298 lines
8.3 KiB
Python
298 lines
8.3 KiB
Python
"""
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算子系统：基础数学算子和技术指标算子的注册与管理
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支持算子的注册、查询、反射调用
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"""
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import numpy as np
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import pandas as pd
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from typing import Dict, Callable, List, Optional, Any
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from abc import ABC, abstractmethod
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import inspect
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class Operator(ABC):
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"""算子基类"""
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def __init__(self, name: str, func: Callable, description: str = ""):
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"""
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Parameters:
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-----------
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name : str
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算子名称（唯一标识）
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func : Callable
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算子函数
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description : str
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算子描述
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"""
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self.name = name
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self.func = func
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self.description = description
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self._signature = inspect.signature(func)
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def __call__(self, *args, **kwargs):
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"""调用算子函数"""
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return self.func(*args, **kwargs)
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def get_signature(self):
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"""获取函数签名"""
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return self._signature
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def __repr__(self):
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return f"Operator(name='{self.name}', description='{self.description}')"
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class OperatorRegistry:
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"""算子注册表"""
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def __init__(self):
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self._operators: Dict[str, Operator] = {}
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def register(self, operator: Operator):
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"""注册算子"""
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if operator.name in self._operators:
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raise ValueError(f"算子 '{operator.name}' 已存在")
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self._operators[operator.name] = operator
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def register_function(self, name: str, func: Callable, description: str = ""):
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"""直接注册函数为算子"""
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operator = Operator(name, func, description)
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self.register(operator)
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def get(self, name: str) -> Optional[Operator]:
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"""获取算子"""
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return self._operators.get(name)
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def has(self, name: str) -> bool:
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"""检查算子是否存在"""
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return name in self._operators
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def list_all(self) -> List[str]:
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"""列出所有算子名称"""
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return list(self._operators.keys())
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def get_all(self) -> Dict[str, Operator]:
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"""获取所有算子"""
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return self._operators.copy()
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# 全局算子注册表
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_registry = OperatorRegistry()
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def register_operator(name: str, description: str = ""):
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"""装饰器：注册算子"""
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def decorator(func: Callable):
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_registry.register_function(name, func, description)
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return func
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return decorator
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def get_operator(name: str) -> Optional[Operator]:
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"""获取算子"""
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return _registry.get(name)
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def get_registry() -> OperatorRegistry:
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"""获取全局注册表"""
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return _registry
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# ==================== 基础数学算子 ====================
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@register_operator("add", "加法: x + y")
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def _add(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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return x + y
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@register_operator("sub", "减法: x - y")
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def _sub(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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return x - y
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@register_operator("mul", "乘法: x * y")
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def _mul(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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return x * y
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@register_operator("div", "除法: x / y (安全除法)")
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def _div(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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denom = np.where(np.abs(y) < 1e-12, np.nan, y)
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return x / denom
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@register_operator("neg", "取负: -x")
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def _neg(x: np.ndarray) -> np.ndarray:
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return np.negative(x)
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@register_operator("abs", "绝对值: |x|")
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def _abs(x: np.ndarray) -> np.ndarray:
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return np.abs(x)
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@register_operator("log", "对数: log(|x|)")
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def _log(x: np.ndarray) -> np.ndarray:
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return np.log(np.clip(np.abs(x), 1e-12, None))
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@register_operator("sqrt", "平方根: sqrt(x)")
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def _sqrt(x: np.ndarray) -> np.ndarray:
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return np.sqrt(np.clip(x, 0.0, None))
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@register_operator("pow", "幂运算: x^y (限制范围)")
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def _pow(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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y_clip = np.clip(y, -3.0, 3.0)
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with np.errstate(over="ignore", invalid="ignore"):
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out = np.power(np.clip(x, -1e6, 1e6), y_clip)
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out[~np.isfinite(out)] = np.nan
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return out
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# ==================== 时间序列算子 ====================
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def _rolling_mean(x: np.ndarray, window: int) -> np.ndarray:
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s = pd.Series(x)
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return s.rolling(window, min_periods=max(2, window // 2)).mean().to_numpy()
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def _rolling_std(x: np.ndarray, window: int) -> np.ndarray:
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s = pd.Series(x)
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return s.rolling(window, min_periods=max(2, window // 2)).std().to_numpy()
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def _ts_delta(x: np.ndarray, period: int) -> np.ndarray:
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s = pd.Series(x)
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return s.diff(period).to_numpy()
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def _ts_rank(x: np.ndarray, window: int) -> np.ndarray:
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s = pd.Series(x)
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return (
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s.rolling(window, min_periods=max(2, window // 2))
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.apply(lambda a: pd.Series(a).rank(pct=True).iloc[-1], raw=False)
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.to_numpy()
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)
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def _delay(x: np.ndarray, period: int) -> np.ndarray:
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s = pd.Series(x)
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return s.shift(period).to_numpy()
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# 注册时间序列算子（带不同窗口）
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for w in (3, 6, 12, 24, 48, 96):
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_registry.register_function(
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f"sma{w}", lambda x, w=w: _rolling_mean(x, w), f"简单移动平均: SMA(x, {w})"
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)
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_registry.register_function(
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f"std{w}", lambda x, w=w: _rolling_std(x, w), f"滚动标准差: STD(x, {w})"
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)
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_registry.register_function(
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f"rank{w}", lambda x, w=w: _ts_rank(x, w), f"滚动排名: RANK(x, {w})"
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)
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_registry.register_function(
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f"delta{w}", lambda x, w=w: _ts_delta(x, w), f"差分: DELTA(x, {w})"
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)
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_registry.register_function(
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f"delay{w}", lambda x, w=w: _delay(x, w), f"延迟: DELAY(x, {w})"
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)
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# ==================== 因子公式解析与计算 ====================
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class FactorFormula:
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"""因子公式：支持序列化和反序列化"""
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def __init__(self, expression: str, feature_names: List[str]):
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"""
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Parameters:
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-----------
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expression : str
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因子表达式（使用算子名称）
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feature_names : List[str]
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特征名称列表
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"""
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self.expression = expression
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self.feature_names = feature_names
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def compute(self, features: Dict[str, np.ndarray]) -> np.ndarray:
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"""
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计算因子值
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Parameters:
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-----------
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features : Dict[str, np.ndarray]
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特征字典，key为特征名称
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Returns:
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--------
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np.ndarray: 因子值
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"""
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# 构建计算环境
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env = {}
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# 添加特征
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for name in self.feature_names:
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if name not in features:
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raise KeyError(f"特征 '{name}' 不存在")
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env[name] = features[name]
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# 添加算子
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for op_name in _registry.list_all():
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op = _registry.get(op_name)
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if op:
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env[op_name] = op.func
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# 添加numpy和pandas（用于某些表达式）
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env["np"] = np
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env["pd"] = pd
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# 执行表达式
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try:
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# 限制可用的内置函数
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safe_builtins = {
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"abs": abs,
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"min": min,
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"max": max,
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"sum": sum,
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"len": len,
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}
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result = eval(self.expression, {"__builtins__": safe_builtins}, env)
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# 确保结果是numpy数组
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if not isinstance(result, np.ndarray):
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if isinstance(result, (int, float)):
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# 标量转换为数组（广播）
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result = np.full(len(features[self.feature_names[0]]), result)
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else:
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result = np.array(result)
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# 确保长度一致
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expected_len = len(features[self.feature_names[0]])
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if len(result) != expected_len:
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raise ValueError(
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f"表达式结果长度 {len(result)} 与特征长度 {expected_len} 不匹配"
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)
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return result
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except Exception as e:
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raise RuntimeError(f"计算因子表达式失败: {e}\n表达式: {self.expression}")
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def to_dict(self) -> Dict:
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"""序列化为字典"""
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return {"expression": self.expression, "feature_names": self.feature_names}
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@classmethod
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def from_dict(cls, data: Dict) -> "FactorFormula":
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"""从字典反序列化"""
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return cls(data["expression"], data["feature_names"])
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def __repr__(self):
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return f"FactorFormula(expression='{self.expression}', features={self.feature_names})"
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