big data for finance – challenges in high-frequency trading

16
THINK BIG INTERNATIONAL Matt Cooke - Principal Manager [email protected] om Martin Oberhuber - Senior Data Scientist martin.oberhuber@thinkbiganaly

Upload: think-big-a-teradata-company

Post on 18-Aug-2015

199 views

Category:

Data & Analytics


0 download

TRANSCRIPT

Page 1: Big Data for Finance – Challenges in High-Frequency Trading

THINK BIG INTERNATIONAL

Matt Cooke - Principal Manager

[email protected]

Martin Oberhuber - Senior Data Scientist

[email protected]

Page 2: Big Data for Finance – Challenges in High-Frequency Trading

© 2015 Think Big, a Teradata Company

WHO IS THINK BIG? © 2015 Think Big, a Teradata

Company

Page 3: Big Data for Finance – Challenges in High-Frequency Trading

THINK BIG

© 2015 Think Big, a Teradata Company

• First and leading professional services firm exclusively focused on big data

• End to End Services: Strategy, Design, Implementation, IP/Software, Support and Managed Services

• Academy to scale delivery capability

• Extend and integrate open source with UDA

• Team-based delivery with Solution Center

• Trusted Analytics Services Provider to Fortune 1000

Proven, Team-based MethodologyExperiment-Driven Short Sprints with Quick Release Cycles

We will be the trusted Big Analytics provider to the Fortune 1000and become the #1 Global Brand in Big Data analytics consulting.

Our Mission

Page 4: Big Data for Finance – Challenges in High-Frequency Trading

eCommerce2 of Global Top 5

Internet Transaction SecurityGlobal #1

Retail2 of Global Top 5

Brokerage & Mutual Funds2 of Global Top 5

Social NetworkingGlobal #1

Asset ManagementGlobal #1

Credit Issuer2 of Global Top 5

Semiconductor2 of Global Top 5

Banking4 of Global Top 10

Data Storage Devices3 of Global Top 5

Financial Data Services2 of Global Top 5

Disk ManufacturingGlobal #1

Financial ExchangesGlobal #2

Telecommunications2 of Global Top 5

Media & Advertising2 of Global Top 5

THINK BIG CLIENTSTrusted Analytics Service Provider to the Fortune 1000

© 2015 Think Big, a Teradata Company

Page 5: Big Data for Finance – Challenges in High-Frequency Trading

Think Big Academy

© 2015 Think Big, a Teradata Company

VELOCITY METHODOLGY

Managed Services

Data Engineering

Big Data Program Mgt

• Solution Focus• Planning & Design• Team Prioritization• Engineering

• Engineering• Software Dev• Agile Sprint(s)• Optimization

• Quality Assurance & Test• Managed Support• Break Fix• Sustaining Engineering

• New Models• New Analytics• New Insights• New Data Requirements

• Big Data Approach• Use Cases• Refine Roadmap• Org & Process

• Data Science• Discovery• R&D• Machine Learning

Big Data Strategy

Business Analytics

Big Data Lab

Hands on Training• Data Science• Data Engineering• Operations

Think Big engages with it’s clients business, technical, analyst and support teams in an agile inspired VELOCITY methodology to continuously develop big data solutions.

© 2015 Think Big, a Teradata Company

Page 6: Big Data for Finance – Challenges in High-Frequency Trading

ENGAGEMENT MODEL

Think Big offers end-to-end Big Data strategy, implementation and support services focused on helping customers quickly achieve ROI on their Big Data investments

STRATEGY IMPLEMENTATION SOLUTION SUPPORT

ENTERPRISE DATA LAKE SOFTWARE FRAMEWORKS

ManagedServices

Big DataAnalyticsRoadmap

Data Lake Optimisatio

n

EstablishData Lake

AnalyticSolutions

© 2015 Think Big, a Teradata Company

Page 7: Big Data for Finance – Challenges in High-Frequency Trading

Big Data for Finance – Challenges in High-Frequency Trading

Graphic by Stamen

Page 8: Big Data for Finance – Challenges in High-Frequency Trading

Using computer algorithms to rapidly trade securities• Positions are held for seconds to minutes

• Reaction times to market changes are sub-millisecond.

• HFT accounts for more than 60% of all trading volume in some markets

© 2015 Think Big, a Teradata Company

WHAT IS HIGH-FREQUENCY TRADING?

Page 9: Big Data for Finance – Challenges in High-Frequency Trading

•SpeedLatencies in electronic trading are usually measured in

microsecondsHFT firms co-locate with exchanges to reduce latency

•Strong predictionsDue to market efficiency it is challenging to come up with robust predictive models

© 2015 Think Big, a Teradata Company

WHAT IS IMPORTANT?

Page 10: Big Data for Finance – Challenges in High-Frequency Trading

Fibre Optic MicrowaveRefractive Index (v) 1.5 1.0003Round-trip time Chicago/New Jersey

~8ms ~13ms

© 2015 Think Big, a Teradata Company

LOW LATENCY

Page 11: Big Data for Finance – Challenges in High-Frequency Trading

© 2015 Think Big, a Teradata Company

MICROWAVE VS FIBRE OPTIC

Page 12: Big Data for Finance – Challenges in High-Frequency Trading

time price order_flag size1336732593.051448 571.00 Ask_cancel 1081336732593.096281 571.06 Ask_add 9221336732593.138566 571.19 Bid_add 2301336732593.179509 571.26 Bid_add 7311336732593.249253 571.28 Trade 2801336732593.321581 571.33 Bid_cancel 9331336732593.369489 571.36 Ask_cancel 6761336732593.396394 571.37 Trade 4891336732593.403784 571.39 Bid_cancel 7801336732593.471040 571.48 Trade 4651336732593.485026 571.54 Bid_cancel 6681336732593.585481 571.55 Ask_cancel 8141336732593.699121 571.63 Ask_cancel 2861336732593.704077 571.74 Ask_add 4241336732593.820406 571.82 Ask_cancel 7891336732593.865808 571.88 Bid_cancel 2581336732593.912195 571.89 Bid_cancel 5791336732593.916676 571.91 Ask_add 2411336732593.941828 571.95 Bid_add 5281336732593.965397 571.99 Trade 300

• Liquid stocks >10 million messages per day

• NYSE produces ~1TB of market data per day

• Market data from all relevant exchanges is collected

© 2015 Think Big, a Teradata Company

MARKET DATA

Page 13: Big Data for Finance – Challenges in High-Frequency Trading

time price order_flag size1336732593.051448 71.00 Ask_cancel 1081336732593.096281 71.06 Ask_add 9221336732593.138566 71.19 Bid_add 2301336732593.179509 71.26 Bid_add 7311336732593.249253 71.28 Trade 2801336732593.321581 71.33 Bid_cancel 9331336732593.369489 71.36 Ask_cancel 6761336732593.396394 71.37 Trade 4891336732593.403784 71.39 Bid_cancel 7801336732593.471040 71.48 Trade 4651336732593.485026 71.54 Bid_cancel 6681336732593.585481 71.55 Ask_cancel 8141336732593.699121 71.63 Ask_cancel 2861336732593.704077 71.74 Ask_add 4241336732593.820406 71.82 Ask_cancel 7891336732593.865808 71.88 Bid_cancel 2581336732593.912195 71.89 Bid_cancel 5791336732593.916676 71.91 Ask_add 2411336732593.941828 71.95 Bid_add 5281336732593.965397 71.99 Trade 300

© 2015 Think Big, a Teradata Company

Order Book

Spread

Cancel

Add

Priority

Price

Best Bid

Best Ask

Ask

Bid

Page 14: Big Data for Finance – Challenges in High-Frequency Trading

Matching Engine

Exchange 1

ScoringPlatform

News, Twitter…

Compute Cluster, e.g.

Hadoop, Flat files

Data Science

Matching Engine

Exchange 2

ScoringPlatform

MarketData

Orders

Market & scoringdata, orders

Ingestion

Data

Data

Model deployment

Model storage

© 2015 Think Big, a Teradata Company

ARCHITECTURE

Page 15: Big Data for Finance – Challenges in High-Frequency Trading

Questions© 2015 Think Big, a Teradata Company

?

Page 16: Big Data for Finance – Challenges in High-Frequency Trading

WE ARE HIRING© 2015 Think Big, a Teradata Company