Strong / Weak Highs & Lows

Lagging Indicator

Strong / Weak classification requires confirmed swing points and trend context.

Event	Lag	Detail
Detection	swing_lookback bars	A swing high/low must be confirmed by swing_lookback bars on each side

This indicator identifies swing highs and swing lows, then classifies each as Strong or Weak based on whether the swing occurred with or against the prevailing trend and relative volume.

How it works:

1. Pivot detection — Using a scaled lookback (swing_lookback // 5, minimum 1), identify bars that are local highs/lows
2. Trend determination — A rolling window (trend_lookback) checks whether price has broken above the highest high (bullish) or below the lowest low (bearish)
3. Classification:
   • Strong High = swing high that forms in a bearish trend (counter-trend → likely to hold)
   • Weak High = swing high that forms in a bullish trend (with-trend → likely to break)
   • Strong Low = swing low that forms in a bullish trend (counter-trend → likely to hold)
   • Weak Low = swing low that forms in a bearish trend (with-trend → likely to break)
4. Volume enrichment — each swing point stores the volume and relative volume percentile
5. Equilibrium — midpoint between the most recent swing high and swing low

def strong_weak_high_low(
    data: Union[PdDataFrame, PlDataFrame],
    swing_lookback: int = 10,
    trend_lookback: int = 50,
    open_column: str = "Open",
    high_column: str = "High",
    low_column: str = "Low",
    close_column: str = "Close",
    volume_column: str = "Volume",
) -> Union[PdDataFrame, PlDataFrame]:

Example

from pyindicators import (
    strong_weak_high_low,
    strong_weak_high_low_signal,
    get_strong_weak_high_low_stats,
)

# Detect & classify swings
df = strong_weak_high_low(df, swing_lookback=10, trend_lookback=50)

# Generate signal
df = strong_weak_high_low_signal(df)

# Get statistics
stats = get_strong_weak_high_low_stats(df)
print(f"Strong Highs: {stats['strong_highs']}")
print(f"Strong Lows:  {stats['strong_lows']}")
print(f"Weak Highs:   {stats['weak_highs']}")
print(f"Weak Lows:    {stats['weak_lows']}")

Output Columns:

Column	Description
sw_high	1 on bars with a swing high, else 0
sw_low	1 on bars with a swing low, else 0
sw_high_price	Price of the swing high (NaN otherwise)
sw_low_price	Price of the swing low (NaN otherwise)
sw_high_type	"Strong" or "Weak" (empty string otherwise)
sw_low_type	"Strong" or "Weak" (empty string otherwise)
sw_high_volume	Volume at the swing high bar
sw_low_volume	Volume at the swing low bar
sw_high_vol_pct	Volume percentile of the swing high bar (0–100)
sw_low_vol_pct	Volume percentile of the swing low bar (0–100)
sw_equilibrium	Midpoint between the most recent swing high and swing low
sw_signal	+1 on Strong Low, −1 on Strong High, 0 otherwise