Data analysts are becoming increasingly important in today’s world and have skills that have many things in common with the necessary skills of traders or brokers. We have therefore dealt with the question of how the role of data analysts will develop in the future and what opportunities will arise from the more accessible entry into the world of trading.
You can hardly hear it anymore – Big Data. You read and hear about it everywhere, but only some know precisely what Big Data means. Only those already dealing with data today can begin to understand and understand what exponential growth in the field of data entails. The digital transformation is generating more and more data. Not only by people but increasingly also by intelligent systems and machines.
This leads to an exponential increase in data. By 2025, the amount of data worldwide is expected to increase tenfold again. Thanks to the IoT (Internet of Things) and more and more real-time data in the mobile and stationary sectors, analysts expect that by 2025 every person with internet access will interact with networked devices on average almost 5,000 times a day. Capturing this massive amount of data is one thing. The other is to use them efficiently and purposefully.
The Role Of Data Analysts In The Future
Data analysts come into play precisely when humans can no longer process the amount of data. Intelligent systems are needed that are able not only to record the almost unlimited amount of data but also to convert it into usable data sets and process them further.
A data analyst is therefore responsible for merging data sources, training the systems, and using ever-improving algorithms and self-learning systems (AI) to strive for a state in which machines can analyze, evaluate and meaningfully summarize all the data generated so that it is helpful to humans. It is not for nothing that one also speaks of a data scientist in this context.
Forex trading means foreign exchange trading. It traditionally offers traders the most excellent chance of winning. Traditionally, trading on the foreign exchange market only makes sense if you have profound knowledge and experience. Otherwise, the risk is too high and no longer calculable. However, digitization also directly affects forex trading because analyzing large amounts of data is becoming increasingly important here.
This enables the research of complex data and the development of trading-specific algorithms to even better understand the interrelationships in international currency trading. In this way, direct connections between different markets and currency pairs can be recognized more quickly, understood, and thus better predicted. So far, big data has been seen as a bogeyman in forex trading and could be used more efficiently. However, this will change in the future.
What Is A Forex Broker?
One must not necessarily think of a forex broker as a person in the narrower sense. Instead, a forex broker is the same as a financial broker or trading platform, so a system. So, a forex broker is a trading environment that can be built as a forum, as behind every forex broker is a company regulated by authorities and regulations. As part of the significant increase in data collection and demands on the forex market, it is to be expected that brokers will have to comply with ever stricter regulations in the future. Here, too, efficient data processing plays an increasingly important role.
Interfaces Between Forex Brokers And Data Analysts
The data analyst job is very demanding because it requires extensive and well-founded knowledge in linear algebra, statistics, and programming, as well as in system architecture, business intelligence applications, and data structures. If there is a high degree of creativity and a thirst for research, all the skills are available that are primarily responsible for the success of a forex broker.
The acquisition and further processing of complex data makes the existing systems more and more reliable and, above all, faster and more efficient. Of course, forex brokers scramble for the best data scientists. But there are also database specialists in other areas, which simultaneously entail competencies in the field of development and analysis. But depending on personal skills and attitude, it is possible to enter the business yourself as a trader because a data analyst has all the necessary skills – they have to be used sensibly.
Entry Into Trading
Traditionally, trading is very time-consuming. According to the unanimous opinion – as can also be read at finanzscout24 – as a freelance trader beginner, you will not only make losses but also invest a lot of time, so you will inevitably have to do trading full-time. But you can efficiently convert data into conclusions and use them as a helper. In that case, you can find better and better trades with significantly less time and, with the appropriate ambition, work profitably with a broker as an independent trader can.
It is, therefore, still completely open today to what the everyday life of a trader will look like in a few years. Anyone who advances as a data scientist with an innovative spirit and entrepreneurship today may be able to revolutionize the world of trading tomorrow on their own or benefit enormously from it and help shape it.
Day Trading Vs. Position Trading Vs. Swing Trading
As a trader, there are several ways to become active. It currently depends more on personal preferences and which path you choose. However, this could quickly change for a data analyst in the context of digitization and efficient data analysis. We have briefly summarized the types of trading that are available below:
This is a type of trading with positions held for several weeks, months, or years. Due to the high complexity, this type of trading could be more interesting for data analysts.
These are hours, days, or weeks of trades. In the short term, the number of influencing factors is limited and can be better analyzed and assessed. With intelligent systems, this type of trading can be easily optimized, and the risk remains calculable.
Day trading differs from swing trading in that only trades are open and closed within one day. Positions are never held overnight. The potential for data analysis and use is very high here because intelligent systems can keep the risk low. Consistent risk management is, of course, also of crucial importance here.
The work of a scalper is undoubtedly interesting for lateral entry as a data scientist. This is because it is limited to the shortest possible periods; usually, a trade is opened and closed in a time window of 15 minutes. The number of potential influencing factors can be reduced even further, meaning efficient use of existing data can bring great success.
Get To Know Your Limits
Newcomers to trading tend to keep their eyes on the charts. This is one of the significant weaknesses of classic newcomers. However, as a data analyst, instead of looking at the charts, you can develop intelligent systems that make constant viewing unnecessary. Manually scanning the markets should take a backseat as much as possible when looking for lucrative entry opportunities.
In forex trading, volatility is very high, so prices fluctuate considerably. Longer periods of stagnation are rare. As a result, as a trader, you can only leave a trade on this market for a short time. Accordingly, this can lead to a tendency to constantly monitor transactions and the need actively and to switch from pure analytical knowledge to gut feeling and still make a trade that is not well-founded but supposedly profitable and fast.
Discipline Before Emotion
As a data analyst, you know well-founded insights and are better at assessing risk than someone who has nothing to do with analyzing data. As a result, a data scientist is less likely to make hasty decisions and be guided by a “good gut feeling.” This is of great importance for success in forex trading.
Anyone who concentrates strictly on the analysis of patterns learns to understand the connections between systems and develops the necessary discipline not to succumb to the temptation of quick but risky trades can, in conjunction with good stop-loss management, keep losses low and, at the same time the chance of high profits.