finance trading in the cloud - aws michigan meetup

1

Legal Informatics w/ AWS CloudSearch & High-Performance Financial Market Apps

AWS Michigan Meetup

October 9th, 2012

http://www.solidlogic.com

http://www.solidlogic.com/�

Objectives

‣ Introduce Quantitative Trading

‣ Present a case study on AWS usage in Quantitative Trading System Evaluation.

‣ Discuss potential improvements upon our presented architecture.

2 http://www.solidlogic.com


3

About Us



4

Solid Logic Technology develops innovative custom technology solutions across a variety of industries using leading software, infrastructure and business practices.



About us

5

Industry experience ‣ Financial and legal services ‣ Logistics ‣ Automotive ‣ Defense and homeland security ‣ Consumer sales and service ‣ Academic and scientific research

Our expertise Infrastructure and cloud computing ‣ Scalable, programmatic infrastructure

management

‣ Strategic data center design

‣ VMware architecture and management

‣ Multi-cloud development and deployment

‣ Scalable web infrastructure with CDN

‣ Security and compliance methods and implementation

Software development ‣ Analytical solutions - simulation, optimization, big

data, natural language processing, quant. finance

‣ Enterprise content management, workflow solutions, system integration

‣ Oracle Transportation Management

‣ Database technology (Oracle, Vertica, Postgres, Cassandra, etc.)

‣ Web application and website development

Company Information ‣ Founded in 2011 ‣ Entirely mobile company ‣ Develop both internal projects (IP) and

client software solutions



Solid Logic Management Team ‣ Eric Detterman, CEO and Co-Founder

• Professional Experience - Legal IT Business Analyst, Lean Startup, Cloud Computing, Processing Engineering and Consulting

- Researched and developed core investment strategies for Birmingham, MI RIA

- Currently in production and managing > $20M, AUM growth > 50% annually

- Proprietary trading (equities, futures, options), web and software development

• Education: B.S. Economics – Oakland University

‣ Michael Bommarito, CIO and Partner • Relevant Experience

- “Big data” consultant, Oracle ERP architect, Linux cluster administrator. - Software developer - NYC-based quantitative hedge fund

- Consultant - multiple quantitative hedge funds

• Education : M.S.E Financial Engineering, M.S. Political Science, B.S. Mathematics – University of Michigan

‣ Ronald Redmer, Board Member and Lead Technical Advisor • Relevant Experience

- CIO, National Default Exchange (NDeX), a business unit of The Dolan Company (NYSE:DM)

- CEO defense supplier company, Airport systems software, CEO auto testing company, Affina – software dev mgr, EDS - tech lead

6



7

Case Study: Proprietary Trading Simulation




Quantitative Trading and Investment Systems: ‣ (Loose) Definition:

• Rules-based mathematical ‘model’ created by testing and validating a hypothesis about how a tradable market acts or optimizing parameters to create an equation to describe the market.

• The goal is to outperform the broad market (S&P 500) or some benchmark after costs.

‣ Example Strategy: • Investment universe = ~50 Fidelity Mutual Funds • Strategy #1: Invest in the top six ranked mutual funds based on proprietary

momentum (p0 > p-1) based ranking algorithm. Analyze and rank fund universe every 45 days and re-allocate.

• Strategy #2: Invest in the top six ranked mutual funds based on proprietary mean reversion (p0 > p-1) based ranking algorithm. Analyze and rank fund universe every 45 days and re-allocate.




9

Hypothetical Example




10

Challenge: ‣ Characterize the performance and

sensitivity of an equity trading system across input parameters and market conditions

‣ Optimize parameters based on profit and risk measures

‣ Estimated runtime is unacceptable on local workstation (>1 month)

‣ Primary bottlenecks are in dense linear algebra operations

• Spectral decomposition (ARPACK) • Pairwise comparison of higher-order

distribution moments (M-M arithmetic)

Scope: ‣ Assets 62 ‣ Tests/asset 96 ‣ Total tests 5,952

Test Information ‣ Mean components/asset 395 ‣ Points/component 3,135 ‣ Points/test 1,238,325

‣ Total elements 7,370,510,400




Potential solutions: ‣ Run on existing hardware – wait for results ‣ Physical or virtualized servers with supporting job schedulers –

requires hardware, software, and specialized labor ‣ Setup cloud infrastructure to process work – requires software

and specialized labor



Trading Simulation: Architecture

12

This was our initial version – Not overly elegant, but works very well with minimal effort to setup. Easy to improve upon.

US East Region

Availability Zone

Strategy Test Results

(S3 Buckets) Trading System

Source Code and Config Data (Git Repo)

Custom Created

AMIs (x16)



Trading Simulation: Test Process

13

US East Region

Availability Zone

Strategy Test Results (S3

Buckets)

Local Development Environment

Custom Created

AMIs (x16)

Trading System

Source Code (Git Repo)



Trading Simulation: Overview

14

Technology Solution: ‣ Built an optimized simulation

environment as virtual image (AWS EC2 AMI)

‣ Provisioned and configured centralized storage (AWS S3)

• Experiment configuration • Simulation input • Simulation output • Post-processed results

‣ Fully automated deployment of simulation to instances through master source control system (git)

Compute Instance (x16): ‣ 88 Elastic Compute Units (ECU) ‣ 2x Xeon E5-2670s-16 cores ‣ 60.5GB RAM ‣ 10GbE, dual NIC ‣ 3+TB instance scratch

Total Compute Resources: ‣ 1408 ECUs ‣ 512 concurrent threads (HT) ‣ 968GB RAM

(1 ECU~=5GFlops)



Trading Simulation: AMI Creation Process

‣ Use standard Ubuntu Server 12.04.1 LTS for Cluster Instances AMI x64 (ami-eb7bcf82)

• cc2.8xLarge – 88ECUs, 16 cores, 60.5GB RAM

‣ Install git, s3cmd, PostgreSQL JDBC drivers

‣ Install and configure test environment and all dependencies

‣ Create new AMI based on the above



Trading Simulation: Test Execution

‣ For each test instance….. • ssh -X -i /home/ericd/.aws/first/name.pem ubuntu@IP • cd /home/ubuntu/testcode/tradingsystemsales • git pull • cd /usr/local/testcode//bin • sudo ./testcode -nodesktop • parameterSweepSingleNode('Yes','Yes',

'\home\ubuntu\testcode\tradingsystemsales\models\daily\AdaptiveStateSpaceSPY\data\masterlist.mat', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'csv', '', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 1, '\home\ubuntu\testcode\tradingsystemsales\models\daily\AdaptiveStateSpaceSPY\data\ETFsToTest.csv', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'mat', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/src', 'No', 'No')

• parameterSweepSingleNode('No','Yes',

'\home\ubuntu\testcode\tradingsystemsales\models\daily\AdaptiveStateSpaceSPY\data\masterlist.mat', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'csv', '', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 1, '\home\ubuntu\testcode\tradingsystemsales\models\daily\AdaptiveStateSpaceSPY\data\ETFsToTest.csv', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'mat', '/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/src', 'No', 'No')



Trading Simulation: Initial Test Results

‣ Result sets saved to S3 buckets using S3cmd

• Approximately 6000 result sets



Trading Simulation: Output

‣ Run Time:

• Cloud: 45 hours

• Single-seat: 1-2 months

• Order of magnitude improvement in time!



Trading Simulation: Economics

19

On-Site Co-Location

On-Demand

Spot Pricing

Server Hardware / Instance Usage $69,045 $69,045 $1,647 $193

Network Hardware 13,809 13,809 - -

Hardware Maint. 24,856 24,856 - -

Operating System - - - -

Power and Cooling 9,907 - - -

Data Center Construction / Co-Location Expense

8,618 65,136 - -

Admin. / Remote Hands Support 105,000 240 - -

Data Transfer 1 4 1 1

Total $231,237 $173,091 $1,647 $193

Cost Savings1 N/A 25.12% 99.29% 99.92%

$ / Compute Hr.2,3 $26.40 $19.76 $2.40 $0.28

Cost Model Details ‣ Cost estimates using assumptions

and calculations in Cost Comparison Worksheet

‣ Costs represent one year annualized costs. Assumes a useful life of three years for purchased equipment

‣ 1 = Cost savings using On-Site as baseline

‣ 2 = On-Site and Co-Location assume 100% usage

‣ 3 = Based on actual 686 machine hours used


http://d36cz9buwru1tt.cloudfront.net/Amazon_EC2_Cost_Comparison_Calculator_042810.xls�

http://d36cz9buwru1tt.cloudfront.net/Amazon_EC2_Cost_Comparison_Calculator_042810.xls�


Trading Simulation: Next Steps

Potential Improvements: ‣ Develop improved cloud infrastructure management tools

• Allocation of work across instances • Allow user defined completion time and programmatically

scale compute resources to work towards goal • Spread work across unused internal and available external

compute resources



Thank you

21

Eric Detterman CEO, Co-Founder

Michael Bommarito CIO, Partner

[email protected]

Direct: (248) 792 – 8001

[email protected]

Direct: (646) 450 – 3387

(248) 792 – 8000 www.solidlogic.com

330 East Maple Rd. #231 Birmingham, MI 48009

mailto:[email protected]�

mailto:[email protected]�


finance trading in the cloud - aws michigan meetup

Technology

proprietary trading

arithmetic http

case study

website development

specialized labor http

software developmenteducation

eds tech lead http

airport systems software