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Z-Score Predictive Zones

Lagging Indicator

Z-Score smoothing and dynamic reversal levels introduce lag relative to the actual reversal.

EventLagDetail
Z-Score crosses dynamic reversal level≈ 20 barsVWMA smoothing on the raw Z-Score (default smooth=20)
Price band boundaries shift≈ 4 barsVWMA smoothing on the price bands (default band_smooth=4)
RSI gradient updates≈ 14 barsRSI period (default rsi_length=14) with 9-period EMA

Formula for custom params: lag ≈ smooth (dominant smoothing parameter)

Z-Score Predictive Zones detects statistical extremes in price by computing a VWMA-smoothed Z-Score, then tracking pivot-high and pivot-low reversals to build dynamic resistance and support levels. When the Z-Score crosses these levels, price bands are reverse-engineered back into price space, providing clear overbought/oversold zones on the chart.

How it works:

  1. Compute raw Z-Score: (Close - SMA) / StdDev over z_length bars
  2. Smooth via Volume-Weighted Moving Average (VWMA)
  3. Detect pivot highs/lows and filter by threshold
  4. Track rolling average of recent reversal Z-Score levels
  5. Map Z-Score levels back to price using mean ± level × std
  6. Apply VWMA smoothing to produce resistance and support bands
  7. Generate long/short signals on band crossovers
def z_score_predictive_zones(
    data: Union[PdDataFrame, PlDataFrame],
    z_length: int = 144,
    smooth: int = 20,
    history_depth: int = 25,
    threshold: float = 1.5,
    rsi_length: int = 14,
    rsi_ma_length: int = 9,
    band_smooth: int = 4,
    atr_length: int = 30,
    high_column: str = 'High',
    low_column: str = 'Low',
    close_column: str = 'Close',
    volume_column: str = 'Volume',
) -> Union[PdDataFrame, PlDataFrame]:

Example

from pyindicators import (
    z_score_predictive_zones,
    z_score_predictive_zones_signal,
    get_z_score_predictive_zones_stats,
)

# Calculate Z-Score Predictive Zones
df = z_score_predictive_zones(df)

# Generate trading signals (already computed, returned for API consistency)
df = z_score_predictive_zones_signal(df)

# Get statistics
stats = get_z_score_predictive_zones_stats(df)
print(f"Long signals: {stats['long_signals']}")
print(f"Short signals: {stats['short_signals']}")

Output Columns:

  • zspz_z_score: VWMA-smoothed Z-Score oscillator
  • zspz_mean / zspz_std: Rolling SMA and StdDev of close
  • zspz_avg_top_level / zspz_avg_bot_level: Dynamic resistance/support Z-Score levels
  • zspz_res_band_high / zspz_res_band_low: Resistance band in price space
  • zspz_sup_band_high / zspz_sup_band_low: Support band in price space
  • zspz_rsi_ma: EMA-smoothed RSI (used for gradient coloring)
  • zspz_long_signal / zspz_short_signal: Binary (0/1) long and short signals
  • zspz_signal: Combined signal (+1 long, -1 short, 0 neutral)

Signal Values (from z_score_predictive_zones_signal):

  • 1: Long signal — price crosses above support band
  • 0: Neutral
  • -1: Short signal — price crosses below resistance band

Z_SCORE_PREDICTIVE_ZONES