## 4 Data Processing and Features
Figure 4 describes the candlestick in stock trading, candlesticks are helpful since they display four price levels (open, close, high, and low) throughout a certain time frame (Figure 4) [22,40]. **(e) Fundamental data**  
Fundamental analysts focus on the underlying business of a company's stock. They look into how well a firm has performed in the past and how reliable its financial statements are. One example of this is the study of financial statements. Annual Report and Balance Sheet, Profit Loss Statement, Management information, Competitor information, Products information, Economic Environment, Political Factor, and Industrial Relationship. Taking into consideration the above facts and documents for the basic analysis, Table 6 shows the factors that are employed as inputs in the machine learning models being applied, as well as the literature around these topics. **5. Prediction Models-Artificial Intelligence in Quantitative Finance**  
Artificial intelligence (AI) and particularly machine learning (ML) have sparked a significant amount of interest and investment from financial institutions such as banks, asset managers, pension funds, and stock trading. These businesses make it a priority to apply a variety of machine learning (ML), deep learning (DL), and reinforcement learning \begin{table}
\begin{tabular}{l l} \hline \hline
**Features** & **Article** \\ \hline Historical Prices Data & [9,10,26–28,36,43,54,79,80] \\ \hline Technical Indicator and Historical Price Data & [6,29,40,55–58] \\ \hline Text and Historical Price Data & [6,29,40,55–58,81,82] \\ \hline Historical Price Data, Text, and Technical Indicator & [40,58,81,82] \\ \hline Historical Price Data and Fundamental Data & [1,2,4,28,32,68,83,84] \\ \hline Image Data & \\ (e.g., Candle Stick chart) & [22,40] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Input parameters for the various machine learning models that are being employed and their associated articles. Figure 4: A candlestick pattern is a price chart pattern used in technical analysis of the financial markets. (RL) methods, including supervised and unsupervised ML, natural language processing (NLP), and data science, in order to improve their investment strategies, gain new insights into the competitive landscape using the data they collect and purchase, and ultimately boost their profits and outperform their rivals. Figure 5 shows the relationship among AI, ML, DL, RL, and DRL. Both machine learning and deep learning are subsets of artificial intelligence. ML is the subset of AI. DL is the subset of ML. Figure 6 shows Machine learning algorithms are used for Classification, Regression, and Clustering. Many machine learning (ML) systems, particularly deep neural networks, are black boxes since it is difficult to acquire a thorough knowledge of their inner functioning once they have been trained. There are three types of problems such as Classification, Regression, and Clustering which can be solved by the learning techniques such as ML, DL, or RL (Figure 6). Linear Regression (Regression) and Decision Trees (Classification) are two examples of basic models with interpretable structures and few parameters that do not need further explanatory techniques. In contrast, black boxes are often used to describe complicated models such as Deep Neural Networks with millions of parameters (weights), since the model's behavior is incomprehensible even when its structure and weights are known. With the help of ML, computers can analyze massive information in search of trends and patterns. In the business of trading, recognizing patterns and trends is essential. Machine learning algorithms are adept at digesting vast volumes of data to uncover patterns that are difficult for humans to recognize. The area of machine learning (ML) is expanding quickly and is now being used in a wide variety of sectors, including quantitative finance. Quantitative finance is the practice of analyzing financial data through the use of mathematical and statistical methods.