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Jim Foster
“Occasionally high-frequency algo traders want our historical
market data to benchmark the system speed to monitor how
fast we receive and distribute movements in the market in
comparison to other data they are monitoring”
participants that are handling significant volumes of
such data are “quite savvy” in being able to
manipulate databases. “You couldn’t exactly give this
to a total neophyte [a beginner] as they would not
know what to do with it. Algo traders and those who
have super sophisticated strategies typically know
what to do with this type of data. And, these are the
drivers for the offering.”
Minor Huffman, CTO at FXall, explains that are a
number of items that need to be considered in this
regard, including: (1) Data quality, (2) Data coverage,
(3) Analytical tools; and, (4) Hierarchical or other
customised data models.
Data quality comes in to play in respect of the number
of contributing banks or other rate sources, tradable
versus indicative data, as well as error correction
techniques used to cleanse erroneous or off-market data
from the set. Data coverage revolves around currency
pairs, spot prices and forward tenors, bid/offer rates
versus mid rates, time intervals and historical coverage.
Huffman says with regard to analytical tools required
for modelling will create specific requirements for
data availability (e.g. operating system, data format).
“Many clients will want access in order to verify and
back test algorithms, which may drive the choice of
technology used to maintain the data sets. Traditional
relational database models don’t handle time-series
data efficiently,” he adds.
70 | january 2010 e-FOREX
In terms of hierarchical or other customised data
models, the key issues are the tradability of the data
and time stamps. “If a system has a high miss rate for
trading, then the value of its data is reduced, both for
back testing of models and measuring a system,”
notes Huffman.
And, in order for the data to be meaningful it has to
represent tradable data. For example, is the data time
stamped at receipt by the platform, in the matching
engine, or at distribution? “Models will require
improvements in data access speed and increases in
data storage,” he says.
Thomson Reuters’ Doe says that in terms of storing
or sourcing the data, often when firms store it
themselves they can encounter issues around “gaps” in
the data. And, the problem is not just isolated to a
few firms. Largely it is due down to the firms’ own
collection mechanisms and where they are storing it,
he explains. That is why firms come to a source to
obtain this data (e.g. Thomson Reuters).
Cleanliness
Bloomberg’s Brittan assets that data cleanliness is the
“most important factor” in this regard and is pertinent
to all types of users. The questions here are:
• Is the data free from ‘spikes’?; and,
• Is the frequency of updates acceptable?
Minor Huffman
“If a system has a high miss rate for trading, then the
value of its data is reduced, both for back testing of
models and measuring a system”