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feat(backtest): add Strategy base class with DataProxy and crossover
- 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>
This commit is contained in:
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co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
f37b75ea42
commit
687851fc67
@@ -0,0 +1,450 @@
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"""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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@@ -0,0 +1,467 @@
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"""测试策略基类和数据代理。"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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import pytest
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from easy_tdx.backtest.strategy import (
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Strategy,
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StrategyDataProxy,
|
||||
_SeriesAccessor,
|
||||
crossover,
|
||||
)
|
||||
from easy_tdx.backtest.types import Signal
|
||||
|
||||
|
||||
# ── 辅助函数 ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _make_df(n: int = 20, seed: int = 42) -> pd.DataFrame:
|
||||
"""构造随机 OHLCV DataFrame。
|
||||
|
||||
Args:
|
||||
n: K线数量
|
||||
seed: 随机种子
|
||||
|
||||
Returns:
|
||||
DataFrame with columns: datetime, open, close, high, low, vol, amount
|
||||
"""
|
||||
rng = np.random.default_rng(seed)
|
||||
base = 10.0
|
||||
prices = base + rng.random(n) * 5
|
||||
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"datetime": pd.date_range("2024-01-01", periods=n, freq="D"),
|
||||
"open": prices,
|
||||
"close": prices + rng.random(n) - 0.5,
|
||||
"high": prices + rng.random(n),
|
||||
"low": prices - rng.random(n),
|
||||
"vol": rng.integers(1000, 10000, n),
|
||||
"amount": rng.integers(100000, 1000000, n),
|
||||
}
|
||||
)
|
||||
# 确保 high >= max(open, close), low <= min(open, close)
|
||||
df["high"] = df[["open", "close", "high"]].max(axis=1)
|
||||
df["low"] = df[["open", "close", "low"]].min(axis=1)
|
||||
return df
|
||||
|
||||
|
||||
def _make_df_with_extras(n: int = 20) -> pd.DataFrame:
|
||||
"""构造带额外列的 DataFrame（MACD_DIF, BOLL_UPPER）。
|
||||
|
||||
Args:
|
||||
n: K线数量
|
||||
|
||||
Returns:
|
||||
DataFrame with standard OHLCV columns + MACD_DIF, BOLL_UPPER
|
||||
"""
|
||||
df = _make_df(n)
|
||||
rng = np.random.default_rng(42)
|
||||
|
||||
# 添加额外列
|
||||
df["MACD_DIF"] = rng.random(n) * 2 - 1
|
||||
df["BOLL_UPPER"] = df["close"] + rng.random(n) * 2
|
||||
|
||||
return df
|
||||
|
||||
|
||||
# ── TestSeriesAccessor ────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestSeriesAccessor:
|
||||
"""测试 _SeriesAccessor 数据访问器。"""
|
||||
|
||||
def test_current_value(self) -> None:
|
||||
"""测试获取当前值 [0]。"""
|
||||
arr = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=2)
|
||||
assert acc[0] == 3.0
|
||||
|
||||
def test_previous_value(self) -> None:
|
||||
"""测试获取前一根 [-1]。"""
|
||||
arr = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=2)
|
||||
assert acc[-1] == 2.0
|
||||
|
||||
def test_previous_two(self) -> None:
|
||||
"""测试获取前两根 [-2]。"""
|
||||
arr = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=2)
|
||||
assert acc[-2] == 1.0
|
||||
|
||||
def test_index_out_of_bounds_negative(self) -> None:
|
||||
"""测试索引越界（负方向）。"""
|
||||
arr = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=0)
|
||||
with pytest.raises(IndexError, match="索引 -1 超出范围"):
|
||||
_ = acc[-1]
|
||||
|
||||
def test_len(self) -> None:
|
||||
"""测试 __len__ 返回数组长度。"""
|
||||
arr = np.array([1.0, 2.0, 3.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=1)
|
||||
assert len(acc) == 3
|
||||
|
||||
def test_array_conversion(self) -> None:
|
||||
"""测试 __array__ 转为 numpy 数组。"""
|
||||
arr = np.array([1.0, 2.0, 3.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=1)
|
||||
result = np.asarray(acc)
|
||||
np.testing.assert_array_equal(result, arr)
|
||||
|
||||
def test_raw_property(self) -> None:
|
||||
"""测试 raw 属性返回原始数组。"""
|
||||
arr = np.array([1.0, 2.0, 3.0])
|
||||
acc = _SeriesAccessor(arr, bar_index=1)
|
||||
np.testing.assert_array_equal(acc.raw, arr)
|
||||
|
||||
|
||||
# ── TestStrategyDataProxy ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestStrategyDataProxy:
|
||||
"""测试 StrategyDataProxy 数据代理。"""
|
||||
|
||||
def test_basic_columns(self) -> None:
|
||||
"""测试标准 OHLCV 列访问。"""
|
||||
df = _make_df(n=10)
|
||||
proxy = StrategyDataProxy(df)
|
||||
proxy._set_index(5)
|
||||
|
||||
assert isinstance(proxy.open[0], float)
|
||||
assert isinstance(proxy.close[0], float)
|
||||
assert isinstance(proxy.high[0], float)
|
||||
assert isinstance(proxy.low[0], float)
|
||||
assert isinstance(proxy.vol[0], float)
|
||||
assert isinstance(proxy.amount[0], float)
|
||||
|
||||
def test_previous_bar(self) -> None:
|
||||
"""测试访问前一根 bar 的数据。"""
|
||||
df = _make_df(n=10)
|
||||
proxy = StrategyDataProxy(df)
|
||||
proxy._set_index(5)
|
||||
|
||||
# close[0] 应该等于 df["close"].iloc[5]
|
||||
assert proxy.close[0] == pytest.approx(df["close"].iloc[5])
|
||||
# close[-1] 应该等于 df["close"].iloc[4]
|
||||
assert proxy.close[-1] == pytest.approx(df["close"].iloc[4])
|
||||
|
||||
def test_extra_columns_via_getattr(self) -> None:
|
||||
"""测试通过 __getattr__ 访问额外列。"""
|
||||
df = _make_df_with_extras(n=10)
|
||||
proxy = StrategyDataProxy(df)
|
||||
proxy._set_index(5)
|
||||
|
||||
# MACD_DIF 和 BOLL_UPPER 应该可以访问
|
||||
assert isinstance(proxy.MACD_DIF[0], float)
|
||||
assert isinstance(proxy.BOLL_UPPER[0], float)
|
||||
|
||||
# 验证值正确
|
||||
assert proxy.MACD_DIF[0] == pytest.approx(df["MACD_DIF"].iloc[5])
|
||||
assert proxy.BOLL_UPPER[0] == pytest.approx(df["BOLL_UPPER"].iloc[5])
|
||||
|
||||
def test_missing_column_raises(self) -> None:
|
||||
"""测试访问不存在的列抛出 AttributeError。"""
|
||||
df = _make_df(n=10)
|
||||
proxy = StrategyDataProxy(df)
|
||||
proxy._set_index(5)
|
||||
|
||||
with pytest.raises(AttributeError, match="列 'NONEXISTENT' 不存在"):
|
||||
_ = proxy.NONEXISTENT[0]
|
||||
|
||||
|
||||
# ── TestCrossover ───────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestCrossover:
|
||||
"""测试 crossover 金叉检测函数。"""
|
||||
|
||||
def test_crossover_true(self) -> None:
|
||||
"""测试金叉检测（a 上穿 b）。"""
|
||||
a = np.array([1, 2, 3, 4, 5])
|
||||
b = np.array([5, 4, 3, 2, 1])
|
||||
mask = crossover(a, b)
|
||||
|
||||
# 索引 3 处：a[2]=3 <= b[2]=3, a[3]=4 > b[3]=2 → 金叉
|
||||
assert mask[3] == True
|
||||
# 其他位置无金叉
|
||||
assert mask[0] == False
|
||||
assert mask[1] == False
|
||||
assert mask[2] == False
|
||||
assert mask[4] == False
|
||||
|
||||
def test_crossover_false_no_cross(self) -> None:
|
||||
"""测试无金叉情况（a 全在 b 下方）。"""
|
||||
a = np.array([1, 2, 3, 4, 5])
|
||||
b = np.array([6, 7, 8, 9, 10])
|
||||
mask = crossover(a, b)
|
||||
|
||||
# 全部 False
|
||||
assert not np.any(mask)
|
||||
|
||||
def test_crossover_series(self) -> None:
|
||||
"""测试接受 pd.Series 参数。"""
|
||||
a = pd.Series([1, 2, 3, 4, 5])
|
||||
b = pd.Series([5, 4, 3, 2, 1])
|
||||
mask = crossover(a, b)
|
||||
|
||||
assert mask[3] == True
|
||||
|
||||
def test_crossover_with_accessor(self) -> None:
|
||||
"""测试接受 _SeriesAccessor 参数。"""
|
||||
arr_a = np.array([1, 2, 3, 4, 5])
|
||||
arr_b = np.array([5, 4, 3, 2, 1])
|
||||
acc_a = _SeriesAccessor(arr_a, bar_index=4)
|
||||
acc_b = _SeriesAccessor(arr_b, bar_index=4)
|
||||
|
||||
mask = crossover(acc_a, acc_b)
|
||||
assert mask[3] == True
|
||||
|
||||
|
||||
# ── TestStrategyBase ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestStrategyBase:
|
||||
"""测试 Strategy 策略基类。"""
|
||||
|
||||
def test_subclass_init_and_next(self) -> None:
|
||||
"""测试子类 init() 和 next() 被调用。"""
|
||||
|
||||
class SimpleStrategy(Strategy):
|
||||
init_called = False
|
||||
next_called_count = 0
|
||||
|
||||
def init(self) -> None:
|
||||
SimpleStrategy.init_called = True
|
||||
|
||||
def next(self) -> None:
|
||||
SimpleStrategy.next_called_count += 1
|
||||
|
||||
df = _make_df(n=10)
|
||||
strategy = SimpleStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
assert SimpleStrategy.init_called is True
|
||||
|
||||
# 模拟引擎遍历所有 bar
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
assert SimpleStrategy.next_called_count == 10
|
||||
|
||||
def test_buy_sell_recording(self) -> None:
|
||||
"""测试 buy() 和 sell() 创建 Signal。"""
|
||||
|
||||
class SignalStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
if self._bar_index == 5:
|
||||
self.buy(size=100, price=10.0, stop_loss=9.0, take_profit=11.0)
|
||||
elif self._bar_index == 8:
|
||||
self.sell(size=100, price=10.5)
|
||||
|
||||
df = _make_df(n=10)
|
||||
strategy = SignalStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
signals = strategy._clear_signals()
|
||||
|
||||
assert len(signals) == 2
|
||||
assert signals[0].direction == "BUY"
|
||||
assert signals[0].size == 100
|
||||
assert signals[0].price == 10.0
|
||||
assert signals[0].stop_loss == 9.0
|
||||
assert signals[0].take_profit == 11.0
|
||||
|
||||
assert signals[1].direction == "SELL"
|
||||
assert signals[1].size == 100
|
||||
assert signals[1].price == 10.5
|
||||
|
||||
def test_I_registers_indicator(self) -> None:
|
||||
"""测试 self.I() 注册指标。"""
|
||||
from easy_tdx.MyTT import MA
|
||||
|
||||
class IndicatorStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
self.ma5 = self.I(MA, self.data.close, 5)
|
||||
self.ma20 = self.I(MA, self.data.close, 20)
|
||||
|
||||
def next(self) -> None:
|
||||
pass
|
||||
|
||||
df = _make_df(n=50)
|
||||
strategy = IndicatorStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
# 验证指标长度正确（MA 会产生 nan，但长度不变）
|
||||
assert len(strategy.ma5) == 50
|
||||
assert len(strategy.ma20) == 50
|
||||
|
||||
# 验证指标已注册
|
||||
assert "MA" in strategy._indicators
|
||||
|
||||
def test_full_position_buy(self) -> None:
|
||||
"""测试全仓买入（size=0）。"""
|
||||
|
||||
class FullPositionStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
if self._bar_index == 5:
|
||||
self.buy(size=0) # 全仓
|
||||
|
||||
df = _make_df(n=10)
|
||||
strategy = FullPositionStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
signals = strategy._clear_signals()
|
||||
|
||||
assert len(signals) == 1
|
||||
assert signals[0].direction == "BUY"
|
||||
assert signals[0].size == 0 # 0 表示全仓
|
||||
|
||||
def test_data_property(self) -> None:
|
||||
"""测试 self.data 属性返回 StrategyDataProxy。"""
|
||||
df = _make_df(n=10)
|
||||
|
||||
class DataAccessStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
# 验证 data 可访问
|
||||
assert hasattr(self.data, "close")
|
||||
assert hasattr(self.data, "open")
|
||||
|
||||
def next(self) -> None:
|
||||
# 验证 next() 中也可访问
|
||||
assert self.data.close[0] > 0
|
||||
|
||||
strategy = DataAccessStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
def test_position_property(self) -> None:
|
||||
"""测试 self.position 属性。"""
|
||||
df = _make_df(n=10)
|
||||
|
||||
class PositionStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
# 初始状态无持仓
|
||||
assert self.position["size"] == 0.0
|
||||
|
||||
strategy = PositionStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
def test_get_datetime(self) -> None:
|
||||
"""测试 _get_datetime() 方法。"""
|
||||
df = _make_df(n=10)
|
||||
|
||||
class DateTimeStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
# 验证 datetime 正确（YYYYMMDD 格式）
|
||||
dt = self._get_datetime()
|
||||
assert isinstance(dt, int)
|
||||
assert 20240101 <= dt <= 20241231
|
||||
|
||||
strategy = DateTimeStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
|
||||
def test_clear_signals(self) -> None:
|
||||
"""测试 _clear_signals() 清空信号列表。"""
|
||||
|
||||
class ClearSignalStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
self.buy(size=100)
|
||||
|
||||
df = _make_df(n=10)
|
||||
strategy = ClearSignalStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
# 第一轮
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
signals1 = strategy._clear_signals()
|
||||
assert len(signals1) == 10
|
||||
|
||||
# 第二轮（无新信号）
|
||||
signals2 = strategy._clear_signals()
|
||||
assert len(signals2) == 0
|
||||
|
||||
def test_buy_without_bind_raises(self) -> None:
|
||||
"""测试未绑定数据时调用 buy() 抛出错误。"""
|
||||
|
||||
class BadStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
self.buy(size=100)
|
||||
|
||||
strategy = BadStrategy()
|
||||
# 未调用 _bind_data()
|
||||
|
||||
with pytest.raises(RuntimeError, match="策略未绑定数据"):
|
||||
strategy._call_init()
|
||||
strategy._set_bar_index(0)
|
||||
strategy._call_next()
|
||||
|
||||
def test_chanlun_property(self) -> None:
|
||||
"""测试 chanlun 属性（预留）。"""
|
||||
df = _make_df(n=10)
|
||||
|
||||
class ChanlunStrategy(Strategy):
|
||||
def init(self) -> None:
|
||||
assert self.chanlun is None
|
||||
|
||||
def next(self) -> None:
|
||||
pass
|
||||
|
||||
strategy = ChanlunStrategy()
|
||||
strategy._bind_data(df)
|
||||
strategy._call_init()
|
||||
|
||||
for i in range(len(df)):
|
||||
strategy._set_bar_index(i)
|
||||
strategy._call_next()
|
||||
Reference in New Issue
Block a user