Since technical analysis focuses only on the movement in the stock price and stock volume, AI and ML can be used very effectively. Looking at the trend, AI will develop an algorithm and can even predict the movement of the stock index very effectively. AI takes into account many types of information and processes the information in such a way that it can ensure safe returns on investment very safely. AI can be used to achieve short-term as well as long-term investment goals. AI always attempts to reduce volatility as AI involves data mining and makes a decision after a complete analysis of past data and the algorithm. When AI and ML are used the results are simple to interpret and help in decision making in the long run. Artificial Intelligence (AI) has been used in practical applications in technical analysis to revolutionize the study and examination of market data by traders for investing. Using AI algorithms and machine learning, technical analysts can browse through massive amounts of past and current market data to identify patterns, trends, and potential trading opportunities.
This equates to greater accuracy, enhanced forecasting, and enhanced trading strategies (Choudhary, 2015). Pattern recognition is among the application uses of technical AI analysis. Algorithms for AI can sort through huge sets of historical prices, i.e., candlesticks and charts, and detect those that repeat as well as identify those that may help predict market movement in the future. The patterns can be double tops, head and shoulders, or triangles. Recognizing this pattern, AI models can advise traders to buy or sell to enable them to make decisions based on historical precedents. Another use of AI in technical analysis is trend detection. Using machine learning algorithms, AI models are able to search through price data to determine the direction and extent of the trend. This allows the trader to spot and take advantage of increasing or decreasing trends in the market.
Through tracking trends, traders can deliberately enter and close positions to maximize profits (Cover, 2007; (Choudhary, 2016)). AI is also used for technical analysis to establish predictions and forecasts. Using past price history and other supporting market data input into AI’s algorithms, the models begin to recognize patterns and data correlations. This enables AI algorithms to project directions of forecast prices into the future, which enables traders to predict market patterns and make successful trading decisions (Choudhary et al., 2012). The predictions can generate useful information and help traders create profitable trading plans. Additionally, AI has proven to be capable of optimizing trading strategies. With sophisticated optimization methods, AI models are able to analyze and optimize parameters such as entry and exit, position size, and risk management to improve trading performance (Davenport and Ronanki (2018)).
These may include techniques such as moving average crossovers, relative strength or stochastics. From their calibration to historical data and prevailing market conditions, AI models are able to enhance the profitability and efficacy of trading systems (Choudhary et al., 2019). It should be observed that the application of AI to technical analysis has its own real-world limitations. One of them is the issue of overfitting, where an AI model will be fitted to past data in such a way that it does not generalise so well to new market conditions. To balance this risk, it is also important to regularly monitor the performance of AI models on out-of-sample data and apply valid validation methodologies in an attempt to guarantee the stability and reliability of the model (Davis and Anderson, 2017). The use of AI for real application in technical analysis has revolutionised the industry, providing traders with convenient tools to analyse and interpret market data. Artificial intelligence models can identify patterns, determine trends, make predictions, and trade at the best levels. Technical analysts, with the implementation of AI, can provide more accurate predictions, identify profitable trades, and improve trading strategies to perform better (Esteva et al., 2017; (Choudhary et al., 2018) to make more informed decisions.
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