Data mining and technical analysis is a growing trend throughout many different fields. It is a relatively simple process at its heart. The use of numbers, statistics, and algorithms has led to much more than a simple new machine or computer program. In fact, the practice is supposed to change how people live, work, and play for decades to come. Data mining will help change how health care is distributed and how countries fight wars.
In addition, it will change how traders make money through their various investments. Data mining can be particularly helpful to technical analysts who use charts, graphs, and statistics to make their decisions. Technical analysis can easily make billions of dollars and bring in thousands of new practitioners with the tools of data mining.
Technical Analysis for Trading
Technical analysis is one of two approaches to stock trading. Fundamental analysis is an incredibly common approach to stock trading. This approach is often used by long-term traders and those who are setting up investment vehicles for retirement accounts. Individuals using fundamental analysis want to make as much money as possible over a long period of time. It involves tracking a set of fundamentals that gauge the overall worth of a stock.
Earnings ratios, cash on hand, and the place of the company being considered in their sector all help with the process of fundamental analysis. This analysis may tell a user to buy a technology company because of a new product they are releasing or a relocation that investors believe will be preferable to their old location. An investor trading on fundamentals may sell a stock because the CEO is incompetent, or the earnings reports make no sense to the trader. The process involves understanding and evaluating the health of a company to determine what sort of position should be taken on it.
Trading Technicals Approach
The study of trading technicals is the less frequent, more complex approach to stock trading. It is more often used by institutional traders and day traders who are particularly interested in maximizing short-term profit. This approach involves analyzing the ways in which stock traders work based on the stock performance in the past. According to the theory of stock analysis, traders of a stock act according to a series of established patterns.
Psychology and statistics help to guide these activities more than the underlying health of a stock. Many of the trades made on a daily basis are not performed by entities interested in holding a stock due to its growth or value. Computers often do not consider the long-term health of a stock after constantly analyzing fundamentals such the products listed or the earnings ratio of the stock. They want to make money as quickly as possible and in high volumes. Therefore, certain ideas and concepts constrain their activities in ways that fundamental analysis does not fully explain.
Examples of Technical Analysis
Technical analysis often requires a considerable amount of education and experience to even begin to follow. However, it is important to know some of the basics in order to figure out the relevance of big data to the process. Two important examples help to explain the nature of stock analysis. One is the resistance level. A resistance level is a level that a stock has gotten close to over a period of time but has not crossed. For example, a stock may have gotten only cents away from $42 several times over a fortnight and never been able to go above it. There may not be any obvious reason why $42 is a special number or a particular expression of a price-to-earnings ratio.
However, stock analysis dictates that the traders of a stock will not want to cross that level on their own. They believe that there are clear reasons why earlier investors did not cross that level. They do not want to appear to be paying too much for a stock. Their decisions then influence the machine algorithms which make thousands of trades per hour. Then, some external event that is both predictable or unpredictable happens. An extra piece of news or earnings report might be released. One market maker may not be noticing the resistance level and may choose to pay for more than $42 per share. Suddenly, the resistance level breaks and the study of technicals dictates that the stock price will soon soar.
Head and Shoulders Trading Pattern
Another example of analyzing charts and patterns is the spotting of a head and shoulders pattern. This pattern occurs when the stock price over a common time frame (like the past month or year) appears to be a reverse parabola. This shape gets its name because of its basic resemblance to a person’s high head and lower shoulders. The proponents of analyzing charts and patterns believe that this is a sign to sell the stock. To technical traders, a head and shoulders mean that a stock will soon plummet. It will be seen as already having achieved a high point and having nowhere else to go but down. The forces that led to the original downturn of the stock are viewed to still be in place by traders. As a result, they often sell and send the price further downward.
These examples are more simplistic than daily trades based off of analyzing charts and patterns. Most technical analysis utilizes terms and concepts that one would need a degree in statistics to fully understand. In order to use these statistics, one needs to utilize the data surrounding stocks. Data mining can help users and interested parties who want a piece of this hundred-billion-dollar industry.
Data Mining and Technical Analysis
The first step of data mining involves gathering all relevant information. Technical analysis runs off information and is the heart of the entire practice, a chart, is basically a visual representation of data. For charts to work properly, they must be filled with a host of relevant information. The vast majority of charts require stock prices and periods of time. More detailed candlestick charts require the stock prices taken at different intervals throughout the day. Many technical charts add more components like volume, volatility, and burgeoning patterns as they emerge and are possibly broken.
All of this information must be collected and compiled in a data mining program. These programs are based on simpler spreadsheets and databases that many people interact with on a daily basis. Many programs will have categories that allow miners to sort and better understand the information that they have. Programs and users can perform scans that will upload thousands of data points in the span of a few seconds. For instance, a technical analyst can study publicly accessible data on a stock’s price swings over a month-long period and enter in the stock price for every minute of every trading day in less than a few hours. This information is critical and at the heart of big data. More data means more examples and a finely tuned system.
Data Mining Processing Big Data
The information gathered must then be processed. Data mining’s power comes from the ability to make sense of massive reams of information. The usefulness of big data comes from the algorithm which is used to interpret data. An algorithm is a mathematical function that has a product when a set of data is entered into it. The algorithm takes hundreds or thousands of factors into consideration much faster than any human being could interpret them. It weights certain factors higher or lower depending on their relevance to the eventual product. Outliers outside of a certain number of standard deviations are removed so that they will not skew the results.
The algorithm distills all of these possible data points into a smooth function or series of outputs which then improve the process of technical analysis. A big data operation may help a user identify technical patterns and then provide a recommendation about how one should trade as a result of those patterns. Data mining may also help a user simply know whether to bet short or long on a particular stock.
Data Mining Attributes
Using big data for the analysis of stocks requires many different attributes over time. First, there needs to be the ability for either human or machine learning. Investment analysis should be performed over a period of months or years. During that time, the algorithm will reveal flaws or imperfections. It may not respond well to a sudden piece of bad economic news. Patterns that it identifies may not pan out the way the algorithm or the user predicted.
In order to correct issues that may arise, the data mining system needs to have a way to continue bringing in new information as much as possible. New information provides a wider time horizon and a greater series of circumstances for the data mining operation to cover. Then, the new information is used to make changes to the algorithm. Those changes are consistently tweaked at the margins until the algorithm is more reflective and hopefully more profitable for the trader.
Data Mining and Investment Vehicles
Data mining for technical analysis requires sophisticated technology and an understanding of mathematics. It also requires people who can write and communicate. This process can be used to dictate how companies spend millions or billions of dollars in investments. Therefore, the data miner must be able to understand and express the activities of their functions. They should, in most, instances, be able to understand the code that eventually led to their results. Data miners must also be sophisticated in the way that they write about those findings. It is not enough to simply run mining operations. The direct results of every big data operation is a jumbled series of numbers. Data miners must make sense of those numbers and then utilize and communicate how one should trade based on those findings.
Technical analysis is not only applicable for stock investments. There are many different forms of stock analysis where data mining can prove useful. Bond funds can often be interpreted using stock analysis. Patterns help point to how bond prices will react when there are economic events and an interest rate increase. The same is true for derivatives trading. Investment analysis can prove helpful for understanding the information that can move the prices of derivatives in one direction or another.
This market involves trillions of dollars in contracts and bets all over the world. The amount of data taken in and money flowing out is astonishing. Credit default swaps in the 2000s dealt with trillions of dollars in potential payments. Options and derivatives also are mostly void of fundamentals. They are not as tangible as a stock or government bond. As a result, the field is ripe both for data mining and stock analysis. These tools help investors figure out where the market is going and how they should take advantage.
Technical Analysis and Cryptocurrency
Technical analysis through data mining can even be used to help predict the activity and future of cryptocurrencies. The field of currencies such as Bitcoin and Ethereum is incredibly difficult to predict and trace. Proper stock analysis tools have not been perfected to track the general performance of these investment vehicles. Data mining has the potential to be a trendsetter.
There is no established wisdom that needs to be superseded by data science and big data techniques. Instead, data mining can be the foundation of stock analysis. It can help bring about the newest generation of moving averages, candlestick graphs, and other tools that investment analysis of stocks has gotten used to over the past several decades. This combination means that the mining of cryptocurrency data can help early practitioners become wealthy trendsetters and develop licensed products that may be used all the way over.
The Revolution of Data Mining
Data mining will continue to revolutionize numerous fields in the economy and society. It has already had a discernible impact in manufacturing and healthcare. As technology becomes more sophisticated, data mining will be used more often to supplement the actions of technical analysts. It will help them make better trades, project better forecasts, and easily interpret and reproduce their results in other locations. Data science will also help change how these analysts view many other forms of investment. All moneymaking and investment opportunities will be changed by the ways in which computers and statisticians are interpreting big data today.