guaiwoluo2020
a705593955
- 新增新闻爬取和监控模块 (news_crawler, news_monitor) - 新增 LLM 分析模块 (llm_analyzer) - 新增持仓管理和交易历史存储 - 新增系统日志功能 - 新增前端页面: News, Positions, Settings, SystemLog - 更新路由和 API 接口 - 更新 .gitignore 排除敏感文件
457 lines
16 KiB
Python
457 lines
16 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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转折点检测模块
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识别K线的高点和低点（分型识别）
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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, Tuple
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import threading
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from .store import KlineData
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class PivotPoint:
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"""转折点数据结构"""
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def __init__(self, symbol: str, period: str, timestamp, price: float,
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direction: str, strength: int = 3):
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self.symbol = symbol
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self.period = period
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self.timestamp = timestamp
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self.price = price
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self.direction = direction # "high" 或 "low"
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self.strength = strength # 转折强度（左右各N根K线）
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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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"price": self.price,
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"direction": self.direction,
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"strength": self.strength
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}
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class PivotDetector:
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"""转折点检测器"""
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# 各周期接近阈值（千分比）
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THRESHOLDS = {
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'H4': 0.0015, # 千分之1.5
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'H1': 0.0015, # 千分之1.5
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'M15': 0.0015, # 千分之1.5
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'M5': 0.0005, # 千分之0.5
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'M1': 0.0002 # 千分之0.2
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}
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# 各周期转折强度（左右各N根K线）
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# M1: 6根K线, M5: 4根K线, M15/H1/H4: 3根K线
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PERIOD_STRENGTH = {
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'M1': 6,
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'M5': 4,
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'M15': 3,
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'H1': 3,
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'H4': 3
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}
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def __init__(self):
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# 存储转折点: {SYMBOL: {PERIOD: [PivotPoint, ...]}}
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# 这是合并后的转折点，用于价格接近检测
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self._pivots = defaultdict(lambda: defaultdict(list))
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# 转折点时间线: {SYMBOL: {PERIOD: [PivotPoint, ...]}}
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# 这是合并前的原始转折点，按时间排序，用于判断趋势方向
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self._pivots_timeline = defaultdict(lambda: defaultdict(list))
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self._lock = threading.RLock()
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# 默认转折强度（左右各N根K线）- 仅作为后备值
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self.default_strength = 3
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print("[PivotDetector] 转折点检测器已初始化")
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print(f"[PivotDetector] 周期强度配置: {self.PERIOD_STRENGTH}")
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def detect_pivots(self, symbol: str, period: str, klines: List[KlineData],
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strength: int = None) -> List[PivotPoint]:
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"""
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检测转折点
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Args:
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symbol: 交易品种
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period: 周期
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klines: K线数据列表
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strength: 转折强度（左右各N根K线），None则使用周期默认值
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Returns:
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检测到的转折点列表
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"""
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# 优先使用传入的strength，否则使用周期配置的strength
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if strength is None:
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strength = self.PERIOD_STRENGTH.get(period, self.default_strength)
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if len(klines) < 2 * strength + 1:
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return []
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pivots = []
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# 遍历K线，检测分型
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for i in range(strength, len(klines) - strength):
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current = klines[i]
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# 检查是否为高点（顶分型）
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is_high = True
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for j in range(1, strength + 1):
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if klines[i - j].high >= current.high or klines[i + j].high >= current.high:
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is_high = False
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break
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if is_high:
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pivot = PivotPoint(
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symbol=symbol,
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period=period,
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timestamp=current.timestamp,
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price=current.high,
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direction="high",
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strength=strength
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)
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pivots.append(pivot)
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# 检查是否为低点（底分型）
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is_low = True
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for j in range(1, strength + 1):
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if klines[i - j].low <= current.low or klines[i + j].low <= current.low:
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is_low = False
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break
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if is_low:
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pivot = PivotPoint(
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symbol=symbol,
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period=period,
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timestamp=current.timestamp,
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price=current.low,
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direction="low",
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strength=strength
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)
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pivots.append(pivot)
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return pivots
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def _merge_pivots(self, pivots: List[PivotPoint], klines: List[KlineData]) -> List[PivotPoint]:
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"""
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合并相近的转折点
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合并规则：
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- 相邻两个同方向转折点
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- 价格相差在万分之四范围内
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- 高点合并：取较高的价格
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- 低点合并：取较低的价格
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Args:
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pivots: 原始转折点列表
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klines: K线数据（用于计算K线索引）
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Returns:
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合并后的转折点列表
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"""
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if len(pivots) < 2:
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return pivots
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# 分开处理高点和低点
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high_pivots = [p for p in pivots if p.direction == "high"]
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low_pivots = [p for p in pivots if p.direction == "low"]
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# 合并高点
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merged_highs = self._merge_same_direction(high_pivots, "high")
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# 合并低点
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merged_lows = self._merge_same_direction(low_pivots, "low")
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# 合并结果
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result = merged_highs + merged_lows
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return result
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def _merge_same_direction(self, pivots: List[PivotPoint], direction: str) -> List[PivotPoint]:
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"""
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合并同方向的转折点
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合并规则：相邻两个转折点价格差距小于万分之四时合并
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"""
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if len(pivots) < 2:
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return pivots
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# 按时间排序
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pivots = sorted(pivots, key=lambda p: str(p.timestamp))
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merged = []
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i = 0
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while i < len(pivots):
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current = pivots[i]
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# 查找需要合并的转折点
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group = [current]
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j = i + 1
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while j < len(pivots):
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next_pivot = pivots[j]
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# 检查价格差距（万分之四）
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if current.price > 0:
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price_diff_pct = abs(next_pivot.price - current.price) / current.price
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if price_diff_pct <= 0.0004: # 万分之四
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group.append(next_pivot)
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j += 1
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continue
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break
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# 从组中选择代表性转折点
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if direction == "high":
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# 高点：取价格最高的
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best = max(group, key=lambda p: p.price)
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else:
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# 低点：取价格最低的
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best = min(group, key=lambda p: p.price)
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merged.append(best)
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i = j
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return merged
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def update_pivots(self, symbol: str, period: str, klines: List[KlineData],
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strength: int = None) -> int:
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"""
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更新转折点数据
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Args:
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symbol: 交易品种
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period: 周期
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klines: K线数据列表
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strength: 转折强度，None则使用周期默认值
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Returns:
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更新后的转折点数量
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"""
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# 使用周期配置的strength
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if strength is None:
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strength = self.PERIOD_STRENGTH.get(period, self.default_strength)
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pivots = self.detect_pivots(symbol, period, klines, strength)
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with self._lock:
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# 保存原始转折点到时间线（按时间排序，用于判断趋势）
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# 高点和低点混合在一起，按时间戳排序
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timeline = sorted(pivots, key=lambda p: self._normalize_timestamp(p.timestamp))
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self._pivots_timeline[symbol][period] = timeline
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# 合并相近的转折点（用于价格接近检测）
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merged_pivots = self._merge_pivots(pivots, klines)
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self._pivots[symbol][period] = merged_pivots
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count = len(merged_pivots)
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original_count = len(pivots)
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timeline_count = len(timeline)
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if original_count != count:
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print(f"[PivotDetector] {symbol} {period} 检测到 {original_count} 个转折点，时间线 {timeline_count} 个，合并后 {count} 个")
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else:
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print(f"[PivotDetector] {symbol} {period} 检测到 {count} 个转折点")
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return count
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def _normalize_timestamp(self, ts) -> str:
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"""标准化时间戳为字符串，用于排序比较"""
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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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def get_pivots(self, symbol: str, period: str, direction: str = None,
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count: int = 50) -> List[Dict]:
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"""
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获取转折点数据
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Args:
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symbol: 交易品种
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period: 周期
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direction: "high" 或 "low"，None表示全部
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count: 返回数量
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Returns:
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转折点列表
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"""
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with self._lock:
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pivots = self._pivots[symbol][period]
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if direction:
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pivots = [p for p in pivots if p.direction == direction]
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# 按时间排序，返回最新的
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pivots = sorted(pivots, key=lambda x: str(x.timestamp), reverse=True)[:count]
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return [p.to_dict() for p in pivots]
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def get_recent_pivots(self, symbol: str, period: str, count: int = 10) -> List[Dict]:
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"""获取最近的转折点（按时间倒序）"""
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with self._lock:
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pivots = self._pivots[symbol][period]
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pivots = sorted(pivots, key=lambda x: str(x.timestamp), reverse=True)[:count]
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return [p.to_dict() for p in pivots]
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def check_near_pivot(self, symbol: str, current_price: float,
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trend_filter: Dict[str, str] = None) -> List[Dict]:
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"""
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检查当前价格是否接近某个转折点
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Args:
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symbol: 交易品种
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current_price: 当前价格
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trend_filter: 趋势过滤，格式 {period: "up"/"down"}
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- "up": 趋势向上，只检查高点
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- "down": 趋势向下，只检查低点
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- 不提供或"unknown": 检查所有
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Returns:
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接近的转折点列表，包含距离信息
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预警逻辑：
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- 接近高点：当前价格 < 高点价格 且 距离在阈值范围内
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- 接近低点：当前价格 > 低点价格 且 距离在阈值范围内
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"""
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near_pivots = []
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with self._lock:
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for period in self._pivots[symbol]:
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pivots = self._pivots[symbol][period]
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threshold = self.THRESHOLDS.get(period, 0.001)
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# 获取该周期的趋势方向
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trend = trend_filter.get(period) if trend_filter else None
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for pivot in pivots:
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if pivot.price == 0 or current_price == 0:
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continue
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# 根据趋势过滤
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if trend == 'up' and pivot.direction != 'high':
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# 趋势向上，只检查高点
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continue
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elif trend == 'down' and pivot.direction != 'low':
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# 趋势向下，只检查低点
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continue
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is_near = False
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alert_type = ""
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if pivot.direction == "high":
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# 高点转折：当前价格低于高点
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if current_price < pivot.price:
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distance_pct = (pivot.price - current_price) / current_price
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if distance_pct <= threshold:
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is_near = True
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alert_type = "near_high"
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elif pivot.direction == "low":
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# 低点转折：当前价格高于低点
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if current_price > pivot.price:
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distance_pct = (current_price - pivot.price) / current_price
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if distance_pct <= threshold:
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is_near = True
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alert_type = "near_low"
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if is_near:
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distance_pct = abs(current_price - pivot.price) / current_price
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near_pivots.append({
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**pivot.to_dict(),
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"current_price": current_price,
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"distance_pct": round(distance_pct * 100, 4),
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"threshold_pct": round(threshold * 100, 4),
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"distance": round(current_price - pivot.price, 2),
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"alert_type": alert_type,
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"trend": trend # 记录趋势方向
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})
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# 按距离排序，最近的优先
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near_pivots.sort(key=lambda x: x['distance_pct'])
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return near_pivots
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def get_threshold(self, period: str) -> float:
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"""获取某个周期的接近阈值"""
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return self.THRESHOLDS.get(period, 0.001)
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def get_trend_direction(self, symbol: str, period: str = None) -> Dict[str, str]:
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"""
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根据最近的转折点判断趋势方向
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原理：
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- 最近是高点 → 价格刚从高点下来 → 趋势向下 → 应检查低点
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- 最近是低点 → 价格刚从低点上去 → 趋势向上 → 应检查高点
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Args:
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symbol: 交易品种
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period: 指定周期，如果为None则判断所有周期
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Returns:
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{period: "up"/"down"/"unknown"}
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- up: 趋势向上，应检查高点
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- down: 趋势向下，应检查低点
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"""
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result = {}
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periods_to_check = [period] if period else list(self._pivots_timeline[symbol].keys())
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with self._lock:
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for p in periods_to_check:
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timeline = self._pivots_timeline[symbol][p]
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if not timeline:
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result[p] = 'unknown'
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continue
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# 时间线已按时间排序，最后一个就是最近的转折点
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latest_pivot = timeline[-1]
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if latest_pivot.direction == 'high':
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# 最近是高点，价格往下走，趋势向下
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result[p] = 'down'
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else:
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# 最近是低点，价格往上走，趋势向上
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result[p] = 'up'
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return result
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def clear_symbol(self, symbol: str):
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"""清除某个Symbol的转折点数据"""
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with self._lock:
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if symbol in self._pivots:
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del self._pivots[symbol]
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if symbol in self._pivots_timeline:
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del self._pivots_timeline[symbol]
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def get_status(self) -> Dict:
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"""获取状态"""
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with self._lock:
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status = {}
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for symbol in self._pivots:
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status[symbol] = {}
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for period in self._pivots[symbol]:
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count = len(self._pivots[symbol][period])
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strength = self.PERIOD_STRENGTH.get(period, self.default_strength)
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status[symbol][period] = {
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"pivot_count": count,
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"strength": strength
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}
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return status
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def get_strength(self, period: str) -> int:
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"""获取某个周期的转折强度"""
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return self.PERIOD_STRENGTH.get(period, self.default_strength) |