Stream. Base Case Study Automated Trading www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 1
I. The Problem § Background: - Successful Buy-side firm successful in conventional buy/hold strategies wanted to apply learnings to intraday trading § Business Drivers: - Making money: from short-lived trading opportunities in real-time market data feeds, and reducing transaction costs - Customer retention www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 2
The Approach: Application Overview § Data/events stream from real-time market data feeds § Data is filtered (watch-list) and processed § Trading rules/logic applied to real-time streams to make buy/sell decisions - Spread pairs, Bollinger bands, limit rules - Store and retrieve latest market data § Maintain execution state of trades, check continuously § Buy/sell orders sent to execution engine § Recent addition of block-trading and best execution application - Run algorithms across multiple liquidity sources to determine best price and optimize execution (price, transaction fees) www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 3
Event Sources, Types, Interfaces § Event sources: - NYSE Arca - Nasdaq - Instinet - 15 other global exchanges § Event types: - Message format: contains string, int, datetime, Boolean, and decimal/float data types § Market data: e. g. Symbol, bid_price, ask_price, bid_size, ask_size, last_price, last_size, timestamp § Daily market condition data: symbol, market cap, sector, 52 -week § Message rates: - Market Data providers: up to 10, 000 messages per second. - < 20 ms from input to output § Interfaces: - Tibco EMS, MS SQL Server adapter. -. Net adapter for EMS leveraging existing Microsoft/. Net development work www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 4
Example of Application Logic § Query table look-up and filter for watch-list § Calculate and store Bollinger Bands/moving average, (Aggregate operator) § Apply Bollinger rule: current price much reach lower band (Filter) § Apply 52 -week rule: current price must reach 52 -week low (Filter) § Apply daily volume rule: quote must reach 150% of daily volume (Filter) § Union all orders and add timestamp § Output stream with orders to submit www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 5
Application Module: Quote to Order www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 6
Example Code Create order if the last_price on the Quote. And. Market. Ref stream is less than the 52 week low. CREATE STREAM Low 52 Wk. Orders AS SELECT symbol, timestamp, watchlist_position_threshold as position_threshold, "off" AS new_order_type, bid_price AS new_order_price, int(watchlist_position_threshold / bid_price) AS new_order_size FROM Quote. And. Market. Ref WHERE last_price < w 52_low; www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 7
III. Results, Costs and Benefits § Application in production § Built by in-house staff in 30 days (2 -3 people, including QA/testing). - Estimated to take 8 months with team of 3 -5 people via customcoding - Easy for non-expert developer to build, understand, and modify § ROI - Trading profitability (not disclosed) - Customer retention and new acquisition - Deployed in 1/8 the time and resources vs custom-coding - Visibility to whole organization for event/application flow www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 8
IV. Conclusions § Alternative approaches would not have offered value of Stream. Base - Custom-coding (too costly in terms of time/resources) to get high performance - Full-blown order management system (OMS) too expensive and too featurerich § Lessons learned - Strong business drivers (not just an IT project) - Up-front architectural planning paid-off in time-to-deployment www. streambase. com © Copyright Stream. Base®. Proprietary & Confidential. 9
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