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Intelligent Trading

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  1. Intelligent Trading Prepared by: Jerry Gao, Ph.D. Date: 11/1/2000 Vanguard Software LLC.

  2. Intelligent Trading • Introduction to Intelligent trading • Why intelligent trading? • What is Intelligent Trading? • Benefits and applications • Intelligent trading Processing • Intelligent trading methods, models, and metrics • Basics of intelligent trading • Trading models and methods • Trading analysis metrics • Intelligent trading services • Model-based information presentation for real-time trading • Intelligent decision making for a trading process

  3. Intelligent Trading - Introduction Why Intelligent Trading ? The major current problems in e-commerce real-time trading under: • lack of systematic intelligent support for traders in the aspects of making fast and intelligent decisions in e-commerce trading • Select and screen out the trading parties in a fast way • Make the intelligent trading decisions to help in reaching deals • Provide intelligent assistance in update of proposals or bidding • Provide real-time, effective and multiple views to presents trading information and trading positions for all involved parties • lack of intelligent mechanisms and methods to help traders to make deals

  4. Intelligent Trading - Introduction What is Intelligent Trading ? The major goal is to provide effective solutions to solve the existing e-commerce trading problems. The major objective of intelligent trading is to provide intelligent assistance to trader parties in e-commerce trading based on various intelligent trading models in e-commerce trading . Basic scope of intelligent trading: • Trading decision models and algorithms • Trading information models and analysis methods • Model-based trading services

  5. Intelligent Trading - Introduction Major advantages and benefits in e-trading: • Deal-oriented trading models increase the successful rating of e-trading transactions. • Provide a fair and efficient e- trading market . • Support intelligent trading assistance and service Major benefits of e-traders: • Reduce tedious efforts of trading parties • Provide various model-based intelligent trading models • Provide real-time trading information for traders in multiple views, including their competitors • Support traders to make smart decisions in selecting trading parties, making decisions, and updating proposals.

  6. Intelligent Trading - Processing Rule-based trading registration Trading process for sellers: Send New Proposals to Buyers Post Products For Sale Update Trading Proposals Receive Proposals from Buyers Decision Analysis and Recommendations Comparative Report & Analysis Smart Selection of Trading Parties

  7. Intelligent Trading - Processing Rule-based trading registration Trading process for buyers: Send New Proposals to Sellers Post Wanted Products Update Trading Proposals Receive Proposals from Sellers Decision Analysis and Recommendations Comparative Report & Analysis Smart Selection of Trading Parties

  8. Intelligent Trading - Models, Methods, and Metrics Four types of trading models: • Trade information model • Trading proposal analysis models and metrics • Trading party selection models and metrics • Trading decision models • Static target-oriented decision analysis model • Dynamic target-oriented decision analysis model • Adoptive decision model • Design experience model • Decision analysis models and metrics

  9. Intelligent Trading- Trade Information Model Trading Proposal Product Features Others Delivery Schedule Product Cost Product Quality Product Quantity Feature Factors Other Factors Schedule Factors Cost Factors Quality Factors Quantity Factors FF CF QF QAF OF SF Sub-Feature factors SFF

  10. Intelligent Trading- Trade Information Model Formal trade information model (FTIM): FTIM (Pi) = (FF, SF, CF, QF, QAF, OF), where • Pi is a received proposal • FF is a set of product feature factors • SF is a set of factors relating to product delivery schedule • CF is a set of product cost factors • QF is a set of product quality factors • QAF is a set of product quantity factors • OF is a set of other factors relating to shipping, payment, and liability insurance

  11. Intelligent Trading- Trade Information Model Factors in a proposal refer to conditions like Boolean expression and operation expression. All factors in each category can be classified into the following groups. They are: • Desirable factors (DF) • Important factors (IMF) • Required and necessary factors [RF] • Alternative factors [AF] • Optional or not required factors [OF] • Unimportant factors [UF] • Undesirable factors [UDF]

  12. Intelligent Trading- Trade Information Model Data types involved in factors: • Enumerate type, • such as color = green, red, …. • Boolean type, • e.g. CPU model = MODEL #1 • Date and time type, • e.g. delivery schedule date= 10/1/00 • Float data type, • e.g. total cost = $400.99 • Integer data type, • e.g. memory size = 256K • Operational expression, • e.g. total cost > $4000

  13. Intelligent Trading- Trading Proposal Analysis & Metrics Trading proposal analysis is used to assist traders to evaluate received proposals against a targeted proposal. There are several types of targeted proposals: - Initial targeted proposal - Minimum targeted proposal - Desirable targeted proposal - Reasonable targeted proposal Idea: Evaluate a pair of proposals (Rpi, Tpi) based on its information model and pre-defined analysis metrics. - Rpi is a received proposal - Tpi is a targeted proposal

  14. Intelligent Trading- Trading Proposal Analysis & Metrics Analysis metrics are a set of metrics which can be used to measure how well two proposals match each other. Let’s define the following proposal analysis ranking metrics for a pair of proposals (Pi, Pj): M [GFF(Pi, Pj)] - Match ranking metric based on product feature factors. M [GSF(Pi, Pj)] - Match ranking metric based on delivery schedule factors. M [GCF(Pi, Pj)] - Match ranking metric based on product cost factors. M [GQF(Pi, Pj)] - Match ranking metric based on product quality factors. M [GOF(Pi,Pj)] - Match ranking metric based on other product factors. They can be used to find out the distances between two proposals in each area.

  15. Intelligent Trading- Trading Proposal Analysis & Metrics A total match ranking metric can be used to measure the overall distance between two proposals. Let’s define TM[Pi, Pj] as the total distance ranking metric below: TM[Pi,Pj]= M [GFF(Pi, Pj)] * WFF + M [GSF(Pi, Pj)] *WSF + M [GCF(Pi, Pj)] *GCF + M [GQF(Pi, Pj)] *WQF + M [GOF(Pi, Pj)] *GOF Where WFF , WSF ,WCF ,WQF ,WOF are the per-defined and configurable weights for different categories respectively.

  16. Intelligent Trading- Trading Proposal Analysis & Metrics Since we can use the same way to compute them, here we just use M [GFF(Pi,Pj)] as an example to show the basic idea. We first classify all product feature factors into the following groups. Closely matched factors No matched factors Strongly matched factors Weakly matched factors Unmatched factors Pi RF AF OF UMF UDF IMF DF Pj RF IMF AF OF UMF DF UDF

  17. Intelligent Trading- Trading Proposal Analysis & Metrics Since we can use the same way to compute them, here we just use M [GFF(Pi,Pj)] as an example to show the basic idea. We can compute the following metrics first: - Strongly matched number (SMN): SM [GFF(Pi,Pj)] = total no. of strongly matched factors between Pi and Pj. - Closely matched number(CMN): CM [GFF(Pi,Pj)] = total no. of closely matched factors between Pi and Pj. - Weakly matched number(WMN): WM [GFF(Pi,Pj)] = total no. of closely matched factors between Pi and Pj. - Unmatched number(UMN): UM [GFF(Pi,Pj)] = total no. of unmatched factors between Pi and Pj. - No matched number(NMN): NM [GFF(Pi,Pj)] = total no. of no matched factors between Pi and Pj.

  18. Intelligent Trading- Trading Proposal Analysis & Metrics The matched rate for GFF(Pi,Pj) is defined as: SM [GFF(Pi,Pj)] + CM [GFF(Pi,Pj)] + WM [GFF(Pi,Pj)] MR [GFF(Pi,Pj)] = ------------------------------------------------------------------ UM [GFF(Pi,Pj)] + NM [GFF(Pi,Pj)] The unmatched rate for GFF(Pi,Pj) is defined as: UM [GFF(Pi,Pj)] + NM [GFF(Pi,Pj)] UMR [GFF(Pi,Pj)] = ---------------------------------------------------------------- SM [GFF(Pi,Pj)] + CM [GFF(Pi,Pj)] + WM [GFF(Pi,Pj)] M [GFF(Pi,Pj)] = MGFF(Pi,Pj) / ( MGFF(Pi,Pj) + UMGFF(Pi,Pj) )

  19. Intelligent Trading- Trading Proposal Analysis & Metrics Generate multiple comparative views based on proposal analysis metrics: • Table view • Line curve view • Bar chart view • Pie chart view • Polygon view

  20. Intelligent Trading- Trading Proposal Analysis & Metrics M MGR UMGR SM CM WM UM NM P0 X XX XX XX XX XX XX XX XX P1 X XX XX XX XX XX XX XX XX P1 X XX XX XX XX XX XX XX XX ..…. …...

  21. Intelligent Trading- Trading Proposal Analysis & Metrics Polygon View P0 GFF GOF Pi P3 P1 GQF GCF P2 GSF P4

  22. Intelligent Trading- Trading Party Selection Models Design experience decision model: • Idea: • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  23. Intelligent Trading- Trade Decision Analysis Model Target-oriented decision model: • Idea: • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  24. Intelligent Trading- Trade Decision Analysis Model Adoptive decision model: • Idea: to come out new decision based on the data in the given proposal. • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  25. Intelligent Trading- Trade Decision Analysis Model Design experience decision model: • Idea: • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  26. Intelligent Trading- Proposal Analysis Models and Metrics Design experience decision model: • Idea: • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  27. Intelligent Trading- Decision Analysis Methods & Metrics Trading decision analysis methods are used to : • Idea: • Strategy: • Methods: • Decedent • Random • Decision analysis models and metrics

  28. Intelligent Trading Services