1. ONETICK ® Accelerating Quant Research and Trading Principal Component Analysis & Multi-Factor Modeling Tests with OneTick & R Historical & Real-Time 7 Minute Crash Course Maria Belianina, Director of Pre-Sales Engineering Jianwen Luo, Director, Trading System Engineering

2. ONETICK ® Accelerating Quant Research and Trading What is OneTick? ONETICK time series database & analytics Tick data management and super fast analytics for Finance. Capture, store, retrieve and analyze real-time and historical tick data for any asset class, any size & period of time, any granulairty ONETICK CEP real-time analytics Low latency Complex Event Processing seamlessly integrating the analysis of real-time streaming and historical market data ONETICK reference data file + OneQuantData Smooth historical time series data with Corporate actions, take into account symbol name changes, look at earnings, daily prices and more Archives: History History: Data Archives: History In-memory Database: Today’s TAQ TAQ Loaders Real-time market feeds Collectors Reference Data CEP Server Tick Server Analytics Markets

3. ONETICK ® Accelerating Quant Research and Trading  Hedge Funds & Proprietary Trading Firms  Market Makers  Large Asset Managers  Banks & Brokers  Marketplaces / Exchanges  Technology & Information Providers  Universities  Backtesting & Quantitative Research  High frequency trading signal generation  Pre- & Post- Trade TCA  Venue Analysis  Backbone for Charting / Time and Sales  Compliance & Regulatory Reporting  Risk, Portfolio Analytics, PnL  Generic time series analysis Our clients: Who is using OneTick and why? Business Cases:

4. OneTick GUI: Query Language Query Example: Bollinger Bands Buy/Sell Signals Runs Historical (for research & backtesting) or Real-Time (alerts & signal generation) A “Nested query” for Bollinger Bands calculations A “Nested query” for Bollinger Bands calculations NOTE: One of the nodes can be a custom code in R or C++, C#, Java, Python, Perl, MatLab Trades

5. Principal Component Analysis (PCA) Based Multi-Factor Trading  Based on “Developing High-Frequency Equities Trading Models” by Leandro Infantino and Savion Itzhaki, 2010  Claim: By filtering out “noise” in a portoflio of stocks through PCA, we might be able to “predict the future” and make some gains  Testing approach: 1.OneTick price and quote history. Portfolio of stocks. Test 1 day at a time. 2.Define PCAWindowSize (e.g., 15 min); Run PCA on the initial PCAWindowSize; Run regression with given MemoryWindowSize (e.g., 30 sec); Calculate betas 3.For the rest of the day: Run PCA over sliding PCAWindowSize buckets; Forecast using calculated betas, Try to generate trading signals; Update betas per MemoryWindowSize bucket; keep track of returns and PnL OneTick + R OneTick+ R Sample Use Case:

6. Use nested query _get_log_return as a source OneTick+ R Testing approach Pass sliding sub-matrices into R for PCA, etc “Nested” query Merge all portfolio log returns and transpose into matrix BBO.. MID.. Log Returns

7. OneTick+ R: Built-In R Integration Generic OneTick bucket aggregation parameters R in-process call parameters Full Strategy in OneTick+R - Work in Progress Sample ETA is next month. Contact us for details.

8. PCA Based Statistic Multi-Factor Trading Initial Tests  Preliminary test results. Testing: 1 day of TAQ for December 15 th 2010 quotes, 25-symbol portfolio. To be tested further… Cumulative PnL assuming position limit of 1 unit per stock Cumulative Returns assuming equal weights

9. ONETICK ® Accelerating Quant Research and Trading STOP BY OUR STAND FOR A LIVE DEMO! THANK YOU Contacts: info@onetick.com