Focused on the issue that noisy data, single period hypothesis and nonstationary prediction are not fully considered in the existing mean reversion strategy, an efficient OnLine Autoregressive moving average Reversion (OLAR) algorithm based on multi-period was proposed. Firstly, a stock price forecasting model was given by using the autoregressive moving average algorithm, and it was converted into an autoregressive model by a reasonable assumption. Then, an objective function was given by combining the loss function and a regular term, and a closed solution was obtained by using the second-order information of the loss function. The portfolio's closed-form update was obtained by using the online Passive Aggressive (PA) algorithm. Theoretical analysis and experimental results show that, compared with Robust Median Reversion (RMR), the accumulated profits of OLAR increase by 455.6%, 221.5%, 11.2% and 50.3% on NYSE (N), NYSE (N), Dow Jones Industrial Average (DJIA) and MSCI datasets respectively. Meanwhile, the results of statistical test show that the superior performance of OLAR is not caused by random factors. In addition, compared with algorithms such as RMR and Online Moving Average Reversion (OLMAR), OLAR achieves the highest annualized percentage yield, Sharpe ratio and Calmar ratio. Finally, the running time of OLAR is almost the same as that of RMR and OLMAR, therefore OLAR is suitable for large-scale real-time applications.