Leverage is position exposure divided by account equity.

Leverage is position exposure divided by account equity. If a strategy has $100,000 of market exposure against $50,000 of equity, it is running 2 times leverage. This ratio matters because it scales the effect of price changes on equity. When exposure is larger relative to equity, the same market move produces a larger gain or loss in percentage terms on the account.

The mechanism is straightforward. A strategy earns or loses returns on its exposure, but survival is determined by its equity. As leverage increases, losses consume a larger fraction of equity per adverse move. That makes drawdowns deeper and recovery harder, because a larger percentage loss requires a disproportionately larger percentage gain to return to the starting equity level. In practical terms, higher leverage shortens the distance between ordinary variation in returns and a loss large enough to impair or end the trading process.

For a given trading edge, increasing leverage does not change the underlying signal quality. It changes the mapping from strategy returns to equity returns. If the expected edge is positive but noisy, leverage amplifies both sides of that distribution. The positive outcomes become larger, but so do the negative outcomes, including the left tail that drives severe drawdowns and ruin. This is why leverage is central to risk management rather than signal design.

Leverage is defined as exposure relative to capital and magnifies gains and losses alike. That framing is important because the main consequence of leverage is not only faster growth in favorable paths, but also faster deterioration in unfavorable ones. Since trading outcomes arrive as a sequence, the order and size of losses matter. Higher leverage leaves less equity buffer to absorb that sequence.

To evaluate the claim that increasing leverage raises the probability of account ruin for a given trading edge, the evidence requested would need to compare the same representative strategy across multiple leverage levels. The comparison should hold the underlying signals, instruments, and transaction assumptions fixed while varying only the exposure relative to equity. For each leverage level, Monte Carlo resampling or historical path analysis would then estimate the distribution of drawdowns and the frequency with which equity breaches a ruin threshold. That is the relevant design because it isolates leverage as the changing variable.

Sonar Sciences provides a backtest overfitting audit to examine whether a backtest result is likely to be inflated by selection over many trials. That matters here because a leverage study built on a single attractive backtest can be misleading if the strategy was chosen after extensive searching. An overfitting audit is a statistical control on the validity of the underlying strategy evidence before leverage conclusions are drawn from it.

The deflated Sharpe ratio adjusts the interpretation of a reported Sharpe ratio for non normality and for the fact that many strategies may have been tested before one was selected. That adjustment is relevant because leverage studies often summarize outcomes with risk adjusted performance statistics. If those statistics are biased upward by multiple testing or distributional effects, then the apparent safety of a leveraged strategy can also be overstated. Using a deflated Sharpe ratio helps reduce that bias when judging whether the edge persists after realistic statistical correction.

Leverage is exposure divided by equity, and raising it increases the sensitivity of equity to return fluctuations. That makes drawdowns larger and leaves less room for error, which is the basic mechanism by which leverage increases the likelihood of ruin.

Covered in depth in the Strategy research fundamentals pillar hub.