Rekey
edc50e8809
- 数据层: build_aggregates_sql 新增 2h/6h 聚合视图，默认起始时间调整为 2017-05 - 模型层: KlineInterval 类型扩展 2h/6h，DataService 新增对应表名和毫秒映射 - 指标层: 新增 incremental.py 增量指标模块 (EmaInc/AtrInc/RsiInc/BbInc)，O(1) per bar - 策略重构: long_short.py 和 regime_all.py 从批量 ema/atr 迁移至增量指标，避免每 bar 重复全量计算 - regime 探测器: RegimeDetector3 改为增量 EMA200，detect() 接口简化 - 回测扩展: regime_timeframe_comparison 从 4h/1d 扩展至 2h/4h/6h/1d - 新增示例: multi_strategy_report, vol_break_compare/periods, intraday_explore, top3_trades 等分析脚本
245 lines
7.2 KiB
Python
245 lines
7.2 KiB
Python
"""
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增量指标 — O(1) 每 bar 更新，避免每次从头重算整条序列
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策略在 on_kline 中对每根 bar 调用 update()，内部只计算增量值，
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对外暴露 values 属性（完整序列，支持索引回溯），兼顾性能与易用性。
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用法：
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from engine.indicators.incremental import EmaInc, AtrInc
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e200 = EmaInc(200)
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for price in prices:
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e200.update(price)
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print(e200[-1]) # 最新 EMA 值
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print(e200[-20]) # 20 根前的 EMA 值（斜率计算用）
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"""
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from typing import Optional
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class EmaInc:
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"""增量 EMA
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内部维护完整序列，update() 为 O(1)，values 为 list[float] 可直接索引。
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"""
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def __init__(self, period: int):
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self.period = period
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self.k = 2.0 / (period + 1)
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self._values: list[float] = []
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self._warm: list[float] = []
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self._ready = False
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def update(self, price: float) -> float:
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"""输入新价格，返回最新 EMA 值（不足周期时返回 0）"""
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if not self._ready:
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self._warm.append(price)
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self._values.append(0.0)
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if len(self._warm) == self.period:
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self._values[-1] = sum(self._warm) / self.period
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self._warm.clear()
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self._ready = True
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return self._values[-1]
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return 0.0
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val = price * self.k + self._values[-1] * (1 - self.k)
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self._values.append(val)
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return val
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@property
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def values(self) -> list[float]:
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return self._values
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@property
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def current(self) -> float:
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return self._values[-1] if self._values else 0.0
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def __getitem__(self, idx: int) -> float:
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return self._values[idx]
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def __len__(self) -> int:
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return len(self._values)
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class AtrInc:
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"""增量 ATR（Wilder 平滑）
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内部维护完整序列，update() 为 O(1)，values 为 list[float] 可直接索引。
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"""
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def __init__(self, period: int = 14):
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self.period = period
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self._values: list[float] = []
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self._tr_buffer: list[float] = []
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self._prev_close: Optional[float] = None
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self._ready = False
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def update(self, high: float, low: float, close: float) -> float:
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"""输入新 bar 的 HLC，返回最新 ATR 值（不足周期时返回 0）"""
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# 第一根 bar：记录收盘价，无法计算 TR
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if self._prev_close is None:
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self._prev_close = close
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self._values.append(0.0)
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return 0.0
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tr = max(high - low, abs(high - self._prev_close), abs(low - self._prev_close))
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self._prev_close = close
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if not self._ready:
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self._tr_buffer.append(tr)
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self._values.append(0.0)
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if len(self._tr_buffer) == self.period:
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atr_val = sum(self._tr_buffer) / self.period
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self._values[-1] = atr_val
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self._tr_buffer.clear()
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self._ready = True
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return atr_val
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return 0.0
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# Wilder 平滑
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atr_val = (self._values[-1] * (self.period - 1) + tr) / self.period
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self._values.append(atr_val)
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return atr_val
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@property
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def values(self) -> list[float]:
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return self._values
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@property
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def current(self) -> float:
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return self._values[-1] if self._values else 0.0
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def __getitem__(self, idx: int) -> float:
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return self._values[idx]
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def __len__(self) -> int:
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return len(self._values)
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class RsiInc:
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"""增量 RSI（Wilder 平滑）
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内部维护完整序列，update() 为 O(1)。
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"""
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def __init__(self, period: int = 14):
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self.period = period
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self._values: list[float] = []
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self._prev_price: Optional[float] = None
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self._avg_gain: float = 0.0
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self._avg_loss: float = 0.0
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self._changes: list[float] = []
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self._ready = False
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def update(self, price: float) -> float:
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if self._prev_price is None:
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self._prev_price = price
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self._values.append(0.0)
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return 0.0
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change = price - self._prev_price
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self._prev_price = price
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if not self._ready:
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self._changes.append(change)
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self._values.append(0.0)
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if len(self._changes) == self.period:
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gains = [max(c, 0.0) for c in self._changes]
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losses = [abs(min(c, 0.0)) for c in self._changes]
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self._avg_gain = sum(gains) / self.period
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self._avg_loss = sum(losses) / self.period
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self._changes.clear()
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self._ready = True
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rs = self._avg_gain / self._avg_loss if self._avg_loss > 0 else float("inf")
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rsi = 100.0 - (100.0 / (1.0 + rs)) if self._avg_loss > 0 else 100.0
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self._values[-1] = rsi
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return rsi
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return 0.0
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gain = max(change, 0.0)
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loss = abs(min(change, 0.0))
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self._avg_gain = (self._avg_gain * (self.period - 1) + gain) / self.period
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self._avg_loss = (self._avg_loss * (self.period - 1) + loss) / self.period
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if self._avg_loss == 0:
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rsi = 100.0
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else:
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rs = self._avg_gain / self._avg_loss
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rsi = 100.0 - (100.0 / (1.0 + rs))
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self._values.append(rsi)
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return rsi
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@property
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def values(self) -> list[float]:
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return self._values
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@property
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def current(self) -> float:
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return self._values[-1] if self._values else 0.0
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def __getitem__(self, idx: int) -> float:
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return self._values[idx]
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def __len__(self) -> int:
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return len(self._values)
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class BbInc:
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"""增量布林带
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内部维护完整序列，update() 返回 (upper, mid, lower) 三元组。
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"""
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def __init__(self, period: int = 20, std: float = 2.0):
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self.period = period
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self.std = std
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self._upper: list[float] = []
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self._mid: list[float] = []
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self._lower: list[float] = []
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self._window: list[float] = []
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self._window_sum: float = 0.0
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self._window_sum_sq: float = 0.0
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def update(self, price: float) -> tuple[float, float, float]:
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self._window.append(price)
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self._window_sum += price
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self._window_sum_sq += price * price
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if len(self._window) < self.period:
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self._upper.append(0.0)
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self._mid.append(0.0)
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self._lower.append(0.0)
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return 0.0, 0.0, 0.0
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if len(self._window) > self.period:
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old = self._window.pop(0)
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self._window_sum -= old
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self._window_sum_sq -= old * old
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mean = self._window_sum / self.period
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variance = (self._window_sum_sq / self.period) - (mean * mean)
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stdev = max(variance, 0.0) ** 0.5
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upper = mean + self.std * stdev
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lower = mean - self.std * stdev
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self._upper.append(upper)
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self._mid.append(mean)
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self._lower.append(lower)
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return upper, mean, lower
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@property
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def upper(self) -> list[float]:
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return self._upper
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@property
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def mid(self) -> list[float]:
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return self._mid
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@property
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def lower(self) -> list[float]:
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return self._lower
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def __len__(self) -> int:
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return len(self._mid)
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