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GitHubandClaude Opus 4.8
687851fc67
- Add _SeriesAccessor for relative indexed data access ([0] current, [-1] previous) - Add StrategyDataProxy for efficient DataFrame column access via numpy arrays - Add crossover() function for golden cross detection (fast line crosses above slow line) - Add Strategy abstract base class with: - init() for indicator registration via self.I() - next() for signal generation via buy()/sell() - Internal engine hooks (_bind_data, _call_init, _set_bar_index, etc.) - All code is mypy strict compliant with full type annotations - 25 unit tests covering all components Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
451 lines
15 KiB
Python
451 lines
15 KiB
Python
"""策略基类和数据代理。
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提供策略编写的声明式 API：
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- StrategyDataProxy: K线数据访问层，支持 OHLCV + 自定义指标列
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- Strategy: 策略基类，init() 注册指标，next() 生成信号
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- crossover: 金叉检测辅助函数
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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from easy_tdx.backtest.types import Signal
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if TYPE_CHECKING:
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from collections.abc import Callable
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NDArray = npt.NDArray[np.float64]
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else:
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NDArray = np.ndarray
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# ── 数据序列访问器 ─────────────────────────────────────────────────────────────
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class _SeriesAccessor:
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"""数据序列访问器，支持相对索引和 numpy 数组转换。
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Examples:
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>>> close = data.close
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>>> close[0] # 当前 bar 的收盘价
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>>> close[-1] # 前一根 bar 的收盘价
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>>> ma = MyTT.MA(close.raw, 20) # 传入 numpy 数组
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"""
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__slots__ = ("_series", "_bar_index")
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def __init__(self, series: NDArray, bar_index: int) -> None:
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self._series = series
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self._bar_index = bar_index
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def __getitem__(self, key: int) -> float:
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"""获取相对索引的值。
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Args:
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key: 0=当前值, -1=前一根, -2=前两根,依此类推
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Returns:
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对应位置的 float 值
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"""
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idx = self._bar_index + key
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if idx < 0:
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raise IndexError(f"索引 {key} 超出范围（bar_index={self._bar_index}）")
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return float(self._series[idx])
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def __len__(self) -> int:
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"""返回数组长度。"""
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return len(self._series)
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def __array__(self) -> NDArray:
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"""允许传入 MyTT 函数（自动解包为 numpy 数组）。"""
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return self._series
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@property
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def raw(self) -> NDArray:
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"""获取完整原始 numpy 数组。"""
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return self._series
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# ── K线数据代理 ────────────────────────────────────────────────────────────────
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class StrategyDataProxy:
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"""K线数据代理，将 DataFrame 转为高效的 numpy 数组访问。
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内部将所有列（除 datetime）转为 numpy 数组，通过 _SeriesAccessor
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提供相对索引访问（[0] 当前, [-1] 前一根）。
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支持标准 OHLCV 列和任意自定义列（如 MACD_DIF, BOLL_UPPER）。
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"""
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__slots__ = ("_arrays", "_bar_index")
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def __init__(self, df: pd.DataFrame) -> None:
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"""初始化数据代理。
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Args:
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df: K线 DataFrame，必须包含 datetime, open, close, high, low, vol, amount
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可包含额外列（如 MACD_DIF, BOLL_UPPER）
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"""
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self._arrays: dict[str, NDArray] = {}
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self._bar_index = 0
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# 将所有列（除 datetime）转为 numpy 数组
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for col in df.columns:
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if col == "datetime":
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continue
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arr = df[col].to_numpy()
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if len(arr) > 0 and isinstance(arr[0], (np.datetime64, pd.Timestamp)):
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# datetime 列转为 int (YYYYMMDD)
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self._arrays[col] = _datetime_to_int(arr)
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else:
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self._arrays[col] = arr.astype(np.float64)
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def _set_index(self, idx: int) -> None:
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"""设置当前 bar 索引（引擎调用）。"""
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self._bar_index = idx
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@property
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def open(self) -> _SeriesAccessor:
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"""开盘价序列。"""
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return _SeriesAccessor(self._arrays["open"], self._bar_index)
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@property
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def close(self) -> _SeriesAccessor:
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"""收盘价序列。"""
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return _SeriesAccessor(self._arrays["close"], self._bar_index)
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@property
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def high(self) -> _SeriesAccessor:
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"""最高价序列。"""
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return _SeriesAccessor(self._arrays["high"], self._bar_index)
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@property
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def low(self) -> _SeriesAccessor:
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"""最低价序列。"""
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return _SeriesAccessor(self._arrays["low"], self._bar_index)
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@property
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def vol(self) -> _SeriesAccessor:
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"""成交量序列。"""
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return _SeriesAccessor(self._arrays["vol"], self._bar_index)
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@property
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def amount(self) -> _SeriesAccessor:
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"""成交额序列。"""
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return _SeriesAccessor(self._arrays["amount"], self._bar_index)
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def __getattr__(self, name: str) -> _SeriesAccessor:
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"""访问额外列（如 MACD_DIF, BOLL_UPPER）。
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Raises:
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AttributeError: 列不存在
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"""
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if name not in self._arrays:
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raise AttributeError(f"列 '{name}' 不存在于数据中")
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return _SeriesAccessor(self._arrays[name], self._bar_index)
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# ── 金叉检测 ─────────────────────────────────────────────────────────────────────
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def crossover(
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a: NDArray | pd.Series | _SeriesAccessor,
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b: NDArray | pd.Series | _SeriesAccessor,
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) -> NDArray:
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"""检测 a 从下方穿越 b（金叉）。
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Args:
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a: 快线序列
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b: 慢线序列
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Returns:
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bool 数组，True 表示发生金叉
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Examples:
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>>> ma5 = MyTT.MA(data.close.raw, 5)
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>>> ma20 = MyTT.MA(data.close.raw, 20)
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>>> cross = crossover(ma5, ma20) # ma5 上穿 ma20
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>>> if cross[bar_index]:
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... strategy.buy(size=100)
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"""
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# 解包 _SeriesAccessor
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if isinstance(a, _SeriesAccessor):
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a = a.raw
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if isinstance(b, _SeriesAccessor):
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b = b.raw
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# pd.Series 转 numpy
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if isinstance(a, pd.Series):
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a = a.to_numpy().astype(np.float64)
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if isinstance(b, pd.Series):
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b = b.to_numpy().astype(np.float64)
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# 金叉：前一根 a <= b，当前 a > b
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mask = np.zeros(len(a), dtype=bool)
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mask[1:] = (a[:-1] <= b[:-1]) & (a[1:] > b[1:])
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return mask
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# ── 策略基类 ───────────────────────────────────────────────────────────────────
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class Strategy(ABC):
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"""策略基类，提供声明式回测 API。
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用户子类实现：
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- init(): 注册指标（通过 self.I()）
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- next(): 生成交易信号（通过 self.buy()/self.sell()）
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内部状态：
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- self.data: StrategyDataProxy，访问 K线数据
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- self.position: {"size": float}，当前持仓
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- self.chanlun: 缠论分析结果（预留）
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示例:
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>>> class MyStrategy(Strategy):
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... def init(self):
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... self.ma5 = self.I(MyTT.MA, self.data.close, 5)
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... self.ma20 = self.I(MyTT.MA, self.data.close, 20)
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... self.cross = crossover(self.ma5, self.ma20)
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...
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... def next(self):
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... if self.cross[self._bar_index]:
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... self.buy(size=100)
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... elif self.position["size"] > 0:
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... self.sell(size=0)
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"""
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def __init__(self) -> None:
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"""初始化策略内部状态。"""
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self._data_proxy: StrategyDataProxy | None = None
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self._bar_index = 0
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self._signals: list[Signal] = []
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self._indicators: dict[str, NDArray] = {}
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self._chanlun_result: dict[str, Any] | None = None
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self._position_size = 0.0
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self._cash = 0.0
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self._datetime_array: NDArray | None = None
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# ── 用户实现方法 ─────────────────────────────────────────────────────────────
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@abstractmethod
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def init(self) -> None:
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"""注册指标。
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在回测开始前调用一次，用户通过 self.I() 注册技术指标。
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"""
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@abstractmethod
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def next(self) -> None:
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"""生成交易信号。
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每根 bar 调用一次，用户通过 self.buy()/self.sell() 生成信号。
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"""
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# ── 指标注册 ───────────────────────────────────────────────────────────────
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def I(
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self, func: Callable[..., NDArray], *args: Any, **kwargs: Any
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) -> NDArray:
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"""注册指标。
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将 _SeriesAccessor 参数自动解包为 numpy 数组，调用 func 计算指标。
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返回的数组存入 self._indicators，可在 next() 中访问。
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Args:
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func: 指标函数（如 MyTT.MA）
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*args: 参数（可能是 _SeriesAccessor，会自动解包）
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**kwargs: 关键字参数
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Returns:
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指标值数组（numpy ndarray）
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Examples:
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>>> ma5 = self.I(MyTT.MA, self.data.close, 5)
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>>> macd = self.I(MyTT.MACD, self.data.close, self.data.low, self.data.high)
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"""
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# 解包 _SeriesAccessor 参数
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unpacked_args = []
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for arg in args:
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if isinstance(arg, _SeriesAccessor):
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unpacked_args.append(arg.raw)
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else:
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unpacked_args.append(arg)
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# 调用函数
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result = func(*unpacked_args, **kwargs)
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# 存储指标（用于调试/日志）
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func_name = getattr(func, "__name__", str(func))
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self._indicators[func_name] = result
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return result
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# ── 交易信号生成 ─────────────────────────────────────────────────────────────
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def buy(
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self,
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size: float = 0,
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price: float | None = None,
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stop_loss: float | None = None,
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take_profit: float | None = None,
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) -> None:
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"""生成买入信号。
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Args:
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size: 交易数量（0 = 全仓，由引擎计算）
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price: 限价（None = 市价单）
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stop_loss: 止损价（None = 不设置）
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take_profit: 止盈价（None = 不设置）
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"""
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if self._data_proxy is None:
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raise RuntimeError("策略未绑定数据，请先调用 _bind_data()")
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if self._datetime_array is None:
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raise RuntimeError("数据未正确初始化")
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signal = Signal(
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datetime=int(self._datetime_array[self._bar_index]),
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direction="BUY",
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size=size,
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price=price,
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stop_loss=stop_loss,
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take_profit=take_profit,
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)
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self._signals.append(signal)
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def sell(
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self,
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size: float = 0,
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price: float | None = None,
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stop_loss: float | None = None,
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take_profit: float | None = None,
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) -> None:
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"""生成卖出信号。
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Args:
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size: 交易数量（0 = 全仓，由引擎计算）
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price: 限价（None = 市价单）
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stop_loss: 止损价（None = 不设置）
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take_profit: 止盈价（None = 不设置）
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"""
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if self._data_proxy is None:
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raise RuntimeError("策略未绑定数据，请先调用 _bind_data()")
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if self._datetime_array is None:
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raise RuntimeError("数据未正确初始化")
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signal = Signal(
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datetime=int(self._datetime_array[self._bar_index]),
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direction="SELL",
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size=size,
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price=price,
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stop_loss=stop_loss,
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take_profit=take_profit,
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)
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self._signals.append(signal)
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# ── 状态访问 ─────────────────────────────────────────────────────────────────
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@property
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def data(self) -> StrategyDataProxy:
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"""K线数据代理。"""
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if self._data_proxy is None:
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raise RuntimeError("策略未绑定数据，请先调用 _bind_data()")
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return self._data_proxy
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@property
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def position(self) -> dict[str, float]:
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"""当前持仓（简化 dict 格式）。
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Returns:
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{"size": float}，正=多头，负=空头，0=空仓
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"""
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return {"size": self._position_size}
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@property
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def chanlun(self) -> dict[str, Any] | None:
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"""缠论分析结果（预留）。"""
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return self._chanlun_result
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# ── 内部方法（引擎调用） ─────────────────────────────────────────────────────
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def _bind_data(self, df: pd.DataFrame) -> None:
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"""绑定 K线数据（引擎调用）。
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Args:
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df: K线 DataFrame
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"""
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self._data_proxy = StrategyDataProxy(df)
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# 提取 datetime 数组
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if "datetime" in df.columns:
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dt_arr = df["datetime"].to_numpy()
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self._datetime_array = _datetime_to_int(dt_arr)
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else:
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raise ValueError("DataFrame 必须包含 datetime 列")
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def _call_init(self) -> None:
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"""调用用户 init() 方法（引擎调用）。"""
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self.init()
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def _set_bar_index(self, idx: int) -> None:
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"""设置当前 bar 索引（引擎调用）。
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Args:
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idx: bar 索引
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"""
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self._bar_index = idx
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if self._data_proxy is not None:
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self._data_proxy._set_index(idx)
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def _call_next(self) -> None:
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"""调用用户 next() 方法（引擎调用）。"""
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self.next()
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def _get_datetime(self) -> int:
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"""获取当前 bar 的 datetime（引擎调用）。
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Returns:
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datetime int (YYYYMMDD)
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"""
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if self._datetime_array is None:
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raise RuntimeError("数据未正确初始化")
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return int(self._datetime_array[self._bar_index])
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def _clear_signals(self) -> list[Signal]:
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"""清空并返回已生成的信号（引擎调用）。
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Returns:
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当前累积的信号列表
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"""
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signals = self._signals
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self._signals = []
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return signals
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# ── 辅助函数 ─────────────────────────────────────────────────────────────────────
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def _datetime_to_int(arr: NDArray) -> NDArray:
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"""将 datetime 数组转为 int (YYYYMMDD)。
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Args:
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arr: datetime 数组（np.datetime64 或 pd.Timestamp）
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Returns:
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int 数组，格式 YYYYMMDD
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"""
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result = np.zeros(len(arr), dtype=np.float64)
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for i, val in enumerate(arr):
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if isinstance(val, (np.datetime64, pd.Timestamp)):
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ts = pd.Timestamp(val)
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result[i] = float(ts.strftime("%Y%m%d"))
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else:
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# 已经是 int 或可转为 int
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result[i] = float(val)
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return result
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