259 lines
8.6 KiB
Python
259 lines
8.6 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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K线数据存储模块
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按周期和Symbol存储K线数据
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"""
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from collections import defaultdict
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from datetime import datetime
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from typing import List, Dict, Optional
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import threading
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def normalize_symbol(symbol: str) -> str:
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"""
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标准化品种名称（保持原样）
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"""
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return symbol if symbol else ""
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class KlineData:
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"""K线数据结构"""
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def __init__(self, symbol: str, period: str, timestamp, open_price: float,
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high: float, low: float, close: float, volume: float = 0):
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self.symbol = normalize_symbol(symbol)
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self.period = period # H4, H1, M15, M5, M1
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self.timestamp = timestamp
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self.open = open_price
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self.high = high
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self.low = low
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self.close = close
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self.volume = volume
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def to_dict(self) -> Dict:
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"""转换为字典"""
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ts = self.timestamp
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if isinstance(ts, datetime):
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ts_str = ts.strftime("%Y-%m-%d %H:%M:%S")
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else:
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ts_str = str(ts)
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return {
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"symbol": self.symbol,
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"period": self.period,
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"timestamp": ts_str,
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"open": self.open,
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"high": self.high,
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"low": self.low,
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"close": self.close,
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"volume": self.volume
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}
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class MarketStore:
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"""K线数据存储"""
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# 支持的周期
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PERIODS = ['H4', 'H1', 'M15', 'M5', 'M1']
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# 各周期最大存储条数
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MAX_KLINES = {
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'H4': 1500, # 4小时，6个月约1100根，留余量
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'H1': 1000, # 1小时，1个月约720根
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'M15': 500, # 15分钟，3天约288根
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'M5': 400, # 5分钟，24小时288根
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'M1': 100 # 1分钟，1小时60根
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}
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def __init__(self):
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# 存储结构: {SYMBOL: {PERIOD: [KlineData, ...]}}
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self._klines = defaultdict(lambda: defaultdict(list))
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self._lock = threading.RLock()
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# 标记每个symbol每个周期是否已收到全量数据
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# 结构: {SYMBOL: {PERIOD: True/False}}
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self._initialized = defaultdict(lambda: defaultdict(bool))
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print("[MarketStore] K线存储已初始化")
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def save_klines(self, symbol: str, period: str, klines: List[Dict],
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is_full: bool = False) -> Dict:
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"""
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保存K线数据
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Args:
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symbol: 交易品种
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period: 周期 (H4/H1/M15/M5/M1)
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klines: K线数据列表
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is_full: 是否为全量数据
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Returns:
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{"status": "ok", "count": N, "is_full": bool}
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"""
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symbol = normalize_symbol(symbol)
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period = period.upper()
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if period not in self.PERIODS:
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return {"status": "error", "message": f"不支持的周期: {period}"}
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with self._lock:
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if is_full:
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# 全量数据，直接覆盖
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self._klines[symbol][period] = []
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# 解析并存储K线数据
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new_count = 0
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for k in klines:
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kline = KlineData(
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symbol=symbol,
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period=period,
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timestamp=k.get('timestamp') or k.get('time'),
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open_price=float(k.get('open', 0)),
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high=float(k.get('high', 0)),
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low=float(k.get('low', 0)),
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close=float(k.get('close', 0)),
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volume=float(k.get('volume', 0))
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)
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# 检查是否已存在相同时间戳的数据
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existing = self._klines[symbol][period]
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ts = kline.timestamp
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# 查找是否已存在
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found_idx = -1
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for i, existing_kline in enumerate(existing):
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if self._normalize_timestamp(existing_kline.timestamp) == self._normalize_timestamp(ts):
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found_idx = i
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break
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if found_idx >= 0:
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# 更新已有数据
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existing[found_idx] = kline
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else:
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# 添加新数据
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existing.append(kline)
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new_count += 1
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# 按时间排序
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self._klines[symbol][period].sort(
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key=lambda x: self._normalize_timestamp(x.timestamp)
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)
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# 限制最大条数，保留最新的
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max_count = self.MAX_KLINES.get(period, 500)
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if len(self._klines[symbol][period]) > max_count:
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self._klines[symbol][period] = self._klines[symbol][period][-max_count:]
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# 标记已初始化
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self._initialized[symbol][period] = True
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total = len(self._klines[symbol][period])
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print(f"[MarketStore] {symbol} {period} 保存了 {new_count} 条新数据, 当前共 {total} 条")
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return {
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"status": "ok",
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"count": new_count,
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"total": total,
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"is_full": is_full
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}
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def get_klines(self, symbol: str, period: str, count: int = 100) -> List[Dict]:
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"""获取K线数据"""
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symbol = normalize_symbol(symbol)
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period = period.upper()
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with self._lock:
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klines = self._klines[symbol][period][-count:]
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return [k.to_dict() for k in klines]
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def get_all_klines(self, symbol: str, period: str) -> List[Dict]:
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"""获取所有K线数据"""
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symbol = normalize_symbol(symbol)
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period = period.upper()
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with self._lock:
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return [k.to_dict() for k in self._klines[symbol][period]]
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def get_latest_price(self, symbol: str) -> Optional[float]:
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"""获取最新价格（从K线的最新close，优先M1，依次尝试其他周期）"""
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symbol = normalize_symbol(symbol)
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with self._lock:
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# 尝试找到匹配的symbol（支持带#后缀的symbol）
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actual_symbol = None
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if symbol in self._klines:
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actual_symbol = symbol
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else:
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# 尝试添加#后缀
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for s in self._klines:
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if s.upper().startswith(symbol.upper()):
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actual_symbol = s
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break
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if not actual_symbol:
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return None
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# 按优先级尝试各周期（M1优先，然后更短周期）
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for period in ['M1', 'M5', 'M15', 'H1', 'H4']:
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klines = self._klines[actual_symbol][period]
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if klines:
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return klines[-1].close
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return None
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def is_initialized(self, symbol: str, period: str) -> bool:
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"""检查某个周期的数据是否已初始化"""
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symbol = normalize_symbol(symbol)
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period = period.upper()
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return self._initialized[symbol][period]
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def check_all_initialized(self, symbol: str) -> bool:
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"""检查所有周期是否都已初始化"""
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symbol = normalize_symbol(symbol)
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return all(self._initialized[symbol][p] for p in self.PERIODS)
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def clear_symbol(self, symbol: str):
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"""清除某个Symbol的数据"""
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symbol = normalize_symbol(symbol)
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with self._lock:
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if symbol in self._klines:
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del self._klines[symbol]
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if symbol in self._initialized:
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del self._initialized[symbol]
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def get_status(self) -> Dict:
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"""获取存储状态"""
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with self._lock:
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status = {}
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for symbol in self._klines:
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status[symbol] = {}
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for period in self.PERIODS:
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count = len(self._klines[symbol][period])
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initialized = self._initialized[symbol][period]
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status[symbol][period] = {
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"count": count,
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"initialized": initialized
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}
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return status
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def get_symbols(self) -> List[str]:
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"""获取所有有实际数据的symbol列表"""
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with self._lock:
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symbols = []
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for symbol in self._klines:
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# 检查是否有实际数据（任一周期有K线数据）
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has_data = False
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for period in self.PERIODS:
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if len(self._klines[symbol][period]) > 0:
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has_data = True
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break
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if has_data:
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symbols.append(symbol)
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return symbols
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def _normalize_timestamp(self, ts) -> str:
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"""标准化时间戳为字符串"""
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if isinstance(ts, datetime):
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return ts.strftime("%Y-%m-%d %H:%M:%S")
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return str(ts)
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