MACD Seasons in Python

Python

import numpy as np
import polars as pl


def rl_endpoint(values: np.ndarray, n: int = 10) -> np.ndarray:
    """Linear-regression endpoint over a trailing window of `n` bars.

    Fits a least-squares line to each window [i-n+1 .. i] and returns the
    fitted value at the last position. Warmup bars (< n) are NaN. Vectorized
    via np.correlate with precomputed endpoint weights (matches edge-transform
    CoreIndicators.rl_series).
    """
    values = np.asarray(values, dtype=float)
    length = len(values)
    out = np.full(length, np.nan)
    if n <= 0 or length < n:
        return out
    x = np.arange(n, dtype=float)
    x_mean = (n - 1) / 2.0
    x_diff = x - x_mean
    denom = float(np.sum(x_diff ** 2))
    if denom == 0:
        return out
    # Endpoint weight: intercept term (1/n) + slope contribution at x=n-1.
    w = 1.0 / n + x_mean * x_diff / denom
    out[n - 1:] = np.correlate(values, w, mode="valid")
    return out


def macd_rl(close, short: int = 10, long: int = 30, rl_n: int = 10) -> pl.Series:
    """MACD of the regression line: SMA(RL, short) - SMA(RL, long).

    macdrl = SMA(RL(close, rl_n), short) - SMA(RL(close, rl_n), long)
    (matches edge-transform CoreIndicators.calc_macdrl).
    """
    rl = pl.Series("rl", rl_endpoint(np.asarray(close, dtype=float), n=rl_n))
    sma_short = rl.rolling_mean(short)
    sma_long = rl.rolling_mean(long)
    return (sma_short - sma_long).alias("macdrl")


def macd_seasons(close, short: int = 10, long: int = 30, rl_n: int = 10) -> pl.Series:
    """4-season MACD classification from MACDRL sign and slope.

    Seasons follow a market cycle:
      1 = Spring : macdrl < 0 and rising  (recovery)
      2 = Summer : macdrl > 0 and rising  (expansion)
      3 = Fall   : macdrl > 0 and falling (distribution)
      4 = Winter : macdrl < 0 and falling (contraction)

    Slope is approximated as: rising = macdrl >= SMA(macdrl, 2)
    (matches edge-transform CoreIndicators.calc_macdseason4).
    """
    macdrl = macd_rl(close, short=short, long=long, rl_n=rl_n)
    sma2 = macdrl.rolling_mean(2)
    positive = macdrl > 0
    rising = macdrl >= sma2
    df = pl.DataFrame({"positive": positive, "rising": rising})
    season = df.select(
        pl.when(positive.not_() & rising).then(1)        # Spring
        .when(positive & rising).then(2)                  # Summer
        .when(positive & rising.not_()).then(3)           # Fall
        .when(positive.not_() & rising.not_()).then(4)    # Winter
        .otherwise(None)
        .alias("season")
    )["season"]
    return season