sanguo_vnpy_v2/sanguo_factor/fast_ops.py

"""
Fast polars-native drop-in replacements for vnpy's slow rolling operators.

This module provides native polars implementations that override vnpy's
rolling_map-based operators through the EXPRESSION_FUNCTIONS extension point.
Optimized for 10M+ row datasets on 2-core NAS infrastructure.

Registration: Call register_fast_ops() once before batch evaluation.
Idempotent and thread-safe.
"""

import sys
from typing import cast

import polars as pl
import numpy as np

# Inject vnpy source path (follow sanguo_factor/universe.py pattern)
_VNPY_SRC = "/Users/chufeng/.openclaw/sanguo_projects/sanguo_vnpy_v2/vnpy_v4.4.0"
if _VNPY_SRC not in sys.path:
    sys.path.insert(0, _VNPY_SRC)

from vnpy.alpha.dataset.utility import DataProxy, EXPRESSION_FUNCTIONS


def fast_ts_rank(feature: DataProxy, window: int) -> DataProxy:
    """Percentile rank of current value within window [0,1] (polars native).

    Replicates: scipy.stats.percentileofscore(s, s[-1]) / 100
    Strategy: Count values <= current, divide by window size.
    Handles NaN by treating NaN > everything (consistent with scipy).
    Returns None for partial windows (like original).
    """
    current = pl.col("data")

    # Count values <= current for each shift k in 0..window-1
    # Fill null with False to treat null comparisons as not <=
    le_count = pl.sum_horizontal([
        pl.when(current.shift(k).over("vt_symbol").le(current).fill_null(False))
        .then(pl.lit(1))
        .otherwise(pl.lit(0))
        for k in range(window)
    ])

    # Rank = count / window
    result = pl.when(pl.int_range(pl.len()).over("vt_symbol") >= window - 1) \
        .then(le_count / pl.lit(window)) \
        .otherwise(None)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        result.alias("data")
    )

    return DataProxy(df)


def fast_ts_corr(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Correlation between two features over rolling window (optimized native).

    Uses correlation identity: corr(x,y) = cov(x,y) / (std_x * std_y)
    """
    # 两操作数源自同一父df且全算子保长保序(rolling/shift/cs_rank 均保长)——
    # 按位拼接等价于按键join,免 10M 行 hash join(py-spy 实锤分钟级)
    h1, h2 = feature1.df.height, feature2.df.height
    if h1 != h2:
        raise ValueError(f"ts_corr 操作数长度不一致: {h1} vs {h2}(行序对齐前提被破坏)")
    df_merged = feature1.df.with_columns(feature2.df["data"].alias("data_right"))

    # Use native polars rolling correlation for exact match
    corr_result = pl.rolling_corr(
        pl.col("data"),
        pl.col("data_right"),
        window_size=window,
        min_samples=1
    ).over("vt_symbol")

    df = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.when(corr_result.is_infinite() | corr_result.is_nan())
        .then(None)
        .otherwise(corr_result)
        .alias("data")
    )

    return DataProxy(df)


def fast_ts_cov(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Covariance between two features over rolling window (native).

    Uses identity: cov(x,y) = corr(x,y) * std_x * std_y
    """
    # Get correlation and standard deviations, then compute covariance
    corr_result = fast_ts_corr(feature1, feature2, window)

    # Extract std deviations from the merged dataframe
    # 两操作数源自同一父df且全算子保长保序——按位拼接等价于按键join
    h1, h2 = feature1.df.height, feature2.df.height
    if h1 != h2:
        raise ValueError(f"ts_cov 操作数长度不一致: {h1} vs {h2}(行序对齐前提被破坏)")
    df_merged = feature1.df.with_columns(feature2.df["data"].alias("data_right"))

    std_x = pl.col("data").rolling_std(window, min_samples=1, ddof=0).over("vt_symbol")
    std_y = pl.col("data_right").rolling_std(window, min_samples=1, ddof=0).over("vt_symbol")

    # Covariance = corr * std_x * std_y
    df = df_merged.with_columns([
        std_x.alias("std_x"),
        std_y.alias("std_y"),
        corr_result.df["data"].alias("corr")
    ])

    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        (pl.col("corr") * pl.col("std_x") * pl.col("std_y")).alias("data")
    )

    # Handle infinite/NaN values
    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.when(pl.col("data").is_infinite() | pl.col("data").is_nan())
        .then(None)
        .otherwise(pl.col("data"))
        .alias("data")
    )

    return DataProxy(df)


def fast_ts_decay_linear(feature: DataProxy, window: int) -> DataProxy:
    """Linear decay weighted average: weights (w, w-1, ..., 1) / sum (polars native).

    Optimized by expanding the weighted sum as:
    Σ_k (k+1) * shift(k) for k in 0..window-1
    where shift(0) gets weight 1 (last element), shift(window-1) gets weight w (first element)
    Returns None for partial windows (like original).
    """
    current = pl.col("data")

    # Calculate weighted sum: Σ_k (k+1) * shift(k) for k in 0..window-1
    # shift(k) goes backward in time, so higher k = earlier element = higher weight
    weighted_sum = pl.sum_horizontal([
        pl.lit(k + 1) * current.shift(k).over("vt_symbol")
        for k in range(window)
    ])

    denominator = window * (window + 1) // 2

    result = pl.when(pl.int_range(pl.len()).over("vt_symbol") >= window - 1) \
        .then(weighted_sum / pl.lit(denominator)) \
        .otherwise(None)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        result.alias("data")
    )

    return DataProxy(df)


def fast_ts_slope(feature: DataProxy, window: int) -> DataProxy:
    """OLS slope over rolling window: cov(x,t) / var(t) where t = 0..w-1 (native).

    Replicates the optimized original formula but with cleaner native polars syntax.
    x = time index (0, 1, 2, ..., w-1)
    slope = cov(y, x) / var(x)
    """
    n = window
    mean_x = (n - 1) / 2.0
    var_x = (n**2 - 1) / 12.0  # Variance of [0, 1, ..., n-1]

    # E[y*x] using weighted sum
    mean_yx_expr = pl.sum_horizontal([
        i * pl.col("data").shift(window - 1 - i).over("vt_symbol")
        for i in range(n)
    ]) / n

    mean_y = pl.col("data").rolling_mean(window, min_samples=window).over("vt_symbol")

    # cov(y, x) = E[yx] - E[y]E[x]
    cov_yx = mean_yx_expr - mean_y * mean_x

    slope = cov_yx / var_x

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        slope.alias("data")
    )

    return DataProxy(df)


def fast_ts_rsquare(feature: DataProxy, window: int) -> DataProxy:
    """R-squared of linear regression over rolling window (native).

    r² = slope² * var(x) / var(y)
    where var(x) is constant for window size.
    """
    n = window
    mean_x = (n - 1) / 2.0
    var_x = (n**2 - 1) / 12.0  # Variance of [0, 1, ..., n-1]

    # E[y*x]
    mean_yx_expr = pl.sum_horizontal([
        i * pl.col("data").shift(window - 1 - i).over("vt_symbol")
        for i in range(n)
    ]) / n

    mean_y = pl.col("data").rolling_mean(window, min_samples=window).over("vt_symbol")

    cov_yx = mean_yx_expr - mean_y * mean_x
    var_y = pl.col("data").rolling_var(window, min_samples=window, ddof=0).over("vt_symbol")

    # r² = cov²(x,y) / (var(x) * var(y))
    rsquare = (cov_yx.pow(2)) / (var_x * var_y)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.when(rsquare.is_infinite() | rsquare.is_nan())
        .then(None)
        .otherwise(rsquare)
        .alias("data")
    )

    return DataProxy(df)


def fast_ts_resi(feature: DataProxy, window: int) -> DataProxy:
    """Residuals from linear regression over rolling window (native).

    residual = y - (intercept + slope * x_last)
    where x_last = window - 1 (time index of last point)
    """
    n = window
    mean_x = (n - 1) / 2.0
    var_x = (n**2 - 1) / 12.0
    x_last = n - 1

    # E[y*x]
    mean_yx_expr = pl.sum_horizontal([
        i * pl.col("data").shift(window - 1 - i).over("vt_symbol")
        for i in range(n)
    ]) / n

    mean_y = pl.col("data").rolling_mean(window, min_samples=window).over("vt_symbol")

    cov_yx = mean_yx_expr - mean_y * mean_x
    slope = cov_yx / var_x
    intercept = mean_y - slope * mean_x

    # residual = y - (intercept + slope * x_last)
    residual = pl.col("data") - (intercept + slope * x_last)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        residual.alias("data")
    )

    return DataProxy(df)


def fast_ts_quantile(feature: DataProxy, window: int, quantile: float) -> DataProxy:
    """Quantile value over rolling window (native polars).

    Uses rolling_quantile with linear interpolation to match original behavior.
    Returns None for partial windows (like original).
    """
    current = pl.col("data")

    # Use native rolling_quantile with linear interpolation
    quantile_result = current.rolling_quantile(
        quantile=quantile,
        interpolation="linear",
        window_size=window
    ).over("vt_symbol")

    # Apply window gating - null for partial windows
    result = pl.when(pl.int_range(pl.len()).over("vt_symbol") >= window - 1) \
        .then(quantile_result) \
        .otherwise(None)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        result.alias("data")
    )

    return DataProxy(df)


def fast_ts_mean(feature: DataProxy, window: int) -> DataProxy:
    """Mean over rolling window with shift expansion (over-free).

    Uses: mean = Σ shift_k / count_nonnull(shift_k) for k in 0..window-1
    Replicates: rolling_map(lambda s: np.nanmean(s), window, min_samples=1).over("vt_symbol")
    """
    current = pl.col("data")

    # Create shifted versions with symbol boundary protection
    shifts = [current.shift(k).over("vt_symbol") for k in range(window)]

    # Sum all shifts, then count non-null values
    sum_expr = pl.sum_horizontal(shifts)
    count_expr = pl.sum_horizontal([pl.when(s.is_not_null()).then(1).otherwise(0) for s in shifts])

    # Mean = sum / count (handles partial windows automatically)
    mean_expr = sum_expr / count_expr

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        mean_expr.alias("data")
    )
    return DataProxy(df)


def fast_ts_std(feature: DataProxy, window: int) -> DataProxy:
    """Standard deviation over rolling window with shift expansion (over-free).

    Uses: std = sqrt(E[x²] - E[x]²) with shift-based mean computation
    Replicates: rolling_map(lambda s: np.nanstd(s, ddof=0), window, min_samples=1).over("vt_symbol")
    """
    current = pl.col("data")

    # Create shifted versions with symbol boundary protection
    shifts = [current.shift(k).over("vt_symbol") for k in range(window)]

    # Count non-null values
    count_expr = pl.sum_horizontal([pl.when(s.is_not_null()).then(1).otherwise(0) for s in shifts])

    # Mean
    sum_expr = pl.sum_horizontal(shifts)
    mean_expr = sum_expr / count_expr

    # E[x²] = sum(shift²) / count
    sum_sq_expr = pl.sum_horizontal([s.pow(2) for s in shifts])
    mean_sq_expr = sum_sq_expr / count_expr

    # std = sqrt(E[x²] - E[x]²)
    std_expr = (mean_sq_expr - mean_expr.pow(2)).sqrt()

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        std_expr.alias("data")
    )
    return DataProxy(df)


def fast_ts_sum(feature: DataProxy, window: int) -> DataProxy:
    """Sum over rolling window with shift expansion (over-free).

    Uses: sum = Σ shift_k for k in 0..window-1
    Replicates: rolling_sum(window).over("vt_symbol") (no min_samples, so partial windows are NaN)
    """
    current = pl.col("data")

    # Create shifted versions and sum them
    shifts = [current.shift(k).over("vt_symbol") for k in range(window)]
    sum_expr = pl.sum_horizontal(shifts)

    # Apply boundary masking - null for partial windows (first window-1 rows per symbol)
    boundary_mask = pl.int_range(pl.len()).over("vt_symbol") >= (window - 1)
    result = pl.when(boundary_mask).then(sum_expr).otherwise(None)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        result.alias("data")
    )
    return DataProxy(df)


def fast_ts_min(feature: DataProxy, window: int) -> DataProxy:
    """Minimum over rolling window with shift expansion (over-free).

    Uses: min = min(shift_0, shift_1, ..., shift_{w-1})
    Replicates: rolling_min(window, min_samples=1).over("vt_symbol")
    """
    current = pl.col("data")

    # Create shifted versions and find minimum
    shifts = [current.shift(k).over("vt_symbol") for k in range(window)]
    min_expr = pl.min_horizontal(shifts)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        min_expr.alias("data")
    )
    return DataProxy(df)


def fast_ts_max(feature: DataProxy, window: int) -> DataProxy:
    """Maximum over rolling window with shift expansion (over-free).

    Uses: max = max(shift_0, shift_1, ..., shift_{w-1})
    Replicates: rolling_max(window, min_samples=1).over("vt_symbol")
    """
    current = pl.col("data")

    # Create shifted versions and find maximum
    shifts = [current.shift(k).over("vt_symbol") for k in range(window)]
    max_expr = pl.max_horizontal(shifts)

    df = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        max_expr.alias("data")
    )
    return DataProxy(df)


def fast_ts_corr_v2(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Correlation with shift expansion (over-free).

    Uses: corr = (E[xy] - E[x]E[y]) / (std_x * std_y)
    All statistics computed via shift-based mean/std
    """
    # 两操作数源自同一父df且全算子保长保序(rolling/shift/cs_rank 均保长)——
    # 按位拼接等价于按键join,免 10M 行 hash join(py-spy 实锤分钟级)
    h1, h2 = feature1.df.height, feature2.df.height
    if h1 != h2:
        raise ValueError(f"ts_corr_v2 操作数长度不一致: {h1} vs {h2}(行序对齐前提被破坏)")
    df_merged = feature1.df.with_columns(feature2.df["data"].alias("data_right"))

    x = pl.col("data")
    y = pl.col("data_right")

    # Create shifted versions for both series
    shifts_x = [x.shift(k).over("vt_symbol") for k in range(window)]
    shifts_y = [y.shift(k).over("vt_symbol") for k in range(window)]

    # Count non-null pairs
    count_expr = pl.sum_horizontal([
        pl.when(sx.is_not_null() & sy.is_not_null()).then(1).otherwise(0)
        for sx, sy in zip(shifts_x, shifts_y)
    ])

    # Means
    mean_x = pl.sum_horizontal(shifts_x) / count_expr
    mean_y = pl.sum_horizontal(shifts_y) / count_expr

    # E[xy]
    sum_xy = pl.sum_horizontal([sx * sy for sx, sy in zip(shifts_x, shifts_y)])
    mean_xy = sum_xy / count_expr

    # Standard deviations
    sum_x_sq = pl.sum_horizontal([sx.pow(2) for sx in shifts_x])
    sum_y_sq = pl.sum_horizontal([sy.pow(2) for sy in shifts_y])
    var_x = (sum_x_sq / count_expr) - mean_x.pow(2)
    var_y = (sum_y_sq / count_expr) - mean_y.pow(2)
    std_x = var_x.sqrt()
    std_y = var_y.sqrt()

    # Correlation
    corr_expr = (mean_xy - mean_x * mean_y) / (std_x * std_y)

    df = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.when(corr_expr.is_infinite() | corr_expr.is_nan())
        .then(None)
        .otherwise(corr_expr)
        .alias("data")
    )
    return DataProxy(df)


def fast_ts_cov_v2(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Covariance with shift expansion (over-free).

    Uses: cov = corr * std_x * std_y
    Delegates to fast_ts_corr_v2 for correlation
    """
    # Get correlation
    corr_result = fast_ts_corr_v2(feature1, feature2, window)

    # Get individual std deviations
    std_x_result = fast_ts_std(feature1, window)
    std_y_result = fast_ts_std(feature2, window)

    # Merge and compute covariance
    # 三操作数源自同一父df且全算子保长保序——按位拼接等价于按键join
    h_corr, h_x = corr_result.df.height, std_x_result.df.height
    if h_corr != h_x:
        raise ValueError(f"ts_cov_v2 corr与std_x长度不一致: {h_corr} vs {h_x}")
    h_y = std_y_result.df.height
    if h_corr != h_y:
        raise ValueError(f"ts_cov_v2 corr与std_y长度不一致: {h_corr} vs {h_y}")
    df_merged = corr_result.df.with_columns([
        std_x_result.df["data"].alias("std_x"),
        std_y_result.df["data"].alias("std_y")
    ])

    df = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        (pl.col("data") * pl.col("std_x") * pl.col("std_y")).alias("data")
    )

    # Handle infinite/NaN values
    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.when(pl.col("data").is_infinite() | pl.col("data").is_nan())
        .then(None)
        .otherwise(pl.col("data"))
        .alias("data")
    )
    return DataProxy(df)


def register_fast_ops() -> list[str]:
    """Register fast polars operators into vnpy's EXPRESSION_FUNCTIONS.

    Returns list of overridden operator names for verification.
    Idempotent: safe to call multiple times.

    Usage:
        overrides = register_fast_ops()
        print(f"Registered {len(overrides)} fast operators")
    """
    operators = {
        "ts_mean": fast_ts_mean,
        "ts_std": fast_ts_std,
        "ts_sum": fast_ts_sum,
        "ts_min": fast_ts_min,
        "ts_max": fast_ts_max,
        "ts_corr": fast_ts_corr_v2,
        "ts_cov": fast_ts_cov_v2,
        "ts_rank": fast_ts_rank,
        "ts_decay_linear": fast_ts_decay_linear,
        "ts_slope": fast_ts_slope,
        "ts_rsquare": fast_ts_rsquare,
        "ts_resi": fast_ts_resi,
        "ts_quantile": fast_ts_quantile,
    }

    # Register all operators
    for name, func in operators.items():
        EXPRESSION_FUNCTIONS[name] = func

    return list(operators.keys())


# Auto-registration on import is intentional for batch_eval usage
# But also expose explicit registration for testing
__all__ = [
    "register_fast_ops",
    "fast_ts_rank",
    "fast_ts_corr",
    "fast_ts_cov",
    "fast_ts_decay_linear",
    "fast_ts_slope",
    "fast_ts_rsquare",
    "fast_ts_resi",
    "fast_ts_quantile",
    "fast_ts_mean",
    "fast_ts_std",
    "fast_ts_sum",
    "fast_ts_min",
    "fast_ts_max",
    "fast_ts_corr_v2",
    "fast_ts_cov_v2",
]