> ## Documentation Index > Fetch the complete documentation index at: https://docs.trinigence.com/llms.txt > Use this file to discover all available pages before exploring further. # Trade Frequency & Exposure > How often your strategy trades, how long it stays in the market, and why both define real risk. ## 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. *** ## 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 *** ## 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 *** ## 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 *** ## 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 *** ## 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 *** ## 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 Exposure explains many confusing results. *** ## 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 Learn how filters change behavior. *** ## 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. *** ## 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 *** ## What to read next Frequency amplifies sizing risk. Exposure metrics in context. Frequency-driven traps. Balance frequency and robustness. How often you trade defines how often you feel risk.