Algorithmic trading heavily relies on the optimization of rule-based strategies to maximize profitability and ensure robustness under volatile market conditions. Traditional optimization methods often face limitations when dealing with the nonlinear, high-dimensional, and dynamic nature of financial search spaces. This study introduces a Metaheuristic-based framework for financial strategy optimization that focuses on the modeling and resolution of the problem through population-based search algorithms. The framework evaluates four Metaheuristic optimization techniques within a unified design, enabling a consistent and fair comparison of their performance in optimizing trading rules. To ensure realistic and time-consistent evaluation, the experimental setup incorporates a Rolling Windows Validation approach, allowing the assessment of model performance across successive market periods. Beyond improving convergence behavior, Diversity is employed as a metric to assess the quality and exploration capability of the search process, providing deeper insight into algorithmic performance. Experimental results, obtained from real market data, demonstrate substantial improvements in profitability consistency and risk-adjusted performance compared to conventional optimization approaches. The findings confirm that Metaheuristic optimization offers a robust and flexible alternative for the design and refinement of algorithmic trading systems in complex and dynamic financial environments. Interestingly, Differential Evolution exhibited persistently high Diversity, suggesting the presence of multiple distant yet competitive optima in the financial search space, where functional convergence coexists with geometric dispersion.

The Black-Scholes model revolutionized option pricing under ideal market assumptions (minimal trading friction, no short-selling restrictions). However, its applicability in emerging markets with critical institutional constraints can be limited. This paper studies how short-selling bans in China violate the Black-Scholes models assumptions and cause systematic option mispricing in China. Regulatory implications for Chinas markets, such as Qualified Securities for Short-sale Refinancing program, T+1 intra-day trading restriction, daily limit on price movements, etc., are studied for their regulatory effects on pricing and arbitrage. The study makes a comparative institutional study on Chinas markets with US and Indias markets that have more flexible market settings. the results show that the restrictive settings in China caused a reverse IMVD and driven significant mispricing, whereas US and Indian markets benefit from freer short selling and improved pricing accuracy. The study provides insight for policymakers to carefully expand the short-selling business in mainland Chinas capital market, reduce the costs of securities lending, as well as re-arrange trading rules with efficiency as the main target. This study highlights the influence of market mechanism on the Black-Scholes option pricing theory, as well as inspirations and implications for policymakers seeking more efficient, transparent and deeply liquid derivative markets as well as the capital market.

This study aims to determine how to increase sales volume at Desi Store through promotional strategies. This study aims to formulate promotional strategies as an effort to increase sales volume at Desi Store which requires business actors to be honest, fair and responsible, understand the rules of trading according to sharia, conduct buying and selling based on divinity because Muslim sellers believe that all forms of transactions they make will be held accountable by Allah SWT. Desi Store is located in Nagari Pinaga, Aua Kuniang, Pasaman District, West Pasaman Regency. The type of research used is Qualitative Descriptive and field research , namely data collected directly by the author through structured interviews with the owner and employees of Desi Store. Data analysis used is SWOT analysis, namely analysis of data obtained to identify strengths, weaknesses, opportunities, and threats . The results of the study indicate that the SWOT analysis conducted indicates that Toko Desi's position is in Quadrant I (SO) with the highest score of 3.5, which means Toko Desi has excellent internal strengths and simultaneously faces various significant opportunities from the external environment. In conclusion, the SO strategy from a sharia perspective places Toko Desi in a strong position to grow aggressively while maintaining Islamic values. With consistent and innovative implementation, Toko Desi has the potential not only to increase sales volume but also to obtain blessings (barakah) in its business. The results of the overall analysis indicate that Toko Desi's promotional strategy is incomplete and still relies on traditional promotions. The IFE and EFE analyses prove that although there are many strengths and opportunities, weaknesses in the digital promotion aspect and threats from online competition need to be addressed immediately. Recommended promotional strategies include social media, digital training, event collaborations, and loyalty programs to increase sales sustainably and implement sharia marketing principles.

The advancement of computational modeling, data systems, and digital infrastructure has enabled the rise of agent-based computational finance (ACF). This study models interactions among heterogeneous investors. By embedding behavioral logics such as environmental, social, and governance (ESG) preferences and volatility thresholds, the model captures microstructural dynamics under different trading rules. Using ACF, the authors compare transaction plus 0 day (T+0) to transaction plus 1 day (T+1). Results show that T+0 improves price discovery, deepens liquidity, and reduces transaction costs. From a computational perspective, this research contributes to ACF by showing how policy logic and investor heterogeneity can be encoded and tested in a replicable simulation environment. The simulation method offers a scalable approach for regulators to assess sustainability-aligned reforms under diverse institutional settings. The study bridges computational finance and digital governance, highlighting the potential of integrating advanced IT into ACF to support adaptive, climate-conscious market design.

Profitability of technical trading rules in the Chinese yuan-based foreign exchange market

This study examines the relations among feeder cattle, corn, and live cattle futures listed on the Chicago Mercantile Exchange (CME), the components of the cattle feeding spread. We find that the three series are cointegrated, and that the cointegration relationship is characterized by strong seasonality and uptrend. Further, there is evidence of one-way information flow and volatility spillover from the feeder cattle and corn markets to the live cattle market, and bidirectional volatility spillover between the feeder cattle and corn markets. Our in-sample trading simulations that utilize this cointegration relationship are able to generate profits that are both statistically and economically significant, suggesting inefficiency exists in these commodity futures markets. This is, to a certain extent, corroborated by the results from our out-of-sample trading simulations that employ similar trading rules.

This paper examines arbitrage opportunities available in one of the largest wholesale electricity markets in the world, the Midcontinent Independent System Operator (MISO) electricity exchange. While prior research suggests that market efficiency on the exchange has increased over time, this study reveals that historical pricing information can still be used to generate positive returns. I find that a trading rule based on prior spot and forward prices generates statistically and economically significant risk-adjusted returns across the entire MISO footprint. These returns may in part be explained by the relatively small number of financial traders in the market and the ability of generation owners to exercise market power.

This study explores an innovative approach to incorporating option strategies into equity portfolios. It presents an alternative direction that institutional investors could take to overcome their current challenges, in a context where traditionally diversified portfolios of only equity and fixed-income assets have shown weaknesses that make it difficult for these investors to achieve their performance goals within their risk limits. We test whether a set of well-known backward-looking signals from equities markets and less-researched forward-looking ones from options markets can be used to improve the efficiency of two option strategies, namely covered call and protective put. The trend signal appears to be the one that adds the most value to both strategies. This study also shows that increasing complexity through additional trading rules does not improve the results of the more basic option strategies that make use of the signals.

Islam is a religion that requires trading, the Prophet is a good role model in teaching trading rules, in this research we examine the regulation of market mechanisms with the perception of hisbah using the review literature review method, and study the comparison of sharia and conventional trading objects in Indonesia, so as to create a fair trading system and healthy competition in the trading market.

An efficiency index as a predictor of the performance of some popular technical trading rules

This study examines the impact that fat-tailed distributions of the spread residuals have on the optimal orders for pairs trading of stocks and cryptocurrencies. Using daily data from selected pairs, the spread dynamics has been modeled through a mean-reverting Ornstein–Uhlenbeck process and investigates how deviations from normality affect strategy design and profitability. Specifically, we compared four fat-tailed distributions—Lévy stable, generalized hyperbolic, Johnson’s SU, and non-centered Student’s t—and showed how they modify optimal entry and exit thresholds, trading frequency, and performance metrics. The main findings reveal that the proposed pairs trading strategy correctly captures some key stylized facts of residual spreads such as large jumps, skewness, and excess Kurtosis. Interestingly, we considered regime-switching behaviors to account for structural changes in market dynamics, providing empirical evidence that optimal trading rules are regime-dependent and significantly influenced by the residual distribution’s tail behavior. Unlike conventional approaches, we optimized the entry signal and link heavy tails not only to volatility clustering but also to the nonlinearity in switching regimes. These findings suggest the need to account for distributional properties and dynamic regimes when designing robust pairs trading strategies, providing a more realistic and effective framework of these strategies in highly volatile and non-normal markets.

In the context of heightened oil price volatility, mastering technical trading strategies is essential for informed investment and sound decision making. This study explores the effectiveness of contrarian technical trading strategies in the Brent crude oil market, aiming to enhance returns in the face of persistent market fluctuations. Utilizing historical price data, this research formulates trading rules based on overbought and oversold signals derived from the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI). It assesses their performance through a range of Average Holding Period Return (AHPR) metrics, emphasizing the 250-day AHPR as a proxy for one-year returns. The findings show that RSI-based strategies, especially those using a threshold of 25, are most effective in oversold conditions, achieving peak profitability of over 40% in Quarter 2. The conclusions highlight the importance of parameter flexibility, strategic timing, and responsiveness to market dynamics in optimizing the contrarian strategy performance. The implications suggest investors and managers can refine strategies by accounting for behavioral biases, market timing, and flexible parameters, while enhancing big data analytics in technical trading.

The market for foreign currency is the largest on the planet. One would imagine that Neoclassicism would have an easy time explaining this prime example of market capitalism, but the opposite has been true. They have struggled to develop even one model that captures the salient features of the international monetary system. Fortunately, theirs is not the only game in town. For the past thirty years or so, Post Keynesian scholars have been developing theories of exchange rate determination that do explain the currency price volatility, chronic payments imbalances, and financial crises characteristic of the post-Bretton Woods period. That work has been quite varied, including discussions of agents’ expectation formation processes, technical trading rules, currency hierarchies, Minskyan processes, stock-flow consistency, speculation, financial capital flows, and more. In many ways, however, this is still a relatively new literature, one in need of careful summary and review so that we may take stock of what has been accomplished thus far and consider what remains to be done. This critical survey provides a starting point for this process.

We propose a novel strategy-proof dynamic auction for efficiently allocating heterogeneous indivisible commodities. The auction applies to all unimodular demand types of Baldwin and Klemperer’s necessary and sufficient condition for the existence of competitive equilibrium which accommodate a wide variety of complements, substitutes, gross substitutes and complements, and any other kinds. Although bidders are not assumed to be price takers so they can act strategically, this auction induces bidders to bid truthfully, yielding efficient outcomes. Sincere bidding is shown to be an ex post perfect Nash equilibrium of the auction. The trading rules are simple, detail free, privacy preserving, error tolerant, and independent of any probability distribution assumption. Funding: This work was supported by the Japan Society for the Promotion of Science [Grants 19K11839, and 22K11922] and the Research Institute for Mathematical Sciences at Kyoto University.

Abstract We develop a simple nonlinear stock market model in which speculators switch between technical and fundamental trading rules depending on market conditions. Additionally, we assume that agents are unaware of the true current fundamental value and, thus, use a weighted average of the current price and the known long-run fundamental value as an estimate of the fundamental price. Using analytical and numerical methods, we demonstrate that an increase in the reaction parameter of technical traders may cause boom-bust dynamics. Moreover, we show that a heightened belief among agents that the fundamental value is more sensitive to deviations of the current price from its long-run fundamental value can cause the price to become trapped above or below this long-run value, oscillate within a higher price range, and prolong the duration of a bubble. In two model extensions, we assume that agents compute the current fundamental value based on the deviation between the average price and the known long-run fundamental value, using a moving average of the past k prices and an exponential moving average, respectively. These robustness checks show that, in these cases, price and perceived fundamental value fluctuate less statically around the long-run fundamental value.

This paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP) rules. RL4CEP uses Double Deep Q-Networks to update the threshold values used by CEP rules. It is implemented using Apache Flink as a CEP engine and Apache Kafka for message distribution. RL4CEP is a generic approach for scenarios in which CEP rules need to be updated dynamically. In this paper, we use RL4CEP in a financial trading use case. Our experimental results based on three financial trading rules and eight financial datasets demonstrate the merits of RL4CEP in improving the overall profit, when compared to baseline and state-of-the-art approaches, with a reasonable consumption of resources, i.e., RAM and CPU. Finally, our experiments indicate that RL4CEP is executed quite fast compared to traditional CEP engines processing static rules.

Efficiency and Predictive Power of Technical Trading Rules in Post-COVID-19 Era : A Study of BRICS Stock Market

The article defines the role and place of exchange trading in grain futures contracts in the general structure of exchange trading in agricultural products and commodity assets in general. Summarizing the existing views, it is stated that grain futures contracts contribute to the effective balancing of demand and supply for grain, ensuring economic stability in the spot agricultural markets. The economic essence of grain futures is presented as a type of standardized exchange-traded term instruments that provide for obligations to buy and sell grain with delivery in the future under certain conditions of the exchange trading rules. It is assumed that grain futures contracts provide several important functions for organized agricultural markets, including the following: transparent pricing for the main types of grain crops at the national and global levels; transfer of price risks; information and analytical support for market participants through open access to exchange quotes for the main types of grain crops. Based on the assessment, it was found that among the main grain crops that serve as the underlying assets of futures contracts on leading international commodity exchanges by trading volume in 2024 were: corn (306 million transactions); wheat (74 million transactions). The mandatory standardization of futures contracts in the form of exchange specifications on commodity exchanges has provided us with the opportunity to conduct a comparative assessment of the specifications of futures contracts for corn and wheat by key indicators. An assessment of the indicators of international exchange trading in grain futures contracts on leading commodity exchanges, conducted on the basis of statistical data from the Futures Industry Association, indicates an increase in the number of concluded agreements in the period 2015-2024 by 1.83 times. At the same time, a significant drop occurred in 2022 – 22%, compared to 2021, against the background of the beginning of the war in Ukraine and the lack of export supplies until mid-2022, and then the search for new logistical routes. In the following two years, the volume of international exchange trading in grain futures contracts recovered, in particular in 2023 – by 16%, in 2024 – by 2%. Prospects for the development of international exchange trading in grain futures are presented.

Excessive stock market volatility amplifies financial risks, necessitating AI-driven decision-support systems for risk mitigation. Integrating AI and big data analytics enables real-time market monitoring and actionable insights for investors and regulators, enhancing global financial profitability through improved forecasting and risk management. This study combines game theory principles with AI technologies to develop market stabilization strategies, simulating multi-agent interactions to optimize trading rules and regulatory frameworks. Through price game modeling, it examines price dynamics and investor strategy equilibria across time horizons, highlighting AI’s dual role in predicting volatility and balancing short-term gains with long-term stability. While addressing stakeholder conflicts, the research acknowledges AI’s complex challenges alongside its transformative potential. The findings offer practical guidance for leveraging AI’s benefits in finance while managing risks, contributing to sustainable economic growth through enhanced understanding of intelligent systems’ operational mechanisms.

This review examines the growing literature on pairs trading frameworks, which involve relative value arbitrage strategies between two or more securities. Existing research is categorized into five main categories: distance methods use nonparametric distance measures to identify pairs trading opportunities; cointegration methods rely on formal cointegration tests to reveal stationary time series of spreads; time series methods focus on finding optimal trading rules for mean-reverting spreads; stochastic control methods aim to determine the optimal portfolio holdings in pairs trading relative to other available securities; and the "Other Methods" category encompasses other relevant pairs trading frameworks, albeit with a more limited supporting literature. Through a comprehensive review of over 100 papers published between 2016 and 2023, the survey identifies the key strengths and weaknesses of each approach, providing insights relevant for future research and practical implementation.