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๐ŸŽฏ Module 2: Core SMC Setups ๐Ÿ“ˆ Intermediate

Fair Value Gaps (FVGs): Formation, Filtering, and Trading Mechanics

Master Fair Value Gaps from theory to execution. Learn why FVGs form, the 5-point quality filter, precise entry mechanics, and common mistakes to avoid.

โฑ 18 min๐Ÿ“ˆ Intermediate๐ŸŽ“ Quantum Trading Academyโœ… Free with any plan

A Fair Value Gap (FVG) is one of the most powerful concepts in Smart Money trading. It represents a price imbalance โ€” a zone where institutional orders moved price so aggressively that no two-way auction occurred. When price returns to fill this gap, it creates one of the highest-probability entry setups available.

How FVGs Form

An FVG is a three-candle pattern where the wick of candle 1 and the wick of candle 3 don't overlap. The space between them is the Fair Value Gap. The middle candle (candle 2) is the displacement candle โ€” a moment of aggressive institutional order flow that left an imbalance in the order book.

For a bullish FVG: the gap sits between the high of candle 1 and the low of candle 3 during an upward move. For a bearish FVG: the gap sits between the low of candle 1 and the high of candle 3 during a downward move.

Why FVGs Get Filled

The reason is rooted in market microstructure. Market-making algorithms are designed to seek price efficiency. When price moves too fast in one direction, it creates an inefficiency โ€” a range where buyers and sellers never properly matched. These algorithms will guide price back to these zones to rebalance the order book. Research shows approximately 70-80% of FVGs on the 1H timeframe and above get at least partially filled within 20 candles.

The 5-Point Quality Filter

1. HTF Alignment: The FVG must form in the direction of the higher-timeframe bias. A bullish FVG on the 15M chart is far more reliable when the 4H structure is also bullish.

2. Displacement Strength: The middle candle should show strong momentum โ€” a large body relative to recent candles, ideally closing near its extreme. Weak, small-bodied candles produce unreliable FVGs.

3. Unmitigated Status: A FVG that has already been tested is "mitigated" and loses most of its edge. Focus on fresh, untested gaps.

4. Confluence: The best FVGs overlap with an order block, key liquidity level, or Fibonacci zone. Multiple confluences dramatically increase win probability.

5. Session Context: FVGs formed during high-volume sessions (London open, NY open) are more reliable than those formed during low-volume periods (late Asian session for forex).

Entry Mechanics

The standard entry is at the 50% mark of the FVG โ€” the midpoint between the top and bottom of the gap. This gives optimal risk-to-reward. Stop loss goes beyond the entire gap. If the full gap gets swept with a candle body close, the thesis is invalidated. Target the next significant structure level or opposing liquidity pool.

Common Mistakes

1. Entering on first touch without confirmation. Wait for a rejection candle (pin bar, engulfing) at the FVG boundary. 2. Trading FVGs against HTF trend. This dramatically reduces your edge. 3. Ignoring volume context. Low-volume FVGs get swept entirely rather than providing clean entries. 4. Trading already-mitigated FVGs. First touch is the highest probability โ€” don't trade the second or third retest.

Quantum Algo FVG Detection

Quantum Algo automatically identifies every valid FVG across all timeframes on your TradingView chart. It color-codes by type (bullish/bearish), tracks mitigation status in real time, and filters low-quality imbalances using structural context. The multi-timeframe panel shows HTF FVGs directly on your entry timeframe so you never miss a high-quality setup.

Continue Learning

๐ŸŽฏ Liquidity in SMC: How Institutions Hunt Your Stop Loss โ†’ ๐ŸŽฏ Multi-Timeframe Analysis: The Top-Down Framework That Wins โ†’ โšก The Complete SMC Entry Model: Combining OBs, FVGs, and Liquidity โ†’ โ† Back to Full Academy

See these concepts live on your chart

Quantum Algo automates institutional order flow detection directly on TradingView. Every concept in this lesson โ€” detected in real time.

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