[Development Log] Drawdown is not an abnormality but a premise. Interpreting the stagnation period from the perspective of verification

Even with a superior logic, there will be periods when assets stall or decline in value. Whether you regard this phenomenon as a flaw or abnormality in the logic or as a statistically embedded premise greatly influences the quality of judgment during operation. This time, I would like to reorganize the phenomenon called drawdown from a verification perspective. The discussion is about how to quantify this stagnation period, which always exists behind a flashy asset curve with a strong right tail.

1. Drawdown is an inevitable probabilistic phenomenon

Drawdown is an indicator that shows the magnitude of decline from the asset’s peak, expands as unrealized losses and losing streaks accumulate, and returns to zero when new highs are reached. This value does not indicate a flaw in the logic; it is a probabilistically inevitable phenomenon. It is treated as a major metric that is always reported in backtest reports because this value is not an abnormal value to be avoided but a premise to be evaluated.

For example, even a logic with a 60% win rate has a nonzero probability of experiencing a 10-loss sequence if trials are assumed to be independent. As the number of trades increases, the probability of such losing streaks accumulates to a non-negligible level. Treating drawdown as “abnormal” often reflects a misunderstanding of statistical premises. A superior logic and the complete absence of losing streaks are two completely different issues.

2. Another factor: regime changes in the market

In addition to the statistical probability of drawdown, another factor that causes drawdown is regime change in the market. A logic that excels in trending markets naturally becomes harder to operate in range-bound markets, and vice versa. Since the structure of the entry logic itself embeds conditions based on a specific market environment, it is only natural that performance changes when regimes switch.

Market regimes are constantly changing, and it is inherently impossible for any logic to function in all environments all the time. Designs and evaluations that assume favorable and unfavorable environments alternate are a realistic perspective for actual operation. Operating under the premise that such a premise is missing and expecting constant victory is a fundamental reason for overly emphasizing stagnation.

3. Resilience is the essential evaluation axis

In verification, what matters is not the absolute value of the maximum drawdown itself, but the resilience to recover from it. How quickly a system can reach new highs after a decline is a crucial indicator of the durability of the logic’s edge.

If two logics record similar maximum drawdowns, but one recovers in a few weeks while the other remains subdued for months, their practical reliability is completely different. Evaluating both the depth of drawdown and the speed and stability of the recovery greatly influences the quality of verification. A logic with a long recovery period also bears higher capital constraint costs and psychological burden, so risks not evident from a simple maximum drawdown figure may be lurking.

4. Typical misjudgment structures during stagnation

There is a common structure to misjudgments that operators are prone to during stagnation. They confuse a streak of losses when statistical samples are not yet enough with an essential degradation of the logic.

Ideally, one should compare with the predefined threshold of “expected maximum drawdown” identified in advance during backtests and stay quiet if within that range. In reality, many cases start operation without this threshold, overreact to stagnation within expectations, and make weakly justified interventions such as changing parameters or adjusting lots. Such interventions often undermine the reproducibility of the logic and, in operation, destroy the edge that existed at the time of verification.

5. Handling drawdown in the lab verification

In the lab verification process, we evaluate not only the maximum drawdown value but also the time taken to recover and the volatility of price movement during the recovery. This is one of the elements we place particular emphasis on within the axis of “survival and friction” that we previously recorded.

A logic with shallow drawdown but slow recovery and a logic with deep drawdown but fast recovery have completely different psychological burdens and capital efficiency in real operation. Visualizing these differences numerically and presenting them as facts is the role of verification. Rather than displaying a single figure like “maximum drawdown ◯%,” disclosing the recovery behavior behind that number as a set provides a basis for evidence-based judgments.

Semura Lab.’s Approach to Drawdown

We, Semura Lab., treat drawdown not as an abnormal event but as a statistically embedded premise. A superior logic inevitably experiences stagnation. What matters is whether that stagnation stays within the pre-anticipated range, clearly stated at the time of verification. Whether one can endure stagnation within expectations is often more a matter of the operator’s preparation than the performance of the logic. In this development log, we will continue to honestly record these subtle yet essential verification perspectives.