(Edwin Hautus) A DSL for normalization of financial data sets

Report
The Science of Finance
Case study: A DSL for normalization of
financial data sets
Software Development Automation 2014
Edwin Hautus
Agenda
— Problem Domain
— First iteration
— Second iteration
— Lessons learned
\
2
Problem Domain
We are a leading global diversified provider
of financial information services
2003
Founded
3,000+
We help our customers reduce risk, improve
operational efficiency and benefit from
enhanced transparency
Employees
3,000+
Customers
20+
Our customers include investment banks,
hedge funds, asset managers, central banks,
regulators, auditors, fund administrators and
insurance companies
Offices
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3
Problem Domain
Input
Input
Normalized
Model
Input
Database
Output
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4
Problem Domain
Normalization questions
— Identification - is it the same object?
— Field mapping - do these fields have the same semantics?
— Structural changes – how can the data be transformed?
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5
Problem Domain
Workflow & Roles
Data
Analyst
Input
Specification
Developer
Normalization
Specification
Software
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6
Problem Domain
Problem Statement
Define a framework to improve efficiency and quality of normalization
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7
First iteration
First iteration (2007)
— Standardize writing of normalization specifications
— Create a domain model to describe the data structures
— Create a XML-based DSL to define mappings
— Use a runtime engine to execute the mappings
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8
First iteration
Results
— Good improvement in efficiency and quality
— Applied in over 75 projects
— The XML DSL is too technical for data analysts
— Quite a few iterations required to make sure mappings meet the
requirements
— Mappings can become really complicated!
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9
Second iteration
Second Iteration (2013)
— Have analysts write mappings directly
— Create parsed language to replace XML (based on ANTLR)
— Simplify business logic
— Allow analyst to verify mappings with runnable tests before
handover
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10
Second iteration
Field name mapping
input Bond bond
Instrument
Bond
isin
isinCode
Instrument instrument =
from bond
{
isin = isinCode
}
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11
Second iteration
Field mapping with simple transformation
input Sedol sedol
InstrumentType
Sedol
cfi
cfiCode
InstrumentType instrumentType =
from sedol
{
cfi = cfiCode.substring(0,2) + “XXXX”
}
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12
Second iteration
Field mapping via map
input Anna anna
Anna
InstrumentType
InstrumentType instrumentType =
debtEquityCode
cfi
from Anna
{
cfi = debtEquityCodeToCfi[debtEquityCode]
Map
}
Map debtEquityCodeToCfi =
{
"D"
= "DBXXXX"
"E"
= "ESXXXX"
>> "MMXXXX“
}
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13
Second iteration
Structural change: unfold
input MarkitMap markitMap
Company company =
Company
MarkitMap
from markitMap
{
lei
lei
from lei
{
lei = leiCode
Lei
}
leiCode
}
\
14
Second iteration
Structural change: fold
input Company company
MarkitMap
Company
MarkitMap markitMap =
Lei lei
lei
from company
{
lei =
Lei
{
leiCode
leiCode = lei
}
}
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15
Second iteration
Structural change: fold list field
input Instrument instrument
Sedol
Instrument
Sedol sedol =
details[]
sedols[]
from instrument
{
details =
SedolDetails
from sedols
sedolCode
{
sedolCode = it
actionIdentifier
actionIdentifier = “I”
}
}
\
16
Second iteration
Conditional mapping
input Bond bond
Instrument instrument =
from bond
{
identifier
if isin
Bond
Instrument
?
{
isin
identifier = isin
}
cusip
else
{
identifier = cusip
}
}
\
17
Second iteration
Annotations
input Bond bond
Instrument
Bond
isin
isinCode
name
name
Instrument instrument =
from bond
{
isin = isinCode
@Manual
name = name
}
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18
Second iteration
Additional language features
— Asserts
— Functions
— Imports
— Comments
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19
Lessons learned
Lessons learned on DSL development
— Using a syntax based approach is worth the effort
— DSLs need to be periodically refined as you learn about the domain
— Provide integrated testing solution
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20
Lessons learned
Key factors in adoption of the DSL by business users
— Requires more effort from users in order to be precise
— Provides feeling of empowerment
— Immediate feedback loop through test functionality is crucial
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21
mines data
pools intelligence
surfaces information
enables transparency
builds platforms
provides access
scales volume
extends networks
& transforms business.
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