claude_dev
3fbe6c9be5
- 添加 vnpy_v4.4.0/ 目录到版本控制 - 删除嵌入的 .git 目录避免嵌套仓库 - 添加 README_SANGUO.md 说明来源和用途 - 更新 .gitignore 移除 vnpy_v4.4.0/ 忽略规则 上游代码信息: - 项目: VeighNa 量化交易平台 - 版本: 4.4.0 - 来源: https://github.com/vnpy/vnpy 用途: 版本参考、代码学习、差异对比、离线开发 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
286 lines
9.4 KiB
Python
286 lines
9.4 KiB
Python
from datetime import datetime
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from enum import Enum
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from numbers import Real
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from typing import Union, cast
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from collections.abc import Callable
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import polars as pl
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EXPRESSION_FUNCTIONS: dict[str, Callable] = {}
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def register_functions(functions: list[Callable]) -> None:
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"""Register custom expression functions by function name."""
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for func in functions:
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EXPRESSION_FUNCTIONS[func.__name__] = func
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class DataProxy:
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"""Feature data proxy"""
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def __init__(self, df: pl.DataFrame) -> None:
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"""Constructor"""
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self.name: str = df.columns[-1]
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self.df: pl.DataFrame = df.rename({self.name: "data"})
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# Note that for numerical expressions, variables should be placed before numbers. e.g. a * 2
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@staticmethod
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def _as_series(value: object) -> pl.Series:
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"""Normalize an operator result to a Polars series."""
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if isinstance(value, pl.Series):
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return value
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return cast(pl.Series, value)
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def _comparison_series(self, value: object) -> pl.Series:
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"""Normalize comparison results to an Int32 series."""
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if isinstance(value, pl.Series):
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return value.cast(pl.Int32)
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if isinstance(value, bool):
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return pl.Series(name="data", values=[int(value)] * len(self.df))
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if isinstance(value, Real):
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return pl.Series(name="data", values=[int(bool(value))] * len(self.df))
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raise TypeError(f"Unsupported comparison result type: {type(value)!r}")
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def result(self, s: pl.Series) -> "DataProxy":
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"""Convert series data to feature object"""
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result: pl.DataFrame = self.df[["datetime", "vt_symbol"]]
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result = result.with_columns(other=s)
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return DataProxy(result)
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def __add__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Addition operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] + other.df["data"])
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else:
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s = self._as_series(self.df["data"] + other)
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return self.result(s)
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def __radd__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Right addition operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(other.df["data"] + self.df["data"])
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else:
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s = self._as_series(other + self.df["data"])
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return self.result(s)
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def __sub__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Subtraction operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] - other.df["data"])
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else:
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s = self._as_series(self.df["data"] - other)
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return self.result(s)
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def __rsub__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Right subtraction operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(other.df["data"] - self.df["data"])
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else:
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s = self._as_series(other - self.df["data"])
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return self.result(s)
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def __mul__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Multiplication operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] * other.df["data"])
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else:
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s = self._as_series(self.df["data"] * other)
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return self.result(s)
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def __rmul__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Right multiplication operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] * other.df["data"])
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else:
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s = self._as_series(self.df["data"] * other)
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return self.result(s)
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def __truediv__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Division operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] / other.df["data"])
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else:
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s = self._as_series(self.df["data"] / other)
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return self.result(s)
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def __rtruediv__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Right division operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(other.df["data"] / self.df["data"])
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else:
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s = self._as_series(other / self.df["data"])
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return self.result(s)
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def __floordiv__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Floor division operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] // other.df["data"])
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else:
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s = self._as_series(self.df["data"] // other)
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return self.result(s)
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def __mod__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Modulo operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"] % other.df["data"])
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else:
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s = self._as_series(self.df["data"] % other)
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return self.result(s)
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def __pow__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Power operation"""
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if isinstance(other, DataProxy):
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s = self._as_series(self.df["data"].pow(other.df["data"]))
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else:
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s = self._as_series(self.df["data"].pow(cast(int | float, other)))
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return self.result(s)
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def __abs__(self) -> "DataProxy":
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"""Get absolute value"""
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s: pl.Series = self.df["data"].abs()
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return self.result(s)
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def __neg__(self) -> "DataProxy":
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"""Negation operation"""
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s: pl.Series = -self.df["data"]
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return self.result(s)
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def __gt__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Greater than comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] > other.df["data"]
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else:
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s = self.df["data"] > other
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return self.result(self._comparison_series(s))
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def __ge__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Greater than or equal comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] >= other.df["data"]
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else:
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s = self.df["data"] >= other
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return self.result(self._comparison_series(s))
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def __lt__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Less than comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] < other.df["data"]
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else:
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s = self.df["data"] < other
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return self.result(self._comparison_series(s))
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def __le__(self, other: Union["DataProxy", Real]) -> "DataProxy":
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"""Less than or equal comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] <= other.df["data"]
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else:
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s = self.df["data"] <= other
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return self.result(self._comparison_series(s))
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def __eq__(self, other: Union["DataProxy", Real]) -> "DataProxy": # type: ignore[override]
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"""Equal comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] == other.df["data"]
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else:
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s = self.df["data"] == other
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return self.result(self._comparison_series(s))
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def __ne__(self, other: Union["DataProxy", Real]) -> "DataProxy": # type: ignore[override]
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"""Not equal comparison"""
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if isinstance(other, DataProxy):
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s: object = self.df["data"] != other.df["data"]
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else:
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s = self.df["data"] != other
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return self.result(self._comparison_series(s))
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def calculate_by_expression(df: pl.DataFrame, expression: str) -> pl.DataFrame:
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"""Execute calculation based on expression"""
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# Import operators locally to avoid polluting global namespace
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from .ts_function import ( # noqa
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ts_delay,
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ts_min, ts_max,
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ts_argmax, ts_argmin,
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ts_rank, ts_sum,
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ts_mean, ts_std,
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ts_slope, ts_quantile,
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ts_rsquare, ts_resi,
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ts_corr,
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ts_less, ts_greater,
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ts_log, ts_abs,
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ts_delta, ts_cov,
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ts_decay_linear,
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ts_product
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)
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from .cs_function import ( # noqa
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cs_rank,
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cs_mean,
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cs_std,
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cs_sum,
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cs_scale
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)
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from .ta_function import ( # noqa
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ta_rsi,
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ta_atr
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)
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from .math_function import ( # noqa
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less, greater, log, abs,
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sign, pow1, pow2,
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quesval, quesval2
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)
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# Extract feature objects to local space
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d: dict = locals()
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d.update(EXPRESSION_FUNCTIONS)
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for column in df.columns:
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# Filter index columns
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if column in {"datetime", "vt_symbol"}:
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continue
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# Cache feature df
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column_df = df[["datetime", "vt_symbol", column]]
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d[column] = DataProxy(column_df)
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# Use eval to execute calculation
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other: DataProxy = eval(expression, {}, d)
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# Return result DataFrame
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return other.df
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def calculate_by_polars(df: pl.DataFrame, expression: pl.expr.expr.Expr) -> pl.DataFrame:
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"""Execute calculation based on Polars expression"""
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return df.select([
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"datetime",
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"vt_symbol",
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expression.alias("data")
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])
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def to_datetime(arg: datetime | str) -> datetime:
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"""Convert time data type"""
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if isinstance(arg, str):
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if "-" in arg:
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fmt: str = "%Y-%m-%d"
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else:
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fmt = "%Y%m%d"
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return datetime.strptime(arg, fmt)
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else:
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return arg
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class Segment(Enum):
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"""Data segment enumeration values"""
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TRAIN = 1
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VALID = 2
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TEST = 3
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