•Gated Recurrent Unit is proposed to extract informative features from raw financial data.•Reward function is designed with risk-adjusted ratio for trading strategies for stable returns in the ...volatile condition.•Two adaptive stock trading strategies are proposed for quantitative stock trading.•The system outperforms the Turtle trading strategy and achieve more stable returns.
The increasing complexity and dynamical property in stock markets are key challenges of the financial industry, in which inflexible trading strategies designed by experienced financial practitioners fail to achieve satisfactory performance in all market conditions. To meet this challenge, adaptive stock trading strategies with deep reinforcement learning methods are proposed. For the time-series nature of stock market data, the Gated Recurrent Unit (GRU) is applied to extract informative financial features, which can represent the intrinsic characteristics of the stock market for adaptive trading decisions. Furthermore, with the tailored design of state and action spaces, two trading strategies with reinforcement learning methods are proposed as GDQN (Gated Deep Q-learning trading strategy) and GDPG (Gated Deterministic Policy Gradient trading strategy). To verify the robustness and effectiveness of GDQN and GDPG, they are tested both in the trending and in the volatile stock market from different countries. Experimental results show that the proposed GDQN and GDPG not only outperform the Turtle trading strategy but also achieve more stable returns than a state-of-the-art direct reinforcement learning method, DRL trading strategy, in the volatile stock market. As far as the GDQN and the GDPG are compared, experimental results demonstrate that the GDPG with an actor-critic framework is more stable than the GDQN with a critic-only framework in the ever-evolving stock market.
This study proposes a novel reinforcement learning (RL) framework, professional trader RL (Pro Trader RL), which mimics the decision-making patterns and trading philosophy of professional traders in ...stock trading. By exploiting the characteristics of RL, the framework aims to learn efficient trading strategies while mimicking the trading philosophy and risk management methods of professional traders. The framework takes into account the complex nature of the stock market and presents an integrated approach to RL, from data pre-processing to buying, selling and stop-loss. Pro Trader RL consists of four main modules. Data Preprocessing, Buy Knowledge RL, Sell Knowledge RL and Stop Loss Rule, each of which plays the role of professional traders knowledge. The results of three experiments show that the framework achieves high returns and Sharpe ratio regardless of market conditions and has stable performance with low maximum drawdown (MDD), which is superior to the state-of-the-art research. The proposed framework provides a novel approach to applying RL to the stock market and is expected to be useful and applicable in real-world trading settings.
With the continuous reform of the electricity market in the whole world, internal trading models in parks are generally studied in terms of load peaking, load flexibility and energy management. In ...this paper, the regional differential trading method is adopted for industrial users with different energy needs, and the interests of all parties within the industrial park are considered. Spot transactions involving surplus energy on both the supply side and the demand side are considered, and the weight allocation of transactions in each park is coordinated. Firstly, the way of circulating waste heat and power within the industrial park is analyzed from the composition of the trading system of the industrial park. The demand side is transported heat and electricity while the energy storage device is considered. The contact line is modeled on the basis of the energy transmission line characteristics. Secondly, the industrial park is disaggregated regionally using cascade analysis within the target area to decide load regulation mode between systems and modules. Thirdly, regional differences in trading are adopted for industrial users with different energy needs while the interests of all parties within the park are considered. Both sides of the supply side and the demand side participate in spot trading of surplus energy. The allocation of weights is coordinated for trading in the various industrial parks. Finally, The DICOPT solver optimizes operating costs and takes into account energy abandonment penalties. In the waste heat power transaction in the industrial park, the remaining power can be saved by 9.78 %, and the waste heat can be saved by 4.35 %. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research.
•The DICOPT solver is used to optimize the energy operating costs of industrial parks.•By considering the penalty of waste electricity and waste heat, the economy of industrial park can be improved.•The strategy of bilateral thermal power cooperation trading in industrial park is put forward.
•Continuous double auction is applied to facilitate prosumer-consumer interactions.•A market prediction model is designed to model the market from a data perspective.•The market predictor is ...successfully integrated into the prosumer operation model.•The proposed model simultaneously improves the prosumer operation and market profit.
With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.
To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental ...indicators. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. Genetic operators, including crossover, mutation, and inversion, are applied on the population to form a new offspring. Evolution is continued until the stop conditions are reached. Lastly, experiments were conducted on two real datasets with different trends to show that the advantages and the effectiveness of the presented approach.
Climate change compels the development and enforcement of policies and regulations designed to diminish carbon emissions, imposing substantial implications on the energy sector. Given the ...contribution of crude oil prices to carbon emissions, developing precise forecasting methods is imperative. However, existing studies often overlook the inherent uncertainty in price movements by focusing solely on point forecasting. To address this limitation, this paper constructs a threshold autoregressive interval-valued model with interval sentiment indexes for climate change (TARIX) to analyze and forecast interval-valued crude oil prices. We have found that the interval climate sentiment index, derived from social media, can significantly enhance the accuracy in forecasting interval crude oil prices. Moreover, we propose an interval-based trading strategy that can effectively reduce volatility and enhance returns. Our empirical results demonstrate that our interval-valued forecast model outperforms traditional forecasting methods in terms of forecasting accuracy and profit generation.
•Construct interval sentiment indexes for comprehensive emotion understanding.•The TARIX model, considering nonlinear characteristics, yields accurate forecasts.•The interval-based strategy can mitigate trading volatility and enhance profits.
The linkage of neighboring multi-energy microgrids may overcome the disadvantage of one single microgrid since the surplus/deficient energy can meet internal balance and realize self-satisfaction. In ...order to realize successful cooperation of such a complicated energy system, optimal trading strategy and reasonable trading price among all entities are important. In this study, a bi-level optimization model combined of two trading modes is proposed under different scenarios: an intermediary agent-based trading mode where the intermediary agent acts as an intermediate operator, as well as a direct trading mode without intermediary agent. The Stackelberg game theory-based method and the supply/demand ratio method are used for price determination, respectively. In addition, the integrated demand response is also considered in this study. As an illustrative example, a comparative study is implemented in a neighboring energy cluster combing three park-level multi-energy microgrids. The results show that, compared with the situation that each multi-energy microgrid acts alone, a cluster obviously increases total benefits, which may contribute to the development of tailored policy instruments. In addition, although direct trading among multi-energy microgrids enjoys higher benefits, its self-sufficient ability is lower than the situation considering intermediary agent, which may help policymakers to predict the merits and demerits associated with different energy trading modes among multi-energy microgrids in a more accurate way.
•A bi-level model is proposed for the multi-energy microgrid cluster.•Both electricity and heat energy trading are taken into consideration.•The model considers interactions of demand response and dynamic trading price.•Two kinds of approaches are proposed to deal with the price determination problem.
Pairs trading is typically implemented using two assets. The copula approach can allow us to consider the dependency among multiple assets and use multivariate pairs in this strategy. The goal of ...this article is to investigate this strategy under the copula approach for a group of assets that have mixture distributions. Increasing the consideration of multivariate pairs, especially in the trivariate case, enhances the amount of dependent information. In fact, the results show that multivariate pairs increase trading opportunities. Computational pieces of evidence are brought forward to support the proposed algorithm of this work.
•We discuss traditional pairs trading by enabling the use of multivariate pairs in the trading strategy.•Using copula functions, we extract joint distributions, enabling direct calculation of the joint probability for each pair of data observations in the trading strategy.•Computational evidence with mixture marginal distributions supports the applicability of the proposed algorithm.
•This paper examines the day of the week effect in the cryptocurrency market.•It using a variety of statistical techniques – as well as a trading simulation approach.•Most crypto currencies ...(LiteCoin, Ripple, Dash) are found not to exhibit this anomaly.•The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. Trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.
This paper examines the day of the week effect in the cryptocurrency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal–Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.
•A P2P trading strategy is proposed for the energy balance service provider (EBSP).•The EBSP market power and market elasticity are considered in optimized pricing.•The EBSP could enhance the energy ...resource allocation ability of the P2P market.•The EBSP could obtain profit without harming the overall benefit of the microgrid.
The peer-to-peer (P2P) energy trading technology can effectively promote the mutual exchange and complementation of energy among diversified prosumers in the community microgrid. To further improve the energy resource allocation performance of the P2P energy trading, an energy balance service provider (EBSP) can be involved in the market. EBSP is an independent commercial entity configured with high-capacity energy storage systems (ESS) or auxiliary power sources, participating in the P2P market for profit. The energy shifting ability of EBSP can further improve the overall benefit of the microgrid. However, the scale effect endows the EBSP with comparatively strong market power, which may impose influence on the equilibrium of the P2P market. From the perspective of EBSP, the P2P market can be regarded as a market with elasticity. In the existing researches on optimized pricing and trading strategies of the P2P market, such a characteristic was not fully considered, which may have negative impacts on the economic profit of the EBSP and the overall benefit of the microgrid. Therefore, a P2P energy trading strategy for EBSP considering market elasticity is proposed in this paper. A market equilibrium model for the community microgrid with EBSP is first established, which reflects the elasticity of the market. Then, an optimized pricing model and trading strategy are established and solved to maximize the EBSP’s profit. Numerical results show that the proposed method can improve the profit of EBSP, meanwhile reducing the energy dispatching cost of prosumers, achieving peak load shifting and renewable energy self-consumption of the community microgrid.
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