  Algorithmic trading refers to executing buy and sell orders for specific
assets based on automatically identified trading opportunities. Strategies
based on reinforcement learning (RL) have demonstrated remarkable capabilities
in addressing algorithmic trading problems. However, the trading patterns
differ among market conditions due to shifted distribution data. Ignoring
multiple patterns in the data will undermine the performance of RL. In this
paper, we propose MOT,which designs multiple actors with disentangled
representation learning to model the different patterns of the market.
Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate
samples to the appropriate actor by introducing a regularization loss term.
Additionally, we propose Pretrain Module to facilitate imitation learning by
aligning the outputs of actors with expert strategy and better balance the
exploration and exploitation of RL. Experimental results on real futures market
data demonstrate that MOT exhibits excellent profit capabilities while
balancing risks. Ablation studies validate the effectiveness of the components
of MOT.
