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Optimizing Cryptocurrency Trading Tactics Using Self-service Business Intelligence
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Ai Optimizing Crypto Exchange Functions — Bitget Exec
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By Sherin M. Omran Sherin M. Omran Scilit Preprints.org Google Scholar View Publication 1, * , Wessam H. El-Behaidy Wessam H. El-Behaidy Scilit Preprints.org Google Scholar View Publication 1 and Aliaa A. A. Youssif Aliaa A. A. Youssif Scilit Preprints.org Google Scholar View Publication 2
College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport (AASTMT), Giza 12577, Egypt
Mastering Bitcoin Mining: Insights, Strategies, And The Future
The cryptocurrency market environment has emerged as the most important application of blockchain innovation in financial trading. Cryptocurrencies are advanced or virtual forms of money supported by cryptographic frameworks. They enable secure online payments without the use of third parties or intermediaries. The term “cryptographic” refers to the various encryption calculations and cryptographic strategies that protect these payment operations. The crypto market is very volatile due to large fluctuations in the value of assets in a single day [1]. This volatility has a significant impact on traders’ returns. Therefore, there is always a need for new, powerful algorithms that help traders improve their results.
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Despite its importance and widespread use, trade rule optimization is not often found in the literature. One of the main goals of this research is to test the ability to optimize algorithmic trading strategies to keep up in different market conditions.
Cryptocurrency trading is a Multi-Objective Optimization (MOO) problem where there is more than one objective to be optimized. Among these measures are returns, trading risk, number of exchanges, positive negative exchange ratio, Maximum Draw-Down (MDD), which quantifies the maximum decline in the value of an investment or asset in a certain period, etc. [18].
}. Due to the conflict between the objectives, the solution to these problems is not a single point, but rather a series of non-dominated points, i.e. Pareto set (PS) [21, 22].
A solution is considered non-dominated when the promotion of one goal leads to the weakening of at least one of the other goals. For any solution
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To be Pareto Optimal (PO), it should not be dominated by another solution. You could say that the solution
Better than B for at least one measure [21]. Pareto front (PF) is the set constructed by four non-dominated points in the objective space, while PS contains the PO points in the decision space.
The multi-objective evolutionary algorithm using decomposition (MOEA/D) is a promising algorithm used to solve multi- and multi-objective optimization problems (i.e., MOO problems and
). Zhang and Li [21] first introduced MOEA / D to overcome the problems of dominance based on MOEAs, where they sometimes do not handle many objective problems without a reduction in performance. MOEA/D has demonstrated its simplicity and superiority in solving complex problems. It separates the complex MOO problem into a set of subproblems (SP) using a scalarization function (SF), also called an aggregation function. A well-generated set of weight vectors and a well-chosen SF are the main factors affecting the performance of such an algorithm [ 21 , 22 ].
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Due to the good performance shown by MOEA/D, several researchers have investigated some improvements to the original algorithm. Research studies for MOEA/D can be classified into four different categories: weight generation strategies, adapting one or more SFs, implementing different variants of the original algorithm to solve more complex problem challenges, and applying MOEA/D algorithms to the problem . the real world.
Some new aspects of adaptive Pareto generation have been introduced, such as paλ-MOEA/D [23], AWD-MOEA/D [24], MOEA/D-AWG [25], and MOEA/D-URAW [26].
New degradation mechanisms have also been found in the literature, either using new SFs, as shown in [ 27 , 28 , 29 ], or using a collection of different SFs [ 30 , 31 ].
The decomposition method has been extended to a larger number of EAs, such as MOEA/DD-CMA [32] and MOEA/D-ACO [33]. The work of a number of new operators to handle the trade-off between diversity and convergence has also been found in the literature, such as Differential-Evolution (DE) [34] , a two-phase and MOEA/D-TPN niche mechanism. [35] , and hierarchical decomposition (HD) [36] .
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MOEA/D has also been applied in various application areas, such as network routing [ 37 ], portfolio optimization [ 38 , 39 ], image segmentation [ 40 ], and aerospace applications [ 41 ].
This study aims to evaluate the ability of decomposition-based algorithms to improve the performance of cryptocurrency trading rules to help traders and investors improve their results and compare them with original trading rules or standards on a hardly predictable market condition, i.e. . The covid-19 pandemic.
In this paper, a particle swarm optimization algorithm based on decomposition (MOPSO/D) using a new normalized linear scalarization method Augmented-Weighed-Tchebycheff (AWTCH) was proposed and applied to cryptocurrency trading strategy optimization. AWTCH [42] is an updated version of WTCH proposed by Zhang [21]. Normalization is used here because the objective functions have widely different intervals. The algorithm is used to optimize the control parameters for a set of four named trading strategies (Linear Weighted Moving Average, Bollinger Bands, Stochastic Relative-Strength-Index and Smoothed Rate-of-Change). In addition, a new hybrid weight generation strategy that combines both
In the dynamic world of cryptocurrency trading, staying ahead of market trends and making informed decisions are key to success. The advent of self-service business intelligence (Business Intelligence ) tools has revolutionized this landscape, offering traders an edge in analyzing market data and optimizing their trading tactics. This article explores the integration of self-service Business Intelligence tools in cryptocurrency trading, highlighting how they can be leveraged to enhance decision-making and profitability.
Understanding Cryptocurrency Market Dynamics
Cryptocurrency markets are known for their volatility and unpredictability. This makes it challenging for traders to consistently make profitable decisions. Understanding market trends, historical data, and real-time analytics is crucial in this context.
The Role of Self-Service Business Intelligence in Cryptocurrency Trading
Self-service Business Intelligence tools empower traders to analyze complex data without needing specialized IT skills. These tools offer functionalities such as:
- Data Visualization: Transforming complex market data into understandable charts and graphs.
- Predictive Analytics: Using historical data to predict future market trends.
- Real-Time Data Access: Providing up-to-the-minute market information for timely decision-making.
Optimizing Trading Strategies with Business Intelligence Tools
To effectively utilize Business Intelligence tools in cryptocurrency trading, traders should focus on:
- Customized Data Analysis: Tailoring the analysis to fit individual trading strategies and goals.
- Integrating Multiple Data Sources: Combining market data with other relevant information for a comprehensive view.
- Continuous Learning: Regularly updating strategies based on insights gained from Business Intelligence tools.
Challenges and Solutions in Business Intelligence -Driven Cryptocurrency Trading
While Business Intelligence tools offer significant advantages, traders may face challenges like data overload and the complexity of choosing the right tools. To overcome these, traders should:
- Prioritize Relevant Data: Focus on data that directly impacts their trading decisions.
- Choose User-Friendly Tools: Select Business Intelligence tools that are easy to use and understand.
- Stay Updated on Market Trends: Regularly update their knowledge and strategies to align with current market dynamics.
The Future of Cryptocurrency Trading with Self-Service Business Intelligence
The future of cryptocurrency trading with self-service Business Intelligence tools looks promising. Advancements in AI and machine learning are expected to make these tools more intuitive and insightful, providing traders with even more sophisticated data analysis capabilities.
Self-service business intelligence tools represent a powerful resource for cryptocurrency traders. By effectively leveraging these tools, traders can gain deeper insights into the market, optimize their strategies, and enhance their profitability. As the field of Business Intelligence continues to evolve, its integration into cryptocurrency trading will undoubtedly become more profound, paving the way for more informed and successful trading decisions.
