Data Mining in Financial Markets and an Open Enterprise Infrastructure
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1 Data Mining in Financial Markets and an Open Enterprise Infrastructure Supporting Trading & Mining Chengqi Zhang, Longbing Cao, Jiaqi Wang, Li Lin, Jiarui Ni, Wanli Chen E-Intelligence Group at FIT, UTS Data Mining Program at Capital Market CRC F-TRADE: E-Intelligence Group: Centre of Excellence in Data Mining:
2 Some problems from financial markets Online accessing real huge capital data crossing markets Evaluating trading and mining strategies from industry & research in financial markets Stock & rules (parameters) association, selection, recommendation, optimization, and integration Pattern discovery of (large) market/limit orders in stock markets Cross market analysis Applications as investment decision support
3 Overview of the Data Mining Program Datamining Program UTS: Broadway/CMCRC: City F-Trade Infrastructure (integrating DataSources, trading/mining algorithms, offers personalized services in terms of system, data and algorithms) Infrastructure Longbing & Jiarui s work Team Leader: Prof Chengqi Zhang Multi-agent & Data-mining ITR&D-Enabled Capital Market Multiple * Remote Data Sources CMCRC/Tricom/ABN: CBD (Industrial requirements; Users: Anybody, anytime, anywhere, from KDD & Finance; Services: System, algorithms, data) Industry Brokers, retailors Applications like Wanli s work Researchers (Data mining, financial researchers, financial analysis, decision support analysis ) Data miners Jiaqi & Li s work Australian Technology Park: Redfern; FIT,UTS (Diff. Providers: AC3, HK market, ABN, CSFB, etc. Diff. Formats: FAV, ODBC, JDBC, OLEDB, etc.) Data& resources
4 Data Mining in Financial Markets Infrastructure supporting Trading & Data Mining
5 Data Mining in Financial Markets
6 Overview Optimization of trading rules Correlation analysis in/cross markets Pattern analysis of large orders Cross market mining Platform supporting mining in financial markets
7 Optimization of trading rules Parameter optimization of trading rules Rule optimization of technical trading strategies
8 Optimization of trading rules
9 Optimization of trading rules Moving average contraints.. when spot is above the s/r and l/r ma when spot is above the s/r when spot is above the l/r ma when s/r and l/r ma etc etc Generate a new optimal moving average
10 Optimization of trading rules
11 Correlation analysis in/cross markets Correlation between securities Correlation between securities and trading rules Cross market correlation mining
12 Correlation analysis in/cross markets
13 Correlation analysis in/cross markets Pairs trading CBA/GMF
14 Pattern analysis of large orders Pattern analysis of market order and limit order Pattern discovery of large orders
15 Pattern analysis of large orders MarketOrder LargeMarketOrder January February
16 Infrastructure supporting Trading & Data Mining
17 Overview What is F-Trade? Objectives to build F-Trade Benefits for different parties System architecture of F-Trade Functionalities of F-Trade F-Trade Conclusions and future work
18 What is F-Trade? F-Trade is an automated enterprise (agent services-based) infrastructure supporting trading and mining currently on capital markets. Online connection to huge, historical, realistic stock information from anywhere. Online plug and play, and optimization of algorithms. Service provider based on subscription
19 Objectives to Build F-Trade As a trading system (virtual broker) Trading services for data, rules, optimization, evaluation, and reporting As a data mining system Mining services for data gateway and preparation, algorithms evaluation, and reporting As a virtual service provider Services of system, algorithms, data for mining or trading
20 Overview What is F-Trade? Objectives to build F-Trade Benefits for different parties System architecture of F-Trade Functionalities of F-Trade F-Trade Conclusions and future work
21 Benefit for Individual Investors Back-testing: Individual investors can choose any built-in trading algorithm (or strategy) for any stock they choose to evaluate the performance of their strategy in history. Parameter optimization: recommend combinations of rule parameters for the best performance of a trading strategy. Signal alerts: recommend signals for traders
22 Benefit for Brokers Plug and play & privacy: Brokers can build their own trading algorithms to plug-in the platform for their private use to evaluate the performance Huge data: Broker can benefit from the huge historic real stock information under the platform Recommendation: recommend interesting watchlist of securities, rules, or pairs between securities and rules cross markets Cross-market: Brokers can evaluate their trading rules by crossing international markets
23 Benefit for Data Miners Data gateway service: access and extract heterogeneous data sources transparently and remotely if required Data transformer: transform data as required by some data mining purpose Data stream processing: data stream preprocessing, mining, and post-processing algorithms with online connectivity to huge amount of stock order stream Multiple data source mining:
24 Benefit for Data Miners Privacy-preserving data mining: Data securityenhancing techniques Integration of data mining: with database systems, data warehouse systems, and reporting systems Plug-and-play: Plug-and-play of mining algorithms Evaluation: back-test/evaluate the plugged mining algorithms Privacy: keep privacy of the data and the algorithms
25 Overview What is F-Trade? Objectives to build F-Trade Benefits for different parties System architecture of F-Trade Functionalities of F-Trade F-Trade Conclusions and future work
26 The System Hierarchy End Users Models F-Trade DW/DB/KB Users from diff. fields, at diff. levels, for diff. requests may benefit from the system. Algorithms for diff. fields can be registered to the system and evaluated. Multiple programming language supported. Seamless integration of DS, algorithm, security and components. Offering personalized services to users/programs. Data warehouse, databases, knowledge base... ETL Data Sources Data extraction, cleaning, transformation, loading. User specific data sources can be plugged into the system.
27 Technologies underneath F-Trade
28 F-Trade: the Story So Far Administration Center Algorithms Center Control Center Services Center User Center
29 Overview What is F-Trade? Objectives to build F-Trade Benefits for different parties System architecture of F-Trade Functionalities of F-Trade F-Trade Conclusions and future work
30 Functionalities of F-Trade Data services: Data source connections Data plug-in Data preprocessing and post-processing Algorithm services: Algorithm plug and play Trading rules, mining algorithms, etc. Evaluation (back-testing), optimization, recommendation System services Agent service-based module plug and play Online accessing Automated interface configuration System personalization and reconstruction Trading/mining service provider: Subscription-based services utilization Huge stock data Trading/mining system
31 Functionalities of F-Trade Transparent: users, data sources, algorithms, services, operational systems Flexible: plug in, reconstruction, interfaces, user profiles Safe: your privacy of data and algorithms can be kept Online: registration, plug in, subscription, and iterative evaluation of algorithms, data sources, components Distributed: platform, data sources, users Profitable: High opportunities for commercialization Customizable: F-Trade can be personalized, customized or re-constructed online on your demand
32 Overview What is F-Trade? Objectives to build F-Trade Benefits for different parties System architecture of F-Trade Functionalities of F-Trade F-Trade Conclusions and future work
33
34 Intelligent System Reconstruction: Function Tree
35 Intelligent System Reconstruction: User Permission
36 Plug in & Intelligent Interface Configuration of Algorithms
37 Iterative Running, Testing, Evaluation of Algorithms
38 Online services-based usage of algorithms
39 VWAP Execution IV
40 Summary & Future Work F-Trade as an infrastructure supporting trading and mining in stock markets Data mining in stock markets through the infrastructure As a trading system Semi-language for coding trading strategies in the F-Trade Trading services As a data mining system Mining services Data mining in stock markets Stock order stream pre-processing & post-processing (visualization) Optimization of trading strategies using data mining Correlation analysis of stocks/rules/stock-rule pairs Pattern discovery of (large) orders based on order book Risk analysis and management using data mining Cross market mining
41 Thank you for your attention! Any suggestion is welcome! Contact us: Chengqi Zhang Longbing Cao www-staff.it.uts.edu.au/~chengqi www-staff.it.uts.edu.au/~lbcao
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