  Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and
Information ratios, have been pivotal in portfolio asset allocation, focusing
on minimizing risk while maximizing profit. Nevertheless, these metrics often
fail to account for the distinct characteristics of bull and bear markets,
leading to sub-optimal investment decisions. This paper introduces a novel
approach called the Market-adaptive Ratio, which was designed to adjust risk
preferences dynamically in response to market conditions. By integrating the
$\rho$ parameter, which differentiates between bull and bear markets, this new
ratio enables a more adaptive portfolio management strategy. The $\rho$
parameter is derived from historical data and implemented within a
reinforcement learning framework, allowing the method to learn and optimize
portfolio allocations based on prevailing market trends. Empirical analysis
showed that the Market-adaptive Ratio outperformed the Sharpe Ratio by
providing more robust risk-adjusted returns tailored to the specific market
environment. This advance enhances portfolio performance by aligning investment
strategies with the inherent dynamics of bull and bear markets, optimizing risk
and return outcomes.
