Whole Prestito Trading System

Sistema e metodo per ottimizzare Tasso Fisso intero prestito Trading US 20140188692 A1 Ottimizzazione tasso fisso di trading intero prestito. In particolare, l'invenzione fornisce sistemi e metodi basati su computer per il confezionamento in modo ottimale una popolazione di prestiti interi in obbligazioni sia in una struttura di legame seniorsubordinate o in pool di passaggio attraverso titoli garantiti da un ente governativo. Modelli per ogni tipo di struttura legame vengono elaborati sulla popolazione di prestiti fino a trovare sia un pacchetto legame ottimale o un utente determina che una soluzione sufficientemente elevata qualità è trovato. Inoltre, i modelli possono spiegare le offerte per i prestiti interi assegnando tutta prestiti che soddisfano i requisiti dell'offerta, ma sono meno favorevoli da cartolarizzare. (25) Ciò che si rivendica è: 1. Un computer attuate metodo comprendendo: la creazione, da un computer, un modello comprendente una funzione obiettivo per un valore totale di mercato della struttura di legame seniorsubordinate per la pluralità di impieghi e massimizzando, dal computer, la funzione obiettivo da massimizzare il valore totale di mercato della struttura legame seniorsubordinate. 2. Il metodo elaboratori elettronici secondo la rivendicazione 1, in cui la fase di massimizzare la funzione obiettivo comprende: determinare un prezzo di mercato di ciascun prestito determinare una prima media ponderata promozionale esecuzione per la pluralità di impieghi corrispondenti al prezzo di mercato di ciascun credito che determinano il mercato totale valore della struttura seniorsubordinate al primo ponderata coupon medio di esecuzione iterando la media ponderata coupon esecuzione e la determinazione di un valore di mercato totale per la struttura seniorsubordinate ad ogni iterazione e determina il coupon ponderata media d'esecuzione con i più alti valori di mercato totale per la struttura seniorsubordinate. 3. Il metodo elaboratori elettronici secondo la rivendicazione 1. comprendente inoltre sviluppare e massimizzare una funzione obiettivo per dividere in modo ottimale almeno uno dei prestiti in due prestiti pseudo impedire una creazione di un interesse solo legame o un unico vincolo principale, i due prestiti pseudo comprendente diversi valori coupon. 4. Procedimento elaboratori elettronici di gestione comune in modo ottimale una popolazione di prestiti in passaggio attraverso pool obbligazioni, il metodo comprendendo: selezionare la popolazione di crediti determinanti, dal computer, un'esecuzione ottimale di ciascun prestito dalla popolazione dei crediti per un acquisto o una acquistare in basso di una commissione di garanzia che determina una o più vasche per il quale ogni prestito è ammissibile la costruzione di un modello basato su almeno un vincolo per almeno un pool determinato e assegnare i crediti verso l'una o più pass through piscine obbligazionari. 5. Il metodo elaboratori elettronici secondo la rivendicazione 4 comprende inoltre la determinazione, dal computer, almeno un modulo di uno o più moduli che è configurato per riunire la popolazione di prestiti in passaggio attraverso pool obbligazioni basate su un ingresso ricevuto, in cui l'almeno un modulo comprende un modulo pass-through. 6. Il metodo elaboratori elettronici secondo la rivendicazione 4. in cui il modello comprende una funzione obiettivo comprendente una combinazione lineare di un valore di mercato di ciascuna della popolazione dei crediti. 7. Il metodo elaboratori elettronici secondo la rivendicazione 6. cui ripartizione dei crediti comprende l'esecuzione del modello di massimizzare la funzione obiettivo. 8. Il metodo elaboratori elettronici secondo la rivendicazione 4. comprendente inoltre trasformando l'almeno una sollecitazione di ogni passaggio attraverso piscina vincolo in un vincolo condizionale. 9. Il metodo elaboratori elettronici secondo la rivendicazione 4. comprendente inoltre convertire almeno una porzione dell'almeno un vincolo di ogni passaggio attraverso piscina vincolo in un vincolo condizionale prima di elaborare il modello per garantire che il modello è risolvibile. 10. Il metodo elaboratori elettronici secondo la rivendicazione 4. comprendente inoltre trasformare ciascuno dei vincoli almeno uno in un vincolo condizionale per consentire vincoli di essere applicabile a passare solo attraverso pool obbligazioni allocate. 11. Il metodo elaboratori elettronici secondo la rivendicazione 4. comprendente inoltre allocare almeno una popolazione di prestiti a una piscina allocato. 12. Il metodo elaboratori elettronici secondo la rivendicazione 4. comprendente inoltre ripartizione prestiti in una piscina allocato se ciascuno di pass attraverso pool obbligazioni non può essere assegnato con la popolazione dei crediti, in cui prestiti nel pool allocato sono riportate valore di mercato nullo ed in cui l'elaborazione del modello comprende inoltre minimizzando il numero di crediti assegnati al pool allocato. 13. Il metodo elaboratori elettronici secondo la rivendicazione 4. in cui il modello non preveda il vincolo di ogni passaggio piscina vincolo e payup associato ad ogni passaggio piscina vincolo. 14. Un sistema comprendendo: una memoria comprendente un insieme di istruzioni per allocare una porzione di una pluralità di crediti per un pacchetto di prestito e un computer accoppiato alla memoria e configurato per eseguire il set di istruzioni per: determinare quale della pluralità di prestiti soddisfa uno o più vincoli del pacchetto di prestiti determinano un prezzo di mercato di ciascuna della pluralità dei prestiti sulla base di un modello di cartolarizzazione modello di una funzione obiettivo per determinare quali prestiti nella pluralità di prestiti che soddisfi i uno o più vincoli sono meno redditizi per la cartolarizzazione nel modello di cartolarizzazione e di destinare i prestiti che soddisfi i uno o più vincoli e sono meno redditizi per la cartolarizzazione nel pacchetto di prestito. 15. Il sistema secondo la rivendicazione 14. in cui il modello cartolarizzazione comprende un modello seniorsubordinate. 16. Il sistema secondo la rivendicazione 14. in cui la funzione obiettivo è modellato per minimizzare un differenziale tra un prezzo medio ponderato dei mutui nel pacchetto prestito e un Annuncio a (TBA) prezzo del titolo del tagliando media ponderata dei prestiti nel pacchetto prestito . 17. Il sistema secondo la rivendicazione 14. in cui la funzione obiettivo è modellato per minimizzare un valore monetario di un differenziale tra un prezzo medio ponderato dei mutui nel pacchetto prestito e un Annuncio a (TBA) prezzo del titolo del tagliando media ponderata dei crediti nel pacchetto di prestito. 18. Un metodo di ottimizzazione tasso fisso di trading intero prestito, il procedimento comprendendo: la selezione di una popolazione di prestiti selezionare, da un computer, uno o più prestiti che soddisfano un vincolo di un'offerta determinare, dal computer, un prezzo per ogni prestito che soddisfa la vincolo basato su un modello cartolarizzato determinazione, dal computer, se utilizzare un modello efficiente per selezionare quale degli uno o più prestiti sono meno favorevoli da cartolarizzare e se si utilizza il modello efficiente, quindi selezionando, dal computer, quale dei i una o più prestiti sono meno favorevoli da cartolarizzare dal valore minimo in dollari di diffusione. 19. Procedimento secondo la rivendicazione 18 comprendente inoltre: determinare, dal computer, almeno un modulo di uno o più moduli che ottimizza tasso fisso negoziazione intero prestito basato su un ingresso ricevuto, in cui l'almeno un modulo comprende un modulo di prestito intero. 20. Il metodo della rivendicazione 18. comprendente inoltre la fase di assegnazione, dal computer, una porzione della pluralità di prestiti interi ad un pacchetto di prestiti interi per vendere prestiti come interi, la porzione comprendente intera riunione prestiti almeno una sollecitazione ed essendo meno vantaggioso rispetto agli altri prestiti interi quando eseguito in un legame nella struttura legame se il modello efficiente non viene utilizzato, quindi selezionando, dal computer, che degli uno o più prestiti sono meno favorevoli da cartolarizzare minimizzando diffusione. 21. Un sistema comprendendo: un computer communicably accoppiato alla rete e configurato per: creare un modello corrispondente ad una pluralità di vasche obbligazioni coupon eccesso e una piscina allocato, ogni pool coupon bond eccesso comprendente almeno una sollecitazione ed elaborare il modello di assegnare ciascuno di i prestiti in una piscina o coupon bond eccesso o in piscina non assegnate al fine di massimizzare il valore di mercato totale del coupon in eccesso che viene assegnata alle piscine dei titoli di coupon in eccesso. 22. Il sistema secondo la rivendicazione 21. in cui il modello comprende una funzione obiettivo che rappresenta il valore totale di mercato del coupon eccesso che viene assegnato alle piscine obbligazioni coupon eccesso. 23. Il sistema secondo la rivendicazione 21. in cui il computer è inoltre configurato per trasformare ogni dei vincoli almeno uno in un vincolo condizionale. 22. Il sistema secondo la rivendicazione 21. in cui il computer è inoltre configurato per trasformare ogni dei vincoli almeno uno in un vincolo condizionale per consentire vincoli di essere applicabile solo piscine obbligazioni coupon eccesso allocate. 24. Il sistema secondo la rivendicazione 21. in cui il computer è inoltre configurato per: identificare le piscine promozionale eccesso per cui ciascun prestito può essere assegnata in base agli attributi collaterali degli impieghi e comprimere ogni prestito identificato per un pool promozionale eccesso in un unico prestito ridurre il numero di prestiti nel modello. Questa applicazione è un divisionale di domanda di brevetto statunitense. No. 12533.315, depositata lug. 31, 2009, che rivendica il beneficio di brevetto provvisoria domanda di brevetto 61191.011, depositata Sep. 3, 2009, entrambi i quali sono qui integralmente incorporati per riferimento. La presente invenzione si riferisce in generale a sistemi e metodi per ottimizzare la negoziazione prestito e più in particolare a sistemi computerizzati e metodi mediante elaboratore per ottimizzare pacchetti di crediti intere per l'esecuzione in obbligazioni o vendita come pacchetti di prestito interi. Le istituzioni finanziarie, come le banche di investimento, comprare prestiti e portafogli di crediti da parte di banche o creatori di prestito in primo luogo per la cartolarizzazione dei crediti in obbligazioni e poi vendere le obbligazioni agli investitori. Questi legami sono considerati titoli garantiti da attività in quanto sono garantiti da attivi dei prestiti. Molti tipi di prestiti possono essere cartolarizzati in obbligazioni, inclusi mutui residenziali, ipoteche commerciali, prestiti auto, e dei crediti di carte di credito. Una varietà di strutture obbligazionari può essere creato da una popolazione di prestiti, ogni struttura con caratteristiche e dei vincoli che devono essere valutate al fine di massimizzare il profitto che un istituto finanziario può realizzare da cartolarizzazione dei prestiti in obbligazioni. Il raggruppamento ottimale o pool di prestiti in obbligazioni per una data struttura legame e una data popolazione prestito possono dipendere dalle caratteristiche di ciascun prestito nella popolazione. Inoltre, la piscina vincolo o l'esecuzione coupon che un singolo credito esegue in può dipendere dalla piscina vincolo o migliore esecuzione di ogni altro prestito nella popolazione. Come la popolazione tipica prestito considerato per cartolarizzare in obbligazioni è molto grandi (ad esempio 10.000 prestiti o più), determinando un raggruppamento ottimale degli impieghi per cartolarizzare in obbligazioni può essere difficile. Di conseguenza, ciò che è necessario sono sistemi e metodi per ottimizzare il confezionamento di una popolazione di prestiti in obbligazioni per una data struttura legame. L'invenzione fornisce sistemi computerizzati e metodi implementati informatici per l'ottimizzazione tasso fisso di trading intero prestito per una popolazione di prestiti interi. Un aspetto della presente invenzione fornisce un sistema per l'ottimizzazione tasso fisso negoziazione intero prestito. Questo sistema comprende un sistema di calcolo che include un'applicazione software comprendente uno o più moduli atti a sviluppare un modello per determinare una strategia cartolarizzazione per una popolazione di prestiti interi, la strategia cartolarizzazione comprese obbligazioni e azionabile per elaborare il modello finché una strategia cartolarizzazione ottimale la popolazione dei crediti interi viene trovato e un'interfaccia utente per ricevere input dell'utente per l'uno o più moduli e per emettere la strategia ottimale cartolarizzazione, l'interfaccia utente essendo in comunicazione con l'applicazione software. Un altro aspetto della presente invenzione fornisce un metodo attuata per per determinare un coupon legame esecuzione ottimale di ciascun credito in un gruppo di impieghi in una struttura legame seniorsubordinate. Il metodo include la creazione di un modello comprendente una funzione obiettivo che rappresenta un valore di mercato totale della struttura di legame seniorsubordinate per i prestiti. Inoltre, il metodo comprende massimizzare la funzione obiettivo da massimizzare il valore totale di mercato della struttura legame seniorsubordinate. Un altro aspetto dell'invenzione fornisce un metodo per attuata per riunire ottimale prestiti in passaggio attraverso i pool obbligazioni. Il metodo comprende la selezione di una popolazione di prestiti. Inoltre, per ogni prestito della popolazione selezione dei prestiti, il metodo comprende la determinazione un'esecuzione ottimale di ciascun prestito da un acquisto o un buy in basso della commissione di garanzia. Inoltre, il metodo comprende la determinazione di uno o più piscine per i quali ogni prestito è ammissibile. Inoltre, il metodo comprende la costruzione di un modello basato su almeno una sollecitazione per almeno un pool determinato e ripartizione prestiti al uno o più passaggio attraverso piscine obbligazioni. Un altro aspetto dell'invenzione fornisce un sistema che comprende una memoria che ha una serie di istruzioni per allocare una porzione di un gruppo di crediti per un pacchetto di prestito. Inoltre, il sistema comprende un computer accoppiato alla memoria. Al momento dell'esecuzione del set di istruzioni del computer determina quale dei prestiti soddisfano uno o più vincoli del pacchetto di prestito. Inoltre, il computer determina un prezzo di mercato di ciascuno dei prestiti sulla base di un modello di cartolarizzazione. Inoltre, il computer può modellare una funzione obiettivo per determinare quali prestiti nel gruppo di prestiti che soddisfi l'uno o più vincoli sono meno redditizi per cartolarizzazione nel modello cartolarizzazione e allocare i prestiti che soddisfa uno o più vincoli e sono meno vantaggioso per cartolarizzazione nel pacchetto di prestito. Un altro aspetto della presente invenzione fornisce un metodo di ottimizzazione tasso fisso negoziazione intero prestito. Questo metodo include le fasi di selezione di una popolazione di prestiti selezionando uno o più prestiti che soddisfano un vincolo di un'offerta determinare un prezzo di ciascun prestito che soddisfa il vincolo sulla base di un modello di cartolarizzato determinare se utilizzare un modello efficiente di scegliere quale dei uno o più prestiti sono meno favorevoli da cartolarizzare. Inoltre, se si utilizza il modello efficiente, il metodo comprende selezionare quale delle pone o più prestiti sono meno favorevoli da cartolarizzare dal valore minimo dollaro di diffusione. Un altro aspetto della presente invenzione fornisce un sistema di scambio ottimale eccesso coupon risultante da prestiti cartolarizzazione. Il sistema include e rete e un comunicabile calcolatore accoppiato alla rete. Inoltre, il computer crea un modello corrispondente alle piscine obbligazioni coupon eccesso e una piscina allocato, ogni pool eccesso coupon bond includente almeno un vincolo ed elabora il modello di assegnare ciascuno dei prestiti in entrambi una piscina coupon bond eccesso o in piscina allocato al fine di massimizzare il valore totale di mercato del coupon eccesso che viene assegnato alle piscine obbligazioni coupon eccesso. Questi e altri aspetti, caratteristiche e forme di realizzazione dell'invenzione saranno evidenti ad una persona di ordinaria esperienza nel ramo dalla considerazione della seguente descrizione dettagliata delle forme di realizzazione illustrate esemplificative la modalità migliore per realizzare l'invenzione come attualmente percepito. BREVE DESCRIZIONE DEI DISEGNI Per una comprensione più completa delle forme di realizzazione esemplificative della presente invenzione ed i suoi vantaggi, si fa ora riferimento alla descrizione che segue, in congiunzione con le figure allegate brevemente descritti come segue. FIGURA. 1 è uno schema a blocchi illustrante un sistema per ottimizzare tasso fisso intero negoziazione prestito secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 2 è un diagramma di flusso che illustra un metodo di ottimizzazione tasso fisso intero negoziazione prestito secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 3 è un diagramma di flusso che illustra un metodo per determinare una strategia cartolarizzazione per una popolazione di prestiti secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 4 è un diagramma di flusso che illustra un metodo per il confezionamento di una popolazione di prestiti in una struttura seniorsubordinate secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 5 è un diagramma di flusso che illustra un metodo per il confezionamento di una popolazione di prestiti in una struttura seniorsubordinate secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 6 è un diagramma di flusso che illustra un metodo per il confezionamento di una popolazione di prestiti in passaggio attraverso legami secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 7 è un diagramma di flusso che illustra un metodo per il confezionamento prestiti interi secondo una forma di realizzazione esemplificativa della presente invenzione. FIGURA. 8 è un diagramma di flusso che illustra un metodo per riunire eccesso promozionale secondo una forma di realizzazione esemplificativa della presente invenzione. DESCRIZIONE DETTAGLIATA DELLE FORME DI REALIZZAZIONE ESEMPLIFICATIVE L'invenzione fornisce sistemi e metodi basati su computer per ottimizzare tasso fisso negoziazione intero prestito. In particolare, l'invenzione fornisce sistemi e metodi basati su computer per il confezionamento in modo ottimale una popolazione di prestiti interi in obbligazioni sia in una struttura di legame seniorsubordinate o in pool di passaggio attraverso titoli garantiti da un ente governativo. Modelli per ogni tipo di struttura legame vengono elaborati sulla popolazione di prestiti fino a trovare sia un pacchetto legame ottimale o un utente determina che una soluzione sufficientemente elevata qualità è trovato. Inoltre, i modelli possono spiegare le offerte per i prestiti interi assegnando tutta prestiti che soddisfano i requisiti dell'offerta, ma sono meno favorevoli da cartolarizzare. Sebbene le forme di realizzazione esemplificative dell'invenzione sono discussi in termini di impieghi intere (mutui residenziali a tasso fisso particolarmente), aspetti dell'invenzione possono anche essere applicati alla negoziazione altri tipi di prestiti e attività, quali prestiti a tasso variabile e debiti girevoli. L'invenzione può comprendere un programma per computer che incorpora le funzioni qui descritte e illustrate nei diagrammi di flusso allegati. Tuttavia, dovrebbe essere evidente che ci potrebbero essere molti modi diversi di attuazione dell'invenzione in programmazione di computer, e l'invenzione non deve essere interpretata come limitata a una serie di istruzioni di programma per computer. Inoltre, un programmatore esperto sarebbe in grado di scrivere un tale programma di computer per realizzare una forma di realizzazione dell'invenzione descritta sulla base dei diagrammi di flusso e la descrizione associata nel testo dell'applicazione. Pertanto, la rivelazione di un particolare insieme di istruzioni di codice programma non è ritenuto necessario per un'adeguata comprensione di come realizzare e utilizzare l'invenzione. La funzionalità inventivo del programma per elaboratore secondo sarà spiegato più in dettaglio nella seguente descrizione letta in combinazione con le figure illustrano il flusso del programma. Inoltre, si comprenderà agli esperti del ramo che una o più delle fasi descritte possono essere eseguite da hardware, software, o una loro combinazione, come può essere incorporato in uno o più sistemi informatici. Passando ora ai disegni, in cui numeri uguali rappresentano elementi uguali in tutte le figure, aspetti delle forme di realizzazione esemplificative saranno descritte in dettaglio. FIGURA. 1 è uno schema a blocchi illustrante un sistema 100 per ottimizzare tasso fisso intero negoziazione prestito secondo una forma di realizzazione esemplificativa della presente invenzione. Facendo riferimento alla FIG. 1. il sistema 100 include un sistema di calcolo 110 collegato ad una rete distribuita 140. Il sistema di calcolo 110 può essere un personal computer collegato alla rete distribuita 140. Il sistema di calcolo 110 può includere una o più applicazioni, come ad esempio il commercio di prestito applicazione ottimizzatore 120. Questo esemplare ottimizzatore negoziazione prestito 120 comprende quattro moduli 121 - 124 che possono operare singolarmente o interagire con l'altro per fornire un imballaggio ottimale di prestiti in una o più strutture di legame e pacchetti di prestito interi. Un modulo seniorsubordinate 121 distribuisce prestiti in una struttura legame seniorsubordinate con legami con diversi rating e diversi valori netti coupon. Come sarà discusso in maggior dettaglio con riferimento alle figg. 4-5. il modulo seniorsubordinate 121 distribuisce i prestiti in titoli aventi un rating AAA, obbligazioni subordinate con rating inferiori e, a seconda delle prestiti ei valori promozionali di obbligazioni AAA e obbligazioni subordinate, interessi solo obbligazioni e principali solo legami. Un pass-thru Modulo 122 distribuisce prestiti in passaggio attraverso obbligazioni garantite da un ente governativo, come Freddie Mac e Fannie Mae. Il pass-thru Modulo 122 piscine in modo ottimale i prestiti in To Be Announced (TBA) passano attraverso titoli sulla base di una serie di vincoli. Il pass-through modulo 122 è discusso più dettagliatamente in seguito con riferimento alla FIG. 6. Un intero modulo prestito 123 assegna prestiti per soddisfare le offerte per portafogli di crediti che soddisfano i requisiti e vincoli dell'offerta specifici. L'intero modulo prestito 123 può interagire sia con il modulo seniorsubordinate 121 o il pass-through modulo 122 per allocare prestiti che soddisfano i requisiti delle offerte, ma sono meno favorevoli da cartolarizzare. Il modulo prestito intero 123 è discusso di seguito in maggior dettaglio con riferimento alla fig. 7. Un modulo coupon eccesso 124 distribuisce cedole in eccesso di crediti cartolarizzati in diverse tranche di obbligazioni o piscine. Il modulo promozionale eccesso 124 può unire coupon eccesso derivanti dalla struttura legame seniorsubordinate creato dal modulo seniorsubordinate 121 eo coupon eccesso derivanti dal passaggio attraverso titoli creati dal passaggio attraverso il modulo 122. Il modulo promozionale eccesso 124 è discusso di seguito in maggior dettaglio con riferimento alla fig. 8. Gli utenti possono inserire le informazioni in un'interfaccia utente 115 del sistema di calcolo 110. Queste informazioni possono includere un tipo di struttura legame per ottimizzare, vincoli associati con strutture obbligazionari e dei titoli di piscine, informazioni associate con le offerte di prestito, e ogni altra informazione richiesta dal commercio di prestito ottimizzatore 120. Dopo che l'informazione è ricevuta dall'interfaccia utente 115. le informazioni sono memorizzate in una memoria dati 125. che può essere un database del software o altra struttura di memoria. Gli utenti possono anche selezionare una popolazione di prestiti a prendere in considerazione per l'ottimizzazione attraverso l'interfaccia utente 115. I prestiti possono essere memorizzati in un database memorizzato su o accoppiato al sistema di calcolo 110 o ad una fonte di dati 150 collegata alla rete distribuita 140. L'interfaccia utente 115 può anche uscita ad un utente i pacchetti di obbligazioni e pacchetti di prestito intere determinati dal commercio di prestito ottimizzatore 120. L'ottimizzatore di trading prestito 120 può comunicare con i dati di più fonti 150 attraverso la rete distribuita 140. Ad esempio, l'ottimizzatore di trading prestito 120 può comunicare con una sorgente di dati 150 per determinare Fannie Mae prezzi TBA e un'altra fonte di dati 150 per determinare i prezzi degli Stati Uniti del Tesoro. In un altro esempio, l'ottimizzatore di trading prestito 120 può comunicare con una sorgente di dati 150 di accedere alle informazioni associate a offerte per pacchetti di prestito interi. La rete distribuita 140 può essere una rete locale (LAN), wide area network (WAN), Internet o altro tipo di rete. FIGURA. 2 è un diagramma di flusso che illustra un metodo 200 per ottimizzare tasso fisso intero negoziazione prestito secondo una forma di realizzazione esemplificativa della presente invenzione. Con riferimento alle figg. 1 e 2. al punto 205. l'interfaccia utente 115 riceve input da un utente. Questo input dell'utente viene utilizzato dal commercio di prestito ottimizzatore 120 per determinare la struttura legame che dovrebbe essere ottimizzato per una popolazione di prestiti. Ad esempio, se l'utente desidera trovare il raggruppamento ottimale dei prestiti per passaggio attraverso legami, l'utente può inserire i vincoli per ogni pool legame. Esempi di vincoli per passare attraverso le piscine dei titoli comprendono i vincoli sui saldi di prestito, il numero totale di prestiti per una piscina, e l'equilibrio del prestito totale per una piscina. Al passo 210. una popolazione di prestiti è stato selezionato per l'ottimizzazione. La popolazione di prestiti può essere selezionato da prestiti memorizzati in un database memorizzato su prestito o accoppiato al sistema di calcolo 110 o da un database a un'origine dati 150 collegata alla rete distribuita 140. La popolazione di prestiti può includere prestiti attualmente di proprietà dell'utente (ad esempio, banca d'investimento) delle negoziazioni del prestito ottimizzatore 120 prestiti eo che sono su per l'offerta da un'altra banca, prestito ordinante, o altro istituto. Ad esempio, un utente può impiegare il prestito di trading ottimizzatore 120 per trovare il massimo valore di mercato di un portafoglio crediti attualmente in vendita al fine di determinare un'offerta ottimale per il portafoglio crediti. Inoltre, un utente può selezionare la popolazione di prestiti specificando determinati criteri, come il bilanciamento del prestito massimo, la posizione dei prestiti, e il punteggio FICO. Nel passo 215. l'ottimizzatore di trading prestito 120 determina una strategia di cartolarizzazione per la popolazione di prestiti selezionati al punto 210. A seconda gli input dell'utente ricevuti nel passo 205. l'ottimizzatore di trading prestito 120 impiega uno o più del modulo seniorsubordinate 121. il pass-through modulo 122. e l'intero modulo prestito 123 per determinare la strategia di cartolarizzazione per la popolazione di prestiti. Fase 215 è discussa in maggior dettaglio con riferimento alle figure. 3-7. Nel passo 220. l'ottimizzatore di negoziazione prestito 120 determina se la strategia cartolarizzazione restituito al passo 215 è di qualità sufficientemente elevata. In questa forma di realizzazione esemplificativa, l'ottimizzatore di negoziazione prestito 120 itera la fase di determinare una strategia cartolarizzazione per la frazione di prestiti fino a quando una soluzione ottimale viene trovato o l'utente determina che la strategia cartolarizzazione è di qualità sufficientemente elevata. Affinché l'all'utente di determinare se la strategia di cartolarizzazione, se sufficientemente alta qualità, il commercio di prestito ottimizzatore 120 può restituire i risultati all'utente a titolo dell'interfaccia utente 115. La negoziazione prestito ottimizzatore 120 può emettere questi risultati in base a un numero di iterazioni del passo 215 (ad esempio ogni 100 iterazioni) oppure quando viene trovato un certo livello di qualità. L'interfaccia utente 115 può quindi ricevere input dall'utente che indica se la strategia cartolarizzazione è sufficiente alta qualità. Se la strategia cartolarizzazione è sufficiente alta qualità o ottimale, il ricavato metodo 200 al passo 225. Altrimenti, i rendimenti metodo 200 al passo 215. In un esempio, la qualità è misurata in termini di valore del dollaro totale della popolazione dei prestiti. Ad esempio, l'utente potrebbe desiderare di vendere una popolazione di prestiti per almeno dieci milioni di dollari al fine di fare offerte sui prestiti. L'utente può impostare una soglia per il prestito negoziazione ottimizzatore 120 per restituire solo una soluzione che soddisfi tale soglia o una soluzione che è la soluzione ottimale se la soluzione ottimale è inferiore a questa soglia. Nel passo 225. il modulo di buono in eccesso 124 del commercio di prestito ottimizzatore 120 può unire ogni eccesso cedola risultante dalla strategia di cartolarizzazione determinata al punto 215. Questo passaggio è opzionale ed è discusso di seguito in maggior dettaglio con riferimento alla fig. 8. Al passo 230. l'ottimizzatore di trading prestito 120 comunica la strategia di cartolarizzazione finale per l'interfaccia utente 115 per l'output ad un utente. L'interfaccia utente 115 può visualizzare la strategia cartolarizzazione finale e facoltativamente altri possibili strategie di capitalizzazione con livelli simili di qualità. FIGURA. 3 è un diagramma di flusso che illustra un metodo 215 per determinare una strategia cartolarizzazione per una popolazione di prestiti secondo una forma di realizzazione esemplificativa della presente invenzione. Con riferimento alle figg. 1 e 3. nel passo 305. l'ottimizzatore di trading prestito 120 determina quali modelli da utilizzare per la determinazione delle strategie di cartolarizzazione. In questa forma di realizzazione esemplificativa, l'ottimizzatore di negoziazione prestito 120 include un modulo 121 seniorsubordinate. un passare-attraverso modulo 122. e il modulo 123 di un prestito intero. Ciascuno dei moduli 121 - 123 può costruire ed elaborare un modello per determinare un confezionamento ottimale degli impieghi come discusso di seguito. L'ottimizzatore negoziazione prestito 120 determina quali moduli 121 - 123 per utilizzare in base all'ingresso ricevuto dall'utente nel passaggio 205 di FIG. 2. Ad esempio, l'utente può specificare che solo una struttura seniorsubordinate dovrebbe essere ottimizzato per la popolazione di prestiti. In alternativa, se l'utente ha inserito le informazioni offerta per un portafoglio di prestiti interi, l'ottimizzatore di trading prestito 120 può eseguire l'intero modulo prestito 123 con il modulo seniorsubordinate 121 Andor modulo pass-thru 122 per determinare quale dei prestiti soddisfare le esigenze di l'offerta e sono meno favorevoli per la cartolarizzazione. Inoltre, un utente può specificare che sia una struttura legame seniorsubordinate ottimale e una condivisione ottimale di passaggio attraverso legami devono essere determinati per la popolazione dei crediti. Se l'utente ha selezionato che una struttura legame seniorsubordinate dovrebbe essere ottimizzato, il ricavato metodo 215 al passo 310. Al passo 310. il modulo seniorsubordinate 121 sviluppa un modello per il confezionamento la popolazione di prestiti in una struttura legame seniorsubordinate ed elabora il modello per determinare una struttura di legame seniorsubordinate ottimale per la popolazione dei crediti. Fase 310 è discussa in maggior dettaglio con riferimento alle figg. 4 e 5. Dopo che la struttura seniorsubordinate è determinata, il ricavato metodo 215 al passo 220 (FIG. 2). Se l'utente ha selezionato che la popolazione di prestiti dovrebbero essere ottimamente raggruppata all'interno passaggio attraverso legami, il ricavato metodo 215 al passo 315. Al passo 315. il pass-through modulo 122 sviluppa un modello per riunire la popolazione di prestiti in più vasche obbligazioni ed elabora il modello per determinare un pool ottimale per la popolazione dei crediti. Fase 315 è discussa in maggior dettaglio con riferimento alla FIG. 6. Dopo la messa in comune è determinata, il ricavato metodo 215 al passo 220 (FIG. 2). Se l'utente ha selezionato che prestiti interi dovrebbero essere assegnati a un pacchetto di prestiti interi per essere venduto, il ricavato metodo 215 al passo 320. Al passo 320. l'intero modulo prestito 123 sviluppa un modello di ripartizione prestiti integrali che soddisfano determinati vincoli e meno favorevoli per cartolarizzare in un pacchetto di prestito intero ed elabora il modello per determinare quali prestiti sono più adatti per l'intero pacchetto prestito. Fase 320 è discussa in maggior dettaglio con riferimento alla fig. 7. Dopo l'intero pacchetto di prestito è determinato, il ricavato metodo 215 al passo 220 (FIG. 2). FIGURA. 4 è un diagramma di flusso che illustra un metodo 310 per il confezionamento di una popolazione di prestiti in una struttura legame seniorsubordinate secondo una forma di realizzazione esemplificativa della presente invenzione. Come discusso brevemente sopra con riferimento alla FIG. 1. una struttura legame seniorsubordinate è una struttura in cui sono creati legami con diversi rating. Tipicamente, la struttura di legame seniorsubordinate include una quota maggiore di obbligazioni di AAA o simile rating e un segmento subordinato di titoli aventi un rating inferiore. La tranche senior è protetto da un certo livello di perdita da parte del segmento subordinato come il segmento subordinato incorre le prime perdite che possono verificarsi. La trance di alto livello può essere venduto agli investitori che desiderano un investimento più prudente avere una resa inferiore, mentre la tranche subordinata può essere venduto a investitori disposti ad assumersi maggiori rischi per un rendimento più elevato. For the purpose of this application, a AAA rated bond refers to a bond in the senior tranche, but not necessarily a bond having a credit rating of AAA. Additionally, interest only (IO) and principal only (PO) bonds may be created in a seniorsubordinate structure. An IO bond is created when the net coupon of a loan is more than the coupon of the bond in which the loan executes. Thus, the difference in the loan coupon and the bond coupon creates an interest only cash flow. Similarly, when the loan coupon is less than the bond coupon, a PO bond is created which receives only principal payments. Referring to FIGS. 1 and 4. at step 405 . the seniorsubordinate module 121 determines the bond coupons that are available for executing the loans into. The seniorsubordinate module 121 may obtain the available bond coupons from a data source 150 or may receive the available bond coupons from the user by way of the user interface 115 in step 205 of FIG. 2. For example, the user may desire to execute the loans into bonds having coupon values between 4.5 and 7.0. At step 410 . the seniorsubordinate module 121 selects a first bond coupon value from the range of available bond coupon values. This first coupon value can be the lowest bond coupon value, the highest coupon value, or any other bond coupon value in the range of available bond coupon values. At step 415 . the seniorsubordinate module 121 determines the execution price of each loan in the population of loans at the selected coupon value. Each loan in the population of loans is structured as a bond. The cash flow of each loan is distributed into symbolic AAA and subordinate bonds, and depending on the coupon of the loan and the selected bond coupon, an IO or PO bond. The principal payment and interest cash flows of each loan is generated in each period accounting for loan characteristics of the loan, such as IO period, balloon terms, and prepayment characteristics. The cash flow generated in each period is distributed to all bonds that the loan executes taking into account shifting interest rules that govern the distribution of prepayments between the AAA and the subordinate bonds in each period. The proportion in which the principal payments are distributed depends on the subordination levels of the AAA and the subordinate bonds. The subordination levels are a function of the loan attributes and are supplied by rating agencies for each loan through an Application Program Interface (API) coupled to the computing device 110 . Prepayments are first distributed pro rata to the PO bond and then between the AAA and the subordinate bonds based on the shifting interest rules. Any remaining prepayment is distributed proportionally among all the subordinate bonds. The interest payment for each of the bonds is a direct function of the coupon value for the bond. After the cash flows of each of the bonds for each of the loans have been generated, the present value of these cash flows is determined. For fixed rate loans, the AAA bonds can be priced as a spread to the To Be Announced (TBA) bond prices. However, the subordinate bond cash flows are discounted by a spread to the U. S. Treasury Yield Curve. The IO and PO bonds are priced using the Trust IO and PO prices. Finally, the price of the AAA bond, the subordinate bonds, and the IO or PO bond is combined proportionally for each loan based on the bond sizes to get the final bond price for each loan. This final bond price is the price of the loan executing into the bond given the selected coupon value of the bond. At step 420 . the seniorsubordinate module 121 determines if there are more bond coupon values in the range of available bond coupon values. If there are more bond coupon values, the method 310 proceeds to step 425 . Otherwise, the method 310 proceeds to step 430 . At step 425 . the next bond coupon value in the range of available bond coupon values is selected. In one exemplary embodiment, the seniorsubordinate module 121 can increment from the previous selected bond coupon value (e. g. 0.5 increments) to determine the next bond coupon value. In an alternative embodiment, the seniorsubordinate module 121 can progress through a fixed list of bond coupon values. For example, the user may select specific bond coupon values to execute the loans into, such as only 4.0, 5.0, and 6.0. After the next bond coupon value is selected, the method 310 returns to step 415 to determine the execution price of each loan in the population of loans at the new coupon value. At step 430 . the seniorsubordinate module 121 determines, for each loan in the population of loans, which bond coupon value yielded the highest final bond price for that particular loan. At step 435 . the seniorsubordinate module 121 groups the loans according to the bond coupon value that yielded the highest final bond price for each loan. For example, if the available bond coupon values are 4.0, 5.0, and 6.0, each loan that has a highest final bond price at 4.0 are grouped together, while each loan that has a highest final bond price at 5.0 are grouped together, and each loan that has a final bond price at 6.0 are grouped together. After step 435 is complete, the method proceeds to step 220 ( FIG. 2 ). In the embodiment of FIG. 4. the subordinate bonds for each loan execute at the same bond coupon value as the corresponding AAA bond. For example, if a first loan of 6.25 best executes into a bond having a coupon value of 6.0, then a AAA bond of 6.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 6.0 is created. If a second loan of 5.375 best executes into a bond having a coupon value of 5.0, then a AAA bond of 5.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 5.0 is created. This creates two AAA bonds and two subordinate bonds at two different coupon values. Typically, when loans are packaged in a seniorsubordinate bond structure, multiple AAA bonds with multiple coupon values are created with a common set of subordinate bonds that back all of the AAA bonds. This set of subordinate bonds is priced at the weighted average (WA) execution coupon of all of the AAA bonds created for the loan package. Pricing the subordinate bonds at the WA execution coupon implies that the spread to the benchmark U. S. Treasury curve, which is a function of the bond rating and the execution coupon of the subordinate bond, has to be chosen appropriately. In order to know the WA execution coupon of all the AAA bonds for the population of loans, the best execution coupon for each loan in the population of loans has to be known. In order to know the best execution coupon of each loan, the loan has to be priced at different bond coupon values and the AAA and subordinate bonds created at those coupons also have to be priced. However, the subordinate bond cash flows are discounted with spreads to the U. S. Treasury, with spreads taken at the WA best execution coupon which is still unknown. This creates a circular dependency as the best execution of each loan in the population of loans now depends on all the other loans in the population. FIG. 5 is a flow chart depicting a method 500 for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. The method 500 is an alternative method to that of method 310 of FIG. 4. accounting for pricing subordinate bonds at the WA execution coupon and provides a solution to the circular dependency discussed above. The WA execution coupon for a population of loans can be calculated by: In Equation 1, x ij is a binary variable with a value of either 0 or 1, whereby a value of 1 indicates that the i th loan is optimally executing at the j th execution coupon value. The parameters d 0 to d j represent the j execution coupon values. For example, the coupons values could range from 4.5 to 7.0. Finally, the parameter b i represents the balance of the i th loan. If q o to q j are the weights of the j execution coupons, then: where q 0 to q 1 are special ordered sets of type two, which implies that at most two are non-zero and the two non-zero weights are adjacent. Let Pa ij be the price of the AAA bond when loan i executes at coupon j. Next, let Ps ij be the overall price of all of the subordinate bonds combined when loan i executes at coupon j. Finally, let Pio ij and Ppo ij be the prices of the IO and PO bonds respectively when loan i executes at coupon j. The AAA bond prices and the IO and PO bond price components of loan i executing at coupon j are linear functions of x ij . The AAA priced as a spread to the TBA is a function of the execution coupon of the AAA bond and the IOPO prices are a lookup based on collateral attributes of the loan. However, pricing the subordinate bonds is complicated because the subordinate cash flows are discounted at the WA execution coupon. Let P i be a matrix of size jj that contains the prices of the subordinate bonds. The (m, n) entry of the matrix represents the price of the subordinate cash flows when the cash flow of loan i is generated assuming that loan i executes at the m th coupon and is discounted using subordinate spreads for the n th coupon. Subordinate spreads to the U. S. Treasury are a function of the execution coupon and any product definition, such as the size (e. g. JumboConforming), maturity (e. g. 1530 years), etc. The price of the subordinate bond of the i th loan can be written as: which is a non linear expression as the equation contains a product of q and x ij . both of which are variables in this equation. FIG. 5 provides a method 500 for overcoming this non-linearity. Referring to FIG. 5. at step 505 . the seniorsubordinate module 121 determines the optimal execution price for each loan in the population of loans independent of the WA execution coupon. In one exemplary embodiment, the seniorsubordinate module 121 employs the method 310 of FIG. 4 to find the optimal execution price for each loan. At step 510 . the seniorsubordinate module 121 determines the WA execution coupon corresponding to the optimal execution price for each loan. This WA execution coupon can be found using Equation 1 above. At step 515 . the seniorsubordinate module 121 determines the weights (i. e. q 0 8722q j ) of each execution coupon for the WA execution coupon found in step 510 . These weights can be found using Equation 3 above. At step 520 . the seniorsubordinate module 121 builds a model including an objective function to determine the optimal execution coupon for each loan to maximize the total market value of all of the bonds in the seniorsubordinate structure. The expression of the objective function contains ij terms, where the ij term represents the market value of executing the i th loan at the j th execution coupon. After inserting the values of the weights of the execution coupons (i. e. qs) into the expression for subordinate bond price (Equation 4), only two of the terms will be non-zero for the sub-price of the i th loan executing at the j th execution coupon. As the method 200 of FIG. 2 iterates step 215 . different WA execution coupons can be used to maximize the objective function. The iterations can begin with the WA execution coupon found in step 510 and the seniorsubordinate module 121 can search around this WA execution coupon until either the optimal solution is found or the user decides that a solution of sufficient high quality is found in step 220 of FIG. 2. In other words, the seniorsubordinate module 121 searches for an optimal solution by guessing several values of the WA execution coupon around an initial estimate of the optimal execution coupon. After a final solution is found by the seniorsubordinate module 121 . the loans can be grouped based on the coupon values for each loan in the final solution to the objective function. In some instances, one of the undesirable effects of the seniorsubordinate bond structure is the creation of IO andor PO bonds, which may not trade as rich as AAA bonds. In some exemplary embodiments, the seniorsubordinate module 121 can ameliorate this issue by considering a loan as two pseudo loans. For example, a loan having a net rate of 6.125 and a balance of 100,000 can be considered equivalent to two loans of balance b1 and b2 and coupons 6 and 6.5 such that the following conditions are satisfied: The first condition conserves the original balance, while the second condition is to set the WA coupon of the two pseudo loans to equal the net rate of the original loan. Solving these equations for b1 and b2, we find that b175,000 and b225,000. These two loans, when executed at 6.0 and 6.5 bond coupons respectively, avoids the creation of either an IO bond or a PO bond. Although in the above example two adjacent half point coupons were used to create the two pseudo loans, two coupons from any of the half point bond coupons that are being used to create the bonds can be used. For example, if only bond coupons from 4.5 to 7.0 are being used to create the bonds, there would be fifteen combinations to consider (6C215). In some cases, the best solution is not to split the loan into two adjacent half point bond coupons. For example, this split may not be optimal if the AAA spreads at the two adjacent half point coupons are far higher than the ones that are not adjacent to the net balance of the loan. The seniorsubordinate module 121 can construct a linear program or linear objective function to determine the optimal split into pseudo loans. The output of the linear program is the optimal splitting of the original loan into pseudo loans such that the overall execution of the loan is maximized, subject to no IO bond or PO bond creation. For each loan i, let variable x ij indicate the balance of loan i allocated to the jth half point coupon, subject to the constraint that the sum of over x ij for all j equals to the balance of loan i and the WA coupon expressed as a function of the x ij s equals to the net coupon of loan i, similar to Equation 6 above. Let the execution coupons be r 0 to r n . Thus, this equation becomes: where b i is the balance of loan i and c i is the net coupon of loan i. The price of loan i executing at coupon j is the sum of the price of the AAA bond and the subordinate bonds. No IO or PO bonds are created when the coupons are split. The seniorsubordinate module 121 calculates the price of the AAA bond as a spread to the TBA, where the spread is a function of the execution coupon j. In one embodiment, the seniorsubordinate module 121 also calculates the price of the subordinate bond as a spread to the TBA for simplification of the problem. Cash flows are not generated as the split of the balances to different execution coupons is not yet known. The seniorsubordinate module 121 combines the price of the subordinate bond and the AAA bond in proportion to the subordination level of loan i, which can be input by a user in step 205 of FIG. 2 or input by an API. At this point, the seniorsubordinate module 121 has calculated the price of loan i (P ij ) for each execution coupon j. To determine the optimal splitting of the original loan into pseudo loans, the seniorsubordinate module 121 creates the following objective function and works to maximize this objective function: Equation 8 is a simple linear program with two constraints and can be solved optimally. The solution gives the optimal split of the loan into at most two coupons and thus, a bond can be structured without creating any IO or PO bonds. The user can determine if the bond should be split or not based on the optimal execution and other business considerations. FIG. 6 is a flow chart depicting a method 315 for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. A pass through bond is a fixed income security backed by a package of loans or other assets. Typically, as briefly discussed above with reference to FIG. 1. a pass through bond is guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The government agency guarantees the pass through bond in exchange for a guarantee fee (Gfee). The Gfee can be an input provided by the agencies for a specific set of loans or can be specified as a set of rules based on collateral characteristics. Regardless of how the Gfee is obtained, the Gfee for a loan set is known. When loans are securitized as a pass through bond, one has the option to buy up or buy down the Gfee in exchange for an equivalent fee to the agencies. Buying up the Gfee reduces the net coupon and thus the price of the bond as well. This upfront buy up fee is exchanged in lieu of the increased Gfee coupon. Similarly, buying down the Gfee reduces the Gfee and increases the net coupon and therefore increases the bond price. An upfront fee is paid to the agencies to compensate for the reduced Gfee. The Fannie Mae and Freddie Mac agencies typically provide buy up and buy down grids each month. Referring to FIG. 1. these grids can be stored in a data source 150 or in the data storage unit 125 for access by the pass-thru module 122 of the loan trading optimizer 120 . If the Gfee is bought up or bought down, an excess coupon is created. The amount of buy up or buy down of Gfee can vary based on collateral attributes of the loan and can also be subject to a minimum and maximum limit. Referring now to FIGS. 1 and 6. at step 605 . the pass-thru module 122 determines the optimal execution of each loan by buy up or buy down of the Gfee. In one exemplary embodiment, the optimal execution of each loan is determined by finding the overall price of the loan for each available buy up and buy down of the Gfee. Typically, a Gfee can be bought up or down in increments of 1100 th of a basis point. The pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy up with a step size of 1100 th of a basis point. Similarly, the pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy down with a step size of 1100 th of a basis point. In each iteration, the amount of Gfee buy up or buy down is added to the current net rate of the loan. From this modified net rate of the loan, the TBA coupon is determined as the closest half point coupon lower than or equal to the modified net rate. The excess coupon is equal to the modified net rate of the TBA coupon and the price of the excess coupon is a lookup in the agency grid. The fee for the buy up or buy down is also a lookup in the agency grid. The price of the TBA coupon is a lookup from the TBA price curve. When the Gfee is bought up, the cost is added to the overall price and when the Gfee is bought down, the cost is subtracted from the overall price. The pass-thru module 122 determines the overall price of execution for the loan at each iteration and determines the optimal execution for the loan as the execution coupon of the TBA for which the overall price is maximized. This overall cost is the combination of the price of the TBA coupon, the price of the excess coupon, and the cost of the Gfee (added if buy up, subtracted if buy down). At step 610 . the pass-thru module 122 determines which TBA pools each loan is eligible for. Pooling loans into TBA bonds is a complex process with many constraints on pooling. Furthermore, different pools of loans have pool payups based on collateral characteristics. For example, low loan balance pools could prepay slower and thus may trade richer. Also, loan pools with geographic concentration known to prepay faster may trade cheaper and thus have a negative pool payup. Thus, pooling optimally taking into account both the constraints and the pool payups can lead to profitable execution that may not be captured otherwise. Each of the TBA pools for which a loan can be allocated has a set of pool eligibility rules and a pool payup or paydown. Non-limiting examples of pools can be a low loan balance pool (e. g. loan balances less than 80K), a medium loan balance pool (e. g. loan balance between 80K and 150K), a high loan balance pool (e. g. loan balances above 150K), a prepay penalty loan pool, and an interest only loan pool. For a loan to be allocated to a specific pool by the pass-thru module 122 . the loan has to satisfy both the eligibility rules of the pool and also best execute at the execution coupon for that pool. The pass-thru module 122 applies the eligibility rules of the TBA bond pools to the loans to determine the TBA bond pools for which each loan is eligible. The pass-thru module 122 can utilize pool priorities to arbitrate between multiple pools if a loan is eligible for more than one pool. If a loan is eligible to be pooled into a higher and lower priority pool, the pass-thru module 122 allocates the loan to the higher priority pool. However, if a loan is eligible for multiple pools having the same priority, the pass-thru module 122 can allocate the loan into either of the pools having the same priority. At step 615 . the pass-thru module 122 builds a model for allocating the loans into TBA pools based on the constraints of each TBA bond pool. Let x ij be a binary variable with a value of 1 or 0 which has a value of 1 when loan i is allocated to TBA bond pool j. The total loan balance and loan count constraints of the TBA pools are linear functions of the x ij variables. The objective function for this model is also a linear combination of the market values of each loan. The primary problem in this model is that the given loan population selected in step 210 of FIG. 2 may not be sufficient to allocate all TBA loan pools, as some of the pools may not have loans to satisfy the balance and count constraints or the loans may not be eligible for those pools. In such cases, it is desirable for the pools to have the constraints when applicable. If there are some pools for which there are not enough loans in the population of loans to form a pool, then such pools are not subjected to the specified constraints while the other pools are. However, it is not possible to know a-priori which pools do not have enough loans to satisfy the constraints. Thus, the model employs conditional constraints to allow constraints to be applicable to only those pools which are allocated. The pooling model is modified to allow for some loans to not be allocated to any pool. This non-allocation will ensure that the model is always solvable and is similar to introducing a slack variable in linear programming. Thus, for each loan in the population of loans, there is an additional binary variable representing the unallocated pool into which the loan can be allocated. Those loans allocated to the unallocated pool are given a zero costmarket value, thus encouraging the pass-thru module 122 to allocate as many loans as possible. The next step in building this pooling model is to introduce p binary variables for the p possible TBA pools. A value of 1 indicates that this pool is allocated with loans satisfying the pool constraints and a value of 0 indicates that this pool is not allocated. These variables are used to convert simple linear constraints into conditional constraints. Each constraint of each pool is converted to conditional constraints for the pooling model. To detail this conversion, a maximum loan count constraint is considered for pool P. Let x 1 to x n be binary variable where x i are the loans eligible for pool P. Next, let x 1 . x n U, where U equals the total number of loans in pool P. Finally, let w be the binary variable to indicate if pool P is allocated. The user constraint for maximum loan count is specified as U8806K, where K is given by the user. In order to impose this constraint conditionally, this constraint is transformed to the following two constraints: U8806K w U8806M w where M is a constant such that the sum of all x i s is bounded by M. Consider both the cases when pool P is allocated (w1) and when pool P is not allocated (w0) below: w1: U8806K (required) U8806M (redundant) w0: U88060 U88060 The only way for U88060 would be when all the x i s are 0 and thus, pool P will be unallocated. Other constraints, such as minimum count, minimum balance, maximum balance, average balance, and weighted average constraints can be transformed similarly for the pooling model. After all of the constraints are transformed to conditional constraints, the pooling model is ready to handle constraints conditionally. At step 620 . the pass-thru module 122 executes the pooling model to allocate the loans into TBA pools. After the pass-thru module 122 executes the model for one iteration, the method 315 proceeds to step 220 ( FIG. 2 ). As the method 200 of FIG. 2 iterates step 215 . different TBA pool allocations are produced by the pass-thru module 122 until either the optimal TBA pool allocation is found or until the user decides that a solution of sufficient high quality is found in step 220 ( FIG. 2 ). FIG. 7 is a flow chart depicting a method 320 for packaging whole loans in accordance with one exemplary embodiment of the present invention. The method 320 identifies an optimal package of loans meeting a set of constraints given by a customer or investor. In this embodiment, the loan package is optimized by determining which loans, among the population of loans that meet the constraints, are least favorable to be securitized. Although the method 320 of FIG. 7 is discussed in terms of the seniorsubordinate bond structure, other bonds structures or models can be used. Referring to FIG. 7. at step 705 . the whole loan module 123 determines which loans in the population of loans meets constraints of a bid for whole loans. Investment banks and other financial institutions receive bids for whole loans meeting specific requirements. These requirements can be entered into the user interface 115 at step 205 of FIG. 2 andor stored in the data storage unit 125 or a data source 150 . The constraints can include requirements that the loans must satisfy, such as, for example, minimum and maximum balance of the total loan package, constraints on the weighted average coupon, credit ratings of the recipients of the loans (e. g. FICO score), and loan-to-value (LTV) ratio. The constraints can also include location based constraints, such as no more than 10 of the loan population be from Florida and no zip code should have more than 5 of the loan population. After the whole loan module 123 selects the loans that meet the constraints, at step 710 . the whole loan module 123 determines the price of each loan that meets the constraints based on a securitization module. For example, the price of the loans may be calculated based on the seniorsubordinate structure discussed above with reference to FIGS. 4 and 5 . At step 715 . the whole loan module 123 determines whether to use an efficient model to select loans least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on a securitization model or a less efficient model to select loans least favorable to be securitized by minimizing the spread of execution of the loans based on a securitization model. In one exemplary embodiment, this determination can be based on the total number of loans in the population or chosen by a user. If the whole loan module 123 determines to use the efficient model, the method 320 proceeds to step 725 . Otherwise, the method 320 proceeds to step 720 . At step 720 . the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the spread of execution of the loans based on the seniorsubordinate bond structure. The whole loan module 123 builds a model to select a subset of the loans that meet the constraints such that the WA price of the loans of this subset net of the TBA price of the WA coupon of this subset is minimized. The TBA price of the WA coupon of the subset is typically higher as the TBA typically has a better credit quality and hence the metric chosen will have a negative value. The objective function that needs to be minimized is given by: In Equation 9, x 1 to x n are binary variables with a value of either 0 or 1, whereby a value of 1 indicates that the loan is allocated and 0 otherwise. The variables b 1 to b n are the balances of the loans and p 1 to p n are the prices of the loans as determined in step 710 . The variables q 1 to q m are the weights for each of the half point coupons and px 1 to px m are the TBA prices for the half point coupons. The weights are special ordered sets of type two, which as discussed above, implies that at most two are non-zero and the two non-zero weights are adjacent. Thus, the expression (q 1 px 1 . q m px m ) is the price of the WA coupon of the allocated loans. The weights (q 1 - q m ) are subject to the constraints: The equations above are analyzed when z i is set to 1 and z i is set to 0 and which shows that y i will be y 0 or zero within a tolerance of eps. Eps is a model specific constant and is suitably small to account for lack of numerical precision in a binary variable. The tolerance eps is utilized in this model as although binary variables are supposed to be 0 or 1, the binary variables suffer from precision issues and thus, the model should accommodate numerical difficulties. The source of this precision issue is the way y 0 has been defined. The denominator of y 0 M(x 1 b 1 . x n b n ) is essentially the sum of the balances of all loans in the pool, which can be a very large number resulting in a small y 0 . After building the model, the whole loan module 123 minimizes the objective function in Equation 13 with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations 17- 21 . The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 720 is completed, the method 320 proceeds to step 220 ( FIG. 2 ). At step 725 . the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on the seniorsubordinate bond structure. Thus, the difference of the market value of the allocated loans and the notional market value of the loan pool using the price of the WA execution coupon is minimized. The objective function that needs to be minimized for this model is given by: After building the model, the whole loan module 123 minimizes the objective function in Equation 24 with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations 25-29. The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 725 is completed, the method 320 proceeds to step 220 of FIG. 2. FIG. 8 is a flow chart depicting a method 225 for pooling excess coupon in accordance with one exemplary embodiment of the present invention. The excess coupon module 124 can pool the excess coupon of securitized loans into different tranches or pools. The excess coupon module 124 can take a large population of loans (e. g. 100 thousand or more), each with some excess coupon, and pool the loans into different pools, each pool with a different coupon and specified eligibility rules. Each of the pools can also have a minimum balance constraint. Pools that are created with equal contribution of excess coupon from every loan that is contributing to that pool typically trades richer than pools that have a dispersion in the contribution of excess from different loans. Therefore, it is profitable to create homogeneous pools. Referring to FIG. 8. at step 805 . the excess coupon module 124 converts the pool constraints into conditional constraints as some of the pools defined in this excess coupon model may not have loans to satisfy the pool constraints. This conversion is similar to the conversion of constraints discussed above with reference to FIG. 6. At step 810 . the excess coupon module 124 builds a model to determine the optimal pooling for the excess coupons. Let x ij be the contribution of excess coupon from loan i to pool j. Unlike the pooling model in FIG. 6 above, this variable is not a binary variable. However, an unallocated pool is added to the set of user defined pools which enables the pass-thru module 122 to always solve the model and produce partial allocations. The first constraint of this excess coupon model is the conservation of excess coupon allocated among all the pools for each loan. Any loan that does not get allocated to a user defined pool is placed in the unallocated pool, and thus the unallocated pool is also included in the conservation constraint. In this embodiment, the unallocated pool does not have any other constraint. The objective function of this excess coupon model is to maximize the total market value of the excess that gets allocated. Unallocated excess coupon is assigned a zero market value and thus the solver tries to minimize the unallocated excess coupon. In this model, the excess coupon module 124 tries to create the maximum possible pools with equal excess contribution. Any leftover excess from all the loans can be lumped into a single pool and a WA coupon pool can be created from this pool. An aspect of this excess coupon model is to enforce equality of the excess coupon that gets allocated from a loan to a pool. Furthermore, it is not necessary that all loans allocate excess to a given pool. Thus, the equality of excess is enforced only among loans that have a non-zero contribution of excess to this pool. Let xp 0 to xp p be p real variables that indicate the amount of excess in each pool. Also, let w ij be a binary variable that indicates if loan i is contributing excess to pool. For each eligible loan i, for pool j, the following constraints are added: When M is chosen to be the maximum excess coupon of all loans in the allocation, the expression xp j 8722M is negative. Thus, from x ij 88060 and that all excess coupons have to be zero or positive, this implies that x ij 0 when w ij 0. This excess coupon model can be difficult to solve because of its complexity level. In order to reduce the complexity, the excess coupon module 124 employs dimensionality reduction. The first step of this process is to identify the pools into which a loan can be allocated. Eligibility filters in this excess coupon model specify the mapping of the collateral attributes of the loans to the coupons of the pools that the attributes can go into. For example, loans with a net coupon between 4.375 and 5.125 can go into pools of 4.5 or 5.0. Unlike the pooling model discussed above with reference to FIG. 6. there are no pool priorities. At step 815 . the excess coupon module 124 identifies the pool into which a given loan can be allocated based on the collateral attributes of the loan and independent of the pool execution coupon. This gives a one to one mapping between the loans and the pools. At step 820 . the excess coupon module 124 collapses all loans having the same excess coupon within a given pool definition into a single loan. This approach can significantly reduce the number of loans in the loan population. After the population of loans is reduced, the excess coupon module 124 maximizes the objective function at step 825 . The excess coupon module 124 can iteratively determine solutions to the objective function until an optimal solution is found or until a user decides that a solution of sufficient high quality is found. One of ordinary skill in the art would appreciate that the present invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. Although specific embodiments of the invention have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects of the invention were described above by way of example only and are not intended as required or essential elements of the invention unless explicitly stated otherwise. Various modifications of, and equivalent steps corresponding to, the disclosed aspects of the exemplary embodiments, in addition to those described above, can be made by a person of ordinary skill in the art, having the benefit of this disclosure, without departing from the spirit and scope of the invention defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures. Patent application title: System And Method For Optimizing Fixed Rate Whole Loan Trading Patent application title: System And Method For Optimizing Fixed Rate Whole Loan Trading Inventors: Harsha Nagesh Rajan Godse Agents: KING SPALDINGCREDIT SUISSE SECURITIES (USA) LLC Assignees: Credit Suisse Securities (USA) LLC Origin: ATLANTA, GA US IPC8 Class: AG06Q4000FI USPC Class: 705 36 R Patent application number: 20100057635 Optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. 1. A system for optimizing fixed rate whole loan trading, comprising:a computing system comprising a software application comprising one or more modules operable to:develop a model for determining a securitization strategy for a population of whole loans, the securitization strategy comprising a plurality of bonds andprocess the model until an optimal securitization strategy for the population of whole loans is found anda user interface for receiving user input for the one or more modules and for outputting the optimal securitization strategy, the user interface being in communication with the software application. 2. The system of claim 1, wherein the one or more modules comprise a seniorsubordinate module operable to group the population of loans into a seniorsubordinate bond structure comprising at least one senior tranche of bonds and at least one subordinate tranche of bonds. 3. The system of claim 1, wherein the one or more module comprise a pass-thru module operable to pool a population of loans into one or more pools of pass through bonds. 4. The system of claim 1, further comprising one or more data sources communicably coupled to the computing system, the one or more data sources comprising information for use by the software application. 5. A computer program product comprising:a computer-readable medium having computer-readable program code embodied therein for determining an optimal execution bond coupon for each loan in a plurality of loans in a seniorsubordinate bond structure, the computer-readable medium comprising:computer-readable program code for creating a model comprising an objective function representing a total market value of the seniorsubordinate bond structure for the plurality of loans andcomputer-readable program code for maximizing the objective function to maximize the total market value of the seniorsubordinate bond structure. 6. The computer program product of claim 5, wherein the computer-readable program code for maximizing the objective function comprises computer-readable program code for:determining a market price of each loandetermining a first weighted average execution coupon for the plurality of loans corresponding to the market price of each loandetermining the total market value of the seniorsubordinate structure at the first weighted average execution couponiterating the weighted average execution coupon and determining a total market value for the seniorsubordinate structure at each iteration anddetermining the weighted average execution coupon having the highest total market values for the seniorsubordinate structure. 7. The computer program product of claim 5, further comprising computer-readable program code for developing and maximizing an objective function to optimally split at least one of the loans into two pseudo loans to prevent the creation of an interest only bond or a principal only bond, the two pseudo loans comprising different coupon values. 8. A computer program product comprising:a computer-readable medium having computer-readable program code embodied therein for optimally pooling a plurality of loans into pass through bond pools, the computer-readable medium comprising:computer-readable program code for creating a model corresponding to a plurality of pass through bond pools, each pass through bond pool comprising at least one constraintcomputer-readable program code for applying the at least one constraint of each pass through bond pool to each of the plurality of loans to determine which pass through bond pools each of the plurality of loans is eligible andcomputer-readable program code for processing the model to determine the optimal pooling. 9. The computer program product of claim 8, wherein the model comprises an objective function comprising a linear combination of a market value of each of the plurality of loans. 10. The computer program product of claim 9, wherein processing the model comprises maximizing the objective function. 11. The computer program product of claim 8, further comprising computer-readable program code for transforming the at least one constraint of each pass through bond pool into a conditional constraint. 12. The computer program product of claim 8, further comprising computer-readable program code for converting at least a portion of the at least one constraint of each pass through bond pool into a conditional constraint prior to processing the model to ensure that the model is solvable. 13. The computer program product of claim 18, further comprising computer-readable program code for transforming each of the at least one constraints into a conditional constraint to allow constraints to be applicable to only pass through bond pools that are allocated. 14. The computer program product of claim 8, further comprising computer-readable program code for allocating at least one of the plurality of loans to an unallocated pool. 15. The computer program product of claim 8, further comprising computer-readable program code for allocating loans into an unallocated pool if each of the plurality of pass through bond pools can not be allocated with the plurality of loans, wherein loans in the unallocated pool are given zero market value and wherein processing the model further comprises minimizing the number of loans allocated to the unallocated pool. 16. The computer program product of claim 8, wherein the model accounts for the constraint of each pass through bond pool and a payup associated with each pass through bond pool. 17. A computer program product comprising:a computer-readable medium having computer-readable program code embodied therein for allocating a portion of a plurality of loans to a loan package, the computer-readable medium comprising:computer-readable program code for determining which of the plurality of loans meet one or more constraints of the loan packagecomputer-readable program code for determining a market price of each of the plurality of loans based on a securitization modelcomputer-readable program code for modeling an objective function to determine which loans in the plurality of loans that meets the one or more constraints are least profitable for securitization in the securitization model andcomputer-readable program code for allocating the loans that meets the one or more constraints and are least profitable for securitization into the loan package. 18. The computer program product of claim 17, wherein the securitization model comprises a seniorsubordinate model. 19. The computer program product of claim 17, wherein the objective function is modeled to minimize a spread between a weighted average price of the loans in the loan package and a To Be Announced (TBA) bond price of the weighted average coupon of the loans in the loan package. 20. The computer program product of claim 17, wherein the objective function is modeled to minimize a dollar value of a spread between a weighted average price of the loans in the loan package and a To Be Announced (TBA) bond price of the weighted average coupon of the loans in the loan package. 21. A method for optimizing fixed rate whole loan trading, wherein each step is implemented on a computer system, the method comprising the steps of:determining a bond structure to securitize a plurality of whole loansdeveloping a model comprising an objective function that represents a total market value for the plurality of whole loans when executed into bonds corresponding to the bond structureprocessing the model to determine which of a group of available bonds should be generated and into which bonds of the generated bonds that each of the plurality of whole loans best executes into. 22. The method of claim 21, wherein the bond structure comprises a seniorsubordinate bond structure. 23. The method of claim 21, wherein the bond structure comprises an agency secured pass through bond structure. 24. The method of claim 21, further comprising the step of allocating a portion of the plurality of whole loans to a package of whole loans for selling as whole loans, the portion comprising whole loans meeting at least one constraint and being less profitable than the other whole loans when executed into a bond in the bond structure. 25. A computer program product comprising:a computer-readable medium having computer-readable program code embodied therein for optimally pooling excess coupon resulting from securitizing a plurality of loans, the computer-readable medium comprising:computer-readable program code for creating a model corresponding to a plurality of excess coupon bond pools and an unallocated pool, each excess coupon bond pool comprising at least one constraint andcomputer-readable program code for processing the model to allocate each of the loans into either an excess coupon bond pool or into the unallocated pool in order to maximize the total market value of the excess coupon that gets allocated to the excess coupon bond pools. 26. The computer program product of claim 25, wherein the model comprises an objective function representing the total market value of the excess coupon that gets allocated to the excess coupon bond pools. 27. The computer program product of claim 25, further comprising computer-readable program code for transforming each of the at least one constraints into a conditional constraint. 28. The computer program product of claim 25, further comprising computer-readable program code for transforming each of the at least one constraints into a conditional constraint to allow constraints to be applicable to only excess coupon bond pools that are allocated. 29. The computer program product of claim 25, further comprising:computer-readable program code for identifying the excess coupon pools for which each of the loans can be allocated based on collateral attributes of the loans andcomputer-readable program code for collapsing each loan identified for an excess coupon pool into a single loan to reduce the number of loans in the model. Description: 0001 This non-provisional patent application claims priority under 35 U. S.C. 119 to U. S. Provisional Patent Application No. 61191,011, entitled, System and Method for Optimizing Fixed Rate Whole Loan Trading, which is hereby fully incorporated herein by reference. 0002 The present invention relates generally to systems and methods for optimizing loan trading and more specifically to computerized systems and computer implemented methods for optimizing packages of whole loans for execution into bonds or sale as whole loan packages. 0003 Financial institutions, such as investment banks, buy loans and loan portfolios from banks or loan originators primarily to securitize the loans into bonds and then sell the bonds to investors. These bonds are considered asset-backed securities as they are collateralized by the assets of the loans. Many types of loans can be securitized into bonds, including residential mortgages, commercial mortgages, automobile loans, and credit card receivables. 0004 A variety of bond structures can be created from a population of loans, each structure having characteristics and constraints that need to be accounted for in order to maximize the profit that a financial institution can realize by securitizing the loans into bonds. The optimal grouping or pooling of loans into bonds for a given bond structure and a given loan population can depend on the characteristics of each loan in the population. Furthermore, the bond pool or execution coupon that an individual loan executes into can depend on the bond pool or best execution of each other loan in the population. As the typical loan population considered for securitizing into bonds is very large (e. g. 10,000 loans or more), determining an optimal pooling of loans for securitizing into bonds can be challenging. 0005 Accordingly, what is needed are systems and methods for optimizing the packaging of a population of loans into bonds for a given bond structure. 0006 The invention provides computerized systems and computer implemented methods for optimizing fixed rate whole loan trading for a population of whole loans. 0007 An aspect of the present invention provides a system for optimizing fixed rate whole loan trading. This system includes a computing system that includes a software application including one or more modules operable to develop a model for determining a securitization strategy for a population of whole loans, the securitization strategy including bonds and operable to process the model until an optimal securitization strategy for the population of whole loans is found and a user interface for receiving user input for the one or more modules and for outputting the optimal securitization strategy, the user interface being in communication with the software application. 0008 Another aspect of the present invention provides a computer-program product including a computer-readable medium having computer-readable program code embodied therein for determining an optimal execution bond coupon for each loan in a group of loans in a seniorsubordinate bond structure. This computer-readable medium includes computer-readable program code for creating a model comprising an objective function representing a total market value of the seniorsubordinate bond structure for the loans and computer-readable program code for maximizing the objective function to maximize the total market value of the seniorsubordinate bond structure. 0009 Another aspect of the invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for optimally pooling loans into pass through bond pools. This computer-readable medium includes computer-readable program code for creating a model corresponding to pass through bond pools, each pass through bond pool including a constraint computer-readable program code for applying the constraint of each pass through bond pool to each of the loans to determine which pass through bond pools each of the loans is eligible and computer-readable program code for processing the model to determine the optimal pooling. 0010 Another aspect of the invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for allocating a portion of a group of loans to a loan package. This computer-readable medium includes computer-readable program code for determining which of the loans meet one or more constraints of the loan package computer-readable program code for determining a market price of each of the loans based on a securitization model computer-readable program code for modeling an objective function to determine which loans in the group of loans that meets the one or more constraints are least profitable for securitization in the securitization model and computer-readable program code for allocating the loans that meets the one or more constraints and are least profitable for securitization into the loan package. 0011 Another aspect of the present invention provides a method for optimizing fixed rate whole loan trading. This method includes the steps of determining a bond structure to securitize whole loans developing a model comprising an objective function that represents a total market value for the whole loans when executed into bonds corresponding to the bond structure processing the model to determine which of a group of available bonds should be generated and into which bonds of the generated bonds that each of the whole loans best executes into. 0012 Another aspect of the present invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for optimally pooling excess coupon resulting from securitizing loans. This computer-readable medium includes computer-readable program code for creating a model corresponding to excess coupon bond pools and an unallocated pool, each excess coupon bond pool including at least one constraint and computer-readable program code for processing the model to allocate each of the loans into either an excess coupon bond pool or into the unallocated pool in order to maximize the total market value of the excess coupon that gets allocated to the excess coupon bond pools. 0013 These and other aspects, features and embodiments of the invention will become apparent to a person of ordinary skill in the art upon consideration of the following detailed description of illustrated embodiments exemplifying the best mode for carrying out the invention as presently perceived. BRIEF DESCRIPTION OF THE DRAWINGS 0014 For a more complete understanding of the exemplary embodiments of the present invention and the advantages thereof, reference is now made to the following description, in conjunction with the accompanying figures briefly described as follows. 0015 FIG. 1 is a block diagram depicting a system for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. 0016 FIG. 2 is a flow chart depicting a method for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. 0017 FIG. 3 is a flow chart depicting a method for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention. 0018 FIG. 4 is a flow chart depicting a method for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. 0019 FIG. 5 is a flow chart depicting a method for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. 0020 FIG. 6 is a flow chart depicting a method for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. 0021 FIG. 7 is a flow chart depicting a method for packaging whole loans in accordance with one exemplary embodiment of the present invention. 0022 FIG. 8 is a flow chart depicting a method for pooling excess coupon in accordance with one exemplary embodiment of the present invention. DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 0023 The invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. Although the exemplary embodiments of the invention are discussed in terms of whole loans (particularly fixed rate residential mortgages), aspects of the invention can also be applied to trading other types of loans and assets, such as variable rate loans and revolving debts. 0024 The invention can comprise a computer program that embodies the functions described herein and illustrated in the appended flow charts. However, it should be apparent that there could be many different ways of implementing the invention in computer programming, and the invention should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed invention based on the flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use the invention. The inventive functionality of the claimed computer program will be explained in more detail in the following description read in conjunction with the figures illustrating the program flow. Further, it will be appreciated to those skilled in the art that one or more of the stages described may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. 0025 Turning now to the drawings, in which like numerals represent like elements throughout the figures, aspects of the exemplary embodiments will be described in detail. FIG. 1 is a block diagram depicting a system 100 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIG. 1, the system 100 includes a computing system 110 connected to a distributed network 140. The computing system 110 may be a personal computer connected to the distributed network 140. The computing system 110 can include one or more applications, such as loan trading optimizer application 120. This exemplary loan trading optimizer 120 includes four modules 121-124 that can operate individually or interact with each other to provide an optimal packaging of loans into one or more bond structures and whole loan packages. 0026 A seniorsubordinate module 121 distributes loans into a seniorsubordinate bond structure with bonds having different credit ratings and different net coupon values. As will be discussed in more detail with reference to FIGS. 4-5, the seniorsubordinate module 121 distributes the loans into bonds having a AAA rating, subordinate bonds with lower credit ratings, and, depending on the loans and the coupon values of the AAA bonds and the subordinate bonds, interest only bonds and principal only bonds. 0027 A pass-thru module 122 distributes loans into pass through bonds guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The pass-thru module 122 optimally pools the loans into To Be Announced (TBA) pass through securities based on a variety of constraints. The pass-thru module 122 is discussed in more detail below with reference to FIG. 6. 0028 A whole loan module 123 allocates loans to meet bids for loan portfolios meeting specific requirements and constraints of the bid. The whole loan module 123 can interact with either the seniorsubordinate module 121 or the pass-thru module 122 to allocate loans that meet the requirements of the bids but are less favorable to be securitized. The whole loan module 123 is discussed below in more detail with reference to FIG. 7. 0029 An excess coupon module 124 distributes excess coupons of securitized loans into different bond tranches or pools. The excess coupon module 124 can pool excess coupons resulting from seniorsubordinate bond structure created by the seniorsubordinate module 121 andor excess coupons resulting from pass through securities created by the pass-thru module 122. The excess coupon module 124 is discussed below in more detail with reference to FIG. 8. 0030 Users can enter information into a user interface 115 of the computing system 110. This information can include a type of bond structure to optimize, constraints associated with bond structures and bond pools, information associated with loan bids, and any other information required by the loan trading optimizer 120. After the information is received by the user interface 115, the information is stored in a data storage unit 125, which can be a software database or other memory structure. Users can also select a population of loans to consider for optimization by way of the user interface 115. The loans can be stored in a database stored on or coupled to the computing system 110 or at a data source 150 connected to the distributed network 140. The user interface 115 can also output to a user the bond packages and whole loan packages determined by the loan trading optimizer 120. 0031 The loan trading optimizer 120 can communicate with multiple data sources 150 by way of the distributed network 140. For example, the loan trading optimizer 120 can communicate with a data source 150 to determine Fannie Mae TBA prices and another data source 150 to determine U. S. Treasury prices. In another example, the loan trading optimizer 120 can communicate with a data source 150 to access information associated with bids for whole loan packages. The distributed network 140 may be a local area network (LAN), wide area network (WAN), the Internet or other type of network. 0032 FIG. 2 is a flow chart depicting a method 200 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 2, at step 205, the user interface 115 receives input from a user. This user input is used by the loan trading optimizer 120 to determine the bond structure that should be optimized for a population of loans. For example, if the user desires to find the optimal pooling of loans for pass through bonds, the user can input the constraints for each bond pool. Examples of constraints for pass through bond pools include constraints on loan balances, total number of loans for a pool, and total loan balance for a pool. 0033 At step 210, a population of loans is selected for optimization. The population of loans can be selected from loans stored in a loan database stored on or coupled to the computing system 110 or from a database at a data source 150 connected to the distributed network 140. The population of loans can include loans currently owned by the user (e. g. investment bank) of the loan trading optimizer 120 andor loans that are up for bid by another bank, loan originator, or other institution. For example, a user may employ the loan trading optimizer 120 to find the maximum market value of a loan portfolio currently for sale in order to determine an optimal bid for the loan portfolio. Additionally, a user can select the population of loans by specifying certain criteria, such as maximum loan balance, location of the loans, and FICO score. 0034 At step 215, the loan trading optimizer 120 determines a securitization strategy for the population of loans selected in step 210. Depending upon the user inputs received in step 205, the loan trading optimizer 120 employs one or more of the seniorsubordinate module 121, the pass-thru module 122, and the whole loan module 123 to determine the securitization strategy for the population of loans. Step 215 is discussed in more detail with reference to FIGS. 3-7. 0035 At step 220, the loan trading optimizer 120 determines whether the securitization strategy returned at step 215 is of sufficiently high quality. In this exemplary embodiment, the loan trading optimizer 120 iterates the step of determining a securitization strategy for the population of loans until either an optimal solution is found or the user determines that the securitization strategy is of sufficiently high quality. In order for the user to determine if the securitization strategy if of sufficient high quality, the loan trading optimizer 120 can output the results to the user by way of the user interface 115. The loan trading optimizer 120 can output these results based on a number of iterations of step 215 (e. g. every 100 iterations) or when a certain level of quality is found. The user interface 115 can then receive input from the user indicating whether the securitization strategy is of sufficient high quality. If the securitization strategy is of sufficient high quality or optimal, the method 200 proceeds to step 225. Otherwise, the method 200 returns to step 215. 0036 In one exemplary embodiment, quality is measured in terms of the total dollar value of the population of loans. For example, the user may desire to sell a population of loans for at least ten million dollars in order to bid on the loans. The user can set a threshold for the loan trading optimizer 120 to only return a solution that meets this threshold or a solution that is the optimal solution if the optimal solution is below this threshold. 0037 At step 225, the excess coupon module 124 of the loan trading optimizer 120 can pool any excess coupon resulting from the securitization strategy determined in step 215. This step is optional and is discussed below in more detail with reference to FIG. 8. 0038 At step 230, the loan trading optimizer 120 communicates the final securitization strategy to the user interface 115 for outputting to a user. The user interface 115 can display the final securitization strategy and optionally other possible securitization strategies with similar quality levels. 0039 FIG. 3 is a flow chart depicting a method 215 for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 3, at step 305, the loan trading optimizer 120 determines which models to use for determining the securitization strategies. In this exemplary embodiment, the loan trading optimizer 120 includes a seniorsubordinate module 121, a pass-thru module 122, and a whole loan module 123. Each of the modules 121-123 can build and process a model for determining an optimal packaging of loans as discussed below. The loan trading optimizer 120 determines which modules 121-123 to use based on the input received from the user in step 205 of FIG. 2. For example, the user may specify that only a seniorsubordinate structure should be optimized for the population of loans. Alternatively, if the user has entered bid information for a portfolio of whole loans, the loan trading optimizer 120 can execute the whole loan module 123 with the seniorsubordinate module 121 andor the pass-thru module 122 to determine which of the loans meet the requirements of the bid and are least favorable for securitization. Additionally, a user may specify that both an optimal seniorsubordinate bond structure and an optimal pooling of pass through bonds should be determined for the population of loans. 0040 If the user selected that a seniorsubordinate bond structure should be optimized, the method 215 proceeds to step 310. At step 310, the seniorsubordinate module 121 develops a model for packaging the population of loans into a seniorsubordinate bond structure and processes the model to determine an optimal seniorsubordinate bond structure for the loan population. Step 310 is discussed in more detail with reference to FIGS. 4 and 5. After the seniorsubordinate structure is determined, the method 215 proceeds to step 220 (FIG. 2). 0041 If the user selected that the population of loans should be optimally pooled into pass through bonds, the method 215 proceeds to step 315. At step 315, the pass-thru module 122 develops a model for pooling the population of loans into multiple bond pools and processes the model to determine an optimal pooling for the loan population. Step 315 is discussed in more detail with reference to FIG. 6. After the pooling is determined, the method 215 proceeds to step 220 (FIG. 2). 0042 If the user selected that whole loans should be allocated to a package of whole loans to be sold, the method 215 proceeds to step 320. At step 320, the whole loan module 123 develops a model for allocating whole loans that meet certain constraints and are less favorable to be securitized into a whole loan package and processes the model to determine which loans are best suited for the whole loan package. Step 320 is discussed in more detail with reference to FIG. 7. After the whole loan package is determined, the method 215 proceeds to step 220 (FIG. 2). 0043 FIG. 4 is a flow chart depicting a method 310 for packaging a population of loans into a seniorsubordinate bond structure in accordance with one exemplary embodiment of the present invention. As briefly discussed above with reference to FIG. 1, a seniorsubordinate bond structure is a structure where bonds with different credit ratings are created. Typically, the seniorsubordinate bond structure includes a senior tranche of bonds having a AAA or similar credit rating and a subordinate tranche of bonds having a lower credit rating. The senior tranche is protected from a certain level of loss by the subordinate tranche as the subordinate tranche incurs the first losses that may occur. The senior trance can be sold to investors desiring a more conservative investment having a lower yield, while the subordinated tranche can be sold to investors willing to take on more risk for a higher yield. For the purpose of this application, a AAA rated bond refers to a bond in the senior tranche, but not necessarily a bond having a credit rating of AAA. 0044 Additionally, interest only (IO) and principal only (PO) bonds may be created in a seniorsubordinate structure. An IO bond is created when the net coupon of a loan is more than the coupon of the bond in which the loan executes. Thus, the difference in the loan coupon and the bond coupon creates an interest only cash flow. Similarly, when the loan coupon is less than the bond coupon, a PO bond is created which receives only principal payments. 0045 Referring to FIGS. 1 and 4, at step 405, the seniorsubordinate module 121 determines the bond coupons that are available for executing the loans into. The seniorsubordinate module 121 may obtain the available bond coupons from a data source 150 or may receive the available bond coupons from the user by way of the user interface 115 in step 205 of FIG. 2. For example, the user may desire to execute the loans into bonds having coupon values between 4.5 and 7.0. 0046 At step 410, the seniorsubordinate module 121 selects a first bond coupon value from the range of available bond coupon values. This first coupon value can be the lowest bond coupon value, the highest coupon value, or any other bond coupon value in the range of available bond coupon values. 0047 At step 415, the seniorsubordinate module 121 determines the execution price of each loan in the population of loans at the selected coupon value. Each loan in the population of loans is structured as a bond. The cash flow of each loan is distributed into symbolic AAA and subordinate bonds, and depending on the coupon of the loan and the selected bond coupon, an IO or PO bond. The principal payment and interest cash flows of each loan is generated in each period accounting for loan characteristics of the loan, such as IO period, balloon terms, and prepayment characteristics. The cash flow generated in each period is distributed to all bonds that the loan executes taking into account shifting interest rules that govern the distribution of prepayments between the AAA and the subordinate bonds in each period. The proportion in which the principal payments are distributed depends on the subordination levels of the AAA and the subordinate bonds. The subordination levels are a function of the loan attributes and are supplied by rating agencies for each loan through an Application Program Interface (API) coupled to the computing device 110. Prepayments are first distributed pro rate to the PO bond and then between the AAA and the subordinate bonds based on the shifting interest rules. Any remaining prepayment is distributed proportionally among all the subordinate bonds. The interest payment for each of the bonds is a direct function of the coupon value for the bond. 0048 After the cash flows of each of the bonds for each of the loans have been generated, the present value of these cash flows is determined. For fixed rate loans, the AAA bonds can be priced as a spread to the To Be Announced (TBA) bond prices. However, the subordinate bond cash flows are discounted by a spread to the U. S. Treasury Yield Curve. The TO and PO bonds are priced using the Trust TO and PO prices. Finally, the price of the AAA bond, the subordinate bonds, and the TO or PO bond is combined proportionally for each loan based on the bond sizes to get the final bond price for each loan. This final bond price is the price of the loan executing into the bond given the selected coupon value of the bond. 0049 At step 420, the seniorsubordinate module 121 determines if there are more bond coupon values in the range of available bond coupon values. If there are more bond coupon values, the method 310 proceeds to step 425. Otherwise, the method 310 proceeds to step 430. 0050 At step 425, the next bond coupon value in the range of available bond coupon values is selected. In one exemplary embodiment, the seniorsubordinate module 121 can increment from the previous selected bond coupon value (e. g. 0.5 increments) to determine the next bond coupon value. In an alternative embodiment, the seniorsubordinate module 121 can progress through a fixed list of bond coupon values. For example, the user may select specific bond coupon values to execute the loans into, such as only 4.0, 5.0, and 6.0. After the next bond coupon value is selected, the method 310 returns to step 415 to determine the execution price of each loan in the population of loans at the new coupon value. 0051 At step 430, the seniorsubordinate module 121 determines, for each loan in the population of loans, which bond coupon value yielded the highest final bond price for that particular loan. 0052 At step 435, the seniorsubordinate module 121 groups the loans according to the bond coupon value that yielded the highest final bond price for each loan. For example, if the available bond coupon values are 4.0, 5.0, and 6.0, each loan that has a highest final bond price at 4.0 are grouped together, while each loan that has a highest final bond price at 5.0 are grouped together, and each loan that has a final bond price at 6.0 are grouped together. After step 435 is complete, the method proceeds to step 220 (FIG. 2). 0053 In the embodiment of FIG. 4, the subordinate bonds for each loan execute at the same bond coupon value as the corresponding AAA bond. For example, if a first loan of 6.25 best executes into a bond having a coupon value of 6.0, then a AAA bond of 6.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 6.0 is created. If a second loan of 5.375 best executes into a bond having a coupon value of 5.0, then a AAA bond of 5.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 5.0 is created. This creates two AAA bonds and two subordinate bonds at two different coupon values. 0054 Typically, when loans are packaged in a seniorsubordinate bond structure, multiple AAA bonds with multiple coupon values are created with a common set of subordinate bonds that back all of the AAA bonds. This set of subordinate bonds is priced at the weighted average (WA) execution coupon of all of the AAA bonds created for the loan package. Pricing the subordinate bonds at the WA execution coupon implies that the spread to the benchmark U. S. Treasury curve, which is a function of the bond rating and the execution coupon of the subordinate bond, has to be chosen appropriately. In order to know the WA execution coupon of all the AAA bonds for the population of loans, the best execution coupon for each loan in the population of loans has to be known. In order to know the best execution coupon of each loan, the loan has to be priced at different bond coupon values and the AAA and subordinate bonds created at those coupons also have to be priced. However, the subordinate bond cash flows are discounted with spreads to the U. S. Treasury, with spreads taken at the WA best execution coupon which is still unknown. This creates a circular dependency as the best execution of each loan in the population of loans now depends on all the other loans in the population. 0055 FIG. 5 is a flow chart depicting a method 500 for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. The method 500 is an alternative method to that of method 310 of FIG. 4, accounting for pricing subordinate bonds at the WA execution coupon and provides a solution to the circular dependency discussed above. 0056 The WA execution coupon for a population of loans can be calculated by: 0057 In Equation 1, x ij is a binary variable with a value of either 0 or 1, whereby a value of 1 indicates that the i th loan is optimally executing at the j th execution coupon value. The parameters d 0 to d j represent the j execution coupon values. For example, the coupons values could range from 4.5 to 7.0. Finally, the parameter b i represents the balance of the i th loan. 0058 If q o to q j are the weights of the j execution coupons, then: 0059 where q 0 to q 1 are special ordered sets of type two, which implies that at most two are non-zero and the two non-zero weights are adjacent. 0060 Let Pa ij be the price of the AAA bond when loan i executes at coupon j. Next, let Ps ij be the overall price of all of the subordinate bonds combined when loan i executes at coupon j. Finally, let Pio ij and Ppo ij be the prices of the IO and PO bonds respectively when loan i executes at coupon j. 0061 The AAA bond prices and the TO and PO bond price components of loan i executing at coupon j are linear functions of x ij . The AAA priced as a spread to the TBA is a function of the execution coupon of the AAA bond and the IOPO prices are a lookup based on collateral attributes of the loan. However, pricing the subordinate bonds is complicated because the subordinate cash flows are discounted at the WA execution coupon. 0062 Let p i be a matrix of size jj that contains the prices of the subordinate bonds. The (m, n) entry of the matrix represents the price of the subordinate cash flows when the cash flow of loan i is generated assuming that loan i executes at the m th coupon and is discounted using subordinate spreads for the n th coupon. Subordinate spreads to the U. S. Treasury are a function of the execution coupon and any product definition, such as the size (e. g. JumboConforming), maturity (e. g. 1530 years), etc. The price of the subordinate bond of the i th loan can be written as: 0063 which is a non linear expression as the equation contains a product of q and x ij . both of which are variables in this equation. 0064 FIG. 5 provides a method 500 for overcoming this non-linearity. Referring to FIG. 5, at step 505, the seniorsubordinate module 121 determines the optimal execution price for each loan in the population of loans independent of the WA execution coupon. In one exemplary embodiment, the seniorsubordinate module 121 employs the method 310 of FIG. 4 to find the optimal execution price for each loan. 0065 At step 510, the seniorsubordinate module 121 determines the WA execution coupon corresponding to the optimal execution price for each loan. This WA execution coupon can be found using Equation 1 above. 0066 At step 515, the seniorsubordinate module 121 determines the weights (i. e. q 0 - q j ) of each execution coupon for the WA execution coupon found in step 510. These weights can be found using Equation 3 above. 0067 At step 520, the seniorsubordinate module 121 builds a model including an objective function to determine the optimal execution coupon for each loan to maximize the total market value of all of the bonds in the seniorsubordinate structure. The expression of the objective function contains ij terms, where the ij term represents the market value of executing the i th loan at the j th execution coupon. After inserting the values of the weights of the execution coupons (i. e. qs) into the expression for subordinate bond price (Equation 4), only two of the terms will be non-zero for the sub-price of the i th loan executing at the j th execution coupon. 0068 As the method 200 of FIG. 2 iterates step 215, different WA execution coupons can be used to maximize the objective function. The iterations can begin with the WA execution coupon found in step 510 and the seniorsubordinate module 121 can search around this WA execution coupon until either the optimal solution is found or the user decides that a solution of sufficient high quality is found in step 220 of FIG. 2. In other words, the seniorsubordinate module 121 searches for an optimal solution by guessing several values of the WA execution coupon around an initial estimate of the optimal execution coupon. After a final solution is found by the seniorsubordinate module 121, the loans can be grouped based on the coupon values for each loan in the final solution to the objective function. 0069 In some instances, one of the undesirable effects of the seniorsubordinate bond structure is the creation of IO andor PO bonds, which may not trade as rich as AAA bonds. In some exemplary embodiments, the seniorsubordinate module 121 can ameliorate this issue by considering a loan as two pseudo loans. For example, a loan having a net rate of 6.125 and a balance of 100,000 can be considered equivalent to two loans of balance b1 and b2 and coupons 6 and 6.5 such that the following conditions are satisfied: 0070 The first condition conserves the original balance, while the second condition is to set the WA coupon of the two pseudo loans to equal the net rate of the original loan. Solving these equations for b1 and b2, we find that b175,000 and b225,000. These two loans, when executed at 6.0 and 6.5 bond coupons respectively, avoids the creation of either an IO bond or a PO bond. 0071 Although in the above example two adjacent half point coupons were used to create the two pseudo loans, two coupons from any of the half point bond coupons that are being used to create the bonds can be used. For example, if only bond coupons from 4.5 to 7.0 are being used to create the bonds, there would be fifteen combinations to consider (6C215). In some cases, the best solution is not to split the loan into two adjacent half point bond coupons. For example, this split may not be optimal if the AAA spreads at the two adjacent half point coupons are far higher than the ones that are not adjacent to the net balance of the loan. 0072 The seniorsubordinate module 121 can construct a linear program or linear objective function to determine the optimal split into pseudo loans. The output of the linear program is the optimal splitting of the original loan into pseudo loans such that the overall execution of the loan is maximized, subject to no IO bond or PO bond creation. For each loan i, let variable x ij indicate the balance of loan i allocated to the jth half point coupon, subject to the constraint that the sum of over x ij for all j equals to the balance of loan i and the WA coupon expressed as a function of the x ij s equals to the net coupon of loan i, similar to Equation 6 above. Let the execution coupons be r 0 to r n . Thus, this equation becomes: 0073 where b i is the balance of loan i and c i is the net coupon of loan i. The price of loan i executing at coupon j is the sum of the price of the AAA bond and the subordinate bonds. No IO or PO bonds are created when the coupons are split. The seniorsubordinate module 121 calculates the price of the AAA bond as a spread to the TBA, where the spread is a function of the execution coupon j. In one embodiment, the seniorsubordinate module 121 also calculates the price of the subordinate bond as a spread to the TBA for simplification of the problem. Cash flows are not generated as the split of the balances to different execution coupons is not yet known. The seniorsubordinate module 121 combines the price of the subordinate bond and the AAA bond in proportion to the subordination level of loan i, which can be input by a user in step 205 of FIG. 2 or input by an API. At this point, the seniorsubordinate module 121 has calculated the price of loan i (P ij ) for each execution coupon j. To determine the optimal splitting of the original loan into pseudo loans, the seniorsubordinate module 121 creates the following objective function and works to maximize this objective function: 0074 Equation 8 is a simple linear program with two constraints and can be solved optimally. The solution gives the optimal split of the loan into at most two coupons and thus, a bond can be structured without creating any IO or PO bonds. The user can determine if the bond should be split or not based on the optimal execution and other business considerations. 0075 FIG. 6 is a flow chart depicting a method 315 for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. A pass through bond is a fixed income security backed by a package of loans or other assets. Typically, as briefly discussed above with reference to FIG. 1, a pass through bond is guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The government agency guarantees the pass through bond in exchange for a guarantee fee (Gfee). The Gfee can be an input provided by the agencies for a specific set of loans or can be specified as a set of rules based on collateral characteristics. Regardless of how the Gfee is obtained, the Gfee for a loan set is known. 0076 When loans are securitized as a pass through bond, one has the option to buy up or buy down the Gfee in exchange for an equivalent fee to the agencies. Buying up the Gfee reduces the net coupon and thus the price of the bond as well. This upfront buy up fee is exchanged in lieu of the increased Gfee coupon. Similarly, buying down the Gfee reduces the Gfee and increases the net coupon and therefore increases the bond price. An upfront fee is paid to the agencies to compensate for the reduced Gfee. 0077 The Fannie Mae and Freddie Mac agencies typically provide buy up and buy down grids each month. Referring to FIG. 1, these grids can be stored in a data source 150 or in the data storage unit 125 for access by the pass-thru module 122 of the loan trading optimizer 120. If the Gfee is bought up or bought down, an excess coupon is created. The amount of buy up or buy down of Gfee can vary based on collateral attributes of the loan and can also be subject to a minimum and maximum limit. 0078 Referring now to FIGS. 1 and 6, at step 605, the pass-thru module 122 determines the optimal execution of each loan by buy up or buy down of the Gfee. In one exemplary embodiment, the optimal execution of each loan is determined by finding the overall price of the loan for each available buy up and buy down of the Gfee. Typically, a Gfee can be bought up or down in increments of 1100 th of a basis point. The pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy up with a step size of 1100 th of a basis point. Similarly, the pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy down with a step size of 1100 th of a basis point. In each iteration, the amount of Gfee buy up or buy down is added to the current net rate of the loan. From this modified net rate of the loan, the TBA coupon is determined as the closest half point coupon lower than or equal to the modified net rate. The excess coupon is equal to the modified net rate of the TBA coupon and the price of the excess coupon is a lookup in the agency grid. The fee for the buy up or buy down is also a lookup in the agency grid. The price of the TBA coupon is a lookup from the TBA price curve. When the Gfee is bought up, the cost is added to the overall price and when the Gfee is bought down, the cost is subtracted from the overall price. The pass-thru module 122 determines the overall price of execution for the loan at each iteration and determines the optimal execution for the loan as the execution coupon of the TBA for which the overall price is maximized. This overall cost is the combination of the price of the TBA coupon, the price of the excess coupon, and the cost of the Gfee (added if buy up, subtracted if buy down). 0079 At step 610, the pass-thru module 122 determines which TBA pools each loan is eligible for. Pooling loans into TBA bonds is a complex process with many constraints on pooling. Furthermore, different pools of loans have pool payups based on collateral characteristics. For example, low loan balance pools could prepay slower and thus may trade richer. Also, loan pools with geographic concentration known to prepay faster may trade cheaper and thus have a negative pool payup. Thus, pooling optimally taking into account both the constraints and the pool payups can lead to profitable execution that may not be captured otherwise. 0080 Each of the TBA pools for which a loan can be allocated has a set of pool eligibility rules and a pool payup or paydown. Non-limiting examples of pools can be a low loan balance pool (e. g. loan balances less than 80K), a medium loan balance pool (e. g. loan balance between 80K and 150K), a high loan balance pool (e. g. loan balances above 150K), a prepay penalty loan pool, and an interest only loan pool. For a loan to be allocated to a specific pool by the pass-thru module 122, the loan has to satisfy both the eligibility rules of the pool and also best execute at the execution coupon for that pool. 0081 The pass-thru module 122 applies the eligibility rules of the TBA bond pools to the loans to determine the TBA bond pools for which each loan is eligible. The pass-thru module 122 can utilize pool priorities to arbitrate between multiple pools if a loan is eligible for more than one pool. If a loan is eligible to be pooled into a higher and lower priority pool, the pass-thru module 122 allocates the loan to the higher priority pool. However, if a loan is eligible for multiple pools having the same priority, the pass-thru module 122 can allocate the loan into either of the pools having the same priority. 0082 At step 615, the pass-thru module 122 builds a model for allocating the loans into TBA pools based on the constraints of each TBA bond pool. Let x ij be a binary variable with a value of 1 or 0 which has a value of 1 when loan i is allocated to TBA bond pool j. The total loan balance and loan count constraints of the TBA pools are linear functions of the x ij variables. The objective function for this model is also a linear combination of the market values of each loan. The primary problem in this model is that the given loan population selected in step 210 of FIG. 2 may not be sufficient to allocate all TBA loan pools, as some of the pools may not have loans to satisfy the balance and count constraints or the loans may not be eligible for those pools. In such cases, it is desirable for the pools to have the constraints when applicable. If there are some pools for which there are not enough loans in the population of loans to form a pool, then such pools are not subjected to the specified constraints while the other pools are. However, it is not possible to know a-priori which pools do not have enough loans to satisfy the constraints. Thus, the model employs conditional constraints to allow constraints to be applicable to only those pools which are allocated. 0083 The pooling model is modified to allow for some loans to not be allocated to any pool. This non-allocation will ensure that the model is always solvable and is similar to introducing a slack variable in linear programming. Thus, for each loan in the population of loans, there is an additional binary variable representing the unallocated pool into which the loan can be allocated. Those loans allocated to the unallocated pool are given a zero costmarket value, thus encouraging the pass-thru module 122 to allocate as many loans as possible. 0084 The next step in building this pooling model is to introduce p binary variables for the p possible TBA pools. A value of 1 indicates that this pool is allocated with loans satisfying the pool constraints and a value of 0 indicates that this pool is not allocated. These variables are used to convert simple linear constraints into conditional constraints. 0085 Each constraint of each pool is converted to conditional constraints for the pooling model. To detail this conversion, a maximum loan count constraint is considered for pool P. Let x 1 to x n be binary variable where x i are the loans eligible for pool P. Next, let x 1 . x n U, where U equals the total number of loans in pool P. Finally, let w be the binary variable to indicate if pool P is allocated. The user constraint for maximum loan count is specified as UK, where K is given by the user. In order to impose this constraint conditionally, this constraint is transformed to the following two constraints: 0086 UK w 0087 UM w 0088 where M is a constant such that the sum of all x i s is bounded by M. Consider both the cases when pool P is allocated (w1) and when pool P is not allocated (w0) below: 0089 w1: UK (required) 0090 UM (redundant) 0091 w0: U0 0092 U0 0093 The only way for U0 would be when all the x i s are 0 and thus, pool P will be unallocated. 0094 Other constraints, such as minimum count, minimum balance, maximum balance, average balance, and weighted average constraints can be transformed similarly for the pooling model. After all of the constraints are transformed to conditional constraints, the pooling model is ready to handle constraints conditionally. 0095 At step 620, the pass-thru module 122 executes the pooling model to allocate the loans into TBA pools. After the pass-thru module 122 executes the model for one iteration, the method 315 proceeds to step 220 (FIG. 2). As the method 200 of FIG. 2 iterates step 215, different TBA pool allocations are produced by the pass-thru module 122 until either the optimal TBA pool allocation is found or until the user decides that a solution of sufficient high quality is found in step 220 (FIG. 2). 0096 FIG. 7 is a flow chart depicting a method 320 for packaging whole loans in accordance with one exemplary embodiment of the present invention. The method 320 identifies an optimal package of loans meeting a set of constraints given by a customer or investor. In this embodiment, the loan package is optimized by determining which loans, among the population of loans that meet the constraints, are least favorable to be securitized. Although the method 320 of FIG. 7 is discussed in terms of the seniorsubordinate bond structure, other bonds structures or models can be used. 0097 Referring to FIG. 7, at step 705, the whole loan module 123 determines which loans in the population of loans meets constraints of a bid for whole loans. Investment banks and other financial institutions receive bids for whole loans meeting specific requirements. These requirements can be entered into the user interface 115 at step 205 of FIG. 2 andor stored in the data storage unit 125 or a data source 150. The constraints can include requirements that the loans must satisfy, such as, for example, minimum and maximum balance of the total loan package, constraints on the weighted average coupon, credit ratings of the recipients of the loans (e. g. FICO score), and loan-to-value (LTV) ratio. The constraints can also include location based constraints, such as no more than 10 of the loan population be from Florida and no zip code should have more than 5 of the loan population. 0098 After the whole loan module 123 selects the loans that meet the constraints, at step 710, the whole loan module 123 determines the price of each loan that meets the constraints based on a securitization module. For example, the price of the loans may be calculated based on the seniorsubordinate structure discussed above with reference to FIGS. 4 and 5. 0099 At step 715, the whole loan module 123 determines whether to use an efficient model to select loans least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on a securitization model or a less efficient model to select loans least favorable to be securitized by minimizing the spread of execution of the loans based on a securitization model. In one exemplary embodiment, this determination can be based on the total number of loans in the population or chosen by a user. If the whole loan module 123 determines to use the efficient model, the method 320 proceeds to step 725. Otherwise, the method 320 proceeds to step 720. 0100 At step 720, the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the spread of execution of the loans based on the seniorsubordinate bond structure. The whole loan module 123 builds a model to select a subset of the loans that meet the constraints such that the WA price of the loans of this subset net of the TBA price of the WA coupon of this subset is minimized. The TBA price of the WA coupon of the subset is typically higher as the TBA typically has a better credit quality and hence the metric chosen will have a negative value. The objective function that needs to be minimized is given by: 0101 In Equation 9, x i to x n are binary variables with a value of either 0 or 1, whereby a value of 1 indicates that the loan is allocated and 0 otherwise. The variables b 1 to b n are the balances of the loans and p i to p n are the prices of the loans as determined in step 710. The variables q 1 to q m are the weights for each of the half point coupons and px 1 to px m are the TBA prices for the half point coupons. The weights are special ordered sets of type two, which as discussed above, implies that at most two are non-zero and the two non-zero weights are adjacent. Thus, the expression (q 1 px 1 . q mpx m ) is the price of the WA coupon of the allocated loans. 0102 The weights (q 1 - q m ) are subject to the constraints: 0103 where the c i s are the net coupons on the loans and the r i s are the half point coupons of the TBA curve. 0104 An illustrated weighted average constraint (Kwac) on the coupon could be: 0105 Let y 0 M(x 1 b 1 . x nb n ) and y j x j y 0 where M is a scaling constant to keep the model scaled sensibly. Rewriting the equations, the objective function to minimize is:Optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. RELATED APPLICATION This non-provisional patent application claims priority under 35 U. S.C. 119 to U. S. Provisional Patent Application No. 61191,011, filed Sep. 3, 2008, entitled, System and Method for Optimizing Fixed Rate Whole Loan Trading, which is hereby fully incorporated herein by reference. What is claimed is: 1. A system for optimizing fixed rate whole loan trading, comprising: a computer comprising a non-transitory storage medium comprising a software application comprising one or more modules operable to: a) receive input from a user b) select a population of loans c) determine, by the computer system, at least one module of the one or more modules that optimizes fixed rate whole loan trading based, at least in part, on the received input d) select the at least one module, wherein the at least one module comprises a seniorsubordinate module e) determine available bond coupon values from a plurality of bond coupons f) select a first bond coupon value from the available bond coupon values g) determine a price of each loan in the population of loans at the first bond coupon value h) repeat steps f) and g) for a plurality of additional bond coupon values i) determine the bond coupon value from the first bond coupon value and the plurality of additional bond coupon values that yield the a highest final bond price for each loan and j) group the loans in the population of loans according to the bond coupon values that yield the highest final bond price. 2. The system of claim 1, further comprising one or more data sources communicably coupled to the computing system, the one or more data sources comprising information for use by the software application. 3. The system of claim 1, wherein the received input comprises a constraint associated with a securitization strategy of the population of loans. 4. The system of claim 3, wherein the securitization strategy comprises packaging the population of loans into a tranche of senior bonds and a tranche of subordinate bonds. 5. The system of claim 3, wherein the securitization strategy comprises packaging the population of loans into pass through bonds. 6. The system of claim 1, wherein the population of loans selected in step b) comprises loans owned by the user. 7. The system of claim 1, wherein the population of loans selected in step b) comprises loans that are placed for bidding. 8. The system of claim 1, wherein the population of loans selected in step b) comprises loans that match a criterion selected by the user. 9. The system of claim 1, wherein the population of loans are structured into one or more bonds, and wherein each bond is associated with one or more loans of the population of loans. 10. The system of claim 9, further comprises one or more modules operable to: generate a cash flow of each bond based on a cash flow of the one or more loans associated with the respective bond responsive to generating the cash flow of each bond, determine a present value of the cash flow of each bond set a price associated with each bond based on the type of bond and for each loan, proportionally combine the price associated with each bond based on a size of each bond to determine a final bond price for each loan. 11. The system of claim 10, further comprises one or more modules operable to: distribute the cash flow associated with each loan of the population of loans into senior bonds having a high credit rating, subordinate bonds having a low credit rating, interest only bonds and principal only bonds and generate a principal payment cash flow and an interest cash flow of each loan for a loan period associated with each loan. 12. The system of claim 11: wherein for fixed rate loans, the senior bonds are priced as a spread to the To Be Announced (TBA) bond prices, wherein for fixed rate loans, the subordinate bonds are priced as a spread to the United States Treasury Yield Curve, and wherein for fixed rate loans, the Principal Only bonds and the Interest Only bonds are priced based on Trust Principal Only prices and Trust Interest Only prices. 13. A non-transitory computer readable medium comprising a set of executable instructions that when executed by a processor is configured to optimize fixed rate whole loan trading by performing a method comprising: a) receiving an input b) selecting a population of loans c) determining and selecting at least one module from one or more modules that optimizes fixed rate whole loan trading based, at least in part, on the received input, wherein the at least one module comprises a seniorsubordinate module d) determining available bond coupon values from a plurality of bond coupons e) selecting a first bond coupon value from the available bond coupon values f) determining a price of each loan in the population of loans at the first bond coupon value g) repeating steps e) and f) for a plurality of additional bond coupon values h) determining the bond coupon value from the first bond coupon value and the plurality of additional bond coupon values that yield a highest final bond price for each loan and i) grouping the loans in the population of loans according to the bond coupon values that yield the highest final bond price. 14. The non-transitory computer readable medium of claim 13, wherein the received input comprises a constraint associated with a securitization strategy for the population of loans. 15. The non-transitory computer readable medium of claim 14, wherein the securitization strategy comprises packaging the population of loans into a tranche of senior bonds and a tranche of subordinate bonds. 16. The non-transitory computer readable medium of claim 13, wherein the population of loans selected in step b) comprises loans that are placed for bidding. 17. The non-transitory computer readable medium of claim 13, wherein the population of loans selected in step b) comprises loans that match a criterion selected from a plurality of criteria. 18. The non-transitory computer readable medium of claim 13, wherein the population of loans are structured into one or more bonds, and wherein each bond is associated with one or more loans of the population of loans. 19. The non-transitory computer readable medium of claim 18, wherein the method performed by the set of executable instructions when executed by a processor further comprising: generating a cash flow of each bond based on a cash flow of the one or more loans associated with the respective bond responsive to generating the cash flow of each bond, determining a present value of the cash flow of each bond setting a price associated with each bond based on the type of bond and for each loan, proportionally combining the price associated with each bond based on a size of each bond to determine a final bond price for each loan. 20. The non-transitory computer readable medium of claim 18, wherein the method performed by the set of executable instructions when executed by a processor further comprising: distribute the cash flow associated with each loan of the population of loans into senior bonds having a high credit rating, subordinate bonds having a low credit rating, interest only bonds and principal only bonds and generate a principal payment cash flow and an interest cash flow of each loan for a loan period associated with each loan, wherein for fixed rate loans, the senior bonds are priced as a spread to the To Be Announced (TBA) bond prices, wherein for fixed rate loans, the subordinate bonds are priced as a spread to the United States Treasury Yield Curve, and wherein for fixed rate loans, the Principal Only bonds and the Interest Only bonds are priced based on Trust Principal Only prices and Trust Interest Only prices. TECHNICAL FIELD The present invention relates generally to systems and methods for optimizing loan trading and more specifically to computerized systems and computer implemented methods for optimizing packages of whole loans for execution into bonds or sale as whole loan packages. BACKGROUND Financial institutions, such as investment banks, buy loans and loan portfolios from banks or loan originators primarily to securitize the loans into bonds and then sell the bonds to investors. These bonds are considered asset-backed securities as they are collateralized by the assets of the loans. Many types of loans can be securitized into bonds, including residential mortgages, commercial mortgages, automobile loans, and credit card receivables. A variety of bond structures can be created from a population of loans, each structure having characteristics and constraints that need to be accounted for in order to maximize the profit that a financial institution can realize by securitizing the loans into bonds. The optimal grouping or pooling of loans into bonds for a given bond structure and a given loan population can depend on the characteristics of each loan in the population. Furthermore, the bond pool or execution coupon that an individual loan executes into can depend on the bond pool or best execution of each other loan in the population. As the typical loan population considered for securitizing into bonds is very large (e. g. 10,000 loans or more), determining an optimal pooling of loans for securitizing into bonds can be challenging. Accordingly, what is needed are systems and methods for optimizing the packaging of a population of loans into bonds for a given bond structure. The invention provides computerized systems and computer implemented methods for optimizing fixed rate whole loan trading for a population of whole loans. An aspect of the present invention provides a system for optimizing fixed rate whole loan trading. This system includes a computing system that includes a software application including one or more modules operable to develop a model for determining a securitization strategy for a population of whole loans, the securitization strategy including bonds and operable to process the model until an optimal securitization strategy for the population of whole loans is found and a user interface for receiving user input for the one or more modules and for outputting the optimal securitization strategy, the user interface being in communication with the software application. Another aspect of the present invention provides a computer-program product including a computer-readable medium having computer-readable program code embodied therein for determining an optimal execution bond coupon for each loan in a group of loans in a seniorsubordinate bond structure. This computer-readable medium includes computer-readable program code for creating a model comprising an objective function representing a total market value of the seniorsubordinate bond structure for the loans and computer-readable program code for maximizing the objective function to maximize the total market value of the seniorsubordinate bond structure. Another aspect of the invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for optimally pooling loans into pass through bond pools. This computer-readable medium includes computer-readable program code for creating a model corresponding to pass through bond pools, each pass through bond pool including a constraint computer-readable program code for applying the constraint of each pass through bond pool to each of the loans to determine which pass through bond pools each of the loans is eligible and computer-readable program code for processing the model to determine the optimal pooling. Another aspect of the invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for allocating a portion of a group of loans to a loan package. This computer-readable medium includes computer-readable program code for determining which of the loans meet one or more constraints of the loan package computer-readable program code for determining a market price of each of the loans based on a securitization model computer-readable program code for modeling an objective function to determine which loans in the group of loans that meets the one or more constraints are least profitable for securitization in the securitization model and computer-readable program code for allocating the loans that meets the one or more constraints and are least profitable for securitization into the loan package. Another aspect of the present invention provides a method for optimizing fixed rate whole loan trading. This method includes the steps of determining a bond structure to securitize whole loans developing a model comprising an objective function that represents a total market value for the whole loans when executed into bonds corresponding to the bond structure processing the model to determine which of a group of available bonds should be generated and into which bonds of the generated bonds that each of the whole loans best executes into. Another aspect of the present invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for optimally pooling excess coupon resulting from securitizing loans. This computer-readable medium includes computer-readable program code for creating a model corresponding to excess coupon bond pools and an unallocated pool, each excess coupon bond pool including at least one constraint and computer-readable program code for processing the model to allocate each of the loans into either an excess coupon bond pool or into the unallocated pool in order to maximize the total market value of the excess coupon that gets allocated to the excess coupon bond pools. These and other aspects, features and embodiments of the invention will become apparent to a person of ordinary skill in the art upon consideration of the following detailed description of illustrated embodiments exemplifying the best mode for carrying out the invention as presently perceived. BRIEF DESCRIPTION OF THE DRAWINGS For a more complete understanding of the exemplary embodiments of the present invention and the advantages thereof, reference is now made to the following description, in conjunction with the accompanying figures briefly described as follows. FIG. 1 is a block diagram depicting a system for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. FIG. 2 is a flow chart depicting a method for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. FIG. 3 is a flow chart depicting a method for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention. FIG. 4 is a flow chart depicting a method for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. FIG. 5 is a flow chart depicting a method for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. FIG. 6 is a flow chart depicting a method for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. FIG. 7 is a flow chart depicting a method for packaging whole loans in accordance with one exemplary embodiment of the present invention. FIG. 8 is a flow chart depicting a method for pooling excess coupon in accordance with one exemplary embodiment of the present invention. DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS The invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. Although the exemplary embodiments of the invention are discussed in terms of whole loans (particularly fixed rate residential mortgages), aspects of the invention can also be applied to trading other types of loans and assets, such as variable rate loans and revolving debts. The invention can comprise a computer program that embodies the functions described herein and illustrated in the appended flow charts. However, it should be apparent that there could be many different ways of implementing the invention in computer programming, and the invention should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed invention based on the flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use the invention. The inventive functionality of the claimed computer program will be explained in more detail in the following description read in conjunction with the figures illustrating the program flow. Further, it will be appreciated to those skilled in the art that one or more of the stages described may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. Turning now to the drawings, in which like numerals represent like elements throughout the figures, aspects of the exemplary embodiments will be described in detail. FIG. 1 is a block diagram depicting a system 100 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIG. 1, the system 100 includes a computing system 110 connected to a distributed network 140 . The computing system 110 may be a personal computer connected to the distributed network 140 . The computing system 110 can include one or more applications, such as loan trading optimizer application 120 . This exemplary loan trading optimizer 120 includes four modules 121 - 124 that can operate individually or interact with each other to provide an optimal packaging of loans into one or more bond structures and whole loan packages. A seniorsubordinate module 121 distributes loans into a seniorsubordinate bond structure with bonds having different credit ratings and different net coupon values. As will be discussed in more detail with reference to FIGS. 4-5, the seniorsubordinate module 121 distributes the loans into bonds having a AAA rating, subordinate bonds with lower credit ratings, and, depending on the loans and the coupon values of the AAA bonds and the subordinate bonds, interest only bonds and principal only bonds. A pass-thru module 122 distributes loans into pass through bonds guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The pass-thru module 122 optimally pools the loans into To Be Announced (TBA) pass through securities based on a variety of constraints. The pass-thru module 122 is discussed in more detail below with reference to FIG. 6. A whole loan module 123 allocates loans to meet bids for loan portfolios meeting specific requirements and constraints of the bid. The whole loan module 123 can interact with either the seniorsubordinate module 121 or the pass-thru module 122 to allocate loans that meet the requirements of the bids but are less favorable to be securitized. The whole loan module 123 is discussed below in more detail with reference to FIG. 7. An excess coupon module 124 distributes excess coupons of securitized loans into different bond tranches or pools. The excess coupon module 124 can pool excess coupons resulting from seniorsubordinate bond structure created by the seniorsubordinate module 121 andor excess coupons resulting from pass through securities created by the pass-thru module 122 . The excess coupon module 124 is discussed below in more detail with reference to FIG. 8. Users can enter information into a user interface 115 of the computing system 110 . This information can include a type of bond structure to optimize, constraints associated with bond structures and bond pools, information associated with loan bids, and any other information required by the loan trading optimizer 120 . After the information is received by the user interface 115 . the information is stored in a data storage unit 125 . which can be a software database or other memory structure. Users can also select a population of loans to consider for optimization by way of the user interface 115 . The loans can be stored in a database stored on or coupled to the computing system 110 or at a data source 150 connected to the distributed network 140 . The user interface 115 can also output to a user the bond packages and whole loan packages determined by the loan trading optimizer 120 . The loan trading optimizer 120 can communicate with multiple data sources 150 by way of the distributed network 140 . For example, the loan trading optimizer 120 can communicate with a data source 150 to determine Fannie Mae TBA prices and another data source 150 to determine U. S. Treasury prices. In another example, the loan trading optimizer 120 can communicate with a data source 150 to access information associated with bids for whole loan packages. The distributed network 140 may be a local area network (LAN), wide area network (WAN), the Internet or other type of network. FIG. 2 is a flow chart depicting a method 200 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 2, at step 205 . the user interface 115 receives input from a user. This user input is used by the loan trading optimizer 120 to determine the bond structure that should be optimized for a population of loans. For example, if the user desires to find the optimal pooling of loans for pass through bonds, the user can input the constraints for each bond pool. Examples of constraints for pass through bond pools include constraints on loan balances, total number of loans for a pool, and total loan balance for a pool. At step 210 . a population of loans is selected for optimization. The population of loans can be selected from loans stored in a loan database stored on or coupled to the computing system 110 or from a database at a data source 150 connected to the distributed network 140 . The population of loans can include loans currently owned by the user (e. g. investment bank) of the loan trading optimizer 120 andor loans that are up for bid by another bank, loan originator, or other institution. For example, a user may employ the loan trading optimizer 120 to find the maximum market value of a loan portfolio currently for sale in order to determine an optimal bid for the loan portfolio. Additionally, a user can select the population of loans by specifying certain criteria, such as maximum loan balance, location of the loans, and FICO score. At step 215 . the loan trading optimizer 120 determines a securitization strategy for the population of loans selected in step 210 . Depending upon the user inputs received in step 205 . the loan trading optimizer 120 employs one or more of the seniorsubordinate module 121 . the pass-thru module 122 . and the whole loan module 123 to determine the securitization strategy for the population of loans. Step 215 is discussed in more detail with reference to FIGS. 3-7. At step 220 . the loan trading optimizer 120 determines whether the securitization strategy returned at step 215 is of sufficiently high quality. In this exemplary embodiment, the loan trading optimizer 120 iterates the step of determining a securitization strategy for the population of loans until either an optimal solution is found or the user determines that the securitization strategy is of sufficiently high quality. In order for the user to determine if the securitization strategy if of sufficient high quality, the loan trading optimizer 120 can output the results to the user by way of the user interface 115 . The loan trading optimizer 120 can output these results based on a number of iterations of step 215 (e. g. every 100 iterations) or when a certain level of quality is found. The user interface 115 can then receive input from the user indicating whether the securitization strategy is of sufficient high quality. If the securitization strategy is of sufficient high quality or optimal, the method 200 proceeds to step 225 . Otherwise, the method 200 returns to step 215 . In one exemplary embodiment, quality is measured in terms of the total dollar value of the population of loans. For example, the user may desire to sell a population of loans for at least ten million dollars in order to bid on the loans. The user can set a threshold for the loan trading optimizer 120 to only return a solution that meets this threshold or a solution that is the optimal solution if the optimal solution is below this threshold. At step 225 . the excess coupon module 124 of the loan trading optimizer 120 can pool any excess coupon resulting from the securitization strategy determined in step 215 . This step is optional and is discussed below in more detail with reference to FIG. 8. At step 230 . the loan trading optimizer 120 communicates the final securitization strategy to the user interface 115 for outputting to a user. The user interface 115 can display the final securitization strategy and optionally other possible securitization strategies with similar quality levels. FIG. 3 is a flow chart depicting a method 215 for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 3, at step 305 . the loan trading optimizer 120 determines which models to use for determining the securitization strategies. In this exemplary embodiment, the loan trading optimizer 120 includes a seniorsubordinate module 121 . a pass-thru module 122 . and a whole loan module 123 . Each of the modules 121 - 123 can build and process a model for determining an optimal packaging of loans as discussed below. The loan trading optimizer 120 determines which modules 121 - 123 to use based on the input received from the user in step 205 of FIG. 2. For example, the user may specify that only a seniorsubordinate structure should be optimized for the population of loans. Alternatively, if the user has entered bid information for a portfolio of whole loans, the loan trading optimizer 120 can execute the whole loan module 123 with the seniorsubordinate module 121 andor the pass-thru module 122 to determine which of the loans meet the requirements of the bid and are least favorable for securitization. Additionally, a user may specify that both an optimal seniorsubordinate bond structure and an optimal pooling of pass through bonds should be determined for the population of loans. If the user selected that a seniorsubordinate bond structure should be optimized, the method 215 proceeds to step 310 . At step 310 . the seniorsubordinate module 121 develops a model for packaging the population of loans into a seniorsubordinate bond structure and processes the model to determine an optimal seniorsubordinate bond structure for the loan population. Step 310 is discussed in more detail with reference to FIGS. 4 and 5. After the seniorsubordinate structure is determined, the method 215 proceeds to step 220 (FIG. 2). If the user selected that the population of loans should be optimally pooled into pass through bonds, the method 215 proceeds to step 315 . At step 315 . the pass-thru module 122 develops a model for pooling the population of loans into multiple bond pools and processes the model to determine an optimal pooling for the loan population. Step 315 is discussed in more detail with reference to FIG. 6. After the pooling is determined, the method 215 proceeds to step 220 (FIG. 2). If the user selected that whole loans should be allocated to a package of whole loans to be sold, the method 215 proceeds to step 320 . At step 320 . the whole loan module 123 develops a model for allocating whole loans that meet certain constraints and are less favorable to be securitized into a whole loan package and processes the model to determine which loans are best suited for the whole loan package. Step 320 is discussed in more detail with reference to FIG. 7. After the whole loan package is determined, the method 215 proceeds to step 220 (FIG. 2). FIG. 4 is a flow chart depicting a method 310 for packaging a population of loans into a seniorsubordinate bond structure in accordance with one exemplary embodiment of the present invention. As briefly discussed above with reference to FIG. 1, a seniorsubordinate bond structure is a structure where bonds with different credit ratings are created. Typically, the seniorsubordinate bond structure includes a senior tranche of bonds having a AAA or similar credit rating and a subordinate tranche of bonds having a lower credit rating. The senior tranche is protected from a certain level of loss by the subordinate tranche as the subordinate tranche incurs the first losses that may occur. The senior trance can be sold to investors desiring a more conservative investment having a lower yield, while the subordinated tranche can be sold to investors willing to take on more risk for a higher yield. For the purpose of this application, a AAA rated bond refers to a bond in the senior tranche, but not necessarily a bond having a credit rating of AAA. Additionally, interest only (IO) and principal only (PO) bonds may be created in a seniorsubordinate structure. An IO bond is created when the net coupon of a loan is more than the coupon of the bond in which the loan executes. Thus, the difference in the loan coupon and the bond coupon creates an interest only cash flow. Similarly, when the loan coupon is less than the bond coupon, a PO bond is created which receives only principal payments. Referring to FIGS. 1 and 4, at step 405 . the seniorsubordinate module 121 determines the bond coupons that are available for executing the loans into. The seniorsubordinate module 121 may obtain the available bond coupons from a data source 150 or may receive the available bond coupons from the user by way of the user interface 115 in step 205 of FIG. 2. For example, the user may desire to execute the loans into bonds having coupon values between 4.5 and 7.0. At step 410 . the seniorsubordinate module 121 selects a first bond coupon value from the range of available bond coupon values. This first coupon value can be the lowest bond coupon value, the highest coupon value, or any other bond coupon value in the range of available bond coupon values. At step 415 . the seniorsubordinate module 121 determines the execution price of each loan in the population of loans at the selected coupon value. Each loan in the population of loans is structured as a bond. The cash flow of each loan is distributed into symbolic AAA and subordinate bonds, and depending on the coupon of the loan and the selected bond coupon, an IO or PO bond. The principal payment and interest cash flows of each loan is generated in each period accounting for loan characteristics of the loan, such as IO period, balloon terms, and prepayment characteristics. The cash flow generated in each period is distributed to all bonds that the loan executes taking into account shifting interest rules that govern the distribution of prepayments between the AAA and the subordinate bonds in each period. The proportion in which the principal payments are distributed depends on the subordination levels of the AAA and the subordinate bonds. The subordination levels are a function of the loan attributes and are supplied by rating agencies for each loan through an Application Program Interface (API) coupled to the computing device 110 . Prepayments are first distributed pro rate to the PO bond and then between the AAA and the subordinate bonds based on the shifting interest rules. Any remaining prepayment is distributed proportionally among all the subordinate bonds. The interest payment for each of the bonds is a direct function of the coupon value for the bond. After the cash flows of each of the bonds for each of the loans have been generated, the present value of these cash flows is determined. For fixed rate loans, the AAA bonds can be priced as a spread to the To Be Announced (TBA) bond prices. However, the subordinate bond cash flows are discounted by a spread to the U. S. Treasury Yield Curve. The TO and PO bonds are priced using the Trust TO and PO prices. Finally, the price of the AAA bond, the subordinate bonds, and the TO or PO bond is combined proportionally for each loan based on the bond sizes to get the final bond price for each loan. This final bond price is the price of the loan executing into the bond given the selected coupon value of the bond. At step 420 . the seniorsubordinate module 121 determines if there are more bond coupon values in the range of available bond coupon values. If there are more bond coupon values, the method 310 proceeds to step 425 . Otherwise, the method 310 proceeds to step 430 . At step 425 . the next bond coupon value in the range of available bond coupon values is selected. In one exemplary embodiment, the seniorsubordinate module 121 can increment from the previous selected bond coupon value (e. g. 0.5 increments) to determine the next bond coupon value. In an alternative embodiment, the seniorsubordinate module 121 can progress through a fixed list of bond coupon values. For example, the user may select specific bond coupon values to execute the loans into, such as only 4.0, 5.0, and 6.0. After the next bond coupon value is selected, the method 310 returns to step 415 to determine the execution price of each loan in the population of loans at the new coupon value. At step 430 . the seniorsubordinate module 121 determines, for each loan in the population of loans, which bond coupon value yielded the highest final bond price for that particular loan. At step 435 . the seniorsubordinate module 121 groups the loans according to the bond coupon value that yielded the highest final bond price for each loan. For example, if the available bond coupon values are 4.0, 5.0, and 6.0, each loan that has a highest final bond price at 4.0 are grouped together, while each loan that has a highest final bond price at 5.0 are grouped together, and each loan that has a final bond price at 6.0 are grouped together. After step 435 is complete, the method proceeds to step 220 (FIG. 2). In the embodiment of FIG. 4, the subordinate bonds for each loan execute at the same bond coupon value as the corresponding AAA bond. For example, if a first loan of 6.25 best executes into a bond having a coupon value of 6.0, then a AAA bond of 6.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 6.0 is created. If a second loan of 5.375 best executes into a bond having a coupon value of 5.0, then a AAA bond of 5.0 and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 5.0 is created. This creates two AAA bonds and two subordinate bonds at two different coupon values. Typically, when loans are packaged in a seniorsubordinate bond structure, multiple AAA bonds with multiple coupon values are created with a common set of subordinate bonds that back all of the AAA bonds. This set of subordinate bonds is priced at the weighted average (WA) execution coupon of all of the AAA bonds created for the loan package. Pricing the subordinate bonds at the WA execution coupon implies that the spread to the benchmark U. S. Treasury curve, which is a function of the bond rating and the execution coupon of the subordinate bond, has to be chosen appropriately. In order to know the WA execution coupon of all the AAA bonds for the population of loans, the best execution coupon for each loan in the population of loans has to be known. In order to know the best execution coupon of each loan, the loan has to be priced at different bond coupon values and the AAA and subordinate bonds created at those coupons also have to be priced. However, the subordinate bond cash flows are discounted with spreads to the U. S. Treasury, with spreads taken at the WA best execution coupon which is still unknown. This creates a circular dependency as the best execution of each loan in the population of loans now depends on all the other loans in the population. FIG. 5 is a flow chart depicting a method 500 for packaging a population of loans into a seniorsubordinate structure in accordance with one exemplary embodiment of the present invention. The method 500 is an alternative method to that of method 310 of FIG. 4, accounting for pricing subordinate bonds at the WA execution coupon and provides a solution to the circular dependency discussed above. In Equation 1, x ij is a binary variable with a value of either 0 or 1, whereby a value of 1 indicates that the i th loan is optimally executing at the j th execution coupon value. The parameters d 0 to d j represent the j execution coupon values. For example, the coupons values could range from 4.5 to 7.0. Finally, the parameter b i represents the balance of the i th loan. where q 0 to q 1 are special ordered sets of type two, which implies that at most two are non-zero and the two non-zero weights are adjacent. Let Pa ij be the price of the AAA bond when loan i executes at coupon j. Next, let Ps ij be the overall price of all of the subordinate bonds combined when loan i executes at coupon j. Finally, let Pio ij and Ppo ij be the prices of the IO and PO bonds respectively when loan i executes at coupon j. The AAA bond prices and the TO and PO bond price components of loan i executing at coupon j are linear functions of x ij . The AAA priced as a spread to the TBA is a function of the execution coupon of the AAA bond and the IOPO prices are a lookup based on collateral attributes of the loan. However, pricing the subordinate bonds is complicated because the subordinate cash flows are discounted at the WA execution coupon. Let P i be a matrix of size jj that contains the prices of the subordinate bonds. The (m, n) entry of the matrix represents the price of the subordinate cash flows when the cash flow of loan i is generated assuming that loan i executes at the m th coupon and is discounted using subordinate spreads for the n th coupon. Subordinate spreads to the U. S. Treasury are a function of the execution coupon and any product definition, such as the size (e. g. JumboConforming), maturity (e. g. 1530 years), etc. The price of the subordinate bond of the i th loan can be written as: q 0 ( x i0 P i (0,0) . x ij P i (0,j) ). q j ( x i0 P i (j,0) . x ij P i (j, j) ) 4 which is a non linear expression as the equation contains a product of q and x ij . both of which are variables in this equation. FIG. 5 provides a method 500 for overcoming this non-linearity. Referring to FIG. 5, at step 505 . the seniorsubordinate module 121 determines the optimal execution price for each loan in the population of loans independent of the WA execution coupon. In one exemplary embodiment, the seniorsubordinate module 121 employs the method 310 of FIG. 4 to find the optimal execution price for each loan. At step 510 . the seniorsubordinate module 121 determines the WA execution coupon corresponding to the optimal execution price for each loan. This WA execution coupon can be found using Equation 1 above. At step 515 . the seniorsubordinate module 121 determines the weights (i. e. q 0 q j ) of each execution coupon for the WA execution coupon found in step 510 . These weights can be found using Equation 3 above. At step 520 . the seniorsubordinate module 121 builds a model including an objective function to determine the optimal execution coupon for each loan to maximize the total market value of all of the bonds in the seniorsubordinate structure. The expression of the objective function contains ij terms, where the ij term represents the market value of executing the i th loan at the j th execution coupon. After inserting the values of the weights of the execution coupons (i. e. qs) into the expression for subordinate bond price (Equation 4), only two of the terms will be non-zero for the sub-price of the i th loan executing at the j th execution coupon. As the method 200 of FIG. 2 iterates step 215 . different WA execution coupons can be used to maximize the objective function. The iterations can begin with the WA execution coupon found in step 510 and the seniorsubordinate module 121 can search around this WA execution coupon until either the optimal solution is found or the user decides that a solution of sufficient high quality is found in step 220 of FIG. 2. In other words, the seniorsubordinate module 121 searches for an optimal solution by guessing several values of the WA execution coupon around an initial estimate of the optimal execution coupon. After a final solution is found by the seniorsubordinate module 121 . the loans can be grouped based on the coupon values for each loan in the final solution to the objective function. In some instances, one of the undesirable effects of the seniorsubordinate bond structure is the creation of IO andor PO bonds, which may not trade as rich as AAA bonds. In some exemplary embodiments, the seniorsubordinate module 121 can ameliorate this issue by considering a loan as two pseudo loans. For example, a loan having a net rate of 6.125 and a balance of 100,000 can be considered equivalent to two loans of balance b 1 and b 2 and coupons 6 and 6.5 such that the following conditions are satisfied: b 1 b 2100,000 5 (( b 16.0)( b 26.5))( b 1 b 2)6.125 6 The first condition conserves the original balance, while the second condition is to set the WA coupon of the two pseudo loans to equal the net rate of the original loan. Solving these equations for b 1 and b 2 . we find that b 1 75,000 and b 2 25,000. These two loans, when executed at 6.0 and 6.5 bond coupons respectively, avoids the creation of either an IO bond or a PO bond. Although in the above example two adjacent half point coupons were used to create the two pseudo loans, two coupons from any of the half point bond coupons that are being used to create the bonds can be used. For example, if only bond coupons from 4.5 to 7.0 are being used to create the bonds, there would be fifteen combinations to consider (6C215). In some cases, the best solution is not to split the loan into two adjacent half point bond coupons. For example, this split may not be optimal if the AAA spreads at the two adjacent half point coupons are far higher than the ones that are not adjacent to the net balance of the loan. The seniorsubordinate module 121 can construct a linear program or linear objective function to determine the optimal split into pseudo loans. The output of the linear program is the optimal splitting of the original loan into pseudo loans such that the overall execution of the loan is maximized, subject to no IO bond or PO bond creation. For each loan i, let variable x ij indicate the balance of loan i allocated to the jth half point coupon, subject to the constraint that the sum of over x ij for all j equals to the balance of loan i and the WA coupon expressed as a function of the x ij s equals to the net coupon of loan i, similar to Equation 6 above. Let the execution coupons be r 0 to r n . Thus, this equation becomes: ( x i0 r 0 . x in r n ) b i c i 7 where b i is the balance of loan i and c i is the net coupon of loan i. The price of loan i executing at coupon j is the sum of the price of the AAA bond and the subordinate bonds. No IO or PO bonds are created when the coupons are split. The seniorsubordinate module 121 calculates the price of the AAA bond as a spread to the TBA, where the spread is a function of the execution coupon j. In one embodiment, the seniorsubordinate module 121 also calculates the price of the subordinate bond as a spread to the TBA for simplification of the problem. Cash flows are not generated as the split of the balances to different execution coupons is not yet known. The seniorsubordinate module 121 combines the price of the subordinate bond and the AAA bond in proportion to the subordination level of loan i, which can be input by a user in step 205 of FIG. 2 or input by an API. At this point, the seniorsubordinate module 121 has calculated the price of loan i (P ij ) for each execution coupon j. To determine the optimal splitting of the original loan into pseudo loans, the seniorsubordinate module 121 creates the following objective function and works to maximize this objective function: Max: P i0 x i0 . P in x in 8 Equation 8 is a simple linear program with two constraints and can be solved optimally. The solution gives the optimal split of the loan into at most two coupons and thus, a bond can be structured without creating any IO or PO bonds. The user can determine if the bond should be split or not based on the optimal execution and other business considerations. FIG. 6 is a flow chart depicting a method 315 for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. A pass through bond is a fixed income security backed by a package of loans or other assets. Typically, as briefly discussed above with reference to FIG. 1, a pass through bond is guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The government agency guarantees the pass through bond in exchange for a guarantee fee (Gfee). The Gfee can be an input provided by the agencies for a specific set of loans or can be specified as a set of rules based on collateral characteristics. Regardless of how the Gfee is obtained, the Gfee for a loan set is known. When loans are securitized as a pass through bond, one has the option to buy up or buy down the Gfee in exchange for an equivalent fee to the agencies. Buying up the Gfee reduces the net coupon and thus the price of the bond as well. This upfront buy up fee is exchanged in lieu of the increased Gfee coupon. Similarly, buying down the Gfee reduces the Gfee and increases the net coupon and therefore increases the bond price. An upfront fee is paid to the agencies to compensate for the reduced Gfee. The Fannie Mae and Freddie Mac agencies typically provide buy up and buy down grids each month. Referring to FIG. 1, these grids can be stored in a data source 150 or in the data storage unit 125 for access by the pass-thru module 122 of the loan trading optimizer 120 . If the Gfee is bought up or bought down, an excess coupon is created. The amount of buy up or buy down of Gfee can vary based on collateral attributes of the loan and can also be subject to a minimum and maximum limit. Referring now to FIGS. 1 and 6, at step 605 . the pass-thru module 122 determines the optimal execution of each loan by buy up or buy down of the Gfee. In one exemplary embodiment, the optimal execution of each loan is determined by finding the overall price of the loan for each available buy up and buy down of the Gfee. Typically, a Gfee can be bought up or down in increments of 1100 th of a basis point. The pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy up with a step size of 1100 th of a basis point. Similarly, the pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy down with a step size of 1100 th of a basis point. In each iteration, the amount of Gfee buy up or buy down is added to the current net rate of the loan. From this modified net rate of the loan, the TBA coupon is determined as the closest half point coupon lower than or equal to the modified net rate. The excess coupon is equal to the modified net rate of the TBA coupon and the price of the excess coupon is a lookup in the agency grid. The fee for the buy up or buy down is also a lookup in the agency grid. The price of the TBA coupon is a lookup from the TBA price curve. When the Gfee is bought up, the cost is added to the overall price and when the Gfee is bought down, the cost is subtracted from the overall price. The pass-thru module 122 determines the overall price of execution for the loan at each iteration and determines the optimal execution for the loan as the execution coupon of the TBA for which the overall price is maximized. This overall cost is the combination of the price of the TBA coupon, the price of the excess coupon, and the cost of the Gfee (added if buy up, subtracted if buy down). At step 610 . the pass-thru module 122 determines which TBA pools each loan is eligible for. Pooling loans into TBA bonds is a complex process with many constraints on pooling. Furthermore, different pools of loans have pool payups based on collateral characteristics. For example, low loan balance pools could prepay slower and thus may trade richer. Also, loan pools with geographic concentration known to prepay faster may trade cheaper and thus have a negative pool payup. Thus, pooling optimally taking into account both the constraints and the pool payups can lead to profitable execution that may not be captured otherwise. Each of the TBA pools for which a loan can be allocated has a set of pool eligibility rules and a pool payup or paydown. Non-limiting examples of pools can be a low loan balance pool (e. g. loan balances less than 80K), a medium loan balance pool (e. g. loan balance between 80K and 150K), a high loan balance pool (e. g. loan balances above 150K), a prepay penalty loan pool, and an interest only loan pool. For a loan to be allocated to a specific pool by the pass-thru module 122 . the loan has to satisfy both the eligibility rules of the pool and also best execute at the execution coupon for that pool. The pass-thru module 122 applies the eligibility rules of the TBA bond pools to the loans to determine the TBA bond pools for which each loan is eligible. The pass-thru module 122 can utilize pool priorities to arbitrate between multiple pools if a loan is eligible for more than one pool. If a loan is eligible to be pooled into a higher and lower priority pool, the pass-thru module 122 allocates the loan to the higher priority pool. However, if a loan is eligible for multiple pools having the same priority, the pass-thru module 122 can allocate the loan into either of the pools having the same priority. At step 615 . the pass-thru module 122 builds a model for allocating the loans into TBA pools based on the constraints of each TBA bond pool. Let x ij be a binary variable with a value of 1 or 0 which has a value of 1 when loan i is allocated to TBA bond pool j. The total loan balance and loan count constraints of the TBA pools are linear functions of the x ij variables. The objective function for this model is also a linear combination of the market values of each loan. The primary problem in this model is that the given loan population selected in step 210 of FIG. 2 may not be sufficient to allocate all TBA loan pools, as some of the pools may not have loans to satisfy the balance and count constraints or the loans may not be eligible for those pools. In such cases, it is desirable for the pools to have the constraints when applicable. If there are some pools for which there are not enough loans in the population of loans to form a pool, then such pools are not subjected to the specified constraints while the other pools are. However, it is not possible to know a-priori which pools do not have enough loans to satisfy the constraints. Thus, the model employs conditional constraints to allow constraints to be applicable to only those pools which are allocated. The pooling model is modified to allow for some loans to not be allocated to any pool. This non-allocation will ensure that the model is always solvable and is similar to introducing a slack variable in linear programming. Thus, for each loan in the population of loans, there is an additional binary variable representing the unallocated pool into which the loan can be allocated. Those loans allocated to the unallocated pool are given a zero costmarket value, thus encouraging the pass-thru module 122 to allocate as many loans as possible. The next step in building this pooling model is to introduce p binary variables for the p possible TBA pools. A value of 1 indicates that this pool is allocated with loans satisfying the pool constraints and a value of 0 indicates that this pool is not allocated. These variables are used to convert simple linear constraints into conditional constraints. Each constraint of each pool is converted to conditional constraints for the pooling model. To detail this conversion, a maximum loan count constraint is considered for pool P. Let x 1 to x n be binary variable where x i are the loans eligible for pool P. Next, let x 1 . x n U, where U equals the total number of loans in pool P. Finally, let w be the binary variable to indicate if pool P is allocated. The user constraint for maximum loan count is specified as UK, where K is given by the user. In order to impose this constraint conditionally, this constraint is transformed to the following two constraints: UK w UM w where M is a constant such that the sum of all x i s is bounded by M. Consider both the cases when pool P is allocated (w1) and when pool P is not allocated (w0) below: w1: UK (required) UM (redundant) w0: U0 U0 The only way for U0 would be when all the x i s are 0 and thus, pool P will be unallocated. Other constraints, such as minimum count, minimum balance, maximum balance, average balance, and weighted average constraints can be transformed similarly for the pooling model. After all of the constraints are transformed to conditional constraints, the pooling model is ready to handle constraints conditionally. At step 620 . the pass-thru module 122 executes the pooling model to allocate the loans into TBA pools. After the pass-thru module 122 executes the model for one iteration, the method 315 proceeds to step 220 (FIG. 2). As the method 200 of FIG. 2 iterates step 215 . different TBA pool allocations are produced by the pass-thru module 122 until either the optimal TBA pool allocation is found or until the user decides that a solution of sufficient high quality is found in step 220 (FIG. 2). FIG. 7 is a flow chart depicting a method 320 for packaging whole loans in accordance with one exemplary embodiment of the present invention. The method 320 identifies an optimal package of loans meeting a set of constraints given by a customer or investor. In this embodiment, the loan package is optimized by determining which loans, among the population of loans that meet the constraints, are least favorable to be securitized. Although the method 320 of FIG. 7 is discussed in terms of the seniorsubordinate bond structure, other bonds structures or models can be used. Referring to FIG. 7, at step 705 . the whole loan module 123 determines which loans in the population of loans meets constraints of a bid for whole loans. Investment banks and other financial institutions receive bids for whole loans meeting specific requirements. These requirements can be entered into the user interface 115 at step 205 of FIG. 2 andor stored in the data storage unit 125 or a data source 150 . The constraints can include requirements that the loans must satisfy, such as, for example, minimum and maximum balance of the total loan package, constraints on the weighted average coupon, credit ratings of the recipients of the loans (e. g. FICO score), and loan-to-value (LTV) ratio. The constraints can also include location based constraints, such as no more than 10 of the loan population be from Florida and no zip code should have more than 5 of the loan population. After the whole loan module 123 selects the loans that meet the constraints, at step 710 . the whole loan module 123 determines the price of each loan that meets the constraints based on a securitization module. For example, the price of the loans may be calculated based on the seniorsubordinate structure discussed above with reference to FIGS. 4 and 5. At step 715 . the whole loan module 123 determines whether to use an efficient model to select loans least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on a securitization model or a less efficient model to select loans least favorable to be securitized by minimizing the spread of execution of the loans based on a securitization model. In one exemplary embodiment, this determination can be based on the total number of loans in the population or chosen by a user. If the whole loan module 123 determines to use the efficient model, the method 320 proceeds to step 725 . Otherwise, the method 320 proceeds to step 720 . At step 720 . the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the spread of execution of the loans based on the seniorsubordinate bond structure. The whole loan module 123 builds a model to select a subset of the loans that meet the constraints such that the WA price of the loans of this subset net of the TBA price of the WA coupon of this subset is minimized. The TBA price of the WA coupon of the subset is typically higher as the TBA typically has a better credit quality and hence the metric chosen will have a negative value. The objective function that needs to be minimized is given by: ( x 1 b 1 p 1 . x n b n p n )( x 1 b 1 . x n b n )( q 1 px 1 . q m px m ) 9 In Equation 9, x 1 to x n are binary variables with a value of either 0 or 1, whereby a value of 1 indicates that the loan is allocated and 0 otherwise. The variables b 1 to b n are the balances of the loans and p 1 to p n are the prices of the loans as determined in step 710 . The variables q 1 to q m are the weights for each of the half point coupons and px 1 to px m are the TBA prices for the half point coupons. The weights are special ordered sets of type two, which as discussed above, implies that at most two are non-zero and the two non-zero weights are adjacent. Thus, the expression (q 1 px 1 . q m px m ) is the price of the WA coupon of the allocated loans. where the c i s are the net coupons on the loans and the r i s are the half point coupons of the TBA curve. Additionally, other constraints for loan balance and ratio balance can similarly be transformed into linear constraints. In this exemplary embodiment, the ys are real numbers and the ys should be equal to y 0 when that loan is allocated, else the y should equal 0. This requirement can be enforced by adding additional constraints and variables: y i 0 18 y i Kz i 19 y i y 0 Kz i K (1 eps ) 20 y i y 0 Kz i K (1 eps ) 21 The equations above are analyzed when z i is set to 1 and z i is set to 0 and which shows that y i will be y 0 or zero within a tolerance of eps. Eps is a model specific constant and is suitably small to account for lack of numerical precision in a binary variable. The tolerance eps is utilized in this model as although binary variables are supposed to be 0 or 1, the binary variables suffer from precision issues and thus, the model should accommodate numerical difficulties. The source of this precision issue is the way y 0 has been defined. The denominator of y 0 M(x 1 b 1 . x n b n ) is essentially the sum of the balances of all loans in the pool, which can be a very large number resulting in a small y 0 . After building the model, the whole loan module 123 minimizes the objective function in Equation 13 with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations 17-21. The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 720 is completed, the method 320 proceeds to step 220 (FIG. 2). At step 725 . the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on the seniorsubordinate bond structure. Thus, the difference of the market value of the allocated loans and the notional market value of the loan pool using the price of the WA execution coupon is minimized. The objective function that needs to be minimized for this model is given by: Min:( x 1 b 1 p 1 . x n b n p n )( x 1 b 1 . q m px m ) 22 Let y i q i ( x 1 b 1 . x n b n ) 23 After building the model, the whole loan module 123 minimizes the objective function in Equation 24 with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations 25-29. The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 725 is completed, the method 320 proceeds to step 220 of FIG. 2. FIG. 8 is a flow chart depicting a method 225 for pooling excess coupon in accordance with one exemplary embodiment of the present invention. The excess coupon module 124 can pool the excess coupon of securitized loans into different tranches or pools. The excess coupon module 124 can take a large population of loans (e. g. 100 thousand or more), each with some excess coupon, and pool the loans into different pools, each pool with a different coupon and specified eligibility rules. Each of the pools can also have a minimum balance constraint. Pools that are created with equal contribution of excess coupon from every loan that is contributing to that pool typically trades richer than pools that have a dispersion in the contribution of excess from different loans. Therefore, it is profitable to create homogeneous pools. Referring to FIG. 8, at step 805 . the excess coupon module 124 converts the pool constraints into conditional constraints as some of the pools defined in this excess coupon model may not have loans to satisfy the pool constraints. This conversion is similar to the conversion of constraints discussed above with reference to FIG. 6. At step 810 . the excess coupon module 124 builds a model to determine the optimal pooling for the excess coupons. Let x ij be the contribution of excess coupon from loan i to pool j. Unlike the pooling model in FIG. 6 above, this variable is not a binary variable. However, an unallocated pool is added to the set of user defined pools which enables the pass-thru module 122 to always solve the model and produce partial allocations. The first constraint of this excess coupon model is the conservation of excess coupon allocated among all the pools for each loan. Any loan that does not get allocated to a user defined pool is placed in the unallocated pool, and thus the unallocated pool is also included in the conservation constraint. In this embodiment, the unallocated pool does not have any other constraint. The objective function of this excess coupon model is to maximize the total market value of the excess that gets allocated. Unallocated excess coupon is assigned a zero market value and thus the solver tries to minimize the unallocated excess coupon. In this model, the excess coupon module 124 tries to create the maximum possible pools with equal excess contribution. Any leftover excess from all the loans can be lumped into a single pool and a WA coupon pool can be created from this pool. An aspect of this excess coupon model is to enforce equality of the excess coupon that gets allocated from a loan to a pool. Furthermore, it is not necessary that all loans allocate excess to a given pool. Thus, the equality of excess is enforced only among loans that have a non-zero contribution of excess to this pool. Let xp 0 to xp p be p real variables that indicate the amount of excess in each pool. Also, let w ij be a binary variable that indicates if loan i is contributing excess to pool. For each eligible loan i, for pool j, the following constraints are added: x ij Mw ij x ij xp j 31 x ij xp j M (1 w ij ) 32 When M is chosen to be the maximum excess coupon of all loans in the allocation, the expression xp j M is negative. Thus, from x ij 0 and that all excess coupons have to be zero or positive, this implies that x ij 0 when w ij 0. This excess coupon model can be difficult to solve because of its complexity level. In order to reduce the complexity, the excess coupon module 124 employs dimensionality reduction. The first step of this process is to identify the pools into which a loan can be allocated. Eligibility filters in this excess coupon model specify the mapping of the collateral attributes of the loans to the coupons of the pools that the attributes can go into. For example, loans with a net coupon between 4.375 and 5.125 can go into pools of 4.5 or 5.0. Unlike the pooling model discussed above with reference to FIG. 6, there are no pool priorities. At step 815 . the excess coupon module 124 identifies the pool into which a given loan can be allocated based on the collateral attributes of the loan and independent of the pool execution coupon. This gives a one to one mapping between the loans and the pools. At step 820 . the excess coupon module 124 collapses all loans having the same excess coupon within a given pool definition into a single loan. This approach can significantly reduce the number of loans in the loan population. After the population of loans is reduced, the excess coupon module 124 maximizes the objective function at step 825 . The excess coupon module 124 can iteratively determine solutions to the objective function until an optimal solution is found or until a user decides that a solution of sufficient high quality is found. One of ordinary skill in the art would appreciate that the present invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a seniorsubordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. Although specific embodiments of the invention have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects of the invention were described above by way of example only and are not intended as required or essential elements of the invention unless explicitly stated otherwise. Various modifications of, and equivalent steps corresponding to, the disclosed aspects of the exemplary embodiments, in addition to those described above, can be made by a person of ordinary skill in the art, having the benefit of this disclosure, without departing from the spirit and scope of the invention defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures. Sentry LT Loan Trading System Sentry LT represents the next generation of Syndicated Loan trading platforms and offers both the features and functionality to successfully streamline your loan trading operation. At ClearStructure Financial Technology, we have worked with Syndicated Loans for over ten years. Our Sentry LT system brings this experience and expertise together into a robust web-based bank loan trading platform, which eliminates many challenges loan trading desks face by automating tasks and improving efficiency. Sentry LT is built using the latest technology and is positioned to easily scale as your business grows. Sentry LTs customizable workflows and configurable screens ensure that users see data that is important to them by making this information easily accessible. Sentry LT Featured Functionality Customizable PampL and trade blotter views. Ability to generate reports and export data with a single click. User-defined tradesettlement workflows, which allow users to adapt the system to current processes and compliance procedures. Pre-trade allocation and trade eligibility through rules-based compliance engine for multiple accounts, including counterparty limitations. Calculation of trading fees (such as delayed compensation, break funding, cost of carry, etc.) with drill down into detailed formulas, showing exactly how each fee was calculated. Ability to generate par amp distressed LSTALMA trade documentation (Trade Confirm, Funding Memo, Pricing Letter, PSA, Assignment Agreements, Netting Letters, etc.). Full audit trail and user navigation log tracks every change that occurs within the application. Powerful user permission system, through which the system administrator can create groups, as well as restrict and control access to features and screens by various levels.