Deep Reinforcement Learning (DRL) algorithms have been increasingly used to construct stock trading strategies, but they often face performance challenges when applied to financial data with low ...signal-to-noise ratios and unevenness, as these methods were originally designed for the gaming community. To address this issue, we propose a DRL-based stock trading system that leverages Cascaded Long Short-Term Memory (CLSTM-PPO Model) to capture the hidden information in the daily stock data. Our model adopts a cascaded structure with two stages of carefully designed deep LSTM networks: it uses one LSTM to extract the time-series features from a sequence of daily stock data in the first stage, and then the features extracted are fed to the agent in the reinforcement learning algorithm for training, while the actor and the critic in the agent also use a LSTM network. We conduct experiments on stock market datasets from four major indices: the Dow Jones Industrial index (DJI) in the US, the Shanghai Stock Exchange 50 (SSE50) in China, S&P BSE Sensex Index (SENSEX) in India, and the Financial Times Stock Exchange 100 (FTSE100) in the UK. We compare our model with several benchmark models, including: (i) a model based on a buy-and-hold strategy; (ii) a Proximal Policy Optimization (PPO) model with Multilayer Perceptron (MLP) policy; (iii) some up-to-date models like the MLP model, LSTM model, Light Gradient Boosting Machine (LGBM) model, and histogram-based gradient boosting model; and (iv) an ensemble strategy model. The experimental results show that our model outperforms the baseline models in several key metrics, such as cumulative returns, maximum earning rate, and average profitability per trade. The improvements range from 5% to 52%, depending on the metric and the stock index. This indicates that our proposed method is a promising way to build an automated stock trading system.
Presently, the volatile and dynamic aspects of stock prices are significant research challenges for stock markets or any other financial sector to design accurate and profitable trading strategies in ...all market situations. To meet such challenges, the usage of computer-aided stock trading techniques has grown in prominence in recent decades owing to their ability to rapidly and accurately analyze stock market situations. In the recent past, deep reinforcement learning (DRL) methods and trading bots are commonly utilized for algorithmic trading. However, in the existing literature, the trading agents employ the historical and present trends of stock prices as an observing state to make trading decisions without taking into account the long-term market future pattern of stock prices. Therefore, in this study, we proposed a novel decision support system for automated stock trading based on deep reinforcement learning that observes both past and future trends of stock prices whether single and multi-step ahead as an observing state to make the optimal trading decisions of buying, selling, and holding the stocks. More specifically, at every time step, future trends are monitored concurrently using a forecasting network whose output is concatenated with past trends of stock prices. The concatenated vectors are subsequently supplied to the DRL agent as an observation state. In addition, the suggested forecasting network is built on a Gated Recurrent Unit (GRU). The GRU-based agent captures more informative and inherent aspects of time-series financial data. Furthermore, the suggested decision support system has been tested on several stock markets such as Tesla, IBM, Amazon, CSCO, and Chinese Stocks as well as equity markets i-e SSE Composite Index, NIFTY 50 Index, US Commodity Index Fund, and has achieved encouraging profit values while trading.
Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term ...memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period.
There are several automated stock trading programs using reinforcement learning, one of which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL agents and make an ...ensemble with three different actor–critic algorithms: Advantage Actor–Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). This novel idea was the concept mainly used in this paper. However, we did not stop there, but we refined the automated stock trading in two areas. First, we made another DRL-based ensemble and employed it as a new trading agent. We named it Remake Ensemble, and it combines not only A2C, DDPG, and PPO but also Actor–Critic using Kronecker-Factored Trust Region (ACKTR), Soft Actor–Critic (SAC), Twin Delayed DDPG (TD3), and Trust Region Policy Optimization (TRPO). Furthermore, we expanded the application domain of automated stock trading. Although the existing stock trading method treats only 30 Dow Jones stocks, ours handles KOSPI stocks, JPX stocks, and Dow Jones stocks. We conducted experiments with our modified automated stock trading system to validate its robustness in terms of cumulative return. Finally, we suggested some methods to gain relatively stable profits following the experiments.
Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive ...deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in this study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we assessed the performance of eight deep learning models: long short-term memory (LSTM), a convolutional neural network (CNN), bidirectional LSTM (BiLSTM), CNN Attention, a bidirectional gated recurrent unit (BiGRU) CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble ...strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.
The key challenges of the financial industry are the volatility and complexity of the stock market, so how to make optimal trading strategy to maximize the total profit in all market conditions has ...become an important issue to the professional researchers and investors. This paper describes a hybrid stock trading strategy model based on long short-term memory (LSTM) networks. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and sample entropy (SE), combined with LSTM, are used to construct the integrated prediction model, which has dramatically improved the forecast precision. On the premise of accurate prediction, the extreme value theory (EVT) is introduced to improve the predictive ability of dynamic value at risk (VaR), which can manage the risk of portfolio. To forecast stock trends, the approach of analytic hierarchy process (AHP) is applied to assign weights to related factors. The final trading decisions are made by establishing trading signals and scoring models. Based on models above, the integrated trading strategy model is constructed as an automated trading decision tool. Taking Gold and Crude oil as examples, the profit results are proved to be decent through trading simulations.
Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial ...neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).
PDF
