"""
Pure-function technical indicators.  All operate on pandas Series/DataFrame
and return pandas objects so NaN propagation is handled automatically.
"""

import numpy as np
import pandas as pd


def ema(series: pd.Series, period: int) -> pd.Series:
    return series.ewm(span=period, min_periods=period, adjust=False).mean()


def rsi(close: pd.Series, period: int = 14) -> pd.Series:
    delta = close.diff()
    gain  = delta.clip(lower=0)
    loss  = (-delta).clip(lower=0)
    avg_g = gain.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
    avg_l = loss.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
    rs    = avg_g / avg_l.replace(0, np.inf)
    return 100 - (100 / (1 + rs))


def atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
    hi, lo, pc = df["high"], df["low"], df["close"].shift(1)
    tr = pd.concat([(hi - lo), (hi - pc).abs(), (lo - pc).abs()], axis=1).max(axis=1)
    return tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()


def adx(df: pd.DataFrame, period: int = 14) -> pd.Series:
    """Average Directional Index."""
    hi, lo, pc = df["high"], df["low"], df["close"].shift(1)
    tr = pd.concat([(hi - lo), (hi - pc).abs(), (lo - pc).abs()], axis=1).max(axis=1)
    dm_pos = (hi - hi.shift(1)).clip(lower=0).where((hi - hi.shift(1)) > (lo.shift(1) - lo), 0)
    dm_neg = (lo.shift(1) - lo).clip(lower=0).where((lo.shift(1) - lo) > (hi - hi.shift(1)), 0)
    atr_s  = tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
    di_pos = 100 * dm_pos.ewm(alpha=1 / period, min_periods=period, adjust=False).mean() / atr_s
    di_neg = 100 * dm_neg.ewm(alpha=1 / period, min_periods=period, adjust=False).mean() / atr_s
    dx     = (100 * (di_pos - di_neg).abs() / (di_pos + di_neg).replace(0, np.nan))
    return dx.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()