This presentation, given at AWS Michigan Meetup on 10-09-2012 provides an overview of how we used Amazon Web Services to conduct a quantitative trading system simulation on Amazon Web Services (AWS). We demonstrate an improvement in processing time of an order of magnitude and cost savings of greater than 99% compared to a traditional, in-house physical infrastructure.

1. Legal Informatics w/ AWS CloudSearch &
High-Performance Financial Market Apps
AWS Michigan Meetup
October 9th, 2012
http://www.solidlogic.com
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2. Objectives
‣ Introduce Quantitative Trading
‣ Present a case study on AWS usage in Quantitative Trading System
Evaluation.
‣ Discuss potential improvements upon our presented architecture.
http://www.solidlogic.com
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3. About
Us
http://www.solidlogic.com
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4. Solid Logic Technology develops innovative custom
technology solutions across a variety of industries
using leading software, infrastructure and
business practices.
http://www.solidlogic.com
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5. About us
Our expertise Industry experience
Infrastructure and cloud computing ‣ Financial and legal services
‣ Scalable, programmatic infrastructure ‣ Logistics
management
‣ Automotive
‣ Strategic data center design
‣ Defense and homeland security
‣ VMware architecture and management
‣ Consumer sales and service
‣ Multi-cloud development and deployment
‣ Scalable web infrastructure with CDN
‣ Academic and scientific research
‣ Security and compliance methods and
implementation
Software development Company Information
‣ Analytical solutions - simulation, optimization, big ‣ Founded in 2011
data, natural language processing, quant. finance
‣ Entirely mobile company
‣ Enterprise content management, workflow
solutions, system integration ‣ Develop both internal projects (IP) and
‣ Oracle Transportation Management client software solutions
‣ Database technology (Oracle, Vertica, Postgres,
Cassandra, etc.)
‣ Web application and website development
http://www.solidlogic.com
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6. 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 http://www.solidlogic.com
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7. Case Study:
Proprietary Trading Simulation
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8. 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. http://www.solidlogic.com
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9. Case Study: Proprietary Trading Simulation
Hypothetical Example
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10. Case Study: Proprietary Trading Simulation
Challenge: Scope:
‣ Characterize the performance and ‣ Assets 62
sensitivity of an equity trading
‣ Tests/asset 96
system across input parameters and
market conditions ‣ Total tests 5,952
‣ Optimize parameters based on profit
and risk measures Test Information
‣ Estimated runtime is unacceptable
‣ Mean components/asset 395
on local workstation (>1 month)
‣ Primary bottlenecks are in dense ‣ Points/component 3,135
linear algebra operations ‣ Points/test 1,238,325
• Spectral decomposition (ARPACK)
Pairwise comparison of higher-order
Total elements 7,370,510,400
•
distribution moments (M-M arithmetic) ‣
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11. Case Study: Proprietary Trading Simulation
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
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12. Trading Simulation: Architecture
This was our initial version – Not overly elegant, but works very well
with minimal effort to setup. Easy to improve upon.
Strategy Test
Trading System Results
Source Code and Custom (S3 Buckets)
Config Data Created
(Git Repo) AMIs (x16)
Availability Zone
US East Region
http://www.solidlogic.com
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13. Trading Simulation: Test Process
Trading
System
Source Code
(Git Repo)
Strategy Test
Results (S3
Custom Buckets)
Created
AMIs (x16)
Availability Zone
US East Region
Local
Development
Environment
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14. Trading Simulation: Overview
Technology Solution: Compute Instance (x16):
‣ Built an optimized simulation ‣ 88 Elastic Compute Units (ECU)
environment as virtual image ‣ 2x Xeon E5-2670s-16 cores
(AWS EC2 AMI)
‣ 60.5GB RAM
‣ Provisioned and configured
‣ 10GbE, dual NIC
centralized storage (AWS S3)
‣ 3+TB instance scratch
• Experiment configuration
• Simulation input Total Compute Resources:
• Simulation output ‣ 1408 ECUs
• Post-processed results ‣ 512 concurrent threads (HT)
‣ Fully automated deployment of ‣ 968GB RAM
simulation to instances through
master source control system (1 ECU~=5GFlops)
(git) http://www.solidlogic.com
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15. 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
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16. 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',
'homeubuntutestcodetradingsystemsalesmodelsdailyAdaptiveStateSpaceSPYdatamasterlist.mat',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'csv', '',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 1,
'homeubuntutestcodetradingsystemsalesmodelsdailyAdaptiveStateSpaceSPYdataETFsToTest.csv',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'mat',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/src', 'No', 'No')
• parameterSweepSingleNode('No','Yes',
'homeubuntutestcodetradingsystemsalesmodelsdailyAdaptiveStateSpaceSPYdatamasterlist.mat',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'csv', '',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 1,
'homeubuntutestcodetradingsystemsalesmodelsdailyAdaptiveStateSpaceSPYdataETFsToTest.csv',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/data/etfs', 'mat',
'/home/ubuntu/testcode/tradingsystemsales/models/daily/AdaptiveStateSpaceSPY/src', 'No', 'No')
http://www.solidlogic.com
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17. Trading Simulation: Initial Test Results
‣ Result sets saved to S3 buckets using S3cmd
• Approximately 6000 result sets
http://www.solidlogic.com
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18. Trading Simulation: Output
‣ Run Time:
• Cloud: 45 hours
• Single-seat: 1-2 months
• Order of magnitude improvement in time!
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19. Trading Simulation: Economics
Co- On- Spot
On-Site
Location Demand Pricing Cost Model Details
Server Hardware / ‣ Cost estimates using assumptions
$69,045 $69,045 $1,647 $193
Instance Usage
and calculations in Cost Comparison
Network Hardware 13,809 13,809 - -
Worksheet
Hardware Maint. 24,856 24,856 - - ‣ Costs represent one year annualized
costs. Assumes a useful life of three
Operating System - - - -
years for purchased equipment
Power and Cooling 9,907 - - - ‣ 1= Cost savings using On-Site as
Data Center baseline
Construction / Co- 8,618 65,136 - -
Location Expense
‣ 2= On-Site and Co-Location assume
Admin. / Remote
100% usage
105,000 240 - -
Hands Support ‣ 3= Based on actual 686 machine
Data Transfer 1 4 1 1 hours used
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
http://www.solidlogic.com
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20. 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
http://www.solidlogic.com
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21. Thank you
Eric Detterman Michael Bommarito
CEO, Co-Founder CIO, Partner
Eric.Detterman@solidlogic.com Michael.Bommarito@solidlogic.com
Direct: (248) 792 – 8001 Direct: (646) 450 – 3387
(248) 792 – 8000
www.solidlogic.com
330 East Maple Rd. #231
Birmingham, MI 48009 21