  Quantitative markets are characterized by swift dynamics and abundant
uncertainties, making the pursuit of profit-driven stock trading actions
inherently challenging. Within this context, reinforcement learning (RL), which
operates on a reward-centric mechanism for optimal control, has surfaced as a
potentially effective solution to the intricate financial decision-making
conundrums presented. This paper delves into the fusion of two established
financial trading strategies, namely the constant proportion portfolio
insurance (CPPI) and the time-invariant portfolio protection (TIPP), with the
multi-agent deep deterministic policy gradient (MADDPG) framework. As a result,
we introduce two novel multi-agent RL (MARL) methods, CPPI-MADDPG and
TIPP-MADDPG, tailored for probing strategic trading within quantitative
markets. To validate these innovations, we implemented them on a diverse
selection of 100 real-market shares. Our empirical findings reveal that the
CPPI-MADDPG and TIPP-MADDPG strategies consistently outpace their traditional
counterparts, affirming their efficacy in the realm of quantitative trading.
