feat: 纳入 VeighNa 4.4.0 上游源码作为版本参考

- 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>
2026-07-01 21:40:21 +08:00
parent 36ec0d38e3
commit 3fbe6c9be5
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+"""
+Math Functions
+"""
+
+import polars as pl
+
+from .utility import DataProxy
+
+
+def less(feature1: DataProxy, feature2: DataProxy | float) -> DataProxy:
+    """Return the minimum value between two features"""
+    if isinstance(feature2, DataProxy):
+        df_merged: pl.DataFrame = feature1.df.join(feature2.df, on=["datetime", "vt_symbol"])
+    else:
+        df_merged = feature1.df.with_columns(pl.lit(feature2).alias("data_right"))
+
+    df: pl.DataFrame = df_merged.select(
+        pl.col("datetime"),
+        pl.col("vt_symbol"),
+        pl.min_horizontal("data", "data_right").over("vt_symbol").alias("data")
+    )
+
+    return DataProxy(df)
+
+
+def greater(feature1: DataProxy, feature2: DataProxy | float) -> DataProxy:
+    """Return the maximum value between two features"""
+    if isinstance(feature2, DataProxy):
+        df_merged: pl.DataFrame = feature1.df.join(feature2.df, on=["datetime", "vt_symbol"])
+
+    else:
+        df_merged = feature1.df.with_columns(pl.lit(feature2).alias("data_right"))
+
+    df: pl.DataFrame = df_merged.select(
+        pl.col("datetime"),
+        pl.col("vt_symbol"),
+        pl.max_horizontal("data", "data_right").over("vt_symbol").alias("data")
+    )
+
+    return DataProxy(df)
+
+
+def log(feature: DataProxy) -> DataProxy:
+    """Calculate the natural logarithm of the feature"""
+    df: pl.DataFrame = feature.df.select(
+        pl.col("datetime"),
+        pl.col("vt_symbol"),
+        pl.col("data").log().over("vt_symbol")
+    )
+    return DataProxy(df)
+
+
+def abs(feature: DataProxy) -> DataProxy:
+    """Calculate the absolute value of the feature"""
+    df: pl.DataFrame = feature.df.select(
+        pl.col("datetime"),
+        pl.col("vt_symbol"),
+        pl.col("data").abs().over("vt_symbol")
+    )
+    return DataProxy(df)
+
+
+def sign(feature: DataProxy) -> DataProxy:
+    """Calculate the sign of the feature"""
+    df: pl.DataFrame = feature.df.select(
+        pl.col("datetime"),
+        pl.col("vt_symbol"),
+        pl.when(pl.col("data") > 0).then(1).when(pl.col("data") < 0).then(-1).otherwise(0).alias("data")
+    )
+    return DataProxy(df)
+
+
+def quesval(threshold: float, feature1: DataProxy, feature2: DataProxy | float | int, feature3: DataProxy | float | int) -> DataProxy:
+    """Return feature2 if threshold < feature1, otherwise feature3"""
+    df_merged = feature1.df
+
+    if isinstance(feature2, DataProxy):
+        df_merged = df_merged.join(feature2.df, on=["datetime", "vt_symbol"], suffix="_true")
+    else:
+        df_merged = df_merged.with_columns(pl.lit(feature2).alias("data_true"))
+
+    if isinstance(feature3, DataProxy):
+        df_merged = df_merged.join(feature3.df, on=["datetime", "vt_symbol"], suffix="_false")
+    else:
+        df_merged = df_merged.with_columns(pl.lit(feature3).alias("data_false"))
+
+    df: pl.DataFrame = df_merged.with_columns(
+        pl.when(threshold < pl.col("data"))
+        .then(pl.col("data_true"))
+        .otherwise(pl.col("data_false"))
+        .alias("data")
+    ).select(["datetime", "vt_symbol", "data"])
+
+    return DataProxy(df)
+
+
+def quesval2(threshold: DataProxy, feature1: DataProxy, feature2: DataProxy | float | int, feature3: DataProxy | float | int) -> DataProxy:
+    """Return feature2 if threshold < feature1, otherwise feature3 (DataProxy threshold version)"""
+    df_merged: pl.DataFrame = threshold.df.join(feature1.df, on=["datetime", "vt_symbol"], suffix="_cond")
+
+    if isinstance(feature2, DataProxy):
+        df_merged = df_merged.join(feature2.df, on=["datetime", "vt_symbol"], suffix="_true")
+    else:
+        df_merged = df_merged.with_columns(pl.lit(feature2).alias("data_true"))
+
+    if isinstance(feature3, DataProxy):
+        df_merged = df_merged.join(feature3.df, on=["datetime", "vt_symbol"], suffix="_false")
+    else:
+        df_merged = df_merged.with_columns(pl.lit(feature3).alias("data_false"))
+
+    df: pl.DataFrame = df_merged.with_columns(
+        pl.when(pl.col("data_cond") < pl.col("data"))
+        .then(pl.col("data_true"))
+        .otherwise(pl.col("data_false"))
+        .alias("data")
+    ).select(["datetime", "vt_symbol", "data"])
+
+    return DataProxy(df)
+
+
+def pow1(base: DataProxy, exponent: float) -> DataProxy:
+    """Safe power operation for DataProxy (handles negative base values)"""
+    df: pl.DataFrame = base.df.with_columns(
+        pl.when(pl.col("data") > 0)
+        .then(pl.col("data").pow(exponent))
+        .when(pl.col("data") < 0)
+        .then(pl.lit(-1) * pl.col("data").abs().pow(exponent))
+        .otherwise(0)
+        .alias("data")
+    )
+
+    return DataProxy(df)
+
+
+def pow2(base: DataProxy, exponent: DataProxy) -> DataProxy:
+    """Power operation between two DataProxy objects (base^exponent)
+
+    handle logic:
+    - base > 0: calculate base^exponent
+    - base < 0 and exponent is integer: calculate -1 * |base|^exponent
+    - other cases (base = 0, exponent is NaN, negative base and non-integer exponent): return 0
+
+    Note: use floor method to check integer rather than cast(Int64) method, because NaN cannot be converted to integer will report an error
+    """
+    base_renamed = base.df.rename({"data": "base_data"})
+    exp_renamed = exponent.df.rename({"data": "exp_data"})
+
+    df_merged: pl.DataFrame = base_renamed.join(exp_renamed, on=["datetime", "vt_symbol"], how="left")
+
+    df: pl.DataFrame = df_merged.with_columns(
+        pl.when(pl.col("base_data") > 0)
+        .then(pl.col("base_data").pow(pl.col("exp_data")))
+        .when(
+            (pl.col("base_data") < 0) &
+            (~pl.col("exp_data").is_nan()) &
+            (pl.col("exp_data").floor() == pl.col("exp_data"))
+        )
+        .then((-1) * pl.col("base_data").abs().pow(pl.col("exp_data")))
+        .otherwise(pl.lit(None))
+        .fill_nan(None)
+        .fill_null(0)
+        .alias("data")
+    ).select(["datetime", "vt_symbol", "data"])
+
+    return DataProxy(df)
+
+