What is a high speed data stream?
High speed data streams, which transmit data such as quotes and returns in real time and without delay, are used in high frequency trading for real time data analysis.
Key points to remember
- High-speed data flows provide computerized algorithmic traders with faster and more reliable data.
- Lawyers claim that HFT has a beneficial role in the market, deepening market liquidity and valuing securities more efficiently than other intermediaries, and lowering transaction costs for everyone by narrowing spreads.
- The stock market now consists of a vast, fragmented network of interconnected and automated trading systems.
How a high speed data stream works
High-speed data flows provide computerized algorithmic traders with faster and more reliable data. Because high frequency trading (HFT) is spurred by faster access to data, there has been a technology arms race as data flows and transactions approach the speed of light. The HFT creates natural monopolies in market data, which critics say has given high-frequency traders an unfair advantage over institutional and retail investors.
Lawyers claim that HFT has a beneficial role in the market, deepening market liquidity and valuing securities more efficiently than other intermediaries, and lowering transaction costs for everyone by narrowing spreads. To maintain a fair and orderly market, the New York Stock Exchange introduced designated market makers in 2008 to facilitate price discovery and provide liquidity to institutional and retail investors, largely electronically via HFT.
The HFT industry has used many controversial predatory trading practices – like our guide to HFT terminology – like front running, where traders detect incoming orders and jump before them before they can be executed. Investors say that because there are so many HFTs on the market, it reduces long-term returns because they take a share of the profits.
Traders in banks and institutions began to see the effects of HFT on their large orders in the 2000s. Traders began to notice how their order flow seemed to be exploited, as stocks would rise immediately after a trader started buying the stock. This forced institutional investors to hunt the stock to fill up. HFT companies would see the demand for order flow and buy stocks ahead of time in hopes of reselling the stocks to the investor at a higher price. It wasn’t until years later that many investors learned exactly what was going on, so they had to learn to manage HFTs in the years that followed.
High-speed data flows are here to stay
The stock market now consists of a vast, fragmented network of interconnected and automated trading systems. High frequency transactions, characterized by high speeds, ultra-short holding periods and high trading order ratios, represent a significant portion of the volume of transactions in US stocks, although this share has declined somewhat to around 50%. Lower volumes, low market volatility and higher regulatory costs have squeezed HFT margins and led to consolidation in the industry.
To solve the problems of market competition, regulators have introduced retarders, which randomize entry times and introduce processing times for random orders. After the new IEX exchange introduced its alternative trading system, which slows orders by 350 microseconds to neutralize the advantage of high-frequency traders, the New York Stock Exchange followed suit in 2020 when it traded for small and medium caps.
Bloomberg’s B-PIPE data stream, the corresponding binary multicast stream from Thomson Reuters and Ultra from EBS Brokertec are examples of high-speed streams, which provide investors and providers with extremely low latency market data – the time that elapses from the moment a signal is sent upon receipt.