DRL-Based Quantitative Algorithms for Gold and Bitcoin Portfolio Decisions

Abstract

Maximizing returns is always people’s investment goal. Gold and Bitcoin have some hedging ability, and their prices fluctuate greatly, making them popular varieties for investors. However, the market is risky, and different economic, political and environmental factors will impact the market. As a result, Bitcoin and gold prices fluctuate sharply, leading to uncertain investment and uncertain returns. To maximize returns, we model time and price, analyze and model the price trends of gold and bitcoin problems, give the best trading model, and analyze the transaction cost sensitivity of transaction risk and trading strategies. Predictive techniques are used to move the future and to build some heuristic-based robots to make decisions. Use (DRL) deep reinforcement learning to simulate stock trading based on the Markov decision-making process of avoiding risk aversion, reducing trading costs, maintaining liquidity, and assuming that the stock market is not affected by enhanced trading agents.