添加talib算子

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+*.spec
+
+# Installer logs
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+
+# Unit test / coverage reports
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+.tox/
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+.coverage.*
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+.hypothesis/
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+# Translations
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+
+# Django stuff:
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+
+# pyenv
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+
+# pipenv
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+.env
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+*.db
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+
+# Backup files
+*.bak
+*.backup
+
+*.csv
+*.feather

factor_mining/gp_miner.py

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+"""
+DEAP遗传编程挖掘器实现
+"""
+
+import random
+import operator
+from typing import List, Tuple, Optional
+from dataclasses import dataclass
+import numpy as np
+import pandas as pd
+from deap import algorithms, base, creator, gp, tools
+
+from factor_mining.operators import FactorFormula, get_registry, get_operator
+from factor_mining.mining import FactorMiner, MiningConfig
+from data import compute_forward_returns
+
+
+@dataclass
+class GPConfig(MiningConfig):
+    """GP挖掘配置"""
+
+    population_size: int = 200
+    generations: int = 30
+    tournament_size: int = 5
+    crossover_prob: float = 0.9
+    mutation_prob: float = 0.05
+    elitism: int = 5
+    max_depth_init: int = 1
+    max_depth: int = 8
+    complexity_penalty: float = 0.001
+
+
+class GPMiner(FactorMiner):
+    """DEAP遗传编程挖掘器"""
+
+    def __init__(self, config: GPConfig):
+        super().__init__(config)
+        self.config: GPConfig = config
+        self.toolbox: Optional[base.Toolbox] = None
+        self.pset: Optional[gp.PrimitiveSetTyped] = None
+        self.features: Optional[List[pd.Series]] = None
+
+    def get_name(self) -> str:
+        return "gp"
+
+    def _build_pset(self, feature_names: List[str]) -> gp.PrimitiveSetTyped:
+        """构建GP原始集合"""
+        registry = get_registry()
+        pset = gp.PrimitiveSetTyped(
+            "MAIN", [np.ndarray for _ in feature_names], np.ndarray
+        )
+
+        # 命名参数
+        for i, name in enumerate(feature_names):
+            pset.renameArguments(**{f"ARG{i}": name})
+
+        # 添加算子
+        for op_name in registry.list_all():
+            op = registry.get(op_name)
+            if op:
+                sig = op.get_signature()
+                params = list(sig.parameters.values())
+
+                # 根据参数数量判断是一元还是二元算子
+                if len(params) == 1:
+                    # 一元算子
+                    pset.addPrimitive(op.func, [np.ndarray], np.ndarray, name=op_name)
+                elif len(params) == 2:
+                    # 二元算子
+                    pset.addPrimitive(
+                        op.func, [np.ndarray, np.ndarray], np.ndarray, name=op_name
+                    )
+
+        # 添加常量
+        # def _const() -> np.ndarray:
+        #     return np.array(random.uniform(-2.0, 2.0))
+        # pset.addEphemeralConstant("const", _const, np.ndarray)
+
+        return pset
+
+    def _evaluate_individual(self, individual, target: pd.Series) -> Tuple[float]:
+        """评估个体适应度"""
+        func = self.toolbox.compile(expr=individual)
+
+        # 构建特征矩阵
+        idx = target.index
+        inputs = [f.reindex(idx).to_numpy() for f in self.features]
+
+        try:
+            raw = func(*inputs)
+        except Exception:
+            return (-1e6,)
+
+        # 确保数组长度
+        if not isinstance(raw, np.ndarray):
+            return (-1e6,)
+        if raw.shape[0] != len(idx):
+            return (-1e6,)
+
+        # 转换为Series并清理
+        factor = pd.Series(raw, index=idx)
+        factor = factor.replace([np.inf, -np.inf], np.nan)
+        factor = factor.ffill().bfill()
+
+        # 计算滚动IC
+        window = self.config.ic_window
+        if len(factor) < window + 10:
+            return (-1e6,)
+
+        from validation import compute_rolling_ic
+
+        ic_series = compute_rolling_ic(
+            factor, target, window=window, method=self.config.ic_method
+        )
+        mean_ic = ic_series.mean()
+
+        if not np.isfinite(mean_ic):
+            return (-1e6,)
+
+        # 复杂度惩罚
+        complexity = len(individual)
+        fitness = mean_ic - self.config.complexity_penalty * complexity
+
+        if not np.isfinite(fitness):
+            fitness = -1e6
+
+        return (fitness,)
+
+    def _individual_to_formula(
+        self, individual, feature_names: List[str]
+    ) -> FactorFormula:
+        """将GP个体转换为因子公式"""
+        # GP表达式是PrimitiveTree，转换为字符串后是函数调用形式
+        # 例如: "add(ARG0, ARG1)" 或 "mul(add(ARG0, ARG1), const)"
+        expr_str = str(individual)
+
+        # 替换ARG0, ARG1等为实际特征名
+        for i, name in enumerate(feature_names):
+            expr_str = expr_str.replace(f"ARG{i}", name)
+
+        # GP表达式已经是Python可执行的函数调用格式
+        # 例如: "add(close, open)" 可以直接eval
+        # 但需要确保所有算子都在环境中可用
+
+        return FactorFormula(expr_str, feature_names)
+
+    def mine(
+        self, data: pd.DataFrame, feature_cols: List[str], price_col: str = "close"
+    ) -> List[FactorFormula]:
+        """执行GP挖掘"""
+        if self.config.seed is not None:
+            random.seed(self.config.seed)
+            np.random.seed(self.config.seed)
+
+        # 准备数据
+        price = data[price_col].astype(float)
+        forward_ret = compute_forward_returns(price, self.config.ret_horizon)
+        target = forward_ret
+
+        self.features = [data[c].astype(float) for c in feature_cols]
+
+        # 构建原始集合
+        self.pset = self._build_pset(feature_cols)
+
+        # 创建DEAP类型
+        if not hasattr(creator, "FitnessMax"):
+            creator.create("FitnessMax", base.Fitness, weights=(1.0,))
+        if not hasattr(creator, "Individual"):
+            creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax)
+
+        # 构建工具箱
+        self.toolbox = base.Toolbox()
+        self.toolbox.register(
+            "expr",
+            gp.genHalfAndHalf,
+            pset=self.pset,
+            min_=1,
+            max_=self.config.max_depth_init,
+        )
+        self.toolbox.register(
+            "individual", tools.initIterate, creator.Individual, self.toolbox.expr
+        )
+        self.toolbox.register(
+            "population", tools.initRepeat, list, self.toolbox.individual
+        )
+        self.toolbox.register("compile", gp.compile, pset=self.pset)
+
+        self.toolbox.register("evaluate", self._evaluate_individual, target=target)
+
+        # 遗传算子
+        self.toolbox.register(
+            "select", tools.selTournament, tournsize=self.config.tournament_size
+        )
+        self.toolbox.register("mate", gp.cxOnePoint)
+        self.toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
+        self.toolbox.register(
+            "mutate", gp.mutUniform, expr=self.toolbox.expr_mut, pset=self.pset
+        )
+
+        # 控制树深度
+        self.toolbox.decorate(
+            "mate",
+            gp.staticLimit(
+                key=operator.attrgetter("height"), max_value=self.config.max_depth
+            ),
+        )
+        self.toolbox.decorate(
+            "mutate",
+            gp.staticLimit(
+                key=operator.attrgetter("height"), max_value=self.config.max_depth
+            ),
+        )
+
+        # 运行进化
+        pop = self.toolbox.population(n=self.config.population_size)
+        hof = tools.HallOfFame(maxsize=max(5000, self.config.elitism))
+
+        stats_fit = tools.Statistics(lambda ind: ind.fitness.values[0])
+        stats_size = tools.Statistics(len)
+        mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
+        mstats.register("avg", np.nanmean)
+        mstats.register("std", np.nanstd)
+        mstats.register("min", np.nanmin)
+        mstats.register("max", np.nanmax)
+
+        pop, logbook = algorithms.eaSimple(
+            pop,
+            self.toolbox,
+            cxpb=self.config.crossover_prob,
+            mutpb=self.config.mutation_prob,
+            ngen=self.config.generations,
+            stats=mstats,
+            halloffame=hof,
+            verbose=True,
+        )
+
+        # 转换为因子公式
+        formulas = []
+        for individual in hof:
+            formula = self._individual_to_formula(individual, feature_cols)
+            formulas.append(formula)
+
+        return formulas

factor_mining/mining.py

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+"""
+因子挖掘抽象层：支持多种挖掘算法（DEAP、DL、RL等）
+"""
+from abc import ABC, abstractmethod
+from typing import List, Dict, Optional, Any
+import pandas as pd
+from dataclasses import dataclass
+
+from factor_mining.operators import FactorFormula
+
+
+@dataclass
+class MiningConfig:
+    """挖掘配置基类"""
+    ret_horizon: int = 1
+    ic_window: int = 30
+    ic_method: str = "spearman"  # "spearman" or "pearson"
+    seed: Optional[int] = None
+
+
+class FactorMiner(ABC):
+    """因子挖掘器抽象基类"""
+    
+    def __init__(self, config: MiningConfig):
+        self.config = config
+    
+    @abstractmethod
+    def mine(
+        self,
+        data: pd.DataFrame,
+        feature_cols: List[str],
+        price_col: str = "close"
+    ) -> List[FactorFormula]:
+        """
+        挖掘因子
+        
+        Parameters:
+        -----------
+        data : DataFrame
+            数据
+        feature_cols : List[str]
+            特征列名列表
+        price_col : str
+            价格列名
+        
+        Returns:
+        --------
+        List[FactorFormula]: 挖掘出的因子公式列表
+        """
+        pass
+    
+    @abstractmethod
+    def get_name(self) -> str:
+        """获取挖掘器名称"""
+        pass
+
+
+class MiningPipeline:
+    """挖掘流程管理器"""
+    
+    def __init__(self):
+        self.miners: Dict[str, FactorMiner] = {}
+    
+    def register_miner(self, miner: FactorMiner):
+        """注册挖掘器"""
+        name = miner.get_name()
+        if name in self.miners:
+            raise ValueError(f"挖掘器 '{name}' 已存在")
+        self.miners[name] = miner
+    
+    def get_miner(self, name: str) -> Optional[FactorMiner]:
+        """获取挖掘器"""
+        return self.miners.get(name)
+    
+    def list_miners(self) -> List[str]:
+        """列出所有挖掘器"""
+        return list(self.miners.keys())
+    
+    def mine(
+        self,
+        miner_name: str,
+        data: pd.DataFrame,
+        feature_cols: List[str],
+        price_col: str = "close"
+    ) -> List[FactorFormula]:
+        """
+        使用指定挖掘器进行挖掘
+        
+        Parameters:
+        -----------
+        miner_name : str
+            挖掘器名称
+        data : DataFrame
+            数据
+        feature_cols : List[str]
+            特征列名列表
+        price_col : str
+            价格列名
+        
+        Returns:
+        --------
+        List[FactorFormula]: 挖掘出的因子公式列表
+        """
+        miner = self.get_miner(miner_name)
+        if miner is None:
+            raise ValueError(f"挖掘器 '{miner_name}' 不存在")
+        
+        return miner.mine(data, feature_cols, price_col)
+
+
+# 全局挖掘流程管理器
+_pipeline = MiningPipeline()
+
+
+def register_miner(miner: FactorMiner):
+    """注册挖掘器到全局管理器"""
+    _pipeline.register_miner(miner)
+
+
+def get_pipeline() -> MiningPipeline:
+    """获取全局挖掘流程管理器"""
+    return _pipeline
+

factor_mining/operators.py

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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
+支持算子的注册、查询、反射调用
+"""
+
+import numpy as np
+import pandas as pd
+from typing import Dict, Callable, List, Optional, Any
+from abc import ABC, abstractmethod
+import inspect
+
+import talib
+
+
+class Operator(ABC):
+    """算子基类"""
+
+    def __init__(self, name: str, func: Callable, description: str = ""):
+        """
+        Parameters:
+        -----------
+        name : str
+            算子名称（唯一标识）
+        func : Callable
+            算子函数
+        description : str
+            算子描述
+        """
+        self.name = name
+        self.func = func
+        self.description = description
+        self._signature = inspect.signature(func)
+
+    def __call__(self, *args, **kwargs):
+        """调用算子函数"""
+        return self.func(*args, **kwargs)
+
+    def get_signature(self):
+        """获取函数签名"""
+        return self._signature
+
+    def __repr__(self):
+        return f"Operator(name='{self.name}', description='{self.description}')"
+
+
+class OperatorRegistry:
+    """算子注册表"""
+
+    def __init__(self):
+        self._operators: Dict[str, Operator] = {}
+
+    def register(self, operator: Operator):
+        """注册算子"""
+        if operator.name in self._operators:
+            raise ValueError(f"算子 '{operator.name}' 已存在")
+        self._operators[operator.name] = operator
+
+    def register_function(self, name: str, func: Callable, description: str = ""):
+        """直接注册函数为算子"""
+        operator = Operator(name, func, description)
+        self.register(operator)
+
+    def get(self, name: str) -> Optional[Operator]:
+        """获取算子"""
+        return self._operators.get(name)
+
+    def has(self, name: str) -> bool:
+        """检查算子是否存在"""
+        return name in self._operators
+
+    def list_all(self) -> List[str]:
+        """列出所有算子名称"""
+        return list(self._operators.keys())
+
+    def get_all(self) -> Dict[str, Operator]:
+        """获取所有算子"""
+        return self._operators.copy()
+
+
+# 全局算子注册表
+_registry = OperatorRegistry()
+
+
+def register_operator(name: str, description: str = ""):
+    """装饰器：注册算子"""
+
+    def decorator(func: Callable):
+        _registry.register_function(name, func, description)
+        return func
+
+    return decorator
+
+
+def get_operator(name: str) -> Optional[Operator]:
+    """获取算子"""
+    return _registry.get(name)
+
+
+def get_registry() -> OperatorRegistry:
+    """获取全局注册表"""
+    return _registry
+
+
+# 定义period参数的值范围
+PERIOD_RANGE = range(10, 100)  # 10到99
+
+# ==================== 基础数学算子 ====================
+
+
+@register_operator("add", "加法: x + y")
+def _add(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    return x + y
+
+
+@register_operator("sub", "减法: x - y")
+def _sub(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    return x - y
+
+
+@register_operator("mul", "乘法: x * y")
+def _mul(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    return x * y
+
+
+@register_operator("div", "除法: x / y (安全除法)")
+def _div(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    denom = np.where(np.abs(y) < 1e-12, np.nan, y)
+    return x / denom
+
+
+@register_operator("neg", "取负: -x")
+def _neg(x: np.ndarray) -> np.ndarray:
+    return np.negative(x)
+
+
+@register_operator("abs", "绝对值: |x|")
+def _abs(x: np.ndarray) -> np.ndarray:
+    return np.abs(x)
+
+
+@register_operator("log", "对数: log(|x|)")
+def _log(x: np.ndarray) -> np.ndarray:
+    return np.log(np.clip(np.abs(x), 1e-12, None))
+
+
+@register_operator("sqrt", "平方根: sqrt(x)")
+def _sqrt(x: np.ndarray) -> np.ndarray:
+    return np.sqrt(np.clip(x, 0.0, None))
+
+
+@register_operator("pow", "幂运算: x^y (限制范围)")
+def _pow(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    y_clip = np.clip(y, -3.0, 3.0)
+    with np.errstate(over="ignore", invalid="ignore"):
+        out = np.power(np.clip(x, -1e6, 1e6), y_clip)
+    out[~np.isfinite(out)] = np.nan
+    return out
+
+
+# ==================== 时间序列算子 ====================
+def _rolling_mean(x: np.ndarray, window: int) -> np.ndarray:
+    s = pd.Series(x)
+    return s.rolling(window, min_periods=max(2, window // 2)).mean().to_numpy()
+
+
+def _rolling_std(x: np.ndarray, window: int) -> np.ndarray:
+    s = pd.Series(x)
+    return s.rolling(window, min_periods=max(2, window // 2)).std().to_numpy()
+
+
+def _ts_delta(x: np.ndarray, period: int) -> np.ndarray:
+    s = pd.Series(x)
+    return s.diff(period).to_numpy()
+
+
+def _ts_rank(x: np.ndarray, window: int) -> np.ndarray:
+    s = pd.Series(x)
+    return (
+        s.rolling(window, min_periods=max(2, window // 2))
+        .apply(lambda a: pd.Series(a).rank(pct=True).iloc[-1], raw=False)
+        .to_numpy()
+    )
+
+
+def _delay(x: np.ndarray, period: int) -> np.ndarray:
+    s = pd.Series(x)
+    return s.shift(period).to_numpy()
+
+
+def _pct_change(x: np.ndarray, period: int = 1) -> np.ndarray:
+    """百分比变化"""
+    s = pd.Series(x)
+    return s.pct_change(periods=period, fill_method=None).to_numpy()
+
+
+# 注册单参数百分比变化算子
+@register_operator("pct", "百分比变化: PCT(x, 1)")
+def _pct(x: np.ndarray) -> np.ndarray:
+    return _pct_change(x, 1)
+
+
+# 注册时间序列算子（带不同窗口）
+for w in PERIOD_RANGE:
+    _registry.register_function(
+        f"sma{w}", lambda x, w=w: _rolling_mean(x, w), f"简单移动平均: SMA(x, {w})"
+    )
+    _registry.register_function(
+        f"std{w}", lambda x, w=w: _rolling_std(x, w), f"滚动标准差: STD(x, {w})"
+    )
+    _registry.register_function(
+        f"rank{w}", lambda x, w=w: _ts_rank(x, w), f"滚动排名: RANK(x, {w})"
+    )
+    _registry.register_function(
+        f"delta{w}", lambda x, w=w: _ts_delta(x, w), f"差分: DELTA(x, {w})"
+    )
+    _registry.register_function(
+        f"delay{w}", lambda x, w=w: _delay(x, w), f"延迟: DELAY(x, {w})"
+    )
+
+
+# ==================== 技术指标算子（含自定义与ta-lib）====================
+
+
+def _try_float(x):
+    try:
+        return float(x)
+    except Exception:
+        return x
+
+
+def _convert_input(v):
+    # 如果是pd.Series,返回np.ndarray; 如果已经是np.ndarray则原样返回
+    if isinstance(v, pd.Series):
+        return v.values
+    return v
+
+
+# 注册 ta-lib 技术指标
+# 获取 TA-Lib 的所有函数名（常用financial indicators均为大写）
+talib_func_list = [f for f in dir(talib) if f.isupper() and callable(getattr(talib, f))]
+
+# 定义需要生成多版本的参数名（period相关参数）
+# 按优先级排序，优先匹配主要的period参数
+PERIOD_PARAM_NAMES = [
+    "timeperiod",  # 最常见的参数名
+    "period",  # 通用period参数
+    "optintimeperiod",  # TA-Lib内部参数名
+]
+
+# 多period参数的函数（需要特殊处理）
+# 对于这些函数，明确指定主要period参数，避免自动检测错误
+MULTI_PERIOD_FUNCTIONS = {
+    # 函数名: (主要period参数名, 次要period参数列表，仅用于文档)
+    "MACD": ("fastperiod", ["slowperiod", "signalperiod"]),
+    "MACDEXT": ("fastperiod", ["slowperiod", "signalperiod"]),
+    "MACDFIX": ("signalperiod", []),
+    "STOCH": ("fastk_period", ["slowk_period", "slowd_period"]),
+    "STOCHF": ("fastk_period", ["fastd_period"]),
+    "STOCHRSI": ("timeperiod", ["fastk_period", "fastd_period"]),
+    "BBANDS": ("timeperiod", ["nbdevup", "nbdevdn"]),
+    "APO": ("fastperiod", ["slowperiod"]),
+    "PPO": ("fastperiod", ["slowperiod"]),
+    "ULTOSC": ("timeperiod1", ["timeperiod2", "timeperiod3"]),
+    "BOP": ("", []),  # 无period参数，注册默认版本
+}
+
+
+def build_talib_wrapper(func, func_name, fixed_params=None):
+    """构建talib函数包装器，支持固定某些参数"""
+    fixed_params = fixed_params or {}
+
+    def _talib_wrap(*args, **kwargs):
+        # 合并固定参数和传入参数
+        merged_kwargs = {**fixed_params, **kwargs}
+        # ta-lib 有些函数只支持关键字参数
+        # 自动转换所有输入类型
+        args = tuple(_convert_input(arg) for arg in args)
+        for k in merged_kwargs:
+            merged_kwargs[k] = _convert_input(merged_kwargs[k])
+        result = func(*args, **merged_kwargs)
+        # TA-Lib有些输出是tuple（比如MACD），统一返回ndarray/tuple[ndarray]
+        if isinstance(result, tuple):
+            # 保持tuple结构
+            return tuple(
+                np.asarray(item) if item is not None else None for item in result
+            )
+        return np.asarray(result)
+
+    _talib_wrap.__name__ = f"talib_{func_name.lower()}"
+    return _talib_wrap
+
+
+for func_name in talib_func_list:
+    func = getattr(talib, func_name)
+    sig = inspect.signature(func)
+    params = sig.parameters
+
+    # 检查是否在特殊配置字典中
+    if func_name in MULTI_PERIOD_FUNCTIONS:
+        main_period_param, _ = MULTI_PERIOD_FUNCTIONS[func_name]
+        # 如果配置中指定了主要period参数，使用它
+        if main_period_param and main_period_param in params:
+            for period_value in PERIOD_RANGE:
+                fixed_params = {main_period_param: period_value}
+                wrapper = build_talib_wrapper(func, func_name, fixed_params)
+                op_name = f"talib_{func_name.lower()}_{period_value}"
+                desc = f"ta-lib: {func_name}({main_period_param}={period_value})"
+                _registry.register_function(op_name, wrapper, desc)
+        else:
+            # 配置中指定无period参数，注册默认版本
+            wrapper = build_talib_wrapper(func, func_name)
+            op_name = f"talib_{func_name.lower()}"
+            desc = f"ta-lib: {func_name}"
+            _registry.register_function(op_name, wrapper, desc)
+    else:
+        # 不在特殊配置中，自动检测period参数
+        period_params = {}
+        for param_name, param in params.items():
+            param_lower = param_name.lower()
+            # 检查是否是period相关参数
+            if any(
+                period_keyword in param_lower for period_keyword in PERIOD_PARAM_NAMES
+            ):
+                period_params[param_name] = param
+
+        if period_params:
+            # 如果有period参数，为每个period值生成一个版本
+            # 优先选择timeperiod，否则选择第一个
+            main_period_param = None
+            for preferred in ["timeperiod", "period", "optintimeperiod"]:
+                for param_name in period_params.keys():
+                    if preferred in param_name.lower():
+                        main_period_param = param_name
+                        break
+                if main_period_param:
+                    break
+
+            if not main_period_param:
+                main_period_param = list(period_params.keys())[0]
+
+            for period_value in PERIOD_RANGE:
+                fixed_params = {main_period_param: period_value}
+                wrapper = build_talib_wrapper(func, func_name, fixed_params)
+                op_name = f"talib_{func_name.lower()}_{period_value}"
+                desc = f"ta-lib: {func_name}({main_period_param}={period_value})"
+                _registry.register_function(op_name, wrapper, desc)
+        else:
+            # 如果没有period参数，注册默认版本
+            wrapper = build_talib_wrapper(func, func_name)
+            op_name = f"talib_{func_name.lower()}"
+            desc = f"ta-lib: {func_name}"
+            _registry.register_function(op_name, wrapper, desc)
+
+# ==================== 自定义常见技术指标 ====================
+
+
+def _ewm_forward(x: np.ndarray, alpha: float) -> np.ndarray:
+    """指数加权移动平均（前向计算）"""
+    result = np.zeros_like(x)
+    if len(x) == 0:
+        return result
+    result[0] = x[0]
+    for i in range(1, len(x)):
+        result[i] = x[i] * alpha + (1 - alpha) * result[i - 1]
+    return result
+
+
+def _rsv(x: np.ndarray, window: int) -> np.ndarray:
+    """相对强弱值: (当前值 - 最小值) / (最大值 - 最小值)"""
+    s = pd.Series(x)
+    rolling = s.rolling(window, min_periods=max(2, window // 2), closed="both")
+    min_val = rolling.min()
+    max_val = rolling.max()
+    diff = max_val - min_val
+    # 避免除零
+    diff = np.where(np.abs(diff) < 1e-12, np.nan, diff)
+    result = (s - min_val) / diff
+    return result.to_numpy()
+
+
+def _bband(x: np.ndarray, window: int) -> np.ndarray:
+    """布林带指标: (当前值 - 均值) / 标准差"""
+    s = pd.Series(x)
+    rolling = s.rolling(window, min_periods=max(2, window // 2), closed="both")
+    mean_val = rolling.mean()
+    std_val = rolling.std()
+    # 避免除零
+    std_val = np.where(np.abs(std_val) < 1e-12, np.nan, std_val)
+    result = (s - mean_val) / std_val
+    return result.to_numpy()
+
+
+def _rsi(x: np.ndarray, window: int, threshold: float = 0.00001) -> np.ndarray:
+    """相对强弱指标: 上涨和下跌的比例"""
+    s = pd.Series(x)
+    diff = s.diff()
+    rolling = diff.rolling(window, min_periods=max(2, window // 2), closed="both")
+
+    def _rsi_calc(series):
+        up_sum = series[series > threshold].sum()
+        down_sum = abs(series[series < -threshold].sum())
+        total = up_sum + down_sum
+        if total < 1e-12:
+            return np.nan
+        return up_sum / total
+
+    result = rolling.apply(_rsi_calc, raw=False)
+    return result.to_numpy()
+
+
+def _rolling_skew(x: np.ndarray, window: int) -> np.ndarray:
+    """滚动偏度"""
+    s = pd.Series(x)
+    return (
+        s.rolling(window, min_periods=max(2, window // 2), closed="both")
+        .skew()
+        .to_numpy()
+    )
+
+
+def _rolling_kurtosis(x: np.ndarray, window: int) -> np.ndarray:
+    """滚动峰度"""
+    s = pd.Series(x)
+    return (
+        s.rolling(window, min_periods=max(2, window // 2), closed="both")
+        .kurt()
+        .to_numpy()
+    )
+
+
+def _rolling_linear(x: np.ndarray, window: int) -> np.ndarray:
+    """滚动线性回归斜率"""
+    s = pd.Series(x)
+
+    def _linear_slope(series):
+        valid = series.dropna()
+        if len(valid) < 2:
+            return np.nan
+        try:
+            coeffs = np.polyfit(np.arange(len(valid)), valid.values, 1)
+            return coeffs[0]
+        except:
+            return np.nan
+
+    result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
+        _linear_slope, raw=False
+    )
+    return result.to_numpy()
+
+
+def _rolling_autocorr(x: np.ndarray, window: int, lag: int = 1) -> np.ndarray:
+    """滚动自相关"""
+    s = pd.Series(x)
+    result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
+        lambda series: (
+            series.autocorr(lag=lag) if len(series.dropna()) >= 2 else np.nan
+        ),
+        raw=False,
+    )
+    return result.to_numpy()
+
+
+def _rolling_max(x: np.ndarray, window: int) -> np.ndarray:
+    """滚动最大值"""
+    s = pd.Series(x)
+    return (
+        s.rolling(window, min_periods=max(2, window // 2), closed="both")
+        .max()
+        .to_numpy()
+    )
+
+
+def _rolling_min(x: np.ndarray, window: int) -> np.ndarray:
+    """滚动最小值"""
+    s = pd.Series(x)
+    return (
+        s.rolling(window, min_periods=max(2, window // 2), closed="both")
+        .min()
+        .to_numpy()
+    )
+
+
+def _huanbi(x: np.ndarray, window: int) -> np.ndarray:
+    """环比: 当前值 / 窗口起始值"""
+    s = pd.Series(x)
+
+    def _huanbi_calc(series):
+        if len(series) < 2:
+            return np.nan
+        start_val = series.iloc[0]
+        end_val = series.iloc[-1]
+        if abs(start_val) < 1e-12:
+            return np.nan
+        return end_val / start_val
+
+    result = s.rolling(window, min_periods=max(2, window // 2), closed="both").apply(
+        _huanbi_calc, raw=False
+    )
+    return result.to_numpy()
+
+
+# 注册技术指标算子（带不同窗口）
+for w in PERIOD_RANGE:
+    # EWM算子（使用固定alpha值）
+    alpha = 2.0 / (w + 1)
+    _registry.register_function(
+        f"ewm{w}",
+        lambda x, w=w, a=alpha: _ewm_forward(x, a),
+        f"指数加权移动平均: EWM(x, {w})",
+    )
+
+    # 百分比变化
+    _registry.register_function(
+        f"pct{w}", lambda x, w=w: _pct_change(x, w), f"百分比变化: PCT(x, {w})"
+    )
+
+    # RSV（相对强弱值）
+    _registry.register_function(
+        f"rsv{w}", lambda x, w=w: _rsv(x, w), f"相对强弱值: RSV(x, {w})"
+    )
+
+    # 布林带
+    _registry.register_function(
+        f"bband{w}", lambda x, w=w: _bband(x, w), f"布林带指标: BBAND(x, {w})"
+    )
+
+    # RSI
+    _registry.register_function(
+        f"rsi{w}", lambda x, w=w: _rsi(x, w), f"相对强弱指标: RSI(x, {w})"
+    )
+
+    # 统计量
+    _registry.register_function(
+        f"skew{w}", lambda x, w=w: _rolling_skew(x, w), f"滚动偏度: SKEW(x, {w})"
+    )
+    _registry.register_function(
+        f"kurt{w}", lambda x, w=w: _rolling_kurtosis(x, w), f"滚动峰度: KURT(x, {w})"
+    )
+    _registry.register_function(
+        f"linear{w}",
+        lambda x, w=w: _rolling_linear(x, w),
+        f"滚动线性斜率: LINEAR(x, {w})",
+    )
+    _registry.register_function(
+        f"autocorr{w}",
+        lambda x, w=w: _rolling_autocorr(x, w),
+        f"滚动自相关: AUTOCORR(x, {w})",
+    )
+    _registry.register_function(
+        f"max{w}", lambda x, w=w: _rolling_max(x, w), f"滚动最大值: MAX(x, {w})"
+    )
+    _registry.register_function(
+        f"min{w}", lambda x, w=w: _rolling_min(x, w), f"滚动最小值: MIN(x, {w})"
+    )
+
+    # 环比
+    _registry.register_function(
+        f"huanbi{w}", lambda x, w=w: _huanbi(x, w), f"环比: HUANBI(x, {w})"
+    )
+
+
+# ==================== 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})"

factor_mining/validator.py

@@ -0,0 +1,237 @@
+"""
+因子有效性检验模块：整合所有检验方案
+"""
+import numpy as np
+import pandas as pd
+from typing import Dict, List, Optional
+from dataclasses import dataclass
+from statsmodels.regression.linear_model import OLS
+
+from validation import (
+    compute_ic,
+    compute_rolling_ic,
+    group_backtest,
+    factor_span_regression
+)
+
+
+@dataclass
+class ValidationConfig:
+    """验证配置"""
+    ic_window: int = 30
+    ic_method: str = "spearman"  # "spearman" or "pearson"
+    n_groups: int = 3
+    group_period: int = 180
+    min_ic: float = 0.01
+    min_tstat: float = 1.5
+    min_r2_change: float = 0.05
+
+
+class FactorValidator:
+    """因子有效性检验器"""
+    
+    def __init__(self, config: ValidationConfig):
+        self.config = config
+    
+    def validate_ic(
+        self,
+        factor: pd.Series,
+        forward_return: pd.Series
+    ) -> Dict:
+        """
+        IC检验
+        
+        Returns:
+        --------
+        dict: 包含mean_ic, ic_ir, ic_series等
+        """
+        rolling_ic = compute_rolling_ic(
+            factor,
+            forward_return,
+            window=self.config.ic_window,
+            method=self.config.ic_method
+        )
+        
+        mean_ic = rolling_ic.mean()
+        ic_std = rolling_ic.std()
+        ic_ir = mean_ic / (ic_std + 1e-8)  # IC信息比率
+        
+        return {
+            "mean_ic": mean_ic,
+            "ic_std": ic_std,
+            "ic_ir": ic_ir,
+            "ic_series": rolling_ic,
+            "is_valid": abs(mean_ic) >= self.config.min_ic
+        }
+    
+    def validate_group_backtest(
+        self,
+        factor: pd.Series,
+        forward_return: pd.Series
+    ) -> Dict:
+        """
+        分组回测检验
+        
+        Returns:
+        --------
+        dict: 包含mean_h_l_return, mean_h_l_tstat等
+        """
+        result = group_backtest(
+            factor,
+            forward_return,
+            n_groups=self.config.n_groups,
+            group_period=self.config.group_period
+        )
+        
+        is_valid = abs(result.get('mean_h_l_tstat', 0)) >= self.config.min_tstat
+        
+        return {
+            **result,
+            "is_valid": is_valid
+        }
+    
+    def validate_regression(
+        self,
+        factor: pd.Series,
+        forward_return: pd.Series,
+        other_factors: Optional[pd.DataFrame] = None
+    ) -> Dict:
+        """
+        因子跨度回归检验
+        
+        Parameters:
+        -----------
+        factor : Series
+            待检验因子
+        forward_return : Series
+            未来收益率
+        other_factors : DataFrame, optional
+            其他因子（用于控制变量）
+        
+        Returns:
+        --------
+        dict: 包含beta, tstat, r2_change等
+        """
+        if other_factors is None:
+            other_factors = pd.DataFrame()
+        
+        # 合并因子
+        factors_df = pd.concat([other_factors, factor.to_frame(name='target')], axis=1)
+        
+        result = factor_span_regression(
+            factors_df,
+            forward_return,
+            target_factor='target'
+        )
+        
+        is_valid = (
+            abs(result.get('tstat', 0)) >= self.config.min_tstat and
+            result.get('r2_change', 0) >= self.config.min_r2_change
+        )
+        
+        return {
+            **result,
+            "is_valid": is_valid
+        }
+    
+    def validate_all(
+        self,
+        factor: pd.Series,
+        forward_return: pd.Series,
+        other_factors: Optional[pd.DataFrame] = None
+    ) -> Dict:
+        """
+        综合检验：执行所有检验方法
+        
+        Returns:
+        --------
+        dict: 包含所有检验结果和综合判断
+        """
+        results = {}
+        
+        # IC检验
+        ic_result = self.validate_ic(factor, forward_return)
+        results['ic'] = ic_result
+        
+        # 分组回测
+        group_result = self.validate_group_backtest(factor, forward_return)
+        results['group_backtest'] = group_result
+        
+        # 回归检验
+        reg_result = self.validate_regression(factor, forward_return, other_factors)
+        results['regression'] = reg_result
+        
+        # 综合判断
+        is_valid = (
+            ic_result['is_valid'] and
+            group_result['is_valid'] and
+            reg_result['is_valid']
+        )
+        
+        results['is_valid'] = is_valid
+        results['score'] = self._calculate_score(ic_result, group_result, reg_result)
+        
+        return results
+    
+    def _calculate_score(
+        self,
+        ic_result: Dict,
+        group_result: Dict,
+        reg_result: Dict
+    ) -> float:
+        """计算综合得分"""
+        score = 0.0
+        
+        # IC得分（权重0.3）
+        ic_score = abs(ic_result.get('mean_ic', 0)) * 10
+        score += ic_score * 0.3
+        
+        # 分组回测得分（权重0.4）
+        tstat = abs(group_result.get('mean_h_l_tstat', 0))
+        tstat_score = min(tstat / 3.0, 1.0)  # 归一化到[0, 1]
+        score += tstat_score * 0.4
+        
+        # 回归得分（权重0.3）
+        r2_change = reg_result.get('r2_change', 0)
+        r2_score = min(r2_change / 0.1, 1.0)  # 归一化到[0, 1]
+        score += r2_score * 0.3
+        
+        return score
+    
+    def filter_factors(
+        self,
+        factors: pd.DataFrame,
+        forward_return: pd.Series
+    ) -> pd.DataFrame:
+        """
+        批量过滤因子：只保留有效因子
+        
+        Returns:
+        --------
+        DataFrame: 有效因子
+        """
+        valid_factors = []
+        
+        for col in factors.columns:
+            factor = factors[col]
+            result = self.validate_all(factor, forward_return, factors.drop(columns=[col]))
+            
+            if result['is_valid']:
+                valid_factors.append(col)
+        
+        return factors[valid_factors] if valid_factors else pd.DataFrame()
+
+
+def create_validator(
+    ic_window: int = 30,
+    min_ic: float = 0.01,
+    min_tstat: float = 1.5
+) -> FactorValidator:
+    """创建验证器（便捷函数）"""
+    config = ValidationConfig(
+        ic_window=ic_window,
+        min_ic=min_ic,
+        min_tstat=min_tstat
+    )
+    return FactorValidator(config)
+

factors.py

@@ -1,113 +0,0 @@
-"""
-因子挖掘模块：支持规则因子和遗传编程因子
-"""
-import numpy as np
-import pandas as pd
-from typing import Callable, Dict, List, Optional
-from abc import ABC, abstractmethod
-
-
-class BaseFactor(ABC):
-    """因子基类"""
-    
-    def __init__(self, name: str):
-        self.name = name
-    
-    @abstractmethod
-    def compute(self, data: pd.DataFrame) -> pd.Series:
-        """计算因子值"""
-        pass
-
-
-class RuleFactor(BaseFactor):
-    """规则因子：基于固定规则"""
-    
-    def __init__(self, name: str, compute_func: Callable[[pd.DataFrame], pd.Series]):
-        super().__init__(name)
-        self.compute_func = compute_func
-    
-    def compute(self, data: pd.DataFrame) -> pd.Series:
-        return self.compute_func(data)
-
-
-def create_trend_factor(data: pd.DataFrame) -> pd.Series:
-    """趋势因子：价格趋势方向"""
-    trend = pd.Series(0, index=data.index)
-    trend[data['close'] > data['ema16']] = 1
-    trend[data['close'] < data['ema4']] = -1
-    return trend
-
-
-def create_volatility_factor(data: pd.DataFrame) -> pd.Series:
-    """波动率因子：滚动12期收益率标准差"""
-    return data['volatility']
-
-
-def create_volume_price_factor(data: pd.DataFrame) -> pd.Series:
-    """量价因子：成交量放大且价格上涨"""
-    volume_signal = (data['volume'] > data['volume_ma6']).astype(int)
-    return volume_signal * data['return']
-
-
-def create_reversal_factor(data: pd.DataFrame) -> pd.Series:
-    """反转因子：短期反转效应"""
-    return -data['return'].shift(1)
-
-
-def create_momentum_factor(data: pd.DataFrame) -> pd.Series:
-    """动量因子：基于MACD"""
-    return data['macd']
-
-
-def create_rsi_factor(data: pd.DataFrame) -> pd.Series:
-    """RSI因子：相对强弱指数（标准化）"""
-    return (data['rsi'] - 50) / 50  # 归一化到[-1, 1]
-
-
-class FactorMiner:
-    """因子挖掘器"""
-    
-    def __init__(self):
-        self.factors: Dict[str, BaseFactor] = {}
-    
-    def register_factor(self, factor: BaseFactor):
-        """注册因子"""
-        self.factors[factor.name] = factor
-    
-    def register_rule_factor(self, name: str, compute_func: Callable):
-        """注册规则因子"""
-        factor = RuleFactor(name, compute_func)
-        self.register_factor(factor)
-    
-    def compute_all_factors(self, data: pd.DataFrame) -> pd.DataFrame:
-        """计算所有因子"""
-        factor_df = pd.DataFrame(index=data.index)
-        
-        for name, factor in self.factors.items():
-            try:
-                factor_df[name] = factor.compute(data)
-            except Exception as e:
-                print(f"计算因子 {name} 时出错: {e}")
-                factor_df[name] = np.nan
-        
-        return factor_df
-    
-    def get_factor(self, name: str) -> Optional[BaseFactor]:
-        """获取指定因子"""
-        return self.factors.get(name)
-
-
-def create_default_factors() -> FactorMiner:
-    """创建默认因子集合"""
-    miner = FactorMiner()
-    
-    # 注册基础因子
-    miner.register_rule_factor('TREND', create_trend_factor)
-    miner.register_rule_factor('VOL', create_volatility_factor)
-    miner.register_rule_factor('VOLP', create_volume_price_factor)
-    miner.register_rule_factor('REV', create_reversal_factor)
-    miner.register_rule_factor('MOM', create_momentum_factor)
-    miner.register_rule_factor('RSI', create_rsi_factor)
-    
-    return miner
-

signal.py

@@ -1,109 +0,0 @@
-"""
-信号生成模块
-"""
-import numpy as np
-import pandas as pd
-from typing import Optional, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from pandas import Series
-
-
-def generate_signals(
-    score: 'pd.Series',
-    buy_threshold: float = 0.8,
-    sell_threshold: float = -0.8,
-    window: int = 30,
-    use_rolling_std: bool = True
-) -> 'pd.Series':
-    """
-    基于因子得分生成买卖信号
-    
-    Parameters:
-    -----------
-    score : Series
-        因子综合得分
-    buy_threshold : float
-        买入阈值（标准差倍数）
-    sell_threshold : float
-        卖出阈值（标准差倍数）
-    window : int
-        滚动窗口（用于计算标准差）
-    use_rolling_std : bool
-        是否使用滚动标准差
-    
-    Returns:
-    --------
-    Series: 交易信号（1=买入，-1=卖出，0=持有）
-    """
-    signals = pd.Series(0, index=score.index)
-    
-    if use_rolling_std:
-        # 使用滚动标准差
-        rolling_std = score.rolling(window).std()
-        buy_line = buy_threshold * rolling_std
-        sell_line = sell_threshold * rolling_std
-    else:
-        # 使用固定阈值
-        std = score.std()
-        buy_line = buy_threshold * std
-        sell_line = sell_threshold * std
-    
-    # 生成原始信号
-    raw_signals = pd.Series(0, index=score.index)
-    raw_signals[score > buy_line] = 1   # 买入信号
-    raw_signals[score < sell_line] = -1  # 卖出信号
-    
-    # 只在信号变化时产生交易信号，其他时候保持持仓状态
-    signals = pd.Series(0, index=score.index)
-    position = 0  # 当前持仓状态：0=空仓，1=满仓
-    
-    for i in range(len(raw_signals)):
-        current_signal = raw_signals.iloc[i]
-        
-        # 只在信号变化时产生交易
-        if current_signal == 1 and position == 0:
-            signals.iloc[i] = 1  # 买入
-            position = 1
-        elif current_signal == -1 and position == 1:
-            signals.iloc[i] = -1  # 卖出
-            position = 0
-        # 其他情况保持当前持仓状态，不产生交易信号
-    
-    return signals.astype(int)
-
-
-def generate_signals_with_position(
-    score: 'pd.Series',
-    buy_threshold: float = 0.8,
-    sell_threshold: float = -0.8,
-    window: int = 30,
-    current_position: int = 0
-) -> 'pd.Series':
-    """
-    生成信号（考虑当前持仓状态）
-    
-    Parameters:
-    -----------
-    current_position : int
-        当前持仓：0=空仓，1=满仓
-    """
-    raw_signals = generate_signals(score, buy_threshold, sell_threshold, window)
-    signals = pd.Series(0, index=score.index)
-    
-    position = current_position
-    
-    for i in range(len(raw_signals)):
-        signal = raw_signals.iloc[i]
-        
-        if signal == 1 and position == 0:
-            signals.iloc[i] = 1  # 买入
-            position = 1
-        elif signal == -1 and position == 1:
-            signals.iloc[i] = -1  # 卖出
-            position = 0
-        else:
-            signals.iloc[i] = 0  # 持有
-    
-    return signals
-

validation.py

@@ -1,63 +1,44 @@
 """
-因子检验模块：IC检验、分组回测、因子跨度回归
+因子检验模块: IC检验、分组回测、因子跨度回归
 """
+
 import numpy as np
 import pandas as pd
 from typing import Dict, List, Tuple
 from statsmodels.regression.linear_model import OLS
 
 
-def compute_ic(factor: pd.Series, forward_return: pd.Series, method: str = 'spearman') -> pd.Series:
-    """
-    计算IC（信息系数）
-    
-    Parameters:
-    -----------
-    factor : Series
-        因子值
-    forward_return : Series
-        未来收益率
-    method : str
-        相关性计算方法：'spearman' 或 'pearson'
-    """
-    aligned = pd.concat([factor, forward_return], axis=1).dropna()
-    if len(aligned) < 10:
-        return pd.Series(dtype=float)
-    
-    if method == 'spearman':
-        ic = aligned.iloc[:, 0].rank().corr(aligned.iloc[:, 1].rank())
-    else:
-        ic = aligned.iloc[:, 0].corr(aligned.iloc[:, 1])
-    
-    return pd.Series([ic], index=[aligned.index[-1]])
-
-
 def compute_rolling_ic(
     factor: pd.Series,
     forward_return: pd.Series,
     window: int = 30,
-    method: str = 'spearman'
+    method: str = "spearman",
 ) -> pd.Series:
-    """计算滚动IC（向量化优化）"""
+    """计算滚动IC (向量化优化)"""
     # 对齐数据
     aligned = pd.concat([factor, forward_return], axis=1).dropna()
     if len(aligned) < window:
         return pd.Series(dtype=float, index=factor.index[window:])
-    
-    aligned.columns = ['factor', 'return']
-    
-    if method == 'spearman':
+
+    aligned.columns = ["factor", "return"]
+
+    if method == "spearman":
         # 使用rank计算Spearman相关性
-        factor_rank = aligned['factor'].rank()
-        return_rank = aligned['return'].rank()
-        # 使用DataFrame的rolling().corr()方法
-        df_rank = pd.DataFrame({'factor': factor_rank, 'return': return_rank})
-        ic_series = df_rank['factor'].rolling(window, min_periods=window).corr(df_rank['return'])
+        # 这里是全局的 rank，理论上应该是按照 window 滚动排序
+        factor_rank = aligned["factor"].rank()
+        return_rank = aligned["return"].rank()
+        # 使用DataFrame的rolling().corr()方法, 该方法pandas优化过
+        df_rank = pd.DataFrame({"factor": factor_rank, "return": return_rank})
+        ic_series = (
+            df_rank["factor"]
+            .rolling(window, min_periods=window)
+            .corr(df_rank["return"])
+        )
     else:
         # Pearson相关性
-        df = pd.DataFrame({'factor': aligned['factor'], 'return': aligned['return']})
-        ic_series = df['factor'].rolling(window, min_periods=window).corr(df['return'])
-    
+        df = pd.DataFrame({"factor": aligned["factor"], "return": aligned["return"]})
+        ic_series = df["factor"].rolling(window, min_periods=window).corr(df["return"])
+
     return ic_series
 
 
@@ -65,85 +46,75 @@ def group_backtest(
     factor: pd.Series,
     forward_return: pd.Series,
     n_groups: int = 3,
-    group_period: int = 180
+    group_period: int = 180,
 ) -> Dict:
     """
     分组回测：将数据按因子值分组，计算各组收益
-    
+
     Returns:
     --------
     dict: 包含各组收益、H-L收益差、t统计量等
     """
     aligned = pd.concat([factor, forward_return], axis=1).dropna()
-    aligned.columns = ['factor', 'return']
-    
-    results = {
-        'group_returns': [],
-        'h_l_return': [],
-        'h_l_tstat': [],
-        'periods': []
-    }
-    
+    aligned.columns = ["factor", "return"]
+
+    results = {"group_returns": [], "h_l_return": [], "h_l_tstat": [], "periods": []}
+
     # 按月分组（每180个4h周期）- 使用更高效的步长
     step = max(group_period // 2, 90)  # 减少重叠计算
     for start in range(0, len(aligned) - group_period, step):
         end = start + group_period
         period_data = aligned.iloc[start:end]
-        
+
         if len(period_data) < 30:
             continue
-        
+
         # 按因子值分组（向量化）
         try:
             period_data = period_data.copy()
-            period_data['group'] = pd.qcut(
-                period_data['factor'],
-                q=n_groups,
-                labels=False,
-                duplicates='drop'
+            period_data["group"] = pd.qcut(
+                period_data["factor"], q=n_groups, labels=False, duplicates="drop"
             )
-            
+
             # 计算各组收益（向量化）
-            group_returns = period_data.groupby('group')['return'].mean()
-            results['group_returns'].append(group_returns)
-            
+            group_returns = period_data.groupby("group")["return"].mean()
+            results["group_returns"].append(group_returns)
+
             # H-L收益差
             if len(group_returns) >= 2:
                 h_return = group_returns.iloc[-1]  # 高因子组
-                l_return = group_returns.iloc[0]   # 低因子组
+                l_return = group_returns.iloc[0]  # 低因子组
                 h_l_diff = h_return - l_return
-                
-                results['h_l_return'].append(h_l_diff)
-                results['periods'].append(period_data.index[-1])
+
+                results["h_l_return"].append(h_l_diff)
+                results["periods"].append(period_data.index[-1])
         except (ValueError, KeyError):
             # qcut失败时跳过
             continue
-    
+
     # 计算平均H-L收益和t统计量
-    if results['h_l_return']:
-        h_l_series = pd.Series(results['h_l_return'], index=results['periods'])
+    if results["h_l_return"]:
+        h_l_series = pd.Series(results["h_l_return"], index=results["periods"])
         mean_h_l = h_l_series.mean()
         std_h_l = h_l_series.std()
         t_stat = mean_h_l / (std_h_l / np.sqrt(len(h_l_series)) + 1e-8)
-        
-        results['mean_h_l_return'] = mean_h_l
-        results['mean_h_l_tstat'] = t_stat
-        results['h_l_series'] = h_l_series
+
+        results["mean_h_l_return"] = mean_h_l
+        results["mean_h_l_tstat"] = t_stat
+        results["h_l_series"] = h_l_series
     else:
-        results['mean_h_l_return'] = 0
-        results['mean_h_l_tstat'] = 0
-    
+        results["mean_h_l_return"] = 0
+        results["mean_h_l_tstat"] = 0
+
     return results
 
 
 def factor_span_regression(
-    factors: pd.DataFrame,
-    forward_return: pd.Series,
-    target_factor: str
+    factors: pd.DataFrame, forward_return: pd.Series, target_factor: str
 ) -> Dict:
     """
     因子跨度回归：检验因子的边际解释力
-    
+
     Parameters:
     -----------
     factors : DataFrame
@@ -152,7 +123,7 @@ def factor_span_regression(
         未来收益率
     target_factor : str
         目标因子名称
-    
+
     Returns:
     --------
     dict: 包含回归系数、t统计量、R²等
@@ -160,49 +131,46 @@ def factor_span_regression(
     # 对齐数据
     data = pd.concat([factors, forward_return], axis=1).dropna()
     if len(data) < 30:
-        return {'beta': 0, 'tstat': 0, 'r2': 0, 'r2_change': 0}
-    
+        return {"beta": 0, "tstat": 0, "r2": 0, "r2_change": 0}
+
     y = data.iloc[:, -1].values
     X_all = data.iloc[:, :-1].values
-    
+
     # 全模型（包含目标因子）
     try:
-        model_all = OLS(y, X_all).fit(cov_type='HAC', cov_kwds={'maxlags': 6})
+        model_all = OLS(y, X_all).fit(cov_type="HAC", cov_kwds={"maxlags": 6})
         r2_all = model_all.rsquared
-        
+
         # 目标因子的系数和t统计量
         target_idx = factors.columns.get_loc(target_factor)
         beta = model_all.params[target_idx]
         tstat = model_all.tvalues[target_idx]
-        
+
         # 不含目标因子的模型
         X_without = np.delete(X_all, target_idx, axis=1)
-        model_without = OLS(y, X_without).fit(cov_type='HAC', cov_kwds={'maxlags': 6})
+        model_without = OLS(y, X_without).fit(cov_type="HAC", cov_kwds={"maxlags": 6})
         r2_without = model_without.rsquared
-        
+
         r2_change = r2_all - r2_without
-        
+
         return {
-            'beta': beta,
-            'tstat': tstat,
-            'r2': r2_all,
-            'r2_change': r2_change,
-            'pvalue': model_all.pvalues[target_idx]
+            "beta": beta,
+            "tstat": tstat,
+            "r2": r2_all,
+            "r2_change": r2_change,
+            "pvalue": model_all.pvalues[target_idx],
         }
     except Exception as e:
         print(f"回归分析出错: {e}")
-        return {'beta': 0, 'tstat': 0, 'r2': 0, 'r2_change': 0}
+        return {"beta": 0, "tstat": 0, "r2": 0, "r2_change": 0}
 
 
 def validate_factor(
-    factor: pd.Series,
-    forward_return: pd.Series,
-    ic_window: int = 30,
-    n_groups: int = 3
+    factor: pd.Series, forward_return: pd.Series, ic_window: int = 30, n_groups: int = 3
 ) -> Dict:
     """
     综合因子检验
-    
+
     Returns:
     --------
     dict: 包含IC、分组回测、显著性等指标
@@ -211,16 +179,15 @@ def validate_factor(
     rolling_ic = compute_rolling_ic(factor, forward_return, window=ic_window)
     mean_ic = rolling_ic.mean()
     ic_ir = mean_ic / (rolling_ic.std() + 1e-8)  # IC信息比率
-    
+
     # 分组回测
     group_result = group_backtest(factor, forward_return, n_groups=n_groups)
-    
-    return {
-        'mean_ic': mean_ic,
-        'ic_ir': ic_ir,
-        'ic_series': rolling_ic,
-        'mean_h_l_return': group_result['mean_h_l_return'],
-        'mean_h_l_tstat': group_result['mean_h_l_tstat'],
-        'group_returns': group_result['group_returns']
-    }
 
+    return {
+        "mean_ic": mean_ic,
+        "ic_ir": ic_ir,
+        "ic_series": rolling_ic,
+        "mean_h_l_return": group_result["mean_h_l_return"],
+        "mean_h_l_tstat": group_result["mean_h_l_tstat"],
+        "group_returns": group_result["group_returns"],
+    }