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Liquidity Pools

Warmup Window

Minimum bars needed: ≥ contact_count × gap_bars bars (default params: Varies (contact_count=2))

Zone requires contact_count wick touches with sufficient spacing (gap_bars). The exact warmup depends on when enough contacts occur. After zones are formed, signals fire 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

Pool zones appear after enough wick contacts are observed.

EventLagDetail
Pool zone createddepends on contact_count + gap_barsZone requires contact_count wick touches with sufficient spacing
Price enters pool zone (signal)0 barsInstant once the zone exists

Liquidity Pools is a Smart Money Concept indicator that identifies zones where resting orders cluster, detected by tracking areas where price repeatedly bounces (wicks) from a level.

A bullish pool (support) forms when price wicks below a body-bottom level multiple times without closing below it. A bearish pool (resistance) forms when price wicks above a body-top level multiple times without closing above it. Zones are mitigated (invalidated) when price closes through them on two consecutive bars.

Key parameters:

  • Contact Count – minimum wick bounces required to form a pool (default: 2). Higher = fewer, more reliable zones.
  • Gap Bars – minimum bars between contacts to prevent double-counting (default: 5).
  • Confirmation Bars – bars price must stay away before confirming the zone (default: 10).
def liquidity_pools(
    data: Union[PdDataFrame, PlDataFrame],
    contact_count: int = 2,
    gap_bars: int = 5,
    confirmation_bars: int = 10,
    high_column: str = "High",
    low_column: str = "Low",
    open_column: str = "Open",
    close_column: str = "Close",
    volume_column: Optional[str] = "Volume",
    bull_pool_top_column: str = "liq_pool_bull_top",
    bull_pool_bottom_column: str = "liq_pool_bull_bottom",
    bear_pool_top_column: str = "liq_pool_bear_top",
    bear_pool_bottom_column: str = "liq_pool_bear_bottom",
    bull_pool_formed_column: str = "liq_pool_bull_formed",
    bear_pool_formed_column: str = "liq_pool_bear_formed",
    bull_pool_mitigated_column: str = "liq_pool_bull_mitigated",
    bear_pool_mitigated_column: str = "liq_pool_bear_mitigated",
) -> Union[PdDataFrame, PlDataFrame]:

Example

import pandas as pd
from pyindicators import (
    liquidity_pools,
    liquidity_pool_signal,
    get_liquidity_pool_stats
)

# Create sample OHLC data
df = pd.DataFrame({
    'Open': [...],
    'High': [...],
    'Low': [...],
    'Close': [...]
})

# Detect liquidity pools
df = liquidity_pools(df, contact_count=2, gap_bars=5, confirmation_bars=10)
print(df[['liq_pool_bull_top', 'liq_pool_bull_bottom',
          'liq_pool_bear_top', 'liq_pool_bear_bottom']])

# Generate trading signals
# 1 = bullish pool formed (support), -1 = bearish pool formed (resistance)
df = liquidity_pool_signal(df)
pool_events = df[df['liq_pool_signal'] != 0]

# Get statistics
stats = get_liquidity_pool_stats(df)
print(f"Bull pools formed: {stats['total_bull_formed']}")
print(f"Bear pools formed: {stats['total_bear_formed']}")
print(f"Total mitigated: {stats['total_mitigated']}")

The function returns:

  • liq_pool_bull_top / liq_pool_bull_bottom: Boundaries of the most recent active bullish pool (NaN if none)
  • liq_pool_bear_top / liq_pool_bear_bottom: Boundaries of the most recent active bearish pool (NaN if none)
  • liq_pool_bull_formed / liq_pool_bear_formed: 1 when a new pool forms
  • liq_pool_bull_mitigated / liq_pool_bear_mitigated: 1 when a pool is mitigated (broken)

Trading Strategy:

  • Bullish pools are support zones where institutional buyers accumulate—look for long entries near the zone
  • Bearish pools are resistance zones where institutional sellers distribute—look for short entries near the zone
  • Mitigation signals a change in market structure; the zone is no longer valid
  • Increase contact_count for higher-quality, more reliable zones

LIQUIDITY_POOLS

Chart Parameters

The image above uses the following parameters:

ParameterValue
contact_count2