Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Trading in Fast-ChangingMarkets withMeta-Reinforcement Learning

How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively.

An intelligent market making strategy in algorithmic trading

Market making （MM） strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange （TSE） and Shanghai Stock Exchange （SSE）, and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1） strategies with signals perform better than strategies without any signal in terms of average daily profit and loss （PnL） and sharpe ratio （SR）, and 2） correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.

Practical Meta-Reinforcement Learning of Evolutionary Strategy with Quantum Neural Networks for Stock Trading

We show the practicality of two existing meta-learning algorithms Model- Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our quantum meta-learning models on a simulated quantum computer with portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs have a similar learning pattern to our much larger classical meta-learned models that have over 250,000 model parameters and 2500 training epochs. Given these findings, we also discuss the benefits of scaling up our experiments from a simulated quantum computer to a real quantum computer. To the best of our knowledge, we are the first to apply the ideas of both classical meta-learning as well as quantum meta-learning to enhance stock trading.