
21/08/2023
Integration of artificial intelligence (AI) in trading is one of the trends in stock market trading. In theory, neural networks can "revolutionize" traders' approach to the market, automate many trading and analysis processes. In this article, we will examine the main paths of neural network development in trading. We will also discuss the flip side of the coin - addressing the current negative aspects of AI application in trading.
Attention! This article is purely informational and does not contain investment recommendations and trading advice.
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Table of Contents
How neural networks can be used in trading
Weaknesses of neural networks in trading
Conclusion
How neural networks can be used in trading
Advanced sentiment analysis
Artificial intelligence can conduct advanced analysis of market sentiments using natural language processing (NLP) and machine learning algorithms. Traders can specify aspects for classifying data related to specific cryptocurrencies or projects. This will help better understand market sentiments and tailor trading strategies to them. With the help of algorithmic sentiment analysis, it is also possible to detect changes in sentiments before they become widespread.
Combatting fraud
Neural network-based systems can constantly monitor transactions and activity on the blockchain, detecting anomalies and fraudulent actions. Algorithms can timely report double spending or suspicious transaction patterns.
Social media monitoring
AI can scan massive amounts of data from social networks, identify popular topics, discussions, and sentiments related to cryptocurrencies. Based on this information, the neural network can predict potential market changes, assess the prospects of new projects, and opportunities for investments.
Individual trading bots
Artificial intelligence can create personalized trading bots. These bots can be customized to the goals, preferences, and risk profile of traders. Bots can analyze the market and make trading decisions based on each user's profile. Feedback can be provided to the algorithm, making its usage more flexible.
Portfolio optimization
AI-based systems can rebalance a cryptocurrency portfolio. Market conditions, risk factors, and individual user preferences are taken into account as criteria. This can make asset diversification highly efficient. The neural network can also model various market scenarios, providing recommendations for portfolio adjustments based on them.
Integration with IoT (Internet of Things) devices
AI-based trading platforms can be integrated with IoT devices. This will open new horizons for neural networks in trading. For example, users may be able to make trading decisions and perform operations using voice commands or other interfaces. This can create a more flexible user experience, making trading accessible to a wider range of users.
Education and training
Depending on the user's progress and learning style, AI-based platforms can expedite the process of training traders. Educational content can be dynamically adjusted to the trader's personal characteristics, goals, and learning style on the go.
Automated market making
Trading algorithms have long been used by market makers and liquidity providers. Artificial intelligence can enhance the market-making process, ensuring greater liquidity and stability in the cryptocurrency market. For example, AI can analyze data from the order book and adjust the bid-ask spread. In theory, this can provide narrow spreads, fast order ex*****on, and overall market efficiency improvement.
Weaknesses of neural networks in trading
Limited understanding of market nuances.