batch of 1s candles ending at specific time"""
|
|
try:
|
|
binance_symbol = symbol.replace('/', '').upper()
|
|
|
|
# Convert end_time to milliseconds
|
|
end_ms = int(end_time.timestamp() * 1000)
|
|
|
|
# API request
|
|
url = "https://api.binance.com/api/v3/klines"
|
|
params = {
|
|
'symbol': binance_symbol,
|
|
'interval': '1s',
|
|
'endTime': end_ms,
|
|
'limit': limit
|
|
}
|
|
|
|
headers = {
|
|
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
|
|
'Accept': 'application/json'
|
|
}
|
|
|
|
response = requests.get(url, params=params, headers=headers, timeout=10)
|
|
response.raise_for_status()
|
|
|
|
data = response.json()
|
|
|
|
if not data:
|
|
return None
|
|
|
|
# Convert to DataFrame
|
|
df = pd.DataFrame(data, columns=[
|
|
'timestamp', 'open', 'high', 'low', 'close', 'volume',
|
|
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
|
|
'taker_buy_quote', 'ignore'
|
|
])
|
|
|
|
# Process columns with proper timezone handling
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
|
|
# Keep in UTC to match COB WebSocket data (no timezone conversion)
|
|
for col in ['open', 'high', 'low', 'close', 'volume']:
|
|
df[col] = df[col].astype(float)
|
|
|
|
# Keep only OHLCV columns
|
|
df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
|
|
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error fetching 1s batch for {symbol}: {e}")
|
|
return None
|
|
|
|
def _fetch_1m_data_range(self, symbol: str, start_time: datetime, end_time: datetime) -> Optional[pd.DataFrame]:
|
|
"""Fetch 1m candles for a specific time range with efficient batching"""
|
|
try:
|
|
# Convert symbol format for Binance API
|
|
if '/' in symbol:
|
|
api_symbol = symbol.replace('/', '')
|
|
else:
|
|
api_symbol = symbol
|
|
|
|
logger.info(f"Fetching 1m data for {symbol} from {start_time} to {end_time}")
|
|
|
|
all_candles = []
|
|
current_start = start_time
|
|
batch_size = 1000 # Binance limit
|
|
api_calls_made = 0
|
|
|
|
while current_start < end_time and api_calls_made < 50: # Safety limit for 30 days
|
|
try:
|
|
# Calculate end time for this batch
|
|
batch_end = min(current_start + timedelta(minutes=batch_size), end_time)
|
|
|
|
# Convert to milliseconds
|
|
start_timestamp = int(current_start.timestamp() * 1000)
|
|
end_timestamp = int(batch_end.timestamp() * 1000)
|
|
|
|
# Binance API call
|
|
url = "https://api.binance.com/api/v3/klines"
|
|
params = {
|
|
'symbol': api_symbol,
|
|
'interval': '1m',
|
|
'startTime': start_timestamp,
|
|
'endTime': end_timestamp,
|
|
'limit': batch_size
|
|
}
|
|
|
|
headers = {
|
|
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
|
|
'Accept': 'application/json'
|
|
}
|
|
|
|
response = requests.get(url, params=params, headers=headers, timeout=10)
|
|
response.raise_for_status()
|
|
|
|
data = response.json()
|
|
api_calls_made += 1
|
|
|
|
if not data:
|
|
logger.warning(f"No data returned for batch {current_start} to {batch_end}")
|
|
break
|
|
|
|
# Convert to DataFrame
|
|
batch_df = pd.DataFrame(data, columns=[
|
|
'timestamp', 'open', 'high', 'low', 'close', 'volume',
|
|
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
|
|
'taker_buy_quote', 'ignore'
|
|
])
|
|
|
|
# Process columns with proper timezone handling
|
|
batch_df['timestamp'] = pd.to_datetime(batch_df['timestamp'], unit='ms', utc=True)
|
|
# Keep in UTC to match COB WebSocket data (no timezone conversion)
|
|
for col in ['open', 'high', 'low', 'close', 'volume']:
|
|
batch_df[col] = batch_df[col].astype(float)
|
|
|
|
# Keep only OHLCV columns
|
|
batch_df = batch_df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]
|
|
|
|
all_candles.append(batch_df)
|
|
|
|
# Move to next batch (add 1 minute to avoid overlap)
|
|
current_start = batch_end + timedelta(minutes=1)
|
|
|
|
# Rate limiting (Binance allows 1200/min)
|
|
time.sleep(0.05) # 50ms delay
|
|
|
|
# Progress logging
|
|
if api_calls_made % 10 == 0:
|
|
total_candles = sum(len(df) for df in all_candles)
|
|
logger.info(f"Progress: {api_calls_made} API calls, {total_candles} candles collected")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in batch {current_start} to {batch_end}: {e}")
|
|
current_start += timedelta(minutes=batch_size)
|
|
time.sleep(1) # Wait longer on error
|
|
continue
|
|
|
|
if not all_candles:
|
|
logger.error(f"No data collected for {symbol}")
|
|
return None
|
|
|
|
# Combine all batches
|
|
df = pd.concat(all_candles, ignore_index=True)
|
|
df = df.sort_values('timestamp').drop_duplicates(subset=['timestamp']).reset_index(drop=True)
|
|
|
|
logger.info(f"Successfully fetched {len(df)} 1m candles for {symbol} ({api_calls_made} API calls)")
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error fetching 1m data range for {symbol}: {e}")
|
|
return None
|
|
|
|
def _extract_pivot_bounds_from_monthly_data(self, symbol: str, monthly_data: pd.DataFrame) -> Optional[PivotBounds]:
|
|
"""Extract pivot bounds using Williams Market Structure analysis"""
|
|
try:
|
|
logger.info(f"Analyzing {len(monthly_data)} candles for pivot extraction...")
|
|
|
|
# Convert DataFrame to numpy array format expected by Williams Market Structure
|
|
ohlcv_array = monthly_data[['timestamp', 'open', 'high', 'low', 'close', 'volume']].copy()
|
|
|
|
# Convert timestamp to numeric for Williams analysis
|
|
ohlcv_array['timestamp'] = ohlcv_array['timestamp'].astype(np.int64) // 10**9 # Convert to seconds
|
|
ohlcv_array = ohlcv_array.to_numpy()
|
|
|
|
# Initialize Williams Market Structure analyzer
|
|
try:
|
|
|
|
williams = WilliamsMarketStructure(1)
|
|
|
|
# Calculate 5 levels of recursive pivot points
|
|
logger.info("Running Williams Market Structure analysis...")
|
|
pivot_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
|
|
|
|
except ImportError:
|
|
logger.warning("Williams Market Structure not available, using simplified pivot detection")
|
|
pivot_levels = self._simple_pivot_detection(monthly_data)
|
|
|
|
# Extract bounds from pivot analysis
|
|
bounds = self._extract_bounds_from_pivot_levels(symbol, monthly_data, pivot_levels)
|
|
|
|
return bounds
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error extracting pivot bounds for {symbol}: {e}")
|
|
return None
|
|
|
|
def _extract_bounds_from_pivot_levels(self, symbol: str, monthly_data: pd.DataFrame,
|
|
pivot_levels: Dict[str, Any]) -> PivotBounds:
|
|
"""Extract normalization bounds from Williams pivot levels"""
|
|
try:
|
|
# Initialize bounds
|
|
price_max = monthly_data['high'].max()
|
|
price_min = monthly_data['low'].min()
|
|
volume_max = monthly_data['volume'].max()
|
|
volume_min = monthly_data['volume'].min()
|
|
|
|
support_levels = []
|
|
resistance_levels = []
|
|
|
|
# Extract pivot points from all Williams levels
|
|
for level_key, level_data in pivot_levels.items():
|
|
if level_data and hasattr(level_data, 'swing_points') and level_data.swing_points:
|
|
# Get prices from swing points
|
|
level_prices = [sp.price for sp in level_data.swing_points]
|
|
|
|
# Update overall price bounds
|
|
price_max = max(price_max, max(level_prices))
|
|
price_min = min(price_min, min(level_prices))
|
|
|
|
# Extract support and resistance levels
|
|
if hasattr(level_data, 'support_levels') and level_data.support_levels:
|
|
support_levels.extend(level_data.support_levels)
|
|
|
|
if hasattr(level_data, 'resistance_levels') and level_data.resistance_levels:
|
|
resistance_levels.extend(level_data.resistance_levels)
|
|
|
|
# Remove duplicates and sort
|
|
support_levels = sorted(list(set(support_levels)))
|
|
resistance_levels = sorted(list(set(resistance_levels)))
|
|
|
|
# Extract trend context from pivot levels
|
|
pivot_context = {
|
|
'nested_levels': len(pivot_levels),
|
|
'level_details': {}
|
|
}
|
|
|
|
# Get trend info from primary level (level_0)
|
|
if 'level_0' in pivot_levels and pivot_levels['level_0']:
|
|
level_0 = pivot_levels['level_0']
|
|
pivot_context['trend_direction'] = getattr(level_0, 'trend_direction', 'UNKNOWN')
|
|
pivot_context['trend_strength'] = getattr(level_0, 'trend_strength', 0.0)
|
|
else:
|
|
pivot_context['trend_direction'] = 'UNKNOWN'
|
|
pivot_context['trend_strength'] = 0.0
|
|
|
|
# Add details for each level
|
|
for level_key, level_data in pivot_levels.items():
|
|
if level_data:
|
|
level_info = {
|
|
'swing_points_count': len(getattr(level_data, 'swing_points', [])),
|
|
'support_levels_count': len(getattr(level_data, 'support_levels', [])),
|
|
'resistance_levels_count': len(getattr(level_data, 'resistance_levels', [])),
|
|
'trend_direction': getattr(level_data, 'trend_direction', 'UNKNOWN'),
|
|
'trend_strength': getattr(level_data, 'trend_strength', 0.0)
|
|
}
|
|
pivot_context['level_details'][level_key] = level_info
|
|
|
|
# Create PivotBounds object
|
|
bounds = PivotBounds(
|
|
symbol=symbol,
|
|
price_max=float(price_max),
|
|
price_min=float(price_min),
|
|
volume_max=float(volume_max),
|
|
volume_min=float(volume_min),
|
|
pivot_support_levels=support_levels,
|
|
pivot_resistance_levels=resistance_levels,
|
|
data_period_start=monthly_data['timestamp'].min(),
|
|
data_period_end=monthly_data['timestamp'].max(),
|
|
total_candles_analyzed=len(monthly_data)
|
|
)
|
|
|
|
logger.info(f"Extracted pivot bounds for {symbol}:")
|
|
logger.info(f" Price range: ${bounds.price_min:.2f} - ${bounds.price_max:.2f}")
|
|
logger.info(f" Volume range: {bounds.volume_min:.2f} - {bounds.volume_max:.2f}")
|
|
logger.info(f" Support levels: {len(bounds.pivot_support_levels)}")
|
|
logger.info(f" Resistance levels: {len(bounds.pivot_resistance_levels)}")
|
|
|
|
return bounds
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error extracting bounds from pivot levels: {e}")
|
|
# Fallback to simple min/max bounds
|
|
return PivotBounds(
|
|
symbol=symbol,
|
|
price_max=float(monthly_data['high'].max()),
|
|
price_min=float(monthly_data['low'].min()),
|
|
volume_max=float(monthly_data['volume'].max()),
|
|
volume_min=float(monthly_data['volume'].min()),
|
|
pivot_support_levels=[],
|
|
pivot_resistance_levels=[],
|
|
pivot_context={},
|
|
created_timestamp=datetime.now(),
|
|
data_period_start=monthly_data['timestamp'].min(),
|
|
data_period_end=monthly_data['timestamp'].max(),
|
|
total_candles_analyzed=len(monthly_data)
|
|
)
|
|
|
|
def _simple_pivot_detection(self, monthly_data: pd.DataFrame) -> Dict[str, Any]:
|
|
"""Simple pivot detection fallback when Williams Market Structure is not available"""
|
|
try:
|
|
# Simple high/low pivot detection using rolling windows
|
|
highs = monthly_data['high']
|
|
lows = monthly_data['low']
|
|
|
|
# Find local maxima and minima using different windows
|
|
pivot_highs = []
|
|
pivot_lows = []
|
|
|
|
for window in [5, 10, 20, 50]:
|
|
if len(monthly_data) > window * 2:
|
|
# Rolling max/min detection
|
|
rolling_max = highs.rolling(window=window, center=True).max()
|
|
rolling_min = lows.rolling(window=window, center=True).min()
|
|
|
|
# Find pivot highs (local maxima)
|
|
high_pivots = monthly_data[highs == rolling_max]['high'].tolist()
|
|
pivot_highs.extend(high_pivots)
|
|
|
|
# Find pivot lows (local minima)
|
|
low_pivots = monthly_data[lows == rolling_min]['low'].tolist()
|
|
pivot_lows.extend(low_pivots)
|
|
|
|
# Create proper pivot level structure
|
|
pivot_level = SimplePivotLevel(
|
|
swing_points=[],
|
|
support_levels=list(set(pivot_lows)),
|
|
resistance_levels=list(set(pivot_highs))
|
|
)
|
|
|
|
return {'level_0': pivot_level}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in simple pivot detection: {e}")
|
|
return {}
|
|
|
|
def _should_refresh_pivot_bounds(self, symbol: str) -> bool:
|
|
"""Check if pivot bounds need refreshing"""
|
|
try:
|
|
if symbol not in self.pivot_bounds:
|
|
return True
|
|
|
|
bounds = self.pivot_bounds[symbol]
|
|
age = datetime.now() - bounds.created_timestamp
|
|
|
|
return age > self.pivot_refresh_interval
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error checking pivot bounds refresh: {e}")
|
|
return True
|
|
|
|
def _refresh_pivot_bounds_for_symbol(self, symbol: str):
|
|
"""Refresh pivot bounds for a specific symbol"""
|
|
try:
|
|
# Collect monthly 1m data
|
|
monthly_data = self._collect_monthly_1m_data(symbol)
|
|
if monthly_data is None or monthly_data.empty:
|
|
logger.warning(f"Could not collect monthly data for {symbol}")
|
|
return
|
|
|
|
# Extract pivot bounds
|
|
bounds = self._extract_pivot_bounds_from_monthly_data(symbol, monthly_data)
|
|
if bounds is None:
|
|
logger.warning(f"Could not extract pivot bounds for {symbol}")
|
|
return
|
|
|
|
# Store bounds
|
|
self.pivot_bounds[symbol] = bounds
|
|
|
|
# Save to cache
|
|
self._save_pivot_bounds_to_cache(symbol, bounds)
|
|
|
|
logger.info(f"Successfully refreshed pivot bounds for {symbol}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error refreshing pivot bounds for {symbol}: {e}")
|
|
|
|
def _add_pivot_context_features(self, df: pd.DataFrame, symbol: str) -> pd.DataFrame:
|
|
"""Add pivot-derived context features for normalization"""
|
|
try:
|
|
if symbol not in self.pivot_bounds:
|
|
logger.warning("Pivot bounds missing for %s; access will be blocked until real data is ready (guideline: no stubs)", symbol)
|
|
return df
|
|
|
|
bounds = self.pivot_bounds[symbol]
|
|
current_prices = df['close']
|
|
|
|
# Distance to nearest support/resistance levels (normalized)
|
|
df['pivot_support_distance'] = current_prices.apply(bounds.get_nearest_support_distance)
|
|
df['pivot_resistance_distance'] = current_prices.apply(bounds.get_nearest_resistance_distance)
|
|
|
|
# Price position within pivot range (0 = price_min, 1 = price_max)
|
|
df['pivot_price_position'] = current_prices.apply(bounds.normalize_price).clip(0, 1)
|
|
|
|
# Add binary features for proximity to key levels
|
|
price_range = bounds.get_price_range()
|
|
proximity_threshold = price_range * 0.02 # 2% of price range
|
|
|
|
df['near_pivot_support'] = 0
|
|
df['near_pivot_resistance'] = 0
|
|
|
|
for price in current_prices:
|
|
# Check if near any support level
|
|
if any(abs(price - s) <= proximity_threshold for s in bounds.pivot_support_levels):
|
|
df.loc[df['close'] == price, 'near_pivot_support'] = 1
|
|
|
|
# Check if near any resistance level
|
|
if any(abs(price - r) <= proximity_threshold for r in bounds.pivot_resistance_levels):
|
|
df.loc[df['close'] == price, 'near_pivot_resistance'] = 1
|
|
|
|
logger.debug(f"Added pivot context features for {symbol}")
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error adding pivot context features for {symbol}: {e}")
|
|
return df
|
|
|
|
def _extract_symbol_from_dataframe(self, df: pd.DataFrame) -> Optional[str]:
|
|
"""Extract symbol from dataframe context (basic implementation)"""
|
|
# This is a simple implementation - in a real system, you might pass symbol explicitly
|
|
# or store it as metadata in the dataframe
|
|
for symbol in self.symbols:
|
|
# Check if this dataframe might belong to this symbol based on current processing
|
|
return symbol # Return first symbol for now - can be improved
|
|
return None
|
|
|
|
# === CACHE MANAGEMENT ===
|
|
|
|
def _auto_fix_corrupted_cache(self):
|
|
"""Automatically fix corrupted cache files on startup"""
|
|
try:
|
|
from utils.cache_manager import get_cache_manager
|
|
cache_manager = get_cache_manager()
|
|
|
|
# Quick health check
|
|
health_summary = cache_manager.get_cache_summary()
|
|
|
|
if health_summary['corrupted_files'] > 0:
|
|
logger.warning(f"Found {health_summary['corrupted_files']} corrupted cache files, cleaning up...")
|
|
|
|
# Auto-cleanup corrupted files (no confirmation needed)
|
|
deleted_files = cache_manager.cleanup_corrupted_files(dry_run=False)
|
|
|
|
deleted_count = 0
|
|
for cache_dir, files in deleted_files.items():
|
|
for file_info in files:
|
|
if "DELETED:" in file_info:
|
|
deleted_count += 1
|
|
|
|
logger.info(f"Auto-cleaned {deleted_count} corrupted cache files")
|
|
else:
|
|
logger.debug("Cache health check passed - no corrupted files found")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Cache auto-fix failed: {e}")
|
|
|
|
# === PIVOT BOUNDS CACHING ===
|
|
|
|
def _load_all_pivot_bounds(self):
|
|
"""Load all cached pivot bounds on startup"""
|
|
try:
|
|
for symbol in self.symbols:
|
|
bounds = self._load_pivot_bounds_from_cache(symbol)
|
|
if bounds:
|
|
self.pivot_bounds[symbol] = bounds
|
|
logger.info(f"Loaded cached pivot bounds for {symbol}")
|
|
except Exception as e:
|
|
logger.error(f"Error loading pivot bounds from cache: {e}")
|
|
|
|
def _load_pivot_bounds_from_cache(self, symbol: str) -> Optional[PivotBounds]:
|
|
"""Load pivot bounds from cache"""
|
|
try:
|
|
cache_file = self.pivot_cache_dir / f"{symbol.replace('/', '')}_pivot_bounds.pkl"
|
|
if cache_file.exists():
|
|
with open(cache_file, 'rb') as f:
|
|
bounds = pickle.load(f)
|
|
|
|
# Check if bounds are still valid (not too old)
|
|
age = datetime.now() - bounds.created_timestamp
|
|
if age <= self.pivot_refresh_interval:
|
|
return bounds
|
|
else:
|
|
logger.info(f"Cached pivot bounds for {symbol} are too old ({age.days} days)")
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error loading pivot bounds from cache for {symbol}: {e}")
|
|
return None
|
|
|
|
def _save_pivot_bounds_to_cache(self, symbol: str, bounds: PivotBounds):
|
|
"""Save pivot bounds to cache"""
|
|
try:
|
|
cache_file = self.pivot_cache_dir / f"{symbol.replace('/', '')}_pivot_bounds.pkl"
|
|
with open(cache_file, 'wb') as f:
|
|
pickle.dump(bounds, f)
|
|
logger.debug(f"Saved pivot bounds to cache for {symbol}")
|
|
except Exception as e:
|
|
logger.warning(f"Error saving pivot bounds to cache for {symbol}: {e}")
|
|
|
|
def _load_monthly_data_from_cache(self, symbol: str) -> Optional[pd.DataFrame]:
|
|
"""Load monthly 1m data from cache"""
|
|
try:
|
|
cache_file = self.monthly_data_cache_dir / f"{symbol.replace('/', '')}_monthly_1m.parquet"
|
|
if cache_file.exists():
|
|
try:
|
|
df = pd.read_parquet(cache_file)
|
|
# Ensure cached monthly data has proper timezone (UTC to match COB WebSocket data)
|
|
if not df.empty and 'timestamp' in df.columns:
|
|
if df['timestamp'].dt.tz is None:
|
|
# If no timezone info, assume UTC and keep in UTC
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True)
|
|
elif str(df['timestamp'].dt.tz) != 'UTC':
|
|
# Convert to UTC if different timezone
|
|
df['timestamp'] = df['timestamp'].dt.tz_convert('UTC')
|
|
logger.info(f"Loaded {len(df)} 1m candles from cache for {symbol}")
|
|
return df
|
|
except Exception as parquet_e:
|
|
# Handle corrupted Parquet file - expanded error detection
|
|
error_str = str(parquet_e).lower()
|
|
corrupted_indicators = [
|
|
"parquet magic bytes not found",
|
|
"corrupted",
|
|
"couldn't deserialize thrift",
|
|
"don't know what type",
|
|
"invalid parquet file",
|
|
"unexpected end of file",
|
|
"invalid metadata"
|
|
]
|
|
|
|
if any(indicator in error_str for indicator in corrupted_indicators):
|
|
logger.warning(f"Corrupted Parquet cache file for {symbol}, removing and returning None: {parquet_e}")
|
|
try:
|
|
cache_file.unlink() # Delete corrupted file
|
|
logger.info(f"Deleted corrupted monthly cache file: {cache_file}")
|
|
except Exception as delete_e:
|
|
logger.error(f"Failed to delete corrupted monthly cache file: {delete_e}")
|
|
return None
|
|
else:
|
|
raise parquet_e
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error loading monthly data from cache for {symbol}: {e}")
|
|
return None
|
|
|
|
def _save_monthly_data_to_cache(self, symbol: str, df: pd.DataFrame):
|
|
"""Save monthly 1m data to cache"""
|
|
try:
|
|
cache_file = self.monthly_data_cache_dir / f"{symbol.replace('/', '')}_monthly_1m.parquet"
|
|
df.to_parquet(cache_file, index=False)
|
|
logger.info(f"Saved {len(df)} monthly 1m candles to cache for {symbol}")
|
|
except Exception as e:
|
|
logger.warning(f"Error saving monthly data to cache for {symbol}: {e}")
|
|
|
|
def get_pivot_bounds(self, symbol: str) -> Optional[PivotBounds]:
|
|
"""Get pivot bounds for a symbol"""
|
|
return self.pivot_bounds.get(symbol)
|
|
|
|
def get_pivot_normalized_features(self, symbol: str, df: pd.DataFrame) -> Optional[pd.DataFrame]:
|
|
"""Get dataframe with pivot-normalized features"""
|
|
try:
|
|
if symbol not in self.pivot_bounds:
|
|
logger.warning(f"No pivot bounds available for {symbol}")
|
|
return df
|
|
|
|
bounds = self.pivot_bounds[symbol]
|
|
normalized_df = df.copy()
|
|
|
|
# Normalize price columns using pivot bounds
|
|
price_range = bounds.get_price_range()
|
|
for col in ['open', 'high', 'low', 'close']:
|
|
if col in normalized_df.columns:
|
|
normalized_df[col] = (normalized_df[col] - bounds.price_min) / price_range
|
|
|
|
# Normalize volume using pivot bounds
|
|
volume_range = bounds.volume_max - bounds.volume_min
|
|
if volume_range > 0 and 'volume' in normalized_df.columns:
|
|
normalized_df['volume'] = (normalized_df['volume'] - bounds.volume_min) / volume_range
|
|
|
|
return normalized_df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error applying pivot normalization for {symbol}: {e}")
|
|
return df
|
|
|
|
def build_base_data_input(self, symbol: str) -> Optional['BaseDataInput']:
|
|
"""
|
|
Build BaseDataInput from cached data (optimized for speed)
|
|
|
|
Args:
|
|
symbol: Trading symbol
|
|
|
|
Returns:
|
|
BaseDataInput with consistent data structure
|
|
"""
|
|
try:
|
|
from .data_models import BaseDataInput
|
|
|
|
# Get OHLCV data directly from optimized cache (no validation checks for speed)
|
|
ohlcv_1s_list = self._get_cached_ohlcv_bars(symbol, '1s', 300)
|
|
ohlcv_1m_list = self._get_cached_ohlcv_bars(symbol, '1m', 300)
|
|
ohlcv_1h_list = self._get_cached_ohlcv_bars(symbol, '1h', 300)
|
|
ohlcv_1d_list = self._get_cached_ohlcv_bars(symbol, '1d', 300)
|
|
|
|
# Get BTC reference data
|
|
btc_symbol = 'BTC/USDT'
|
|
btc_ohlcv_1s_list = self._get_cached_ohlcv_bars(btc_symbol, '1s', 300)
|
|
if not btc_ohlcv_1s_list:
|
|
# Use ETH data as fallback
|
|
btc_ohlcv_1s_list = ohlcv_1s_list
|
|
|
|
# Get cached data (fast lookups)
|
|
technical_indicators = self._get_latest_technical_indicators(symbol)
|
|
cob_data = self._get_latest_cob_data_object(symbol)
|
|
last_predictions = {}
|
|
|
|
# Build BaseDataInput (no validation for speed - assume data is good)
|
|
base_data = BaseDataInput(
|
|
symbol=symbol,
|
|
timestamp=datetime.now(),
|
|
ohlcv_1s=ohlcv_1s_list,
|
|
ohlcv_1m=ohlcv_1m_list,
|
|
ohlcv_1h=ohlcv_1h_list,
|
|
ohlcv_1d=ohlcv_1d_list,
|
|
btc_ohlcv_1s=btc_ohlcv_1s_list,
|
|
technical_indicators=technical_indicators,
|
|
cob_data=cob_data,
|
|
last_predictions=last_predictions
|
|
)
|
|
|
|
return base_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error building BaseDataInput for {symbol}: {e}")
|
|
return None
|
|
|
|
def _get_cached_ohlcv_bars(self, symbol: str, timeframe: str, max_count: int) -> List['OHLCVBar']:
|
|
"""Get OHLCV data list from cached data"""
|
|
try:
|
|
from .data_models import OHLCVBar
|
|
data_list = []
|
|
|
|
# Get cached data
|
|
if symbol in self.cached_data and timeframe in self.cached_data[symbol]:
|
|
cached_df = self.cached_data[symbol][timeframe]
|
|
if not cached_df.empty:
|
|
# Convert cached data to OHLCVBar objects
|
|
for idx, row in cached_df.tail(max_count).iterrows():
|
|
bar = OHLCVBar(
|
|
symbol=symbol,
|
|
timestamp=idx if hasattr(idx, 'to_pydatetime') else datetime.now(),
|
|
open=float(row['open']),
|
|
high=float(row['high']),
|
|
low=float(row['low']),
|
|
close=float(row['close']),
|
|
volume=float(row['volume']),
|
|
timeframe=timeframe
|
|
)
|
|
data_list.append(bar)
|
|
|
|
return data_list
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting cached OHLCV bars for {symbol}/{timeframe}: {e}")
|
|
return []
|
|
|
|
def _get_latest_technical_indicators(self, symbol: str) -> Dict[str, float]:
|
|
"""Get latest technical indicators for a symbol"""
|
|
try:
|
|
# Get latest data and calculate indicators
|
|
df = self.get_historical_data(symbol, '1h', limit=50)
|
|
if df is not None and not df.empty:
|
|
df_with_indicators = self._add_technical_indicators(df)
|
|
if not df_with_indicators.empty:
|
|
# Return the latest indicators as a dict
|
|
latest_row = df_with_indicators.iloc[-1]
|
|
indicators = {}
|
|
for col in df_with_indicators.columns:
|
|
if col not in ['open', 'high', 'low', 'close', 'volume', 'timestamp']:
|
|
indicators[col] = float(latest_row[col]) if pd.notna(latest_row[col]) else 0.0
|
|
return indicators
|
|
return {}
|
|
except Exception as e:
|
|
logger.error(f"Error getting technical indicators for {symbol}: {e}")
|
|
return {}
|
|
|
|
def _get_latest_cob_data_object(self, symbol: str) -> Optional['COBData']:
|
|
"""Get latest COB data as COBData object"""
|
|
try:
|
|
from .data_models import COBData
|
|
|
|
# Get latest COB data from cache
|
|
cob_data = self.get_latest_cob_data(symbol)
|
|
if cob_data:
|
|
# Determine current price (prefer explicit field, fallback to stats.mid_price)
|
|
stats = cob_data.get('stats', {}) if isinstance(cob_data.get('stats', {}), dict) else {}
|
|
current_price = cob_data.get('current_price') or stats.get('mid_price', 0.0)
|
|
bucket_size = 1.0 if 'ETH' in symbol else 10.0
|
|
|
|
# Ensure price buckets exist; compute from bids/asks if missing
|
|
price_buckets = cob_data.get('price_buckets') or {}
|
|
if (not price_buckets) and current_price:
|
|
price_buckets = self._compute_price_buckets_from_snapshot(
|
|
current_price=current_price,
|
|
bucket_size=bucket_size,
|
|
bids=cob_data.get('bids', []),
|
|
asks=cob_data.get('asks', [])
|
|
)
|
|
|
|
# Build imbalance map (price -> imbalance) if not provided
|
|
bid_ask_imbalance = cob_data.get('bid_ask_imbalance') or {}
|
|
if not bid_ask_imbalance and price_buckets:
|
|
tmp = {}
|
|
for price, bucket in price_buckets.items():
|
|
bid_vol = float(bucket.get('bid_volume', 0.0) or 0.0)
|
|
ask_vol = float(bucket.get('ask_volume', 0.0) or 0.0)
|
|
denom = bid_vol + ask_vol
|
|
tmp[price] = (bid_vol - ask_vol) / denom if denom > 0 else 0.0
|
|
bid_ask_imbalance = tmp
|
|
|
|
return COBData(
|
|
symbol=symbol,
|
|
timestamp=datetime.now(),
|
|
current_price=float(current_price or 0.0),
|
|
bucket_size=bucket_size,
|
|
price_buckets=price_buckets,
|
|
bid_ask_imbalance=bid_ask_imbalance,
|
|
volume_weighted_prices=cob_data.get('volume_weighted_prices', {}),
|
|
order_flow_metrics=cob_data.get('order_flow_metrics', {}),
|
|
ma_1s_imbalance=cob_data.get('ma_1s_imbalance', {}),
|
|
ma_5s_imbalance=cob_data.get('ma_5s_imbalance', {}),
|
|
ma_15s_imbalance=cob_data.get('ma_15s_imbalance', {}),
|
|
ma_60s_imbalance=cob_data.get('ma_60s_imbalance', {})
|
|
)
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB data object for {symbol}: {e}")
|
|
return None
|
|
|
|
def _compute_price_buckets_from_snapshot(
|
|
self,
|
|
current_price: float,
|
|
bucket_size: float,
|
|
bids: List[List[float]],
|
|
asks: List[List[float]]
|
|
) -> Dict[float, Dict[str, float]]:
|
|
"""Compute ±20 price buckets around current price from raw bids/asks.
|
|
|
|
Returns dict: price -> {bid_volume, ask_volume, total_volume, imbalance}
|
|
"""
|
|
try:
|
|
# Initialize bucket map for ±20 buckets
|
|
bucket_map: Dict[float, Dict[str, float]] = {}
|
|
if not current_price or bucket_size <= 0:
|
|
return bucket_map
|
|
|
|
# Center-aligned bucket prices
|
|
bucket_count = 20
|
|
for i in range(-bucket_count, bucket_count + 1):
|
|
price = (round(current_price / bucket_size) * bucket_size) + (i * bucket_size)
|
|
bucket_map[price] = {
|
|
'bid_volume': 0.0,
|
|
'ask_volume': 0.0,
|
|
'total_volume': 0.0,
|
|
'imbalance': 0.0,
|
|
}
|
|
|
|
# Aggregate bids
|
|
for level in (bids or [])[:200]:
|
|
try:
|
|
price, size = float(level[0]), float(level[1])
|
|
except Exception:
|
|
continue
|
|
bucket_price = round(price / bucket_size) * bucket_size
|
|
if bucket_price in bucket_map:
|
|
bucket_map[bucket_price]['bid_volume'] += size
|
|
|
|
# Aggregate asks
|
|
for level in (asks or [])[:200]:
|
|
try:
|
|
price, size = float(level[0]), float(level[1])
|
|
except Exception:
|
|
continue
|
|
bucket_price = round(price / bucket_size) * bucket_size
|
|
if bucket_price in bucket_map:
|
|
bucket_map[bucket_price]['ask_volume'] += size
|
|
|
|
# Compute totals and imbalance
|
|
for price, bucket in bucket_map.items():
|
|
bid_vol = float(bucket['bid_volume'])
|
|
ask_vol = float(bucket['ask_volume'])
|
|
total = bid_vol + ask_vol
|
|
bucket['total_volume'] = total
|
|
bucket['imbalance'] = (bid_vol - ask_vol) / total if total > 0 else 0.0
|
|
|
|
return bucket_map
|
|
except Exception as e:
|
|
logger.debug(f"Error computing price buckets: {e}")
|
|
return {}
|
|
|
|
|
|
|
|
def _add_basic_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""Add basic indicators for small datasets"""
|
|
try:
|
|
df = df.copy()
|
|
|
|
# Basic moving averages
|
|
if len(df) >= 20:
|
|
df['sma_20'] = ta.trend.sma_indicator(df['close'], window=20)
|
|
df['ema_12'] = ta.trend.ema_indicator(df['close'], window=12)
|
|
|
|
# Basic RSI - using our own implementation to avoid ta library deprecation warnings
|
|
if len(df) >= 14:
|
|
df['rsi_14'] = self._calculate_rsi(df['close'], period=14)
|
|
|
|
# Basic volume indicators
|
|
if len(df) >= 10:
|
|
df['volume_sma_10'] = df['volume'].rolling(window=10).mean()
|
|
|
|
# Basic price action
|
|
df['price_position'] = (df['close'] - df['low']) / (df['high'] - df['low'])
|
|
df['price_position'] = df['price_position'].fillna(0.5) # Default to middle
|
|
|
|
# Fill NaN values
|
|
df = df.ffill().bfill().fillna(0)
|
|
|
|
return df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error adding basic indicators: {e}")
|
|
return df
|
|
|
|
def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> float:
|
|
"""Calculate RSI (Relative Strength Index) - custom implementation to avoid ta library deprecation warnings"""
|
|
try:
|
|
if len(prices) < period + 1:
|
|
return 50.0 # Default neutral value
|
|
|
|
# Calculate price changes
|
|
delta = prices.diff()
|
|
|
|
# Separate gains and losses
|
|
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
|
|
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
|
|
|
|
# Calculate RS and RSI
|
|
rs = gain / loss
|
|
rsi = 100 - (100 / (1 + rs))
|
|
|
|
# Return the last value, or 50 if NaN
|
|
last_rsi = rsi.iloc[-1]
|
|
return float(last_rsi) if not pd.isna(last_rsi) else 50.0
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Error calculating RSI: {e}")
|
|
return 50.0 # Default neutral value
|
|
|
|
def _load_from_cache(self, symbol: str, timeframe: str) -> Optional[pd.DataFrame]:
|
|
"""Load data from cache"""
|
|
try:
|
|
cache_file = self.cache_dir / f"{symbol.replace('/', '')}_{timeframe}.parquet"
|
|
if cache_file.exists():
|
|
# Check if cache is recent - stricter rules for startup
|
|
cache_age = time.time() - cache_file.stat().st_mtime
|
|
|
|
# For 1m data, use cache only if less than 5 minutes old to avoid gaps
|
|
if timeframe == '1m':
|
|
max_age = 300 # 5 minutes
|
|
else:
|
|
max_age = 3600 # 1 hour for other timeframes
|
|
|
|
if cache_age < max_age:
|
|
try:
|
|
df = pd.read_parquet(cache_file)
|
|
# Ensure cached data has proper timezone (UTC to match COB WebSocket data)
|
|
if not df.empty and 'timestamp' in df.columns:
|
|
if df['timestamp'].dt.tz is None:
|
|
# If no timezone info, assume UTC and keep in UTC
|
|
df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True)
|
|
elif str(df['timestamp'].dt.tz) != 'UTC':
|
|
# Convert to UTC if different timezone
|
|
df['timestamp'] = df['timestamp'].dt.tz_convert('UTC')
|
|
logger.debug(f"Loaded {len(df)} rows from cache for {symbol} {timeframe} (age: {cache_age/60:.1f}min)")
|
|
return df
|
|
except Exception as parquet_e:
|
|
# Handle corrupted Parquet file - expanded error detection
|
|
error_str = str(parquet_e).lower()
|
|
corrupted_indicators = [
|
|
"parquet magic bytes not found",
|
|
"corrupted",
|
|
"couldn't deserialize thrift",
|
|
"don't know what type",
|
|
"invalid parquet file",
|
|
"unexpected end of file",
|
|
"invalid metadata"
|
|
]
|
|
|
|
if any(indicator in error_str for indicator in corrupted_indicators):
|
|
logger.warning(f"Corrupted Parquet cache file for {symbol} {timeframe}, removing and returning None: {parquet_e}")
|
|
try:
|
|
cache_file.unlink() # Delete corrupted file
|
|
logger.info(f"Deleted corrupted cache file: {cache_file}")
|
|
except Exception as delete_e:
|
|
logger.error(f"Failed to delete corrupted cache file: {delete_e}")
|
|
return None
|
|
else:
|
|
raise parquet_e
|
|
else:
|
|
logger.debug(f"Cache for {symbol} {timeframe} is too old ({cache_age/60:.1f}min > {max_age/60:.1f}min)")
|
|
return None
|
|
except Exception as e:
|
|
logger.warning(f"Error loading cache for {symbol} {timeframe}: {e}")
|
|
return None
|
|
|
|
def _save_to_cache(self, df: pd.DataFrame, symbol: str, timeframe: str):
|
|
"""Save data to cache"""
|
|
try:
|
|
cache_file = self.cache_dir / f"{symbol.replace('/', '')}_{timeframe}.parquet"
|
|
df.to_parquet(cache_file, index=False)
|
|
logger.debug(f"Saved {len(df)} rows to cache for {symbol} {timeframe}")
|
|
except Exception as e:
|
|
logger.warning(f"Error saving cache for {symbol} {timeframe}: {e}")
|
|
|
|
async def start_real_time_streaming(self):
|
|
"""Start real-time data streaming using COBIntegration"""
|
|
if self.is_streaming:
|
|
logger.warning("Real-time streaming already active")
|
|
return
|
|
|
|
self.is_streaming = True
|
|
logger.info("Starting real-time streaming via COBIntegration")
|
|
# COBIntegration is started in the constructor
|
|
|
|
async def stop_real_time_streaming(self):
|
|
"""Stop real-time data streaming"""
|
|
if not self.is_streaming:
|
|
return
|
|
|
|
logger.info("Stopping Enhanced COB WebSocket streaming")
|
|
self.is_streaming = False
|
|
|
|
# Stop COB Integration
|
|
if self.cob_integration:
|
|
try:
|
|
await self.cob_integration.stop()
|
|
logger.info("COB Integration stopped")
|
|
except Exception as e:
|
|
logger.error(f"Error stopping COB Integration: {e}")
|
|
|
|
# Stop Enhanced COB WebSocket
|
|
if self.enhanced_cob_websocket:
|
|
try:
|
|
await self.enhanced_cob_websocket.stop()
|
|
self.enhanced_cob_websocket = None
|
|
logger.info("Enhanced COB WebSocket stopped")
|
|
except Exception as e:
|
|
logger.error(f"Error stopping Enhanced COB WebSocket: {e}")
|
|
|
|
# Cancel any remaining WebSocket tasks
|
|
for symbol, task in self.websocket_tasks.items():
|
|
if not task.done():
|
|
task.cancel()
|
|
try:
|
|
await task
|
|
except asyncio.CancelledError:
|
|
pass
|
|
|
|
self.websocket_tasks.clear()
|
|
|
|
# === COB DATA ACCESS METHODS ===
|
|
|
|
def get_cob_raw_ticks(self, symbol: str, count: int = 1000) -> List[Dict]:
|
|
"""Get raw COB ticks for a symbol (up to 15 minutes of data)"""
|
|
try:
|
|
if symbol in self.cob_raw_ticks:
|
|
return list(self.cob_raw_ticks[symbol])[-count:]
|
|
return []
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB raw ticks for {symbol}: {e}")
|
|
return []
|
|
|
|
def get_cob_1s_aggregated(self, symbol: str, count: int = 300) -> List[Dict]:
|
|
"""Get 1s aggregated COB data with $1 price buckets"""
|
|
try:
|
|
if symbol in self.cob_1s_aggregated:
|
|
return list(self.cob_1s_aggregated[symbol])[-count:]
|
|
return []
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB 1s aggregated for {symbol}: {e}")
|
|
return []
|
|
|
|
def get_latest_cob_data(self, symbol: str) -> Optional[Dict]:
|
|
"""Get latest COB raw tick for a symbol"""
|
|
try:
|
|
if symbol in self.cob_raw_ticks and self.cob_raw_ticks[symbol]:
|
|
return self.cob_raw_ticks[symbol][-1]
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error getting latest COB data for {symbol}: {e}")
|
|
return None
|
|
|
|
def get_latest_cob_aggregated(self, symbol: str) -> Optional[Dict]:
|
|
"""Get latest 1s aggregated COB data for a symbol"""
|
|
try:
|
|
if symbol in self.cob_1s_aggregated and self.cob_1s_aggregated[symbol]:
|
|
return self.cob_1s_aggregated[symbol][-1]
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error getting latest COB aggregated for {symbol}: {e}")
|
|
return None
|
|
|
|
def subscribe_to_cob_raw_ticks(self, callback: Callable[[str, Dict], None]) -> str:
|
|
"""Subscribe to raw COB tick updates"""
|
|
subscriber_id = str(uuid.uuid4())
|
|
self.cob_data_callbacks.append(callback)
|
|
logger.info(f"COB raw tick subscriber added: {subscriber_id}")
|
|
return subscriber_id
|
|
|
|
def subscribe_to_cob_aggregated(self, callback: Callable[[str, Dict], None]) -> str:
|
|
"""Subscribe to 1s aggregated COB updates"""
|
|
subscriber_id = str(uuid.uuid4())
|
|
self.cob_aggregated_callbacks.append(callback)
|
|
logger.info(f"COB aggregated subscriber added: {subscriber_id}")
|
|
return subscriber_id
|
|
|
|
def get_cob_price_buckets(self, symbol: str, timeframe_seconds: int = 60) -> Dict:
|
|
"""Get price bucket analysis for a timeframe"""
|
|
try:
|
|
# Get aggregated data for the timeframe
|
|
count = min(timeframe_seconds, 900) # Max 15 minutes
|
|
aggregated_data = self.get_cob_1s_aggregated(symbol, count)
|
|
|
|
if not aggregated_data:
|
|
return {}
|
|
|
|
# Combine buckets across the timeframe
|
|
combined_bid_buckets = {}
|
|
combined_ask_buckets = {}
|
|
|
|
for data in aggregated_data:
|
|
bid_buckets = data.get('bid_buckets', {})
|
|
ask_buckets = data.get('ask_buckets', {})
|
|
|
|
for bucket, volume in bid_buckets.items():
|
|
if bucket not in combined_bid_buckets:
|
|
combined_bid_buckets[bucket] = 0
|
|
combined_bid_buckets[bucket] += volume
|
|
|
|
for bucket, volume in ask_buckets.items():
|
|
if bucket not in combined_ask_buckets:
|
|
combined_ask_buckets[bucket] = 0
|
|
combined_ask_buckets[bucket] += volume
|
|
|
|
return {
|
|
'symbol': symbol,
|
|
'timeframe_seconds': timeframe_seconds,
|
|
'bucket_size_usd': 1.0,
|
|
'bid_buckets': dict(sorted(combined_bid_buckets.items(), reverse=True)),
|
|
'ask_buckets': dict(sorted(combined_ask_buckets.items())),
|
|
'total_bid_volume': sum(combined_bid_buckets.values()),
|
|
'total_ask_volume': sum(combined_ask_buckets.values()),
|
|
'bucket_count': len(combined_bid_buckets) + len(combined_ask_buckets)
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB price buckets for {symbol}: {e}")
|
|
return {}
|
|
|
|
def get_cob_websocket_status(self) -> Dict[str, Any]:
|
|
"""Get COB WebSocket status"""
|
|
try:
|
|
if self.cob_integration:
|
|
return {
|
|
'status': 'active',
|
|
'symbols': self.symbols,
|
|
'websocket_status': self.cob_integration.get_websocket_status(),
|
|
'raw_tick_counts': {symbol: len(self.cob_raw_ticks[symbol]) for symbol in self.symbols},
|
|
'aggregated_counts': {symbol: len(self.cob_1s_aggregated[symbol]) for symbol in self.symbols}
|
|
}
|
|
else:
|
|
return {
|
|
'status': 'inactive',
|
|
'symbols': self.symbols,
|
|
'error': 'COB integration not initialized'
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB WebSocket status: {e}")
|
|
return {
|
|
'status': 'error',
|
|
'error': str(e)
|
|
}
|
|
|
|
async def _start_fallback_websocket_streaming(self):
|
|
"""Fallback to old WebSocket method if Enhanced COB WebSocket fails"""
|
|
try:
|
|
logger.warning("⚠️ Starting fallback WebSocket streaming")
|
|
|
|
# Start old WebSocket for each symbol
|
|
for symbol in self.symbols:
|
|
task = asyncio.create_task(self._websocket_stream(symbol))
|
|
self.websocket_tasks[symbol] = task
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error starting fallback WebSocket: {e}")
|
|
|
|
def get_cob_websocket_status(self) -> Dict[str, Any]:
|
|
"""Get COB WebSocket status for dashboard"""
|
|
try:
|
|
if self.enhanced_cob_websocket:
|
|
return self.enhanced_cob_websocket.get_status_summary()
|
|
else:
|
|
return {
|
|
'overall_status': 'not_initialized',
|
|
'symbols': {},
|
|
'websockets_available': False
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"Error getting COB WebSocket status: {e}")
|
|
return {
|
|
'overall_status': 'error',
|
|
'symbols': {},
|
|
'error': str(e)
|
|
}
|
|
|
|
def get_latest_cob_data(self, symbol: str) -> Optional[Dict]:
|
|
"""Get latest COB data from Enhanced WebSocket"""
|
|
try:
|
|
# First try the websocket data cache
|
|
if symbol in self.cob_websocket_data and self.cob_websocket_data[symbol]:
|
|
return self.cob_websocket_data[symbol]
|
|
|
|
# Fallback to raw ticks
|
|
if symbol in self.cob_raw_ticks and len(self.cob_raw_ticks[symbol]) > 0:
|
|
return self.cob_raw_ticks[symbol][-1] # Get latest raw tick
|
|
|
|
# No COB data available
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error getting latest COB data for {symbol}: {e}")
|
|
return None
|
|
|
|
async def _websocket_stream(self, symbol: str):
|
|
"""WebSocket stream for a single symbol using trade stream for better granularity"""
|
|
binance_symbol = symbol.replace('/', '').upper()
|
|
url = f"wss://stream.binance.com:9443/ws/{binance_symbol.lower()}@trade"
|
|
|
|
while self.is_streaming:
|
|
try:
|
|
logger.info(f"Connecting to WebSocket for {symbol}: {url}")
|
|
|
|
async with websockets.connect(url) as websocket:
|
|
logger.info(f"WebSocket connected for {symbol}")
|
|
|
|
async for message in websocket:
|
|
if not self.is_streaming:
|
|
break
|
|
|
|
try:
|
|
await self._process_trade_message(binance_symbol, message)
|
|
except Exception as e:
|
|
logger.warning(f"Error processing trade message for {symbol}: {e}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"WebSocket error for {symbol}: {e}")
|
|
self.distribution_stats['distribution_errors'] += 1
|
|
|
|
if self.is_streaming:
|
|
logger.info(f"Reconnecting WebSocket for {symbol} in 5 seconds...")
|
|
await asyncio.sleep(5)
|
|
|
|
async def _process_trade_message(self, symbol: str, message: str):
|
|
"""Process incoming trade message and distribute to subscribers"""
|
|
try:
|
|
trade_data = json.loads(message)
|
|
|
|
# Extract trade information
|
|
price = float(trade_data.get('p', 0))
|
|
quantity = float(trade_data.get('q', 0))
|
|
timestamp = datetime.fromtimestamp(int(trade_data.get('T', 0)) / 1000)
|
|
is_buyer_maker = trade_data.get('m', False)
|
|
trade_id = trade_data.get('t', '')
|
|
|
|
# Calculate volume in USDT
|
|
volume_usdt = price * quantity
|
|
|
|
# Data validation
|
|
if not self._validate_tick_data(symbol, price, volume_usdt):
|
|
logger.warning(f"Invalid tick data for {symbol}: price={price}, volume={volume_usdt}")
|
|
return
|
|
|
|
# Process raw tick through aggregator
|
|
side = 'sell' if is_buyer_maker else 'buy'
|
|
raw_tick, completed_bar = self.tick_aggregator.process_tick(
|
|
symbol=symbol,
|
|
timestamp=timestamp,
|
|
price=price,
|
|
volume=volume_usdt,
|
|
quantity=quantity,
|
|
side=side,
|
|
trade_id=str(trade_id)
|
|
)
|
|
|
|
# Update statistics
|
|
self.distribution_stats['total_ticks_received'] += 1
|
|
self.distribution_stats['ticks_per_symbol'][symbol] += 1
|
|
self.distribution_stats['last_tick_time'][symbol] = timestamp
|
|
self.last_prices[symbol] = price
|
|
|
|
if raw_tick:
|
|
self.distribution_stats['raw_ticks_processed'] += 1
|
|
|
|
# Notify raw tick callbacks
|
|
for callback in self.raw_tick_callbacks:
|
|
try:
|
|
callback(raw_tick)
|
|
except Exception as e:
|
|
logger.error(f"Error in raw tick callback: {e}")
|
|
|
|
if completed_bar:
|
|
self.distribution_stats['ohlcv_bars_created'] += 1
|
|
|
|
# Notify OHLCV bar callbacks
|
|
for callback in self.ohlcv_bar_callbacks:
|
|
try:
|
|
callback(completed_bar)
|
|
except Exception as e:
|
|
logger.error(f"Error in OHLCV bar callback: {e}")
|
|
|
|
# Create standardized tick for legacy compatibility
|
|
tick = MarketTick(
|
|
symbol=symbol,
|
|
timestamp=timestamp,
|
|
price=price,
|
|
volume=volume_usdt,
|
|
quantity=quantity,
|
|
side=side,
|
|
trade_id=str(trade_id),
|
|
is_buyer_maker=is_buyer_maker,
|
|
raw_data=trade_data
|
|
)
|
|
|
|
# Add to buffer
|
|
self.tick_buffers[symbol].append(tick)
|
|
|
|
# Update current prices and candles
|
|
await self._process_tick(symbol, tick)
|
|
|
|
# Distribute to all subscribers
|
|
self._distribute_tick(tick)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing trade message for {symbol}: {e}")
|
|
|
|
async def _process_tick(self, symbol: str, tick: MarketTick):
|
|
"""Process a single tick and update candles"""
|
|
try:
|
|
# Update current price
|
|
with self.data_lock:
|
|
self.current_prices[symbol] = tick.price
|
|
|
|
# Initialize real-time data structure if needed
|
|
if symbol not in self.real_time_data:
|
|
self.real_time_data[symbol] = {}
|
|
for tf in self.timeframes:
|
|
self.real_time_data[symbol][tf] = deque(maxlen=1000)
|
|
|
|
# Create tick record for candle updates
|
|
tick_record = {
|
|
'timestamp': tick.timestamp,
|
|
'price': tick.price,
|
|
'volume': tick.volume
|
|
}
|
|
|
|
# Update all timeframes
|
|
for timeframe in self.timeframes:
|
|
self._update_candle(symbol, timeframe, tick_record)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing tick for {symbol}: {e}")
|
|
|
|
def _update_candle(self, symbol: str, timeframe: str, tick: Dict):
|
|
"""Update candle for specific timeframe"""
|
|
try:
|
|
timeframe_secs = self.timeframe_seconds.get(timeframe, 3600)
|
|
current_time = tick['timestamp']
|
|
|
|
# Calculate candle start time using proper datetime truncation
|
|
if isinstance(current_time, datetime):
|
|
timestamp_seconds = current_time.timestamp()
|
|
else:
|
|
timestamp_seconds = current_time.timestamp() if hasattr(current_time, 'timestamp') else current_time
|
|
|
|
# Truncate to timeframe boundary
|
|
candle_start_seconds = int(timestamp_seconds // timeframe_secs) * timeframe_secs
|
|
candle_start = datetime.fromtimestamp(candle_start_seconds)
|
|
|
|
# Get current candle queue
|
|
candle_queue = self.real_time_data[symbol][timeframe]
|
|
|
|
# Check if we need a new candle
|
|
if not candle_queue or candle_queue[-1]['timestamp'] != candle_start:
|
|
# Create new candle
|
|
new_candle = {
|
|
'timestamp': candle_start,
|
|
'open': tick['price'],
|
|
'high': tick['price'],
|
|
'low': tick['price'],
|
|
'close': tick['price'],
|
|
'volume': tick['volume']
|
|
}
|
|
candle_queue.append(new_candle)
|
|
else:
|
|
# Update existing candle
|
|
current_candle = candle_queue[-1]
|
|
current_candle['high'] = max(current_candle['high'], tick['price'])
|
|
current_candle['low'] = min(current_candle['low'], tick['price'])
|
|
current_candle['close'] = tick['price']
|
|
current_candle['volume'] += tick['volume']
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating candle for {symbol} {timeframe}: {e}")
|
|
|
|
def get_latest_candles(self, symbol: str, timeframe: str, limit: int = 100) -> pd.DataFrame:
|
|
"""Get the latest candles from cached data only"""
|
|
try:
|
|
# Get cached data
|
|
cached_df = self.get_historical_data(symbol, timeframe, limit=limit)
|
|
|
|
# Get real-time data if available
|
|
with self.data_lock:
|
|
if symbol in self.real_time_data and timeframe in self.real_time_data[symbol]:
|
|
real_time_candles = list(self.real_time_data[symbol][timeframe])
|
|
|
|
if real_time_candles:
|
|
# Convert to DataFrame
|
|
rt_df = pd.DataFrame(real_time_candles)
|
|
|
|
if cached_df is not None and not cached_df.empty:
|
|
# Combine cached and real-time
|
|
# Remove overlapping candles from cached data
|
|
if not rt_df.empty:
|
|
cutoff_time = rt_df['timestamp'].min()
|
|
cached_df = cached_df[cached_df.index < cutoff_time]
|
|
|
|
# Concatenate
|
|
combined_df = pd.concat([cached_df, rt_df], ignore_index=True)
|
|
else:
|
|
combined_df = rt_df
|
|
|
|
return combined_df.tail(limit)
|
|
|
|
# Return just cached data if no real-time data
|
|
return cached_df.tail(limit) if cached_df is not None else pd.DataFrame()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting latest candles for {symbol} {timeframe}: {e}")
|
|
return pd.DataFrame()
|
|
|
|
def get_current_price(self, symbol: str) -> Optional[float]:
|
|
"""Get current price for a symbol from cached data"""
|
|
try:
|
|
# Try to get from 1s candle first (most recent)
|
|
for tf in ['1s', '1m', '1h', '1d']:
|
|
if symbol in self.cached_data and tf in self.cached_data[symbol]:
|
|
df = self.cached_data[symbol][tf]
|
|
if not df.empty:
|
|
return float(df.iloc[-1]['close'])
|
|
|
|
logger.warning(f"No cached price data available for {symbol}")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting current price for {symbol}: {e}")
|
|
return None
|
|
|
|
def calculate_williams_pivot_points(self, symbol: str, force_recalculate: bool = False) -> Dict[int, TrendLevel]:
|
|
"""
|
|
Calculate Williams Market Structure pivot points for a symbol
|
|
|
|
Args:
|
|
symbol: Trading symbol (e.g., 'ETH/USDT')
|
|
force_recalculate: Force recalculation even if cache is fresh
|
|
|
|
Returns:
|
|
Dictionary of trend levels with pivot points
|
|
"""
|
|
try:
|
|
# Check if we need to recalculate
|
|
now = datetime.now()
|
|
if (not force_recalculate and
|
|
symbol in self.last_pivot_calculation and
|
|
now - self.last_pivot_calculation[symbol] < self.pivot_calculation_interval):
|
|
# Return cached results
|
|
return self.pivot_points_cache.get(symbol, {})
|
|
|
|
# Get 1s OHLCV data for Williams Market Structure calculation
|
|
df_1s = self.get_historical_data(symbol, '1s', limit=1000)
|
|
if df_1s is None or len(df_1s) < 50:
|
|
logger.warning(f"Insufficient 1s data for Williams pivot calculation: {symbol}")
|
|
return {}
|
|
|
|
# Convert DataFrame to numpy array for Williams calculation
|
|
# Format: [timestamp_ms, open, high, low, close, volume]
|
|
ohlcv_array = np.column_stack([
|
|
df_1s.index.astype(np.int64) // 10**6, # Convert to milliseconds
|
|
df_1s['open'].values,
|
|
df_1s['high'].values,
|
|
df_1s['low'].values,
|
|
df_1s['close'].values,
|
|
df_1s['volume'].values
|
|
])
|
|
|
|
# Calculate recursive pivot points using Williams Market Structure
|
|
williams = self.williams_structure[symbol]
|
|
pivot_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
|
|
|
|
# Cache the results
|
|
self.pivot_points_cache[symbol] = pivot_levels
|
|
self.last_pivot_calculation[symbol] = now
|
|
|
|
logger.debug(f"Calculated Williams pivot points for {symbol}: {len(pivot_levels)} levels")
|
|
return pivot_levels
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating Williams pivot points for {symbol}: {e}")
|
|
return {}
|
|
|
|
def get_pivot_features_for_ml(self, symbol: str) -> np.ndarray:
|
|
"""
|
|
Get pivot point features for machine learning models
|
|
|
|
Returns a 250-element feature vector containing:
|
|
- Recent pivot points (price, strength, type) for each level
|
|
- Trend direction and strength for each level
|
|
- Time since last pivot for each level
|
|
"""
|
|
try:
|
|
# Ensure we have fresh pivot points
|
|
pivot_levels = self.calculate_williams_pivot_points(symbol)
|
|
|
|
if not pivot_levels:
|
|
logger.warning(f"No pivot points available for {symbol}")
|
|
return np.zeros(250, dtype=np.float32)
|
|
|
|
# Use Williams Market Structure to extract ML features
|
|
williams = self.williams_structure[symbol]
|
|
features = williams.get_pivot_features_for_ml(symbol)
|
|
|
|
return features
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting pivot features for ML: {e}")
|
|
return np.zeros(250, dtype=np.float32)
|
|
|
|
def get_market_structure_summary(self, symbol: str) -> Dict[str, Any]:
|
|
"""
|
|
Get current market structure summary for dashboard display
|
|
|
|
Returns:
|
|
Dictionary containing market structure information
|
|
"""
|
|
try:
|
|
# Ensure we have fresh pivot points
|
|
pivot_levels = self.calculate_williams_pivot_points(symbol)
|
|
|
|
if not pivot_levels:
|
|
return {
|
|
'symbol': symbol,
|
|
'levels': {},
|
|
'overall_trend': 'sideways',
|
|
'overall_strength': 0.0,
|
|
'last_update': datetime.now().isoformat(),
|
|
'error': 'No pivot points available'
|
|
}
|
|
|
|
# Use Williams Market Structure to get summary
|
|
williams = self.williams_structure[symbol]
|
|
structure = williams.get_current_market_structure()
|
|
structure['symbol'] = symbol
|
|
|
|
return structure
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting market structure summary for {symbol}: {e}")
|
|
return {
|
|
'symbol': symbol,
|
|
'levels': {},
|
|
'overall_trend': 'sideways',
|
|
'overall_strength': 0.0,
|
|
'last_update': datetime.now().isoformat(),
|
|
'error': str(e)
|
|
}
|
|
|
|
def get_recent_pivot_points(self, symbol: str, level: int = 1, count: int = 10) -> List[PivotPoint]:
|
|
"""
|
|
Get recent pivot points for a specific level
|
|
|
|
Args:
|
|
symbol: Trading symbol
|
|
level: Pivot level (1-5)
|
|
count: Number of recent pivots to return
|
|
|
|
Returns:
|
|
List of recent pivot points
|
|
"""
|
|
try:
|
|
pivot_levels = self.calculate_williams_pivot_points(symbol)
|
|
|
|
if level not in pivot_levels:
|
|
return []
|
|
|
|
trend_level = pivot_levels[level]
|
|
recent_pivots = trend_level.pivot_points[-count:] if len(trend_level.pivot_points) >= count else trend_level.pivot_points
|
|
|
|
return recent_pivots
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting recent pivot points for {symbol} level {level}: {e}")
|
|
return []
|
|
|
|
def get_price_at_index(self, symbol: str, index: int, timeframe: str = '1m') -> Optional[float]:
|
|
"""Get price at specific index for backtesting from cached data"""
|
|
try:
|
|
if symbol in self.cached_data and timeframe in self.cached_data[symbol]:
|
|
df = self.cached_data[symbol][timeframe]
|
|
if not df.empty and 0 <= index < len(df):
|
|
return float(df.iloc[index]['close'])
|
|
return None
|
|
except Exception as e:
|
|
logger.error(f"Error getting price at index {index}: {e}")
|
|
return None
|
|
|
|
def get_feature_matrix(self, symbol: str, timeframes: List[str] = None,
|
|
window_size: int = 20) -> Optional[np.ndarray]:
|
|
"""
|
|
Get comprehensive feature matrix for multiple timeframes with technical indicators
|
|
|
|
Returns:
|
|
np.ndarray: Shape (n_timeframes, window_size, n_features)
|
|
Each timeframe becomes a separate channel for CNN
|
|
"""
|
|
try:
|
|
if timeframes is None:
|
|
timeframes = self.timeframes
|
|
|
|
feature_channels = []
|
|
common_feature_names = None
|
|
|
|
# First pass: determine common features across all timeframes
|
|
timeframe_features = {}
|
|
for tf in timeframes:
|
|
logger.debug(f"Processing timeframe {tf} for {symbol}")
|
|
# Use cached data directly
|
|
if symbol in self.cached_data and tf in self.cached_data[symbol]:
|
|
df = self.cached_data[symbol][tf]
|
|
if not df.empty and len(df) >= window_size:
|
|
df = df.tail(window_size + 100) # Get enough data for indicators
|
|
else:
|
|
logger.warning(f"Insufficient cached data for {symbol} {tf}: {len(df) if not df.empty else 0} rows")
|
|
continue
|
|
else:
|
|
logger.warning(f"No cached data for {symbol} {tf}")
|
|
continue
|
|
|
|
# Get feature columns
|
|
basic_cols = ['open', 'high', 'low', 'close', 'volume']
|
|
indicator_cols = [col for col in df.columns
|
|
if col not in basic_cols + ['timestamp'] and not col.startswith('unnamed')]
|
|
|
|
selected_features = self._select_cnn_features(df, basic_cols, indicator_cols)
|
|
timeframe_features[tf] = (df, selected_features)
|
|
|
|
if common_feature_names is None:
|
|
common_feature_names = set(selected_features)
|
|
else:
|
|
common_feature_names = common_feature_names.intersection(set(selected_features))
|
|
|
|
if not common_feature_names:
|
|
logger.error(f"No common features found across timeframes for {symbol}")
|
|
return None
|
|
|
|
# Convert to sorted list for consistent ordering
|
|
common_feature_names = sorted(list(common_feature_names))
|
|
# logger.info(f"Using {len(common_feature_names)} common features: {common_feature_names}")
|
|
|
|
# Second pass: create feature channels with common features
|
|
for tf in timeframes:
|
|
if tf not in timeframe_features:
|
|
continue
|
|
|
|
df, _ = timeframe_features[tf]
|
|
|
|
# Use only common features
|
|
try:
|
|
tf_features = self._normalize_features(df[common_feature_names].tail(window_size), symbol=symbol)
|
|
|
|
if tf_features is not None and len(tf_features) == window_size:
|
|
feature_channels.append(tf_features.values)
|
|
logger.debug(f"Added {len(common_feature_names)} features for {tf}")
|
|
else:
|
|
logger.warning(f"Feature normalization failed for {tf}")
|
|
except Exception as e:
|
|
logger.error(f"Error processing features for {tf}: {e}")
|
|
continue
|
|
|
|
if not feature_channels:
|
|
logger.error(f"No valid feature channels created for {symbol}")
|
|
return None
|
|
|
|
# Verify all channels have the same shape
|
|
shapes = [channel.shape for channel in feature_channels]
|
|
if len(set(shapes)) > 1:
|
|
logger.error(f"Shape mismatch in feature channels: {shapes}")
|
|
return None
|
|
|
|
# Stack all timeframe channels
|
|
feature_matrix = np.stack(feature_channels, axis=0)
|
|
|
|
logger.debug(f"Created feature matrix for {symbol}: {feature_matrix.shape} "
|
|
f"({len(feature_channels)} timeframes, {window_size} steps, {len(common_feature_names)} features)")
|
|
|
|
return feature_matrix
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating feature matrix for {symbol}: {e}")
|
|
import traceback
|
|
logger.error(traceback.format_exc())
|
|
return None
|
|
|
|
def _select_cnn_features(self, df: pd.DataFrame, basic_cols: List[str], indicator_cols: List[str]) -> List[str]:
|
|
"""Select the most important features for CNN training"""
|
|
try:
|
|
selected = []
|
|
|
|
# Always include basic OHLCV (normalized)
|
|
selected.extend(basic_cols)
|
|
|
|
# Priority indicators (most informative for CNNs)
|
|
priority_indicators = [
|
|
# Trend indicators
|
|
'sma_10', 'sma_20', 'sma_50', 'ema_12', 'ema_26', 'ema_50',
|
|
'macd', 'macd_signal', 'macd_histogram',
|
|
'adx', 'adx_pos', 'adx_neg', 'psar',
|
|
|
|
# Momentum indicators
|
|
'rsi_14', 'rsi_7', 'rsi_21',
|
|
'stoch_k', 'stoch_d', 'williams_r', 'ultimate_osc',
|
|
|
|
# Volatility indicators
|
|
'bb_upper', 'bb_lower', 'bb_middle', 'bb_width', 'bb_percent',
|
|
'atr', 'keltner_upper', 'keltner_lower', 'keltner_middle',
|
|
|
|
# Volume indicators
|
|
'volume_sma_10', 'volume_sma_20', 'obv', 'vpt', 'mfi', 'ad_line', 'vwap',
|
|
|
|
# Price action
|
|
'price_position', 'true_range', 'roc',
|
|
|
|
# Custom composites
|
|
'trend_strength', 'momentum_composite', 'volatility_regime'
|
|
]
|
|
|
|
# Add available priority indicators
|
|
for indicator in priority_indicators:
|
|
if indicator in indicator_cols:
|
|
selected.append(indicator)
|
|
|
|
# Add any other technical indicators not in priority list (limit to avoid curse of dimensionality)
|
|
remaining_indicators = [col for col in indicator_cols if col not in selected]
|
|
if remaining_indicators:
|
|
# Limit to 10 additional indicators
|
|
selected.extend(remaining_indicators[:10])
|
|
|
|
# Verify all selected features exist in dataframe
|
|
final_selected = [col for col in selected if col in df.columns]
|
|
|
|
logger.debug(f"Selected {len(final_selected)} features from {len(df.columns)} available columns")
|
|
return final_selected
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error selecting CNN features: {e}")
|
|
return basic_cols # Fallback to basic OHLCV
|
|
|
|
def _normalize_features(self, df: pd.DataFrame, symbol: str = None) -> Optional[pd.DataFrame]:
|
|
"""Normalize features for CNN training using unified normalization across all timeframes"""
|
|
try:
|
|
df_norm = df.copy()
|
|
|
|
# Get unified normalization bounds for all timeframes
|
|
if symbol and symbol in self.pivot_bounds:
|
|
bounds = self.pivot_bounds[symbol]
|
|
price_range = bounds.get_price_range()
|
|
volume_range = bounds.volume_max - bounds.volume_min
|
|
|
|
logger.debug(f"Using unified pivot-based normalization for {symbol} (price_range: {price_range:.2f})")
|
|
else:
|
|
df_norm = df_norm.fillna(0)
|
|
|
|
# Ensure all values are in reasonable range for neural networks
|
|
df_norm = np.clip(df_norm, -10, 10)
|
|
|
|
return df_norm
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in unified feature normalization: {e}")
|
|
return None
|
|
|
|
return df_norm
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in symbol-grouped normalization for {symbol}: {e}")
|
|
return df
|
|
|
|
def get_historical_data_for_inference(self, symbol: str, timeframe: str, limit: int = 300) -> Optional[pd.DataFrame]:
|
|
"""Get normalized historical data specifically for model inference"""
|
|
try:
|
|
# Get raw historical data
|
|
raw_df = self.get_historical_data(symbol, timeframe, limit)
|
|
|
|
if raw_df is None or raw_df.empty:
|
|
return None
|
|
|
|
# Apply normalization for inference
|
|
normalized_df = self._normalize_features(raw_df, symbol)
|
|
|
|
logger.debug(f"Retrieved normalized historical data for inference: {symbol} {timeframe} ({len(normalized_df)} records)")
|
|
return normalized_df
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting normalized historical data for inference: {symbol} {timeframe}: {e}")
|
|
return None
|
|
|
|
def get_multi_symbol_features_for_inference(self, symbols_timeframes: List[Tuple[str, str]], limit: int = 300) -> Dict[str, Dict[str, pd.DataFrame]]:
|
|
"""Get normalized multi-symbol feature matrices for model inference"""
|
|
try:
|
|
logger.info("Preparing normalized multi-symbol features for model inference...")
|
|
|
|
symbol_features = {}
|
|
|
|
for symbol, timeframe in symbols_timeframes:
|
|
if symbol not in symbol_features:
|
|
symbol_features[symbol] = {}
|
|
|
|
# Get normalized data for inference
|
|
normalized_df = self.get_historical_data_for_inference(symbol, timeframe, limit)
|
|
|
|
if normalized_df is not None and not normalized_df.empty:
|
|
symbol_features[symbol][timeframe] = normalized_df
|
|
logger.debug(f"Prepared normalized features for {symbol} {timeframe}: {len(normalized_df)} records")
|
|
else:
|
|
logger.warning(f"No normalized data available for {symbol} {timeframe}")
|
|
symbol_features[symbol][timeframe] = None
|
|
|
|
return symbol_features
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating multi-symbol feature matrix: {e}")
|
|
return None
|
|
|
|
def get_cnn_features_for_inference(self, symbol: str, timeframe: str = '1m', window_size: int = 60) -> Optional[np.ndarray]:
|
|
"""Get normalized CNN features for a specific symbol and timeframe"""
|
|
try:
|
|
# Get normalized data
|
|
df = self.get_historical_data_for_inference(symbol, timeframe, limit=300)
|
|
|
|
if df is None or df.empty:
|
|
return None
|
|
|
|
# Extract recent window for CNN
|
|
recent_data = df.tail(window_size)
|
|
|
|
# Extract OHLCV features
|
|
features = recent_data[['open', 'high', 'low', 'close', 'volume']].values
|
|
|
|
logger.debug(f"Extracted CNN features for {symbol} {timeframe}: {features.shape}")
|
|
return features.flatten()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error extracting CNN features for {symbol} {timeframe}: {e}")
|
|
return None
|
|
|
|
def get_dqn_state_for_inference(self, symbols_timeframes: List[Tuple[str, str]], target_size: int = 100) -> Optional[np.ndarray]:
|
|
"""Get normalized DQN state vector combining multiple symbols and timeframes"""
|
|
try:
|
|
state_components = []
|
|
|
|
for symbol, timeframe in symbols_timeframes:
|
|
df = self.get_historical_data_for_inference(symbol, timeframe, limit=50)
|
|
|
|
if df is not None and not df.empty:
|
|
# Extract key features for state
|
|
latest = df.iloc[-1]
|
|
state_features = [
|
|
latest['close'], # Current price (normalized)
|
|
latest['volume'], # Current volume (normalized)
|
|
df['close'].pct_change().iloc[-1] if len(df) > 1 else 0, # Price change
|
|
]
|
|
state_components.extend(state_features)
|
|
|
|
if state_components:
|
|
# Pad or truncate to expected DQN state size
|
|
if len(state_components) < target_size:
|
|
state_components.extend([0] * (target_size - len(state_components)))
|
|
else:
|
|
state_components = state_components[:target_size]
|
|
|
|
state_vector = np.array(state_components, dtype=np.float32)
|
|
logger.debug(f"Created DQN state vector: {len(state_vector)} dimensions")
|
|
return state_vector
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating DQN state for inference: {e}")
|
|
return None
|
|
|
|
def get_transformer_sequences_for_inference(self, symbols_timeframes: List[Tuple[str, str]], seq_length: int = 150) -> List[np.ndarray]:
|
|
"""Get normalized sequences for transformer inference"""
|
|
try:
|
|
sequences = []
|
|
|
|
for symbol, timeframe in symbols_timeframes:
|
|
df = self.get_historical_data_for_inference(symbol, timeframe, limit=300)
|
|
|
|
if df is not None and not df.empty:
|
|
# Use last seq_length points as sequence
|
|
sequence = df.tail(seq_length)[['open', 'high', 'low', 'close', 'volume']].values
|
|
sequences.append(sequence)
|
|
logger.debug(f"Created transformer sequence for {symbol} {timeframe}: {sequence.shape}")
|
|
|
|
return sequences
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating transformer sequences for inference: {e}")
|
|
return []
|