Scaling Financial Analytics from the Desktop to the Cloud ... Vlitos_FINCAD... · Scaling Financial Analytics from the Desktop ... SAS, Simcorp, SS&C, Fidessa, Infosys, MarketAxess,

©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


Introductions - About FINCAD

Desktop to Cloud (a stepwise approach)

Discussion


• 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


• 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


• 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


• 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


About FINCAD Desktop to Cloud (a stepwise

approach) Discussion


Separation of concepts: • Generic model free description

of financial contracts

• Model of ‘my’ financial world

• Configurable calculation

• Values on request

9


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


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


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


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


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


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


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


Worker

Call Result


But it has limitations: • Synchronous interface

• Concurrency can be an issue

• Larger data sets still a problem • Limited ability to share results

17


Platform Controller


Worker

User


Worker

Call Result

User User


The in-memory cache: • Asynchronous interface

• Non-blocking

• Improves computational

efficiency - reducing repetition • Can deploy cache

independently

• Improved concurrency

18

Users


Worker

Call Notify

In-memory Cache

Result


Platform Controller


Worker


Once again it has limitations : • Limited options scaling the

calculation server

• Concurrency still an issue

• Larger data sets still a problem

19

Users


Worker

Call Notify

In-memory Cache

Result


Platform Controller


Worker


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


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


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


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


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

Documents

Scaling Financial Analytics from the Desktop to the Cloud ... Vlitos_FINCAD... · Scaling Financial Analytics from the Desktop ... SAS, Simcorp, SS&C, Fidessa, Infosys, MarketAxess,