With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researchers and practitioners . In this research, we propose a novel portfolio management strategy based on the framework of Deep Deterministic Policy Gradient, a policy-based reinforcement learning framework, and compare its performance to that of other trading strategies . In our framework, two Long Short-Term Memory neural networks and two fully connected neural networks are constructed . We also investigate the performance of our strategy with and without transaction costs . Experimentally, we choose eight US stocks consisting of four low-volatility stocks and four high-volatility stocks . We compare the compound annual return rate of our strategy against seven other strategies, e.g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios . In our case, the compound annual return rate is 14.12%, outperforming all other strategies . Furthermore, in terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of the second-best performing strategy.