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©2012- Proprietary and Confidential Information of FINCAD 1
Scaling Financial Analytics from the Desktop
to the Cloud
Dr Marc Vlitos
©2012- Proprietary and Confidential Information of FINCAD 2 ©2012- Proprietary and Confidential Information of FINCAD 2
Introductions Desktop to Cloud (a stepwise
approach) Discussion
©2012- Proprietary and Confidential Information of FINCAD 3 ©2012- Proprietary and Confidential Information of FINCAD 3
Introductions - About FINCAD
Desktop to Cloud (a stepwise approach)
Discussion
©2012- Proprietary and Confidential Information of FINCAD 4
• Founded in 1990 • The value leader for Derivatives and Fixed Income Analytics solutions
for valuation, pricing and risk analysis - Comprehensive cross-asset class analytics - Industry standard financial models - Proven, accurate and trusted
• Global brand and market penetration - 4000+ clients - 80+ countries
• Diversified client base across multiple segments • HQ in Vancouver with a sales & support office in Dublin and channel
relationships in Asia
©2012- Proprietary and Confidential Information of FINCAD 5
• Banks - 8 of the top 10 banks globally (by total Assets, 2011)
• Hedge Funds: - 7 of the 10 largest hedge funds globally (by total AUM 2010)
• Insurance companies - 5 of the top 10 insurance companies globally (by 2009 revenues)
• Corporates - 6 of the top 10 Fortune 500 companies
• FINCAD Alliance Program: - 70 technology and service vendors; OEM, Solution, Service partners
- Top 3 most-used OMS systems; Fidessa, Eze Castle, Charles River
• Auditing Firms - The big 4 auditing firms globally
©2012- Proprietary and Confidential Information of FINCAD 6
• Alliance Capital, Desjardins, Fidelity Investments, HSBC, Investors Group, Massachusetts Financial , OTTP
Asset Managers
• Blackstone, Balyasny, Caxton, Citadel, Fortress, GLG, Moore Capital, Renaissance, SAC
Hedge Funds
• AIG, Great West Life, London Life, Pacific Life, Prudential Insurance, Sun Life, Standard Life
Insurance Companies
• Used by most leading Investment Banks
Investment Banks
• ABN Amro, Bank of Montreal, Citibank, Credit Suisse, Credit Lyonnais, Deutsche Bank, ING, JP Morgan, RBC, UBS
Commercial & Retail Banks
• Asian, African and European Development Banks, Bank of Canada, Bank of Greece, Banca d’Italia, BIS, International Finance Corp, World Bank, US Federal Reserve
Central Banks
©2012- Proprietary and Confidential Information of FINCAD 7
• Alcoa, Bombardier , Canadian Pacific, Dell, Diageo, IBM, Intel, McDonalds, Microsoft, WalMart Duke Energy, PG&E, Reliant Energy, Suncor, TXU Energy,
Corporate Treasuries
• BDO Dunwoody, Deloitte Touche, Ernst & Young, Grant Thornton, KPMG, McKinsey & Co, PricewaterhouseCoopers
Professional Services firms
• Governments of United Kingdom, Colombia, State of New York, Provinces of Alberta, BC, Quebec, Saskatchewan
Govts. & Agencies
• Moody’s Analytics, SAS, Simcorp, SS&C, Fidessa, Infosys, MarketAxess, Misys, Citco Fund Services, Butterfield Fulcrum, Charles River Development, DST, Eze Castle, Tradar , Asset Control, Techila
Alliance Partners
©2012- Proprietary and Confidential Information of FINCAD 8 ©2012- Proprietary and Confidential Information of FINCAD 8
About FINCAD Desktop to Cloud (a stepwise
approach) Discussion
©2012- Proprietary and Confidential Information of FINCAD 9
Separation of concepts: • Generic model free description
of financial contracts
• Model of ‘my’ financial world
• Configurable calculation
• Values on request
9
©2012- Proprietary and Confidential Information of FINCAD 10
Generic Product Description: • Legal agreement between
collection of parties (usually 2)
• Terms of agreement are expressed in a document (the term sheet)
• Define rights and obligations
• Product encapsulates those rights and obligations
• A portfolio is a collection of products
10
The Product Concept • Obligations to make payments of cash or other
assets and
• Rights to other products
The Index Concept • The definition of a quantity on which a payment
in contingent
©2012- Proprietary and Confidential Information of FINCAD 11
Arbitrage-free Financial Model: • Contains a snapshot of all
relevant market quotes to which model parameters are to be calibrated.
• Pay-as-you-go calibration (i.e. lazy evaluation)
• Consistent view
• Arbitrage-free – one and only one version of artifacts
11
The Model Concept
• A consistent, arbitrage-free view of the market in which a given collection of Products is to be traded and hedged, at the time of valuation
©2012- Proprietary and Confidential Information of FINCAD 12
Valuation Method (AKA ValSpec): • Define how:
• Monte Carlo • Closed-Form • Backwards Evolution
• Define where:
• Local (on my machine) • Off-load to server • Distribute to Cloud • Blended approach
• Multithreaded distributed
computing
• Automatic simulation generation
12
The Valuation Method Concept
• How valuation is to proceed, numerically
©2012- Proprietary and Confidential Information of FINCAD 13
Output Request: • Configurable based on:
• Need • Product Type
• One and only one valuation
function
13
Output Request Concept
• List of valid value and information requests for a given combination of product, model and valuation specification
©2012- Proprietary and Confidential Information of FINCAD 14
Start with Microsoft® Excel MATLAB® and/or R also common Main advantages: • Low Cost of Entry
• Ease of Deployment
• Great Flexibility
• ‘Loved’ by Traders
• Adequate performance
14
©2012- Proprietary and Confidential Information of FINCAD 15
Performance becomes an issue with: • Large data sets
• Complex analysis required
• Need to run simulations
• Some things are slow
• Others VERY slow
Eventually you hit the ‘brick wall’
15
©2012- Proprietary and Confidential Information of FINCAD 16
Offload calculations: • Keeps same user interface
• Can be blended with local
processing
• Relatively simple
• Often all that is needed
16
Offload Calculations
User
Analytics Calculation
Worker
Platform Controller
Analytics Calculation
Worker
Call Result
©2012- Proprietary and Confidential Information of FINCAD 17
But it has limitations: • Synchronous interface
• Concurrency can be an issue
• Larger data sets still a problem • Limited ability to share results
17
Offload Calculations
Platform Controller
Analytics Calculation
Worker
User
Analytics Calculation
Worker
Call Result
User User
©2012- Proprietary and Confidential Information of FINCAD 18
The in-memory cache: • Asynchronous interface
• Non-blocking
• Improves computational
efficiency - reducing repetition • Can deploy cache
independently
• Improved concurrency
18
Users
Analytics Calculation
Worker
Call Notify
In-memory Cache
Result
Offload Calculations
Platform Controller
Analytics Calculation
Worker
©2012- Proprietary and Confidential Information of FINCAD 19
Once again it has limitations : • Limited options scaling the
calculation server
• Concurrency still an issue
• Larger data sets still a problem
19
Users
Analytics Calculation
Worker
Call Notify
In-memory Cache
Result
Offload Calculations
Platform Controller
Analytics Calculation
Worker
©2012- Proprietary and Confidential Information of FINCAD 20
Distributed Calculations: • Asynchronous interface
• Non-blocking
• Introduces a broker
• Broker distributes work to
multiple works
• Improved concurrency
• Can scale to handle: • Intensive calculation • Very large data sets
20
Users
Broker
Call Notify
In-memory Cache
Calculation Worker
Calculation Worker
Calculation Worker
Calculation Worker
Call Notify
©2012- Proprietary and Confidential Information of FINCAD 21
Distributed Calculations: • Data distribution overhead
• Some calculations do not
parallelise
21
Users
Broker
Call Notify
In-memory Cache
Calculation Worker
Calculation Worker
Calculation Worker
Calculation Worker
Call Notify
©2012- Proprietary and Confidential Information of FINCAD 22
Automatic ‘Sharding’: • Local calculation where
appropriate
• BUT STILL • Non-blocking • Introduces a broker • Broker distributes work
to multiple works • Improved concurrency • Can scale to handle:
• Intensive calculation
• Very large data sets
22
Users
In-memory Cache
Broker
Call Notify
Calculation Worker
Calculation Worker
Calculation Worker
Calculation Worker
Call Notify
Calculation Worker
©2012- Proprietary and Confidential Information of FINCAD 23 23
Portfolio of n trades
Broker n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
Calculation Worker
Model(s)
Mar
ket
Dat
a an
d C
alib
rati
on
s
The data problem: • Marshalling data takes time
• Significant overhead in
distribution
• Most data is held in legacy systems (RDBMS)
• Added complexity that data comes from multiple sources
• Model calibration can be 60% or more of total
©2012- Proprietary and Confidential Information of FINCAD 24
Data Storage
Calculation Services In-memory
Risk architecture: • Move data in-memory
• Event stream processing for
market data and trades
• Cache all results
• Keep calculations local
• Cloud on demand
• Long term storage provided by physical disk
24
Portfolio of n trades
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
n/m
Model(s)
Mar
ket
Dat
a an
d C
alib
rati
on
s
Results
result
result
result
result
n/m
result
result
result
n/m
result
result
result
Broker
Call Notify
CalculationWorker
CalculationWorker
CalculationWorker
CalculationWorker
Call Notify
CalculationWorker
Stream Market / Trade Data With CEP
©2012- Proprietary and Confidential Information of FINCAD 25
High Performance EAP • Designed for emerging
compute-intensive requirements
Smart Platform Services
• Automatic sharding • Calculations performed in
the most appropriate place Seamless Cloud Integration
• Cloud services are an extension of the platform
Seamless User Experience
• Not just for developers
25
Calculation
Server
Calculation
Server
Cache Server
Calculation
Server
Calculation
Server
Market Data
Gateway
Calculation
Server
Calculation
Server Repository
Platform Controller
Calculation
Server
Calculation
Server
Calculation Server
Users
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