Trading Rules Research Articles

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Short-term contrarian in the carbon emission market

We consider the short-term contrarian effects in daily data from carbon emission markets. Technical analysis is conducted with three contrarian trading rules on four carbon emission markets of China. Various individual and joint test statistics are developed for testing the contrarian profits from individual trading path as well as a group of trading paths. Empirical evidence shows that the contrarian profits are statistically and economically significant across the technical rules. Particularly, the contrarian moving average group trading generates average returns of 3.8 to 13.8 % per month across the different carbon markets during the period of 2013–2023 and the corresponding Sharpe ratios range from 0.9 to 2.01, while the Sharpe ratios from the buy-and-hold strategy are all below 0.12. Further tests support that the contrarian profits are not eliminated by transaction costs, and not dominated by extreme returns. We also emphasize on the explanations of the contrarian profit. Among the considered variables, the market volatility is the most significant explanatory variable for contrarian profits across four markets, whereas the market illiquidity and non-trading breaks are also significant in certain markets. The finding supports the explanation of the behavioural bias of overreaction for the short-term contrarian effect. Other explanations, including short-selling constraint, clearing stress, price limits, government interventions, etc. are also discussed in the paper.

EXPRESS: On the Valuation and Monetization Roles of the Rolling Intrinsic Policy for Merchant Commodity Storage

Merchants use the rolling intrinsic policy to value commodity storage assets and monetize the values they attribute to these assets. A recent study ascribes to informational inconsistency two discrepancies between its partial monetization findings and the known excellent valuation performance of this method: (i) The rolling intrinsic policy can perform worse than the intrinsic policy and (ii) it appears to be far from optimal. This paper contends that this claim generally confounds informational inconsistency and the risk adjustment that underpins the common no arbitrage valuation of this policy. In particular, lack of risk adjustment in that investigation explains at least the first difference and informational inconsistency cannot be associated with the second dissimilarity in about four percent of its instances. Further, this work establishes that equipping the rolling intrinsic policy with forward trading rules out the former disparity, whereas the latter one in general can persist. Finally, it uses known results to argue that in realistic settings this extended policy incurs small monetization losses on average for storage assets acquired at a fair price and outlines alternative monetization methods with potentially improved effectiveness.

Lesson from Crude Oil Treasure Failure in 2020

After the 2020 pandemic, the Chicago Mercantile Exchange (CME) changed its trading rules, allowing the negative oil price mechanism in the futures market. That same month, Bank of China's (BOC) "Crude Oil Bao" investment product brought huge losses to investors, exposing the product's risk management flaws. The purpose of this report is to conduct a comprehensive study of the factors that led to the losses suffered by Chinese investors in international crude oil product trading. It delves into the operational dynamics of these financial industry institutions, as well as the perspectives of Chinese banks and retail investors affected by the incident. Finally, recommendations are made to strengthen risk management measures in response to events such as the Crude Oil Treasure, with the aim of helping commercial banks to improve their product design methodologies. The article provides valuable insights into understanding financial product failures and offers guidance on mitigating risk.

The Predictive Ability and Profitability of Moving Average Rules in the Saudi Stock Market

Abstract Following the implementation of capital market reforms, Saudi Arabia has opened up its stock market to international investors. This liberalisation has led to the inclusion of the stock exchange into leading global indices, which is expected to lead to an influx of foreign investment. Thus, it is important and timely to examine the efficiency of the Saudi stock market. To that end, this paper conducts a comprehensive investigation of the predictive ability and profitability of a popular trading rule, the moving average rule, using firm-level data for the Saudi stock market over the period 1st January 2008 to 31st December 2017. The results show that the moving average rule has predictive ability; the active rules for a majority of the sample companies outperformed the corresponding passive strategy. Furthermore, the results were economically significant as the rules were profitable even after the consideration of transaction costs. The results also showed that the most profitable moving average rules were those with short-moving average lengths, and that the introduction of a bandwidth, which serves to eliminate weak trading signals, had a positive impact on rule profitability. Importantly, the analysis showed that the most profitable component of the rule related to short selling as these trades resulted in higher profits than the long positions. A disaggregated analysis of sectors showed that the trading rules outperformed the buy-and-hold strategy in all seven industries considered, while the analysis of covariance revealed the importance of careful selection of filter size. Overall, the analysis documents significant inefficiencies in the Saudi stock market and suggests that the employment of a simple trading rule, based on past price data, can yield substantial profits across companies and sectors even in a costly trading environment. These findings suggest that the recent reforms that were implemented to improve the efficiency of the Saudi stock market have been suboptimal, and that further regulatory reform is required.

Overnight return reversal in the Chinese stock market

ABSTRACT This article shows that the overnight return reversal is strong at a firm level in the Chinese stock market: the overnight return negatively predicts the first half-hour return of individual stocks. We explain this phenomenon from the perspective of the pre-open auction mechanism. Taking a step further, we find that the Chinese stock market shows a pattern with average negative overnight return and positive first half-hour return. Additionally, the predictability is stronger on negative overnight returns and negative informational shocks compared with positive overnight returns and positive informational shocks. Both phenomena may be led by the combined effects of the pre-open auction mechanism and T + 1 trading rule.

Information, sentiment, and margin trading of Chinese stock market

AbstractThis paper examines whether information or sentiment drives margin trading in the Chinese stock market. At the aggregate level, no conclusive evidence indicates a dominance of either information‐driven or sentiment‐driven margin trading activities. However, at the individual firm level, we observe both types of margin trading, underscoring the need to consider the heterogeneity of margin trading when reconciling previous research findings. Moreover, we find that the likelihood of sentiment‐driven margin trading significantly declined following regulators' implementation of stricter rules for margin trading in 2015.

Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things

By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.

Optimizing Genetic Algorithm With Momentum Strategy for Technical Trading Rules: Evidence From Futures Markets

ABSTRACTThis paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

Investigating profitability of Technical Trading Rules around COVID pandemic: Evidence from PSX

This paper investigates the role of technical analysis in predicting the stock prices of listed companies on the Pakistan Stock Exchange (PSX). Technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Bollinger Bands (BB), and Money Flow Index (MFI) are evaluated to explore their significance for stock gains in the Pakistan Stock Exchange. For this purpose, daily price data from 29 companies listed on the PSX was collected during the period from 2018 to 2022. This sample period provides a unique opportunity to analyze the impact of Covid-19 on the accuracy of technical trading signals. MACD, RSI, BB, and MFI are calculated, and the accuracy of the results is then assessed. Evidence from the PSX highlights that these indicators perform differently across various sectors. The t-test outcomes indicate significant differences in results before and after Covid-19, emphasizing the impact of the pandemic on stock movements. Based on these results an index has been developed and proposed to aid future decision-making processes.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Post-neoliberal globalization: international trade rules for global prosperity

Abstract This paper analyses frameworks for the design of the rules for international trading, assuming that it is possible to have some rule of law. In the Arrow–Debreu benchmark, where there is no economic power and political power is seemingly irrelevant, there is no need for trade agreements—free trade is the optimal policy for each country. But under even minimal deviations from that benchmark, trade agreements matter. We focus on environments in which there are market failures, technology is endogenous, and there is political power. Power dynamics play, for instance, a critical role in the design, implementation, and enforcement of agreements, with the latter being a critical difference between international agreements and domestic contracts and a key determinant of the feasibility and consequences of agreements. With endogenous technology, trade rules proscribing industrial policies may lead to lower growth and greater cross-country inequalities. Finally, we develop a normative framework which may be useful in the design and implementation of trade rules.

Multi-microgrid Energy Management Based on Finite Horizon Twin Delayed Deep Deterministic Policy Gradient

Background: With the increasing integration of renewable energies into smart microgrids, the parameter space of the microgrid system model has increased dramatically, leading to critical computational challenges in achieving the dispatch strategy. Objective: Additionally, in remote areas, connecting renewable energy resources to the national grid is unavailable due to the high costs. Consequently, we have introduced microgrids as a viable alternative solution. Microgrids are small-scale distributed power grids that rely on renewable energy sources and generators to balance the load demand. The aim of this paper is to improve the reliability of microgrids by integrating machine learning techniques into their control. This technology ensures that all loads in the microgrid are met through energy trading. Methods: This algorithm utilizes reinforcement learning as the control mechanism for the trading process. We have established a set of trading rules that facilitate energy trades among three microgrids. We designate one of these three microgrids as the primary microgrid, while the other two function as trading microgrids. Firstly, a deep reinforcement learning algorithm (FH-TD3) is developed as a solution. Subsequently, the performances obtained in the three regions are compared with those derived from traditional algorithms. Finally, the effectiveness of the proposed energy management strategy was validated through simulations. Results: FH-TD3 has shown superior performance to DDPG in three different microgrid experiments, with the FH-TD3 algorithm consistently exhibiting shorter iteration times compared to the DDPG algorithm. Conclusion: The research shows that the algorithm realizes the balance of the power without the loss of the power grid, and realizes the profit in the transaction process.

Design Algorithmic Trading Strategies with Expert Advisor Using Linear Weighted Moving Average (LWMA) and Stochastic Oscillator Technical Indicators

Earlier this decade, the financial sector saw a paradigm change, with automated trading (AT) systems gaining popularity as essential tools for traders and investors. This research explores deeply the design and implementation of Expert Advisors (EAs) for automated financial built using a combination of the Linear Weighted Moving Average (LWMA) and Stochastic oscillator technical indicators. The EAs are built using the programming language MetaQuotes Language 4 (MLQ4) at Metatrader 4 (MT4) platform, enabling automated trade execution. It implements a machine learning genetic algorithm, trained on historical data to optimize the parameters of the LWMA and Stochastic trading rules. The core strategy relies on the LWMA to identify the overall market trend direction while the Stochastic oscillator provides additional signals for timing entry and exit points based on momentum. The EAs were coded to generate automated buy and sell signals for algorithmic trading (AT) based on a set of defined rules using thresholds for each indicators. Extensive historical backtesting using Gold (XAU/USD) currency pair across multiple timeframes from 5-minute (M5) up to 4-hour (H4) charts was conducted using 5 years of price data from 2019 to 2023 for evaluation. The goal of this study is to assess if the EAs could potentially produce consistent profits over time while minimizing drawdowns in different market conditions. The results demonstrate that the system was able to generate annual returns ranging from 3.04% up to 232.19% depending on the aggression of the timeframe settings. Meanwhile, maximum drawdowns were controlled to reasonable levels between 0.5% to 8.27% which is below 10% of potential loss throughout the backtests. An hourly timeframe configuration provided a balanced blend between strong profitability and drawdown control based on the backtest analysis. All the timeframes used for the test show positive results and the M5 timeframe is the best chosen timeframe to trade using this EAs implementation.

Mean-variance portfolio with wealth and volatility dependent risk aversion

Risk aversion rate plays a significant role in mean-variance portfolio selection. Most existing literature assumes it to be constant or wealth dependent, which is unrealistic. In this study, I contend that it is both wealth and volatility dependent because it varies across economic status: either steady or fluctuated. In addition, I decompose the risk aversion rate into a wealth prudence rate and a volatility prudence rate and investigate their mutual effect on portfolio selection under a continuous-time mean-variance framework. Using Game theoretic approach and asymptotic expansion, I derive the optimal trading rule analytically.

Deep reinforcement learning for pairs trading: Evidence from China black series futures

Pair trading is one of the main methods of statistical arbitrage, mainly by taking advantage of the temporary price anomalies between related financial products with long-term equilibrium relationships to obtain arbitrage opportunities. In this paper, based on the co-integration method for the selection of allotment, the deep reinforcement learning method is integrated. The deep reinforcement learning method establishes an autonomous learning model and develops paired trading rules under different cycles. At the same time, the deep neural network model is used to implement the learning and training algorithm. This paper uses the data of Shanghai Commodity Exchange and Dalian Commodity Exchange on black futures, and the period is from January 2, 2014 to December 31, 2021. Three different periods were set for five models, namely simple threshold method (ST), simple threshold method based on pairwise cointegration (CA-ST), simple threshold method based on tripartite Cointegration (CA-ST-ALL), deep reinforcement learning method (DRL), and Deep reinforcement learning method based on pairwise cointegration (CA-DRL), and economic indicators were compared. The final results show that, combining the results of the three time periods, the deep reinforcement learning method based on pairwise cointegration performs better regarding return and risk control, validating the feasibility of deep reinforcement learning in China's future market.

Are simple technical trading rules profitable in bitcoin markets?

This paper examines the profitability of simple technical trading rules in bitcoin markets comprehensively, by taking into account realistic investor behaviour and transaction costs, and data mining problems. Realistic investor behaviour is replicated by first employing 75,360 simple technical trading rules, divided over 6 commonly used trading rule classes and daily and intraday frequencies. Next, we select the best performing rules after transaction costs using a multiple hypothesis procedure. Finally, we form portfolios combining the selected rules and analyse their out-of-sample performance. We find that, especially risk–return wise, simple technical trading rules can outperform a buy-and-hold strategy in the bitcoin market out-of-sample.

Do loosened trading rules restore the stock index futures price discovery ability in China?

In response to the severe liquidity depletion caused by extremely stringent trading restrictions, which were initially designed to deter speculative activities, China's regulatory authorities progressively loosened the trading rules governing the stock index futures market from 2017 to 2019. This paper explores the evolution of price discovery and volatility spillover between the index futures and its spot market amidst the four-phased loosening of trading rules. Utilizing a high-frequency dataset (5-min intervals), our findings unearth that the initial restrictive regulatory framework notably hinders the index futures market's functions of price discovery and volatility spillover. These functions then begin to exhibit signs of recovery as the restrictions are incrementally loosened. However, our analysis also uncovers that an excessive loosening of trading rules conversely impairs these critical market functions. The findings underscore the necessity for regulatory bodies to develop adaptable and reactive regulatory frameworks that can align with the market's continuous evolution.

Explaining the Stylized Facts of Foreign Exchange Markets with a Simple Agent-based Version of Paul de Grauwe’s Chaotic Exchange Rate Model

AbstractWe propose a simple agent-based version of Paul de Grauwe’s chaotic exchange rate model. In particular, we assume that each speculator follows his own technical and fundamental trading rule. Moreover, a speculator’s choice between these two trading philosophies depends on his individual assessment of current market circumstances. Our agent-based model setup is able to explain a number of important stylized facts of foreign exchange markets, including bubbles and crashes, excess volatility, fat-tailed return distributions, serially uncorrelated returns and volatility clustering. A stability and bifurcation analysis of its deterministic skeleton provides us with useful insights that foster our understanding of exchange rate dynamics.

A Voltage-Aware P2P Power Trading System Aimed at Eliminating Unfairness Due to the Interconnection Location

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract’s availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior.