Simplifying the Approach to Complex Regulation Achieving Dynamic Compliance in today’s Regulatory Landscape 25th January 2017

simple solution Implement a future proofed solution and still meet the deadlines smart

Functional Requirements for a Regulatory Solution Alerts Flexible CEP engine and alerts definition for instant deployment and detection of trading patterns indicating market abuse, erroneous trading or malfunctioning algorithms Multi Asset & Multi Market High performance data capture (rates, trades, orders, news) across multiple asset classes and markets Back-testing & Calibration Allows surveillance analysts to test new alert algorithms against historical data and to recalibrate alerts to minimize false positives Reporting & Visualization Highly flexible dashboards for surveillance analysts to monitor real-time trading activity and perform post-trade analytics and reporting Market Replay To reconstruct market conditions and step forward and backward through order book activity for cross-market and single-market analysis Regulatory data model (extensible, includes exeuctions/trades and market data) APIs, manual plus automated, RTS; best ex. Customers telling us Priority list . Are they on yours?

For the Techies -- Solution Architecture!! Scalability: [Horizontal/Vertical] and Standby Manual Entry Native Lambda/HTAP Architecture Stream for Kx Reference Data Core Batch Loader Batch Loader Stream Engine Ticker Plant Stream Engine Ticker Plant Stream/ Complex Event Processing Micro-services Queries Transforms Event-triggered Time-synched Sequenced ... In Memory Database In Memory Database eComms Historical Database Historical Database Market Data Stream Feed Handler Stream Feed Handler Orders & Executions q language & scripting Pricing Third-Party Interoperability pub/sub, web services, change data capture,… R, Python, Matlab, Java, ODBC, web sockets… Everything has architecture….. But often its an afterthought ( deliver a fix first, then worry about how you did it) ... So you end up with mutiple incompatible systems AND THEN CONSTANT DUPLICATION..

Key Non-technical requirements Dynamic, End user oriented, Interactive Simple architecture Performance is essential Fast to implement and proven Future Proof Dynamic - more than flexible --- adaptable and EVOLUTIONARY End User Oriented – not dependent on techies or suplliers to make changes Simple Architecture – rubbish hadoop! NOT A SCIENCE PROJECT Performance -- big issue -- VAST DATA, but still expect interactive response Fast to implement/ Proven -- YOU CAN’T TAKE RISKS WITH MONTHS TO GO Future proof

Compliance out of the Box MiFID II Systematic Internaliser Best Ex RTS 27/28 MAR Alerts and more Alerts Dodd Frank

Solutions Using Kx - Mifid II Examples European Best Bid & Offer Trade Reconstruction Transaction Cost Analysis Best Execution SI Determination PnL Impact Analysis Fast to ingest data, fast to retrieve data, fast to build modules and applications

Report Templates for Best Execution Reporting Requirements under MiFID II

Mifid: Systematic Internaliser Highlights Market Share per Instrument Market Share, Trade Size , SI Max and SI Alert level Instrument breakdown – Volume, Size & Market Share In-house Volume vs Market total A systematic internaliser (SI), recently brought into the limelight by MiFID II/MiFIR, is an investment firm or exchange that deals on its own account on an organised, frequent, systematic and substantial basis. They use their own internal liquidity pools to match orders. The SI dashboards have been designed to help companies determine whether they meet regulatory requirements for trading as a systematic internaliser. The display takes trading data from that trading venue, for either equities or FX, and compares this to market data to allow comparison of trading volumes and market share of in-house trading vs the wider market. The SI dashboard provides a trader with insight into their current market participation in line with MiFID2 regulations.

MAR: Abuse examples FX Equities Fixed Income Commodities Rates Credit Derivatives 1 Abusive Squeeze Yes NA 2 Inappropriate Barrier Running 3 Creating Floor or Ceiling in Price Pattern 4 Inappropriate Stop Loss Triggering 5 Prearranged Trading 6 Ping Orders 7 High Order Rate 8 Front Running 9 Inappropriate Client Allocation during Benchmark Window 10 Inappropriate Assignment (Client Orders) 11 Large Order 12 Bait & Switch 13 Benchmark Manipulation 14 Trade Cancel/Amend (Fixing Window) 15 Marking the Close 16 Pre-Auction Spoofing 17 Mark Up Breach 18 Trade Out of Bounds 19 RFQ Out of Bounds

More: Abuse examples FX Equities Fixed Income Commodities Rates Credit Derivatives 20 Order to Trade Ratio Yes NA 21 Parking 22 Partial Fills 23 Rapid Price Dislocation & Reversion Alert 24 Unusual Price Movement 25 Breaking the Market 26 Spoofing 27 Time-Order Priority 28 High Order Rate 29 Unusual Trade Volume 30 Wash Trades 31 Inter Trading (Cross Instrument Manipulation) 32 Phishing 33 Painting the Tape 34 Pump and Dump / Trash and Cash 35 Fictitious Quotes 36 Momentum Ignition 37 Smoking 38 Insider Trading 39 Attempts to Inside Trade

MAR: Alert Dashboard Market Replay Action Tracker

Reconstruction

Simple, Powerful Architecture  Proven and Future Proofed Solutions 3 q(uicks)

Contact James Corcoran www.kx.com www.kxcommunity.com Senior Vice President, Engineering jcorcoran@kx.com +44-7841-912572 www.kx.com www.kxcommunity.com www.firstderivatives.com