CA2331775A1 - System and method for an automated exchange - Google Patents

Abstract

The invention provides a system, computerized method, and computer program for operating an automated combinatorial exchange for trading items. Functions or steps of the invention include receiving a plurality of orders (a "package"
that may include logically grouped items) offering to sell items and a package of orders offering to buy items. The method includes steps of allocating trading quantities of items to a portion of the orders (14), and assigning trading prices for each item allocated a trading quantity in an order (16).

Description

SYSTEM AND METHOD FOR
AN AUTOMATED EXCHANGE
CLAIM OF PRIORITY
This application claims the benefit of priority to U.S. Provisional Application No.
60/119,888, filed February 12, 1999.
TECHNICAL FIELD
This invention relates generally to trading markets and, more particularly, to automated methods and systems for trading items in a combined value exchange.
BACKGROUND
An investor often holds a portfolio containing a mixture of types of tradable items, such as securities (e.g., stocks, bonds, futures), pollution credits, power resources, commodities, resource allocations, etc. The particular mixture gives the portfolio a variety of properties, such as yield and long-term stability. Typically, the investor structures trades so that these properties are maintained or improved. Maintaining or improving the properties often means that the investor wants to trade several different types of items in each trade.
Such a market is known as a combined value trading market or a combinatorial exchange.
~5 As an example, the traditional bond market operates by bilateral transactions in which each transaction is for one type of bond. To trade several types of bonds, an investor makes a series of bilateral transactions with different investors or brokers, each transaction being for a portion of the trading goal. Since the trade progresses through a series of transactions, the investor takes a new contractual risk at each step without the assurance that the investor will 2o finally achieve a defined trading goal.
The series of transactions involves a second risk, because information is disclosed at each transaction. Information from earlier transactions alerts other market traders to the investor's goals. By making a series of transactions, the investor risks less favorable deals from later traders who may adjust their trading prices based on the previously disclosed 25 information.

To avoid adverse market consequences from information disclosure, an investor frequently hedges bid and/or asking prices to hide the investor's true trading goal. The investor offers to trade at prices below the maximum price at which the investor is willing to buy and above the minimum price at which the investor is willing to sell.
Hedging deprives other investors of information but is generally used in an attempt to produce better prices for the investor. Unfortunately, hedging also lowers the probability that each transaction will be consummated.
For complex mixtures of trading items, the probability that a trader can achieve a desired trading mixture decreases rapidly. Hedging further frustrates prospects for obtaining complicated mixtures by a series of separate bilateral transactions for single items.
The present invention is directed to reducing or overcoming the effects of one or more of the problems set forth above.
SUMMARY
The invention provides a system, computerized method, and computer program for ~5 operating automated one- or two-sided combinatorial exchanges for trading items. Functions or steps of the invention include receiving any of a plurality of orders (a "package" that may include logically grouped items) offering to sell items, a package of orders offering to buy items, and a package of orders offering to buy and sell items. The method includes the principal steps of allocating trading quantities of items to a portion of the orders, and 20 assigning trading prices for each item allocated a trading° quantity in an order.
In various embodiments, at least one of the orders is selected from a group consisting of a proportional order, an indifferent order, and a complex order. In various embodiments, the items traded are selected from a group consisting of securities, commodities, property rights, contracts, futures, etc.
25 In one aspect, the invention includes a computerized method of operating an automated market for trading items, including receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to trade a plurality of types of items; allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders; assigning trading prices to each order for each item therein allocated a nonzero trading quantity using at least a direct accommodation algorithm; and outputting information indicative of the allocated trading quantities and assigned trading prices.
In another aspect, the invention includes a computerized method of operating an automated market for trading items, including receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being an indifferent order offering to trade between a selection of different items among a plurality of types of items; allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders; assigning trading prices to each order for each item therein allocated a nonzero trading quantity; and outputting information indicative of the allocated trading quantities and assigned trading prices.
In another aspect, the invention includes a computerized method of operating an automated market for trading items, including receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to t5 trade a plurality of types of items; allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders; assigning trading prices to each order for each item therein allocated a nonzero trading quantity using a plurality of pricing paths; and outputting information indicative of the allocated trading quantities and assigned trading prices.
2o In another aspect, the invention includes a computerized method of operating an automated market for trading items, including receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items from a plurality of traders, at least one of the orders offering to trade a plurality of types of items; allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the 2s orders; assigning trading prices to each order for each item therein allocated a nonzero trading quantity; re-pricing each order of a trading entity having a per unit surplus value by redistributing such per unit surplus value to other units of such order; and outputting information indicative of the allocated trading quantities and assigned trading prices.
In another aspect, the invention includes a computerized method of operating an 3o automated market for trading items, including receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being selected from a group consisting of a proportional order, a relaxed proportional order, and a complex order; allocating trading quantities of items to a portion of the orders using a method to increase a total market gain from trading, the trading quantities satisfying trading constraints imposed by the orders; assigning trading prices to each order for each item therein allocated a nonzero trading quantity; and outputting information indicative of the allocated trading quantities and assigned trading prices.
The embodiments include computerized systems and program storage media encoding executable programs for performing the above-described methods.
1o The details of one or more embodiments of the invention are set forth in the accompa-nying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.
DESCRIPTION OF DRAWINGS
FIG. 1 shows a system for trading a class of items.
~5 FIG. 2 is a flow chart illustrating a method for trading items in a call market.
FIG. 3 graphically represents two exemplary simple orders.
FIG. 4A graphically represents an exemplary proportional order.
FIG. 4B graphically represents an exemplary relaxed proportional order.
FIG. 5 graphically represents an exemplary indifferent order.
2o FIG. 6 is a flow chart illustrating a method for allocating trading quantities of items to orders according to the method of FIG. 2.
FIG. 7 is a flow chart for a method of eliminating orders with low probabilities of trading according to the method of FIG. 6.
FIG. 8 graphically illustrates allocation and pricing of trading items in a single price 25 call market.
FIG. 9 is a flow chart illustrating a method of separating orders into partitions to optimize potentials for intra-partition trades.
FIG. 10 is a flow chart illustrating a method of allocating trading quantities according to the method of FIG. 9.

FIG. 11 graphically illustrates how order trading conditions on price restrict the market price assigned to trading items.
FIG. 12A is a graph allowing direct accommodation for a trade in a single item call market where apportioning of costs to satisfy minimum fill conditions occurs among trading orders A-D and F-H.
FIG. I2B is a graph requiring general accommodation for a trade in a single item call market where apportioning of costs to satisfy minimum fill conditions occurs among trading orders A-D and F-H.
FIG. 13 is a flow chart for a method of pricing previously allocated trading quantities of items.
FIG. 14 is a flow chart illustrating a method of ticketing allocated and priced quantities of items.
FIG. 15 is a flow chart illustrating a method of assigning trading buys and sells for direct ticketing and indirect ticketing, respectively.
~5 FIG. 16 is a flow chart illustrating a method ofmatching up buys and sells for either direct or indirect ticketing.
FIG. 17 is a flowchart showing an overview of the processes of a matching engine suitable for use in a second embodiment of the invention.
FIGS. 18A, 18B, and 18C are related flowcharts showing details of the embodiment 2o described in FIG. 17.
Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION
Glossary The detailed description of the embodiments may use the following terms.
Accommodation - The process whereby Marginal Orders that are also Inflexible Orders compensate the market for their inflexibility. Accommodating these orders (and Extra-Marginal Orders) into the market adds liquidity to the market. Denying an order the opportunity to accommodate the market for the order's inflexibility and thus denying that order the opportunity to trade, can cause other orders to be also no longer able to trade. Because of the potential interconnectedness of the orders and items in the market, it is possible that the entire trading session would collapse. This partial or complete collapse of trading robs the market of liquidity, which is an undesirable event for all traders in the market.
Allocation - The process of matching buys and sells by volume (observing all Trading Conditions for the orders being traded).
All-or-Nothing (AON) - A completely inflexible trade requiring that a trade meet the ~ 5 maximum requirements of the order, or that nothing in the order trades at all.
ANDI Logic (AND Indifferent) - This logic is used to force all orders connected by the ANDI logic to execute to some extent, or else no order in the group will execute. Typically, if any one order cannot execute, then none of the orders executes. However, if an order has no special trading conditions on it, that is, the order is fully flexible, then that order is considered to 2o be executed, even if no item in the order is traded, for purposes of determining the ANDI logic execution.
ANDP Logic (AND proportional) - This logic is used to force all orders in the group to execute in strict proportion to each other. If the orders cannot be traded in proportion, then none of the orders executes. The proportion between orders is measured by the percent fill of each 2s order.
AND Group - A set of orders and/or groups linked together with AND logic.
Asking Price - The sale price offered by a seller for specified items.
Bid Price - The purchase price offered by a buyer for specified items.

Budget Limit - This specifies the maximum desired cash flow for an Indifferent Order.
The Budget Limit limits the total volume traded by an Indifferent Order. Note that Proportional and Relaxed Proportional Orders specify their Budget Limits indirectly through their Unit Offers and Maximum Quantities.
Call Period - A period for submitting orders to buy and/or sell items. The call is the set of orders received during the call period.
Canonical Order - The most basic form of an order. It is a set of one or more firm offers submitted to buy and/or sell items. An order may include trading conditions.
Order types include Simple Orders, Proportional Orders, Relaxed Proportional Orders, and Indifferent Orders.
Canonical Orders may be logically connected with other Orders using Inter-Order Logic to create Strategies.
Child Order - This is an order that is a member of a Parent Order. A Child Order may itself be a Parent Order, with Child Orders beneath it. A Bottom Level Child is always a Canonical Order.
~5 Clearance & Settlement-The escrow process used to physically consummate trades.
Clearing Price (Market Price) - In a call market having a single trading price, the clearing price is the calculated price at which those items trade. The clearing price is determined by the bid and asking prices of the orders with items trading at the margin (Marginal Orders).
Combinatorial Trading -- The opportunity to engage in the simultaneous buying and 2o selling of many combinations of things.
Contra - short for contraposition. The contra of an order to buy an item is an offer to sell the item.
Delivery-sized Data File - a performance benchmark set of data consisting of 50,000 orders and 2,000 commodities.
25 Delta - This parameter delineates how far from an ideal proportion line an order is allowed to deviate in an attempt to trade. As used, delta specifies the percent deviation from the ideal proportion line. Delta is used in conjunction with Relaxed Proportional Orders.
Direct Ticket - A trading ticket for a trade made directly between the buyer and seller.
No intermediaries are involved.

Execute - An order that meets its minimum Trading Conditions trades or executes. An order that meets its maximum Trading Conditions fully executes.
Extra-Marginal Order-An order that is allocated and resides to the right ofthe Market Cross. These orders are allocated solely to accommodate an inflexible marginal order.
Flexibility Parameters - A set of parameters that may be used to describe ranges of acceptable trading conditions for an order, such as quantities, total monetary amounts, and item compositions. Flexible orders are orders that may be allocated over a wide range of possible trades. Inflexible orders are orders that may trade in a very narrow range of possible conditions.
Flexibility Parameters include Delta, Minimum Fill, Minimum Quantity, and Odd Lot.
Flexible Order - An order where the trader places minimal limitations on the degree to which an order executes. Fully Flexible Order is an order where the trader is willing to accept any degree of execution of his order from none to full. The more flexible an order is, the more likely it is to trade (provided it specifies competitive unit offers).
Flexibly Allocated, Zero Surplus - FZS. The order is in the Flexible Allocation, but has zero surplus. The order may be fully or partially flexibly allocated.
Flexibly Fully Allocated Order - FA. This is an order that trades to its allocated limits during the flexible allocation phase.
Flexibly Partially Allocated Order - FPA. This is an order that has traded, but not to its allocated limits during the flexible allocation phase.
Flexibly Rejected Order - FR. This is an order that did not trade during the flexible allocation phase.
Fully Allocated Order - This is an order that has traded to its maximum limits.
Gains-from-Trade - This is a calculated value that gives an indication of the efficiency of the market, and how much improvement an order achieved over its monetary trading conditions. The gain of an item being bought is the value of the bid price minus the market price, times the quantity traded. The gain of an item being sold is the value of the market price minus the asking price, times the quantity traded. The gain for an entire order is the sum of the gains of the items bought and sold therein. The gain for the market as a whole is the sum of the gains of all of the orders being traded.
_g_ Indifferent Order - An order that allows the trader to benefit from his willingness to trade between a selection of different items, where the trader perceives those items as being functionally equivalent. For example, if a trader wishes to buy a dozen muffins and does not care whether they are banana nut, cranberry, or bran muffins, then an Indifferent Order allows the trader to specify all three equivalent muffin types and end up with the best available mix of the three muffin types. With Indifferent Orders, the total Trading Quantity is determined by the Indifferent Order's budget, and the individual items are not guaranteed to trade in any particular ratio. An Indifferent Order must be a pure buy or pure sell.
Indirect Ticket - A trading ticket for a trade in which an intermediary acts as the buyer for the seller, and the seller for the buyer. Usually there is a difference in the buy and sell price and that is why a Direct Ticket cannot be used. This price differential requires an intermediary to act as a clearing agent.
Inflexible Order - An order where the trader places stricter limitations on the degree to which his order executes. A completely inflexible order is an all-or-nothing (A01~ order. The less flexible an order is, the less likely it is to trade.
Infra-Marginal Order - An order which is allocated and resides to the left of the Market Cross. This order does not trade at or through the cross when a supply and demand curve is drawn using the allocated orders.
Inter-order Logic - The ANDI, ANDP, XOR, or KOR logic used to connect orders into 2o a strategy group.
Item - A commodity to be traded. An item can be any tangible or intangible thing for which a property right can be defined.
Item Count/List - The number and identification of the items in an order.
Key Order - The primary (most desired to execute by the trader creating the group) order in a KOR group. The Key Order is usually a package order. Generally, the Key Order is priced as a more attractive trade than the conglomeration of Minor Orders in a KOR group.
KOR Logic (Key Order OR) - This logic is used to allow a trader to trade an entire package, or to trade as many pieces of a package as possible. The KOR group is composed of one Key Order and a series of Minor Orders. The logic is performed such .that either the Key 3o Order executes, and none of the Minor Orders execute; or the Key Order does not execute, and at least one {perhaps all) of the Minor Orders execute. It is also possible that neither the Key Order nor any of the Minor Orders trade. Note that while KOR logic exhibits similarities to XOR
groups, they are not the same.
Liquidity Providing Strategy - a group of orders ANDed together to bring additional volume/liquidity to a market, provided certain price and contra-side volume requirements are met.
LP {Linear Program) - A system of linear equations used to model a problem.
Marginal Order - An order that trades at least one item at the Market Cross or extra-marginally. Marginal Orders can be single item or package orders. Marginal orders are useful in setting the market price for the items being traded.
Market Cross - The point at which the supply and demand curves for an item meet.
Traditionally in a single item market, all orders to the left of the cross trade, and no orders to the right of the cross trade.
Market Prices - The final prices at which various items trade. The market prices depend ~ 5 upon the item being traded and the orders involved in trading that item.
The market price is the single price reported or quoted to the world at large. Note, however, that different orders may trade the same item at prices different from the market price. These are the various orders' Trading Prices. These differences are due to the system accommodating the rigidity of certain orders in order to add liquidity to the market as a whole.
2o Maximum Quantity - This specifies the upper limit for the number of units that an order is willing to trade of an item.
Minimum Fill - This specifies the minimum degree to which an order executes.
For a proportional or relaxed proportional order, the Minimum Fill generally applies to the total volume traded, but may apply to the Budget Limit. For an Indifferent Order, the Minimum Fill 25 applies to the Budget Limit.
Minimum Increment - This is the minimum quantity step that an order is willing to trade of an item.
Minimum Quantity - This describes the minimum acceptable trading quantity for an item in an Indifferent Order. That is, an item must trade at least the Minimum Quantity in 3o volume, or it will not trade at all.

Minor Orders - The orders representing a less desirable trading mix than the Key Order.
Typically, Minor Orders are created by making a series of simple orders or orders of smaller packages of items using the same items specified in the Key Order. However, the Minor Orders can be orders with little or no overlap in items with the Key Order. The composition of the Minor Orders is entirely up to the trader.
MIP (Mixed Integer Program) - An LP in which one or more of the variables are integers.
Natural Decomposition -- The process where a partition of orders can be cleanly split into two or more independent groups where none of the groups share any items in common.
Odd Lot - This describes the minimum residual quantity acceptable in an item sold by an Indifferent Order. That is, an item for sale in an order with an Odd Lot for that item must trade the Maximum Quantity for that item, or zero quantity, or between the Minimum Quantity and (Maximum Quantity - Odd Lot).
OR Group - Shorthand term for XOR Group: a set of Canonical Orders and/or Order Groups linked together with XOR Logic.
Order - A set of one or more firm offers submitted to buy and/or sell items.
An order may include trading conditions. Order types include Simple Orders, Proportional Orders, Relaxed Proportional Orders, and Indifferent Orders. Orders may be logically connected with other orders to form Strategies.
2o Order Group - Any set of orders linked together with Inter-Order Logic. An Order Group may be composed entirely of Canonical Orders, or entirely of Order Groups, or a mixture of both.
Order Logic - The Boolean-style logic used to connect two or more orders together to create a strategy. The basic logic types are exclusive OR and AND.
2s Order Type - The general character of an order: simple, proportional, relaxed proportional, or indifferent.
Package Order - This is the same as a combinatorial order. It is composed of at least two items for trading at specified prices per unit traded.
Partition - A group of orders.

Parent Order - Canonical Orders and Order Groups may be nested together, with no theoretical limit to the depth of nesting. A Parent Order is an order that defines which orders are logically linked together. A Parent Order may have a Parent Order above it. A
Top Level Parent Order is the order at the top of the nesting hierarchy.
Partially Allocated Order - This is an order that has traded between its minimum and maximum limits.
Portfolio - This is a complete group of items or investments held by a trader.
A portfolio trade occurs when a trader acts to change the composition of his portfolio.
Pricing - The action of determining Market Prices for all Allocated Items in a Call.
Proportional Order - This is an order offering to trade two or more items in conjunction. These items are offered to trade in strict quantity ratios between the items. For example with a two item order having a ratio of 2:3, for every two units of item A traded there must be three units of item B traded; or with a three item order, the three items might trade in a ratio of 3:2:17. Any number of items may be included in a proportional order.
A proportional order may be a pure buy, a pure sell, or a swap order.
Pure Buy Order - An order composed only of items offered for purchase.
Pure Sell Order - An order composed only of items offered for sale.
Relaxed Proportional Order - This order is similar to a proportional order in that it is an order offering to trade two or more items in conjunction. However, the items offered for trade 2o are expected to trade in approximate proportion as opposed to strict proportion. For example, a three item order may be offered to trade in an ideal ratio of 3:2:10, but the trader is willing to trade in ratios within 10% of the ideal ratios. In this example, the trader is willing to trade in ratios of 2.7:2:9, or 3:1.8: I0, and so on over all possible ratio combinations that are within 10%
of the trader's ideal ratio.
Re-pricing - An action whereby the Gains from Trade for an individual order are redistributed equally among all items (units) trading. Normally, the Gains from Trade are distributed so that the order as a whole has positive gains, but some items may have negative gains when viewed in isolation. With re-pricing, the Gains from Trade are distributed by re calculating the Trading Price so that each Item has positive Gains from Trade relative to the order's original Unit Offers.

Simple Order - This order is one side of a traditional bilateral trade. There is a single item offered for sale or to purchase at a specified price per unit traded.
Standalone Order - This is a Canonical Order that has no Parent Order. That is, the order is not logically linked to any other order.
Strategies - Trading strategies are implemented by logically grouping two or more orders together in an attempt to achieve a more complex trade sequence. For example, a trader may want to execute a liquidity providing strategy. The trader offers to sell more volume in an item or a series of items if the trader can get a better price and if there is additional demand for the items) at the better price.
1o Swap Order - This is an order in which various items are offered for sale, and other items for purchase within a single order.
Thick Item - This is an item where a relatively large number of orders are offering to trade the item.
Thin Item - This is an item where a relatively small number of orders are offering to trade the item.
Tickets - Bilateral contracts for one item in which a buyer buys and a seller sells the same quantity of a traded item at the same price. Tickets are used to facilitate the integration of a combinatorial market's results into traditional clearance and settlement mechanisms.
Trading Conditions - These are conditions imposed on the offers of an order by the 2o trader. The conditions may set minimum asking prices, maximum bidding prices, maximum and/or minimum quantities to trade, item compositions of trades, and logical relations between other orders. Trading conditions may be fixed values or set ranges of acceptable values. Trading Conditions include Budget Limit, Inter-Order Logic, Item CountlList, Maximum Quantity, Minimum Increment, Order Type, and Unit Offer, along with all Flexibility Parameters.
Trading Price - This is the actual price per unit paid or received by an order for a particular item being traded by that order. The trading price for an item in an order can be different from the clearing price for that same item in the open market.
Trading Quantity - This is the actual volume trading for an item in an order.
This number is always less than or equal in magnitude to the Maximum Quantity of the item.

Unallocated Order -An order that did not trade either because its minimum acceptable limits could not be met or other more attractive orders beat it out.
Unit - This is a standard single quantity of volume in an item, e.g., a pound, a bond, a lane. Items need not trade in integer units, as some applications may permit trading a half ton of an item where Maximum Quantities are normally specified in tons.
Unit Offer - The target bid or ask price that an order presents per unit volume of item.
XOR Logic (exclusive OR) - This logic is used to force only a single order in the group to execute. No more than one order in an XOR group may ever execute.
Your Price - This is the same as Trading Price.
1o Overview of Operational Environment FIG. 1 shows a system 2 for trading items in a market. The items traded may belong to any class of items tradable together on a market, such as securities (e.g., stocks, bonds, futures), pollution credits, power resources, commodities, resource allocations, etc.
During a preselected call period, the system 2 accepts orders submitted through client 15 computer tenminals 3-5. The computer terminals 3-5 execute (either directly or through an attached server computer) a program for submitting orders to and fox receiving trading information from a central processing system 6 {e.g., a server). The trading information includes data on previously received orders, e.g., offered buy/sell quantities and/or prices. A
trader controls the availability of information about the trader's order (e.g., prices, quantities, 2o etc.) to other traders through commands submitted with the order. The computer terminals 3-may execute software providing a graphical user interface (GUI) to submit orders and receive trading information and results. One such GUI suitable for trading bonds is the BR117GESTATION GUI available from Bridge Information Systems.
The.processing system 6 includes at least one program storage device 7 for storing 25 software and, preferably, one or more high speed processors 8 for executing portions of the program. A plurality of processors 8 may be configured as a loosely or tightly coupled parallel processing system. The software program determines trading quantities, trading prices, and, optionally, bilateral settlement tickets for the items of each order through methods described below.

The invention may be implemented in hardware or software, or a combination of both (e.g., programmable logic arrays). Unless otherwise specified, the algorithms included as part of the invention are not inherently related to any particular computer or other apparatus. In particular, various general-purpose machines may be used with programs written in accordance with the teachings herein, or it may be more convenient to construct more specialized apparatus to perform the functions described herein. However, preferably, the invention is implemented in one or more computer programs executing on one or more programmable computer systems each including at least one processor, at least one data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device or port, and at least one output device or port. Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices, in known fashion.
Each such program may be implemented in any desired computer language (including machine, assembly, or high level procedural, logical, or object oriented programming languages) ~5 to communicate with a computer system. In any case, the language may be a compiled or interpreted language.
Each such computer program is preferably stored on a storage media or device (e.g., magnetic, optical, or solid-state media) readable by a general or special purpose programmable computer system, for configuring and operating the computer when the 2o storage media or device is read by the computer system to perform the functions described herein. The inventive system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer system to operate in a specific and predefined manner to perform the functions described herein.
25 Overview of Trading System and Method FIG. 2 illustrates an automated method 10 of trading items on a market according to one embodiment of the invention. After a predetermined time, the processing system fi closes the call period for receiving orders and starts processing the orders received, i.e., the orders of the call (step 12). The processing system 6 allocates trading quantities for each item type to each received order (step 14). The processing system 6 determines the trading quantities using a method that maximizes the overall market gain from the entire set of trades without violating trading conditions imposed by orders. The processing system 6 may allocate zero trading quantities to some item types in some or all of the orders. Next, the processing system 6 assigns a trading price to each trading quantity already allocated to an order (step 16). The processing system 6 assigns trading prices and quantities so that each trading order has a non-negative total gain. The steps of allocating and assigning price may include creating a table in the storage device 7. Such a table preferably lists the trading quantities and prices for each order without listing which trader will accept the offers in the orders.
Finally, the processing system 6 determines the identities of the accepting traders and assigns the trading quantities to bilateral transactions at a single trading price between individual offerors (step 18). Optionally, "tickets" (enforceable trading contracts) may be output. More generally, the system may simply output a report of matched trades.
For ease of understanding, the remainder of the description will discuss markets for ~5 trading securities. Nevertheless, the scope of the invention includes any class of items having a market for combined value trading.
Order Types The system 2 and method 10 of FIGS. 1 and 2 can process the following types of orders: simple orders, proportional orders, relaxed proportional orders, and indifferent orders.
2o An order may include trading conditions. Each order can include offers either to buy or to sell, and some orders can include both simultaneously. Orders may be logically connected with other orders to form strategies.
Simple orders are offers to buy or sell one type of security. A simple order specifies the item type, the asking or bid price, and a maximum quantity. The offer is for a buy or a 25 sell according to whether the maximum quantity is positive or negative, respectively. The trader's order is a firm offer to trade the specified item subject to conditions. The trade quantity cannot exceed the specified maximum, and the price must satisfy the conditions. For offers to sell, the price condition is to sell at any price no less than the asking price. For offers to buy, the price condition is to buy at any price no greater than the bid price.

A simple order includes one flexibility condition expressing the offeror's flexibility to trade quantities. The flexibility condition is a minimum fill percentage. The minimum fill percentage times the specified maximum quantity is the minimum quantity that can trade.
The offer is conditioned on an acceptance of, at least, the specified minimum percentage times the maximum quantity in the simple order. Of course, the entire order can be rejected.
FIG. 3 graphically represents two simple orders C and D, made by the same offeror, for securities A and B, respectively. Before each trade, the offeror initially has 500 units of both security A and security B. In order C, the offeror offers to buy between S00 and 1,000 units of security A. The solid line between 1000 and 1500 units of security A
and located at 1o the pre-trade 500 units of security B illustrates the order C. Thus, the maximum trading quantity for order C is 1,000 units of security A, and the minimum fill is 50%. In order D, the offeror offers to buy between 250 and S00 units of security B. The solid line between 750 and 1000 units of security B and located at the pre-trade 500 units of security A illustrates the order D. Thus, the maximum trading quantity is 500 units of security B, and the minimum fill 15 is again 50%.
A proportional order offers to buy and/or sell several types of securities. A
proportional order specifies the types of securities offered, the asking or bid price for each type, the maximum quantity to trade for each type, and a mix for the various types. The percentage mix fixes the numerical percentages of the various securities offered. FIG. 4A
2o graphically represents a proportional order using the quantities from FIG
3. Security types A
and B trade in strict proportion, resulting in a straight line S of permissible trading values.
A relaxed proportional order also has two flexibility parameters. The first flexibility parameter is the minimum fill percentage, F. The minimum fill times the sum of all maximum quantities offered for each security defines the minimum quantity of securities 25 upon which the offer is conditioned. The second flexibility parameter, 8, is a range for the percentage mix of the individual securities in the order. Alternatively, the minimum fill for relaxed proportional orders may be based on a budget limit rather than on total quantity.
FIG. 4B graphically represents a relaxed proportional order E for a mixture of the security types A and B by the crosshatched region E. The order E is an offer to buy a 3o maximum of up to 1,000 units of security A and up to 500 units of security B at a mixture of 66.67% security A and 33.33 % security B. The dashed line of FIG. 4B labeled "Mix"
indicates the precise mixture of 66.67% A and 33.33% B values. Given an initial portfolio position of 500 units of A and 500 units of B, the order's maximum buy limits of 1,000 units of A and 500 units of B would give the offeror a maximum of 1,500 units of A
and 1,000 units of B. These upper limits are indicated by boundaries "Max A" and "Max $"
of the region E of FIG. 4B. The order E gives the minimum fill constraint F as 50%
and is thus, conditioned on trading, at least, 750 units of securities A and B together as indicated by the boundary line MF in FIG. 4B. Finally, the order E gives the flexibility 8 of the mixture as 50%. Thus, the order offers to trade any mixture including between 50% less of security A, as indicated by boundary L1, to 50% less of security B, as indicated by boundary L2.
A relaxed proportional order is similar to a proportional order in that it is an order offering to trade two or more items in conjunction. However, the items offered for trade are expected to trade in approximate proportion as opposed to strict proportion.
For example, a three-item order may be offered to trade in an ideal ratio of 3:2:10, but the trader is willing to ~5 trade in ratios within 10% of the ideal ratios. In this example, the trader is willing to trade in ratios of 2.7:2:9, or 3:1.8:10, and so on over all possible ratio combinations that are within 10% of the trader's ideal ratio.
An indifferent order is a unique mufti-security offer conditioned on total trading dollar amounts rather than security composition. The offers for the individual securities of 2o the order are either all buys or all sells. An indifferent order gives the types of securities offered, the asking or bid prices for each type as appropriate, the maximum quantity to trade for each type, and the maximum dollar amount for the trade.
An indifferent order also has two flexibility parameters. The first flexibility parameter, F, gives a minimum fill percentage of the total dollar amount on which the offer is 25 conditioned. The second flexibility parameter, minQty, specifies a minimum quantity of each type of security to trade. The second flexibility parameter must be less than the smallest of the specified maximum trading quantities for the various~securities. A sell order may have a minimum leftover (odd lot) quantity restriction as well.
FIG. 5 illustrates the allowed trading region "F" of an indifferent order for a sell.
3o Before the sell, the seller's pre-trade situation is 1,500 units of security A and 1,250 units of security B. The trading region F has boundaries "I" and "J" indicating the respective maximum sale quantities of 1,000 units of security A and 750 units of security B. The trading region F also has boundaries "G" and "H" indicating minimum odd lot quantities of 125 units for each of A and B. Finally, the trading region has boundaries "K" and "L"
indicating a maximum dollar amount to sell of $1,250 and that the minimum fill of the maximum dollar amount is 50%. Note that this example is drawn using equal unit offers; the slopes of boundaries "K" and "L" will vary at different unit offers. The unit offers for each item in the order and the maximum Budget Limit must be known in order to draw "K "; "L" is a function of F times the Budget Limit. "K " and "L" will always be parallel to each other. The slopes of "K" and "L" are determined by the unit offers for "A" and "B", and the Budget Limit. With indifferent orders, it is possible to have one or more of the items in the order not trade, while at least one item does trade. This is shown in FIG. 5 by means of solid line segment "M" and "N" drawn on the dotted lines outside of boundaries "G" and "H". The line segments "M" and "N" are bounded by extensions of boundary "L" out to the dotted lines ~5 and extensions of boundaries "J" and "I" out to the dotted lines.
Traders can also submit complex orders that group together smaller orders with OR
and AND logic, examples of which are shown in TABLE 1 below. The OR and AND
logic correlates acceptance of orders connected by the logic and is preferably implemented by additional discrete valued (i.e., integer or binary) order variables.
2o Two types of OR logic can couple orders. The first type is an exclusive OR
(XOR). If XOR couples two or more orders, the system 2 of FIG. 1 only accepts the one of the coupled orders that maximizes the market gain or no order in the group trades at all.
The second type of OR logic, KOR logic, couples a "key" offer to "minor" offers. The system 2 only accepts either the key order or a subset of the minor orders or none of the orders trade. The selection 25 is made to maximize the overall market gain.
There are also two types of AND logic, which either trade all orders coupled by the logic or no orders coupled by the logic. For the first type of AND logic, i.e., AND Indifferent (ANDI) logic, the system 2 accepts all orders coupled by AND if each order so coupled at least partially trades. For the second type of AND logic, i.e., AND
Proportional (ANDP) logic, the system 2 accepts orders coupled by the logic if each order so coupled trades at the same fill percentage.
TABLE

LOGIC ORDER RESULT

A1 XOR A2 Either Al trades or A2 trades, or neither, but not both A1 KOR {B1, B2, B3, Either A1 trades or part or all of {Bl, ...} B2, B3, ...} trades, or none trade Al AND A2 Either A1 and A2 both trade, or neither trades If AND = ANDP Trade only occurs if A 1 and A2 have same fill percentage If AND = ANDI Trade only occurs if A 1 and A2 fill to any allowed percentage {A1 AND AZ} XOR B1 Either A1 and A2 both trade, or B1 trades, or nothing trades The various types of intra-order logic among offers may be implemented with logical connectors or discrete valued variables. In suitable situations, integer variables rnay be used.
First Embodiment A first embodiment of the invention is described in this section. This embodiment also provides an overview of the general functions performed by the second embodiment described below.
Allocating Trading Quantities FIG. 6 is a flow chart illustrating one method 14 (corresponding to step 14 of FIG. 2) for allocating the trading quantities to orders. First, the processing system 6 determines which orders have a low probability of trading and marks such orders as non-trading (step ~5 22). Next, the processing system 6 assigns the remaining orders to separate partitions by a method which assigns orders with higher potentials for trading among themselves to the same partition (step 24). Next, the processing system 6 finds a separate trading solution for each partition (step 26). Each separate solution fixes potential quantities of securities traded among the orders of the same partition. To find the separate solutions, the processing system 20 6 uses a method for separately optimizing the sum of the gains of the orders of each partition.

Next, the processing system 6 determines whether the step of determining separate solutions has used more than an allotted processing time for finding such solutions (step 27).
If not, the processor finds a new trading solution for the orders found to trade in step 26, by globally optimizing the gain (step 28). Then, the new trading solution determines trading quantities for the individual orders. If more than the allotted time has been used, the processor 6 determines the trading quantities for the individual orders from the separate solutions for each partition (step 29).
Eliminating Low-Probability Orders FIG. 7 illustrates a method 22 (corresponding to step 22 of FIG. 6) of eliminating orders with low probabilities to trade. First, the processing system 6 eliminates orders not satisfying volume conditions on trading quantities (step 32). For example, such orders include buys where minimum buy quantities are greater than the total quantities offered for sale. Next, the processing system 6 separates each order into suborders for single security types (step 34). For the suborders of each security type, the processing system 6 finds hypothetical trading quantities and prices that would result if the suborders formed a call submitted to a single-price call market for that security where each suborder is treated as "fully" flexible (step 36). The hypothetical trading prices are the clearing prices of these call markets. Next, the system 6 calculates a hypothetical gain for each order as the sum of the gains of the suborders of the order (step 38). Finally, the processing system 6 eliminates 20 orders with gains below a preselected threshold value (step 40). Only the remaining orders can trade, i.e., orders having higher potentials to trade.
Within indifferent orders, individual security types can also be eliminated.
Using the same scoring process within an order, the potential gain contributed to the order by each security type is compared to the pre-selected threshold. Those security types below the 25 threshold are eliminated from the indifferent order.
In some embodiments, the processor 6 performs a second elimination of orders not satisfying trading volume conditions after step 40. For the second elimination, volume conditions can be fulfilled only through trades with other orders having gains above the preselected threshold.

Single-Price Call Market Algorithm FIG. 8 is a graph 42 illustrating how the single-price call markets of step 36 in FIG. 7 determine the hypothetical trading quantities and prices the calls of fully flexible orders. The orders include offers to buy A-E and offers to sell F-J for a single item at the order prices and maximum quantities illustrated in FIG. 8. From left to right, the graph 42 displays offers to buy A-E and offers to sell F-J with respective bid and asking prices arranged in decreasing and increasing orders. Since the marginal orders are fully flexible, orders and portions thereof trade if they are to the left of crossing point 44 of the lines plotting the offers to buy A-E and offers to sell F-J. Thus, the offers to buy for the highest prices and to sell for the lowest prices trade (i.e., orders A-D and F-H). The clearing price is located in a region 46 between the asking price 48 and the bid price 49 of orders H and D at the margin of the trading region. In particular, the call markets of step 36 in FIG. 7 set the hypothetical trade price at point 50 halfway between the marginal asking and bid prices 48, 49.
Since the orders A-J are all fully flexible, the trading orders A-D and F-H trade at a trading price equal to the clearing price 50. The trading prices for trading orders A-D and F-H satisfy trading conditions, i.e., trading prices 49 are less than the bid prices and greater than the asking prices 48.
If the order (demand) and offer (supply) curves do not cross (e.g., only orders I, J, D, & E are in the market, but might trade due to combinatorial constraints), the price is 2o preferably set by taking the average price of the highest buy & lowest sell.
Assigning Orders to Separate Partitions FIG. 9 is a flow chart illustrating one method 24 (corresponding to step 24 of FIG. 6) of decomposing the orders of the call that remain after step 40 of FIG. 7 into separate partitions. The processing system 6 starts the decomposition by constructing a graph of nodes 25 and arcs (step 53). The graph is a connected set of arcs and nodes in which each arc connects one pair of nodes and each node corresponds to a single order. To represent orders that offer to trade several securities, a separate arc connects a pair of nodes of the graph for each type of security that the corresponding orders can trade. For example, one arc connects a pair of nodes for two orders that can trade one type of security, two arcs connect a pair of nodes for two orders that can trade two types of securities, etc.
The processing system 6 preferably constructs the graph as a stored data table. For example, the table may have rows and columns indexed by nodes or orders. Then, the number of arcs connecting a pair of nodes of the graph is indicated in the table by an integer stored at the row and column for the two nodes. A graph can also be visualized as a connected drawing of the nodes and arcs, as known in the general art of graph decomposition.
After constructing the graph, the processing system 6 assigns weights to the nodes and arcs of the graph (step 54). One way to assign weights is to assign each of the nodes and arcs a weight of one. Other ways to assign weights give different weights to the arcs than to the nodes.
After assigning weights to the nodes and arcs, the processing system 6 finds the node that produces the largest combined weight when combined with another node and the arcs connecting the two nodes (step 55). The weight of a combined node is defined as the sum of the weights of the original arcs and nodes combined therein. After finding the pair of nodes that produces the largest combined weight, the processing system 6 determines whether the combined weight is above a preselected threshold value (step 56). If the combined weight is not above the threshold value, the processing system 6 combines the two nodes from step 55 20 (step 57) and then loops back to fmd a new largest node. If the combined weight is above the threshold value, the processing system 6 determines whether a remaining third node exists to combine with the first node of step 56 (step 58). If no third node exists, the processing system 6 separates off the first node as a new partition (step 59). If there is a third node, the processing system 6 finds the third node that produces the highest weight when combined 25 with the first node (step 60). Then, the processing system 6 loops back to step 56 to determine whether the new combined weight is above or below preselected threshold.
The processing system 6 repeats steps 55-60 until the first node with the largest below-threshold weight is found. The processing system 6 separates off this first node as a separate partition in accordance with the step 59. The method 24 distributes the various 30 orders into separate partitions for which infra-partition potentials for trades are increased.

WO 00/48109 PCT/US00/0359~
Optimizing Trading Gains The processing system 6 handles minimum fill trading conditions by using both discrete and continuous order-defining variables. For example, a fully inflexible offer (also known as All-Or-Nothing or AON) to sell A securities is an offer to sell XA
securities with X = 0 or 1, i.e., X is a binary integer variable. Partial fill conditions and logical nesting conditions also involve integer valued variables. To determine the trading quantities in the method 14 of FIG. 6, the processing system 6 optimizes total trading gains generally depending on both integer and continuous variables.
FIG. 10 illustrates a method 26 (corresponding to step 26 of FIG. 6) of finding the trading quantities of securities allocated to each order after partitioning in step 24 of FIG. 6.
First, the processing system 6 performs a mixed integer optimization to determine which orders of each partition group trade (step 82). The optimization procedure maximizes the total gain of each partition. Since many orders may not trade, the optimization on partitions usually eliminates many orders from further consideration. Most such orders are marked as ~ 5 non-trading for later processing steps. If time remains, the processing system 6 recombines the trading orders from the optimization procedure to produce super partitions of orders (step 84). In some embodiments, different super partitions do not include orders with the same security types, i.e., trades may not occur between super partitions. The processing system 6 then repeats the integer optimization procedure to fmd a trading solution that maximizes total 2o gains for the different super partitions (step 86). The trading solutions for the super partitions provide the allocations of trading quantities to the various orders at step 14 of FIG. 2 that allow consistent pricing.
To find which orders will trade, the processing system 6 preferably employs an integer optimization procedure to find trading solutions that maximize total gains in each 25 partition. One such procedure is a branch and bound procedure. The branch and bound procedure starts by finding a trading solution in which integer order variables are treated as continuous variables. The procedure uses a linear program technique to find this first trading solution. Next, the procedure sequentially replaces continuous variables defining the orders and individual offers by discrete variables if the variables are in fact discrete-valued. For 3o example, discrete variables define the minimum fills for offers, offer coupling logic, and other variables that define trading conditions. The processing system b recalculates total gains after each replacement of a continuous variable by a discrete variable.
Each replacement produces two branches for possible trading solutions associated with the discrete values of zero or one. The procedure stops replacements in a branch if a solution assigns values to the variables that satisfy all discreteness conditions imposed by the various order conditions. The running largest gain solution that also satisfies all discreteness conditions is a lower bound for further branching. Repeating the branching procedure finally produces a trading solution in which all order conditions are satisfied.
To optimize the allocations of trading quantities of securities in FIG. 2, a bank of processors 8 (FIG. 1 ) may determine trading solutions on various partitions or super partitions concurrently. For example, a separate processor of the bank 8 may determine the trading solution for each partition. The processing system 6 may also find a trading solution for a partition of orders into smaller groups in parallel with the above described procedure.
The integer optimization procedure determines a trading solution more quickly on smaller ~5 groups. The processing system 6 uses the trading solution from the smaller groups if the optimization procedure on the larger groups does not provide a trading solution within the processing time allocated to finding such solutions.
Pricing Allocated Trading Quantities FIG. 11 is a graph 90 illustrating an allowed region 92 for the trading prices of a trade 2o involving four trading orders and two traded securities. Lines 94-97 illustrate price conditions imposed by the submitted orders. Possible trades occur on the side of each line 94-97 indicated by arrows. Each line represents a pricing constraint from an order to which a nonzero trading quantity of security A or B has been allocated at step 14 of FIG. 2. The price conditions require that the processing system 6 select a trading price for A
and B securities in 25 the crosshatched overlap region 92 of the price conditions for the trading orders.
The crosshatched region 92 is the region (solution space) for all feasible prices for items A and B. Any single point within that region represents prices at which A and B can trade, given the set of matched orders. The rules for picking a "fair" point within that region are based on economic theory. For a two-sided market, the preferred approach is to find an "equal distribution of trading surplus at the margin", so that all traders benefit as equally as possible from the price point selection. This generally means picking a point near the center of the crosshatched region. In a one-sided market, such as the US Treasury auction, the surplus is handed almost entirely to the Treasury. This generally means picking a price point at the vertex that gives the monopolist (or monopsonist) the greatest income (or lowest outlay).
FIG. 12A is a graph 100 allowing direct accommodation for a trade in a single item call market where apportioning of costs to satisfy minimum fill conditions occurs among trading orders A-D and F-H. The allocation step of FIG. 2 assigns trading quantities to orders A-D, F-I. If all orders were fully flexible, orders A-C, F-H and the portion of order D to the left of line 1 O1 would trade at the clearing price 102 for the call market, in this case midway between order D and order H. In the illustrated example, order D is an inflexible (AON) offer to buy to which allocation has assigned a maximum buy quantity. Thus, order I
must sell a portion of the quantity of the trading item, which has been allocated to order D, at a price above the clearing price 102. To absorb the added cost, the inflexible order D
must buy at above the clearing price 102.
The crosshatched area 103 represents order D's trading surplus. The crosshatched area 104 represents order D's trading deficit. Given that order D's surplus is greater than or equal to its deficit, order D must directly accommodate (subsidize) its trade with order I.
2o FIG. 12B is a graph 105 requiring general accommodation for a trade in a single item call market where apportioning of costs to satisfy minimum fill conditions occurs among trading orders A-D and F-H. The crosshatched area 106 represents a general accommodation subsidy. This only occurs if order D's deficit 104 is greater than its surplus 103. (Note that the figure exaggerates the size of area 106 for clarity. The area of region 106 should equal the difference between the deficit area 104 and the surplus area 103). Techniques for handling general accommodations are known in the art.
FIG. 13 is a flow chart for a method 16 (corresponding to step 16 of FIG. 2) of pricing the allocated quantities of securities. First, the processing system 6 finds a fictional allocation (the flexible allocation) for each allocated order (step 1 i0). The flexible allocation 3o is done by assuming that no allocated order uses minimum trading conditions such as 1~0 00/48109 PCT/US00/03594 minimum fill, minimum quantity, etc. Then the set of reference prices are computed by treating each flexibly allocated order as fully flexible in each security (step 112). If the fully flexible treatment does not produce a supply-demand cross, the original allocated quantities are used instead to find the reference price (step 114). Next, the processing system 6 determines whether trading at the reference prices would attribute a non-negative gain to each flexibly trading order (step 116). If all flexibly trading orders have non-negative gains, the processing system 6 defines the market prices to be the reference prices (step 118).
Otherwise, the processing system 6 selects a price in the overlap region closest to each reference price as the corresponding market price (step 120).
Next the processing system 6 determines whether any extra-marginal orders trade (step 122). If no extra-marginal trades occur, the processing system 6 defines the trading prices to be the market prices (step 124). Otherwise, the processing system 6 obtains trading prices by adding extra costs of extra-marginal trades to the market prices of marginal orders needing the extra-marginal trades to satisfy fill conditions (step 126). The processing system ~5 6 may adjust trading prices above the bid prices or below the asking prices for the marginal orders. Any order, as a whole, must have non-negative total surplus. However, individual securities within an order may have traded with negative surplus result. If the marginal orders do not have enough positive surplus to cover the negative surplus of the extra-marginal orders (step 128), the processing system 6 apportions any remaining extra costs for extra-2ti marginal trades to the market prices of intra-marginal orders to obtain the trading prices for those orders (step 130). The inclusion of the extra-marginal orders may require that some of the trading prices differ from the market prices.
During pricing, the processing system 6 of FIG. 1 writes a pricing table to the storage device 7. One example of a pricing table is shown in TABLE 2.

Security Types ~

Orders/OffersA A B B N N Swap? Mkt.
Qty. Price Qty. Price Qty. Price Price?

Y/1 50 105 y N

Y/2 -100 87.50 Y N

Zl3 /lll lI N Y
lI

1~ABL~ 2 Each block of rows of the pricing table identifies an individual offeror of one of the orders and lists the final trading quantities and trading prices for each type of security offered by that offeror. Each row of the pricing table also identifies whether the offer traded at the market price or another price. The offer may have traded at a non-market price for several reasons. The offer may be an extra-marginal offer, e.g., the order I of FIG.
12A, which traded at its asking or bid price so that a trading condition of a marginal offer could be satisfied. The offer may be a marginal offer(e.g., order D in FIG. 12A) which traded at a non-market price to accommodate the cost of an extra-marginal offer. Finally, the offer may be an infra-marginal offer (e.g., orders A-C and F-H of FIG. 12A) which traded at a non-market price to generally accommodate an extra-marginal offer.
In some embodiments, the pricing table also labels certain paired buy and sell offers of the same offeror as "swap offers". Swap offers may trade at non-market prices, but the total gain of the pair is equal to the gain that would result if each offer of the pair had traded ~5 at its market price. An example of a pair of swap buy and sell pair is a buy by offeror Y of 50 units of security A at $105 per unit and a sell (indicated by a negative quantity) by the same offeror Y of 100 units of security B at $87.50 per unit, where the market prices of securities A and B are $100 and $90 per unit, respectively.
-28_ Ticketing Allocated And Priced Quantities At the completion of pricing, the processing system 6 still needs to determine which trading offerors accept which trading offers. That is, the trading offerors must be assigned status as "offerees." In the illustrated embodiment, the processing system 6 assigns offeree status to the trading offerors in a manner complying with practices of the market for fixed income securities, i.e., the bond market.
The fixed-income securities market traditionally trades securities using bilateral contracts in which a seller sells a quantity of one type of security to a buyer at a single price.
Each bilateral contract is processed by an agent who forms a sell ticket and a buy ticket for the seller and buyer, respectively. The sell and buy tickets are for the same quantity of one type of security and have the same sell and buy prices. The paired tickets are enforceable contracts enabling either the buyer or the seller to pursue damages if the paired seller or buyer defaults. Both the buyer and seller of the pair pay the agent a fixed per ticket fee.
FIG. 14 is a flow chart illustrating a method 18 (corresponding to one embodiment of ~ 5 step 18 of FIG. 2) for ticketing the trading offers with paired tickets in compliance with the practices of the fixed-income securities market. First, the processing system 6 selects a security type to ticket (step i 42). Next, the processing system 6 determines, on an order by order basis, how much of the traded quantity of the selected security will be directly ticketed and how much will be indirectly ticketed (step 144).
2o The need of both indirect and direct ticketing arises from the market practice that pairs buy and sell offers in tickets at the same price. To produce such tickets, a trading offer to sell or buy must be matched up with a counter offer to buy or sell having the same price.
However, a trading offer does not generally have such a counter trading offer if the trading price is not the market price(e.g., see order I shown in FIG. 12A). Offers trading at prices 25 different from the market price must generally pair with counter offers at a different price.
Such paired offers ticket "indirectly" through a designated market middleman.
The middleman may be a large financial institution supporting the automated trading system 2.
The middleman is a special trader whose sole purpose is to trade in a pair of buy/sell tickets with each offeror of a pair being indirectly ticketed. The middleman participates as a 3o seller with the buyer of the pair, and as a buyer with the seller of the pair. Each pair of tickets with the middleman is for the same trading quantity, but is at the trading price of the offeror with which the middleman is trading. As an example, suppose that offers from offeror I and offeror 2 are being indirectly ticketed and that offeror 1 is selling 100 units of security A at $10 per unit and offeror 2 is buying the I00 units of security A at $9 per unit. The middleman will buy the 100 units from offeror 1 at $10 per unit and sell the 100 units to offeror 2 at $9 per unit. The middleman is party to a pair of tickets with offeror 1 and to a separate pair of tickets with offeror 2. Though each ticket with the middleman conforms to the market practice, the inequality of the buy and sell prices of the pair means that the middleman takes a loss of $100 in this example. The middleman has a surplus or loss with each pair of indirectly ticketed offers. Nevertheless, the surpluses and deficits net to zero, because the pricing method 16 of FIG. 13 assures the sum of all trading costs net to zero between buyers and sellers. The middleman's role is to assume contractual trading risks that an indirectly ticketed trader may default.
To implement both indirect and direct ticketing, the processing system 6 matches up ~ 5 trading sells and buys for equal quantities of the selected security (step 146). The matches pair up the quantities for indirectly and directly trading orders separately so that the selected security appears in equal quantities on paired buy and sell tickets. Next, the processing system 6 determines whether other security types remain (step 148). If other types remain, the processing system 6 loops back to step 142 to process the next security to ticket.
2o The processing system 6 generates an output of the match results (Step 150) One form of output is a ticketing table, an example of which is shown in TABLE 3 below. The ticketing table lists the seller, buyer, security type, quantity and price for each trading pair, and is essentially a double-entry balance sheet. In practice, the matched items may be output in any convenient format, such as a comma-delimited ASCII file.

_ Ticketing Table Seller-Buyer Security Qty Price Type Order 1-Order B q 1 p 1 Order 1-Order B q2 p2 Order 2-Order B q3 p3 Order 3-Order A q4 p4 Order 1-MiddlemanA q5 p5 Middleman-Order A q6 p6 l Al3L>r 3 FIG. 15 is a flow chart illustrating one method 144 (corresponding to step 144 of FIG.
14) of determining which trading quantities ticket directly and which quantities ticket indirectly. First, the processing system 6 selects a trading offer from the pricing table (step 152). Next, the processing system 6 determines whether the selected offer trades at the market price from the information in the pricing table (step 154). If the selected offer does not trade the selected security at the market price, the processing system assigns the trading quantity for indirect ticketing (step 156). If the selected offer does trade the selected security at the market price, the processing system 6 temporarily assigns the trading offer for direct ticketing (step 158). Next, the processing system 6 reads the pricing table to determine to whether trading offers remain to be processed (step 160). If other offers remain, the processing system 6 loops back to step 152 to process another offer. If other offers do not remain, the processing system 6 determines whether the total quantities of the selected security bought and sold match up among the offers temporarily assigned for direct ticketing (step 162). If the total quantities do not match, the processing system 6 reassigns a portion of ~5 the offers to buy or sell, as appropriate, from the group for direct ticketing to the group for indirect ticketing, thereby equalizing amounts bought and sold separately in the two groups (step 164). This step may include separating one offer into a portion that is reassigned for indirect ticketing and a portion that remains assigned for direct ticketing.
The method for determining which offers ticket indirectly and which offers ticket 2o directly preferably reassigns the largest offers to the indirectly ticketed group after first looking for an equality offer. Such a reassignment priority minimizes the final number of indirectly ticketed offers. This minimizes the total number of tickets and per ticket agent fees, because paired directly ticketed offers need only two tickets while paired indirectly ticketed offers need four tickets.
FIG. 16 is a flow chart illustrating a method 146 (corresponding to step 146 of FIG.
14) for ticketing trading offers, which is performed separately on the group for direct ticketing and on the group for indirect ticketing. First, the processing system 6 separately ranks offers to buy and offers to sell in order of decreasing trading quantities (step 172).
Next, the processing system 6 preferably pairs up buy and sell offers that trade equal quantities of the selected security (step 174). Pairing up offers for equal trading quantities reduces the number of tickets and per ticket agent fees. Next, the processing system 6 1o preferably matches up a portion of the trading quantity from the largest buy offer with the trading quantity from the largest sell offer (step 176). Again, this matching step minimizes the total number of tickets and per agent ticketing fees. This is repeated until the buy offer is completely matched. Any remaining sell offer is applied to the next buy (step 178).
After matches are found (steps 174, 176), the processing unit 6 updates the ~s corresponding ticket table (step 180). The processing system 6 tickets the offers from the entries of the completed ticket table by sending data from the ticketing table to agents who actually write the paired tickets in a manner already described.
In embodiments with swap pairs, the methods 18, 144, and 146 of FIGS. 14, 1 S, and 16 may be modified to reduce the number of tickets and agent per ticket fees.
Even though 2o the buy and sell offers of swap pairs do not sell at market price, swap pairs can be ticketed quasi-directly by using the offeror's agent as a middleman. Such ticketing is possible because the deficit that the agent incurs by ticketing one offer of a swap pair is offset exactly by the surplus the same agent incurs by ticketing the other offer of the swap pair to the same offeror.
Thus, the methods of FIGS. 14, 15, and 16 can be modified to reduce the total number of 2s tickets by treating both offers of a swap pair as directly ticketed offers.
Offers of a swap pair can only be directly ticketed in the method 144 of FIG.
15 if neither member is reassigned to the group of indirectly ticketed offers in step 164. Thus, one embodiment modifies the reassignment step 164 so that offers of swap pairs are only reassigned to the group of indirectly ticketed offers if other directly ticketed offers are 3o unavailable.

Second Embodiment FIG. 17 is a flowchart showing an overview of the processes of a matching engine suitable for use in a second embodiment of the invention. The matching engine consists of two main steps: allocation and pricing. .A number of optional steps (indicated by hatched lines) can be used to enhance performance. These optional steps can enhance the speed of operation, the optimality of the trading, or both. In addition, there are several "bookkeeping"
steps used to input or output data and to verify the proper operation of the system and data integrity. These phases are data load, logic simplification, data verification, matched order output, and ticketing.
1 o FIG. 17 shows the program flow through the various processing phases. The steps include data loading (with optional verification and logic simplification) (step 200), an optional preprocessing step (step 202), an optional decomposition step (step 204), allocation (step 206), an optional iterative allocation step (step 208), pricing (step 210), matched order output (step 212), and an optional ticketing step (step 214). FIGS. 18A, 18B, and 18C are related flowcharts showing details of the embodiment described in FIG. 17.
In the preferred embodiment, each processing phase is designed to be implemented as a standalone module. The sequence of modules can be easily re-arranged to adapt to differing operational constraints, such as the number of orders and items, time limits, and so on. The modules can be used in a strictly linear sequence, or an iterative fashion, or in parallel, or in 2o any combination of these methods. The modules are also easily used in a single CPU serial mode, or in complex arrangements spanning multiple parallel CPUs in a monolithic or clustered environment. In addition, the parameters used to guide each module can be easily changed to handle differing patterns of input order data. In one embodiment, the system is designed to accommodate 50,000 orders and 2,000 items within an 8-minute time limit 25 (referenced to mid-1998 commodity computers).
Data Load Phase (step 200 - FIG. 17) The Data Load phase of FIG. 17 inputs initial data into the system, and may include optional verification and logic simplification phases.

Data Input (step 220) Refernng to FIG. 18A, data is initially loaded into the implementing program by a data load module. The data may be read from a local storage device, or be received from a remote data source by means of a communications channel, including local area networks and wide area networks (e.g., the Internet). The data loading process is repeated for each call of a call market.
Verification (step 222) The input data preferably is checked for inconsistencies and discrepancies.
Any bad data is logged and discarded. The discarded data may cause other logically connected orders to be discarded according to certain Discard Rules. In the illustrated embodiment, the Verification module is called repeatedly throughout the overall process of FIGS. 18A-C. This is done to identify immediately, report, and remove corrupt data and avoid spending future resources on bad data. (Technically, verification is optional to the application of the allocation and pricing model, but presence of this function follows good programming ~ 5 practices.) Discard Rules are driven by the constraints of Boolean AND and XOR logic. For example, for an AND group to execute, all members of the group must execute.
If any one member cannot execute, no member may execute. Thus, if a member of an AND
group is corrupt and must be discarded, all members of the AND group must be discarded.
In an XOR
2o group, only one member may execute. Thus, if a member of an XOR group is corrupt and must be discarded, no other member of the group is affected. The extension of these Discard Rules to strategies and hierarchical groupings of orders and groups is straightforward. There are additional caveats with discarded zero-minimum-fill-members of AND groups, and conversion of KOR groups to standalone orders when the Key Order is discarded.
Again, the 25 rules are straightforward.
Logic Simplification (step 224) The Logic Simplification module is essentially a logic-reduction module. The logical groupings within orders are scanned and simplified where possible, in known fashion. This reduces problem complexity and serves to speed up all of the following (later) modules. An example of logic simplification is shown in TABLE 4:
XOR
XOR
XOR D reduces to: ~ I I \
\ A B C D
A B C

In the illustrated embodiment, the Logic Simplification module is called repeatedly throughout the overall process of FIGS. 18A-C. This is done in an attempt to keep the logical complexity of the problem as simple as possible at all times.
Preprocessing Phase (step 202 - FIG. 17) The Preprocessing Phase is an optional step performed to weed out orders that cannot possibly execute and those with little probability of executing. While some orders that would execute in the optimal trading mix may be discarded, a very large improvement in solution speed compensates for a relatively small loss of efficiency. In the preferred embodiment, the parameters used by the preprocessing filters can be changed to make the filtering tighter or looser as needed. In the illustrated embodiment, there are two main preprocessing filters:
volume matching and scoring. Other generic and application specific filters can be added as ~ 5 data patterns emerge.
Volume Matching (step 226) Volume matching is done by comparing an order's minimum volume requirements of items against the maximum available contra-side volume. If there is insufficient contra-side volume, the order is discarded and its volumes of items are removed from the potential 20 volume tallies.
Scoring (step 228) Scoring is performed by calculating an order's potential Gains-from-Trade relative to the relevant reference prices. The reference price of an item is calculated by using the unit offers and volumes of all orders offering to trade that item. The order's potential Gains-from-Trade are then scored or ranked. Those orders falling below a selected threshold value are discarded. In addition, the unit offers of individual items in an indifferent order are checked against the reference prices. Those items whose unit offers are below a threshold value are dropped from the order.
In the illustrated embodiment, there is also a secondary filter within the scoring module. It is used to identify those orders with a potential to act as fill-in orders during the allocation process. These fill-in orders can add significantly to the valid solution space of the MIP problem during the Allocation Phase. Highly flexible orders which were discarded by the primary scoring threshold but have a score higher than this secondary threshold are retained and not discarded.
Verification (step 230) - Same as step 222.
Logic Simplification (step 232) - Same as step 224.
Decomposition Phase (step 204 - FIG. 17) 15 The Decomposition Phase is a major phase made up of several modules. While optional, the purpose of decomposition is to break up the problem into pieces of more manageable size. Since the items and orders are conceivably all interwoven, the cost of decomposition is decreased efficiency. The benefit is a faster solution time.
Decomposition can be accomplished using any of a variety of methods. These include 2o spectral analysis, graph decomposition, brute force comparison among orders, and many other mechanisms. The illustrated embodiment uses a form of graph decomposition as implemented in the software package METIS (D 1997 Regents of the University of Minnesota).
Arc 8c Node Construction (step 234) 2s In preparation for using METIS, a graph of nodes and arcs is constructed such that each order is a node. Arcs are constructed between a node and all other nodes containing contra-sides to the items in the node. That is, if a node (order) is selling item A, arcs are created between the node and all other nodes (orders) which are buying item A.
In addition, if the node (order) is also buying item B, arcs are created between the node and all other nodes (orders) which are selling item B. Each node is given a node weight equal to the number of binary variables in the order. Each arc is given a weight of one in the preferred s embodiment. The weights for each of the arcs and nodes could easily be made into more complex functions involving the orders' and items' characteristics. Note that a node that shares more than one item in contra with another node will have one arc for each item in contra. This is equivalent to assigning an arc-weight of n to an arc between the two nodes, where n is the number of items in contra between the two nodes.
For large data sets, say 50,000 orders across 2,000 items with each order having an average of four items up for trade, the number of arcs between nodes becomes computationally difficult. That is, it is not unusual for a decomposition of this size to take more than an hour using METIS in the illustrated computational system. This is far in excess of the allowable time limit. Therefore, in the preferred embodiment, a selection rule is ~5 applied during the arc and node setup to identify which items will have arcs drawn between them and which will not. Those items between which arcs will not be drawn are called "Very Thick" items. These are items in which many more orders are trying to trade than average.
For these items, no arcs are drawn at all. Typically, 1-3% of all items is identified as being very thick. This elimination of arcs has been found to be sufficient in the illustrated 20 computational system to provide the requisite performance.
The numbers used in the following discussion of decomposition and partitioning are based on a typical large data set (50,000 orders and 2,000 items).
Graph Decomposition (step 236) Once all nodes and arcs are in place, METIS is invoked with parameters designed to 25 split the problem into pieces (partitions). METIS can split a graph into n well-overlapped partitions of roughly equal weight, or an unknown number of partitions with a node weight equal to or less than a target node weight. The decomposed partitions are not necessarily equal in size as far as the number of orders in the partitions. When the graph is broken into an unknown number of partitions, it is not unusual for the partitions to vary significantly in size.

Once the forced decomposition is complete, there are typically 100-200 partitions.
The resulting partitions can be statistically analyzed. Many partitions are very small, containing a single order or strategy group. This is a consequence of the Very Thick items, where some orders are composed entirely of Very Thick items only and hence had no connecting arcs drawn to other orders. Many other partitions contain orders without contras.
This is a result of the forced decomposition and an example of efficiency loss, since orders without contras are less likely to trade. Other partitions are significantly smaller in binary count than the target decomposition size.
While a good solution can be found to each of these partitions, which results in a reasonable overall efficiency (better than 40% in the illustrated embodiment), a significantly better overall solution can be found. This is done by re-arranging the contents of the partitions to a more efficient distribution. There are a number of ways to accomplish this re-arrangement. The goal is to maximize overall efficiency. The following three optional steps provide one method of accomplishing this goal. In one implementation, application of these ~5 steps has resulted in a factor of 100+ in speedup of the overall process.
Partition Troweling (step 238) The troweling step divides the partitions into standard and reserve partitions based on the number of orders in the partition. Standard partitions are the larger partitions, and reserve partitions are the smallest partitions. Both types of partitions are statistically analyzed to 2o determine which items are being traded and the buy or sell volume surpluses in those items.
Each reserve partition is then compared with every standard partition. A
reserve partition is merged with the standard partition with the best fit. The standard partition's statistics are updated and the process repeats until all reserve partitions have been processed. If a reserve partition does not fit with any standard partition, the reserve partition is added to the end of 25 the standard partition list. In the illustrated embodiment, there are typically 40-80 partitions after troweling.
Partition Build Up (step 240) In the illustrated embodiment, the remaining partitions are built up into larger partitions to approximate a "Primary Partition" target size (based on binary variable count).

This is done by comparing the partitions with each other to determine their contra overlap.
The partitions which have a high degree of complementary overlap and which have a combined binary variable count below the Primary Partition target size are merged together.
Typically, this reduces the partition count to 8-16 in the illustrated embodiment.
Order Redistribution (step 242) Frequently, the remaining partitions still contain orders that cannot be matched because there are no contras for those orders within their own partition. Such orders may have better prospects if they were redistributed to another partition.
Accordingly, as an optional step, orders without contras are removed from their current partitions and set into their own private partition. The partitions are run through the troweling process once again to find the best fit partitions for the isolated orders.
Verification (step 244) - Same as step 222.
Allocation Phase (step 206 - FIG. 17) The main purpose of the Allocation Phase is to determine which orders trade and at ~5 what volumes. This is preferably done through a series of modules including order trimming, allocation computation (which includes problem constraint construction and linear prograrnming/mixed integer linear programming (LP/MIP) solving of the constrained problem), solution verification, and order removal. The heart of the Allocation Phase resides in the constraint equations, which model the maximization of the matching process. In 2o general, solution time is a function of the number of variables and constraints in the model.
Therefore, anything that can be done to cut down on the number of variables usually increases the chance of finding a high efficiency solution within the time limit.
After the Decomposition Phase, the Primary Partitions are ready for the Allocation Phase. These partitions have been shaped to solve to a high degree of efficiency in the 25 allowed time limit. However, there is no guarantee that any valid solution will be found, let alone a high efficiency solution. Operating under the assumption that some solution is better than no solution, the preferred system hedges by performing a secondary decomposition on these Primary Partitions.

Secondary Decomposition (step 246) The secondary decompositions are performed in parallel with the start of the primary Allocation Phase. In the illustrated embodiment, each Primary Partition is split into n equal sized pieces using the METIS decomposition (without the Very Thick item step).
Typically, the Primary Partition is split into three Secondary Partitions. The Secondary Partitions usually solve quickly and frequently solve to completion. The sum of the objective values of the Secondary Partitions is compared to the objective value of the corresponding Primary Partition, and the higher-valued solution is retained. This helps ensure maximum overall efficiency.
Order Trimming (step 250) Order trimming works toward the elimination of variables and constraints from the LP/MIP problem. This is done by analyzing the partition statistically to see which orders and items have or do not have contra sides. Proportional and Relaxed Proportional orders where one or more items do not have contras are deleted. Items in an indifferent order that do not ~5 have contras are deleted from the order. Each of these deleted orders and items reduces the number of variables in the LP/MIP problem, simplifies, and speeds up the problem solution.
Allocation Computation (step 252) The actual allocation process consists of parsing through the orders in a partition.
Each order is converted into components of an objective function and a series of limiting 2o constraint equations. The objective function measures the Gains-from-Trade for a given set of trades. The constraint equations specify bounding limits for the solution set of valid trades, and require that supply balances with demand for each item being traded. By maximizing the objective function and staying within the constraint equation boundaries, the optimal or nearly optimal trading mix of orders can be found.
25 In the preferred embodiment, the problem of maximizing the Gains-from-Trade subject to the constraints is formulated as a mixed integer linear programming (MIP). The integer components arise from having certain variables that must have binary (0 or 1) values.
There is a whole field of mathematics devoted to solving these types of problems. This type of problem belongs to a field known as Operations Research. Several public domain and commercial software packages are available to solve these problems, including UNDO, OSL, CPLEX, XPRESS, and XA. They typically employ the "branch and bound"
method by repeatedly invoking a linear programming (LP) solver. The LP solver selection depends on specific application data patterns and other considerations.
The selected LP/MIP solver is fed the problem formulation, and control parameters that include a time limit. The LP/MIP solver reports back solution status on certain conditions, such as finding a feasible solution. Upon solving to optimal, solving close-to-optimal, or being halted by the application for other reasons, the LP/MIP
solver reports back its best integer solution.
The Allocation Phase is a parallel processing opportunity. In the preferred embodiment, the Primary Partitions are ranked by approximate difficulty of solution. While actual computation difficulty for a partition is not known before the LP/MIP
solution is in progress, there is a correlation between binary variable count and length of time to and efficiency of solution. Generally, the greater the number of binary variables, the more is difficult it is to find a solution.
In the preferred embodiment, the Primary Partitions are sorted by binary variable count and placed in an Allocation queue. The Primary Partitions are also placed in a Secondary Decomposition queue. The available CPUs are assigned to each queue.
Some of the CPUs start working from the top of the Allocation queue. Some of the CPUs start 2o working from the bottom of the Allocation queue. The remaining CPUs work from the top of the Secondary Decomposition queue. This mechanism guarantees that the difficult partitions run for as long as possible, and thus increases the probability of finding a solution and of improving on any solution found. Likewise, if a solution is not found to the difficult partitions, there should be ample opportunity to solve that partition's Secondary Partitions, 2s since many of the CPUs start working on the easy partitions. Typically, a division of CPUs is 1/3 to each starting point for Decomposition, Secondary Decomposition, and Allocation.
In the preferred embodiment, as a CPU finishes a job, it returns to its queue to get the next job. When a Decomposition CPU finishes, it places the Secondary Partitions into a standby queue. When there are no more Decomposition jobs, the Decomposition CPUs) take 3o work from the Allocation queue. When there are no more Allocation jobs, the Secondary Partitions from the Primary Partitions that have not finished are loaded into the Allocation queue. When all jobs are completed or the overall processing time limit is reached, the Allocation Phase ends. Note that if a Primary Partition job completes before its Secondary Partitions are loaded into the Allocation queue, then those Secondary Partition jobs are never queued up, as that would be unnecessary work.
If there are no jobs in a queue, all running jobs are allowed to run to completion or until the phase time limit is reached. If there are jobs in a queue, running jobs are halted once their efficiency passes a proximity threshold. The threshold varies with time and job count.
With a serial application, each partition is solved to the time limit. If no solution is to found, the Primary Partition undergoes a Secondary Decomposition, and each Secondary Partition is solved to the time limit. If the Primary Partition solves to completion, no Secondary Decomposition is performed. Similarly, if the Primary Partition solves to a minimum proximity efficiency, no Secondary Decomposition is performed.
Solution Verification (step 254) ~5 The solution is converted into executed orders and quantities. The solution is checked for validity and consistency. If an error or inconsistency is found, the solution is thrown out.
Unallocated Order Deletion (step 256) With a valid solution, orders that did not allocate are filtered out. The filtering is based on the orders' lack of likelihood of matching during the optional Iterative Allocation 2o Phase.
Iterative Allocation Phase (step 208 - FIG. 17) Having split the problem into manageable pieces at the expense of efficiency, it has been found useful to try to regain some of that lost efficiency. This can be done by merging together highly complementary partitions and running an Iterative Allocation (IA) Phase. By 25 the time processing reaches this point, here, the number of surviving orders has been whittled down significantly. However, there may still be too many orders (variables and constraints) simply to merge all of the partitions back together and rerun the allocation process from scratch. Some fraction of these orders are fully allocated, the remainder are partially allocated. The Iterative Allocation Phase attempts to eliminate some of the inflexible efficiency loss and the extra-marginal status of some of these orders by trying to find a more feasible allocation from the combined partitions. The Iterative Allocation Phase first builds up partitions, "fixes" certain known values, and then performs essentially the same process steps as the Allocation Phase.
Partition Build Up (step 258) The process for building the allocated partitions into larger well-overlapped partitions is the same as was used to build up larger partitions in the Decomposition Phase (step 240).
However, in the preferred embodiment, the target maximum binary count for the build up of the IA Phase partitions is three times the target maximum binary count used in the Decomposition Phase. This increase in binary count has been determined through trial and error. Smaller values tend to give little improvement in solution, while larger values tend either to not solve in the remaining time or the solutions are not as good as the combined solution of the partitions from the Allocation Phase.
~5 These higher binary count partitions could not be used in the Allocation Phase because good solutions could not be found reliably in the allotted time in the illustrated system. The larger partitions can be used in IA for several reasons. Most of the non-tradable orders and items have been filtered out by this time. This cuts down on the number of extraneous variables and constraints, leaving primarily the variables and constraints that are 2o involved in a feasible solution.
Order Trimming (step 260) - Same as step 250.
Fixing of Orders (step 262) Knowing the Allocation Phase solutions of the smaller partitions, it is possible to determine some or all of the orders that are guaranteed to execute fully. It is possible to "fix"
25 these orders by making their variables and constraints into constants. This reduces the variable and constraint count, and improves the LP/MIP computational response.

Allocation Computation (step 264) The LP/MIP problem for the IA Phase is constructed exactly the same as for the Allocation Phase. The exception is the fixed orders, which have their allocated data entered as constants rather than as variables and constraints. The problem is fed to the LP/MIP
solving software as before. If a solution is found, the solution is converted into executed orders and trading quantities.
Verify Allocation (step 266) Any solution is checked for validity and consistency. If an error or inconsistency is found, the solution is thrown out (for an effective objective value of 0). The new solution objective value is compared with the sum of the objective values of the component Primary Partitions, and the higher valued solution is kept.
Unallocated Order Deletion (step 268) - Same as step 256.
Pricing Phase (step 210 - FIG. 17) At this point in the process of FIG. 17, the allocation phases are complete.
The set of orders that are trading is known, along with the quantity trading in each item in each order.
The system can now assign prices to each item in every order and set market prices for all items in a Pricing Phase.
Partition Recomposition (step 270) To perform pricing and guarantee that only one market price exists for each item, all 20 orders trading in a particular item must be considered together. Due to the forced decomposition, orders trading the same item can be scattered across any or all partitions. The first pricing step is to merge all partitions back into a single partition containing all surviving orders.
Orderlltem Elimination (step 272) 25 There are still a number of unallocated orders and unallocated items within allocated orders (indifferent and RP) in the recomposed single partition. These orders and items should be~deleted for two reasons. First, because only allocated orders and items are of interest in the Pricing Phase, non-trading orders and items merely add clutter. Second, the pricing mechanism (LPs and other modules) operate faster with less data. Determining and removing unallocated orders and unallocated items within allocated orders is straightforward, and may be accomplished, for example, by using the same process as Order Trimming (step 250).
Logic Simplificatian (step 274) - Same as step 224.
Natural Decomposition (step 276) In order to process the remaining data in parallel, it is desirable to partition the data.
However, the system cannot do a forced decomposition to make smaller partitions, because that would cause items to be distributed between two or more partitions, and that would yield more than one price point for an item. In effect, forced partitions undue the function of the recomposition step 270. Only "natural" decompositions can be relied upon to cut down on computational complexity. By eliminating all non-trading orders and non-trading items in the allocated orders, the overlap and interconnectedness of the data can be significantly reduced.
Accordingly, in the preferred embodiment, a natural decomposition is performed on ~ 5 the remaining data. In the illustrated example, this usually yields more than 20 independent partitions from a typical bond market data set. These partitions can be priced completely separate and in parallel with each other.
Deletion of Select Partitions (step 278) It should be noted that some of the naturally decomposed partitions might have 2o negative Gains-from-Trade. This happens because the Allocation and Iterative Allocation Phases might solve to sub-optimal solutions. In a sub-optimal solution, it is not unusual to have orders that detract from the desired goal. However, due to the combinatorial nature of the problem, it is not always easy to find those detracting orders. The natural decomposition step can isolate some of those orders. Those partitions composed of detracting orders have a 25 net negative Gains-from-Trade value, as the trades they represent are not feasible on their own. These partitions can be deleted. This increases the overall efficiency of the allocation by eliminating negative contributors.

WO 00/4$109 PCT/US00/03594 Pricing Logic Simpliftcation (step 280) The same as step 224, with the additional step of separating Liquidity Providing Strategies into stand-alone canonical orders.
Flexible Allocation (step 282) The Pricing Phase begins in earnest with the flexible allocation step. If there were no minimum allocation scale or minimum quantity requirements in any of the allocated orders, where would the natural market equilibrium be? The flexible allocation determines this equilibrium. In doing so, the flexible solution may violate some of the orders' constraints, such as not trading at least the minimum quantity, or violating an odd lot quantity. These "violations" are acceptable and have no impact on the actual trading volumes for any order.
The flexible allocation provides the necessary information to determine the prices paid and received by the orders for the items that they are trading. In other words, the results of the flexible allocation are used to determine which orders and traded quantities pay at their unit offers and which orders and quantities are to be directly accommodated in the ~ 5 accommodation step.
The flexible allocation is an LP problem, not an MIP problem. An LP problem solves very quickly especially compared to a similar size MIP problem. In the preferred embodiment, the formulation of the LP problem is very similar to the Allocation Phase MIP
step (step 252). The problem is handed to the LP solver and the optimal solution converted 2o into flexibly allocated orders and quantities. Based on the results from the flexible allocation, orders are given a preliminary classification as FR, FPA, or FA.
Pricing Computation (step 284) With the allocated orders and quantities, the "ideal" flexible allocation, and having eliminated all of the non-trading orders, a very accurate reference price can be calculated.
25 This reference price is used to help produce "fair" and "realistic" prices that fall into the price ranges expected by the traders.
Pricing Mechanisms - The preferred system strives for three or more of several goals when determining clearing prices of the items traded. In order of precedence, these goals are:

~ Speed - The prices must be determined within the allotted time limit.
~ Non-negative surplus - The allocated orders and flexibly allocated orders must have positive or zero surplus.
~ Expected Prices - The selected prices must make sense.
~ Marginal Orders Determine Prices.
~ Gains from Trade are distributed fairly (based on an even split of gains at the margin).
Achieving the last two goals is not always possible due to the time constraint imposed by the first goal.
To achieve these goals, the preferred embodiment uses three independent parallel paths. Obviously, more and different mechanisms could be used here in order to improve the results. However, the three paths in the preferred embodiment seem to give the best results as a function of the resources required to program the pricing mechanism and the resources available in the target system. The three pricing paths are (in reverse order of preference and t5 precedence):
~ Reference-Priced - Fastest mechanism, least concerned with surplus distribution.
L2-Priced - Slowest mechanism, in which the Euclidean distances to reference prices are minimized.
~ L1-Priced - Single pass or iterative mechanism, in which the sum of the absolute 2o value of the difference between market prices and reference prices is minimized.
Reference-Priced. The Referenced-Priced path is simple: the reference prices calculated immediately after the Flexible Allocation are considered the market clearing prices. Based on those.prices, each allocated order or group is checked for surplus. If every order has non-negative surplus, then the reference prices are indeed a set of valid clearing 25 prices.
An extension of this very fast mechanism is to identify those orders with negative surplus, those orders with zero surplus, and those orders with positive surplus. Using this information, some (or all) items can be identified that could have their reference prices adjusted in order to make sure all orders boasted non-negative surplus. This process can easily be crafted to converge toward an even distribution of surplus at the margin.
L2-Priced. The L2-Priced path is significantly more mathematically involved than the Reference-Priced path. The L2-Priced path seeks the set of prices that is "closest" to the reference prices and yields non-negative surplus for all flexibly allocated orders. In the L2-priced path, the distance of the market prices to the reference prices is measured in the Euclidean norm - the sum of squares of price differences between the market prices and the reference prices. An extension of this mechanism is to use a weighted version of norms. The weighting can be a function of price, volume traded, potential volume, and so on.
In the L2-Priced path, the illustrated system solves a quadratic programming (QP) problem. The weighted sum of squares of the price differences is minimized subject to the non-negative surplus constraint far each flexibly allocated order/group. There are several ways to solve this problem including using a commercially available QP-solver, successively solving LP's until the solution converges to a solution, or solving the system of ~5 complementary conditions for the optimality. In the illustrated embodiment, the complementary conditions are expressed as a set of integer linear constraints and solved by an MIP-solver. The results from this MIP solver are used to set the market prices. If the solution to the MIP is infeasible, or a solution cannot be found within the time limit, the L2-Priced path is rejected.
20 Ll-Priced Path. As with the L2-Priced path, the L1-Priced path calculates the closeness of the market prices to the reference prices by measuring the L1-norm - the sum of the absolute value of the price differences between the market prices and reference prices. As with the L2-Priced path, an extension of the mechanism is to use a weighted version of the norms.
2s In the L1-Priced path, the weighted L1-norm is minimized subject to the non-negative surplus constraint for each flexibly allocated order/group. The problem is formulated as an LP problem and solved by an LP-solver. This can be a single pass or iterative process. The results from the LP solver are used to set the market prices. This mechanism always succeeds.

Pricing Mechanism Selection. Which of the pricing path results is finally chosen to set the Market Clearing Prices is dependent on several factors. For consideration, the path must yield a valid pricing solution (all flexibly allocated orders and groups must show non-negative surplus). A path may fail to have a valid pricing solution due to insufficient time (which happens most often to the L2-Priced path), or because the pricing numbers do not work out (which happens most often to the Reference-Priced path). The L1-Priced path rarely, if ever, fails. The pricing solution is then selected by precedence from those pricing paths that produced valid solutions. In the preferred embodiment, the L2-Priced path is the first choice; the Ll-Priced path is second; and the Reference-Priced path is third.
In the preferred embodiment, the speed of the pricing mechanism is still the overriding concern. Because of this, the preferred embodiment gives the Reference-Priced path an execution advantage over the other pricing paths.
In a serial solver, if a pricing path succeeds, the remaining paths are not attempted. If a pricing path fails, the next pricing path in sequence is tried. This continues until a pricing ~5 path succeeds or all pricing paths have failed. The first successful pricing path is used for setting prices. If all pricing paths fail, the partition is rejected. The preferred pricing paths are executed in the following sequence: Reference-Priced; L2-Priced; and L1-Priced.
In a parallel solver, a slightly different sequence is preferred. All Reference-Priced jobs are queued up. Next, all L1-Priced jobs are queued up. Finally, all L2-Priced jobs are 2o queued up. As each job finishes, it is checked for a valid solution. If the solution is not valid, no action is taken. If the solution is valid, all other pricing jobs for this same partition are de-queued. Then, any other running jobs for this same partition are aborted. This is done to make sure that all partitions have a good chance of being priced. In the illustrated embodiment, with natural decomposition there are generally many more than 20 partitions 25 (in a delivery-sized file). Each partition has three jobs queued up, and this large number of jobs strains the parallel resources available in a given time limit. So, once a partition has a valid pricing solution, the parallel resources are best devoted to those partitions not yet priced.

Rounding & Classification (step 286) The trading volumes calculated in the solutions of the allocation phases are all decimal values. However, usually a minimum standard trading unit must be observed. This requires the calculated volumes to be rounded from decimal values to the precision of the s minimum standard trading unit. This unit is currently any whole integer.
Brute Force Rounding. The rounding decision can be accomplished in any of a number of ways. One method is a brute force technique. The decision rules are:
~ Demand must equal Supply.
~ Do not violate order quantity limits.
~ Fractional supply is rounded up (in magnitude).
~ Fractional demand is rounded down (in magnitude).
This approach yields excess supply. The system then attempts to balance supply and demand by going through all of the rounded down buys and rounded some of them up (by adding "one" to the buy volume) until there is no more excess supply. If there is still excess ~5 supply, the system starts back through the rounded sells, and rounds them down until there is no excess supply.
Intelligent Rounding. A better rounding mechanism intelligently picks the rounding direction for each item in each order. This can be made into a very sophisticated selection process, but much improved results can be achieved using these rounding rules:
20 ~ Demand must equal Supply.
~ Round in the direction that adds surplus to the order.
~ Do not violate order constraints.
With this approach, a preferably solution to the rounding problem consists of four basic steps:
25 (1) Round all fractional quantities based upon the spread between unit offer and market price. The intent is to maximize the gains from trade for each item rounded. Buys with a unit offer higher than the market price are rounded up (this decreases supply). Buys with a unit offer lower than the market price are rounded down (this increases supply). Sells -SO-with a unit offer higher than the market price are rounded down (this decreases supply). Sells with a unit offer lower than the market price are rounded up (this increases supply).
(2) Make sure that no illegal rounding occurred. That is, no orders may have quantity or budget violations. If they do, the rounding is reversed.
{3) The trading volume is balanced for each item being traded. The system goes through the orders and sums the rounded quantities for each item. For an item with excess supply (demand), the system follows a sequence for reversing the rounding of each order that increased supply (decreased supply) for an item due to rounding. Flexibly rejected (FR) orders are checked first, then flexibly partially allocated (FPA) orders, and finally fully flexibly allocated (FA) orders are checked. Each order that is having its rounding reversed is checked to make sure that all quantity limits are observed and to avoid any negative surplus in the case of FPA and FA orders. This step is halted as soon as there is no excess supply or demand for the item, i.e., the supply and demand balance.
(4) Finally, if excess supply or demand still exists after the reverse rounding, the ~5 system drops the quantity limit and negative surplus checks. The system then forces the rounding, again beginning with FR, then FPA, then FA orders. The process halts as soon as supply and demand balance. If the original unrounded allocation is correct, this step will always result in balanced supply/demand. In any case, the result from this forced rounding is no worse {and usually better) than the brute force rounding mechanism.
2o Surplus Check. After rounding is complete, the surplus (gain) for each order that was rounded has changed. In most cases, the surplus for an order has increased.
However, in some cases, the surplus for an order has decreased, with some orders actually becoming negative. Based on the market clearing prices set in the Pricing Path phase, the surplus of each order is recalculated. Any order found to possess negative surplus is removed from the 25 list of flexibly allocated orders.
Class~cation. All of the orders must be classified before setting individual prices for all items in all orders. Each order is classified based on its surplus and flexible allocation status. The categories are:
FR - Flexibly Rejected. The order did not trade in the Flexible Allocation phase.

~ FZS - Flexibly Allocated, Zero Surplus. The order is in the Flexible Allocation, but has zero surplus. The order may be fully or partially flexibly allocated.
~ FPA - Flexibly Partially Allocated. This is an order that has traded, but not to its allocated limits during the flexible allocation phase.
~ FA - Flexibly Fully Allocated. This is an order that trades to its allocated limits during the flexible allocation phase.
If there are only FA orders, there is no need for accommodation, and all orders are assigned market prices.
Accommodation (step 288) The Accommodation step actually assigns trading prices to all orders. Not every order trades at the market clearing prices. In thick markets, most orders trade at the clearing prices.
Some orders trade at their bid prices. Marginal orders trade between their bid prices and the market price. The thinner the market, the fewer the orders that trade at the market clearing prices. The prices assigned to an order are dependent on its flexible allocation classification ~5 and a relationship between its trading and flexible allocation quantities.
For pricing in general, FR and FZS orders price at their bids; FA orders price at the market-clearing price; and FPA orders price somewhere between their bid and the market puce.
There are three paths used to price the orders, as described below. A
particular path is 2o selected based on the relationship between the total negative surplus in the orders and the expendable surplus in the orders:
TotalSuprlus = NegativeSurplus + ExpendableSurplus The negative surplus value is assessed by calculating the amount of surplus that was accommodated by the MIP allocation compared to the flexible allocation. This value is the 25 area between the supply and demand curves for the volume that trades to the right of the market cross. This is found by summing the cash flow imbalance due to the FR
and FPA
orders. In FIGS. 12A and 12B, the negative surplus is the hatched area 104 between D and I.

The expendable surplus is assessed by calculating the amount of positive surplus that is contributed to the system by the FPA orders. This value is the area of the FPA order's volume to the left of the market cross between the FPA order's unit offers and the market price. This is found by summing the product of the flexibly allocated quantity and the delta between the unit offer and market price for all items in the FPA order. For example, in FIG.
12A, the expendable surplus is the crosshatched area 103.
The negative surplus is added to the expendable surplus, and the pricing path is chosen based on the sign of the resulting value. If the value is zero, Flat Pricing is used. If the value is positive, FPA-Split Pricing is used. If the value is negative, Split Market Pricing is used.
Flat Pricing. Flat Pricing is done by pricing all FZS, FR, and FPA orders at their unit offers. All FA orders are priced at the market price. This is the simplest pricing path, as there is no need to calculate accommodation. This is because the FPA orders are able to compensate exactly for their inflexibilities. This appears to be a rare occurrence in the real world, FPA-Split Pricing. FPA-Split Pricing is done by pricing all FR and FZS orders at their unit offers. All FA orders are priced at the market price. FPA orders are priced somewhere between their unit offers and the market price.
FPA orders are essentially the inflexible "problem" orders. That is, in an ideal world, 2o they trade at lower volumes than in the real world. These orders generally have trader-specified restrictions that make them more difficult to match. These orders force other orders to trade that would not have traded in an ideal world. These "other orders"
are extra-marginal orders.
Such extra-marginal orders must be accommodated, because they bring liquidity to the market. If these extra-marginal orders did not trade, it is quite likely that many other trades would collapse. This collapsed trading can spread across a few, many, or all of the items involved in the market due to the combinatorial/interconnected nature of the market. In many cases, without the extra-marginal orders, a significant portion or potentially all of the trades would collapse.

An important function of the entire system is to trade and maximize the Gains-from-Trade for all traders. To accomplish this, the extra-marginal orders must be included. Since the extra-marginal orders introduce a small deficit to the system (that would not be involved if the extra-marginal orders were not included), that deficit must be balanced. Since the trader-imposed restrictions on the FPA orders cause the extra-marginal orders to be included, it is preferably that the FPA orders pay to restore the balance. That is, the FPA orders are "taxed" to accommodate the extra-marginal orders. This accommodation tax is split proportionately across all FPA orders.
Accordingly, in the preferred embodiment, the price for an item in an FPA
order is set 1 o by:
UnitOffer * (TradeQty - FlexAllocQty) + FlexAllocQty * FPAMarketprice itemprice =
TradeQty The FPA Order's Trading Price is set as an offset based on the FPA order's per unit surplus. The offset is the FPA order's share of the accommodation tax. For a buy order, this 1S:
FPAMarketprice = Marketprice + FPAUnitSurplus * - NegativeSurplus ExpendableSurplus where FPAUnitSurplus = TotalOrderSurplus ~~ FlexiblyAllocatedQuantity ~
In case of a sell, it is possible that an item's market price will be less than the per unit 2o accommodation delta for an order. If this happens, the sell price for that item is set to zero, and the unfulfilled accommodation for that item is spread evenly over the remaining items in the order.
Split Market Pricing. Split Market Pricing is done by pricing all FR, FZS, and FPA
orders at their unit offers. All FA orders are priced off of a spread from the calculated market clearing price. In this case, FA orders are forced to contribute to the accommodation process.

The process of accommodating FA orders for the excess negative surplus is known as general accommodation. The negative surplus is divided across all FA orders equally by trading volumes. Essentially, the excess negative surplus is divided by the sum total buy and sell volumes of all FA orders to get a per unit volume value, the accommodation delta. This delta is effectively added to the market price to get the market buy price, and subtracted from the market price to get the market sell price. This computation yields a spread between the market buy and sell prices. This spread across all of the FA volume accommodates the excess negative surplus.
If the total FA volume is relatively small and the excess negative surplus is relatively large, then the spread can be significant. If the total FA volume is relatively large and the excess negative surplus is relatively small, then the spread can be insignificant.
Some special cases must be handled. These cases are:
~ FA orders where their per unit surplus is less than the accommodation delta (cannot force an order to have negative surplus) ~ Items whose market prices are less than the delta (cannot have a negative market sell price).
The preferred mechanism for accommodating the excess negative surplus is:
(1) Determine the total buy and sell volume for all FA orders.
(2) Determine the per unit accommodation required.
(3) Determine the minimum per unit surplus of all FA orders.
(4) Determine the minimum market sell price for all items.
(5) If the per unit accommodation delta is less than the minimum per unit surplus and minimum market sell price, then adjust the market buy and sell prices by the accommodation delta, and price all orders at the appropriate market price;
Split Market Pricing is done.
(6) If the per unit accommodation delta is greater than either or both of the two minimum values, take the smallest minimum value as the per unit accommodation delta.

(7) Adjust the remaining excess negative surplus by the product of the remaining FA trading volume and the accommodation delta. This product is how much negative surplus is accommodated by the accommodation delta.

(8) Adjust the market price of all items still being traded by FA orders by the s accommodation delta.

(9) Downward adjust the per unit surplus of all FA orders by the accommodation delta. Remove any orders from the FA list, and mark them as FR, whose per unit surplus is less than or equal to the accommodation delta.

(10) Mark any items being sold with a market sell price of 0 (zero) as no longer involved in the volume distribution of the remaining excess negative surplus.

(11) Repeat steps 1-10 until the process finally finishes at step 5.
More complex schemes can be implemented for distributing the excess negative surplus. These could include rationing relative to market price, relative to item trading volumes, combinations of these methods, or other methods. For the illustrated embodiment, it ~ 5 has been found that uniform distribution across trading volume works best.
Reprising (step 290) In certain situations, it may be requested to adjust the market prices at which an order was reported to have traded. This is called "reprising". An example of this in bond trading, is an order known as a Spread Swap, where a trader swaps two bonds as a function of the 2o trader's expectations of yield on the bonds.
An order that is re-priced in no way gains or loses any advantage over other orders.
The re-pricing mechanism simply takes the surplus earned by an order and redistributes that surplus relative to the trader's unit offers instead of relative to the market prices. For an order with zero surplus (an FR or FZS order), no change in reported price occurs.
Those orders 25' must pay at their unit offers. Only FPA and FA orders may have different prices reported when re-priced.
A new surplus value is calculated using the order's unit offers relative to the order's reported market prices. This surplus value is divided by the total trading volume for the orders, to get a per unit "surplus". The order's market prices are set by adjusting the unit offers by the per-unit "surplus". This gives the trader uniform per unit price improvement from the trader's original unit offers for all items trading in the order.
Again, this affords the order no advantage or disadvantage over its originally calculated and reported trading prices. The net cash flow is the same. The new prices are generated and reported purely as a bookkeeping procedure.
Output Phase (step 212 - FIG. 17) To be useful, the matched orders, prices, and volumes must be communicated outside the application. In the illustrated embodiment, this is currently done by generating text files that are written to a storage device (e.g., magnetic disk).
Recomposition (step 292) Once the Pricing Phase is complete, all of the various partitions are merged back together into a single partition. This partition contains all of the matched orders and trading information to be reported.
Balance Verification (step 294) 15 In the illustrated embodiment, a final check is performed to make sure all trading volumes in all items balance and net out to zero. Also, the total cash flow must balance and net out to zero. In some embodiments, due to the floating point nature of the monetary calculations and imprecision in the calculations (including round-off errors), a small tolerance is allowed in the total balance calculations for cash flow. In the illustrated 20 embodiment, this tolerance amounts to 0.01 % of the gross of buyers' payments.
Data Output (step 296) Files are written out which contain ail of the trading information. This includes which orders traded, which items traded in those orders, and at what volumes and prices for each order. Also, data is reported on the total trading volume for each item and market prices for 25 those items.

Ticketing Phase (step 214 - FIG. 17) In certain embodiments, such as bond trading, the final procedure in the order matching process is the matching of individual buyers and sellers for each trade transaction.
Since trading is done from a pool, this step is necessary to create the explicit bilateral trades that make up the "paper-trail" accounting for trades. In the illustrated embodiment, ticketing goes through four steps to produce trader-to-trader bilateral tickets:
~ Creation of a Item/Trader List ~ Forced Matching of Direct and Indirect Trading of Buy/Sell Volumes ~ Building of Ticket Pairs ~ Generation of a Ticket File These steps attempt to minimize the total number of tickets generated in the final output as each ticket incurs a transaction charge and it is desirable to minimize the costs of the transactions.
ItemlTrader List. The first step in this process is to create a list of all the traders and ~5 their volumes for each item. Each volume is marked as directly or indirectly traded. If the price set for a trader for an item is the market price, then the item can be ticketed directly with another trader who is a contra for that same item and trading at the market price. These exchanges are marked as "directly" traded volumes. However, if the trade was repriced or involved in the accommodation process, the price paid by the trader for that item differs from 2o the market price. In this case, there will be a funds "delta" between buyer and seller for that trade. Hence, that order must be traded through a central clearing agency in order to account for the funds delta. These are marked as "indirectly" traded volumes. The net funds delta of all indirect trades will sum to zero, as discussed above.
Directllndirect Volume Matching. The indirect buy volume does not always equal the 25 indirect sell volume. The indirect supply and demand must be balanced in order to prevent the central clearing agency from being responsible for residual volume. To accomplish this, trading volume is moved from the direct side to the indirect side until the indirect buy and sell volumes balance. If necessary, all the direct volume will be marked as indirect volume, in which case buy/sell volume is guaranteed to match. By forcing the indirect volumes to balance, the direct volumes are also forced into balance.
Building Ticket Pairs. Once the direct/indirect buy/sell volumes have been balanced, the individual buyers and sellers are paired up to create bilateral transactions. Where necessary, a trade is split over two or more contra trades. Two direct traders cause two tickets to be created, one for buyer-to-seller and one for seller-to-buyer. Trades going through one intermediary (i.e., a middleman) generate four tickets. Trades going through two intermediaries can generate up to six tickets. The pairing algorithm organizes the direct and indirect trades in order to minimize the number of tickets created.
Generating a Ticket File. Once all the ticket pairs have been computed, a file is generated that lists each ticket. This can then be used to generate print out or electronic trade tickets that are forwarded on to the clearance and settlement agents.
A number of embodiments of the present invention have been described. Neverthe-less, it will be understood that various modifications may be made without departing from ~s the spirit and scope of the invention. For example, the steps of some of the methods may be order-independent, and thus may be carried out in one or more sequences different from those shown. Further, although the embodiments above have focused on two-sided markets, the invention may be applied as well to one-sided markets, auctions, reverse auctions, and exchanges in general. Accordingly, other embodiments are within the scope of the following 2o claims.

Claims (28)

WHAT IS CLAIMED IS:

1. A computerized method of operating an automated market for trading items, including:
(a) receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to trade a plurality of types of items;
(b) allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assigning trading prices to each order for each item therein allocated a nonzero trading quantity using at least a direct accommodation algorithm; and (d) outputting information indicative of the allocated trading quantities and assigned trading prices.

2. A computerized method of operating an automated market for trading items, including:
(a) receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being an indifferent order offering to trade between a selection of different items among a plurality of types of items;
(b) allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assigning trading prices to each order for each item therein allocated a nonzero trading quantity; and (d) outputting information indicative of the allocated trading quantities and assigned trading prices.

3. A computerized method of operating an automated market for trading items, including:
(a) receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to trade a plurality of types of items;
(b) allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assigning trading prices to each order for each item therein allocated a nonzero trading quantity using a plurality of pricing paths; and (d) outputting information indicative of the allocated trading quantities and assigned trading prices.

4. A computerized method of operating an automated market for trading items, including:
(a) receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items from a plurality of traders, at least one of the orders offering to trade a plurality of types of items;
(b) allocating trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assigning trading prices to each order for each item therein allocated a nonzero trading quantity;
(d) re-pricing each order of a trading entity having a per unit surplus value by redistributing such per unit surplus value to other units of such order; and (e) outputting information indicative of the allocated trading quantities and assigned trading prices.

5. A computerized method of operating an automated market for trading items, including:
(a) receiving a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being selected from a group consisting of a proportional order, a relaxed proportional order, and a complex order;
(b) allocating trading quantities of items to a portion of the orders using a method to increase a total market gain from trading, the trading quantities satisfying trading constraints imposed by the orders;
(c) assigning trading prices to each order for each item therein allocated a nonzero trading quantity; and (d) outputting information indicative of the allocated trading quantities and assigned trading prices.

6. The method of claims 1, 2, 3, 4, or 5, wherein allocating trading quantities includes:
(a) dividing the orders into a plurality of partitions with a process that increases the potential for trades between the orders of a partition; and (b) finding trading quantities for the orders of each partition.

7. The method of claims 1, 2, 3, 4, or 5, wherein assigning trading prices includes:
(a) finding a clearing price for each item traded; and (b) assigning a trading price for one of the items of a trading marginal order different from the clearing pricing in response to an extra-marginal order being allocated a trading quantity of the one of the items.

8. The method of claims 1, 2, 3, 4, or 5, wherein allocating trading quantities includes designating orders as non-trading in response to the designated orders having below-threshold probabilities of trading.

9. The method of claim 8, wherein allocating trading quantities further includes:
(a) assigning the orders not designated as non-trading into a plurality of partitions by a method to increase the potential for trades between the orders of a partition;
and (b) finding trading quantities for each partition by optimizing the total trade gain for each partition separately.

10. The method of claim 9, wherein assigning the orders creates partitions below a preselected threshold size, the size depending on the number of potential trades therein.

11. The method of claim 9, further including:
(a) recomposing trading orders of the partitions into one or more super partitions in response to the step of finding trading quantities completing within a preselected time; and (b) determining a final trading solution for each super partition, the final solution assigning a non-negative gain to each trading order and determining trading quantities of items for each order.

12. The method of claim 9, wherein assigning the orders includes:
(a) building one or more graphs of nodes and arcs, one order assigned to each node and each arc associated with a potential trade of an item type between the orders assigned to the nodes attached thereto; and (b) assigning to the nodes and arcs weights.

13. The method of claim 12, further including:
(a) forming combined nodes for each graph; and (b) forming a new partition in response to a newly combined node having a maximal below threshold weight.

14. The method of claims 1, 2, 3, 4, or 5, further including ticketing the allocating trading quantities at the trading prices by apportioning the allocated trading quantities among pairs sellers and buyers, one of the buyer and the seller of each pair having made the order allocated the trading quantity being indirectly or directly ticketed.

15. A computer program, stored on a computer-readable medium, for operating an automated market for trading items, the computer program comprising instructions for causing a computer to:
(a) receive a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to trade a plurality of types of items;
(b) allocate trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assign trading prices to each order for each item therein allocated a nonzero trading quantity using at least a direct accommodation algorithm; and (d) output information indicative of the allocated trading quantities and assigned trading prices.

16. A computer program, stored on a computer-readable medium, for operating an automated market for trading items, the computer program comprising instructions for causing a computer to:
(a) receive a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being an indifferent order offering to trade between a selection of different items among a plurality of types of items;
(b) allocate trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assign trading prices to each order for each item therein allocated a nonzero trading quantity; and (d) output information indicative of the allocated trading quantities and assigned trading prices.

17. A computer program, stored on a computer-readable medium, for operating an automated market for trading items, the computer program comprising instructions for causing a computer to:
(a) receive a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders offering to trade a plurality of types of items;
(b) allocate trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assign trading prices to each order for each item therein allocated a nonzero trading quantity using a plurality of pricing paths; and (d) output information indicative of the allocated trading quantities and assigned trading prices.

18. A computer program, stored on a computer-readable medium, for operating an automated market for trading items, the computer program comprising instructions far causing a computer to:
(a) receive a plurality of orders offering to sell items and a plurality of orders offering to buy items from a plurality of traders, at least one of the orders offering to trade a plurality of types of items;
(b) allocate trading quantities of items to a portion of the orders, the trading quantities satisfying trading constraints imposed by the orders;
(c) assign trading prices to each order for each item therein allocated a nonzero trading quantity;
(d) re-price each order of a trading entity having a per unit surplus value by redistributing such per unit surplus value to other units of such order; and (e) output information indicative of the allocated trading quantities and assigned trading prices.

19. A computer program, stored on a computer-readable medium, for operating an automated market for trading items, the computer program comprising instructions for causing a computer to:
(a) receive a plurality of orders offering to sell items and a plurality of orders offering to buy items, at least one of the orders being selected from a group consisting of a proportional order, a relaxed proportional order, and a complex order;
(b) allocate trading quantities of items to a portion of the orders using a method to increase a total market gain from trading, the trading quantities satisfying trading constraints imposed by the orders;
(c) assign trading prices to each order for each item therein allocated a nonzero trading quantity; and (d) output information indicative of the allocated trading quantities and assigned trading prices.

20. The computer program of claims 15, 16, 17, 18, or 19, wherein allocating trading quantities includes instructions for causing the computer to:
(a) divide the orders into a plurality of partitions with a process that increases the potential for trades between the orders of a partition; and (b) find trading quantities for the orders of each partition.

21. The computer program of claims 15, 16, 17, 18, or 19, wherein assigning trading prices includes instructions for causing the computer to:
(a) find a clearing price for each item traded; and (b) assign a trading price for one of the items of a trading marginal order different from the clearing pricing in response to an extra-marginal order being allocated a trading quantity of the one of the items.

22. The computer program of claims 1, 2, 3, 4, or 5, wherein allocating trading quantities includes instructions for causing the computer to designate orders as non-trading in response to the designated orders having below-threshold probabilities of trading.

23. The computer program of claim 22, wherein allocating trading quantities further includes instructions for causing the computer to:
(a) assign the orders not designated as non-trading into a plurality of partitions by a method to increase the potential for trades between the orders of a partition;
and (b) find trading quantities for each partition by optimizing the total trade gain for each partition separately.

24. The computer program of claim 23, wherein assigning the orders creates partitions below a preselected threshold size, the size depending on the number of potential trades therein.

25. The computer program of claim 23, further including instructions for causing the computer to:
(a) recompose trading orders of the partitions into one or more super partitions in response to the function of finding trading quantities completing within a preselected time; and (b) determine a final trading solution for each super partition, the final solution assigning a non-negative gain to each trading order and determining trading quantities of items for each order.

26. The computer program of claim 23, wherein assigning the orders includes instructions for causing the computer to:
(a) build one or more graphs of nodes and arcs, one order assigned to each node and each arc associated with a potential trade of an item type between the orders assigned to the nodes attached thereto; and (b) assign to the nodes and arcs weights.

27. The computer program of claim 26, further including:
(a) forming combined nodes for each graph; and (b) forming a new partition in response to a newly combined node having a maximal below threshold weight.

28. The computer program of claims 15, 16, 17, 18, or 19, further including instructions for causing a computer to ticket the allocating trading quantities at the trading prices by apportioning the allocated trading quantities among pairs sellers and buyers, one of the buyer and the seller of each pair having made the order allocated the trading quantity being indirectly or directly ticketed.