1. by Mark Goetsch

2. The Pieces

3. AnalyticsTrading OpportunityRouting & ExecutionConfirmation & MatchingClearing & Settlement Arbitrage Quotes Order Matching STP

4. 7/2/2016 4

5. “I enter orders[1] in an order book[2] for a specific market[3]. The orders are matched at discrete time time intervals that are controlled by a heartbeat[4]. The orders can be either buy[5] or sell[6] orders[1]. The order matcher will will check whenever a new order[1] is entered into the market. The time will be marked by the heartbeat[4]. If there is a match[7] then the order matcher will either fully match the order[8] or partially match the order[9].” Patterns Used 1.Orders 2.Orderbook 3.Market 4.Heartbeat 5.Buy Order 6.Sell Order 7.Match 8.Full Match 9.Partial Match 7/2/2016 5

6. Time Beat Invalid Order Match Match RulesSell Orders Buy Orders Full MatchPartial Match Orders 7/2/2016 6

7. A customer authenticates[1] to a particular contract[2] which belongs to a market[3]. An order[5] is then presented to the orderbook[4] but not before checking the order[5] against the customers margin[6] which is different for every contract[2]. Gateway Analysis Pattern Patterns Used 1.Authenticate 2.Contract 3.Market 4.Orderbook 5.Order 6.Margin 7/2/2016 7

8. Authenticatio n Margin Contract Order Market Order Book 7/2/2016 8

9. Rules that are used for every matching possibility. 7/2/2016 9

10. 10 Opening Rules => How are orders entered before the opening Closing Rules => How are orders handled at the end Trading Rules => How are orders matched

11. 7/2/2016 11 Limit Order Against Limit Order Buy Limit in book >= incoming sell Limit Sell Limit in book <= incoming buy Limit Match buy quantity Best Buy Limit Match sell quantity Best Sell Limit “The incoming order is a limit order. It matches against the best limit in the book (bid price >= ask price) for the incoming limit order. The best limit in the book determines the price” Remaining buy quantity Remaining sell quantity

12. 7/2/2016 12 Pre-Opening State of BookConditionsSettle ExistsNo Settle Exists No Entries--nothing--Settle--nothing-- Bids OnlyBid > Settle Bid = Settle Bid < Settle Bid Settle Bid Offers OnlyOffer > Settle Offer = Settle Offer < Settle Settle Offer Bids and Offers No Trades Possible Settle = Offer Bid < Settle < Offer Bid = Settle Bid > Settle Offer Settle Bid Bids and Offers Trades Possible Bid > OfferIOP (Indicative Opening Price) IOP (Indicative Opening Price) Three Stages to Opening the Market 1.Staging Period – Orders are entered and a countdown begins which calculates the Indicative Opening Price (IOP) is possible (see table below). 2.Non-Cancel Period – Can enter new orders but not cancel orders. The final IOP is calculated. 3.Opening – Orders that can be matched are matched.

13. 7/2/2016 13 Order Matcher Design I

14. Order Matcher CMI FIX 4.2 Ilink FIX 4.2 FIX 4.2 FIX 2.3 Express Confirm Record CMTA/ Allocation Trade Correctio n Confirm Record GUS/ Allocation Trade Correctio n Firm Back Office Systems FCM Back Office Systems OCC CME Clearing Front End Connectivity CME GLOBEX API CBOEdirect API CBOE Trade Match CME GLOBEX Trade Match Trade Processing and Clearing 7/2/2016 14

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16. Knowing financial theory

17. How to Test the Engine Testing EngineTrading Engine Sample Buy and Sell Transactions Various prices above and below the book are generated according to a random distribution.

18. https://www.khanacademy.org/economics-finance-domain/core- finance/derivative-securities/Black-Scholes/v/introduction-to- the-black-scholes-formula https://www.khanacademy.org/economics-finance-domain/core- finance/derivative-securities/forward-futures- contracts/v/motivation-for-the-futures-exchange

19. Random Walk

20. public static double SimulateAsset(double s0, double mu, double sigma,double tau, double delta_t,MCG g) { //Purpose: Simulates an Asset Price run using a random walk and returns a final asset price. // so = Price of the asset at time 0 (current time) // mu = Historical Mean // sigma = Historical Volatility (variance) // delta_t = period of time (% of a year or a day) // g = Random variable double s = s0; // Made the steps = to the number of days which is the same as daily changes. double nSteps =tau; for (int i=0; i < (int)nSteps; i++) { // s = s0 * (1 + mean + standard deviation * gaussian random number * squareRoot of the time period. s= s * (1 + mu * delta_t + sigma * g.gaussian() * Math.sqrt(delta_t)); } //Returns the final Price return s; } Simulating an Asset as a Random Walk (or drunkards walk)

21. public static double MeasureVolatilityFromHistoric(double[] historic, double delta_t, int length) { // Purpose: Measures the Volatility for scaled prices. double sum = 0; double variance = 0; double volatility = 0; // length - 1 instead of length since n prices generates n-1 returns for (int i=0; i< length -1; i++) { //Random variable X^2 sum = sum + Math.pow((historic[i+1]-historic[i])/historic[i],2); } // E[X^2] - E[X]^2 variance = sum / (length -1) - Math.pow(MeasureMeanFromHistoric( historic, delta_t,length) * delta_t,2); // Volatility = SquareRoot(variance/ dt) which is the standard deviation scaled for a time increment volatility = Math.sqrt(variance/delta_t); return volatility; } Measuring the Volatility Associated with the Trade

22. public static double MeasureMeanFromHistoric(double[] historic, double delta_t, int length) { //Purpose: Measures the mean of the scaled prices. (Scaled indicates that the level of the // Prices is not important. double sum = 0; double average = 0; double waverage = 0; double returns = 0; //length-1 because the scaling requires n prices to generate a sequence of n-1 scaled returns. for (int i=0;i < (length-1); i++) { // Scales the returns and sums them returns = (historic[i+1]-historic[i])/historic[i]; sum = sum + returns; } //computes the average of the returns average = sum/(length-1); // divides the average by dt so that the average applies to each time increment waverage = average/delta_t; return waverage; } Measuring the averages

23. Defining your engine

24. Patterns Used 1.Quote Instrument, bid, offer, number, spread (bid-offer), mid (bid+offer / 2), one-way quote, two-way quote. Board Exercise “To understand the value of a contract, we need to understand the price of the goods being traded. Goods are often priced differently depending on whether they are bought or sold. This two-way pricing behavior can be captured by a quote[1]” Martin Fowler, Analysis Patterns 1997

25. Patterns Used 1.Scenario Instrument, Quote, Timepoint, Price, Quote, Party, Information Source, Market Indicator Board Exercise “In volatile markets, prices can change rapidly. Traders need to value goods against a range of possible changes. The scenario[1] puts together a combination of conditions that can act as a single state for the market for valuation. Scenarios can be complex, and we need a way to define their construction so we can use the same scenario construction at different times in a consistent manner. Scenarios are useful for any domain with complex price changes.” Martin Fowler, Analysis Patterns 1997