In finance, market data is price and other related data for a financial instrument reported by a trading venue such as a stock exchange. Market data allows traders and investors to know the latest price and see historical trends for instruments such as equities, fixed-income products, derivatives, and currencies. [1]
The market data for a particular instrument would include the identifier of the instrument and where it was traded such as the ticker symbol and exchange code plus the latest bid and ask price and the time of the last trade. It may also include other information such as volume traded, bid, and offer sizes and static data about the financial instrument that may have come from a variety of sources. It is used in conjunction with the related financial reference data that is typically distributed ahead of market data. There are a number of financial data vendors that specialize in collecting, cleaning, collating, and distributing market data and this has become the most common way that traders and investors get access to market data. [1]
Delivery of price data from exchanges to users, such as traders, is highly time-sensitive and involves specialized technologies designed to handle collection and throughput of massive data streams are used to distribute the information to traders and investors. The speed that market data is distributed can become critical when trading systems are based on analyzing the data before others are able to, such as in high-frequency trading. [2]
Market price data is not only used in real-time to make on-the-spot decisions about buying or selling, but historical market data can also be used to project pricing trends and to calculate market risk on portfolios of investments that may be held by an individual or an institutional investor.
A typical equity market data message or business object furnished from NYSE, TSX, or NASDAQ might appear something like this:
Ticker symbol | IBM |
Bid | 89.02 |
Ask | 89.08 |
Bid size | 300 |
Ask size | 1000 |
Last sale | 89.06 |
Last size | 200 |
Quote time | 14:32:45.152 |
Trade time | 14.32.44.096 |
exchange | NYSE |
volume | 7808 |
The above example is an aggregation of different sources of data, as quote data (bid, ask, bid size, ask size) and trade data (last sale, last size, volume) are often generated over different data feeds.
Delivery of price data from exchanges to users is highly time-sensitive. Specialized software and hardware systems called ticker plants are designed to handle collection and throughput of massive data streams, displaying prices for traders and feeding computerized trading systems fast enough to capture opportunities before markets change. When stored, historical market data is a type of time series data.
Latency is the time lag in delivery of real-time data, i.e. the lower the latency, the faster the data transmission speed. Processing of large amounts of data with minimal delay is low latency. The delivery of data has increased in speed dramatically since 2010, with "low" latency delivery meaning delivery under 1 millisecond. The competition for low latency data has intensified with the rise of algorithmic and high frequency trading and the need for competitive trade performance.
Market data generally refers to either real-time or delayed price quotations. The term also includes static or reference data, that is, any type of data related to securities that is not changing in real time.
Reference data includes identifier codes such as ISIN codes, the exchange a security trades on, end-of-day pricing, name and address of the issuing company, the terms of the security (such as dividends or interest rate and maturity on a bond), and the outstanding corporate actions (such as pending stock splits or proxy votes) related to the security.
While price data generally originates from the exchanges, reference data generally originates from the issuer. Before investors and traders receive price or updated reference data, financial data vendors may reformat, organize, and attempt to correct obvious outliers due to data feed or other real-time collection based errors. [3]
For consumers of market data, which are primarily the financial institutions and industry utilities serving the capital markets, the complexity of managing market data rose with the increase in the number of issued securities, number of exchanges and the globalization of capital markets. Beyond the rising volume of data, the continuing evolution of complex derivatives and indices, along with new regulations designed to contain risk and protect markets and investors, created more operational demands on market data management.
Initially, individual financial data vendors provided data for software applications in financial institutions that were specifically designed for one data feed; thus, giving that financial data vendor control of that area of operations. Next, many of the larger investment banks and asset management firms started to design systems that would integrate market data into one central store. This drove investments in large-scale enterprise data management systems which collect, normalize and integrate feeds from multiple financial data vendors, with the goal of building a "single version of the truth" of data repository supporting every kind of operation throughout the institution. Beyond the operational efficiency gained, this data consistency became increasingly necessary to enable compliance with regulatory requirements, such as Sarbanes Oxley, Regulation NMS, and the Basel 2 accord. [4]
There are various industry bodies that focus on market data:
The business of providing technology solutions to financial institutions for data management has grown over the past decade, as market data management has emerged from a little-known discipline for specialists to a high-priority issue for the entire capital markets industry and its regulators. Providers range from middleware and messaging vendors, vendors of cleansing and reconciliation software and services, and vendors of highly scalable solutions for managing the massive loads of incoming and stored reference data that must be maintained for daily trading, accounting, settlement, risk management and reporting to investors and regulators. [5]
The market data distribution platforms are designed to transport over the network large amounts of data from financial markets. They are intended to respond to the fast changes on the financial markets, compressing or representing data using specially designed protocols to increase throughput and/or reduce latency. Most market data servers run on Solaris or Linux as main targets. However, some have versions for Windows.
A typical usage can be a "feed handler" solution. Applications (sources) receive data from specific feed and connect to a server (authority) which accepts connections from clients (destinations) and redistributes data further. When a client (Destination) wants to subscribe for an instrument (to open an instrument), it sends a request to the server (authority) and if the server has not got the information in its cache it forwards the request to the source(s). Each time a server (authority) receives updates for an instrument, it sends them to all clients (destinations), subscribed for it.
Notes:
Market data requirements depend on the need for customization, latency sensitivity, and market depth.
Customization: How much operational control a firm has over its market data infrastructure.
Latency sensitivity: The measure of how important high-speed market data is to a trading strategy.
Market depth: the volume of quotes in a market data feed. [6]
There are 5 market data fee types charged by exchanges and financial data vendors. These fees are access fees, user fees, non-display fees, redistribution fees, and market data provider fees. [6]
Market data is expensive (global expenditure yearly exceeds $50 billion) and complex (data variety, functionality, technology, billing). Therefore, it needs to be managed professionally. [7] Professional market data management deals with issues such as:
Financial data vendors typically also offer mobile applications that provide market data in real time to financial institutions and consumers.
Day trading is a form of speculation in securities in which a trader buys and sells a financial instrument within the same trading day, so that all positions are closed before the market closes for the trading day to avoid unmanageable risks and negative price gaps between one day's close and the next day's price at the open. Traders who trade in this capacity are generally classified as speculators. Day trading contrasts with the long-term trades underlying buy-and-hold and value investing strategies. Day trading may require fast trade execution, sometimes as fast as milli-seconds in scalping, therefore direct-access day trading software is often needed.
A binary option is a financial exotic option in which the payoff is either some fixed monetary amount or nothing at all. The two main types of binary options are the cash-or-nothing binary option and the asset-or-nothing binary option. The former pays some fixed amount of cash if the option expires in-the-money while the latter pays the value of the underlying security. They are also called all-or-nothing options, digital options, and fixed return options (FROs).
The Bloomberg Terminal is a computer software system provided by the financial data vendor Bloomberg L.P. that enables professionals in the financial service sector and other industries to access Bloomberg Professional Services through which users can monitor and analyze real-time financial market data and place trades on the electronic trading platform. It was developed by employees working for businessman Michael Bloomberg. The system also provides news, price quotes, and messaging across its proprietary secure network. It is well known among the financial community for its black interface, which has become a recognizable trait of the service. The first version of the terminal was released in December 1982.
In finance, a contract for difference (CFD) is a legally binding agreement that creates, defines, and governs mutual rights and obligations between two parties, typically described as "buyer" and "seller", stipulating that the buyer will pay to the seller the difference between the current value of an asset and its value at contract time. If the closing trade price is higher than the opening price, then the seller will pay the buyer the difference, and that will be the buyer's profit. The opposite is also true. That is, if the current asset price is lower at the exit price than the value at the contract's opening, then the seller, rather than the buyer, will benefit from the difference.
An electronic communication network (ECN) is a type of computerized forum or network that facilitates the trading of financial products outside traditional stock exchanges. An ECN is generally an electronic system that widely disseminates orders entered by market makers to third parties and permits the orders to be executed against in whole or in part. The primary products that are traded on ECNs are stocks and currencies. ECNs are generally passive computer-driven networks that internally match limit orders and charge a very small per share transaction fee.
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.
A trading room gathers traders operating on financial markets. The trading room is also often called the front office. The terms "dealing room" and "trading floor" are also used, the latter being inspired from that of an open outcry stock exchange. As open outcry is gradually replaced by electronic trading, the trading room becomes the only remaining place that is emblematic of the financial market. It is also the likeliest place within the financial institution where the most recent technologies are implemented before being disseminated in its other businesses.
An order management system, or OMS, is a computer software system used in a number of industries for order entry and processing.
Direct market access (DMA) in financial markets is the electronic trading infrastructure that gives investors wishing to trade in financial instruments a way to interact with the order book of an exchange. Normally, trading on the order book is restricted to broker-dealers and market making firms that are members of the exchange. Using DMA, investment companies and other private traders use the information technology infrastructure of sell side firms such as investment banks and the market access that those firms possess, but control the way a trading transaction is managed themselves rather than passing the order over to the broker's own in-house traders for execution. Today, DMA is often combined with algorithmic trading giving access to many different trading strategies. Certain forms of DMA, most notably "sponsored access", have raised substantial regulatory concerns because of the possibility of a malfunction by an investor to cause widespread market disruption.
ICE Data Services was an American 1960s-founded Time-sharing services company that later became known for providing financial market data, analytics and related solutions to financial institutions, active traders and individual investors. The company's businesses supply real-time market data, time-sensitive pricing, evaluations and reference data for securities trading, including hard-to-value instruments. The company was acquired by and folded into Intercontinental Exchange in December 2015.
In finance, a dark pool is a private forum for trading securities, derivatives, and other financial instruments. Liquidity on these markets is called dark pool liquidity. The bulk of dark pool trades represent large trades by financial institutions that are offered away from public exchanges like the New York Stock Exchange and the NASDAQ, so that such trades remain confidential and outside the purview of the general investing public. The fragmentation of electronic trading platforms has allowed dark pools to be created, and they are normally accessed through crossing networks or directly among market participants via private contractual arrangements. Generally, dark pools are not available to the public, but in some cases, they may be accessed indirectly by retail investors and traders via retail brokers.
In finance, an electronic trading platform also known as an online trading platform, is a computer software program that can be used to place orders for financial products over a network with a financial intermediary. Various financial products can be traded by the trading platform, over a communication network with a financial intermediary or directly between the participants or members of the trading platform. This includes products such as stocks, bonds, currencies, commodities, derivatives and others, with a financial intermediary such as brokers, market makers, Investment banks or stock exchanges. Such platforms allow electronic trading to be carried out by users from any location and are in contrast to traditional floor trading using open outcry and telephone-based trading. Sometimes the term trading platform is also used in reference to the trading software alone.
High-frequency trading (HFT) is a type of algorithmic trading in finance characterized by high speeds, high turnover rates, and high order-to-trade ratios that leverages high-frequency financial data and electronic trading tools. While there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, co-location, and very short-term investment horizons in trading securities. HFT uses proprietary trading strategies carried out by computers to move in and out of positions in seconds or fractions of a second.
Flash trading, otherwise known as a flash order, is a marketable order sent to a market center that is not quoting the industry's best price or that cannot fill that order in its entirety. The order is then flashed to recipients of the venue's proprietary data feed to see if any of those firms wants to take the other side of the order.
A financial data vendor provides market data to financial firms, traders, and investors. The data distributed is collected from sources such as stock exchange feeds, brokers and dealer desks or regulatory filings.
MT4 ECN Bridge is a technology that allows a user to access the interbank foreign exchange market through the MetaTrader 4 (MT4) electronic trading platform. MT4 was designed to allow trading between a broker and its clients, so it did not provide for passing orders through to wholesale forex market via electronic communication networks (ECNs). In response, a number of third-party software companies developed Straight-through processing bridging software to allow the MT4 server to pass orders placed by clients directly to an ECN and feed trade confirmations back automatically.
LMAX Group is a global financial technology company which operates multiple institutional execution venues for electronic foreign exchange (FX) and crypto currency trading. The Group's portfolio includes LMAX Exchange, LMAX Global and LMAX Digital.
In finance, quote stuffing refers to a form of market manipulation employed by high-frequency traders (HFT) that involves quickly entering and withdrawing a large number of orders in an attempt to flood the market. This can create confusion in the market and trading opportunities for high-speed algorithmic traders. The term is relatively new to the financial market lexicon and was coined by Nanex in studies on HFT behavior during the 2010 Flash Crash.
Fondex Global is a Cyprus registered online brokerage that provides financial trading in contracts for difference (CFD) on the currency markets, shares, ETFs, major indices and commodities such as precious metals; gold and crude oil.
Unlisted Trading Privileges (UTP) oversees the Securities Information Processor for securities listed on Nasdaq and other securities that do not meet the requirements for listing on an exchange.