refactor(archive): move unused modules to archive/
Archive legacy framework and utility modules that are no longer referenced by the active core (datasource/ and rotation/): - framework/ -> archive/framework/ - framework_v2/ -> archive/framework_v2/ - strategies/ -> archive/strategies/ - config/ -> archive/config/ - visualization/ -> archive/visualization/ - scripts/ -> archive/scripts/ - tests/ -> archive/tests/ - run_rotation.py, run_us_rotation.py -> archive/single_files/ - compare_*.py, test_api_dates.py -> archive/single_files/
This commit is contained in:
19
archive/framework_v2/core/__init__.py
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19
archive/framework_v2/core/__init__.py
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@@ -0,0 +1,19 @@
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"""
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核心抽象接口层（纯ABC，零实现）
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只定义策略框架的标准接口，不包含任何业务逻辑
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"""
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from framework_v2.core.strategy import StrategyBase
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from framework_v2.core.factor import FactorBase
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from framework_v2.core.signal import SignalGenerator
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from framework_v2.core.executor import Executor
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from framework_v2.core.data import DataFetcher
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__all__ = [
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'StrategyBase',
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'FactorBase',
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'SignalGenerator',
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'Executor',
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'DataFetcher',
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]
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97
archive/framework_v2/core/data.py
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97
archive/framework_v2/core/data.py
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@@ -0,0 +1,97 @@
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"""
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数据获取器抽象基类
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional
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import pandas as pd
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class DataFetcher(ABC):
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"""
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数据获取器抽象基类
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所有数据获取器必须实现必要方法
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"""
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name: str = "base"
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def __init__(self, **params):
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"""
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初始化数据获取器参数
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Args:
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**params: 数据源参数（如 api_url, ssh_config 等）
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"""
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self._params = params
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@abstractmethod
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def fetch_indices(
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self,
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codes: List[str],
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start: str,
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end: str
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) -> Dict[str, pd.DataFrame]:
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"""
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获取指数 OHLCV 数据
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Args:
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codes: 指数代码列表
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start: 开始日期 (YYYY-MM-DD)
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end: 结束日期 (YYYY-MM-DD)
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Returns:
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{code: DataFrame} 字典
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"""
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pass
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@abstractmethod
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def fetch_etf(
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self,
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codes: List[str],
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start: str,
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end: str
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) -> Dict[str, pd.DataFrame]:
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"""
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获取 ETF 数据（价格 + 净值）
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Args:
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codes: ETF 代码列表
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start: 开始日期
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end: 结束日期
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Returns:
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{code: DataFrame} 字典
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"""
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pass
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@abstractmethod
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def get_trading_calendar(self, market: str = 'A') -> pd.Index:
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"""
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获取交易日历
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Args:
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market: 市场代码（'A', 'US', 'HK' 等）
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Returns:
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交易日历 Index
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"""
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pass
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def get_benchmark(self, code: str, start: str, end: str) -> pd.Series:
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"""
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获取基准数据（可选）
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Args:
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code: 基准代码
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start: 开始日期
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end: 结束日期
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Returns:
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基准收盘价 Series
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"""
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raise NotImplementedError("Optional method")
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def __repr__(self) -> str:
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params_str = ', '.join([f"{k}={v}" for k, v in self._params.items()])
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return f"{self.__class__.__name__}(name={self.name})"
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46
archive/framework_v2/core/executor.py
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46
archive/framework_v2/core/executor.py
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@@ -0,0 +1,46 @@
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"""
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执行器抽象基类
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"""
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from abc import ABC, abstractmethod
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import pandas as pd
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class Executor(ABC):
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"""
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执行器抽象基类
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所有执行器必须实现 execute 方法
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"""
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mode: str = "base"
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def __init__(self, **params):
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"""
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初始化执行器参数
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Args:
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**params: 执行参数（如 initial_capital, trade_cost 等）
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"""
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self._params = params
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@abstractmethod
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def execute(self, signals: pd.DataFrame, data: pd.DataFrame) -> dict:
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"""
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执行信号
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Args:
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signals: 信号 DataFrame
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data: 收益率数据 DataFrame
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Returns:
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回测结果字典，包含：
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- result: 回测 DataFrame（含净值、收益率）
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- portfolio: 组合对象（可选）
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- metrics: 绩效指标（可选）
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"""
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pass
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def __repr__(self) -> str:
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params_str = ', '.join([f"{k}={v}" for k, v in self._params.items()])
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return f"{self.__class__.__name__}({params_str})"
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59
archive/framework_v2/core/factor.py
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59
archive/framework_v2/core/factor.py
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@@ -0,0 +1,59 @@
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"""
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因子抽象基类
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"""
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from abc import ABC, abstractmethod
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import pandas as pd
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class FactorBase(ABC):
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"""
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因子抽象基类
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所有因子必须实现 compute 方法
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"""
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name: str = "base"
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category: str = "unknown"
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def __init__(self, **params):
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"""
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初始化因子参数
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Args:
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**params: 因子参数（如 n_days, weighted 等）
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"""
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self._params = params
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@abstractmethod
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""
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计算因子值
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Args:
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data: OHLCV 数据，必须包含 'close' 列
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Returns:
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因子值序列（与 data 同索引）
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"""
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pass
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def validate_data(self, data: pd.DataFrame) -> bool:
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"""
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验证数据是否满足计算要求
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Args:
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data: OHLCV 数据
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Returns:
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True 如果数据有效
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"""
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if 'close' not in data.columns:
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return False
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min_periods = self._params.get('min_periods', 20)
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return len(data) >= min_periods
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def __repr__(self) -> str:
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params_str = ', '.join([f"{k}={v}" for k, v in self._params.items()])
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return f"{self.__class__.__name__}({params_str})"
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57
archive/framework_v2/core/signal.py
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57
archive/framework_v2/core/signal.py
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@@ -0,0 +1,57 @@
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"""
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信号生成器抽象基类
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"""
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from abc import ABC, abstractmethod
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import pandas as pd
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class SignalGenerator(ABC):
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"""
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信号生成器抽象基类
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所有信号生成器必须实现 generate 方法
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"""
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mode: str = "base"
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def __init__(self, **params):
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"""
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初始化信号生成器参数
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Args:
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**params: 信号参数（如 select_num, rebalance_days 等）
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"""
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self._params = params
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@abstractmethod
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def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
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"""
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生成交易信号
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Args:
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factor_data: 因子数据 DataFrame
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Returns:
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信号 DataFrame，必须包含 'signal' 列
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"""
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pass
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def validate_factor_data(self, factor_data: pd.DataFrame) -> bool:
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"""
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验证因子数据是否有效
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Args:
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factor_data: 因子数据
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Returns:
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True 如果数据有效
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"""
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if factor_data.empty:
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return False
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return True
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def __repr__(self) -> str:
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params_str = ', '.join([f"{k}={v}" for k, v in self._params.items()])
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return f"{self.__class__.__name__}({params_str})"
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260
archive/framework_v2/core/strategy.py
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260
archive/framework_v2/core/strategy.py
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@@ -0,0 +1,260 @@
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"""
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策略抽象基类
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所有策略必须继承此类并实现必要方法
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Optional, Any
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import pandas as pd
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from framework_v2.config.schemas import StrategyConfig
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class StrategyBase(ABC):
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"""
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策略抽象基类（V2 增强版）
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定义策略的标准生命周期：
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1. 初始化配置（使用 Pydantic Schema）
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2. 获取数据（支持多数据源）
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3. 计算因子（使用框架因子库）
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4. 生成信号（策略特定逻辑）
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5. 执行回测（框架通用执行器）
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子类必须实现：
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- get_codes(): 获取标的列表
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- compute_factors(): 计算因子
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- generate_signals(): 生成信号
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- manage_positions(): 仓位管理
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"""
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INTERFACE_VERSION = 2 # V2 版本
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def __init__(self, config: StrategyConfig):
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"""
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初始化策略
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Args:
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config: 策略配置（Pydantic Schema）
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"""
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self.config = config
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self.name = config.metadata.strategy
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# 组件将在子类中初始化
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self._data_fetcher = None
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self._executor = None
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@abstractmethod
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def get_codes(self) -> list:
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"""
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获取标的列表（策略必须实现）
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Returns:
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标的代码列表，如 ['399006.SZ', 'NDX', 'GC=F']
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"""
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pass
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@abstractmethod
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def compute_factors(self, data: Dict[str, pd.DataFrame]) -> Dict[str, pd.Series]:
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"""
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计算因子（策略必须实现）
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Args:
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data: 数据字典 {code: DataFrame}
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Returns:
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因子字典 {code: Series}
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"""
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pass
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@abstractmethod
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def generate_signals(self, factors: Dict[str, pd.Series]) -> pd.DataFrame:
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"""
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生成信号（策略必须实现）
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Args:
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factors: 因子字典 {code: Series}
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Returns:
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信号 DataFrame（包含 'signal' 列，1=买入，0=空仓）
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"""
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pass
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@abstractmethod
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def manage_positions(self, signals: pd.DataFrame) -> pd.DataFrame:
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"""
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仓位管理（策略必须实现）
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Args:
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signals: 信号 DataFrame
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Returns:
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仓位 DataFrame（包含 'weight' 列，权重和为 1）
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"""
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pass
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def get_data(self) -> Dict[str, pd.DataFrame]:
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"""
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获取数据（框架实现，策略可覆盖）
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Returns:
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数据字典 {code: DataFrame}
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"""
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if self._data_fetcher is None:
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self._data_fetcher = self._create_data_fetcher()
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codes = self.get_codes()
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# 处理 end_date 为 None 的情况（使用今天）
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from datetime import date
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start = self.config.backtest.start_date
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end = self.config.backtest.end_date
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if end is None:
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end = date.today().strftime('%Y-%m-%d')
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# 批量获取数据（fetch_indices 返回 {code: DataFrame}）
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try:
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data = self._data_fetcher.fetch_indices(
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codes=codes,
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start=start,
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end=end
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)
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return data
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except Exception as e:
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print(f" 错误: 数据获取失败 - {e}")
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return {}
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def compute_factors(self, data: Dict[str, Any]) -> pd.DataFrame:
|
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"""
|
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计算因子（可选覆盖）
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Args:
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data: 数据字典
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Returns:
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因子 DataFrame（日期 × 标的）
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"""
|
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if self._factor is None:
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self._factor = self.init_factors()
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# 默认实现：遍历标的计算因子
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factor_values = {}
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for code in data.get('valid_codes', []):
|
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if code in data.get('index_data', {}):
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factor_values[code] = self._factor.compute(data['index_data'][code])
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return pd.DataFrame(factor_values)
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||||
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||||
def generate_signals(self, factor_df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
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生成信号
|
||||
|
||||
Args:
|
||||
factor_df: 因子 DataFrame
|
||||
|
||||
Returns:
|
||||
信号 DataFrame（包含 'signal' 列）
|
||||
"""
|
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if self._signal_generator is None:
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||||
self._signal_generator = self.init_signal_generator()
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||||
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||||
return self._signal_generator.generate(factor_df)
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||||
def run(self, data: Optional[Dict[str, pd.DataFrame]] = None,
|
||||
export_detail: bool = False, detail_path: str = None) -> Dict[str, Any]:
|
||||
"""
|
||||
运行完整回测流程（框架标准流程）
|
||||
|
||||
Args:
|
||||
data: 可选，如不提供则自动获取
|
||||
export_detail: 是否导出逐日明细（默认 False）
|
||||
detail_path: 明细 JSON 文件路径（export_detail=True 时必需）
|
||||
|
||||
Returns:
|
||||
回测结果字典，包含：
|
||||
- equity_curve: 净值曲线
|
||||
- trades: 交易记录
|
||||
- metrics: 绩效指标
|
||||
"""
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# 1. 获取数据并保存
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if data is None:
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print("[1/5] 获取数据...")
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||||
data = self.get_data()
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||||
self._data = data # 保存数据供导出使用
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||||
print(f" 获取 {len(data)} 个标的")
|
||||
else:
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||||
self._data = data
|
||||
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||||
# 2. 计算因子
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||||
print("[2/5] 计算因子...")
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||||
factors = self.compute_factors(data)
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||||
print(f" 计算 {len(factors)} 个因子")
|
||||
|
||||
# 3. 生成信号
|
||||
print("[3/5] 生成信号...")
|
||||
signals = self.generate_signals(factors)
|
||||
print(f" 生成 {signals.shape[0]} 个信号")
|
||||
|
||||
# 4. 仓位管理
|
||||
print("[4/5] 仓位管理...")
|
||||
positions = self.manage_positions(signals)
|
||||
# positions 可能是信号矩阵或权重矩阵，计算平均仓位
|
||||
if hasattr(positions, 'sum'):
|
||||
avg_position = positions.sum(axis=1).mean() if hasattr(positions.sum(axis=1), 'mean') else 0
|
||||
print(f" 平均仓位: {avg_position:.2%}")
|
||||
else:
|
||||
print(f" 仓位管理完成")
|
||||
|
||||
# 5. 执行回测
|
||||
print("[5/5] 执行回测...")
|
||||
result = self._execute_backtest(positions, data)
|
||||
print(f" 回测完成")
|
||||
|
||||
# 6. 可选：导出逐日明细
|
||||
if export_detail:
|
||||
if not detail_path:
|
||||
raise ValueError("export_detail=True 时需要指定 detail_path")
|
||||
|
||||
print("\n[额外] 导出逐日明细...")
|
||||
self._export_backtest_detail(
|
||||
factors=factors,
|
||||
signals=signals,
|
||||
positions=positions,
|
||||
result=result,
|
||||
output_path=detail_path
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
def _execute_backtest(self, signals: pd.DataFrame, data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
执行回测（子类可覆盖）
|
||||
|
||||
Args:
|
||||
signals: 信号 DataFrame
|
||||
data: 数据字典
|
||||
|
||||
Returns:
|
||||
回测结果
|
||||
"""
|
||||
raise NotImplementedError("Subclasses must implement _execute_backtest()")
|
||||
|
||||
def _create_data_fetcher(self):
|
||||
"""
|
||||
创建数据获取器（框架实现）
|
||||
|
||||
Returns:
|
||||
DataFetcher 实例
|
||||
"""
|
||||
from framework_v2.shared.data import FlaskAPIFetcher
|
||||
|
||||
# 使用配置中的第一个启用的数据源
|
||||
for source_config in self.config.data.sources:
|
||||
if source_config.enabled and source_config.type.value == 'flask_api':
|
||||
return FlaskAPIFetcher(
|
||||
base_url=source_config.url,
|
||||
timeout=source_config.timeout
|
||||
)
|
||||
|
||||
raise ValueError("未找到可用的数据源配置")
|
||||
Reference in New Issue
Block a user