Expanding the Strategy Validation Passage

In this article, I shall talk about the validation of a strategy.

In the intricate process of strategy formulation, a key step that often follows is validation. Strategy validation is pivotal as it provides insight into the feasibility and potential success of the strategy implemented. Within the realm of time-series analysis, validation poses its own unique set of challenges. Traditional methods often involve using historical data, segmenting it so that the majority forms the training set, while the final segment serves as the validation or test set. This approach assumes that past patterns will hold into this last chunk of data, giving an idea of the strategy’s efficiency.

However, while this conventional approach has its merits, it’s essential to note that financial markets are ever-evolving. Relying solely on historical data may not capture the nuances and shifts that markets can experience. Fortunately, with the availability and ease of accessing new market data, there’s an opportunity to adopt a more forward-looking validation process. Instead of just leveraging the past, why not use future data to stress-test our strategy? One method to employ is simulated trading. By running our strategy in a simulated environment for a duration - say, 6 months - we can closely monitor its performance in near real-time scenarios. Though this method might require a bit more patience, the time invested can provide a wealth of information about the strategy’s robustness.

But as we scrutinize performance, it’s crucial to be precise about the metrics we’re evaluating. Simply observing whether a strategy is profitable over the 6 months doesn’t provide the full picture. Profits, after all, can be significantly influenced by market conditions, often represented by the market’s beta. To get a clearer sense of a strategy’s genuine merit, it’s beneficial to analyze its excessive profit in relation to a chosen benchmark. This allows for a more apples-to-apples comparison, accounting for prevailing market conditions.

A particularly informative metric in this context is the maximum drawdown of the excessive return curve. This metric provides insights into the strategy’s downside risk, indicating how much one can expect the strategy’s value to decline from a peak before a new peak is achieved. If during our validation period, we witness the maximum drawdown being surpassed, it’s a telling sign. Such a scenario strongly hints that the strategy might have been influenced by survivorship bias or, even worse, is a mere product of overfitting. These are red flags, suggesting that the strategy may not be as viable as initially perceived. In such cases, it’s prudent to halt the simulated trading and revisit the drawing board.