Stock Market and Cryptocurrency Prediction: Data Analysis Techniques
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Introduction: Stock Market and Cryptocurrency Prediction: Data Analysis Techniques
The stock market is a place where everybody invests their money according to their knowledge. Because the market is a place where lots of money will come and go, it all depends on the stock. What type of stock is this? In the stock market, there are multiple companies to invest money. These companies are publicly listed, meaning everybody can buy or sell the stock in the market. These days, with the help of the stock market, people are reaching and crossing the limit of success with the help of these markets if they have perfect knowledge about the stock market's ups and downs. If they are researching perfectly before investing the money, then it is damn sure that money will come in the form of multiple additions. These are the reasons nowadays why people think this is also a kind of business or job People are leaving their jobs after seeing the success of stock marketing and investing. Because if someone is investing in the stock market and earning a huge amount of money, then they must have invested their time also in these lines. Without focusing on the company's stocks if someone is investing the money, it may go through the risk area. And the stocks of the company are also called equities of the company, which means it represent the ownership in that company buying a reserve on any company, which means the user buying the shares of a company. Nowadays companies raise capital with the help of the stock market. The stock market will help the company to reach its goal in the way of stock market prediction of stock. It also helps people's personal wealth. The stock market is a kind of indicator. For the state economy. If the company raising and everything with the help of stock then it will affect the economy of any state.
The procedure of guessing ups and down price movements of the digital cryptocurrency prediction using different analyses and arithmetic is called cryptocurrency prediction. Cryptocurrency prediction is not so easy to predict. It needs various types of knowledge.
Literature Review
According to Chiang et al. 2021, After coming to Bitcoin to the market so many people from different backgrounds came, to increase the cryptocurrency market there are various types of computerized structures inserting all the data with this thing the investment and financing are also admitted continuously. In this research paper, the cryptocurrency price is continuously changing to Recurrent Neural Network (RNN). Along with these things in this paper report there various graphs are made to analyze and predict cryptocurrency prediction with the of this guess the future. After this, the business process cryptocurrency investment and it depends on High-Level Fuzzy Petri Nets (HLFPNs) to improve the best investment agreement. With the help of this, every investor will be able to understand the prediction of cryptocurrency so easily. According to Chiang in this article, the era of cryptocurrency can become the most popular earning source in just recent coming years and with these things, the world also will change. According to a researcher, there are 6000 or more than this count of cryptocurrencies all over the world. Investing in cryptocurrencies may affect the assembled wealth, but on the other hand, money may be lost, because there are risks created for riding with this investor has to research and use all the different investment tools. Using this the risks will be reduced and also this will make it easy to search for good ways to make money in the market. According to the author of this report, cryptocurrency is a way of exchanging money for security using the cryptocurrency principle. Digital and online money both are Combined and made the cryptography.
Figure 1: Research Architecture
In this architecture, the method is applied d HLFPN model for the cryptocurrency.
Before this the cryptocurrency money prices are guided by the use of LSTM along with the coming prices of the cryptocurrency are predicted depending on the guided structure. Every data is calculated with actual guesses by the fuzzifier and then insert into the HLFPN. All the things are built to make predictions and then turn into the decision for buying the stocks else selling the stocks.
Exploratory data analysis
The data science method has an important stage such as “Exploratory data analysis”. This technique is essential to explore the displaying and simplifying key features of the dataset. “Exploratory data analysis” is an essential milestone for any consumer working in data science.
The main goals of the of the “Exploratory data analysis”.
To develop the graphical representation with the help of software.
To improve the coding part to get the perfect outcomes.
To improve the test theories on the connections between variables of the dataset.
Method
The researcher taken various kinds methods are used to make this research report perfect. These provided methods are very important for the software work to make the output authentic. The steps are mentioned below with proper definitions.
Data collection
Data visualisation
Encoding the label of the dataset
Tries to scale the dataset’s feature
Select the proper model, and evaluate this
Hyperparameter fine-tuning
Data collection
This section is very important for the research because the whole research is based on the dataset which is related to cryptocurrency. The researcher used various kinds of currencies which is very important to get the proper outcomes. The main objective of Data visualization is to convey information to the consumers in a style that is perceptive to grasp. The data is collected by the researcher's own with the help of a dataset and with the help of this dataset all analysis has been done. The figures show various graphical representations such as line graphs, point graphs, scatter plots, etc.
Figure 2: First 10-row display
The figure shows the first ten row’s dataset and the researcher took these values for analysis purposes.
Figure 3: Represent the other coin
The figure shows the Represent the other coins dataset and the researcher took these values for analysis purposes. In this dataset, years are mentioned and also quantity is presented here.
Model training
The Researcher used various modeling to develop the graphical representation. The researcher get the graphical representation with the help of analysis which is done by the VS code.
Figure 4: Data modeling
This output screen represents the modeling, which means that after implementing the method the dataset was trained and tested and that this screen presents this model screen.
Figure 5: Main Class
Error messages can be displayed on the web page because the application runs in debug mode with debug=True set in the app. run method.
Figure 6: Home Page in HTML
This HTML code addresses a site layout for a stock gadget application. It includes a form for entering a stock symbol, a Highchairs-based chart, and information about the application.
Figure 7: Creating Bar Graph
This code represents the dataset and it also represents the CSV file and after analysis, this creates a bar graph. This program mainly helps to create the bar graph and this program is very important. The of X variable is volume.
Data visualisation
In this section, the graphical representation is shown by the researcher with the help of VS Code. various graphical presentations are provided here with the help of proper code with the help of VS Code software (Chiang et al. 2021). The researcher provided the information with the help of graphs, charts, graphical representations, etc.
Figure 8: Bar Graph
This bar graph is based on the CSV file and it represents the bar graph and the graphical representation.
Figure 9: Line Graph Input
This program reads cryptocurrency data from a CSV file, extracts the "Open" and "Close" columns, and employs matplotlib to draw a line graph of the two columns. Before plt. show() is used to display the graph, a title, x-axis label, and y-axis label are added.
Figure 10: Line Graph Output
This is the line graph; it represents the lining. This lining is above each line because all values are frequently closed.
Figure 11: LSTM Implementation
This code is an execution of a LSTM model for time series expectation utilizing digital currency information (Jeribi et al. 2019). It loads the data from a CSV file, scales it with the MinMaxScaler, divides it into training and testing sets, and creates LSTM input and output sequences.
Figure 12 : LSTM Implementation Output
A digital currency-based LSTM model for predicting time series expectations. It scales the data using the MinMaxScaler after loading it from a CSV file.
Figure 13: Candlestick Input
Plotting a candlestick chart with financial information from a CSV file is made possible by this Python code that imports the necessary libraries (Jeribi et al., 2021). Plotting the candlestick chart makes use of the mpl_finance library.
Figure 14: Candlestick Chart
This output makes subplots and plots the candle diagram, utilising the candlestick_ohlc capability. The X-axis shows the date and the Y-axis shows the price of the coin.
Figure 15: Time Chart Input
This code loads digital money cost information from a CSV record, preprocesses the information, plots the first-time series, fits an ARIMA model to the information, creates forecasts utilising the fitted model, and plots the first-time series and anticipated values on a similar plot.
Figure 16: Original Time Series
This graph represents a graph, and it represents the blue line based on the CSV file or provided dataset.
Label Encoding
Label encoding is one of the of the greatest and most popular ways to differentiate the data. In label encoding, there are some techniques for all those things, and every single dataset variable is provided a different integer depending on all the dataset queues.
Model Evaluation
Since machine learning has become one of the finest and most successful methods for predicting stock markets by guiding various structures, its application is too risky to predict the data very easily; its application may be restrictive. There are so many reasons why the price of cryptocurrency currencies is so obvious. There are many technical factors applied to that. It will be affected the markets of crypto, economic growth problem, some privacy issues will be generated, etc.
Hyperparameter fine-tuning
Hyperparameter is a way of relating a medium to a machine learning structure. Generally, people's creativity is very necessary for the model. With the help and creativity of humans, the model will be able to tune. Hyperparameter fine-tuning is used to improve the machine-learning algorithm. It also helps in the performance of a particular data set. Before making this model, the hyperparameter is built with all the settings before the structure.
Graphic User Interface (GUI)
Figure 17: Input code of “Graphic User Interface”
The figure shows the Input code of “Graphic User Interface”. Various numerical data is presented here that helps increase the graphical interface's accuracy. There is various
Figure 18: Candlestick in graphical representation
This graphical representation shows all the data about grading the dataset, all based on the values of the dataset.
The code creates a candle diagram of the stock information. It uses Pandas to make a data frame to address opening, shutting, high, and low costs of stock (Wang et al., 2022). Then, the diagram statement is up and down, which depends on the market. at that point, plots the information utilising matplotlib, involving various tones for "up" and "down" information,
Feature Importance
The features of the stock market and cryptocurrency make it easy to access the data and there are so many things to invest in and then make money through. Lots of companies here come and use the prediction of the stock market and make their business so high (Gajamannage et al. 2019). People can make it their personal earnings source all over life because, in cryptocurrency and the stock market area, everybody has become the richest person through perfect knowledge of stock and crypto. For predicting the stock, which is also called equity of the company, the company bar goes to the upward side of the graph or the downside of the graph. To predict this bar inverter, one must first learn about the company and the price of the stocks. Important features are used for building the models of crypto and stock markets from tree-based and machine-based models.
Feature Scaling
Feature scaling is a type of thing that generates the efficiency of the model It is an important technique to increase the feasibility of the machine learning algorithm. Every single piece of data goes into every feature of the prediction. In the everyday life of people, there are so many data sets features with huge amounts of values. For example, suppose there is one smart building. So in that building, there must be so many features to modify, like a lift and hall and so many bedrooms With these things, how many floors are there? In a guess system like this if the user predicts the number of floors in the building, how many floors are there in between what numbers? Along with that, their bedroom size will definitely be different, like 700 and 2500. Both feature scales are different from one another. Maximum absolute scaling is finding the dataset values from the different model's datasets. And the next step of maximum scaling is to divide all the dataset structures in the form of column through that highest dataset.
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Conclusion
In this article the writer is explains how the prediction will do and how many types of prediction is there. How everybody can take the knowledge of the other f crypto and the stock market and how they can apply their individual investment are all thing done in this s report. With help of LSTM neural network,. From this article, invite ester will able to catch the things very quickly. There So many ups and down graph are available. From there people will learn the prediction things how the graph will vary. From there, users can also learn the dataset
Limitation
The limitation is very important for this researcher to get the perfect analysis with the help of software. The researcher always tries to analysis the proper dataset to get the perfect result.
References
Journals
Chiang, D.L., Wang, S.K., Lin, Y.N., Yang, C.Y., Shen, V.R., Juang, T.T.Y. and Liao, T.Y., 2021. Development and evaluation of a novel investment decision system in cryptocurrency market. Applied Artificial Intelligence, 35(14), pp.1169-1195.
Chiang, D.L., Wang, S.K., Lin, Y.N., Yang, C.Y., Shen, V.R., Juang, T.T.Y. and Liao, T.Y., 2021. Development and evaluation of a novel investment decision system in cryptocurrency market. Applied Artificial Intelligence, 35(14), pp.1169-1195.
Abdul-Rahim, R., Khalid, A., Karim, Z.A. and Rashid, M., 2022. Exploring the driving forces of stock-cryptocurrency comovements during COVID-19 pandemic: An analysis using wavelet coherence and seemingly unrelated regression. Mathematics, 10(12), p.2116.
Gajamannage, K. and Park, Y., 2022. Real-time forecasting of time series in financial markets using sequentially trained many-to-one LSTMs. arXiv preprint arXiv:2205.04678.
Jeribi, A. and Ghorbel, A., 2021. Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis. International Journal of Emerging Markets, 17(9), pp.2290-2320.
Almeida, J. and Gonçalves, T.C., 2022. Portfolio diversification, hedge and safe-haven properties in cryptocurrency investments and financial economics: A systematic literature review. Journal of Risk and Financial Management, 16(1), p.3.
Thanekar, G.S. and Shaikh, Z.S., 2021. Analysis and Evaluation of Technical Indicators for Prediction of Stock Market (Doctoral dissertation, University of Mumbai).
Jeribi, A. and Masmoudi, W.K., 2021. Investigating dynamic interdependencies between traditional and digital assets during the COVID-19 outbreak: Implications for G7 and Chinese financial investors. Journal of Research in Emerging Markets, 3(3), pp.60-80.
Wang, Y., Li, K. and Wang, G.G., 2022. Combining key-points-based transfer learning and hybrid prediction strategies for dynamic multi-objective optimization. Mathematics, 10(12), p.2117.
Arnoldus, T., 2019. Ex-ante uncertainty as a determinant of Initial Coin Offering underpricing (Master's thesis, University of Twente).
