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By Thomas Rodgers Matlock Capital, LLC
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Privately held trading firm specializing in Stock and Stock Options market making Focus on sophisticated analytics and highly automated trading Member of the Chicago Board of Trade, Chicago Board Options Exchange, New York Stock Exchange, International Securities Exchange
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Usually targeted at trading logic specification Vendor proprietary grammars Leverage tools like Matlab and Mathematica Other examples include Specifying “over the counter” (non-exchange traded) options contracts Risk scenario specification Report generation
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Too specific Little opportunity for customization Useful only for the most simplistic of trading strategies Poorly designed grammars Lack of debugging and testing tools Poor performance Particularly with DSLs based on mathematics packages High cost You’ll pay (a lot) to not have a debugger
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Issue ~500,000 orders per day ▪ We expect to increase this to ~2M orders per day over the next year Issue ~4M option quotes per day ▪ We expect to increase this to ~10M quotes per day over the next year Majority of trading decisions are expected to complete within 5-10ms of a price tick System should require no more than one or two traders to monitor
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Sophisticated analysis and filtering of price feeds Minimize risk of catastrophic loss in adverse market conditions Real time risk exposure monitoring is used to modify quotes to make trades more or less likely to happen Automated hedge trading ensures our position stays within acceptable risk limits Trade sentinels monitor order flow and preempt routing to exchanges We do not want trading machines trading with themselves Ensure no more than some number of a given type of trade are working simultaneously Poor price quality from an exchange means we shouldn’t be sending orders to it Ensure that order flow rate does not exceed a configured threshold
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Distributed asynchronous message driven architecture Timing sensitive Exhibits non-linear behavior Trading strategy growth Tends to be fairly organic Attempts to codify trader intuition in many cases Reaction to short lived market conditions Often easier to specify what the system clearly should not do Not get run over in a fast market Violate exchange or SEC rules Fail to manage market exposure due to market volume
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Smaller surface area to cover than a trading logic DSL Match on system events Generate Prices, Quotes, Orders, Market Execution Reports, Volatilities and Interest Rates Basic assertions and comparison operators We would like to make test scenarios business readable Minimize friction by hijacking the language of open out-cry trading Apply Behavior Driven Design to trading strategy development
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Individual tick data feed testFeed 10 { MSFT Jul 15.50 put 5:1.20@10:1.30 MSFT 500:15.35@300:15.45 AAPL 500:83.50 } Generated tick data feed testFeed2 10 { MSFT Jul 15.50 call { step 500 from [5,50] 1.15@1.45 to 1.35@1.60 to 1.25@1.40 } }
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Most trading systems are message driven Typically use JMS, TibcoRV, or FIX internally FIX protocol is the financial industry standard for message interchange between institutions Engine executing test scenarios should Interface with the system under test using these messaging protocols Handle asynchronous nature of a MDA Match and respond to particular events using expressions that are easily understood by traders/financial analysts
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Trading system Listens to a simulated price feed Executes a buy/write transaction Verification The call must be covered, stock options are contracts for 100 shares of stock, so option contract size should be number of stocks/100 Recovering from an error Fail the stock order and fill the option Expect the system to trade out of the option position within a specified time frame
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CBOE Learning Center - http://www.cboe.com/LearnCenter/default.aspx http://www.cboe.com/LearnCenter/default.aspx Financial Information eXchange (“FIX”) Protocol – http://www.fixprotocol.org/ Matlock Capital – http://www.matcap.com/ QuickFIX Engine http://www.quickfixengine.org/
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Thomas Rodgers trodgers@matcap.com
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