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A properly performing and efficient bond market is widely considered important for the smooth functioning of trading systems in general. An important feature of the bond market for investors is its liquidity. High-frequency trading employs sophisticated algorithms to explore numerous markets, such as fixed-income markets. In this trading, transacti...
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Backtesting would be an example of a situation that allows the doctor to have an idea of the diagnosis by questioning the patient's history of the disease and considering a few complaints he or she has had. Backward performance metrics play an important role in the evaluation process of a trading strategy. When evaluating a trading strategy, key me...
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... From a scalability standpoint, the framework was crafted to effectively adjust to market shifts, even when faced with rigorous computational limitations. Clapham (2023) [48] and Alaminos (2024) [49] emphasize that these issues are particularly vital in high-frequency trading environments, where cutting-edge technology is crucial for enhancing performance and minimizing latency. To meet these requirements in real-time trading situations, the following strategies were employed: [49], where machine learning architectures were adapted to handle high-frequency trading data across multiple markets effectively. ...
... From a scalability standpoint, the framework was crafted to effectively adjust to market shifts, even when faced with rigorous computational limitations. Clapham (2023) [48] and Alaminos (2024) [49] emphasize that these issues are particularly vital in high-frequency trading environments, where cutting-edge technology is crucial for enhancing performance and minimizing latency. To meet these requirements in real-time trading situations, the following strategies were employed: [49], where machine learning architectures were adapted to handle high-frequency trading data across multiple markets effectively. ...
... Clapham (2023) [48] and Alaminos (2024) [49] emphasize that these issues are particularly vital in high-frequency trading environments, where cutting-edge technology is crucial for enhancing performance and minimizing latency. To meet these requirements in real-time trading situations, the following strategies were employed: [49], where machine learning architectures were adapted to handle high-frequency trading data across multiple markets effectively. ...
In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes.
With the increasing complexity and volatility of modern power markets, high-frequency trading (HFT) has emerged as a promising approach to enhance market efficiency and liquidity. This paper proposes a high-frequency trading methodology and system tailored for power market transactions. The approach leverages advanced algorithmic techniques, including predictive modeling, real-time data analytics, and adaptive decision-making processes, to capitalize on micro-level price movements and optimize trade execution. We introduce a novel system architecture that integrates market data feeds, trade execution modules, and risk management tools, enabling rapid responses to market dynamics and improving overall trading performance. Through simulations and case studies, we demonstrate the effectiveness of this method in capturing arbitrage opportunities, reducing transaction costs, and enhancing market stability. The proposed HFT framework offers a potential solution to address the growing challenges in power markets, particularly in terms of price discovery, liquidity provision, and market efficiency.
This review explores the dynamic intersection of algorithmic trading and artificial intelligence (AI) within financial markets. It delves into the evolution, strategies, and broader market impact of algorithmic trading fueled by AI technologies. Examining the symbiotic relationship between advanced algorithms and AI, the review navigates through the various strategies employed, shedding light on their implications for market efficiency, liquidity, and overall stability. From high-frequency trading to machine learning-driven predictive analytics, this review unveils the multifaceted landscape of algorithmic trading in the era of AI, presenting both opportunities and challenges for financial markets. The review begins by tracing the historical development of algorithmic trading, emphasizing the paradigm shift with the integration of AI. From traditional programmatic trading to the emergence of sophisticated algorithms driven by machine learning and deep learning, the evolution sets the stage for a comprehensive understanding of the subject. An in-depth analysis of diverse algorithmic trading strategies unfolds, covering areas such as trend following, statistical arbitrage, market making, and sentiment analysis. The incorporation of AI introduces adaptive learning capabilities, enabling algorithms to evolve and optimize strategies based on real-time market conditions. Exploring the impact of algorithmic trading on financial markets, the review examines how AI-driven strategies contribute to market efficiency, liquidity provision, and price discovery. It dissects the implications for traditional market structures, regulatory considerations, and the potential risks associated with algorithmic dominance. Acknowledging the transformative power of algorithmic trading with AI, the review critically assesses the challenges and ethical considerations. From algorithmic bias to systemic risks, the review delves into the darker corners of this technological advancement, prompting a reflection on the need for responsible and transparent practices. The review concludes by peering into the future trajectory of algorithmic trading fueled by AI. Anticipated innovations, regulatory responses, and the evolving landscape of financial markets are discussed, offering insights into the ongoing transformation and potential disruptions in the realm of algorithmic trading. In essence, this review provides a nuanced perspective on the intricate relationship between algorithmic trading and AI, offering a comprehensive understanding of their strategies and the transformative impact on financial markets.
