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Analysis of Fair Value Gap (FVG) Trading Strategies: Retest Dilemma & Market Context

#fair_value_gap #futures_trading #trading_strategies #market_context #ai_bubble #mechanical_trading
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November 23, 2025
Analysis of Fair Value Gap (FVG) Trading Strategies: Retest Dilemma & Market Context
Integrated Analysis

The analysis originates from a Reddit discussion [0] where traders debate waiting for Fair Value Gap (FVG) retests. FVGs are untraded price zones from impulsive three-candle moves [1][2]. Key arguments:

  1. Consistency over retests
    : Execution consistency matters more than rigidly waiting for retests [0].
  2. Market context impact
    : AI bubble nervousness reduces retests, leading to faster, unretracted moves [0][4].
  3. Mechanical rules
    : A rule-based approach (HTF bias + 1-min structure shift + confirmation) eliminates emotional decisions [0].
    The AI bubble context (45% of fund managers view AI as a bubble [4]) creates volatility where retests are less frequent [0][5].
Key Insights
  • Sentiment-strategy interaction
    : AI bubble nervousness directly alters FVG behavior, requiring adaptive strategies.
  • Emotional risk mitigation
    : Mechanical rules reduce FOMO, a common pitfall when waiting for retests [0].
  • Contextual flexibility
    : No single rule applies—traders must balance FVG signals with market conditions [0][3].
Risks & Opportunities
  • Risks
    : Waiting for retests misses 1-2 continuation trades per fast leg [0]; emotional decisions lead to poor entries.
  • Opportunities
    : Rule-based strategies capture more trades in volatile markets [0]; adapting to AI bubble sentiment aligns with current dynamics [4].
Key Information Summary

FVGs are untraded zones from impulsive moves [1]. Traders face a dilemma: wait for retests (risking missed trades) or use mechanical rules (capturing opportunities). Market context (AI bubble) reduces retests, making adaptive, rule-based strategies relevant [0][4]. Consistency in execution is prioritized over rigid retest rules [0].

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Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.