414 lines
14 KiB
Python
414 lines
14 KiB
Python
|
#!/usr/bin/env python3
|
|||
|
|
# -*- coding: utf-8 -*-
|
|||
|
|
"""
|
|||
|
|
转折点检测模块
|
|||
|
|
识别K线的高点和低点（分型识别）
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
from collections import defaultdict
|
|||
|
|
from datetime import datetime
|
|||
|
|
from typing import List, Dict, Optional, Tuple
|
|||
|
|
import threading
|
|||
|
|
|
|||
|
|
from .store import KlineData, normalize_symbol
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PivotPoint:
|
|||
|
|
"""转折点数据结构"""
|
|||
|
|
|
|||
|
|
def __init__(self, symbol: str, period: str, timestamp, price: float,
|
|||
|
|
direction: str, strength: int = 3):
|
|||
|
|
self.symbol = normalize_symbol(symbol)
|
|||
|
|
self.period = period
|
|||
|
|
self.timestamp = timestamp
|
|||
|
|
self.price = price
|
|||
|
|
self.direction = direction # "high" 或 "low"
|
|||
|
|
self.strength = strength # 转折强度（左右各N根K线）
|
|||
|
|
|
|||
|
|
def to_dict(self) -> Dict:
|
|||
|
|
"""转换为字典"""
|
|||
|
|
ts = self.timestamp
|
|||
|
|
if isinstance(ts, datetime):
|
|||
|
|
ts_str = ts.strftime("%Y-%m-%d %H:%M:%S")
|
|||
|
|
else:
|
|||
|
|
ts_str = str(ts)
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"symbol": self.symbol,
|
|||
|
|
"period": self.period,
|
|||
|
|
"timestamp": ts_str,
|
|||
|
|
"price": self.price,
|
|||
|
|
"direction": self.direction,
|
|||
|
|
"strength": self.strength
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
class PivotDetector:
|
|||
|
|
"""转折点检测器"""
|
|||
|
|
|
|||
|
|
# 各周期接近阈值（千分比）
|
|||
|
|
THRESHOLDS = {
|
|||
|
|
'H4': 0.0015, # 千分之1.5
|
|||
|
|
'H1': 0.0015, # 千分之1.5
|
|||
|
|
'M15': 0.0015, # 千分之1.5
|
|||
|
|
'M5': 0.0005, # 千分之0.5
|
|||
|
|
'M1': 0.0002 # 千分之0.2
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def __init__(self):
|
|||
|
|
# 存储转折点: {SYMBOL: {PERIOD: [PivotPoint, ...]}}
|
|||
|
|
self._pivots = defaultdict(lambda: defaultdict(list))
|
|||
|
|
self._lock = threading.RLock()
|
|||
|
|
|
|||
|
|
# 默认转折强度（左右各N根K线）
|
|||
|
|
self.default_strength = 3
|
|||
|
|
|
|||
|
|
print("[PivotDetector] 转折点检测器已初始化")
|
|||
|
|
|
|||
|
|
def detect_pivots(self, symbol: str, period: str, klines: List[KlineData],
|
|||
|
|
strength: int = None) -> List[PivotPoint]:
|
|||
|
|
"""
|
|||
|
|
检测转折点
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
symbol: 交易品种
|
|||
|
|
period: 周期
|
|||
|
|
klines: K线数据列表
|
|||
|
|
strength: 转折强度（左右各N根K线）
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
检测到的转折点列表
|
|||
|
|
"""
|
|||
|
|
if strength is None:
|
|||
|
|
strength = self.default_strength
|
|||
|
|
|
|||
|
|
if len(klines) < 2 * strength + 1:
|
|||
|
|
return []
|
|||
|
|
|
|||
|
|
pivots = []
|
|||
|
|
|
|||
|
|
# 遍历K线，检测分型
|
|||
|
|
for i in range(strength, len(klines) - strength):
|
|||
|
|
current = klines[i]
|
|||
|
|
|
|||
|
|
# 检查是否为高点（顶分型）
|
|||
|
|
is_high = True
|
|||
|
|
for j in range(1, strength + 1):
|
|||
|
|
if klines[i - j].high >= current.high or klines[i + j].high >= current.high:
|
|||
|
|
is_high = False
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
if is_high:
|
|||
|
|
pivot = PivotPoint(
|
|||
|
|
symbol=symbol,
|
|||
|
|
period=period,
|
|||
|
|
timestamp=current.timestamp,
|
|||
|
|
price=current.high,
|
|||
|
|
direction="high",
|
|||
|
|
strength=strength
|
|||
|
|
)
|
|||
|
|
pivots.append(pivot)
|
|||
|
|
|
|||
|
|
# 检查是否为低点（底分型）
|
|||
|
|
is_low = True
|
|||
|
|
for j in range(1, strength + 1):
|
|||
|
|
if klines[i - j].low <= current.low or klines[i + j].low <= current.low:
|
|||
|
|
is_low = False
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
if is_low:
|
|||
|
|
pivot = PivotPoint(
|
|||
|
|
symbol=symbol,
|
|||
|
|
period=period,
|
|||
|
|
timestamp=current.timestamp,
|
|||
|
|
price=current.low,
|
|||
|
|
direction="low",
|
|||
|
|
strength=strength
|
|||
|
|
)
|
|||
|
|
pivots.append(pivot)
|
|||
|
|
|
|||
|
|
return pivots
|
|||
|
|
|
|||
|
|
def _merge_pivots(self, pivots: List[PivotPoint], klines: List[KlineData]) -> List[PivotPoint]:
|
|||
|
|
"""
|
|||
|
|
合并相近的转折点
|
|||
|
|
|
|||
|
|
合并规则：
|
|||
|
|
- K线距离小于26根
|
|||
|
|
- 价格相差在万分之三范围内
|
|||
|
|
- 高点合并：取较高的价格
|
|||
|
|
- 低点合并：取较低的价格
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
pivots: 原始转折点列表
|
|||
|
|
klines: K线数据（用于计算K线索引）
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
合并后的转折点列表
|
|||
|
|
"""
|
|||
|
|
if len(pivots) < 2:
|
|||
|
|
return pivots
|
|||
|
|
|
|||
|
|
# 建立K线时间戳到索引的映射
|
|||
|
|
kline_index = {str(k.timestamp): i for i, k in enumerate(klines)}
|
|||
|
|
|
|||
|
|
# 按时间排序
|
|||
|
|
pivots = sorted(pivots, key=lambda p: str(p.timestamp))
|
|||
|
|
|
|||
|
|
# 分开处理高点和低点
|
|||
|
|
high_pivots = [p for p in pivots if p.direction == "high"]
|
|||
|
|
low_pivots = [p for p in pivots if p.direction == "low"]
|
|||
|
|
|
|||
|
|
# 合并高点
|
|||
|
|
merged_highs = self._merge_same_direction(
|
|||
|
|
high_pivots, kline_index, "high"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 合并低点
|
|||
|
|
merged_lows = self._merge_same_direction(
|
|||
|
|
low_pivots, kline_index, "low"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 合并结果
|
|||
|
|
result = merged_highs + merged_lows
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
def _merge_same_direction(self, pivots: List[PivotPoint],
|
|||
|
|
kline_index: Dict[str, int],
|
|||
|
|
direction: str) -> List[PivotPoint]:
|
|||
|
|
"""
|
|||
|
|
合并同方向的转折点
|
|||
|
|
"""
|
|||
|
|
if len(pivots) < 2:
|
|||
|
|
return pivots
|
|||
|
|
|
|||
|
|
merged = []
|
|||
|
|
i = 0
|
|||
|
|
|
|||
|
|
while i < len(pivots):
|
|||
|
|
current = pivots[i]
|
|||
|
|
current_idx = kline_index.get(str(current.timestamp), -1)
|
|||
|
|
|
|||
|
|
if current_idx < 0:
|
|||
|
|
i += 1
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 查找需要合并的转折点
|
|||
|
|
group = [current]
|
|||
|
|
|
|||
|
|
j = i + 1
|
|||
|
|
while j < len(pivots):
|
|||
|
|
next_pivot = pivots[j]
|
|||
|
|
next_idx = kline_index.get(str(next_pivot.timestamp), -1)
|
|||
|
|
|
|||
|
|
if next_idx < 0:
|
|||
|
|
j += 1
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 检查K线距离
|
|||
|
|
kline_distance = abs(next_idx - current_idx)
|
|||
|
|
|
|||
|
|
if kline_distance >= 26:
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 检查价格差距（万分之三）
|
|||
|
|
if current.price > 0:
|
|||
|
|
price_diff_pct = abs(next_pivot.price - current.price) / current.price
|
|||
|
|
if price_diff_pct <= 0.0003: # 万分之三
|
|||
|
|
group.append(next_pivot)
|
|||
|
|
j += 1
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 从组中选择代表性转折点
|
|||
|
|
if direction == "high":
|
|||
|
|
# 高点：取价格最高的
|
|||
|
|
best = max(group, key=lambda p: p.price)
|
|||
|
|
else:
|
|||
|
|
# 低点：取价格最低的
|
|||
|
|
best = min(group, key=lambda p: p.price)
|
|||
|
|
|
|||
|
|
merged.append(best)
|
|||
|
|
i = j
|
|||
|
|
|
|||
|
|
return merged
|
|||
|
|
|
|||
|
|
def update_pivots(self, symbol: str, period: str, klines: List[KlineData],
|
|||
|
|
strength: int = None) -> int:
|
|||
|
|
"""
|
|||
|
|
更新转折点数据
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
更新后的转折点数量
|
|||
|
|
"""
|
|||
|
|
symbol = normalize_symbol(symbol)
|
|||
|
|
|
|||
|
|
pivots = self.detect_pivots(symbol, period, klines, strength)
|
|||
|
|
|
|||
|
|
# 合并相近的转折点
|
|||
|
|
merged_pivots = self._merge_pivots(pivots, klines)
|
|||
|
|
|
|||
|
|
with self._lock:
|
|||
|
|
self._pivots[symbol][period] = merged_pivots
|
|||
|
|
count = len(merged_pivots)
|
|||
|
|
|
|||
|
|
original_count = len(pivots)
|
|||
|
|
if original_count != count:
|
|||
|
|
print(f"[PivotDetector] {symbol} {period} 检测到 {original_count} 个转折点，合并后 {count} 个")
|
|||
|
|
else:
|
|||
|
|
print(f"[PivotDetector] {symbol} {period} 检测到 {count} 个转折点")
|
|||
|
|
return count
|
|||
|
|
|
|||
|
|
def get_pivots(self, symbol: str, period: str, direction: str = None,
|
|||
|
|
count: int = 50) -> List[Dict]:
|
|||
|
|
"""
|
|||
|
|
获取转折点数据
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
symbol: 交易品种
|
|||
|
|
period: 周期
|
|||
|
|
direction: "high" 或 "low"，None表示全部
|
|||
|
|
count: 返回数量
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
转折点列表
|
|||
|
|
"""
|
|||
|
|
symbol = normalize_symbol(symbol)
|
|||
|
|
|
|||
|
|
with self._lock:
|
|||
|
|
pivots = self._pivots[symbol][period]
|
|||
|
|
|
|||
|
|
if direction:
|
|||
|
|
pivots = [p for p in pivots if p.direction == direction]
|
|||
|
|
|
|||
|
|
# 按时间排序，返回最新的
|
|||
|
|
pivots = sorted(pivots, key=lambda x: str(x.timestamp), reverse=True)[:count]
|
|||
|
|
|
|||
|
|
return [p.to_dict() for p in pivots]
|
|||
|
|
|
|||
|
|
def get_recent_pivots(self, symbol: str, period: str, count: int = 10) -> List[Dict]:
|
|||
|
|
"""获取最近的转折点（按时间倒序）"""
|
|||
|
|
symbol = normalize_symbol(symbol)
|
|||
|
|
|
|||
|
|
with self._lock:
|
|||
|
|
pivots = self._pivots[symbol][period]
|
|||
|
|
pivots = sorted(pivots, key=lambda x: str(x.timestamp), reverse=True)[:count]
|
|||
|
|
return [p.to_dict() for p in pivots]
|
|||
|
|
|
|||
|
|
def check_near_pivot(self, symbol: str, current_price: float) -> List[Dict]:
|
|||
|
|
"""
|
|||
|
|
检查当前价格是否接近某个转折点
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
symbol: 交易品种
|
|||
|
|
current_price: 当前价格
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
接近的转折点列表，包含距离信息
|
|||
|
|
|
|||
|
|
预警逻辑：
|
|||
|
|
- 接近高点：当前价格 < 高点价格 且 距离在阈值范围内
|
|||
|
|
- 接近低点：当前价格 > 低点价格 且 距离在阈值范围内
|
|||
|
|
- 突破高点：当前价格超过高点价格的万分之一点二（基于实时价格）
|
|||
|
|
- 突破低点：当前价格低于低点价格的万分之一点二（基于实时价格）
|
|||
|
|
- 超过千分之一不再提示
|
|||
|
|
"""
|
|||
|
|
symbol = normalize_symbol(symbol)
|
|||
|
|
near_pivots = []
|
|||
|
|
|
|||
|
|
# 突破阈值：万分之一点二
|
|||
|
|
BREAKTHROUGH_THRESHOLD = 0.00012
|
|||
|
|
# 最大提示范围：千分之一
|
|||
|
|
MAX_ALERT_THRESHOLD = 0.001
|
|||
|
|
|
|||
|
|
with self._lock:
|
|||
|
|
for period in self._pivots[symbol]:
|
|||
|
|
pivots = self._pivots[symbol][period]
|
|||
|
|
threshold = self.THRESHOLDS.get(period, 0.001)
|
|||
|
|
|
|||
|
|
for pivot in pivots:
|
|||
|
|
if pivot.price == 0 or current_price == 0:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# 基于实时价格计算阈值
|
|||
|
|
breakthrough_value = current_price * BREAKTHROUGH_THRESHOLD # 万分之一点二
|
|||
|
|
max_alert_value = current_price * MAX_ALERT_THRESHOLD # 千分之一
|
|||
|
|
|
|||
|
|
# 判断是接近还是突破
|
|||
|
|
is_near = False
|
|||
|
|
is_breakthrough = False
|
|||
|
|
alert_type = ""
|
|||
|
|
|
|||
|
|
if pivot.direction == "high":
|
|||
|
|
# 高点转折
|
|||
|
|
if current_price > pivot.price:
|
|||
|
|
# 当前价格高于高点，判断是否突破
|
|||
|
|
# 突破：超过高点的距离在万分之一点二到千分之一之间
|
|||
|
|
distance = current_price - pivot.price
|
|||
|
|
if distance >= breakthrough_value and distance < max_alert_value:
|
|||
|
|
is_breakthrough = True
|
|||
|
|
alert_type = "breakthrough_high"
|
|||
|
|
# 超过千分之一不再提示
|
|||
|
|
else:
|
|||
|
|
# 当前价格低于高点
|
|||
|
|
distance_pct = (pivot.price - current_price) / current_price
|
|||
|
|
if distance_pct <= threshold:
|
|||
|
|
is_near = True
|
|||
|
|
alert_type = "near_high"
|
|||
|
|
|
|||
|
|
elif pivot.direction == "low":
|
|||
|
|
# 低点转折
|
|||
|
|
if current_price < pivot.price:
|
|||
|
|
# 当前价格低于低点，判断是否突破
|
|||
|
|
# 突破：低于低点的距离在万分之一点二到千分之一之间
|
|||
|
|
distance = pivot.price - current_price
|
|||
|
|
if distance >= breakthrough_value and distance < max_alert_value:
|
|||
|
|
is_breakthrough = True
|
|||
|
|
alert_type = "breakthrough_low"
|
|||
|
|
# 超过千分之一不再提示
|
|||
|
|
else:
|
|||
|
|
# 当前价格高于低点
|
|||
|
|
distance_pct = (current_price - pivot.price) / current_price
|
|||
|
|
if distance_pct <= threshold:
|
|||
|
|
is_near = True
|
|||
|
|
alert_type = "near_low"
|
|||
|
|
|
|||
|
|
if is_near or is_breakthrough:
|
|||
|
|
distance_pct = abs(current_price - pivot.price) / current_price
|
|||
|
|
near_pivots.append({
|
|||
|
|
**pivot.to_dict(),
|
|||
|
|
"current_price": current_price,
|
|||
|
|
"distance_pct": round(distance_pct * 100, 4),
|
|||
|
|
"threshold_pct": round(threshold * 100, 4),
|
|||
|
|
"distance": round(current_price - pivot.price, 2),
|
|||
|
|
"alert_type": alert_type,
|
|||
|
|
"is_breakthrough": is_breakthrough
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 按距离排序，最近的优先
|
|||
|
|
near_pivots.sort(key=lambda x: x['distance_pct'])
|
|||
|
|
|
|||
|
|
return near_pivots
|
|||
|
|
|
|||
|
|
def get_threshold(self, period: str) -> float:
|
|||
|
|
"""获取某个周期的接近阈值"""
|
|||
|
|
return self.THRESHOLDS.get(period, 0.001)
|
|||
|
|
|
|||
|
|
def clear_symbol(self, symbol: str):
|
|||
|
|
"""清除某个Symbol的转折点数据"""
|
|||
|
|
symbol = normalize_symbol(symbol)
|
|||
|
|
with self._lock:
|
|||
|
|
if symbol in self._pivots:
|
|||
|
|
del self._pivots[symbol]
|
|||
|
|
|
|||
|
|
def get_status(self) -> Dict:
|
|||
|
|
"""获取状态"""
|
|||
|
|
with self._lock:
|
|||
|
|
status = {}
|
|||
|
|
for symbol in self._pivots:
|
|||
|
|
status[symbol] = {}
|
|||
|
|
for period in self._pivots[symbol]:
|
|||
|
|
count = len(self._pivots[symbol][period])
|
|||
|
|
status[symbol][period] = {"pivot_count": count}
|
|||
|
|
return status
|