How to Transform Your Business into a Fully Automated Trading Company

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Clement Morrison
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1 How to Transform Your Business into a Fully Automated Trading Company Andy Flury (AlgoTrader GmbH)

2 Andy Flury Founder & CEO AlgoTrader Ltd. Enterprise-level Software Systems Projects for various major banks and insurance companies Projects for the Swiss Intelligence Agency Head of Algorithmic Trading at Linard Capital AG Started AlgoTrader Ltd. In 2013 Masters Industrial Management (ETH Zurich) Executive MBA (University St. Gallen)

3 AlgoTrader Ltd. AlgoTrader Product Automation of complex, quantitative trading strategies Provides everything a typical trading company needs to run a fully automated operation Professional Services Automation and migration of existing strategies Improving and optimizing existing strategies Prototyping and back-testing of new strategies Developing customized functionality Coaching & Training

4 Excel, MatLab and Bloomberg Development & Backtesting Complex Excel Sheets Macros and/or Visual Basic code Combined with MatLab and/or R for back testing Historical Data from Bloomberg stored in Files or Database Reference Data manually managed Spread, Slippage and Commissions? No code versioning Live Trading Strategy Logic in C#, Python, Java, etc. Hand written interface for historical and live data Hand written interface(s) for trading (e.g. Fix or Bloomberg EMSX) Hand written reconciliation & P&L calculation Manual handling of holidays Manual reporting Future/Options rolling logic?

5 Alternative Approach? Integrated Trading Software Solution Benefits / Features Same Code for Development / Back Testing and Live Trading Interfaces for Historical Data, Live Data and Trading Integrated Storage of Market Data Integrated Reporting & Reconciliation Integrated Monitoring and Valuation Integrated Handling of Holidays and Trading Hours Automated Hedging Available Products AlgoTrader NinjaTrader Apama ProTrader Deltix QuantHouse FlexTrade RightEdge MarketCetera Streambase MetaTrader TradeStation

6 Required Skills Quantitative Knowledge Trading Knowledge Market Knowledge Programming Languages Connectivity (API, FIX, etc.) Databases IT & Hardware In-house Solution Partially Outsourced Outsourced Design the trading models (in-house) Develop the trading models (in-house) Develop an execution system (in-house) Design the trading models (in-house) Develop the trading models (in-house) Use a 3rd party execution system Design the trading models (in-house) Develop the trading models (external) Use a 3rd party execution system

7 External Development Partner Fee Structure Required Competence (Trading and Software Development) Intellectual Property Service Level Agreement Support Geographic Presence

8 Choosing the Right Broker and Market Data Provider Commissions Supported Exchanges / Instruments API Interfaces (fix, native etc.) Redundancy Round-trip Latency Quality of Data Reporting and Reconciliation Support

9 Hardware and Performance Requirements Decision Drivers Number of Market Data Subscriptions Number of Orders per Day Complexity of Trading Logic Specific Latency Requirements Hosting Preferences IT Knowhow

10 Hardware and Performance Requirements EOD Trading Low Frequency Intraday Trading High Frequency Trading Runs once a day subscriptions trades / day Continuously running subscriptions 1k 10k trades / day Continuously running 1k 10k L2 subscriptions > 10k trades / day In-house or cloud hosted Ram: 2-4 GB CPU: 1-2 Cores Low network throughput Latency: low millisecond In-house or cloud hosted Ram: 16 32GB CPU: Cores Med. network throughput Latency: low millisecond Co-located Ram: > 32GB CPU: > 20 Cores High network throughput Latency: low microsecond

11 Cons Pros Hosting On-Premise Cloud Hosting Co-Location Privacy Cost Latency Availability Scalability Flexibility Security Privacy Cost Cost Latency Availability Flexibility Flexibility Latency Availability

12 Historical Data Management Challenges Storage of historical data for back testing and traceability Record historical data Purchase historical data Historical data providers Storage Options Flat Files / Compressed Files Relational Databases Size Limitations Time Series Databases KDB OneTick Open TSDB on Apache HBase

13 Best Practices Coding Standards & Development Process Test Driven Development (Unit & Integration Tests) Make Extensive Use of Logging Use a Source Code Management (e.g. Git) Deploy New Features in Small Chunks Prepare a User Manual and give Training to Users

14 Lessons Learned Test and production environments must be as similar as possible Expect issues with brokers and data providers Be prepared to handle all use-cases, especially the unlikely ones Proper user training and user manual documentation Treat the paper trading phase as real money trading Have alerts in place Handle partial fills, limited liquidity, slippage, etc. appropriately Expect misunderstandings between traders, quants and developers Expect budget overflows Expect bugs that only show up in live trading Start simple and add complexity only if needed

15 AlgoTrader Benefits Automated Any quantitative trading strategy can be fully automated Customizable Open-source architecture can be customized for user-specific requirements Reliable Built on the robust architecture and state-of-the-art technology Fully-Supported Guidance available for installation and customization. Onsite and remote training and consulting available Features Multiple broker interfaces and market data providers Support for forex, options, futures, stocks, ETFs, Commodities etc. Write trading strategies in C#, Python, JavaScript, MatLab or R Excel based back test report Several built-in execution algorithms Support for synthetic instruments and custom derivative spreads Options Pricing Engine Automated Hedging

THANK YOU FOR YOUR ATTENTION AlgoTrader GmbH Aeschstrasse 6 8834

16 THANK YOU FOR YOUR ATTENTION AlgoTrader GmbH Aeschstrasse Schindellegi Switzerland Phone: info@algotrader.ch

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