Volumetric Supply & Demand Zones

Real-time Indicator

Zone detection is triggered immediately when a pivot is confirmed with a qualifying impulse move.

Event	Lag	Detail
Zone creation	≈ swing_length bars	Pivot confirmation requires swing_length bars on each side (default 8)
Volume profile	0 bars	Built from base candles at time of zone creation
Mitigation	0 bars	Checked in real-time as price crosses zone boundaries

Formula for custom params: lag ≈ swing_length (pivot confirmation delay)

Volumetric Supply and Demand Zones detects key supply (resistance) and demand (support) zones at significant swing points, enriched with volume distribution and buy/sell delta analysis within each zone.

How it works:

1. Detect pivot highs and lows using swing_length bar lookback
2. Validate that the subsequent price move exceeds impulse_mult × ATR
3. Build zone boundaries from the base_lookback candles around the pivot
4. Distribute volume across profile_rows horizontal slices within the zone
5. Calculate buy vs sell delta per row (bullish candles = buy, bearish = sell)
6. Identify the Point of Control (POC) — the price row with highest volume
7. Track zone lifecycle: Fresh → Tested → Mitigated
8. Optionally merge overlapping same-type zones

def volumetric_supply_demand_zones(
    data: Union[PdDataFrame, PlDataFrame],
    swing_length: int = 8,
    impulse_mult: float = 1.2,
    base_lookback: int = 3,
    atr_length: int = 14,
    max_zone_atr: float = 4.0,
    max_zones: int = 10,
    merge_zones: bool = True,
    merge_gap_atr: float = 0.3,
    mitigation_type: str = "Wick",
    profile_rows: int = 10,
    high_column: str = 'High',
    low_column: str = 'Low',
    open_column: str = 'Open',
    close_column: str = 'Close',
    volume_column: str = 'Volume',
) -> Union[PdDataFrame, PlDataFrame]:

Example

from pyindicators import (
    volumetric_supply_demand_zones,
    volumetric_supply_demand_zones_signal,
    get_volumetric_supply_demand_zones_stats,
)

# Calculate Volumetric Supply & Demand Zones
df = volumetric_supply_demand_zones(df)

# Extract signal (already computed, returned for API consistency)
df = volumetric_supply_demand_zones_signal(df)

# Get statistics
stats = get_volumetric_supply_demand_zones_stats(df)
print(f"Demand zones: {stats['total_demand_zones']}")
print(f"Supply zones: {stats['total_supply_zones']}")
print(f"Avg zone volume: {stats['avg_zone_volume']}")

Output Columns:

• vsdz_demand: 1 on demand zone formation bar, else 0
• vsdz_supply: 1 on supply zone formation bar, else 0
• vsdz_zone_top / vsdz_zone_bottom: Active zone boundaries
• vsdz_poc: Point of Control — price level with highest volume
• vsdz_zone_type: 1 (demand) or -1 (supply)
• vsdz_volume: Total volume accumulated in the zone
• vsdz_delta: Net buy-sell volume delta in the zone
• vsdz_status: Zone lifecycle status (Fresh / Tested / Mitigated)
• vsdz_touches: Number of times the zone has been tested
• vsdz_signal: Trading signal (+1 demand entry, -1 supply entry, 0 neutral)

Zone Lifecycle:

Status	Meaning
Fresh	Newly created, price has not revisited
Tested	Price has touched the zone at least once
Mitigated	Price broke through the zone boundary

Mitigation Types:

Type	Rule
Wick	Zone broken when any wick crosses the boundary
Close	Zone broken when candle closes beyond the boundary

Signal Values (from volumetric_supply_demand_zones_signal):

• 1: Price enters a demand zone (potential long)
• 0: Neutral
• -1: Price enters a supply zone (potential short)