A Guide to Data Analytics Standards in Finance and Trading

By Srikanth 8 Min Read
8 Min Read
A Guide to Data Analytics Standards in Finance and Trading 1

The world of financial markets and trading is ever-changing and complex. Data analytics is how investors can make informed decisions, gaining a competitive edge in the markets to minimize risks and maximize returns. Analyzing data also benefits financial institutions like brokers and exchanges that provide platforms for clients to trade. Let’s explore more about data analytics and the many practical applications it offers.

A Brief Overview of Data Analytics in Finance and Trading

Data analytics is applicable in many industries, allowing for the collection and transformation of data to attain conclusions and make predictions. On the trader side, it empowers investors to uncover patterns, correlations, and trends to drive more innovative strategies and decisions.

The MT4 trading platform is one of the popular software packages traders use across many financial markets.

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On the business side, data analytics plays a role in numerous circumstances, like fraud detection, consumer analytics, and more.

What Are the Use Cases for Data Analytics Within Finance and Trading?

So, how many ways does data analytics apply to finance and trading?

Algorithmic Trading

Algo-based trading continues to grow thanks to the advancement in computers and software programs that process vast amounts of data. The MT4 trading platform is one of the most straightforward packages for creating ‘robots’ that execute positions automatically.

Data analytics allows developers to code a complete strategy that automates the trading process from start to finish with zero manual intervention. This is derived through predetermined mathematical parameters from profitable existing back-tested strategies and historical data.

A considerable element of algorithmic trading for large financial companies like hedge funds and banks is high-frequency trading (HFT). HFT involves using complex algorithms to transact huge orders at ultra-high speeds. Through this approach, institutions can perform ‘market making’ for their clients, profit from news trading and arbitrage, and engage in manipulative tactics like spoofing and layering.

Machine learning and artificial intelligence (AI) are emerging sectors within algorithmic trading. Experts predict these inventions will have the most predictive power in the markets once fully developed.

Real-time Insights

The speed at which events and price changes happen in traded assets is rapid. Those accessing this data in real time have the best chance of reacting to these fluctuations. Investment bank Goldman Sachs approved a three-year plan in 2019 to spend over $100 million to shave milliseconds off stock trades.

Hence, it’s clear that timing is everything. The real-time data used in the financial traded markets includes the economic calendar, live price quotes, order books, volume information, etc.

Social Media Sentiment

Billions of people are on one or more social media platforms. It’s among the best ways to gauge the sentiment of real humans trading various financial markets, as people are pretty honest about their feelings. Hence, analyzing such data is valuable for the short and long term.

Fortunately, there are social media sentiment tools for anyone to use. Commonly, they gather data by connecting an API (application programming interface) scraping tool on networks like Twitter and Reddit. They would observe specific market-related keywords, hashtags, and interactions to determine whether users feel bullish or bearish.

Automated Risk Management

Here, we refer to the many internal and external risks a financial institution may face. A common one is fraud, particularly with credit cards. Data analytics can observe unusual trends by monitoring things like a card being used in an uncommon location or withdrawal patterns typical of fraudsters.

Once machine learning improves, the data can get advanced to detect anomalous financial behavior across many organizations. In a nutshell, data analytics can save any financial company lots of money.

Consumer Analytics

It’s challenging for clients to commit their loyalty to a broker or financial institution. These companies leverage data analytics to understand their customers and direct effective marketing.

Market segmentation is increasingly being implemented, using demographic data like age, geographic location, socio-economic status, and specific interests, among other factors.

Data analytics can predict how much potential profit an individual client can provide a financial company based on their interactions.

The Relevance and Integration of Data Analytics in MT4

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Having mentioned the MT4 trading software several times, let’s look at how it integrates the data analytics discussed.

News Feed

MT4 offers a simple yet efficient news feed. Users can find real-time news updates on breaking news, economic indicators, and market events. This ensures they are kept abreast of the latest financial market developments like central bank announcements and political events. MT4 also has a dedicated economic calendar, offering a schedule of specific economic events.

Traders have various customization options, with the ability to filter news according to preferred symbols and news sources. MetaTrader’s news feed flawlessly integrates within its charts and trading terminals so traders can view news alongside their trading activities.

Real-time Alerts

Real-time alerts on the MT4 trading platform emphasize the necessity of receiving data as it occurs. Here, traders get alerts on preset price levels where they will make a trading decision like an entry or exit.

Not only price levels, MT4 even offers alerts based on specific indicator conditions. For instance, one can get notified when the Relative Strength Index (RSI) crosses below or above a certain level.

Real-time Quotes

MT4 has the ‘Market Watch’ feature, where users monitor real-time prices of different traded markets. Here, they can customize this window according to their desired instruments. This feature is helpful for quick and accurate order execution.

Automated Trading and Backtesting

Finally, MT4 is revered for having accessible, powerful, automated trading functionalities. Using its proprietary MQL4 programming language, one can code their own robots or ‘expert advisors.’ The software lets traders access extensive historical price data, which is helpful for backtesting strategies before they deploy them in the live markets.

The Value of Data Analytics Remains Priceless

Mark Twain once comically said, “Data is like garbage. You’d better know what you are going to do with it before you collect it.” There is much data to analyze in finance and trading, as explored here. The most necessary part is the use case. Is it for real-time insights? Are you building a trading robot? Regardless, MT4 is an accessible piece of software for data analytics within many traded financial markets.

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