时间序列算子拆出来;
This commit is contained in:
@@ -10,6 +10,7 @@ from abc import ABC, abstractmethod
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import inspect
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import talib
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from factor_mining.time_series_op import register_time_series_operator
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class Operator(ABC):
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@@ -101,9 +102,6 @@ def get_registry() -> OperatorRegistry:
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return _registry
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# 定义period参数的值范围
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PERIOD_RANGE = range(10, 100) # 10到99
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# ==================== 基础数学算子 ====================
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@@ -158,496 +156,4 @@ def _pow(x: np.ndarray, y: np.ndarray) -> np.ndarray:
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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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def _pct_change(x: np.ndarray, period: int = 1) -> np.ndarray:
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"""百分比变化"""
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s = pd.Series(x)
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return s.pct_change(periods=period, fill_method=None).to_numpy()
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# 注册单参数百分比变化算子
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@register_operator("pct", "百分比变化: PCT(x, 1)")
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def _pct(x: np.ndarray) -> np.ndarray:
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return _pct_change(x, 1)
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# 注册时间序列算子（带不同窗口）
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for w in PERIOD_RANGE:
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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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# ==================== 技术指标算子（含自定义与ta-lib）====================
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def _try_float(x):
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try:
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return float(x)
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except Exception:
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return x
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def _convert_input(v):
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# 如果是pd.Series,返回np.ndarray; 如果已经是np.ndarray则原样返回
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if isinstance(v, pd.Series):
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return v.values
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return v
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# 注册 ta-lib 技术指标
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# 获取 TA-Lib 的所有函数名（常用financial indicators均为大写）
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talib_func_list = [f for f in dir(talib) if f.isupper() and callable(getattr(talib, f))]
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# 定义需要生成多版本的参数名（period相关参数）
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# 按优先级排序，优先匹配主要的period参数
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PERIOD_PARAM_NAMES = [
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"timeperiod", # 最常见的参数名
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"period", # 通用period参数
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"optintimeperiod", # TA-Lib内部参数名
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]
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# 多period参数的函数（需要特殊处理）
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# 对于这些函数，明确指定主要period参数，避免自动检测错误
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MULTI_PERIOD_FUNCTIONS = {
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# 函数名: (主要period参数名, 次要period参数列表，仅用于文档)
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"MACD": ("fastperiod", ["slowperiod", "signalperiod"]),
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"MACDEXT": ("fastperiod", ["slowperiod", "signalperiod"]),
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"MACDFIX": ("signalperiod", []),
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"STOCH": ("fastk_period", ["slowk_period", "slowd_period"]),
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"STOCHF": ("fastk_period", ["fastd_period"]),
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"STOCHRSI": ("timeperiod", ["fastk_period", "fastd_period"]),
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"BBANDS": ("timeperiod", ["nbdevup", "nbdevdn"]),
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"APO": ("fastperiod", ["slowperiod"]),
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"PPO": ("fastperiod", ["slowperiod"]),
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"ULTOSC": ("timeperiod1", ["timeperiod2", "timeperiod3"]),
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"BOP": ("", []), # 无period参数，注册默认版本
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}
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def build_talib_wrapper(func, func_name, fixed_params=None):
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"""构建talib函数包装器，支持固定某些参数"""
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fixed_params = fixed_params or {}
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def _talib_wrap(*args, **kwargs):
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# 合并固定参数和传入参数
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merged_kwargs = {**fixed_params, **kwargs}
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# ta-lib 有些函数只支持关键字参数
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# 自动转换所有输入类型
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args = tuple(_convert_input(arg) for arg in args)
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for k in merged_kwargs:
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merged_kwargs[k] = _convert_input(merged_kwargs[k])
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result = func(*args, **merged_kwargs)
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# TA-Lib有些输出是tuple（比如MACD），统一返回ndarray/tuple[ndarray]
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if isinstance(result, tuple):
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# 保持tuple结构
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return tuple(
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np.asarray(item) if item is not None else None for item in result
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)
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return np.asarray(result)
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_talib_wrap.__name__ = f"talib_{func_name.lower()}"
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return _talib_wrap
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for func_name in talib_func_list:
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func = getattr(talib, func_name)
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sig = inspect.signature(func)
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params = sig.parameters
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# 检查是否在特殊配置字典中
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if func_name in MULTI_PERIOD_FUNCTIONS:
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main_period_param, _ = MULTI_PERIOD_FUNCTIONS[func_name]
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# 如果配置中指定了主要period参数，使用它
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if main_period_param and main_period_param in params:
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for period_value in PERIOD_RANGE:
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fixed_params = {main_period_param: period_value}
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wrapper = build_talib_wrapper(func, func_name, fixed_params)
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op_name = f"talib_{func_name.lower()}_{period_value}"
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desc = f"ta-lib: {func_name}({main_period_param}={period_value})"
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_registry.register_function(op_name, wrapper, desc)
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else:
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# 配置中指定无period参数，注册默认版本
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wrapper = build_talib_wrapper(func, func_name)
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op_name = f"talib_{func_name.lower()}"
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desc = f"ta-lib: {func_name}"
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_registry.register_function(op_name, wrapper, desc)
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else:
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# 不在特殊配置中，自动检测period参数
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period_params = {}
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for param_name, param in params.items():
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param_lower = param_name.lower()
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# 检查是否是period相关参数
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if any(
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period_keyword in param_lower for period_keyword in PERIOD_PARAM_NAMES
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):
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period_params[param_name] = param
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if period_params:
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# 如果有period参数，为每个period值生成一个版本
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# 优先选择timeperiod，否则选择第一个
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main_period_param = None
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for preferred in ["timeperiod", "period", "optintimeperiod"]:
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for param_name in period_params.keys():
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if preferred in param_name.lower():
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main_period_param = param_name
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break
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if main_period_param:
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break
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if not main_period_param:
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main_period_param = list(period_params.keys())[0]
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for period_value in PERIOD_RANGE:
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fixed_params = {main_period_param: period_value}
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wrapper = build_talib_wrapper(func, func_name, fixed_params)
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op_name = f"talib_{func_name.lower()}_{period_value}"
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desc = f"ta-lib: {func_name}({main_period_param}={period_value})"
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_registry.register_function(op_name, wrapper, desc)
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else:
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# 如果没有period参数，注册默认版本
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wrapper = build_talib_wrapper(func, func_name)
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op_name = f"talib_{func_name.lower()}"
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desc = f"ta-lib: {func_name}"
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_registry.register_function(op_name, wrapper, desc)
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# ==================== 自定义常见技术指标 ====================
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def _ewm_forward(x: np.ndarray, alpha: float) -> np.ndarray:
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"""指数加权移动平均（前向计算）"""
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result = np.zeros_like(x)
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if len(x) == 0:
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return result
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result[0] = x[0]
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for i in range(1, len(x)):
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result[i] = x[i] * alpha + (1 - alpha) * result[i - 1]
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return result
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def _rsv(x: np.ndarray, window: int) -> np.ndarray:
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"""相对强弱值: (当前值 - 最小值) / (最大值 - 最小值)"""
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s = pd.Series(x)
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rolling = s.rolling(window, min_periods=max(2, window // 2), closed="both")
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min_val = rolling.min()
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max_val = rolling.max()
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diff = max_val - min_val
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# 避免除零
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diff = np.where(np.abs(diff) < 1e-12, np.nan, diff)
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result = (s - min_val) / diff
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return result.to_numpy()
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def _bband(x: np.ndarray, window: int) -> np.ndarray:
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"""布林带指标: (当前值 - 均值) / 标准差"""
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s = pd.Series(x)
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rolling = s.rolling(window, min_periods=max(2, window // 2), closed="both")
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mean_val = rolling.mean()
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std_val = rolling.std()
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# 避免除零
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std_val = np.where(np.abs(std_val) < 1e-12, np.nan, std_val)
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result = (s - mean_val) / std_val
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return result.to_numpy()
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def _rsi(x: np.ndarray, window: int, threshold: float = 0.00001) -> np.ndarray:
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"""相对强弱指标: 上涨和下跌的比例"""
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s = pd.Series(x)
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diff = s.diff()
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rolling = diff.rolling(window, min_periods=max(2, window // 2), closed="both")
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def _rsi_calc(series):
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up_sum = series[series > threshold].sum()
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down_sum = abs(series[series < -threshold].sum())
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total = up_sum + down_sum
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if total < 1e-12:
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return np.nan
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return up_sum / total
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result = rolling.apply(_rsi_calc, raw=False)
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return result.to_numpy()
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def _rolling_skew(x: np.ndarray, window: int) -> np.ndarray:
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"""滚动偏度"""
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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), closed="both")
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.skew()
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.to_numpy()
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)
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def _rolling_kurtosis(x: np.ndarray, window: int) -> np.ndarray:
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"""滚动峰度"""
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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), closed="both")
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.kurt()
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.to_numpy()
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)
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def _rolling_linear(x: np.ndarray, window: int) -> np.ndarray:
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"""滚动线性回归斜率"""
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s = pd.Series(x)
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def _linear_slope(series):
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valid = series.dropna()
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if len(valid) < 2:
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return np.nan
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try:
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coeffs = np.polyfit(np.arange(len(valid)), valid.values, 1)
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return coeffs[0]
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except:
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return np.nan
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result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
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_linear_slope, raw=False
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)
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return result.to_numpy()
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def _rolling_autocorr(x: np.ndarray, window: int, lag: int = 1) -> np.ndarray:
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"""滚动自相关"""
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s = pd.Series(x)
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result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
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lambda series: (
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series.autocorr(lag=lag) if len(series.dropna()) >= 2 else np.nan
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),
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raw=False,
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)
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return result.to_numpy()
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def _rolling_max(x: np.ndarray, window: int) -> np.ndarray:
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"""滚动最大值"""
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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), closed="both")
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.max()
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.to_numpy()
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)
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def _rolling_min(x: np.ndarray, window: int) -> np.ndarray:
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"""滚动最小值"""
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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), closed="both")
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.min()
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.to_numpy()
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)
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def _huanbi(x: np.ndarray, window: int) -> np.ndarray:
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"""环比: 当前值 / 窗口起始值"""
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s = pd.Series(x)
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def _huanbi_calc(series):
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if len(series) < 2:
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return np.nan
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start_val = series.iloc[0]
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end_val = series.iloc[-1]
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if abs(start_val) < 1e-12:
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return np.nan
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return end_val / start_val
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result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
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_huanbi_calc, raw=False
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)
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return result.to_numpy()
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# 注册技术指标算子（带不同窗口）
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for w in PERIOD_RANGE:
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# EWM算子（使用固定alpha值）
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alpha = 2.0 / (w + 1)
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_registry.register_function(
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f"ewm{w}",
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lambda x, w=w, a=alpha: _ewm_forward(x, a),
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f"指数加权移动平均: EWM(x, {w})",
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)
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# 百分比变化
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_registry.register_function(
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f"pct{w}", lambda x, w=w: _pct_change(x, w), f"百分比变化: PCT(x, {w})"
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)
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# RSV（相对强弱值）
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_registry.register_function(
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f"rsv{w}", lambda x, w=w: _rsv(x, w), f"相对强弱值: RSV(x, {w})"
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)
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# 布林带
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_registry.register_function(
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f"bband{w}", lambda x, w=w: _bband(x, w), f"布林带指标: BBAND(x, {w})"
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)
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# RSI
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_registry.register_function(
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f"rsi{w}", lambda x, w=w: _rsi(x, w), f"相对强弱指标: RSI(x, {w})"
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)
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# 统计量
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_registry.register_function(
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f"skew{w}", lambda x, w=w: _rolling_skew(x, w), f"滚动偏度: SKEW(x, {w})"
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)
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_registry.register_function(
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f"kurt{w}", lambda x, w=w: _rolling_kurtosis(x, w), f"滚动峰度: KURT(x, {w})"
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)
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_registry.register_function(
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f"linear{w}",
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lambda x, w=w: _rolling_linear(x, w),
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f"滚动线性斜率: LINEAR(x, {w})",
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)
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_registry.register_function(
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f"autocorr{w}",
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lambda x, w=w: _rolling_autocorr(x, w),
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f"滚动自相关: AUTOCORR(x, {w})",
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)
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_registry.register_function(
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f"max{w}", lambda x, w=w: _rolling_max(x, w), f"滚动最大值: MAX(x, {w})"
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)
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_registry.register_function(
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f"min{w}", lambda x, w=w: _rolling_min(x, w), f"滚动最小值: MIN(x, {w})"
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)
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# 环比
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_registry.register_function(
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f"huanbi{w}", lambda x, w=w: _huanbi(x, w), f"环比: HUANBI(x, {w})"
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)
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||||
|
||||
# ==================== 因子公式解析与计算 ====================
|
||||
|
||||
|
||||
class FactorFormula:
|
||||
"""因子公式：支持序列化和反序列化"""
|
||||
|
||||
def __init__(self, expression: str, feature_names: List[str]):
|
||||
"""
|
||||
Parameters:
|
||||
-----------
|
||||
expression : str
|
||||
因子表达式（使用算子名称）
|
||||
feature_names : List[str]
|
||||
特征名称列表
|
||||
"""
|
||||
self.expression = expression
|
||||
self.feature_names = feature_names
|
||||
|
||||
def compute(self, features: Dict[str, np.ndarray]) -> np.ndarray:
|
||||
"""
|
||||
计算因子值
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
features : Dict[str, np.ndarray]
|
||||
特征字典，key为特征名称
|
||||
|
||||
Returns:
|
||||
--------
|
||||
np.ndarray: 因子值
|
||||
"""
|
||||
# 构建计算环境
|
||||
env = {}
|
||||
|
||||
# 添加特征
|
||||
for name in self.feature_names:
|
||||
if name not in features:
|
||||
raise KeyError(f"特征 '{name}' 不存在")
|
||||
env[name] = features[name]
|
||||
|
||||
# 添加算子
|
||||
for op_name in _registry.list_all():
|
||||
op = _registry.get(op_name)
|
||||
if op:
|
||||
env[op_name] = op.func
|
||||
|
||||
# 添加numpy和pandas（用于某些表达式）
|
||||
env["np"] = np
|
||||
env["pd"] = pd
|
||||
|
||||
# 执行表达式
|
||||
try:
|
||||
# 限制可用的内置函数
|
||||
safe_builtins = {
|
||||
"abs": abs,
|
||||
"min": min,
|
||||
"max": max,
|
||||
"sum": sum,
|
||||
"len": len,
|
||||
}
|
||||
result = eval(self.expression, {"__builtins__": safe_builtins}, env)
|
||||
|
||||
# 确保结果是numpy数组
|
||||
if not isinstance(result, np.ndarray):
|
||||
if isinstance(result, (int, float)):
|
||||
# 标量转换为数组（广播）
|
||||
result = np.full(len(features[self.feature_names[0]]), result)
|
||||
else:
|
||||
result = np.array(result)
|
||||
|
||||
# 确保长度一致
|
||||
expected_len = len(features[self.feature_names[0]])
|
||||
if len(result) != expected_len:
|
||||
raise ValueError(
|
||||
f"表达式结果长度 {len(result)} 与特征长度 {expected_len} 不匹配"
|
||||
)
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"计算因子表达式失败: {e}\n表达式: {self.expression}")
|
||||
|
||||
def to_dict(self) -> Dict:
|
||||
"""序列化为字典"""
|
||||
return {"expression": self.expression, "feature_names": self.feature_names}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict) -> "FactorFormula":
|
||||
"""从字典反序列化"""
|
||||
return cls(data["expression"], data["feature_names"])
|
||||
|
||||
def __repr__(self):
|
||||
return f"FactorFormula(expression='{self.expression}', features={self.feature_names})"
|
||||
register_time_series_operator(_registry)
|
||||
|
||||
Reference in New Issue
Block a user