Fair Value Gap (FVG) Indicator

Fair Value Gaps are price imbalances that occur when there’s a gap between candlestick wicks, indicating areas where price may return to “fill the gap.”

Overview

FVGs are a core concept in Smart Money Concepts (SMC) trading. They represent areas where aggressive buying or selling created an imbalance, leaving a “gap” that price often revisits.

How It Works

Bullish FVG

A bullish FVG forms when:

Candle 1’s high is below Candle 3’s low
Creates an unfilled gap between the wicks


        ┌─┐ Candle 3
        │ │
      ──┴─┴── Candle 3 Low
   ░░░░░░░░░░ ← BULLISH FVG ZONE
      ──┬─┬── Candle 1 High
        │ │
        └─┘ Candle 1

Bearish FVG

A bearish FVG forms when:

Candle 1’s low is above Candle 3’s high
Creates an unfilled gap between the wicks


        ┌─┐ Candle 1
        │ │
      ──┴─┴── Candle 1 Low
   ░░░░░░░░░░ ← BEARISH FVG ZONE
      ──┬─┬── Candle 3 High
        │ │
        └─┘ Candle 3

Implementation


# indicators/fvg.py
 
import pandas as pd
from smartmoneyconcepts import smc
 
def calculate_fvg(
    df: pd.DataFrame,
    min_gap_percent: float = 0.001,
    max_age_bars: int = 20
) -> pd.DataFrame:
    """
    Calculate Fair Value Gaps.
 
    Args:
        df: DataFrame with OHLCV data
        min_gap_percent: Minimum gap size as percentage
        max_age_bars: Maximum age before FVG expires
 
    Returns:
        DataFrame with FVG data
    """
    # Detect FVGs using smartmoneyconcepts library
    fvg_data = smc.fvg(df, join_consecutive=True)
 
    # Filter by minimum size
    fvg_data = fvg_data[
        fvg_data['FVG_Size'] >= min_gap_percent * df['close'].iloc[-1]
    ]
 
    # Filter by age
    current_bar = len(df) - 1
    fvg_data = fvg_data[
        (current_bar - fvg_data['FVG_Bar']) <= max_age_bars
    ]
 
    return fvg_data

Signal Interpretation

FVG States

State	Description
Active	Gap not yet tested by price
Mitigated	Price has partially filled the gap
Filled	Price has completely filled the gap

Trading Zones

Zone Type	Direction	Trading Action
Bullish FVG	Support	Look for long entries when price touches
Bearish FVG	Resistance	Look for short entries when price touches

Role in VRVP Strategy

FVGs provide confluence zones for entries:

Long Entries

Price bouncing off a bullish FVG zone (support)

Short Entries

Price bouncing off a bearish FVG zone (resistance)


def check_fvg_confluence(price: float, fvg_data: pd.DataFrame) -> dict:
    """Check if price is near an FVG zone."""
 
    for _, fvg in fvg_data.iterrows():
        # Check bullish FVG (support)
        if fvg['FVG_Type'] == 'bullish':
            if fvg['FVG_Bottom'] <= price <= fvg['FVG_Top']:
                return {
                    'type': 'bullish',
                    'zone': (fvg['FVG_Bottom'], fvg['FVG_Top']),
                    'strength': fvg['FVG_Size']
                }
 
        # Check bearish FVG (resistance)
        elif fvg['FVG_Type'] == 'bearish':
            if fvg['FVG_Bottom'] <= price <= fvg['FVG_Top']:
                return {
                    'type': 'bearish',
                    'zone': (fvg['FVG_Bottom'], fvg['FVG_Top']),
                    'strength': fvg['FVG_Size']
                }
 
    return {'type': 'none', 'zone': None, 'strength': 0}

Configuration


@dataclass
class FVGConfig:
    min_gap_percent: float = 0.001  # 0.1% minimum gap
    max_age_bars: int = 20          # Maximum 20 bars old
    mitigation_pct: float = 0.5     # 50% fill = mitigated

Parameters

Parameter	Default	Range	Description
`min_gap_percent`	0.1%	0.05%-0.5%	Minimum gap size
`max_age_bars`	20	10-50	Maximum age in bars
`mitigation_pct`	50%	30%-70%	Percentage fill for mitigation

Parameter Effects

Minimum Gap:

Higher values → Only significant gaps, fewer signals
Lower values → More gaps detected, more noise

Maximum Age:

Higher values → Older gaps still valid, more zones
Lower values → Only fresh gaps, fewer but more relevant

Usage Example


from indicators.fvg import calculate_fvg
from config.settings import FVGConfig
 
# Load price data
df = load_ohlcv_data("EUR_USD", "1H")
 
# Configure indicator
config = FVGConfig(
    min_gap_percent=0.001,
    max_age_bars=20
)
 
# Calculate FVGs
fvg_data = calculate_fvg(df, config.min_gap_percent, config.max_age_bars)
 
# Display active FVGs
print("Active Fair Value Gaps:")
for _, fvg in fvg_data.iterrows():
    print(f"  {fvg['FVG_Type'].upper()}: {fvg['FVG_Bottom']:.5f} - {fvg['FVG_Top']:.5f}")
 
# Check current price against FVGs
current_price = df['close'].iloc[-1]
confluence = check_fvg_confluence(current_price, fvg_data)
 
if confluence['type'] != 'none':
    print(f"Price in {confluence['type']} FVG zone!")

Visual Representation


Price Chart (1H)
│
│     ┌─┐
│     │ │
│   ──┴─┴──
│  ░░░░░░░░░  ← Bullish FVG Zone (Support)
│   ──┬─┬──
│     │ │      ╭───╮
│     └─┘     ╱     ╲
│            ╱       ╲     Price bounces
│           ╱         ╲    off FVG zone
│          ╱           ╲
│  ░░░░░░░░░░░░░░░░░░░░░░░░  ← Price enters FVG
│         ╱
│        ╱    ← Bullish reaction
│       ╱
│──────────────────────────────────────────►

FVG Quality Factors

High-Quality FVGs

Size: Larger gaps indicate stronger imbalances
Volume: High volume during gap formation
Trend alignment: FVG in direction of higher timeframe trend
Fresh: Recently formed, not yet tested

Low-Quality FVGs

Small gaps (noise)
Old, multiple times tested
Counter-trend
Low volume formation

Best Practices

Tips for Using FVGs:

Trade only in trend direction - Bullish FVGs in uptrends, bearish in downtrends
Wait for price to reach the zone - Don’t anticipate
Combine with other indicators - Volume Profile, StochRSI confirmation
Use proper risk management - Stop below/above the FVG zone

Volume Profile - Additional S/R zones
StochRSI - Entry timing
Supertrend - Trend direction