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Why frequency and exposure matter

Many traders focus on entries and exits, but ignore how often and how long a strategy trades. Two strategies with identical metrics can feel completely different because:
  • one trades rarely and stays flat most of the time
  • the other trades constantly and is always exposed
Frequency and exposure define lived risk, not just statistical risk.

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Trade frequency

Trade frequency is how often a strategy opens trades. It depends on:
  • timeframe
  • entry logic strictness
  • filters (trend, session, volatility)
  • market volatility
Examples:
  • 2 trades per month → low frequency
  • several trades per day → high frequency

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What low frequency implies

Low-frequency strategies:
  • have fewer trades
  • rely on larger moves
  • often look cleaner in backtests
  • have higher uncertainty per result
Risks:
  • results depend heavily on a few trades
  • harder to validate statistically
  • long periods of inactivity

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What high frequency implies

High-frequency strategies:
  • produce many trades
  • rely on small edges
  • are sensitive to costs and slippage
  • reveal weaknesses faster
Risks:
  • noise sensitivity
  • overtrading
  • execution assumptions matter more

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Exposure time

Exposure time measures how long the strategy is in a position. Example:
  • exposure = 80% → strategy is in the market most of the time
  • exposure = 20% → strategy is mostly flat
High exposure increases:
  • market risk
  • drawdown sensitivity
  • emotional pressure

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Frequency vs exposure

These are related but not the same. Examples:
  • low frequency + high exposure → long-term trend following
  • high frequency + low exposure → scalping / fast mean reversion
  • high frequency + high exposure → often dangerous
  • low frequency + low exposure → very selective strategies

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Why exposure often hides risk

A strategy can look safe because:
  • win rate is high
  • drawdowns look moderate
But:
  • it may be exposed during every major market move
  • risk compounds during volatility spikes

How to read results

Exposure explains many confusing results.

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How filters affect frequency

Adding filters almost always:
  • reduces trade count
  • reduces exposure
  • increases selectivity
But too many filters can:
  • eliminate valid trades
  • create misleading backtests
  • cause long flat periods

Schedule & filters

Learn how filters change behavior.

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Common mistakes

High win rate strategies often trade frequently with small edges and hidden exposure.
Long drawdowns feel worse when the strategy is always in the market.
Always normalize expectations when comparing low- vs high-frequency systems.

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Best practices

  • Know how often your strategy trades
  • Check exposure before trusting returns
  • Ensure trade frequency matches your psychology
  • Compare strategies with similar frequency profiles

Position sizing basics

Frequency amplifies sizing risk.

Metrics explained

Exposure metrics in context.

Common pitfalls

Frequency-driven traps.

Improving a strategy

Balance frequency and robustness.
How often you trade defines how often you feel risk.