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The set of Pareto optimal solutions found by SPEA2 (a), MOEA/D (b), MOPSO/D−Sys (c), and MOPSO/D−Hyb (d) for the Viennet problem.
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A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile...
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Citations
... Cryptocurrency markets have welcomed a new frontier for AT applications. Omran et al. (2023) present a multi-objective particle swarm optimization algorithm undertaking the cryptocurrency algorithmic trading of Litecoin. The algorithm will help traders find optimal trading strategies, balancing return on investment, risk measures such as the Sortino ratio, and the number of trades executed. ...
This chapter stresses how algorithmic trading has transformed portfolio management, underscoring the resultant ability to optimize risk-adjusted returns, enhance decision processes, and sustain efficient asset allocation. Advanced computational methods utilized in this research examine algorithmic strategies as a means to address the complexities of today’s financial markets in their ability to handle risk management, diversification, and periodic rebalancing. The results of the optimization of a portfolio consisting of six Nasdaq 100 stocks—Amazon, Apple, AMD, Tesla, Google, and NVIDIA—for ten years, from 2014 to 2024, are shown here. These assets have been selected based on their historical performance and variable risk-return profile as a sample to evaluate algorithmic trading strategies. In this paper, SLSQP is used to optimize the weights of each portfolio according to the Sharpe ratio, with efficient capital allocation considering the realistic constraint of no short-selling on the historical price data. Annual rebalancing was adopted to dampen the drifting of weights and to make the weights given at any period closer to the target weights. The performance of the portfolio is measured concerning the Nasdaq 100 through a set of key metrics: the cumulative return, the annualized return, volatility, and the Sharpe ratio. Hereby, the optimized portfolio gains an annualized return of 46.89% with a cumulative return of 4576.56% throughout the period under review. Although the portfolio demonstrated higher volatility (40.89%) in comparison to the Nasdaq 100, its Sharpe ratio of 1.12 surpassed that of the benchmark (0.90), thereby illustrating superior risk-adjusted performance. The rebalancing process effectively maintained the efficiency of the portfolio, although the concentration of risk in high-growth assets, such as NVIDIA, was brought to light. The findings highlight the inherent trade-offs between return maximization and risk management, offering valuable insights for investors, practitioners, and policymakers.
... Strategies more closely related to the fields of optimization and automated trading have also been proposed. In [17], a new normalized decomposition-based multi-objective particle swarm optimization (N-MOPSO/D) algorithm is implemented, demonstrating strong performance in terms of Return on Investment (ROI), Sortino Ratio (SOR), and the number of trades (TR). However, the comparative analysis is limited, as it does not include the buy-and-hold baseline based on cumulative returns. ...
... Parameter optimization is performed using a grid search over ranges for the MACD short EMA (e.g., [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], MACD long EMA (e.g., 10-30), signal line EMA (e.g., [5][6][7][8][9][10][11][12][13][14][15], and ADX window size (e.g., [10][11][12][13][14][15][16][17][18][19][20]. Each parameter combination is evaluated on historical performance, and the one with the highest return is selected as optimal. ...
... Parameter optimization is performed using a grid search over ranges for the MACD short EMA (e.g., [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], MACD long EMA (e.g., 10-30), signal line EMA (e.g., [5][6][7][8][9][10][11][12][13][14][15], and ADX window size (e.g., [10][11][12][13][14][15][16][17][18][19][20]. Each parameter combination is evaluated on historical performance, and the one with the highest return is selected as optimal. ...
In this note, we make a comparison between a novel machine learning method, Long Short-Term Memory (LSTM), and two trading strategies using technical analysis: Exponential Moving Average (EMA) crossing and Moving Average Convergence/Divergence with Average Directional Index (MACD+ADX). The purpose is to use trading signals to maximize profits in the Bitcoin digital commodity. The comparison was motivated by the approval of the first spot Bitcoin exchange-traded funds (ETFs) by the U.S. Securities and Exchange Commission (SEC) on January 9, 2024. The results show that the LSTM algorithm delivers a cumulative return of approximately 65.23% over a testing period of less than nine months, significantly outperforming both the EMA and MACD+ADX strategies, as well as the baseline buy-and-hold approach typically followed by fundamental investors. Our work highlights the potential for further integration between machine learning and technical analysis in the evolving landscape of cryptocurrency markets.
Surrounding the world, cryptocurrencies are an exciting phenomenon. Whether for quick profits on cryptocurrency marketplaces or future gains via holdings of assets, rapidly growing virtual currency remains a very profitable economic tool. Artificial intelligence, or AI, is an additional subject which is now gaining a lot of interest. This is because artificial intelligence (AI) has produced a broad and positive influence on many different businesses. One industry that has benefited from AI in several ways is cryptocurrencies. To improve cryptocurrency's efficiency and assist financiers, this article examines a variety of automated tools. Numerous scholarly articles, website pages, blogs, and other additional materials are reviewed for this purpose. The results demonstrate the importance of market research, sentiment monitoring, and automated trading platforms as tools that assist users in making price predictions and increasing profits. The digital currency sector benefits greatly from the various areas of artificial intelligence.
Surrounding the world, cryptocurrencies are an exciting phenomenon. Whether for quick profits on cryptocurrency marketplaces or future gains via holdings of assets, rapidly growing virtual currency remains a very profitable economic tool. Artificial intelligence, or AI, is an additional subject which is now gaining a lot of interest. This is because artificial intelligence (AI) has produced a broad and positive influence on many different businesses. One industry that has benefited from AI in several ways is cryptocurrencies. To improve cryptocurrency's efficiency and assist financiers, this article examines a variety of automated tools. Numerous scholarly articles, website pages, blogs, and other additional materials are reviewed for this purpose. The results demonstrate the importance of market research, sentiment monitoring, and automated trading platforms as tools that assist users in making price predictions and increasing profits. The digital currency sector benefits greatly from the various areas of artificial intelligence.
