ENTERPRISE DATA WAREHOUSING
A storehouse for your proprietary data
A storehouse for your proprietary data
With an ever-expanding set of data and analytics creating a greater challenge to find alpha, quants demand accurate time series data and can benefit from removing data management responsibilities. Enterprise data warehousing is a valuable well of efficiency companies can pull from in such times.
Big Data is nothing new to the financial services industry as the markets produce over 50 TB of data per day. The ability to collect and aggregate the data once for all use cases is a daunting challenge as each application requires different fields and frequencies of the data
OneTick is the ideal solution for the Enterprise which is seeking to aggregate and manage their time series data and analytics as an enterprise service, enabling traders, analysts, wealth managers and clients to access the same content delivered with the specific analytics relevant to each application.
Quants apply an empirically-tested and rules-based approach to exploit perceived market inefficiencies manifested by human behavior, geo-political events and market structure. With tighter spreads, thinner margins and lower risk appetite, quantitative traders are exploring more cross asset trading models and cross asset hedging. Consequently, the quest for new and revised models is never ending. The side effect of this is increasing demands for deep data over longer time periods across a multiplicity of markets -equities, futures, options and of course cross border currencies. This data dump is the fuel feeding automation technology, quant’s research and strategy modeling tools. That technology plays a critical role in the trade lifecycle. Its fast paced evolution goes hand-in-hand with innovations in trading.
Data accuracy is vital to determining outcomes; asset prices cannot be inaccurate or missing. It means dealing with the vagaries of multiple data sources, mapping ticker symbols across a global universe, tying indices to their constituents, tick-level granularity, ingesting cancellations and corrections, inserting corporation action price and symbol changes and detecting gaps in history. Any and all of these factors are vital to the science of quantitative trade modeling. With over five billion options contracts traded in 2014, the reliability of the resulting analytics such as implied volatility, delta and gamma for option strategies depend on underlying data accuracy and reliability. Big Data is about linking disparate data sets under some common thread to tease out intelligible answers to drive the creation of smarter trading models.
Enable Effective Signal Generation
Today’s markets are pervaded by economic turmoil and regulatory overhang which have wrought havoc for some time. No longer can this be seen as a phase in the rapidly changing world of algorithmic trading - it is the new normal, and to navigate these volatile seas demands the ability to process massive amounts of data rapidly and efficiently, and, beyond that, to make sense of it all.
OneTick has been adopted by Algo trading leaders across four continents precisely because of its ability to do so. OneTick is an enterprise solution capable of capturing, storing and analyzing Big Data across any asset class, even the massive Options OPRA feed, which has spewed as many as four million messages a second. But it is also a focused solution for financial big data providing the scalable database, the analytics functions and user tools to uncover the narrative in the big data dump. Combined, these benefits form a potent tool for finding alpha and developing and testing strategies for algorithmic trading in today’s turbulent markets.
Data collection and aggregation can no longer reliably provide financial institutions with a competitive edge as each firm replicates the same collection of sources and formats for use in common applications. Customers bear the overhead of managing the collection for historical purposes and must dedicate skilled technical resources to maintain the environment. Public and private cloud platforms are increasingly being used by financial institutions to reduce costs and increase agility to address rapidly changing market conditions.
Optimize Strategy Profitability
Research the quest for knowledge, an empirical investigation to discover the truth, establish factual evidence opinion aside, to solve existing problems and provide insight into new theories. With technological automation advancing rapidly the need to understand market dynamics, market micro-structure, the influences of geopolitical economics and central bank policy is more important than ever. The tumultuous market turmoil from events such as Knight Capital’s software glitch has sent jitters through market participants and motivated a renewed interested in strategy testing.
Backtesting is to measure profitability as strategies are optimized – this could be targets for profit factor, Sharpe ratio, max drawdown or an equity curve to improve the quality of execution decisions. Historical data sets provide “what if” market conditions as strategy parameters are varied.
Analyzing historical time series data is to use observations from the past to characterize data relationships. Those historical relationships can explain future developments with an assumption that the future behaves like the past. This forms the basis of backtesting trade models.
One Tick provides a fully featured strategy development and large-scale backtesting platform for the most discerning quant: