Arquitetura do sistema de comércio de energia

Sistemas e soluções de comércio de energia.
No setor de energia acelerada, os comerciantes precisam visualizar o mercado com rapidez e precisão.
Temos a experiência de construir plataformas de negociação de energia de alta qualidade, visualmente atraentes e intuitivas que oferecem uma variedade de opções de visualização de dados, desde visualizações de mercado personalizadas e análise de tendências até informações de preços para uma variedade de commodities e em plataformas múltiplas.
Muitos de nossos clientes usam nossos aceleradores de visualização de dados e iOS, que fornecem:
suporte total para dados de mercado históricos e intra-dia. sistemas de troca de castiçal, lotes de volume e preço. análise de tendências usando Bandas de Bollinger, Índice de Força Relativa, médias móveis e muito mais.
ARQUITETURA FLEXÍVEL.
Os serviços avançados e integrados de back-end são essenciais para uma compensação comercial precisa e atempada, reduzindo erros e custos. Ajudamos os clientes a desenvolver arquiteturas modulares flexíveis, ligando as plataformas de negociação às câmaras de compensação e possuem uma vasta experiência em implementar STP (Straight Through Processing) para comércio de commodities usando o protocolo FIX.
DANDO-LHE A BORDA EM MERCADOS TURBULENTES.
Nosso histórico no setor financeiro nos dá uma compreensão abrangente das questões técnicas envolvidas na entrega de soluções de comércio de commodities e soluções de gerenciamento de risco. À medida que a demanda por soluções multiplataforma cresce, nossos clientes estão cada vez mais chamando nossa experiência com HTML5 e JavaScript para fornecer sistemas e soluções de negociação adaptáveis ​​e rápidos, capazes de lidar com grandes quantidades de dados. Essas habilidades são complementadas pela nossa vasta experiência com tecnologias mais tradicionais, como Java e Java. Nossa Prática de Design de Experiência de Usuário também pode ajudar a desenvolver a mais eficaz e bela interface de usuário para dar-lhe uma vantagem em mercados turbulentos.
Nosso povo.
Chefe do Desenvolvimento & # 8211; Edimburgo.
& # 8220; A crescente complexidade e globalização do mercado de energia torna imperativo que os comerciantes possam reconhecer rapidamente tendências e padrões. Para permitir que essa consideração cuidadosa seja dada à apresentação dinâmica dos dados de mercado. & # 8221;
TRANSFORMANDO A EXPERIÊNCIA DO CLIENTE EM MERCADOS DE POTÊNCIA.
Como uma abordagem liderada pela UX ajudou o mercado de energia líder da Europa a mudar o jogo na experiência do cliente online.
SISTEMA DE CLEARING DE NEGOCIAÇÃO DE ENERGIA.
Nosso cliente se aproximou da Scott Logic para substituir um sistema de limpeza em envelhecimento local com uma solução hospedada moderna.
CARREGAMENTO PARA NEGOCIAÇÃO ENERGÉTICA.
Scott Logic foi abordado por um fornecedor de sistemas de comércio de commodities para adicionar gráficos de alta qualidade para sua plataforma de comércio de energia.
Saiba mais sobre a experiência da Scott Logic em soluções de comércio e limpeza de energia.
Setores.
Carreiras.
Copyright © 2018 Scott Logic Ltd. Todos os direitos reservados.

arquitetura do sistema de comércio de energia
A ETRM Systems, LLC oferece soluções e serviços criativos que combinam e suportam suas estratégias comerciais, comerciais e de gestão de riscos.
soluções atendidas às suas necessidades.
Especialização líder na indústria.
Serviços profissionais abrangentes que são conhecidos em toda a indústria de energia definem a ETRM Systems, LLC acima do restante.
soluções globais bem sucedidas de comércio de energia.
Estratégias Multi-Commodity.
Oferecendo soluções de negociação multi-commodities eficientes através de design e serviços profissionais excepcionais.
funcionalidade de negociação de ponta.
Tecnologia do estado da arte.
Abordagens inovadoras e serviços profissionais excepcionais para o setor de energia.
arquitetura de negócios e integrações de sistemas.
ETRM Systems, LLC começou em 2008.
Nós fornecemos serviços de consultoria experientes para.
Por que selecionar ETRM Systems, LLC para suas soluções de gerenciamento de risco?
Uma visão sobre as indústrias do mercado de energia.
O mais recente de notícias de energia dos EUA e além.
ETRM Systems, LLC oferece ótimas oportunidades para desenvolver e.
A ETRM Systems, LLC orgulha-se de patrocinar e se voluntariar em diversas iniciativas.
Contate-Nos.
Para mais informações, entre em contato conosco.
ETRM Systems, LLC | Soluções de TI para Comércio de Energia e Gerenciamento de Riscos.
Sobre a nossa empresa.
ETRM, gerenciamento de riscos, gerenciamento de risco de comércio de energia, negociação de energia, consultoria de negociação de energia, design de software de energia.
Atualizações recentes.
Sistemas ETRM selecionados pela Horizon Trades Technologies para sua plataforma de negociação multi-commodities global. Consulte Mais informação. ETRM Systems, LLC anuncia o lançamento de seu novo site! ETRM Systems, LLC tem um novo endereço de escritório no Energy Corridor de Houston, Texas!
Links úteis.
Informação de contato.
1001 S. Dairy Ashford Rd.
Houston, Texas 77077.
Copyright 2018. ETRM Systems, LLC. Todos os direitos reservados.
Copyright 2018. ETRM Systems, LLC. Todos os direitos reservados.

A WePower é uma plataforma de negociação de energia verde baseada em blocos.
Permite que os produtores de energia verde obtenham capital através da emissão de tokens de energia comercializáveis.
O que é WePower?
pessoas que já aguardam venda de token de poder.
Junte-se a nós:
Apoiado por parceiros globais.
Parceiros de energia.
Parceiros estratégicos.
Como visto em.
A WePower está tornando essa potência comercializável e acessível para qualquer um. E isso está dando às pessoas mais controle.
É aqui que a cadeia de bloqueios pode afetar as indústrias, mas também o bem-estar do nosso planeta.
A WePower é que liga os consumidores diretamente aos produtores de energia verde.
A facilidade de comprar e vender tokens permite o comércio global de energia tokenizada em uma cadeia de blocos.
Isso se tornará uma usina de energia virtual baseada em ethereum.
O valor real dos toques WPR crescerá à medida que o WePower se expandir e mais produtores de energia renovável começam a usar a plataforma.
Modelo de token revolucionário.
Ou assista ao vídeo abaixo.
WPR token oferece múltiplos usos e benefícios.
Leilão de energia verde.
Os titulares de toques WPR obtêm acesso prioritário a novos leilões de venda de token de energia na plataforma WePower. Quanto mais WPR tokens você tem, maior alocação de energia que você recebe.
A prioridade do leilão permite a compra de energia ao melhor preço. Também aumenta o valor de troca de toques WPR devido à demanda adicional de grandes compradores de energia.
Bacia de doação de energia verde.
O desenvolvedor de projetos de energia verde anuncia uma venda de token de planta na plataforma WePower e libera tokens de energia.
0,9% desses tokens de energia são automaticamente colocados nas piscinas contribuintes. Somente os titulares de toques WPR têm acesso a esse grupo.
Uma vez que a planta é construída e a energia é produzida, os detentores de toques WPR, assim como os detentores de token de energia, podem usar a energia, retirar ou reinvestir para aumentar o pool de contribuições.
As doações crescem com a plataforma.
Aqui está o crescimento do volume de contribuição de energia verde nos primeiros 6 anos com base em nossas projeções. Você pode ver claramente que o crescimento do pool de contribuições está diretamente relacionado com o crescimento da plataforma. Sua recompensa em energia verde quebraria mesmo em 3 anos pelo seu valor contábil e, em seguida, aceleraria seu crescimento ainda mais.
Observe que o tamanho do pool de doações dependerá do número de projetos verdes dispostos a usar a plataforma WePower. A tabela fornecida é apenas uma projeção.
Junte-se à nossa comunidade em Telegram.
Objetivos de arrecadação de fundos.
Plataforma WePower.
A WePower permite que produtores de energia renovável obtenham capital através da emissão de seus próprios tokens de energia. Esses tokens representam energia que eles comprometem-se a produzir e entregar. A padronização de energia tokenizada simplifica e abre o ecossistema de investimento de energia atualmente existente atualmente. Como resultado, os produtores de energia podem negociar diretamente com os compradores de energia verde (consumidores e investidores) e aumentar o capital vendendo energia antecipada, abaixo das taxas de mercado. A tokenização de energia garante liquidez e amplia o acesso ao capital. A solução WePower blockchain atualmente é reconhecida pela Elering, um dos mais inovadores Operadores de Sistemas de Transmissão na Europa.
Tokenização de energia e venda de tokens de energia.
Explore o futuro próximo!
Junte-se à revolução verde.
Nosso time.
Nick tem 10 anos de experiência trabalhando no setor de energia. Nick estava construindo energia e plantas solares, comprando / vendendo energia verde e certificados verdes. Além disso, ele estava envolvido na importação / exportação de energia entre países.
Art. E «ras é praticante de direito em um dos maiores escritórios de advocacia dos países bálticos, onde é responsável por todos os negócios e regulamentos fintech blockchain e criptográficos. Ele também é presidente da Associação de Fintech da Lituânia e duas vezes reconhecido como o patrocinador da Lituânia pela Comissão Européia.
A Kaspar tem sido líder no setor de serviços públicos e DSO nos últimos 10 anos. Como palestrante, estrategista tecnológico e solucionador de problemas complexos, ele tem sido muito ativo não só na Estônia natal, onde ele foi mais recentemente o CTO da Elektrilevi & # 8211; Maior DSO dos estões, mas em toda a Europa. Manter o cargo de CTO tinha sido uma progressão natural dado suas posições anteriores e deveres dentro Elektrilevi. Ele foi responsável pelo desenvolvimento e execução do Estratégico.
Mapa roteiro e tecnológico, bem como o gerenciamento da arquitetura geral da empresa de Tecnologia da Informação para infra-estrutura e aplicações. Antes desse papel, ele estava liderando o processo de integração da tecnologia digital com a infra-estrutura de distribuição de eletricidade como chefe de departamento. Ele foi responsável pela visão de desenvolvimento Elektrilevi Smart Grid, metodologia de execução, arquiteturas técnicas e segurança cibernética. Ele ajudou a construir o departamento de Tecnologia de Rede Digital como um workshop interdisciplinar de engenharia e operações de sistemas de energia. O lado das operações do departamento tratou as operações do Smart Meter, os sistemas de suporte OT e a coordenação de terceirização de TI.
Jon Matonis é diretor fundador da Fundação Bitcoin e presidente da Globitex, uma plataforma de câmbio criptográfica. Sua carreira incluiu posts influentes em VISA International, VeriSign, Sumitomo Bank e Hushmail.
Empresário de tecnologia apoiado por Venture por mais de 20 anos. Fundador da MetaCafe, o site de compartilhamento de vídeo de mais rápido crescimento de Israel atingindo mais de 50 milhões de pessoas no seu auge. Anteriormente, a Eyal fundou a Contact Networks, uma das primeiras redes sociais em 1999. Eyal tem foi um líder de pensamento franco em cryptocurrency em Israel e é um talentoso músico de piano e baixo.
David A. Cohen é o fundador e presidente da Dcntral, uma empresa baseada em blocos da Cybersecurity. Ele é reconhecido internacionalmente pelo seu trabalho pioneiro nas plataformas de software Smart Systems. Em 2018, David foi nomeado como um dos Top 100 Movers e Shakers no SmartGrid pela Greentech Media. David foi o fundador e CEO da Infotility, onde foi pioneiro na Grid Edge e desbloqueou um mercado SmartGrid de vários bilhões de dólares. David é o membro emérito do Conselho de Arquitetura da GridWise (GWAC), que foi fundamental no lançamento da visão para o setor SmartGrid. David era um membro da Fundação IOTA e da IOTA Token Cryptocurrency.
Arquitetura complexa do sistema. Heikki é o consultor principal da equipe de serviços de consultoria Catapult Lab. Nessa função, lidera a atividade geral de nossas três ofertas de serviços de consultoria; Integração de Sistemas, Suporte de Avaliação de Risco de Pós-Segurança e Análise de Negócios & # 038; Suporte de Governança de TI. Antes de trabalhar na Catapult Labs, a Heikki trabalhou para a Elektrilevi, o maior operador de sistemas de distribuição na Estônia, ocupando diferentes cargos ao longo de 10 anos. Ele começou como um engenheiro SCADA, participando e liderando diferentes projetos, como transição para redes SCADA baseadas em IP e atualizando o sistema SCADA da Elektrilevi. Mais tarde, ele também foi responsável por definir os casos de uso do centro de controle em todos os principais desenvolvimentos de TI e projetos de implementação na Elektrilevi, incluindo o sistema de informações do cliente, vários sistemas de gerenciamento de ativos e sistemas de medição inteligente. Enquanto ele era o arquiteto da Smart Grid, a Heikki consultou colegas na definição de novas soluções como análises avançadas, demandas de demanda e plataformas de gerenciamento de geração distribuída.
Liraz Siri é um hacker Whitehat profissionalmente paranóico, apoiador adiantado de Bitcoin e co-fundador do TurnKey Linux que poderes e amp; protege mais de 100.000 servidores em todo o mundo. Aos 18 anos ele digitalizou toda a Internet para detectar vulnerabilidades. Mais tarde, nas forças armadas, ele co-fundou uma unidade cibernética israelense. Hoje, ele evangeliza código aberto, cripto e amp; descentralização, enquanto comercializam segurança tolerante a falhas para aplicações de alto risco (WO / 2007/066333).
Steven fornece conselhos de investimentos, transações e negócios para fundos de investimento, empresas de energia e comerciantes independentes nos mercados globais de energia, projetos recentes incluem os campos de suporte de investimento / transação e estratégia de negociação.
Brad é um empreendedor, investidor, mentor e conselheiro que iniciou e iniciou várias empresas desde o início até o vencimento em 20 anos. Brad atualmente é co-fundador & # 038; Parceiro Gerente da Krowd Mentor, uma empresa de consultoria de crowdfunding estratégica focada em organizações de ICOs, cryptocurrencies, block-chains e token powered.
Aaron Bichler é um ex-profissional de poker, que tem usado seus conhecimentos profundos de teoria de jogos e gerenciamento de riscos com sucesso no mercado de criptografia por vários anos.
Ele fundou duas florescentes agências de marketing digital com grandes clientes internacionais, incluindo empresas Fortune 500.
Saber Aria é CEO e fundador de duas agências de marketing digital proeminentes, cada uma com um portfólio diversificado de clientes, incluindo várias empresas Fortune 500.
A Saber tem uma paixão por procurar e ajudar as novas start-ups como consultor e investidor. Ele concentra o seu conselho não apenas em ideias de negócios brilhantes, mas tão importante, as equipes por trás de cada projeto. A experiência de aconselhamento levou-o a ser um palestrante em vários eventos, como o Afiliado World Asia, onde ele inspirou a multidão com seu painel "0 a 7 Figuras em um ano".
Co-fundador e CEO da Simplex e membro do conselho da Associação Bitcoin de Israel. Simplex é um fintech & amp; empresa de segurança cibernética que apresenta comerciantes a um mundo sem fraude.
Um programa de desenvolvimento de empreendimentos e desenvolvimento de exportação (SEED), ex Googler, Business Geek e Startup (SEED) para os novos mercados europeus. Ao longo dos anos, ele trabalhou com uma série de projetos de transformação digital dentro e fora do Google.
Trabalhando na principal agência de desempenho digital iProspect, a Gytis desenvolve o desenvolvimento de estratégias de vendas e aquisições de usuários digitais das marcas mundiais e locais mais conhecidas, incluindo AirBaltic, Admiral Markets e outros.
Fundadora da plataforma de crowdfunding sem fins lucrativos da Hooandja, CEE region, com mais de 2 milhões de euros para projetos criativos e de ONGs. Mais de uma década de experiência em software líder e projetos de desenvolvimento web, incluindo iniciativas como Arenguidee. ee e Rahvakogu. ee. Membro do conselho do Fundo Estoniano para a Natureza. Membro do Conselho de Pensamento do Presidente da Estônia. Co-fundador do Let & # 8217; s Do It! Iniciativa mundial que coordenou 50 mil pessoas para limpar até 10.000 toneladas de lixo ilegal em um dia. Em 2017, mais de 10 milhões de pessoas participaram de Let's Fez isso! ações de limpeza em mais de 100 países.
Geoffrey é um contador de histórias internacional e marketing # 038; profissional de comunicação com base no sul da França. Com mais de 20 anos de experiência trabalhando com B-to-B e marcas de consumidores, ele ajuda as empresas que querem fazer negócios a nível internacional alcançar seus objetivos de marcom. Ele tem uma vasta experiência em Telecomunicações, IoT, Smart Cities, Smart Grid / Metering, Energy e trabalhando com DSO europeu # 8217; s em questões de segurança cibernética.
Darius Rugevicius é experiente na construção e crescimento de negócios baseados em tecnologia bem sucedida, tendo vendido duas de suas anteriores empresas iniciantes nos últimos 4 anos sozinhos.
Usando suas habilidades para implementar estratégias de crescimento efetivas, impulsionar a execução e cumprir os prazos, ele ajudou as empresas que operam nos setores de cadeias de blocos, tecnologia de fintas, robótica e biotecnologia. No ano passado, Darius trabalhou com vários projetos de ICO, ajudando-os a desenvolver uma estratégia de ICO bem-sucedida, plano de marketing e modelo de token.
Parceiro na consultoria de estratégia e marca Be & amp; Do. Viveu e trabalhou em mais de 20 países como diretor criativo, estrategista, empresário e consultor. Seus clientes apresentam qualquer indústria imaginável, e se estendem de campeões locais para grandes multinacionais.
O Dr. Tadas Jucikas é responsável pela análise de dados e aprendizagem de máquinas dentro da plataforma. Ele é um fundador e CEO da Genus AI, que é uma empresa de inteligência artificial, permitindo que as empresas interajam com seus clientes de forma emocionalmente inteligente.
Anteriormente, a Tadas criou e gerenciou equipes de ciência de dados nos setores privado e público e foi pioneira na aplicação de algoritmos de aprendizado de máquinas em diversos conjuntos de dados, como dados de redes sociais não estruturados, dados da cadeia de suprimentos ou dados do processo do governo.
Ele completou seu Ph. D. em Neurociências Computacionais na Universidade de Cambridge com descobertas de pesquisa nas principais revistas científicas mundiais, como Nature e PNAS.
Blockchain e desenvolvedor e consultor de contratos inteligentes. Ele tem interesse pessoal na construção de novos produtos e sistemas alimentados pela tecnologia Blockchain e ajuda a equipe com seus assessores sobre como desenvolver redes descentralizadas.
Mais de 10 anos de experiência colorida trazendo marcas e clientes mais próximos um do outro, polindo identidades e vozes de marcas, capacitando as empresas para desenvolver um diálogo com seus clientes.
7 anos de prática prática e acadêmica no campo da comunicação, com foco em redes sociais e gerenciamento de reputação on-line. A Asta tem experiência de trabalhar com as principais marcas locais e globais, esculpir a mensagem certa, ouvir atentamente os clientes e criar um diálogo benéfico para ambas as partes.
Senior Java e MongoDB profissionais certificados com forte conhecimento e habilidades de UI. Experiente com o trabalho em equipes e ambientes distribuídos internacionais, participou com sucesso do desenvolvimento de sistemas de arquitetura distribuída em larga escala com um conjunto diversificado de tecnologias.

Arquitetura do piso comercial.
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Índice.
Arquitetura do piso comercial.
Visão geral executiva.
O aumento da concorrência, o maior volume de dados do mercado e as novas exigências regulatórias são algumas das forças motrizes que deram origem às mudanças na indústria. As empresas estão tentando manter sua vantagem competitiva mudando constantemente suas estratégias de negociação e aumentando a velocidade de negociação.
Uma arquitetura viável deve incluir as tecnologias mais recentes dos domínios de rede e de aplicativos. Tem que ser modular para fornecer um caminho gerenciável para evoluir cada componente com uma interrupção mínima no sistema geral. Portanto, a arquitetura proposta por este artigo é baseada em uma estrutura de serviços. Examinamos serviços como mensagens de latência ultra-baixa, monitoramento de latência, multicast, computação, armazenamento, virtualização de dados e aplicativos, resiliência comercial, mobilidade comercial e thin client.
A solução para os requisitos complexos da plataforma de negociação da próxima geração deve ser construída com uma mentalidade holística, cruzando os limites dos silos tradicionais, como negócios e tecnologia ou aplicativos e redes.
O objetivo principal deste documento é fornecer diretrizes para a construção de uma plataforma de negociação de latência ultra baixa, ao mesmo tempo em que otimizamos o débito bruto e a taxa de mensagens tanto para os dados de mercado como para os pedidos de negociação FIX.
Para conseguir isso, estamos propondo as seguintes tecnologias de redução de latência:
• Conexão de alta velocidade interconectada ou InfiniBand ou 10 Gbps para o cluster de negociação.
• Autocarro de mensagens de alta velocidade.
• Aceleração de aplicativos via RDMA sem reconexão de aplicativo.
• Monitoramento de latência em tempo real e re-direção do tráfego comercial para o caminho com menor latência.
Tendências e desafios da indústria.
As arquiteturas de negociação de próxima geração precisam responder ao aumento das demandas de velocidade, volume e eficiência. Por exemplo, espera-se que o volume de dados de mercado de opções seja duplicado após a introdução das opções de negociação de penny em 2007. Existem também exigências regulatórias para a melhor execução, que exigem o manuseio de atualizações de preços a taxas que se aproximam de 1M msg / seg. para trocas. Eles também exigem visibilidade sobre o frescor dos dados e prova de que o cliente obteve a melhor execução possível.
No curto prazo, a velocidade de negociação e inovação são diferenciadores-chave. Um número crescente de negociações é tratada por aplicativos de negociação algorítmica colocados o mais próximo possível do local de execução comercial. Um desafio com estas "caixa preta" Os motores comerciais são que eles compõem o aumento de volume ao emitir ordens apenas para cancelá-los e enviá-los novamente. A causa desse comportamento é a falta de visibilidade em que local oferece melhor execução. O comerciante humano é agora um "engenheiro financeiro", & quot; um "quot" (analista quantitativo) com habilidades de programação, que pode ajustar modelos de negociação sobre a marcha. As empresas desenvolvem novos instrumentos financeiros, como derivados do tempo ou transações de classe de ativos cruzados, e precisam implementar os novos aplicativos de forma rápida e escalável.
A longo prazo, a diferenciação competitiva deve ser feita a partir da análise, não apenas do conhecimento. Os comerciantes de estrelas de amanhã assumem riscos, conseguem uma verdadeira visão do cliente e sempre vencem o mercado (fonte IBM: www-935.ibm/services/us/imc/pdf/ge510-6270-trader. pdf).
A resiliência empresarial tem sido uma das principais preocupações das empresas comerciais desde 11 de setembro de 2001. As soluções nesta área variam de centros de dados redundantes situados em diferentes regiões geográficas e conectados a vários locais de negociação para soluções de comerciantes virtuais que oferecem aos comerciantes de energia a maior parte da funcionalidade de um piso comercial em um local remoto.
O setor de serviços financeiros é um dos mais exigentes em termos de requisitos de TI. A indústria está experimentando uma mudança arquitetônica para Arquitetura Orientada a Serviços (SOA), serviços da Web e virtualização de recursos de TI. A SOA aproveita o aumento da velocidade da rede para permitir a ligação dinâmica e a virtualização de componentes de software. Isso permite a criação de novas aplicações sem perder o investimento em sistemas e infra-estrutura existentes. O conceito tem o potencial de revolucionar a forma como a integração é feita, permitindo reduções significativas na complexidade e custo dessa integração (gigaspaces / download / MerrilLynchGigaSpacesWP. pdf).
Outra tendência é a consolidação de servidores em fazendas de servidores de centros de dados, enquanto as lojas de comerciantes possuem apenas extensões KVM e clientes ultrafinos (por exemplo, soluções de lâminas SunRay e HP). As redes de área metropolitana de alta velocidade permitem que os dados de mercado sejam multicast entre diferentes locais, possibilitando a virtualização do piso comercial.
Arquitetura de alto nível.
A Figura 1 representa a arquitetura de alto nível de um ambiente comercial. A planta ticker e os mecanismos de negociação algorítmica estão localizados no cluster de negócios de alto desempenho no centro de dados da empresa ou na troca. Os comerciantes humanos estão localizados na área de aplicativos do usuário final.
Funcionalmente, existem dois componentes de aplicativos no ambiente comercial da empresa, editores e assinantes. O ônibus de mensagens fornece o caminho de comunicação entre editores e assinantes.
Existem dois tipos de tráfego específicos para um ambiente comercial:
• Dados de mercado - Realiza informações de preços para instrumentos financeiros, notícias e outras informações de valor agregado, como a análise. É unidirecional e muito sensível à latência, tipicamente entregue ao multicast UDP. É medido em atualizações / seg. e em Mbps. Os fluxos de dados do mercado de um ou vários feeds externos, provenientes de provedores de dados de mercado, como bolsas de valores, agregadores de dados e ECNs. Cada provedor tem seu próprio formato de dados de mercado. Os dados são recebidos por manipuladores de alimentação, aplicativos especializados que normalizam e limpam os dados e enviam-no aos consumidores de dados, como motores de preços, aplicativos de negociação algorítmica ou comerciantes humanos. As empresas que vendem também enviam os dados de mercado para seus clientes, empresas de compra como fundos de investimento, hedge funds e outros gerentes de ativos. Algumas empresas compradoras podem optar por receber feeds diretos dos intercâmbios, reduzindo a latência.
Figura 1 Arquitetura de negociação para uma empresa Side Side / Sell Side.
Não existe um padrão industrial para formatos de dados de mercado. Cada troca tem seu formato proprietário. Os provedores de conteúdo financeiro, como Reuters e Bloomberg, agregam diferentes fontes de dados de mercado, normalizam e adicionam notícias ou análises. Exemplos de feeds consolidados são RDF (Reuters Data Feed), RWF (Reuters Wire Format) e Bloomberg Professional Services Data.
Para entregar dados de mercado de baixa latência, ambos os fornecedores lançaram feeds de dados de mercado em tempo real que são menos processados ​​e têm menos análises:
- Bloomberg B-Pipe-Com B-Pipe, a Bloomberg desloca o feed de dados do mercado de sua plataforma de distribuição porque um terminal Bloomberg não é necessário para obter B-Pipe. Wombat e Reuters Feed Handlers anunciaram apoio para a B-Pipe.
Uma empresa pode decidir receber feeds diretamente de uma troca para reduzir a latência. Os ganhos na velocidade de transmissão podem variar entre 150 milissegundos e 500 milissegundos. Esses feeds são mais complexos e mais caros e a empresa tem que construir e manter sua própria planta de ticker (financetech / featured / showArticle. jhtml? ArticleID = 60404306).
• Negociação de encomendas: este tipo de tráfego carrega os negócios reais. É bidirecional e muito sensível à latência. É medido em mensagens / seg. e Mbps. Os pedidos originam-se de uma empresa compradora ou comercial e são enviados para locais de negociação como um Exchange ou ECN para execução. O formato mais comum para o transporte de pedidos é FIX (Financial Information eXchange-fixprotocol /). As aplicações que manipulam mensagens FIX são chamadas de motores FIX e eles se interagem com sistemas de gerenciamento de pedidos (OMS).
Uma otimização para FIX é denominada FAST (Fix Adapted for Streaming), que usa um esquema de compressão para reduzir o comprimento da mensagem e, de fato, reduzir a latência. FAST é direcionado mais para a entrega de dados de mercado e tem potencial para se tornar um padrão. FAST também pode ser usado como um esquema de compressão para formatos de dados de mercado proprietários.
Para reduzir a latência, as empresas podem optar por estabelecer acesso direto ao mercado (DMA).
O DMA é o processo automatizado de rotear uma ordem de valores mobiliários diretamente para um local de execução, evitando assim a intervenção de um terceiro (towergroup / research / content / glossary. jsp? Page = 1 e glossaryId = 383). DMA requer uma conexão direta com o local de execução.
O barramento de mensagens é um software de middleware de fornecedores como Tibco, 29West, Reuters RMDS, ou uma plataforma de código aberto como a AMQP. O barramento de mensagens usa um mecanismo confiável para entregar mensagens. O transporte pode ser feito através de TCP / IP (TibcoEMS, 29West, RMDS e AMQP) ou UDP / multicast (TibcoRV, 29West e RMDS). Um conceito importante na distribuição de mensagens é o "fluxo de tópicos", "quot; que é um subconjunto de dados de mercado definidos por critérios como símbolo de ticker, indústria ou uma certa cesta de instrumentos financeiros. Os assinantes se juntam a grupos de tópicos mapeados para um ou vários sub-tópicos para receber apenas as informações relevantes. No passado, todos os comerciantes receberam todos os dados do mercado. Nos atuais volumes de tráfego, isso seria sub-ótimo.
A rede desempenha um papel crítico no ambiente comercial. Os dados de mercado são levados ao balcão onde os comerciantes humanos estão localizados através de uma rede de alta velocidade Campus ou Metro Area. Alta disponibilidade e baixa latência, bem como alto rendimento, são as métricas mais importantes.
O ambiente de negociação de alto desempenho tem a maioria de seus componentes no farm de servidores do Data Center. Para minimizar a latência, os mecanismos de negociação algorítmica precisam estar localizados na proximidade dos manipuladores de alimentação, dos motores FIX e dos sistemas de gerenciamento de pedidos. Um modelo de implantação alternativo possui os sistemas de negociação algorítmica localizados em uma troca ou um provedor de serviços com conectividade rápida para trocas múltiplas.
Modelos de implantação.
Existem dois modelos de implantação para uma plataforma de negociação de alto desempenho. As empresas podem optar por ter uma mistura dos dois:
• Centro de dados da empresa comercial (Figura 2) - Este é o modelo tradicional, onde uma plataforma de negociação de pleno direito é desenvolvida e mantida pela empresa com links de comunicação para todos os locais de negociação. A latência varia com a velocidade dos links e o número de lúpulos entre a empresa e os locais.
Figura 2 Modelo de implantação tradicional.
• Co-localização no local de negociação (trocas, provedores de serviços financeiros (FSP)) (Figura 3)
A empresa comercial implementa sua plataforma de negociação automatizada o mais próximo possível dos locais de execução para minimizar a latência.
Figura 3 Modelo de implantação hospedado.
Arquitetura de negociação orientada para serviços.
Estamos propondo uma estrutura orientada a serviços para a construção da arquitetura de negociação da próxima geração. Esta abordagem fornece uma estrutura conceitual e um caminho de implementação baseado em modularização e minimização de interdependências.
Este quadro fornece às empresas uma metodologia para:
• Avalie seu estado atual em termos de serviços.
• Priorizar os serviços com base no seu valor para o negócio.
• Evolua a plataforma de negociação para o estado desejado usando uma abordagem modular.
A arquitetura de negociação de alto desempenho depende dos seguintes serviços, conforme definido pelo quadro de arquitetura de serviços representado na Figura 4.
Figura 4 Estrutura de Arquitetura de Serviços para Negociação de Alto Desempenho.
Tabela 1 Descrições e tecnologias de serviços.
Mensagens de latência ultra baixa.
Instrumento-aparelhos, agentes de software e módulos de roteador.
SO e virtualização de E / S, Remote Direct Memory Access (RDMA), TCP Offload Engines (TOE)
Middleware que paraleliza o processamento de aplicativos.
Middleware que acelera o acesso a dados para aplicativos, por exemplo, armazenamento em cache na memória.
Replicação multicast assistida por hardware através da rede; Optimizações multicast Layer 2 e Layer 3.
Virtualização de hardware de armazenamento (VSANs), replicação de dados, backup remoto e virtualização de arquivos.
Resiliência comercial e mobilidade.
Rede local e local de balanceamento e redes de campus de alta disponibilidade.
Serviços de aplicativos de área ampla.
Aceleração de aplicações através de uma conexão WAN para comerciantes que residem fora do campus.
Serviço de cliente fino.
Desacoplamento dos recursos de computação dos terminais enfrentados pelo usuário final.
Serviço de Mensagens de Latência Ultra-Baixa.
Esse serviço é fornecido pelo barramento de mensagens, que é um sistema de software que resolva o problema de conectar muitas aplicações. O sistema consiste em:
• Um conjunto de esquemas de mensagens pré-definidos.
• Um conjunto de mensagens de comando comuns.
• Uma infra-estrutura de aplicativos compartilhados para enviar as mensagens aos destinatários. A infra-estrutura compartilhada pode ser baseada em um corretor de mensagens ou em um modelo de publicação / assinatura.
Os principais requisitos para o barramento de mensagens de próxima geração são (fonte 29West):
• menor latência possível (por exemplo, menos de 100 microssegundos)
• Estabilidade sob carga pesada (por exemplo, mais de 1,4 milhões de msg / seg.)
• Controle e flexibilidade (controle de taxa e transportes configuráveis)
Há esforços na indústria para padronizar o ônibus de mensagens. O Advanced Message Queuing Protocol (AMQP) é um exemplo de um padrão aberto defendido por J. P. Morgan Chase e apoiado por um grupo de fornecedores, tais como Cisco, Envoy Technologies, Red Hat, TWIST Process Innovations, Iona, 29West e iMatix. Dois dos principais objetivos são fornecer um caminho mais simples para a interoperabilidade para aplicações escritas em diferentes plataformas e modularidade para que o middleware possa ser facilmente desenvolvido.
Em termos muito gerais, um servidor AMQP é análogo a um servidor de E-mail com cada troca atuando como um agente de transferência de mensagens e cada fila de mensagens como caixa de correio. As ligações definem as tabelas de roteamento em cada agente de transferência. Os editores enviam mensagens para agentes de transferência individuais, que então roteiam as mensagens para as caixas de correio. Os consumidores tomam mensagens de caixas de correio, o que cria um modelo poderoso e flexível que é simples (fonte: amqp / tikiwiki / tiki-index. php? Page = OpenApproach # Why_AMQP_).
Serviço de Monitoramento de Latência.
Os principais requisitos para este serviço são:
• Granularidade sub-milissegundo de medidas.
• Visibilidade em tempo real sem adicionar latência ao tráfego comercial.
• Capacidade de diferenciar a latência do processamento de aplicativos da latência de trânsito da rede.
• Capacidade de lidar com altas taxas de mensagens.
• Fornecer uma interface programática para aplicativos de negociação para receber dados de latência, permitindo que os mecanismos de negociação algorítmica se adaptem às condições de mudança.
• Correlação de eventos de rede com eventos de aplicativos para fins de solução de problemas.
A latência pode ser definida como o intervalo de tempo entre quando uma ordem comercial é enviada e quando a mesma ordem é reconhecida e agendada pela parte receptora.
Abordar o problema de latência é um problema complexo, que requer uma abordagem holística que identifique todas as fontes de latência e aplique diferentes tecnologias em diferentes camadas do sistema.
A Figura 5 mostra a variedade de componentes que podem introduzir latência em cada camada da pilha OSI. 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 ability 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 the 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-minute 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 (tradingmetrics/).
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 (tradingmetrics/TM_brochure. pdf).
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 (cisco/go/ipsla).
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 responder 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 ì 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 Deployment.
Computing Services.
Computing services cover a wide range of technologies with the goal of eliminating 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 intel/technology/ioacceleration/306517.pdf).
• 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 intel/technology/ioacceleration/306517.pdf).
• 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): cisco/application/pdf/en/us/guest/netsol/ns500/c643/cdccont_0900aecd804c35cb. pdf.
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 application 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 (networkworld/supp/2005/ndc1/022105virtual. html? page=2).
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 (gridtoday/03/0210/101061.html).
This is called a data grid and is the first step in the process of creating what Gartner calls Extreme Transaction Processing (XTP) (gartner/DisplayDocument? ref=g_search&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 is 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 the 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 large 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 CSCO. last. 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 filtering 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 only 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:
Another example is the Nasdaq Totalview service, broken up this way:
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 and 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 have 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 multicast 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 protocol 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.
Details of Anycast RP can be found in:
The classic high availability design for Tibco in the brokerage network is documented in:
Bidirectional PIM.
PIM-Bidir is an optimization of PIM Sparse Mode for many-to-many applications. It has several key advantages over a PIM-SM deployment:
• Better support for intermittent sources.
• No data-triggered events.
One of the weaknesses of PIM-SM is that the network continually needs to react to active data flows. This can cause non-deterministic behavior that may be hard to troubleshoot. PIM-Bidir has the following major protocol differences over PIM-SM:
– No source registration.
Source traffic is automatically sent to the RP and then down to the interested receivers. There is no unicast encapsulation, PIM joins from the RP to the first hop router and then registration stop messages.
All PIM-Bidir traffic is forwarded on a *,G forwarding entry. The router does not have to monitor the traffic flow on a *,G and then send joins when the traffic passes a threshold.
– No need for an actual RP.
The RP does not have an actual protocol function in PIM-Bidir. The RP acts as a routing vector in which all the traffic converges. The RP can be configured as an address that is not assigned to any particular device. This is called a Phantom RP.
– No need for MSDP.
MSDP provides source information between RPs in a PIM-SM network. PIM-Bidir does not use the active source information for any forwarding decisions and therefore MSDP is not required.
Bidirectional PIM is ideally suited for the brokerage network in the data center of the exchange. In this environment there are many sources sending to a relatively few set of groups in a many-to-many traffic pattern.
The key components of the PIM-Bidir implementation are:
Further details about Phantom RP and basic PIM-Bidir design are documented in:
Source Specific Multicast.
PIM-SSM is an optimization of PIM Sparse Mode for one-to-many applications. In certain environments it can offer several distinct advantages over PIM-SM. Like PIM-Bidir, PIM-SSM does not rely on any data-triggered events. Furthermore, PIM-SSM does not require an RP at all—there is no such concept in PIM-SSM. The forwarding information in the network is completely controlled by the interest of the receivers.
Source Specific Multicast is ideally suited for market data delivery in the financial service provider. The FSP can receive the feeds from the exchanges and then route them to the end of their network.
Many FSPs are also implementing MPLS and Multicast VPNs in their core. PIM-SSM is the preferred method for transporting traffic in VRFs.
When PIM-SSM is deployed all the way to the end user, the receiver indicates his interest in a particular S, G with IGMPv3. Even though IGMPv3 was defined by RFC 2236 back in October, 2002, it still has not been implemented by all edge devices. This creates a challenge for deploying an end-to-end PIM-SSM service. A transitional solution has been developed by Cisco to enable an edge device that supports IGMPv2 to participate in an PIM-SSM service. This feature is called SSM Mapping and is documented in:
Storage Services.
The service provides storage capabilities into the market data and trading environments. Trading applications access backend storage to connect to different databases and other repositories consisting of portfolios, trade settlements, compliance data, management applications, Enterprise Service Bus (ESB), and other critical applications where reliability and security is critical to the success of the business. The main requirements for the service are:
Storage virtualization is an enabling technology that simplifies management of complex infrastructures, enables non-disruptive operations, and facilitates critical elements of a proactive information lifecycle management (ILM) strategy. EMC Invista running on the Cisco MDS 9000 enables heterogeneous storage pooling and dynamic storage provisioning, allowing allocation of any storage to any application. High availability is increased with seamless data migration. Appropriate class of storage is allocated to point-in-time copies (clones). Storage virtualization is also leveraged through the use of Virtual Storage Area Networks (VSANs), which enable the consolidation of multiple isolated SANs onto a single physical SAN infrastructure, while still partitioning them as completely separate logical entities. VSANs provide all the security and fabric services of traditional SANs, yet give organizations the flexibility to easily move resources from one VSAN to another. This results in increased disk and network utilization while driving down the cost of management. Integrated Inter VSAN Routing (IVR) enables sharing of common resources across VSANs.
Figure 18 High Performance Computing Storage.
Replication of data to a secondary and tertiary data center is crucial for business continuance. Replication offsite over Fiber Channel over IP (FCIP) coupled with write acceleration and tape acceleration provides improved performance over long distance. Continuous Data Replication (CDP) is another mechanism which is gaining popularity in the industry. It refers to backup of computer data by automatically saving a copy of every change made to that data, essentially capturing every version of the data that the user saves. It allows the user or administrator to restore data to any point in time. Solutions from EMC and Incipient utilize the SANTap protocol on the Storage Services Module (SSM) in the MDS platform to provide CDP functionality. The SSM uses the SANTap service to intercept and redirect a copy of a write between a given initiator and target. The appliance does not reside in the data path—it is completely passive. The CDP solutions typically leverage a history journal that tracks all changes and bookmarks that identify application-specific events. This ensures that data at any point in time is fully self-consistent and is recoverable instantly in the event of a site failure.
Backup procedure reliability and performance are extremely important when storing critical financial data to a SAN. The use of expensive media servers to move data from disk to tape devices can be cumbersome. Network-accelerated serverless backup (NASB) helps you back up increased amounts of data in shorter backup time frames by shifting the data movement from multiple backup servers to Cisco MDS 9000 Series multilayer switches. This technology decreases impact on application servers because the MDS offloads the application and backup servers. It also reduces the number of backup and media servers required, thus reducing CAPEX and OPEX. The flexibility of the backup environment increases because storage and tape drives can reside anywhere on the SAN.
Trading Resilience and Mobility.
The main requirements for this service are to provide the virtual trader:
• Fully scalable and redundant campus trading environment.
• Resilient server load balancing and high availability in analytic server farms.
• Global site load balancing that provide the capability to continue participating in the market venues of closest proximity.
A highly-available campus environment is capable of sustaining multiple failures (i. e., links, switches, modules, etc.), which provides non-disruptive access to trading systems for traders and market data feeds. Fine-tuned routing protocol timers, in conjunction with mechanisms such as NSF/SSO, provide subsecond recovery from any failure.
The high-speed interconnect between data centers can be DWDM/dark fiber, which provides business continuance in case of a site failure. Each site is 100km-200km apart, allowing synchronous data replication. Usually the distance for synchronous data replication is 100km, but with Read/Write Acceleration it can stretch to 200km. A tertiary data center can be greater than 200km away, which would replicate data in an asynchronous fashion.
Figure 19 Trading Resilience.
A robust server load balancing solution is required for order routing, algorithmic trading, risk analysis, and other services to offer continuous access to clients regardless of a server failure. Multiple servers encompass a "farm" and these hosts can added/removed without disruption since they reside behind a virtual IP (VIP) address which is announced in the network.
A global site load balancing solution provides remote traders the resiliency to access trading environments which are closer to their location. This minimizes latency for execution times since requests are always routed to the nearest venue.
Figure 20 Virtualization of Trading Environment.
A trading environment can be virtualized to provide segmentation and resiliency in complex architectures. Figure 20 illustrates a high-level topology depicting multiple market data feeds entering the environment, whereby each vendor is assigned its own Virtual Routing and Forwarding (VRF) instance. The market data is transferred to a high-speed InfiniBand low-latency compute fabric where feed handlers, order routing systems, and algorithmic trading systems reside. All storage is accessed via a SAN and is also virtualized with VSANs, allowing further security and segmentation. The normalized data from the compute fabric is transferred to the campus trading environment where the trading desks reside.
Wide Area Application Services.
This service provides application acceleration and optimization capabilities for traders who are located outside of the core trading floor facility/data center and working from a remote office. To consolidate servers and increase security in remote offices, file servers, NAS filers, storage arrays, and tape drives are moved to a corporate data center to increase security and regulatory compliance and facilitate centralized storage and archival management. As the traditional trading floor is becoming more virtual, wide area application services technology is being utilized to provide a "LAN-like" experience to remote traders when they access resources at the corporate site. Traders often utilize Microsoft Office applications, especially Excel in addition to Sharepoint and Exchange. Excel is used heavily for modeling and permutations where sometime only small portions of the file are changed. CIFS protocol is notoriously known to be "chatty," where several message normally traverse the WAN for a simple file operation and it is addressed by Wide Area Application Service (WAAS) technology. Bloomberg and Reuters applications are also very popular financial tools which access a centralized SAN or NAS filer to retrieve critical data which is fused together before represented to a trader's screen.
Figure 21 Wide Area Optimization.
A pair of Wide Area Application Engines (WAEs) that reside in the remote office and the data center provide local object caching to increase application performance. The remote office WAEs can be a module in the ISR router or a stand-alone appliance. The data center WAE devices are load balanced behind an Application Control Engine module installed in a pair of Catalyst 6500 series switches at the aggregation layer. The WAE appliance farm is represented by a virtual IP address. The local router in each site utilizes Web Cache Communication Protocol version 2 (WCCP v2) to redirect traffic to the WAE that intercepts the traffic and determines if there is a cache hit or miss. The content is served locally from the engine if it resides in cache; otherwise the request is sent across the WAN the initial time to retrieve the object. This methodology optimizes the trader experience by removing application latency and shielding the individual from any congestion in the WAN.
WAAS uses the following technologies to provide application acceleration:
• Data Redundancy Elimination (DRE) is an advanced form of network compression which allows the WAE to maintain a history of previously-seen TCP message traffic for the purposes of reducing redundancy found in network traffic. This combined with the Lempel-Ziv (LZ) compression algorithm reduces the number of redundant packets that traverse the WAN, which improves application transaction performance and conserves bandwidth.
• Transport Flow Optimization (TFO) employs a robust TCP proxy to safely optimize TCP at the WAE device by applying TCP-compliant optimizations to shield the clients and servers from poor TCP behavior because of WAN conditions. By running a TCP proxy between the devices and leveraging an optimized TCP stack between the devices, many of the problems that occur in the WAN are completely blocked from propagating back to trader desktops. The traders experience LAN-like TCP response times and behavior because the WAE is terminating TCP locally. TFO improves reliability and throughput through increases in TCP window scaling and sizing enhancements in addition to superior congestion management.
Thin Client Service.
This service provides a "thin" advanced trading desktop which delivers significant advantages to demanding trading floor environments requiring continuous growth in compute power. As financial institutions race to provide the best trade executions for their clients, traders are utilizing several simultaneous critical applications that facilitate complex transactions. It is not uncommon to find three or more workstations and monitors at a trader's desk which provide visibility into market liquidity, trading venues, news, analysis of complex portfolio simulations, and other financial tools. In addition, market dynamics continue to evolve with Direct Market Access (DMA), ECNs, alternative trading volumes, and upcoming regulation changes with Regulation National Market System (RegNMS) in the US and Markets in Financial Instruments Directive (MiFID) in Europe. At the same time, business seeks greater control, improved ROI, and additional flexibility, which creates greater demands on trading floor infrastructures.
Traders no longer require multiple workstations at their desk. Thin clients consist of keyboard, mouse, and multi-displays which provide a total trader desktop solution without compromising security. Hewlett Packard, Citrix, Desktone, Wyse, and other vendors provide thin client solutions to capitalize on the virtual desktop paradigm. Thin clients de-couple the user-facing hardware from the processing hardware, thus enabling IT to grow the processing power without changing anything on the end user side. The workstation computing power is stored in the data center on blade workstations, which provide greater scalability, increased data security, improved business continuance across multiple sites, and reduction in OPEX by removing the need to manage individual workstations on the trading floor. One blade workstation can be dedicated to a trader or shared among multiple traders depending on the requirements for computer power.
The "thin client" solution is optimized to work in a campus LAN environment, but can also extend the benefits to traders in remote locations. Latency is always a concern when there is a WAN interconnecting the blade workstation and thin client devices. The network connection needs to be sized accordingly so traffic is not dropped if saturation points exist in the WAN topology. WAN Quality of Service (QoS) should prioritize sensitive traffic. There are some guidelines which should be followed to allow for an optimized user experience. A typical highly-interactive desktop experience requires a client-to-blade round trip latency of <20ms for a 2Kb packet size. There may be a slight lag in display if network latency is between 20ms to 40ms. A typical trader desk with a four multi-display terminal requires 2-3Mbps bandwidth consumption with seamless communication with blade workstation(s) in the data center. Streaming video (800x600 at 24fps/full color) requires 9 Mbps bandwidth usage.
Figure 22 Thin Client Architecture.
Management of a large thin client environment is simplified since a centralized IT staff manages all of the blade workstations dispersed across multiple data centers. A trader is redirected to the most available environment in the enterprise in the event of a particular site failure. High availability is a key concern in critical financial environments and the Blade Workstation design provides rapid provisioning of another blade workstation in the data center. This resiliency provides greater uptime, increases in productivity, and OpEx reduction.
Advanced Encryption Standard.
Advanced Message Queueing Protocol.
Application Oriented Networking.
The Archipelago® Integrated Web book gives investors the unique opportunity to view the entire ArcaEx and ArcaEdge books in addition to books made available by other market participants.
ECN Order Book feed available via NASDAQ.
Chicago Board of Trade.
Class-Based Weighted Fair Queueing.
Continuous Data Replication.
Chicago Mercantile Exchange is engaged in trading of futures contracts and derivatives.
Central Processing Unit.
Distributed Defect Tracking System.
Acesso direto ao mercado.
Data Redundancy Elimination.
Dense Wavelength Division Multiplexing.
Rede de Comunicação Eletrônica.
Enterprise Service Bus.
Enterprise Solutions Engineering.
FIX Adapted for Streaming.
Fibre Channel over IP.
Financial Information Exchange.
Financial Services Latency Monitoring Solution.
Financial Service Provider.
Information Lifecycle Management.
Instinet Island Book.
Internetworking Operating System.
Keyboard Video Mouse.
Low Latency Queueing.
Metro Area Network.
Multilayer Director Switch.
Diretoria de Mercados em Instrumentos Financeiros.
Message Passing Interface is an industry standard specifying a library of functions to enable the passing of messages between nodes within a parallel computing environment.
Network Attached Storage.
Network Accelerated Serverless Backup.
Network Interface Card.
Nasdaq Quotation Dissemination Service.
Order Management System.
Open Systems Interconnection.
Protocol Independent Multicast.
PIM-Source Specific Multicast.
Qualidade de serviço.
Random Access Memory.
Reuters Data Feed.
Reuters Data Feed Direct.
Remote Direct Memory Access.
Regulation National Market System.
Remote Graphics Software.
Reuters Market Data System.
RTP Control Protocol.
Real Time Protocol.
Reuters Wire Format.
Storage Area Network.
Small Computer System Interface.
Sockets Direct Protocol—Given that many modern applications are written using the sockets API, SDP can intercept the sockets at the kernel level and map these socket calls to an InfiniBand transport service that uses RDMA operations to offload data movement from the CPU to the HCA hardware.
Server Fabric Switch.
Secure Financial Transaction Infrastructure network developed to provide firms with excellent communication paths to NYSE Group, AMEX, Chicago Stock Exchange, NASDAQ, and other exchanges. It is often used for order routing.

QuantStart.
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By Michael Halls-Moore on July 26th, 2018.
One of the most frequent questions I receive in the QS mailbag is "What is the best programming language for algorithmic trading?". The short answer is that there is no "best" language. Strategy parameters, performance, modularity, development, resiliency and cost must all be considered. This article will outline the necessary components of an algorithmic trading system architecture and how decisions regarding implementation affect the choice of language.
Firstly, the major components of an algorithmic trading system will be considered, such as the research tools, portfolio optimiser, risk manager and execution engine. Subsequently, different trading strategies will be examined and how they affect the design of the system. In particular the frequency of trading and the likely trading volume will both be discussed.
Once the trading strategy has been selected, it is necessary to architect the entire system. This includes choice of hardware, the operating system(s) and system resiliency against rare, potentially catastrophic events. While the architecture is being considered, due regard must be paid to performance - both to the research tools as well as the live execution environment.
What Is The Trading System Trying To Do?
Before deciding on the "best" language with which to write an automated trading system it is necessary to define the requirements. Is the system going to be purely execution based? Will the system require a risk management or portfolio construction module? Will the system require a high-performance backtester? For most strategies the trading system can be partitioned into two categories: Research and signal generation.
Research is concerned with evaluation of a strategy performance over historical data. The process of evaluating a trading strategy over prior market data is known as backtesting . The data size and algorithmic complexity will have a big impact on the computational intensity of the backtester. CPU speed and concurrency are often the limiting factors in optimising research execution speed.
Signal generation is concerned with generating a set of trading signals from an algorithm and sending such orders to the market, usually via a brokerage. For certain strategies a high level of performance is required. I/O issues such as network bandwidth and latency are often the limiting factor in optimising execution systems. Thus the choice of languages for each component of your entire system may be quite different.
Type, Frequency and Volume of Strategy.
The type of algorithmic strategy employed will have a substantial impact on the design of the system. It will be necessary to consider the markets being traded, the connectivity to external data vendors, the frequency and volume of the strategy, the trade-off between ease of development and performance optimisation, as well as any custom hardware, including co-located custom servers, GPUs or FPGAs that might be necessary.
The technology choices for a low-frequency US equities strategy will be vastly different from those of a high-frequency statistical arbitrage strategy trading on the futures market. Prior to the choice of language many data vendors must be evaluated that pertain to a the strategy at hand.
It will be necessary to consider connectivity to the vendor, structure of any APIs, timeliness of the data, storage requirements and resiliency in the face of a vendor going offline. It is also wise to possess rapid access to multiple vendors! Various instruments all have their own storage quirks, examples of which include multiple ticker symbols for equities and expiration dates for futures (not to mention any specific OTC data). This needs to be factored in to the platform design.
Frequency of strategy is likely to be one of the biggest drivers of how the technology stack will be defined. Strategies employing data more frequently than minutely or secondly bars require significant consideration with regards to performance.
A strategy exceeding secondly bars (i. e. tick data) leads to a performance driven design as the primary requirement. For high frequency strategies a substantial amount of market data will need to be stored and evaluated. Software such as HDF5 or kdb+ are commonly used for these roles.
In order to process the extensive volumes of data needed for HFT applications, an extensively optimised backtester and execution system must be used. C/C++ (possibly with some assembler) is likely to the strongest language candidate. Ultra-high frequency strategies will almost certainly require custom hardware such as FPGAs, exchange co-location and kernal/network interface tuning.
Research Systems.
Research systems typically involve a mixture of interactive development and automated scripting. The former often takes place within an IDE such as Visual Studio, MatLab or R Studio. The latter involves extensive numerical calculations over numerous parameters and data points. This leads to a language choice providing a straightforward environment to test code, but also provides sufficient performance to evaluate strategies over multiple parameter dimensions.
Typical IDEs in this space include Microsoft Visual C++/C#, which contains extensive debugging utilities, code completion capabilities (via "Intellisense") and straightforward overviews of the entire project stack (via the database ORM, LINQ); MatLab, which is designed for extensive numerical linear algebra and vectorised operations, but in an interactive console manner; R Studio, which wraps the R statistical language console in a fully-fledged IDE; Eclipse IDE for Linux Java and C++; and semi-proprietary IDEs such as Enthought Canopy for Python, which include data analysis libraries such as NumPy, SciPy, scikit-learn and pandas in a single interactive (console) environment.
For numerical backtesting, all of the above languages are suitable, although it is not necessary to utilise a GUI/IDE as the code will be executed "in the background". The prime consideration at this stage is that of execution speed. A compiled language (such as C++) is often useful if the backtesting parameter dimensions are large. Remember that it is necessary to be wary of such systems if that is the case!
Interpreted languages such as Python often make use of high-performance libraries such as NumPy/pandas for the backtesting step, in order to maintain a reasonable degree of competitiveness with compiled equivalents. Ultimately the language chosen for the backtesting will be determined by specific algorithmic needs as well as the range of libraries available in the language (more on that below). However, the language used for the backtester and research environments can be completely independent of those used in the portfolio construction, risk management and execution components, as will be seen.
Portfolio Construction and Risk Management.
The portfolio construction and risk management components are often overlooked by retail algorithmic traders. This is almost always a mistake. These tools provide the mechanism by which capital will be preserved. They not only attempt to alleviate the number of "risky" bets, but also minimise churn of the trades themselves, reducing transaction costs.
Sophisticated versions of these components can have a significant effect on the quality and consistentcy of profitability. It is straightforward to create a stable of strategies as the portfolio construction mechanism and risk manager can easily be modified to handle multiple systems. Thus they should be considered essential components at the outset of the design of an algorithmic trading system.
The job of the portfolio construction system is to take a set of desired trades and produce the set of actual trades that minimise churn, maintain exposures to various factors (such as sectors, asset classes, volatility etc) and optimise the allocation of capital to various strategies in a portfolio.
Portfolio construction often reduces to a linear algebra problem (such as a matrix factorisation) and hence performance is highly dependent upon the effectiveness of the numerical linear algebra implementation available. Common libraries include uBLAS, LAPACK and NAG for C++. MatLab also possesses extensively optimised matrix operations. Python utilises NumPy/SciPy for such computations. A frequently rebalanced portfolio will require a compiled (and well optimised!) matrix library to carry this step out, so as not to bottleneck the trading system.
Risk management is another extremely important part of an algorithmic trading system. Risk can come in many forms: Increased volatility (although this may be seen as desirable for certain strategies!), increased correlations between asset classes, counter-party default, server outages, "black swan" events and undetected bugs in the trading code, to name a few.
Risk management components try and anticipate the effects of excessive volatility and correlation between asset classes and their subsequent effect(s) on trading capital. Often this reduces to a set of statistical computations such as Monte Carlo "stress tests". This is very similar to the computational needs of a derivatives pricing engine and as such will be CPU-bound. These simulations are highly parallelisable (see below) and, to a certain degree, it is possible to "throw hardware at the problem".
Sistemas de Execução.
The job of the execution system is to receive filtered trading signals from the portfolio construction and risk management components and send them on to a brokerage or other means of market access. For the majority of retail algorithmic trading strategies this involves an API or FIX connection to a brokerage such as Interactive Brokers. The primary considerations when deciding upon a language include quality of the API, language-wrapper availability for an API, execution frequency and the anticipated slippage.
The "quality" of the API refers to how well documented it is, what sort of performance it provides, whether it needs standalone software to be accessed or whether a gateway can be established in a headless fashion (i. e. no GUI). In the case of Interactive Brokers, the Trader WorkStation tool needs to be running in a GUI environment in order to access their API. I once had to install a Desktop Ubuntu edition onto an Amazon cloud server to access Interactive Brokers remotely, purely for this reason!
Most APIs will provide a C++ and/or Java interface. It is usually up to the community to develop language-specific wrappers for C#, Python, R, Excel and MatLab. Note that with every additional plugin utilised (especially API wrappers) there is scope for bugs to creep into the system. Always test plugins of this sort and ensure they are actively maintained. A worthwhile gauge is to see how many new updates to a codebase have been made in recent months.
Execution frequency is of the utmost importance in the execution algorithm. Note that hundreds of orders may be sent every minute and as such performance is critical. Slippage will be incurred through a badly-performing execution system and this will have a dramatic impact on profitability.
Statically-typed languages (see below) such as C++/Java are generally optimal for execution but there is a trade-off in development time, testing and ease of maintenance. Dynamically-typed languages, such as Python and Perl are now generally "fast enough". Always make sure the components are designed in a modular fashion (see below) so that they can be "swapped out" out as the system scales.
Architectural Planning and Development Process.
The components of a trading system, its frequency and volume requirements have been discussed above, but system infrastructure has yet to be covered. Those acting as a retail trader or working in a small fund will likely be "wearing many hats". It will be necessary to be covering the alpha model, risk management and execution parameters, and also the final implementation of the system. Before delving into specific languages the design of an optimal system architecture will be discussed.
Separation of Concerns.
One of the most important decisions that must be made at the outset is how to "separate the concerns" of a trading system. In software development, this essentially means how to break up the different aspects of the trading system into separate modular components.
By exposing interfaces at each of the components it is easy to swap out parts of the system for other versions that aid performance, reliability or maintenance, without modifying any external dependency code. This is the "best practice" for such systems. For strategies at lower frequencies such practices are advised. For ultra high frequency trading the rulebook might have to be ignored at the expense of tweaking the system for even more performance. A more tightly coupled system may be desirable.
Creating a component map of an algorithmic trading system is worth an article in itself. However, an optimal approach is to make sure there are separate components for the historical and real-time market data inputs, data storage, data access API, backtester, strategy parameters, portfolio construction, risk management and automated execution systems.
For instance, if the data store being used is currently underperforming, even at significant levels of optimisation, it can be swapped out with minimal rewrites to the data ingestion or data access API. As far the as the backtester and subsequent components are concerned, there is no difference.
Another benefit of separated components is that it allows a variety of programming languages to be used in the overall system. There is no need to be restricted to a single language if the communication method of the components is language independent. This will be the case if they are communicating via TCP/IP, ZeroMQ or some other language-independent protocol.
As a concrete example, consider the case of a backtesting system being written in C++ for "number crunching" performance, while the portfolio manager and execution systems are written in Python using SciPy and IBPy.
Performance Considerations.
Performance is a significant consideration for most trading strategies. For higher frequency strategies it is the most important factor. "Performance" covers a wide range of issues, such as algorithmic execution speed, network latency, bandwidth, data I/O, concurrency/parallelism and scaling. Each of these areas are individually covered by large textbooks, so this article will only scratch the surface of each topic. Architecture and language choice will now be discussed in terms of their effects on performance.
The prevailing wisdom as stated by Donald Knuth, one of the fathers of Computer Science, is that "premature optimisation is the root of all evil". This is almost always the case - except when building a high frequency trading algorithm! For those who are interested in lower frequency strategies, a common approach is to build a system in the simplest way possible and only optimise as bottlenecks begin to appear.
Profiling tools are used to determine where bottlenecks arise. Profiles can be made for all of the factors listed above, either in a MS Windows or Linux environment. There are many operating system and language tools available to do so, as well as third party utilities. Language choice will now be discussed in the context of performance.
C++, Java, Python, R and MatLab all contain high-performance libraries (either as part of their standard or externally) for basic data structure and algorithmic work. C++ ships with the Standard Template Library, while Python contains NumPy/SciPy. Common mathematical tasks are to be found in these libraries and it is rarely beneficial to write a new implementation.
One exception is if highly customised hardware architecture is required and an algorithm is making extensive use of proprietary extensions (such as custom caches). However, often "reinvention of the wheel" wastes time that could be better spent developing and optimising other parts of the trading infrastructure. Development time is extremely precious especially in the context of sole developers.
Latency is often an issue of the execution system as the research tools are usually situated on the same machine. For the former, latency can occur at multiple points along the execution path. Databases must be consulted (disk/network latency), signals must be generated (operating syste, kernal messaging latency), trade signals sent (NIC latency) and orders processed (exchange systems internal latency).
For higher frequency operations it is necessary to become intimately familiar with kernal optimisation as well as optimisation of network transmission. This is a deep area and is significantly beyond the scope of the article but if an UHFT algorithm is desired then be aware of the depth of knowledge required!
Caching is very useful in the toolkit of a quantitative trading developer. Caching refers to the concept of storing frequently accessed data in a manner which allows higher-performance access, at the expense of potential staleness of the data. A common use case occurs in web development when taking data from a disk-backed relational database and putting it into memory. Any subsequent requests for the data do not have to "hit the database" and so performance gains can be significant.
For trading situations caching can be extremely beneficial. For instance, the current state of a strategy portfolio can be stored in a cache until it is rebalanced, such that the list doesn't need to be regenerated upon each loop of the trading algorithm. Such regeneration is likely to be a high CPU or disk I/O operation.
However, caching is not without its own issues. Regeneration of cache data all at once, due to the volatilie nature of cache storage, can place significant demand on infrastructure. Another issue is dog-piling , where multiple generations of a new cache copy are carried out under extremely high load, which leads to cascade failure.
Dynamic memory allocation is an expensive operation in software execution. Thus it is imperative for higher performance trading applications to be well-aware how memory is being allocated and deallocated during program flow. Newer language standards such as Java, C# and Python all perform automatic garbage collection , which refers to deallocation of dynamically allocated memory when objects go out of scope .
Garbage collection is extremely useful during development as it reduces errors and aids readability. However, it is often sub-optimal for certain high frequency trading strategies. Custom garbage collection is often desired for these cases. In Java, for instance, by tuning the garbage collector and heap configuration, it is possible to obtain high performance for HFT strategies.
C++ doesn't provide a native garbage collector and so it is necessary to handle all memory allocation/deallocation as part of an object's implementation. While potentially error prone (potentially leading to dangling pointers) it is extremely useful to have fine-grained control of how objects appear on the heap for certain applications. When choosing a language make sure to study how the garbage collector works and whether it can be modified to optimise for a particular use case.
Many operations in algorithmic trading systems are amenable to parallelisation . This refers to the concept of carrying out multiple programmatic operations at the same time, i. e in "parallel". So-called "embarassingly parallel" algorithms include steps that can be computed fully independently of other steps. Certain statistical operations, such as Monte Carlo simulations, are a good example of embarassingly parallel algorithms as each random draw and subsequent path operation can be computed without knowledge of other paths.
Other algorithms are only partially parallelisable. Fluid dynamics simulations are such an example, where the domain of computation can be subdivided, but ultimately these domains must communicate with each other and thus the operations are partially sequential. Parallelisable algorithms are subject to Amdahl's Law, which provides a theoretical upper limit to the performance increase of a parallelised algorithm when subject to $N$ separate processes (e. g. on a CPU core or thread ).
Parallelisation has become increasingly important as a means of optimisation since processor clock-speeds have stagnated, as newer processors contain many cores with which to perform parallel calculations. The rise of consumer graphics hardware (predominently for video games) has lead to the development of Graphical Processing Units (GPUs), which contain hundreds of "cores" for highly concurrent operations. Such GPUs are now very affordable. High-level frameworks, such as Nvidia's CUDA have lead to widespread adoption in academia and finance.
Such GPU hardware is generally only suitable for the research aspect of quantitative finance, whereas other more specialised hardware (including Field-Programmable Gate Arrays - FPGAs) are used for (U)HFT. Nowadays, most modern langauges support a degree of concurrency/multithreading. Thus it is straightforward to optimise a backtester, since all calculations are generally independent of the others.
Scaling in software engineering and operations refers to the ability of the system to handle 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 designed 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 simply 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 operating 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 ability 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 processors 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 within 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 various 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 enforcing 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. Existem vantagens e desvantagens para ambas as abordagens. 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. Further, 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 products 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 Linux 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, HTTP, operating system interaction, GUIs, regular expressions (regex), iteration and basic algorithms.
C++ is famed for its Standard Template Library (STL) which contains a wealth 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++), JDBC (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. Se for necessário um alto desempenho, as corretoras suportarão o protocolo FIX.
Conclusão.
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 of 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.
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