Arquiteto Do Sistema De Negociação

Algorithmic Trading System Architecture. Previamente neste blog eu escrevi sobre a arquitetura conceitual de um sistema de negociação algorítmica inteligente, bem como os requisitos funcionais e não funcionais de uma produção de sistema de negociação algorítmica Desde então, tenho projetado uma arquitetura de sistema que eu acredito que poderia Satisfazer os requisitos arquitetônicos Neste post vou descrever a arquitetura seguindo as diretrizes da ISO IEC IEEE 42010 sistemas e arquitetura de engenharia de software padrão de descrição De acordo com este padrão uma descrição de arquitetura deve. Conter múltiplas visões arquitetônicas padronizadas, por exemplo, UML and. Maintain rastreabilidade entre Decisões de projeto e requisitos arquitetônicos. Definição de arquitetura de software. Ainda não há consenso sobre o que é a arquitetura de um sistema. No contexto deste artigo, é definida como a infra-estrutura dentro da qual componentes de aplicativo que satisfazem requisitos funcionais podem ser especificados, Implementados e executados Os requisitos funcionais são as funções esperadas do sistema e seus componentes Os requisitos não funcionais são medidas através das quais a qualidade do sistema pode ser medida. Um sistema que satisfaz plenamente os seus requisitos funcionais pode ainda não atender às expectativas se os requisitos não funcionais São deixados insatisfeito Para ilustrar este conceito considere o seguinte cenário um sistema de negociação algorítmica que você acabou de comprar construído faz excelentes decisões comerciais, mas é completamente inoperável com as organizações de gestão de riscos e sistemas de contabilidade Este sistema atender às suas expectativas. Conceptual Architecture. A conceitual View descreve conceitos de alto nível e mecanismos que existem no sistema no nível mais alto de granularidade. Neste nível, o sistema de negociação algorítmica segue uma arquitetura orientada a eventos EDA dividida em quatro camadas e dois aspectos arquitetônicos. Para cada camada e arquiteturas de referência de aspecto e Padrões ar E usados ​​Padrões arquitetônicos são comprovadas, estruturas genéricas para a obtenção de requisitos específicos Os aspectos arquitetônicos são preocupações transversais que abrangem vários componentes. A arquitetura orientada a eventos - uma arquitetura que produz, detecta, consome e reage a eventos Os eventos incluem movimentos de mercado em tempo real, Eventos ou tendências e eventos comerciais, por exemplo, apresentação de uma ordem. Este diagrama ilustra a arquitetura conceitual do sistema de negociação algorítmica. Referência arquiteturas. Para usar uma analogia, uma arquitetura de referência é semelhante aos planos para uma parede de suporte de carga Este blueprint Pode ser reutilizada para projetos de edifícios múltiplos, independentemente do edifício que está sendo construído, uma vez que satisfaz um conjunto de requisitos comuns. De modo semelhante, uma arquitetura de referência define um modelo contendo estruturas genéricas e mecanismos que podem ser usados ​​para construir uma arquitetura de software concreta que satisfaça Requisitos específicos A arquitetura para o algoritmo tr O sistema ading utiliza uma arquitetura baseada em espaço SBA e um controlador de exibição de modelo MVC como referências. As boas práticas como o ODS operacional, o extrato de transformação e carga ETL padrão, e um data warehouse DW também são usados. Separa a representação da informação da interação do usuário com ela. A arquitetura baseada no espaço - especifica uma infra-estrutura onde unidades de processamento acopladas frouxamente interagem uns com os outros através de uma memória associativa compartilhada chamada espaço mostrado abaixo. A visão estrutural de uma arquitetura mostra os componentes e subcomponentes do sistema de negociação algorítmica. Ele também mostra como esses componentes são implantados na infra-estrutura física. Os diagramas UML usados ​​nesta visão incluem diagramas de componentes e diagramas de implantação. Os diagramas de implantação do sistema global de negociação algorítmica e os p Unidades de rocessing na arquitetura de referência SBA, bem como diagramas de componentes relacionados para cada um as camadas. Algorithmic diagrama de distribuição de alto nível do sistema de negociação Diagrama de implantação de unidades de processamento SBA Diagrama de componente de camada de processamento de pedidos Diagrama de componente de processamento de eventos automatizado do comerciante Fonte de dados e camada de pré - Diagrama de componente diagrama de componente de interface de usuário baseado em MVC. Técnicas arquitetônicas. De acordo com o instituto de engenharia de software uma tática arquitetônica é um meio de satisfazer um requisito de qualidade, manipulando algum aspecto de um modelo de atributo de qualidade através de decisões de design arquitetônico Um exemplo simples usado na negociação algorítmica A arquitetura do sistema está manipulando um armazenamento de dados operacional ODS com um componente de consulta contínua Este componente iria analisar continuamente o ODS para identificar e extrair eventos complexos As táticas a seguir são usadas na arquitetura. O padrão de disruptor nas filas de evento e ordem. As filas de evento e ordem. Continua consulta CQL sobre a ODS. Data filtragem com o padrão de design de filtro em dados de entrada. Congestion evitação algoritmos em todas as conexões de entrada e saída. Active gestão de filas AQM e congestionamento explícito notificationmodity recursos de computação com capacidade de atualização escalável . Redundância ativa para todos os únicos pontos de falha. Indexação e estruturas de persistência otimizadas no ODS. Schedule regular backup de dados e scripts de limpeza para ODS. Transaction históricos em todos os bancos de dados. Checksums para todas as ordens para detectar falhas. Anotar eventos com timestamps para Ignorar eventos viciados. Order regras de validação, por exemplo, as quantidades máximas trade. Automated comerciante componentes usam um banco de dados na memória para a análise. Dois estágio de autenticação para interfaces de usuário conectando-se ao ATs. Encryption em interfaces de usuário e conexões para o ATs. Observer padrão de design para o MVC para gerenciar views. The lista acima são apenas algumas decisões de design que eu identifiquei durante o Design da arquitetura Não é uma lista completa de táticas Como o sistema está sendo desenvolvido táticas adicionais devem ser empregados em vários níveis de granularidade para atender aos requisitos funcionais e não funcionais Abaixo estão três diagramas descrevendo o padrão de design disruptor, padrão de design de filtro, E o componente de consulta contínua. Continuous Querying Diagrama de componentes Disruptor padrão de design padrão fonte de diagrama de classe Padrão de filtro diagrama de classe de padrão. Behavioural View. This vista de uma arquitetura mostra como os componentes e camadas devem interagir uns com os outros Isso é útil ao criar cenários para testar a arquitetura Desenhos e para a compreensão do sistema de ponta a ponta Esta visão consiste em diagramas de seqüência e diagramas de atividade Diagramas de atividade mostrando o processo interno do sistema de negociação algorítmica e como os comerciantes são supostos interagir com o sistema de negociação algorítmica são mostrados abaixo. Interação comerciante algorítmica Negociação algorítmica de ponta a ponta Processo. Tecnologias e frameworks. The passo final na concepção de uma arquitetura de software é identificar potenciais tecnologias e estruturas que poderiam ser utilizados para realizar a arquitetura Como um princípio geral é melhor aproveitar as tecnologias existentes, desde que satisfaçam adequadamente tanto funcional E requisitos não funcionais Uma estrutura é uma arquitetura de referência realizada, por exemplo, JBoss é uma estrutura que realiza a arquitetura de referência JEE As seguintes tecnologias e estruturas são interessantes e devem ser considerados ao implementar um sistema de negociação algorítmica. CUDA - NVidia tem um número de produtos que suportam alta Desempenho de modelagem de finanças computacionais Pode-se conseguir até 50x melhorias de desempenho na execução de simulações de Monte Carlo na GPU em vez da CPU. River River é um kit de ferramentas usado para desenvolver sistemas distribuídos Foi usado como um framework para construir aplicações baseadas No padrão SBA. Apache Hadoop - no e Então o uso de Hadoop oferece uma solução interessante para o problema de grandes dados Hadoop pode ser implantado em um ambiente em cluster suportando tecnologias CUDA. AlgoTrader - uma plataforma de negociação algorítmica de código aberto AlgoTrader poderia potencialmente ser implantado no FIX, FAST e FIXatd. Apesar de não ser uma tecnologia ou uma estrutura, os componentes devem ser construídos com uma API de interface de programação de aplicativo para melhorar a interoperabilidade Do sistema e seus componentes. A arquitetura proposta foi projetada para satisfazer requisitos muito genéricos identificados para sistemas de negociação algorítmica Geralmente falando sistemas de negociação algorítmica são complicados por três fatores que variam com cada implementação. Dependências em sistemas de empresa externa e troca. Desafiando requisitos não funcionais E. Ev Portanto, a arquitetura de software proposta precisaria ser adaptada caso a caso, a fim de satisfazer requisitos organizacionais e regulatórios específicos, bem como superar restrições regionais. A arquitetura do sistema de negociação algorítmica deve ser vista como apenas um Ponto de referência para indivíduos e organizações que desejam projetar seus próprios sistemas de negociação algorítmicos. Para obter uma cópia completa e fontes usadas, por favor, faça o download de uma cópia do meu relatório Obrigado. Melhor linguagem de programação para Algorithmic Trading Systems. One das perguntas mais freqüentes que recebo em O mailbag QS é Qual é a melhor linguagem de programação para negociação algorítmica A resposta curta é que não há melhor linguagem Estratégia parâmetros, desempenho, modularidade, desenvolvimento, resiliência e custo devem ser considerados Este artigo descreverá os componentes necessários de uma negociação algorítmica Arquitetura do sistema e como as decisões relativas à implementação afetam a escolha As principais componentes de um sistema de negociação algorítmico serão consideradas, tais como as ferramentas de pesquisa, otimizador de portfólio, gerente de risco e mecanismo de execução. Subseqüentemente, serão examinadas diferentes estratégias de negociação e como elas afetarão o projeto do sistema. A freqüência de negociação eo volume de negociação provável serão discutidos. Uma vez que a estratégia de negociação foi selecionada, é necessário arquitetar todo o sistema Isso inclui a escolha de hardware, sistema operacional e resiliência do sistema contra eventos raros, potencialmente catastróficos. A arquitetura está sendo considerada, devida consideração deve ser dada ao desempenho - tanto para as ferramentas de pesquisa, bem como o ambiente de execução ao vivo. O que é o sistema de negociação tentando Do. Before decidir sobre o melhor idioma com o qual escrever um sistema de comércio automatizado que É necessário definir os requisitos Será que o sistema vai ser puramente baseado em execução Será que o sistema requer uma gestão de risco Nt ou módulo de construção de portfólio O sistema exigirá um backtester de alto desempenho Para a maioria das estratégias o sistema de negociação pode ser dividido em duas categorias Pesquisa e geração de sinal. Research está preocupado com a avaliação de um desempenho de estratégia sobre dados históricos O processo de avaliação de uma estratégia comercial Sobre os dados do mercado anterior é conhecido como backtesting O tamanho dos dados ea complexidade algorítmica terá um grande impacto sobre a intensidade computacional do backtester velocidade da CPU e simultaneidade são muitas vezes os fatores limitantes na otimização da velocidade de execução de pesquisa. Signal geração está preocupada com a geração de um conjunto de Negociar sinais de um algoritmo e enviar tais ordens para o mercado, geralmente através de uma corretora Para certas estratégias de um alto nível de desempenho é necessário IO questões como largura de banda de rede e latência são muitas vezes o fator limitante na otimização de sistemas de execução Assim, a escolha de idiomas para Cada componente de todo o seu sistema pode ser bastante diferente O tipo de estratégia algorítmica empregada terá um impacto substancial na concepção do sistema. Será necessário considerar os mercados que estão sendo negociados, a conectividade com fornecedores de dados externos, a freqüência e o volume da estratégia. A estratégia, o trade-off entre facilidade de desenvolvimento e otimização de desempenho, bem como qualquer hardware personalizado, incluindo servidores personalizados co-localizados, GPUs ou FPGAs que possam ser necessárias. As opções de tecnologia para uma estratégia de ações de baixa freqüência nos EUA será Muito diferente daqueles de uma estratégia de arbitragem estatística de alta freqüência negociação no mercado de futuros Antes da escolha da linguagem muitos fornecedores de dados devem ser avaliados que pertencem a uma estratégia em hand. It será necessário considerar conectividade para o vendedor, a estrutura De qualquer APIs, a oportunidade dos dados, os requisitos de armazenamento e resiliência na cara de um fornecedor indo off-line Também é sábio para possuir acesso rápido a vários fornecedores Va Rious todos os seus próprios caprichos de armazenamento, exemplos dos quais incluem símbolos ticker múltiplos para as ações e datas de vencimento para futuros, para não mencionar qualquer OTC dados específicos Isso precisa ser tidos em conta para a plataforma design. Frequency da estratégia é provável que seja um dos Os maiores condutores de como a pilha de tecnologia será definida Estratégias que empregam dados mais freqüentemente do que minuciosamente ou barras de segunda necessidade consideração significativa com relação ao desempenho. Uma estratégia que excede barras secundárias isto é carrapato dados conduz a um desempenho dirigido design como a exigência principal Para alta freqüência Estratégias uma quantidade substancial de dados de mercado precisará ser armazenado e avaliado Software como HDF5 ou kdb são comumente usados ​​para estas funções. Para processar os volumes extensos de dados necessários para aplicações de HFT, um backtester extensivamente otimizado e sistema de execução deve ser Usado CC possivelmente com algum montador é provável que o candidato de língua mais forte Ultra-alta Frequência estratégias quase certamente exigem hardware personalizado, como FPGAs, intercâmbio co-localização e interface de rede kernal tuning. Research Systems. Research sistemas normalmente envolvem uma mistura de desenvolvimento interativo e scripting automatizado O primeiro muitas vezes ocorre dentro de um IDE, como Visual Studio, MatLab ou R Studio O último envolve cálculos numéricos extensos sobre vários parâmetros e pontos de dados Isso leva a uma escolha de idioma fornecendo um ambiente simples para testar código, mas também fornece desempenho suficiente para avaliar estratégias sobre dimensões de vários parâmetros. IDEs típicos neste espaço incluem Microsoft Visual CC, que contém utilitários de depuração extensa, capacidades de conclusão de código via Intellisense e visões gerais diretas de toda a pilha de projeto através da base de dados ORM, LINQ MatLab que é projetado para álgebra linear numerosa extensa e operações vectorizadas, mas em um console interativo R Studio que Envolve O console de linguagem estatística R em IDE Eclipse IDE totalmente desenvolvido para Linux Java e C e IDEs semi-proprietários como o Enthought Canopy para Python, que incluem bibliotecas de análise de dados como SciPy SciPy scikit-learn e pandas em um único ambiente de console interativo . Para backtesting numérico, todos os idiomas acima são adequados, embora não seja necessário utilizar um GUI IDE como o código será executado em segundo plano. A principal consideração nesta fase é a da velocidade de execução. Uma linguagem compilada como C é Muitas vezes útil se as dimensões do parâmetro backtesting forem grandes. Lembre-se que é necessário ser cauteloso com esses sistemas, se for esse o caso. Linguagens interpretadas como Python freqüentemente fazem uso de bibliotecas de alto desempenho como pandas NumPy para a etapa de backtesting, Para manter um grau razoável de competitividade com equivalentes compilados Em última análise, a linguagem escolhida para o backtesting será determinada por necessidades algorítmicas específicas Assim como o leque de bibliotecas disponíveis na linguagem mais abaixo. No entanto, a linguagem utilizada para o backtester e os ambientes de pesquisa pode ser completamente independente daqueles utilizados nos componentes de construção de portfólio, gerenciamento de risco e execução, como veremos. Construção e Gestão de Riscos. A construção de carteiras e componentes de gestão de risco são muitas vezes ignorados pelos comerciantes algorítmicos de varejo Isso é quase sempre um erro Essas ferramentas fornecem o mecanismo pelo qual o capital será preservado Eles não só tentam aliviar o número de apostas arriscadas, Reduzindo os custos de transação. Versões sofisticadas desses componentes podem ter um efeito significativo na qualidade e consistência da lucratividade. É fácil criar uma estratégia estável, pois o mecanismo de construção da carteira eo gerenciador de riscos podem ser facilmente modificados para Sistemas múltiplos. Assim, eles devem ser considerados essenciais Componentes no início da concepção de um sistema de negociação algorítmica. O trabalho do sistema de construção de carteira é ter um conjunto de negócios desejados e produzir o conjunto de negócios reais que minimizam o churn, manter a exposição a vários fatores, como setores, classes de ativos , Volatilidade etc e otimizar a alocação de capital para várias estratégias em um portfolio. Portfolio construção muitas vezes se reduz a um problema de álgebra linear, como um factorization matriz e, portanto, o desempenho é altamente dependente da eficácia da implementação de álgebra linear numérica disponível bibliotecas comuns incluem uBLAS LAPACK e NAG para C MatLab também possui operações de matriz amplamente otimizadas Python utiliza NumPy SciPy para tais cálculos Um portfólio freqüentemente reequilibrado exigirá uma biblioteca de matriz compilada e bem otimizada para levar este passo para fora, de modo a não encolher o sistema de negociação. Outra parte extremamente importante de um sistema de negociação algorítmica Ris K pode vir de muitas formas Aumento da volatilidade, embora isso possa ser visto como desejável para certas estratégias, correlações aumentadas entre classes de ativos, padrão de contraparte, interrupções de servidor, eventos de cisne preta e bugs não detectados no código de negociação, para citar alguns. Os componentes de gestão tentam antecipar os efeitos da volatilidade excessiva e correlação entre as classes de activos e os seus efeitos subsequentes sobre o capital de negociação Muitas vezes isto reduz a um conjunto de cálculos estatísticos, tais como testes de stress Monte Carlo Isto é muito semelhante às necessidades computacionais de um preço de derivados E, como tal, será CPU-bound Estas simulações são altamente paralelizáveis ​​ver abaixo e, em certo grau, é possível jogar hardware no problema. Execution Systems. O trabalho do sistema de execução é receber sinais de negociação filtrada a partir do Construção de carteiras e componentes de gestão de risco e enviá-los para uma corretora ou outros meios de acesso ao mercado. Estratégias de negociação algorítmicas de varejo envolve uma conexão API ou FIX para uma corretora como Interactive Brokers As considerações principais ao decidir sobre uma linguagem incluem a qualidade da API, a disponibilidade do wrapper de linguagem para uma API, a freqüência de execução e a antecipação do deslizamento. A API refere-se a quão bem documentada é, que tipo de desempenho fornece, se precisa de software autônomo para ser acessado ou se um gateway pode ser estabelecido de uma forma sem cabeça, ou seja, sem GUI No caso de Interactive Brokers, a ferramenta Trader WorkStation Precisa ser executado em um ambiente GUI para acessar sua API Eu tive que instalar uma edição Desktop Ubuntu em um servidor de nuvem Amazon para acessar remotamente Interactive Brokers, puramente por esta razão. A maioria das APIs fornecerá uma interface C e ou Java É normalmente a comunidade para desenvolver invólucros específicos de linguagem para C, Python, R, Excel e MatLab Note que com cada plugin adicional utilizado espe Cially API wrappers há espaço para bugs para rastejar para o sistema Sempre testar plugins deste tipo e garantir que eles são ativamente mantidos Um indicador vale a pena é ver quantas novas atualizações para um codebase foram feitas nos últimos meses. Execução freqüência é do A maior importância no algoritmo de execução Note que centenas de ordens podem ser enviadas a cada minuto e como tal desempenho é crítico Slippage será incorrido através de um sistema de execução mal executando e isso terá um impacto dramático sobre a rentabilidade. Como C Java são geralmente ideais para a execução, mas há um trade-off no tempo de desenvolvimento, testes e facilidade de manutenção linguagens dinamicamente tipados, como Python e Perl são agora geralmente rápido o suficiente Certifique-se sempre os componentes são projetados de forma modular Ver abaixo para que eles podem ser trocados para fora como o sistema escalas. Architectural Planejamento e Desenvolvimento Process. The componentes de um sistema de comércio, a sua freq As necessidades de volume e de volume têm sido discutidas acima, mas a infra-estrutura do sistema ainda não foi coberta. Aqueles que atuam como um comerciante varejista ou que trabalham em um pequeno fundo provavelmente usarão muitos chapéus. Será necessário cobrir o modelo alfa, gerenciamento de riscos e execução Parâmetros e também a implementação final do sistema. Antes de aprofundar em linguagens específicas, o projeto de uma arquitetura de sistema ótima será discutido. Separação de Preocupações. Uma das decisões mais importantes que devem ser feitas no início é como separar as preocupações de Um sistema de negociação No desenvolvimento de software, isso significa essencialmente como dividir os diferentes aspectos do sistema de comércio em componentes modulares separados. Ao expor interfaces em cada um dos componentes é fácil trocar partes do sistema por outras versões que ajudam o desempenho , Confiabilidade ou manutenção, sem modificar nenhum código de dependência externa. Esta é a melhor prática para tais sistemas. Freqüências tais práticas são aconselhadas Para a negociação de freqüência ultra alta o livro de regras pode ter que ser ignorado à custa de ajustar o sistema para ainda mais desempenho Um sistema mais fortemente acoplado pode ser desejável. Criar um mapa de componente de um sistema de negociação algorítmica vale um artigo Em si Porém, uma abordagem ótima é certificar-se de que há componentes separados para as entradas de dados históricos e em tempo real do mercado, armazenamento de dados, API de acesso a dados, backtester, parâmetros de estratégia, construção de portfólio, gerenciamento de risco e sistemas automatizados de execução. , Se o armazenamento de dados que está sendo usado está subjacente, mesmo em níveis significativos de otimização, pode ser trocado com rewrites mínimos para a ingestão de dados ou API de acesso de dados. Tanto quanto o backtestter e componentes subseqüentes estão em causa, não há diferença. Outro benefício de componentes separados é que ele permite que uma variedade de linguagens de programação para ser usado no sistema global Lá Não é necessário ser restrito a um único idioma se o método de comunicação dos componentes é independente da linguagem Isso será o caso se eles estão se comunicando via TCP IP, ZeroMQ ou algum outro protocolo independente de linguagem. Como um exemplo concreto, considere o caso De um sistema de backtesting sendo escrito em C para o desempenho de crunching número, enquanto o gestor de portfólio e sistemas de execução são escritos em Python usando SciPy e IBPy. Performance Considerações. Performance é uma consideração significativa para a maioria das estratégias de negociação Para estratégias de maior freqüência é o mais importante Factor Desempenho abrange uma vasta gama de questões, tais como a velocidade de execução algorítmica, latência de rede, largura de banda, IO de dados, paralelismo de concorrência e dimensionamento Cada uma dessas áreas são individualmente abrangidos por grandes manuais, por isso este artigo apenas arranhar a superfície de cada tópico Arquitetura E a escolha da língua serão agora discutidas em termos de seus efeitos sobre o desempenho. A sabedoria prevalecente Como afirmado por Donald Knuth um dos pais da Ciência da Computação, é que a otimização prematura é a raiz de todos os mal Esta é quase sempre o caso - exceto quando a construção de um algoritmo de negociação de alta freqüência Para aqueles que estão interessados ​​em estratégias de baixa freqüência, um comum Abordagem é construir um sistema da maneira mais simples possível e só otimizar como gargalos começam a aparecer. Ferramentas de perfil são usadas para determinar onde gargalos surgem Perfis podem ser feitas para todos os fatores listados acima, seja em um ambiente MS Windows ou Linux Lá Existem muitas ferramentas de sistema operacional e linguagem disponíveis para fazê-lo, bem como utilitários de terceiros. A escolha de idioma será agora discutida no contexto de performance. C, Java, Python, R e MatLab contêm bibliotecas de alto desempenho ou como parte de suas Padrão ou externamente para estrutura de dados básica e trabalho algorítmico C vem com a Biblioteca de Modelo Padrão, enquanto Python contém NumPy SciPy Tarefas matemáticas comuns devem ser Encontrado nestas bibliotecas e é raramente benéfico escrever uma implementação nova. Uma exceção é se a arquitetura de hardware altamente customized é requerida e um algoritmo está fazendo o uso extensivo de extensões proprietárias tais como caches feitos sob encomenda contudo, frequentemente a reinvenção da roda desperdiça o tempo que poderia Ser melhor gasto desenvolvimento e otimização de outras partes da infra-estrutura de negociação Tempo de desenvolvimento é extremamente precioso, especialmente no contexto de desenvolvedores exclusivos. A freqüência é muitas vezes uma questão do sistema de execução como as ferramentas de pesquisa são geralmente situados na mesma máquina Para o primeiro, a latência Pode ocorrer em vários pontos ao longo do caminho de execução bancos de dados devem ser consultados latência da rede de disco, os sinais devem ser gerados sistema operacional, latência de mensagens kernal, sinais comerciais enviados latência NIC e ordens processadas sistemas de troca de latência interna. Para operações de freqüência mais alta é necessário tornar - Intimamente familiarizado com otimização kernal, bem como a otimização Da transmissão de rede Esta é uma área profunda e está significativamente além do escopo do artigo, mas se um algoritmo UHFT é desejado, em seguida, estar ciente da profundidade de conhecimento required. Caching é muito útil no conjunto de ferramentas de um desenvolvedor de negócios quantitativos Caching refere-se ao Um conceito de armazenamento de dados acessados ​​com freqüência de uma maneira que permite um acesso de alto desempenho, à custa de potencial staleness dos dados Um caso de uso comum ocorre no desenvolvimento da web ao tomar dados de um banco de dados relacional e colocá-lo em memória Solicitações para os dados não têm que bater o banco de dados e, portanto, os ganhos de desempenho pode ser significativo. Para situações de negociação cache pode ser extremamente benéfico Por exemplo, o estado atual de um portfólio de estratégia pode ser armazenado em um cache até que seja reequilibrado, A lista não precisa ser regenerada em cada loop do algoritmo de negociação. Tal regeneração é provável que seja uma CPU alta ou operação de disco IO. No entanto, cachi Ng não está sem seus próprios problemas Regeneração de dados de cache todos de uma vez, devido à natureza volatilie de armazenamento em cache, pode colocar demanda significativa sobre a infra-estrutura Outra questão é dog-piling onde várias gerações de uma nova cópia de cache são realizadas sob extremamente alta Carga, o que leva a falha em cascata. Dynamic alocação de memória é uma operação cara na execução de software Assim, é imperativo para aplicações de negociação de maior desempenho estar bem consciente como a memória está sendo alocados e desalocados durante o fluxo de programa Padrões de linguagem mais recentes, E Python todos executam a coleta de lixo automática que se refere à desalocação da memória alocada dinamicamente quando os objetos saem do escopo. A coleta de lixo é extremamente útil durante o desenvolvimento, pois reduz os erros e facilita a legibilidade. No entanto, é sub-ótimo para certas estratégias de negociação de alta freqüência A coleta de lixo personalizada é freqüentemente desejada para esses casos. Em Java, por exemplo, ajustando a aparência Coletor de idade e configuração de heap, é possível obter alto desempenho para HFT strategies. C doesn t fornecer um coletor de lixo nativo e por isso é necessário para lidar com todos alocação de memória alocação como parte da implementação de um objeto Embora potencialmente propenso a erros potencialmente levando a Dangling ponteiros é extremamente útil ter controle de grãos finos de como objetos aparecem no heap para certas aplicações Ao escolher um idioma certifique-se de estudar como o coletor de lixo funciona e se ele pode ser modificado para otimizar para um caso de uso particular. Muitos Operações em sistemas de negociação algorítmicos são passíveis de paralelização Refere-se ao conceito de realizar operações programáticas múltiplas ao mesmo tempo, ou seja, em paralelo Os chamados algoritmos embarassingly paralelos incluem etapas que podem ser calculadas de forma totalmente independente de outras etapas Determinadas operações estatísticas, como Como simulações de Monte Carlo, são um bom exemplo de algoritmos embarassingly paralelos Como cada desenho aleatório e operação de caminho subseqüente pode ser computado sem conhecimento de outros caminhos. Outros algoritmos são apenas parcialmente paralelizáveis ​​simulações de dinâmica de fluidos são um exemplo, onde o domínio de computação pode ser subdividido, mas em última instância estes domínios devem comunicar uns com os outros e Assim as operações são parcialmente sequenciais Algoritmos Parallelisable estão sujeitos a Lei de Amdahl que fornece um limite superior teórico para o aumento de desempenho de um algoritmo paralelizado quando sujeito a N processos separados, por exemplo, em um núcleo de CPU ou thread. Parallelisation tem se tornado cada vez mais importante como um meio De processadores mais recentes contêm muitos núcleos com os quais realizar cálculos paralelos O aumento de hardware de gráficos de consumo predominantemente para jogos de vídeo levou ao desenvolvimento de GPUs Gráficos de Unidades de Processamento, que contêm centenas de núcleos para altamente Operações concorrentes Tais GPUs são agora v Tais como Nvidia s CUDA têm levar a adopção generalizada na academia e finanças. Tal hardware GPU é geralmente adequado apenas para o aspecto de investigação de finanças quantitativas, Considerando que outro hardware mais especializado, incluindo Field-Programmable Gate Arrays - FPGAs São usados ​​para U HFT Hoje em dia, a maioria de langauges modernos suportam um grau de multithreading da simultaneidade Assim é direto para otimizar um backtester, desde que todos os cálculos são geralmente independentes dos outros. Escala na engenharia e em operações de software consulta à abilidade do sistema segurar consistently increasing loads in the form of greater requests, higher processor usage and more memory allocation In algorithmic trading a strategy is able to scale if it can accept larger quantities of capital and still produce consistent returns The trading technology stack scales if it can endure larger trade volumes and increased latency, without bottlenecking. While systems must be desig ned to scale, it is often hard to predict beforehand where a bottleneck will occur Rigourous logging, testing, profiling and monitoring will aid greatly in allowing a system to scale Languages themselves are often described as unscalable This is usually the result of misinformation, rather than hard fact It is the total technology stack that should be ascertained for scalability, not the language Clearly certain languages have greater performance than others in particular use cases, but one language is never better than another in every sense. One means of managing scale is to separate concerns, as stated above In order to further introduce the ability to handle spikes in the system i e sudden volatility which triggers a raft of trades , it is useful to create a message queuing architecture This simply means placing a message queue system between components so that orders are stacked up if a certain component is unable to process many requests. Rather than requests being lost they are si mply kept in a stack until the message is handled This is particularly useful for sending trades to an execution engine If the engine is suffering under heavy latency then it will back up trades A queue between the trade signal generator and the execution API will alleviate this issue at the expense of potential trade slippage A well-respected open source message queue broker is RabbitMQ. Hardware and Operating Systems. The hardware running your strategy can have a significant impact on the profitability of your algorithm This is not an issue restricted to high frequency traders either A poor choice in hardware and operating system can lead to a machine crash or reboot at the most inopportune moment Thus it is necessary to consider where your application will reside The choice is generally between a personal desktop machine, a remote server, a cloud provider or an exchange co-located server. Desktop machines are simple to install and administer, especially with newer user friendly operati ng systems such as Windows 7 8, Mac OSX and Ubuntu Desktop systems do possess some significant drawbacks, however The foremost is that the versions of operating systems designed for desktop machines are likely to require reboots patching and often at the worst of times They also use up more computational resources by the virtue of requiring a graphical user interface GUI. Utilising hardware in a home or local office environment can lead to internet connectivity and power uptime problems The main benefit of a desktop system is that significant computational horsepower can be purchased for the fraction of the cost of a remote dedicated server or cloud based system of comparable speed. A dedicated server or cloud-based machine, while often more expensive than a desktop option, allows for more significant redundancy infrastructure, such as automated data backups, the ability to more straightforwardly ensure uptime and remote monitoring They are harder to administer since they require the abi lity to use remote login capabilities of the operating system. In Windows this is generally via the GUI Remote Desktop Protocol RDP In Unix-based systems the command-line Secure SHell SSH is used Unix-based server infrastructure is almost always command-line based which immediately renders GUI-based programming tools such as MatLab or Excel to be unusable. A co-located server, as the phrase is used in the capital markets, is simply a dedicated server that resides within an exchange in order to reduce latency of the trading algorithm This is absolutely necessary for certain high frequency trading strategies, which rely on low latency in order to generate alpha. The final aspect to hardware choice and the choice of programming language is platform-independence Is there a need for the code to run across multiple different operating systems Is the code designed to be run on a particular type of processor architecture, such as the Intel x86 x64 or will it be possible to execute on RISC process ors such as those manufactured by ARM These issues will be highly dependent upon the frequency and type of strategy being implemented. Resilience and Testing. One of the best ways to lose a lot of money on algorithmic trading is to create a system with no resiliency This refers to the durability of the sytem when subject to rare events, such as brokerage bankruptcies, sudden excess volatility, region-wide downtime for a cloud server provider or the accidental deletion of an entire trading database Years of profits can be eliminated within seconds with a poorly-designed architecture It is absolutely essential to consider issues such as debuggng, testing, logging, backups, high-availability and monitoring as core components of your system. It is likely that in any reasonably complicated custom quantitative trading application at least 50 of development time will be spent on debugging, testing and maintenance. Nearly all programming languages either ship with an associated debugger or possess well-respected third-party alternatives In essence, a debugger allows execution of a program with insertion of arbitrary break points in the code path, which temporarily halt execution in order to investigate the state of the system The main benefit of debugging is that it is possible to investigate the behaviour of code prior to a known crash point. Debugging is an essential component in the toolbox for analysing programming errors However, they are more widely used in compiled languages such as C or Java, as interpreted languages such as Python are often easier to debug due to fewer LOC and less verbose statements Despite this tendency Python does ship with the pdb which is a sophisticated debugging tool The Microsoft Visual C IDE possesses extensive GUI debugging utilities, while for the command line Linux C programmer, the gdb debugger exists. Testing in software development refers to the process of applying known parameters and results to specific functions, methods and objects wit hin a codebase, in order to simulate behaviour and evaluate multiple code-paths, helping to ensure that a system behaves as it should A more recent paradigm is known as Test Driven Development TDD , where test code is developed against a specified interface with no implementation Prior to the completion of the actual codebase all tests will fail As code is written to fill in the blanks , the tests will eventually all pass, at which point development should cease. TDD requires extensive upfront specification design as well as a healthy degree of discipline in order to carry out successfully In C , Boost provides a unit testing framework In Java, the JUnit library exists to fulfill the same purpose Python also has the unittest module as part of the standard library Many other languages possess unit testing frameworks and often there are multiple options. In a production environment, sophisticated logging is absolutely essential Logging refers to the process of outputting messages, with var ious degrees of severity, regarding execution behaviour of a system to a flat file or database Logs are a first line of attack when hunting for unexpected program runtime behaviour Unfortunately the shortcomings of a logging system tend only to be discovered after the fact As with backups discussed below, a logging system should be given due consideration BEFORE a system is designed. Both Microsoft Windows and Linux come with extensive system logging capability and programming languages tend to ship with standard logging libraries that cover most use cases It is often wise to centralise logging information in order to analyse it at a later date, since it can often lead to ideas about improving performance or error reduction, which will almost certainly have a positive impact on your trading returns. While logging of a system will provide information about what has transpired in the past, monitoring of an application will provide insight into what is happening right now All aspects of the system should be considered for monitoring System level metrics such as disk usage, available memory, network bandwidth and CPU usage provide basic load information. Trading metrics such as abnormal prices volume, sudden rapid drawdowns and account exposure for different sectors markets should also be continuously monitored Further, a threshold system should be instigated that provides notification when certain metrics are breached, elevating the notification method email, SMS, automated phone call depending upon the severity of the metric. System monitoring is often the domain of the system administrator or operations manager However, as a sole trading developer, these metrics must be established as part of the larger design Many solutions for monitoring exist proprietary, hosted and open source, which allow extensive customisation of metrics for a particular use case. Backups and high availability should be prime concerns of a trading system Consider the following two questions 1 If an entire production database of market data and trading history was deleted without backups how would the research and execution algorithm be affected 2 If the trading system suffers an outage for an extended period with open positions how would account equity and ongoing profitability be affected The answers to both of these questions are often sobering. It is imperative to put in place a system for backing up data and also for testing the restoration of such data Many individuals do not test a restore strategy If recovery from a crash has not been tested in a safe environment, what guarantees exist that restoration will be available at the worst possible moment. Similarly, high availability needs to be baked in from the start Redundant infrastructure even at additional expense must always be considered, as the cost of downtime is likely to far outweigh the ongoing maintenance cost of such systems I won t delve too deeply into this topic as it is a large area, but make sure it is one of the first considerations given to your trading system. Choosing a Language. Considerable detail has now been provided on the various factors that arise when developing a custom high-performance algorithmic trading system The next stage is to discuss how programming languages are generally categorised. Type Systems. When choosing a language for a trading stack it is necessary to consider the type system The languages which are of interest for algorithmic trading are either statically - or dynamically-typed A statically-typed language performs checks of the types e g integers, floats, custom classes etc during the compilation process Such languages include C and Java A dynamically-typed language performs the majority of its type-checking at runtime Such languages include Python, Perl and JavaScript. For a highly numerical system such as an algorithmic trading engine, type-checking at compile time can be extremely beneficial, as it can eliminate many bugs that would otherwise lead to numerical errors However, type-checking doesn t catch everything, and this is where exception handling comes in due to the necessity of having to handle unexpected operations Dynamic languages i e those that are dynamically-typed can often lead to run-time errors that would otherwise be caught with a compilation-time type-check For this reason, the concept of TDD see above and unit testing arose which, when carried out correctly, often provides more safety than compile-time checking alone. Another benefit of statically-typed languages is that the compiler is able to make many optimisations that are otherwise unavailable to the dynamically - typed language, simply because the type and thus memory requirements are known at compile-time In fact, part of the inefficiency of many dynamically-typed languages stems from the fact that certain objects must be type-inspected at run-time and this carries a performance hit Libraries for dynamic languages, such as NumPy SciPy alleviate this issue due to enforc ing a type within arrays. Open Source or Proprietary. One of the biggest choices available to an algorithmic trading developer is whether to use proprietary commercial or open source technologies There are advantages and disadvantages to both approaches It is necessary to consider how well a language is supported, the activity of the community surrounding a language, ease of installation and maintenance, quality of the documentation and any licensing maintenance costs. The Microsoft stack including Visual C , Visual C and MathWorks MatLab are two of the larger proprietary choices for developing custom algorithmic trading software Both tools have had significant battle testing in the financial space, with the former making up the predominant software stack for investment banking trading infrastructure and the latter being heavily used for quantitative trading research within investment funds. Microsoft and MathWorks both provide extensive high quality documentation for their products Furthe r, the communities surrounding each tool are very large with active web forums for both The software allows cohesive integration with multiple languages such as C , C and VB, as well as easy linkage to other Microsoft products such as the SQL Server database via LINQ MatLab also has many plugins libraries some free, some commercial for nearly any quantitative research domain. There are also drawbacks With either piece of software the costs are not insignificant for a lone trader although Microsoft does provide entry-level version of Visual Studio for free Microsoft tools play well with each other, but integrate less well with external code Visual Studio must also be executed on Microsoft Windows, which is arguably far less performant than an equivalent Linux server which is optimally tuned. MatLab also lacks a few key plugins such as a good wrapper around the Interactive Brokers API, one of the few brokers amenable to high-performance algorithmic trading The main issue with proprietary p roducts is the lack of availability of the source code This means that if ultra performance is truly required, both of these tools will be far less attractive. Open source tools have been industry grade for sometime Much of the alternative asset space makes extensive use of open-source Linux, MySQL PostgreSQL, Python, R, C and Java in high-performance production roles However, they are far from restricted to this domain Python and R, in particular, contain a wealth of extensive numerical libraries for performing nearly any type of data analysis imaginable, often at execution speeds comparable to compiled languages, with certain caveats. The main benefit of using interpreted languages is the speed of development time Python and R require far fewer lines of code LOC to achieve similar functionality, principally due to the extensive libraries Further, they often allow interactive console based development, rapidly reducing the iterative development process. Given that time as a developer is extremely valuable, and execution speed often less so unless in the HFT space , it is worth giving extensive consideration to an open source technology stack Python and R possess significant development communities and are extremely well supported, due to their popularity Documentation is excellent and bugs at least for core libraries remain scarce. Open source tools often suffer from a lack of a dedicated commercial support contract and run optimally on systems with less-forgiving user interfaces A typical Linux server such as Ubuntu will often be fully command-line oriented In addition, Python and R can be slow for certain execution tasks There are mechanisms for integrating with C in order to improve execution speeds, but it requires some experience in multi-language programming. While proprietary software is not immune from dependency versioning issues it is far less common to have to deal with incorrect library versions in such environments Open source operating systems such as Linu x can be trickier to administer. I will venture my personal opinion here and state that I build all of my trading tools with open source technologies In particular I use Ubuntu, MySQL, Python, C and R The maturity, community size, ability to dig deep if problems occur and lower total cost ownership TCO far outweigh the simplicity of proprietary GUIs and easier installations Having said that, Microsoft Visual Studio especially for C is a fantastic Integrated Development Environment IDE which I would also highly recommend. Batteries Included. The header of this section refers to the out of the box capabilities of the language - what libraries does it contain and how good are they This is where mature languages have an advantage over newer variants C , Java and Python all now possess extensive libraries for network programming, operating system interaction, GUIs, regular expressions regex , iteration and basic algorithms. C is famed for its Standard Template Library STL which contains a wealt h of high performance data structures and algorithms for free Python is known for being able to communicate with nearly any other type of system protocol especially the web , mostly through its own standard library R has a wealth of statistical and econometric tools built in, while MatLab is extremely optimised for any numerical linear algebra code which can be found in portfolio optimisation and derivatives pricing, for instance. Outside of the standard libraries, C makes use of the Boost library, which fills in the missing parts of the standard library In fact, many parts of Boost made it into the TR1 standard and subsequently are available in the C 11 spec, including native support for lambda expressions and concurrency. Python has the high performance NumPy SciPy Pandas data analysis library combination, which has gained widespread acceptance for algorithmic trading research Further, high-performance plugins exist for access to the main relational databases, such as MySQL MySQL C , J DBC Java MatLab , MySQLdb MySQL Python and psychopg2 PostgreSQL Python Python can even communicate with R via the RPy plugin. An often overlooked aspect of a trading system while in the initial research and design stage is the connectivity to a broker API Most APIs natively support C and Java, but some also support C and Python, either directly or with community-provided wrapper code to the C APIs In particular, Interactive Brokers can be connected to via the IBPy plugin If high-performance is required, brokerages will support the FIX protocol. As is now evident, the choice of programming language s for an algorithmic trading system is not straightforward and requires deep thought The main considerations are performance, ease of development, resiliency and testing, separation of concerns, familiarity, maintenance, source code availability, licensing costs and maturity of libraries. The benefit of a separated architecture is that it allows languages to be plugged in for different aspects o f a trading stack, as and when requirements change A trading system is an evolving tool and it is likely that any language choices will evolve along with it. Just Getting Started with Quantitative Trading. Trading Floor Architecture. Trading Floor Architecture. Executive Overview. Increased competition, higher market data volume, and new regulatory demands are some of the driving forces behind industry changes Firms are trying to maintain their competitive edge by constantly changing their trading strategies and increasing the speed of trading. A viable architecture has to include the latest technologies from both network and application domains It has to be modular to provide a manageable path to evolve each component with minimal disruption to the overall system Therefore the architecture proposed by this paper is based on a services framework We examine services such as ultra-low latency messaging, latency monitoring, multicast, computing, storage, data and application virtualization, tra ding resiliency, trading mobility, and thin client. The solution to the complex requirements of the next-generation trading platform must be built with a holistic mindset, crossing the boundaries of traditional silos like business and technology or applications and networking. This document s main goal is to provide guidelines for building an ultra-low latency trading platform while optimizing the raw throughput and message rate for both market data and FIX trading orders. To achieve this, we are proposing the following latency reduction technologies. High speed inter-connect InfiniBand or 10 Gbps connectivity for the trading cluster. High-speed messaging bus. Application acceleration via RDMA without application re-code. Real-time latency monitoring and re-direction of trading traffic to the path with minimum latency. Industry Trends and Challenges. Next-generation trading architectures have to respond to increased demands for speed, volume, and efficiency For example, the volume of options market data is expected to double after the introduction of options penny trading in 2007 There are also regulatory demands for best execution, which require handling price updates at rates that approach 1M msg sec for exchanges They also require visibility into the freshness of the data and proof that the client got the best possible execution. In the short term, speed of trading and innovation are key differentiators An increasing number of trades are handled by algorithmic trading applications placed as close as possible to the trade execution venue A challenge with these black-box trading engines is that they compound the volume increase by issuing orders only to cancel them and re-submit them The cause of this beh avior is lack of visibility into which venue offers best execution The human trader is now a financial engineer, a quant quantitative analyst with programming skills, who can adjust trading models on the fly Firms develop new financial instruments like weather derivatives or cross-asset class trades and they need to deploy the new applications quickly and in a scalable fashion. In the long term, competitive differentiation should come from analysis, not just knowledge The star traders of tomorrow assume risk, achieve true client insight, and consistently beat the market source IBM. Business resilience has been one main concern of trading firms since September 11, 2001 Solutions in this area range from redundant data centers situated in different geographies and connected to multiple trading venues to virtual trader solutions offering power traders most of the functionality of a trading floor in a remote location. The financial services industry is one of the most demanding in terms of IT requirements The industry is experiencing an architectural shift towards Services-Oriented Architecture SOA , Web services, and virtualization of IT resources SOA takes advantage of the increase in network speed to enable dynamic binding and virtualization of software components This allows the creation of new applications without losing the investment in existing systems and infrastructure The concept has the potential to revolutionize the way integration is done, enabling significant reductions in the complexity and cost of such integration. Another trend is the consolidation of servers into data center server farms, while trader desks have only KVM extensions and ultra-thin clients e g SunRay and HP blade solutions High-speed Metro Area Networks enable market data to be multicast between different locations, enabling the virtualization of the trading floor. High-Level Architecture. Figure 1 depicts the high-level architecture of a trading environment The ticker plant and the algorithmi c trading engines are located in the high performance trading cluster in the firm s data center or at the exchange The human traders are located in the end-user applications area. Functionally there are two application components in the enterprise trading environment, publishers and subscribers The messaging bus provides the communication path between publishers and subscribers. There are two types of traffic specific to a trading environment. Market Data Carries pricing information for financial instruments, news, and other value-added information such as analytics It is unidirectional and very latency sensitive, typically delivered over UDP multicast It is measured in updates sec and in Mbps Market data flows from one or multiple external feeds, coming from market data providers like stock exchanges, data aggregators, and ECNs Each provider has their own market data format The data is received by feed handlers, specialized applications which normalize and clean the data and then send it to data consumers, such as pricing engines, algorithmic trading applications, or human traders Sell-side firms also send the market data to their clients, buy-side firms such as mutual funds, hedge funds, and other asset managers Some buy-side firms may opt to receive direct feeds from exchanges, reducing latency. Figure 1 Trading Architecture for a Buy Side Sell Side Firm. There is no industry standard for market data formats Each exchange h as their proprietary format Financial content providers such as Reuters and Bloomberg aggregate different sources of market data, normalize it, and add news or analytics Examples of consolidated feeds are RDF Reuters Data Feed , RWF Reuters Wire Format , and Bloomberg Professional Services Data. To deliver lower latency market data, both vendors have released real-time market data feeds which are less processed and have less analytics. Bloomberg B-Pipe With B-Pipe, Bloomberg de-couples their market data feed from their distribution platform because a Bloomberg terminal is not required for get B-Pipe Wombat and Reuters Feed Handlers have announced support for B-Pipe. A firm may decide to receive feeds directly from an exchange to reduce latency The gains in transmission speed can be between 150 milliseconds to 500 milliseconds These feeds are more complex and more expensive and the firm has to build and maintain their own ticker plant. Trading Orders This type of traffic carries the actual trades It is bi-directional and very latency sensitive It is measured in messages sec and Mbps The orders originate from a buy side or sell side firm and are sent to trading venues like an Exchange or ECN for execution The most common format for order transport is FIX Financial Information The applications which handle FIX messages are called FIX engines and they interface with order management systems OMS. An optimization to FIX is called FAST Fix Adapted for Streaming , which uses a compression schema to reduce message length and, in effect, reduce latency FAST is targeted more to the delivery of market data and has the potential to become a standard FAST can also be used as a compression schema for proprietary market data formats. To reduce latency, firms may opt to establish Direct Market Access DMA. DMA is the automated process of routing a securities order directly to an execution venue, therefore avoiding the intervention by a third-party glossaryId 383 DMA requires a direct connection to the execution venue. The messaging bus is middleware software from vendors such as Tibco, 29West, Reuters RMDS, or an open source platform such as AMQP The messaging bus uses a reliable mechanism to deliver messages The transport can be done over TCP IP TibcoEMS, 29West, RMDS, and AMQP or UDP multicast TibcoRV, 29West, and RMDS One important concept in message distribution is the topic stream, which is a subset of market data defined by criteria such as ticker symbol, industry, or a certain basket of financial instruments Subscribers join topic groups mapped to one or multiple sub-topics in order to receive only the relevant information In the past, all traders received all market data At the current volumes of traffic, this would be sub-optimal. The network plays a critical role in the trading environment Market data is carried to the trading floor where the human traders are located via a Campus or Metro Area high-speed net work High availability and low latency, as well as high throughput, are the most important metrics. The high performance trading environment has most of its components in the Data Center server farm To minimize latency, the algorithmic trading engines need to be located in the proximity of the feed handlers, FIX engines, and order management systems An alternate deployment model has the algorithmic trading systems located at an exchange or a service provider with fast connectivity to multiple exchanges. Deployment Models. There are two deployment models for a high performance trading platform Firms may chose to have a mix of the two. Data Center of the trading firm Figure 2 This is the traditional model, where a full-fledged trading platform is developed and maintained by the firm with communication links to all the trading venues Latency varies with the speed of the links and the number of hops between the firm and the venues. Figure 2 Traditional Deployment Model. Co-location at the trading venue exchanges, financial service providers FSP Figure 3.The trading firm deploys its automated trading platform as close as possible to the execution venues to minimize latency. Figure 3 Hosted Deployment Model. Services-Oriented Trading Architecture. We are proposing a services-oriented framework for building the next-generation trading architecture This approach provides a conceptual framework and an implementation path based on modularization and minimization of inter-dependencies. This framework provides firms with a methodology to. Evaluate their current state in terms of services. Prioritize services based on their value to the business. Evolve the trading platform to the desired state using a modular approach. The high performance trading architecture relies on the following services, as defined by the services architecture framework represented in Figure 4.Figure 4 Service Architecture Framework for High Performance Trading. Ultra-Low Latency Messaging Service. This service is provided by the messaging bus, which is a software system that solves the problem of connecting many-to-many applications The system consists of. A set of pre-defined message schemas. A set of common command messages. A shared application infrastructure for sending the messages to recipients The shared infrastructure can be based on a message broker or on a publish subscribe model. The key requirements for the next-generation messaging bus are source 29West. Lowest possible latency e g less than 100 microseconds. Stability under heavy load e g more than 1 4 million msg sec. Control and flexibility rate control and configurable transports. There are efforts in the industry to standardize the messaging bus Advanced Message Queueing Protocol AMQP is an example of an open standard championed by J P Morgan Chase and supported by a group of vendors such as Cisco, Envoy Technologies, Red Hat, TWIST Process Innovations, Iona, 29West, and iMatix Two of the main goals are to provide a more simple path to inter-operability for applications written on different platforms and modularity so that the middleware can be easily evolved. In very general terms, an AMQP server is analogous to an E-mail server with each exchange acting as a message transfer agent and each message queue as a mailbox The bindings define the routing tables in each transfer agent Publishers send messages to individual transfer agents, which then route the messages into mailboxes Consumers take messages from mailboxes, which creates a powerful and flexible model that is simple source. Latency Monitori ng Service. The main requirements for this service are. Sub-millisecond granularity of measurements. Near-real time visibility without adding latency to the trading traffic. Ability to differentiate application processing latency from network transit latency. Ability to handle high message rates. Provide a programmatic interface for trading applications to receive latency data, thus enabling algorithmic trading engines to adapt to changing conditions. Correlate network events with application events for troubleshooting purposes. Latency can be defined as the time interval between when a trade order is sent and when the same order is acknowledged and acted upon by the receiving party. Addressing the latency issue is a complex problem, requiring a holistic approach that identifies all sources of latency and applies different technologies at different layers of the system. Figure 5 depicts the variety of components that can introduce latency at each layer of the OSI stack It also maps each source of latency with a possible solution and a monitoring solution This layered approach can give firms a more structured way of attacking the latency issue, whereby each component can be thought of as a service and treated consistently across the firm. Maintaining an accurate measure of the dynamic state of this time interval across alternative routes and destinations can be of great assistance in tactical trading decisions The ability to identify the exact location of delays, whether in the customer s edge network, the central processing hub, or the transaction application level, significantly determines the ability of service providers to meet their trading service-level agreements SLAs For buy-side and sell-side forms, as well as for market-data syndicators, the quick identification and removal of bottlenecks translates directly into enhanced trade opportunities and revenue. Figure 5 Latency Management Architecture. Cisco Low-Latency Monitoring Tools. Traditional network monitoring tools operate with minutes or seconds granularity Next-generation trading platforms, especially those supporting algorithmic trading, require latencies less than 5 ms and extremely low levels of packet loss On a Gigabit LAN, a 100 ms microburst can cause 10,000 transactions to be lost or excessively delayed. Cisco offers its customers a choice of tools to measure latency in a trading environment. Bandwidth Quality Manager BQM OEM from Corvil. Cisco AON-based Financial Services Latency Monitoring Solution FSMS. Bandwidth Quality Manager. Bandwidth Quality Manager BQM 4 0 is a next-generation network application performance management product that enables customers to monitor and provision their network for controlled levels of latency and loss performance While BQM is not exclusively targeted at trading networks, its microsecond visibility combined with intelligent bandwidth provisioning features make it ideal for these demanding environments. Cisco BQM 4 0 implements a broad set of patented and patent-pending traffic measurement and network analysis technologies that give the user unprecedented visibility and understanding of how to optimize the network for maximum application performance. Cisco BQM is now supported on the product family of Cisco Application Deployment Engine ADE The Cisco ADE product family is the platform of choice for Cisco network management applications. BQM Benefits. Cisco BQM micro-visibility is the abilit y to detect, measure, and analyze latency, jitter, and loss inducing traffic events down to microsecond levels of granularity with per packet resolution This enables Cisco BQM to detect and determine the impact of traffic events on network latency, jitter, and loss Critical for trading environments is that BQM can support latency, loss, and jitter measurements one-way for both TCP and UDP multicast traffic This means it reports seamlessly for both trading traffic and market data feeds. BQM allows the user to specify a comprehensive set of thresholds against microburst activity, latency, loss, jitter, utilization, etc on all interfaces BQM then operates a background rolling packet capture Whenever a threshold violation or other potential performance degradation event occurs, it triggers Cisco BQM to store the packet capture to disk for later analysis This allows the user to examine in full detail both the application traffic that was affected by performance degradation the victims and th e traffic that caused the performance degradation the culprits This can significantly reduce the time spent diagnosing and resolving network performance issues. BQM is also able to provide detailed bandwidth and quality of service QoS policy provisioning recommendations, which the user can directly apply to achieve desired network performance. BQM Measurements Illustrated. To understand the difference between some of the more conventional measurement techniques and the visibility provided by BQM, we can look at some comparison graphs In the first set of graphs Figure 6 and Figure 7 , we see the difference between the latency measured by BQM s Passive Network Quality Monitor PNQM and the latency measured by injecting ping packets every 1 second into the traffic stream. In Figure 6 we see the latency reported by 1-second ICMP ping packets for real network traffic it is divided by 2 to give an estimate for the one-way delay It shows the delay comfortably below about 5ms for almost all of the time. Figure 6 Latency Reported by 1-Second ICMP Ping Packets for Real Network Traffic. In Figure 7 we see the latency reported by PNQM for the same traffic at the same time Here we see that by measuring the one-way latency of the actual application packets, we get a radically different picture Here the latency is seen to be hovering around 20 ms, with occasional bursts far higher The explanation is that because ping is sending packets only every second, it is completely missing most of the application traffic latency In fact, ping results typically only indicate round trip propagation delay rather than realistic application latency across the network. Figure 7 Latency Reported by PNQM for Real Network Traffic. In the second example Figure 8 , we see the difference in reported link load or saturation levels between a 5-minute average view and a 5 ms microburst view BQM can report on microbursts down to about 10-100 nanosecond accuracy The green line shows the average utilization at 5-minut e averages to be low, maybe up to 5 Mbits s The dark blue plot shows the 5ms microburst activity reaching between 75 Mbits s and 100 Mbits s, the LAN speed effectively BQM shows this level of granularity for all applications and it also gives clear provisioning rules to enable the user to control or neutralize these microbursts. Figure 8 Difference in Reported Link Load Between a 5-Minute Average View and a 5 ms Microburst View. BQM Deployment in the Trading Network. Figure 9 shows a typical BQM deployment in a trading network. Figure 9 Typical BQM Deployment in a Trading Network. BQM can then be used to answer these types of questions. Are any of my Gigabit LAN core links saturated for more than X milliseconds Is this causing loss Which links would most benefit from an upgrade to Etherchannel or 10 Gigabit speeds. What application traffic is causing the saturation of my 1 Gigabit links. Is any of the market data experiencing end-to-end loss. How much additional latency does the failover data center experience Is this link sized correctly to deal with microbursts. Are my traders getting low latency updates from the market data distribution layer Are they seeing any delays greater than X milliseconds. Being able to answer these questions simply and effectively saves time and money in running the trading network. BQM is an essential tool for gaining visibility in market data and trading environments It provides granular end-to-end latency measurements in complex infrastructures that experience high-volume data movement Effectively detecting microbursts in sub-millisecond levels and receiving expert analysis on a particular event is invaluable to trading floor architects Smart bandwidth provisioning recommendations, such as sizing and what-if analysis, provide greater agility to respond to volatile market conditions As the explosion of algorithmic trading and increasing message rates continues, BQM, combined with its QoS tool, provides the capability of implementing QoS policies that can protect critical trading applications. Cisco Financial Services Latency Monitoring Solution. Cisco and Trading Metrics have collaborated on latency monitoring solutions for FIX order flow and market data monitoring Cisco AON technology is the foundation for a new class of network-embedded products and solutions that help merge intelligent networks with application infrastructure, based on either service-oriented or traditional architectures Trading Metrics is a leading provider of analytics software for network infrastructure and application latency monitoring purposes. The Cisco AON Financial Services Latency Monitoring Solution FSMS correlated two kinds of events at the point of observation. Network events correlated directly with coincident application message handling. Trade order flow and matching market update events. Using time stamps asserted at the point of capture in the network, real-time analysis of these correlated data streams permits precise identification of bottlenecks across the infrastructure while a trade is being executed or market data is being distributed By monitoring and measuring latency early in the cycle, financial companies can make better decisions about which network service and which intermediary, market, or counterparty to select for routing trade orders Likewise, this knowledge allows more streamlined access to updated market data stock quotes, economic news, etc , which is an important basis for initiating, withdrawing from, or pursuing market opportunities. The components of the solution are. AON hardware in three form factors. AON Network Module for Cisco 2600 2800 3700 3800 routers. AON Blade for the Cisco Catalyst 6500 series. AON 8340 Appliance. Trading Metrics M A 2 0 software, which provides the monitoring and alerting application, displays latency graphs on a dashboard, and issues alerts when slowdowns occur. Figure 10 AON-Based FIX Latency Monitoring. Cisco IP SLA. Cisco IP SLA is an embedded network management tool in Cisco IOS which allows routers and switches to generate synthetic traffic streams which can be measured for latency, jitter, packet loss, and other criteria. Two key concepts are the source of the generated traffic and the target Both of these run an IP SLA responder, which has the responsibility to timestamp the control traffic before it is sourced and returned by the target for a round trip measurement Various traffic types can be sourced within IP SLA and they are aimed at different metrics and target different services and applications The UDP jitter operation is used to measure one-way and round-trip delay and report variations As the traffic is time stamped on both sending and target devices using the resp onder capability, the round trip delay is characterized as the delta between the two timestamps. A new feature was introduced in IOS 12 3 14 T, IP SLA Sub Millisecond Reporting, which allows for timestamps to be displayed with a resolution in microseconds, thus providing a level of granularity not previously available This new feature has now made IP SLA relevant to campus networks where network latency is typically in the range of 300-800 microseconds and the ability to detect trends and spikes brief trends based on microsecond granularity counters is a requirement for customers engaged in time-sensitive electronic trading environments. As a result, IP SLA is now being considered by significant numbers of financial organizations as they are all faced with requirements to. Report baseline latency to their users. Trend baseline latency over time. Respond quickly to traffic bursts that cause changes in the reported latency. Sub-millisecond reporting is necessary for these customers, since many campus and backbones are currently delivering under a second of latency across several switch hops Electronic trading environments have generally worked to eliminate or minimize all areas of device and network latency to deliver rapid order fulfillment to the business Reporting that network response times are just under one millisecond is no longer sufficient the granularity of latency measurements reported across a network segment or backbone need to be closer to 300-800 micro-seconds with a degree of resolution of 100 seconds. IP SLA recently added support for IP multicast test streams, which can measure market data latency. A typical network topology is shown in Figure 11 with the IP SLA shadow routers, sources, and responders. Figure 11 IP SLA Deploymentputing Servicesputing services cover a wide range of technologies with the goal of elim inating memory and CPU bottlenecks created by the processing of network packets Trading applications consume high volumes of market data and the servers have to dedicate resources to processing network traffic instead of application processing. Transport processing At high speeds, network packet processing can consume a significant amount of server CPU cycles and memory An established rule of thumb states that 1Gbps of network bandwidth requires 1 GHz of processor capacity source Intel white paper on I O acceleration. Intermediate buffer copying In a conventional network stack implementation, data needs to be copied by the CPU between network buffers and application buffers This overhead is worsened by the fact that memory speeds have not kept up with increases in CPU speeds For example, processors like the Intel Xeon are approaching 4 GHz, while RAM chips hover around 400MHz for DDR 3200 memory source Intel. Context switching Every time an individual packet needs to be processed, the CPU performs a context switch from application context to network traffic context This overhead could be reduced if the switch would occur only when the whole application buffer is complete. Figure 12 Sources of Overhead in Data Center Servers. TCP Offload Engine TOE Offloads transport processor cycles to the NIC Moves TCP IP protocol stack buffer copies from system memory to NIC memory. Remote Direct Memory Access RDMA Enables a network adapter to transfer data directly from application to application without involving the operating system Eliminates intermediate and application buffer copies memory bandwidth consumption. Kernel bypass Direct user-level access to hardware Dramatically reduces application context switches. Figure 13 RDMA and Kernel Bypass. InfiniBand is a point-to-point switched fabric bidirectional serial communication link which implements RDMA, among other features Cisco offers an InfiniBand switch, the Server Fabric Switch SFS. Figure 14 Typical SFS Deployment. Trading applications benefit from the reduction in latency and latency variability, as proved by a test performed with the Cisco SFS and Wombat Feed Handlers by Stac Research. Application Virtualization Service. De-coupling the application from the underlying OS and server hardware enables them to run as network services One application can be run in parallel on multiple servers, or multiple applications can be run on the same server, as the best resource allocation dictates This decoupling enables better load balancing and disaster recovery for business continuance strategies The process of re-allocating computing resources to an a pplication is dynamic Using an application virtualization system like Data Synapse s GridServer, applications can migrate, using pre-configured policies, to under-utilized servers in a supply-matches-demand process. There are many business advantages for financial firms who adopt application virtualization. Faster time to market for new products and services. Faster integration of firms following merger and acquisition activity. Increased application availability. Better workload distribution, which creates more head room for processing spikes in trading volume. Operational efficiency and control. Reduction in IT complexity. Currently, application virtualization is not used in the trading front-office One use-case is risk modeling, like Monte Carlo simulations As the technology evolves, it is conceivable that some the trading platforms will adopt it. Data Virtualization Service. To effectively share resources across distributed enterprise applications, firms must be able to leverage data across multiple sources in real-time while ensuring data integrity With solutions from data virtualization software vendors such as Gemstone or Tangosol now Oracle , financial firms can access heterogeneous sources of data as a single system image that enables connectivity between business processes and unrestrained application access to distributed caching The net result is that all users have instant access to these data resources across a distributed network. This is called a data grid and is the first step in the process of creating what Gartner calls Extreme Transaction Processing XTP id 500947 Technologies such as data and applications virtualization enable financial firms to perform real-time complex analytics, event-driven applications, and dynamic resource allocation. One example of data virtualization in action is a global order book application An order book is the repository of active orders that is published by the exchange or other market makers A global order book aggregates orders from around the world from markets that operate independently The biggest challenge for the application is scalability over WAN connectivity because it has to maintain state Today s data grids are localized in data centers connected by Metro Area Networks MAN This is mainly because the applications themselves have limits they have been developed without the WAN in mind. Figure 15 GemStone GemFire Distributed Caching. Before data virtualization, applications used database clustering for failover and scalability This solution is limited by the performance of the underlying database Failover i s slower because the data is committed to disc With data grids, the data which is part of the active state is cached in memory, which reduces drastically the failover time Scaling the data grid means just adding more distributed resources, providing a more deterministic performance compared to a database cluster. Multicast Service. Market data delivery is a perfect example of an application that needs to deliver the same data stream to hundreds and potentially thousands of end users Market data services have been implemented with TCP or UDP broadcast as the network layer, but those implementations have limited scalability Using TCP requires a separate socket and sliding window on the server for each recipient UDP broadcast requires a separate copy of the stream for each destination subnet Both of these methods exhaust the resources of the servers and the network The server side must transmit and service each of the streams individually, which requires larger and larger server farms On th e network side, the required bandwidth for the application increases in a linear fashion For example, to send a 1 Mbps stream to 1000recipients using TCP requires 1 Gbps of bandwidth. IP multicast is the only way to scale market data delivery To deliver a 1 Mbps stream to 1000 recipients, IP multicast would require 1 Mbps The stream can be delivered by as few as two servers one primary and one backup for redundancy. There are two main phases of market data delivery to the end user In the first phase, the data stream must be brought from the exchange into the brokerage s network Typically the feeds are terminated in a data center on the customer premise The feeds are then processed by a feed handler, which may normalize the data stream into a common format and then republish into the application messaging servers in the data center. The second phase involves injecting the data stream into the application messaging bus which feeds the core infrastructure of the trading applications The larg e brokerage houses have thousands of applications that use the market data streams for various purposes, such as live trades, long term trending, arbitrage, etc Many of these applications listen to the feeds and then republish their own analytical and derivative information For example, a brokerage may compare the prices of CSCO to the option prices of CSCO on another exchange and then publish ratings which a different application may monitor to determine how much they are out of synchronization. Figure 16 Market Data Distribution Players. The delivery of these data streams is typically over a reliable multicast transport protocol, traditionally Tibco Rendezvous Tibco RV operates in a publish and subscribe environment Each financial instrument is given a subject name, such as Each application server can request the individual instruments of interest by their subject name and receive just a that subset of the information This is called subject-based forwarding or filtering Subject-based f iltering is patented by Tibco. A distinction should be made between the first and second phases of market data delivery The delivery of market data from the exchange to the brokerage is mostly a one-to-many application The only exception to the unidirectional nature of market data may be retransmission requests, which are usually sent using unicast The trading applications, however, are definitely many-to-many applications and may interact with the exchanges to place orders. Figure 17 Market Data Architecture. Design Issues. Number of Groups Channels to Use. Many application developers consider using thousand of multicast groups to give them the ability to divide up products or instruments into small buckets Normally these applications send many small messages as part of their information bus Usually several messages are sent in each packet that are received by many users Sending fewer messages in each packet increases the overhead necessary for each message. In the extreme case, sending onl y one message in each packet quickly reaches the point of diminishing returns there is more overhead sent than actual data Application developers must find a reasonable compromise between the number of groups and breaking up their products into logical buckets. Consider, for example, the Nasdaq Quotation Dissemination Service NQDS The instruments are broken up alphabetically. This approach allows for straight forward network application management, but does not necessarily allow for optimized bandwidth utilization for most users A user of NQDS that is interested in technology stocks, and would like to subscribe to just CSCO and INTL, would have to pull down all the data for the first two groups of NQDS Understanding the way users pull down the data and then organize it into appropriate logical groups optimizes the bandwidth for each user. In many market data applications, optimizing the data organization would be of limited value Typically customers bring in all data into a few machines a nd filter the instruments Using more groups is just more overhead for the stack and does not help the customers conserve bandwidth Another approach might be to keep the groups down to a minimum level and use UDP port numbers to further differentiate if necessary The other extreme would be to use just one multicast group for the entire application and then have the end user filter the data In some situations this may be sufficient. Intermittent Sources. A common issue with market data applications are servers that send data to a multicast group and then go silent for more than 3 5 minutes These intermittent sources may cause trashing of state on the network and can introduce packet loss during the window of time when soft state and then hardware shorts are being created. PIM-Bidir or PIM-SSM. The first and best solution for intermittent sources is to use PIM-Bidir for many-to-many applications and PIM-SSM for one-to-many applications. Both of these optimizations of the PIM protocol do not ha ve any data-driven events in creating forwarding state That means that as long as the receivers are subscribed to the streams, the network has the forwarding state created in the hardware switching path. Intermittent sources are not an issue with PIM-Bidir and PIM-SSM. Null Packets. In PIM-SM environments a common method to make sure forwarding state is created is to send a burst of null packets to the multicast group before the actual data stream The application must efficiently ignore these null data packets to ensure it does not affect performance The sources must only send the burst of packets if they have been silent for more than 3 minutes A good practice is to send the burst if the source is silent for more than a minute Many financials send out an initial burst of traffic in the morning and then all well-behaved sources do not have problems. Periodic Keepalives or Heartbeats. An alternative approach for PIM-SM environments is for sources to send periodic heartbeat messages to the mu lticast groups This is a similar approach to the null packets, but the packets can be sent on a regular timer so that the forwarding state never expires. S,G Expiry Timer. Finally, Cisco has made a modification to the operation of the S, G expiry timer in IOS There is now a CLI knob to allow the state for a S, G to stay alive for hours without any traffic being sent The S, G expiry timer is configurable This approach should be considered a workaround until PIM-Bidir or PIM-SSM is deployed or the application is fixed. RTCP Feedback. A common issue with real time voice and video applications that use RTP is the use of RTCP feedback traffic Unnecessary use of the feedback option can create excessive multicast state in the network If the RTCP traffic is not required by the application it should be avoided. Fast Producers and Slow Consumers. Today many servers providing market data are attached at Gigabit speeds, while the receivers are attached at different speeds, usually 100Mbps This creates the potential for receivers to drop packets and request re-transmissions, which creates more traffic that the slowest consumers cannot handle, continuing the vicious circle. The solution needs to be some type of access control in the application that limits the amount of data that one host can request QoS and other network functions can mitigate the problem, but ultimately the subscriptions need to be managed in the application. Tibco Heartbeats. TibcoRV has had the ability to use IP multicast for the heartbeat between the TICs for many years However, there are some brokerage houses that are still using very old versions of TibcoRV that use UDP broadcast support for the resiliency This limitation is often cited as a reason to maintain a Layer 2 infrastructure between TICs located in different data centers These older versions of TibcoRV should be phased out in favor of the IP multicast supported versions. Multicast Forwarding Options. PIM Sparse Mode. The standard IP multicast forwarding protoco l used today for market data delivery is PIM Sparse Mode It is supported on all Cisco routers and switches and is well understood PIM-SM can be used in all the network components from the exchange, FSP, and brokerage. There are, however, some long-standing issues and unnecessary complexity associated with a PIM-SM deployment that could be avoided by using PIM-Bidir and PIM-SSM These are covered in the next sections. The main components of the PIM-SM implementation are. PIM Sparse Mode v2. Shared Tree spt-threshold infinity. A design option in the brokerage or in the exchange.