As per Ruey S. Tsay et al., Time-series data gathered at a very fine scale is referred to as high-frequency data. Recent advances in computing power have made it possible to correctly and quickly collect high-frequency data for processing. High-frequency data offers intraday insights that may be utilised to comprehend market behaviours, dynamics, and microstructures. It is widely used in financial analysis and high-frequency trading for basing financial and trading decisions.
As per Nikolaus Hautsch, Tick-by-tick market data, in which each individual "event" (transaction, quotation, price change, etc.) is described by a "tick," or one logical unit of information, was first used to mass produce high-frequency data sets. Great frequency data collections often include a lot of data since there are so many ticks in a day, which allows for high statistical accuracy. A day's worth of high-frequency observations on a liquid market may be equivalent to 30 years' worth of daily data. This large data set helps traders and high-frequency trading bots make trading decisions at lightning speed with great accuracy.
High-frequency data has become considerably more accessible due to the development of electronic trading methods and Internet-based data providers, and it is now possible to watch price formation in real-time. Consequently, a significant new area of high-frequency data study has emerged, where academics and researchers exploit the features of high-frequency data to create models suitable for forecasting future market movements. Numerous market characteristics, such as volume, volatility, price movement, and placement optimization, are covered by model forecasts, forming the underlying data set for such models.
The use of transaction data and limit order book data, which have broader implications for trade and market behaviours as well as market outcomes and dynamics, is of continued interest to regulatory bodies and academics. Because liquidity and price concerns are not completely understood in terms of emerging types of automated trading apps, regulatory bodies are quite interested in these models to understand how to regulate such automated trading algorithms.
Studies using high-frequency data are valuable because they can track erroneous market activity over time. This information makes a better comprehension of pricing, trade activity, and behaviour possible. High-frequency data must be analysed using point processes, which rely on observations and history to describe random occurrences of events, due to the significance of timing in market events.
Auros, a company specialising in algorithmic trading and market making, and Pyth Network will provide access to high-frequency data in real-time to blockchain protocols. Auros' sophisticated high-frequency trading system will provide Pyth with price information for various cryptocurrencies.
The institutional trading activity is being brought on-chain by Pyth's network with over 70 data sources, making it the premier oracle solution for latency-sensitive financial data. Auros's best-in-class price data will strengthen Pyth's capacity to provide aggregated data to various blockchain protocols. To guarantee pricing is responsive to market changes in microseconds, Auros gathers data from many different sources and filters it for quality and accuracy. The company's data is fundamental to its key offerings, from providing long-term liquidity for joint ventures to supporting its high-frequency trading and arbitrage operations. Auros has incorporated over 60 controlled and decentralised exchanges, allowing it to control a large portion of daily global trade and amass over $1.5 trillion in trading volume.
More than 90 price feeds for cryptocurrencies, equities, foreign exchange, and metals are aggregated and published by Pyth in sub-second time intervals, with the data made accessible through the Wormhole messaging protocol for use by blockchain protocols. This technological development is set to make the cryptocurrency market more mature and institutionalized with a focus on developing models that are developed on rich data models updated at a sub-second speed.
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With a daily notional turnover in the billions of dollars, Auros is a market-making and algorithmic trading organisation created by derivatives traders and trading system builders with over 20 years of expertise. The technical pedigree of Auros incorporates complex pricing models with cutting-edge execution capabilities, guaranteeing dependable trading results. Their innovative method of forming strategic alliances to provide access to external finance for token projects has quickly made them a market leader in the space.
The Pyth Network is a specialized oracle solution for latency-sensitive financial data that is typically kept behind the “walled gardens’’ of centralized institutions. Pyth is focused on finding a new and inexpensive way to bring this unique data on-chain and aggregating it securely. The Pyth Data Association was created in support of the Pyth Network and is overseen by a board of directors elected by members of the Pyth Network.
In my opinion, high-frequency data for blockchain protocols will be a game changer for brokerage firms, quantitative traders, market makers and cryptocurrency exchanges who would be able to provide real-time data to their clients and make trading decisions based on rich data sets. This move may increase the liquidity of cryptocurrencies with more market makers providing their services based on such high-frequency data models.
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