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The Best Data Science Applications and Tools to Manage a Quantitative Hedge Fund by@mikhailkirilin

The Best Data Science Applications and Tools to Manage a Quantitative Hedge Fund

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Mikhail Kirilin

Copywriter, community manager, editor. Interested in fintech, investing, fund management.

Modern-day hedge fund management is precisely data-driven, uses information and AI as the fuel to process the entire buy and sell operation in trading. Primarily these data-driven metrics reflect the value and growth of the correlated trades and provide indications to the data analysts.

Further, the hedge fund managers use those indications to employ complex data analytics practices and tools as instruments for examining the market and executing trading decisions. Hence, the better the data indication, the possibility of profitability increases.

The following article will discuss some essential data science practices and tools to implement in a hedge fund and start driving on the road toward profitability.

What Is Data Science?

Data scienceis a field of data analysis where the data are cleaned, aggregated, processed, manipulated, and organized for advanced data analysis.  The area of research relies on statistics, AI, data analysis, and other scientific methods to study and prepare patterns and insights analyzing raw data collected from the web, clients, devices, sensors, etc., in different formats like text, images, audio, or videos.

In the trading world, data science is used to study market volatility, optimize portfolios, manage risks, and as a critical factor in making investment decisions. The fund managers use data science, AI and Machine Learning to implement clear and compelling analytical decisions based on the collected data about the market rather than exerting a random judgment.

The Best Data Science Applications for Hedge Fund Management

Setting up or managing a quantitative hedge fund requires the flow of information and proper analysis to make decisions. Several applications and data science tools are used in hedge fund management, and we will discuss a few of them here.

Risk Management:

Using data science to analyze, identify, and predict subjective risks and tailoring decisions for future market trends significantly decreases the risk of squandering capital.

Through studying consumer behavior data, market trends data, market price fluctuations data, and economic data, an analyst can build an advanced risk management strategy that can predict the potential hazards in the market and react to avoiding or lowering the risk.

So, using data science in risk management for a hedge fund may influence customer trust, secure investments, and lead to profit.

Consumer Analysis:

Studying the customer’s real-time insights, their account, financial institution, values, and identities can open new opportunities for their service. They can use such data science analysis to make better decisions related to customer behavior and trading patterns.

Algorithmic Trading:

Algorithmic trading implants instant and accurate order placement, automatically determining the trade timing to evade price fluctuations to make the trade profitable and risk-free investment. And the entire process depends on fascinating automatic data analysis based on the trade pricing, volume, trading time, etc.

The Best Data Science Tools for Hedge Fund Management

Here are some of the basic data science tools for Hedge fund management:

Trading Platforms with Integrated Analytical Systems

Your trading platform is the most primary instrument for your hedge fund trading. Thus, select your trading platform wisely. The most pleasant option is choosing a trading platform with integrated data science tools to monitor the fund's progress and technical analytics.

MetaTrader 5

The trading platform offers an automated technical analysis opportunity and trading operations that assist you in managing your fund. Besides, you get a single exchange terminal with integrated risk management and data analytics. Furthermore, If you look for algorithmic trading opportunities, you'll get to customize your trading robots specialized in MQL5 IDE supporting Python, R, and other efficient languages.

Bloomberg Terminal

The Bloomberg terminal provides you a proficient cover of the market and securities along with information across different classes. Asides it offers different cutting edge technology tools to monitor the portfolio, market indications, real-time global financial data, news feed texts and so many more. Also, the investors can use the Bloomberg trading terminal trading systems to facilitate financial transactions.

Analytics and Machine Learning Tools:

R Language: R language provides different objects, functions, and operators vital in modeling and visualizing data. Besides, it can handle, store and analyze data needed for hedge fund analytics and statistical monitoring.

Python: Python is the most common name in data science technology. However, it is easy, flexible, and open source makes it a vital instrument in hedge fund management. For example, Python is vividly used for fundamental research, data gathering and processing, backtesting, and data analysis.

Market Volume Tools

There are tools to maintain scalability and keep regular records of the data. Considering the file size capability, you should choose the tool from the list. If you are handling data less than 10 GB, here are some of the choices for you:

Microsoft Excel: One of the most popular and most accessible to use data handling tools. It can handle 16,380 columns at the time and a shade over one million rows. For the entry phase, this Microsoft Excel should be a great choice in data handling.

SQL: If you want to use the most popular data management for a few decades, SQL is the tool for you. Despite being so popular, it has the drawback of being difficult to scale when it grows big.

If you are handling data more than 10 GB, here are the best choices for you:

Hadoop: It allows the processing of large and complex data sets over computers utilizing simple programming modules. In addition, it can scale up one single server to a few thousand servers.

Hive: Considering other tools, Hive is a warehouse of data built on the top of the Hadoop. It can store data like SQL and query and call data as SQL database file systems.

Business Intelligence Tools

Business intelligence tools allow you to run different types of hedge fund trading analysis, starting on data analytics, MIS, visible dashboard, and numerous regular business information analysis both online and offline.

QlikView: It is an analytics solution to analyze all your fund data sources within a few clicks. It compresses data and keeps them in the memory. Hence repetitious users can utilize the data instantly.

Tableau: Tableau is one of the finest data analysis tools in modern computing. It converts big data to small data and makes small data insightful and actionable.

Bottomline

We must be acquainted with the fact that there is no particular method of trading that applies particularly to hedge funds. DIfferent fund managers employ their exceptional understanding, knowledge, and skills about the market. Likewise, the implementation of data science in hedge funds entirely depends on the fund managers themselves. Hence, the corresponding data science applications or tools may provide inconsistent results for separate funds.

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