Premium / Discount Zones

Warmup Window

Minimum bars needed: 2 × swing_length + 1 bars (default params: 21 bars (swing_length=10))

Swing high/low confirmation needs swing_length bars on each side. After warmup, zone boundaries update in real-time.

✅ After the warmup window is filled, this indicator produces a new value on every incoming bar in real-time.

Real-time Indicator

Zones update after swing confirmation, not after smoothing delay.

Event	Lag	Detail
Zone boundaries update	≈ swing_length bars after the swing	Zones computed from most recent confirmed swing high/low
Price enters premium / discount zone	0 bars	Instant comparison of close vs zone levels

Identifies Premium, Discount, and Equilibrium zones based on the current market range defined by swing highs and swing lows. Inspired by Smart Money Concepts (SMC) trading, the market is divided into zones relative to the most recent significant swing range:

• Premium Zone -- the upper half of the range (above equilibrium). Price is considered expensive; smart money is more likely to sell.
• Discount Zone -- the lower half of the range (below equilibrium). Price is considered cheap; smart money is more likely to buy.
• Equilibrium -- the exact midpoint (50%) of the range, acting as the decision boundary.

def premium_discount_zones(
    data: Union[PdDataFrame, PlDataFrame],
    swing_length: int = 10,
    high_column: str = "High",
    low_column: str = "Low",
    close_column: str = "Close",
) -> Union[PdDataFrame, PlDataFrame]:

Returns the following columns:

• pdz_range_high: Current swing range high
• pdz_range_low: Current swing range low
• pdz_equilibrium: Midpoint of the range
• pdz_zone: "premium", "discount", or "equilibrium"
• pdz_zone_pct: How deep into the zone (0-100%)

Signal function:

• pdz_signal: 1 = discount zone (potential buy), -1 = premium zone (potential sell), 0 = equilibrium / no range

Example

from investing_algorithm_framework import download

from pyindicators import (
    premium_discount_zones,
    premium_discount_zones_signal,
    get_premium_discount_zones_stats,
)

pd_df = download(
    symbol="btc/eur",
    market="bitvavo",
    time_frame="1d",
    start_date="2024-01-01",
    end_date="2024-06-01",
    pandas=True,
)

# Calculate Premium / Discount Zones
pd_df = premium_discount_zones(pd_df, swing_length=10)
pd_df = premium_discount_zones_signal(pd_df)

# Get summary statistics
stats = get_premium_discount_zones_stats(pd_df)
print(stats)

pd_df[["Close", "pdz_range_high", "pdz_range_low", "pdz_equilibrium", "pdz_zone", "pdz_zone_pct", "pdz_signal"]].tail(10)

Chart Parameters

The image above uses the following parameters:

Parameter	Value
swing_length	10