In contrast to conventional trend-following approaches, this method relies on the mean reversion aspect of financial markets. Leveraging an online passive-aggressive learning technique, the portfolio selection strategy effectively capitalizes on the mean reversion property in markets. Through an analysis of PAMR's update scheme, it is observed that the strategy adeptly balances between portfolio return and volatility risk, aligning with mean reversion trading principles. The results are promising, indicating that in most scenarios, the PAMR strategy surpasses benchmark portfolios and nearly all cutting-edge portfolio selection strategies based on diverse performance metrics. Aside from its outstanding performance, PAMR demonstrates high-speed execution, making it well-suited for practical online trading applications.
The write up for the algorithm can be found at this link:
