Many quantitative trading styles require a significant amount of market data and financial engineering.
Algorithmic trading strategies use market data to make near-instantaneous decisions about buying or selling assets, project pricing trends, and calculate market risk. How quant traders select their market data providers can make a difference to the success of their strategies and long-term prospects.
Delivery of market data from exchanges to traders is highly time sensitive. Nanoseconds can matter when executing ultra-low-latency trading strategies. Understanding how to choose a data provider can be a critical decision that significantly affects a trader’s business operations and overhead.
Market data is like a race car. The faster you want to go, usually the more expensive it will be. Let’s look at how market data is structured, the types of market data, why market data can be a significant operational issue for quantitative trading strategies, and why your broker can help you determine what best fits your market data needs.
Types of Market Data
Nearly 20 U.S. exchanges provide algorithmic traders with real-time market data. Each exchange will have an order book for each instrument that features all the orders at any given time, ranked according to various criteria, such as time received, bid/offer levels, or quote amounts.
The most common type of order book is a central limit order book, where Level 1 data refers to the latest or best bids and offers in the order book, sometimes referred to as the “top of the book.” The top of the book is where you’ll find the highest bid and lowest ask prices. These point to the predominant market and price that need to get an order executed.
Level 2 data typically refers to all the other data in a book. Upward and downward moves in prices constitute “ticks.” Tick data is generally used by quantitative analysts in models, such as when a trading strategy is backtested. Market data vendors may provide level 1 market data, level 2 market data, or both.
The way you set up your market data can greatly affect the performance of an algorithmic trading strategy. For instance, if a trader has a low-latency strategy on Nasdaq, they likely would benefit from direct access to Nasdaq’s matching engine and subscribe to the exchange’s market data feed. Otherwise, the slower data speeds will limit the strategy’s effectiveness.
How Market Data Is Structured
Market data falls into two broad categories: raw market data and normalized market data.
• Raw market data is generated by exchanges, such as the New York Stock Exchange, and other organizations. For example, the NYSE captures one terabyte of information each day. Raw feeds are essentially a massive data firehose put out by the exchange and received by the trader. As a result, with a raw market data feed, algorithmic and quant traders either need to process the data themselves to make it useful for their trading strategies or use normalized data.
• Normalized market data is produced by market data vendors by simplifying the various raw feeds into one protocol, making the data simpler, less costly, and easier to manage for clients. A normalized market data feed from some vendors can add latency to the trading strategy, which can hurt the performance of high-frequency trading strategies.
Most exchanges will provide a market data vendor list with details of the various products they offer, but it can be difficult for traders to navigate without a trusted partner.
Market Data Is a Consideration Cost to Algorithmic Trading
Market data costs can be substantial for quant traders. For example, a quant trader can easily spend $25,000 per month on market data alone.
Nearly 80% of systematic and algorithmic fund managers said they expected to increase their budget for market data over the next few years, according to a survey by technology provider SigTech. One in five quants said they are prepared for a significant rise in market data spending.
The depth and breadth of market data has expanded rapidly, as alternative data, with its plethora of sources, has entered into many algorithmic models. In fact, 84% of quants surveyed by SigTech said that they work with at least four data sources.
Partner With Experts Who Can Improve Your Performance
Quant traders have enough to do without worrying about the rising cost of high-quality and high-speed market data. They need a broker that understands the challenges of algorithmic trading.
Lime Financial is both a technology company and a broker-dealer focused on serving the market data needs of its clients. For nearly 20 years, Lime Direct has helped quants connect with a fast, reliable, direct market access gateway to the U.S. equity and options markets.
The specialists for Lime Direct guide quant traders through the market data minefield to find the best configuration for their strategies.
As a Lime Direct user, you can use normalized Nasdaq market data at a discount. For example, market data costs can be as low as $1,000 per month instead of $10,000 per month or more. Discounted market data is available to clients who use Lime Direct’s execution services or its Virtual Private Server, which allows you to test your algorithmic strategies while cost-effectively scaling your business.
Tom Anderson is the editor-in-chief of Lime Insights. His work has appeared in CNBC.com, Forbes, Kiplinger’s Personal Finance, Money, Monocle, and Wired. He was a 2008-09 Knight-Bagehot Fellow in Economics and Business Journalism at Columbia University.