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Welcome to the final installment of our series on choosing the right data partner. In our previous post, we examined the total cost of ownership (TCO) and the complexities and expenses involved in acquiring, managing, and maintaining an options data infrastructure....

In the last installment of our five-part series on finding the right data partner we explored provider service models and stability. Now, let's delve into another crucial aspect: the total cost of ownership (TCO). Understanding TCO is vital when considering the...

The last installment of our five-part blog series focused on provider experience and expertise and how these aspects affect the quality of your data. In this installment, we will examine the importance of a provider's service model and organizational stability, and...

In the last part of our five-part series, we discussed options data considerations to take while evaluating your options data partner. In this part, we will focus on your provider experience and expertise and how these aspects affect the quality of your data.   A...

Options data is complex and multifaceted. Good options market data can make the difference between strong, profitable strategies and the rest of the pack. Your choice of data partner should be driven by a clear understanding of your organization's specific needs and...

When institutional investors entrust data to a trading platform, they must be confident it is securely and privately stored and available when needed. Due diligence demands scrutiny of third-party infrastructure and software to ensure it conforms to security, privacy,...

Portfolio margining is a margining strategy used to determine how much risk a broker can take on when lending to an investor. The margin is the collateral an investor must deposit to cover the risk of buying or borrowing financial instruments with cash borrowed from...

It is estimated that roughly 90% of trading on U.S. public markets uses algorithmic quantitative trading strategies, a number likely to grow as investors leverage technology and modeling techniques to exploit the smallest market inefficiencies.  But what is a...

Quantitative analysts and the strategies they develop are often called alpha generators. Alpha is an investment’s ability to beat the market, exploiting market inefficiencies and thereby generating excess returns relative to a benchmark index. For as long as markets...

Although the term “machine learning” was coined at IBM in the late 1950s, and the methods and models that underpin machine learning applications were developed in the following decades, only since the turn of the century has it exerted significant influence outside of...

Algorithmic trading is becoming ever more prevalent in the current market environment. Algorithmic traders use historical or live market data to discover repeatable trends and take advantage of them. Firms are now relying on machines to make seemingly instantaneous...