  Price movement prediction has always been one of the traders' concerns in
financial market trading. In order to increase their profit, they can analyze
the historical data and predict the price movement. The large size of the data
and complex relations between them lead us to use algorithmic trading and
artificial intelligence. This paper aims to offer an approach using
Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading
strategy in the stock and cryptocurrency markets. Unlike previous studies using
a discrete action space reinforcement learning algorithm, the TD3 is
continuous, offering both position and the number of trading shares. Both the
stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this
research to evaluate the performance of the proposed algorithm. The achieved
strategy using the TD3 is compared with some algorithms using technical
analysis, reinforcement learning, stochastic, and deterministic strategies
through two standard metrics, Return and Sharpe ratio. The results indicate
that employing both position and the number of trading shares can improve the
performance of a trading system based on the mentioned metrics.
